Identification and Clinical Significance of Differentially Expressed Genes in the Tumour Microenvironment of Clear Cell Renal Cell Carcinoma.

Objective

To perform a bioinformatics analysis of differentially expressed genes (DEGs) in clear cell renal cell carcinoma (ccRCC), evaluate their clinical significance, and identify potential therapeutic targets for ccRCC. STUDY

Design

An analytical study. Place and Duration of the Study: Department of Clinical Medicine, Inner Mongolia Medical University, Hohhot, China, from February 2024 to March 2026. METHODOLOGY RNA-sequencing data from 601 patients with ccRCC in the Cancer Genome Atlas (TCGA) were analysed using R. Immune and stromal scores were estimated using ESTIMATE, and immune-cell fractions were calculated using CIBERSORT. Associations with survival and clinicopathological features were assessed. Overlapping DEGs from ImmuneScore and StromalScore groups underwent Gene Ontology (GO) and Kyoto Encyclopaedia of Genes and Genomes (KEGG) enrichment analyses. A protein-protein interaction (PPI) network and univariate Cox regression were used to identify prognostic genes. Core genes were identified by intersecting PPI hub nodes with significant Cox predictors, followed by immune-infiltration analysis.

Results

The immune score showed significant relationships with both overall survival and clinicopathological characteristics in ccRCC (p <0.05). Differential expression screening retained 375 shared DEGs. Functional enrichment identified 114 significant GO terms and 28 significant KEGG pathways (p <0.05). Plasminogen (PLG) emerged as the core candidate associated with survival and clinicopathological stage (p <0.05). CIBERSORT analysis further demonstrated significant differences in resting dendritic cells, resting mast cells, plasma cells, M0 macrophages, and activated memory CD4⁺ T cells between the high- and low-PLG-expression groups (p <0.05).

Conclusion

Bioinformatics analysis identified PLG as a core gene associated with ccRCC prognosis and immune-cell infiltration, thereby providing a basis for subsequent mechanistic investigation and external validation. KEY WORDS Clear cell renal cell carcinoma, Tumour microenvironment, Plasminogen, The Cancer Genome Atlas, ESTIMATE, CIBERSORT.