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• W3083803729 abstract "Cervical cancer is the fourth most common cancer in women worldwide. The current approaches still have limitations in predicting the therapy outcome of each individual because of cancer heterogeneity. The goal of this study was to establish a gene expression signature that could help when choosing the right therapeutic method for the treatment of advanced-stage cervical cancer. The 666 patients were collected from four independent datasets. The 70-gene expression signature was established using univariate Cox proportional hazard regression analysis. The 70-gene signature was significantly different between low- and high-risk groups in the training dataset (p = 4.24e−6) and in the combined three validation datasets (p = 4.37e−3). Treatment of advanced-stage cancer patients in the high-risk group with molecular-targeted therapy combined with chemoradiotherapy yielded a better survival rate than with only chemoradiotherapy (p = 0.0746). However, treatment of the patients in the low-risk group with the combined therapy resulted in significantly lower survival (p = 0.00283). Functional classification of 70 genes revealed involvement of the angiogenesis pathway, specifically phosphatidylinositol 3-kinase signaling (p = 0.040), extracellular matrix organization (p = 0.0452), and cell adhesion (p = 0.011). The 70-gene signature could predict the prognosis and indicate an optimal therapeutic modality in molecular-targeted therapy or chemotherapy for advanced-stage cervical cancer. Cervical cancer is the fourth most common cancer in women worldwide. The current approaches still have limitations in predicting the therapy outcome of each individual because of cancer heterogeneity. The goal of this study was to establish a gene expression signature that could help when choosing the right therapeutic method for the treatment of advanced-stage cervical cancer. The 666 patients were collected from four independent datasets. The 70-gene expression signature was established using univariate Cox proportional hazard regression analysis. The 70-gene signature was significantly different between low- and high-risk groups in the training dataset (p = 4.24e−6) and in the combined three validation datasets (p = 4.37e−3). Treatment of advanced-stage cancer patients in the high-risk group with molecular-targeted therapy combined with chemoradiotherapy yielded a better survival rate than with only chemoradiotherapy (p = 0.0746). However, treatment of the patients in the low-risk group with the combined therapy resulted in significantly lower survival (p = 0.00283). Functional classification of 70 genes revealed involvement of the angiogenesis pathway, specifically phosphatidylinositol 3-kinase signaling (p = 0.040), extracellular matrix organization (p = 0.0452), and cell adhesion (p = 0.011). The 70-gene signature could predict the prognosis and indicate an optimal therapeutic modality in molecular-targeted therapy or chemotherapy for advanced-stage cervical cancer. Cervical cancer is the fourth most frequent cancer in women, and about 570,000 new cases were diagnosed in 2018, representing 6.6% of all female cancers.1World Health OrganizationCervical cancer.2018http://www.who.int/health-topics/cervical-cancerGoogle Scholar Early-stage cervical cancer rate increased because of the development of diagnosis technique and screening test. However, many patients are still diagnosed in the advanced stage with poor prognosis. Therefore, it is very important for cervical cancer patients to get a timely, accurate diagnosis and appropriate treatment to increase the survival rate. The National Comprehensive Cancer Network (NCCN) and the International Federation of Gynecology and Obstetrics (FIGO) suggested standard treatment guidelines, such as surgery, radiotherapy, and chemotherapy, for treatment of the patients since the time of diagnosis and according to the disease stage. Even though patients are treated based on these guidelines, each patient has a different