scholarly journals Identification of an Immune-related Seven-lncRNA Signature Predicting Prognosis and Tumor-infiltrating Immune Cells in Lung Adenocarcinoma

2020 ◽  
Author(s):  
Jinchun Wu ◽  
Yanhua Mou ◽  
Chunfang Zhang ◽  
Chaojun Duan ◽  
Bin Li
Keyword(s):  
Lung Adenocarcinoma ◽  
T Cell Activation ◽  
Immune Cells ◽  
Antigen Processing ◽  
Risk Group ◽  
Pearson Correlation ◽  
Low Risk ◽  
Gene Set Enrichment Analysis ◽  
Prognostic Signature ◽  
Immune Pathways

Abstract Background: Lung adenocarcinoma (LUAD) is a common cancer. Immunotherapy is one of the major treatments but showing diverse efficacy in LUAD. Long non-coding RNAs (lncRNAs) are emerging as important players in immune regulation in cancer. Herein, we identified and validated a prognostic signature of immune-related lncRNAs in LUAD and explored its correlation with tumor-infiltrating immune cells (TIICs) by bioinformatics analysis.Methods: Immune-related lncRNAs were acquired using Pearson correlation analysis between lncRNAs from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases and immune genes from the ImmPort website and Molecular Signatures Database. The risk signature was constructed in the TCGA group through univariable Cox, lasso and multivariable Cox regression analyses. The prognostic value of the established risk signature was validated in both TCGA and GEO datasets. The interacted TIICs and immune pathways with each single lncRNA and the risk signature were investigated respectively in ImmLnc database, Cibersortx database and gene set enrichment analysis (GSEA) analyses.Results: A seven immune-related lncRNAs prognostic signature was constructed and it stratified LUAD into high and low risk groups. High risk group showed poorer overall survival (OS) in comparison with low risk group via survival analysis.The seven-lncRNAs signature was closely correlated with various TIICs and immune pathways mostly involved in T cell activation, antigen processing and presentation, chemokines and cytokine receptors.Conclusions: The seven lncRNAs model was identified as a predictable signature for prognosis of LUAD patients probably due to its immunomodulation role. This study might provide a new target for enhancing the efficacy of immunotherapy in this mortal disease.

scholarly journals m5C-Related lncRNAs Predict Overall Survival of Patients and Regulate the Tumor Immune Microenvironment in Lung Adenocarcinoma

2021 ◽  
Vol 9 ◽  
Author(s):  
Junfan Pan ◽  
Zhidong Huang ◽  
Yiquan Xu
Keyword(s):  
Lung Adenocarcinoma ◽  
Risk Score ◽  
Immune Cells ◽  
Prognostic Value ◽  
Risk Model ◽  
Risk Group ◽  
Risk Groups ◽  
Low Risk ◽  
Gene Set Enrichment Analysis ◽  
Rna Methylation

Long non-coding RNAs (lncRNAs), which are involved in the regulation of RNA methylation, can be used to evaluate tumor prognosis. lncRNAs are closely related to the prognosis of patients with lung adenocarcinoma (LUAD); thus, it is crucial to identify RNA methylation-associated lncRNAs with definitive prognostic value. We used Pearson correlation analysis to construct a 5-Methylcytosine (m5C)-related lncRNAs–mRNAs coexpression network. Univariate and multivariate Cox proportional risk analyses were then used to determine a risk model for m5C-associated lncRNAs with prognostic value. The risk model was verified using Kaplan–Meier analysis, univariate and multivariate Cox regression analysis, and receiver operating characteristic curve analysis. We used principal component analysis and gene set enrichment analysis functional annotation to analyze the risk model. We also verified the expression level of m5C-related lncRNAs in vitro. The association between the risk model and tumor-infiltrating immune cells was assessed using the CIBERSORT tool and the TIMER database. Based on these analyses, a total of 14 m5C-related lncRNAs with prognostic value were selected to build the risk model. Patients were divided into high- and low-risk groups according to the median risk score. The prognosis of the high-risk group was worse than that of the low-risk group, suggesting the good sensitivity and specificity of the constructed risk model. In addition, 5 types of immune cells were significantly different in the high-and low-risk groups, and 6 types of immune cells were negatively correlated with the risk score. These results suggested that the risk model based on 14 m5C-related lncRNAs with prognostic value might be a promising prognostic tool for LUAD and might facilitate the management of patients with LUAD.

