权重基因共表达网络分析在生物医学中的应用

高通量生物监测方法可以同时检测同一样本的上千个参数,其在生物医学中的应用越来越广泛,但如何系统地分析并从高通量数据中挖掘有用信息,仍是一项重要的课题。网络生物学的出现使人们对复杂生物系统有了更深刻的理解,组织/细胞功能执行具有模块化特点。目前,相关网络(Correlation network)被越来越多地应用于生物信息学,权重基因共表达网络分析(Weighted gene co-expression network analysis,WGCNA)是描述样品基因表达相关模式的一种系统生物学工具。在此,对WGCNA在疾病分型及预后、发病机制和其他相关领域研究进展作一个较为系统的综述。首先,对WGCNA的原理、分析流程和优势缺点进行总结。其次,介绍如何用WGCNA研究疾病、正常组织、药物、进化和基因组注释。最后,结合新高通量技术展望WGCNA应用新空间。以期科研工作者能够对WGCNA的应用有所了解。     

应用GSEA和WGCNA方法分析细菌性败血症宿主关键差异基因及意义

【目的】采用生物信息学方法分析公共数据库来源的细菌性败血症患者全血转录组学表达谱,探讨细菌败血症相关的宿主关键差异基因及意义。【方法】基于GEO数据库中GSE80496和GSE72829全血转录组基因数据集,采用GEO2R、基因集富集分析(GSEA)联用加权基因共表达网络分析(WGCNA)筛选细菌性败血症患者相比健康人群显著改变的差异基因,通过R软件对交集基因进行GO功能分析和KEGG富集分析。同时,通过String 11.0和Cytoscape分析枢纽基因,验证枢纽基因在数据集GSE72809(Health组52例,Definedsepsis组52例)全血标本中的表达情况,并探讨婴儿性别、月(胎)龄、出生体重、是否接触抗生素等因素与靶基因表达谱间的关系。【结果】分析GSE80496和GSE72829数据集分别筛选得到932个基因和319个基因,联合WGCNA枢纽模块交集得到与细菌性败血症发病相关的10个枢纽基因(MMP9、ITGAM、CSTD、GAPDH、PGLYRP1、FOLR3、OSCAR、TLR5、IL1RN和TIMP1);GSEA分析获得关键通路(氨基酸糖类-核糖代谢、PPAR信号通路、聚糖生物合成通路、自噬调控通路、补体、凝血因子级联反应、尼古丁和烟酰胺代谢、不饱和脂肪酸生物合成和阿尔兹海默症通路)及生物学过程(类固醇激素分泌、腺苷酸环化酶的激活、细胞外基质降解和金属离子运输)。【结论】本项研究通过GEO2R、GSEA联用WGCNA分析,筛选出与细菌性败血症发病相关的2个枢纽模块、10个枢纽基因以及一些关键信号通路和生物学过程,可为后续深入研究细菌性败血症致病机制奠定理论依据。     

基于转录组和加权基因共表达网络的广叶绣球菌木质纤维素降解特性分析

【目的】分析广叶绣球菌（Sparassis latifolia）在不同木质纤维素诱导条件下基因表达差异,为广叶绣球菌木质纤维素降解关键基因和分子机制研究提供参考。【方法】以松木、杉木、甘蔗渣和天然堆积发酵后的杉木和发酵后的甘蔗渣为碳源,在液体培养条件下培养诱导广叶绣球菌,对其转录组进行测序研究,并对不同木质纤维素诱导样本进行加权基因共表达网络分析（weighted gene co-expression network analysis,WGCNA）。【结果】杉木培养与松木培养比较组差异表达基因最少（20个）,蔗渣培养与松木培养比较组差异表达基因最多（486个）。基因本体（gene ontology,GO）富集分析结果表明,差异表达基因主要涉及氧化还原酶活性、单加氧酶活性和铁离子结合活性等,京都基因和基因组百科全书（Kyoto encyclopedia of genes and genomes,KEGG）通路富集分析结果表明,差异表达基因主要涉及戊糖和葡萄糖醛酸转换、甲烷代谢和乙醛酸盐和二羧酸盐代谢等通路。发酵甘蔗渣为碳源培养时,纤维素和半纤维素降解相关的糖苷水解酶基因表达量总体上较高,而未发酵的松木、杉木和甘蔗渣为碳源培养时木质素降解或修饰相关的碳水化合物辅助酶基因表达量总体上较高。利用WGCNA共鉴定出10个共表达模块,其中green模块与未发酵蔗渣诱导显著正相关,blue模块与发酵甘蔗渣诱导显著正相关,magenta和turquoise模块与发酵杉木诱导显著正相关。GO富集分析结果表明,turquoise模块内基因显著富集到尿素跨膜转运子活性、甲基转移酶活性和单加酶活性等,blue模块基因显著富集到水解酶活性和β-甘露糖苷酶活性。KEGG通路富集分析结果表明,blue模块内基因显著富集的通路有半乳糖代谢、果糖和甘露糖代谢、苯丙氨酸代谢、精氨酸和脯氨酸代谢等。通过构建互作网络图挖掘到12个核心基因,其可能参与了基质降解及相关基因的表达调控。【结论】不同木质纤维素类型显著影响了广叶绣球菌木质纤维素降解基因的差异表达轮廓,这种差异反映了广叶绣球菌对不同木质纤维素特异的降解策略。     

