Gene Signature And Prognostic Values of m5C-Related Regulators In Colon Adenocarcinoma

2021 ◽  
Author(s):  
Yuancheng Huang ◽  
Chaoyuan Huang ◽  
Xiaotao Jiang ◽  
Yanhua Yan ◽  
Kunhai Zhuang ◽  
...  
Keyword(s):  
Prognostic Value ◽  
Cox Regression ◽  
Colon Adenocarcinoma ◽  
Predictive Biomarkers ◽  
Roc Curves ◽  
The Cancer Genome Atlas ◽  
Risk Scores ◽  
Rna Modification ◽  
Regression Analyses ◽  
Prognostic Features

Abstract Objectives: The purpose of this study was to investigate the role of 13 m5C-related regulators in colon adenocarcinoma (COAD) and determine their prognostic value.Main Methods: Gene expression and clinicopathological data were obtained from The Cancer Genome Atlas (TCGA) datasets. The expression of m5C-related regulators were analyzed with clinicopathological characteristics and alterations within m5C-related regulators. Subsequently, different subtypes of patients with COAD were identified. Then, the prognostic value of m5C-related regulators in COAD were confirmed via univariate Cox regression and least absolute shrinkage and selection operator (LASSO) Cox regression analyses. The prognostic value of risk scores was evaluated using the Kaplan-Meier method, receiver operating characteristic (ROC) curves, and univariate and multivariate regression analyses. Additionally, Gene Set Enrichment Analysisc (GSEA), Kyoto Encyclopedia of Genes and Genomes c (KEGG) pathways, and Gene Ontologyc (GO) analysis were performed for biological functional analysis.Results: m5C-related regulators were found to be differentially expressed in COAD with different clinicopathological features. We observed a high alteration frequency in these genes, which were significantly correlated with their mRNA expression levels. Two clusters with different prognostic features were identified. Based on two independent prognostic m5C-related regulators (NSUN6 and ALYREF), a risk signature with good predictive significance was constructed. Univariate and multivariate Cox regression analyses suggested that the risk score was an independent prognostic factor. Biological processes and pathways associated with cancer, immune response, and RNA processing were identified.Conclusion: We revealed the genetic signatures and prognostic values of m5C-related regulators in COAD. Together, this has improved our understanding of m5C RNA modification and provided novel insights to identify predictive biomarkers and develop molecular targeted therapy for COAD.

scholarly journals Effect of Different Expression of Immune-Related lncRNA on Colon Adenocarcinoma and Its Relation to Prognosis

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Meiwei Mu ◽  
Yi Tang ◽  
Zheng Yang ◽  
Yuling Qiu ◽  
Xiaohong Li ◽  
...  
Keyword(s):  
Cox Regression ◽  
Colon Adenocarcinoma ◽  
The Cancer Genome Atlas ◽  
Risk Scores ◽  
Future Research ◽  
Sequencing Data ◽  
Tissue Samples ◽  
Gene Sets ◽  
Cancer Genome Atlas ◽  
Immune Related Genes

Objective. To explore the expression of immune-related lncRNAs in colon adenocarcinoma and find out the effect on how these lncRNAs influence the development and prognosis of colon adenocarcinoma. Method. Transcriptome data of colon adenocarcinoma from The Cancer Genome Atlas (TCGA) were downloaded, and gene sets “IMMUNE RESPONSE” and “IMMUNE SYSTEM PROCESS” were sought from the Molecular Signatures Database (MSigDB). The expression of immune-related genes was extracted that were immune-related mRNAs. Then, the immune-related lncRNAs were sought out by utilizing of the above data. Clinical traits were combined with immune-related lncRNAs, so that prognostic-related lncRNAs were identified by Cox regression. Multivariate Cox regression was built to calculate risk scores. Relationships between clinical traits and immune-related lncRNAs were also calculated. Result. A total of 480 colorectal adenocarcinoma patients and 41 normal control patients’ transcriptome sequencing data of tissue samples were obtained from TCGA database. 918 immune-related lncRNAs were screened. Cox regression showed that 34 immune-related lncRNAs were associated with colon adenocarcinoma prognosis. Seven lncRNAs were independent risk factors. Conclusion. This study revealed that some lncRNAs can affect the development and prognosis of colon adenocarcinoma. It may provide new theory evidence of molecular mechanism for the future research and molecular targeted therapy of colon adenocarcinoma.

scholarly journals Mining novel cell glycolysis related gene markers that can predict the survival of colon adenocarcinoma patients