scholarly journals Prognostic Signature for Lung Adenocarcinoma Patients Based on Cell-Cycle-Related Genes

2021 ◽  
Vol 9 ◽  
Author(s):  
Wei Jiang ◽  
Jiameng Xu ◽  
Zirui Liao ◽  
Guangbin Li ◽  
Chengpeng Zhang ◽  
...  
Keyword(s):  
Cell Cycle ◽  
Lung Adenocarcinoma ◽  
Cox Regression ◽  
Training Group ◽  
Risk Groups ◽  
Low Risk ◽  
Gene Set Enrichment Analysis ◽  
Prognostic Signature ◽  
Cox Regression Analysis ◽  
External Test

ObjectiveTo screen lung adenocarcinoma (LUAC)-specific cell-cycle-related genes (CCRGs) and develop a prognostic signature for patients with LUAC.MethodsThe GSE68465, GSE42127, and GSE30219 data sets were downloaded from the GEO database. Single-sample gene set enrichment analysis was used to calculate the cell cycle enrichment of each sample in GSE68465 to identify CCRGs in LUAC. The differential CCRGs compared with LUAC data from The Cancer Genome Atlas were determined. The genetic data from GSE68465 were divided into an internal training group and a test group at a ratio of 1:1, and GSE42127 and GSE30219 were defined as external test groups. In addition, we combined LASSO (least absolute shrinkage and selection operator) and Cox regression analysis with the clinical information of the internal training group to construct a CCRG risk scoring model. Samples were divided into high- and low-risk groups according to the resulting risk values, and internal and external test sets were used to prove the validity of the signature. A nomogram evaluation model was used to predict prognosis. The CPTAC and HPA databases were chosen to verify the protein expression of CCRGs.ResultsWe identified 10 LUAC-specific CCRGs (PKMYT1, ETF1, ECT2, BUB1B, RECQL4, TFRC, COCH, TUBB2B, PITX1, and CDC6) and constructed a model using the internal training group. Based on this model, LUAC patients were divided into high- and low-risk groups for further validation. Time-dependent receiver operating characteristic and Cox regression analyses suggested that the signature could precisely predict the prognosis of LUAC patients. Results obtained with CPTAC, HPA, and IHC supported significant dysregulation of these CCRGs in LUAC tissues.ConclusionThis prognostic prediction signature based on CCRGs could help to evaluate the prognosis of LUAC patients. The 10 LUAC-specific CCRGs could be used as prognostic markers of LUAC.

scholarly journals Prognostic value of an autophagy-related gene expression signature for endometrial cancer patients

2020 ◽  
Author(s):  
Hui Wang ◽  
Xiaoling Ma ◽  
Jinhui Liu ◽  
Yicong Wan ◽  
Yi Jiang ◽  
...  
Keyword(s):  
Endometrial Cancer ◽  
High Risk ◽  
Risk Score ◽  
Risk Group ◽  
Low Risk ◽  
Gene Set Enrichment Analysis ◽  
Prognostic Signature ◽  
Gene Set Enrichment ◽  
Test Set ◽  
Gene Set

