ArticleFrontiers in cell and developmental biology2021
Identification and Validation of the Pyroptosis-Related Molecular Subtypes of Lung Adenocarcinoma by Bioinformatics and Machine Learning.
Article in Frontiers in cell and developmental biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.
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37 citing papers in PubMed, 50 citations in OpenAlex.
- A multi-algorithm machine learning framework identifies FGD5, LRRC36, C8B, and MYOC as novel diagnostic biomarkers in lung adenocarcinoma.Discover oncology · 2026Article
- DeepCMS: A Feature Selection-Driven Model for Cancer Molecular Subtyping with a Case Study on Testicular Germ Cell Tumors.Diagnostics (Basel, Switzerland) · 2025Article
- In-depth exploration of programmed cell death-related subtypes and development of a prognostic signature model in lung adenocarcinoma.Scientific reports · 2025Article
- Feasibility of machine learning-based modeling and prediction to assess osteosarcoma outcomes.Scientific reports · 2025Article
- Molecular Subtyping and Therapeutic Targeting of IFNG-Driven Immunogenic Cell Death in Lung Adenocarcinoma.Cancer medicine · 2025Article
- Identification of potential biomarkers for lung cancer using integrated bioinformatics and machine learning approaches.PloS one · 2025Article
- Knowledge map of artificial intelligence in neurodegenerative diseases: a decade-long bibliometric and visualization study.Frontiers in aging neuroscience · 2025Article
- Construction of ferroptosis and pyroptosis model to assess the prognosis of gastric cancer patients based on bioinformatics.Translational cancer research · 2024Article
- Recent advances in lung cancer organoid (tumoroid) research (Review).Experimental and therapeutic medicine · 2024Review
- Artificial intelligence in lung cancer: current applications, future perspectives, and challenges.Frontiers in oncology · 2024Review
- Clinical-mediated discovery of pyroptosis in CD8Cell death & disease · 2023Article
- Identification of pyroptosis-related genes and potential drugs in diabetic nephropathy.Journal of translational medicine · 2023Article
- A novel defined pyroptosis-related gene signature predicts prognosis and correlates with the tumour immune microenvironment in lung adenocarcinoma.Scientific reports · 2023Article
- Pyroptosis: A promising target for lung cancer therapy.Chinese medical journal pulmonary and critical care medicine · 2023Review
- Article
- The predictive accuracy of CT radiomics combined with machine learning in predicting the invasiveness of small nodular lung adenocarcinoma.Translational lung cancer research · 2023Article
- A five-collagen-based risk model in lung adenocarcinoma: prognostic significance and immune landscape.Frontiers in oncology · 2023Article
- Pyroptosis-based risk score predicts prognosis and drug sensitivity in lung adenocarcinoma.Open medicine (Warsaw, Poland) · 2023Article
- Comprehensive analysis of a pyroptosis-related gene signature of clinical and biological values in spinal cord injury.Frontiers in neurology · 2023Article
- Proposing new early detection indicators for pancreatic cancer: Combining machine learning and neural networks for serum miRNA-based diagnostic model.Frontiers in oncology · 2023Article
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Authors and funding
8 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Lung cancer remains the leading cause of cancer death globally, with lung adenocarcinoma (LUAD) being its most prevalent subtype. Due to the heterogeneity of LUAD, patients given the same treatment regimen may have different responses and clinical outcomes. Therefore, identifying new subtypes of LUAD is important for predicting prognosis and providing personalized treatment for patients. Pyroptosis-related genes play an essential role in anticancer, but there is limited research investigating pyroptosis in LUAD. In this study, 33 pyroptosis gene expression profiles and clinical information were collected from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. By bioinformatics and machine learning analyses, we identified novel subtypes of LUAD based on 10 pyroptosis-related genes and further validated them in the GEO dataset, with machine learning models performing up to an AUC of 1 for classifying in GEO. A web-based tool was established for clinicians to use our clustering model (http://www.aimedicallab.com/tool/aiml-subphe-luad.html). LUAD patients were clustered into 3 subtypes (A, B, and C), and survival analysis showed that B had the best survival outcome and C had the worst survival outcome. The relationships between pyroptosis gene expression and clinical characteristics were further analyzed in the three molecular subtypes. Immune profiling revealed significant differences in immune cell infiltration among the three molecular subtypes. GO enrichment and KEGG pathway analyses were performed based on the differential genes of the three subtypes, indicating that differentially expressed genes (DEGs) were involved in multiple cellular and biological functions, including RNA catabolic process, mRNA catabolic process, and pathways of neurodegeneration-multiple diseases. Finally, we developed an 8-gene prognostic model that accurately predicted 1-, 3-, and 5-year overall survival. In conclusion, pyroptosis-related genes may play a critical role in LUAD, and provide new insights into the underlying mechanisms of LUAD.
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