Identification of dynamic signatures associated with smoking‐related squamous cell lung cancer and chronic obstructive pulmonary disease

Chronic obstructive pulmonary disease (COPD) is a risk factor for the development of lung cancer. The aim of this study was to identify early diagnosis biomarkers for lung squamous cell carcinoma (SQCC) in COPD patients and to determine the potential pathogenetic mechanisms. The GSE12472 data set was downloaded from the Gene Expression Omnibus database. Differentially co‐expressed links (DLs) and differentially expressed genes (DEGs) in both COPD and normal tissues, or in both SQCC + COPD and COPD samples were used to construct a dynamic network associated with high‐risk genes for the SQCC pathogenetic process. Enrichment analysis was performed based on Gene Ontology annotations and Kyoto Encyclopedia of Genes and Genomes pathway analysis. We used the gene expression data and the clinical information to identify the co‐expression modules based on weighted gene co‐expression network analysis (WGCNA). In total, 205 dynamic DEGs, 5034 DLs and one pathway including CDKN1A, TP53, RB1 and MYC were found to have correlations with the pathogenetic progress. The pathogenetic mechanisms shared by both SQCC and COPD are closely related to oxidative stress, the immune response and infection. WGCNA identified 11 co‐expression modules, where magenta and black were correlated with the “time to distant metastasis.” And the “surgery due to” was closely related to the brown and blue modules. In conclusion, a pathway that includes TP53, CDKN1A, RB1 and MYC may play a vital role in driving COPD towards SQCC. Inflammatory processes and the immune response participate in COPD‐related carcinogenesis.     

Network cluster analysis of protein–protein interaction network identified biomarker for early onset colorectal cancer

Colorectal cancer (CRC) is a major cause of morbidity and mortality throughout the world. However, the genetic alterations and molecular mechanism of the early onset CRCs are not fully investigated. The present study aimed to characterize early onset CRC by analyzing its gene expression compared with normal controls and to identify network-based biomarkers of early onset CRC. The gene expression profiles of early onset CRC were downloaded from Gene Expression Omnibus and the differentially expressed genes (DEGs) in CRC patients were identified. Then, a protein–protein interaction (PPI) network was constructed and the clusters in PPI were analyzed by ClusterONE. Furthermore, the gene ontology functional analysis and pathway enrichment analysis were conducted to the modules in PPI network. A systems biology approach integrating microarray data and PPI was further applied to construct a PPI network in CRC. Total 631 DEGs were identified from the early onset CRC compared to healthy controls. These genes were found to be involved in several biological processes, including cell communication, cell proliferation, cell shape and apoptosis. Five functional modules which may play important roles in the initiation of early onset CRC were identified from the PPI network. Functional annotation revealed that these five modules were involved in the pathways of signal transduction, carcinogenesis and metastasis. The hub nodes of these five modules, CDC42, TEX11, QKI, CAV1 and FN1, may serve as the biomarkers of early onset CRC and have the potential to be targets for therapeutic intervention. However, further investigations are still needed to confirm our findings.     

PNME – A gene-gene parallel network module extraction method

In the domain of gene-gene network analysis, construction of co-expression networks and extraction of network modules have opened up enormous possibilities for exploring the role of genes in biological processes. Through such analysis, one can extract interesting behaviour of genes and would help in the discovery of genes participating in a common biological process. However, such network analysis methods in sequential processing mode often have been found time-consuming even for a moderately sized dataset.It is observed that most existing network construction techniques are capable of handling only positive correlations in gene-expression data whereas biologically-significant genes exhibit both positive and negative correlations. To address these problems, we propose a faster method for construction and analysis of gene-gene network and extraction of modules using a similarity measure which can identify both negatively and positively correlated co-expressed patterns. Our method utilizes General-purpose computing on graphics processing units (GPGPU) to provide fast, efficient and parallel extraction of biologically relevant network modules to support biomarker identification for breast cancer. The modules extracted are validated using p-value and q-value for both metastasis and non-metastasis stages of breast cancer. PNME has been found capable of identifying interesting biomarkers for this critical disease. We identified six genes with the interesting behaviours which have been found to cause breast cancer in homo-sapiens.     