Abstract Background: Autophagy is associated with cancer development. Autophagy-related genes play significant roles in endometrial cancer (EC), a major gynecological malignancy worldwide, but little was known about their value as prognostic markers. Here we evaluated the value of a prognostic signature based on autophagy-related genes for EC.Methods: First, various autophagy-related genes were obtained via the Human Autophagy Database and their expression profiles were downloaded from The Cancer Genome Atlas. Second, key prognostic autophagy-related genes were identified via univariat, LASSO, and multivariate Cox regression analyses. Finally, a risk score to predict the prognosis of EC was calculated and validated by using the test and the entire data sets. Besides, gene set enrichment and somatic mutation analyses were also used for these prognostic autophagy-related genes. Results: A total of 40 differentially expressed autophagy-related genes in EC were screened and five of them were prognosis-related (CDKN1B, DLC1, EIF4EBP1, ERBB2 and GRID1). A prognostic signature was constructed based on these five genes using the train set, which stratified EC patients into high-risk and low-risk groups (P<0.05). In terms of overall survival, the analyses of the test set and the entire set yielded consistent results (test set: p < 0.05; entire set: p < 0.05). Time-dependent ROC analysis suggested that the risk score predicted EC prognosis accurately and independently (0.674 at 1 year, 0.712 at 3 years and 0.659 at 5 years). A nomogram with clinical utility was built. Patients in the high-risk group displayed distinct mutation signatures compared with those in the low-risk group. Gene set enrichment analysis revealed high risk score was associated with tumor initiation and progression associated pathways.Conclusions: Based on five autophagy-related genes (CDKN1B, DLC1, EIF4EBP1, ERBB2 and GRID1), our model can independently predict the OS of EC patients by combining molecular signature and clinical characteristics.

scholarly journals Prognostic value of an autophagy-related gene expression signature for endometrial cancer patients

2020 ◽  
Author(s):  
Hui Wang ◽  
Xiaoling Ma ◽  
Jinhui Liu ◽  
Yicong Wan ◽  
Yi Jiang ◽  
...  
Keyword(s):  
Endometrial Cancer ◽  
High Risk ◽  
Risk Score ◽  
Risk Group ◽  
Low Risk ◽  
Gene Set Enrichment Analysis ◽  
Prognostic Signature ◽  
Gene Set Enrichment ◽  
Test Set ◽  
Gene Set

Abstract Background: Autophagy is associated with cancer development. Autophagy-related genes play significant roles in endometrial cancer (EC), a major gynecological malignancy worldwide, but little was known about their value as prognostic markers. Here we evaluated the value of a prognostic signature based on autophagy-related genes for EC. Methods: First, various autophagy-related genes were obtained via the Human Autophagy Database and their expression profiles were downloaded from The Cancer Genome Atlas. Second, key prognostic autophagy-related genes were identified via univariat, LASSO, and multivariate Cox regression analyses. Finally, a risk score to predict the prognosis of EC was calculated and validated by using the test and the entire data sets. Besides, gene set enrichment and somatic mutation analyses were also used for these prognostic autophagy-related genes. Results: A total of 40 differentially expressed autophagy-related genes in EC were screened and five of them were prognosis-related (CDKN1B, DLC1, EIF4EBP1, ERBB2 and GRID1). A prognostic signature was constructed based on these five genes using the train set, which stratified EC patients into high-risk and low-risk groups (P<0.05). In terms of overall survival, the analyses of the test set and the entire set yielded consistent results (test set: p < 0.05; entire set: p < 0.05). Time-dependent ROC analysis suggested that the risk score predicted EC prognosis accurately and independently (0.674 at 1 year, 0.712 at 3 years and 0.659 at 5 years). A nomogram with clinical utility was built. Patients in the high-risk group displayed distinct mutation signatures compared with those in the low-risk group. Gene set enrichment analysis revealed high risk score was associated with tumor initiation and progression associated pathways. Conclusions: Based on five autophagy-related genes (CDKN1B, DLC1, EIF4EBP1, ERBB2 and GRID1), our model can independently predict the OS of EC patients by combining molecular signature and clinical characteristics.

scholarly journals Identification of A Novel Six Autophagy-Related Genes Signature for The Prognostic and A miRNA-Related Autophagy Predictor for Anti-PD-1 Therapy Responses in Prostate Cancer

