Mining featured biomarkers associated with prostatic carcinoma based on bioinformatics

Abstract

Objective: To analyze the differentially expressed genes and identify featured biomarkers from prostatic carcinoma.

Methods: The software “Significance Analysis of Microarray” (SAM) was used to identify the differentially coexpressed genes (DCGs). The DCGs existed in two datasets were analyzed by GO (Gene Ontology) functional annotation.

Results: A total of 389 DCGs were obtained. By GO analysis, we found these DCGs were closely related with the acinus development, TGF-β receptor and signal transduction pathways. Furthermore, five featured biomarkers were discovered by interaction analysis.

Conclusion: These important signal pathways and oncogenes may provide potential therapeutic targets for prostatic carcinoma.     

Immune-associated biomarkers for early diagnosis of Parkinson’s disease based on hematological lncRNA–mRNA co-expression

Early stage diagnosis of Parkinson’s disease (PD) is challenging without significant motor symptoms. The identification of effective molecular biomarkers as a hematological indication of PD may help improve the diagnostic timelines and accuracy. In the present paper, we analyzed and compared the blood samples of PD and control (CTR) patients to identify the disease-related changes and determine the putative biomarkers for PD diagnosis. Based on the RNA sequencing analysis, differentially expressed genes (DEGs) were identified, and the co-expression network of DEGs was constructed using the weighted gene correlation network analysis (WGCNA). The analysis leads to the identification of 87 genes that were exclusively regulated in the PD group, whereas 66 genes were significantly increased and 21 genes were significantly decreased in contrast with the control group. The results indicate that the core lncRNA–mRNA co-expression network greatly changes the immune response in PD patients. Specifically, the results showed that Prader Willi Angelman Region RNA6 (PWAR6), LINC00861, AC83843.1, IRF family, IFIT family and calcium/calmodulin-dependent protein kinase IV (CaMK4) may play important roles in the immune system of PD. Based on the findings from the present study, future research aims at identifying novel therapeutic strategies for PD.     

结节性甲状腺肿circRNA-miRNA-mRNA调控网络的构建

基于生物信息分析筛选结节性甲状腺肿中差异表达的环状RNA(circRNA),并揭示circRNA-miRNA-mRNA调控网络在结节性甲状腺肿中的作用。从GEO数据库中检索结节性甲状腺肿组织基因芯片数据,利用R软件筛选出差异表达的circRNA。联合多个生物信息数据库预测差异表达circRNA下游的miRNA及mRNA, 并对靶mRNA进行GO及KEGG富集分析。利用STRING在线数据库及Cytoscape软件筛选核心基因。确定了2个circRNA,42个miRNA及546个mRNA。GO及KEGG富集分析表明靶mRNA主要涉及细胞生长及基因表达调控过程。基于Cytoscape软件筛选出了14个核心基因(SP1、IGF1R、RPS6KB1、SMAD2、SMAD3、SMAD4、VEGFA、CCND1、CDK2、HSPA4、HIF1A、CREB1,NR3C1和STAT5A)。最终基于2个circRNA、11个miRNA和14个核心mRNA构建了circRNA-miRNA-mRNA调控网络。结节性甲状腺肿组织中异常表达的circRNA及相关的circRNA-miRNA-mRNA调控网络可能成为结节性甲状腺肿诊断与治疗的新靶点。     

基于转录组的肝豆状核变性调控网络的构建和分析

本研究旨在利用生物信息学方法构建经铜诱导的ATP7B基因敲除HepG2细胞系的转录调控网络。探讨关键转录因子在肝豆状核变性发生、发展中的潜在作用机制。收集公共基因表达数据库(gene expression omnibus, GEO)中包含野生型、ATP7B基因敲除型、铜诱导的野生型和铜诱导的ATP7B基因敲除型HepG2细胞系数据。筛选由铜诱导产生的差异表达基因(differentially expressed genes,DEGs)后进行基因本体论(gene ontology,GO)、京都基因和基因组百科全书(Kyoto encyclopedia of genes and genomes, KEGG)富集分析。基于蛋白相互作用网络,识别疾病关键基因和功能模块,并对关键功能模块中的基因进行富集分析。最后,构建转录调控网络,筛选核心转录因子。共筛选出1 034个差异表达基因,其中上调525个,下调509个。上、下调关键功能模块分别包括了3785个和3931个基因。关键功能模块中的基因主要定位于细胞-基质连接、染色体、剪接复合体、核糖体等区域,共同参与了mRNA加工、组蛋白修饰、RNA剪切...