以LPAR3为中心的2型糖尿病相关基因功能富集及蛋白互作分析

本研究目的是查找与2型糖尿病相关的潜在基因及其参与的生物过程、信号通路和蛋白互作网络。利用GEO数据库中GSE20966数据集,采用GENE-E平台,筛选出与LPAR3共表达的基因,结合生物信息学工具GOC、DAVID、COREMINE、Gene Mania对共表达基因进行基因功能富集分析、文本挖掘及蛋白互作分析。我们筛选出LPAR3在2型糖尿病患者胰腺β细胞中表达值显著低于正常对照组,其共表达基因603个,主要涉及代谢过程、信号调控等生物过程和Hippo信号通路。共表达相关系数高的8个基因与2型糖尿病相关,且均与肿瘤相关,涉及代谢过程、信号转导、发病机理、细胞增殖、细胞粘附等生物过程。LPAR3与20个已报道的2型糖尿病基因存在互作关系。本研究发现LPAR3及其共表达基因可能与2型糖尿病相关,并可能增加患者罹患各系统肿瘤的风险。     

MTA1调控基因网络在胃腺癌中的作用

本研究利用TCGA数据库提供的胃腺癌数据集,结合cBio Cancer Genomics Portal数据库和GeneCards数据库筛选出与肿瘤转移相关基因(MTA1)存在共表达和相互作用的83个基因。利用DAVID、STRING等分析软件发现这些基因主要富集在细胞周期、WNT通路、P53信号通路、胃癌和DNA修复等癌症相关通路上,并进一步利用String数据库和Cytoscape筛选出与MTA1紧密联系基因,同时结合大样本临床数据的生存曲线分析认为这些基因与胃腺癌生存率密切相关,MTA1可能与这些基因相互作用调控细胞增殖,影响胃腺癌细胞侵袭转移。通过研究胃腺癌组织中MTA1调控的基因网络,有助于揭示胃腺癌发病机制。找到有效生物学标记物组合作为胃癌的预测指标,可以为相关药物研发及临床诊断治疗提供新的思路和依据。     

调控通路内基因表达的相关性分析

本研究从基因表达调控通路的角度分析了基因功能与基因表达之间的关系,利用7套酿酒酵母基因芯片表达谱数据和通路数据库（KEGG和CYGD）所提供的信息,应用我们研制的Genehub软件分析研究了同一基因表达调控通路内的基因在mRNA表达水平上的相关性,共涉及16条通路,495个基因。通过Pearson相关系数和Spearman相关系数两种相似性测度的分析,我们发现有94%（15条）的基因表达调控通路内的基因在大于等于4套的表达谱数据中是共表达的,以上结果从基因表达调控通路的角度,证实了基因功能与基因表达之间存在着一定的相关性。     

肺癌中Hsa-miR-98靶基因生物信息学分析

[目的]分析肺鳞癌中Hsa-miR-98的靶基因及参与的生物学过程和信号通路。[方法]分析TCGA中收录的肺鳞癌患者的Hsa-miR-98及生存数据,通过4个靶基因数据库和表达数据分析Hsa-miR-98靶基因,利用Bi NGO富集分析靶基因功能,通过DAVID数据库富集分析靶基因信号通路。[结果]高表达Hsa-miR-98延长肺鳞癌患者总生存率和无复发生存率。综合4个数据库分析结果,得到Hsa-miR-98的113个靶基因,其中82个在肺鳞癌患者癌与癌旁差异表达。GO分析表明4个基因参与细胞胶原的组成,59个基因参与细胞的生命活动,KEGG通路富集到7条通路,包括肿瘤发生发展、阿米巴运动、胞外基质的形成等。[结论]82个Hsa-miR-98靶基因通过7条信号通路,影响细胞生命活动,进而调控肿瘤发生发展,使高表达Hsa-miR-98患者生存期延长。     

DLL4和Notch4在膀胱尿路上皮癌中的表达及临床意义

目的探讨DLL4和Notch4在膀胱尿路上皮癌中的表达及其临床意义。方法收集福建医科大学附属第一医院2008-2010年有完整临床病理资料的膀胱尿路上皮癌存档蜡块85例和27例癌旁组织,采用组织芯片免疫组织化学法检测膀胱尿路上皮癌和癌旁组织DLL4和Notch4的表达水平。对DLL4和Notch4的表达进行半定量分析,并用SPSSl3．0软件对各组免疫组织化学反应阳性率采用x。检验或Fisher精确概率法分析。结果（1）DLL4和Notch4在膀胱尿路上皮癌中呈高表达,癌旁组织中呈低表达,差异有显著性（P〈O．05）。（2）Notch4和DLL4在膀胱尿路上皮癌中的表达与患者性别、年龄、临床分期、病理分级及初复发均无显著相关性（P〉0．05）。结论DLL4和Notch4在膀胱尿路上皮癌中的高表达与膀胱尿路上皮癌血管生成密切相关,阻断DLL4-Noteh4信号通路有望抑制膀胱尿路上皮癌的发生发展。     

