Predictive relevance of miR‑34a, miR‑224 and miR‑342 in patients with advanced squamous cell carcinoma of the lung undergoing palliative chemotherapy

  • Authors:
    • Vlastimil Kulda
    • Martin Svaton
    • Petr Mukensnabl
    • Kristyna Hrda
    • Pavel Dvorak
    • Zbynek Houdek
    • Katerina Houfkova
    • Radana Vrzakova
    • Vaclav Babuska
    • Milos Pesek
    • Martin Pesta
  • View Affiliations

  • Published online on: November 2, 2017     https://doi.org/10.3892/ol.2017.7337
  • Pages: 592-599
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Abstract

Attributing to their pathophysiological role and stability in biological samples, microRNAs (miRNAs) have the potential to become valuable predictive markers for non‑small cell lung cancer (NSCLC). Samples of biopsy tissue constitute suitable material for miRNA profiling with the aim of predicting the effect of palliative chemotherapy. The present study group included 81 patients (74 males, 7 females, all smokers or former smokers) with the squamous cell carcinoma (SCC) histological subtype of NSCLC at a late stage (3B or 4). All patients received palliative chemotherapy based on platinum derivatives in combination with paclitaxel or gemcitabine. The expression of 17 selected miRNAs was measured by reverse transcription‑quantitative polymerase chain reaction in tumor tissue macrodissected from formalin‑fixed paraffin‑embedded (FFPE) tissue samples. To predict the effect of palliative chemotherapy, the association between gene expression levels and overall survival (OS) time was analyzed. From the 17 miRNAs of interest, low expression levels of miR‑342 and high expression levels of miR‑34a and miR‑224 were associated with a reduced OS time in subgroups of patients based on smoking status and treatment modality. Using cluster analysis, associations between combinations of miR‑34a, ‑224 and ‑342 expression levels with patient survival were identified. The present study revealed that patients with the simultaneous high expression of miR‑224 and ‑342 had a similar prognostic outcome to those with the low expression of miR‑224 and ‑342, which was significantly reduced, compared with patients exhibiting high expression of either miR‑224 or miR‑342 with low expression of the other. We hypothesize that the effect of a particular miRNA is dependent on the expression level of other members of the miRNA network. This finding appears to complicate survival analyses based on individual miRNAs as markers. In conclusion, the present study provides evidence that specific miRNAs were associated with OS time, which may be candidate predictors for the effectiveness of palliative treatment in SCC lung cancer patients. This objective can be better achieved by combining more markers together than by using individual miRNAs.

Introduction

Lung cancer is the most common type of cancer, with high mortality rates worldwide (1); the incidence in the Czech Republic was 86.9 cases in men and 38.0 in women per 100,000 people in 2011 (2). Approximately 85% of all lung cancer cases are non-small cell lung cancer (NSCLC), which includes two major histological subtypes: Squamous cell carcinoma (SCC) and adenocarcinoma. SCC represents ~25–30% of cases of NSCLC (3). The prognosis for patients with advanced SCC is poorer than that of those with adenocarcinoma (4).

Chemotherapy is an essential modality of palliative treatment for inoperable SCC at advanced stages. The response rate to chemotherapy varies widely from patient to patient; therefore, it is of interest to find biomarkers that predict the effect of cytostatic therapeutics. The resistance of cancer cells to chemotherapy can be caused by the increased export of anticancer drugs out of the cells, improved DNA repair ability or apoptosis resistance (5). The expression of genes participating in these processes is regulated by the microRNA (miRNA/miR) network. miRNAs are small non-coding RNA molecules of ~22 nucleotides that participate in the post-transcriptional regulation of gene expression (6). The human genome encodes >2,500 miRNAs (7), which target ~60% of mammalian genes and are abundant in a number of human cell types (8) (see miRNA database available online at www.mirbase.org).

The aim of the present study was to evaluate the association of the expression of miRNAs involved in the processes resulting in chemotherapy resistance with the overall survival (OS) time of patients with advanced SCC receiving palliative care. All patients in the cohort of the present study received palliative chemotherapy based on platinum derivatives (cisplatin or carboplatin) in combination with paclitaxel or gemcitabine.

On the basis of previously published literature, the present study focused on miRNAs whose effect on the processes involved in chemotherapy resistance may be expected (miR-15b, miR-21, miR-27a, miR-34a, miR-99a, miR-106a, miR-107, miR-143, miR-150, miR-192, miR-193, miR-211, miR-218, miR-221, miR-224, miR-342 and miR-375). A list of the main characteristics of miRNAs of interest, including references, is included in Table I.

Table I.

Analyzed miRNAs and their involvement in pathogenesis and treatment of NSCLC.

Table I.

Analyzed miRNAs and their involvement in pathogenesis and treatment of NSCLC.

