生物信息学分析食管癌关键基因的表达及其临床意义

目的：采用生物信息学技术，从基因表达综合数据库(GEO)中挖掘食管癌(ESCA)异常表达基因，探讨该基因在食管癌中的表达及临床意义。方法：用R语言中的GEOquery包从GEO数据库中下载ESCA芯片数据集GSE38129、GSE20347，经sva包对数据集去除批次效应后，使用Limma包对标准化数据集进行差异表达基因(DEGs)筛选，利用cluster Profiler包对DEGs进行基因本体(GO)功能富集分析和京都基因与基因组百科全书(KEGG)通路富集分析，在STRING网站对DEGs进行蛋白互作网络分析(PPI)，利用MCODE和Cyto Hubba插件提取核心模块及核心基因(Hub gene)。在阿拉巴马伯明翰分校癌症数据库(UALCAN)输入Hub gene分析其表达水平与食管癌分期、甲基化水平及TP53突变等的关系，最后借助芯片数据GSE70409对核心基因进行验证。结果：在标准化数据集中筛选出390个DEGs，其中上调166个，下调224个。GO分析得出，它们主要参与有丝分裂细胞周期相变、细胞外基质生成、表皮发育等生物学过程。KEGG富集分析显示DEGs与细胞周期、...     

胃癌基因表达谱芯片差异基因的生物信息学分析

目的：筛选胃癌发生发展过程中的关键基因和信号通路,为寻找有价值的胃癌分子标志物提供依据。方法：从GEO数据库下载5个胃癌基因芯片数据集：GSE35809、GSE54129、GSE79973、GSE66229和GSE51105。合并5个数据集中的样本,去除数据集间的批次效应,对合并后的基因表达数据进行标准化,并通过主成分分析监测数据标准化情况。利用R语言中的limma包筛选胃癌组织和正常组织中表达差异的基因。利用DAVID数据库对胃癌发生发展过程中的差异基因进行功能富集分析,并通过STRING数据库和Cytoscape分析差异基因编码蛋白之间的相互作用网络并进行可视化。结果：总共筛选出1 205个差异基因,包括480个上调基因,725个下调基因。差异基因的生物学功能主要富集于细胞-细胞信号传导、炎症反应的调节、细胞粘附、细胞凋亡和离子的跨膜转运。KEGG信号通路分析显示差异基因主要富集于p53信号通路、PI3K-Akt信号通路、NF-κB信号通路。通过构建蛋白质相互作用网络筛选出了CENPE、KIF15、MELK、KIF2C、CENPF、KIF11、NUSAP1、UBE2C、TTK、AURKB、DLGAP5、TOP2A等29个Hub基因。结论：通过合并不同数据集,利用生物信息学方法筛选出胃癌发生发展过程中的关键基因和信号通路,为胃癌的诊疗提供新的候选标志物。     

基于生物信息学筛选胰腺导管腺癌关键基因

目的:利用生物信息学方法分析胰腺导管腺癌（PDAC）基因表达谱芯片并筛选关键基因。方法:从公共数据库基因表达数据库（GEO）中下载PDAC基因表达谱芯片GSE28735、GSE15471、GSE101448,共纳入108例PDAC样本和97例癌旁组织样本。应用R语言limma包和impute包筛选差异表达基因。利用DAVID数据库和在线分析工具Kobas分别对差异基因进行GO功能富集分析和KEGG通路富集分析。利用STRING数据库和Cytoscape软件构建差异蛋白互作网络并进一步筛选关键基因。结果:3个基因表达谱芯片共有161个差异表达基因（|log2 fold-change(FC)|>2,P<0.05）,包括54个上调基因,107个下调基因。GO功能富集分析显示差异基因与extracellular exosome、extracellular space、extracellular matrix organization密切相关。KEGG通路分析显示差异基因主要富集在protein digestion and absorption、ECM-receptor interaction和focal adhesion等通路。蛋白质相互作用网络图中显示节点最多的10个枢纽基因分别是ALB、COL11A1、COL3A1、FN1、EGF、COL1A1、MMP9、COL5A2、ITGA2、COL6A3。结论:筛选所得的10个关键基因可能在PDAC发生发展中发挥重要作用,有望成为PDAC诊断及治疗的生物学靶标,为进一步研究PDAC发生发展的分子机制提供了理论依据。     

