Renal biopsy is the gold standard for defining renal fibrosis which causes calcium deposits in the kidneys. Persistent calcium deposition leads to kidney inflammation, cell necrosis, and is related to serious kidney diseases. However, it is invasive and involves the risk of complications such as bleeding, especially in patients with end-stage renal diseases. Therefore, it is necessary to identify specific diagnostic biomarkers for renal fibrosis. This study aimed to develop a predictive drug target signature to diagnose renal fibrosis based on m6A subtypes. We then performed an unsupervised consensus clustering analysis to identify three different m6A subtypes of renal fibrosis based on the expressions of 21 m6A regulators. We evaluated the immune infiltration characteristics and expression of canonical immune checkpoints and immune-related genes with distinct m6A modification patterns. Subsequently, we performed the WGCNA analysis using the expression data of 1,611 drug targets to identify 474 genes associated with the m6A modification. 92 overlapping drug targets between WGCNA and DEGs (renal fibrosis vs. normal samples) were defined as key drug targets. A five target gene predictive model was developed through the combination of LASSO regression and stepwise logistic regression (LASSO-SLR) to diagnose renal fibrosis. We further performed drug sensitivity analysis and extracellular matrix analysis on model genes. The ROC curve showed that the risk score (AUC = 0.863) performed well in diagnosing renal fibrosis in the training dataset. In addition, the external validation dataset further confirmed the outstanding predictive performance of the risk score (AUC = 0.755). These results indicate that the risk model has an excellent predictive performance for diagnosing the disease. Furthermore, our results show that this 5-target gene model is significantly associated with many drugs and extracellular matrix activities. Finally, the expression levels of both predictive signature genes EGR1 and PLA2G4A were validated in renal fibrosis and adjacent normal tissues by using qRT-PCR and Western blot method.
Background
Renal cell cacinoma (RCC) accounts for 3% of human cancers, and clear cell renal cell carcinoma (ccRCC) is the most common pathological type of RCC. Cell surface proteins have been shown to play an important role in the occurrence and progression of various cancers. In this study, we focused on plasma membrane proteins (PMPs), to explore their potential value in ccRCC.
Methods
The PMPs expression profiles and ccRCC patients’ clinical information were downloaded from TCGA database. Through a series of bioinformatic methods, we established a plasma membrane proteins prognostic model.
Results
Multivariate cox regression analysis and area under receiver operating characteristic curve indicated that this model was an effective independent predictor of ccRCC clinical outcomes. Combined with other two clinical characteristics, a nomogram was constructed to predict patient survival at 1, 3, and 5 years.
Conclusions
Our study is the first to explore the prognostic value of plasma membrane proteins in clear cell renal cell carcinoma. We hope our work could provide a new viewpoint for ccRCC prognosis and drawn people’s attention to plasma membrane proteins in clear cell renal cell carcinoma.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.