We identified 44 necroptosis-related DEGs and 12 risk NRGs based on expression differences between tumour and normal tissues. GSEA was used to uncover latent signalling pathways implicated in the development and progression of KIRC, and lasso regression was used to build a suitable prediction model.
3.1 Differentially expressed NRGs
Compared to normal samples, we identified 44 DEGs associated with necroptosis (8 downregulated and 36 upregulated; Table S2) (Figure.2a). The univariate COX research identified 22 significant NRGs, which were then incorporated in the multivariate COX analysis. In all, 22 distinct NRGs (TRAF2, RBCK1, SLC25A4, SLC25A5, MAPK10, JMJD7PLA2G4B, PLA2G4B, PLA2G4D, PYCARD, TNFRSF10B, FASLG, IFNG, IFNAR2, IFNGR2, JAK3, STAT4, IRF9, TLR3, ZBP1, BID, H2AC17, H2AC7) were identified to be (Table S4). As a result, we computed risk ratings for the NRGs and created a prognostic signature. We explored their genetic alterations because these NRGs have significant clinical effects and observed that truncating and missense mutations were the two most common types of mutations (Figure.2c). A total of 6 genes showed a 1% mutation rate, with CAMK2A being the most often modified (14%).
3.2 Enrichment Analysis of necroptosis-related genes
526 core targets were discovered by GO enrichment analysis, including MF, CC, and BP. The MF mainly involves signaling receptor activator activity (GO:0030546), receptor ligand activity (GO:0048018), ubiquitin-like protein transferase activity (GO:0019787), phospholipid binding (GO:0005543). The CC mainly involves membrane raft (GO:0045121), membrane microdomain (GO:0098857), protein-DNA complex (GO:0032993), DNA packaging complex (GO:0044815). The BP mainly involves positive regulation of anion transport (GO:1903793), signal release (GO:0023061), T cell activation (GO:0042110), regulation of immune effector process (GO:0002697). In addition, the main signaling pathways were identified by KEGG enrichment analysis, revealed the over-expressed genes were mainly involved in Herpes simplex virus 1 infection (hsa05168), Pathways of neurodegeneration-multiple diseases (hsa05022), Alzheimer disease (hsa05010), PI3K-Akt signaling pathway (hsa04151), Huntington disease (hsa05016), Cytokine-cytokine receptor interaction (hsa04060), MAPK signaling pathway (hsa04010) (Figure.3 and Table S3).
3.3 Survival results and multivariate examination
A Kaplan-Meier analysis showed that high-risk NRGs signatures were associated with a shorter survival time (P<0.001. Figure.4a). Meanwhile, the AUC for NRG signature was 0.769, indicating that it outperformed standard clinicopathological characteristics in predicting KIRC prognosis (Figure.4b-c). We observed that the patient's risk score was inversely related to the survival of KIRC patients using a risk survival status plot. Surprisingly, most of the new NRGs discovered in this study showed a negative relationship with our risk model, indicating that more research is needed (Figure.4d). For 1, 2, and 3-year survival rates, the AUC predictive value of the unique NRGs signature was 0.769, 0.737, and 0.736, respectively (Figure.4e). NRGs, PLA2G4D, H2AC17, H2AC7, IRF9, IFNG, FASLG, STAT4, TLR3, JAK3, IFNAR2, and BID were all substantially represented in a high-risk group, indicating that all of them may be detrimental to KIRC patients' prognosis (Figure.4f). COX analysis revealed that the NRGs signature (HR: 1.195, 95CI: 1.113-1.282) and Age (HR: 1.032, 95CI: 1.011-1.052) were the most important independent predictors of KIRC patients' survival (Figure.5a-b). Figure.5c demonstrates the connection between Necroptosis and RNA. The hybrid nomogram (Figure.6) that integrated clinicopathological features and the NRGs prognostic signal was stable and reliable, and therefore may be employed in the therapy of KIRC patients.
3.4 The necroptosis-related signature is an independent prognostic factor for KIRC patients
Clinicopathological studies were carried out to examine the connections between clinical markers and the risk profile (Figures.7a–d). The signature was associated with tumor stage (p=1.875e-08), T stage (p=1.977e-10), M stage (p=2.871e-06), and N stage (p=1.348e-08). The survival rates differed significantly between the high-risk and low-risk groups. Patients in the high-risk group had shorter overall survival (OS) than those in the low-risk group. Using OS ROC curves, the prediction performance of the NRGs risk signature was stated (Figure.7e). Thus, by combining university and multivariate Cox-regression analysis data, we discovered that the necroptosis-related signature might be used as an independent predictor in clinical practice. Furthermore, we created a heatmap of clinical characteristics for the NRGs. We discovered that patients' Gender, Grade, Stage, T, M, and N were distributed differently across the low- and high-risk groupings (Figure.7f).
3.5 Gene set enrichment analyses
The majority of NRGs prognostic signature regulated immunological and tumor-related pathways such as homologous recombination, ribosome, primary immunodeficiency, intestinal immune network for iga synthesis, proteasome, p53 signaling pathway, and so on, according to gene set enrichment analysis (GSEA). Figure 8 depicts the top six enriched functions or pathways for each cluster (Table S5). Both the FDR q-value and the FWER p-value were <0.05. As a result, the 'p53 signaling pathway' was shown to be the most enriched, and some of the genes were found to be positively associated with H or L.
3.6 Analysis of the correlation between NRGs with immune checkpoints and m6A
Given the significance of checkpoint inhibitor-based immunotherapies, we investigated differences in immune checkpoint expression between the two groups. We discovered a significant difference in the face of CD44, TNFRSF8, CD27, TMIGD2, HHLA2, LGALS9, and other genes between the two patient groups (Figure.9a). When the expression of NRGs was compared between the high and low-risk groups, YTHDC1, YTHDC2, YTHDF2, ZC3H13, FTOALKBH5, METTL14, METTL3, and RBM15 were shown to be significant (Figure.9b).