Systematic prediction of key genes for ovarian cancer by co-expression network analysis

Ovarian cancer (OC) is the most lethal gynaecological malignancy, characterized by high recurrence and mortality. However, the mechanisms of its pathogenesis remain largely unknown, hindering the investigation of the functional roles. This study sought to identify key hub genes that may serve as biomarkers correlated with prognosis. Here, we conduct an integrated analysis using the weighted gene co-expression network analysis (WGCNA) to explore the clinically significant gene sets and identify candidate hub genes associated with OC clinical phenotypes. The gene expression profiles were obtained from the MERAV database. Validations of candidate hub genes were performed with RNASeqV2 data and the corresponding clinical information available from The Cancer Genome Atlas (TCGA) database. In addition, we examined the candidate genes in ovarian cancer cells. Totally, 19 modules were identified and 26 hub genes were extracted from the most significant module (R² = .53) in clinical stages. Through the validation of TCGA data, we found that five hub genes (COL1A1, DCN, LUM, POSTN and THBS2) predicted poor prognosis. Receiver operating characteristic (ROC) curves demonstrated that these five genes exhibited diagnostic efficiency for early-stage and advanced-stage cancer. The protein expression of these five genes in tumour tissues was significantly higher than that in normal tissues. Besides, the expression of COL1A1 was associated with the TAX resistance of tumours and could be affected by the autophagy level in OC cell line. In conclusion, our findings identified five genes could serve as biomarkers related to the prognosis of OC and may be helpful for revealing pathogenic mechanism and developing further research.     

基于集成的共表达网络分析方法研究3种癌症的肿瘤相关模块

在肿瘤/癌旁基因表达数据中,差异表达 (DE,differential expression) 代表各种生物条件下基因表达水平的变化,而差异共表达 (DC,differential co-expression) 代表基因对之间相关系数的变化。单独的DC和DE研究方法已经被广泛应用于人类疾病研究中。但是,目前仍然缺乏有效整合DC和DE的分析方法。文中提出一个新颖的分析框架DC&DEmodule,该框架可以基于共表达模块整合DC和DE的特征,并同时整合多个肿瘤/癌旁表达谱的信息,用以识别与疾病相关的基因共表达模块,包括激活模块 (肿瘤样本中上调且共表达增强) 和失能模块 (肿瘤样本中下调且失去共表达)。将该框架用于分析肝癌、胃癌和结直肠癌各两组微阵列数据,分别得到肝癌、胃癌和结直肠癌的2、5和2个激活模块以及5、5和1个失能模块。富集分析表明与同类方法相比,文中的方法在检测已知的肿瘤相关通路和发现新通路方面均具有更高的灵敏度。然后,进一步从这3种癌症的激活模块中鉴定出17、69和11个模块关键基因,其中包含53个已报道的预后生物标志物以及3个分别与3种癌症存活率显著相关的新预后标志物。基于关键基因训练了3种癌症的随机森林模型,用于区分TCGA(The Cancer Genome Atlas) 和GEO (Gene Expression Omnibus)数据库中的肿瘤和癌旁样本,结果显示其分类的平均准确性达到了93%。三种癌症的比较为不同癌症的共有和组织特异性机制提供了新的见解。一系列评估表明,DC&DEmodule框架能够整合公共数据库中快速积累的表达谱,发现更多疾病中功能失调的生物过程。     

