Identification and Validation of a Prognostic Model Based on Tumour Necrosis Factor-Related mRNAs for Kidney Renal Clear Cell Carcinoma

Abstract

Tumour necrosis factor (TNF) plays a critical role in tumour progression, but the specific involvement of mRNA in this process, particularly in kidney renal clear cell carcinoma (KIRC) remains insufficiently understood. Our study aims to develop a TNF-related mRNA (TRmRNA) model to predict prognosis and inform treatment strategies in KIRC. KIRC expression data from The Cancer Genome Atlas (TCGA) and TNF-related genes (TRGs) from the Genecards database were used to construct and validate a TRmRNA prognostic model. A nomogram integrating clinical features with the risk model was also developed to enhance prognostic accuracy. Enrichment analysis, drug sensitivity analysis and RT-qPCR validation were performed to further explore the biological mechanisms and clinical applicability of the model. A prognostic signature consisting of nine TRmRNAs was identified. Kaplan-Meier analysis showed that the high-risk (HRK) group had significantly shorter overall survival (OS) compared to the low-risk (LRG) group (p < 0.001). The nomogram, incorporating the risk model, yielded an area under the curve (AUC) of 0.766, indicating robust prognostic accuracy. Enrichment analysis identified solute sodium symporter and proximal tubule transport pathways enriched in the LRG group, whereas the HRK group exhibited enrichment in CD22-mediated BCR regulation and immunoglobulin complex pathways. The HRK group also showed a higher tumour mutational burden (TMB), correlating with a poorer prognosis. RT-qPCR confirmed the differential expression of mRNAs in KIRC cells. The TRmRNA-based prognostic model holds significant promise for predicting patient outcomes and guiding personalised treatment strategies in KIRC.

Keywords: kidney clear cell carcinoma; messenger RNA; prognostic model; tumour necrosis factor.

FIGURE 1

Flow Chart.

FIGURE 2

PPI network (A); Filter for TNF‐related mRNAs. Volcano plot of differentially expressed TNF‐associated mRNAs (B); LASSO regression analysis (C, D).

FIGURE 3

The model prediction effect is validated by the train group, test group, and entire group. K‐M analysis (A–C) and time‐dependent ROC curves (D–F) to compare the survival of the high‐risk group and low‐risk group.

FIGURE 4

PCA and independent prognostic analysis of the signature. PCA based on all genes (A), all mRNAs (B), TNF‐related mRNAs (C), and risk signature (D); univariate (E) and multivariate (F) independent prognostic analysis.

FIGURE 5

Further validation of model effects. Survival curves of patients in different clinical states (A–F).

FIGURE 6

Nomogram predicts patient prognosis. Decision curve to test for forecast value (A) ROC curves containing different clinical information (B) A clinical prognosis nomogram is constructed by age, gender, risk, and stage together (C). Nomogram with (D) and without (E) risk model.

FIGURE 7

Tumour mutation burden in different risk groups. Percentage bar graph showing TMB for different risk subgroups (A) High‐risk group waterfall chart (B) Low‐risk group waterfall chart (C).

FIGURE 8

Analysis of tumour immune microenvironment. Violin plots of differences in ESTIMATE scores (A), stromal scores (B), immune scores (C) and tumour purity (D) for different risk subgroups; Bubble plots of correlations between immune cells and risk scores under six algorithms (E); Proportions of 22 immune cells in two subgroups under the CIBERSORT algorithm (F); single sample gene set enrichment analysis (G). *p < 0.05, **p < 0.01, ***p < 0.001.