基于生物信息学方法识别非小细胞肺癌(NSCLC)潜在治疗靶基因

本研究运用生物信息学方法识别非吸烟女性非小细胞肺癌(NSCLC)潜在的靶基因,并从分子水平探索其潜在的发病机制。从GEO数据库下载非吸烟女性非小细胞肺癌相关基因芯片数据集,经癌症组和癌旁对照组差异表达基因识别,并利用R软件对差异基因进行层次聚类分析,DAVID进行基因本体(gene ontology)和KEGG通路富集分析,STRING和Cytoscape软件构建蛋白-蛋白交互(PPI)网络,以及运用PASTAA分析,识别NSCLC相关转录因子,构建转录因子-基因共表达网络。结果表明,185个基因在NSCLC中差异表达,其中40个上调,145个下调;通过PASTAA分析识别出5个NSCLC基因相关转录因子。差异基因与胶原分解代谢过程、炎症反应的正调控等生物过程密切相关,基因的产物主要参与蛋白质细胞外基质、胶原三聚体等细胞组分,且主要发挥调节金属内肽酶活性、肝素结合和调节受体活性等分子功能;KEGG通路富集分析表明差异基因显著富集到胞外基质-受体信号通路、粘着斑信号通路、PPAR信号通和PI3K-Akt信号通路等,与非小细胞肺癌的发生发展密切相关。通过生物信息学方法,最终筛选到4个NSCLC关键基因:IL6、MMP1、COL1A1、CD36,其可能是非吸烟女性NSCLC潜在的治疗靶点。     

基于生物信息学分析的非小细胞肺癌诊断预后相关基因的筛选

为了从基因层面探讨非小细胞肺癌(non-small cell lung cancer, NSCLC)发生发展的内在机制,筛选与NSCLC诊断、预后相关的基因,为NSCLC分子机制的进一步研究提供生物信息学依据,利用生物信息学方法对GEO数据库和TCGA数据库的数据集进行合并分析,筛选NSCLC组织与正常肺组织之间的差异表达基因(differentially expressed genes, DEGs),并对所取交集的DEGs进行基因集富集分析(gene set enrichment analysis, GSEA)、基因本体论(gene ontology, GO)分析、KEGG (kyoto encyclopedia of genes and genomes)通路富集分析、蛋白质相互作用(protein-protein interaction, PPI)分析、ROC曲线诊断效能分析及LASSO生存分析。文中共筛选出240个DEGs,主要涉及核分裂、染色体分离等生物学过程。GSEA分析结果显示,富集的通路主要涉及DNA修复和细胞周期。从PPI网络中筛选出20个hub基因, ROC结果显示, UBE2C (AUC=0.939)、TOP2A(AUC=0.927)、RRM2 (AUC=0.927)、CCNB1 (AUC=0.928)、MKI67 (AUC=0.930)、AURKA (AUC=0.931)、MELK(AUC=0.950)相对具有较高的诊断价值, LASSO COX回归结果则显示IL6、KIAA0101、MKI67、TPX2、AURKA、CDKN3及CDCA5与NSCLC患者的预后强相关。本研究结果表明, ZWINT、KIF2C、MELK、CDCA5可能在NSCLC中发挥着重要的作用,为阐明NSCLC的分子机制提供了新思路。     

