Open Access

Aging‑associated genes TNFRSF12A and CHI3L1 contribute to thyroid cancer: An evidence for the involvement of hypoxia as a driver

  • Authors:
    • Meng Lian
    • Hongbao Cao
    • Ancha Baranova
    • Kamil Can Kural
    • Lizhen Hou
    • Shizhi He
    • Qing Shao
    • Jugao Fang
  • View Affiliations

  • Published online on: April 10, 2020     https://doi.org/10.3892/ol.2020.11530
  • Pages: 3634-3642
  • Copyright: © Lian et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Abstract

The prevalence of thyroid cancer (TC) is high in the elderly. The present study was based on the hypothesis that genes, which have increased activity with aging, may play a role in the development of TC. A large‑scale literature‑based data analysis was conducted to explore the genes that are implicated in both TC and aging. Subsequently, a mega‑analysis of 16 RNA expression datasets (1,222 samples: 439 healthy controls, and 783 patients with TC) was conducted to test a set of genes associated with aging but not TC. To uncover a possible link between these genes and TC, a functional pathway analysis was conducted, and the results were validated by analysis of gene co‑expression. A multiple linear regression (MLR) model was employed to study the possible influence of sample size, population region and study age on the gene expression levels in TC. A total of 262 and 816 genes were identified to have increased activity with aging and TC, respectively; with a significant overlap of 63 genes (P<3.82x10‑35). The mega‑analysis revealed two aging‑associated genes (CHI3L1 and TNFRSF12A) to be significantly associated with TC (P<2.05x10‑8), and identified the association with multiple hypoxia‑driven pathways through functional pathway analysis, also confirmed by the co‑expression analysis. The MLR analysis identified population region as a significant factor contributing to the expression levels of CHI3L1 and TNFRSF12A in TC samples (P<3.24x10‑4). The determination of genes that promote aging was warranted due to their possible involvement in TC. The present study suggests CHI3L1 and TNFRSF12A as novel common risk genes associated with both aging and TC.

Introduction

Aging is a fundamental biological process accompanied by alterations in the regulatory activities performed by the endocrine system, including the thyroid gland (1). On the other hand, with aging, the prevalence of hypo- and hyperthyroidism increases (2). Both hypo- and hyperpituitarism are believed to affect longevity (3). Defining a physiological norm for the thyroid hormones is complicated in the elderly by the gradual resetting of the hypothalamic-pituitary-thyroid axis, which leads to increased levels of thyroid stimulating hormone (4).

The prevalence of thyroid neoplasms is increased in the elderly; these tumors are more aggressive in men than in women (5). The mortality rate of thyroid cancer (TC) gradually increases with age, from the ages of 40–45 (6). Notably, young survivors of TC have increased risks for other aging-related diseases, including diabetes, disorders of lipid metabolism, eye disorders, ear conditions and diseases of the musculoskeletal system and connective tissue (7). A majority of TC arise from the epithelial elements of the gland, including thyrocytes and follicular cells. The classification of thyroid tumors into two major groups, differentiated (including papillary, follicular, and medullary) or undifferentiated (anaplastic) carcinoma, based on clinical features and morphology was supported by advances in molecular studies (8).

Importantly, a recent study on DNA methylation and histone modification patterns in >2,000 tumors collected from patients of various ages revealed that most tumor types did not demonstrate age-associated changes in DNA methylation (9), which is in agreement with the theory that the epigenetic clock in cancer cells is reprogrammed (9). Remarkably, this theory does not hold for thyroid tumors, which displayed age-associated differential methylation of CpGs (9). In fact, age at diagnosis serves as a strong indicator of prognosis in TC, particularly in well-differentiated tumors (10,11), with an order of magnitude difference in hazard of death from cancer between the youngest and oldest cohorts. It is also of note that thyroid tissue displays the lowest level of DNA methylation changes in cancer and one of the lowest with aging (9).

