Introduction

Oncology Letters

1792-1074 1792-1082

D.A. Spandidos

10.3892/ol.2025.15141

OL-30-2-15141

Articles

Telomere length and skin cancer risk: A systematic review and meta-analysis of melanoma, basal cell carcinoma and squamous cell carcinoma

Andreikos

Dimitrios A.

1 Spandidos

Demetrios A.

1School of Medicine, Democritus University of Thrace, 68100 Alexandroupoli, Greece 2Laboratory of Clinical Virology, School of Medicine, University of Crete, 71003 Heraklion, Greece

Correspondence to: Dr Dimitrios A. Andreikos, School of Medicine, Democritus University of Thrace, 6th km Alexandroupoli-Makri, Dragana, 68100 Alexandroupoli, Greece, E-mail: jim.andrei@outlook.com

082025

12062025

30 2

395

06122024 15052025

2025

This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

Skin cancer is one of the most prevalent types of cancer worldwide, with its global incidence rising despite prevention efforts. Telomere length (TL) has emerged as a potential biomarker for cancer risk; however, its relationship with skin cancer risk remains incompletely understood. To explore the association between TL and the risk of melanoma, basal cell carcinoma (BCC) and squamous cell carcinoma (SCC), a systematic review and meta-analysis was conducted. Longer TL was significantly associated with an increased risk in melanoma (pooled odds ratio: 0.51; 95% confidence interval: 0.38–0.69; P<0.0001). A significant association between longer TL and increased melanoma risk was identified in both familial melanoma and the general population. Subgroup analyses revealed consistent associations across sex, population source and adjustments for confounding factors. Geographic stratification indicated stronger associations in studies conducted in the USA compared with those from European populations. A meta-analysis of BCC and SCC studies did not achieve statistical significance, although qualitative synthesis suggested a potential association between shortened TL and increased risk. The significant association of longer TL and increased melanoma risk diverges from the conventional hypothesis that telomere shortening elevates cancer risk, highlighting a cancer-type specific telomeric relationship. The inconclusive findings for BCC and SCC underscore the necessity for further detailed investigation. Large-scale prospective studies with standardized methodologies are imperative to validate these findings and explore the underlying mechanisms. The present findings suggested that TL could potentially serve as a valuable biomarker for melanoma risk stratification in dermatologic oncology.

telomere length telomere biology biomarker cancer skin cancer melanoma basal cell carcinoma squamous cell carcinoma

Funding: No funding was received.

Introduction

Skin cancer is the most frequently diagnosed cancer among Caucasian individuals, and despite extensive public health campaigns and preventative initiatives, its incidence continues to rise globally (1–3). Skin cancer comprises malignant melanoma and non-melanoma skin cancers (NMSCs), with basal cell carcinoma (BCC) and squamous cell carcinoma (SCC) being the most clinically significant NMSCs due to their potential for local invasion and metastasis (4,5).

BCC, originating from basal epidermal cells, represents 70–80% of skin cancer diagnoses, making it one of the most prevalent types of cancer worldwide (6). BCC is histologically characterised by basophilic nests of basal-like cells arranged in a peripheral palisade pattern and typically exhibits slow growth (7). Although BCC is a malignant tumour, it exhibits almost no metastatic potential (8). SCC constitutes ~20% of NMSC cases and carries a higher risk of metastasis compared with BCC, reported to. Be between 0.1 and 13.7% (9,10). The lifetime risk of SCC in the United States is estimated to be 7–11%, with its incidence increasing over recent decades; risk factors include advanced age, fair skin and cumulative ultraviolet (UV) radiation exposure (11–14). Clinically, SCC may present as enlarging plaques or nodules that may ulcerate, and often arises from the precursor lesion actinic keratosis (AK) (15). Early diagnosis and surgical intervention typically result in excellent outcomes (16).

Melanoma, while less common, is the most aggressive form of skin cancer and poses the greatest mortality risk due to its high metastatic potential and resistance to therapy (17). Cutaneous melanoma originates from melanocytes and constitutes >90% of melanoma cases in Caucasian populations. Pathophysiologically, it is predominantly associated with UV radiation-induced mutations, with UV radiation accounting for >75% of melanoma cases in Caucasian populations; however, non-UV-related subtypes exist (18–20). Risk factors include sun exposure, high nevus count, the presence of atypical moles, genetic predisposition and family history (21,22). The incidence of melanoma has risen sharply since the 1950s, with current estimates reporting ~25 new cases per 100,000 individuals annually in Europe, 30 in the United States, and 60 in Australia and New Zealand (23–25).

Telomeres are specialized nucleoprotein structures capping the ends of eukaryotic chromosomes, consisting of repetitive hexameric sequences (5′-TTAGGG-3′) that span 10–15 kilobases in humans (26–28). Telmoeres serve a critical role in maintaining genomic integrity by safeguarding chromosome ends from degradation and preventing end-to-end fusions (26,29). The shelterin complex ensures telomere protection, regulates their length and affects telomerase activity (27,28). During DNA replication, the inability of DNA polymerase to fully replicate 3′ends results in progressive telomere shortening by 30–200 base pairs per cell division (30). The progressive telomere attrition results in a cellular replicative limit, termed the Hayflick limit (31). In cells with functional cell cycle checkpoints, critical telomere shortening results in replicative senescence, whereas in cells with disrupted checkpoints, telomere crisis ensues, which is resolved either by apoptosis or the activation of telomere maintenance mechanisms (31,32). Telomerase, a ribonucleoprotein enzyme, permits the elongation of telomeres and is tightly regulated, being predominantly active in stem cells, germ cells and certain highly proliferative somatic cell populations, whereas most somatic cells exhibit low or negligible telomerase activity (32–34). The aberrant reactivation of telomerase is found in most malignancies and is considered a critical tumourigenic event (35–37).

The pathophysiology of telomere length (TL) in cancer is complex, with research underscoring its dual role as both a tumour suppressor via induction of apoptosis and senescence, and an enabler of carcinogenesis via genomic instability (38–40). The primary proposed mechanism in carcinogenesis consists of short telomeres inducing chromosomal instability and promoting genomic aberrations, subsequently TL is stabilized above the apoptotic threshold through telomerase activation or alternative lengthening mechanisms, enabling unlimited replicative potential (41,42). The relationship between TL and cancer risk is an area of active investigation, with findings varying by cancer type. Short telomeres have been associated with an increased risk in urological, head and neck, and digestive system cancers (43–46). Studies on breast and colorectal cancers, and NMSCs, have yielded inconclusive results, failing to demonstrate a statistically significant association, whereas studies have indicated a significant association between long TL and increased risk of lung cancer and melanoma (47–51).

Corollary to the rising incidence of skin cancer is the need for novel risk stratification and early detection strategies aimed at prevention and timely therapeutic intervention. Given that TL can be readily assessed through peripheral leukocyte measurements and its centrality to cancer biology, it constitutes a promising cancer biomarker (52–54). The present systematic review and meta-analysis aimed to evaluate and synthesise the current evidence on the association between TL and skin cancer, specifically in melanoma, BCC and SCC, elucidating the potential of TL as a risk biomarker in dermatologic oncology.

Materials and methods <sec> <title>Search strategy

A systematic review and meta-analysis was conducted to investigate the association between TL and skin cancer risk, specifically focusing on melanoma, BCC and SCC. The present study was developed in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines (55).

A comprehensive literature search was performed to identify relevant studies published up to October 2024 across the electronic databases PubMed (https://pubmed.ncbi.nlm.nih.gov/), Cochrane Library (https://www.cochranelibrary.com/) and Scopus (https://www.scopus.com/). The search strategy utilized a combination of Medical Subject Headings terms and key words related to TL and skin cancer risk. The following terms were used: ‘telomere’, ‘telomere length’, ‘skin cancer’, ‘melanoma’, ‘basal cell carcinoma’, ‘squamous cell carcinoma’ and ‘skin neoplasms’. Logical operators ‘AND’ and ‘OR’ were employed to combine the search terms effectively. Additionally, bibliographies of relevant reviews were examined to identify any further eligible studies.

Eligibility criteria

Studies were included based on predefined eligibility criteria, with additional criteria applied for inclusion in the quantitative synthesis. The eligibility criteria were structured based on the PICO framework, as follows:

P (population), eligible studies investigated individuals at risk for developing melanoma, SCC or BCC. Studies focusing on non-malignant or premalignant conditions, such as AK and Bowen's disease (BD), were excluded. Similarly, extracutaneous melanoma, including mucosal and ocular melanoma, and malignancies of the skin other than melanoma, BCC and SCC, such as cutaneous lymphoma, were excluded from both the quantitative and qualitative analyses. All included studies involved human populations, studies involving non-human subjects or cell lines were excluded.

I (intervention/exposure), to be eligible, studies had to explore the relationship between TL and the risk of skin cancer by directly measuring TL in any human biological sample, including tissue or peripheral blood leukocytes (PBLs), while studies reporting indirect measurements, such as genetic risk score, were excluded.

C (comparison), the comparison groups included individuals with and without skin cancer, or patient samples from malignant tissue and normal skin tissue. Studies that lacked a control group or comparator were excluded.

O (outcome), for the qualitative synthesis, a descriptive analysis of the relevant association was required. For inclusion in the quantitative analysis of risk, studies were required to report relative risk estimates, including odds ratios (ORs), risk ratios or hazard ratios (HRs), with corresponding 95% confidence intervals (CIs).

Eligible studies followed an observational design employing a cohort, case-control or nested case-control design. Review articles, meta-analyses, case reports, conference abstracts and dissertations were excluded. Only articles published in English were included, but no restrictions were applied regarding the publication date.

Study selection and data extraction

All retrieved articles were initially screened against the eligibility criteria based on titles and abstracts. Full-text articles were thoroughly examined in studies that appeared to meet the inclusion criteria or when eligibility was uncertain. Two independent reviewers conducted the initial screening and the evaluation of the full-text articles to determine inclusion. Any conflicts were resolved through discussion and consensus.

