Identification and Validation of Angiogenesis-Related Gene Expression for Predicting Prognosis in Patients With Ovarian Cancer

Ovarian cancer (OC) is a highly heterogeneous disease with different cellular origins reported; thus, precise prognostic strategies and effective new therapies are urgently needed for patients with OC. A growing number of studies have shown that most malignancies have intensive angiogenesis and rapid growth. Therefore, angiogenesis plays an important role in the development of tumor metastasis. However, the prognostic value of angiogenesis-related genes (ARGs) in OC remains to be further elucidated. In this study, the expression data and corresponding clinical data from patients with OC and normal control samples were downloaded with UCSC XENA. A total of 1,960 differentially expressed ARGs were screened and functionally annotated through Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Univariate Cox regression analysis was performed to identify ARGs associated with prognosis. New ARGs signatures (including ESM1, CXCL13, TPCN2, PTPRD, FOXO1, and ELK3) were constructed for the prediction of overall survival (OS) in OC based on the least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analysis. Patients were divided based on their median risk score. In the The Cancer Genome Atlas (TCGA) training dataset, the survival analysis showed that overall survival was lower in the high-risk group than that in the low-risk group (p < 0.0001). The International Cancer Genome Consortium (ICGC) database was used for validation, and the receiver operating characteristic (ROC) curves showed good performance. Univariate and multivariate Cox analyses were conducted to identify independent predictors of OS. The nomogram, including the risk score, age, stage, grade, and position, can not only show good predictive ability but also can explore the correlation analysis based on ARGs for immunogenicity, immune components, and immune phenotypes with risk score. Risk scores were correlated strongly with the type of immune infiltration. Furthermore, homologous recombination defect (HRD), NtAIscore, LOH score, LSTm score, stemness index (mRNAsi), and stromal cells were significantly correlated with risk score. The present study suggests that the novel signature constructed from six ARGs may serve as effective prognostic biomarkers for OC and contribute to clinical decision making and personalized prognostic monitoring of OC.

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Prognostic model of head and neck squamous cell carcinoma based on 12 ferroptosis-related genes

10.21203/rs.3.rs-349666/v1 ◽

2021 ◽

Author(s):

Sijia Li ◽

Hongyang Zhang ◽

Wei Li

Keyword(s):

Regression Analysis ◽

Risk Score ◽

Cox Regression ◽

Risk Group ◽

High Risk Group ◽

Risk Scores ◽

Cox Regression Analysis ◽

Normal Tissues ◽

Model Based ◽

Cox Analysis

Abstract Background: The purpose of our study is establishing a model based on ferroptosis-related genes predicting the prognosis of patients with head and neck squamous cell carcinoma (HNSCC).Methods: In our study, transcriptome and clinical data of HNSCC patients were from The Cancer Genome Atlas, ferroptosis-related genes and pathways were from Ferroptosis Signatures Database. Differentially expressed genes (DEGs) were screened by comparing tumor and adjacent normal tissues. Functional enrichment analysis of DEGs, protein-protein interaction network and gene mutation examination were applied. Univariate Cox regression analysis and least absolute shrinkage and selection operator (LASSO) regression were used to identified DEGs. The model was constructed by multivariate Cox regression analysis and verified by Kaplan-Meier analysis. The relationship between risk scores and other clinical features was also analyzed. Univariate and multivariate Cox analysis was used to verified the independence of our model. The model was evaluated by receiver operating characteristic analysis and calculation of the area under the curve (AUC). A nomogram model based on risk score, age, gender and TNM stages was constructed.Results: We analyzed data including 500 tumor tissues and 44 adjacent normal tissues and 259 ferroptosis-related genes, then obtained 73 DEGs. Univariate Cox regression analysis screened out 16 genes related to overall survival, and LASSO analysis fingered out 12 of them with prognostic value. A risk score model based on these 12 genes was constructed by multivariate Cox regression analysis. According to the median risk score, patients were divided into high-risk group and low-risk group. The survival rate of high-risk group was significantly lower than that of low-risk group in Kaplan-Meier curve. Risk scores were related to T and grade. Univariate and multivariate Cox analysis showed our model was an independent prognostic factor. The AUC was 0.669. The nomogram showed high accuracy predicting the prognosis of HNSCC patients.Conclusion: Our model based on 12 ferroptosis-related genes performed excellently in predicting the prognosis of HNSCC patients. Ferroptosis-related genes may be promising biomarkers for HNSCC treatment and prognosis.

