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. 2021 May 17;21(1):553.
doi: 10.1186/s12885-021-08319-0.

Identification of 4-genes model in papillary renal cell tumor microenvironment based on comprehensive analysis

Liang Luo  1 , Haiyi Zhou  2 , Hao Su  3
Affiliations

Identification of 4-genes model in papillary renal cell tumor microenvironment based on comprehensive analysis

Liang Luo et al. BMC Cancer. .

Abstract

Background: The tumor microenvironment acts a pivotal part in the occurrence and development of tumor. However, there are few studies on the microenvironment of papillary renal cell carcinoma (PRCC). Our study aims to explore prognostic genes related to tumor microenvironment in PRCC.

Methods: PRCC expression profiles and clinical data were extracted from The Cancer Gene Atlas (TCGA) and Gene Expression Omnibus (GEO) database. Immune/stromal scores were performed utilizing the ESTIMATE algorithm. Three hundred fifty-seven samples were split into two groups on the basis of median immune/stromal score, and comparison of gene expression was conducted. Intersect genes were obtained by Venn diagrams. Hub genes were selected through protein-protein interaction (PPI) network construction, and relevant functional analysis was conducted by DAVID. We used Kaplan-Meier analysis to identify the correlations between genes and overall survival (OS) and progression-free survival (PFS). Univariate and multivariate cox regression analysis were employed to construct survival model. Cibersort was used to predict the immune cell composition of high and low risk group. Combined nomograms were built to predict PRCC prognosis. Immune properties of PRCC were validated by The Cancer Immunome Atlas (TCIA).

Results: We found immune/stromal score was correlated with T pathological stages and PRCC subtypes. Nine hundred eighty-nine differentially expressed genes (DEGs) and 1169 DEGs were identified respectively on the basis of immune and stromal score. Venn diagrams indicated that 763 co-upregulated genes and 4 co-downregulated genes were identified. Kaplan-Meier analysis revealed that 120 genes were involved in tumor prognosis. Then PPI network analysis identified 22 hub genes, and four of which were significantly related to OS in patients with PRCC confirmed by cox regression analysis. Finally, we constructed a prognostic nomogram which combined with influence factors.

Conclusions: Four tumor microenvironment-related genes (CD79A, CXCL13, IL6 and CCL19) were identified as biomarkers for PRCC prognosis.

Keywords: Hub genes; Papillary renal cell carcinoma; Prognosis; Tumor microenvironment.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
The correlations between the stromal score and clinical characteristics, and OS analysis. a–e. The relationship between stromal score and T stage, N stage, M stage, clinical stage and grade. f, g. The relationship between immune/stromal score and PRCC subtypes. h. Kaplan-Meier analysis of stromal score with PRCC patients
Fig. 2
Fig. 2
Kaplan-Meier survival and ROC analysis.. OS and PFS were compared respectively between low and high risk group of TCGA samples (a, c), OS was compared between low and high risk group of GEO samples (e), ROC of 4-gene model of OS (c) and PFS (d) analysis of TCGA samples and OS analysis of GEO samples (f)
Fig. 3
Fig. 3
Immune cell composition analysis and the difference analysis of IPS. a Box plot of 22 immune cells proportion between low and high risk group of 4-gene model. b the difference analysis of IPS between low and high risk group of 4-gene model
Fig. 4
Fig. 4
Nomogram for predicting OS (a) and PFS (c) for PRCC patients of TCGA based on risk score and stage. The calibration plot for internal validation of the nomogram (b, d)
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