Identification of prognostic genes after platinum-based chemotherapy in ovarian cancer

2020 ◽  
Author(s):  
Jianyang Feng ◽  
Lijiang Xu ◽  
Yangping Chen ◽  
Weifeng Li ◽  
Yuyuan Zhu ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Roc Curve ◽  
Kegg Pathway ◽  
Expression Profiles ◽  
Operative Therapy ◽  
Time Dependent ◽  
Rank Test ◽  
Survival Prediction ◽  
Pathway Enrichment ◽  
Platinum Based Chemotherapy

Abstract Background: Platinum-based chemotherapy plays a crucial role in pre- and post-operative therapy in advanced stage ovarian cancer (OC). The objective of this study was to explore differentially expression genes (DEGs) and their survival impact after exposing to platinum-based chemotherapy in OC patient via integrated bioinformatics analysis.Methods: Gene expression profiles of RNA-seq data in OC were extracted from the GEO and TCGA databases respectively. DEGs were sent to perform functional Gene Ontology and KEGG pathway enrichment analyses. Survival analysis was processed to identify significant prognostic genes. After overlapping between DEGs and prognostic genes, univariate and multivariate Cox proportion hazards models were utilized to estimate the hazard ratio of the potential genes with a 95% confidence interval. Finally, the Kaplan-Meier log rank test and the time-dependent receiver operating characteristic (ROC) curve were performed to evaluate the potential prognostic prediction in platinum-based chemotherapy OC patients.Results: A total of 484 up-regulated and 495 down-regulated DEGs were identified. Down-regulated DEGs remarkedly enriched in the cell cycle, oocyte meiosis, progesterone-mediated oocyte maturation, homologous recombination in KEGG pathway enrichment analyses. After overlapping with survival genes in the TCGA cohort, 64 DEGs were demonstrated prognostic potential. Then 29 genes were further identified by univariate analyses. Multivariate analyses indicated eight of 29 genes (DHRS9, OVOS2, STAC2, TCF15, AADAC, LOC730183, LOC440910, ARHGDIG) demonstrated survival prediction potential in platinum-based chemotherapy OC patients. The area under the curve of the time-dependent ROC curve was 0.725 for 5-year survival prediction based on those eight genes.Conclusion: These prognostic genes identified in this study indicate some significance for prognosis prediction in platinum-based chemotherapy OC patients.

scholarly journals Identifying a ten-microRNA signature as a superior prognosis biomarker in colon adenocarcinoma

2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Rong Ma ◽  
Yanyun Zhao ◽  
Miao He ◽  
Hongliang Zhao ◽  
Yifan Zhang ◽  
...  
Keyword(s):  
Roc Curve ◽  
Expression Profiles ◽  
Differential Expression Analysis ◽  
Colon Adenocarcinoma ◽  
Functional Enrichment ◽  
Time Dependent ◽  
Survival Prediction ◽  
Aberrant Expression ◽  
Cox Regression Analysis ◽  
Kaplan Meier

Abstract Background Increasing studies have suggested that aberrant expression of microRNAs might play essential roles in the progression of cancers. In this study, we sought to construct a high-specific and superior microRNAs signature to improve the survival prediction of colon adenocarcinoma (COAD) patients. Methods The genome-wide miRNAs, mRNA and lncRNA expression profiles and corresponding clinical information of COAD were collected from the TCGA database. Differential expression analysis, Kaplan–Meier curve and time-dependent ROC curve were calculated and performed using R software and GraphPad Prism7. Univariate and multivariate Cox analysis was performed to evaluate the prognostic ability of signature. Functional enrichment analysis was analyzed using STRING database. Results We identified ten prognosis-related microRNAs, including seven risky factors (hsa-miR-197, hsa-miR-32, hsa-miR-887, hsa-miR-3199-2, hsa-miR-4999, hsa-miR-561, hsa-miR-210) and three protective factors (hsa-miR-3917, hsa-miR-3189, hsa-miR-6854). The Kaplan–Meier survival analysis showed that the patients with high risk score had shorter overall survival (OS) in test series. And the similar results were observed in both validation and entire series. The time-dependent ROC curve suggested this signature have high accuracy of OS for COAD. The Multivariate Cox regression analysis and stratification analysis suggested that the ten-microRNA signature was an independent factor after being adjusted with other clinical characteristics. In addition, we also found microRNA signature have higher AUC than other signature. Furthermore, we identified some miRNA-target genes that affect lymphatic metastasis and invasion of COAD patients. Conclusion In this study, we established a ten-microRNA signature as a potentially reliable and independent biomarker for survival prediction of COAD patients.