prognosis because of the tumor heterogeneity. Therefore, proper stratification of patients, depending on their clinical conditions, is required. In this way, a more effective treatment could be provided to the patient. Many clinical and pathological studies have descried a number of prognostic factors for cervical cancer, such as clinical stage, tumor histology, depth of invasion, tumor grade, size of primary tumor, lymph node involvement, parametrium involvement, and lymph-vascular space invasion.2Aoki Y. Sasaki M. Watanabe M. Sato T. Tsuneki I. Aida H. Tanaka K. High-risk group in node-positive patients with stage IB, IIA, and IIB cervical carcinoma after radical hysterectomy and postoperative pelvic irradiation.Gynecol. Oncol. 2000; 77: 305-309Abstract Full Text PDF PubMed Scopus (116) Google Scholar, 3Ayhan A. Al R.A. Baykal C. Demirtas E. Yüce K. Ayhan A. A comparison of prognoses of FIGO stage IB adenocarcinoma and squamous cell carcinoma.Int. J. Gynecol. Cancer. 2004; 14: 279-285Crossref PubMed Scopus (43) Google Scholar, 4Eifel P.J. Burke T.W. Morris M. Smith T.L. Adenocarcinoma as an independent risk factor for disease recurrence in patients with stage IB cervical carcinoma.Gynecol. Oncol. 1995; 59: 38-44Abstract Full Text PDF PubMed Scopus (231) Google Scholar, 5Sevin B.U. Nadji M. Lampe B. Lu Y. Hilsenbeck S. Koechli O.R. Averette H.E. Prognostic factors of early stage cervical cancer treated by radical hysterectomy.Cancer. 1995; 76: 1978-1986Crossref PubMed Scopus (115) Google Scholar, 6Trattner M. Graf A.H. Lax S. Forstner R. Dandachi N. Haas J. Pickel H. Reich O. Staudach A. Winter R. Prognostic factors in surgically treated stage ib-iib cervical carcinomas with special emphasis on the importance of tumor volume.Gynecol. Oncol. 2001; 82: 11-16Abstract Full Text PDF PubMed Scopus (63) Google Scholar, 7Kristensen G.B. Abeler V.M. Risberg B. Trop C. Bryne M. Tumor size, depth of invasion, and grading of the invasive tumor front are the main prognostic factors in early squamous cell cervical carcinoma.Gynecol. Oncol. 1999; 74: 245-251Abstract Full Text PDF PubMed Scopus (145) Google Scholar A more successful result was obtained when the therapeutic methods, such as surgery, radiotherapy, and chemotherapy, were applied to patients with certain characteristics. NCCN recommends either surgery or radiotherapy for the treatment of patients with FIGO stage IA1–IIA1 called early stage. In contrast, the treatment of choice for advanced cancer with stages IIB–IVA consists of a combined therapy including external beam radiation therapy (EBRT), cisplatin-containing chemotherapy, and brachytherapy.8Monk B.J. Tewari K.S. Koh W.J. Multimodality therapy for locally advanced cervical carcinoma: state of the art and future directions.J. Clin. Oncol. 2007; 25: 2952-2965Crossref PubMed Scopus (192) Google Scholar, 9Gaffney D.K. Soisson A.P. Simple or complex: optimal therapy for cancer of the cervix.Gynecol. Oncol. 2010; 119: 401-403Abstract Full Text Full Text PDF PubMed Scopus (8) Google Scholar, 10Abu-Rustum N.R. Yashar C.M. Bradley K. Campos S.M. Chon H.S. Chu C. et al.Cervical cancer (version 1. 2018). NCCN, 2018https://www.nccn.org/store/login/login.aspx?ReturnURL=https://www.nccn.org/professionals/physician_gls/pdf/cervical.pdfGoogle Scholar In case of metastasis, targeted therapy and immunotherapy have been applied.11Rotman J. Mom C.H. Jordanova E.S. de Gruijl T.D. Kenter G.G. ‘DURVIT’: a phase-I trial of single low-dose durvalumab (Medi4736) IntraTumourally injected in cervical cancer: safety, toxicity and effect on the primary tumour- and lymph node microenvironment.BMC Cancer. 