Dataset Information

Identification of key miRNAs in papillary thyroid carcinoma based on data mining and bioinformatics methods.

ABSTRACT: MicroRNAs (miRNAs) are a class of short (approximately 22 nucleotides), non-coding and endogenous RNA molecules that play pivotal roles in the occurrence and development of cancer. The present study aimed to investigate key miRNAs involved in papillary thyroid carcinoma (PTC). Two independent datasets (GSE73182 and GSE113629) were obtained from the GEO database. The differentially expressed miRNAs (DEmiRNAs) between PTC tissues and normal thyroid tissues were analyzed by GEO2R with the Limma R package. Key miRNAs in PTC were identified by the VennDiagram R package. The targets of the key miRNAs were predicted by miRWalk and were functionally enriched by clusterProfiler R package. Five miRNAs including hsa-miR-146b-5p, hsa-miR-15a-5p, hsa-miR-21-5p, hsa-miR-221-3p and hsa-miR-222-3p were identified as key miRNAs in PTC. The expression levels of these key miRNAs were upregulated in PTC. This finding was also confirmed in the other dataset. Target prediction of miRNAs indicated that hsa-miR-146b-5p, hsa-miR-15a-5p, hsa-miR-21-5p, hsa-miR-221-3p and hsa-miR-222-3p exhibited 2, 41, 3, 14 and 8 target genes, respectively. Enrichment analysis indicated that these key miRNAs were mainly involved in nine biological processes, such as regulation of MAP kinase activity, JNK cascade signaling and regulation of protein serine/threonine kinase activity) and in 28 pathways, including the mitogen associated protein kinase, the sphingolipid, ErbB, Ras and the C-type lectin receptor signaling pathways. In conclusion, the present study identified several key miRNAs in PTC, which serve as potential targets for PTC diagnosis and treatment.

SUBMITTER: Wang J

PROVIDER: S-EPMC6906535 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Identification of key miRNAs in papillary thyroid carcinoma based on data mining and bioinformatics methods.

Wang Jianguo J Wu Liping L Jin Yuxia Y Li Suping S Liu Xiaodan X

Biomedical reports 20191121 1

MicroRNAs (miRNAs) are a class of short (approximately 22 nucleotides), non-coding and endogenous RNA molecules that play pivotal roles in the occurrence and development of cancer. The present study aimed to investigate key miRNAs involved in papillary thyroid carcinoma (PTC). Two independent datasets (GSE73182 and GSE113629) were obtained from the GEO database. The differentially expressed miRNAs (DEmiRNAs) between PTC tissues and normal thyroid tissues were analyzed by GEO2R with the Limma R p ...[more]

PMID: 31839944

Similar Datasets

Project description:Medullary thyroid carcinoma (MTC) is an endocrine tumor and comprises 5‑10% of all primary thyroid malignancies. However, the biomechanical contribution to the development and progression of MTC remains unclear. In this study, To discover the key microRNAs (miRNAs or miRs) and their potential roles in the tumorigenesis of MTC, the microarray datasets GSE97070, GSE40807 and GSE27155 were analyzed. The datasets were downloaded from the Gene Expression Omnibus (GEO) database. The differentially expressed miRNAs (DEMs) and genes (DEGs) were accessed by R. Targets of DEMs and predicted using starBase, and functional and pathway enrichment analyses were performed using Metascape. A protein‑protein interaction (PPI) network and an analysis of modules were constructed using NetworkAnalyst. Finally, a network was constructed to show the regulatory association between transcription factors (TFs), DEMs and downstream genes. A total of 5 DEMs were found both in GSE97070 and GSE40807, including 3 upregulated DEMs and 2 downregulated DEMs. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses from Metascape revealed that the target genes of upregulated DEMs were significantly enriched in adherens junction, kinase and protein binding, while the target genes of downregulated DEMs were mainly involved in non‑canonical Wnt signaling pathway and RNA transport. From the PPI network, 13 nodes were screened as hub genes. Pathway enrichment analysis revealed that the top 5 modules were mostly enriched in the neurotrophin signaling pathway, mRNA surveillance pathway and MAPK signaling pathway. In addition, the TF‑DEMs‑target gene and DEGs regulatory network revealed that 17 TFs regulated 2 miRNAs, including upregulated or downregulated DEMs, CREB1 regulated all upregulated DEMs, and TGFB1 was an activator of hsa‑miR‑199a‑3p and a repressor of hsa‑miR‑429. Taken together, the present study identified several miRNAs and potential biological mechanisms involved in the tumorigenesis of MTC. This study identified the key DEMs and potential mechanisms underlying the development of MTC, and provided a series of biomarkers and targets for the management of MTC.

