The Role of ERBB Signaling Pathway-Related Genes in Kidney Renal Clear Cell Carcinoma and Establishing a Prognostic Risk Assessment Model for Patients

Abstract

Objective: We aimed to investigate the potential role of ERBB signaling pathway–related genes in kidney renal clear cell carcinoma (KIRC) and establish a new predictive risk model using various bioinformatics methods.Methods: We downloaded the KIRC dataset and clinicopathological information from The Cancer Genome Atlas database. Univariate Cox analysis was used to identify essential genes significantly associated with KIRC progression. Next, we used the STRING website to construct a protein–protein interaction network of ERBB signaling pathway–related molecules. We then used the least the absolute shrinkage and selection operator (LASSO) regression analysis to build a predictive risk model for KIRC patients. Next, we used multiple bioinformatics methods to analyze the copy number variation, single-nucleotide variation, and overall survival of these risk model genes in pan-cancer. At last, we used the Genomics of Drug Sensitivity in Cancer to investigate the correlation between the mRNA expression of genes associated with this risk model gene and drug sensitivity.Results: Through the LASSO regression analysis, we constructed a novel KIRC prognosis–related risk model using 12 genes: SHC1, GAB1, SOS2, SRC, AKT3, EREG, EIF4EBP1, ERBB3, MAPK3, transforming growth factor-alpha, CDKN1A, and PIK3CD. Based on this risk model, the overall survival rate of KIRC patients in the low-risk group was significantly higher than that in the high-risk group (p = 1.221 × 10−15). Furthermore, this risk model was associated with cancer metastasis, tumor size, node, stage, grade, sex, and fustat in KIRC patients. The receiver operating characteristic curve results showed that the model had better prediction accuracy. Multivariate Cox regression analysis showed that the model’s risk score was an independent risk factor for KIRC. The Human Protein Atlas database was used to validate the protein expression of risk model–associated molecules in tumors and adjacent normal tissues. The validation results were consistent with our previous findings.Conclusions: We successfully established a prognostic-related risk model for KIRC, which will provide clinicians with a helpful reference for future disease diagnosis and treatment.

Machine learning-based on cytotoxic T lymphocyte evasion gene develops a novel signature to predict prognosis and immunotherapy responses for kidney renal clear cell carcinoma patients

“…We collected 101 signatures in KIRC involving various biological pathways. These models are rarely applied in clinical practice and rarely verified by external data sets; alternatively, the validation performance of the external data sets is poor (45)(46)(47)(48). The generalization and applicability of these signatures are poor.…”

Glycolysis‑related lncRNA may be associated with prognosis and immune activity in grade II‑III glioma

“…Drug information was obtained from the Genomics of Cancer Drug Sensitivity (GDSC) website ( 30 ) ( ), and the R package ‘pRRophetic’ ( 31 ) was used to predict IC 50 values to explore possible clinical adjuvant drugs for the treatment of grade II–III glioma ( ). To determine if the risk model was associated with immunotherapy, the expression levels of key genes in the two groups of immune checkpoints were compared.…”