2018; 18: 888Crossref PubMed Scopus (18) Google Scholar,12Menderes G. Black J. Schwab C.L. Santin A.D. Immunotherapy and targeted therapy for cervical cancer: an update.Expert Rev. Anticancer Ther. 2016; 16: 83-98Crossref PubMed Scopus (75) Google Scholar Despite the development of many treatment options, the survival rate in advanced-stage cancer remains poor. Recently, the gene expression profiles obtained from microarray, next generation sequencing (NGS), and subsequent oncogenic signaling pathway analysis have become useful for predicting prognosis of the disease and for discovering therapeutic targets in various cancers.13Dang Y. Wang Y.C. Huang Q.J. Microarray and next-generation sequencing to analyse gastric cancer.Asian Pac. J. Cancer Prev. 2014; 15: 8033-8039Crossref PubMed Scopus (0) Google Scholar, 14Nguyen M.N. Choi T.G. Nguyen D.T. Kim J.H. Jo Y.H. Shahid M. Akter S. Aryal S.N. Yoo J.Y. Ahn Y.J. et al.CRC-113 gene expression signature for predicting prognosis in patients with colorectal cancer.Oncotarget. 2015; 6: 31674-31692Crossref PubMed Scopus (29) Google Scholar, 15Shahid M. Choi T.G. Nguyen M.N. Matondo A. Jo Y.H. Yoo J.Y. Nguyen N.N. Yun H.R. Kim J. Akter S. et al.An 8-gene signature for prediction of prognosis and chemoresponse in non-small cell lung cancer.Oncotarget. 2016; 7: 86561-86572Crossref PubMed Scopus (25) Google Scholar In estrogen receptor-positive breast cancer, a 21-gene signature has been widely used to improve disease-free survival and to predict chemo-resistance to anthracyclines and a beneficial outcome with tamoxifen administration.16Arranz E.E. Vara J.A. Gámez-Pozo A. Zamora P. Gene signatures in breast cancer: current and future uses.Transl. Oncol. 2012; 5: 398-403Crossref PubMed Scopus (19) Google Scholar,17Paik S. Shak S. Tang G. Kim C. Baker J. Cronin M. Watson D. Bryant J. Costontino J. Wolmark N. Expression of the 21 genes in the Recurrence Score assay and tamoxifen clinical benefit in the NSABP study B-14 of node negative, estrogen receptor positive breast cancer.J. Clin. Oncol. 2005; 23: 510Crossref PubMed Google Scholar In this regard, microarray data have been studied for diagnosis, prognosis, or prediction of therapeutic response in cervical cancer as well.18Kitahara O. Katagiri T. Tsunoda T. Harima Y. Nakamura Y. Classification of sensitivity or resistance of cervical cancers to ionizing radiation according to expression profiles of 62 genes selected by cDNA microarray analysis.Neoplasia. 2002; 4: 295-303Crossref PubMed Scopus (127) Google Scholar, 19Harima Y. Ikeda K. Utsunomiya K. Shiga T. Komemushi A. Kojima H. Nomura M. Kamata M. Sawada S. Identification of genes associated with progression and metastasis of advanced cervical cancers after radiotherapy by cDNA microarray analysis.Int. J. Radiat. Oncol. Biol. Phys. 2009; 75: 1232-1239Abstract Full Text Full Text PDF PubMed Scopus (29) Google Scholar, 20Buttarelli M. Babini G. Raspaglio G. Filippetti F. Battaglia A. Ciucci A. Ferrandina G. Petrillo M. Marino C. Mancuso M. et al.A combined ANXA2-NDRG1-STAT1 gene signature predicts response to chemoradiotherapy in cervical cancer.J. Exp. Clin. Cancer Res. 2019; 38: 279Crossref PubMed Scopus (10) Google Scholar, 21Xia L. Wang H. Cai S. Su X. Shen J. Meng Q. Chen Y. Li L. Yan J. Zhang C. Xu M. Integrated Analysis of a Competing Endogenous RNA Network Revealing a Prognostic Signature for Cervical Cancer.Front. Oncol. 2018; 8: 368Crossref PubMed Scopus (12) Google Scholar However, no favorable results have been obtained yet. Therefore, more reliable gene signatures are warranted to further achieve predictive accuracy of prognosis and effective treatments. In this study, we established a novel prognostic gene signature to distinguish low- and high-risk patients. In addition, we assessed the association between the gene signatures and clinicopathological factors. Finally, we provided a patient profile that could receive the most benefit from molecular-targeted therapy. In order to identify a prognostic gene signature that distinguished low- and high-risk patients, we analyzed gene expression profiles based on survival data. Gene Expression Omnibus (GEO): GSE44001 was assigned as the training dataset; meanwhile, The Cancer Genome Atlas (TCGA) RNA sequencing (RNA-seq) and microarray data (GEO: GSE39001 and GSE52904) corresponded to validation datasets. Patients were classified into low- (n = 150) and high-risk (n = 150) groups by the expression pattern of 70 genes and