Dataset Information

Exploration of predictive and prognostic alternative splicing signatures in lung adenocarcinoma using machine learning methods.

ABSTRACT:

Background

Alternative splicing (AS) plays critical roles in generating protein diversity and complexity. Dysregulation of AS underlies the initiation and progression of tumors. Machine learning approaches have emerged as efficient tools to identify promising biomarkers. It is meaningful to explore pivotal AS events (ASEs) to deepen understanding and improve prognostic assessments of lung adenocarcinoma (LUAD) via machine learning algorithms.

Method

RNA sequencing data and AS data were extracted from The Cancer Genome Atlas (TCGA) database and TCGA SpliceSeq database. Using several machine learning methods, we identified 24 pairs of LUAD-related ASEs implicated in splicing switches and a random forest-based classifiers for identifying lymph node metastasis (LNM) consisting of 12 ASEs. Furthermore, we identified key prognosis-related ASEs and established a 16-ASE-based prognostic model to predict overall survival for LUAD patients using Cox regression model, random survival forest analysis, and forward selection model. Bioinformatics analyses were also applied to identify underlying mechanisms and associated upstream splicing factors (SFs).

Results

Each pair of ASEs was spliced from the same parent gene, and exhibited perfect inverse intrapair correlation (correlation coefficient?=?-?1). The 12-ASE-based classifier showed robust ability to evaluate LNM status of LUAD patients with the area under the receiver operating characteristic (ROC) curve (AUC) more than 0.7 in fivefold cross-validation. The prognostic model performed well at 1, 3, 5, and 10 years in both the training cohort and internal test cohort. Univariate and multivariate Cox regression indicated the prognostic model could be used as an independent prognostic factor for patients with LUAD. Further analysis revealed correlations between the prognostic model and American Joint Committee on Cancer stage, T stage, N stage, and living status. The splicing network constructed of survival-related SFs and ASEs depicts regulatory relationships between them.

Conclusion

In summary, our study provides insight into LUAD researches and managements based on these AS biomarkers.

SUBMITTER: Cai Q

PROVIDER: S-EPMC7720605 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Exploration of predictive and prognostic alternative splicing signatures in lung adenocarcinoma using machine learning methods.

Cai Qidong Q He Boxue B Zhang Pengfei P Zhao Zhenyu Z Peng Xiong X Zhang Yuqian Y Xie Hui H Wang Xiang X

Journal of translational medicine 20201207 1

<h4>Background</h4>Alternative splicing (AS) plays critical roles in generating protein diversity and complexity. Dysregulation of AS underlies the initiation and progression of tumors. Machine learning approaches have emerged as efficient tools to identify promising biomarkers. It is meaningful to explore pivotal AS events (ASEs) to deepen understanding and improve prognostic assessments of lung adenocarcinoma (LUAD) via machine learning algorithms.<h4>Method</h4>RNA sequencing data and AS data ...[more]

PMID: 33287830

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Project description:BackgroundLung adenocarcinoma (LUAD) stands as the most prevalent histological subtype of lung cancer, exhibiting heterogeneity in outcomes and diverse responses to therapy. CD8 T cells are consistently present throughout all stages of tumor development and play a pivotal role within the tumor microenvironment (TME). Our objective was to investigate the expression profiles of CD8 T cell marker genes, establish a prognostic risk model based on these genes in LUAD, and explore its relationship with immunotherapy response.MethodsBy leveraging the expression data and clinical records from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) cohorts, we identified 23 consensus prognostic genes. Employing ten machine-learning algorithms, we generated 101 combinations, ultimately selecting the optimal algorithm to construct an artificial intelligence-derived prognostic signature named riskScore. This selection was based on the average concordance index (C-index) across three testing cohorts.ResultsRiskScore emerged as an independent risk factor for overall survival (OS), progression-free interval (PFI), disease-free interval (DFI), and disease-specific survival (DSS) in LUAD. Notably, riskScore exhibited notably superior predictive accuracy compared to traditional clinical variables. Furthermore, we observed a positive correlation between the high-risk riskScore group and tumor-promoting biological functions, lower tumor mutational burden (TMB), lower neoantigen (NEO) load, and lower microsatellite instability (MSI) scores, as well as reduced immune cell infiltration and an increased probability of immune evasion within the TME. Of significance, the immunophenoscore (IPS) score displayed significant differences among risk subgroups, and riskScore effectively stratified patients in the IMvigor210 and GSE135222 immunotherapy cohort based on their survival outcomes. Additionally, we identified potential drugs that could target specific risk subgroups.ConclusionsIn summary, riskScore demonstrates its potential as a robust and promising tool for guiding clinical management and tailoring individualized treatments for LUAD patients.

Dataset Information

Exploration of predictive and prognostic alternative splicing signatures in lung adenocarcinoma using machine learning methods.

Background

Method

Results

Conclusion

Publications

Exploration of predictive and prognostic alternative splicing signatures in lung adenocarcinoma using machine learning methods.

Similar Datasets

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