Overall, previous studies indicate the presence of an association between thyroid carcinogenesis and aging. The present study explored whether genes that promote aging may also play a role in the development of TC, Therefore, besides the identification of previously identified common genes, those that are known to be associated with aging but not with TC are worthy of further study. A mega-analysis of thyroid cancer datasets obtained from Gene Expression Omnibus (GEO) identified two genes, TNFRSF12A and CHI3L1, as likely contributors to both the process of aging and the thyroid carcinogenesis.

Materials and methods

Study workflow

The workflow was organized as follows. Firstly, the large-scale literature-based mining effort for thyroid cancer (TC)- and aging-associated gene sets was undertaken; these gene sets were compared. For each gene from the list implicated in aging alone, a mega-analysis was conducted in 16 publicly available expression datasets retrieved from GEO. For genes that showed significant change in expression across analyzed datasets, functional pathway analysis and protein-protein interaction (PPI) by co-expression analysis were conducted, then conclusions on their pathogenic significance in TC were made.

Literature-based relation data

Relation data for both aging and TC were extracted and analyzed using Pathway Studio (www.pathwaystudio.com), and the results were downloaded into a genetic database Aging_TC, hosted at http://database.gousinfo.com. The downloadable format of the database was available at http://gousinfo.com/database/Data_Genetic/Aging_TC.xlsx, with different section of the results presented in different worksheets (e.g., age-specific genes were presented in workseheet ‘Aging_alone genes’). In this study, we refered to these worksheets in the form of Aging_TC→‘worksheet name’. Beside the list of analyzed genes (Aging_TC→Aging_alone genes, Aging_TC→TC_alone genes, and Aging_TC→Common genes), supporting references for each disease-gene relation are presented (Aging_TC→Ref for Aging_alone genes, Aging_TC→Ref for TC_alone genes, and Aging_TC→Ref for Common genes), including titles of the references and the sentences describing identified disease-gene relationships. The information could be used to locate a detailed description of an association of a candidate gene with aging and/or TC. Please refer to Aging_TC→DataNote for the decryption of each worksheet.

Data selection for mega-analysis

The relevant expression datasets available at GEO (https://www.ncbi.nlm.nih.gov/geo/) were retrieved with the keyword ‘thyroid cancer’ (n=91). The following criteria were applied: i) Organism, Homo sapiens; ii) data type, RNA expression; iii) sample size, ≥10; and iv) the studies were performed according to case-control design. A total of 16 datasets remained available for the mega-analysis.

Mega-analysis models

To discern the effect sizes of the selected genes in a case vs. control expression comparison, both fixed-effect and random-effects models were employed. The expression log fold change (LFC) was used as the effect size. The results derived from both models were compared. In order to assess the variance within and between different studies, the heterogeneity of the mega-analysis was analyzed. In the case that total variance Q was equal to or smaller than the expected between-study variance df, the statistic ISq=100% × (Q-df)/Q was set as 0, and a fixed-effect model was selected for the mega-analysis. Otherwise, a random-effects model was selected. The genes with significant effects were identified according to the following criteria: P<1×10−7 and effect size (LFC) >1 or <-1.

Multiple linear regression analysis

A multiple linear regression (MLR) model was employed to study the possible influence of three factors on the changes in gene expression in TC: Sample size, population region, and study age. P-values and 95% confidence interval (CI) were reported for each of the factors.

Pathway analysis

For the set of genes identified through expression mega-analysis described above, a functional pathway analysis was conducted with an aim to identify potential biological associations between the selected genes and the TC. The analysis was performed using the ‘Shortest Path’ module of Pathway Studio (www.pathwaystudio.com).

Co-expression analysis

For each pair of the genes/proteins identified in the pathway analysis, a study of their co-expression was executed. The purpose of this analysis was to identify possible protein-protein interaction (PPI) of the proteins involved. The Fisher's Z was employed as the effect size for the mega-analysis, as shown in the equation below:

FisherZ=0.5xlog(1+correlation1-correlation)

All co-expression associations with P-value <1×10−4 and Fisher's Z-value ≥0.3 or ≤-0.3 were identified as significant, and then presented as a Cytoscape-imaged PPI network.

