OL Oncology Letters 1792-1074 1792-1082 D.A. Spandidos 10.3892/ol.2018.9044 OL-0-0-9044 Articles Prognostic value of IL-6R mRNA in lung adenocarcinoma and squamous cell carcinoma XuBin 1 2 * ChenQu 3 * YueCuihua 1 2 * LanLei 3 JiangJingting 1 2 ShenYueping 3 LuBinfeng 1 2 4 Department of Tumor Biological Treatment, The Third Affiliated Hospital of Soochow University, Changzhou, Jiangsu 213000, P.R. China Jiangsu Engineering Research Center for Tumor Immunotherapy, The Third Affiliated Hospital of Soochow University, Changzhou, Jiangsu 213000, P.R. China Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Department of Epidemiology and Biostatistics, School of Public Health, Soochow University, Suzhou, Jiangsu 215123, P.R. China Department of Immunology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15106, USA Correspondence to: Professor Yueping Shen, Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Department of Epidemiology and Biostatistics, School of Public Health, Soochow University, 199 Ren-Ai Road, Suzhou, Jiangsu 215123, P.R. China, E-mail: shenyueping@suda.edu.cn Professor Binfeng Lu, Department of Immunology, University of Pittsburgh School of Medicine, E1040 Biomedical Science Tower, 200 Lothrop Street, Pittsburgh, PA 15106, USA, E-mail: binfeng@pitt.edu

Contributed equally

 09 2018 28 06 2018 16 3 2935 2948 04112017 22052018 Copyright: © Xu et al. 2018 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.

Previous studies have demonstrated that the interleukin (IL)-6/ IL-6 receptor (IL-6R) signaling pathway contributes to the pathogenesis of lung cancer. Lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) are the two major pathological subtypes of non-small cell lung cancer (NSCLC). The present study aimed to elucidate the potential clinical prognosis and biological function of IL-6R mRNA expression in LUAD and LUSC. The search term ‘lung cancer’ was used to search through the Gene Expression Omnibus database. Including LUAD and LUSC datasets in The Cancer Genome Atlas database, a total of 8 LUAD and 6 LUSC datasets were included in the present analysis. It was observed that a higher expression level of IL-6R mRNA in tumor tissues was a significant positive prognostic factor for overall survival in LUAD [pooled hazard ratio (HR), 0.48 and P<0.001 for univariate analysis; pooled HR, 0.50 and P<0.001 for multivariate analysis] while there was no similar association in LUSC (pooled HR, 1.59 and P=0.062 for univariate analysis; pooled HR, 1.58 and P=0.079 for multivariate analysis). Correlation analysis revealed that IL-6 and IL-6R were negatively correlated in LUAD and positively correlated in LUSC. IL-6R and its most correlated genes were primarily involved in cell cycle progression in LUAD and primarily involved in tumor angiogenesis, invasion and metastasis in LUSC. These results suggest a possible role of tumoral expression for IL-6R in LUAD, which means it may have potential as a prognostic marker for this type of cancer.

 lung adenocarcinoma lung squamous cell carcinoma IL-6R overall survival enrichment analysis Introduction

Lung cancer is the leading cause of cancer-related deaths in men and the second leading cause of cancer death in women worldwide and presents a serious problem to global health (1). It was estimated that 1.8 million new lung cancer cases and 1.6 million lung cancer related deaths occurred in 2012 worldwide, accounting for approximately 19% of all cancer deaths (2). In recent decades, despite of great research effort in diagnosis and treatment for lung cancer, progress in the treatment is still slow (3). Non-small cell lung cancer (NSCLC) is the major lung cancer, accounts for approximately 85% of lung cancer. Lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) are the major two pathological subtypes of NSCLC and in many ways they are different, such as originate, biological patterns and molecular characteristics (4–6). Related studies on NSCLC should be analyzed separately according to histological type.

Interleukin-6 (IL-6) signaling through IL-6 receptor (IL-6R) regulates cell growth and differentiation and plays an important role in the immune response (7). This signaling pathway can also promote tumor growth which has both pro-inflammatory and anti-inflammatory effect (8). Evidence has shown that the IL-6 signaling pathway contribute to the pathogenesis of NSCLC (9). Several studies have revealed the role of IL-6 in NSCLC and suggested that it promotes tumor growth and survival (10–12). Increased expression of IL-6R in human LUSC-derived cells (HARA-B) has been shown in vitro (13) and in a murine model of brain metastasis (14). Tocilizumab is an anti-IL-6R antibody, upon application, the stimulated growth of HARA-B cells was significantly inhibited and when it was injected to the animal model, the volume of metastatic focus was significantly smaller (14). Studies also have demonstrated that blockade of IL-6R can significantly suppress the proliferation of NSCLC cell and reduce the mRNA levels of IL-6R (15,16). Meanwhile, tocilizumab also seems to be effective for lung cancer cachexia (17,18). However, there was no study investigating the prognostic effect of IL-6R on lung cancer, which is addressed by our current study.

 Materials and methods <sec> <title>Search strategy

We searched public databases such as The Cancer Genome Atlas (TCGA) and Gene Expression Ominibus (GEO database; last update by July 03, 2017) using the keywords ‘Lung cancer’. The search strategy is designed as follows: The study type was set as ‘expression profiling by array. The entry type was set as ‘datasets’. The sample size of all selected datasets should be greater or equal to 100. The organism was homo sapiens. Database searching was carried out by two researchers independently.

 Data extraction and quality assessment

Data from all eligible datasets were abstracted independently by two authors, using information recorded as follows: First author's surname, publication year, origin of population, sample number, tumor stage, follow-up period and clinic outcome. We separate those microarray datasets into LUAD and LUSC. HRs and 95% CIs were evaluated by Cox's proportional hazards model.