scholarly journals Identification of a Glycolysis-Related Gene Signature for Survival Prediction of Ovarian Cancer Patients

2020 ◽  
Author(s):  
Dai Zhang ◽  
Si Yang ◽  
Yiche Li ◽  
Meng Wang ◽  
Jia Yao ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Risk Model ◽  
Cox Regression ◽  
Expression Profiles ◽  
Gynecological Cancer ◽  
Predictive Ability ◽  
Gene Signature ◽  
The Cancer Genome Atlas ◽  
Survival Prediction ◽  
Test Sets

Abstract Background: Ovarian cancer (OV) is deemed as the most lethal gynecological cancer in women. The aim of this study was construct an effective gene prognostic model for OV patients.Methods: The expression profiles of glycolysis-related genes (GRGs) and clinical data of patients with OV were extracted from The Cancer Genome Atlas (TCGA) database. Univariate, multivariate, and least absolute shrinkage and selection operator Cox regression analyses were conducted, and a prognostic signature based on GRGs was constructed. The predictive ability of the signature was analyzed in training and test sets.Results: Based on nine GRGs (ISG20, CITED2, PYGB, IRS2, ANGPTL4, TGFBI, LHX9, PC, and DDIT4), a gene risk signature was identified to predict the outcome of patients with OV. The signature showed a good prognostic ability for OV, particularly high-grade OV, in the TCGA dataset, with areas under the curve of 0.709, 0.762, and 0.808 for 3-, 5- and 10-year survival, respectively. Similar results were found in the test sets, and the signature was also an independent prognostic factor. Moreover, a nomogram combining the prediction model and clinical factors was constructed.Conclusion: Our study established a nine-GRG risk model and a nomogram to better perform on OV patients’ survival prediction. The risk model represents a promising and independent prognostic predictor for OV patients. Moreover, our study of GRGs could offer guidances for underlying mechanisms explorations in the future.

Randomized phase III study of erlotinib versus observation in patients with no evidence of disease progression after first-line platin-based chemotherapy for ovarian carcinoma: A GCIG and EORTC-GCG study.

2012 ◽  
Vol 30 (18_suppl) ◽  
pp. LBA5000-LBA5000 ◽  
Author(s):  
Ignace B. Vergote ◽  
Florence Joly ◽  
Dionyssios Katsaros ◽  
Corneel Coens ◽  
Alexander Reinthaller ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Kinase Inhibitor ◽  
Progression Free Survival ◽  
Phase Iii ◽  
Egfr Mutations ◽  
Rank Test ◽  
First Line ◽  
Fallopian Tube Cancer ◽  
Egfr Expression ◽  
Platinum Based Chemotherapy

LBA5000 Background: The epidermal growth factor receptor (EGFR) has been found to be overexpressed in 55-98% of advanced epithelial ovarian cancer. This trial evaluated the efficacy of maintenance erlotinib, an EGFR tyrosine kinase inhibitor, after first-line chemotherapy. Methods: Eligible patients (pts) had high-risk FIGO stage I or stage II-IV epithelial ovarian, peritoneal or fallopian tube cancer and were not selected for EGFR expression. All patients underwent first line therapy (6-9 cycles of 3-weekly platinum-based chemotherapy (CT)) and showed no signs of progression at the end of CT. Patients were randomised to maintenance erlotinib 150 mg daily for 2 years or observation. Primary endpoint was progression-free survival (PFS) by RECIST in combination with GCIG CA125 criteria. The final design provided 80% power to detect a PFS hazard ratio (HR) of 0.80 with 2-sided log-rank test at 5% after 632 events in 830 patients. Stratifications factors were stage, institution, age, response to and type of first-line CT. Immunohistochemistry (IHC) and FISH for EGFR, and EGFR mutation analyses were performed in 330 patients. The study was registered as NCT00263822 and EudraCT number 2004-004333-34. Results: Between Oct 2005 and Feb 2008, 835 pts were randomised by 125 institutions from 10 countries. The most important baseline characteristics, PFS and OS are summarized in the table. Median follow-up was 51 months. 25% of the patients stopped erlotinib due to side effects (of these 67% due to rash). The predictive value of IHC and FISH for EGFR, and EGFR mutations are being evaluated and will be presented at the meeting. Conclusions: In the overall study populationmaintenance erlotinib after first-line treatment in ovarian cancer did not improve progression-free or overall survival. [Table: see text]

