加权共表达网络分析确定甲状腺乳头状癌淋巴结转移和远处转移相关核心基因

目的:筛选甲状腺乳头状癌(papillary thyroid carcinoma,PTC)淋巴结转移和远处转移相关核心基因.方法:对TCGA数据库中PTC的数据样本进行加权基因共表达网络分析(weighted gene co-expression network analysis,WGCNA),筛选出与PTC发生淋巴结...     

基因共表达网络分析及蛋白质相互作用筛选核分裂周期蛋白80与肺腺癌分期及预后的相关性

目的 通过基因共表达网络分析(weighted gene co-expression network analaysis,WGCNA)及蛋白质相互作用(protein-protein interaction,PPI)识别肺腺癌发生发展中的枢纽基因.方法 从TCGA数据库中下载497例肺腺癌组织和54例正常肺组织的RNA...     

空间与加权基因共表达分析揭示跨种属的缺血性心力衰竭机制

目的]从空间与基因共表达网络的角度揭示跨种属的缺血性心力衰竭机制。 [方法]从美国国立生物技术信息中心基因表达数据库(NCBI-GEO)中检索获得GSE210374和GSE57338高通量测序数据,利用R语言软件程序包分析筛选在心肌梗死大鼠不同心肌区域中差异表达基因(DEG)以及缺血性心力衰竭患者与健康对照者的心肌样本间的DEG,分析共同基因的分区域表达情况。利用加权基因共表达网络分析(WGCNA)筛选心肌梗死相关的基因并进行富集分析,构建蛋白质-蛋白质相互作用网络(PPI)筛选核心基因(HG)。 [结果]在心肌梗死大鼠和正常对照组中共筛选出4 835个差异基因,在缺血性心力衰竭患者和正常对照样本共筛选出51个差异基因,揭示心肌梗死后左心室心肌梗死区(I区)、边缘区(BZ区)及远端区(R区)的代表性基因集。空间表达分析发现两个种属样本:在每个心肌分区均有20个共表达基因,其中16个在3个分区均有表达,在I区、BZ区和R区特异表达的基因数分别为2、0和2个。富集分析显示:各分区的共表达基因功能各异,I、BZ区与胶原纤维组装、应激诱导的心肌细胞肥大、下调c-Jun氨基末端激酶(JNK信号)与细胞增殖等功能以及补体信号通路相关;而I、R区则富集于Wnt和胶原结合;作为非缺血的远端R区,共表达基因显著富集于细胞外基质的抗压性、细胞溶解以及抑制T细胞增殖等功能。此外值得注意的是:3个分区的共表达基因的产物大部分位于细胞外空间和细胞外基质当中,提示可能存在活化的细胞分泌与互作调控。进一步PPI分析提示无孢蛋白(ASPN)、骨诱导因子(OGN)和ⅩⅣ型胶原蛋白α链(COL14A1)基因可能是前述机制的核心基因。 [结论]大鼠和人类缺血性心力衰竭的共性机制涉及补体与凝血级联信号、Wnt等多种信号通路;可能与细胞外基质、外泌体介导的细胞凋亡密切相关;ASPN、OGN和COL14A1可能是其中的核心基因。本工作有望为缺血性心力衰竭相关转化研究中的干预靶点与路径选择提供空间与通路参考。     

Novel insights into the molecular mechanisms underlying risk of colorectal cancer from smoking and red/processed meat carcinogens by modeling exposure in normal colon organoids

Tobacco smoke and red/processed meats are well-known risk factors for colorectal cancer (CRC). Most research has focused on studies of normal colon biopsies in epidemiologic studies or treatment of CRC cell lines in vitro. These studies are often constrained by challenges with accuracy of self-report data or, in the case of CRC cell lines, small sample sizes and lack of relationship to normal tissue at risk. In an attempt to address some of these limitations, we performed a 24-hour treatment of a representative carcinogens cocktail in 37 independent organoid lines derived from normal colon biopsies. Machine learning algorithms were applied to bulk RNA-sequencing and revealed cellular composition changes in colon organoids. We identified 738 differentially expressed genes in response to carcinogens exposure. Network analysis identified significantly different modules of co-expression, that included genes related to MSI-H tumor biology, and genes previously implicated in CRC through genome-wide association studies. Our study helps to better define the molecular effects of representative carcinogens from smoking and red/processed meat in normal colon epithelial cells and in the etiology of the MSI-H subtype of CRC, and suggests an overlap between molecular mechanisms involved in inherited and environmental CRC risk.     

