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Single-cell transcriptomic analysis and machine learning identify ATAD3A as a key gene that stabilizes
Henghui Zhang1, Sisi Han2, Zhengming Su1
1Department of Urology, Key Laboratory of Biological Targeting Diagnosis, Therapy and Rehabilitation of Guangdong Higher Education Institutes, The Fifth Affiliated Hospital, Guangzhou Medical University, Guangzhou, Guangdong, 510515, China.
Background:
Mitochondria-associated endoplasmic reticulum membranes (MAM) play a critical regulatory role in cancer, yet their function in bladder cancer (BCa) remains unclear.
Methods:
This study conducted a comprehensive analysis of extensive RNA sequencing and single-cell transcriptomic data. A MAM risk feature model was constructed and evaluated using LASSO-Cox regression, Kaplan-Meier survival curves, and receiver operating characteristic (ROC) analysis. Cell communication networks were decoded using CellChat, and tumor-infiltrating immune cells were quantified through CIBERSORT. Machine learning, OncoPredict, Scissor algorithm, and spatial transcriptomics were employed to analyze chemical reactions. The tumor-promoting functions and molecular mechanisms of ATAD3A were validated through cell and animal models, as well as transmission electron microscopy and fluorescence confocal microscopy.
Results:
This study systematically delineates the gene expression profile of MAM in BCa and constructs a robust MAM prognostic signature that effectively predicts poor patient outcomes. A high MAM score is significantly associated with an immunosuppressive microenvironment and chemotherapy resistance. Using machine learning, we developed a random forest model that successfully identified ATAD3A as a key gene predicting cisplatin resistance, which is significantly correlated with platinum resistance at both single-cell and spatial transcriptomic levels. Further validation shows that ATAD3A is highly expressed in BCa tissue, and its knockdown significantly suppresses tumor growth. Mechanistically, ATAD3A maintains MAM structural integrity, regulates mitochondrial calcium homeostasis and membrane potential, thereby promoting cellular homeostasis and enhancing chemotherapy resistance.
Conclusion:
This study connects MAM to BCa chemotherapy resistance through machine learning and multi-omics analysis, establishing ATAD3A as a prognostic biomarker and potential therapeutic target in BCa.
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