Project description:BackgroudIt has been shown that aberrant expression of microRNAs (miRNAs) and transcriptional factors (TFs) is tightly associated with the development of HCC. Therefore, in order to further understand the pathogenesis of HCC, it is necessary to systematically study the relationship between the expression of miRNAs, TF and genes. In this study, we aim to identify the potential transcriptomic markers of HCC through analyzing common microarray datasets, and further establish the differential co-expression network of miRNAs-TF-mRNA to screen for key miRNAs as candidate diagnostic markers for HCC.MethodWe first downloaded the mRNA and miRNA expression profiles of liver cancer from the GEO database. After pretreatment, we used a linear model to screen for differentially expressed genes (DEGs) and miRNAs. Further, we used weighed gene co-expression network analysis (WGCNA) to construct the differential gene co-expression network for these DEGs. Next, we identified mRNA modules significantly related to tumorigenesis in this network, and evaluated the relationship between mRNAs and TFs by TFBtools. Finally, the key miRNA was screened out in the mRNA-TF-miRNA ternary network constructed based on the target TF of differentially expressed miRNAs, and was further verified with external data set.ResultsA total of 465 DEGs and 215 differentially expressed miRNAs were identified through differential genes expression analysis, and WGCNA was used to establish a co-expression network of DEGs. One module that closely related to tumorigenesis was obtained, including 33 genes. Next, a ternary network was constructed by selecting 256 pairs of mRNA-TF pairs and 100 pairs of miRNA-TF pairs. Network mining revealed that there were significant interactions between 18 mRNAs and 25 miRNAs. Finally, we used another independent data set to verify that miRNA hsa-mir-106b and hsa-mir-195 are good classifiers of HCC and might play key roles in the progression of HCC.ConclusionOur data indicated that two miRNAs-hsa-mir-106b and hsa-mir-195-are identified as good classifiers of HCC.

Project description:BackgroundPapillary thyroid carcinoma (PTC) accounts for most of the proportion of thyroid cancer (TC). The objective of this study was to identify diagnostic, differentially expressed long noncoding RNAs (lncRNAs) and microRNAs (miRNAs), contributing to understanding the epigenetics mechanism of PTC.MethodsThe data of lncRNA, miRNA, and mRNA were downloaded from the Cancer Genome Atlas (TCGA) dataset, followed by functional analysis of differentially expressed mRNAs. Optimal diagnostic lncRNA and miRNA biomarkers were identified via random forest. The regulatory network between optimal diagnostic lncRNA and mRNAs and optimal diagnostic miRNA and mRNAs was identified, followed by the construction of ceRNA network of lncRNA-mRNA-miRNA. Expression validation and diagnostic analysis of lncRNAs, miRNAs, and mRNAs were performed. Overexpression of ADD3-AS1 was performed in PTC-UC3 cell lines, and cell proliferation and invasion assay were used for investigating the role of ADD3-AS1 in PTC.ResultsA total of 107 differentially expressed lncRNAs, 81 differentially expressed miRNAs, and 515 differentially expressed mRNAs were identified. 11 lncRNAs and 6 miRNAs were regarded as the optimal diagnostic biomarkers for PTC. The epigenetic modifications via the above diagnostic lncRNAs and miRNAs were identified, including MIR181A2HG-FOXP2-hsa-miR-146b-3p, BLACAT1/ST7-AS1-RPS6KA5-hsa-miR-34a-5p, LBX2-AS1/MIR100HG-CDHR3-hsa-miR-34a-5p, ADD3-AS1-PTPRE-hsa-miR-9-5p, ADD3-AS1-TGFBR1-hsa-miR-214-3p, LINC00506-MMRN1-hsa-miR-4709-3p, and LOC339059-STK32A-hsa-miR-199b-5p. In the functional analysis, MMRN1 and TGFBR1 were involved in cell adhesion and endothelial cell migration, respectively. Overexpression of ADD3-AS1 inhibited cell growth and invasion in PTC cell lines.ConclusionThe identified lncRNAs/miRNAs/mRNA were differentially expressed between normal and cancerous tissues. In addition, identified altered lncRNAs and miRNAs may be potential diagnostic biomarkers for PTC. Additionally, epigenetic modifications via the above lncRNAs and miRNAs may be involved in tumorigenesis of PTC.

Project description:BACKGROUND To provide a better understanding of anaplastic thyroid carcinoma (ATC) at the molecular level, this study aimed to identify the genes and key pathways associated with ATC by using integrated bioinformatics analysis. MATERIAL AND METHODS Based on the microarray data GSE9115, GSE65144, and GSE53072 derived from the Gene Expression Omnibus, the differentially expressed genes (DEGs) between ATC samples and normal controls were identified. With DEGs, we performed a series of functional enrichment analyses. Then, a protein-protein interaction (PPI) network was constructed and visualized, with which the hub gene nodes were screened out. Finally, modules analysis for the PPI network was performed to further investigate the potential relationships between DEGs and ATC. RESULTS A total of 537 common DEGs were screened out from all 3 datasets, among which 247 genes were upregulated and 275 genes were downregulated. GO analysis indicated that upregulated DEGs were mainly involved in cell division and mitotic nuclear division and the downregulated DEGs were significantly enriched in ventricular cardiac muscle cell action potential. KEGG pathway analysis showed that the upregulated DEGs were mainly enriched in cell cycle and ECM-receptor interaction and the downregulated DEGs were mainly enriched in thyroid hormone synthesis, insulin resistance, and pathways in cancer. The top 10 hub genes in the constructed PPI network were CDK1, CCNB1, TOP2A, AURKB, CCNA2, BUB1, AURKA, CDC20, MAD2L1, and BUB1B. The modules analysis showed that genes in the top 2 significant modules of PPI network were mainly associated with mitotic cell cycle and positive regulation of mitosis, respectively. CONCLUSIONS We identified a series of key genes along with the pathways that were most closely related with ATC initiation and progression. Our results provide a more detailed molecular mechanism for the development of ATC, shedding light on the potential biomarkers and therapeutic targets.

Dataset Information

Identification of key miRNAs in papillary thyroid carcinoma based on data mining and bioinformatics methods.

Publications

Identification of key miRNAs in papillary thyroid carcinoma based on data mining and bioinformatics methods.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets