Revealing the meaning of fibrosis-related genes in the diagnosis, subtype and treatment of renal interstitial fibrosis

Abstract

Abstract Background: During the course of CKD, the patient's renal function continues to deteriorate, eventually progressing to ESRD. Renal interstitial fibrosis (RIF) is the end result of the progression of almost all types of CKD and a major cause of ESRD. However, effective and feasible treatments against RIF are comparatively rare in the clinic. Methods: In the study, we obtained 299 samples from the GEO to investigate the significance of fibrosis-related genes (FRGs). To model RIF, we employed several methods, including SVM-RFE, RF, LASSO analysis, and PPI network analysis to identify crucial FRGs. We constructed a nomogram that included four FRGs to forecast the occurrence of RIF. Besides, we used the consensus clustering algorithm to recognize subtype classifications for RIF. We adopted the ssGSEA method to explore the immune landscape in RIF. Lastly, we performed the PCA method to investigate the FRG expression pattern in RIF patients. Results: A total of 177 FRGs were identified from the genecards database, and the nomogram model was developed from the four hub FRGs (CCL5, TIMP1, ALB, and IFNG) to explore the underlying pathological mechanism of RIF. The calibration curve analysis suggested that the nomogram model possesses accurate predictive ability. The consensus clustering algorithm found that CCL5, TIMP1, and IFNG were more highly expressed in FRG cluster A, while ALB was expressed more highly in FRG cluster B. The ssGSEA results showed that apart from neutrophils, type 17 T helper cells, and immature dendritic cells, the abundance of other immune cells was higher in FRG cluster A. Our results found that FRG cluster A is closely related to RIF features. Conclusion: We identified four hub FRGs (CCL5, TIMP1, ALB, and IFNG) and constructed a nomogram to forecast the occurrence of RIF. The FRG cluster A is strongly linked to RIF characteristics. Our findings provided new insights into identifying RIF progression and early prevention and treatment of CKD.