Identification of molecular subtypes based on chromatin regulator and tumor microenvironment infiltration characterization in papillary renal cell carcinoma

Abstract

Background: Papillary renal cell carcinoma (pRCC) is the second most common histological type of renal cell carcinoma. The prognosis of local pRCC is better than that of ccRCC, but the situation has changed greatly after pRCC metastasis. Chromatin regulators (CRs) are indispensable in epigenetic regulation, and their abnormal expression in tumors leads to the occurrence and development of tumor. However, the role of CRs in pRCC has not been studied yet.

Materials and methods: 291 samples were obtained from TCGA-KIPR cohort. Unsupervised clustering analysis was utilized to divide the patients of pRCC into two subtypes. Lasso Cox regression analysis was performed to construct a CRs_score model for predicting OS. The unique characteristics of different molecular subtypes were determined by TME cell infiltration analysis, GO and KEGG analysis and drug sensitivity analysis. We also carried out drug sensitivity experiments in vitro to verify the effect of signature genes on drug sensitivity to sunitinib.

Results: We described the transcriptional and genetic alteration of 19 prognosis-related CRs genes in 291 cases of TCGA-KIRP cohort. We identified two distinct molecular subtypes, which have significant differences in prognosis, clinicopathological features and tumor immune microenvironment (TME). Then, four signature genes were selected by lasso regression analysis to construct a CRs_score for predicting OS, and its predictive ability for patients with pRCC was verified. A nomogram was established to improve the clinical applicability of CRs_score. We found that there was a significant difference in the proportion of immune cell infiltration between high- and low-CRs_score. In addition, CRs_score was significantly correlated with chemosensitivity. Finally, we found that SK-RC-39 cell lines were more sensitive to sunitinib after knocking down the signature gene CDCA3, PDIA4, or SUCNR1.

Conclusions: Our comprehensive analysis of CRs gene in pRCC showed that CRs gene plays a potential role in TME, prognosis and drug resistance in pRCC. These findings may lay a foundation for further study of the regulatory role of CRs gene in pRCC, and provide a new method for evaluating prognosis and developing more effective targeted therapy.

Keywords: Chromatin regulators; Lasso; Molecular subtypes; Papillary renal cell carcinoma; Tumor microenvironment.

Fig. 1

Genetic and transcriptional alterations of CRs in pRCC. a Expression distributions of 19 CRs between normal and pRCC tissues. b Interactions of CRs in pRCC. The connection of the line represents the interaction of CRs, and the thickness of the line represents the strength of the connection. Pink and blue represent positive correlation and negative correlation, respectively. The size of the circle represents the effect of each CRs on the prognosis of pRCC. The green dot in the circle represents the favorable factor, and the purple dot represents the risk factor. c Mutation frequencies of 19 CRs in 282 KIRP from the TCGA cohort. d Frequencies of CNV gain, loss, and non-CNV among 19 CRs. pRCC, papillary renal cell carcinoma; TCGA, the cancer genome atlas; CNV, copy number variant. ***, p < 0.001

Fig. 2

Identification of CRs-related subtypes in pRCC. a Unsupervised clustering heatmap defining two clusters (k = 2). b heatmap of 19 CRs in CRs subtypes. c PCA analysis showing a remarkable difference in transcriptomes between the two subtypes. d Kaplan–Meier survival curve showed significant differences between subtype A and subtype B (log-rank test, p < 0.001). e Comparison of the proportion of different tumor stages in subtype A and subtype B. f The distribution of two histological subtypes of pRCC in subtype A and subtype B

Fig. 3

Characteristics of TME in different subtypes. a GSVA of biological pathways between two subtypes. Red represents activated pathways and blue inhibited pathways. b ssGSEA analysis showed the differences of immune cell infiltration levels between CRs subtypes. c The TME score between two subtypes. GSVA, gene set variation analysis; ssGSEA, single-sample gene set enrichment analysis; TME, tumor microenvironment; *p < 0.05, **p < 0.01, ***p < 0.001

Fig. 4

Identification of gene subtypes based on DEGs. a, b GO and KEGG enrichment analyses of DEGs among two CRs subtypes. c heatmap showing the difference between the expression of two gene subtypes and their clinicopathological features. d Kaplan–Meier curves showed differences in OS survival between the two gene subtypes. (log-rank tests, p = 0.001) e Differences in the expression of 19 CRs among the two gene subtypes. ***p < 0.001

Fig. 5

Construction and validation of the CRs_score model. a Alluvial diagram of subtype distributions in groups with different CRs_scores and survival outcomes. b Differences in CRs_score between gene subtypes. c Differences in CRs_score between CRs subtypes. d-e Ranked dot and scatter plots showing the CRs_score distribution and the survival status of patients. f Kaplan–Meier analysis of the OS between the two groups in training set. g ROC curves to predict the sensitivity and specificity of 1, 3 and 5 year survival in training set according to the CRs_score. h Nomogram for predicting the1, 3 and 5 year OS of pRCC patients. i Calibration curves of the nomogram for predicting of 1, 3 and 5 year OS

Fig. 6

Relationship between CRs_score and immune cell infiltration. a–j Correlations between CRs_score and different immune cells. k Correlations between CRs_score and both immune and stromal scores. l–o Immunophenoscore comparison between high- and low- CRs_score in patients with pRCC in the PD-1 negative/positive or CTLA4 negative/positive groups. PD1 positive or CTLA4 positive represented anti-PD-1/PD-L1 or anti-CTLA4 therapy, respectively. *p < 0.05

Fig. 7

Relationship between CRs_score and drug susceptibility. a–f Relationships between CRs_score and chemotherapeutic sensitivity. g–j Drug response curves showing drug responses of sunitinib after knockdown of 4 signature genes in SK-RC-39 cell line