生信分析筛选心衰氧化应激相关关键[]基因并预测候选药物

为探究氧化应激相关基因在心力衰竭发生发展中的作用,并发现核心基因进行靶基因药物预测。从GEO数据库下载GSE120895基因表达图谱,通过GEO2R筛选差异表达基因,将差异表达基因与GeneCard数据库中筛选的氧化应激相关基因取交集,得到心力衰竭氧化应激相关差异表达基因,利用R软件对差异表达基因进行GO及KEGG分析,利用Cytoscape进行PPI网络的模块以及关键基因的筛选。之后在GSE17800基因表达图谱中验证关键基因的表达,并针对关键基因进行相互作用药物预测。差异表达基因与氧化应激相关基因取交集后,共筛选出52个上调的氧化应激相关差异表达基因,在此基础上,筛选出ACTB,STAT3,FN1,EDN1,CAT共5个关键基因,在GSE17800基因表达图谱中验证后,针对4个关键基因预测了19个靶基因潜在药物。总之,本研究通过生物信息学方法鉴定关键基因,并预测潜在治疗药物,从而为了解心力衰竭的分子机制及其诊治方法提供新的见解。     

Eotaxin-1在浆液性卵巢癌组织中的表达及临床意义研究

目的:研究嗜酸性粒细胞趋化因子1(Eotaxin-1)在浆液性卵巢癌组织中的表达及临床病理意义。方法:收集2013年4月至2014年5月于我院妇产科手术切除的60例浆液性卵巢癌及对应癌旁组织,采用免疫组织化学染色检测Eotaxin-1表达,分析Eotaxin-1蛋白与肿瘤临床病理资料之间的相关性。结果:浆液性卵巢癌组织中Eotaxin-1蛋白表达水平较对应癌旁组织显著升高(P0.05),浆液性卵巢癌组织中高表达Eotaxin-1蛋白与恶性组织病理分级、淋巴结转移及高TNM分期呈显著正相关(P0.05)。结论:Eotaxin-1蛋白在浆液性卵巢癌组织中表达上调,并与肿瘤恶性临床病理特征有关;Eotaxin-1可能成为浆液性卵巢癌早期诊断的重要标志物和生物靶向治疗的有效靶点之一,具有广阔的临床应用前景。     

肾纤维化关键基因的生信筛选及分析

[目的]利用生物信息学技术筛选与肾纤维化相关基因,并预测与基因相关通路,疾病、细胞类型和有关药物。[方法]在公共基因芯片数据库(GEO)中获取与肾纤维化相关三个基因数据集,利用Venn图鉴定出共表达的差异基因,Metascape工具对差异基因进行功能(GO)和通路富集(KEGG)分析,还利用STRING构建蛋白相互作用网络(PPI)网络以及可视化工具Cytoscape筛选关键基因,最后用及Enrichr和Comparative Toxicogenomics Database(CTD)数据分析有关疾病、细胞类型和药物。[结果]与肾纤维化相关的数据集GSE148420、GSE38117、GSE54441经Venn图共得到83个DEGs。经Cytoscape对STRING工具得到的PPI网络可视化后,筛选出10个与肾纤维化相关的关键基因,分别是F13B、ALDH8A1、A1CF、PAH、KMO、ALDH6A1、SPP2、ACAT1、ABAT、CAT。GO和KEGG富集显示这些基因与氧化还原酶活性,血小板致密颗粒,色氨酸、结氨酸、亮氨酸和异亮氨酸等氨基酸代谢通路有关。利用Enrichr和CTD...     

基于生物信息学分析的肝癌核心基因的筛选及验证

本研究基于GEO数据库,选取由慢性乙型肝炎诱导的肝细胞癌芯片数据GSE121248为研究对象,利用GEO2R软件分析数据,筛选出差异表达基因,利用DAVID数据库进行GO分析和KEGG pathway富集分析.利用STRING数据库构建PPI网络,分析筛选核心基因.利用GEPIA对核心基因的表达进行验证,Kaplan ...     