SymbolmiRBase accession no.Cat. no. 4427975 assay IDRelation to NSCLC(Refs.)
miR-15bMIMAT0000417000390Regulates cisplatin resistance and metastasis by targeting PEBP4 in lung adenocarcinoma cells(35)
miR-21MIMAT0000076000397Regulates NSCLC cell invasion and chemo-sensitivity through SMAD7(36)
miR-27aMIMAT0000084000408Higher expression levels in advanced NSCLC patients resistant to EGFR-TKI(37)
miR-34aMIMAT0000255000426Sensitizes lung cancer cells to cisplatin via p53/miR-34a/MYCN axis(38)
miR-99a-3pMIMAT0004511002141Promotes proliferation, migration and invasion of NSCLC cell lines(39)
miR-106aMIMAT0000103000578Confers cisplatin resistance in non-small cell lung cancer A549 cells(40)
miR-107MIMAT0000104000443Regulates cisplatin chemosensitivity of A549 non small cell lung cancer cell lines by targeting cyclin dependent kinase 8(41)
miR-143MIMAT0000435002249Regulates cell apoptosis in lung cancer by targeting PKCε(42)
miR-150MIMAT0000451000473Downregulation induces cell proliferation inhibition and apoptosis in NSCLC by targeting BAK1(43)
miR-192MIMAT0000222000491Regulates chemo-resistance of lung adenocarcinoma for gemcitabine and cisplatin combined therapy by targeting Bcl-2(44)
miR-193a-3pMIMAT0000459002250Suppresses the metastasis of NSCLC by downregulating the ERBB4/PIK3R3/mTOR/S6K2 signaling pathway(45)
miR-211MIMAT0000268000514Promotes NSCLC proliferation by targeting SRCIN1(46)
miR-218MIMAT0000275000521Regulates cisplatin chemosensitivity in NSCLC by targeting RUNX2(47)
miR-221MIMAT0000278000524Overexpressed in aggressive NSCLC and regulates TRAIL resistance through PTEN and TIMP3(48)
miR-224MIMAT0000281002099Is implicated in lung cancer pathogenesis through targeting caspase-3 and caspase-7(19)
miR-342-3pMIMAT0000753002260Suppresses proliferation and invasion of NSCLC by targeting RAPB2(21)
miR-375MIMAT0000728000564Predictive for response for non-small cell lung cancer treated with cisplatin-vinorelbine A(49)
RNU6B001093Reference gene(50)

[i] miR/miRNA, microRNA; NSCLC, non-small cell lung cancer.

Patients and methods

Ethics statement

This study was approved by the Ethics Committee of the University Hospital in Pilsen (Pilsen, Czech Republic). Written informed consent was obtained from all the subjects. Anonymized data were used to conduct the present study.

Patients

The present study was retrospective. The study group consisted of 81 patients with late-stage (3B or 4) and the SCC histological subtype with an Eastern Cooperative Oncology Group (ECOG) performance status (PS) of 0–2 of NSCLC treated between January 2000 and June 2014 at the Department of Pneumology and Phthisiology of the University Hospital in Pilsen. Stage of disease was determined using the TNM (Tumor Nodus Metastasis) system of the International Union Against Cancer (IUCC; 7th edition) (9). The median patient age was 62.4 years (range, 32.7–79.3 years), and there were 74 males and 7 females. All patients underwent palliative chemotherapy using platinum derivatives in combination with paclitaxel or gemcitabine. The use of sequential radiotherapy was permitted for patients with stage 3B disease; patients with concurrent radiotherapy were excluded from the present study. In certain patients with stage 3B disease, radiotherapy was not indicated due to poor PS. The exclusion criteria for entering the study were >80 years of age, other malignancy and high cardiopulmonary risk. Clinicopathological data, including age at the time of diagnosis, smoking status, clinical disease stage, radiotherapy status and chemotherapy regimen, are listed in Table II.

Table II.

Clinicopathological characteristics of patients with squamous cell carcinoma of the lung (n=81).

Table II.

Clinicopathological characteristics of patients with squamous cell carcinoma of the lung (n=81).

CharacteristicPatients, n (%)
Sex
  Male74 (91.4)
  Female7 (8.6)
Age, years
  <5511 (13.6)
  55–6541 (50.6)
  >6529 (35.8)
Smoking status
  Non-smoker0 (0)
  Ex-smoker42 (51.9)
  Smoker39 (48.1)
Clinical stage
  3B42 (51.9)
  439 (48.1)
Eastern Cooperative Oncology
Group performance status
  02 (2.5)
  158 (71.6)
  218 (22.2)
  33 (3.7)
Radiotherapy
  Yes25 (30.9)
  No56 (69.1)
Chemotherapy
  Paclitaxel and carboplatin35 (43.2)
  Gemcitabine and cisplatin46 (56.8)
Tissue samples and RNA isolation

Biopsy tissue samples were obtained using bronchoscopy for diagnostic purposes prior to chemotherapy and were processed by standard laboratory techniques at the Department of Pathology of the University Hospital in Pilsen. Formalin-fixed paraffin-embedded (FFPE) tissue samples were stored at room temperature until use. Paraffinized sections were stained with hematoxylin and eosin, microscopically verified by pathologists and examined to identify sites with cancer cells for macrodissection. Total RNA (including miRNA) was extracted from 15-µm thick FFPE sections following the macrodissection of tumor tissue using the miRNeasy FFPE kit (Qiagen, Hilden, Germany) as described previously (10).