前列腺癌诊断/预后和耐药分子标志物的筛选及其临床意义

目的：基于已发表的芯片数据通过生物信息学方法筛选差异表达基因,以发现前列腺癌诊断/预后和耐药相关分子标志物。方法：筛选GEO数据库中已发表的前列腺癌mRNA芯片数据GSE6956和前列腺癌细胞多烯紫杉醇耐药mRNA芯片数据GSE33455进行差异表达分析;通过生物学功能注释、基因通路富集分析、蛋白质相互作用网络（protein-protein interaction,PPI）分析等生物信息学方法发现和识别与差异表达基因相关的生物学功能和信号通路;比对TCGA数据库,验证差异表达基因在前列腺癌组织及癌旁组织中的表达,并通过Kaplan-Meier分析差异表达基因对前列腺癌患者生存率的影响;用qPCR方法验证差异表达基因在前列腺癌细胞株PC3及多烯紫杉醇耐药细胞PC3-DTX中的表达情况。结果：共筛选出227个在前列腺癌和前列腺癌多烯紫杉醇耐药细胞芯片数据中共同差异表达基因。差异表达基因主要富集到了癌症相关通路（Lysosome、Sphingolipid、FoxO、Acute myeloid leukemia）,并主要参与细胞黏附、自噬和胞内蛋白转运等生物学过程。构建PPI网络选取18个连接度最高的基因作为Hub基因。Hub基因和共同差异表达基因中,上调基因CITED2、LRP12和RPL17-C18orf32与前列腺癌患者的不良预后显著相关。qPCR验证显示CITED2在多烯紫杉醇耐药细胞PC3-DTX中高表达。结论：通过生物信息学方法筛选出在前列腺癌组织和耐药细胞中共同差异表达,且与前列腺癌患者的不良预后密切相关的基因,为前列腺癌诊断/预后和耐药分子标志物的研究提供了新的思路。     

基于生信分析筛选胆囊癌关键基因#br# 及相关通路的研究#br#

目的通过生信分析筛选胆囊癌治疗的关键基因及癌症相关通路,挖掘胆囊癌患者的差异基因,预测胆囊癌的潜在治疗靶点。方法对GEO数据库中获得的芯片数据进行差异基因（DEGs）分析。选取NCBI 基因表达综合数据库(GEO)中的基因表达芯片 “GSE76633 ”和 “GSE74048”,利用GEO2R在线分析工具对胆囊癌样本和正常胆囊样本中的差异基因进行筛选。在DAVID和KOBAS上对差异基因进行生物过程（GO）分析和通路富集（KEGG）分析。使用蛋白质 蛋白质相互作用（PPI）分析数据库STRING构建靶点互作（PPI）网络模型。结果该研究共筛选了197个差异基因（P<005,|Log2FC|>2）,其中有33个上调基因,164个下调基因。这些基因主要参与了代谢过程的调节,脂肪酸β 氧化、氧化 还原过程等GO生物过程。主要调控代谢途径,甘氨酸、丝氨酸和苏氨酸代谢,缬氨酸亮氨酸和异亮氨酸降解,抗生素的生物合成等。结论该研究利用生物信息学筛选出胆囊癌中的差异基因及相关通路,帮助理解其分子机制及在胆囊癌发病机制和发生、发展过程中的作用,为寻找胆囊癌新的治疗靶点提供思路。     

基于GEO数据库及生物信息学方法筛选食管癌放射抗拒关键基因及通路

目的：基于芯片数据采用生物信息学方法,寻找食管癌放射抗拒关键分子及通路。方法：GEO数据库中下载食管癌放射抗拒mRNA表达谱芯片数据,使用Morpheus软件进行差异基因的统计学分析,利用生物信息学相关软件对这些基因进行生物学过程、信号通路及互做网络分析。结果：在GEO中获得GSE61686芯片数据,199个差异基因与食管癌放射抗拒相关,其中上调表达99个,下调表达基因100个。这些基因参与生物学过程有代谢、刺激反应、细胞黏附、细胞再生及免疫过程。主要涉及23条信号通路。结论：利用生物信息学方法能有效分析基因芯片数据。食管癌放射抗拒的发生是多基因共同参与的结果,为寻找提高食管癌放射敏感性相关靶标提供新思路。     