The quality of all eligible studies was assessed according to the Newcastle-Ottawa Quality Assessment Scale (NOS) by two researchers independently (19). The quality scores span from 0 to 9, and higher the score is, higher the quality is.

 Statistical analysis

For those public microarray data, gene expression was represented by metric variables. We use Cutoff Finder (http://molpath.charite.de/cutoff) to determine a cutoff point and stratify patients into two groups (20). The range of IL-6R mRNA values for each data and the corresponding cutoff values were listed in Table I. HRs and 95% CIs were calculated to measure the effective prognostic value of expression of IL-6R mRNA in LUAD and LUSC patients. Pooled HRs were carried out using STATA software package (version 12.0; Stata Corp LP, College Station, TX, USA). All P-values were obtained upon two tailed analysis.

In The Cancer Genome Atlas (TCGA) lung adenocarcinoma dataset and TCGA lung squamous cell carcinoma dataset, there were gene expression data in both tumor and normal tissues. We used paired test to compare the differences in IL-6R mRNA expression between tumor and adjacent normal tissues.

For each dataset, we calculated the correlation coefficient between IL-6R and the remaining genes, and then matched the coefficients in all datasets. Genes with absolute correlation coefficient which were greater than 0.4 in half or more publications were extracted. 193 genes in LUAD and 101 genes in LUSC were included in subsequent analysis (Table II).

Functional enrichment analysis of genes whose expressions are significantly correlated that of IL-6R was performed to allow the identification of biological processes or functions associated with IL-6R expression. In this study, the Database for Annotation, Visualization and Integrated Discovery (DAVID) was used to analyze gene enrichment and pathway analysis to explore the biological processes of gene enrichment (https://david.ncifcrf.gov/summary.jsp).

 Results <sec> <title>Study characteristics

A total of 7 related studies were identified from the GEO database (GSE14814 (21), GSE30219 (22), GSE37745 (23), GSE42127 (24), GSE50081 (25), GSE68465 (26), GSE68571 (27)). Including TCGA lung adenocarcinoma and TCGA lung squamous cell carcinoma datasets, 9 datasets were included in our analysis. In the initial screening, a total of 779 potentially relevant datasets from the GEO database were selected for keyword retrieval. A total of 680 datasets were retrieved after screening sample size and organism. After examination of summary and the clinic outcome of those data, a total of 7 microarray datasets from the GEO database that met the inclusion criteria were included in the present study. Finally, 8 datasets and 6 datasets for adenocarcinoma and squamous cell carcinoma respectively were further analyzed (Fig. 1). Table III showed the baseline characteristics of all included studies. Date of 1,536 LUAD and 739 LUSC patients from Canada, France, UK and USA were included in this analysis.

A quality assessment of the eligible datasets included in this meta-analysis has been performed according to Newcastle-Ottawa Quality Assessment Scale (NOS). The quality score span was from 6 to 9 and the mean score was 7.63 for LUAD and the quality score span was from 6 to 8 and the mean score was 7.33 for LUSC. The impact factors of the journals where the studies were published were of high caliber (Table III). Thus, all of those studies were included in following analysis.

 Overall survival

Univariate analysis and multivariate analysis were respectively carried out for each dataset. P-values, HRs and 95% CIs of IL-6R mRNA in each article for LUAD and LUSC were shown in Table IV and Figs. 2 and 3.

For LUAD, there was no obvious statistical heterogeneity in all of those datasets both in univariate survival analysis and multivariate survival analysis (I2=0.0%, P=0.806; I2=0.0%, P=0.742), a fixed-effects model was used to calculate the pool HRs. Our analysis demonstrated that a higher expression of IL-6R mRNA was significantly associated with better overall survival (OS) (pooled HR=0.50; 95% CI: 0.33–0.68 in univariate analysis; pooled HR=0.50; 95% CI: 0.35–0.73 in multivariate analysis). The forest plots of study-specific HRs for OS were presented in Fig. 4.

For LUSC, we still used a fixed-effects model to pool the HRs (I2=29.3%, P=0.215; I2=36.5%, P=0.217). Interestingly, there was no association between IL-6 mRNA and OS in patients with LUSC (pooled HR=1.59; 95% CI: 0.98–2.59 in univariate analysis; pooled HR=1.64; 95% CI: 0.98–2.75 in multivariate analysis). The forest plots of study-specific HRs for OS were presented in Fig. 5.

 Correlation between IL-6 and IL-6R, IL-6R and IL-6ST

The IL-6 receptor is a protein complex consisting of an alpha chain, IL-6R, and IL-6 signal transducer (IL-6ST). Relationship between the mRNA expression of IL-6 and IL-6R, and between IL-6R and IL-6ST were all analyzed in those datasets. We then performed a meta-analysis based on Fisher's z transformation. Interestingly, IL-6 and IL-6R were negatively correlated in LUAD (pooled r=−0.199, P<0.001), while they were positively correlated in LUSC (pooled r=0.288, P=0.001). The correlation coefficient between IL-6R and IL-6ST in LUAD was similar with correlation coefficient in LUSC (pooled r=0.331, P<0.001 in LUAD and pooled r=0.334, P<0.001 in LUSC; Table V).

 IL-6R mRNA expression in tumor tissues and adjusted normal tissues

TCGA lung adenocarcinoma and TCGA lung squamous cell carcinoma datasets contains gene expression data both in tumor tissues and normal tissues. There were 57 and 51 pairs in LUAD and LUSC. In both two types of lung cancer, IL-6R mRNA expression level in tumor tissues was less than normal tissues (P<0.001; Fig. 6).

