Characterizing prognostic and immunological traits of kidney renal clear cell carcinoma via mitochondria-associated membranes - using machine learning

Sep 22, 2026

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Mitochondrial-associated membranes (MAMs) regulate metabolism, calcium signaling, and cancer reprogramming, yet their significance in kidney renal clear cell carcinoma (KIRC) remained unclear. Clinical and transcriptomic datasets for KIRC and previously reported MAM-related genes were integrated from TCGA, E-MTAB-1980, and GSE29609. Machine learning was used to build and optimize a MAMs-based prognostic scoring system, which was assessed by survival and Cox analyses. The model was linked to immune infiltration, immune checkpoints, and drug sensitivity, and key genes were experimentally validated in vitro and in vivo.

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Li etal. BMC Cancer (2026) 26:258 BMC Cancer [https://doi.org/10.1186/s12885-026-15603-4](https://doi.org/10.1186/s12885-026-15603-4) RESEARCH Open Access Characterizing prognostic and immunological traits of kidney renal clear cell carcinoma via mitochondria-associated membranesand identifying DNM1L as a potential therapeutic target using machine learning Sheng Li1,2†, Jinkang Lin1,2†, Fucun Zheng1,2†, Xiaoqiang Liu1,2, Situ Xiong1,2, Bin Fu1,2 and Jin Zeng1,2* Abstract Background 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. Methods 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. Results 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. Conclusion 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. †Sheng Li, Jinkang Lin and Fucun Zheng contributed equally to this work. *Correspondence: Jin Zeng [zengjin_31217@163.com](zengjin_31217@163.com) Full list of author information is available at the end of the article © The Author(s) 2026. Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit [http://creati](http://creati)[vecommons.org/licenses/by-nc-nd/4.0/](vecommons.org/licenses/by-nc-nd/4.0/.)[.](vecommons.org/licenses/by-nc-nd/4.0/.) Li et al. BMC Cancer (2026) 26:258 Page 2 of 15 Keywords Kidney renal clear cell carcinoma, Mitochondrial-associated membranes, Machine learning, Prognosis, DNM1L Introduction Renal cell carcinoma (RCC) is a common malignant tumor of the urinary system, with its global incidence steadily rising at an annual rate of approximately 2%[1]. The predominant pathological subtype is kidney renal clear cell carcinoma (KIRC), which accounts for more than 70%

FAQs

What is the biological focus of the study in KIRC?

The study focuses on mitochondrial-associated membranes (MAMs) and their prognostic and immunological roles in kidney renal clear cell carcinoma (KIRC).

How was the prognostic scoring system constructed?

MAM-related gene expression in KIRC was analyzed using machine learning with multiple algorithms to generate and select an optimal MAMs-based scoring system, followed by survival and Cox regression evaluation.

What key gene did the study identify and how was it validated?

DNM1L was identified as a key gene. Its knockdown inhibited KIRC cell proliferation, invasion, and migration in vitro and suppressed tumor growth in BALB/c nude mice with KIRC xenografts.

Sep 22, 2026

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