2020 ◽  
Author(s):  
Lei Wu ◽  
Guojun Yue ◽  
Wen Quan ◽  
Qiong Luo ◽  
Dongxu Peng ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
High Risk ◽  
Regression Model ◽  
Risk Group ◽  
Low Risk ◽  
Gene Set Enrichment Analysis ◽  
Differentially Expressed ◽  
Prognostic Signature ◽  
Potential Biomarker ◽  
Risk Patients

Abstract Background: Autophagy is a highly conserved homeostatic process in the human body that is responsible for the elimination of aggregated proteins and damaged organelles. Several autophagy-related genes (ARGs) contribute to the process of tumorigenesis and metastasis of prostate cancer (PCa). Also, miRNAs have been proven to modulate autophagy by targeting some ARGs. However, their potential role in PCa still remains unclear.Methods: An univariate Cox proportional regression model was used to identify 17 ARGs associated with the overall survival (OS) of PCa. Then, a multivariate Cox proportional regression model was used to construct a 6 autophagy-related prognostic genes signature. Patients were divided into low-risk group and high-risk group using the median risk score as a cutoff value. High-risk patients had shorter OS than low-risk patients. Furthermore, the signature was validated by ROC curves. Regarding mRNA and miRNA, 12 differentially expressed miRNAs (DEMs) and 1073 differentially expressed genes (DEGs) were detected via the GEO database. We found that miR-205, one of the DEMs, was negatively regulated the expression of ARG (NKX2-3). Based on STRING analysis results, we found that the NKX2-3 was moderately related to the part of genes among the 6 autophagy-related genes prognostic signature. Further, NKX 2-3 was significantly correlated with OS and some clinical parameters of PCa by cBioProtal. By gene set enrichment analysis (GSEA). Lastly, we demonstrated that the association between NKX2-3 and tumor mutation burden (TMB) and PDCD1 (programmed cell death 1) of PCa.Results: We identified that the six ARGs expression patterns are independent predictors of OS in PCa patients. Furthermore, our results suggest that ARGs and miRNAs are inter-related. MiR-205 was negatively regulated the expression of ARG (NKX2-3). Further analysis demonstrated that NKX2-3 may be a potential biomarker for predicting the efficacy of anti-PD-1 therapy in PCa.Conclusions: The current study may offer a novel autophagy-related prognostic signature and may identify a promising miRNA-ARG pathway for predicting the efficacy of anti-PD-1 therapy in PCa.

scholarly journals Identification of a nine ferroptosis-related lncRNA prognostic signature for lung adenocarcinoma

2021 ◽  
Author(s):  
Xiwen Tong ◽  
Yujiao Zhang ◽  
Guodong Yang ◽  
Guanghui Yi
Keyword(s):  
Lung Adenocarcinoma ◽  
Signaling Pathways ◽  
Cox Regression ◽  
Risk Group ◽  
Gene Set Enrichment Analysis ◽  
Differentially Expressed ◽  
Rank Test ◽  
Lymph Node Status ◽  
Prognostic Signature ◽  
Test Set