Results

Genes commonly affected by the process of aging and by the carcinogenesis in the thyroid gland

The curated Aging_TC database identified 262 genes with substantially increased expression or activity with aging (supported by 1,495 scientific references) and 816 genes associated with the pathogenesis of TC (supported by 4,169 references). A total of 63 genes were identified to be involved in both aging and TC (right tail Fisher's exact test, P=3.82×10−35; Fig. S1), which accounts for about a quarter of the genes associated with aging (24.05%). The descriptions of these 63 genes are presented in Table I. Further information on these genes was also provided in Aging_TC→Common genes and Aging_TC→Ref for common genes.

Table I.

Common genes associated with aging and thyroid cancer.

Table I.

Common genes associated with aging and thyroid cancer.

NameEntrez Gene IDHuman chromosome position
SERPINC1 462;304917;119051q25.1
IL17A 16171;301289;36056p12.2;6p12
EDN1 1906;24323;136146p24.1
LCN2 16819;170496;39349q34.11;9q34
PTGS2 19225;29527;5743 1q31.1;1q25.2-q25.3
HLA-G 24747;14991;31356p21.3;6p22.1
CSF1 1435;12977;789651p13.3
MIF 4282;17319;8168322q11.23
PTH 19226;24694;5741 11p15.3;11p15.3-p15.1
CYP27B1 1594;13115;11470012q14.1
CYP24A1 25279;1591;1308120q13;20q13.2
HUWE159026;10075Xp11.22
EIF2AK2 5610;54287;19106 2p22-p21;2p22.2
PDGFRB 5159;24629;185965q32;5q33.1
APP 54226;351;1182021q21.3
HYOU1 10525;12282;192235 11q23.1-q23.3;11q23.3
EGF 25313;1950;136454q25
TNF 24835;21926;71246p21.3;6p21.33
HMOX1 15368;3162;24451 22q12.3;22q13.1
MIR2140699117q23.1
MMP9 81687;17395;4318 20q11.2-q13.1;20q13.12
FAS 246097;14102;355 10q23.31;10q24.1
DPP4 25253;1803;134822q24.3;2q24.2
KLK3 18048;18050;354;13648;16619;16618;16613;16624;16612;16623;16622;13646;16617;16616;16615 19q13.33;19q13.41
PROS1 19128;81750;56273q11.2;3q11.1
FASLG 14103;356;253851q23;1q24.3
GPX3 2878;64317;147785q23;5q33.1
ALB 11657;213;241864q13.3
DCN 1634;13179;2913912q21.33
B3GAT1 27087;76898;117108;96411q25
ADIPOQ 9370;246253;114503q27;3q27.3
ARG2 11847;29215;38414q24.1
RUNX3 12399;156726;8641p36.11;1p36
PRDX1 5052;18477;100363379;1172541p34.1
HTRA1 5654;65164;56213 10q26.13;10q26.3
RCAN154720;182721q22.12
CTNNB1 84353;12387;14993p21;3p22.1
BAG3 9531;29810;293524 10q25.2-q26.2;10q26.11
TGFB1 21803;7040;59086 19q13.1;19q13.2
TGFA 24827;21802;70392p13;2p13.3
CTSD 1509;171293;1303311p15.5
MUC14169;45821q21;1q22
TRAP1 68015;10131;28706916p13.3
CDKN1A 12575;114851;10266p21.2
CCNA2 12428;114494;8904q27
CCR2 1231;12772;7292303p21.31
IL21 59067;365769;605054q26-q27;4q27
BAX 12028;24887;581 19q13.33;19q13.3-q13.4
IL4 287287;3565;161895q31.1
ENO1 2023;24333;433182;138061p36.23;1p36.2
HGF 3082;15234;244467q21.11;7q21.1
TNFSF10 246775;22035;87433q26.31;3q26
IL6 24498;3569;161937p15.3;7p21
THRA 7067;81812;21833 17q11.2;17q21.1
ANGPT2 89805;11601;2858p23.1
EP300 170915;2033;32857222q13.2
GSTM1 2944;14863;244241p13.3
CCL2 287562;6347;20293 17q12;17q11.2-q12
TXNIP 117514;56338;106281q21.1
GSTT1295222q11.23
POSTN 50706;361945;1063113q13.3
NQO1 18104;1728;2431416q22.1
IL10 16153;25325;3586 1q31-q32;1q32.1