 Biological processes and pathway analysis

Functional enrichment analysis was performed on IL-6R and the most related genes (all those genes were shown in Table II). Table VI lists the top five biological processes and pathways enriched in IL-6R correlated genes in LUAD. One of the most significant biological processes is cell division (GO:0051301, P=4.038E-20). Results also showed that those genes enriched in mitotic division. GO:0007067, mitotic nuclear division, GO:0007059, chromosome segregation, GO:0000070, mitotic sister chromatid segregation and GO:0045143, homologous chromosome segregation.

The most important pathway in LUAD is cell cycle (bta04110, P=8.584E-15). As with the results of biological processes, pathway analysis also shows that these genes are involved in cell division, eg. oocyte meiosis (bta04114, P=2.573E-07), progesterone-mediated oocyte maturation (bta04914, P=3.845E-06). The other two important pathways are p53 signaling pathway (bta04115, P<0.001) and HTLV-I infection (bta05166, P=0.008).

Table VII lists the top five biological processes and pathways enriched in LUSC. The most significant biological processes are regulation of cell proliferation (GO:0042127, P=0.001) and positive regulation of osteoclast differentiation (GO:0045672, P=0.003). Result also showed that those genes are involved in immune response (GO:0006955, P=0.003). The other two biological processes are trans-membrane receptor protein tyrosine kinase signaling pathway (GO:0007169, P=0.006) and integrin-mediated signaling pathway (GO:0007229, P=0.006).

The most important pathway in LUSC is natural killer cell mediated cytotoxicity (ptr04650, P<0.001). And the others are osteoclast differentiation (ptr04380, P=0.002), platelet activation (ptr04611, P=0.011), cytokine-cytokine receptor interaction (ptr04060, P=0.015) and HTLV-I infection (ptr05166, P=0.028).

 Discussion

Lung cancer is a serious threat to public health in the world. Cytokines play important roles in tumorigenesis as well as immune surveillance of lung cancer. LUAD and LUSC are two main pathological subtypes of NSCLC. To study the association of various cytokines and their receptors with clinical parameters of NSCLC is an important step for further mechanistic investigations and provides insight into new therapeutic targets. Our study revealed that higher expression levels of IL-6R mRNA in tumor tissues were positively associated with better overall survival in LUAD. These data suggest an antitumoral role of IL-6R signaling.

Recent works have demonstrated that IL-6 signaling pathway plays an important role in the immune response (28). Several studies have demonstrated the pro-tumor effect of IL-6 in NSCLC. Our study revealed a predictive value of IL-6R mRNA expression in LUAD. It suggested that higher IL-6R mRNA was associated with better survival. However, the prognostic value of IL-6R was not shown in LUSC. It is speculated that LUAD and LUSC arise from distinct cells based on the histopathological appearance and gene expression signatures. It is generally accepted that LUAD originates mainly from alveolar epithelial cells and LUSC is possibly derived from basal cells (29,30). LUAD and LUSC undergo distinct developmental processes. Several articles also have showed different result between LUAD and LUSC. One study showed that an increased expression of the embryonic stem cells gene set was associated with overall survival in LUAD. However, there was no correlation in LUSC (31). Meanwhile, other study found that the expression levels of PTN1 genes were associated with survival in LUAD but not LUSC (32). The different results may mainly due to its different cellular origins, developmental stages and tumor microenvironment.

In LUAD, enrichment analysis of IL-6R and its most relevant genes showed that the most significant biological processes were cell division and mitotic division. That means those genes mainly involved in cell cycle progression in LUAD. While in LUSC, the most significant biological processes were regulation of cell proliferation and several signaling pathway. Pathways analysis revealed that those genes were involved in natural killer cell mediated cytotoxicity and platelet activation, meaning that they were mostly involved in tumor angiogenesis, invasion and metastasis. Genes that mostly related with IL-6R and the most significant biological processes were all different in LUAD and LUSC. That means the tumor microenvironment in both cancers were discriminate. This prompts us that studies on LUAD and LUSC should be analyzed separately.

In our report, although there was no statistical significance between IL-6R mRNA expression and OS in LUSC, the lower 95% CI limit of HR (0.98) were very close to 1, showing a trend that IL-6R may be a risk factor for LUSC. Considering the small sample size in each study, further investigations with a larger scale of samples are needed to confirm this result.

IL-6R is a part of the receptor for IL-6 which binds to IL-6 with low affinity, but does not transduce a signal (33). IL-6ST is necessary for this signal activation. Correlation analysis showed that IL-6 and IL-6R were negative correlated in LUAD while they were positive correlated in LUSC, indicating that the higher the expression of IL-6R, the lesser the expression of IL-6 in LUAD and the higher the expression of IL-6 in LUSC. And the pooled correlation coefficient of IL-6R and IL-6ST were positive both in LUAD and LUSC. However, Brooks et al have found that IL-6R protein displayed a positive correlation with IL-6 in LUAD (34). The correlation coefficient of IL-6 and IL-6R in LUAD and LUSC were −0.199 and 0.288, respectively. Both of them were less than 0.3, showing a weak correlation. In our report, the correlation between IL-6 and IL-6R was calculated based on public datasets, showing a possible phenomenon. The possible biological process or interaction between these cytokines should be verified at a tissue, cellular or molecular level by subsequent experimental verification.

In TCGA dataset, we found that IL-6R mRNA expression level in tumor tissues was less than normal tissues in both LUAD and LUSC. Balabko et al have demonstrated the same trend, and they also found that IL-6R mRNA was found significantly induced in the tumoural region of LUAD as compared to LUSC (35). STAT3, a transcription factor downstream of IL-6R, has also been found increased and phosphorylated in LUAD while there was no phosphorylation in LUSC (35). Considering the different effect of IL-6R mRNA in LUAD and LUSC, we deduce that the expression level of IL-6R mRNA can affect the expression level of IL-6 and the activation of downstream pathways and can affect the most important biological processes it involved in.