Abstract Background Recently, mounting of studies has shown that lncRNA affects tumor progression through the regulation of ferroptosis. The current study aims to construct a robust ferroptosis-related lncRNAs signature to increase the predicted value of lung adenocarcinoma (LUAD) by bioinformatics analysis. Methods The transcriptome data were abstracted from The Cancer Genome Atlas (TCGA). Differentially expressed lncRNAs were screened by comparing 535 LAUD tissues with 59 adjacent non-LAUD tissues. Univariate Cox regression, lasso regression, multivariate Cox regression were conducted to design a ferroptosis-related lncRNA signature. This signature’s prognosis was verified by the log-rank test of Kaplan-Meier curve and the area under curve (AUC) of receiver operating characteristic (ROC) in train set, test set, and entire set. Furthermore, univariate and multivariate Cox regression were used to analyze its independent prognostic ability. The relationship of the ferroptosis-linked lncRNAs' expression and clinical variables was demonstrated by Wilcoxon rank-sum test and Kruskal-Wallis test. Gene set enrichment analysis (GSEA) was performed to signaling pathways it may involve. Results 1224 differentially expressed lncRNAs were idendified, of which 195 are ferroptosis-related lncRNAs. A nine ferroptosis-related lncRNAs (AC099850.3, NAALADL2-AS2, AL844908.1, AL365181.2, SMIM25, FAM83A-AS1, LINC01116, AL049836.1, C20orf197) prognostic signature was constucted. This model's prognosis in the high-risk group is obviously worse than that of the low-risk group in train set, test set, and entire set. The AUC of ROC predicting the three years survival in the train set, test set, and entire set was 0.754, 0.716, and 0.738, respectively. Moreover, the designed molecular signature was found to be an independent prognostic variable. The expression of these lncRNAs and the lncRNA signature are related to clinical stage, T stage, Lymph-node status, distant metastasis. Finally, GSEA analysis results show that the signature is involved in eight tumor-related and metabolism-related signaling pathways Conclusion The current study constructed, validated, and evaluated a nine ferroptosis-related lncRNA signature which can independently be used to predict the prognosis of LAUD patients, and may become a new therapeutic target.

scholarly journals Construction of a Ferroptosis-Related Long Non-coding RNA Prognostic Signature and Competing Endogenous RNA Network in Lung Adenocarcinoma

2021 ◽  
Vol 9 ◽  
Author(s):  
Xiang Fei ◽  
Congli Hu ◽  
Xinyu Wang ◽  
Chaojing Lu ◽  
Hezhong Chen ◽  
...  
Keyword(s):  
High Risk ◽  
Lung Adenocarcinoma ◽  
Risk Score ◽  
Cox Regression ◽  
Malignant Tumors ◽  
Pearson Correlation ◽  
Low Risk ◽  
Clinical Samples ◽  
Prognostic Signature ◽  
Competing Endogenous Rna

Ferroptosis-related genes play an important role in the progression of lung adenocarcinoma (LUAD). However, the potential function of ferroptosis-related lncRNAs in LUAD has not been fully elucidated. Thus, to explore the potential role of ferroptosis-related lncRNAs in LUAD, the transcriptome RNA-seq data and corresponding clinical data of LUAD were downloaded from the TCGA dataset. Pearson correlation was used to mine ferroptosis-related lncRNAs. Differential expression and univariate Cox analysis were performed to screen prognosis related lncRNAs. A ferroptosis-related lncRNA prognostic signature (FLPS), which included six ferroptosis-related lncRNAs, was constructed by the least absolute shrinkage and selection operator (LASSO) Cox regression. Patients were divided into a high risk-score group and low risk-score group by the median risk score. Receiver operating characteristic (ROC) curves, principal component analysis (PCA), and univariate and multivariate Cox regression were performed to confirm the validity of FLPS. Enrichment analysis showed that the biological processes, pathways and markers associated with malignant tumors were more common in high-risk subgroups. There were significant differences in immune microenvironment and immune cells between high- and low-risk groups. Then, a nomogram was constructed. We further investigated the relationship between six ferroptosis-related lncRNAs and tumor microenvironment and tumor stemness. A competing endogenous RNA (ceRNA) network was established based on the six ferroptosis-related lncRNAs. Finally, we detected the expression levels of ferroptosis-related lncRNAs in clinical samples through quantitative real-time polymerase chain reaction assay (qRT-PCR). In conclusion, we identified the prognostic ferroptosis-related lncRNAs in LUAD and constructed a prognostic signature which provided a new strategy for the evaluation and prediction of prognosis in LUAD.

scholarly journals Identification of a nine ferroptosis-related lncRNA prognostic signature for lung adenocarcinoma