CEACAM6高表达在胃癌患者中的潜在临床价值

目的 CEACAM6可促进胃癌进展,但机制尚不明确,本研究旨在评价CEACAM6表达意义并初步探究其潜在分子通路。方法下载胃癌芯片和高通量测序数据,提取并计算CEACAM6 mRNA表达值标准化平均差（SMD）,结合人类蛋白质图谱免疫组化染色结果,从基因和蛋白层面验证胃癌CEACAM6表达水平。用汇总受试者工作特征曲线法评估CEACAM6表达对甄别胃癌的潜在价值。用基因差异表达分析鉴定胃癌中异常表达的CEACAM6共表达基因,并注释功能;基于富集通路构建蛋白质交互网络,寻找枢纽基因。最后比较胃癌患者CEACAM6和枢纽基因突变。结果 本研究共纳入胃癌数据集24个,整合了来自世界各地的1531例胃癌组织样本及686例正常胃组织样本。CEACAM6在胃癌组织中被明显上调[SMD=0.72(0.47～0.97)],对胃癌有中等区分能力[AUC=0.82(0.78～0.85)],但其高表达水平在不同性别、年龄及肿瘤分期的胃癌患者中无显著差异。CEACAM6共表达基因显著富集于“中间丝细胞骨架”“金属离子跨膜转运活性”与“胰腺分泌”中。SPRR3可能为CEACAM6共表达枢纽基因,与CEACAM...     

Construction of a co-expression network and prediction of metastasis markers in colorectal cancer patients with liver metastasis

BackgroundColorectal cancer (CRC) is a common global malignancy associated with high invasiveness, high metastasis, and poor prognosis. CRC commonly metastasizes to the liver, where the treatment of metastasis is both difficult and an important topic in current CRC management.MethodsMicroarrays data of human CRC with liver metastasis (CRCLM) were downloaded from the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) database to identify potential key genes. Differentially expressed (DE) genes (DEGs) and DEmiRNAs of primary CRC tumor tissues and metastatic liver tissues were identified. Microenvironment Cell Populations (MCP)-counter was used to estimate the abundance of immune cells in the tumor micro-environment (TME), and weighted gene correlation network analysis (WGCNA) was used to construct the co-expression network analysis. Gene Ontology and Kyoto Encyclopaedia of Gene and Genome (KEGG) pathway enrichment analyses were conducted, and the protein-protein interaction (PPI) network for the DEGs were constructed and gene modules were screened.ResultsThirty-five pairs of matched colorectal primary cancer and liver metastatic gene expression profiles were screened, and 610 DEGs (265 up-regulated and 345 down-regulated) and 284 DEmiRNAs were identified. The DEGs were mainly enriched in the complement and coagulation cascade pathways and renin secretion. Immune infiltrating cells including neutrophils, monocytic lineage, and cancer-associated fibroblasts (CAFs) differed significantly between primary tumor tissues and metastatic liver tissues. WGCN analysis obtained 12 modules and identified 62 genes with significant interactions which were mainly related to complement and coagulation cascade and the focal adhesion pathway. The best subset regression analysis and backward stepwise regression analysis were performed, and eight genes were determined, including F10, FGG, KNG1, MBL2, PROC, SERPINA1, CAV1, and SPP1. Further analysis showed four genes, including FGG, KNG1, CAV1, and SPP1 were significantly associated with CRCLM.ConclusionsOur study implies complement and coagulation cascade and the focal adhesion pathway play a significant role in the development and progression of CRCLM, and FGG, KNG1, CAV1, and SPP1 may be metastatic markers for its early diagnosis.     