In order to assess the functional profile of the 63 genes associated with both aging and TC, a gene set enrichment analysis (GSEA) against the GO and Pathway Studio Ontology was conducted. The GSEA showed that these common genes were mainly involved in the protein kinase domain, cell proliferation, tissue development, gland development and response to hormone processes. Specifically, this analysis uncovered a total of three pathways/gene sets associated with cell apoptosis (26 unique genes) and 2 pathways/gene sets associated with cell growth proliferation (25 unique genes). A bar plot of the 39 pathways and enriched genes out of the 63 common genes are presented in Fig. S2.

Gene expression analysis result

Although there was a significant overlap between aging- and TC-associated gene sets (n=63; P=3.82×10−35), a majority of the aging-associated genes (n=199 or 75.95%) have not been yet implicated in the pathogenesis of TC. Therefore, the association between each of these 199 genes with TC was examined, using 16 gene expression datasets shown in Table II (1226). The detailed description of the results is presented in Aging_TC→Mega-analysis. The significance of association criteria (P<1×10−7 and absolute LFC>1) was met by two genes and presented in Table III.

Table II.

Datasets used for thyroid cancer-aging relation mega-analysis.

Table II.

Datasets used for thyroid cancer-aging relation mega-analysis.

StudyDataset GEO IDControl, nCase, nCountryRefs.
Jarzab et al, 2015GSE355705165Poland(12)
Rusinek et al, 2015GSE585451827Poland(13)
Swierniak et al, 2015GSE586891827Poland(13)
Tarabichi et al, 2015GSE605423433Belgium(14)
von Roemeling et al, 2015GSE651441312USA(15)
Versteyhe et al, 2013GSE391561648Belgium(16)
Pita et al, 2013GSE53157324Portugal(17)
Tomas et al, 2012GSE292652029BelgiumN/A
Tomas et al, 2012GSE336304560Belgium(18,19)
Giordano et al, 2011GSE27155495USA(20,21)
Yu et al, 2008GSE536458270Singapore(22)
Fontaine et al, 2007GSE633913548France(23)
Salvatore et al, 2007GSE9115415USA(24)
Reyes et al, 2006GSE367877USAN/A
Vasko et al, 2006GSE6004414USA(25)
Liyanarachchi et al, 2005GSE346799USA(26)

[i] N/A, not applicable.

Table III.

Significant genes from mega-analysis [P<1×10−7 and abs(LFC)>1] involved in aging and thyroid cancer.

Table III.

Significant genes from mega-analysis [P<1×10−7 and abs(LFC)>1] involved in aging and thyroid cancer.

Mega-analysis resultsMultiple linear regression analysis for three factors


Gene nameRandom effects modelDatasets includedLFCSTH of LFCP-valueSample sizePopulation regionYear of study
CHI3L10162.880.42 5.10×10−120.24 2.77×10−40.15
TNFRSF12A1141.790.33 2.05×10−80.35 3.24×10−40.56

[i] LFC, log fold change. STD, standard deviation.

For each gene, a LFC was estimated from the majority of the 16 studies (a total of 16 and 14 studies for CHI3L1 and TNFRSF12A, respectively). As shown in Fig. 1, the mRNA expression levels of TNFRSF12A demonstrated strong between-study variances (ISq=54.07% and Q test P=8.2×10−3); therefore, the random-effects model was selected for the mega-analysis. In contrast, no significant between-study variance was observed for the gene CHI3L1 (ISq=14.73% and Q test P>0.28); therefore, the fixed-effect model was selected for analysis of its mRNA expression levels. This identified the sample population region (country) as a significant factor that influences the LFC of both genes in the case of TC (P<3.24×10−4; Table III).

Functional pathway analysis

According to the de novo approach selected for the identification of novel TC-associated genes, no prior direct link to the pathogenesis of TC were known. However, Pathway Studio-guided ‘shortest path analysis’ revealed plausible associations of CHI3L1 and TNFRSF12A genes with TC, with a set of common interactions (Fig. 2A).

