Single-Cell RNA Sequencing Reveals Metabolic Reprogramming and Heterogeneity of Proximal Tubular Cells in Renal Cancer

Abstract Objective To investigate the metabolic heterogeneity and metabolic reprogramming characteristics of proximal tubular cells in renal cancer tissues, and to elucidate the cellular metabolic regulatory mechanisms during renal cancer initiation and progression. Methods Single-cell RNA sequencing data of kidney tissue from the Gene Expression Omnibus (GEO) database (accession number: GSM6395361). Cell types were identified through quality control, principal component analysis, UMAP dimensionality reduction, and unsupervised clustering methods. Metabolic state classification was performed based on oxidative phosphorylation (OXPHOS) and glycolysis pathway enrichment scores. Differential gene expression analysis, ligand-receptor interaction analysis, pseudotemporal trajectory analysis, and machine learning classification models were systematically applied to evaluate metabolic reprogramming patterns in proximal tubular cells. Furthermore, quantitative real-time PCR (qRT-PCR) assays were conducted in normal renal epithelial cells (HK-2) and renal carcinoma cell lines (786-O and Caki-1) to experimentally validate the expression trends of representative metabolic and tubular marker genes identified by single-cell analysis. Results More than 18 cell subpopulations were successfully identified, with proximal tubular cells accounting for approximately 70% of total cells. Quality control analysis revealed significant differences between Renal cancer proximal tubular cells and controls in metrics such as mitochondrial gene content. Metabolic state classification revealed three major metabolic phenotypes: OXPHOS_high (oxidative metabolism-dominant), Glycolysis_high (glycolytic metabolism-dominant), and Mixed (mixed metabolic state). Proximal tubular cells exhibited significant metabolic heterogeneity, with 10 PT subclusters (clusters 0–9) displaying different metabolic preferences, and some subclusters showing aerobic glycolysis features similar to the Warburg effect. Metabolic enzyme expression analysis demonstrated differential expression patterns of key metabolic enzymes including ALDOB, HK1, GPI, ENO1, LDHA/LDHB, and IDH1/IDH2 across subclusters. Functional enrichment analysis identified subcluster-specific metabolic signatures, and ligand-receptor analysis revealed active cell communication networks between proximal tubular cells and other kidney cell populations. Pseudotemporal analysis indicated that PT cells undergo continuous transitions along metabolic reprogramming trajectories. The machine learning model achieved high-performance metabolic state classification (ROC AUC = 0.873, PR-AUC = 0.689). The qRT-PCR validation further confirmed the expression consistency of ALDOB, LDHA, IDH1, IDH2, ATP5F1B, SLC34A1, SLC5A2, and CUBN, supporting the reliability of the single-cell transcriptomic findings. Conclusion Proximal tubular cells in renal cancer tissues exhibit significant metabolic heterogeneity and metabolic reprogramming, with different subclusters displaying metabolic plasticity between oxidative phosphorylation and glycolysis. This study provides important single-cell level evidence for understanding the mechanisms of metabolic reprogramming in renal cancer and developing targeted metabolic therapeutic strategies. The integration of scRNA-seq analysis with experimental validation provides robust evidence for understanding metabolic reprogramming mechanisms in renal cancer and offers potential molecular targets for metabolic therapy.