ArticleAging2021
A risk model based on autophagy-related lncRNAs for predicting prognosis and efficacy of immunotherapy and chemotherapy in gastric cancer patients.
Article in Aging, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
6 citing papers in PubMed, 8 citations in OpenAlex.
- Mechanistic roles of long non-coding RNAs in gastric cancer therapy resistance.Non-coding RNA research · 2026Review
- Article
- Long Non-Coding RNAs in Drug Resistance of Gastric Cancer: Complex Mechanisms and Potential Clinical Applications.Biomolecules · 2024Review
- Construction of N-7 methylguanine-related mRNA prognostic model in uterine corpus endometrial carcinoma based on multi-omics data and immune-related analysis.Scientific reports · 2022Article
- Construction of a Novel LncRNA Signature Related to Genomic Instability to Predict the Prognosis and Immune Activity of Patients With Hepatocellular Carcinoma.Frontiers in immunology · 2022Article
- MicroRNA-34c-5p exhibits anticancer properties in gastric cancer by targeting MAP2K1 to inhibit cell proliferation, migration, and invasion.BioMed research international · 2022Article
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Long non-coding RNAs (lncRNAs) are a class of non-protein-coding RNAs essential to the occurrence and development of gastric cancer (GC). We aimed to identify critical lncRNA pairs to construct a prognostic model and assess its performances in prognosis and efficacy prediction in GC patients receiving immunotherapy and chemotherapy. We searched transcriptome and clinical data of GC patients from The Cancer Genome Atlas (TCGA) database. Autophagy-related lncRNAs were identified using co-expression network analysis, and lncRNA pairs with prognostic value were selected using pairwise transcriptome analysis. The gene pairs were subjected to LASSO algorithm for identification of optimal gene pairs for risk model construction. Patients were classified into the low-risk and high-risk groups with the RiskScore as a cutoff. Finally, 9 optimal gene pairs were identified in the LASSO algorithm model for construction of a lncRNA prognostic risk model. For predictive performances, it successfully predicted a shorter survival of high-risk patients than that obtained in low-risk individuals (
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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.