Evidence map›Paper›PMID 39269650›Full record

ArticleDiscover oncology2024

Unveiling the impact of cell death-related genes and immune dynamics on drug resistance in lung adenocarcinoma: a risk score model and functional insights.

Yuanyuan Xiao, Shancheng He, Baochang Xie, Wenqi Zhao, Dengliang Ji

Abstract read
In one paragraph

Article in Discover oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Yuanyuan XiaoDepartment of Critical Care Medicine, The Fifth People's Hospital of Ganzhou City, Ganzhou, 341000, China.
Shancheng HeDepartment of Critical Care Medicine, The Fifth People's Hospital of Ganzhou City, Ganzhou, 341000, China.
Baochang XieDepartment of Critical Care Medicine, The Fifth People's Hospital of Ganzhou City, Ganzhou, 341000, China.
Wenqi ZhaoDepartment of Critical Care Medicine, The Fifth People's Hospital of Ganzhou City, Ganzhou, 341000, China.
Dengliang JiDepartment of Critical Care Medicine, The Fifth People's Hospital of Ganzhou City, Ganzhou, 341000, China. jdl090239@sina.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Lung adenocarcinoma (LUAD), characterized by its heterogeneity and complex pathogenesis, is the focus of this study which investigates the association between cell death-related genes and LUAD. Through machine learning, a risk score model was developed using the Coxboost rsf algorithm, demonstrating strong prognostic accuracy in both validation (GSE30219, GSE31210, GSE72094) and training (TCGA-LUAD) datasets with C-indices of 0.93, 0.67, 0.68, and 0.64, respectively. The study reveals that the expression of Keratin 18 (KRT18), a key cytoskeletal protein, varies across LUAD cell lines (DV-90, PC-9, A549) compared to normal bronchial epithelial cells (BEAS-2B), suggesting its potential role in LUAD's pathogenesis. Kaplan-Meier survival curves further validate the model, indicating longer survival in the low-risk group. A comprehensive analysis of gene expression, functional differences, immune infiltration, and mutations underscores significant variations between risk groups, highlighting the high-risk group's immunological dysfunction. This points to a more intricate tumor immune environment and the possibility of alternative therapeutic strategies. The study also delves into drug sensitivity, showing distinct responses between risk groups, underscoring the importance of risk stratification in treatment decisions for LUAD patients. Additionally, it explores KRT18's epigenetic regulation and its correlation with immune cell infiltration and immune regulatory molecules, suggesting KRT18's significant role in the tumor immune landscape. This research not only offers a valuable prognostic tool for LUAD but also illuminates the complex interplay between cell death-related genes, drug sensitivity, and immune infiltration, positioning KRT18 as a potential therapeutic or prognostic target to improve patient outcomes by personalizing LUAD treatment strategies.

Indexed as

Cell death-related genesDrug sensitivityImmune infiltrationLung adenocarcinomaRisk score model

Identifiers

PMID39269650
PMCPMC11399484

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Registered trials

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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.