Identification of candidate disease genes in patients with common variable immunodeficiency

Background: Common variable immunodeficiency (CVID), the most prevalent form of primary immunodeficiency (PID), is characterized by hypogammaglobulinemia and recurrent infections. Understanding protein-protein interaction (PPI) networks of CVID genes and identifying candidate CVID genes are critical steps in facilitating the early diagnosis of CVID. Here, the aim was to investigate PPI networks of CVID genes and identify candidate CVID genes using computation techniques. Methods: Network density and biological distance were used to study PPI data for CVID and PID genes obtained from the STRING database. Gene expression data of patients with CVID were obtained from the Gene Expression Omnibus, and then Pearson’s correlation coefficient, a PPI database, and Kyoto Encyclopedia of Genes and Genomes were used to identify candidate CVID genes. We then evaluated our predictions and identified differentially expressed CVID genes. Results: The majority of CVID genes are characterized by a high network density and small biological distance, whereas most PID genes are characterized by a low network density and large biological distance, indicating that CVID genes are more functionally similar to each other and closely interact with one other compared with PID genes. Subsequently, we identified 172 CVID candidate genes that have similar biological functions to known CVID genes, and eight genes were recently reported as CVID-related genes. MYC, a candidate gene, was down-regulated in CVID duodenal biopsies, but up-regulated in blood samples compared with levels in healthy controls. Conclusion: Our findings will aid in a better understanding of the complex of CVID genes, possibly further facilitating the early diagnosis of CVID.     

Fast and accurate method for identifying high-quality protein-interaction modules by clique merging and its application to yeast

Molecular networks in cells are organized into functional modules, where genes in the same module interact densely with each other and participate in the same biological process. Thus, identification of modules from molecular networks is an important step toward a better understanding of how cells function through the molecular networks. Here, we propose a simple, automatic method, called MC(2), to identify functional modules by enumerating and merging cliques in the protein-interaction data from large-scale experiments. Application of MC(2) to the S. cerevisiae protein-interaction data produces 84 modules, whose sizes range from 4 to 69 genes. The majority of the discovered modules are significantly enriched with a highly specific process term (at least 4 levels below root) and a specific cellular component in Gene Ontology (GO) tree. The average fraction of genes with the most enriched GO term for all modules is 82% for specific biological processes and 78% for specific cellular components. In addition, the predicted modules are enriched with coexpressed proteins. These modules are found to be useful for annotating unknown genes and uncovering novel functions of known genes. MC(2) is efficient, and takes only about 5 min to identify modules from the current yeast gene interaction network with a typical PC (Intel Xeon 2.5 GHz CPU and 512 MB memory). The CPU time of MC(2) is affordable (12 h) even when the number of interactions is increased by a factor of 10. MC(2) and its results are publicly available on http://theory.med.buffalo.edu/MC2.     

转录因子及miRNA调控食管癌耐药机制研究

由于耐药性的存在,不同患者在使用相同药物时会导致治疗效果的差异.因此识别耐药性相关的关键生物学标记,有助于临床医生快速选择出适合的药物,延长患者的生存时间,对药物研发以及药物的作用机制的详细研究具有重要意义.首先在食管癌细胞系中筛选不同药物的耐药及敏感细胞系,从中找到不同药物耐药相关的基因,将这些计算得到的耐药相关基因...     

LncRNAs related key pathways and genes in ischemic stroke by weighted gene co-expression network analysis (WGCNA)

BackgroundIschemic stroke (IS) was a significant public health concern and long-chain noncoding RNAs (lncRNAs) were gaining particular importance in stroke biology, however, the potential mechanism of lncRNAs in IS was not fully understood.MethodsIn this study, three diagnosed patients with IS and three controls were selected to establish the lncRNA library. Weighted gene co-expression network analysis (WGCNA) was applied to screen key lncRNA modules associated with IS. The key lncRNAs were identified by module membership (MM) and gene significance (GS). The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was used to identify the key pathways and protein-protein interaction (PPI) network method was used to identify the key genes.ResultsA total of 3627 lncRNAs were investigated, followed by an analysis of 17 modules, and only one module was highly associated with the IS. The top 10 lncRNAs were identified based on GS and MM. KEGG pathways analysis revealed the top two pathways of the Human T cell Lymphotropic Virus-1 (HTLV-1) infection and the mTOR signaling pathway might influence the progress of IS. Further, genes meeting the top two degree (AKT1 and MAPK14) were selected as the hub genes in the PPI network.ConclusionTo summarize, this study identified the key pathways and genes, which might serve as biomarkers and targets for precise diagnosis and treatment of IS in the future.     