Data extraction was conducted independently by two reviewers using a standardized data extraction form developed for the present study. Any discrepancies were resolved by consensus. For all eligible studies, the extracted data included: Study name, first author name, year of publication, location, cancer type, the racial or ethnic composition of the study population, information about the source of cases, study design, age and sex distribution among both cases and controls, number of cases and controls, method of TL measurement, tissue source of the DNA, cut-off values used for categorizing TL, relative risk measure and the corresponding 95% CIs. If an article included multiple non-overlapping datasets, they were considered independent individual studies. Between studies with overlapping samples for the same outcome, the study with the largest number of cases was included in the meta-analysis. If sample sizes were similar, the study that adjusted for the greatest number of covariates was selected.

Qualitative synthesis

All studies that met the inclusion criteria, including those incorporated into the quantitative synthesis (meta-analysis), were included in the qualitative synthesis. This synthesis involved systematically reviewing and interpreting the findings of each study.

Quantitative synthesis Data preparation

In the quantitative synthesis, the relative risk for comparing patients with the longest TLs to the shortest was utilized, with the longest being used as the reference. In studies reporting TL as a continuous variable, the reported effect estimate per unit decrease in TL was utilized. To maintain consistency in the direction of the effect, effect estimates were transformed when necessary, so that long TL was always the reference. In studies with multiple relative risk measures, the most fully adjusted risk measure was included in the meta-analysis.

Meta-analysis

The statistical heterogeneity between studies was assessed using Cochran's Q test and quantified with the I² statistic. P<0.10 for the Q test was considered indicative of significant heterogeneity. The I² statistic, describing the percentage of total variation across studies due to heterogeneity rather than chance, was utilised to quantify heterogeneity, with values of 25, 50 and 75% representing low, moderate and high heterogeneity, respectively (56,57). The random-effects model, based on the DerSimonian and Laird method, which accounts for both within-study and between-study variability was used (58). When pooling risk measures, they were transformed by taking the natural logarithm and their standard errors were utilised for the meta-analysis (57).

To explore potential sources of heterogeneity and to assess the robustness of the findings, subgroup analyses were performed based on sex, location, subtype by genetic predisposition (familial vs. sporadic), population source (hospital vs. general population), and adjustment for skin cancer specific confounders. Sensitivity analyses were conducted by sequentially removing each study to evaluate its influence on the overall pooled estimates.

All statistical analyses were conducted using R software (version R 4.4.1; http://www.r-project.org/) with the ‘meta’ (https://cran.r-project.org/package=meta) and ‘metafor’ (https://cran.r-project.org/package=metafor) packages. Two-tailed P<0.05 was considered to indicate a statistically significant difference, except where otherwise specified.

Assessment of publication bias

Publication bias was evaluated using funnel plots for visual inspection, and Egger's regression asymmetry test was conducted to statistically assess the presence of small-study effects (59,60). P<0.05 was considered indicative of potential publication bias.

Quality assessment

Risk of bias was independently assessed by two reviewers using the Newcastle-Ottawa Scale (NOS) for observational studies (61). The NOS evaluates studies based on three domains: Selection of participants, comparability of study groups, and ascertainment of exposure and outcomes. The maximum score a study could receive is nine stars. Studies scoring seven or more stars were considered high quality, those scoring five to six were considered moderate quality, and those scoring below five were considered low quality. Any discrepancies in quality assessments were resolved by consensus.

Results <sec> <title>Study selection

The systematic search yielded a total of 1,688 articles from PubMed, Cochrane Library and Scopus (Fig. 1). One additional study was identified through cross-referencing. After removing duplicates, 1,187 articles were screened based on title and abstract, and 1,138 were excluded as irrelevant to the research question. The full manuscripts for the remaining 49 articles were acquired. Upon thorough examination, 34 articles were excluded based on the eligibility criteria, including lack of a control group, non-human subjects and insufficient information on TL.

Subsequently, all studies with overlapping samples for the same outcome were excluded from the present analysis. Specifically, the study by Han et al (2009) (62) on BCC in the Nurses' Health Study (NHS) population, the study by Nan et al (2011) (63) on melanoma in the NHS population, and the study by Weischer et al (2013) (64) on melanoma in the Copenhagen City Heart Study & Copenhagen General Population Study population were not included, thus the included studies were 24 derived from 15 articles.

Study characteristics

The key characteristics of the included studies are detailed in Table ITable IITable III [country, study type and design, population demographics (ethnicity, sex distribution, age of cases and controls), sample sizes, TL measurement methods, DNA source, effect estimates with 95% confidence intervals, TL categorization and covariates adjusted for]. Among the 24 studies included, 12 focused on melanoma, seven on BCC, and five on SCC. The studies eligible for quantitative analysis comprised 19 studies, among which 12 focused on melanoma, four on BCC and three on SCC.

The majority of the studies included in the present qualitative synthesis were conducted in the United States (11 studies), with additional studies from Ireland (four studies), Italy (three studies), Germany (one study), UK (two studies), Denmark (one study), Spain (one study) and Hungary/Romania/Slovakia (one study). In the meta-analysis, the United States (11 studies) was the most prevalent location, followed by Italy (three studies), Denmark (one study), Spain (one study), Hungary/Romania/Slovakia (one study), Germany (one study) and the UK (one study). Participants in the included studies were predominantly of European and American ancestry. In the meta-analysis, the population was exclusively Western. The age of participants ranged from mid-50s to early 70s. Two studies focused exclusively on male patients and five exclusively on female patients. In studies included in the meta-analysis, a combination of nested case-control designs (seven studies), prospective cohort studies (two studies) and case-control studies (10 studies) was observed. The source of DNA was PBL in 19 studies and malignant tissue samples in five studies. Quantitative polymerase chain reaction (qPCR) was the primary method for TL determination (19 studies), followed by telomere fluorescence in situ hybridization (TEL-FISH) (four studies) and Southern blotting (one study). All studies in the meta-analysis measured TL using qPCR techniques on PBLs, ensuring methodological consistency.

Relative risk measures were reported as ORs with 95% CIs provided, or as HRs in some melanoma studies, where the rare disease principle applies, thus HRs were considered equivalent to ORs (65). In four studies where the short TL was used as the reference, the relative risk was inverted to achieve homogeneity (66,67). Two studies analysed TL as a continuous variable, assessing risk per unit decrease or increase in TL. In studies reporting risk per increase, the relative risk was inverted to maintain homogeneity in the direction of the effect (50,68).

Qualitative synthesis

The association between TL and melanoma risk was investigated in the study by Han et al (2009) (62), where no significant association was found between TL and melanoma risk (OR: 0.62; 95% CI: 0.31–1.23). Similarly, Bodelon et al (2012) (69) reported no significant association (OR: 0.87; 95% CI: 0.46–1.64).

Nan et al (2011) (63) revealed a significant inverse association in the Women's Health Initiative Observational Study cohort (OR: 0.40; 95% CI: 0.18–0.88) and the Health Professionals Follow-up Study cohort (OR: 0.18; 95% CI: 0.06–0.56), indicating that shorter telomeres were associated with reduced melanoma risk. Anic et al (2013) (66) also observed a strong inverse association (OR: 0.22; 95% CI: 0.13–0.38). Llorca-Cardeñosa et al (2014) (70) reported a significant inverse association between shorter telomeres and melanoma risk (OR: 0.04; 95% CI: 0.01–0.10). Rachakonda et al (2018) (67) also demonstrated a significant association (OR: 0.41; 95% CI: 0.33–0.52), reinforcing the hypothesis that shorter telomeres reduce melanoma risk. By contrast, Menin et al (2016) (71) reported that, in sporadic melanoma cases, shorter telomeres were associated with an increased risk (OR: 1.74; 95% CI: 1.00–3.04; P=0.051), although the association did not reach statistical significance, whereas in familial cases, shorter telomeres were associated with a reduced risk (OR: 0.46; 95% CI: 0.23–0.95). Burke et al (2013) (72) also identified an inverse association between short TL and familial melanoma risk (OR: 0.36; 95% CI: 0.13–0.98). Large cohort studies provided mixed results. Rode et al (2016) (50) found no significant association (OR: 0.98; 95% CI: 0.95–1.01) in a Danish population-based cohort. Schneider et al (2022) (68), analysing the UK Biobank data, observed that shorter telomeres were associated with decreased melanoma risk (OR: 0.80; 95% CI: 0.71–0.89).

In studies in BCC, Perrem et al (2008) (73) found significantly shorter telomeres in BCC in renal transplant recipients compared with in normal skin; however, in non-transplant patients the relationship did not reach significance. Anic et al (2013) (66) observed a strong significant association, suggesting that shorter telomeres may increase BCC risk (OR: 10.00; 95% CI: 5.26–16.67). Srinivas et al (2019) (74) concurred, finding a significant association, with shorter telomeres increasing BCC risk (OR: 4.09; 95% CI: 2.86–5.85). By contrast, Liang et al (2011) (75) conducted two nested case-control studies within the NHS and found no significant association (NHS 1: OR: 0.96; 95% CI: 0.49–1.87; NHS 2: OR: 0.91; 95% CI: 0.66–1.25). Wainwright et al (1995) (76) found that in BCC tissue samples, telomeres were longer than in the adjacent epidermis, but no measure of significance was reported.

In SCC, Perrem et al (2007) (77) found significantly shorter telomeres in SCC in both renal transplant recipients and non-transplant patients compared with that in normal skin. Anic et al (2013) (66) observed a strong significant association (OR: 25.00; 95% CI: 11.11–50.00), indicating shorter telomeres increase SCC risk. By contrast, Han et al (2009) (62) found no significant association between TL and SCC risk (OR: 0.72; 95% CI: 0.33–1.55). Liang et al (2011) (75) also reported no significant association (OR: 1.09; 95% CI: 0.62–1.93).