核不均一核糖核蛋白R基因沉默抑制非小细胞肺癌细胞恶性转化

核不均一核糖核蛋白R(hnRNPR)是一种与mRNA生物学功能密切相关的RNA结合蛋白质,与多种肿瘤细胞的恶性转化相关。然而,在非小细胞肺癌(NSCLC)中的作用机制尚不清楚。本研究通过检索公共数据库发现,hnRNPR蛋白主要在肺癌细胞核中表达,hnRNPR mRNA在非小细胞肺癌组织中高表达,并且与肺腺癌患者的生存率呈负相关;hnRNPR的表达与非小细胞肺癌患者的性别、T分期显著相关(P<0.05)。构建hnRNPR基因沉默的非小细胞肺癌稳定细胞株,检测细胞功能变化,结果显示,hnRNPR基因沉默抑制了细胞增殖、迁移和侵袭能力以及上皮-间质转化(EMT),并将细胞周期阻滞在G₁期(P＜0.01)。生物信息学分析显示,非小细胞肺癌中hnRNPR基因与9 310个基因的表达正相关(皮尔逊相关系数>0,P<0.05),与10 680个基因的表达负相关(皮尔逊相关系数<0,P<0.05)。综上所述,hnRNPR在非小细胞肺癌中高表达,可能作为剪接体的组分,通过调节相关基因的表达,促进了NSCLC细胞的恶性转化。     

基于拓扑分析方法鉴定与胃癌相关的生物标志物

胃癌(gastric cancer, GC)是最常见的恶性肿瘤之一。由于GC发病隐匿的特性,其早期检测困难。因此,研究与GC早期诊断和预后相关的生物标志物至关重要。从GEO数据库下载了3组基因表达数据集GSE79973、 GSE19826和GSE13911,通过Limma包筛选差异表达基因(differentially expressed genes, DEGs),并使用DEGs、 STRING V11数据库和Cytoscape构建了DEGs的蛋白质-蛋白质相互作用(protein-protein interaction, PPI)网络,通过4种拓扑分析方法取交集筛选hub基因,并通过单变量Cox分析、多变量Cox分析、 Lasso回归分析、生存分析、通路分析以及文献法验证hub基因。从3个数据集中分别筛选了1 599个、 333个和662个DEGs。通过拓扑分析方法筛选了4个hub基因,即CDK1、AURKA、PTTG1和UBE2C。GO和KEGG富集分析结果表明4个hub基因参与了细胞外基质-受体相互作用、糖尿病并发症中的AGE-RAGE信号通路、小细胞肺癌和蛋白质消化吸收等通路。...     

强直性脊柱炎差异表达基因的生物信息学分析

目的探讨强直性脊柱炎(AS)患者差异表达基因,并基于差异基因探讨强直性脊柱炎发病相关的可能生物学过程和信号通路。方法检索基因表达谱数据库(GEO)并筛选AS相关基因表达谱数据集。应用GEO在线分析功能GEO2R分析AS组和正常对照组的差异表达基因,用Cytoscape软件clueGO插件进行基因本体论和京都基因与基因组百科全书分析,采用String蛋白-蛋白相互作用(PPI)数据库分析差异表达基因编码蛋白间的相互作用;应用Cytoscape绘制蛋白相互作用网络图,并软件筛选信号通路关键基因分析。结果选取AS患者全血表达数据集GSE25101为研究对象,分析获得差异表达基因72个。72个差异表达基因分子功能主要为参与高迁移率族盒染色体蛋白1(HMGB1)转导机制;生物学过程主要富集于巨噬细胞迁移、骨髓细胞凋亡过程、线粒体呼吸链复合体装配、ATP合成偶联电子传输、线粒体ATP合成耦合电子输运等;细胞成分主要富集于呼吸链复合体、线粒体呼吸体等。信号通路富集于氧化磷酸化信号通路和帕金森综合征相关信号通路。PPI网络经过cytohubba插件筛选,ATP5J、NDUFS4、UQCRB、UQCRH、NDUFB3、COX7B、LSM3、ATP5EP2、ENY2、PSMA4被筛选为网络中的核心基因。结论通过生物信息学方法进行预测了AS的潜在机制,并筛选出10个潜在的与AS相关的重要分子,其中氧化磷酸化可能在AS发病机制中发挥了重要的作用。     

