A novel autophagy-related long non-coding RNAs prognostic risk score for clear cell renal cell carcinoma

Abstract

Background As the main histological subtype of renal cell carcinoma, clear cell renal cell carcinoma (ccRCC) places a heavy burden on health worldwide. Autophagy-related long non-coding RNAs (ARlncRs) have shown tremendous potential as prognostic signatures in several studies, but the relationship between them and ccRCC still has to be demonstrated. Methods The RNA-sequencing and clinical characteristics of 483 ccRCC patients were downloaded download from the Cancer Genome Atlas and International Cancer Genome Consortium. ARlncRs were determined by Pearson correlation analysis. Univariate and multivariate Cox regression analyses were applied to establish a risk score model. A nomogram was constructed considering independent prognostic factors. The Harrell concordance index calibration curve and the receiver operating characteristic analysis were utilized to evaluate the nomogram. Furthermore, functional enrichment analysis was used for differentially expressed genes between the two groups of high- and low-risk scores. Results A total of 9 SARlncRs were established as a risk score model. The Kaplan–Meier survival curve, principal component analysis, and subgroup analysis showed that low overall survival of patients was associated with high-risk scores. Age, M stage, and risk score were identified as independent prognostic factors to establish a nomogram, whose concordance index in the training cohort, internal validation, and external ICGC cohort was 0.793, 0.671, and 0.668 respectively. The area under the curve for 5-year OS prediction in the training cohort, internal validation, and external ICGC cohort was 0.840, 0.706, and 0.708, respectively. GO analysis and KEGG analysis of DEGs demonstrated that immune- and inflammatory-related pathways are likely to be critically involved in the progress of ccRCC. Conclusions We established and validated a novel ARlncRs prognostic risk model which is valuable as a potential therapeutic target and prognosis indicator for ccRCC. A nomogram including the risk model is a promising clinical tool for outcomes prediction of ccRCC patients and further formulation of individualized strategy.

IKBIP promotes tumor development via the akt signaling pathway in esophageal squamous cell carcinoma

“…In recent years, an increasing number of studies have shown that IKBIP can serve as a predictive biomarker for many cancers, such as glioma [12,13], renal cell carcinoma [14] and gastrointestinal cancer [26]. However, whether IKBIP can be used as a biomarker for ESCC is unknown.…”

“…Additionally, IKBIP has been reported to maintain the abnormal proliferation of glioblastoma cells by inhibiting the degradation of CDK4 [12]. Importantly, a growing number of studies have shown that IKBIP may serve as a predictive biomarker and a potential therapeutic target for several cancers, such as glioblastoma [13], renal cancer [14] and lung cancer [15]. However, the role of IKBIP in ESCC has not yet been reported.…”

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

“…), or did not thoroughly established potentially significant treatments that could be used to treat the different risk groups of patients their model could stratify. For instance, Tang et al established a prognostic model on 9 autophagy-related long non-coding RNA 42 , Xu et al constructed a panel of 10 cuproptosis-associated lncRNAs 43 , Ming et al designed a panel comprising 12 N7-Methylguanosine (m7G)-related long non-coding RNAs (lncRNAs) 44 , Sun et al constructed a ferroptosis-related prognostic signature based on 19 ferroptosis-related genes 45 , Liu et al identified a 13-gene risk signature related to ccRCC patients' metabolism 46 , Zhao et al described their finding on 3 metabolic genes that were used to build a risk score model 47 , and Peng et al proposed a 3-gene methylation signature that could be used as a risk stratification tool to predict patient's outcomes and treatment response 48 . Compared these previous literature, to our existing knowledge, this is the first study to thoroughly investigate a panel of 13 proteins in ccRCC based on which we established an algorithm able to differentiate high-risk from low-risk patients, predict their 1-, 3- and 5-year OS, perform immune-related functional analysis to investigate the potential effect of immunotherapeutics in these 2 groups of patients and screen for potential drugs that could have be effective in the high and low-risk groups.…”