Quantitative estimation of microRNA expression

A quantitative estimation of 17 selected miRNAs (Table I) was performed by the reverse transcription-quantitative polymerase chain reaction (RT-qPCR) method using TaqMan® MicroRNA assays (Applied Biosystems; Thermo Fisher Scientific, Inc., Waltham, MA, USA) in technical duplicates on the Stratagene Mx3005P apparatus (Agilent Technologies, Santa Clara, CA, USA) according to the manufacturer's protocol. The two-step protocol included reverse transcription with a miRNA-specific primer, followed by qPCR with TaqMan® probes. Briefly, 5 ng of RNA was reverse transcribed in a 20-µl reaction containing 2.5 µl of primers specific to particular miRNA (Applied Biosystems; Thermo Fisher Scientific, Inc.). The 20-µl PCR reactions included 2.5 µl of RT product. The reactions were incubated in 96-well plates at 95°C for 15 min and then followed by 48 cycles of 95°C for 15 sec and 60°C for 60 sec. The 2−ΔΔCq method was used for the quantification of qPCR data, as described previously (11); the expression was normalized to RNU6B (U6snRNA). Details of all kits for estimation of all miRNAs and RNU6B are included in Table I.

Statistical analysis

SAS version 9.3 statistical software (SAS Institute, Inc., Cary, NC, USA) was used to perform all statistical calculations. Although the group of patients in the present study was as homogenous as possible (SCC subtype of NSCLC, stages 3B and 4), certain patients underwent radiotherapy, which may have caused confounders. The potential effect of treatment inconsistency was mitigated by evaluating miRNA expression in the subgroups of patients.

The evaluation of prognostic significance (the association between markers and time to recurrence) was performed as a univariate analysis of maximum likelihood estimates using the Cox regression hazard model. For markers significant in the Cox model, an optimal cut off was identified. There is no standard method for biomarker cut-off determination to split continuous variables into two groups; the simplest approach used in exploratory studies is to set the median as a cut-off value. However, in the case of unequal distribution of events in the studied group, this approach is far from optimal. The present study used continuous search for the cut-off value by searching for the lowest P-value of the log-rank test, as described previously (12,13).

miRNAs identified as significant by univariate analysis were incorporated into a multivariate analysis and the Kaplan-Meier survival distribution functions were generated for combinations of miRNA expression levels (clustering) with the cut-off values from univariate analysis and later, the median. P<0.05 was considered to indicate a statistically significant difference. DIANA-TarBase v7.0 and DIANA-miRPath v3.0 bioinformatic tools were used to identify overlapped target genes of the miRNAs of interest (14,15).

Results

Effect of treatment modalities on OS time

Prior to the analysis of the association between gene expression and OS, the outcomes for subgroups of patients undergoing different treatment were compared. A significantly longer OS time was identified in the subgroup of patients who underwent chemotherapy combined with radiotherapy in comparison with patients who underwent chemotherapy alone (P=0.0498; Fig. 1A). There were no significant differences in OS between subgroups of patients with different chemotherapy regimens (Fig. 1B).

Association of miRNA expression with OS time

The Cox regression hazard model was used to determine the association between the levels of miRNA expression with OS time. From the 17 miRNAs of interest, in the subgroup of smokers, the low expression of miR-342 (P=0.0500) and high expression level of miR-34a and miR-224 (P=0.0338 and P=0.0400, respectively) were associated with a shorter OS time. High expression levels of miR-34a were associated with shorter OS time in the subgroup of patients treated with platinum derivate-based chemotherapy in combination with gemcitabine (P=0.0364). High expression levels of miR-224 were associated with shorter OS time in the subgroup of patients who underwent chemotherapy combined with radiotherapy (P=0.0250).

For the statistically significant miRNA markers, optimal cut-off values were identified and Kaplan-Meier survival distribution functions for OS were generated. Statistically significant differences in OS time between subgroups with marker expression levels below and above the cut-off value were obtained for miR-342 in the subgroup of smokers (P=0.0243; Fig. 2A), miR-34a in a subgroup of patients that were treated with gemcitabine in chemotherapy regimen (P=0.0239; Fig. 2B) and miR-224 in the subgroup of patients that underwent chemotherapy combined with radiotherapy (P=0.0093; Fig. 2C). Statistical values obtained from the Kaplan-Meier analyses are summarized in Table III.

Table III.

Association between the level of miRs and overall survival time as determined by Kaplan-Meier estimation.

Table III.

Association between the level of miRs and overall survival time as determined by Kaplan-Meier estimation.

Below cut-offAbove cut-off


Patient groupTreatmentMarkerPatients, nCut-offnMedian, daysnMedian, daysP-value
Smokers onlyChemotherapymiR-342362.4473229847480.0243
Smokers and ex-smokersGemcitabine and cisplatinmiR-34a415.2853631351810.0239
Smokers and ex-smokersChemotherapy and radiotherapymiR-224250.7931746282620.0093

[i] miR, microRNA.

miRNAs associated with OS (miR-34a, −224 and −342) were the subject of the subsequent cluster analysis. Pairs of these miRNAs (miR-34a and −224, miR-34a and −342, and miR-224 and −342) were analyzed for their association with OS time. For each pair of miRNAs, patients were stratified into groups according to the miRNA expression being above (high) or below (low) the cut-off value: Group A (high miR-224 and high miR-342); group B (high miR-224 and low miR-342); group C (low miR-224 and high miR-342); and group D (low miR-224 and low miR-342). Initially, the cut-off value obtained from univariate analysis was used also for cluster analysis; however, this led to a highly disproportional distribution of patients among subgroups. Therefore, a median was used as a cut off value for cluster analysis.