Meta-analysis results

The meta-analysis was conducted using the random-effects model, based on the DerSimonian and Laird method, due to the substantial heterogeneity observed among the studies, as indicated by high I² statistics and significant Cochran's Q tests. This model accounts for both within-study and between-study variability attributable to heterogeneity across studies (56,58).

For melanoma, the pooled OR from 12 studies using the random-effects model was 0.51 (95% CI: 0.38–0.69), demonstrating a significant association between shorter TL and a reduced risk of melanoma (P<0.0001) (Fig. 2). For BCC, the data from four studies yielded a pooled OR of 2.45 (95% CI: 0.81–7.38), indicating no significant association between TL and BCC risk (P=0.1124) (Fig. 3). The meta-analysis of SCC included three studies, and resulted in a pooled OR of 2.68 (95% CI: 0.34–21.47), which was not statistically significant (P=0.3519) (Fig. 4).

Subgroup analysis results

To further elucidate the relationship between TL and melanoma risk, subgroup analyses were conducted based on geographic location, sex, genetic predisposition, population source and level of adjustment. Subgroup analyses for BCC and SCC were not performed due to paucity of data.

Geographic location

In subgroup analyses based on geographic location, a significant inverse association between short TL and melanoma risk was observed across geographic locations. Studies conducted in the USA demonstrated a stronger association, with a pooled OR of 0.33 (95% CI: 0.21–0.52), compared with studies conducted in Europe, which reported a pooled OR of 0.65 (95% CI: 0.47–0.89) (Fig. 5). The test for subgroup differences was statistically significant (P=0.0175), indicating that while the association was significant in both groups, the magnitude of the effect may vary by geographic region.

Sex

In studies restricted to female patients, a significant inverse association between shorter TL and melanoma risk, with a pooled OR of 0.51 (95% CI: 0.31–0.86) was reported (Fig. 6). Mixed-sex studies demonstrated a similar association, with a pooled OR of 0.55 (95% CI: 0.40–0.75). The test for subgroup differences between female-only studies and mixed-sex studies was not statistically significant (P=0.833), suggesting that the effect of TL on melanoma risk was consistent across the sexes.

Genetic predisposition

In familial melanoma, a significant inverse association between shorter TL and melanoma risk was shown (pooled OR: 0.43; 95% CI: 0.26–0.72) (Fig. 7). In the general population studies, the pooled OR was 0.53 (95% CI: 0.39–0.73), indicating a similar significant association. The test for subgroup differences was not significant (P=0.497). Additionally, to examine the potential impact of familial melanoma cases that may have been included in the general population studies, an analysis was performed assuming 10% familial melanoma cases in the general population. It was assumed that familial cases in the general population had an effect of OR=0.43, as indicated by the subgroup analysis, and sporadic cases had an OR of 1, indicating no association. As ORs are multiplicative, the calculation was performed on the logarithmic scale: A weighted average of the log(OR) values was computed, with weights reflecting the assumed proportions of familial and sporadic cases. The resulting weighted log(OR) was then exponentiated to obtain the expected overall OR of 0.92.

Population source

Subgroup analysis based on the source of the sample reported a significant inverse association between shorter TL and melanoma risk, with a pooled OR of 0.40 (95% CI: 0.22–0.75) in hospital-based studies (Fig. 8). Similarly, in population-based studies, a significant protective association, with a pooled OR of 0.75 (95% CI: 0.59–0.96) was shown. The test for subgroup differences between hospital- and population-based studies was not significant (P=0.066).

Adjustment level

Subgroup analysis based on stratification by adjustment level demonstrated significant inverse associations between TL and melanoma risk, with a pooled OR of 0.42 (95% CI: 0.34–0.52) in the melanoma-specific subgroup and a pooled OR of 0.56 (95% CI: 0.41–0.75) in the subgroup with general adjustments [non-melanoma-specific adjustments, such as demographic factors (e.g., age, sex) and lifestyle variables (e.g., smoking, alcohol consumption)] (Fig. 9). The test for subgroup differences was not significant (P=0.151).

Publication bias

The visual inspection of the funnel plot suggested some asymmetry, especially at the lower end among the smaller studies (Fig. 10). Publication bias was evaluated quantitatively using Egger's test for funnel plot asymmetry. For melanoma studies, Egger's test indicated significant evidence of funnel plot asymmetry, which can be suggestive of publication bias, with a bias estimate of −2.94 (SE=0.89), suggesting potential small-study effects (59,60). For BCC and SCC, publication bias could not be assessed due to the small number of included studies (n=4 studies for BCC, n=3 studies for SCC); this limitation reflects the methodological constraints that require a minimum of 10 studies to produce reliable results (59,60).

Sensitivity analysis

A sensitivity analysis was conducted to evaluate the robustness of the meta-analysis by systematically omitting each study and recalculating the pooled effect size and heterogeneity measures.

In melanoma, the pooled OR ranged from 0.44 (95% CI: 0.28–0.70) to 0.58 (95% CI: 0.44–0.77), indicating that no single study disproportionately influenced the overall findings (Fig. 11). The sensitivity analysis confirmed the stability of the meta-analysis, with the association between shorter TL and reduced melanoma risk remaining statistically significant under all scenarios.

For BCC, the sensitivity analysis showed a wide range of pooled ORs, from 1.55 (95% CI: 0.52–4.57) to 3.46 (95% CI: 1.13–10.58) (Fig. S1). In the case of SCC, the pooled OR ranged from 0.94 (95% CI: 0.60–1.49) to 5.17 (95% CI: 0.24–111.31) (Fig. S2). The results for BCC and SCC reflected the substantial variability and suggested that the findings may not be robust.

Quality assessment

Quality assessment using the NOS showcased a predominance of high-quality studies. All studies included in the meta-analysis were high quality, demonstrating robust methodologies. Among the studies included only in the qualitative analysis, most were categorized as of moderate quality (Table SI, Table SII, Table SIII).

Discussion

The present meta-analysis revealed a significant association between longer TL and increased melanoma risk, whereas the associations for BCC and SCC, did not reach statistical significance. Nonetheless, a trend towards increased risk being linked with shorter telomeres was observed in the qualitative synthesis for BCC and SCC.

The association between longer TL and melanoma risk demonstrated in the current meta-analysis may appear counterintuitive, as telomere shortening is generally associated with genomic instability and increased cancer risk (28,33,78,79). Telomere biology exhibits a complex role in oncogenesis; while short telomeres can promote chromosomal instability and tumourigenesis, they may also inhibit tumour progression by enforcing replicative limits (40,80). The present findings align with those of a previous meta-analysis in melanoma by Caini et al (2015) (49), and similar associations have been noted in other types of cancer (49–51). Further exploration of the underlying biological mechanisms is warranted.

Melanocytes are characterised by low proliferative rates and limited apoptotic capacity, and they may be prone to senescence in response to oncogenic stress rather than apoptosis (62,63,81). Longer telomeres might extend the proliferative lifespan of melanocytes harbouring oncogenic mutations, thereby increasing the likelihood of accumulating additional genetic alterations that could precipitate malignant transformation (Fig. 12) (19,62,63). While an association has been demonstrated between increased TL and total number of nevi, there is evidence that longer TL is associated with increased melanoma risk independently of nevus count, as the protective role of shorter telomeres persists even after adjusting for nevus count and other risk factors (22,62,63,82).

Genetic contributions to telomere dynamics also serve a crucial role in melanoma susceptibility, with genetic loci regulating TL implicated in melanoma pathogenesis (83–85). Evidence has suggested that telomere elongation appears to be the mechanism linking telomerase reverse transcriptase (TERT) promoter and shelterin mutations to cancer, rather than telomere deprotection (86). Carriers of TERT promoter and protection of telomeres 1 (POT1) mutations exhibit longer telomeres compared with unaffected relatives, and families with POT1 and TPP1 mutations demonstrate genetic anticipation for both cancer onset and mortality, suggesting successive telomere elongation across generations (67,86,87). The association between long telomere-associated single nucleotide polymorphisms and increased cancer risk has been observed in multiple types of cancer, including melanoma, glioma and chronic lymphocytic leukaemia (50,86,88,89). Concurrently, short telomeres have been associated with poorer survival outcomes in patients with melanoma (85,90). A potential explanation for this is that longer telomeres confer an increased melanoma risk; however, once the cancer has developed, shorter telomeres may lead to increased genomic instability, thus contributing to worse survival outcomes (85,90).

It has been suggested that the mechanism of interaction between telomere dynamics and melanoma risk may vary between sporadic and familial melanoma (71). Menin et al (2016) (71) demonstrated the association between increased familial melanoma risk and longer telomeres, concurring with evidence in familial melanoma from another study (72); however, Menin et al (71) also reported an inverse relationship in sporadic melanoma, with shorter TL being associated with increased risk. Studies have observed a similar phenomenon in other types of cancer, including breast and ovarian cancers, where short telomeres are linked to increased risk in familial but not in sporadic cancer (91,92).

In the subgroup analysis of exclusively familial melanoma and melanoma in the general population, the test for subgroup differences was not significant. In both subgroups, there was a significant inverse relation between TL and melanoma risk, and the difference in magnitude of the effect did not reach significance. The exact distribution of familial cases in each general population study was not available; however, familial melanoma constitutes 5–10% of melanoma cases (93,94). To further examine the validity of the present findings, an analysis was performed assuming 10% familial melanoma cases in the general population and concluded that familial cases alone cannot account for the observed overall association. The analysis showed that familial cases alone could not account for the overall association as the expected overall OR would have been 0.92 not the observed 0.51 (95% CI: 0.38–0.68). Therefore, the present study indicated that there may be no significant difference in the association of TL and melanoma risk between sporadic and familial melanoma cases. Nevertheless, this analysis is speculative in nature and further large cohort studies that explicitly delineate between familial and sporadic melanoma are imperative to validate the results.

