Identification of a Three-Glycolysis-Related lncRNA Signature Correlated With Prognosis and Metastasis in Clear Cell Renal Cell Carcinoma

Abstract

The clear cell renal cell carcinoma (ccRCC) is not only a malignant disease but also an energy metabolic disease, we aimed to identify a novel prognostic model based on glycolysis-related long non-coding RNA (lncRNAs) and explore its mechanisms. With the use of Pearson correlation analysis between the glycolysis-related differentially expressed genes and lncRNAs from The Cancer Genome Atlas (TCGA) dataset, we identified three glycolysis-related lncRNAs and successfully constructed a prognostic model based on their expression. The diagnostic efficacy and the clinically predictive capacity of the signature were evaluated by univariate and multivariate Cox analyses, Kaplan–Meier survival analysis, and principal component analysis (PCA). The glycolysis-related lncRNA signature was constructed based on the expressions of AC009084.1, AC156455.1, and LINC00342. Patients were grouped into high- or low-risk groups according to risk score demonstrated significant differences in overall survival (OS) period, which were validated by patients with ccRCC from the International Cancer Genome Consortium (ICGC) database. Univariate Cox analyses, multivariate Cox analyses, and constructed nomogram-confirmed risk score based on our signature were independent prognosis predictors. The CIBERSORT algorithms demonstrated significant correlations between three-glycolysis-related lncRNAs and the tumor microenvironment (TME) components. Functional enrichment analysis demonstrated potential pathways and processes correlated with the risk model. Clinical samples validated expression levels of three-glycolysis-related lncRNAs, and LINC00342 demonstrated the most significant aberrant expression. in vitro, the general overexpression of LINC00342 was detected in ccRCC cells. After silencing LINC00342, the aberrant glycolytic levels and migration abilities in 786-O cells were decreased significantly, which might be explained by suppressed Wnt/β-catenin signaling pathway and reversed Epithelial mesenchymal transformation (EMT) process. Collectively, our research identified a novel three-glycolysis-related lncRNA signature as a promising model for generating accurate prognoses for patients with ccRCC, and silencing lncRNA LINC00342 from the signature could partly inhibit the glycolysis level and migration of ccRCC cells.

Prediction of the Prognosis of Clear Cell Renal Cell Carcinoma by Cuproptosis-Related lncRNA Signals Based on Machine Learning and Construction of ceRNA Network

“…Te performance and precision of the model in the training set were assessed by using the timedependent ROC curve and the C-index, and the performance of the risk score as an independent prognostic indicator was confrmed utilizing univariate and multivariate Cox regression analysis. In order to more thoroughly assess the performance of our prognostic model, we gathered a number of lncRNA-based prognostic risk scoring models developed based on the TCGA-KIRC cohort in recent years, computed the time-dependent ROC curve and C index of the lncRNA model based on the entire TCGA-KIRC cohort, and compared them with our developed prognostic model [32][33][34][35][36][37].…”

Guidelines on lung adenocarcinoma prognosis based on immuno-glycolysis-related genes

“…One of the most common and important changes to metabolism is aerobic glycolysis, widely referred to as the "Warburg effect". Warburg effect represents a shift in glucose utilization by tumor cells from oxidative phosphorylation to glycolysis, characterized by increased glucose uptake and lactate secretion, occurs even under normal oxygen content, and is now considered as one of the hallmarks of tumors [11][12][13]. Many glycolysis-related enzymes, such as hexokinase 2, phosphofructokinase, and pyruvate kinase, are overexpressed in lung cancer cells compared to normal cells [14][15][16].…”

Construction of a prognostic glycolysis‐related lncRNA signature for patients with colorectal cancer

“… 20 For instance, a signature consisting of three GRLs was found to be correlated with metastasis and patients' survival in clear cell renal cell carcinoma. 21 Similarly, Tong et al set up a glycolysis‐related lncRNA (GRL) signature which divided ovarian cancer patients into two subgroups with distinct prognosis and immune microenvironment. 22 Immune checkpoint inhibitors (ICIs) have been approved to treat CRC and have provided reliable responses and disease control in patients with mismatch repair deficiency (MMR‐D) and/or microsatellite instability‐high (MSI‐H).…”

“… 24 , 25 The relationship between TME and GRL signature was explored in some types of cancers. 21 , 22 However, relevant research is absent in CRC. Hence, we constructed a GRL model which could forecast patients' survival and provide promising therapeutic guidance for managing CRC.…”

“…In relation to this, it has been shown that lncRNAs are important in TME remodeling and can affect the infiltration landscape of CRC, suggesting that lncRNA expression can reflect the TME of CRC and guide immunotherapy 24,25 . The relationship between TME and GRL signature was explored in some types of cancers 21,22 . However, relevant research is absent in CRC.…”