their prognostic indexes (PIs) (Figures 1A and 1B ). There was a significant survival difference between the low- and high-risk groups (p = 4.24e−6; Figure 1C). The 70-gene signature was applied to other datasets for validation, and a statistically significant difference of survival between the two groups was observed (p = 0.00437; Figure 1D). Healthy controls had lower PI than cervical cancer patients (p = 1.102e−12; Figure 1E). In both RNA-seq and microarray datasets, low-risk patients had a better survival rate than high-risk patients (p = 0.0311 and p = 0.0466, respectively; Figures S1A and S1B). The expression pattern of 70 genes was divided by low- and high-risk patients in both microarray and TCGA datasets (Figures S1C and S1D). Next, cervical cancer patients were divided based on NCCN guidelines and FIGO staging system into two groups: early stage (stage I–IIA) and advanced stage (stage IIB–IV). Patients in the early-stage group can be cured by surgery and/or radiotherapy, whereas those with advanced-stage cancer require combined therapy, including radiation and chemotherapy. As previously reported, the survival rate in our patient data was also different between the early and advanced stages (p = 3.35e−13; Figure 2A). Our 70-gene signature stratified patients’ survival in all stages (p = 8.61e−6; Figure 2B). Importantly, the low-risk group had a significantly higher survival rate than the high-risk group in the early stage (p = 4.65e−6; Figure 2C). However, advanced-stage patients did not show the statistical significance (p = 0.0919; Figure 2D), but patients showed significant survival difference between the two groups after 9 months (p = 0.0421; Figure 2E). To evaluate the prognostic accuracy of the 70-gene signature in relation to the cancer stage, we performed univariate and multivariate Cox proportional hazards regression analyses using all of the datasets. As shown in Table S1, stage was significantly associated with overall survival (OS) in both analyses (univariate: hazard ratio [HR], 3.547, 95% confidence interval [CI]: 2.466–5.103, p = 8.8e−12; multivariate: HR, 3.691, 95% CI: 2.564–5.313, p = 2.2e−12). The 70-gene signature was also significantly associated with OS according to univariate and multivariate analyses (univariate: HR, 2.265, 95% CI: 1.566–3.275, p = 1.4e−5; multivariate: HR, 2.369, 95% CI: 1.637–3.428, p = 4.8e−6). The tumor size is an important clinical diagnosis factor; the threshold size for our study was 4 cm in diameter. As reported, cervical cancer patients with tumor over 4 cm had a worse prognosis than under 4 cm (p = 2.91e−5; Figure 3A). The patients in the low-risk group had lower PI values than those of the high-risk group, even though they carried tumors of over 4 cm in size (Figure 3B). Regardless of tumor size, the high-risk group had poor survival rate in both groups: under and over 4 cm (p = 0.0049 and p = 0.00735, respectively; Figures 3C and 3D). Cox proportional hazard regression test was performed when analyzing the association with tumor size (univariate: HR, 3.703, 95% CI: 1.917–7.152, p = 9.7e−5; multivariate: HR, 2.724, 95% CI: 1.397–5.311, p = 0.00326) and 70-gene signature (univariate: HR, 6.769, 95% CI: 2.632–17.409, p = 7.3e−5; multivariate: HR, 5.491, 95% CI: 2.108–14.304, p = 0.00049) (Table S2). Patients younger than 65 years of age at the time of diagnosis of cervical cancers have higher chances of longer survival.22Yost S. Hoekstra A. Cervical cancer in women over 65: An analysis of screening.Gynecol. Oncol. Rep. 2018; 25: 48-51Crossref PubMed Scopus (11) Google Scholar The 70-gene signature further stratified patients according to age under 65 years into low- and high-risk groups (p = 0.00153; Figure S2A). In this age group, patients were stratified regardless of early or advanced stage (p = 0.0126 and p = 0.0653, respectively; Figures S2B and S2C). The Cox proportional hazard regression test was performed in advanced stage with under 65 years old (univariate: HR, 2.144, 95% CI: 1.346–3.416, p = 0.0013; multivariate: HR, 2.048, 95% CI: 