New biomarkers to predict platinum resistance in ovarian cancer patients.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. e17085-e17085
Author(s):  
Oana Trifanescu ◽  
Laurentia Minea Gales ◽  
Maria Iuliana Gruia ◽  
Bianca Andreea Gusoiu ◽  
Florina Torliceanu ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Cancer Patients ◽  
Roc Curve ◽  
Advanced Ovarian Cancer ◽  
Progression Free Survival ◽  
Initial Response ◽  
Platinum Resistance ◽  
Platinum Sensitive ◽  
Platinum Salts ◽  
Platinum Based Chemotherapy

e17085 Background: Epithelial ovarian cancer is the second most common gynecologic malignancy and is characterized by the highest mortality of all gynecological cancers. Despite of initial response, platinum resistance develops and contributes to the poor outcome of advanced stage ovarian cancer patients. The aim of the study was to identify biomarkers helpful in predicting treatment response to platinum salts. Methods: Forty eight patients with advanced ovarian (stage II, III and IV) cancer were prospectively enrolled between 2014 and 2017. All patients underwent surgery followed by platinum-based chemotherapy. Serum reactive oxygen species parameters such as malondialdehyde, ceruloplasmine, and serum VEGF were measured before each cycle of chemotherapy. Results: Mean age at diagnostic was 51.3 +/- 8.1 years, (range 42 - 78). Median follow up was 39 months (range 12-56). Twenty tree percent were platinum resistance. Median progression free survival was 22 months and estimated median overall survival was 84 months, 77% of patients being alive at 3 years. VEGF levels were significantly higher in patients with platinum resistance disease (1210 pg/ml) compare to platinum sensitive (mean VEGF levels 945pg/ml, p = 0.0003). We used a ROC curve to estimate the sensitivity and specificity of VEGF as a predictor to platinum response and find out that the aria under the curve (AUC) was 0.874, p = 0.003, 95% CI 0.734-1 and cut-off value (80% sensibility, 80% specificity) was 1085pg/ml. Malondialdehyde levels were statistically significant higher in patients with platinum resistance disease (mean value 11.1 μmol/100 ml vs. 7.4 μmol/100 ml in platinum sensitive, p = 0.02. The ROC curve for malondialdehyde identify an aria under the curve of 0.818, p = 0.0001 and CI 95% (0.744-0.893) and a cut-off value of 7.74 μmol/100 ml to estimate with 81.3% sensitivity and 64% specificity platinum response validating this bio markers as predicting platinum response. For Ceruloplasmine AUC was 0.706, p = 0.0001, 95% CI (0.617,-0.796). Conclusions: Malondialdehyde, ceruloplasmine and VEGF can estimate with precision the resistance to platinum salts in advanced ovarian cancer patients.

scholarly journals Oestrogen receptor 1 mRNA is a prognostic factor in ovarian cancer patients treated with neo-adjuvant chemotherapy: determination by array and kinetic PCR in fresh tissue biopsies

2009 ◽  
Vol 16 (4) ◽  
pp. 1241-1249 ◽  
Author(s):  
Claudio Zamagni ◽  
Ralph M Wirtz ◽  
Pierandrea De Iaco ◽  
Marta Rosati ◽  
Elke Veltrup ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Adjuvant Chemotherapy ◽  
Prognostic Factor ◽  
Cox Regression ◽  
Endocrine Treatment ◽  
Rank Test ◽  
Mrna Levels ◽  
Cox Regression Analysis ◽  
Platinum Based Chemotherapy ◽  
Neo Adjuvant Chemotherapy