人肝细胞癌预后不良相关基因的生物信息学分析及其临床意义

目的:筛选肝细胞癌(HCC)预后不良相关基因,并探讨其临床意义。方法:在基因表达综合数据库(GEO)中获取符合分析条件的肝细胞癌全基因组表达谱数据并分析得到差异表达基因(DEGs),再运用生物学信息注释及可视化数据库 (DAVID) 和蛋白相互作用数据库 (String) 分别进行功能富集分析和蛋白质互作用网络的构建。利用癌症基因组图谱数据库(TCGA)和Cox比例风险回归模型对相关差异基因进行预后分析。结果:找到一个符合条件的人类HCC数据库 (GSE84402),共筛选出1141个差异表达基因(DEGs),其中上调基因720个,下调基因421个。基因功能富集分析和蛋白质互作用分析结果显示CDK1、CDC6、CCNA2、CHEK1、CENPE 、PIK3R1、RACGAP1、BIRC5、KIF11和CYP2B6为HCC预后的关键基因。TCGA数据库和Cox回归模型分析显示CDC6、PIK3R1、RACGAP1和KIF11的表达升高,CENPE的表达降低与HCC预后不良密切相关。结论:CDC6、CENPE、PIK3R1、RACGAP1和KIF11可能和HCC的预后不良相关,可作为未来HCC预后研究的参考标志物。     

基于SNP共表达网络肝癌分子分型及预后分析

基于SNP突变数据与mRNA表达谱关联分析,构建一种肝癌分子分型方法并对比不同分型预后的差异,并对不同分型肝癌的发生发展机制进一步研究。首先通过TCGA数据库收集359例肝细胞癌患者的SNP突变数据和mRNA表达数据,采用Wilcoxon秩和检验,筛选突变后差异表达基因,并通过生物信息学工具String和Cytoscape 构建差异表达基因的蛋白互作网络,筛选连接度最高的10个Hub基因。利用Consensus Cluster Plus软件包,基于Hub基因mRNA表达水平构建NMF分子分型模型,再结合生存数据评估各分型患者的预后。最后利用加权基因共表达网络分析(WGCNA),识别与肝癌分子分型相关的模块,并针对关键模块的基因进行通路富集,从而对不同分型肝癌的基因表达谱进行比较。结果:NMF模型将肝癌分为高危、低危2个分型,其中CDKN2A和FOXO1基因对分型贡献度高。生存分析显示低危组患者的生存情况显著优于高危组,高危组富集多个与肿瘤细胞侵蚀、转移、复发过程相关的信号通路,低危组则与细胞周期和胰液分泌相关。本研究在无先验性信息的前提下,基于突变后显著差异表达的Hub基因表达水平构建的肝癌分子分型对肝癌患者预后评估具有一定的指导意义,其中CDKN2A和FOXO1突变是肝癌患者的不良预后因素,针对二者的靶向药研发,可能为肝癌患者提供新的治疗策略。     

肝细胞癌患者自噬相关基因的预后作用

构建由自噬相关基因组成的预后模型,预测肝细胞癌(HCC)患者的生存预后情况,为其个性化诊疗和临床研究提供依据.利用TCGA数据库中HCC的测序信息与人类自噬数据库联合,筛选差异表达的自噬相关基因,对其进行GO富集与KEGG通路分析;通过单因素与多因素Cox分析筛选与患者生存预后明显相关的风险基因,构建预后风险评分模型;...     

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...     

幽门螺杆菌感染与胃癌相关基因的筛选及预后分析

目的通过分析幽门螺杆菌感染胃黏膜组织和胃癌细胞系后的差异基因变化,并在癌症基因组图谱(The Cancer Genome Atlas,TCGA)数据库和肿瘤基因芯片(Oncomine)数据库进行验证,探究幽门螺杆菌导致胃癌发生、发展的分子机制。方法分析基因表达汇编(Gene Expression Omnibus,GEO)数据库幽门螺杆菌感染相关芯片集GSE5081与GSE70394,绘制维恩图查找幽门螺杆菌感染后共同上调的差异基因。对共同上调的差异基因进行功能富集分析。通过TCGA和Oncomine数据库验证差异基因在胃癌中的表达。利用Kaplan-Meier Plotter数据库和GEPIA数据库分析差异基因表达高低与胃癌患者预后是否存在相关性。结果通过差异基因筛选和维恩分析,两个芯片集共有21个共同上调差异基因。GO分析发现共同上调差异基因主要富集在对细菌来源分子的反应、趋化因子CXCR受体结合、中性粒细胞趋化作用等相关的基因功能上;KEGG通路主要富集在癌症通路、TNF信号通路、趋化因子信号通路等。STRING以及PPI数据库分析发现21个基因中PRDM1、IL10、NRP1、BIRC3、GNG13、CXCL1、CXCL2、CXCL3、CXCL8基因存在有网络关系,属于关键枢纽基因。通过TCGA和Oncomine数据库筛选及验证,发现在胃癌组织中NRP1、CXCL1、CXCL8基因明显上调,结果差异有统计学意义(TCGA数据库中,三者P值均小于0.05,Oncomine数据库中,NRP1:t=4.607,P0.01;CXCL1:t=5.854,P0.01;CXCL8:t=5.316,P0.01)。在Kaplan-Meier Plotter数据库(210615-at芯片:P0.01;210510-s-at芯片:P0.01;212298-at芯片:P0.01)以及GEPIA数据库(P0.01)两个数据库中,NRP1的高表达均与胃癌的预后负相关。结论不同的数据库均显示NRP1、CXCL1、CXCL8三个基因与幽门螺杆菌感染相关,同时在胃癌中高表达,并且NRP1的高表达与胃癌的不良预后相关,这些结果为进一步探究幽门螺杆菌导致胃癌发生、发展的分子机制提供了重要基础。     