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Systemic characterization of alternative splicing related to prognosis and immune infiltration in malignant mesothelioma

BMC Cancer ◽

10.1186/s12885-021-08548-3 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Jinzhi Lai ◽

Hainan Yang ◽

Tianwen Xu

Keyword(s):

Overall Survival ◽

Regression Analysis ◽

Risk Score ◽

Regulatory Network ◽

Immune Cell ◽

Cox Regression ◽

Risk Group ◽

High Risk Group ◽

Immune Cell Infiltration ◽

Cox Regression Analysis

Abstract Background Malignant mesothelioma (MM) is a relatively rare and highly lethal tumor with few treatment options. Thus, it is important to identify prognostic markers that can help clinicians diagnose mesothelioma earlier and assess disease activity more accurately. Alternative splicing (AS) events have been recognized as critical signatures for tumor diagnosis and treatment in multiple cancers, including MM. Methods We systematically examined the AS events and clinical information of 83 MM samples from TCGA database. Univariate Cox regression analysis was used to identify AS events associated with overall survival. LASSO analyses followed by multivariate Cox regression analyses were conducted to construct the prognostic signatures and assess the accuracy of these prognostic signatures by receiver operating characteristic (ROC) curve and Kaplan–Meier survival analyses. The ImmuCellAI and ssGSEA algorithms were used to assess the degrees of immune cell infiltration in MM samples. The survival-related splicing regulatory network was established based on the correlation between survival-related AS events and splicing factors (SFs). Results A total of 3976 AS events associated with overall survival were identified by univariate Cox regression analysis, and ES events accounted for the greatest proportion. We constructed prognostic signatures based on survival-related AS events. The prognostic signatures proved to be an efficient predictor with an area under the curve (AUC) greater than 0.9. Additionally, the risk score based on 6 key AS events proved to be an independent prognostic factor, and a nomogram composed of 6 key AS events was established. We found that the risk score was significantly decreased in patients with the epithelioid subtype. In addition, unsupervised clustering clearly showed that the risk score was associated with immune cell infiltration. The abundances of cytotoxic T (Tc) cells, natural killer (NK) cells and T-helper 17 (Th17) cells were higher in the high-risk group, whereas the abundances of induced regulatory T (iTreg) cells were lower in the high-risk group. Finally, we identified 3 SFs (HSPB1, INTS1 and LUC7L2) that were significantly associated with MM patient survival and then constructed a regulatory network between the 3 SFs and survival-related AS to reveal potential regulatory mechanisms in MM. Conclusion Our study provided a prognostic signature based on 6 key events, representing a better effective tumor-specific diagnostic and prognostic marker than the TNM staging system. AS events that are correlated with the immune system may be potential therapeutic targets for MM.

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A methylation-based nomogram for predicting survival in patients with lung adenocarcinoma

BMC Cancer ◽

10.1186/s12885-021-08539-4 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Xuelong Wang ◽

Bin Zhou ◽

Yuxin Xia ◽

Jianxin Zuo ◽

Yanchao Liu ◽

...

Keyword(s):