1.284–3.265, p = 0.0033) and 70-gene signature (univariate: HR: 2.134, 95% CI: 1.328–3.432, p = 0.0017; multivariate: HR, 2.095, 95% CI: 1.303–3.368, p = 0.0023) (Table S3). Next, we analyzed the treatment method, such as hysterectomy, radiotherapy, chemotherapy, and molecular-targeted therapy, that was applied to early-stage or late-stage cancer patients. Following the NCCN guidelines, the first-line treatment is hysterectomy and/or radiotherapy in early-stage patients. In early-stage patients, there was no difference in patient survival depending on the applied conventional therapy, such as hysterectomy and/or radiotherapy (Figure S3A). Chemotherapy did not affect the survival even in combination with conventional therapies (Figure S3B). However, chemoradiation therapy is the first-line therapy in advanced-stage patients. The chemoradiation therapy showed a significant difference between the low- and high-risk groups (p = 0.0341; Figure S4). Recently, molecular-targeted therapy was provided to increase survival of patients. Survival differences were observed among the various combination therapies in both low- and high-risk patients with advanced-stage cancer (p = 7.04e−5 and p = 0.0208, respectively; Figures 4A and 4B ). However, molecular-targeted therapy resulted in the opposite effect between low- and high-risk patients (p = 0.00283 and p = 0.0746, respectively; Figures 4C and 4D). The combined molecular-targeted therapy, hysterectomy, and chemoradiation therapy affected the survival of patients in only the high-risk group (p = 0.705, and p = 0.0374, respectively; Figures 4E and 4F). In this study, the term hysterectomy included radical hysterectomy, simple hysterectomy, and radical trachelectomy. Gene Ontology enrichment analysis in DAVID (Database for Annotation, Visualization, and Integrated Discovery, National Institutes of Health, Bethesda, MD, USA) was used to identify the biological functions of the genes in the 70-gene signature (Table S4) and identified 31 significant terms (biological processes) (Table 1). These terms consist of 29% of cellular process, 16.8% metabolic process, 10.3% biological regulation, and 10.3% response to stimulus (Figure 5A). The 70 genes were analyzed to see the relationship with functional pathway using the PANTHER system. They were involved in various pathways (Figure 5B). The functions of 70 genes were related to binding (44.0%), catalytic activity (34.0%), and other processes (Figure 5C).Table 1Gene Ontology AnalysisTermNameCountp ValueGenesGO:0031623receptor internalization40.000698039GRB2, GHR, DNM2, BTN1A1GO:0040014regulation of multicellular organism growth30.004932049FGFR2, STAT5A, GHRGO:0001764neuron migration40.008871214CXCL12, CDK5R2, BAX, NTRK2GO:0007155cell adhesion70.010592445CXCL12, CX3CL1, NCAN, PCDHB6, APLP1, HAPLN1, COL19A1GO:0032526response to retinoic acid30.011967854RBP4, NCOA1, SCAMP3GO:0006549isoleucine metabolic process20.01210023STAT5A, GHRGO:0000255allantoin metabolic process20.01210023STAT5A, GHRGO:0007595lactation30.012533686NCOA1, STAT5A, GHRHRGO:0006573valine metabolic process20.016101492STAT5A, GHRGO:0046449creatinine metabolic process20.016101492STAT5A, GHRGO:0001501skeletal system development40.018107062ZBTB16, NCAN, HAPLN1, COL19A1GO:0006417regulation of translation30.018809178DDX25, EIF4E, TYMSGO:0019530taurine metabolic process20.020086785STAT5A, GHRGO:0048562embryonic organ morphogenesis20.020086785RBP4, FGFR2GO:0021987cerebral cortex development30.021620051NCOA1, BAX, NTRK2GO:0060041retina development in camera-type eye30.023086905BAX, SERPINF1, NTRK2GO:0018108peptidyl-tyrosine phosphorylation40.0241554BTC, FGFR2, STAT5A, NTRK2GO:0006101citrate metabolic process20.028009715STAT5A, GHRGO:0051384response to glucocorticoid30.028530929TYMS, GHR, GHRHRGO:0007568aging40.02932512GRB2, SERPINF1, TYMS, BTN1A1GO:0010518positive regulation of phospholipase activity20.031947477ARHGAP6, FGFR2GO:0060670branching involved in labyrinthine layer morphogenesis20.03586952GRB2, FGFR2GO:0060068vagina development20.03586952RBP4, BAXGO:0030324lung