子宫肉瘤差异表达基因生物信息学分析

目的:筛选子宫肉瘤（uterine carcinosarcoma,UCS）进展相关的核心差异基因（differentially expressed genes,DEGs),探讨其生物学作用并筛选预后相关生物标志物。方法:从美国国立生物数据中心下的 GEO数据库获取包含子宫肉瘤和正常组织的表达数据集GSE64763,使用Limma包筛选差异基因。对筛选得到的差异基因运用ClusterProfiler包进行GO和KEGG分析,并通过蛋白互作网络(protein protein interaction network,PPI)在线平台String和Cytoscape（3.7.2）软件对DEGs分析,筛选核心基因。再基于GEPIA（gene expression profiling interactive analysis)数据库,验证核心基因的表达与预后关系。结果:共筛选出861个DEGs,其中上调DEGs 426个,下调DEGs 435个。富集GO主要生物活性信号15条,主要包括染色质结合、DNA转录活性激活、细胞外基质组成等生物过程。富集KEGG信号15 条,主要包括细胞循环通路、DNA复制通路、p53信号通路。成功筛选出核心基因网络,包含DEGs 10个,均为上调基因。通过GEPIA数据库验证后得到与UCS预后相关的差异基因CENPA。结论:UCS差异表达基因主要集中在染色体结合活性、DNA复制活性、细胞循环通路与p53信号通路等。CENPA基因可能为UCS早期诊断的生物标志物和治疗的潜在靶点。     

基于生物信息数据挖掘对卵巢浆液性癌差异表达基因筛选及分析

目的:利用生物信息学对卵巢浆液性癌的差异表达基因进行筛选及分析,探索浆液性卵巢癌的潜在治疗靶点。方法:从GEO数据库下载卵巢癌数据集GSE10971、GSE54388、GSE14407,用GEO2R筛选差异表达基因,DAVID数据库进行GO及KEGG富集分析,String数据库构建蛋白互作网络,同时利用Cytoscape获取关键基因,GEPIA数据库分析关键基因的表达情况,UCSC Xena对关键基因进行分层聚类分析,并通过cBioPortal分析关键基因的共表达网络。结果:筛选获得114个差异表达基因,包括41个下调基因及73个上调基因。主要涉及调整细胞周期、有丝分裂、染色体分离等细胞学过程,富集于细胞周期、p53信号通路、细胞衰老等信号通路。从差异表达基因筛选出49个关键基因,在卵巢癌中均呈高表达,其中21个基因的表达与卵巢癌分期相关,BIRC5基因的表达与卵巢癌患者的总生存期相关。结论:利用生物信息学对卵巢浆液性癌差异表达基因功能及信号通路的相关研究,为改善卵巢浆液性癌的预后提供了治疗靶点。     

生物信息学筛选分析前列腺癌发生及转移相关的枢纽基因

目的通过生物信息学分析前列腺癌基因表达芯片谱, 寻找与前列腺癌发生及转移相关的枢纽基因。方法使用GEO2R分析基因芯片数据集GSE27616, 分别获得4个良性前列腺和5个局限性前列腺癌组织样本的差异表达基因(DEGs)、5个局限性前列腺癌和4个转移性前列腺癌组织样本配对的DEGs, 将两组DEGs取交集后得到最终DEGs。使用DAVID数据库进行GO分析、KEGG通路富集分析, 使用STRING数据库进行蛋白互作网络分析。应用Cytoscape软件中的Cytohubba 筛选得到20个枢纽基因。通过GEPIA数据库进行验证和生存分析比较。结果最终获得DEGs 388个, 其中上调基因60个, 下调基因328个。KEGG分析发现DEGs主要富集于局灶性粘连、cGMP-PKG、Rap1、cAMP等信号通路。GEPIA分析20个枢纽基因, 发现TOP2A、BIRC5、CENPF、FGF2在前列腺癌和良性组织中表达水平不同(P<0.05)。进一步筛选发现CDK1、EZH2、TOP2A、HMMR、CCNB2、CENPA、CDC45、FOXM1、BUB1、CDCA8、DLGAP5、NUSA...     