Fig. 3 demonstrates the Kaplan-Meier survival distribution functions of patients stratified into groups according to the expression of miR-224 and miR-342. There are two pairs of groups with similar OS distributions; Fig. 4 includes a comparison of the OS of two groups of patients created by combining the groups from Fig. 3 with similar survival outcomes (group A/D vs. group B/C). There was a significant difference in survival between these groups (P=0.0018), as detailed in Table IV. The same approach was used to analyze the other pairs of miRNAs; however, no significance was identified. All three miRNAs were analyzed together in the same manner (miR-34a, −224 and −342). Patterns of expression associating patients with significantly shorter survival times were identified (Fig. 5; Table IV).

Table IV.

Association between combinations of miRs and OS (Kaplan-Meier estimation).

Table IV.

Association between combinations of miRs and OS (Kaplan-Meier estimation).

Expression patternPatients, nMedian OS, days
miR-342 and −224
  High miR-224 and −342, or low miR-224 and −34248267
  High miR-224 and low miR-342, or low miR-224 and high miR-34230432
miR-342, −224 and −34a
  High miR-224, −342 and −34a, or low miR-224, −342 and −34a39250
  Other combinations35451

[i] OS, overall survival; miR, microRNA.

Identification of potential target genes for miR-34a, −224 and −342

Using DIANA-TarBase v7.0 and DIANA-miRPath v3.0 bioinformatic tools (11,12), 6 overlapping target genes with P<0.05 were identified between miR-34a, −224 and −342. These genes, including GNAS complex locus (GNAS), insulin like growth factor 1 receptor (IGF1R), cyclin D1 (CCND1), cyclin G2 (CCNG2), serpin family E member 1 (SERPINE1) and ribonucleotide reductase regulatory subunit M2 (RRM2), are associated with cell cycle regulation, p53 signaling and DNA repair.

Discussion

miRNAs may have the potential to become accurate, easily measurable biomarkers, with features convenient for diagnostic testing methods, including stability in FFPE tissue blocks, blood, and potentially, other bodily fluids (16). The present study focused on patients with the NSCLC SCC subtype with an advanced-stage SCC. The included patients were unable to undergo surgical resection and received palliative treatment only. For these patients, there were multiple treatment modalities. The main clinical concern in such cases is deciding which therapeutic regimen is indicated. However, in the group of patients in the present study, it was only possible to analyze the potential predictors for the treatment response to platinum base derivatives in combination with either paclitaxel or gemcitabine, with or without the application of radiotherapy.

Initially, the present study focused on a univariate analysis of the association between miRNA expression and OS time. Subsequently, a multivariate analysis was performed that included the miRNAs that had been identified to exhibit associations with OS. On the basis of the results of the present study, we hypothesize that the effect of a single miRNA may depend on the level of expression of other members of the miRNA network, to be further discussed.

Higher levels of miR-224 indicated shorter OS times for patients with chemotherapy combined with radiotherapy in the present study. Cui et al (17) reported that miR-224 expression was significantly upregulated in NSCLC tissues and suggested it performed its oncogenic role in lung cancer pathogenesis through targeting caspase-3 and −7. Wang et al (18) identified through microarray analysis that miR-224 expression was upregulated in cisplatin-resistant cell lines, and demonstrated that miR-224 could promote cisplatin resistance via regulating the G1/S cell cycle transition and apoptosis by targeting p21. These findings indicated the association of miR-224 with the effect of chemotherapy based on DNA damage, and its potential as a predictor for the response to treatment. However, Zhu et al (19) reported that miR-224 expression levels were downregulated in NSCLC compared with non-cancerous lung tissue. These authors also observed that decreased miR-224 expression was significantly associated with lymph node metastasis, an advanced tumor-node-metastasis stage and a reduced OS time (19). Furthermore, Wang et al (20) recently identified that miR-224 was significantly upregulated in NSCLC tissues and hypothesized that miR-224 expression promotes NSCLC cell proliferation by downregulating Ras association domain family member 8 expression; the inconsistency of these studies will be discussed in the following paragraph.

In the present study, low levels of miR-342 indicated a poorer outcome in patients with a history of smoking, independent of treatment modality. Xie et al (21) demonstrated that miR-342 was downregulated in NSCLC and acted as a tumor suppressor through the repression of RAP2B, member of RAS oncogene family. Similarly, Tai et al (22) identified that miR-342 was capable of indirectly regulating MYC activity via the direct repression of E2F transcription factor 1. Takahashi et al (23) investigated how cigarette smoking altered plasma miRNA profiles; they identified that there was a decrease in plasma miR-342 in subjects who quit smoking, compared with smokers.

miR-34a is a member of the miR-34 family that is associated with the p53 pathway, and is implicated in cell death/survival signaling (24). The miR-34 family is transcriptionally activated by p53; in turn, p53 is a direct miR-34a target. However, the effect of miR-34a on p53 depends on the cellular context (25). miR-34a can have also a positive effect on p53 transcriptional activity and protein stability by targeting multiple p53 inhibitor genes (including MDM4, p53 regulator, sirtuin 1, metastasis associated 1 family member 2, histone deacetylase 1 and YY1 transcription factor) (26). A previous study identified that miR-34a inhibits cell proliferation (27). Expression of the miR-34 family was downregulated in tumor tissue compared with normal tissue, and low levels of miR-34a expression were associated with a higher probability of relapse in surgically resected NSCLC (28). However, higher levels of circulating miR-34a were observed in patients with NSCLC compared with healthy controls (29). Higher levels of miR-34a indicated a shorter OS time in patients receiving palliative platinum derivate-based chemotherapy in combination with gemcitabine in the present study.