卵巢癌肝转移灶高表达基因SFT2D1的生物信息学分析

目的：研究肿瘤原发灶和转移灶的基因表达差异,并采用生物信息学方法对一条卵巢癌肝转移灶高表达基因SFT2D1进行初步分析。方法：分别将卵巢癌原发灶和肝转移灶组织标本mRNA用Cy3-dUTP和cy5-dUTP标记后与表达谱芯片杂交,通过信号扫描、处理后获得两者的表达差异基因。并用生物信息学方法对一条无功能研究的新基因SFT2D1进行初步分析,阐明了它的基因姑构、染色体定位、编码蛋白质的理化性质、亚细胞定位、蛋白质功能域等信息。并对多物种中的相似性蛋白进行了系统进化分析。结果：表达谱芯片发现了共272条差异表达基因。对新基因SFT2D1的上述性质进行了有效的预测,基本明确了该基因编码蛋白为一内质网跨膜蛋白,可能参与肿瘤转移相关蛋白的合成与加工。结论：表达谱芯片技术是一种研究肿瘤转移基因表达差异的有效的高通量研究方法。通过生物信息学分析,表明新基因SFT2D1是一个有肿瘤转移研究价值的新靶点。     

铁死亡相关基因与结肠癌分型及预后的关系研究

结肠癌是一种常见的发生于结肠部位的消化道恶性肿瘤,它好发于直肠与乙状结肠交界处,恶性程度高,侵袭性强,病情发展快。本研究利用铁死亡相关基因对结肠癌进行分型且对不同分型在生存时间及临床表型方面的差异进行评估,为探索该疾病的发病机制和个性化治疗提供思路。首先,从TCGA-GDC官网下载结肠癌患者的表达数据,通过查找文献检索到60个铁死亡相关基因,筛选有显著差异表达的铁死亡相关基因对结肠癌患者进行无监督聚类分型,同时比较各个分子亚型之间在生存时间和临床特征方面的差异;运用单因素Cox分析法筛选出与预后相关性较高的基因并构建Lasso回归模型,根据回归模型对患者的风险评分将患者分为高风险组和低风险组,比较两组间生存时间的差异并确定风险评分与其他临床特征之间的关联。通过单因素独立预后分析和多因素独立预后分析,筛选出影响结肠癌预后的独立因素。通过无监督聚类将样本分为两种分子亚型,两组间的生存时间差异不显著,不同分子亚型在肿瘤分期这个临床特征间存在一定的差异。使用5个与预后显著相关的基因(FDFT1、HMGCR、CARS1、AKR1C1、ALOX12)构建了Lasso回归模型,根据Lasso回归模型...     

乳腺癌转移相关基因的筛选与生物信息学分析

乳腺癌是女性最常见的恶性肿瘤,转移与复发是乳腺癌患者死亡的主要原因. 研究与乳腺癌细胞转移相关的分子靶点对预防乳腺癌术后复发、提高疗效有重要意义. 本研究以3组乳腺癌转移相关的基因表达谱数据(GSE2034, GSE2603, GSE12276)为分析材料,采用GeneSpring软件筛选乳腺癌原发瘤与转移瘤芯片数据的差异表达基因,结合生物信息学工具PATHER、STRING、pSTIING和文献挖掘工具iHOP对差异基因及其相互作用关系进行分析. 结果显示,共筛选出乳腺癌转移共同差异基因147个,其中表达上调93个,表达下调54个. 这些差异基因主要涉及细胞周期与增殖、细胞粘附、细胞迁移、血管形成及信号转导等生物通路和生物过程. 差异基因编码蛋白间的相互作用主要集中在14个蛋白,且在更为复杂的网络图谱中仍可见其中9个基因（CXCR4、MMP1、MMP2、MMP3、CTGF、COL1A1、MEF2C、PTGS2及SPARC）在重要的节点位置. 文献挖掘发现,COL1A1基因可能为新发现的乳腺癌转移候选基因,为乳腺癌转移的发病机制提供新的思路,也为转移性乳腺癌的分子诊断和个体化治疗奠定基础.     