非小细胞肺癌关键基因的生物信息学分析

目的:通过生物信息学方法挖掘非小细胞肺癌(NSCLC)基因表达谱芯片数据,筛选并验证与NSCLC发生和预后相关的关键基因。方法:从Gene Expression Omnibus(GEO)数据库中下载芯片数据(GSE101929和GSE27262)。采用GEO2R在线工具筛选癌组织和癌旁组织中的差异表达基因(DEGs);采用DAVID在线工具对差异表达基因进行GO和KEGG信号通路分析并用Cytoscape和FunRich软件进行可视化;采用GEPIA在线工具对差异表达基因进行验证和预后分析。结果:共筛选出1816个差异表达基因,其中上调基因数651个,下调基因数1165个。上调基因主要富集在“基质金属肽酶活性”,下调基因主要富集在“受体活性”等分子功能。KEGG信号通路分析显示上调基因主要富集在“有丝分裂前中期”等信号通路,而下调基因主要富集在“上皮-间质转化”信号通路。蛋白-蛋白交互作用(PPI)分析显示,上调基因中的前五位为TOP2A、CDK1、CCNB1、CCNA2和KIF11,而下调基因中的前五位为IL6、FGF2、LRRK2、EDN1和IL1B。总生存率分析显示,KIF11低表达与NSCLC预后呈负相关。结论:本研究鉴定出了与NSCLC相关的关键基因,有望作为NSCLC患者潜在治疗靶点或预后判断相关的生物标志。     

KIF11: A potential prognostic biomarker for predicting bone metastasis-free survival of prostate cancer

Most prostate cancer (PCa) cases remain indolent with a relatively good prognosis. However, bone metastasis of PCa can quickly worsen prognoses and lead to mortality. Metastasis-free survival (MFS), a strong surrogate for overall survival, is widely used in PCa prognosis research. The present study identified molecules that affect bone MFS in PCa, with clinical validation. Three datasets ({"type":"entrez-geo","attrs":{"text":"GSE32269","term_id":"32269"}}GSE32269, {"type":"entrez-geo","attrs":{"text":"GSE74367","term_id":"74367"}}GSE74367 and {"type":"entrez-geo","attrs":{"text":"GSE77930","term_id":"77930"}}GSE77930) were downloaded from the Gene Expression Omnibus database. Hub genes most relevant to clinical traits (bone metastasis-associated morbidity) were identified by weighted gene co-expression network analysis (WGCNA) and subjected to logistic regression analysis. Patient samples were obtained between January 2014 and December 2016, with a clinically annotated follow-up in December 2021. Clinical data and follow-up information for 60 patients with PCa were used in MFS analysis. Tumor samples were retrieved, and immunohistochemistry was performed to detect vascular endothelial growth factor (VEGF). The prognostic potential of the two molecules was assessed using Cox proportional hazards regression analysis. A total of 16 gene modules were obtained via WGCNA, and the tan module, containing 147 genes, was most closely linked to bone metastasis. In total, 877 differentially expressed genes (DEGs) were detected. The DEG-tan module intersection yielded seven hub genes [BUB1, kinesin family member (KIF)2C, RACGAP1, CENPE, KIF11, TTK and KIF20A]. Using univariate and multivariate logistic regression analyses for independent risk factors of bone metastasis, KIF11 and VEGF were found to be significantly associated with a higher T stage, prostate-specific antigen level and Gleason score. In addition, KIF11 and VEGF expression levels were positively correlated (P<0.001). Using univariate Cox analysis, KIF11 and VEGF were found to exhibit a significant association with poor MFS (P<0.05). However, only KIF11 was significantly associated with MFS upon multivariate analysis (P=0.007; hazard ratio, 2.776; 95% confidence interval, 1.315-5.859). Markers of bone metastasis in PCa were identified. Overall, KIF11 is an independent indicator that can predict bone metastasis for patients with PCa, which could be used to guide clinical practice.     