Risk Factors ◽

Dna Methylation ◽

Overall Survival ◽

Regression Analysis ◽

Lung Adenocarcinoma ◽

Risk Score ◽

Cox Regression ◽

Risk Group ◽

Clinical Risk Factors ◽

Cox Regression Analysis

Abstract Background DNA methylation alteration is frequently observed in Lung adenocarcinoma (LUAD) and may play important roles in carcinogenesis, diagnosis, and prognosis. Thus, this study aimed to construct a reliable methylation-based nomogram, guiding prognostic classification screening and personalized medicine for LUAD patients. Method The DNA methylation data, gene expression data and corresponding clinical information of lung adenocarcinoma samples were extracted from The Cancer Genome Atlas (TCGA) database. Differentially methylated sites (DMSs) and differentially expressed genes (DEGs) were obtained and then calculated correlation by pearson correlation coefficient. Functional enrichment analysis and Protein-protein interaction network were used to explore the biological roles of aberrant methylation genes. A prognostic risk score model was constructed using univariate Cox and LASSO analysis and was assessed in an independent cohort. A methylation-based nomogram that included the risk score and the clinical risk factors was developed, which was evaluated by concordance index and calibration curves. Result We identified a total of 1362 DMSs corresponding to 471 DEGs with significant negative correlation, including 752 hypermethylation sites and 610 hypomethylation sites. Univariate cox regression analysis showed that 59 DMSs were significantly associated with overall survival. Using LASSO method, we constructed a three-DMSs signature that was independent predictive of prognosis in the training cohort. Patients in high-risk group had a significant shorter overall survival than patients in low-risk group classified by three-DMSs signature (log-rank p = 1.9E-04). Multivariate cox regression analysis proved that the three-DMSs signature was an independent prognostic factor for LUAD in TCGA-LUAD cohort (HR = 2.29, 95%CI: 1.47–3.57, P = 2.36E-04) and GSE56044 cohort (HR = 2.16, 95%CI: 1.19–3.91, P = 0.011). Furthermore, a nomogram, combining the risk score with clinical risk factors, was developed with C-indexes of 0.71 and 0.70 in TCGA-LUAD and GSE56044 respectively. Conclusions The present study established a robust three-DMSs signature for the prediction of overall survival and further developed a nomogram that could be a clinically available guide for personalized treatment of LUAD patients.

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A Methylation-based nomogram for predicting survival in patients with lung adenocarcinoma

10.21203/rs.3.rs-75403/v1 ◽

2020 ◽

Author(s):

Xuelong Wang ◽

Bin Zhou ◽

Yuxin Xia ◽

Jianxin Zuo ◽

Yanchao Liu ◽

...

Keyword(s):

Risk Factors ◽

Dna Methylation ◽

Overall Survival ◽

Regression Analysis ◽

Lung Adenocarcinoma ◽

Risk Score ◽

Cox Regression ◽

Risk Group ◽

Clinical Risk Factors ◽

Cox Regression Analysis

Abstract Background DNA methylation alteration is frequently observed in Lung adenocarcinoma (LUAD) and may play important roles in carcinogenesis, diagnosis, and prognosis. Thus, this study aimed to construct a reliable methylation-based nomogram, guiding prognostic classification screening and personalized medicine for LUAD patients. Method: The DNA methylation data, gene expression data and corresponding clinical information of lung adenocarcinoma samples were extracted from The Cancer Genome Atlas (TCGA) database. Differentially methylated sites (DMSs) and differentially expressed genes (DEGs) were obtained and then calculated expression correlation by pearson correlation coefficient. Functional enrichment analysis and Protein-protein interaction network were used to explore the biological roles of aberrant methylation genes. A prognostic risk score model was constructed using univariate Cox and LASSO analysis and was assessed in an independent cohort. A methylation-based nomogram that included the risk score and the clinical risk factors was developed, which was evaluated by concordance index and calibration curves. Result We identified a total of 1362 DMSs corresponding to 471 DEGs with significant negative correlation, including 752 hypermethylation sites and 610 hypomethylation sites. Univariate cox regression analysis showed that 59 DMSs were significantly associated with overall survival. Using LASSO method, we constructed a three-DMSs signature that was independent predictive of prognosis in the training cohort. Patients in high-risk group had a significant shorter overall survival than patients in low-risk group classified by three-DMSs signature (log-rank p = 1.9E-04). Multivariate cox regression analysis proved that the three-DMSs signature was an independent prognostic factor for LUAD in TCGA-LUAD cohort (HR = 2.29, 95%CI: 1.47–3.57, P = 2.36E-04) and GSE56044 cohort (HR = 2.16, 95%CI: 1.19–3.91, P = 0.011). Furthermore, a nomogram, combining the risk score with clinical risk factors, was developed with C-indexes of 0.71 and 0.70 in TCGA-LUAD and GSE56044 respectively. Conclusions The present study established a robust three-DMSs signature for the prediction of overall survival and further developed a nomogram that could be a clinically available guide for personalized treatment of LUAD patients.