Identification of key lncRNAs in the carcinogenesis and progression of colon adenocarcinoma by co-expression network analysis

Colon adenocarcinoma (COAD) is one of the most common cancers, and its carcinogenesis and progression is influenced by multiple long non-coding RNAs (lncRNA), especially through the miRNA sponge effect. In this study, more than 4000 lncRNAs were re-annotated from the microarray datasets through probe sequence mapping to obtain reliable lncRNA expression profiles. As a systems biology method for describing the correlation patterns among genes across microarray samples, weighted gene co-expression network analysis was conducted to identify lncRNA modules associated with the five stepwise stages from normal colonic samples to COAD (n = 94). In the most relevant module (R² = −0.78, P = 4E-20), four hub lncRNAs were identified (CTD-2396E7.11, PCGF5, RP11-33O4.1, and RP11-164P12.5). Then, these four hub lncRNAs were validated using two other independent datasets including GSE20916 (n = 145) and GSE39582 (n = 552). The results indicated that all hub lncRNAs were significantly negatively correlated with the three-stage colonic carcinogenesis, as well as TNM stages in COAD (one-way analysis of variance P < 0.05). Kaplan-Meier survival curve showed that patients with higher expression of each hub lncRNA had a significantly higher overall survival rate and lower relapse risk (log-rank P < 0.05). In conclusion, through co-expression analysis, we identified and validated four key lncRNAs in association with the carcinogenesis and progression of COAD, and these lncRNAs might have important clinical implications for improving the risk stratification, therapeutic decision and prognosis prediction in COAD patients.     

Identification of hub genes in colorectal cancer based on weighted gene co-expression network analysis and clinical data from The Cancer Genome Atlas

Colorectal cancer (CRC) is one of the most common tumors worldwide and is associated with high mortality. Here we performed bioinformatics analysis, which we validated using immunohistochemistry in order to search for hub genes that might serve as biomarkers or therapeutic targets in CRC. Based on data from The Cancer Genome Atlas (TCGA), we identified 4832 genes differentially expressed between CRC and normal samples (1562 up-regulated and 3270 down-regulated in CRC). Gene ontology (GO) analysis showed that up-regulated genes were enriched mainly in organelle fission, cell cycle regulation, and DNA replication; down-regulated genes were enriched primarily in the regulation of ion transmembrane transport and ion homeostasis. Weighted gene co-expression network analysis (WGCNA) identified eight gene modules that were associated with clinical characteristics of CRC patients, including brown and blue modules that were associated with cancer onset. Analysis of the latter two hub modules revealed the following six hub genes: adhesion G protein-coupled receptor B3 (BAI3, also known as ADGRB3), cyclin F (CCNF), cytoskeleton-associated protein 2 like (CKAP2L), diaphanous-related formin 3 (DIAPH3), oxysterol binding protein-like 3 (OSBPL3), and RERG-like protein (RERGL). Expression levels of these hub genes were associated with prognosis, based on Kaplan–Meier survival analysis of data from the Gene Expression Profiling Interactive Analysis database. Immunohistochemistry of CRC tumor tissues confirmed that OSBPL3 is up-regulated in CRC. Our findings suggest that CCNF, DIAPH3, OSBPL3, and RERGL may be useful as therapeutic targets against CRC. BAI3 and CKAP2L may be novel biomarkers of the disease.     

Genetic susceptibility to heroin addiction: a candidate gene association study

Heroin addiction is a chronic complex disease with a substantial genetic contribution. This study was designed to identify genetic variants that are associated with susceptibility to develop heroin addiction by analyzing 1350 variants in 130 candidate genes. All subjects had Caucasian ancestry. The sample consisted of 412 former severe heroin addicts in methadone treatment, and 184 healthy controls with no history of drug abuse. Nine variants, in six genes, showed the lowest nominal P values in the association tests (P < 0.01). These variants were in noncoding regions of the genes encoding the mu (OPRM1; rs510769 and rs3778151), kappa (OPRK1; rs6473797) and delta (OPRD1; rs2236861, rs2236857 and rs3766951) opioid receptors; the neuropeptide galanin (GAL; rs694066); the serotonin receptor subtype 3B (HTR3B; rs3758987) and the casein kinase 1 isoform epsilon (CSNK1E; rs1534891). Several haplotypes and multilocus genotype patterns showed nominally significant associations (e.g. OPRM1; P = 0.0006 and CSNK1E; P = 0.0007). Analysis of a combined effect of OPRM1 and OPRD1 showed that rs510769 and rs2236861 increase the risk of heroin addiction (P = 0.0005). None of these associations remained significant after adjustment for multiple testing. This study suggests the involvement of several genes and variants in heroin addiction, which is worthy of future study.     

