Identification of Crucial Genes Associated With Immune Cell Infiltration in Hepatocellular Carcinoma by Weighted Gene Co-expression Network Analysis.

The dreadful prognosis of hepatocellular carcinoma (HCC) is primarily due to the low early diagnosis rate, rapid progression, and high recurrence rate. Valuable prognostic biomarkers are urgently needed for HCC. In this study, microarray data were downloaded from GSE14520, GSE22058, International Cancer Genome Consortium (ICGC), and The Cancer Genome Atlas (TCGA). Differentially expressed genes (DEGs) were identified among GSE14520, GSE22058, and ICGC databases. Weighted gene co-expression network analysis (WGCNA) was used to establish gene co-expression modules of DEGs, and genes of key modules were examined to identify hub genes using univariate Cox regression in the ICGC cohort. Expression levels and time-dependent receiver operating characteristic (ROC) and area under the curve (AUC) were determined to estimate the prognostic competence of the hub genes. These hub genes were also validated in the Gene Expression Profiling Interactive Analysis (GEPIA) and TCGA databases. TIMER algorithm and GSCALite database were applied to analyze the association of the hub genes with immunocytotic infiltration and their pathway enrichment. Altogether, 276 DEGs were identified and WGCNA described a unique and significantly DEGs-associated co-expression module containing 148 genes, with 10 hub genes selected by univariate Cox regression in the ICGC cohort (BIRC5, FOXM1, CENPA, KIF4A, DTYMK, PRC1, IGF2BP3, KIF2C, TRIP13, and TPX2). Most of the genes were validated in the GEPIA databases, except IGF2BP3. The results of multivariate Cox regression analysis indicated that the abovementioned hub genes are all independent predictors of HCC. The 10 genes were also confirmed to be associated with immune cell infiltration using the TIMER algorithm. Moreover, four-gene signature was developed, including BIRC5, CENPA, FOXM1, DTYMK. These hub genes and the model demonstrated a strong prognostic capability and are likely to be a therapeutic target for HCC. Moreover, the association of these genes with immune cell infiltration improves our understanding of the occurrence and development of HCC.

Identification of hub genes, key pathways, and therapeutic agents in Hutchinson-Gilford Progeria syndrome using bioinformatics analysis.

BACKGROUND: Hutchinson-Gilford Progeria syndrome (HGPS) is a rare lethal premature and accelerated aging disease caused by mutations in the lamin A/C gene. Nevertheless, the mechanisms of cellular damage, senescence, and accelerated aging in HGPS are not fully understood. Therefore, we aimed to screen potential key genes, pathways, and therapeutic agents of HGPS by using bioinformatics methods in this study. METHODS: The gene expression profile of GSE113648 and GSE41751 were retrieved from the gene expression omnibus database and analyzed to identify the differentially expressed genes (DEGs) between HGPS and normal controls. Then, gene ontology and the Kyoto encyclopedia of genes and genomes pathway enrichment analysis were carried out. To construct the protein-protein interaction (PPI) network, we used STRING and Cytoscape to make module analysis of these DEGs. Besides, the connectivity map (cMAP) tool was used as well to predict potential drugs. RESULTS: As a result, 180 upregulated DEGs and 345 downregulated DEGs were identified, which were significantly enriched in pathways in cancer and PI3K-Akt signaling pathway. The top centrality hub genes fibroblast growth factor 2, decorin, matrix metallopeptidase2, and Fos proto-oncogene, AP-1 transcription factor subunit were screened out as the critical genes among the DEGs from the PPI network. Dexibuprofen and parthenolide were predicted to be the possible agents for the treatment of HGPS by cMAP analysis. CONCLUSION: This study identified key genes, signal pathways and therapeutic agents, which might help us improve our understanding of the mechanisms of HGPS and identify some new therapeutic agents for HGPS.

MicroRNA-582-5p suppresses non-small cell lung cancer cells growth and invasion via downregulating NOTCH1.

Non-small cell lung cancer (NSCLC) is the most common cancer worldwide. MicroRNAs have been shown to be correlated with biological processes of various tumors. In this study, we observed that the expression of miR-582-5p was lower in NSCLC tissues than that in para-carcinoma tissues. Ectopic expression of miR-582-5p significantly inhibited NCI-H358 cell proliferation and invasion. Knockdown of miR-582-5p showed the opposite results, with cell growth rate and the invasive capacity of PC-9 cells enhanced. Furthermore, we elucidated that NOTCH1 is a target of miR-582-5p and there is an inverse correlation between miR-582-5p and NOTCH1 expression in NSCLC tissues. Overexpression of NOTCH1 in miR-582-5p-overexpressing NCI-H358 cells could partially reverse the inhibition of cell proliferation and invasion by miR-582-5p. Thus, our research demonstrated that miR-582-5p suppresses NSCLC cell lines' growth and invasion via targeting oncoprotein NOTCH1 and restoration of miR-582-5p might be feasible therapeutic strategy for NSCLC.