ArticleNature communications2022
Machine learning-based integration develops an immune-derived lncRNA signature for improving outcomes in colorectal cancer.
Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 583 papers, 1 of them a synthesis that pooled it.
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Who cites it
583 citing papers in PubMed, 1 synthesis or guideline pooled it, 888 citations in OpenAlex.
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- Public Transcriptomic Data Mining for SCLC: From Candidate Ma rkers to Therapeutic Exploration.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026Article
- Targeting galactose metabolic reprogramming overcomes immunotherapy resistance in KRAS-mutant lung adenocarcinoma: Integrative multi-omics and machine learning approaches.Translational oncology · 2026Article
- Machine learning approaches for cancer prognosis and diagnosis via non-coding RNA: a comprehensive review.Briefings in bioinformatics · 2026Review
- Investigating the mechanisms of PhIP-induced colorectal cancer through network toxicology, machine learning, and molecular dynamics simulation.Translational cancer research · 2026Article
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- Integrating machine learning and multi-omics analysis to identify and validate key genes associated with periodontitis.Acta odontologica Scandinavica · 2026Article
- EFEMP2 Is Associated with Shelterin-Related DNA Damage Repair, Immune Microenvironment Features, and Prognosis in Glioblastoma.Biomedicines · 2026Article
- PIP4K2A: A Novel CD8+ T Cell-Related Biomarker Associated with Lung Function Decline in COPD.Biochemical genetics · 2026Article
- Integrative machine learning and multi-omics identify a centromere gene signature and validate B3GALT4 as a tumor suppressor in osteosarcoma.Journal of bone oncology · 2026Article
- Machine learning-derived mast cell-associated angiogenesis features can serve as prognostic targets for clear cell renal cell carcinoma.Translational andrology and urology · 2026Article
- An externally validated lactylation-associated prognostic signature for overall survival prediction in lung adenocarcinoma identifiesTranslational cancer research · 2026Article
- Integrative pan-cancer and COAD-focused multi-omics analysis of non-histone acetylation substrate-related transcriptional landscapes.NPJ precision oncology · 2026Article
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- MIF-Associated Immunosuppressive CAF Remodeling Predicts Poor Prognosis During Lung Adenocarcinoma Progression: A Single-Cell and Multicohort Transcriptomic Study.Biomedicines · 2026Article
523 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
11 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Long noncoding RNAs (lncRNAs) are recently implicated in modifying immunology in colorectal cancer (CRC). Nevertheless, the clinical significance of immune-related lncRNAs remains largely unexplored. In this study, we develope a machine learning-based integrative procedure for constructing a consensus immune-related lncRNA signature (IRLS). IRLS is an independent risk factor for overall survival and displays stable and powerful performance, but only demonstrates limited predictive value for relapse-free survival. Additionally, IRLS possesses distinctly superior accuracy than traditional clinical variables, molecular features, and 109 published signatures. Besides, the high-risk group is sensitive to fluorouracil-based adjuvant chemotherapy, while the low-risk group benefits more from bevacizumab. Notably, the low-risk group displays abundant lymphocyte infiltration, high expression of CD8A and PD-L1, and a response to pembrolizumab. Taken together, IRLS could serve as a robust and promising tool to improve clinical outcomes for individual CRC patients.
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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.