Identification of an extracellular vesicle-related gene signature in the prediction of pancreatic cancer clinical prognosis

Although extracellular vesicles (EVs) in body fluid have been considered to be ideal biomarkers for cancer diagnosis and prognosis, it is still difficult to distinguish EVs derived from tumor tissue and normal tissue. Therefore, the prognostic value of tumor-specific EVs was evaluated through related molecules in pancreatic tumor tissue. NA sequencing data of pancreatic adenocarcinoma (PAAD) were acquired from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC). EV-related genes in pancreatic cancer were obtained from exoRBase. Protein–protein interaction (PPI) network analysis was used to identify modules related to clinical stage. CIBERSORT was used to assess the abundance of immune and non-immune cells in the tumor microenvironment. A total of 12 PPI modules were identified, and the 3-PPI-MOD was identified based on the randomForest package. The genes of this model are involved in DNA damage and repair and cell membrane-related pathways. The independent external verification cohorts showed that the 3-PPI-MOD can significantly classify patient prognosis. Moreover, compared with the model constructed by pure gene expression, the 3-PPI-MOD showed better prognostic value. The expression of genes in the 3-PPI-MOD had a significant positive correlation with immune cells. Genes related to the hypoxia pathway were significantly enriched in the high-risk tumors predicted by the 3-PPI-MOD. External databases were used to verify the gene expression in the 3-PPI-MOD. The 3-PPI-MOD had satisfactory predictive performance and could be used as a prognostic predictive biomarker for pancreatic cancer.     

伴HBV感染性肝癌调控枢纽基因筛选与初步鉴定

慢性乙型肝炎病毒(Hepatitis B virus,HBV)感染引起的原发性肝癌涉及多种基因、转录本和蛋白质的相互作用及调控。从单个基因的角度来看,某个基因的表达量的改变只能对肝癌发生发展的局部作出解释而无法从整体行为进行深入和全面的探索,无法满足高度复杂性的调控研究需要。筛选乙肝相关性肝癌的基因芯片数据获取差异表达基因后,应用加权基因共表达网络分析算法构建基因共表达网络,识别与肝癌发生相关的模块,利用可视化筛选枢纽基因,并针对枢纽基因进行基因本体富集分析和初步验证。富集分析和文献挖掘一致发现,某些枢纽基因确实与多种癌症的发生与发展存在显著的关联。权重基因共表达网络分析方法被证明是一个高效的系统生物学方法,应用该方法发现了新的HBV相关性肝癌枢纽基因。经实验验证,发现枢纽基因SHARPIN促进细胞迁移。该研究对肝癌发生的调控机制以及发现HBV慢性感染导致肝癌的新型诊断标志物和(或)药物作用靶点提供了新的视野。     

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.     

紫外辐照恢复早期耐辐射奇球菌的转录组学分析（英文）

目的 耐辐射奇球菌是一种对紫外线、电离、干燥和化学试剂具有较强抗性的极端微生物。然而，该菌在紫外辐照后恢复早期的分子响应还不完全清楚。本文的目的是揭示耐辐射奇球菌在这一阶段的转录组响应。方法 本研究采用RNA-seq技术，测定了正常和紫外辐照培养条件下耐辐射奇球菌的转录组。为确定关键的差异表达基因及其调控关系，进行了功能富集分析。选取部分关键差异表达基因，进行实时定量PCR实验验证。利用以往研究中的转录组数据，寻找紫外辐照、电离辐射和干燥胁迫条件下公共的差异表达基因。构建了蛋白质-蛋白质相互作用网络；对蛋白质互作网络中的枢纽基因和主要模块进行了鉴定；对这些枢纽基因和模块进行了功能富集分析。结果 紫外辐照后的恢复早期，上调基因数量是下调基因数量的2倍以上，且多数与应激反应和DNA修复有关。恢复早期的修复途径主要有单链退火（SSA）途径（涉及基因：ddr A-D）、非同源端连接（NHEJ）途径（涉及基因：lig B、ppr A）和核苷酸切除修复（NER）途径（涉及基因：uvr A-C），前两种途径为同源重组（HR）做准备，而NER途径去除紫外线照射带来的嘧啶二聚体。通过比较紫外辐照、电离辐...     