Using Pathway Studio, the ‘shortest path’ analysis was conducted to identify associated genes that link CHI3L1 and TNFRSF12A-encoded molecules to the pathogenesis of TC in a unidirectional way. For example, the association between CHI3L1 and transforming growth factor β1 (TGFB1) in TC was identified. YKL-40, also known as chitinase-3-like protein 1 (CHI3L1), is a secreted glycoprotein that binds to interleukin-13 receptor α2 (IL-13Rα2) and subsequently stimulates the production of TGF-β1 (27), a key molecule in thyroid carcinogenesis and considered as a new prognostic and therapeutic target for TC (28,29). The details for all other associations are presented in Fig. 2A, and are described in Aging_TC→TC-2Genes_potential pathways. This reference information included the type of association, the amount of underlying supporting references, and the relevant sentences where these relationships were identified and described. The shortest pathway analysis was conducted using the Pathway Studio (www.pathwaystudio.com). All TC-associated genes reported in the shortest pathway analysis were employed in the identification of the TC-associated genes.

In order to confirm the associations depicted in Fig. 2A, another mega-analysis was conducted using 16 datasets, with the purpose to evaluate the co-expression pattern of mRNAs encoded by CHI3L1 and TNFRSF12A, and 31 genes that link the two genes with TC. The association between CHI3L1 and TNFRSF12A and 13 of the 31 genes presented in Fig. 2A was validated (P<0.005; Fig. 2B). Please refer to Aging_TC→MetaResults_ShortestPath for the detailed description of the associations presented in Fig. 2B. In addition, a PPI network connecting the products of CHI3L1 and TNFRSF12A and the ‘bridge’ genes (n=31) were generated using Cytoscape (Fig. 3). The detailed information of the mega-analysis for the co-expression was presented in Aging-TC→PPI, including a list of the genes (nodes) of the PPI network, and the mega-analysis results for each pair of gene-gene correlation.

Discussion

The present study aimed to identify novel molecular pathways, which connect the process of aging and the development of TC. By removing all known associations between curated sets of genes involved in aging and TC, uncovered aging-associated contributors to TC were identified. According to the pre-selected significance of association criteria (P<1×10−7 and abs(LFC)>1), two aging-associated genes, TNFRSF12A and CHI3L1, were found to be involved in the development of TC.

The role of TNFRSF12A and CHI3L1 in the aging-associated disease is well described, whereas no apparent connections to the malignant processes in the thyroid were reported thus far. TNFRSF12A encodes for an exclusive receptor for tumor necrosis factor-related weak inducer of apoptosis (TWEAK), an interacting pair of molecules involved in age-associated pathological changes in skeletal muscle and other organs (3032). CHI3L1 encodes for YKL-40, a glycoprotein upregulated in a variety of inflammatory conditions commonly found in the elderly, including chronic obstructive pulmonary disease and neurodegenerative diseases (33,34).

Employing a Pathway Studio-guided ‘shortest path analysis’ revealed plausible associations of TNFRSF12A and CHI3L1 with TC, simultaneously highlighting a set of common interactors that included the signaling molecules AKT1, RAC1, MAPK1 and the soluble proteins TNF-α, albumin, TIMP1 and MMP9.

Among these, the association of TNFRSF12A and CHI3L1 with AKT1 was notable, as this molecule was both overexpressed and over-activated in TC (35). Moreover, increased Akt signaling led to increased Bcl-2 promoter activity and cell survival (36). The interaction between TNFRSF12A and TWEAK also positively regulated pro-survival molecules of the Bcl2 family (37,38), and, therefore augmented the pro-survival signal of AKT1 (39).

Among the soluble molecules, angiogenic matrix metalloproteinase (MMP)9 was found to be associated with TNFRSF12A and CHI3L1, which participates in follicular TC cell invasion (40). TIMP-1, acts as an inhibitor for MMP9 expression that is often co-expressed with this molecule in thyroid tumors and serves as a reliable surrogate marker for BRAF-mutated status and likely aggressiveness (41). Notably, TIMP−1 serves as a prominent PPI hub gene in the Cytoscape network built upon co-expression of TNFRSF12A and CHI3L1, along with LGALS3, a specific biomarker of well-differentiated thyroid carcinomas (42), CXCL16, which mediated the involvement of macrophages in the invasion of papillary TCs (43) and fibronectin, an EMT biomarker which promoted migration and invasion of papillary thyroid cancers (44).