生物信息学分析筛选结直肠癌靶基因及评估预后价值

为寻找与结直肠癌发展和预后相关的潜在关键基因及信号通路。从美国国立信息中心NCBI的GEO数据库获得结直肠癌基因表达数据集GSE106582,通过PCA对样本进行分组,利用GEO2R进行综合分析,筛选结直肠癌与癌旁对照组的差异表达基因;通过DAVID在线工具对差异表达基因进行GO本体分析和KEGG通路富集分析,初步分析差异表达基因的生物学作用;基于STRING数据库对差异表达基因进行蛋白质相互作用网络分析,利用Cytoscape软件进行可视化并筛选关键基因;用生存分析和ROC曲线诊断对关键基因进行鉴定并通过数据集GSE21510进行验证。共鉴定出199个差异表达基因,其中53个为上调基因,146个为下调基因;上调的差异表达基因主要富集在与胶原蛋白分解代谢过程、细胞外基质分解、细胞外基质受体相互作用和PI3K/AKT信号通路等生物学过程;下调的差异表达基因主要富集在碳酸氢盐运输、一碳代谢过程、矿物质吸收、药物代谢-细胞色素P450和氮代谢通路等生物学过程;MCODE分析、生存分析和ROC诊断共发现3个基因分别为BGN、COL1A2和TIMP1可能与结直肠癌的发生发展有关,它们在肿瘤组织中的异常高表达与患者较差的生存期呈正相关,GSE21510的验证结果与GSE106582的分析结果相同。本研究采用生物信息学方法对CRC基因芯片数据进行挖掘,从基因水平探讨CRC潜在的发病机制、肿瘤标志物的及患者预后分子的筛选,以及可能的药物治疗靶点提供了一定的参考价值和理论基础。     

基于蛋白质互作网络挖掘自闭症谱系障碍的功能模块与核心基因

自闭症谱系障碍(autism spectrum disorder, ASD)是一种具有高遗传性、临床异质性和生物复杂性的神经行为障碍类疾病。为挖掘ASD发生发展过程中的功能模块与核心基因,本文从自闭症谱系障碍疾病数据库获取ASD相关基因;利用STRING数据库构建ASD相关基因的蛋白质互作网络;通过MCODE算法对蛋白质互作网络进行模块分析并筛选核心基因;最后对各功能模块进行KEGG通路分析,根据富集到的通路类别评估功能模块之间的相互作用。结果显示, 3个疾病基因数据库筛选出182个共有基因,构建的蛋白质互作网络包含171个节点和1 041条边,其中NRXN1、GRIN2B、GRIN2A、DLG4、NLGN3、MECP2、CNTNAP2、BDNF、NLGN4X、FMR1等23个基因具有较高的连通度(degree)。从蛋白质互作网络中分析得到5个功能模块,包括68个核心基因。KEGG富集分析发现功能模块参与多个生物学通路,包括细胞黏附分子、钙离子通路、神经活性的配体-受体相互作用、多巴胺能神经突触等。分析结果提示,挖掘的ASD功能模块和核心基因大多集中在神经元活动、信号分子和信号传导等,且各模块相互作用共同影响ASD的发生发展。     

NSCLC分类及生存分析预测的全基因组特征基因识别

基于NSCLC(非小细胞肺癌)子类分类在临床和生物医学研究方面的意义,利用全基因组基因表达水平(GE)和甲基化(ME)水平的微阵列数据对NSCLC子类分类进行全基因组特征基因识别分析。针对全基因组微阵列数据的高噪声、超高维小样本特性,利用弹性正交贝叶斯算法对全基因组基因进行递归筛选,识别分类精度最优的特征基因集。以TCGA的490的基因表达数据和378个甲基化数据为例,分别识别出52个GE特征基因和25个ME特征基因,相应的分类准确率分别为99%和98%。结合特征基因和临床数据建立的多变量Cox模型明确说明了特征基因在病人生存分析方面的重要作用:仅利用相应的基因表达数据和甲基化数据即可对病人样本的"高/低风险"进行正确分类,显著性水平均低于0.05。特征基因参与的代谢通路与p53、TGF-beta、Wnt等重要的癌症分类和发展的代谢通路的密切关系进一步证实了特征基因对NSCLC分类的重要性。     

