A novel anoikis-related prognostic signature associated with prognosis and immune infiltration landscape in clear cell renal cell carcinoma

Abstract

Background: Clear cell renal cell carcinoma (ccRCC) is the most common histological subtype of renal cell carcinoma (RCC). Anoikis plays an essential function in tumourigenesis, whereas the role of anoikis in ccRCC remains unclear.Methods: Anoikis-related genes (ARGs) were collected from the MSigDB database. According to univariate Cox regression analysis, the least absolute shrinkage and selection operator (LASSO) algorithm was utilized to select the ARGs associated with the overall rate (OS). Multivariate Cox regression analysis was conducted to identify 5 prognostic ARGs, and a risk model was established. The Kaplan-Meier survival analysis was used to evaluate the OS rate of ccRCC patients. Gene ontology (GO), Kyoto encyclopedia of genes and genomes (KEGG), and Gene set enrichment analysis (GSVA) were utilized to investigate the molecular mechanism of patients in the low- and high-risk group. ESTIMATE, CIBERSOT, and single sample gene set enrichment analysis (ssGSEA) algorithms were conducted to estimate the immune infiltration landscape. Consensus clustering analysis was performed to divide the patients into different subgroups.Results: A fresh risk model was constructed based on the 5 prognostic ARGs (CHEK2, PDK4, ZNF304, SNAI2, SRC). The Kaplan-Meier survival analysis indicated that the OS rate of patients with a low-risk score was significantly higher than those with a high-risk score. Consensus clustering analysis successfully clustered the patients into two subgroups, with a remarkable difference in immune infiltration landscape and prognosis. The ESTIMATE, CIBERSORT, and ssGSEA results illustrated a significant gap in immune infiltration landscape of patients in the low- and high-risk group. Enrichment analysis and GSVA revealed that immune-related signaling pathways might mediate the role of ARGs in ccRCC. The nomogram results illustrated that the ARGs prognostic signature was an independent prognostic predictor that distinguished it from other clinical characteristics. TIDE score showed a promising immunotherapy response of ccRCC patients in different risk subgroups and cluster subgroups.Conclusion: Our study revealed that ARGs play a carcinogenic role in ccRCC. Additionally, we firstly integrated multiple ARGs to establish a risk-predictive model. This study highlights that ARGs could be implemented as a stratification factor for individualized and precise treatment in ccRCC patients.

An anoikis-related gene signature predicts prognosis and reveals immune infiltration in hepatocellular carcinoma

“…constructed a 7 anoikis-related genes signature to predict the survival of Low-grade glioma (LGG) patients ( 32 ), Chen et al. identified 5 prognostic anoikis-related genes (CHEK2, PDK4, ZNF304, SNAI2, SRC) to establish a risk-predictive model for clear cell renal cell carcinoma ( 33 ), which all exhibited great predictive performance and implemented as a stratification factor for individualized treatment. Moreover, Sun et al.…”

Construction of a novel anoikis-related prognostic model and analysis of its correlation with infiltration of immune cells in neuroblastoma

“…Anoikis can prevent normal cells and tumor cells from leaving the ECM, suppress their distant colonization and growth, while malignant tumor cells colonizing in organs other than the primary site can get rid of the regulation of anoikis. In recent years, the AR has been found in many solid tumors, including breast cancer ( 37 , 38 ), prostate cancer ( 39 , 40 ), gastric cancer ( 41 , 42 ), liver cancer ( 43 , 44 ), renal cancer ( 45 , 46 ) and so on. Some scholars have also found this phenomenon in NB ( 31 , 32 ), but there is a lack of research on the relevant mechanism.…”

Identification of featured necroptosis-related genes and imbalanced immune infiltration in sepsis via machine learning

“…Additionally, GSE95233 (included 51 septic patients and 22 healthy volunteers) and GSE54514 (included 35 septic patients and 18 healthy volunteers) were merged as the validation cohort to verify the mRNA expression and the diagnostic performances of hub genes. R packages “SVA” was applied to normalize and remove the batch effects of these gene matrices ( Chen Z. et al, 2022 ). According to the dataset probe annotation files, all these dataset probes were transformed into corresponding gene symbols using Perl scripts.…”