2021 ◽  
Author(s):  
Xiwen Tong ◽  
Yujiao Zhang ◽  
Guodong Yang ◽  
Guanghui Yi
Keyword(s):  
Lung Adenocarcinoma ◽  
Signaling Pathways ◽  
Cox Regression ◽  
Risk Group ◽  
Gene Set Enrichment Analysis ◽  
Differentially Expressed ◽  
Rank Test ◽  
Lymph Node Status ◽  
Prognostic Signature ◽  
Test Set

Abstract Background: Recently, mounting of studies has shown that lncRNA affects tumor progression through the regulation of ferroptosis. The current study aims to construct a robust ferroptosis-related lncRNAs signature to increase the predicted value of lung adenocarcinoma (LUAD) by bioinformatics analysis. Methods: The transcriptome data were abstracted from The Cancer Genome Atlas (TCGA). Differentially expressed lncRNAs were screened by comparing 535 LAUD tissues with 59 adjacent non-LAUD tissues. Univariate Cox regression, lasso regression, multivariate Cox regression were conducted to design a ferroptosis-related lncRNA signature. This signature’s prognosis was verified by the log-rank test of Kaplan-Meier curve and the area under curve (AUC) of receiver operating characteristic (ROC) in train set, test set, and entire set. Furthermore, univariate and multivariate Cox regression were used to analyze its independent prognostic ability. The relationship of the ferroptosis-linked lncRNAs' expression and clinical variables was demonstrated by Wilcoxon rank-sum test and Kruskal-Wallis test. Gene set enrichment analysis (GSEA) was performed to signaling pathways it may involve.Results: 1224 differentially expressed lncRNAs were identified, of which 195 are ferroptosis-related lncRNAs. A nine ferroptosis-related lncRNAs (AC099850.3, NAALADL2-AS2, AL844908.1, AL365181.2, SMIM25, FAM83A-AS1, LINC01116, AL049836.1, C20orf197) prognostic signature was constructed. This model's prognosis in the high-risk group is obviously worse than that of the low-risk group in train set, test set, and entire set. The AUC of ROC predicting the three years survival in the train set, test set, and entire set was 0.754, 0.716, and 0.738, respectively. Moreover, the designed molecular signature was found to be an independent prognostic variable. The expression of these lncRNAs and the lncRNA signature are related to clinical stage, T stage, Lymph-node status, distant metastasis. Finally, GSEA analysis results show that the signature is involved in eight tumor-related and metabolism-related signaling pathwaysConclusion: The current study constructed, validated, and evaluated a nine ferroptosis-related lncRNA signature which can independently be used to predict the prognosis of LAUD patients, and may become a new therapeutic target.

scholarly journals Development and Validation of a Robust Pyroptosis-Related Signature for Predicting Prognosis and Immune Status in Patients with Colon Cancer

2021 ◽  
Vol 2021 ◽  
pp. 1-20
Author(s):  
Zhicheng Zhuang ◽  
Huajun Cai ◽  
Hexin Lin ◽  
Bingjie Guan ◽  
Yong Wu ◽  
...  
Keyword(s):  
High Risk ◽  
Immune Cells ◽  
Risk Group ◽  
Risk Groups ◽  
High Risk Group ◽  
Low Risk ◽  
Immune Checkpoints ◽  
Prognostic Signature ◽  
Tumor Purity ◽  
Kaplan Meier