2020 ◽  
Vol 40 (8) ◽  
Author(s):  
Sihan Chen ◽  
Guodong Cao ◽  
Wei Wu ◽  
Yida Lu ◽  
Xiaobo He ◽  
...  
Keyword(s):  
Cox Regression ◽  
Mechanistic Model ◽  
Single Gene ◽  
Colon Adenocarcinoma ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Risk Scores ◽  
Gene Markers ◽  
Cox Regression Analysis

Abstract Colon adenocarcinoma (COAD) is a malignant gastrointestinal tumor, often occurring in the left colon, which is regulated by glycolysis-related processes. In past studies, multiple genes that influence the prognosis for survival have been discovered through bioinformatics analysis. However, the prediction of disease prognosis using a single gene is not an accurate method. In the present study, a mechanistic model was established to achieve better prediction for the prognosis of COAD. COAD-related data downloaded from The Cancer Genome Atlas (TCGA) were correlated with the glycolysis process using gene set enrichment analysis (GSEA) to determine the glycolysis-related genes that regulate COAD. Using COX regression analysis, glycolysis-related genes associated with the prognosis of COAD were identified, and the genes screened to establish a predictive model. The risk scores of this model were correlated with relevant clinical data to obtain a connection diagram between the model and survival rate, tumor characteristic data, etc. Finally, genes in the model were correlated with cells in the tumor microenvironment, finding that they affected specific immune cells in the model. Seven genes related to glycolysis were identified (PPARGC1A, DLAT, 6PC2, P4HA1, STC2, ANKZF1, and GPC1), which affect the prognosis of patients with COAD and constitute the model for prediction of survival of COAD patients.

scholarly journals Comprehensive Analysis and Prognosis Prediction of N6-methyladenosine-related Lncrnas in Immune Microenvironment Infiltration of Gastric Cancer

2021 ◽  
Author(s):  
Jianfeng Huang ◽  
Wenzheng Chen ◽  
Changyu Chen ◽  
Tao Xiao ◽  
Zhigang Jie
Keyword(s):  
Gastric Cancer ◽  
Risk Model ◽  
Cox Regression ◽  
Cytotoxic T Lymphocyte ◽  
The Cancer Genome Atlas ◽  
Risk Scores ◽  
Rna Modification ◽  
Consensus Clustering ◽  
Immune Microenvironment ◽  
Cox Regression Analysis

Abstract BackgroundN6-methyladenosine (m6A) RNA modification plays an important role in regulating tumor microenvironment (TME) infiltration. However, the relationship between the expression pattern of m6A-related long non-coding RNAs (lncRNAs) and the immune microenvironment of gastric cancer (GC) is unclear. MethodsIn this study, 23 m6A-related lncRNAs were identified by Pearson’s correlation analysis and univariate Cox regression analysis. According to the expression of these lncRNAs, we identified two distinct molecular clusters by consensus clustering and compared the differences of the TME and enriched pathways between the two clusters. We further constructed a prognostic risk signature and verified it using The Cancer Genome Atlas training and testing cohorts. ResultsThe results showed that cluster 1 was associated with tumor-related and immune activation-related pathways. In addition, cluster 1 was also associated with higher ImmuneScore, StromalScore, and ESTIMATEScore. The results of the stratified survival analysis and independent prognosis analysis indicated that the risk signature is an independent prognostic indicator for patients with GC. In addition, it can effectively predict survival status in patients with different clinical characteristics. Furthermore, our risk model showed that low risk scores were significantly correlated with high expression of programmed death-1 (PD-1) and cytotoxic T-lymphocyte associated protein 4 (CTLA4), as well as sensitivity to chemotherapeutic drugs (e.g., paclitaxel and oxaliplatin). ConclusionsThis evidence contributes to our understanding of the regulation of TME infiltration by m6A-related lncRNAs and my lead to more effective immunotherapy and chemotherapy for patients with GC.

scholarly journals Identification of key N6-Methyladenosine-Related lncRNAs in Colon Adenocarcinoma using Bioinformatics Analysis

2021 ◽  
Author(s):  
Yuancheng Huang ◽  
Yanhua Yan ◽  
Chaoyuan Huang ◽  
Xiaotao Jiang ◽  
Zehong Yang ◽  
...  
Keyword(s):  
Prognostic Value ◽  
Clinical Characteristics ◽  
Prognostic Model ◽  
Cox Regression ◽  
Colon Adenocarcinoma ◽  
Prognostic Indicator ◽  
Gene Set Enrichment Analysis ◽  
Regression Analyses ◽  
Prognostic Signature ◽  
Cox Regression Analysis