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Prognostic Model of Head and Neck Squamous Cell Carcinoma Based on 12 Ferroptosis-related Genes

10.21203/rs.3.rs-349666/v2 ◽

2021 ◽

Author(s):

Sijia Li ◽

Hongyang Zhang ◽

Wei Li

Keyword(s):

Regression Analysis ◽

Risk Score ◽

Cox Regression ◽

Risk Group ◽

High Risk Group ◽

Risk Scores ◽

Cox Regression Analysis ◽

Normal Tissues ◽

Model Based ◽

Cox Analysis

Abstract Background: The purpose of our study is establishing a model based on ferroptosis-related genes predicting the prognosis of patients with head and neck squamous cell carcinoma (HNSCC).Methods: In our study, transcriptome and clinical data of HNSCC patients were from The Cancer Genome Atlas, ferroptosis-related genes and pathways were from Ferroptosis Signatures Database. Differentially expressed genes (DEGs) were screened by comparing tumor and adjacent normal tissues. Functional enrichment analysis of DEGs, protein-protein interaction network and gene mutation examination were applied. Univariate Cox regression analysis and least absolute shrinkage and selection operator (LASSO) regression were used to identified DEGs. The model was constructed by multivariate Cox regression analysis and verified by Kaplan-Meier analysis. The relationship between risk scores and other clinical features was also analyzed. Univariate and multivariate Cox analysis was used to verified the independence of our model. The model was evaluated by receiver operating characteristic analysis and calculation of the area under the curve (AUC). A nomogram model based on risk score, age, gender and TNM stages was constructed. Our model was also validated in the Gene Expression Omnibus (GEO) verification set. Results: We analyzed data including 500 tumor tissues and 44 adjacent normal tissues and 259 ferroptosis-related genes, then obtained 73 DEGs. Univariate Cox regression analysis screened out 16 genes related to overall survival, and LASSO analysis fingered out 12 of them with prognostic value. A risk score model based on these 12 genes was constructed by multivariate Cox regression analysis. According to the median risk score, patients were divided into high-risk group and low-risk group. The survival rate of high-risk group was significantly lower than that of low-risk group in Kaplan-Meier curve. Risk scores were related to T and grade. Univariate and multivariate Cox analysis showed our model was an independent prognostic factor. The AUC was 0.669. Those all could prove our model had great predictive ability of HNSCC prognosis and it could be validated in GEO dataset. The nomogram showed high accuracy predicting the prognosis of HNSCC patients.Conclusion: Our model based on 12 ferroptosis-related genes performed excellently in predicting the prognosis of HNSCC patients. Ferroptosis-related genes may be promising biomarkers for HNSCC treatment and prognosis.

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Prognostic Signature of Osteosarcoma Based on 14 Autophagy-Related Genes

Pathology & Oncology Research ◽

10.3389/pore.2021.1609782 ◽

2021 ◽

Vol 27 ◽

Author(s):

Wei Qi ◽

Qian Yan ◽

Ming Lv ◽

Delei Song ◽

Xianbin Wang ◽

...

Keyword(s):

Regression Analysis ◽

Risk Score ◽

Clinical Decision Making ◽

Immune Cell ◽

Cox Regression ◽

Clinical Decision ◽

Cell Types ◽

Survival Outcome ◽

Risk Scores ◽

Cox Regression Analysis

Background: Osteosarcoma is a common malignancy of bone with inferior survival outcome. Autophagy can exert multifactorial influence on tumorigenesis and tumor progression. However, the specific function of genes related to autophagy in the prognosis of osteosarcoma patients remains unclear. Herein, we aimed to explore the association of genes related to autophagy with the survival outcome of osteosarcoma patients.Methods: The autophagy-associated genes that were related to the prognosis of osteosarcoma were optimized by LASSO Cox regression analysis. The survival of osteosarcoma patients was forecasted by multivariate Cox regression analysis. The immune infiltration status of 22 immune cell types in osteosarcoma patients with high and low risk scores was compared by using the CIBERSORT tool.Results: The risk score model constructed according to 14 autophagy-related genes (ATG4A, BAK1, BNIP3, CALCOCO2, CCL2, DAPK1, EGFR, FAS, GRID2, ITGA3, MYC, RAB33B, USP10, and WIPI1) could effectively predict the prognosis of patients with osteosarcoma. A nomogram model was established based on risk score and metastasis.Conclusion: Autophagy-related genes were identified as pivotal prognostic signatures, which could guide the clinical decision making in the treatment of osteosarcoma.