Validation of CSN1S1 transcriptional expression,promoter methylation,and prognostic power in breast cancer using independent datasets

Breast cancer ranked second among most frequent cancer in the world playing a significant role in mortality rate. Having prior knowledge on differentially expressed genes in breast cell carcinoma elucidated important indications to understand the molecular mechanism underneath breast carcinogenesis. In this study we have investigated the distinguished CSN1S1 expression in human breast cancer. We have analyzed CSN1S1 mRNA expression between cancer and normal tissues using TCGA datasets. Moreover, analysis including promoter methylation, mutations, prognosis, co-expression, gene ontology, and pathways of CSN1S1 were performed by the TCGA Wanderer, UCSC Xena, cBioPortal, PrognoScan, UALCAN, and Enricher server. We have observed low mRNA expression and high promoter methylation of CSN1S1 in cancer tissues compared to normal tissues. Furthermore, we have also identified low mRNA expression in clinicopathological patients, as well as 9 deleterious mutations with highly co-expressed protein MRC1, and significantly related signaling pathways. We have found a positive correlation between the lower expression of CSN1S1 and patients surviving with breast cancer. Here we have concluded that CSN1S1 acts as a biomarker for the surveillance and prognosis of breast cancer, and also works as a novel therapeutic target at the molecular and pathway levels.     

Development of a novel six-long noncoding RNA signature predicting survival of patients with bladder urothelial carcinoma

Bladder urothelial carcinoma is a malignant tumor with a high incidence in the uropoietic system. Considerable studies have shown that long noncoding RNA (lncRNA) plays an important role in the development and progression of bladder urothelial carcinoma. In this study, the lncRNA expression and clinical data of 377 bladder urothelial carcinoma patients were obtained from The Cancer Genome Atlas database and differentially expressed lncRNAs in cancer and normal groups were evaluated. Univariate COX and multivariate COX regression analyses of prognosis were performed on differentially expressed lncRNAs in the training data sets, six prognosis-related lncRNAs (LINC02195, LINC01484, LINC01468, SMC2-AS1, AC011298.1, and PTPRD-AS1) were assessed, and a six-lncRNA signature was constructed. The predictive capability of this six-lncRNA signature was validated in the testing data sets and entire data sets. The prognostic ability of the six-lncRNA signature was independent of other clinical elements after multivariate COX regression and stratified analyses of with other clinical elements. We performed functional enrichment analysis with the six prognosis-related lncRNAs. Results of functional enrichment revealed that these prognosis-related lncRNAs might promote the development and metastasis of bladder urothelial carcinoma. In summary, the six-lncRNA signature that we developed could effectively predict the prognosis of bladder urothelial carcinoma patients. This six-lncRNA signature might be a novel independent prognostic marker of bladder urothelial carcinoma. Moreover, it also provides novel insights into the mechanism of bladder urothelial carcinoma.     

Identification of immune-related hub genes and construction of an immune-related gene prognostic index for low-grade glioma

Low-grade glioma (LGG) poses significant management challenges and has a dismal prognosis. While immunotherapy has shown significant promise in cancer treatment, its progress in glioma has confronted with challenges. In our study, we aimed to develop an immune-related gene prognostic index (IRGPI) which could be used to evaluate the response and efficacy of LGG patients with immunotherapy. We included a total of 529 LGG samples from TCGA database and 1152 normal brain tissue samples from the GTEx database. Immune-related differentially expressed genes (DEGs) were screened. Then, we used weighted gene co-expression network analysis (WGCNA) to identify immune-related hub genes in LGG patients and performed Cox regression analysis to construct an IRGPI. The median IRGPI was used as the cut-off value to categorize LGG patients into IRGPI-high and low subgroups, and the molecular and immune mechanism in IRGPI-defined subgroups were analysed. Finally, we explored the relationship between IRGPI-defined subgroups and immunotherapy related indicators in patients after immunotherapy. Three genes (RHOA, NFKBIA and CCL3) were selected to construct the IRGPI. In a survival analysis using TCGA cohort as a training set, patients in the IRGPI-low subgroup had a better OS than those in IRGPI-high subgroup, consistent with the results in CGGA cohort. The comprehensive results showed that IRGPI-low subgroup had a more abundant activated immune cell population and lower TIDE score, higher MSI, higher TMB score, lower T cell dysfunction score, more likely benefit from ICIs therapy. IRGPI is a promising biomarker in the field of LGG ICIs therapy to distinguish the prognosis, the molecular and immunological characteristics of patients.