However, some details need to be further refined. Firstly, this study included only 8 eligible datasets for LUAD and 6 studies for LUSC, which resulted in relatively insufficiency data in the subgroup analyses. Secondly, sample size of LUSC in each study was smaller than LUAD, further articles with a larger scale of samples are needed to confirm the result. Thirdly, these results were calculated based on public datasets and these results should be verified using cells or tumor samples.

In conclusion, our results showed that mRNA levels of IL-6R in LUAD was associated with better prognosis and can potentially be used as a prognostic marker for this cancer. While in LUSC, although there was no statistically significance between IL-6R mRNA and OS in LUSC, taking the small sample size of each dataset, the results should be regarded cautiously. Further prospective studies available of pivotal parameters are needed to verify the prognosis value of IL-6R in LUAD and LUSC patients.

 Acknowledgements

Not applicable.

 Funding

This study was supported by funding from the National Science and Technology Support Program (grant no. 2015BAI12B12), the National Natural Science Foundation of China (grant nos. 31570877 and 31570908), the Special Funds of Science and Technology of the People's Livelihood Construction Condition of Jiangsu Province (grant no. BL2014034), the Science and Technology Bureau foundation application project of Changzhou (grant no. CJ20159018), the Key R&D Project of Science and Technology Department of Jiangsu Province (grant no. BE2015633) and the Program of Jiangsu Engineering Research Center for Tumor Immunotherapy (grant no. BM2014404).

 Availability of data and materials

The datasets analyzed during the current study are available in the Cancer Genome Atlas (cancergenome.nih.gov/) and the Gene Expression Ominibus database (ncbi.nlm.nih.gov/gds/).

 Authors' contributions

BL, JJ and YS designed the study and interpreted the results. LL and CY collected the public datasets and reorganized and cleaned the data. QC, CY and BX designed the experiments and helped write the manuscript. QC and BX wrote and organized the manuscript, with editorial input from YS, JJ and BL. All authors read and approved the final manuscript.

 Ethics approval and consent to participate

Not applicable.

 Consent for publication

Not applicable.

 Competing interests

The authors declare that they have no competing interests.

