ArticleMedComm2025
Cell Death and Senescence-Based Molecular Classification and an Individualized Prediction Model for Lung Adenocarcinoma.
Article in MedComm, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 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.
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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
5 citing papers in PubMed.
- A radiotherapy-related fibrosis gene signature-based risk model for predicting prognosis and immunological features in lung adenocarcinoma.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026Article
- A machine learning-driven framework integrating cell death and senescence signatures for multi-target drug design and immunotherapy optimization in ovarian cancer.NPJ precision oncology · 2026Article
- Integrative bioinformatics-machine learning-experimental validation identifies shared immunomodulatory targets in psoriasis and psoriatic arthritis and deciphers allicin's therapeutic mechanism.Naunyn-Schmiedeberg's archives of pharmacology · 2026Article
- Exploration of potential mechanisms and biomarkers related to ERS-associated RCD in steroid-induced osteonecrosis of the femoral head based on bioinformatics, with experimental validation.Frontiers in endocrinology · 2026Article
- CDCSI: a machine learning-based interpretable cell death and cellular senescence index for prognosis improvement, immune landscape characterization, and therapeutic response prediction in head and neck squamous cell carcinoma.Frontiers in immunology · 2026Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The exploration of cell death and cellular senescence (CDS) in cancer has been an area of interest, yet a systematic evaluation of CDS features and their interactions in lung adenocarcinoma (LUAD) to understand tumor heterogeneity, tumor microenvironment (TME) characteristics, and patient clinical outcomes is previously uncharted. Our study characterized the activities and interconnections of 21 CDS features in 1788 LUAD cases across 15 cohorts, employing unsupervised clustering to categorize patients into three CDS subtypes with distinct TME profiles. The CDS index (CDSI), derived from principal component analysis, was developed to assess individual tumor CDS regulation patterns. Twelve CDSI core genes, enriched in proliferating T cells within the TME as per single-cell analysis, were identified and their functional roles and prognostic significance were validated. High CDSI correlated with improved overall survival in discovery cohort, four independent validation cohorts, and subgroup analysis. CDSI-low patients exhibited a favorable clinical response to immunotherapy and potential sensitivity to mitosis pathway drugs, while CDSI-high patients might benefit from drugs targeting ERK/MAPK and MDM2-p53 pathways. The clinical utility of CDSI was further validated using 9185 pan-cancer samples, demonstrating the broad relevance of our prediction model across various cancer types and its potential clinical implications for cancer management.
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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.