Abstract Purpose: The purpose of this study was to investigate the role of m6A-related lncRNAs in colon adenocarcinoma (COAD) and determine their prognostic value.Material and methods: Gene expression and clinicopathological data were obtained from The Cancer Genome Atlas database. Correlation and univariate Cox regression analysis were conducted to identify m6A-related prognostic lncRNAs. A prognostic signature was established via least absolute shrinkage and selection operator (LASSO) Cox regression analyses. The prognostic value of risk scores was evaluated using the Kaplan-Meier method, receiver operating characteristic curves, and univariate and multivariate regression analyses. Whether the prognostic model could serve as a prognostic indicator for overall survival (OS) in subgroups of patients with different clinical characteristics were explored. Next, We established a competing endogenous RNA network. Gene Set Enrichment Analysis, Kyoto Encyclopedia of Genes and Genomes pathway, and Gene Ontology analysis were performed for biological functional analysis.Results: 36 lncRNAs that were highly correlated with OS of patients were identified. A prognostic signature comprising 11 m6A-related lncRNAs was constructed, which had significant value in predicting the OS of patients . Univariate and Multivariate Cox regression analyses suggested that the risk score was an independent prognostic factor. This m6A-related lncRNA prognostic model could serve as a prognostic indicator for OS in subgroups of patients with different clinical characteristics. Biological processes and pathways associated with cancer were identified.Conclusion: We revealed the role and prognostic value of m6A-related lncRNAs in COAD. Our finding refreshed the understanding of m6A-related lncRNAs and provided novel insights to identify predictive biomarkers and develop targeted therapy for COAD.

scholarly journals Identification and Validation of a Ferroptosis-Related Long Non-coding RNA Signature for Predicting the Outcome of Lung Adenocarcinoma

2021 ◽  
Vol 12 ◽  
Author(s):  
Zhiyuan Zheng ◽  
Qian Zhang ◽  
Wei Wu ◽  
Yan Xue ◽  
Shuhan Liu ◽  
...  
Keyword(s):  
Immune Response ◽  
Lung Adenocarcinoma ◽  
Cox Regression ◽  
Information Criteria ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Risk Scores ◽  
Regression Analyses ◽  
Cox Regression Analysis ◽  
Kaplan Meier

BackgroundFerroptosis is a recently recognized type of programmed cell death that is involved in the biological processes of various cancers. However, the mechanism of ferroptosis in lung adenocarcinoma (LUAD) remains unclear. This study aimed to determine the role of ferroptosis-associated long non-coding RNAs (lncRNAs) in LUAD and to establish a prognostic model.MethodsWe downloaded ferroptosis-related genes from the FerrDb database and RNA sequencing data and clinicopathological characteristics from The Cancer Genome Atlas. We randomly divided the data into training and validation sets. Ferroptosis-associated lncRNA signatures with the lowest Akaike information criteria were determined using COX regression analysis and the least absolute shrinkage and selection operator. The risk scores of ferroptosis-related lncRNAs were calculated, and patients with LUAD were assigned to high- and low-risk groups based on the median risk score. The prognostic value of the risk scores was evaluated using Kaplan–Meier curves, Cox regression analyses, and nomograms. We then explored relationships between ferroptosis-related lncRNAs and the immune response using gene set enrichment analysis (GSEA).ResultsTen ferroptosis-related lncRNA signatures were identified in the training group, and Kaplan–Meier and Cox regression analyses confirmed that the risk scores were independent predictors of LUAD outcome in the training and validation sets (all P < 0.05). The area under the curve confirmed that the signatures could determine the prognosis of LUAD. The predictive accuracy of the established nomogram model was verified using the concordance index and calibration curve. The GSEA showed that the 10 ferroptosis-related lncRNAs might be associated with tumor immune response.ConclusionWe established a novel signature involving 10 ferroptosis-related lncRNAs (LINC01843, MIR193BHG, AC091185.1, AC027031.2, AL021707.2, AL031667.3, AL606834.1, AC026355.1, AC124045.1, and AC025048.4) that can accurately predict the outcome of LUAD and are associated with the immune response. This will provide new insights into the development of new therapies for LUAD.

scholarly journals A Four−Gene-Based Risk Score With High Prognostic Value in Gastric Cancer

2021 ◽  
Vol 11 ◽  
Author(s):  
Bingdong Zhang ◽  
Yuerui Li ◽  
Liu Yang ◽  
Yongbing Chen
Keyword(s):  
Overall Survival ◽  
Gastric Adenocarcinoma ◽  
Risk Score ◽  
Prognostic Value ◽  
Cox Regression ◽  
Prognostic Significance ◽  
The Cancer Genome Atlas ◽  
Risk Scores ◽  
Mutation Status ◽  
Risk Formula