Identification of key candidate biomarkers for severe influenza infection by integrated bioinformatical analysis and initial clinical validation

One of the key barriers for early identification and intervention of severe influenza cases is a lack of reliable immunologic indicators. In this study, we utilized differentially expressed genes screening incorporating weighted gene co-expression network analysis in one eligible influenza GEO data set ( GSE111368 ) to identify hub genes associated with clinical severity. A total of 10 genes (PBI, MMP8, TCN1, RETN, OLFM4, ELANE, LTF, LCN2, DEFA4 and HP) were identified. Gene set enrichment analysis (GSEA) for single hub gene revealed that these genes had a close association with antimicrobial response and neutrophils activity. To further evaluate these genes' ability for diagnosis/prognosis of disease developments, we adopted double validation with (a) another new independent data set ( GSE101702 ); and (b) plasma samples collected from hospitalized influenza patients. We found that 10 hub genes presented highly correlation with disease severity. In particular, BPI and MMP8 encoding proteins in plasma achieved higher expression in severe and dead cases, which indicated an adverse disease development and suggested a frustrating prognosis. These findings provide new insight into severe influenza pathogenesis and identify two significant candidate genes that were superior to the conventional clinical indicators. These candidate genes or encoding proteins could be biomarker for clinical diagnosis and therapeutic targets for severe influenza infection.     

Screening of differentially expressed miRNAs related to muscle strain and their target gene

Muscle strain is one of the most common muscle injuries seen in the office of a practicing physician. To get a better understanding of this injury, we identified the differentially expressed miRNAs in muscle stem cells collected from injured muscle tissues of mouse. In this study, we downloaded the gene expression microarray (GSE26780) from Gene Expression Omnibus database. The dataset contained a total of 12 samples (murine muscle stem cells), including normal controls and samples collected from tissues at different time points after the injury. Differentially expreesed miRNAs were identified by LIMMA package and target genes of mmu-miR-143 were found by TargetScan. Then, a protein-protein interaction (PPI) network was constructed for the products of these target genes by using KUPS. Finally, Cytoscape and its plugins were used to identify and analyze the modules in this network. According to the results, 121, 136 and 148 differentially expressed miRNAs were identified in injured samples at each time point, and among them, 60 miRNAs were overlapping between all three groups. The expression values of mmu-miR-143 were most significantly altered over time at 36–72 h after the injury. Therefore, 510 target genes of mmu-miR-143 were found and a PPI network for the products of these target genes was constructed. Moreover, two modules were identified in the PPI network. Together with the previous studies, we suppose that proteins in module B, most of which are collagens or integrins, most likely participate in healing of strain injuries through cell adhesion processes.     

Identification of key genes and pathways of thyroid cancer by integrated bioinformatics analysis

Thyroid cancer is a common endocrine malignancy with a rapidly increasing incidence worldwide. Although its mortality is steady or declining because of earlier diagnoses, its survival rate varies because of different tumour types. Thus, the aim of this study was to identify key biomarkers and novel therapeutic targets in thyroid cancer. The expression profiles of GSE3467, GSE5364, GSE29265 and GSE53157 were downloaded from the Gene Expression Omnibus database, which included a total of 97 thyroid cancer and 48 normal samples. After screening significant differentially expressed genes (DEGs) in each data set, we used the robust rank aggregation method to identify 358 robust DEGs, including 135 upregulated and 224 downregulated genes, in four datasets. Gene Ontology and Kyoto Encyclopaedia of Genes and Genomes pathway enrichment analyses of DEGs were performed by DAVID and the KOBAS online database, respectively. The results showed that these DEGs were significantly enriched in various cancer-related functions and pathways. Then, the STRING database was used to construct the protein–protein interaction network, and modules analysis was performed. Finally, we filtered out five hub genes, including LPAR5, NMU, FN1, NPY1R, and CXCL12, from the whole network. Expression validation and survival analysis of these hub genes based on the The Cancer Genome Atlas database suggested the robustness of the above results. In conclusion, these results provided novel and reliable biomarkers for thyroid cancer, which will be useful for further clinical applications in thyroid cancer diagnosis, prognosis and targeted therapy.