The subgroup analysis based on geographic location revealed significant differences in the magnitude of the association between TL and melanoma risk, with studies conducted in the USA showing a stronger inverse association between TL and melanoma risk (test for subgroup difference P=0.0175). In both groups the subjects were primarily of European or Caucasian descent, so while ethnic differences in TL have been documented, they may not account for the findings (95). Environmental factors may serve a role, particularly UV radiation exposure, the intensity of which varies geographically, and has been documented to impact telomere dynamics and serve a crucial role in melanoma development (21,96–98). Methodological differences among studies, including study design and adjustment for confounders, might also explain part of the variation. Standardization of methodologies is essential to reduce heterogeneity and enable more accurate comparisons across regions, as well as the inclusion of environmental exposure biomarkers.

It has been suggested that telomere dynamics may differ between the sexes, with women having longer telomeres compared with men (48,99); a potential mechanism for this is the potential protective role oestrogen may serve, as it can stimulate telomerase activity through the oestrogen-responsive element present in the human TERT gene (100). In a meta-analysis of the association of colorectal cancer risk and TL, a shift in effect estimates was observed in female patients (48). However, the present subgroup analysis revealed no statistically significant difference in the association between TL and melanoma risk between female-only and mixed-sex studies (test for subgroup differences P=0.833). The similar OR suggested that the reduced risk of melanoma associated with shorter TL is consistent across the sexes, although due to the limited number of male-only studies further research with sex-stratified analyses is warranted to confirm this observation.

Subgroup analysis based on the source of cases revealed significant associations in both hospital-based studies and population-based studies. The test for subgroup differences was not significant (P=0.0658), suggesting that the source of cases may not substantially influence the overall findings. The consistent findings across sample populations indicate the robustness in the association between longer TL and melanoma risk. To explore potential sources of heterogeneity and to assess the potential effect of confounding factors on the robustness of the results, a subgroup analysis was performed on the basis of adjustments for melanoma-specific confounding factors. All studies included in the analysis provided risk estimates adjusted for age, one of the most critical factors affecting TL; however, a minority adjusted for melanoma-specific risk factors (such as number of nevi and UV exposure) (22,31). In the subgroup analysis, the test for the adjustment stratified subgroups found no significant difference (P=0.151), indicating the relationship between melanoma risk and longer telomeres is true and not a confounding artefact. Due to the limited number of melanoma-specific adjusted studies, future research should prioritize collecting comprehensive data on both general and melanoma-specific confounders, enhancing the validity of future findings.

To enable the clinical translation of TL as a skin cancer biomarker, it is recommended that future research implements a multicentre validation framework across geographically and ethnically diverse cohorts. Prospective studies should standardize TL measurement methods and integrate additional biomarkers, such as UV exposure biomarkers, and genomic profiling to refine risk stratification. Measurable clinical endpoints should be prospectively collected to establish the predictive validity of TL. These studies may facilitate the development of comprehensive risk models and the translation of TL measurements into actionable clinical screening and preventative strategies.

In the meta-analysis for associations between TL and BCC risk, and TL and SCC risk, the results did not reach statistical significance and high heterogeneity was observed. Among studies included only in qualitative synthesis, significantly shorter telomeres were detected in SCC tissues compared with in normal skin, whereas the results were mixed in BCC. Given the non-significant quantitative results, caution is warranted in interpreting these findings.

The different associations between TL and skin cancer types may be attributed to the unique proliferative and apoptotic characteristics of melanocytes, basal cells and squamous keratinocytes. Squamous keratinocytes exhibit a high apoptotic threshold and primarily respond to DNA damage by programmed cell death, especially in response to UV-induced genotoxic stress (66,101). Basal cells have a lower senescence rate and reduced apoptotic susceptibility compared with melanocytes (102). These characteristics, combined with the high proliferative demand, may predispose skin cells with shorter telomeres to genomic instability under UV exposure, increasing the risk of malignancy (62,66,102). Conventionally, telomere crisis results in genomic aberrations leading to cancer; however, the specific mechanisms in NMSC remain unclear, the absence of a significant association between TL and telomerase activity suggesting that telomerase-independent mechanisms may serve a role (35,66,101). Moreover, it has been observed that SCC and BCC exhibit significantly shorter TL compared with BD and AK (101,103). Yamada-Hishida et al (2018) (103) suggested that TL may be associated with the malignant potential of NMSCs, with shorter telomeres in SCC and BCC being associated with higher invasive and metastatic risks, positioning TL as a valuable parameter in understanding the biological behaviour of NMSCs (101).

The lack of significant associations for NMSCs in the present meta-analysis may be attributed to the limited number of studies and the substantial heterogeneity observed. Large-scale studies are needed to clarify the relationship between TL and the risk of BCC and SCC, and to ascertain if TL could potentially serve as a biomarker for risk stratification.

Significant heterogeneity was observed in the present meta-analyses, which may stem from numerous factors, including differences in study design, population characteristics and adjustments for confounding variables (104,105). Although subgroup and sensitivity analyses were conducted to explore the sources of heterogeneity, residual heterogeneity remained. A potential contributor to heterogeneity may be the differences in melanoma genomic subtype, unfortunately due to the paucity of data, subgroup analysis on the basis of genomic subtypes was not possible. To address the issue of heterogeneity, future research should aim to standardize methodologies, including genomic subtypes, and to ensure comprehensive adjustment for confounding variables.

Despite the homogeneity in TL measurement methodology and DNA tissue source in the quantitative synthesis, a potential limitation of the present systematic review is the heterogeneity of methods used to measure TL among the qualitative synthesis-only studies. In the quantitative study, qPCR methods were universally employed to measure TL, but studies in the qualitative synthesis used Southern blotting and TEL-FISH analysis, which may vary in precision and sensitivity (106,107). Furthermore, while the quantitative study exclusively used PBLs as the DNA source with independent healthy controls, qualitative synthesis-only studies measured TL in tumour tissue and utilised adjacent normal skin as the control group. These methodological differences could contribute to variability in TL estimates and may partly explain the discrepancy between findings in the quantitative and qualitative synthesis for SCC and BCC.

The present meta-analysis may also be limited by the possibility of publication bias. The results of the Egger's test of melanoma studies suggested funnel plot asymmetry, which can be suggestive of publication bias (59,60). Publication bias may lead to inflation of the estimated effect size in meta-analyses, indicating caution in interpreting the magnitude of the association between TL and melanoma risk (60). However, the reliability of the Egger's test in this context is limited, given the substantial variability in sample sizes, which can inflate the type I error rate. Furthermore, the current analysis included large-scale cohort studies and studies reporting non-significant associations, suggesting that studies with null results were not systematically excluded (60,108). The visual inspection of the funnel plot revealed moderate asymmetry. For BCC and SCC, the small number of included studies (n=4 for BCC, n=3 for SCC) did not allow for formal assessments of publication bias (60). Caution is recommended in interpreting the magnitude of the association as publication bias cannot be entirely ruled out.

Quality assessment using the NOS revealed that almost all studies included in the meta-analysis were of high quality, underscoring the reliability of the present meta-analysis. However, among the qualitative-only studies, most were categorized as of moderate quality. One other potential limitation could be the differences in parametrization of TL, while the majority reported the risk estimates based on quartiles, some studies reported tertiles or continuous measurements. Unfortunately, the studies did not provide sufficient data to recalculate ORs and homogenize TL parametrization, which may contribute to heterogeneity. The present findings may also have limited applicability in diverse ethnic groups, as most studies were conducted in Western countries and had primarily Caucasian patient populations. The lack of diversity highlights the need for further studies with populations from different ethnic backgrounds.

Adjustments for confounding variables also varied among studies. All studies included in the analysis provided risk estimates adjusted for age; however, a minority adjusted for melanoma-specific risk factors. In subgroup analysis, the test for subgroup differences was non-significant (P=0.151), indicating that adjustment for confounders does not influence the observed associations. Nevertheless, due to the limited number of melanoma-specific adjusted studies, future research should prioritize collecting comprehensive data on both general and melanoma-specific confounders, enhancing the validity of future findings.

In conclusion, the findings of the present study suggested a significant association between long TL and increased melanoma risk, challenging the conventional view of an association between telomere shortening and increased cancer risk, and highlighting the complex role of telomere dynamics in carcinogenesis. In melanoma, the robustness of the present findings was indicated by their persistence when adjusting for confounding factors and examining different populations. Furthermore, the association was consistent across the sexes and in familial melanoma. By contrast, the associations between TL and the risks of BCC and SCC were not statistically significant in the quantitative analyses. Qualitative synthesis reported non-significant results in BCC and shorter telomeres in SCC tissues compared with in healthy skin. Further research is imperative to clarify the role of telomere dynamics in BCC and SCC, and to explore the mechanisms underlying the association in melanoma. Future large-scale prospective cohort studies with standardized methodology are essential to validate these findings and to ascertain the utility of TL as a clinically significant skin cancer biomarker.

Supplementary Material

Supporting Data

Acknowledgements

Not applicable.

Availability of data and materials

The data generated in the present study are included in the figures and/or tables of this article.

Authors' contributions

DAA conceptualized the study. Both DAA and DAS developed the methodology. The investigation was conducted by DAA and DAS. Validation of the data and findings was carried out by both authors. DAA prepared the original draft of the manuscript, while both DAA and DAS contributed to the review and editing process. DAS supervised the overall project. DAA and DAS confirm the authenticity of all the raw data. Both authors have read and approved the final manuscript.

Ethics approval and consent to participate

Not applicable.

Patient consent for publication

Not applicable.

Competing interests

DAS is the Editor-in-Chief for the journal, but had no personal involvement in the reviewing process, or any influence in terms of adjudicating on the final decision, for this article. The other authors declare that they have no competing interests.

Use of artificial intelligence tools

During the proofreading stage, artificial intelligence-powered tools were used exclusively to improve the readability and language of the manuscript, and subsequently, the authors revised and edited the content produced as necessary, taking full responsibility for the ultimate content of the present manuscript.

