Study of lung cancer regulatory network that involves erbB4 and tumor marker gene

Our purpose is to screen out serum tumor markers closely correlated to the nature of solitary pulmonary nodule (SPN) and to draw a regulatory network containing genes correlated to lung cancer. Two hundred and sixty cases of SPN patients confirmed through pathological diagnosis were collected as subjects, factors closely correlated to the nature of SPN were screened out from eight tumor markers through Fisher discriminant method, and functional annotation and pathway analysis were conducted on erbB4 as well as its tumor marker genes by GO and KEGG databases. Four key tumor markers: CYFRA21-1, CA125, SCC-Ag and CA153 were successfully screened out and the first three proteins’ corresponding gene were KRT19, MUC16 and SERPINB3 while that of CA153 was not found. GO analysis on erbB4, KRT19, MUC16 and SERPINB3 showed that they covered three domains, cell components, molecular function and biological process; meanwhile, combined with KEGG database and based on signal pathway of erbB4, a regulatory network of lung cancer cells escaping from apoptosis was successfully made. This study indicates that serum tumor marker genes play an important role in the occurrence and development of lung cancer, besides, this study primarily discussed the molecular mechanism of these tumor markers in predicting tumor, which provides a basis for in-depth information about lung cancer.     

Establishment of optimal regulatory network of colorectal cancer based on p42.3 protein

Objective: to establish regulatory network of colorectal cancer involving p42.3 protein and to provide theoretical evidence for deep functional exploration of p42.3 protein in the onset and development of colorectal cancer. Methods: with protein similarity algorithm, reference protein set of p42.3 cell apoptosis was built according to structural features of p42.3. GO and KEGG databases were used to establish regulatory network of tumor cell apoptosis involving p42.3; meanwhile, the largest possible working pathway that involves p42.3 protein was screened out based on Bayesian network theory. Besides, GO and KEGG were used to build regulatory network on early diagnosis gene markers for colorectal cancer including WWOX, K-ras, COX-2, p53, APC, DCC and PTEN, at the same time, a regulatory network of colorectal cancer cell apoptosis which involves p42.3 was established. Results: cell apoptotic regulatory network that p42.3 participates in primarily consists of Bcl-2 family genes and the largest possible pathway is p42.3 → FKBP → Bcl-2 centered as FKBP protein. Combined with colorectal cancer regulatory network that involves early diagnosis gene markers, it can be predicted that p42.3 is most likely to regulate the colorectal cancer cell apoptosis through FKBP → Bcl-2 → Bax → caspase-9 → caspase-3 pathway. Conclusion: the colorectal cancer apoptosis network based on p42.3 established in the study provides theoretical evidence for deep exploration of p42.3 regulatory mechanism and molecular targeting treatment of colorectal cancer.     

Construction of a mitochondrial-associated protein DRP1 and a lung cancer-associated protein Erbb4 combined regulatory network

ObjectiveThis research was to establish a mitochondrial-related Drp1 gene and a lung cancer-related Erbb4 gene to participate in the regulatory network of lung cancer cell apoptosis, and to provide theoretical support for mitochondria to participate in tumor regulation.MethodThe GO and KEGG methods were used to construct the regulatory networks of lung cancer related Drp1 and Erbb4 proteins that involved in the apoptosis of tumor cells, and to combine with the Bayesian network theory to screen out the largest possible action path acting on this network; The information about Drp1 in Oncomine database was collected, and the data in current database were analyzed twice. The role of Drp1 in lung cancer was meta-analyzed.ResultA regulatory network of Drp1 and Erbb4 involved in the apoptosis of tumor cells was successfully constructed; the optimal pathway was optimized using Bayesian theory; a total of 446 different types of research results were collected in the Oncomine database, of which there were 18 studies with statistical differences in Drp1 expression, 13 studies with increased Drp1’s expression, and 5 studies with decreased expression. Compared with the control group, Drp1 was expressed in lung cancer tissues highly (P < 0.05).ConclusionEstablishment and optimization of mitochondrial-related Drp1 and tumor-related Erbb4 genes involved in the regulation of apoptosis of cancer cells. It was proposed that Drp1 was expressed in lung cancer tissues highly through in-depth excavation of tumor-associated gene information in the Oncomine gene chip database.     

