Biostatistics of VHL-Gene Transfection in the Health Informatics Analysis of Renal Cell Carcinoma

Prognostic features of bladder cancer based on five neddylation-related genes

BACKGROUND

Nedylation and tumours are closely linked. The role of nedylation in bladder cancer (BCa) has rarely been reported and this study aims to explore its potential impact on the pathogenesis and progression of BCa.

METHODS

Leveraging gene expression data from the TCGA database, this research employs the limma software package and WGCNA for gene module identification and analysis. Subsequent steps include the construction of a PPI network, the conduct of LASSO and univariate Cox regression analyses, and utilizing GSEA and single-cell sequencing to examine the influence of hub genes in bladder cancer-related biological pathways.

RESULTS

The investigation revealed 11,361 genes with significant differential expression between normal and tumour tissues, and identified 1,500 hub genes through analysis. LASSO regression identified eight critical genes. Univariate Cox regression analysis revealed that COMMD9, GPS1, PSMB5, VHL, and WDR5 are independent prognostic factors for BCa. GSEA and single-cell sequencing highlight the potential of these genes to modulate immune responses and interactions between tumour and immune cells. Meanwhile, GSEA demonstrated that GPS1 can activate the NF-κB signalling pathway, leading to an increase in influenza virus polymerase activity.

CONCLUSION

This study identifies COMMD9, GPS1, PSMB5, VHL, and WDR5 as significant prognostic markers in BCa, thereby underscoring their roles in immune regulation and tumour-immune cell dynamics.

An inflammation-related signature could predict the prognosis of patients with kidney renal clear cell carcinoma

Background: Kidney renal clear cell carcinoma (KIRC) is an inflammation-related carcinoma, and inflammation has been recognized as an important factor in inducing carcinogenesis. To further explore the role of inflammation in KIRC, we developed an inflammation-related signature and verified its correlation with the tumor micro-environment.

Methods: After the differential inflammation-related prognostic genes were screened by Lasso regression, the inflammation-related signature (IRS) was constructed based on the risk score of multivariate Cox regression. Then, the prognostic value of the IRS was evaluated by Kaplan-Meier analysis, receiver operating characteristic (ROC) curve analysis and multivariate Cox regression. Gene set variation analysis (GSVA) was applied to screen out enriched signaling pathways. Infiltrated immune cells, tumor mutational burden (TMB) and immune checkpoints were explored by CIBERSORTx and maftool.

Results: Four genes (TIMP1, PLAUR, CCL22, and IL15RA) were used to construct the IRS in patients with KIRC. Kaplan-Meier analysis and multivariate Cox regression identified that the IRS could independently predict the prognosis of patients with KIRC in the training and validation groups. The diagnostic value of the nomogram increased from 0.811 to 0.845 after adding the IRS to the multiparameter ROC analysis. The GSVA results indicated that IRS was closely related to primary immunodeficiency and antigen processing and presentation. The immune checkpoint LAG3 was highly expressed in patients with high-risk score (p < 0.05), while CD274 (PD-L1) and HAVCR2 were highly expressed in patients with low-risk score (p < 0.001). There was a significant positive correlation between the high-risk score group and CD8⁺ T, activated CD4⁺ memory T, gamma and delta regulatory T and M0 macrophage cells, while the low-risk score group was negatively associated with B memory, plasma, resting CD4⁺ memory T, activated NK, M1 macrophages and resting mast cells.

Conclusion: We found that the IRS might serve as a biomarker to predict the survival of KIRC. Moreover, patients with high or low-risk score might be sensitive to immune drugs at different immune checkpoints.