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. 2022 Jun 29:13:906113.
doi: 10.3389/fgene.2022.906113. eCollection 2022.

Identification of Immune-Related Subtypes and Characterization of Tumor Microenvironment Infiltration in Kidney Renal Clear Cell Carcinoma

Huisheng Qin  1 , Tiancheng Wang  2 , Hui Zhang  3
Affiliations

Identification of Immune-Related Subtypes and Characterization of Tumor Microenvironment Infiltration in Kidney Renal Clear Cell Carcinoma

Huisheng Qin et al. Front Genet. .

Abstract

Background: Tumor microenvironment (TME) plays indisputable role in the progression of cancers. Immune cell infiltration (ICI) in TME was related to the prognosis of tumor patients. In this paper, we identified the pattern of immune-related ICI subtypes based on the TME immune infiltration pattern. Methods: The data from kidney renal clear cell carcinoma data (KIRC) was downloaded from the TCGA database. The distinct ICI subtypes were identified using CIBERSORT and ESTIMATE algorithms. The gene subgroups were identified based on DEGs in ICI subtypes. The single sample gene set enrichment analysis (ssGSEA) was used to ascertain the ICI score. Kaplan-Meier curve with log-rank test was conducted to analyze the survival probability of patients with KIRC in different subtypes. Results: The patients with high ICI scores exhibited a longer survival time and lower expression of checkpoint-related and immune activity-related genes. The high ICI score clusters were positively related to TMB. The patients in the low TMB subgroups have a favorable prognosis. The prediction ICI score did not affect the TMB status, and the low TMB subgroups + low/high ICI score subgroups exhibited better survival. Conclusion: In all, the tumor immune microenvironment, ICI score, and TMB were important determinants of KIRC patients' survival outcomes. The TMB + ICI score may be a potential biomarker for predicting the prognosis of patients and for targeted immunotherapies to treating KIRC.

Keywords: TCGA; TME; immune score; kidney renal clear cell carcinoma; tumor microenvironment.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
(Continued.)The ICI score subtypes in KIRC. (A) The KIRC data were downloaded from the TCGA database and grouped into four ICI subtypes. (B) Kaplan-Meier curve was conducted to analyze the survival probability in four ICI clusters (log-rank test p = 0.003). (C) Immune cellular interaction of the ICI score subtypes. (D) The difference analysis of the fraction of immune cells, immune score, and stromal score in four ICI clusters. *p < 0.05, ***p < 0.001. (E,F) The difference in PD-L1 (E) and PD1 (F) expression among four ICI clusters.,
FIGURE 2
FIGURE 2
(Continued.)The gene subtypes were identified based on DEGs in the TCGA-KIRC cohort. (A) Consensus clustering of DEGs among four ICI subtypes. (B) Kaplan-Meier analysis was conducted to analyze the survival probability of the three gene clusters (log-rank test p = 0.006). (C,D) GO enrichment analysis of signature genes. (E) The infiltration of immune cells among three gene clusters. *p < 0.05, ***p < 0.001. (F,G) The difference in the expression of PD1 (F) and CTLA4 (G) among three gene clusters. ***p < 0.001.,
FIGURE 3
FIGURE 3
ICI score was constructed. (A) Sankey diagram showed the relationship between gene cluster, ICI score, and survival status. (B) The gene set enrichment analysis (GSEA) in the low and high ICI score subgroups. (C) The difference in the expression of immune activity-related genes and immune checkpoint-related genes between low and high ICI score subgroups. (D,E) Kaplan-Meier curve analysis for low and high ICI score subgroups in the KIRC cohort (log-rank test p = 0.023).
FIGURE 4
FIGURE 4
The association between ICI scores and some clinical characteristics of patients with KIRC. (A). The relationship between ICI score and Grade. p = 0.00033. (B) Gender parameter has no significant relationship with ICI score. (C) The association between laterality and ICI scores. (D) The patients with T3/T4 had low scores. p = 0.036. (E) The positive node metastasis (N1) was significantly related to low ICI scores. p = 0.016. (F) The relationship between metastasis (M) with ICI scores. p = 0.0089. (G) The low ICI score was observed in stages III/IV. p = 0.003.
FIGURE 5
FIGURE 5
The association between ICI score and somatic variants. (A) The R package “maftools” analyzed the somatic gene variants in KIRC. (B) A detailed analysis of the variants. (C) The interaction among mutation genes.
FIGURE 6
FIGURE 6
The association of TMB, ICI score, and survival status. (A) The high TMB was related to a high ICI score. (B) Kaplan-Meier curve for low and high TMB subgroups in KIRC cohort (log-rank test p < 0.001). (C) Kaplan-Meier curve analysis for different subgroups (log-rank test p = 0.001). (D,E) The top 20 driver genes in the low (D) and high (E) ICI score subgroups.
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