BackgroundGastric adenocarcinoma is an important contributor to cancer mortality and morbidity. This study aimed to explore the prognostic value of mutation patterns in gastric adenocarcinoma.Materials and MethodsWe extracted somatic mutation data for 437 gastric adenocarcinoma samples from The Cancer Genome Atlas (TCGA) Stomach Adenocarcinoma (STAD) cohort. Kaplan–Meier survival in the R package maftools was used to analyze associations between mutations and survival. Multivariate Cox proportional model was used to establish risk formula. A four-gene-based risk score was developed to predict the overall survival of patients with gastric adenocarcinoma. We used the Tianjin cohort dataset with survival information to further evaluate the clinical value of this mutation signature.ResultsForty-five survival-related mutated genes were identified and verified, most of which were co-occurring in their mutation pattern and co-occurring with MLH3 and polymerase ϵ (POLE) mutations. Gastric adenocarcinoma samples with the 45 mutated genes had a significantly higher mutation count. Four-gene [UTRN, MUC16, coiled-coil domain-containing protein 178 (CCDC178), and HYDIN] mutation status was used to build a prognostic risk score that could be translated into the clinical setting. The association between the four-gene-based signature and overall survival remained statistically significant after controlling for age, sex, TNM stage, and POLE mutation status in the multivariate model [hazard ratio (HR), 1.88; 95% CI, 1.33–2.7; p < 0.001]. The prognostic significance of the four-gene-based risk score identified in TCGA cohort was validated in the Tianjin cohort.ConclusionA four-mutated gene risk formula was developed that correlated with the overall survival of patients with gastric adenocarcinoma using a multivariable Cox regression model. In two independent genomic datasets from TCGA and Tianjin cohorts, low risk scores were associated with higher tumor mutation loads and improved outcome in patients with gastric adenocarcinoma. This finding may have implications for prognostic prediction and therapeutic guidance for gastric adenocarcinoma.

scholarly journals Identification of N6-Methylandenosine-Related lncRNAs for Subtype Identification and Risk Stratification in Gastric Adenocarcinoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Yuancheng Huang ◽  
Zehong Yang ◽  
Chaoyuan Huang ◽  
Xiaotao Jiang ◽  
Yanhua Yan ◽  
...  
Keyword(s):  
Gastric Adenocarcinoma ◽  
Immune Checkpoint ◽  
Prognostic Value ◽  
Cox Regression ◽  
Predictive Biomarkers ◽  
Clinicopathological Characteristics ◽  
Gene Set Enrichment Analysis ◽  
Regression Analyses ◽  
Prognostic Signature ◽  
Cox Regression Analysis

ObjectivesThe purpose of this study was to investigate the role of m6A-related lncRNAs in gastric adenocarcinoma (STAD) and to determine their prognostic value.MethodsGene expression and clinicopathological data were obtained from The Cancer Genome Atlas (TCGA) database. Correlation analysis and univariate Cox regression analysis were conducted to identify m6A-related prognostic lncRNAs. Subsequently, different clusters of patients with STAD were identified via consensus clustering analysis, and a prognostic signature was established by least absolute shrinkage and selection operator (LASSO) Cox regression analyses. The clinicopathological characteristics, tumor microenvironment (TME), immune checkpoint genes (ICGs) expression, and the response to immune checkpoint inhibitors (ICIs) in different clusters and subgroups were explored. The prognostic value of the prognostic signature was evaluated using the Kaplan-Meier method, receiver operating characteristic curves, and univariate and multivariate regression analyses. Additionally, Gene Set Enrichment Analysis (GSEA), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, and Gene Ontology (GO) analysis were performed for biological functional analysis.ResultsTwo clusters based on 19 m6A-related lncRNAs were identified, and a prognostic signature comprising 14 m6A-related lncRNAs was constructed, which had significant value in predicting the OS of patients with STAD, clinicopathological characteristics, TME, ICGs expression, and the response to ICIs. Biological processes and pathways associated with cancer and immune response were identified.ConclusionsWe revealed the role and prognostic value of m6A-related lncRNAs in STAD. Together, our finding refreshed the understanding of m6A-related lncRNAs and provided novel insights to identify predictive biomarkers and immunotherapy targets for STAD.

scholarly journals Identification of a Prognostic Signature of Malignant Melanoma Based on Tumor Immune Microenvironment Exploration

