ArticleCell biology and toxicology2025
Integrated machine learning survival framework develops a prognostic model based on macrophage-related genes and programmed cell death signatures in a multi-sample Kidney renal clear cell carcinoma.
Article in Cell biology and toxicology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed.
- An analysis of succinate dehydrogenase B in pleural mesothelioma.Discover oncology · 2026Article
- A multi-mics exploration of programmed cell death in non-obstructive azoospermia: identifying TLR4 as a central regulator and therapeutic target.Frontiers in cell and developmental biology · 2026Article
- The development and validation of a novel programmed cell death-related signature based on machine learning analysis of 15 programmed cell death patterns for predicting prognosis and therapeutic response in high-grade serous ovarian carcinoma.Translational cancer research · 2025Article
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8 authors.
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Abstract
backgroundMacrophages are closely associated with the progression of Kidney renal clear cell carcinoma (KIRC) and can influence programmed cell death (PCD) of tumour cells. To identify prognostic biomarkers for KIRC, it is essential to investigate the association between macrophage-related genes and PCD characteristics.
methodsClinical details and transcriptome data from 693 KIRC samples were obtained from multiple databases, including TCGA and GEO. Genes associated with macrophages and programmed cell death (PCD) were identified and key regulatory genes and PCD patterns were analyzed. The relationship between macrophages and 18 types of cell death is under investigation with a powerful computational framework. Ten machine learning algorithms, 101 unique combinations of algorithms were utilized to build a macrophage-associated programmed cell death (MacPCD) model to predict KIRC patient survival. Immunohistochemistry and RT-qPCR were used for genetic analysis of MacPCD models.
resultsThe MacPCD model is made up of six genes which showed strong predictive power for the prognosis of patients with KIRC. Immunohistochemistry and RT-qPCR showed that among the MacPCD model genes, BID, SLC25A37 and BNIP3L were highly expressed in tumour tissues, whereas ACSL1, SDHB and ALDH2 were highly expressed in normal tissues. Biologically, the high MacPCD group showed higher tumor mutation burden and increased immune cell infiltration and high expression of immunomodulators. In particular, MacPCD was an independent prognostic indicator of KIRC and was the best predictor of KIRC survival (AUC = 0.920) compared with multiple clinical variables (Age, M, and Stage).
conclusionWe used a powerful machine learning framework to highlight the great potential of MacPCD in providing personalised risk assessment and immunotherapy intervention recommendations for KIRC patients.
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