 References SiegelRLMillerKDJemalACancer statistics, 2016CA Cancer J Clin66730201610.3322/caac.2133226742998 TorreLASiegelRLJemalALung cancer statisticsAdv Exp Med Biol893119201610.1007/978-3-319-24223-1_126667336 JohnsonDHSchillerJHBunnPAJrRecent clinical advances in lung cancer managementJ Clin Oncol32973982201410.1200/JCO.2013.53.122824567433 HerbstRSHeymachJVLippmanSMLung cancerN Engl J Med35913671380200810.1056/NEJMra080271418815398 TravisWDBrambillaENoguchiMNicholsonAGGeisingerKRYatabeYBeerDGPowellCARielyGJVan SchilPEInternational association for the study of lung cancer/American thoracic society/European respiratory society international multidisciplinary classification of lung adenocarcinomaJ Thorac Oncol6244285201110.1097/JTO.0b013e318206a22121252716 ZhanCYanLWangLSunYWangXLinZZhangYShiYJiangWWangQIdentification of immunohistochemical markers for distinguishing lung adenocarcinoma from squamous cell carcinomaJ Thorac Dis713981405201526380766 KishimotoTAkiraSNarazakiMTagaTInterleukin-6 family of cytokines and gp130Blood861243125419957632928 HodgeDRHurtEMFarrarWLThe role of IL-6 and STAT3 in inflammation and cancerEur J Cancer4125022512200510.1016/j.ejca.2005.08.01616199153 HauraEBLivingstonSCoppolaDAutocrine interleukin-6/interleukin-6 receptor stimulation in non-small-cell lung cancerClin Lung Cancer7273275200610.3816/CLC.2006.n.00616512982 YanagawaHSoneSTakahashiYHakuTYanoSShinoharaTOguraTSerum levels of interleukin 6 in patients with lung cancerBr J Cancer7110951098199510.1038/bjc.1995.2127734307 SongürNKuruBKalkanFOzdilekcanCCakmakHHizelNSerum interleukin-6 levels correlate with malnutrition and survival in patients with advanced non-small cell lung cancerTumori90196200200410.1177/03008916040900020715237582 ChangCHHsiaoCFYehYMChangGCTsaiYHChenYMHuangMSChenHLLiYJYangPCCirculating interleukin-6 level is a prognostic marker for survival in advanced nonsmall cell lung cancer patients treated with chemotherapyInt J Cancer13219771985201310.1002/ijc.2789223034889 SeikeTFujitaKYamakawaYKidoMATakiguchiSTeramotoNIguchiHNodaMInteraction between lung cancer cells and astrocytes via specific inflammatory cytokines in the microenvironment of brain metastasisClin Exp Metastasis281325201110.1007/s10585-010-9354-820953899 NodaMYamakawaYMatsunagaNNaoeSJodoiTYamafujiMAkimotoNTeramotoNFujitaKOhdoSIguchiHIL-6 receptor is a possible target against growth of metastasized lung tumor cells in the brainInt J Mol Sci14515526201210.3390/ijms1401051523271367 YiHChoHJChoSMJoKParkJAKimNHAmidonGLKimJSShinHCBlockade of interleukin-6 receptor suppresses the proliferation of H460 lung cancer stem cellsInt J Oncol41310316201222552503 KimNHKimSKKimDSZhangDParkJAYiHKimJSShinHCAnti-proliferative action of IL-6R-targeted antibody tocilizumab for non-small cell lung cancer cellsOncol Lett922832288201510.3892/ol.2015.301926137057 AndoKTakahashiFMotojimaSNakashimaKKanekoNHoshiKTakahashiKPossible role for tocilizumab, an anti-interleukin-6 receptor antibody, in treating cancer cachexiaJ Clin Oncol31e69e72201310.1200/JCO.2012.44.202023129740 AndoKTakahashiFKatoMKanekoNDoiTOheYKoizumiFNishioKTakahashiKTocilizumab, a proposed therapy for the cachexia of Interleukin6-expressing lung cancerPLoS One9e102436201410.1371/journal.pone.010243625010770 StangACritical evaluation of the Newcastle-Ottawa scale for the assessment of the quality of nonrandomized studies in meta-analysesEur J Epidemiol25603605201010.1007/s10654-010-9491-z20652370 BudcziesJKlauschenFSinnBVGyőrffyBSchmittWDDarb-EsfahaniSDenkertCCutoff Finder: A comprehensive and straightforward Web application enabling rapid biomarker cutoff optimizationPLoS One7e51862201210.1371/journal.pone.005186223251644 ZhuCQDingKStrumpfDWeirBAMeyersonMPennellNThomasRKNaokiKLadd-AcostaCLiuNPrognostic and predictive gene signature for adjuvant chemotherapy in resected non-small-cell lung cancerJ Clin Oncol2844174424201010.1200/JCO.2009.26.432520823422 RousseauxSDebernardiAJacquiauBVitteALVesinANagy-MignotteHMoro-SibilotDBrichonPYLantuejoulSHainautPEctopic activation of germline and placental genes identifies aggressive metastasis-prone lung cancersSci Transl Med5186ra66201310.1126/scitranslmed.300572323698379 BotlingJEdlundKLohrMHellwigBHolmbergLLambeMBerglundAEkmanSBergqvistMPonténFBiomarker discovery in non-small cell lung cancer: Integrating gene expression profiling, meta-analysis, and tissue microarray validationClin Cancer Res19194204201310.1158/1078-0432.CCR-12-113923032747 TangHXiaoGBehrensCSchillerJAllenJChowCWSuraokarMCorvalanAMaoJWhiteMAA 12-gene set predicts survival benefits from adjuvant chemotherapy in non-small cell lung cancer patientsClin Cancer Res1915771586201310.1158/1078-0432.CCR-12-232123357979 DerSDSykesJPintilieMZhuCQStrumpfDLiuNJurisicaIShepherdFATsaoMSValidation of a histology-independent prognostic gene signature for early-stage, non-small-cell lung cancer including stage IA patientsJ Thorac Oncol95964201410.1097/JTO.000000000000004224305008 Director's Challenge Consortium for the Molecular Classification of Lung AdenocarcinomaSheddenKTaylorJMEnkemannSATsaoMSYeatmanTJGeraldWLEschrichSJurisicaIGiordanoTJGene expression-based survival prediction in lung adenocarcinoma: A multi-site, blinded validation studyNat Med14822827200810.1038/nm.179018641660 BeerDGKardiaSLHuangCCGiordanoTJLevinAMMisekDELinLChenGGharibTGThomasDGGene-expression profiles predict survival of patients with lung adenocarcinomaNat Med8816824200210.1038/nm73312118244 YuHJoveRThe STATs of cancer-new molecular targets come of ageNat Rev Cancer497105200410.1038/nrc127514964307 HanXLiFFangZGaoYLiFFangRYaoSSunYLiLZhangWTransdifferentiation of lung adenocarcinoma in mice with Lkb1 deficiency to squamous cell carcinomaNat Commun53261201410.1038/ncomms426124531128 SutherlandKDBernsACell of origin of lung cancerMol Oncol4397403201010.1016/j.molonc.2010.05.00220594926 HassanKAChenGKalemkerianGPWichaMSBeerDGAn embryonic stem cell-like signature identifies poorly differentiated lung adenocarcinoma but not squamous cell carcinomaClin Cancer Res1563866390200910.1158/1078-0432.CCR-09-110519808871 LiPZhangLYuXTongRDiXMaoYGaoYZhangKFengLChengSProliferation genes in lung development associated with the prognosis of lung adenocarcinoma but not squamous cell carcinomaCancer Sci109308316201810.1111/cas.1345629168602 Montero-JulianFAThe soluble IL-6 receptors: Serum levels and biological functionCell Mol Biol (Noisy-le-grand)47583597200111502067 BrooksGDMcLeodLAlhayyaniSMillerARussellPAFerlinWRose-JohnSRuwanpuraSJenkinsBJIL6 trans-signaling promotes KRAS-driven lung carcinogenesisCancer Res76866876201610.1158/0008-5472.CAN-15-238826744530 BalabkoLAndreevKBurmannNSchubertMMathewsMTrufaDIReppertSRauTSchichtMSirbuHIncreased expression of the Th17-IL-6R/pSTAT3/BATF/RorγT-axis in the tumoural region of adenocarcinoma as compared to squamous cell carcinoma of the lungSci Rep47396201410.1038/srep0739625491772

Flow diagram of dataset selection process. TCGA, The Cancer Genome Atlas.

Association between IL-6R mRNA expression level and overall survival in patients with LUAD. (A) Kaplan-Meier analysis of datasets (A) GSE14814, (B) GSE30219, (C) GSE37745 (D) GSE42127, (E) GSE50081, (F) GSE68465 and (G) GSE68571. (H) Kaplan-Meier analysis of The Cancer Genome Atlas LUAD data. IL, interleukin; LUAD, lung adenocarcinoma.

Association between IL-6R mRNA expression level and overall survival in LUSC patients. Kaplan-Meier analysis of datasets (A) GSE14814, (B) GSE30219, (C) GSE37745, (D) GSE42127 and (E) GSE50081. (F) Kaplan-Meier analysis of The Cancer Genome Atlas LUSC data. LUSC, lung squamous cell carcinoma; IL, interleukin.

