Dataset Information

MicroRNA related prognosis biomarkers from high throughput sequencing data of kidney renal clear cell carcinoma

ABSTRACT:

Background

Kidney renal clear cell carcinoma (KIRC) is the most common type of kidney cell carcinoma which has the worst overall survival rate. Almost 30% of patients with localized cancers eventually develop to metastases despite of early surgical treatment carried out. MicroRNAs (miRNAs) play a critical role in human cancer initiation, progression, and prognosis. The aim of our study was to identify potential prognosis biomarkers to predict overall survival of KIRC.

Methods

All data were downloaded from an open access database The Cancer Genome Atlas. DESeq2 package in R was used to screening the differential expression miRNAs (DEMs) and genes (DEGs). RegParallel and Survival packages in R was used to analysis their relationships with the KIRC patients. David version 6.8 and STRING version 11 were used to take the Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis.

Results

We found 2 DEGs (TIMP3 and HMGCS1) and 3 DEMs (hsa-miR-21-5p, hsa-miR-223-3p, and hsa-miR-365a-3p) could be prognosis biomarkers for the prediction of KIRC patients. The constructed prognostic model based on those 2 DEGs could effectively predict the survival status of KIRC. And the constructed prognostic model based on those 3 DEMs could effectively predict the survival status of KIRC in 3-year and 5-year.

Conclusion

The current study provided novel insights into the miRNA related mRNA network in KIRC and those 2 DEGs biomarkers and 3 DEMs biomarkers may be independent prognostic signatures in predicting the survival of KIRC patients.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12920-021-00932-z.

SUBMITTER: Huang M

PROVIDER: S-EPMC7941961 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature

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Project description:Numerous studies have shown that microRNA (miRNA) serves as key regulatory factors in the origin and development of cancers. However, the biological mechanisms of miRNAs in kidney renal clear cell carcinoma (KIRC) are still unknown. It is necessary to construct an effective miRNA-clinical model to predict the prognosis of KIRC. In this study, 94 differentially expressed miRNAs were found between para-tumor and tumor tissues based on the Cancer Genome Atlas (TCGA) database. Seven miRNAs (hsa-miR-21-5p, hsa-miR-3613-5p, hsa-miR-144-5p, hsa-miR-376a-5p, hsa-miR-5588-3p, hsa-miR-1269a, and hsa-miR-137-3p) were selected as prognostic indicators. According to their cox coefficient, a risk score formula was constructed. Patients with risk scores were divided into high- and low-risk groups based on the median score. Kaplan-Meier curves analysis showed that the low-risk group had a better survival probability compared to the high-risk group. The area under the ROC curve (AUC) value of the miRNA model was 0.744. In comparison with clinical features, the miRNA model risk score was considered as an independent prognosis factor in multivariate Cox regression analysis. In addition, we built a nomogram including age, metastasis, and miRNA prognostic model based on the results of multivariate Cox regression analysis. The decision curve analysis (DCA) revealed the clinical net benefit of the prognostic model. Gene set enrichment analysis (GSEA) results suggested that several important pathways may be the potential pathways for KIRC. The results of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis for the target genes of 7 miRNAs revealed that miRNAs may participate in KIRC progression via many specific pathways. Additionally, the levels of seven prognostic miRNAs showed a significant difference between KIRC tissues and adjacent non-tumorous tissues. In conclusion, the miRNA-clinical model provides an effective and accurate way to predict the prognosis of KIRC.

Project description:Kidney renal clear cell carcinoma (KIRC) is the most prevalent subtype of renal tumor. The underlying mechanisms governing KIRC initiation and progression are less known. The present study aimed to reveal novel hub genes associated with the initiation and progression of KIRC, which may be utilized as novel molecular biomarkers and therapeutic targets for the treatment of KIRC. The GSE6344 dataset from the Gene Expression Omnibus (GEO) database was integrated to identify differentially expressed genes (DEGs) using the limma package. Then, hub genes were identified and UALCAN, GEPIA, OncoDB, DriverDBv3, GENT2, and HPA databases were employed for the expression, survival, and methylation analyses. cBioPortal tool was used to investigate the genetic alterations, while CancerSEA, TIMER, DAVID, ENCORI, DrugBank, and GSCAlite were utilized to explore a few more hub gene-associated parameters. Finally, targeted bisulfite sequencing (bisulfite-seq), and RT-qPCR techniques were used to validate the expression and methylation level of the hub genes using Human RCC cell line 786-O, A-498, and normal renal tubular epithelial cell line HK-2. In total, 7299 DEGs were found between KIRC and normal samples in the GSE6344 dataset. Using STRING and Cytohubba analysis, four hub genes including VEGFA (vascular endothelial growth factor), ALB (Albumin), ENO2 (enolase 2), and CAVI1 (Caveolin 1) were selected as the hub genes. Further, it was validated through extensive analysis of TCGA datasets that these VEGA, ENO2, and CAV1 hub genes were significantly up-regulated, while ALB was significantly down-regulated in KIRC samples compared to controls. The dysregulation of these genes was found to be associated with the overall survival (OS) of the KIRC patients. Moreover, this study also revealed some novel links between VEGA, ALB, ENO2, and CAV1 expression and genetic alterations, promoter methylation status, immune cell infiltration, miRNAs, gene enrichment terms, and various chemotherapeutic drugs. The present study revealed a panel of four hub genes, which contributed to improving our understanding of the underlying molecular mechanisms of KIRC development and can be utilized as promising novel biomarkers for KIRC diagnosis, prognosis, and treatment.