Dataset Information

Identification and Validation of Novel Diagnostic Biomarkers for Keloid Based on GEO Database

ABSTRACT:

Introduction

Keloid is a pathological scar type, which invades normal surrounding tissue without self-limiting to cause pain, itching, cosmetic disfigurement, etc. Knowledge of the molecular mechanisms underlying keloid remains unclear. This dilemma leads to no biomarker available for diagnosis. Thus, to seek accurate diagnosis, biomarkers are necessary for keloid diagnosis to help control its incidence.

Methods

Gene Expression Omnibus (GEO) database was used to select differentially expressed miRNAs (DE-miRNAs) in GSE113620. miRTarBase miRNA–target tools were used to predict the interactions between miRNAs and their target mRNAs. Target mRNAs that were differentially expressed in keloid were selected by analyzing differentially expressed genes (DEGs) in GSE44270 and GSE92566. PPI network analysis, gene enrichment analysis, cell-specific and tissue-specific expression analyses of DE-target mRNAs were conducted. RT-PCR analysis was conducted to validate our results.

Results

Three novel miRNAs (miR-30b-5p, miR-212-3p, miR-149-5p) and five target mRNAs (SIX1, CCNA2, CCNB1, FOXM1, RUNX2) were identified as potential biomarkers for keloid patients. Additionally, the potential functions of those miRNAs-mRNAs pathways were analyzed.

Discussion

These findings of keloid-related miRNAs, mRNAs, and miRNA–mRNAs regulatory networks may provide insights into the underlying pathogenesis of keloid and serve as potential biomarkers for keloid diagnosis.

SUBMITTER: Nurzati Y

PROVIDER: S-EPMC8801514 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:Background:The molecular mechanisms and genetic markers of thyroid cancer are unclear. In this study, we used bioinformatics to screen for key genes and pathways associated with thyroid cancer development and to reveal its potential molecular mechanisms. Methods:The GSE3467, GSE3678, GSE33630, and GSE53157 expression profiles downloaded from the Gene Expression Omnibus database (GEO) contained a total of 164 tissue samples (64 normal thyroid tissue samples and 100 thyroid cancer samples). The four datasets were integrated and analyzed by the RobustRankAggreg (RRA) method to obtain differentially expressed genes (DEGs). Using these DEGs, we performed gene ontology (GO) functional annotation, pathway analysis, protein-protein interaction (PPI) analysis and survival analysis. Then, CMap was used to identify the candidate small molecules that might reverse thyroid cancer gene expression. Results:By integrating the four datasets, 330 DEGs, including 154 upregulated and 176 downregulated genes, were identified. GO analysis showed that the upregulated genes were mainly involved in extracellular region, extracellular exosome, and heparin binding. The downregulated genes were mainly concentrated in thyroid hormone generation and proteinaceous extracellular matrix. Pathway analysis showed that the upregulated DEGs were mainly attached to ECM-receptor interaction, p53 signaling pathway, and TGF-beta signaling pathway. Downregulation of DEGs was mainly involved in tyrosine metabolism, mineral absorption, and thyroxine biosynthesis. Among the top 30 hub genes obtained in PPI network, the expression levels of FN1, NMU, CHRDL1, GNAI1, ITGA2, GNA14 and AVPR1A were associated with the prognosis of thyroid cancer. Finally, four small molecules that could reverse the gene expression induced by thyroid cancer, namely ikarugamycin, adrenosterone, hexamethonium bromide and clofazimine, were obtained in the CMap database. Conclusion:The identification of the key genes and pathways enhances the understanding of the molecular mechanisms for thyroid cancer. In addition, these key genes may be potential therapeutic targets and biomarkers for the treatment of thyroid cancer.