Multivariate analysis was performed with the miRNAs (miR-34a, −224 and −342) that were identified as associated with OS. The most notable finding was that patients with the high expression of miR-224 and −342 exhibited similar outcomes to those with low expression of miR-224 and −342, which was significantly shorter than that of patients with high expression of either miR-224 or miR-342 and the low expression of the other (Figs. 3 and 4).

We hypothesize that the effect of a single miRNA is dependent on the level of expression of the other members of the miRNA network. It has been established that an miRNA can have a predominantly oncogenic role in one type of cancer and a tumor suppressive role in another; for instance, miR-224 was identified to be a tumor suppressor in prostate cancer (30), whereas in other types of malignancy, including gastric (11,31) and colorectal cancer (32), an oncogenic role for miR-224 was described. The ambiguous role of miR-224 was also observed within the SCC histological subtype of NSCLC in the present study. Tumor progression occurs as a result of the dysregulation of a number of protein-coding genes and epigenetic processes, including the deregulation of a number of miRNAs. Therefore, to understand the role of one particular miRNA, it is necessary to determine the levels of the other ‘co-players’. In the present study, OS time was influenced by the mutual association of miR-224 and −342. The high level of miR-224 can be associated with adverse or favorable outcomes, depending on the simultaneous level of miR-342. These findings could explain the inconsistent results of previously published studies on miR-224 expression in NSCLC. In 2014, Zhu et al (19) reported that miR-224 was significantly downregulated in NSCLC and that a decrease in miR-224 expression was significantly associated with shorter OS time (19). Also in NSCLC, Cui et al (33) identified that miR-224 was significantly upregulated, with the increased expression of miR-224 promoting cell migration, invasion, and proliferation. As aforementioned, the present study also identified that the high expression levels of miR-224 were associated with shorter OS time in one subgroup of patients, specifically those who underwent chemotherapy combined with radiotherapy.

Using bioinformatic tools, the present study identified overlapping experimentally validated target genes for miR-34a, −224 and −342. Notably, all overlapping target genes identified in the present study (GNAS, IGF1R, CCND1, CCNG2, SERPINE1, and RRM2) are involved in processes associated with carcinogenesis, including cell cycle regulation, p53 signaling and DNA repair. This may explain the complicated mutual dependency of those miRNAs in relation to tumor progression and the effectiveness of treatment. We hypothesize that these molecules could be involved in competing endogenous RNA crosstalk, where RNA transcripts co-regulate each other by competing for shared miRNAs, thereby titrating miRNA availability (34). However, one limitation of the present study is the absence of immunoprecipitation data and reporter assays, which are methods that may confirm the interactions among the set of 3 miRNAs and 6 target genes. Nevertheless, the results of the present study may provide a stimulus for further research in this area.

With cluster analysis, novel associations between miR-34a, −224 and −342 that affected patient survival time were identified in the present study. The result may demonstrate that the effort to find a particular miRNA as a perfect marker for a particular event may be fruitless due to the complex interactions between RNA transcripts. In order to understand all aspects of the effect of miRNAs on the regulation of gene expression in cancer and their associations with phenotype and treatment outcome, miRNA profiling and deep bioinformatic analysis will be necessary. Only this approach can facilitate the future application of miRNAs in clinical practice. miRNAs can generally be assessed with more precision and ease than the mRNAs of coding genes, as miRNA analysis is less demanding in terms of the quality and quantity of isolated RNA, features that may be problematic in RNA samples extracted from FFPE tissue (16). FFPE tissue samples are routinely taken and analyzed during standard lung cancer management, which is why miRNAs may become clinically applicable predictors of the effectiveness of palliative treatment in patients with lung cancer. Nevertheless, the findings of the present study demonstrated that, due to the complex network of interactions, this objective could be achieved by combining more markers together rather than by using individual miRNAs. On the basis of the results of the current study, miR-224, −342 and −34a could be members of this panel of predictors of treatment efficacy.

Acknowledgements

The present study was supported by grants from the Ministry of Health of the Czech Republic Conceptual Development of Research Organization (grant nos. 00669806 and AZV 17-30748A), and by the project of Faculty of Medicine in Pilsen (grant no. SVV-2017-260693).

