Clear cell renal cell carcinoma (ccRCC) is the most common and lethal renal malignant tumor in adults. The aim of the present study was to identify the key genes involved in ccRCC metastasis. Expression profiling data for ccRCC patients with metastasis and without metastasis were obtained from The Cancer Genome Atlas database. The datasets were used to identify differentially expressed genes (DEGs) between the metastasis group and the non-metastasis group using the DESeq2 package. Function enrichment analyses of DEGs were performed. The protein-protein interaction (PPI) network was constructed and analyzed using the Search Tool for the Retrieval of Interacting Genes and Cytoscape for further analysis of the identified hub genes. A total of 472 DEGs were identified, including 247 that were upregulated and 225 that were downregulated in the metastasis group. Gene Ontology enrichment analysis revealed that DEGs were mainly enriched in cell transmembrane movement and mitotic cell cycle process. Kyoto Encyclopedia of Genes Genomes pathway analysis revealed that the DEGs were mainly involved in the ‘cell cycle’ (hsa04110), ‘collecting duct acid secretion’ (hsa04966), ‘complement and coagulation cascades’ (hsa04610) and ‘aldosterone-regulated sodium reabsorption’ (hsa04960) pathways. Using the PPI network, 35 hub genes were identified, and the majority of them were upregulated in ccRCC tissue compared with normal kidney tissue. The expression levels of certain hub genes (CDKN3, TPX2, BUB1B, CDCA8, UBE2C, NDC80, RRM2, NCAPG, NCAPH, PTTG1, FAM64A, ANLN, KIF4A, CEP55, CENPF, KIF20A, ASPM and HJURP) were significantly associated with overall survival and recurrence-free survival in ccRCC. The present study has identified key genes associated with the metastasis of ccRCC.
The expression profile of seven lncRNAs can effectively predict ER after surgical resection for HCC.
Background: Tumor mutational burden (TMB) has emerged as an independent biomarker to predict patient responses to treatment with immune checkpoint inhibitors (ICIs) for lung adenocarcinoma (LUAD). MicroRNAs (miRNAs) have a crucial role in the regulation of anticancer immune responses, but the association of miRNA expression patterns and TMB is not clear in LUAD. Methods: Differentially expressed miRNAs in samples with high TMB and low TMB samples were screened in the LUAD dataset in The Cancer Genome Atlas. The least absolute shrinkage and selection operator (LASSO) method was applied to develop a miRNA-based signature classifier for predicting TMB levels in the training set. An test set was used to validate this classifier. The correlation between the miRNA-based classifier index and the expression of three immune checkpoints (PD-1, PD-L1, and CTLA-4) were explored. Functional enrichment analysis was carried out of the miRNAs included in the miRNAbased signature classifier. Results: Twenty-five differentially expressed miRNAs were used to establish a miRNA-based signature classifier for predicting TMB level. The accuracy of the 25-miRNA-based signature classifier was 0.850 in the training set, 0.810 in the test set and 0.840 in the total set. This miRNA-based signature classifier index showed a low correlation with PD-1 and PD-L1, and no correlation with CTLA-4. Enrichment analysis for these 25 miRNA revealed they are involved in many immune-related biological processes and cancer-related pathways. Conclusion: MiRNA expression patterns are associated with tumor mutational burden and a miRNAbased signature classifier may serve as a biomarker for prediction of TMB levels in LUAD.
Objective: The aim of this study was to construct and validate a microRNA (miR)-based signature as a prognostic tool for lung squamous cell carcinoma (LUSC). Materials and methods: With the use of mature miR expression profiles downloaded from The Cancer Genome Atlas database, we identified differentially expressed miRs between LUSC and matched healthy lung tissue. Thereafter, we carried out an evaluation of the association of differentially expressed miRs with overall survival (OS) with the use of univariate and multivariate Cox regression analysis. This analysis was eventually employed for the construction of a miR-based signature, which effectively predicted the prognosis. The functional enrichment analysis of the miRs included in the signature was used to explore their potential molecular mechanism in LUSC. Results: A total of 316 miRs were differentially expressed between LUSC and matched healthy lung tissues in the training set. Following the univariate and multivariate Cox regression analysis, we found that seven miRs were independent prognostic factors. Each patient received a signature index ranging from 0 to 7. Patients with LUSC were divided into high-risk, intermediate-risk, and low-risk groups in accordance with their signature index and the OS in the three groups was significantly different. This finding remains consistent in the validation set. Besides that, this seven-miR signature remained an independent prognostic factor in comparison with routine clinicopathologic features. The seven-miR signature is a promising biomarker for predicting the 5-year survival rate of LUSC with an area under the receiver operating characteristic curveof 0.712 in the training set and 0.688 in the validation set, respectively. The target genes of seven miRs may be involved in various pathways associated with lung cancer, for instance the mitogen-activated protein kinase signaling pathway and the Wnt signaling pathway. Conclusion: Using this signature, patients with LUSC can be divided into high-risk, intermediate-risk, and low-risk groups for more personalized management.
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