基于加权基因共表达网络分析槲皮素抗胃癌的基因模块和分子标志物研究

目的:根据加权基因共表达网络(WGCNA)探索槲皮素抗胃癌的潜在生物靶标。方法:利用TCGA数据库下载胃癌相关转录组和临床数据,分析胃癌转录组表达的差异基因,利用网络药理学找到槲皮素的作用靶点,找出槲皮素与胃癌的hub基因。通过KEGG通路,GO基因富集分析寻找hub基因的靶点及通路,探索hub基因的在胃癌临床数据中的生存分析和性状表达的差异。结果:1)共发现槲皮素142个靶点,共139个蛋白质-蛋白质相互作用关系;2)KEGG通路富集分析发现槲皮素抗胃癌的作用通路可能与细胞衰老、MicroRNAs的表达、P53信号通路有关;3)生存分析发现纤溶酶原激活物抑制剂-1(SERPINE1)、微囊蛋白-1(Caveolin-1)、雄性激素受体(AR)、转录因子E2F2(E2F2)、前列腺素E2受体EP3亚型(PTGER3)在胃癌中的表达差异会影响患者的预后(P<0.05);4)在胃癌的TNM分期中,AR与PTGER3的表达差异与胃癌的分期相关(P<0.05);5)GEPIA数据库分析,AR与PTGER3在胃癌组织中的表达存在一定的相关性(P<0.05)。结论:槲皮素抗胃癌机制可能和细胞凋亡、MicroRNAs表达和P53信号通路有关,通过多靶点作用于SERPINE1、CAV1、AR、E2F2、PTGER3,发挥抑制癌细胞,改善患者预后的作用。     

羊水游离RNA中神经发育关键基因分析

目的分析孕中期羊水游离RNA(AfcfRNA)转录组,筛选神经发育共表达关键基因。方法利用基因共表达网络分析,建立AfcfRNA转录组的基因共表达网络模块。筛选各共表达模块中的神经特异性基因,建立神经系统特异性共表达模块。利用基因间相互作用筛选神经组织特异性共表达模块中的关键基因。结果通过加权基因共表达网络分析共建立27个以颜色命名的共表达模块,在Human Protein Atlas数据库中筛选到832个神经组织特异性基因。在蓝色、棕色、蓝绿色以及黄色模块中富集到前脑发育、神经突触组装和功能、神经递质释放过程、轴突发生以及学习和记忆过程相关的GO术语。通过基因间相互作用以及基因在孕中期的平均表达量分析,共发现蓝色模块(SLC18A3、TACR3、SYT2)、棕色模块(SSTR5、STX1A、SNAP25、GHSR、SSTR4、GABBR2)、蓝绿色模块(DRD2、SLC32A1、GNG3、OPN4、PENK)以及黄色模块(RAB3A、HCRT、GRM5)中的17个关键基因。结论该研究获得了神经系统发育密切相关的并且具有共表达关系的关键基因,可作为潜在的产前诊断中监测神经系统发育的标志物。     

PSMC2, ORC5 and KRTDAP are specific biomarkers for HPV-negative head and neck squamous cell carcinoma

The prognosis of patients with human papillomavirus (HPV)-negative head and neck squamous cell carcinoma (HNSCC) is poorer than those with HPV-positive HNSCC. The present study aimed to identify novel and specific biomarkers of HPV-negative HNSCC using bioinformatics analysis and associated experiments. The gene expression profiles of HPV-negative HNSCC tissues and corresponding clinical data were downloaded from The Cancer Genome Atlas database and used in a weighted gene co-expression network analysis. Genes in clinically significant co-expression modules were used to construct a protein-protein interaction (PPI) network. The genes demonstrating a high degree score in the PPI network and a high correlation with tumor grade were considered hub genes. The diagnostic value of the hub genes associated with HPV-negative and HPV-positive HNSCC was analyzed using differential expression gene (DEG) analysis, immunohistochemical (IHC) staining and a receiver operating characteristic (ROC) curve analysis. Seven genes [Serrate RNA effector molecule (SRRT), checkpoint kinase 2 (CHEK2), small nuclear ribonucleoprotein polypeptide E (SNRPE), proteasome 26S subunit ATPase 2 (PSMC2), origin recognition complex subunit 5 (ORC5), S100 calcium binding protein A7 and keratinocyte differentiation associated protein (KRTDAP)] were demonstrated to be hub genes in clinically significant co-expression modules. DEG, IHC and ROC curve analyses revealed that SRRT, CHEK2 and SNRPE were significantly upregulated in HPV-negative and HPV-positive HNSCC tissues compared with in adjacent tissues, and these genes demonstrated a high diagnostic value for distinguishing HNSCC tissues. However, PSMC2, ORC5 and KRTDAP were the only differentially expressed genes identified in HPV-negative HNSCC tissues, and these genes demonstrated a high diagnostic value for HPV-negative HNSCC. PSMC2, ORC5 and KRTDAP may therefore serve as novel and specific biomarkers for HPV-negative HNSCC, potentially improving the diagnosis and treatment of patients with HPV-negative HNSCC.