Evidence map›Paper›PMID 40175857›Full record

ArticleAnnals of surgical oncology2025

Analysis of Multiple Programmed Cell Death Patterns and Functional Validations of Apoptosis-Associated Genes in Lung Adenocarcinoma.

Yu Peng, Nan Jia, Jingyu Wang, Shilei Dong, Shujun Li, Wei Qin, Hongyun Shi, Kuan Liu

Abstract readMulticenter Study
PubMed Publisher
In one paragraph

Article in Annals of surgical oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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0cells of the map it votes in
2citing papers in PubMed
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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

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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.

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4 · The record

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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

8 authors.

Yu Peng *Department of Radiotherapy, Affiliated Hospital of Hebei University, Baoding, China.
Nan Jia *Department of Neurology, Affiliated Hospital of Hebei University, Baoding, China.
Jingyu Wang *Department of Radiotherapy, Affiliated Hospital of Hebei University, Baoding, China.
Shilei Dong *Department of Radiotherapy, Affiliated Hospital of Hebei University, Baoding, China.
Shujun LiDepartment of Thoracic Surgery, The Second Hospital of Hebei Medical University, Shijiazhuang, China. lishujun2333@163.com.
Wei QinInternal Medicine of Integrated Chinese and Western Medicine, Affiliated Hospital of Hebei University, Baoding, China. qinweiwei1003@163.com.
Hongyun ShiDepartment of Radiotherapy, Affiliated Hospital of Hebei University, Baoding, China. hyshihbuniversity@163.com.
Kuan LiuDepartment of Radiotherapy, Affiliated Hospital of Hebei University, Baoding, China. liukuanhdfs@sina.com.

Funding

Medical Science Foundation of Hebei University 2023A05Science and Technology Plan Project of Hebei Provincial Administration of Traditional Chinese Medicine 2024068
6 · The paper itself

Abstract

backgroundLung adenocarcinoma (LUAD) is marked by its considerable aggressiveness and pronounced heterogeneity. Programmed cell death (PCD) plays a pivotal role in the progression of tumors, their aggressive behavior, resistance to treatment, and recurrence of the disease. PATIENTS AND

methodsUsing expression data from 878 patients across four multicenter cohorts, we identified 13 consensus prognostic genes from 1481 genes associated with PCD. We employed 10 machine-learning algorithms, generating 101 combinations, from which the optimal algorithm was chosen to develop an artificial intelligence-derived cell death index (CDI) on the basis of the average C-index.

resultsThe training cohort and three external validation cohorts consistently demonstrated that CDI could accurately predict LUAD prognosis. Moreover, CDI showed significantly greater accuracy than traditional clinical variables, molecular characteristics, and 22 previously published signatures. Patients in the low-CDI group had a more favorable prognosis, higher levels of immune cell infiltration, better responsiveness to immunotherapy, and a higher likelihood of displaying the "hot tumor" phenotype. Single-cell analysis revealed that neutrophils had the highest CDI scores and exhibited significant differences in marker gene expression.

conclusionsPseudotime trajectory analysis indicated that BCL2L14 plays a crucial role in the developmental pathway of neutrophils, potentially influencing the fate of LUAD cells. Knockdown of BCL2L14 significantly reduced the growth, proliferation, and colony formation abilities of LUAD cells, while also enhancing apoptosis rates.

Indexed as

Adenocarcinoma of LungApoptosisApoptosis Regulatory ProteinsBiomarkers, TumorLung NeoplasmsFemaleFollow-Up StudiesGene Expression ProfilingGene Expression Regulation, NeoplasticHumansMachine LearningMaleMiddle AgedPrognosisSurvival RateApoptosis Regulatory ProteinsBiomarkers, TumorBCL2L14Lung adenocarcinomaMachine learningProgrammed cell deathTumor microenvironment

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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.