Identification of genes of prognostic value in the ccRCC microenvironment from TCGA database

Abstract

Background: Clear cell renal cell carcinoma (ccRCC) is the most common pathological subtype of renal cell carcinoma. Bioinformatics analyses were used to screen candidate genes associated with the prognosis and microenvironment of ccRCC and elucidate the underlying molecular mechanisms of action.

Methods: The gene expression profiles and clinical data of ccRCC patients were downloaded from The Cancer Genome Atlas database. The ESTIMATE algorithm was used to compute the immune and stromal scores of patients. Based on the median immune/stromal scores, all patients were sorted into low- and high-immune/stromal score groups. Differentially expressed genes (DEGs) were extracted from high- versus low-immune/stromal score groups and were described using functional annotations and protein-protein interaction (PPI) network.

Results: Patients in the high-immune/stromal score group had poorer survival outcome. In total, 95 DEGs (48 upregulated and 47 downregulated genes) were screened from the gene expression profiles of patients with high immune and stromal scores. The genes were primarily involved in six signaling pathways. Among the 95 DEGs, 43 were markedly related to overall survival of patients. The PPI network identified the top 10 hub genes-CD19, CD79A, IL10, IGLL5, POU2AF1, CCL19, AMBP, CCL18, CCL21, and IGJ-and four modules. Enrichment analyses revealed that the genes in the most important module were involved in the B-cell receptor signaling pathway.

Conclusion: This study mainly revealed the relationship between the ccRCC microenvironment and prognosis of patients. These results also increase the understanding of how gene expression patterns can impact the prognosis and development of ccRCC by modulating the tumor microenvironment. The results could contribute to the search for ccRCC biomarkers and therapeutic targets.

Keywords: TCGA database; ccRCC; immune scores; microenvironment; stromal scores.

Figure 1

The correlation between (a) immune scores or (b) stromal scores of patients and overall survival. Immune scores and stromal scores were significantly associated with the prognosis of patients (p < .05)

Figure 2

The relationship between immune scores or stromal scores of patients and clinical parameters. Immune scores were differential in gender, grade, clinical stage, T stage, and M stage parameters (p < .05); stromal scores were only differential in age and T stage parameters (p < .05). (a) Immune scores and age; (b) Immune scores and gender (p < .05); (c) Immune scores and grade (p < .05); (d) Immune scores and pathological stage (p < .05); (e) Immune scores and T stage (p < .05); (f) Immune scores and N stage; (g) Immune scores and M stage (p < .05); (h) Stromal scores and age (p < .05); (i) Stromal scores and gender; (j) Stromal scores and grade; (k) Stromal scores and pathological stage; (l) Stromal scores and T stage (p < .05); (m) Stromal scores and N stage; (n) Stromal and M stage

Figure 3

Heatmap of differential gene expression in the low score group (immune scores or stromal scores) and the high score group (immune scores or stromal scores). (a) Immune scores (low score in left and high score in right; |log2 fold change (FC)|> 1, FDR < 0.05); (b) Stromal scores (low score in left and high score in right; |log2 fold change (FC)|> 1, FDR < 0.05)

Figure 4

Differentially expressed genes (DEGs) in (a) immune scores and (b) stromal scores. Red represents upregulated genes, green represents downregulated genes, according to |log2 fold change (FC)|> 1, FDR <0.05

Figure 5

Common differentially expressed genes in immune scores and stromal scores. (a) Common upregulated genes; (b) common downregulated genes

Figure 6

Top ten Kaplan–Meier analysis results of DEGs correlated with overall survival

Figure 7

Top 15 GO enrichment terms of DEGs (a and b) and KEGG pathway analysis of DEGs (c and d)

Figure 8

Constructed PPI network of DEGs and analysis of module. (a) PPI network of DEGs; red nodes represent upregulated DEGs and green nodes represent downregulated DEGs. The blue and red lines indicate the combined score from low to high. (b) The most important module; (c) biological process of all genes in the most important module

Figure 9

Identification and analysis of hub genes. (a) The ten hub genes were identified using Cytoscape. (b) Degree value of the ten hub genes; (c) hub genes and their co‐expression genes were analyzed via cBioPortal database

Figure 10

Kaplan–Meier analysis results of hub genes (p < .05). Three hub genes were found to be correlated with the prognosis of ccRCC patients: (a) IL10, (b) IGLL5, and (c) POU2AF1