Integration of miRNA weighted gene co-expression network and miRNA-mRNA co-expression analyses reveals potential regulatory functions of miRNAs in calf rumen development

This study aimed to explore the roles of microRNAs (miRNAs) in calf rumen development during early life. Rumen tissues were collected from 16 calves (8 at pre-weaning and 8 at post-weaning) for miRNA-sequencing, differential expression (DE), miRNA weighted gene co-expression network (WGCNA) and miRNA-mRNA co-expression analyses. 295 miRNAs were identified. Bta-miR-143, miR-26a, miR-145 and miR-27b were the most abundantly expressed. 122 miRNAs were significantly DE between the pre- and post-weaning periods and the most up- and down-regulated miRNAs were bta-miR-29b and bta-miR-493, respectively. Enrichment analyses of the target genes of DE miRNAs revealed important roles for miRNA in rumen developmental processes, immune system development, protein digestion and processes related to the extracellular matrix. WGCNA indicated that bta-miR-145 and bta-miR-199a-3p are important hub miRNAs in the regulation of these processes. Therefore, bta-miR-143, miR-29b, miR-145, miR-493, miR-26a and miR-199 family members might be key regulators of calf rumen development during early life.     

Identification of gene expression profiles and key genes in subchondral bone of osteoarthritis using weighted gene coexpression network analysis

Osteoarthritis (OA) significantly influences the quality life of people around the world. It is urgent to find an effective way to understand the genetic etiology of OA. We used weighted gene coexpression network analysis (WGCNA) to explore the key genes involved in the subchondral bone pathological process of OA. Fifty gene expression profiles of GSE51588 were downloaded from the Gene Expression Omnibus database. The OA‐associated genes and gene ontologies were acquired from JuniorDoc. Weighted gene coexpression network analysis was used to find disease‐related networks based on 21756 gene expression correlation coefficients, hub‐genes with the highest connectivity in each module were selected, and the correlation between module eigengene and clinical traits was calculated. The genes in the traits‐related gene coexpression modules were subject to functional annotation and pathway enrichment analysis using ClusterProfiler. A total of 73 gene modules were identified, of which, 12 modules were found with high connectivity with clinical traits. Five modules were found with enriched OA‐associated genes. Moreover, 310 OA‐associated genes were found, and 34 of them were among hub‐genes in each module. Consequently, enrichment results indicated some key metabolic pathways, such as extracellular matrix (ECM)‐receptor interaction (hsa04512), focal adhesion (hsa04510), the phosphatidylinositol 3'‐kinase (PI3K)‐Akt signaling pathway (PI3K‐AKT) (hsa04151), transforming growth factor beta pathway, and Wnt pathway. We intended to identify some core genes, collagen (COL)6A3, COL6A1, ITGA11, BAMBI, and HCK, which could influence downstream signaling pathways once they were activated. In this study, we identified important genes within key coexpression modules, which associate with a pathological process of subchondral bone in OA. Functional analysis results could provide important information to understand the mechanism of OA.     

Weighted gene co‐expression network analysis can sort cancer‐associated fibroblast‐specific markers promoting bladder cancer progression

The role of cancer‐associated fibroblasts (CAFs) has been thoroughly investigated in tumour microenvironments but not in bladder urothelial carcinoma (BLCA). The cell fraction of CAFs gradually increased with BLCA progression. Weighted gene co‐expression network analysis (WGCNA) revealed a specific gene expression module of CAFs that are relevant to cancer progression and survival status. Fifteen key genes of the module were consistent with a fibroblast signature in single‐cell RNA sequencing, functionally related to the extracellular matrix, and significant in survival analysis and tumour staging. A comparison of the luminal‐infiltrated versus luminal‐papillary subtypes and fibroblast versus urothelial carcinoma cell lines and immunohistochemical data analysis demonstrated that the key genes were specifically expressed in CAFs. Moreover, these genes are highly correlated with previously reported CAF markers. In summary, CAFs play a major role in the progression of BLCA, and the 15 key genes act as BLCA‐specific CAF markers and can predict CAF changes. WGCNA can, therefore, be used to sort CAF‐specific gene set in cancer tissues.     

