Characterizing prognostic and immunological traits of kidney renal clear cell carcinoma via mitochondria-associated membranes and identifying DNM1L as a potential therapeutic target using machine learning

Mitochondrial-associated membranes (MAMs) participate in cellular metabolism, calcium signaling, and cancer reprogramming, but their role in kidney renal clear cell carcinoma (KIRC) remains unclear. Clinical/transcriptomic data of KIRC and previously reported MAMs-related genes were obtained from TCGA, E-MTAB-1980, and GSE29609. After analyzing MAMs gene expression in KIRC, 101 models were generated via 10 machine learning algorithms to select the optimal MAMs-based scoring system. Survival (Kaplan-Meier) and Cox regression analyses evaluated its prognostic value; associations with immune cell infiltration, checkpoints, and drug sensitivity were explored, and key genes were validated in vitro and in vivo. Forty-two MAMs-related genes were identified, most highly expressed in KIRC tissues. A 9-gene MAMs scoring system was built using the Stepwise Cox model. High-score patients had worse prognosis, and the system was an independent prognostic factor for KIRC. High scores correlated with advanced TNM stage/grade, elevated CTLA4/PD1 (better immunotherapy response in CTLA4+/PD1+ subgroups), and sensitivity to temsirolimus/sunitinib (low scores sensitive to sorafenib). DNM1L was a key gene; its knockdown inhibited KIRC cell proliferation, invasion, and migration in vitro. In vivo experiments further confirmed that DNM1L knockdown suppressed tumor growth in BALB/c nude mice bearing KIRC xenografts. This study identified high MAMs-related gene expression in KIRC, developed a 9-gene MAMs scoring system, and validated DNM1L as a key gene, providing new insights into KIRC prognostic biomarkers and therapeutic targets.