References

1 

Torre LA, Siegel RL and Jemal A: Lung Cancer Statistics. Adv Exp Med Biol. 893:1–19. 2016. View Article : Google Scholar : PubMed/NCBI

2 

Institute of Health Information and Statistics of the Czech Republic, . Czech Health Statistics Year Book 2013UZIS CR. Prague: 2014

3 

Travis WD: Pathology of lung cancer. Clin Chest Med. 32:669–692. 2011. View Article : Google Scholar : PubMed/NCBI

4 

Socinski MA, Obasaju C, Gandara D, Hirsch FR, Bonomi P, Bunn P, Kim ES, Langer CJ, Natale RB, Novello S, et al: Clinicopathologic features of advanced squamous NSCLC. J Thorac Oncol. 11:1411–1422. 2016. View Article : Google Scholar : PubMed/NCBI

5 

Olaussen KA and Postel-Vinay S: Predictors of chemotherapy efficacy in non-small-cell lung cancer: A challenging landscape. Ann Oncol. 27:2004–2016. 2016. View Article : Google Scholar : PubMed/NCBI

6 

Inamura K and Ishikawa Y: MicroRNA in lung cancer: Novel biomarkers and potential tools for treatment. J Clin Med. 5:pii: E362016. View Article : Google Scholar

7 

Griffiths-Jones S, Saini HK, van Dongen S and Enright AJ: miRBase: Tools for microRNA genomics. Nucleic Acids Res. 36(Database Issue): D154–D158. 2008.PubMed/NCBI

8 

Friedman RC, Farh KK, Burge CB and Bartel DP: Most mammalian mRNAs are conserved targets of microRNAs. Genome Res. 19:92–105. 2009. View Article : Google Scholar : PubMed/NCBI

9 

Sobin LH, Gospodarowicz MK and Wittekind Ch: TNM Classification of Malignant Tumours. 7th. Wiley-Blackwell; Chichester: 2010

10 

Kalfert D, Pesta M, Kulda V, Topolcan O, Ryska A, Celakovsky P, Laco J and Ludvikova M: MicroRNA profile in site-specific head and neck squamous cell cancer. Anticancer Res. 35:2455–2463. 2015.PubMed/NCBI

11 

Smid D, Kulda V, Srbecka K, Kubackova D, Dolezal J, Daum O, Kucera R, Topolcan O, Treska V, Skalicky T and Pesta M: Tissue microRNAs as predictive markers for gastric cancer patients undergoing palliative chemotherapy. Int J Oncol. 48:2693–2703. 2016.PubMed/NCBI

12 

Mazumdar M and Glassman JR: Categorizing a prognostic variable: Review of methods, code for easy implementation and applications to decision-making about cancer treatments. Stat Med. 19:113–132. 2000. View Article : Google Scholar : PubMed/NCBI

13 

Faraggi D and Simon R: A simulation study of cross-validation for selecting an optimal cutpoint in univariate survival analysis. Stat Med. 15:2203–2213. 1996. View Article : Google Scholar : PubMed/NCBI

14 

Vlachos IS, Paraskevopoulou MD, Karagkouni D, Georgakilas G, Vergoulis T, Kanellos I, Anastasopoulos IL, Maniou S, Karathanou K, Kalfakakou D, et al: DIANA-TarBase v7.0: Indexing more than half a million experimentally supported miRNA:mRNA interactions. Nucleic Acids Res. 43(Database Issue): D153–D159. 2015. View Article : Google Scholar : PubMed/NCBI

15 

Vlachos IS, Zagganas K, Paraskevopoulou MD, Georgakilas G, Karagkouni D, Vergoulis T, Dalamagas T and Hatzigeorgiou AG: DIANA-miRPath v3.0: Deciphering microRNA function with experimental support. Nucleic Acids Res. 43:W460–W466. 2015. View Article : Google Scholar : PubMed/NCBI

16 

Khan J, Lieberman JA and Lockwood CM: Variability in, variability out: Best practice recommendations to standardize pre-analytical variables in the detection of circulating and tissue microRNAs. Clin Chem Lab Med. 55:608–621. 2017. View Article : Google Scholar : PubMed/NCBI

17 

Cui R, Kim T, Fassan M, Meng W, Sun HL, Jeon YJ, Vicentini C, Tili E, Peng Y and Scarpa A: MicroRNA-224 is implicated in lung cancer pathogenesis through targeting caspase-3 and caspase-7. Oncotarget. 6:21802–21815. 2015. View Article : Google Scholar : PubMed/NCBI

18 

Wang H, Zhu LJ, Yang YC, Wang ZX and Wang R: MiR-224 promotes the chemoresistance of human lung adenocarcinoma cells to cisplatin via regulating G1/S transition and apoptosis by targeting p21(WAF1/CIP1). Br J Cancer. 111:339–354. 2014. View Article : Google Scholar

19 

Zhu D, Chen H, Yang X, Chen W, Wang L, Xu J and Yu L: Decreased microRNA-224 and its clinical significance in non-small cell lung cancer patients. Diagn Pathol. 9:1982014. View Article : Google Scholar

20 

Wang L, Liu W, Zhang YP and Huang XR: The miR-224 promotes non-small cell lung cancer cell proliferation by directly targeting RASSF8. Eur Rev Med Pharmacol Sci. 21:3223–3231. 2017.

21 

Xie X, Liu H, Wang M, Ding F, Xiao H, Hu F, Hu R and Mei J: miR-342-3p targets RAP2B to suppress proliferation and invasion of non-small cell lung cancer cells. Tumour Biol. 36:5031–5038. 2015. View Article : Google Scholar

22 

Tai MC, Kajino T, Nakatochi M, Arima C, Shimada Y, Suzuki M, Miyoshi H, Yatabe Y, Yanagisawa K and Takahashi T: miR-342-3p regulates MYC transcriptional activity via direct repression of E2F1 in human lung cancer. Carcinogenesis. 36:1464–1473. 2015.