Oestrogen receptors (ESRs) regulate the growth and differentiation of normal ovarian epithelia. However, to date their role as biomarkers in the clinical setting of ovarian cancer remains unclear. In view of potential endocrine treatment options, we tested the role of ESR1 mRNA expression in ovarian cancer in the context of a neo-adjuvant chemotherapy trial. Study participants had epithelial ovarian or peritoneal carcinoma unsuitable for optimal upfront surgery and were treated with neo-adjuvant platinum-based chemotherapy before surgery. RNA was isolated from frozen tumour biopsies before treatment. RNA expression of ESR1 was determined by microarray and reverse transcriptase kinetic PCR technologies. The prognostic value of ESR1 was tested using univariate and multivariate Cox proportional hazards models, Kaplan–Meier survival statistics and the log-rank test. ESR1 positively correlates with proliferation markers and histopathological grading. ESR1 was a significant predictor of survival as a continuous variable in the univariate Cox regression analysis. In multivariate analysis, elevated baseline ESR1 mRNA levels predicted prolonged progression-free survival (P=0.041) and overall survival (P=0.01) after neo-adjuvant chemotherapy, independently of pathological grade and age. We conclude that pretreatment ESR1 mRNA is associated with tumour growth and is a strong prognostic factor in ovarian cancer, independent of the strongest clinical parameters used in clinical routine. We suggest that ESR1 mRNA status should be considered in order to minimize possible confounding effects in ovarian cancer clinical trials, and that early treatment with anti-hormonal agents based on reliable hormone receptor status determination is worth investigating.

scholarly journals Integrated analysis of lncRNA-miRNA-mRNA ceRNA network and the potential prognosis indicators in sarcomas

2021 ◽  
Author(s):  
Lu Gao ◽  
Yu Zhao ◽  
Xuelei Ma ◽  
Ling Zhang
Keyword(s):  
Survival Analysis ◽  
Kegg Pathway ◽  
Gene Prediction ◽  
Expression Profiles ◽  
Gene Expression Omnibus ◽  
Integrated Analysis ◽  
Ppi Network ◽  
Tissue Samples ◽  
Pathway Enrichment ◽  
Cerna Network

Abstract Background: Competitive endogenous RNA (ceRNA) has revealed a new mechanism of interaction between RNAs and been demonstrated to play crucial roles in multiple biological processes and in the development of neoplasms that potentially serve as diagnostic and prognosis markers as well as therapeutic targets.Methods：In this work, we identified differentially expressed mRNAs (DEGs), lncRNAs (DELs) and miRNAs (DEMs) in sarcoma by comparing the genes expression profiles between sarcoma samples and normal tissue samples in Gene Expression Omnibus (GEO) datasets. Gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analyses were applied to investigate the primary functions of the overlapped DEGs. Then, lncRNA-miRNA and miRNA-mRNA interactions were predicted, and the ceRNA regulatory network was constructed in Cytoscape. In addition, the protein-protein interaction (PPI) network was constructed and survival analysis was performed.Results: A total of 1296 DEGs were identified in sarcoma samples by combining the GO and KEGG pathway enrichment analyses, 338 DELs were discovered after the probes were reannotated, and 36 DEMs were ascertained through intersecting two different expression miRNAs sets. Further, through target gene prediction, a lncRNA-miRNA-mRNA ceRNA network that contained 113 mRNAs, 69 lncRNAs and 29 miRNAs was constructed. The PPI network identified the six most significant hub proteins. Survival analysis revealed that seven mRNAs, four miRNAs and one lncRNA were associated with overall survival of sarcoma patients.Conclusions: Overall, we constructed a ceRNA network in sarcomas, which likely provides insights for further research on the molecular mechanism and potential prognosis biomarkers.

scholarly journals Identification of a Glycolysis-Related Gene Signature for Survival Prediction of Ovarian Cancer Patients