Deciphering Ascorbic Acid Regulatory Pathways in Ripening Tomato Fruit Using a Weighted Gene Correlation Network Analysis Approach

Genotype is generally determined by the co-expression of diverse genes and multiple regulatory pathways in plants. Gene co-expression analysis combining with physiological trait data provides very important information about the gene function and regulatory mechanism. L-Ascorbic acid （AsA）, which is an essential nutrient component for human health and plant metabolism, plays key roles in diverse biological processes such as cell cycle, cell expansion, stress resistance, hormone synthesis, and signaling. Here, we applied a weighted gene correlation network analysis approach based on gene expression values and AsA content data in ripening tomato （Solanum lycopersicum L.） fruit with different AsA content levels, which leads to identification of AsA relevant modules and vital genes in AsA regulatory pathways. Twenty- four modules were compartmentalized according to gene expression profiling. Among these modules, one negatively related module containing genes involved in redox processes and one positively related module enriched with genes involved in AsA biosynthetic and recycling pathways were further analyzed. The present work herein indicates that redox pathways as well as hormone-signal pathways are closely correlated with AsA accumulation in ripening tomato fruit, and allowed us to prioritize candidate genes for follow-up studies to dissect this interplay at the biochemical and molecular level.     

Identification and functional analysis of spermatogenesis-associated gene modules in azoospermia by weighted gene coexpression network analysis

Nonobstructive azoospermia (NOA) or testicular failure is the most severe form of male infertility. A variety of conditions, both acquired and congenital, can cause azoospermia. However, in a large number of azoospermia patients who are classified as idiopathic cases, the etiology remains poorly understand mainly due to the lack of knowledge of all the genetic causes and molecular mechanisms responsible for spermatogenesis failure. Identification of the key gene modules and pathways-related spermatogenesis failure might help to reveal the mechanisms of idiopathic azoospermia. Therefore, the expression patterns of spermatogenesis-associated genes in NOA were analyzed by weighted gene coexpression network analysis (WGCNA) based on two public microarray data sets (GSE45885 and GSE45887), which included 51 samples and 32,321 genes. We identified a module (turquoise) that was significantly related to the Johnsen score of the testicular samples. In addition, the results of function and pathway enrichment analyses based on the online bioinformatics database Metascape revealed that genes in the turquoise module were mainly related to the process of spermatogenesis and spermatid development. To further identify spermatogenesis-associated genes, a microarray data set (GSE926) of murine testis at different developmental time points was analyzed by WGCNA. The blue module in GSE926 was significantly related to the time of murine testis development. The overlap study and k-core analysis based on protein–protein interaction network revealed that spermatogenesis- and spermatid development–associated genes, including glyceraldehyde-3-phosphate dehydrogenase, ADAM metallopeptidase domain 2, transition protein 1, testis-specific serine kinase 2, transition protein 2, and germ cell-associated 1 (GSG1), were further identified in the selected modules. The expression profile of GSG1 in human testis was chosen for further study using immunochemistry staining. Taken together, these screened gene modules and pathways provided a more detailed genetic and molecular mechanism underlying spermatogenesis failure occurrence and holds promise as potential diagnosis biomarkers and therapeutic targets.     

利用加权基因共表达网络分析方法挖掘香菇发育不同阶段的关键基因

香菇是世界产量第二大食用菌,栽培历史悠久.在木屑袋料栽培模式下,香菇发育可以分为菌丝生长期(G)、菌丝褐化期(B)、原基形成期(P)以及出菇期(FB)4个阶段.褐化期和原基形成期是香菇从营养生长期到生殖生长两个关键发育阶段,对香菇子实体产量和质量至关重要.本研究以3种不同栽培材料为重复,对香菇发育的前3个阶段进行了转录...