Identification of Key Candidate Genes and Pathways in Endometrial Cancer by Integrated Bioinformatical Analysis

Endometrial Cancer is the most common female genital tract malignancy, its pathogenesis is complex, not yetfully described. To identify key genes of Endometrial Cancer we downloaded the gene chip GSE17025 from the GeneExpression Omnibus database. Differentially expressed genes (DEGs) were identified through the GEO2R analysistool. Functional and pathway enrichment analysis were performed for DEGs using DAVID database. The network ofprotein–protein-interaction (PPI) was established by STRING website and visualized by Cytoscape. Then, functionaland pathway enrichment analysis of DEGS were performed by DAVID database. A total of 1000 significant differencesgenes were obtained, contain 362 up-regulated genes and 638 down-regulated genes. PCDH10, SLC6A2, OGN,SFRP4, TRH, ANGPTL, FOSB are down-regulated genes. The gene of IGH, CCL20, ELF5, LTF, ASPM expressionlevel in tumor patients are up-regulated. Biological function of enrichment include metabolism of xenobiotics bycytochrome P450, MAPK signaling pathway, Serotonergic synapse, Protein digestion and absorption, IL-17 signalingpathway, Chemokine signaling pathway, HIF-1 signaling pathway, p53 signaling pathway. All in all, the current studyto determine endometrial differentially expressed genes and biological function, comprehensive analysis of intrauterinemembrane carcinoma pathogenesis mechanism, and might be used as molecular targets and diagnostic biomarkers forthe treatment of endometrial cancer.     

Identification of key pathways and genes in colorectal cancer using bioinformatics analysis

Colorectal cancer (CRC) is the most common malignant tumor of digestive system. The aim of this study was to identify gene signatures during CRC and uncover their potential mechanisms. The gene expression profiles of GSE21815 were downloaded from GEO database. The GSE21815 dataset contained 141 samples, including 132 CRC and 9 normal colon epitheliums. The gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) enrichment analyses were performed, and protein–protein interaction (PPI) network of the differentially expressed genes (DEGs) was constructed by Cytoscape software. In total, 3500 DEGs were identified in CRC, including 1370 up-regulated genes and 2130 down-regulated genes. GO analysis results showed that up-regulated DEGs were significantly enriched in biological processes (BP), including cell cycle, cell division, and cell proliferation; the down-regulated DEGs were significantly enriched in biological processes, including immune response, intracellular signaling cascade and defense response. KEGG pathway analysis showed the up-regulated DEGs were enriched in cell cycle and DNA replication, while the down-regulated DEGs were enriched in drug metabolism, metabolism of xenobiotics by cytochrome P450, and retinol metabolism pathways. The top 10 hub genes, GNG2, AGT, SAA1, ADCY5, LPAR1, NMU, IL8, CXCL12, GNAI1, and CCR2 were identified from the PPI network, and sub-networks revealed these genes were involved in significant pathways, including G protein-coupled receptors signaling pathway, gastrin-CREB signaling pathway via PKC and MAPK, and extracellular matrix organization. In conclusion, the present study indicated that the identified DEGs and hub genes promote our understanding of the molecular mechanisms underlying the development of CRC, and might be used as molecular targets and diagnostic biomarkers for the treatment of CRC.     

Hydrazinobenzoylcurcumin inhibits androgen receptor activity and growth of castration-resistant prostate cancer in mice

There is a critical need for therapeutic agents that can target the amino-terminal domain (NTD) of androgen receptor (AR) for the treatment of castration-resistant prostate cancer (CRPC). Calmodulin (CaM) binds to the AR NTD and regulates AR activity. We discovered that Hydrazinobenzoylcurcumin (HBC), which binds exclusively to CaM, inhibited AR activity. HBC abrogated AR interaction with CaM, suppressed phosphorylation of AR Serine81, and blocked the binding of AR to androgen-response elements. RNA-Seq analysis identified 57 androgen-regulated genes whose expression was significantly (p ≤ 0.002) altered in HBC treated cells as compared to controls. Oncomine analysis revealed that genes repressed by HBC are those that are usually overexpressed in prostate cancer (PCa) and genes stimulated by HBC are those that are often down-regulated in PCa, suggesting a reversing effect of HBC on androgen-regulated gene expression associated with PCa. Ingenuity Pathway Analysis revealed a role of HBC affected genes in cellular functions associated with proliferation and survival. HBC was readily absorbed into the systemic circulation and inhibited the growth of xenografted CRPC tumors in nude mice. These observations demonstrate that HBC inhibits AR activity by targeting the AR NTD and suggest potential usefulness of HBC for effective treatment of CRPC.     

A Systems Biology Approach Provides Deeper Insights into Differentially Expressed Genes in Taxane-Anthracycline Chemoresistant and Non-Resistant Breast Cancers

Objective: To date, numerous studies have been conducted to search for reasons for chemoresistance and differences in survival rates of patients receiving chemotherapy. We have sought to identify differentially expressed genes (DEGs) between predicted chemotherapy resistance and sensitive phenotypes by a network as well as gene enrichment approach. Methods: Functional modules were explored with network analysis of DEGs in predicted neoadjuvant taxane-anthracycline resistance versus sensitive cases in the GSE25066 dataset, including 508 samples. A linear model was created by limma package in R to establish DEGs. Results: A gene set related to phagocytic vesicle membrane was found to be up-regulated in chemoresistance samples. Also, we found GO_CYTOKINE_ACTIVITY and GO_GROWTH_FACTOR BINDING to be up-regulated gene sets with the chemoresistance phenotype. Growth factors and cytokines are two groups of agents that induce the immune system to recruit APCs and promote tolerogenic phagocytosis. Some hub nodes like S100A8 were found to be important in the chemoresistant tumor cell network with associated high rank genes in GSEA. Conclusions: Functional gene sets and hub nodes could be considered as potential treatment targets. Moreover, by screening and enrichment analysis of a chemoresistance network, ligands and chemical agents have been found that could modify significant gene sets like the phagocytic vesicle membrane functional gene set as a key to chemoresistance. They could also impact on down- or up-regulated hub nodes.     

Integrated Bioinformatics Analysis of mRNAs and miRNAs Identified Potential Biomarkers of Oral Squamous Cell Carcinoma

Background: Oral cancer is a frequently encountered neoplasm of the head and neck region, being the eighth most common type of human malignancy worldwide. Despite improvement in its control, morbidity and mortality, rates have improved little in the past decades. The present investigations about gene interaction and pathways still could not clear the appearance and development of oral squamous cell carcinoma (OSCC), completely. The aim of this study is to investigate the key genes and microRNAs interaction in OSCC. Materials and Methods: The microarray datasets GSE13601 and GSE98463, including mRNA and miRNA profiles, were extracted from the GEO database and were analyzed using GEO2R. Functional and pathway enrichment analyses were performed by using the DAVID database. The protein–protein interaction (PPI) network was constructed and analyzed using STRING database and Cytoscape software, respectively. Finally, miRDB was applied to predict the targets of the differentially expressed miRNAs (DEMs). Results: Totally, 97 differentially expressed genes (DEGs) were found in OSCC, including 66 up-regulated and 31 down-regulated genes. The gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses showed that up-regulated genes were significantly enriched in movement of cell or subcellular component, cell adhesion, biological adhesion, cellular localization, apoptotic signaling pathway, while the down-regulated genes were enriched in muscle system process and oxidation-reduction process. From the PPI network, the top 10 nodes with the highest degree were detected as hub genes. In addition, 18 DEMs were screened, which included 7 up-regulated and 11 down-regulated miRNAs. STAT1 was potentially targeted by three miRNAs, including has-miR-6825-5P, has-miR-4495, and has-miR-5580-3P. Conclusion: The roles of DEMs such as hsa-mir-5580-3p in OSCC through interactions with DEGs CD44, ACLY, ACTR3, STAT1, LAMC2 and YWHAZ may offer a suitable candidate biomarker pattern for diagnosis, prognosis and treatment processes in OSCC.