Forest plots of studies evaluating HRs of high IL-6R mRNA expression in lung adenocarcinoma for overall survival. (A) Univariate analysis. HR=0.48, 95% CI: 0.33–0.68. (B) Multivariate analysis. HR=0.50, 95% CI: 0.35–0.73. HR, hazard radio; CI, confidence interval; IL, interleukin.

Forest plots of studies evaluating HRs of high IL-6R mRNA expression in lung squamous cell carcinoma for overall survival. (A) Univariate analysis. HR=1.59, 95% CI: 0.98–2.59. (B) Multivariate analysis. HR=1.64, 95% CI: 0.98–2.75. HR, hazards radio; CI, confidence interval.

IL-6R mRNA expression in tumor tissues and adjusted normal tissues for (A) lung adenocarcinoma and (B) lung squamous cell carcinoma. Data are presented as the mean ± standard deviation and were compared using a two-tailed, paired t-test. ***P<0.001. IL, interleukin.

Cut-off value of IL-6R.

Datasets Minimum P25 Median Mean P75 Maximum Cut-off value
LUAD
  GSE14814 4.211 4.779 5.027 5.134 5.394 6.611 4.607
  GSE30219 5.127 6.343 6.908 7.019 7.686 9.247 7.648
  GSE37745 3.970 7.232 7.698 7.697 8.272 10.200 7.789
  GSE42127 3.670 4.475 4.930 4.945 5.405 7.680 5.515
  GSE50081 5.049 7.281 7.809 7.823 8.405 10.049 7.675
  GSE68465 31.955 246.235 361.102 437.587 543.700 1940.570 393.500
  GSE68571 −41.850 17.188 52.725 69.762 101.213 297.650 44.020
  TCGA 4.360 8.364 9.037 8.970 9.274 11.608 9.125
LUSC
  GSE14814 4.324 4.644 4.912 4.909 5.076 5.871 5.072
  GSE30219 4.232 5.430 6.004 5.946 6.453 7.988 5.741
  GSE37745 4.523 6.395 6.992 6.851 7.458 8.770 7.637
  GSE42127 2.830 4.100 4.390 4.456 4.730 6.260 4.115
  GSE50081 5.475 6.681 7.144 7.125 7.421 9.009 6.678
  TCGA 3.751 7.456 8.286 8.171 8.879 10.982 9.134

IL, interleukin; TCGA, The Cancer Genome Atlas; LUAD, lung adenocarcinoma; LUSC, lung squamous cell carcinoma.

Gene list in LUAD and LUSC.

Cancer Gene names
LUAD ABCC3, ADCY9, AKAP13, ALDH2, ALDH5A1, ALOX15B, APLP2, APOH, AQP3, ARHGAP31, ASF1B, ASPM, ATP13A4, AURKB, BFAR, BIRC5, BUB1, BUB1B, C16orf89, C1orf116, C5orf46, CC2, CCNB1, CCNB2, CCND3, CD302, CD81, CDC20, CDC25A, CDC25C, CDC45, CDC6, CDCA2, CDCA3, CDCA5, CDCA8, CDK1, CDKL2, CDKN3, CDT1, CEBPA, CENPM, CENPN, CENPW, CEP55, CHEK1, CISH, CITED2, CKS2, CLIC3, CLIC5, COL4A3, COL4A4, CPM, CRY2, CTDSPL, CTSH, CYP4B1, DEPDC1, DEPDC1B, DJC9, DLC1, DLGAP5, DYNLL1, ECHDC2, ESPL1, EVPL, EXO1, FAM184A, FAM64A, FAM83D, FANCG, FANCI, FBP1, FBXO5, FCGRT, FEN1, FMO5, FNIP2, FOLR1, FOXM1, GALNT11, GAPDH, GGT1, GGTLC1, GINS1, GPD1L, GPR116, GTSE1, H2AFX, HIP1, HJURP, HLF, HNF1B, ICAM4, ICAM5, IL6R, INMT, KIAA0101, KIF11, KIF18B, KIF20A, KIF23, KIF2C, KIF4A, KIFC1, KNTC1, LOC101930052, LRRK2, MAD2L1, MALL, MAMDC2, MELK, MGLL, MGRN1, MIR3658, MIR6883, MKI67, MLLT11, MTHFD2, MUC1, NBEAL1, NCAPG, NCAPH, NDC80, NEK2, NPC2, NRM, NUF2, NUSAP1, OCLN, OIP5, PARM1, PARPBP, PBK, PC, PCDP1, PEBP4, PI4KB, PLA2G1B, PLK1, PLLP, PMVK, POLE2, PON3, PRC1, PRR15L, PSA, PSRC1, PTS, PTTG1, RAD51AP1, RAD54L, RFC4, RRM2, RSE1, RSEH2A, SELENBP1, SFTA1P, SFTPD, SKA1, SKA3, SLC22A3, SLC34A2, SLC41A1, SNX30, SPAG5, SPAG9, SPC25, ST3GAL5, STAT6, STIL, SUSD2, SYNE1, TLR2, TLR5, TMEM163, TMEM243, TMPRSS2, TPX2, TRIP13, TROAP, TTK, TUBA1C, TUBB, TYMS, UBE2C, UBE2T, UHRF1, USP13, ZBTB4, ZNF385B, ZWINT
LUSC ADAP2, ADCY9, ANXA11, APOBR, ARHGEF3, BTK, BUB1B, C1RL, CCR1, CD37, CD4, CD52, CD93, CD97, CEBPD, COQ3, CORO1A, CRTAP, CSE1L, CTSH, DENND2D, DHX9, DOK2, DUT, ECT2, EPAS1, EZH2, FBXO5, FGR, FHOD1, FOS, GIMAP6, HDAC2, HLA-DPB1, HLA-DQB1, IER2, IFITM1, IL2RB, IL6R, ITGAL, JUNB, KIF15, KLF10, LCP2, LRP10, LSM4, LST1, MAST3, MCM6, MOSPD1, MSH6, MSN, MVP, MYO1F, P2RY13, PAGR1, PDCD5, PDE4A, PLEKHO2, PLIN3, POLA1, PPP1CC, PPP1R16B, PRKCB, PTAFR, PTK2B, PTPN6, RAB27A, RAB31, RAD51AP1, RAD54B, RCN2, RHOG, RPL22, S1PR1, SASH3, SIGLEC1, SLA, SLCO2B1, THEMIS2, TMPO, TNFRSF1A, TNFRSF1B, TPP1, TRANK1, TRPV2, TYROBP, VASP, VWF, ZFYVE26, ARHGAP30, ARHGAP9, C1orf162, CARD6, CXCL16, GIMAP1, LOC100129034, LRRCC1, SARNP, TNFAIP8L2, ZNF664