23 

Takahashi K, Yokota SI, Tatsumi N, Fukami T, Yokoi T and Nakajima M: Cigarette smoking substantially alters plasma microRNA profiles in healthy subjects. Toxicol Appl Pharmacol. 272:154–160. 2013. View Article : Google Scholar

24 

Rokavec M, Li H, Jiang L and Hermeking H: The p53/miR-34 axis in development and disease. J Mol Cell Biol. 6:214–230. 2014. View Article : Google Scholar

25 

Okada N, Lin CP, Ribeiro MC, Biton A, Lai G, He X, Bu P, Vogel H, Jablons DM, Keller AC, et al: A positive feedback between p53 and miR-34 miRNAs mediates tumor suppression. Genes Dev. 28:438–450. 2014. View Article : Google Scholar

26 

Navarro F and Lieberman J: miR-34 and p53: New insights into a complex functional relationship. PLoS One. 10:e01327672015. View Article : Google Scholar

27 

Ma ZL, Hou PP, Li YL, Wang DT, Yuan TW, Wei JL, Zhao BT, Lou JT, Zhao XT, Jin Y and Jin YX: MicroRNA-34a inhibits the proliferation and promotes the apoptosis of non-small cell lung cancer H1299 cell line by targeting TGFβR2. Tumour Biol. 36:2481–2490. 2015. View Article : Google Scholar

28 

Gallardo E, Navarro A, Viñolas N, Marrades RM, Diaz T, Gel B, Quera A, Bandres E, Garcia-Foncillas J, Ramirez J and Monzo M: miR-34a as a prognostic marker of relapse in surgically resected non-small-cell lung cancer. Carcinogenesis. 30:1903–1909. 2009. View Article : Google Scholar

29 

Franchina T, Amodeo V, Bronte G, Savio G, Ricciardi GR, Picciotto M, Russo A, Giordano A and Adamo V: Circulating miR-22, miR-24 and miR-34a as novel predictive biomarkers to pemetrexed-based chemotherapy in advanced non-small cell lung cancer. J Cell Physiol. 229:97–99. 2014.

30 

Goto Y, Nishikawa R, Kojima S, Chiyomaru T, Enokida H, Inoguchi S, Kinoshita T, Fuse M, Sakamoto S, Nakagawa M, et al: Tumour-suppressive microRNA-224 inhibits cancer cell migration and invasion via targeting oncogenic TPD52 in prostate cancer. FEBS Lett. 588:1973–1982. 2014. View Article : Google Scholar

31 

Zhang Y, Li CF, Ma LJ, Ding M and Zhang B: MicroRNA-224 aggrevates tumor growth and progression by targeting mTOR in gastric cancer. Int J Oncol. 49:1068–1080. 2016.

32 

Adamopoulos PG, Kontos CK, Rapti SM, Papadopoulos IN and Scorilas A: miR-224 overexpression is a strong and independent prognosticator of short-term relapse and poor overall survival in colorectal adenocarcinoma. Int J Oncol. 46:849–859. 2015. View Article : Google Scholar

33 

Cui R, Meng W, Sun HL, Kim T, Ye Z, Fassan M, Jeon YJ, Li B, Vicentini C, Peng Y, et al: MicroRNA-224 promotes tumor progression in nonsmall cell lung cancer. Proc Natl Acad Sci USA. 112:pp. E4288–E4297. 2015; View Article : Google Scholar

34 

Tay Y, Rinn J and Pandolfi PP: The multilayered complexity of ceRNA crosstalk and competition. Nature. 505:344–352. 2014. View Article : Google Scholar

35 

Zhao Z, Zhang L, Yao Q and Tao Z: miR-15b regulates cisplatin resistance and metastasis by targeting PEBP4 in human lung adenocarcinoma cells. Cancer Gene Ther. 22:108–114. 2015. View Article : Google Scholar

36 

Lin L, Tu HB, Wu L, Liu M and Jiang GN: MicroRNA-21 regulates non-small cell lung cancer cell invasion and chemo-sensitivity through SMAD7. Cell Physiol Biochem. 38:2152–2162. 2016. View Article : Google Scholar

37 

Wang S, Su X, Bai H, Zhao J, Duan J, An T, Zhuo M, Wang Z, Wu M, Li Z, et al: Identification of plasma microRNA profiles for primary resistance to EGFR-TKIs in advanced non-small cell lung cancer (NSCLC) patients with EGFR activating mutation. J Hematol Oncol. 8:1272015. View Article : Google Scholar

38 

Song C, Lu P, Sun G, Yang L and Wang Z and Wang Z: miR-34a sensitizes lung cancer cells to cisplatin via p53/miR-34a/MYCN axis. Biochem Biophys Res Commun. 482:22–27. 2017. View Article : Google Scholar

39 

Chen C, Zhao Z, Liu Y and Mu D: microRNA-99a is downregulated and promotes proliferation, migration and invasion in non-small cell lung cancer A549 and H1299 cells. Oncol Lett. 9:1128–1134. 2015.