development30.038002374RBP4, EIF4E, FGFR2GO:0006105succinate metabolic process20.039775906STAT5A, GHRGO:0014066regulation of phosphatidylinositol 3-kinase signaling30.039837466GRB2, BTC, FGFR2GO:0006600creatine metabolic process20.043666696STAT5A, GHRGO:0030198extracellular matrix organization40.045163581MFAP2, NCAN, HAPLN1, COL19A1GO:0006107oxaloacetate metabolic process20.047541952STAT5A, GHRGO:0002031G-protein-coupled receptor internalization20.047541952DNM2, BTN1A1GO:0045778positive regulation of ossification20.047541952ZBTB16, BTN1A1 Open table in a new tab The previous studies showed that the gene signatures can predict prognosis in cervical cancer patients. In locally advanced cervical cancer, it was suggested that ANXA2-NDRG1-STAT1 gene signature was a candidate for concurrent chemoradiation treatment.20Buttarelli M. Babini G. Raspaglio G. Filippetti F. Battaglia A. Ciucci A. Ferrandina G. Petrillo M. Marino C. Mancuso M. et al.A combined ANXA2-NDRG1-STAT1 gene signature predicts response to chemoradiotherapy in cervical cancer.J. Exp. Clin. Cancer Res. 2019; 38: 279Crossref PubMed Scopus (10) Google Scholar Fernandez-Retana et al.23Fernandez-Retana J. Zamudio-Meza H. Rodriguez-Morales M. Pedroza-Torres A. Isla-Ortiz D. Herrera L. Jacobo-Herrera N. Peralta-Zaragoza O. López-Camarillo C. 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Oncol. 2018; 31: 403-408Google Scholar In clinic, FIGO stage system classifies cancer depending on tumor size, spread to a lymph node, and metastasis. Stages are further subdivided based on the lesion’s maximum diameter: stage IB3, diameter of ≥4 cm; and stage IIA2, diameter of ≥4 cm.33Berek J.S. Matsuo K. Grubbs B.H. Gaffney D.K. Lee S.I. Kilcoyne A. Cheon G.J. Yoo C.W. Li L. Shao Y. et al.Multidisciplinary perspectives on newly revised 2018 FIGO staging of cancer of the cervix uteri.J. Gynecol. Oncol. 2019; 30: e40Crossref PubMed Scopus (23) Google Scholar Cervical cancer is diagnosed most frequently in middle-aged women between the ages of 35 and 44 years. It rarely develops in women younger than 20 years. The older women, over 65 years old, account for 10% of cervical cancer patients and are more likely to die of the disease because they are at advanced stage when diagnosed. Importantly, we showed that the low-risk patients stratified by our 70-gene signature had better survival even in the same tumor stage, size, and age, suggesting that cervical cancer patients were more accurately subclassified if the 70-gene signature was added to the conventional classification system. Although the prediction of survival and prognosis depends on the FIGO stage, tumor size, and age, it is unknown why prognosis, survival, and drug response are different in the same stage. Radiotherapy and/or hysterectomy is the standard therapy for treatment of early-stage cancer, whereas chemoradiation therapy is generally used for advanced-stage patients. Generally, 80%–90% of patients in early stage are cured using surgery and/or radiotherapy, but it is still difficult to choose the right treatment method for women with advanced-stage cervical cancer. To increase the survival of the patients, chemotherapy and molecular-targeted therapy are currently recommended to early-stage and advanced-stage patients, respectively. In this study, we showed that the 70-gene signature increased accuracy for predicting prognosis of the patients. We observed that chemotherapy had no effect on survival in the early stage of cancer. In contrast, molecular-targeted therapy combined with chemoradiation therapy provided a survival benefit in high-risk patients (p = 0.0746), but had the opposite result in low-risk patients (p = 0.00283). Thus, the 70-gene signature could help to recommend molecular-targeted therapy to high-risk patients suffering from advanced-stage cancer. 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Roemer-Becuwe C. Weber B. Guillemet C. Paraiso D. Pujade-Lauraine E. Cetuximab, topotecan and cisplatin for the treatment of advanced cervical cancer: A phase II GINECO trial.Gynecol. Oncol. 