LUAD, adenocarcinoma; LUSC, squamous cell carcinoma.

Baseline characteristics of microarray data.

Datasets Author Year Country Median duration, months (range) Sample size Stage Outcome measures Quality score Journal IF (Refs.)
LUAD
GSE14814 Zhu CQ 2010 Canada 60.84 (0.36–109.08) 70 I/II (41/29) OS 8 Journal of clinical oncology 24.008 (21)
GSE30219 Rousseaux S 2013 France 68.00 (0–221) 85 NR OS 7 Science translational medicine 16.796 (22)
GSE37745 Botling J 2013 UK 47.80 (0.20–187.79) 106 I/II/III/IV (70/19/13/4) OS 8 Clinical cancer research 9.619 (23)
GSE42127 Tang H 2013 USA 45.60 (0–132) 133 I/II/III/IV (89/22/20/1) OS 7 Clinical cancer research 9.619 (24)
GSE50081 Der SD 2014 Canada 52.44 (1.08–130.56) 129 I/II (93/36) OS 6 Journal of thoracic oncology 6.595 (25)
GSE68465 Shedden K 2008 USA 47.00 (0.03–204.00) 442 NR OS 9 Nature medicine 29.886 (26)
GSE68571 Beer DG 2002 USA 29.50 (1.50–110.60) 86 I/III (67/19) OS 8 Nature medicine 29.886 (27)
TCGA TCGA 2015 USA 15.35 (0.00–223.96) 485 I/II/III/IV (262/118/79/25) OS 8 – –
LUSC
GSE14814 Zhu CQ 2010 Canada 73.56 (8.28–111.48) 52 I/II (25/27) OS 8 Journal of clinical oncology 24.008 (21)
GSE30219 Rousseaux S 2013 France 59.00 (1–256) 61 NR OS 7 Science translational medicine 16.796 (22)
GSE37745 Botling J 2013 UK 36.36 (0.20–179.80) 66 I/II/III (40/15/11) OS 8 Clinical cancer research 9.619 (23)
GSE42127 Tang H 2013 USA 48.00 (0.00–128.40) 43 I/II/III (23/10/10) OS 7 Clinical cancer research 9.619 (24)
GSE50081 Der SD 2014 Canada 62.16 (5.76–144.48) 43 I/II (27/16) OS 6 Journal of thoracic oncology 6.595 (25)
TCGA TCGA 2015 USA 17.27 (0.03–174.08) 474 I/II/III/IV (232148/84/7) OS 8 – –

LUAD, adenocarcinoma; LUSC, squamous cell carcinoma; TCGA, The Cancer Genome Atlas; NR, not report; OS, overall survival; IF, impact factor.

HR of interleukin-6R mRNA for OS.

Univariate analysis Multivariate analysis
Datasets HR LCI UCI P-valuea HR LCI UCI P-valueb
LUAD
  GSE14814c 0.201 0.092 0.443 <0.001 0.190 0.076 0.474 <0.001
  GSE30219d 0.237 0.093 0.603 0.003 0.215 0.081 0.566 0.002
  GSE37745c 0.659 0.416 1.044 0.076 0.589 0.303 1.147 0.120
  GSE42127c 0.559 0.267 1.168 0.122 0.693 0.319 1.505 0.354
  GSE50081e 0.493 0.287 0.846 0.010 0.656 0.370 1.163 0.149
  GSE68465f 0.492 0.375 0.646 <0.001 0.566 0.426 0.752 <0.0001
  GSE68571g 0.492 0.217 1.117 0.089 0.602 0.256 1.418 0.246
  TCGAh 0.617 0.436 0.873 0.006 0.515 0.344 0.772 0.001
LUSC
  GSE14814i 1.701 0.669 4.323 0.264 1.589 0.597 4.227 0.354
  GSE30219d 1.440 0.748 2.775 0.275 1.559 0.797 3.050 0.194
  GSE37745c 2.599 1.344 5.029 0.005 4.071 1.436 11.537 0.008
  GSE42127c 9.906 1.321 74.297 0.026 8.577 1.084 67.868 0.042
  GSE50081j 0.442 0.149 1.312 0.141 0.387 0.119 1.257 0.114
  TCGAh 1.375 0.954 1.981 0.088 1.308 0.899 1.903 0.161

P<0.05 indicated in bold.

