Dataset Information

Transcriptional Regulation Based on Network of Autophagy Identifies Key Genes and Potential Mechanisms in Human Osteoarthritis.

ABSTRACT:

Objective

Osteoarthritis (OA) is a chronic arthropathy that frequently occurs in the middle-aged and elderly population. The aim of this study was to investigate the molecular mechanism of OA based on autophagy theory.

Design

We downloaded the gene expression profile from the Gene Expression Omnibus repository. Differentially expressed genes (DEGs) related to the keyword "autophagy" were identified using the scanGEO online analysis tool. DEGs representing the same expression trend were screened using the MATCH function. Clinical synovial specimens were collected for identification, pathological diagnosis, hematoxylin and eosin staining, and real-time polymerase chain reaction analysis. Differential expression of mRNAs in the synovial membrane tissues and chondrocyte monolayer samples from OA patients was used to identify potential OA biomarkers. Protein-protein interactions were established by the STRING website and visualized with Cytoscape. Functional and pathway enrichment analyses were performed using the Metascape database.

Results

GABARAPL1, GABARAPL2, and ATG13 were obtained as co-expressed autogenes in the 3 data sets. They were all downregulated among OA synovial tissues compared with non-OA synovial tissues (P < 0.01). A protein-protein interaction network was constructed based on these 3 genes and included 63 genes. A functional analysis revealed that these genes were associated with autophagy-related functions. The top hub genes in the protein-protein interaction network were presented. Furthermore, 3 key modules were extracted to be core control modules.

Conclusions

These results offer an important molecular understanding of the key transcriptional regulatory genes and modules based on the network of potential autophagy mechanisms in human OA.

SUBMITTER: Liu J

PROVIDER: S-EPMC8804715 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature

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Publications

Transcriptional Regulation Based on Network of Autophagy Identifies Key Genes and Potential Mechanisms in Human Osteoarthritis.

Liu Jiamei J Fu Qin Q Liu Shengye S

Cartilage 20200820 2_suppl

<h4>Objective</h4>Osteoarthritis (OA) is a chronic arthropathy that frequently occurs in the middle-aged and elderly population. The aim of this study was to investigate the molecular mechanism of OA based on autophagy theory.<h4>Design</h4>We downloaded the gene expression profile from the Gene Expression Omnibus repository. Differentially expressed genes (DEGs) related to the keyword "autophagy" were identified using the scanGEO online analysis tool. DEGs representing the same expression trend ...[more]

PMID: 32819149

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Project description:BackgroundOsteoarthritis (OA) is a degenerative joint disease that acts as a major cause of early disability in the old population. However, the molecular mechanisms of autophagy in osteoclasts involved in OA remain unclear.MethodsThe gene expression profiles were downloaded from the Gene Expression Omnibus (GEO) repository. The NCBI GEO2R and ScanGEO analysis tool were used to identify differentially expressed genes (DEGs). The protein-protein interaction (PPI) network was predicted by the STRING website and visualized with Cytoscape software. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway were performed to enrich GO terms and signaling pathways using Metascape database. To predict LC3-interacting region (LIR) motif among these DEGs, the iLIR database was selected to assess specific short linear sequences. To obtain potential upstream miRNA targets of these DEGs, the mRNA-miRNA interaction networks were predicted by miRWalk database. The knee OA model was performed in mice, and autophagy related mRNAs of osteoclasts were identified. Experimental specimens were further verified with histopathological staining.ResultsBecn1, Atg3, Atg12, Pik3c3, and Gabarapl2 were obtained as coexpressed differential genes. PPI network was constructed and deduced the other 60 related genes. GO and KEGG enrichment networks indicated that autophagy-animal, selective autophagy, and mitophagy mainly participated in autophagy regulation in osteoclasts. The possible LIR sequences were collected to predict motifs. The mRNA-miRNA interaction networks suggested that many miRNAs could regulate autophagy-related genes individually and collectively. The RT-PCR results suggested that these five genes were upregulated in the OA group. Histopathological staining revealed that osteoclasts were increased in subchondral bone, and higher expression of these DEGs in the OA group was compared to the sham group.ConclusionOur results reveal that the role of autophagy in osteoclasts could be a regulatory mechanism in OA and that these autophagy-related genes might be targets for the intervention of OA disease.

Project description:Melanoma is the deadliest skin tumor and is prone to distant metastases. The incidence of melanoma has increased rapidly in the past few decades, and current trends indicate that this growth is continuing. This study was aimed to explore the molecular mechanisms of melanoma pathogenesis and discover underlying pathways and genes associated with melanoma. We used high-throughput expression data to study differential expression profiles of related genes in melanoma. The differentially expressed genes (DEGs) of melanoma in GSE15605, GSE46517, GSE7553, and the Cancer Genome Atlas (TCGA) datasets were analyzed. Differentially expressed genes (DEGs) were identified by paired t-test. Then the DEGs were performed cluster and principal component analyses and protein-protein interaction (PPI) network construction. After that, we analyzed the differential genes through bioinformatics and got hub genes. Finally, the expression of hub genes was confirmed in the TCGA databases and collected patient tissue samples. Total 144 up-regulated DEGs and 16 down-regulated DEGs were identified. A total of 17 gene ontology analysis (GO) terms and 11 pathways were closely related to melanoma. Pathway of pathways in cancer was enriched in 8 DEGs, such as junction plakoglobin (JUP) and epidermal growth factor receptor (EGFR). In the PPI networks, 9 hub genes were obtained, such as loricrin (LOR), filaggrin (FLG), keratin 5 (KRT5), corneodesmosin (CDSN), desmoglein 1 (DSG1), desmoglein 3 (DSG3), keratin 1 (KRT1), involucrin (IVL), and EGFR. The pathway of pathways in cancer and its enriched DEGs may play important roles in the process of melanoma. The hub genes of DEGs may become promising melanoma candidate genes. Five key genes FLG, DSG1, DSG3, IVL, and EGFR were identified in the TCGA database and melanoma tissues. The results suggested that FLG, DSG1, DSG3, IVL, and EGFR might play important roles and potentially be valuable in the prognosis and treatment of melanoma. These hub genes might well have clinical significance as diagnostic markers.

Dataset Information

Transcriptional Regulation Based on Network of Autophagy Identifies Key Genes and Potential Mechanisms in Human Osteoarthritis.

Objective

Design

Results

Conclusions

Publications

Transcriptional Regulation Based on Network of Autophagy Identifies Key Genes and Potential Mechanisms in Human Osteoarthritis.

Similar Datasets

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