基于RNA-seq研究胶质母细胞瘤中差异表达基因及蛋白相互作用网络

本研究基于RNA-seq数据分析了胶质母细胞瘤中的差异表达基因,并对差异表达基因进行了功能(GO term)和通路(KEGG)富集分析。进一步通过蛋白相互作用网络挖掘了胶质母细胞瘤的调控机理。结果表明,405个基因在肿瘤组织中差异表达(p-value≤0.05,|log FC|≥1.5),其中216个(53.3%)基因上调,189个(46.6%)基因下调。基因本体(gene ontology,GO)富集结果表明,这些差异表达基因参与了离子转运,神经冲动传递,细胞信号转导和细胞粘附等。此外,KEGG通路富集结果表明,差异基因参与了许多重要的生物学通路,包括ECM受体相互作用、黏着和钙信号等通路。进一步的蛋白相互作用网络分析鉴定了5个关键的hub基因,包括PTK2B、CDK1、FN1、CCND1和FOS。这5个关键基因对于胶质母细胞瘤的发生和发展可能起到了关键作用,可以作为潜在的调控位点和筛选的标志物。     

GWGS芯片整合分析识别外周血细胞中与骨质疏松症相关的枢纽基因

为了对骨质疏松症基因芯片数据集进行整合分析并识别出外周血细胞中与骨质疏松症相关的枢纽基因,通过检索GEO和ArrayExpress数据库获得骨质疏松症相关的表达谱芯片数据集;运用GWGS (genome-wide global significance)方法对纳入的数据集进行整合分析,筛选出差异表达基因(differentially expressed genes, DEGs);然后,运用GO (gene ontology)富集分析和KEGG (kyoto encyclopedia of genes and genomes)通路富集分析对差异表达基因进行功能注释,并建立蛋白质相互作用(protein-protein interaction, PPI)网络,筛选出骨质疏松症相关的枢纽基因。公共数据库检索得到3个符合纳入排除标准的研究集, GWGS整合分析筛选出排序前200的DEGs,这些基因主要富集的GO条目为脂多糖的细胞反应、凋亡过程和炎症反应,与骨质疏松症相关的KEGG富集通路为破骨细胞分化等。PPI分析进一步检测到与骨质疏松症相关的10个枢纽基因,其中9个基因已有研究报道和骨质疏松症的发生发展相关,而ELANE基因还未有研究报道与骨质疏松症有关。ELANE基因同时在人的骨髓组织、小鼠骨髓和骨组织中高表达,这个基因很可能与骨质疏松症有潜在的联系。本研究的结果将有助于进一步理解骨质疏松症的分子致病机理。     

Urinary exosomal long noncoding RNAs serve as biomarkers for early detection of non-small cell lung cancer

Objective: Increasing the efficiency of early diagnosis using noninvasive biomarkers is crucial for enhancing the survival rate of lung cancer patients. We explore the differential expression of non-small cell lung cancer (NSCLC)-related long noncoding RNAs (lncRNAs) in urinary exosomes in NSCLC patients and normal controls to diagnose lung cancer.Methods: A differential expression analysis between NSCLC patients and healthy controls was performed using microarrays. Gene ontology (GO) term and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were used to predict potential functions of lncRNAs in NSCLC. quantitative real-time PCR (QT-PCR) was used to verify microarray results.Results: A total of 640 lncRNAs (70 up- and 570 down-regulated) were differentially expressed in NSCLC patients in comparison to healthy controls. Six lncRNAs were detected by QT-PCR. GO term and KEGG pathway analyses showed that differential lncRNAs were enriched in cellular component organization or biogenesis, as well as other biological processes and signaling pathways, such as the PI3K-AKT, FOXO, p53, and fatty acid biosynthesis.Conclusions: The differential lncRNAs in urinary exosomes are potential diagnostic biomarkers of NSCLC. The lncRNAs enriched in specific pathways may be associated with tumor cell proliferation, tumor cell apoptosis, and the cell cycle involved in the pathogenesis of NSCLC.     