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Five gene signatures were identified in the prediction of overall survival in resectable pancreatic cancer

BMC Surgery ◽

10.1186/s12893-020-00856-y ◽

2020 ◽

Vol 20 (1) ◽

Author(s):

Chao Wu ◽

Zuowei Wu ◽

Bole Tian

Keyword(s):

Pancreatic Cancer ◽

Overall Survival ◽

Regression Analysis ◽

Risk Score ◽

Cox Regression ◽

Risk Group ◽

Gene Set Enrichment Analysis ◽

Resectable Pancreatic Cancer ◽

Cox Regression Analysis ◽

Validation Set

Abstract Background Although genes have been previously detected in pancreatic cancer (PC), aberrant genes that play roles in resectable pancreatic cancer should be further assessed. Methods Messenger RNA samples and clinicopathological data corrected with PC were downloaded from The Cancer Genome Atlas (TCGA). Resectable PC patients were randomly divided into a primary set and a validation set. Univariable Cox regression analysis, lasso-penalized Cox regression analysis, and multivariable Cox analysis were implemented to distinguish survival-related genes (SRGs). A risk score based on the SRGs was calculated by univariable Cox regression analysis. A genomic-clinical nomogram was established by integrating the risk score and clinicopathological data to predict overall survival (OS) in resectable PC. Results Five survival-related genes (AADAC, DEF8, HIST1H1C, MET, and CHFR) were significantly correlated with OS in resectable PC. The resectable PC patients, based on risk score, were sorted into a high-risk group that showed considerably unfavorable OS (p < 0.001) than the low-risk group, in both the primary set and the validation set. The concordance index (C-index) was calculated to evaluate the predictive performance of the nomogram were respectively in the primary set [0.696 (0.608–0.784)] and the validation set [0.682 (0.606–0.758)]. Additionally, gene set enrichment Analysis discovered several meaningful enriched pathways. Conclusion Our study identified five prognostic gene biomarkers for OS prediction and which facilitate postoperative molecular target therapy for the resectable PC, especially the nomic-clinical nomogram which may be used as an effective model for the postoperative OS evaluation and also an optimal therapeutic tool for the resectable PC.

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Development of an immune-related gene pairs signature for predicting clinical outcome in lung adenocarcinoma

Scientific Reports ◽

10.1038/s41598-021-83120-4 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Chunlei Wu ◽

Quanteng Hu ◽

Dehua Ma

Keyword(s):

Overall Survival ◽

Regression Analysis ◽

Lung Adenocarcinoma ◽

Risk Score ◽

Cox Regression ◽

Related Gene ◽

Cox Regression Analysis ◽

Gene Pairs ◽

Immune Related Gene ◽

Pathological Subtype

AbstractLung adenocarcinoma (LUAD) is the main pathological subtype of Non-small cell lung cancer. We downloaded the gene expression profile and immune-related gene set from the TCGA and ImmPort database, respectively, to establish immune-related gene pairs (IRGPs). Then, IRGPs were subjected to univariate Cox regression analysis, LASSO regression analysis, and multivariable Cox regression analysis to screen and develop an IRGPs signature. The receiver operating characteristic curve (ROC) was applied for evaluating the predicting accuracy of this signature by calculating the area under ROC (AUC) and data from the GEO set was used to validate this signature. The relationship of 22 tumor-infiltrating immune cells (TIICs) to the immune risk score was also investigated. An IRGPs signature with 8 IRGPs was constructed. The AUC for 1- and 3-year overall survival in the TCGA set was 0.867 and 0.870, respectively. Similar results were observed in the AUCs of GEO set 1, 2 and 3 (GEO set 1 [1-year: 0.819; 3-year: 0.803]; GEO set 2 [1-year: 0.834; 3-year: 0.870]; GEO set 3 [1-year: 0.955; 3-year: 0.827]). Survival analysis demonstrated high-risk LUAD patients exhibited poorer prognosis. The multivariable Cox regression indicated that the risk score was an independent prognostic factor. The immune risk score was highly associated with several TIICs (Plasma cells, memory B cells, resting memory CD4 T cells, and activated NK cells). We developed a novel IRGPs signature for predicting 1- and 3- year overall survival in LUAD, which would be helpful for prognosis assessment of LUAD.

