Construction and Validation of Protein Expression-related Prognostic Models in Clear Cell Renal Cell Carcinoma

Abstract

Objective: To construct a prognostic evaluation model for clear cell renal cell carcinoma (ccRCC) patients using bioinformatics method and to screen potential drugs for ccRCC . Methods: ccRCC RNA sequencing data, clinical data, and protein expression data were downloaded from the TCGA database. Univariate Cox and Lasso regression analyses were performed on the combined data to screen out the proteins related to the prognosis, and they were included in a multivariate Cox proportional hazard model. The patients were divided into high and low-risk groups for a survival difference analysis. The predictive power of the model was evaluated on the basis of overall survival, progression-free survival, independent prognostic, clinically relevant receiver operating characteristic (ROC) curve, C-index, principal component, and clinical data statistics analyses. GSEA enrichment and immune function correlation analyses were performed. The samples were divided into different subtypes based on the expression of the risk proteins, and survival analysis of the subtypes was performed. The risk-related protein and RNA sequencing data were analyzed to screen out sensitive drugs with significant differences between the high and low-risk groups. Results: A total of 469 ccRCC-related proteins were screened, of which 13 proteins with independent prognostic significance were screened by univariate Cox, Lasso, and multivariate Cox regression analyses to construct the prognostic model. The sensitivity and accuracy of the model in predicting the survival of patients with ccRCC were high (1 year: 0.811, 3 years: 0.783, 5 years: 0.777). The 13 proteins were closely related to immunity, and the model proteins were different between kidney and tumor tissues according to the HPA database. The samples were divided into three subtypes, and there were obvious clinical characteristics of the three subtypes in the grade and T, N and M stages. According to the IC50 values, CGP-60474, vinorelbine, doxorubicin, etoposide, FTI-277, JQ12, OSU-03012, pyrimethamine, and other drugs were more sensitive in the high-risk group. Conclusions: A prognostic model of protein expression in ccRCC was successfully constructed, which had good predictive ability for the prognosis of ccRCC patients. The ccRCC-related proteins in the model can be used as targets for studying the pathogenesis and targeted therapy.

Exosome‐related lncRNA score: A value‐based individual treatment strategy for predicting the response to immunotherapy in clear cell renal cell carcinoma

“… 39 More studies have found that precision medicine is the key to improving ccRCC outcomes; however, because of the lack of reliable biomarkers, precision medicine is severely limited in the treatment of ccRCC. 40 , 41 , 42 Exosomes have the advantages of easy availability, non‐invasive examination, and tumor specificity. 24 Meanwhile, due to their high mobility and lipid bilayer structure, they can easily passthrough biological membranes and protect rich bioactive substances present inside the membranes from degradation.…”

“…Surgery is the preferred treatment for ccRCC; however, due to the high aggressiveness and drug resistance of ccRCC, 20%–40% of patients with ccRCC experience recurrence and metastasis after surgery 39 . More studies have found that precision medicine is the key to improving ccRCC outcomes; however, because of the lack of reliable biomarkers, precision medicine is severely limited in the treatment of ccRCC 40–42 . Exosomes have the advantages of easy availability, non‐invasive examination, and tumor specificity 24 .…”

The effect of telomeres in cervical cancer

“…In comparison to the low expression group, there was a signi cant increase in mast cell activation (P < 0.001) and macrophage M0 (P < 0.05) in the high expression group. It is evident that various immune in ltrates can impact a patient's telomeres, which in turn can alter prognostic markers and immunotherapy targets for cervical cancer 21,22 . (Figure 6A) Next, we mapped the correlated heat maps of differential genes and immune cells to determine the effects of differential genes on immune function in CC patients.…”