肾透明细胞癌Caki-1细胞系差异表达基因的生物信息学分析

目的比较肾透明细胞癌Caki-1细胞系与正常肾上皮细胞系ASE-5063中的差异表达基因（DEGs）,寻找潜在的肾透明细胞癌特异性分子标志物。 方法利用GEO数据库自带的GEO2R在线分析工具分析基因芯片GSE78179,将筛选出的DEGs分别导入Metascape、STRING以及Cytoscape进行综合分析并筛选出核心基因。最后使用FunRich等软件对筛选出的核心基因进行GO和KEGG富集分析。 结果共筛选出562个DEGs,其中上调基因345个,下调基因217个。进一步使用MCODE筛选出36个关键基因,GO功能分析发现这些基因与细胞粘附分子活性、趋化因子活性、细胞通讯和信号转导等密切相关;KEGG通路富集结果则表明差异基因主要集中在趋化因子信号通路、TNF信号通路以及NF-κB信号通路等多种与肿瘤相关的通路上。 结论运用生物信息学方法筛选出肾透明细胞癌Caki-1细胞系中DEGs,其中数个核心基因广泛参与多种肿瘤的病理进程,但尚未在肾透明细胞癌有相关研究报道,提示其可能是治疗肾透明细胞癌的潜在靶点。     

Glioma stages prediction based on machine learning algorithm combined with protein-protein interaction networks

BackgroundGlioma is the most lethal nervous system cancer. Recent studies have made great efforts to study the occurrence and development of glioma, but the molecular mechanisms are still unclear. This study was designed to reveal the molecular mechanisms of glioma based on protein-protein interaction network combined with machine learning methods. Key differentially expressed genes (DEGs) were screened and selected by using the protein-protein interaction (PPI) networks.ResultsAs a result, 19 genes between grade I and grade II, 21 genes between grade II and grade III, and 20 genes between grade III and grade IV. Then, five machine learning methods were employed to predict the gliomas stages based on the selected key genes. After comparison, Complement Naive Bayes classifier was employed to build the prediction model for grade II-III with accuracy 72.8%. And Random forest was employed to build the prediction model for grade I-II and grade III-VI with accuracy 97.1% and 83.2%, respectively. Finally, the selected genes were analyzed by PPI networks, Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, and the results improve our understanding of the biological functions of select DEGs involved in glioma growth. We expect that the key genes expressed have a guiding significance for the occurrence of gliomas or, at the very least, that they are useful for tumor researchers.ConclusionMachine learning combined with PPI networks, GO and KEGG analyses of selected DEGs improve our understanding of the biological functions involved in glioma growth.     

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。     

Establishment of axon regeneration regulatory network and the role of low intensity pulsed ultrasound in the network

ObjectiveTo establish an axon regeneration regulatory network for optimal selection, and explore the role of low intensity pulsed ultrasound in the network.MethodsThe axon regeneration regulatory network involving axon regeneration-related proteins NGF, BDNF and PirB was constructed by using GO and KEGG. The maximum possible pathway acting on axon regeneration was screened by Bayesian network theory. The node of low - intensity pulsed ultrasound in NGF - involved axon regeneration network was complemented by combining literature methods.ResultsThe NGF, BDNF and PirB-involved axonal regeneration regulatory pathway was successfully constructed. The low intensity pulsed ultrasound played a role in axon regeneration by acting on ERK1/2-CREB pathway and GSK-3β. NGF-TrKA-Rap1-ERK1/2-CREB-Bcl-2 was optimized as optimal pathway by Bayesian theory.ConclusionThe regulatory pathway of axon regeneration involving nerve growth related factors and low intensity pulsed ultrasound was initially established, which provided a theoretical basis for further study of axon regeneration, and also new ideas for action of low intensity pulsed ultrasound on axon regeneration regulatory pathway.     