References 1

Apalla

Nashan

Weller

Castellsagué

Skin cancer: Epidemiology, disease burden, pathophysiology, diagnosis, and therapeutic approaches

Dermatol Ther (Heidelb)7(Suppl 1)S5S192017

10.1007/s13555-016-0165-y

28150105

Lomas

Leonardi-Bee

Bath-Hextall

A systematic review of worldwide incidence of nonmelanoma skin cancer

Br J Dermatol166106910802012

10.1111/j.1365-2133.2012.10830.x

22251204

Smith

Wernham

Patel

When to suspect a non-melanoma skin cancer

BMJ368m6922020

10.1136/bmj.m692

32161002

Grabowski

Saltzstein

Sadler

Tahir

Blair

A Comparison of merkel cell carcinoma and melanoma: Results from the California cancer registry

Clin Med Oncol23273332008

21892294

Yanofsky

Mercer

Phelps

Histopathological variants of cutaneous squamous cell carcinoma: A review

J Skin Cancer20112108132011

10.1155/2011/210813

21234325

Wong

CSM

Basal cell carcinoma

BMJ3277947982003

10.1136/bmj.327.7418.794

14525881

Peterson

Eberl

Vagnozzi

Belkadi

Veniaminova

Verhaegen

Bichakjian

Ward

Dlugosz

Wong

Basal cell carcinoma preferentially arises from stem cells within hair follicle and mechanosensory niches

Cell Stem Cell164004122015

10.1016/j.stem.2015.02.006

25842978

Kim

Armstrong

Nonmelanoma skin cancer

Dermatol Clin301251392012

10.1016/j.det.2011.08.008

22117874

Motley

Kersey

Lawrence

British Association of Dermatologists; British Association of Plastic Surgeons; Royal College of Radiologists, Faculty of Clinical Oncology

Multiprofessional guidelines for the management of the patient with primary cutaneous squamous cell carcinoma

Br J Dermatol14618252002

10.1046/j.0007-0963.2001.04615.x

11841362

Preston

Stern

Nonmelanoma cancers of the Skin

N Engl J Med327164916621992

10.1056/NEJM199212033272307

1435901

Miller

Weinstock

Nonmelanoma skin cancer in the United States: Incidence

J Am Acad Dermatol307747781994

10.1016/S0190-9622(08)81509-5

8176018

Gloster

Brodland

The epidemiology of skin cancer

Dermatol Surg222173261996

10.1016/1076-0512(95)00570-6

8599733

Revenga Arranz

Paricio Rubio

Mar Vázquez Salvado

Del Villar Sordo

Descriptive epidemiology of basal cell carcinoma and cutaneous squamous cell carcinoma in Soria (north-eastern Spain) 1998–2000: A hospital-based survey

J Eur Acad Dermatol Venereol181371412004

10.1111/j.1468-3083.2004.00829.x

15009289

De Vries

Trakatelli

Kalabalikis

Ferrandiz

Ruiz-de-Casas

Moreno-Ramirez

Sotiriadis

Ioannides

Aquilina

Apap

Known and potential new risk factors for skin cancer in European populations: A multicentre case-control study: Risk factors for skin cancer in European populations

Br J Dermatol167(Suppl 2)S1S132012

10.1111/j.1365-2133.2012.11081.x

Fargnoli

Piccioni

Neri

Tambone

Pellegrini

Peris

Long-term efficacy and safety of daylight photodynamic therapy with methyl amninolevulinate for actinic keratosis of the face and scalp

Eur J Dermatol2789912017

10.1684/ejd.2016.2882

27748261

Alam

Ratner

Cutaneous Squamous-cell carcinoma

N Engl J Med3449759832001

10.1056/NEJM200103293441306

11274625

Braeuer

Watson

Mobley

Kamiya

Shoshan

Bar-Eli

Why is melanoma so metastatic?

Pigment Cell Melanoma Res2719362014

10.1111/pcmr.12172

24106873

Cust

Armstrong

Goumas

Jenkins

Schmid

Hopper

Kefford

Giles

Aitken

Mann

Sunbed use during adolescence and early adulthood is associated with increased risk of early-onset melanoma

Int J Cancer128242524352011

10.1002/ijc.25576

20669232

Arnold

De Vries

Whiteman

Jemal

Bray

Parkin

Soerjomataram

Global burden of cutaneous melanoma attributable to ultraviolet radiation in 2012

Int J Cancer143130513142018

10.1002/ijc.31527

29659012

Elder

Bastian

Cree

Massi

Scolyer

The 2018 World Health Organization classification of cutaneous, mucosal, and uveal melanoma: Detailed analysis of 9 distinct subtypes defined by their evolutionary pathway

Arch Pathol Lab Med1445005222020

10.5858/arpa.2019-0561-RA

32057276

Gandini

Sera

Cattaruzza

Pasquini

Picconi

Boyle

Melchi

Meta-analysis of risk factors for cutaneous melanoma: II. Sun exposure

Eur J Cancer4145602005

10.1016/j.ejca.2004.10.016

15617990

Rastrelli

Tropea

Rossi

Alaibac

Melanoma: Epidemiology, risk factors, pathogenesis, diagnosis and classification

In Vivo28100510112014

25398793

Ferlay

Soerjomataram

Dikshit

Eser

Mathers

Rebelo

Parkin

Forman

Bray

Cancer incidence and mortality worldwide: Sources, methods and major patterns in GLOBOCAN 2012

Int J Cancer136E359E862015

10.1002/ijc.29210

25220842

Chang

Murzaku

Penn

Abbasi

Davis

Berwick

Polsky

More skin, more sun, more tan, more melanoma

Am J Public Health104e92e992014

10.2105/AJPH.2014.302185

25211764

Leonardi

Falzone

Salemi

Zanghï

Spandidos

Mccubrey

Candido

Libra

Cutaneous melanoma: From pathogenesis to therapy (Review)

Int J Oncol52107110802018

29532857

Blackburn

Epel

Lin

Human telomere biology: A contributory and interactive factor in aging, disease risks, and protection

Science350119311982015

10.1126/science.aab3389

26785477

Moon

Jarstfer

The human telomere and its relationship to human disease, therapy, and tissue engineering

Front Biosci J Virtual Libr12459546202007

10.2741/2412

17485399

O'Sullivan

Karlseder

Telomeres: Protecting chromosomes against genome instability

Nat Rev Mol Cell Biol1117117812010

10.1038/nrm2848

20125188

De Lange

How telomeres solve the End-protection problem

Science3269489522009

10.1126/science.1170633

19965504

Monaghan

Haussmann

Do telomere dynamics link lifestyle and lifespan?

Trends Ecol Evol2147532006

10.1016/j.tree.2005.11.007

16701469

Shay

Wright

Hayflick, his limit, and cellular ageing

Nat Rev Mol Cell Biol172762000

10.1038/35036093

11413492

Shay

Wright

Role of telomeres and telomerase in cancer

Semin Cancer Biol213493532011

10.1016/j.semcancer.2011.10.001

22015685

Artandi

DePinho

Telomeres and telomerase in cancer

Carcinogenesis319182010

10.1093/carcin/bgp268

19887512

Shay

Bacchetti

A survey of telomerase activity in human cancer

Eur J Cancer337877911997

10.1016/S0959-8049(97)00062-2

9282118

Maciejowski

de Lange

Telomeres in cancer: Tumour suppression and genome instability

Nat Rev Mol Cell Biol181751862017

10.1038/nrm.2016.171

28096526

Robinson

Schiemann

Telomerase in cancer: Function, regulation, and clinical translation

Cancers148082022

10.3390/cancers14030808

35159075

Goldkorn

Telomere and telomerase therapeutics in cancer

Genes7222016

10.3390/genes7060022

27240403

Dratwa

Wysoczańska

Łacina

Kubik

Bogunia-Kubik

TERT-Regulation and roles in cancer formation

Front Immunol115899292020

10.3389/fimmu.2020.589929

33329574

Tsatsakis

Oikonomopoulou

Nikolouzakis

Vakonaki

Tzatzarakis

Flamourakis

Renieri

Fragkiadaki

Iliaki

Bachlitzanaki

Role of telomere length in human carcinogenesis (Review)

Int J Oncol63782023

10.3892/ijo.2023.5526

37232367

López-Otín

Pietrocola

Roiz-Valle

Galluzzi

Kroemer

Meta-hallmarks of aging and cancer

Cell Metab3512352023

10.1016/j.cmet.2022.11.001

36599298

Hanahan

Weinberg

Hallmarks of cancer: The next generation

Cell1446466742011

10.1016/j.cell.2011.02.013

21376230

Okamoto

Seimiya

Revisiting telomere shortening in cancer

Cells81072019

10.3390/cells8020107

30709063

Andreikos

Kyrodimos

Kotsinas

Chrysovergis

Papacharalampous

The Association between telomere length and head and neck cancer risk: A systematic review and Meta-analysis

Int J Mol Sci2590002024

10.3390/ijms25169000

39201686

Karimi

Yunesian

Nabizadeh

Mehdipour

Aghaie

Is leukocyte telomere length related with lung cancer risk?: A Meta-Analysis

Iran Biomed J211421532017

10.18869/acadpub.ibj.21.3.142

27874106

Zhou

Wei

Liu

Pooley

Dunning

Svenson

Roos

Hosgood

IIIShen

Wei

Shortened telomere length is associated with increased risk of cancer: A meta-analysis

PLoS One6e204662011

10.1371/journal.pone.0020466

21695195

Zhu

Han

Xue

Zou

Xie

Jin

The association between telomere length and cancer risk in population studies

Sci Rep6222432016

10.1038/srep22243

26915412

Benites-Zapata

Ulloque-Badaracco

Alarcón-Braga

Fernández-Alonso

López-Baena

Pérez-López

Telomerase activity and telomere length in women with breast cancer or without malignancy: A systematic review and meta-analysis

Maturitas1801078822024

10.1016/j.maturitas.2023.107882

38029511

Naing

Aung

Lai

Mak

Association between telomere length and the risk of colorectal cancer: A meta-analysis of observational studies

