Prognostic impact and immunotherapeutic implications of NETosis-related prognostic model in clear cell renal cell carcinoma

Abstract

Background: The ramifications of necroptosis on the prognostication of clear cell renal cell carcinoma (ccRCC) remain inadequately expounded.

Methods: A prognostic model delineating the facets of necroptosis in ccRCC was constructed, employing a compendium of algorithms. External validation was effectuated using the E-MTAB-1980 dataset. The exploration of immune infiltration scores was undertaken through the exploitation of multiple algorithms. Single-cell RNA sequencing data were procured from the GSE171306 dataset. Real-time quantitative PCR (RT-qPCR) was engaged to scrutinize the differential expression of SLC25A37 across cancer and paracancer tissues, as well as diverse cell lines. Assessments of proliferative and metastatic alterations in 769-P and 786-O cells were accomplished through Cell Counting Kit-8 (CCK8) and wound healing assays.

Results: The necroptosis-related signature (NRS) emerges as a discerning metric, delineating patients' immune attributes, tumor mutation burden, immunotherapy response, and drug susceptibility. Single-cell RNA sequencing analysis unveils the marked enrichment of SLC25A37 in tumor cells. Concurrently, RT-qPCR discloses the overexpression of SLC25A37 in both ccRCC tissues and cell lines. SLC25A37 knockdown mitigates the proliferative and metastatic propensities of 769-P and 786-O cells, as evidenced by CCK8 and wound healing assays.

Conclusion: The NRS assumes a pivotal role in ascertaining the prognosis, tumor mutation burden, immunotherapy response, drug susceptibility, and immune cell infiltration features of ccRCC patients. SLC25A37 emerges as a putative player in immunosuppressive microenvironments, thereby providing a prospective avenue for the design of innovative immunotherapeutic targets for ccRCC.

Keywords: Clear cell renal cell carcinoma; Necroptosis; SLC25A37; Single-cell analysis; Tumor microenvironment.

Fig. 1

Construction and validation of the NETosis-related signature. A The concordance indexes (C-indexes) of 81 machine-learning algorithm combinations in the TCGA and E-MTAB-1980 cohorts; B Kaplan–Meier (KM) survival curve illustrating the NETosis-related signature’s prognostic utility; C risk curve depicting each sample reordered by the NETosis-related signature, accompanied by the scatter plot providing an overview of sample survival; D receiver operating characteristic (ROC) curves delineating the NETosis-related Signature’s predictive accuracy for 1, 2, and 3-year survival rates; E,F univariate and multivariate Cox regression analyses evaluating the prognostic significance of the NETosis-related signature alongside demographic and clinical parameters; G KM survival curve highlighting the NETosis-related signature’s prognostic value in the E-MTAB-1980 cohort; H risk curve of each sample reordered by the NETosis-related signature and the scatter plot of sample survival overview in the E-MTAB-1980 cohort; I ROC curves illustrating the NETosis-related signature’s predictive performance for 1, 2, and 3-year survival rates in the E-MTAB-1980 cohort

Fig. 2

Immune infiltration characteristics of the NETosis-related signature. A Distribution of immune cells in high and low NETosis-related signature groups across multiple algorithms; B correlation analysis depicting the relationship between immune cells and the NETosis-related signature across multiple algorithms; C disparities in NETosis-related signature among different immune subtypes; D distinct expression patterns of immunosuppressive cells between high and low NETosis-related signature groups; E variances in tumor microenvironment scores between high and low NETosis-related signature groups; F contrasts in immune function scores between high and low NETosis-related signature groups; G differential expression patterns of the NETosis-related signature across various tracking tumor immunophenotypes. Asterisks denote statistical significance (* p < 0.05, ** p < 0.01, *** p < 0.001)

Fig. 3

Tumor mutation burden and immunotherapy characteristics of the NETosis-related signature. A,B Waterfall chart visualizing the mutation frequency of the top 20 genes in the high and low NETosis-related signature groups; C disparities in tumor mutation burden (TMB) between high and low NETosis-related signature groups; D correlation analysis assessing the relationship between the NETosis-related signature and TMB; E KM survival curve illustrating prognostic differences between high and low TMB groups; F KM survival curve evaluating the combined prognostic impact of the NETosis-related signature and TMB; G contrasts in the expression of immunosuppressive checkpoints between high and low NETosis-related signature groups; H–K differences in immunotherapy response between high and low NETosis-related signature groups

Fig. 4

Prognostic characteristics of major NETosis genes. A Intersection of differentially expressed genes from GSE171306 and NETosis-related genes; B disparities in expression of intersection genes between ccRCC and paracancer tissues; C diagnostic and predictive value assessment of intersection genes; D–G KM survival analyses for overall survival (OS), progress-free interval (PFI), and disease-specific survival (DSS) of intersection genes

Fig. 5

Expression profile of intersection genes based on single-cell sequencing analysis. A Clustering of ten cell types in GSE171306; B–E expression distribution of intersection genes in each cell; F specific expression patterns of intersection genes in ten cell types

Fig. 6

Correlation of SLC25A37 with clinicopathological characteristics in ccRCC. A Differential expression of SLC25A37 in 33 tumors and adjacent tissues; B–H contrasts in SLC25A37 expression profiles among clinicopathological variables (B PFI; C OS; D DSS; E stage; F T stage; G M stage; H N stage); I–N variances in SLC25A37 expression across different clinicopathological stages in the GEO validation datasets; O nomogram predicting 1-, 3-, and 5-year OS of ccRCC patients; P prognostic risk hypothesis diagram for SLC25A37; Q prognostic calibration curve of SLC25A37

Fig. 7

Identification of immune infiltration characteristics of SLC25A37 in ccRCC. A Disparities in the expression of immune cells between high and low SLC25A37 groups; B correlation analysis assessing the relationship between immune cells and SLC25A37; C contrasts in SLC25A37 expression among different immune subtypes; D differences in the expression of immunosuppressive checkpoints between high and low SLC25A37 groups; E variances in immune function scores between high and low SLC25A37 groups; F expression differences of SLC25A37 among various immunotherapy outcomes

Fig. 8

SLC25A37 knockdown suppresses proliferation and migration in ccRCC cells. A,B RT-qPCR analysis of SLC25A37 expression in ccRCC tissues and cell lines; C,D detection of SLC25A37 knockdown in 786-O and 769-P cells by RT-qPCR; E,F inhibition of proliferation in 769-P and 786-O cells following SLC25A37 knockdown; G,H suppression of migration in 769-P and 786-O cells upon SLC25A37 knockdown