Association between ACE gene polymorphism and carotid stenosisand and construction of related gene regulatory networks

ObjectiveTo investigate the association between DD, ID and II genotypes of ACE gene and carotid stenosis, and to construct a network of ACE-related genes regulating carotid stenosis.ApproachesMeta-analysis was used to study the association between three genotypes DD, ID and II of carotid stenosis susceptibility gene ACE; functional annotation of ACE gene was conducted by GO analysis; and a network of ACE-related genes that regulate the mechanisms of carotid stenosis was established.ResultsMeta-analysis showed that DD and II genotypes of ACE gene were associated with carotid stenosis. GO analysis showed that the main biological processes involved in ACE include: the process of transforming angiotensinogen into mature angiotensin; angiotensin’s mediation of the brain's response to alcohol consumption and thirst control; any chemical reaction involving the regulation of angiotensin; and the process of catalyzing the release of a C-terminal dipeptide from a polypeptide chain. A network of ACE gene regulation of carotid stenosis was constructed in combination with KEGG analysis.ConclusionThe ACE gene is a susceptibility gene for carotid stenosis. Through the functional annotation and pathway analysis of ACE gene, an ACE gene-involved carotid stenosis regulatory mechanisms network was constructed.     

Prognostic value of metabolic genes in lung adenocarcinoma via integrative analyses

BackgroundLung adenocarcinoma (LUAD) is the most common malignant lung tumor. Metabolic pathway reprogramming is an important hallmark of physiologic changes in cancers. However, the mechanisms through which these metabolic genes and pathways function in LUAD as well as their prognostic values have not been fully established.MethodsFour publicly available datasets from GEO and TCGA were used to identify differentially expressed genes (DEGs) in LUAD, which were then subjected to GO and KEGG pathway enrichment analysis. Associations between metabolic gene expressions with overall survival, tumor stage, TP53 mutation status, and infiltrated immune cells were investigated. Protein-protein interactions were evaluated using GeneMANIA and Metascape.ResultsBy integrating four public datasets, 247 DEGs were identified in LUAD. These DEGs were significantly enriched in regulation of chromosome segregation, centromeric region, and histone kinase activity GO terms, as well as in cell cycle, p53 signaling pathway, metabolic pathways, and other KEGG pathways. Elevated expressions of ten metabolic genes in LUAD were significantly associated with poor survival outcomes. These metabolic genes were highly expressed in more advanced tumor stage and TP53 mutated patients. Moreover, expression levels were significantly correlated with tumor-infiltrating immune cells. PPI interaction analysis revealed that the top 20 genes interacting with each metabolic gene were significantly enriched in DNA replication, response to radiation, and central carbon metabolism in cancer.ConclusionThis study elucidates on molecular changes in metabolic genes in LUAD, which may inform the development of genetically oriented diagnostic approaches and effective treatment options.     

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。     

Establishment of apoptotic regulatory network for genetic markers of colorectal cancer and optimal selection of traditional Chinese medicine target

The paper aimed to screen out genetic markers applicable to early diagnosis for colorectal cancer and establish apoptotic regulatory network model for colorectal cancer, and to analyze the current situation of traditional Chinese medicine (TCM) target, thereby providing theoretical evidence for early diagnosis and targeted therapy of colorectal cancer. Taking databases including CNKI, VIP, Wanfang data, Pub Med, and MEDLINE as main sources of literature retrieval, literatures associated with genetic markers that are applied to early diagnosis of colorectal cancer were searched and performed comprehensive and quantitative analysis by Meta analysis, hence screening genetic markers used in early diagnosis of colorectal cancer. KEGG analysis was employed to establish apoptotic regulatory network model based on screened genetic markers, and optimization was conducted on TCM targets. Through Meta analysis, seven genetic markers were screened out, including WWOX, K-ras, COX-2, P53, APC, DCC and PTEN, among which DCC has the highest diagnostic efficiency. Apoptotic regulatory network was built by KEGG analysis. Currently, it was reported that TCM has regulatory function on gene locus in apoptotic regulatory network. The apoptotic regulatory model of colorectal cancer established in this study provides theoretical evidence for early diagnosis and TCM targeted therapy of colorectal cancer in clinic.     

