Exploring the Prognostic Value of Tumour-Associated Genes in Clear Cell Renal Cell Carcinoma Through Single-Cell RNA...

Clear cell renal cell carcinoma (ccRCC) characterised by its diversity and a tendency to defy standard therapeutic approaches. Amidst the advent of immunotherapy, it has become imperative to pinpoint prognostic indicators of the tumour microenvironment (TME) influence the efficacy of treatments. Employing single-cell RNA sequencing (scRNA-seq), this research delved into the diverse landscape of ccRCC, uncovering its complex underpinnings and pinpointing molecular avenues for therapeutic intervention. We constructed a prognostic model using 101 machine learning algorithms and integrated data from multiple cohorts, including TCGA, ICGC, and microarray datasets. The model's efficacy was assessed using the Concordance Index (C-index), and further analyses included pseudotime analysis of tumour cells, mutation analysis and correlation analysis between the prognostic model and tumour immunity. The prognostic model, combining Lasso regression and survival Support Vector Machine (SVM), demonstrated robust discrimination with a C-index of 0.650. Investigation into the TME uncovered pronounced associations between the presence of immune cell infiltrates and patient outcomes, with a notable emphasis on the impact of CCL2-expressing neoplastic cells. The GO Biological Processes (GOBP) encompass the regulation of endothelial cell maturation, the formation of endothelial layers, the enhancement of gene expression controlled by Notch receptors, and the development of endothelial barriers. The research effectively pinpointed critical prognostic markers and crafted a forecasting model that achieved a C-index of 0.650, highlighting the significant impact of immune cell infiltration, especially CCL2+ neoplastic cells, on ccRCC patient prognosis.