BMC Cancer17242017

10.1186/s12885-016-2997-3

28056862

Caini

Raimondi

Johansson

De Giorgi

Zanna

Palli

Gandini

Telomere length and the risk of cutaneous melanoma and non-melanoma skin cancer: A review of the literature and meta-analysis

J Dermatol Sci801681742015

10.1016/j.jdermsci.2015.08.003

26341697

Rode

Nordestgaard

Bojesen

Long telomeres and cancer risk among 95 568 individuals from the general population

Int J Epidemiol45163416432016

10.1093/ije/dyw179

27498151

Fabiani

Chiavarini

Rosignoli

Giacchetta

Leucocyte telomere length and lung cancer risk: A systematic review and Meta-analysis of prospective studies

Cancers (Basel)1632182024

10.3390/cancers16183218

39335189

Chen

Zhou

Cheng

Tong

Chen

Zhang

Telomere-related prognostic biomarkers for survival assessments in pancreatic cancer

Sci Rep13105862023

10.1038/s41598-023-37836-0

37391503

Holesova

Krasnicanova

Saade

Pös

Budis

Gazdarica

Repiska

Szemes

Telomere length changes in cancer: Insights on carcinogenesis and potential for Non-invasive diagnostic strategies

Genes (Basel)147152023

10.3390/genes14030715

36980987

Wentzensen

Mirabello

Pfeiffer

Savage

The association of telomere length and cancer: A meta-analysis

Cancer Epidemiol Biomarkers Prev20123812502011

10.1158/1055-9965.EPI-11-0005

21467229

Page

McKenzie

Bossuyt

Boutron

Hoffmann

Mulrow

Shamseer

Tetzlaff

Akl

Brennan

The PRISMA 2020 statement: An updated guideline for reporting systematic reviews

BMJ372n712021

10.1136/bmj.n71

33782057

Higgins

JPT

Measuring inconsistency in meta-analyses

BMJ3275575602003

10.1136/bmj.327.7414.557

12958120

Green

Higgins

JPT

Cochrane Handbook for Systematic Reviews of Interventions. The Cochrane Collaboration

http://training.cochrane.org/handbook

March202025

DerSimonian

Laird

Meta-analysis in clinical trials

Control Clin Trials71771881986

10.1016/0197-2456(86)90046-2

3802833

Egger

Smith

Schneider

Minder

Bias in meta-analysis detected by a simple, graphical test

BMJ3156296341997

10.1136/bmj.315.7109.629

9310563

Sterne

JAC

Gavaghan

Egger

Publication and related bias in meta-analysis

J Clin Epidemiol53111911292000

10.1016/S0895-4356(00)00242-0

11106885

Wells

Shea

O'Connell

Peterson

Welch

Losos

Tugwell

The Newcastle-Ottawa Scale (NOS) for Assessing the Quality of Nonrandomised Studies in Meta-Analyses. 2013

http://www.ohri.ca/programs/clinical_epidemiology/oxford.asp

March202025

Han

Qureshi

Prescott

Guo

Hunter

De Vivo

A Prospective study of telomere length and the risk of skin cancer

J Invest Dermatol1294154212009

10.1038/jid.2008.238

18668136

Nan

De Vivo

Manson

Liu

McTiernan

Curb

Lessin

Bonner

Guo

Shorter telomeres associate with a reduced risk of melanoma development

Cancer Res71675867632011

10.1158/0008-5472.CAN-11-1988

22028319

Weischer

Short telomere length, cancer survival, and cancer risk in 47102 individuals

J Natl Cancer Inst1054594682013

10.1093/jnci/djt016

23468462

Greenland

Thomas

Morgenstern

The rare-disease assumption revisited

Am J Epidemiol1248698761986

10.1093/oxfordjournals.aje.a114476

3776970

Anic

Sondak

Messina

Fenske

Zager

Cherpelis

Lee

Fulp

Epling-Burnette

Park

Rollison

Telomere length and risk of melanoma, squamous cell carcinoma, and basal cell carcinoma

Cancer Epidemiol374344392013

10.1016/j.canep.2013.02.010

23523330

Rachakonda

Kong

Srinivas

Garcia-Casado

Requena

Fallah

Heidenreich

Planelles

Traves

Schadendorf

Telomere length, telomerase reverse transcriptase promoter mutations, and melanoma risk

Genes Chromosomes Cancer575645722018

10.1002/gcc.22669

30203894

Schneider

Teumer

Rudolph

Hartmann

Rader

Strnad

Association of telomere length with risk of disease and mortality

JAMA Intern Med1822913002022

10.1001/jamainternmed.2021.7804

35040871

Bodelon

Pfeiffer

Bollati

Debbache

Calista

Ghiorzo

Fargnoli

Bianchi-Scarra

Peris

Hoxha

On the interplay of telomeres, nevi and the risk of melanoma

PLoS One7e524662012

10.1371/journal.pone.0052466

23300679

Llorca-Cardeñosa

Peña-Chilet

Mayor

Gomez-Fernandez

Casado

Martin-Gonzalez

Carretero

Lluch

Martinez-Cadenas

Ibarrola-Villava

Ribas

Long telomere length and a TERT-CLPTM1 locus polymorphism association with melanoma risk

Eur J Cancer50316831772014

10.1016/j.ejca.2014.09.017

25457634

Menin

Bojnik

Del Bianco

Elefanti

Gianesin

Keppel

Stagni

Mocellin

Vecchiato

De Rossi

Differences in telomere length between sporadic and familial cutaneous melanoma

Br J Dermatol1759379432016

10.1111/bjd.14652

27061190

Burke

Hyland

Pfeiffer

Prescott

Wheeler

Mirabello

Savage

Burdette

Yeager

Chanock

Telomere length and the risk of cutaneous malignant melanoma in Melanoma-Prone families with and without CDKN2A mutations

PLoS One8e711212013

10.1371/journal.pone.0071121

23990928

Perrem

Lynch

Al Nooh

Leader

Elaine

Kay

The different telomere lengths in basal and squamous cell carcinomas also differ between the nontransplant and renal transplant population

Hum Pathol39103410412008

10.1016/j.humpath.2007.11.008

18482746

Srinivas

Rachakonda

Hielscher

Calderazzo

Rudnai

Gurzau

Koppova

Fletcher

Kumar

Telomere length, arsenic exposure and risk of basal cell carcinoma of skin

Carcinogenesis407157232019

10.1093/carcin/bgz059

30874287

Liang

Qureshi

Guo

De Vivo

Han

No association between telomere length in peripheral blood leukocytes and the risk of nonmelanoma skin cancer

Cancer Epidemiol Biomarkers Prev20104310452011

10.1158/1055-9965.EPI-11-0072

21357377

Wainwright

Rees

Middleton

Changes in mean telomere length in basal cell carcinomas of the skin

Genes Chromosomes Cancer1245491995

10.1002/gcc.2870120108

7534110

Perrem

Lynch

Conneely

Wahlberg

Murphy

Leader

Kay

The higher incidence of squamous cell carcinoma in renal transplant recipients is associated with increased telomere lengths

Hum Pathol383513582007

10.1016/j.humpath.2006.08.019

17134737

De Vitis

Berardinelli

Sgura

Telomere length maintenance in cancer: At the crossroad between telomerase and alternative lengthening of telomeres (ALT)

Int J Mol Sci196062018

10.3390/ijms19020606

29463031

Andreikos

Spandidos

Georgakopoulou

Telomeres and telomerase in mesothelioma: Pathophysiology, biomarkers and emerging therapeutic strategies (Review)

Int J Oncol66232025

10.3892/ijo.2025.5729

39981889

Mooi

Peeper

Oncogene-induced cell Senescence-halting on the road to cancer

N Engl J Med355103710462006

10.1056/NEJMra062285

16957149

Gray-Schopfer

Cheong

Chong

Chow

Moss

Abdel-Malek

Marais

Wynford-Thomas

Bennett

Cellular senescence in naevi and immortalisation in melanoma: A role for p16?

Br J Cancer954965052006

10.1038/sj.bjc.6603283

16880792

Bataille

Kato

Falchi

Gardner

Kimura

Lens

Perks

Valdes

Bennett

Aviv

Spector

Nevus size and number are associated with telomere length and represent potential markers of a decreased senescence in vivo

Cancer Epidemiol Biomarkers Prev16149915022007

10.1158/1055-9965.EPI-07-0152

17627017

Robles-Espinoza

Velasco-Herrera M del

Hayward

Adams

Telomere-regulating genes and the telomere interactome in familial cancers

Mol Cancer Res132112222015

10.1158/1541-7786.MCR-14-0305

25244922

Toussi

Mans

Welborn

Kiuru

Germline mutations predisposing to melanoma

J Cutan Pathol476066162020

10.1111/cup.13689

32249949

Son

Cui

Association between telomere length and skin cancer and aging: A mendelian randomization analysis

Front Genet139317852022

10.3389/fgene.2022.931785

35903361

McNally

Luncsford

Armanios

Long telomeres and cancer risk: The price of cellular immortality

J Clin Invest129347434812019

10.1172/JCI120851

31380804

Stanley

Armanios

The short and long telomere syndromes: Paired paradigms for molecular medicine

Curr Opin Genet Dev33192015

10.1016/j.gde.2015.06.004

26232116

Barrett

ELB

Burke

Hammers

Komdeur

Richardson

Telomere length and dynamics predict mortality in a wild longitudinal study

Mol Ecol222492592013

10.1111/mec.12110

23167566

Speedy

Di Bernardo

Sava

Dyer

MJS

Holroyd

Wang

Sunter

Mansouri

Juliusson

Smedby

A genome-wide association study identifies multiple susceptibility loci for chronic lymphocytic leukemia