间充质干细胞恶性转化相关关键基因的生物信息学分析

筛选间充质干细胞发生恶性转化相关的关键基因,可为进一步的相关性研究提供参考和依据。研究首先基于文献挖掘的方法,从已公开发表的研究中统计出间充质干细胞发生恶性转化的相关基因,随后运用生物信息学方法对上述基因进行分析,同时用在线软件构建基因所表达蛋白质的相互作用网络,并将相应网络进一步可视化处理,计算网络及各个节点的拓扑特性。结果显示,近年内的文献共涉及187个基因或其表达产物,有1 253种GO分类,KEGG通路分析显示,主要参与信号转导通路、细胞粘附、周期调节等;此外,共筛选出关键节点21个。生物信息学筛选的关键基因可用于提示间充质干细胞可能发生恶性转化的趋向,也有助于分析间充质干细胞恶性转化的可能机制。     

卵巢癌肝转移灶高表达基因SFT2D1的生物信息学分析

目的:研究肿瘤原发灶和转移灶的基因表达差异,并采用生物信息学方法对一条卵巢癌肝转移灶高表达基因SFT2D1进行初步分析。方法:分别将卵巢癌原发灶和肝转移灶组织标本mRNA用Cy3-dUTP和Cy5-dUTP标记后与表达谱芯片杂交,通过信号扫描、处理后获得两者的表达差异基因。并用生物信息学方法对一条无功能研究的新基因SFT2D1进行初步分析,阐明了它的基因结构、染色体定位、编码蛋白质的理化性质、亚细胞定位、蛋白质功能域等信息。并对多物种中的相似性蛋白进行了系统进化分析。结果:表达谱芯片发现了共272条差异表达基因。对新基因SFT2D1的上述性质进行了有效的预测,基本明确了该基因编码蛋白为一内质网跨膜蛋白,可能参与肿瘤转移相关蛋白的合成与加工。结论:表达谱芯片技术是一种研究肿瘤转移基因表达差异的有效的高通量研究方法。通过生物信息学分析,表明新基因SFT2D1是一个有肿瘤转移研究价值的新靶点。     

Screening and identification of hub genes in pancreatic cancer by integrated bioinformatics analysis

Pancreatic cancer (Pa) is a malignant tumor of the digestive tract with high degree of malignancy, this study aimed to obtain the hub genes in the tumorigenesis of Pa. Microarray datasets GSE15471, GSE16515, and GSE62452 were downloaded from Gene Expression Omnibus (GEO) database, GEO2R was conducted to screen the differentially expressed genes (DEGs), and functional enrichment analyses were carried out by Database for Annotation, Visualization and Integrated Discovery (DAVID). The protein-protein interaction (PPI) network was constructed with the Search Tool for the Retrieval of Interacting Genes (STRING), and the hub genes were identified by Cytoscape. Totally 205 DEGs were identified, consisting of 51 downregulated genes and 154 upregulated genes enriched in Gene Ontology terms including extracellular matrix (ECM) organization, collagen binding, cell adhesion, and pathways associated with ECM-receptor interaction, focal adhesion, and protein digestion. Two modules in the PPI were chosen and biological process analyses showed that the module genes were mainly enriched in ECM and cell adhesion. Twenty-four hub genes were confirmed, the survival analyses from the cBioPortal online platform revealed that topoisomerase (DNA) II α (TOP2A), periostin (POSTN), plasminogen activator, urokinase (PLAU), and versican (VCAN) may be involved in the carcinogenesis and progression of Pa, and the receiver-operating characteristic curves indicated their diagnostic value for Pa. Among them, TOP2A, POSTN, and PLAU have been previously reported as biomarkers for Pa, and far too little attention has been paid to VCAN. Analysis from R2 online platform showed that Pa patients with high VCAN expression were more sensitive to gemcitabine than those with low level, suggesting that VCAN may be an indicator to guide the use of the chemotherapeutic drug. In vitro experiments also showed that the sensitivity of the VCAN siRNA group to gemcitabine was lower than that of the control group. In conclusion, this study discerned hub genes and pathways related to the development of Pa, and VCAN was identified as a novel biomarker for the diagnose and therapy of Pa.     

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.     

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.     

