Identification of Paraptosis-Related Renal Cell Carcinoma Subtypes, Construction of a Prognostic Signature, and Determination of Tumor Microenvironment Landscape Using Bioinformatic Analysis and Experimental Verification
Mengyuan Qin, Meiting Chen, Yuling Gan, Xiangqian Feng, Ping Huang, Feifei Meng, Yufang Yang

TL;DR
This study identifies two subtypes of kidney cancer based on paraptosis-related genes and develops a four-gene signature to predict patient survival and guide treatment.
Contribution
A novel four-gene prognostic signature and two RCC subtypes linked to paraptosis are identified, offering insights into tumor microenvironment and treatment response.
Findings
A four-gene (COL7A1, RNASE2, SLC10A2, APOLD1) signature accurately predicts survival in renal cell carcinoma patients.
Low-risk patients show enriched immune/stromal infiltration, while high-risk patients have elevated cancer stem cell content and tumor mutation burden.
COL7A1 is upregulated in RCC cell lines and may serve as a potential therapeutic target.
Abstract
Renal cell carcinoma (RCC) is a common and deadly urological cancer, for which there are no robust prognostic biomarkers or personalized treatment strategies. Paraptosis, a distinct form of regulated cell death marked by cytoplasmic vacuolization, is being increasingly recognized for its roles in tumorigenesis and therapy responses, yet its functional implications in RCC remain poorly defined. Transcriptomic profiles and corresponding clinical metadata from the TCGA-KIRC and GSE33371 datasets were systematically analyzed to characterize the paraptosis-related gene (PaRG) expression profile in renal cell carcinoma (RCC). Patients were categorized into two subtypes via consensus clustering, 574 overlapping differentially expressed genes (DEGs) were identified, and a four-gene (COL7A1, RNASE2, SLC10A2, and APOLD1) prognostic signature was constructed using LASSO and multivariate Cox…
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Taxonomy
TopicsFerroptosis and cancer prognosis · Renal cell carcinoma treatment · Cancer Immunotherapy and Biomarkers
