ArticleFrontiers in endocrinology2023
A novel pyroptosis gene expression-based risk score for survival in gastric cancer.
Article in Frontiers in endocrinology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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7 citing papers in PubMed, 11 citations in OpenAlex.
- Reprogramming of cell death in gastric cancer: From molecular mechanisms to therapeutic potential (Review).International journal of molecular medicine · 2026Review
- Targeting Plasma Membrane CaCancers · 2026Review
- Identification and validation of tumor microenvironment-related therapeutic targets in gastric cancer using integrated multi-omics and molecular docking approaches.Frontiers in bioinformatics · 2025Article
- Multiomics integration and machine learning reveal prognostic programmed cell death signatures in gastric cancer.Scientific reports · 2024Article
- Integrated multi-omics analysis and machine learning to refine molecular subtypes, prognosis, and immunotherapy in lung adenocarcinoma.Functional & integrative genomics · 2024Article
- Machine learning-driven prognostic analysis of cuproptosis and disulfidptosis-related lncRNAs in clear cell renal cell carcinoma: a step towards precision oncology.European journal of medical research · 2024Article
- Construction and Validation of a Reliable Disulfidptosis-Related LncRNAs Signature of the Subtype, Prognostic, and Immune Landscape in Colon Cancer.International journal of molecular sciences · 2023Article
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8 authors at 5 institutions in 2 countries.
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Abstract
Background: Gastric cancer (GC) is a highly heterogeneous disease, which makes treatment and prognosis prediction difficult. Pyroptosis plays a vital role in the development of GC and influence the prognosis of GC. Long non-coding RNAs (lncRNAs), as regulators of gene expressions, are among putative biomarkers and therapeutic targets. However, the importance of pyroptosis-associated lncRNAs is still unclear in predicting prognosis in gastric cancer. Methods: In this study, the mRNA expression profiles and clinical data of GC patients were obtained from The Cancer Genome Atlas (TCGA) database and the Gene Expression Omnibus (GEO) database. A pyroptosis-related lncRNA signature was constructed based on TCGA databases by using the Least Absolute Shrinkage and Selection Operator (LASSO) method Cox regression model. GC patients from the GSE62254 database cohort were used for validation. Univariate and multivariate Cox analyses were used to determine the independent predictors for OS. Gene set enrichment analyses were performed to explore the potential regulatory pathways. The immune cell infiltration level was analyzed Results: A four-pyroptosis-related lncRNA (ACVR2B-AS1, PRSS30P, ATP2B1-AS1, RMRP) signature was constructed using LASSO Cox regression analysis. GC patients were stratified into high- and low-risk groups, and patients in the high-risk group showed significant worse prognosis in TNM stage, gender, and age. The risk score was an independent predictor for OS by multivariate Cox analysis. Functional analysis indicated that the immune cell infiltrate was different between high- and low-risk groups. Conclusion: The pyroptosis-related lncRNA prognostic signature can be used for predicting prognosis in GC. Moreover, the novel signature might provide clinical therapeutic intervention for GC patients.
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