Identification of potential key genes in anaplastic thyroid cancer using bioinformatics analysis

Anaplastic thyroid cancer (ATC) has a high degree of malignancy and poor prognosis. The purpose of this study was to determine differentially expressed genes (DEGs) in ATC through biometric analysis technology, clarify potential interactions between them, and screen genes related to the prognosis of ATC. Using obtained DEGs, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Protein-protein interaction (PPI), and survival analysis were performed. After R integration analysis of the four datasets, 764 DEGs were obtained, i.e., 314 upregulated genes and 450 downregulated genes. Among the hub DEGs obtained from the PPI network, the expression levels of TYMS, FN1, CHRDL1, SDC2, ITGA2, COL1A1, COL9A3, and COL23A1 were associated with ATC prognosis. These results showed that the recurrence-free survival (RFS) of ATC was associated with TYMS, FN1, ITGA2, COL23A1, SDC2, and CHRDL1 statistically significantly in the KM plotter (P<0.05). Thus, the study suggests that TYMS, FN1, ITGA2, COL23A1, SDC2, and CHRDL1 may be used as potential biomarkers of ATC. These findings provide new insights for the detection of novel diagnostic and therapeutic biomarkers for ATC.     

基于生物信息学的胃癌早期诊断预测模型研究

利用The Cancer Genome Atlas和Genotype-Tissue Expression公共数据检索收集胃癌(Gastric cancer, GC)基因表达数据集,筛选与早期胃癌密切相关的基因并构建胃癌早期诊断预测模型。运用Deseq2软件包筛选早期胃癌差异基因,并对差异基因进行GO和KEGG富集分析。通过STRING数据库建立其蛋白质相互作用网络并利用Cytoscape软件提取关键子网得到候选关键基因,进一步利用MedCalc软件确认胃癌早期诊断关键基因。根据筛选得到的10个关键基因构建基于支持向量机、随机森林、朴素贝叶斯、K-近邻、极限梯度提升和自适应提升等六种算法的胃癌早期诊断预测模型,依据ROC曲线和准确率等评价指标对各个分类器模型进行评估,通过独立测试集验证得到极致梯度提升诊断预测模型为最优模型。本研究成果为提高结胃癌早期诊断的研究提供了新的思路和方法。     

Proteogenomic analysis of chemo-refractory high-grade serous ovarian cancer

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Identification of key microRNAs and hub genes in non-small-cell lung cancer using integrative bioinformatics and functional analyses

Non-small-cell lung cancer (NSCLC) is an extremely debilitating respiratory malignancy. However, the pathogenesis of NSCLC has not been fully clarified. The main objective of our study was to identify potential microRNAs (miRNAs) and their regulatory mechanism in NSCLC. Using a systematic review, two NSCLC-associated miRNA data sets (GSE102286 and GSE56036) were obtained from Gene Expression Omnibus, and the differentially expressed miRNAs (DE-miRNAs) were accessed by GEO2R. Survival analysis of candidate DE-miRNAs was conducted using the Kaplan-Meier plotter database. To further illustrate the roles of DE-miRNAs in NSCLC, their potential target genes were predicted by miRNet and were annotated by the Database for Annotation, Visualization and Integrated Discovery (DAVID) program. Moreover, the protein-protein interaction (PPI) and miRNA-hub gene regulatory network were established using the STRING database and Cytoscape software. The function of DE-miRNAs in NSCLC cells was evaluated by transwell assay. Compared with normal tissues, a total of eight DE-miRNAs was commonly changed in two data sets. The survival analysis showed that six miRNAs (miR-21-5p, miR-31-5p, miR-708-5p, miR-30a-5p, miR-451a, and miR-126-3p) were significantly correlated with overall survival. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis indicated that target genes of upregulated miRNAs were enriched in pathways in cancer, microRNAs in cancer and proteoglycans in cancer, while the target genes of downregulated miRNAs were mainly associated with pathways in cancer, the PI3K-Akt signaling pathway and HTLV-I infection. Based on the miRNA-hub gene network and expression analysis, PTEN, EGFR, STAT3, RHOA, VEGFA, TP53, CTNNB1, and KRAS were identified as potential target genes. Furthermore, all six miRNAs exhibited significant effects on NSCLC cell invasion. These findings indicate that six DE-miRNAs and their target genes may play important roles in the pathogenesis of NSCLC, which will provide novel information for NSCLC treatments.