Comprehensive bioinformatics analysis identifies lncRNA HCG22 as a migration inhibitor in esophageal squamous cell carcinoma

Esophageal cancer is one of the most lethal malignancies worldwide, and esophageal squamous cell carcinoma (ESCC) is the dominant histological type. However, the long noncoding RNA (lncRNA) alterations in ESCC have not been elucidated to date. In this study, reliable databases from Gene Expression Omnibus (GEO), which analyzed lncRNA expression in ESCC tumor tissues and adjacent normal tissues were searched, and common differentially expressed lncRNAs and genes were analyzed. Next, cis- trans analysis was performed to predict the underlying relationships between altered lncRNAs and mRNAs, and the lncRNA-mRNA regulatory network was established. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses of altered lncRNA-related genes were performed. The promising lncRNA HCG22 was validated by quantitative polymerase chain reaction (qPCR), and clinicopathological data were collected to identify the relationship between lncRNA HCG22 expression level and clinical features. Finally, Transwell assays were performed to explore the biological functions of lncRNA HCG22 in ESCC cells. Two hundred forty-one lncRNAs and 835 mRNAs were observed to be remarkably altered between ESCC tumor tissues and adjacent normal tissues. The lncRNA-mRNA regulatory network showed the coexpression association between lncRNA HCG22 and SPINK7 and ADAMTS12. GO and KEGG analyses showed that HCG22 and ADAMTS12 had potential biological functions in the cell migration of ESCC. The downregulation of lncRNA HCG22 in ESCC tumor tissues was validated by qPCR, and the clinicopathological data showed a noticeable correlation between lncRNA HCG22 expression level and the ESCC differentiational degree and clinical TNM stage. Kaplan-Meier analysis showed that patients with ESCC having low lncRNA HCG22 expression in ESCC tissues had considerably shorter overall survival compared with patients with ESCC having high lncRNA HCG22 expression. Following Transwell assays confirmed the migratory role of lncRNA HCG22 in ESCC cells. In conclusion, lncRNA HCG22 was downregulated in ESCC tissues and can be a migration inhibitor of ESCC cells, and SPINK7 and ADAMTS12 are promising to be the regulatory targets of lncRNA HCG22.     

Identification of biomarkers of chromophobe renal cell carcinoma by weighted gene co-expression network analysis

Background

Chromophobe renal cell carcinoma (ChRCC) is the second common subtype of non-clear cell renal cell carcinoma (nccRCC), which accounting for 4–5% of renal cell carcinoma (RCC). However, there is no effective bio-marker to predict clinical outcomes of this malignant disease. Bioinformatic methods may provide a feasible potential to solve this problem.

Methods

In this study, differentially expressed genes (DEGs) of ChRCC samples on The Cancer Genome Atlas database were filtered out to construct co-expression modules by weighted gene co-expression network analysis and the key module were identified by calculating module-trait correlations. Functional analysis was performed on the key module and candidate hub genes were screened out by co-expression and MCODE analysis. Afterwards, real hub genes were filter out in an independent dataset GSE15641 and validated by survival analysis.

Results

Overall 2215 DEGs were screened out to construct eight co-expression modules. Brown module was identified as the key module for the highest correlations with pathologic stage, neoplasm status and survival status. 29 candidate hub genes were identified. GO and KEGG analysis demonstrated most candidate genes were enriched in mitotic cell cycle. Three real hub genes (SKA1, ERCC6L, GTSE-1) were selected out after mapping candidate genes to GSE15641 and two of them (SKA1, ERCC6L) were significantly related to overall survivals of ChRCC patients.