40 

Ma Y, Li X, Cheng S, Wei W and Li Y: MicroRNA-106a confers cisplatin resistance in non-small cell lung cancer A549 cells by targeting adenosine triphosphatase-binding cassette A1. Mol Med Rep. 11:625–632. 2015. View Article : Google Scholar

41 

Zhang Z, Zhang L, Yin ZY, Fan XL, Hu B, Wang LQ and Zhang D: miR-107 regulates cisplatin chemosensitivity of A549 non small cell lung cancer cell line by targeting cyclin dependent kinase 8. Int J Clin Exp Pathol. 7:7236–7241. 2014.

42 

Zhang N, Su Y and Xu L: Targeting PKCε by miR-143 regulates cell apoptosis in lung cancer. FEBS Lett. 587:3661–3667. 2013. View Article : Google Scholar

43 

Gu XY, Wang J, Luo YZ, Du Q, Li RR, Shi H and Yu TP: Down-regulation of miR-150 induces cell proliferation inhibition and apoptosis in non-small-cell lung cancer by targeting BAK1 in vitro. Tumour Biol. 35:5287–5293. 2014. View Article : Google Scholar

44 

Cao J, He Y, Liu HQ, Wang SB, Zhao BC and Cheng YS: MicroRNA 192 regulates chemo-resistance of lung adenocarcinoma for gemcitabine and cisplatin combined therapy by targeting Bcl-2. Int J Clin Exp Med. 8:12397–12403. 2015.

45 

Yu T, Li J, Yan M, Liu L, Lin H, Zhao F, Sun L, Zhang Y, Cui Y, Zhang F, et al: MicroRNA-193a-3p and −5p suppress the metastasis of human non-small-cell lung cancer by downregulating the ERBB4/PIK3R3/mTOR/S6K2 signaling pathway. Oncogene. 34:413–423. 2015. View Article : Google Scholar

46 

Ye L, Wang H and Liu B: miR-211 promotes non-small cell lung cancer proliferation by targeting SRCIN1. Tumour Biol. 37:1151–1157. 2016. View Article : Google Scholar

47 

Xie J, Yu F, Li D, Zhu X, Zhang X and Lv Z: MicroRNA-218 regulates cisplatin (DPP) chemosensitivity in non-small cell lung cancer by targeting RUNX2. Tumour Biol. 37:1197–1204. 2016. View Article : Google Scholar

48 

Garofalo M, Di Leva G, Romano G, Nuovo G, Suh SS, Ngankeu A, Taccioli C, Pichiorri F, Alder H, Secchiero P, et al: miR-221&222 regulate TRAIL resistance and enhance tumorigenicity through PTEN and TIMP3 downregulation. Cancer Cell. 16:498–509. 2009. View Article : Google Scholar

49 

Berghmans T, Ameye L, Willems L, Paesmans M, Mascaux C, Lafitte JJ, Meert AP, Scherpereel A, Cortot AB, Cstoth I, et al: Identification of microRNA-based signatures for response and survival for non-small cell lung cancer treated with cisplatin-vinorelbine A ELCWP prospective study. Lung Cancer. 82:340–345. 2013. View Article : Google Scholar

50 

Fiedler SD, Carletti MZ and Christenson LK: Quantitative RT-PCR methods for mature microRNA expression analysis. Methods Mol Biol. 630:49–64. 2010. View Article : Google Scholar

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January-2018
Volume 15 Issue 1

Print ISSN: 1792-1074
Online ISSN:1792-1082

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Spandidos Publications style
Kulda V, Svaton M, Mukensnabl P, Hrda K, Dvorak P, Houdek Z, Houfkova K, Vrzakova R, Babuska V, Pesek M, Pesek M, et al: Predictive relevance of miR‑34a, miR‑224 and miR‑342 in patients with advanced squamous cell carcinoma of the lung undergoing palliative chemotherapy. Oncol Lett 15: 592-599, 2018
APA
Kulda, V., Svaton, M., Mukensnabl, P., Hrda, K., Dvorak, P., Houdek, Z. ... Pesta, M. (2018). Predictive relevance of miR‑34a, miR‑224 and miR‑342 in patients with advanced squamous cell carcinoma of the lung undergoing palliative chemotherapy. Oncology Letters, 15, 592-599. https://doi.org/10.3892/ol.2017.7337
MLA
Kulda, V., Svaton, M., Mukensnabl, P., Hrda, K., Dvorak, P., Houdek, Z., Houfkova, K., Vrzakova, R., Babuska, V., Pesek, M., Pesta, M."Predictive relevance of miR‑34a, miR‑224 and miR‑342 in patients with advanced squamous cell carcinoma of the lung undergoing palliative chemotherapy". Oncology Letters 15.1 (2018): 592-599.
Chicago
Kulda, V., Svaton, M., Mukensnabl, P., Hrda, K., Dvorak, P., Houdek, Z., Houfkova, K., Vrzakova, R., Babuska, V., Pesek, M., Pesta, M."Predictive relevance of miR‑34a, miR‑224 and miR‑342 in patients with advanced squamous cell carcinoma of the lung undergoing palliative chemotherapy". Oncology Letters 15, no. 1 (2018): 592-599. https://doi.org/10.3892/ol.2017.7337