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A Metastasis-Related lncRNA Signature Correlates With the Prognosis in Clear Cell Renal Cell Carcinoma.

ABSTRACT: To explore the role of metastasis-related long noncoding RNA (lncRNA) signature for predicting the prognosis of clear cell renal cell carcinoma (ccRCC) patients. Firstly, metastasis-associated genes were identified to establish a metastasis-related lncRNA signature by statistical analysis. Secondly, the ccRCC patients were grouped into high-risk or low-risk group according to the established signature, and the different pathways between the 2 groups were identified by gene set enrichment analysis (GSEA). Finally, investigations involving PCR, transwell migration and invasion assay were carried out to further confirm our findings. The metastasis-related lncRNA signature was successfully constructed according to 7-metastasis-related genes (ADAM12, CD44, IL6, TFPI2, TGF-β1, THBS2, TIMP3). The diagnostic efficacy and the clinically predictive capacity of the signature were evaluated. Most of the values of the area under the time-dependent receiver-operating characteristic (ROC) were greater than 0.70. The nomogram constructed by integrating clinical data and risk scores confirmed that the risk score calculated from our signature was a good prognosis predictor. GSEA analysis showed that some tumor-related pathways were enriched in the high-risk group, while metabolism-related pathways were enriched in the low-risk group. In carcinoma tissues, the SSR3-6, WISP1-2 were highly expressed, but the expression of UBAC2-6 was low there. Knocking down SSR3-6 decreased the ability of migration and invasion in ccRCC cells. In conclusion, we successfully constructed a metastasis-related lncRNA signature, which could accurately predict the survival and prognosis of ccRCC patients.

SUBMITTER: Dou Q

PROVIDER: S-EPMC8209488 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description: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.

Project description:PurposeThis study aimed to construct an m6A-related long non-coding RNAs (lncRNAs) signature to accurately predict the prognosis of kidney clear cell carcinoma (KIRC) patients using data obtained from The Cancer Genome Atlas (TCGA) database.MethodsThe KIRC patient data were downloaded from TCGA database and m6A-related genes were obtained from published articles. Pearson correlation analysis was implemented to identify m6A-related lncRNAs. Univariate, Lasso, and multivariate Cox regression analyses were used to identifying prognostic risk-associated lncRNAs. Five lncRNAs were identified and used to construct a prognostic signature in training set. Kaplan-Meier curves and receiver operating characteristic (ROC) curves were applied to evaluate reliability and sensitivity of the signature in testing set and overall set, respectively. A prognostic nomogram was established to predict the probable 1-, 3-, and 5-year overall survival of KIRC patients quantitatively. GSEA was performed to explore the potential biological processes and cellular pathways. Besides, the lncRNA/miRNA/mRNA ceRNA network and PPI network were constructed based on weighted gene co-expression network analysis (WGCNA). Functional Enrichment Analysis was used to identify the biological functions of m6A-related lncRNAs.ResultsWe constructed and verified an m6A-related lncRNAs prognostic signature of KIRC patients in TCGA database. We confirmed that the survival rates of KIRC patients with high-risk subgroup were significantly poorer than those with low-risk subgroup in the training set and testing set. ROC curves indicated that the prognostic signature had a reliable predictive capability in the training set (AUC = 0.802) and testing set (AUC = 0.725), respectively. Also, we established a prognostic nomogram with a high C-index and accomplished good prediction accuracy. The lncRNA/miRNA/mRNA ceRNA network and PPI network, as well as functional enrichment analysis provided us with new ways to search for potential biological functions.ConclusionsWe constructed an m6A-related lncRNAs prognostic signature which could accurately predict the prognosis of KIRC patients.

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A Metastasis-Related lncRNA Signature Correlates With the Prognosis in Clear Cell Renal Cell Carcinoma.

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