Importantly, many of the molecules discussed above were upregulated in response to hypoxia (45), or stimulated the expression of key mediators of the antihypoxic response (46), or both (47,48). Both HIF-1α and HIF-2α were reported to be expressed in TC (49). Moreover, a growing evidence points out that hypoxia plays a significant role in the maintenance of thyroid cancer stem cells (CSC) (50). General hypoxia due to diminished vascularization is a well-known characteristic of aging tissues, possibly due to endothelial dysfunction as well decreased function of endothelial progenitor cell function (51,52). For more information regarding the hypoxia-gene regulation presented in Fig. 2A, please refer to Aging_TC→TC_2Genes_potential pathway. Therefore, it can be speculated that the age-dependent increase in hypoxia may also contribute to the increased aggressiveness of TC observed in the elderly (10,11).

The results could be influenced by multiple factors, including sample size and sample source. Considering the fact that the pathology of disease could change with time, the publication date/age was checked in the present study. The MLR analysis showed that country region was a significant factor that could influence the gene expression levels of TC genes, but not the other two factors. Other influencing factors of TC gene activities could include mortality or epigenetics. However, due to be the limitation of the metadata, these factors were not amiable for analysis in the datasets employed in the present study, which would be valuable for future studies.

In conclusion, the present study conducted a functional pathway and co-expression analysis, and mined a set of genes associated with aging. TNFRSF12A and CHI3L1 were identified as previously unrecognized contributors to the development of thyroid tumors, which are known for unusual increase in aggressiveness in the elderly. An analysis of the Cytoscape network built upon co-expression of TNFRSF12A and CHI3L1 points towards tissue hypoxia as a bridging factor, which is common for the pathophysiology of aging and the development of TC.

Supplementary Material

Supporting Data

Acknowledgements

Not applicable.

Funding

No funding was received.

Availability of data and material

All data generated or analyzed during this study are included in this published article.

Authors' contributions

ML, HC, AB and QS developed the study design, analyzed the data, and wrote the original manuscript. KCK, LH, SH and JF contributed to data analysis and manuscript writing and revision. All authors read and approved the final version of the manuscript.

Ethics approval and consent to participate

Not applicable.

Patient consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

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June-2020
Volume 19 Issue 6

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Online ISSN:1792-1082

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Spandidos Publications style
Lian M, Cao H, Baranova A, Kural KC, Hou L, He S, Shao Q and Fang J: Aging‑associated genes TNFRSF12A and CHI3L1 contribute to thyroid cancer: An evidence for the involvement of hypoxia as a driver. Oncol Lett 19: 3634-3642, 2020
APA
Lian, M., Cao, H., Baranova, A., Kural, K.C., Hou, L., He, S. ... Fang, J. (2020). Aging‑associated genes TNFRSF12A and CHI3L1 contribute to thyroid cancer: An evidence for the involvement of hypoxia as a driver. Oncology Letters, 19, 3634-3642. https://doi.org/10.3892/ol.2020.11530
MLA
Lian, M., Cao, H., Baranova, A., Kural, K. C., Hou, L., He, S., Shao, Q., Fang, J."Aging‑associated genes TNFRSF12A and CHI3L1 contribute to thyroid cancer: An evidence for the involvement of hypoxia as a driver". Oncology Letters 19.6 (2020): 3634-3642.
Chicago
Lian, M., Cao, H., Baranova, A., Kural, K. C., Hou, L., He, S., Shao, Q., Fang, J."Aging‑associated genes TNFRSF12A and CHI3L1 contribute to thyroid cancer: An evidence for the involvement of hypoxia as a driver". Oncology Letters 19, no. 6 (2020): 3634-3642. https://doi.org/10.3892/ol.2020.11530