2020 ◽  
Author(s):  
lingyan yuan ◽  
Zhitong Bing ◽  
Jianshu Wang ◽  
Jing Li ◽  
Xiaodong Jin ◽  
...  
Keyword(s):  
Malignant Melanoma ◽  
Cox Regression ◽  
Predictive Biomarkers ◽  
Roc Curves ◽  
Risk Groups ◽  
Gene Expression Omnibus ◽  
Low Risk ◽  
The Cancer Genome Atlas ◽  
P Value ◽  
Braf Mutations

Abstract Background: In contrast to identification of well-defined oncogenic alterations like BRAF mutations for malignant melanoma (MM) patient stratification, effective selection of predictive biomarkers remains a challenge in the era of checkpoint blockade.Methods: The differentially expressed genes (DEGs) related to the TME were identified using The Cancer Genome Atlas (TCGA) dataset by Wilcoxon rank sum test. The prognostic effects of immune-related genes (IRGs) were analyzed using univariate Cox regression. Next, the prognostic model was constructed by step multivariate Cox regression and risk score of each sample was calculated. Then, survival and Receiver Operating Characteristic (ROC) analyses were conducted to validate the model using TCGA and the Gene Expression Omnibus (GEO) datasets, respectively. Finally, the overall immune status, tumor purity of high- and low-risk groups was further analyzed to reveal the potential mechanisms of prognostic effects of the model.Results: Twenty eight IRGs were identified, the univariate cox analysis indicated the hazard ratio ranged from 0.796 to 2.621 (p-value < 0.05). 6 genes (SLPI, S100A7, LYZ, CCL19, CXCR4 and CD79A) were screened out by step multivariate cox regression and a 6-IRGs, which can be used as an independent prognostic factor, was constructed. The MM patients in both training (TCGA) and testing (GEO) datasets can be well stratified as high-risk and low-risk groups with the 6-IRGs signature, and the 3-year and 5-year area under curve (AUC) of ROC curves of GEO set were 0.681 and 0.678 (GSE19234). Conclusions: In sum, we identified and constructed a 6-IRGs , which can be used to predict the prognosis of metastasis in MM patients.

scholarly journals Identification of a Three-gene Signature Acting as a Novel Prognostic Biomarker in Intrahepatic Cholangiocarcinoma Patients

2020 ◽  
Author(s):  
Zengwei Tang ◽  
Xing Hunag ◽  
Enliang Li ◽  
Yinan Shen ◽  
Qi Zhang ◽  
...  
Keyword(s):  
Intrahepatic Cholangiocarcinoma ◽  
Prognostic Value ◽  
Cox Regression ◽  
Treatment Options ◽  
Gene Signature ◽  
The Cancer Genome Atlas ◽  
Transcriptional Level ◽  
Regression Analyses ◽  
Operating Characteristics ◽  
Training Cohort

Abstract Background: Intrahepatic cholangiocarcinoma (iCCA) patients have poor outcomes due to the lack of biomarkers for the selection of treatment options. The present study was conducted to find biomarkers with independent prognostic vaule in iCCA patients. Methods: Gene transcriptome profiles of E-MTAB-6389, TCGA-CHOL and GSE26566 were obtained from ArrayExpress, The Cancer Genome Atlas and the Gene Expression Omnibus databases, respectively. Bioinformatic analyses were performed to screen novel biomarkers for predicting the prognosis of iCCA patients. Using multivariate Cox regression analyses, a 3-gene signature (BTD-FER-COL12A1) with potential prognostic value was identified and validated in both a training cohort and two validation cohorts. Results: A total of 177 iCCA patients were included in this study. From the key gene modules significantly associated with liver cirrhosis and overall survival (OS) of iCCA patients, we identified 89 hub genes for functional analyses. Cox-regression analyses in both the training and validation cohort indicate that FER, COL12A1 and BTD were independent risk factors for iCCA patients. A 3-gene signature (BTD-FER-COL12A1) with independent prognostic value in iCCA patients was validated in the training cohort, as well as in two validation cohorts. In terms of predicting the prognosis of iCCA patients, the receiver operating characteristics (ROC) curves showed that this 3-gene signature had superior prediction power to BTD, FER, and COL12A1 alone, as well as known biomarkers (MUC1, MUC13) of iCCA. Immunohistochemical staining of samples from The Human Protein Atlas showed that FER and COL12A1 were positively expressed in iCCA tissue, although BTD was not, while none of these genes was detected in normal tissue. These findings were consistent with the expression status of BTD, FER and COL12A1 at the transcriptional level. In addition, we found that FER and COL12A1 were significantly associated with the degree of infiltration by tumor-infiltrating immune cells. Conclusion: We discovered a three-gene signature with independent prognostic value as a novel biomarker for prediction prognosis of iCCA patients. Our findings may help to find novel therapeutic targets for precision treatment of iCCA.

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

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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