2009; 113: 16-20Abstract Full Text Full Text PDF PubMed Scopus (71) Google Scholar programed cell death (PD-1) and programmed cell death ligand 1 (PD-L1) inhibitors increased the response rate in recurrence and metastatic cervical cancer patients.42Chung H.C. Schellens J.H.M. Delord J.-P. Perets R. Italiano A. Shapira-Frommer R. Manzuk L. Piha-Paul S.A. Wang J. Zeigenfuss S. et al.Pembrolizumab treatment of advanced cervical cancer: Updated results from the phase 2 KEYNOTE-158 study.J. Clin. Oncol. 2018; 36: 5522Crossref Google Scholar In our study, 31 biological processes were identified from Gene Ontology term analysis of 70 genes. Analysis by the PANTHER classification system revealed the angiogenesis pathway as the major target of molecular-targeted therapy. Bevacizumab, an angiogenesis blocker, was associated with regulation of phosphatidylinositol 3-kinase signaling, extracellular matrix organization, and cell adhesion, which were identified in our Gene Ontology analysis (Table 1). Our study had several strengths. First, we showed that the 70-gene signature was a good biomarker for better prognosis prediction of cervical cancer. Second, it allowed for stratification of the patients into two distinctive risk groups even when the patients were also classified in terms of stage, size, and age. Finally, it provided the guidance on how to select patients (high risk in the advanced stage) who could benefit from optimal molecular-targeted therapy. There are a few limitations in this study. Only a small number of patients were analyzed for outcome determination with each therapeutic modality, and more detailed analysis of each molecular-targeted therapy and chemotherapy drugs was not possible to perform due to limited information. The clinical stages used in this study corresponded to the previous FIGO version because the new staging system was announced in 2019. In conclusion, the 70-gene signature could constitute a more accurate biomarker for further stratification of patients besides by stage, size, and age. It also provided guidance for identifying patients who could benefit from molecular-targeted therapy in advanced-stage cervical cancer. Three gene expression datasets were obtained from the National Center for Biotechnology Information GEO database (http://www.ncbi.nlm.nih.gov/geo) and one NGS dataset was from TCGA database (https://www.cancer.gov/about-nci/organization/ccg/research/structural-genomics/tcga). Gene expression data from the GEO: GSE44001 was used as the training dataset. TCGA, GSE39001, and GSE52904 were used as validation datasets (Table S5). Table 2 showed clinicopathological and clinical information of patients in detail, which were provided by TCGA and GEO databases. Healthy controls data were collected from GEO: GSE39001 and GEO: GSE52904.Table 2Clinical Characteristics of Patients in the Training and Validation Datasets with Cervical CancerVariableTraining SetValidation SetGEO: GSE44001TCGAGEO: GSE39001GEO: GSE52094Patients used, n3002902155Median age (range), years46 (20–88)43 (32–67)50 (24–74)Tumor size<4 cm218≥4 cm82FIGO stage (n)I2581551427II426678III042016IV02104DFS (range), months47.5 (0.43–104.13)Follow-up (range), months61 (17–93)58 (1–86)OS (range), months18.4 (0.03–214)HysterectomyYes1551115No91040RadiotherapyYes1751741No63414ChemotherapyYes1451123No1441032Molecular-targeted therapyYes84No44DFS, disease-free survival; FIGO, International Federation of Gynecology and Obstetrics; OS, overall survival; TCGA, The Cancer Genome Atlas. Open table in a new tab DFS, disease-free survival; FIGO, International Federation of Gynecology and Obstetrics; OS, overall survival; TCGA, The Cancer Genome Atlas. The gene expression data in the GEO: GSE44001 dataset were used to develop the gene signature. At first, genes were filtered by more than 1.5-fold absolute value of log2 scale, which represented the same gene expression level. The filtering step leads to 26,156 probes that were selected among various probe sets. In the next step, the disease-free survival (DFS)-associated gene expression signature derived from the training dataset was identified by the univariate Cox proportional hazard regression (p < 0.001). Initially, 175 probes were found from the analysis. However, only 70 genes were common among the four datasets (one training and three validation datasets). Then the 70-gene signature was selected as a prognostic signature. To predict prognosis, we applied selected probes from the survival signature to the survival risk prediction analysis. The PI was computed by the formula:∑iwixi− 0.250394where wi and xi were the weight and logged gene expression for the i-th gene, respectively. The patients were divided into two groups based on a median PI of −0.107633. If the PIs were greater than −0.107633, patients were assigned to the high-risk group. In contrast, patients with PIs equivalent to or less than −0.107633 were assigned to the low-risk group. The cluster analysis was performed with Cluster 3.0. Initially, 175 probes were used for prediction model analysis, and only the 70 genes were common in all of the chip types (Figure S5). The validation of the gene signature was accomplished on independent datasets. Gene expression data from validation datasets were individually adjusted by subtracting the median expression value across the samples. To integrate each validation dataset in order to construct the prediction models, we aligned the 70-gene set in each dataset. To further refine this model and to sub-stratify the predicted outcomes, we used Compound Covariate Predictor (CCP) as a class prediction algorithm. Gene expression data in the training set were combined to form a classifier according to CCP. The robustness of the classifier was determined by the misclassification rate obtained during the leave-one-out cross-validation (LOOCV) in the training set. Kaplan-Meier (KM) survival analyses were performed after the patients were divided into two predicted subgroups, and chi-square (χ2) and log rank tests were used to evaluate the survival risk between the two predicted subgroups of patients. Univariate and multivariate Cox proportional hazard models were used to evaluate independent prognostic factors associated with survival, gene signature, stage, size, and age, as covariates. Heatmap was analyzed using BRB-Array Tools Version 4.3 (National Institutes of Health, MD, USA). All other statistical analyses were accomplished in the R language environment (R Foundation for Statistical Computing, Vienna, Austria) and Statistical Package for Social Sciences (SPSS) software (version 20; IBM, Armonk, NY, USA). Cluster analysis was performed with Cluster 3 and Tree View (Stanford University, CA, USA). Statistical significance was defined as p < 0.05. DAVID was used to classify the genes. DAVID provides a comprehensive set of functional annotation and biological meaning of genes. PANTHER classification system (University of Southern California, CA, USA) was used to classify the proteins, and genes by the function. Protein functions were categorized according to the biological process, protein class, and molecular function through protein’s relationship analysis. N.N.Y.N., Y.H.J., and S.S.K. designed and performed the research, analyzed the data, and wrote the manuscript. T.G.C., J.K., and Y.S. assisted in bioinformatics and statistical analyses. M.H.J. and S.H.K. supervised the study and wrote the manuscript. I.K. and J.H. conceived the project and supervised the study. All authors revised and approved the manuscript. Y.H.J. and S.S.K. shared the corresponding authorship. The authors declare no competing interests. This study was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) grants funded by the Korean Government ( NRF-2017R1A6A3A11028420 and NRF-2020R1I1A1A01065254 to Y.H.J.; NRF-2018R1A6A1A03025124 to S.S.K.). Download .pdf (.57 MB) Help with pdf files Document S1. Figures S1–S5 and Tables S1–S3 and S5 Download .xlsx (.01 MB) Help with xlsx files Table S4. List of 70-Gene Signatures" @default.
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