Dataset Information

Classification of Hepatocellular Carcinoma Based on N6-Methylandenosine-Related lncRNAs Profiling.

ABSTRACT: HCC is one of the most common types of malignancies worldwide and the fourth-leading cause of cancer deaths. Thus, there is an urgent need to search for novel targeted therapies in HCC. 186 m6a-related lncRNAs were screened for subsequent analysis. Two distinct m6A modification clusters were identified to be associated with the overall prognosis in TCGA-LIHC based on the m6A-related lncRNAs profiling, followed by univariate Cox regression analysis. In addition, four m6A-related lncRNAs prognostic signatures were developed and validated that could predict the OS of HCC patients, followed by univariate Cox regression, LASSO regression, and multivariate Cox regression analysis. Moreover, four m6A-related lncRNAs were identified to be related to HCC prognosis. ESTIMATE was used to evaluate the stromal score, immune score, ESTIMATE score, and tumor purity of each HCC sample. ssGSEA was performed to identify the enrichment levels of 29 immune signatures in each sample. Finally, quantitative real-time polymerase chain reaction shown that KDM4A-AS1, BACE1-AS, and NRAV expressions were upregulated in HCC patients. We proved that our m6A-related lncRNAs signature had powerful and robust ability for predicting OS of different HCC subgroups.

SUBMITTER: Yin L

PROVIDER: S-EPMC8854774 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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Classification of Hepatocellular Carcinoma Based on N6-Methylandenosine-Related lncRNAs Profiling.

Yin Lu L Zhou Liuzhi L Gao Shiqi S Feng Yina Y Zhu Hanzhang H Xiang Jingjing J Xu Rujun R

Frontiers in molecular biosciences 20220204

HCC is one of the most common types of malignancies worldwide and the fourth-leading cause of cancer deaths. Thus, there is an urgent need to search for novel targeted therapies in HCC. 186 m6a-related lncRNAs were screened for subsequent analysis. Two distinct m6A modification clusters were identified to be associated with the overall prognosis in TCGA-LIHC based on the m6A-related lncRNAs profiling, followed by univariate Cox regression analysis. In addition, four m6A-related lncRNAs prognosti ...[more]

PMID: 35187083

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Project description:Both lncRNAs and the N6-methyladenosine (m6A) modification are key regulators of tumorigenesis and innate immunity. However, little is known about the m6A modification of lncRNAs and their clinical and immune relevance in bladder cancer. In this study, we identified m6A-related lncRNAs using Pearson correlation analysis in The Cancer Genome Atlas (TCGA) and the IMvigor210 datasets. Next, univariate Cox regression was performed using the TCGA dataset to filter prognostic m6A-related lncRNAs, which were further subjected to the least absolute shrinkage and selection operator (LASSO) Cox regression to establish a 12 m6A-related lncRNA prognostic score (m6A-LRS). The m6A-LRS was validated in the IMvigor210 dataset. In addition, high m6A-LRS tumors, characterized by decreased tumor mutation load and neoantigen load, showed poorer response to immunotherapy than those with low m6A-LRS in the IMvigor210 dataset. Further, we constructed an m6A-LRS-based nomogram that demonstrated a strong ability to predict overall survival in patients with bladder cancer. Moreover, enrichment analysis revealed that tumor-associated biological processes, oncogenic signaling, and tumor hallmarks were commonly associated with a high m6A-LRS. Gene set variation analysis also indicated that high m6A-LRS was associated with activation of canonical oncogenic signatures, such as the epithelial-to-mesenchymal transition, cell cycle regulators, and DNA replication, as well as activation of immunosuppressive signatures, such as the T-cell exhaustion and pan-fibroblast-TGF-β response signatures. Furthermore, we observed distinct tumor microenvironment cell infiltration characteristics between high- and low-risk tumors. High m6A-LRS tumors showed reduced infiltration of CD8+ T-cells and enhanced infiltration of macrophages and fibroblasts. Additionally, we established a competing endogenous RNA network based on the12 m6A-related lncRNAs. Finally, three lncRNAs (SNHG16, SBF2-AS1, and BDNF-AS) were selected for further validation. The qualitative PCR assay on 10 pairs of bladder cancer and adjacent normal control samples validated the differential expression, and methylated RNA immunoprecipitation (MeRIP) analysis demonstrated a robust m6A enrichment in T24 bladder cancer cells compared with normal uroepithelial cells (SVHUC-1). In conclusion, this study introduced an m6A-related lncRNA signature that identified a subgroup of patients with poor prognoses and suboptimal immune responses, thus providing novel approaches for treatment response prediction and patient stratification in bladder cancer.

