Integrated Bioinformatics Analysis for the Screening of Hub Genes and Therapeutic Drugs in Androgen Receptor-Positive TNBC.
As the most invasive and lethal subtype of breast cancer (BC), triple-negative breast carcinoma (TNBC) is of increasing interest. However, the androgen receptor (AR) still has an unclear role in TNBC. The current study is aimed at testing the diagnostic and therapeutic performance of novel biomarkers for AR-positive TNBC. The GSE76124 dataset was analyzed by combining WGCNA and other bioinformatics methods. Subsequently, function enrichment analysis was applied to identify the relationships between these differential expression genes (DEGs). Subsequently, the protein-protein interaction network was established, and the hub genes were identified by Cytoscape software. Eventually, the miRNA-hub gene modulate network was developed and the Drug-Gene Interaction Database (DGIdb) was applied to verify the potential drugs for AR-positive TNBC. In the current research, 88 DEGs in total were selected from the intersection of the purple module genes identified by WGCNA and limma package. TFF1, FOXA1, ESR1, AGR2, TFF3, AGR3, GATA3, XBP1, SPDEF, and TOX3 were selected as hub genes by the MCC method, which were all upregulated. The survival analysis suggested that TFF1 was the only one related to significant lower survival rate in TNBC. Ultimately, hsa-miR-520g-3p and hsa-miR-520h were found taking part in the regulation of TFF1, and 2 small molecules were identified as the potential targets for AR-positive TNBC treatment. As a result, our study suggested that hsa-miR-520g-3p, hsa-miR-520h, and TFF1 might have significant potential values for AR-positive TNBC diagnosis and prognosis prediction. TFF1, hsa-miR-520g-3, and hsa-miR-520h may serve as the novel therapeutic targets, and our findings offer further insights into the therapy of AR-positive TNBC.
Guo Q
，Qiu P
，Yao Q
，Chen J
，Lin J
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Integrated Bioinformatics Analysis for the Screening of Hub Genes and Therapeutic Drugs in Hepatocellular Carcinoma.
Liver cancer is a major medical problem because of its high morbidity and mortality. Hepatocellular carcinoma (HCC) is the most common type of liver cancer. Currently, the mechanism of HCC is unclear, and the prognosis is poor with limited treatment.
The purpose of this study is to identify hub genes and potential therapeutic drugs for HCC.
We used the GEO2R algorithm to analyze the differential expression of each gene in 4 gene expression profiles (GSE101685, GSE62232, GSE46408, and GSE45627) between HCC and normal hepatic tissues. Next, we screened out the differentially expressed genes (DEGs) by corresponding calculation data according to adjusted P-value < 0.05 and | log fold change (FC) | > 1.0. Subsequently, we used the DAVID software to analyze the DEGs by GO and KEGG enrichment analysis. Then, we carried out the protein-protein interaction (PPI) network analysis of DEGs using the STRING tool, and the PPI network was constructed by Cytoscape software. MCODE plugin was used for module analysis, and the hub genes were screened out by the Cyto- Hubba plugin. Meanwhile, we used The Kaplan-Meier plotter, GEPIA2 and HPA databases to exert survival analysis and verify the expression alternation of hub genes. Furthermore, we used ENCORI, TargetScan, miRDB and miRWalk database to predict the upstream regulated miRNA of hub genes and construct a miRNA-hub genes network by Cytoscape software. Finally, we selected potential therapeutic drugs for HCC through DGIdb databases.
A total of 415 DEGs were screened in HCC, including 196 up-regulated DEGs and 219 down-regulated DEGs. The results of KEGG pathway analysis suggested that the up-regulated DEGs can regulate the cell cycle, and DNA replication signal pathway, while the down-regulated DEGs were associated with metabolic pathways. In this study, we identified 11 hub genes (AURKA, BUB1B, TOP2A, MAD2L1, CCNA2, CCNB1, BUB1, KIF11, CDK1, CCNB2 and TPX2), which were independent risk factors of HCCand all up-regulated DEGs. We verified the expression difference of hub genes through the GEPIA2 and HPA database, which was consistent with the results of GEO data. We found that those hub genes were mutations in HCC according to the cBioPortal database. Finally, we used the DGIdb database to select 32 potential therapeutic targeting drugs for hub genes.
In summary, our study provided a new perspective for researching the molecular mechanism of HCC. Hub genes, miRNAs, and candidate drugs provide a new direction for the early diagnosis and treatment of HCC.
Su Q
，Li W
，Zhang X
，Wu R
，Zheng K
，Zhou T
，Dong Y
，He Y
，Wang D
，Ran J
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ResearchGate
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钛学术
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