Impact on disease development, genomic location and biological function of copy number alterations in non-small cell lung cancer

Lung cancer, of which more than 80% is non-small cell, is the leading cause of cancer-related death in the United States. Copy number alterations (CNAs) in lung cancer have been shown to be positionally clustered in certain genomic regions. However, it remains unclear whether genes with copy number changes are functionally clustered. Using a dense single nucleotide polymorphism array, we performed genome-wide copy number analyses of a large collection of non-small cell lung tumors (n = 301). We proposed a formal statistical test for CNAs between different groups (e.g., non-involved lung vs. tumors, early vs. late stage tumors). We also customized the gene set enrichment analysis (GSEA) algorithm to investigate the overrepresentation of genes with CNAs in predefined biological pathways and gene sets (i.e., functional clustering). We found that CNAs events increase substantially from germline, early stage to late stage tumor. In addition to genomic position, CNAs tend to occur away from the gene locations, especially in germline, non-involved tissue and early stage tumors. Such tendency decreases from germline to early stage and then to late stage tumors, suggesting a relaxation of selection during tumor progression. Furthermore, genes with CNAs in non-small cell lung tumors were enriched in certain gene sets and biological pathways that play crucial roles in oncogenesis and cancer progression, demonstrating the functional aspect of CNAs in the context of biological pathways that were overlooked previously. We conclude that CNAs increase with disease progression and CNAs are both positionally and functionally clustered. The potential functional capabilities acquired via CNAs may be sufficient for normal cells to transform into malignant cells.     

Bioinformatics role of the WGCNA analysis and co-expression network identifies of prognostic marker in lung cancer

Lung cancer is the most talked about cancer in the world. It is also one of the cancers that currently has a high mortality rate. The aim of our research is to find more effective therapeutic targets and prognostic markers for human lung cancer. First, we download gene expression data from the GEO database. We performed weighted co-expression network analysis on the selected genes, we then constructed scale-free networks and topological overlap matrices, and performed correlation modular analysis with the cancer group. We screened the 200 genes with the highest correlation in the cyan module for functional enrichment analysis and protein interaction network construction, found that most of them focused on cell division, tumor necrosis factor-mediated signaling pathways, cellular redox homeostasis, reactive oxygen species biosynthesis, and other processes, and were related to the cell cycle, apoptosis, HIF-1 signaling pathway, p53 signaling pathway, NF-κB signaling pathway, and several cancer disease pathways are involved. Finally, we used the GEPIA website data to perform survival analysis on some of the genes with GS > 0.6 in the cyan module. CBX3, AHCY, MRPL12, TPGB, TUBG1, KIF11, LRRC59, MRPL17, TMEM106B, ZWINT, TRIP13, and HMMR was identified as an important prognostic factor for lung cancer patients. In summary, we identified 12 mRNAs associated with lung cancer prognosis. Our study contributes to a deeper understanding of the molecular mechanisms of lung cancer and provides new insights into drug use and prognosis.     

Integration of gene dosage and gene expression in non-small cell lung cancer, identification of HSP90 as potential target

Background

Lung cancer causes approximately 1.2 million deaths per year worldwide, and non-small cell lung cancer (NSCLC) represents 85% of all lung cancers. Understanding the molecular events in non-small cell lung cancer (NSCLC) is essential to improve early diagnosis and treatment for this disease.