Project description:Background: N6-methylandenosine-related long non-coding RNAs (m6A-related lncRNAs) are critically involved in cancer development. However, the roles and clinical significance of m6A-related lncRNAs in soft tissue sarcomas (STS) are inconclusive, thereby warranting further investigations. Methods: Transcriptome profiling data were extracted from The Cancer Genome Atlas (TCGA) database and Genotype-Tissue Expression (GTEx). Consensus clustering was employed to divide patients into clusters and Kaplan-Meier analysis was used to explore the prognostic differences between the subgroups. Gene set enrichment analysis (GSEA) was conducted to identify the biological processes and signaling pathways associated with m6A-Related lncRNAs. Finally, patients were randomly divided into training and validation cohorts, and least absolute shrinkage and selection operator (LASSO) Cox regression was conducted to establish the m6A-related lncRNA-based risk signature. Results: A total of 259 STS patients from TCGA-SARC dataset were enrolled in our study. Thirteen m6A-Related lncRNAs were identified to be closely related to the prognosis of STS patients. Patients were divided into two clusters, and patients in cluster 2 had a better overall survival (OS) than those in cluster 1. Patients in different clusters also showed differences in immune scores, infiltrating immune cells, and immune checkpoint expression. Patients were further classified into high-risk and low-risk subgroups according to risk scores, and high-risk patients were found to have a worse prognosis. The receiver operating characteristic (ROC) curve indicated that the risk signature displayed excellent performance at predicting the prognosis of patients with STS. Further, the risk signature was remarkably connected with the immune microenvironment and chemosensitivity in STS. Conclusion: Our study demonstrated that m6A-related lncRNAs were significantly associated with prognosis and tumor immune microenvironment and could function as independent prognosis-specific predictors in STS, thereby providing novel insights into the roles of m6A-related lncRNAs in STS.

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Project description:BackgroundThe N6-methyladenosine (m6 A) can modify long non-coding RNAs (lncRNAs), thereby influencing a wide array of biological functions. However, the prognosis of m6 A-related lncRNAs (m6 ARLncRNAs) in non-small cell lung cancer (NSCLC) remains largely unknown.MethodsPearson correlation analysis was used to identify m6 ARLncRNAs in 1835 NSCLC patients and with the condition (|Pearson R| > 0.4 and p < 0.001). Univariant Cox regression analysis was conducted to explore the prognostic m6 ARLncRNAs. We filtered prognostic m6 ARLncRNAs by LASSO regression and multivariate Cox proportional hazard regression to construct and validate an m6 ARLncRNAs signature (m6 ARLncSig). We analyzed the correlation between the m6 ARLncSig score and clinical features, immune microenvironment, tumor mutation burden, and therapeutic sensitivity and conducted independence and clinical stratification analysis. Finally, we established and validated a nomogram for prognosis prediction in NSCLC patients.ResultsForty-one m6 ARLncRNAs were identified as prognostic lncRNAs, and 12 m6 ARLncRNAs were selected to construct m6 ARLncSig in the TCGA training dataset. The m6 ARLncSig was further validated in the testing dataset, GSE31210, GSE37745, GSE30219, and our NSCLC samples. In terms of m6 ARLncSig, NSCLC patients were divided into high- and low-risk groups, with significantly different overall survival (OS), clinical features (age, sex, and tumor stage), tumor-infiltrating immune cells, chemotherapeutic sensitivity, radiotherapeutic response, and biological pathways. Moreover, m6 ARLncSig independently predicted the OS of NSCLC patients. Finally, the robustness and clinical practicability for predicting NSCLC patient prognosis was improved by constructing a nomogram containing the m6 ARLncSig, age, gender, and tumor stage.ConclusionsOur study demonstrated that m6 ARLncSig could act as a potential biomarker for evaluating the prognosis and therapeutic efficacy in NSCLC patients.

Dataset Information

Classification of Hepatocellular Carcinoma Based on N6-Methylandenosine-Related lncRNAs Profiling.

Publications

Classification of Hepatocellular Carcinoma Based on N6-Methylandenosine-Related lncRNAs Profiling.

Similar Datasets

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