Background. Pyroptosis has been confirmed as a type of inflammatory programmed cell death in recent years. However, the prognostic role of pyroptosis in colon cancer (CC) remains unclear. Methods. Dataset TCGA-COAD which came from the TCGA portal was taken as the training cohort. GSE17538 from the GEO database was treated as validation cohorts. Differential expression genes (DEGs) between normal and tumor tissues were confirmed. Patients were classified into two subgroups according to the expression characteristics of pyroptosis-related DEGs. The LASSO regression analysis was used to build the best prognostic signature, and its reliability was validated using Kaplan–Meier, ROC, PCA, and t-SNE analyses. And a nomogram based on the multivariate Cox analysis was developed. The enrichment analysis was performed in the GO and KEGG to investigate the potential mechanism. In addition, we explored the difference in the abundance of infiltrating immune cells and immune microenvironment between high- and low-risk groups. And we also predicted the association of common immune checkpoints with risk scores. Finally, we verified the expression of the pyroptosis-related hub gene at the protein level by immunohistochemistry. Results. A total of 23 pyroptosis-related DEGs were identified in the TCGA cohort. Patients were classified into two molecular clusters (MC) based on DEGs. Kaplan–Meier survival analysis indicated that patients with MC1 represented significantly poorer OS than patients with MC2. 13 overall survival- (OS-) related DEGs in MCs were used to construct the prognostic signature. Patients in the high-risk group exhibited poorer OS compared to those in the low-risk group. Combined with the clinical features, the risk score was found to be an independent prognostic factor of CC patients. The above results are verified in the external dataset GSE17538. A nomogram was established and showed excellent performance. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses indicated that the varied prognostic performance between high- and low-risk groups may be related to the immune response mediated by local inflammation. Further analysis showed that the high-risk group has stronger immune cell infiltration and lower tumor purity than the low-risk group. Through the correlation between risk score and immune checkpoint expression, T-cell immunoglobulin and mucin domain-containing protein 3 (TIM-3) was predicted as a potential therapeutic target for the high-risk group. Conclusion. The 13-gene signature was associated with OS, immune cells, tumor purity, and immune checkpoints in CC patients, and it could provide the basis for immunotherapy and predicting prognosis and help clinicians make decisions for individualized treatment.

scholarly journals Characterization of the Prognostic m6A-Related lncRNA Signature in Gastric Cancer

2021 ◽  
Vol 11 ◽  
Author(s):  
Haixu Wang ◽  
Qingkai Meng ◽  
Bin Ma
Keyword(s):  
Gastric Cancer ◽  
High Risk ◽  
Prognostic Factor ◽  
Risk Group ◽  
Pearson Correlation ◽  
High Risk Group ◽  
Independent Prognostic Factor ◽  
Low Risk ◽  
Gene Set Enrichment Analysis ◽  
Receptor Interaction

N6-methyladenosine (m6A) is a common form of mRNA modification regulated by m6A RNA methylation regulators and play an important role in the progression of gastric cancer (GC). However, the prognostic role of m6A-related lncRNA in gastric cancer has not been fully explored. This study aims at exploring the biological function and prognostic roles of the m6A-related lncRNA signature in gastric cancer. A total of 800 m6A-related lncRNAs were identified through Pearson correlation analysis between m6A regulators and all lncRNAs. Eleven m6A-related lncRNA signatures were identified through a survival analysis and the Kaplan-Meier (KM) curve analysis results suggest that patients in the low-risk group have a better overall survival (OS) and disease-free survival (DFS) outcome than the high-risk group. Also, the lncRNA signature can serve as an independent prognostic factor for OS and DFS. The gene set enrichment analysis (GSEA) result suggests that patients in the high-risk group were mainly enriched in the ECM receptor interaction, focal adhesion, and cytokine-cytokine receptor interaction pathway, while the low-risk group was characterized by the base excision repair pathway. We further constructed an individualized prognostic prediction model via the nomogram based on the independent prognostic factor for the OS and DFS, respectively. In addition, some candidate drugs aimed at GC risk group differentiation were identified using the Connective Map (CMAP) database. Lastly, four subgroups (C1, C2, C3, and C4) were identified based on the m6A-related lncRNA expression, through a consensus clustering algorithm. Among them, C1 and C2 have a greater likelihood to respond to immune checkpoint inhibitor immunotherapy, suggesting that the C1 and C2 subgroup might benefit from immunotherapy. In conclusion, the m6A-related lncRNA signature can independently predict the OS and DFS of GC and may aid in development of personalized immunotherapy strategies.

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