ArticleHeliyon2024
Functional exploration and drug prediction on programmed cell death-related biomarkers in lung adenocarcinoma.
Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
2 citing papers in PubMed.
- Exploring hub genes related to adipocytokines in keloids: a combined analysis integrating single-cell, Mendelian randomization and bulk transcriptome data with experimental verification.Frontiers in molecular biosciences · 2026Article
- Computational Analyses Identified Three Diagnostic Biomarkers Associated With Programmed Cell Death for Lung Adenocarcinoma.Human mutation · 2025Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Our study aims to perform functional exploration and drug prediction of programmed cell death (PCD)-related biomarkers in lung adenocarcinoma (LUAD). Methods: UCSC-Xena obtained LUAD-related genes. DESeq2 screened PCD-specific differentially expressed genes (DEGs), and these DEGs were intersected with genes identified by weighted gene co-expression network analysis (WGCNA) to pinpoint the key genes. KOBAS-i was used for enrichment analysis. String and GeneMania were used to construct protein interaction networks and gene-gene interaction networks, respectively. Using two machine learning algorithms to screen for key genes, and taking the intersection as biomarkers, validating via receiver operating characteristic (ROC) and Results: 120 hub genes related to PCD were identified, and an intersection of these genes with DEGs yielded 10 key genes, which were enriched in apoptosis-related pathways. Further machine learning screening of these genes led to the selection of 7 genes, among which 6 genes (FGR, LAPTM5, SIRPA, TLR4, ZEB2, and NLRC4) exhibited significant differences upon ROC validation, ultimately serving as biomarkers, Conclusion: PCD-related biomarkers in LUAD were explored, which may contribute to further understanding on PCD in LUAD.
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Registered trials
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