2020 ◽  
Author(s):  
Dai Zhang ◽  
Si Yang ◽  
Yiche Li ◽  
Meng Wang ◽  
Jia Yao ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Risk Model ◽  
Cox Regression ◽  
Expression Profiles ◽  
Gynecological Cancer ◽  
Predictive Ability ◽  
Gene Signature ◽  
The Cancer Genome Atlas ◽  
Survival Prediction ◽  
Test Sets

Abstract Background: Ovarian cancer (OV) is deemed as the most lethal gynecological cancer in women. The aim of this study was construct an effective gene prognostic model for OV patients.Methods: The expression profiles of glycolysis-related genes (GRGs) and clinical data of patients with OV were extracted from The Cancer Genome Atlas (TCGA) database. Univariate, multivariate, and least absolute shrinkage and selection operator Cox regression analyses were conducted, and a prognostic signature based on GRGs was constructed. The predictive ability of the signature was analyzed in training and test sets.Results: Based on nine GRGs (ISG20, CITED2, PYGB, IRS2, ANGPTL4, TGFBI, LHX9, PC, and DDIT4), a gene risk signature was identified to predict the outcome of patients with OV. The signature showed a good prognostic ability for OV, particularly high-grade OV, in the TCGA dataset, with areas under the curve of 0.709, 0.762, and 0.808 for 3-, 5- and 10-year survival, respectively. Similar results were found in the test sets, and the signature was also an independent prognostic factor. Moreover, a nomogram combining the prediction model and clinical factors was constructed.Conclusion: Our study established a nine-GRG risk model and a nomogram to better perform on OV patients’ survival prediction. The risk model represents a promising and independent prognostic predictor for OV patients. Moreover, our study of GRGs could offer guidance for underlying mechanisms explorations in the future.

scholarly journals Identification of differentially expressed genes and signalling pathways related to ovarian endometriosis based on integrated bioinformatic analysis

2021 ◽  
Author(s):  
Kainan Lin ◽  
Zhenyan Pan ◽  
Renke He ◽  
Hanchu Wang ◽  
Kai Zhou ◽  
...  
Keyword(s):  
Endoplasmic Reticulum ◽  
Kegg Pathway ◽  
Expression Profiles ◽  
Enrichment Analysis ◽  
Protein Processing ◽  
Receptor Interaction ◽  
Pathway Enrichment Analysis ◽  
Channel Activity ◽  
Voltage Gated ◽  
Pathway Enrichment

Abstract Purpose Endometriosis is a common gynaecological disease; however, the specific mechanism and the key molecules involved in endometriosis have not been elucidated. This study aimed to identify key genes associated with poor prognosis and further uncover underlying mechanisms. Methods Data regarding mRNA expression profiles used in this study were retrieved from the Gene Expression Omnibus (GEO) database, and a total of three mRNA expression profiles were included in subsequent analyses (GSE31515, GSE58178 and GSE120103). We divided all differentially expressed genes (DEGs) into up-regulated and down-regulated groups. Then, we conducted Gene Ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis and protein-protein interaction (PPI) analysis using R software.Results A total of 304 DEGs were identified between endometriosis tissues and normal endometrium tissues using integrated analysis, including 185 up-regulated genes and 119 down-regulated genes. GO analysis revealed that the up-regulated DEGs of endometriosis were closely associated with voltage-gated calcium channel activity, whereas the down-regulated DEGs were enriched in uterus development. KEGG pathway enrichment analysis indicated that the up-regulated DEGs were mainly involved in cytokine-cytokine receptor interaction, whereas down-regulated DEGs were enriched in protein processing in the endoplasmic reticulum. In addition, PPIs of these DEGs were visualized using the Cytoscape platform and the Search Tool for the Retrieval of Interacting Genes (STRING). PPI analysis identified 10 potential DEG-related protein targets, including CCND1, IL6, CCL2, COL1A2, PTGS2, VCAM1, COL3A1, ELN, SERPINE1, and HSP90B1. Conclusion In conclusion, the present study reveals that voltage-gated calcium channel activity, uterus development, cytokine-cytokine receptor interaction and protein processing in the endoplasmic reticulum may be involved in the development of endometriosis. In addition, these identified DEGs may exhibit clinical significance for the diagnosis and treatment of endometriosis.

scholarly journals DOCK4 Is a Platinum-Chemosensitive and Prognostic-Related Biomarker in Ovarian Cancer