Nat Genet4656602014

10.1038/ng.2843

24292274

Rachakonda

Srinivas

Mahmoudpour

Garcia-Casado

Requena

Traves

Soriano

Cardelli

Pjanova

Molven

Telomere length and survival in primary cutaneous melanoma patients

Sci Rep8109472018

10.1038/s41598-018-36657-w

30026606

Martinez-Delgado

Yanowsky

Inglada-Perez

De La Hoya

Caldes

Vega

Blanco

Martin

Gonzalez-Sarmiento

Blasco

Shorter telomere length is associated with increased ovarian cancer risk in both familial and sporadic cases

J Med Genet493413442012

10.1136/jmedgenet-2012-100807

22493152

Martinez-Delgado

Yanowsky

Inglada-Perez

Domingo

Urioste

Osorio

Benitez

Genetic anticipation is associated with telomere shortening in hereditary breast cancer

PLoS Genet7e10021822011

10.1371/journal.pgen.1002182

21829373

Rossi

Pellegrini

Cardelli

Ciciarelli

Di Nardo

Fargnoli

Familial melanoma: Diagnostic and management implications

Dermatol Pract Concept910162019

10.5826/dpc.0901a03

30775140

Zocchi

Lontano

Merli

Dika

Nagore

Quaglino

Puig

Ribero

Familial Melanoma and susceptibility genes: A review of the most common clinical and dermoscopic phenotypic aspect, associated malignancies and practical tips for management

J Clin Med1037602021

10.3390/jcm10163760

34442055

Hansen

MEB

Hunt

Stone

Horvath

Herbig

Ranciaro

Hirbo

Beggs

Reiner

Wilson

Shorter telomere length in Europeans than in Africans due to polygenetic adaptation

Hum Mol Genet25232423302016

10.1093/hmg/ddw070

26936823

Chang

Barrett

Bishop

Armstrong

Bataille

Bergman

Berwick

Bracci

Elwood

Ernstoff

Sun exposure and melanoma risk at different latitudes: A pooled analysis of 5700 cases and 7216 controls

Int J Epidemiol388148302009

10.1093/ije/dyp166

19359257

Armstrong

Kricker

The epidemiology of UV induced skin cancer

J Photochem Photobiol B638182001

10.1016/S1011-1344(01)00198-1

11684447

Lundsgaard

Cramp

Franklin

Early exposure to UV radiation causes telomere shortening and poorer condition later in life

J Exp Biol225jeb2439242022

10.1242/jeb.243924

35950364

Gardner

Bann

Wiley

Cooper

Hardy

Nitsch

Martin-Ruiz

Shiels

Sayer

Barbieri

Gender and telomere length: Systematic review and meta-analysis

Exp Gerontol5115272014

10.1016/j.exger.2013.12.004

24365661

100

Kyo

Takakura

Kanaya

Zhuo

Fujimoto

Nishio

Orimo

Inoue

Estrogen activates telomerase

Cancer Res59591759211999

10606235

101

Ventura

Pellegrini

Cardelli

Rocco

Ciciarelli

Peris

Fargnoli

Telomeres and telomerase in cutaneous squamous cell carcinoma

Int J Mol Sci2013332019

10.3390/ijms20061333

30884806

102

Gilchrest

Eller

Geller

Yaar

The pathogenesis of melanoma induced by ultraviolet radiation

N Engl J Med340134113481999

10.1056/NEJM199904293401707

10219070

103

Yamada-Hishida

Nobeyama

Nakagawa

Correlation of telomere length to malignancy potential in non-melanoma skin cancers

Oncol Lett153933992018

29285197

104

Grimes

Schulz

Bias and causal associations in observational research

Lancet3592482522002

10.1016/S0140-6736(02)07451-2

11812579

105

Glasziou

Sanders

Investigating causes of heterogeneity in systematic reviews

Stat Med21150315112002

10.1002/sim.1183

12111916

106

Montpetit

Alhareeri

Montpetit

Starkweather

Elmore

Filler

Mohanraj

Burton

Menzies

Lyon

Jackson-Cook

Telomere length: A review of methods for measurement

Nurs Res632892992014

10.1097/NNR.0000000000000037

24977726

107

Gutierrez-Rodrigues

Santana-Lemos

Scheucher

Alves-Paiva

Calado

Direct comparison of Flow-FISH and qPCR as diagnostic tests for telomere length measurement in humans

PLoS One9e1137472014

10.1371/journal.pone.0113747

25409313

108

Lin

Bias caused by sampling error in meta-analysis with small sample sizes

PLoS One13e02040562018

10.1371/journal.pone.0204056

30212588

Figure 1.

Preferred Reporting Items for Systematic Reviews and Meta-Analyses flow diagram of the systematic review and meta-analysis.

Figure 1. Preferred Reporting Items for Systematic Reviews and Meta–Analyses flow diagram of the systematic review and meta–analysis.

Figure 2.

Forest plot of a meta-analysis depicting the association between telomere length and melanoma risk using a random-effects model. CI, confidence interval; FM, familial melanoma; SM, sporadic melanoma; NHS, Nurses' Health Study; WHI-OS, Women's Health Initiative Observational Study; HPFS, Health Professionals Follow-up Study.

Figure 2. Forest plot of a meta–analysis depicting the association between telomere length and melanoma risk using a random–effects model. CI, confidence interval; FM, familial melanoma; SM, sporadic ...

Figure 3.

Forest plot of a meta-analysis depicting the relationship between telomere length and basal cell carcinoma risk based on a random-effects model. CI, confidence interval; NHS, Nurses' Health Study.

Figure 3. Forest plot of a meta–analysis depicting the relationship between telomere length and basal cell carcinoma risk based on a random–effects model. CI, confidence interval; NHS, Nurses\' Health ...

Figure 4.

Forest plot of a meta-analysis depicting the association between telomere length and squamous cell carcinoma risk using a random-effects model. CI, confidence interval; HPFS, Health Professionals Follow-up Study.

Figure 4. Forest plot of a meta–analysis depicting the association between telomere length and squamous cell carcinoma risk using a random–effects model. CI, confidence interval; HPFS, Health Professi...

Figure 5.

Forest plot of a subgroup analysis depicting the association between telomere length and melanoma risk stratified by geographic location (USA vs. Europe) using a random-effects model. CI, confidence interval; FM, familial melanoma; SM, sporadic melanoma; WHI-OS, Women's Health Initiative Observational Study; HPFS, Health Professionals Follow-up Study.

Figure 5. Forest plot of a subgroup analysis depicting the association between telomere length and melanoma risk stratified by geographic location ( USA vs. Eur ope) using a random–effects model. CI, ...

Figure 6.

Forest plot of a subgroup analysis depicting the association between telomere length and melanoma risk stratified by sex (female-only vs. mixed-sex studies) using a random-effects model. CI, confidence interval; FM, familial melanoma; SM, sporadic melanoma; WHI-OS, Women's Health Initiative Observational Study.

Figure 6. Forest plot of a subgroup analysis depicting the association between telomere length and melanoma risk stratified by sex (female–only vs. mixed–sex studies) using a random–effects model. CI,...

Figure 7.

Forest plot of a subgroup analysis depicting the association between telomere length and melanoma risk stratified by genetic predisposition (familial vs. general population studies) using a random-effects model. CI, confidence interval; FM, familial melanoma; SM, sporadic melanoma; WHI-OS, Women's Health Initiative Observational Study; HPFS, Health Professionals Follow-up Study.

Figure 7. Forest plot of a subgroup analysis depicting the association between telomere length and melanoma risk stratified by genetic predisposition (fa milial vs. gen eral population studies) using ...

Figure 8.

Forest plot of a subgroup analysis depicting the association between telomere length and melanoma risk stratified by population source (hospital-based vs. population-based studies) using a random-effects model. CI, confidence interval; FM, familial melanoma; SM, sporadic melanoma; WHI-OS, Women's Health Initiative Observational Study; HPFS, Health Professionals Follow-up Study.

Figure 8. Forest plot of a subgroup analysis depicting the association between telomere length and melanoma risk stratified by population source (h ospital–based v s. population–based studies) using a...

Figure 9.

Forest plot of a subgroup analysis depicting the association between telomere length and melanoma risk stratified by adjustment level (melanoma-specific vs. general adjustments) using a random-effects model. CI, confidence interval; FM, familial melanoma; SM, sporadic melanoma; WHI-OS, Women's Health Initiative Observational Study; HPFS, Health Professionals Follow-up Study.

Figure 9. Forest plot of a subgroup analysis depicting the association between telomere length and melanoma risk stratified by adjustment level ( melanoma–specific v s. general adjustments) using a ra...

Figure 10.

Funnel plot of studies included in the melanoma meta-analysis.

Figure 10. Funnel plot of studies included in the melanoma meta–analysis.

Figure 11.

Leave-one-out sensitivity analysis for the association between telomere length and melanoma risk. OR, odds ratio; CI, confidence interval; FM, familial melanoma; SM, sporadic melanoma; NHS, Nurses' Health Study; WHI-OS, Women's Health Initiative Observational Study; HPFS, Health Professionals Follow-up Study.

Figure 11. Leave–one–out sensitivity analysis for the association between telomere length and melanoma risk. OR, odds ratio; CI, confidence interval; FM, familial melanoma; SM, sporadic melanoma; NHS,...

Figure 12.

Telomere dynamics in melanoma tumourigenesis. Schematic diagram illustrating a potential mechanism underlying the protective effect of shorter telomeres in melanoma. Exposure to ultraviolet radiation induces mutations in melanocytes, particularly in individuals with familial melanoma or those harbouring predisposing mutations. Mutated melanocytes with shorter telomeres have a restricted proliferative capacity, limiting their potential for malignancy. Mutated melanocytes with long telomeres exhibit an increased proliferative lifespan, allowing for the accumulation of additional mutations, which may be corollary to melanoma development. Elements of the figure were created using components from Servier Medical Art. Servier Medical Art by Servier is licensed under Creative Commons Attribution 4.0 (https://creativecommons.org/licenses/by/4.0/).