Data were compared using Univaruate Cox's proportional hazards model

data were compared using multivariate Cox's proportional hazards model. Multivariate analysis adjusted variables

age, sex, stage, post-treatment

age, sex, T, N

age, sex, stage, T, N

age, sex, T, N, adjuvant-chemotherapy, adjuvant-radio-therapy

age, sex, tumor size, stage

age, sex, stage, T, N, M

age, stage, post-treatment

age, sex, stage, T. HR, hazard ratio; LCI, lower 95% confidence interval; UCI, upper 95% confidence interval; TCGA, The Cancer Genome Atlas; LUAD, adenocarcinoma; LUSC, squamous cell carcinoma.

Correlation between IL-6 and IL-6R, IL-6R and IL-6ST.

IL-6 vs. IL-6R IL-6R vs. IL-6ST
Datasets r P-value r P-value
LUAD
  GSE14814 −0.345   0.004 0.141   0.245
  GSE30219 −0.515 <0.001 0.506 <0.001
  GSE37745 −0.107   0.272 0.294   0.002
  GSE42127   0.041   0.642 0.243   0.005
  GSE50081 −0.207   0.019 0.363 <0.001
  GSE68465 −0.166   0.001 0.271 <0.001
  GSE68571 −0.194   0.073 0.191   0.079
  TCGA −0.165 <0.001 0.527 <0.001
  Pooled r −0.199 <0.001 0.331 <0.001
LUSC
  GSE14814   0.395   0.004 0.073   0.606
  GSE30219   0.036   0.782 0.469   0.000
  GSE37745   0.417   0.001 0.369   0.002
  GSE42127   0.610 <0.001 0.272   0.077
  GSE50081   0.069   0.661 0.155   0.320
  TCGA   0.178 <0.001 0.465   <0.001
  Pooled r   0.288   0.001 0.334 <0.001

P<0.05 indicated in bold. Correlations were calculated using Pearson correlation coefficient and pooled correlations were performed using a meta-analysis based on Fisher's z transformation. IL, interleukin; TCGA, The Cancer Genome Atlas; LUAD, adenocarcinoma; LUSC, squamous cell carcinoma.

Top 5 biological processes and pathway of interleukin-6R and associated genes in lung adenocarcinoma.

Analysis ID Biological processes P-value Genes
Biological processes GO:0051301 Cell division 4.038×10−20 CDK1, CDC6, PSRC1, TPX2, BIRC5, PTTG1, UBE2C, CDC25C, CDC25A, CCNB1, FAM83D, SPC25, NCAPH, CCNB2, CCND3, ZWINT, CDCA2, CKS2, SKA3, SKA1, CDCA5, ASPM, CDCA3
GO:0007067 Mitotic nuclear division 3.084×10−14 CENPN, CDK1, NUF2, PTTG1, CDC25C, CDC25A, FAM83D, SPC25, CCNB2, PLK1, ZWINT, CDCA2, SKA3, CENPW, SKA1, ASPM
GO:0007059 Chromosome segregation 1.529×10−09 SPC25, CENPN, KIF11, OIP5, NEK2, HJURP, SKA3, CENPW, BIRC5, SKA1
GO:0000070 Mitotic sister chromatid segregation 1.186×10−06 CDCA8, PLK1, NEK2, SPAG5, KIF18B, ESPL1
GO:0045143 Homologous chromosome 5.275×10−06 PLK1, ESPL1, PTTG1
Pathway bta04110 Cell cycle 8.584×10−15 CDK1, CDC6, TTK, CDC20, ESPL1, CHEK1, PTTG1, CDC25C, CDC25A, CCNB1, CDC45, CCNB2, MAD2L1, CCND3, PLK1, BUB1, BUB1B
bta04114 Oocyte meiosis 2.573×10−07 CDK1, MAD2L1, LADCY9, PLK1, BUB1, FBXO5, ESPL1, CDC20, PTTG1, CDC25C
bta04914 Progesterone-mediated oocyte maturation 3.845×10−06 CCNB1, CDK1, MAD2L1, CCNB2, LADCY9, PLK1, BUB1, CDC25C, CDC25A
bta04115 p53 signaling pathway <0.001 CCNB1, CDK1, CCNB2, CCND3, RRM2, CHEK1, GTSE1
bta05166 HTLV-I infection 0.008 MAD2L1, CCND3, POLE2, LADCY9, BUB1B, CDC20, CHEK1, PTTG1

Top 5 biological processes and pathway of interleukin-6R and associated genes in lung squamous cell carcinoma.

Analysis ID Biological processes P-value Genes
Biological processes GO:0042127 Regulation of cell proliferation 0.001 TNFRSF1A, TNFRSF1B, FGR, PTK2B, JUNB, BTK
GO:0045672 Positive regulation of osteoclast differentiation 0.003 FOS, KLF10, CCR1
GO:0006955 Immune response 0.003 TNFRSF1A, TNFRSF1B, CCR1, CD4, CTSH, LCP2
GO:0007169 Trans-membrane receptor protein tyrosine kinase signaling pathway 0.006 DOK2, FGR, LCP2, BTK
GO:0007229 Integrin-mediated signaling pathway 0.006 ITGAL, FGR, PTK2B, TYROBP
Pathway ptr04650 Natural killer cell mediated cytotoxicity <0.001 PTPN6, ITGAL, PTK2B, PRKCB, LCP2, TYROBP
ptr04380 Osteoclast differentiation 0.002 FOS, TNFRSF1A, JUNB, LCP2, BTK, TYROBP
ptr04611 Platelet activation 0.011 VWF, LADCY9, PPP1CC, LCP2, BTK
ptr04060 Cytokine-cytokine receptor interaction 0.015 TNFRSF1A, TNFRSF1B, IL2RB, CCR1, CXCL16, IL6R
ptr05166 HTLV-I infection 0.028 FOS, ITGAL, TNFRSF1A, IL2RB, LADCY9, BUB1B