Methodology and Principal Findings

In an attempt to identify novel NSCLC related genes, we performed a genome-wide screening of chromosomal copy number changes affecting gene expression using microarray based comparative genomic hybridization and gene expression arrays on 32 radically resected tumor samples from stage I and II NSCLC patients. An integrative analysis tool was applied to determine whether chromosomal copy number affects gene expression. We identified a deletion on 14q32.2-33 as a common alteration in NSCLC (44%), which significantly influenced gene expression for HSP90, residing on 14q32. This deletion was correlated with better overall survival (P = 0.008), survival was also longer in patients whose tumors had low expression levels of HSP90. We extended the analysis to three independent validation sets of NSCLC patients, and confirmed low HSP90 expression to be related with longer overall survival (P = 0.003, P = 0.07 and P = 0.04). Furthermore, in vitro treatment with an HSP90 inhibitor had potent antiproliferative activity in NSCLC cell lines.

Conclusions

We suggest that targeting HSP90 will have clinical impact for NSCLC patients.     

TRIM59 Promotes the Proliferation and Migration of Non-Small Cell Lung Cancer Cells by Upregulating Cell Cycle Related Proteins

TRIM protein family is an evolutionarily conserved gene family implicated in a number of critical processes including inflammation, immunity, antiviral and cancer. In an effort to profile the expression patterns of TRIM superfamily in several non-small cell lung cancer (NSCLC) cell lines, we found that the expression of 10 TRIM genes including TRIM3, TRIM7, TRIM14, TRIM16, TRIM21, TRIM22, TRIM29, TRIM59, TRIM66 and TRIM70 was significantly upregulated in NSCLC cell lines compared with the normal human bronchial epithelial (HBE) cell line, whereas the expression of 7 other TRIM genes including TRIM4, TRIM9, TRIM36, TRIM46, TRIM54, TRIM67 and TRIM76 was significantly down-regulated in NSCLC cell lines compared with that in HBE cells. As TRIM59 has been reported to act as a proto-oncogene that affects both Ras and RB signal pathways in prostate cancer models, we here focused on the role of TRIM59 in the regulation of NSCLC cell proliferation and migration. We reported that TRIM59 protein was significantly increased in various NSCLC cell lines. SiRNA-induced knocking down of TRIM59 significantly inhibited the proliferation and migration of NSCLC cell lines by arresting cell cycle in G2 phase. Moreover, TRIM59 knocking down affected the expression of a number of cell cycle proteins including CDC25C and CDK1. Finally, we knocked down TRIM59 and found that p53 protein expression levels did not upregulate, so we proposed that TRIM59 may promote NSCLC cell growth through other pathways but not the p53 signaling pathway.     

Gene coexpression analysis offers important modules and pathway of human lung adenocarcinomas

Lung adenocarcinomas injured greatly on the people worldwide. Although clinic experiments and gene profiling analyses had been well performed, to our knowledge, systemic coexpression analysis of human genes for this cancer is still limited to date. Here, using the published data GSE75037, we built the coexpression modules of genes by Weighted Gene Co-Expression Network Analysis (WGCNA), and investigated function and protein–protein interaction network of coexpression genes by Database for Annotation, visualization, and Integrated Discovery (DAVID) and String database, respectively. First, 11 coexpression modules were conducted for 5,000 genes in the 83 samples recently. Number of genes for each module ranged from 90 to 1,260, with the mean of 454. Second, interaction relationships of hub-genes between pairwise modules showed great differences, suggesting relatively high scale independence of the modules. Third, functional enrichment of the coexpression modules showed great differences. We found that genes in modules 8 significantly enriched in the biological process and/or pathways of cell adhesion, extracellular matrix (ECM)–receptor interaction, focal adhesion, and PI3K-Akt signaling pathway, and so forth. It was inferred as the key module underlying lung adenocarcinomas. Furthermore, PPI analysis revealed that the genes COL1A1, COL1A2, COL3A1, CTGF, and BGN owned the largest number of adjacency genes, unveiling that they may functioned importantly during the occurrence of lung adenocarcinomas. To summary, genes involved in cell adhesion, ECM–receptor interaction, focal adhesion, and PI3K-Akt signaling pathway play crucial roles in human lung adenocarcinomas.