Figure 12. Telomere dynamics in melanoma tumourigenesis. Schematic diagram illustrating a potential mechanism underlying the protective effect of shorter telomeres in melanoma. Exposure to ultraviolet...

Table I.

General characteristics of studies included in the meta-analysis.

First author, year	Country	Type	Population	Ethnicity	Male cases	Male controls	Mean age of cases, years	Mean age of controls, years	(Refs.)
Han, 2009	USA	Melanoma	NHS	European	0%	0%	63.3	64.5	(62)
Han, 2009	USA	SCC	NHS	European	0%	0%	64.7	64.5	(62)
Nan, 2011	USA	Melanoma	WHI-OS	European	0%	0%	68.2	63.9	(63)
Nan, 2011	USA	Melanoma	HPFS	European	100%	100%	65.3	59.9	(63)
Liang, 2011	USA	SCC	HPFS	Caucasian	100%	100%	69	63.1	(75)
Liang, 2011	USA	BCC	NHS	Caucasian	0%	0%	66.1	56.3	(75)
Liang, 2011	USA	BCC	NHS	Caucasian	0%	0%	69.3	61	(75)
Bodelon, 2012	Italy	Melanoma	Hospital-based	Mediterranean Caucasian	Mixed-not specified	Mixed-not specified	Not specified	Not specified	(69)
Anic, 2013	USA	Melanoma	Hospital-based	European	57%	35%	58.6	56.3	(66)
Anic, 2013	USA	SCC	Hospital-based	European	67%	35%	64.8	56.3	(66)
Anic, 2013	USA	BCC	Hospital-based	European	60%	35%	63	56.3	(66)
Burke, 2013	USA	Melanoma	Hospital-based	Caucasian	50.40%	41.80%	Not specified	Not specified	(72)
Llorca-Cardeñosa, 2014	Spain	Melanoma	Hospital-based	Caucasian (Spanish origin)	45.40%	42.90%	53	48	(70)
Menin, 2016	Italy	Familial melanoma	Hospital-based	Caucasian	45.00%	43.50%	54.2	53.8	(71)
Menin, 2016	Italy	Sporadic melanoma	Hospital-based	Caucasian	42.00%	43.50%	53.5	53.8	(71)
Rode, 2016	Denmark	Melanoma	CGPS & CCHS/Population	Danish (European)	Mixed-Not specified	Mixed-Not specified	Not specified	Not specified	(50)
Rachakonda, 2018	Germany	Melanoma	Hospital-based	Caucasian	0.405	0.448	54	44	(67)
Srinivas, 2019	Hungary/Romania/Slovakia	BCC	Hospital-based	Central/Eastern European	44.70%	51.40%	67	61	(74)
Schneider, 2022	UK	Melanoma	UK Biobank/Population	Predominantly Caucasian	Mixed-not specified	46%	Not specified	57	(68)

BCC, basal cell carcinoma; SCC, squamous cell carcinoma; NHS, Nurses' Health Study; WHI-OS, Women's Health Initiative Observational Study; HPFS, Health Professionals Follow-up Study; CCHS, Copenhagen City Heart Study; CGPS, Copenhagen General Population Study.

Table II.

Summary of study characteristics and associated risk estimates across studies included in the meta-analysis.

First author, year	Study design	Cases/controls	Measurement method	DNA source	Relative risk (95% CI)	TL categorization	Factors adjusted	(Refs.)
Han, 2009 Melanoma (NHS)	Nested case-control	218/870	RTL (T/S ratio) qPCR	PBLs	0.62 (0.31–1.23)	RTL Q4 (longest)/Q1 (shortest)	Age, number of moles	(62)
Han, 2009 SCC (NHS)	Nested case-control	285/870	RTL (T/S ratio) qPCR	PBLs	0.72 (0.33–1.55)	RTL Q4 (longest)/Q1 (shortest)	Age, number of moles	(62)
Nan, 2011 Melanoma (WHI-OS)	Nested case-control	233/237	RTL (T/S ratio) qPCR	PBLs	0.40 (0.18–0.88)	RTL Q4 (longest)/Q1 (shortest)	Age	(63)
Nan, 2011 Melanoma (HPFS)	Nested case-control	120/120	RTL (T/S ratio) qPCR	PBLs	0.18 (0.06–0.56)	RTL Q4 (longest)/Q1 (shortest)	Age	(63)
Liang, 2011 SCC (HPFS)	Nested case-control	241/241	RTL (T/S ratio) qPCR	PBLs	1.09 (0.62–1.93)	RTL Q4 (longest)/Q1 (shortest)	Age	(75)
Liang, 2011 BCC (NHS 1)	Nested case-control	260/260	RTL (T/S ratio) qPCR	PBLs	0.96 (0.49–1.87)	RTL Q4 (longest)/Q1 (shortest)	Age	(75)
Liang, 2011 BCC (NHS 2)	Nested case-control	363/1683	RTL (T/S ratio) qPCR	PBLs	0.91 (0.66–1.25)	RTL Q4 (longest)/Q1 (shortest)	Age	(75)
Bodelon, 2012 Melanoma	Case-control	172/196	RTL (T/S ratio) qPCR	PBLs	0.87 (0.46–1.64)	RTL as a continuous variable	Age, sex	(69)
Anic, 2013 Melanoma	Case-control	198/372	RTL (T/S ratio) qPCR	PBLs	0.22 (0.13–0.38)	RTL Q3 (longest)/Q1 (shortest)	Age, sex	(66)
Anic, 2013 SCC	Case-control	136/372	RTL (T/S ratio) qPCR	PBLs	25.00 (11.11–50.00)	RTL Q3 (longest)/Q1 (shortest)	Age, sex	(66)
Anic, 2013 BCC	Case-control	185/372	RTL (T/S ratio) qPCR	PBLs	10.00 (5.26–16.67)	RTL Q3 (longest)/Q1 (shortest)	Age, sex	(66)
Burke, 2013 Melanoma	Case-control	119/208	RTL (T/S ratio) qPCR	PBLs	0.36 (0.13–0.98)	RTL Q3 (longest)/Q1 (shortest)	Age, sex, DNA source, CDKN2A status, number of moles, solar injury, MC1R genotype	(72)
Llorca-Cardeñosa, 2014 Melanoma	Case-control	406/406	RTL (T/S ratio) qPCR	PBLs	0.04 (0.01–0.10)	RTL Q4 (longest)/Q1 (shortest)	Age	(70)
Menin, 2016 FM	Case-control	109/216	RTL (T/S ratio) qPCR	PBLs	0.46 (0.23–0.95)	RTL Q4 (longest)/Q1 (shortest)	Age, sex	(71)
Menin, 2016 SM	Case-control	201/216	RTL (T/S ratio) qPCR	PBLs	1.74 (1.00–3.04)	RTL Q4 (longest)/Q1 (shortest)	Age, sex	(71)
Rode, 2016 Melanoma	Cohort study	289/64750	RTL (T/S ratio) qPCR	PBLs	0.98 (0.95–1.01)	RTL as a continuous variable, per 200 base-pairs decrease	Age, sex, body mass index, smoking status, tobacco consumption, alcohol intake	(50)
Rachakonda, 2018 Melanoma	Case-control	1275/904	RTL (T/S ratio) qPCR	PBLs	0.41 (0.33–0.52)	RTL as continuous variable	Age, sex, skin phototype, hair colour, eye colour, sun burns, outdoor occupation, number of nevi	(67)
Srinivas, 2019 BCC	Case-control	506/513	RTL (T/S ratio) qPCR	PBLs	4.09 (2.86–5.85)	RTL Q3 (longest)/Q1 (shortest)	Age, sex, country, arsenic exposure, skin complexion, skin response, MC1R genotype, XRCC3 rs861539	(74)
Schneider, 2022 Melanoma	Cohort study	NR/472 432	RTL (T/S ratio) qPCR	PBLs	0.80 (0.71–0.89)	RTL as a continuous variable, decreased telomere length (per SD)	Age, sex, body mass index, ethnicity, current smoking, cumulative smoking, alcohol intake, Charlson Comorbidity Index	(68)

TL, telomere length; RR, relative risk; CI, confidence interval; qPCR, quantitative polymerase chain reaction; RTL, relative telomere length; T/S ratio, telomere repeat copy number/single-copy gene copy number; FM, familial melanoma; SM, sporadic melanoma; BCC, basal cell carcinoma; SCC, squamous cell carcinoma; NHS, Nurses' Health Study; WHI-OS, Women's Health Initiative Observational Study; HPFS, Health Professionals Follow-up Study; PBLs, peripheral blood leukocytes.

Table III.

General characteristics and findings of studies included in the systematic review but not in the meta-analysis.

First author, year	Country	Type	Cases/controls	Measurement method	DNA source	Findings	(Refs.)
Wainwright, 1995	UK	BCC	20/19	Southern blotting	Fresh/frozen skin tissue	Telomeres in 13/20 BCC cases were longer than in the adjacent epidermis (no measure of significance reported)	(76)
Perrem, 2007	Ireland	SCC	44/10 (RTR)	TEL-FISH	Archival tissue samples	Telomeres in SCC tissue from RTRs were significantly shorter than those in normal skin from RTRs (P=0.0001)	(77)
Perrem, 2007	Ireland	SCC	44/10	TEL-FISH	Archival tissue samples	Telomeres in SCC tissue from non-RTRs were significantly shorter than those in normal skin (P=0.0001)	(77)
Perrem, 2008	Ireland	BCC	35/10 (RTR)	TEL-FISH	Archival tissue samples	Telomeres in BCC tissue from RTRs were significantly shorter than those in normal skin (P=0.0130)	(73)
Perrem, 2008	Ireland	BCC	35/10	TEL-FISH	Archival tissue samples	Telomeres in BCC were shorter than those in normal skin, but the observation did not reach statistical significance (P=0.2530)	(73)

BCC, basal cell carcinoma; SCC, squamous cell carcinoma; TEL-FISH, telomere fluorescence in situ hybridization; RTR, renal transplant recipient.