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Objective To screen key autophagy-related genes in alcoholic hepatitis (AH) and investigate potential biomarkers and therapeutic targets for AH. Methods Two AH gene chips in Gene Expression Omnibus (GEO) and autophagy-related data sets obtained from MSigDB and GeneCards databases were used, and the key genes were verified and obtained by weighted gene co-expression network analysis (WGCNA). The screened key genes were subject to gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), protein-protein interaction (PPI) and immune infiltration analyses. Messenger RNA (mRNA)- microRNA (miRNA) network was constructed to analyze the expression differences of key autophagy-related genes during different stages of AH, which were further validated by real-time fluorescence quantitative polymerase chain reaction (RT-qPCR) in the liver tissues of AH patients and mice. Results Eleven autophagy-related genes were screened in AH (EEF1A2, CFTR, SOX4, TREM2, CTHRC1, HSPB8, TUBB3, PRKAA2, RNASE1, MTCL1 and HGF), all of which were up-regulated. In the liver tissues of AH patients and mice, the relative expression levels of SOX4, TREM2, HSPB8 and PRKAA2 in the AH group were higher than those in the control group. Conclusions SOX4, TREM2, HSPB8 and PRKAA2 may be potential biomarkers and therapeutic targets for AH.
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@#[Abstract] Objective: To investigate the pathogenesis of prostate cancer by analyzing the associated hub gene modules of prostate cancer and identifying key transcription factors and genes that affect these modules. Methods: WGCNA (weighted gene co-expressed network analysis) was used to identify hub gene modules associated with important clinicopathological features of prostate cancer, such as pathological staging, Gleason grading etc. The OPOSSUM online tool was used to analyze the transcription factors enriching and regulating those genes. Pathway enrichment analysis and protein-protein interaction network analysis were used to identify key genes in prostate cancer. Finally, the effects of these genes on clinical features and disease-free survival (DFS) of prostate cancer patients were analyzed. Results: Three hub modules were identified, and they were highly associated with pathologic T stage, pathologic N stage and Gleason grading of prostate cancer, respectively. Further screening revealed 13 key dysregulated transcription factors that participated in the regulation of these three hub modules. The differentially expressed genes regulated by the 13 key transcription factors were significantly enriched in Calcium signaling pathway, cGMP-PKG signaling pathway and cAMP signaling pathway. 14 key genes (PRKG1, PRKG2, CYSLTR2, GRPR, CHRM3, ADCY5, ADRA1D, EDNRA, EDNRB, CYSLTR2, AGTR1, GRPR, GRIA1 and OXT) were at important nodes in the gene network. Among them, the high expression of ADRA1A, PRKG2, CHRM3, ADRA1D and EDN3 significantly extended the DFS of patients with prostate cancer (all P<0.01). Conclusion: ADRA1A, PRKG2, CHRM3, ADRA1D and EDN3 are regulated by key dysregulated transcription factors and highly associated with clinical features of prostate cancer. Their high expressions will significantly prolong the DFS of prostate cancer patients, which may shed light to the discovery of mechanism in prostate adenocarcinoma.
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OBJECTIVE@#In this study, we aimed to expand current knowledge of head and neck squamous cell carcinoma (HNSCC)-associated long noncoding RNAs (lncRNAs), and to discover potential lncRNA prognostic biomarkers for HNSCC based on next-generation RNA-seq.@*METHODS@#RNA-seq data of 546 samples from patients with HNSCC were downloaded from The Cancer Genome Atlas (TCGA), including 43 paired samples of tumor tissue and adjacent normal tissue. An integrated analysis incorporating differential expression, weighted gene co-expression networks, functional enrichment, clinical parameters, and survival analysis was conducted to discover HNSCC-associated lncRNAs. The function of CYTOR was verified by cell-based experiments. To further identify lncRNAs with prognostic significance, a multivariate Cox proportional hazard regression analysis was performed. The identified lncRNAs were validated with an independent cohort using clinical feature relevance analysis and multivariate Cox regression analysis.@*RESULTS@#We identified nine HNSCC-relevant lncRNAs likely to play pivotal roles in HNSCC onset and development. By functional enrichment analysis, we revealed that CYTOR might participate in the multistep pathological processes of cancer, such as ribosome biogenesis and maintenance of genomic stability. CYTOR was identified to be positively correlated with lymph node metastasis, and significantly negatively correlated with overall survival (OS) and disease free survival (DFS) of HNSCC patients. Moreover, CYTOR inhibited cell apoptosis following treatment with the chemotherapeutic drug diamminedichloroplatinum (DDP). HCG22, the most dramatically down-regulated lncRNA in tumor tissue, may function in epidermis differentiation. It was also significantly associated with several clinical features of patients with HNSCC, and positively correlated with patient survival. CYTOR and HCG22 maintained their prognostic values independent of several clinical features in multivariate Cox hazards analysis. Notably, validation either based on an independent HNSCC cohort or by laboratory experiments confirmed these findings.@*CONCLUSIONS@#Our transcriptomic analysis suggested that dysregulation of these HNSCC-associated lncRNAs might be involved in HNSCC oncogenesis and progression. Moreover, CYTOR and HCG22 were confirmed as two independent prognostic factors for HNSCC patient survival, providing new insights into the roles of these lncRNAs in HNSCC as well as clinical applications.