ArticleNPJ precision oncology2024
Machine learning reveals diverse cell death patterns in lung adenocarcinoma prognosis and therapy.
Article in NPJ precision oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 67 papers.
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Who cites it
67 citing papers in PubMed, 66 citations in OpenAlex.
- Synergistic Profiling of Programmed Cell Death and Immune Responses Identifies a Novel Prognostic Index for Cervical Cancer.ACS omega · 2026Article
- Machine learning-driven development of a novel unfolded protein response-related gene signature for predicting lung adenocarcinoma patient prognosis.BMC cancer · 2026Article
- Molecular Analysis of EBUS-TBNA Samples for Nodal Staging in Non-Small Cell Lung Cancer.Cancers · 2026Review
- Identification of diagnostic and therapeutic roles of programmed cell death-related proteins in dilated cardiomyopathy: a multi-omics and experimental validation study.Scientific reports · 2026Article
- Identification of immunogenic cell death-related prognostic genes in gastric cancer.Journal of gastrointestinal oncology · 2026Article
- Construction of chronic inflammation and mitochondrial energy metabolism-associated predictive and therapeutic models for lung adenocarcinoma patients.Discover oncology · 2026Article
- Deciphering lung adenocarcinoma heterogeneity: a multi-omics approach reveals nuclear division fibroblasts as prognosticators and therapeutic targets.Journal of translational medicine · 2026Article
- Single-cell and bulk transcriptomic analyses uncover immune subtypes associated with programmed cell death features in intrahepatic cholangiocarcinoma.Scientific reports · 2026Article
- Development and validation of programmed cell death related genes in intracranial aneurysms.BMC neurology · 2026Article
- Identification and Validation of cGAS-STING Pathway-Associated Predictive and Therapeutic Models for Esophageal Squamous Cell Cancer Patients via Artificial Intelligence and Multi-Omics.Cancer medicine · 2026Article
- Kinic index: an artificial intelligence-driven predictive model and multitarget drug discovery framework for hepatocellular carcinoma patients.NPJ precision oncology · 2026Article
- Harnessing machine learning-driven multiomics integration: deciphering programmed cell death networks for prognostication and immunotherapy prediction in lung adenocarcinoma.Cell biology and toxicology · 2026Article
- Artificial-intelligence- and multi-omics-guided predictive and drug repurposing framework construction for colorectal cancer: evidence from succinylation-neutrophil signatures.Frontiers in cell and developmental biology · 2026Article
- Classifying Molecular Subtypes and Establishing a Prognosis Model using Oxidative Stress-related Genes for Lung Adenocarcinoma.Current medicinal chemistry · 2026Article
- Identification and validation of mitochondrial transport and glycolysis-associated prognostic and therapeutic target for lung adenocarcinoma patients.Frontiers in genetics · 2026Article
- Characterization of ANXA1 in chemotherapy resistance of head and neck squamous cell carcinoma: insights from artificial intelligence and integrative bioinformatics analysis.Frontiers in cell and developmental biology · 2026Article
- Machine learning-based programmed cell death-related index to predict prognosis and immunotherapy response in skin cutaneous melanoma.Oncology letters · 2025Article
- Programmed cell death-related genes define distinct molecular subtypes and risk profiles in hepatocellular carcinoma.Scientific reports · 2025Article
- Demystifying programmed cell death in lung adenocarcinoma: combined prognostic model construction.Translational cancer research · 2025Article
- Review
7 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
6 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cancer cell growth, metastasis, and drug resistance pose significant challenges in the management of lung adenocarcinoma (LUAD). However, there is a deficiency in optimal predictive models capable of accurately forecasting patient prognoses and guiding the selection of targeted treatments. Programmed cell death (PCD) pathways play a pivotal role in the development and progression of various cancers, offering potential as prognostic indicators and drug sensitivity markers for LUAD patients. The development and validation of predictive models were conducted by integrating 13 PCD patterns with comprehensive analysis of bulk RNA, single-cell RNA transcriptomics, and pertinent clinicopathological details derived from TCGA-LUAD and six GEO datasets. Utilizing the machine learning algorithms, we identified ten critical differentially expressed genes associated with PCD in LUAD, namely CHEK2, KRT18, RRM2, GAPDH, MMP1, CHRNA5, TMPRSS4, ITGB4, CD79A, and CTLA4. Subsequently, we conducted a programmed cell death index (PCDI) based on these genes across the aforementioned cohorts and integrated this index with relevant clinical features to develop several prognostic nomograms. Furthermore, we observed a significant correlation between the PCDI and immune features in LUAD, including immune cell infiltration and the expression of immune checkpoint molecules. Additionally, we found that patients with a high PCDI score may exhibit resistance to immunotherapy and standard adjuvant chemotherapy regimens; however, they may benefit from other FDA-supported drugs such as docetaxel and dasatinib. In conclusion, the PCDI holds potential as a prognostic signature and can facilitate personalized treatment for LUAD patients.
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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.