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A Six CT83-Related Genes Based Prognostic Signature for Lung Adenocarcinoma

Combinatorial Chemistry & High Throughput Screening ◽

10.2174/1871520621666210713112630 ◽

2021 ◽

Vol 24 ◽

Author(s):

Yongmei Wang ◽

Guimin Zhang ◽

Ruixian Wang

Keyword(s):

Regression Analysis ◽

Lung Adenocarcinoma ◽

Differential Expression ◽

Risk Score ◽

Prognostic Model ◽

Cox Regression ◽

Differential Expression Analysis ◽

Functional Enrichment ◽

Risk Scores ◽

Cox Regression Analysis

Background: This study aims to explore the prognostic values of CT83 and CT83-related genes in lung adenocarcinoma (LUAD). Methods: We downloaded the mRNA profiles of 513 LUAD patients (RNA sequencing data) and 246 NSCLC patients (Affymetrix Human Genome U133 Plus 2.0 Array) from TCGA and GEO databases. According to the median expression of CT83, the TCGA samples were divided into high and low expression groups, and differential expression analysis between them was performed. Functional enrichment analysis of differential expression genes (DEGs) was conducted. Univariate Cox regression analysis and LASSO Cox regression analysis were performed to screen the optimal prognostic DEGs. Then we established the prognostic model. A Nomogram model was constructed to predict the overall survival (OS) probability of LUAD patients. Results: CT83 expression was significantly correlated to the prognosis of LUAD patients. A total of 59 DEGs were identified, and a predictive model was constructed based on six optimal CT83-related DEGs, including CPS1, RHOV, TNNT1, FAM83A, IGF2BP1, and GRIN2A, could effectively predict the prognosis of LUAD patients. The nomogram could reliably predict the OS of LUAD patients. Moreover, the six important immune checkpoints (CTLA4, PD1, IDO1, TDO2, LAG3, and TIGIT) were closely correlated with the Risk Score, which was also differentially expressed between the LUAD samples with high and low-Risk Scores, suggesting that the poor prognosis of LUAD patients with high-Risk Score might be due to the immunosuppressive microenvironments. Conclusion: A prognostic model based on six optimal CT83 related genes could effectively predict the prognosis of LUAD patients.

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Identification and Validation of a Seven m6A-related lncRNAs Signature Predicting Prognosis of Ovarian Cancer

10.21203/rs.3.rs-915700/v1 ◽

2021 ◽

Author(s):

Yan Li ◽

Xiaoying Wang ◽

Yue Han ◽

Xun Li

Keyword(s):

Ovarian Cancer ◽

Regression Analysis ◽

Correlation Analysis ◽

Prognostic Value ◽

Cox Regression ◽

Risk Group ◽

Pearson Correlation ◽

Regression Analyses ◽

Cox Regression Analysis ◽

Pearson Correlation Analysis

Abstract Background: Long non-coding RNAs (lncRNAs) play an important role in angiogenesis, immune response, inflammatory response and tumor development and metastasis. m6 A (N6 - methyladenosine) is one of the most common RNA modifications in eukaryotes. The aim of our research was to investigate the potential prognostic value of m6A-related lncRNAs in ovarian cancer (OC).Methods: The data we need for our research was downloaded from the Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) database. Pearson correlation analysis between 21 m6A regulators and lncRNAs was performed to identify m6A-related lncRNAs. Univariate Cox regression analysis was implemented to screen for lncRNAs with prognostic value. A least absolute shrinkage and selection operator (LASSO) Cox regression and multivariate Cox regression analyses was used to further reduct the lncRNAs with prognostic value and construct a m6A-related lncRNAs signature for predicting the prognosis of OC patients. Results: 275 m6A-related lncRNAs were obtained using pearson correlation analysis. 29 m6A-related lncRNAs with prognostic value was selected through univariate Cox regression analysis. Then, a seven m6A-related lncRNAs signature was identified by LASSO Cox regression. Each patient obtained a riskscore through multivariate Cox regression analyses and the patients were classified into high-and low-risk group using the median riskscore as a cutoff. Kaplan-Meier curve revealed that the patients in high-risk group have poor outcome. The receiver operating characteristic curve revealed that the predictive potential of the m6A-related lncRNAs signature for OC was powerful. The predictive potential of the m6A-related lncRNAs signature was successfully validated in the GSE9891, GSE26193 datasets and our clinical specimens. Multivariate analyses suggested that the m6A-related lncRNAs signature was an independent prognostic factor for OC patients. Moreover, a nomogram based on the expression level of the seven m6A-related lncRNAs was established to predict survival rate of patients with OC. Finally, a competing endogenous RNA (ceRNA) network associated with the seven m6A-related lncRNAs was constructed to understand the possible mechanisms of the m6A-related lncRNAs involed in the progression of OC.Conclusions: In conclusion, our research revealed that the m6A-related lncRNAs may affect the prognosis of OC patients and identified a seven m6A-related lncRNAs signature to predict the prognosis of OC patients.

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