PPAR Research ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Qianqian Zhao ◽  
Jie Zhong ◽  
Ping Lu ◽  
Xiao Feng ◽  
Ying Han ◽  
...  
Keyword(s):  
Gene Expression ◽  
Ovarian Cancer ◽  
Immune Cell ◽  
Expression Profiles ◽  
The Cancer Genome Atlas ◽  
Cell Infiltration ◽  
Platinum Drugs ◽  
Peroxisome Proliferator ◽  
Chemotherapy Drugs ◽  
Platinum Based Chemotherapy

Ovarian carcinoma (OV) is a lethal gynecological malignancy. Most OV patients develop resistance to platinum-based chemotherapy and recurrence. Peroxisome proliferator-activated receptors (PPARs) are the ligand activating transcription factor of the nuclear receptor superfamily. PPARs as important transcriptional regulators regulate important physiological processes such as lipid metabolism, inflammation, and wound healing. Several reports point out that PPARs can also have an effect on the sensitivity of tumor cells to platinum-based chemotherapy drugs. However, the role of PPAR-target related genes (PPAR-TRGs) in chemotherapeutic resistance of OV remains unclear. The present study is aimed at optimizing candidate genes by integrating platinum-chemotherapy expression data and PPAR family genes with their targets. The gene expression profiles were obtained from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) database. A total of 4 genes (AP2A2, DOCK4, HSDL2, and PDK4) were the candidate differentially expressed genes (DEGs) of PPAR-TRGs with platinum chemosensitivity. After conducting numerous survival analyses using different cohorts, we found that only the upexpression of DOCK4 has important significance with the poor prognosis of OV patients. Meanwhile, DOCK4 is detected in plasma and enriched in neutrophil and monocyte cells of the blood. We further found that there were significant correlations between DOCK4 expression and the levels of CD4+ T cell infiltration, dendritic cell infiltration, and neutrophil infiltration in OV. In addition, we verified the expression level of DOCK4 in OV cell lines treated with platinum drugs and found that DOCK4 is potentially responsive to platinum drugs. In conclusion, DOCK4 is potentially associated with immune cell infiltration and represents a valuable prognostic biomarker in ovarian cancer patients.

scholarly journals Comprehensive pathway-related genes signature for prognosis and recurrence of ovarian cancer

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e10437
Author(s):  
Xinnan Zhao ◽  
Miao He
Keyword(s):  
Ovarian Cancer ◽  
Cox Regression ◽  
Expression Profiles ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Time Dependent ◽  
Prognostic Indicators ◽  
High Recurrence Rate ◽  
Cox Regression Analysis ◽  
Ovarian Tumorigenesis

Background Ovarian cancer (OC) is a highly malignant disease with a poor prognosis and high recurrence rate. At present, there is no accurate strategy to predict the prognosis and recurrence of OC. The aim of this study was to identify gene-based signatures to predict OC prognosis and recurrence. Methods mRNA expression profiles and corresponding clinical information regarding OC were collected from The Cancer Genome Atlas (TCGA) database. Gene set enrichment analysis (GSEA) and LASSO analysis were performed, and Kaplan–Meier curves, time-dependent ROC curves, and nomograms were constructed using R software and GraphPad Prism7. Results We first identified several key signalling pathways that affected ovarian tumorigenesis by GSEA. We then established a nine-gene-based signature for overall survival (OS) and a five-gene-based-signature for relapse-free survival (RFS) using LASSO Cox regression analysis of the TCGA dataset and validated the prognostic value of these signatures in independent GEO datasets. We also confirmed that these signatures were independent risk factors for OS and RFS by multivariate Cox analysis. Time-dependent ROC analysis showed that the AUC values for OS and RFS were 0.640, 0.663, 0.758, and 0.891, and 0.638, 0.722, 0.813, and 0.972 at 1, 3, 5, and 10 years, respectively. The results of the nomogram analysis demonstrated that combining two signatures with the TNM staging system and tumour status yielded better predictive ability. Conclusion In conclusion, the two-gene-based signatures established in this study may serve as novel and independent prognostic indicators for OS and RFS.

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