Conclusions

In summary, our findings identified molecular markers correlated with progression and prognosis of ChRCC, which might provide new implications for improving risk evaluation, therapeutic intervention, and prognosis prediction in ChRCC patients.

     

IgA 肾病患者与膜性肾病患者外周血单核细胞mRNA分析

为了寻找诊断、鉴别IgA肾病(IgAN)和膜性肾病(MN)的血液特异性标记物,利用公共数据库中的IgAN和MN患者的外周血单核细胞(PBMCs)的转录组表达谱数据集识别特异性生物标记物,为诊断和鉴别提供简便、可靠的依据补充。从公共基因表达数据库(GEO)下载IgAN患者组(n=15)和MN患者组(n=8)芯片数据集,筛选前250个差异表达基因(DEGs)。通过分析筛选关键基因和途径,进行基因本体(GO)富集分析、京都基因与基因组百科全书(KEGG)通路分析和蛋白质与蛋白质相互作用关系(PPI)分析等进一步了解DEGs。通过分析共发现75个显著DEGs,其中73个上调基因,2个下调基因。GO富集分析的生物学过程(BP)主要包括蛋白质转运、内溶酶体到溶酶体转运、趋化因子介导的信号通路作用等。显著富集差异表达基因KEGG通路分析包括Endocytosis和Hepatitis B的相关信号通路。PPI筛选出EPS15、STAT4、CCL2、SUN2、SEC24C、SEC31A、GOLGB1、F2R,RAB12和PTK2B等关键基因。成功筛选出核心差异表达基因,为IgAN和MN的诊断和鉴别提供简便、可靠的依据补充,甚至提供治疗的新靶点。     

卵巢高/低级别浆液性癌的定量蛋白质组学比较研究

目的:探讨卵巢高级别浆液性癌和低级别浆液性癌的差异表达蛋白,为阐明卵巢癌发生机制及寻找诊断和预后标志物的提供线索。方法:收集卵巢癌新鲜组织标本冻存于液氮中,经病理学确诊为高级别浆液性癌和低级别浆液性癌,两种类型各收集15例。应用i TRAQ定量蛋白质组学技术筛选及鉴定高/低级别浆液性癌的差异表达蛋白,并进行生物信息学分析。结果:卵巢高级别和低级别浆液性癌组织的定量蛋白质组学比较研究鉴定出差异表达蛋白314个,其中与低级别浆液性癌组比较,高级别浆液性癌组上调蛋白有97种,下调蛋白有217种。GO分析显示这些差异蛋白在分子功能、生物学功能、细胞成分方面均具有一定分布特点。KEGG分析显示这些差异蛋白涉及复杂的信号通路。结论:高/低级别浆液性癌之间存在差异表达蛋白,这些蛋白涉及复杂的功能和信号通路可能在两型卵巢癌发生机制及肿瘤生物学行为差异中具有重要意义。     

基于蛋白互作网络识别非小细胞肺癌相关基因功能模块

本研究对非小细胞肺癌(non-small cell lung carcinoma,NSCLC)基因表达数据进行差异表达分析,并与蛋白质相互作用网络(PPIN)数据进行整合,进一步利用Heinz搜索算法识别NSCLC相关的基因功能模块,并对模块中的基因进行功能(GO term)和通路(KEGG)富集分析,旨在探究肺癌发病分子机制。蛋白互作网络分析得到一个包含96个基因和117个相互作用的功能模块,以及8个对NSCLC的发生和发展起到关键作用候选基因标志物。富集分析结果表明,这些基因主要富集于基因转录催化及染色质调控等生物学过程,并在基础转录因子、黏着连接、细胞周期、Wnt信号通路及HTLV-Ⅰ感染等生物学通路中发挥重要作用。本研究对非小细胞肺癌相关的基因和生物学通路进行预测,可用于肺癌的早期诊断和早期治疗,以降低肺癌死亡率。