ArticleJournal of translational medicine2020
Exploration of predictive and prognostic alternative splicing signatures in lung adenocarcinoma using machine learning methods.
Article in Journal of translational medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it, 17 citations in OpenAlex.
- Machine Learning Models for the Identification of Prognostic and Predictive Cancer Biomarkers: A Systematic Review.International journal of molecular sciences · 2023Pooled it
- Artificial Intelligence in Lung Cancer: From Early Detection to Personalized Therapy.Current oncology reports · 2026Review
- Integration of single-cell and bulk RNA sequencing to identify a distinct tumor stem cells and construct a novel prognostic signature for evaluating prognosis and immunotherapy in LUAD.Journal of translational medicine · 2025Article
- Mechanistic Insights into Alternative Gene Splicing in Oxidative Stress and Tissue Injury.Antioxidants & redox signaling · 2024Review
- Scm6A: A Fast and Low-cost Method for Quantifying m6A Modifications at the Single-cell Level.Genomics, proteomics & bioinformatics · 2024Article
- Genome-wide analyses reveal the regulatory roles of DNA methylation-regulated alternative promoter transcripts in breast cancer.Human genetics · 2024Article
- AI/ML advances in non-small cell lung cancer biomarker discovery.Frontiers in oncology · 2023Review
- Multi-omics analysis reveals RNA splicing alterations and their biological and clinical implications in lung adenocarcinoma.Signal transduction and targeted therapy · 2022Article
- A comprehensive characterization of alternative splicing events related to prognosis and the tumor microenvironment in lung adenocarcinoma.Annals of translational medicine · 2022Article
- Switched alternative splicing events as attractive features in lung squamous cell carcinoma.Cancer cell international · 2022Article
- Development of a Prognostic Alternative Splicing Signature Associated With Tumor Microenvironment Immune Profiles in Lung Adenocarcinoma.Frontiers in oncology · 2022Article
- Changing the landscape of non-small cell lung cancer disparities.Journal of cancer biology · 2021Article
Corrections and comments
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Authors and funding
8 authors at 1 institution in 1 country.
Funding
Abstract
backgroundAlternative 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.
methodRNA 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).
resultsEach 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.
conclusionIn summary, our study provides insight into LUAD researches and managements based on these AS biomarkers.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.