抗肿瘤多肽9R-P201诱导下的肝癌HepG2细胞转录组测序分析

旨在探索多肽9R-P201处理肝癌HepG2细胞后基因融合、单核苷酸多态性(Single nucleotide polymorphism,SNP)突变、可变剪接等事件,并分析差异表达基因所参与的生物学进程与信号通路,以期解析多肽9R-P201在转录组水平对肝癌细胞的调控。通过转录组测序检测9R-P201处理肝癌HepG2细胞前后基因差异表达情况,tophat-fusion软件检测基因融合,SAMTOOLS软件检测SNP位点,r MATS软件鉴定可变剪接,使用基因本体(Gene Ontology,GO)和京都基因与基因组百科全书(Kyoto encyclopedia of genes and genomes,KEGG)富集分析方法对差异表达基因进行功能富集分析。结果共检测到可变剪接事件276个、SNP位点5 557个、基因融合事件45个;同时共得到显著差异表达基因403个,其中上调269个而下调134个,基因的功能富集分析结果显示差异表达基因显著富集细胞生长、迁移等肿瘤相关生物进程,并参与多条与癌症相关的信号通路。研究表明在9R-P201诱导HepG2细胞后,导致表达差异基因显著与肿瘤生物学进程和通路相关,并发生了大量可变剪接、SNP突变、基因融合等事件,这暗示着该多肽有望作为后续肝癌介入治疗潜在药物分子。     

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。     

急性髓系白血病铁死亡相关基因筛选及预后模型的建立

基于急性髓系白血病(Acute Myeloid Leukemia,AML)临床大数据及多组学数据库探讨铁死亡相关基因在AML中的作用,并建立铁死亡基因表达相关预后模型。整合TCGA数据库中151例AML患者和GTEx数据库中337例正常人外周血的临床和转录组数据。将Wilcoxon检验和单因素Cox分析结果取交集,筛选出预后相关差异表达基因(Differential Expression Genes, DEGs),使用Lasso回归建立基因标志物预后模型,利用受试者工作特征曲线(Receiver Operating Characteristic Curve,ROC曲线)评价预测价值,Kaplan-Meier法进行生存分析,对AML患者临床数据进行单因素和多因素Cox回归分析,使用差异基因表达分析等方法比较高、低风险患者间的组学差异,最后,利用BeatAML数据库对基因标志物进行验证。将差异基因表达分析和单因素分析结果取交集,得到13个预后相关DEGs。构建了8个基因标志物的预后评分模型,并将患者分为高、低风险两组;ROC曲线分析证实了模型良好的预测性能;生存分析提示高、低风险组患者的生存率具有显著差异;单因素分析显示年龄和风险评分与患者整体生存显著相关,多因素分析显示,年龄和风险评分是独立预后指标。在2个风险组之间筛选出384个DEGs,GO富集分析结果显示,富集的基因大多与中性粒细胞和白细胞的趋化与迁移等免疫相关分子和通路显著相关,KEGG富集通路主要与TNF信号通路、细胞因子与细胞因子受体相互作用相关。BeatAML数据库验证结果显示,5个基因与预后显著相关。铁死亡相关基因在AML中显著表达,且高风险患者预后较差,该研究对AML铁死亡相关潜在生物标志物的发现和应用奠定了一定的基础。     

smc5基因敲除斑马鱼肝脏转录组学分析

摘要 目的：探讨smc5基因敲除对斑马鱼肝脏基因表达谱的影响,进一步明确smc5突变对斑马鱼代谢的影响。方法：用CRISPR/Cas9技术构建smc5基因敲除斑马鱼模型,取3个月的smc5^-/-和野生型斑马鱼肝脏进行转录组测序,创建基因表达谱文库,观察smc5基因敲除后斑马鱼肝脏基因表达谱的变化,将筛选出的差异表达基因进行功能富集,并运用荧光定量PCR对KEGG通路中显著的差异表达基因进行验证。结果：成功构建出7号外显子上2碱基缺失造成移码突变的smc5基因敲除斑马鱼模型。RNA-seq发现smc5^-/-斑马鱼的肝脏基因表达谱变化显著,包含p53的多个通路激活,如细胞周期和凋亡。糖酵解、脂肪酸降解与代谢、丙酮酸代谢等相关通路显著下调。荧光定量PCR结果与RNA-seq结果一致。结论：smc5基因敲除下调斑马鱼肝脏糖脂代谢。本研究结果为进一步研究SMC5基因在糖脂代谢调控中的潜在机制奠定基础。     

siRNA介导的PRR11表达抑制导致肺癌细胞系基因表达谱变化的分析

Proline rich 11(PRR11)是本课题组鉴定的一个新的肿瘤相关基因。为研究PRR11介导肺癌发生发展相关的分子机制,本研究分析了PRR11表达被抑制后人肺癌细胞系H1299的全基因组基因表达谱的变化。首先,采用siRNA抑制H1299细胞中PRR11的表达,提取总RNA,采用基因芯片分析全基因组基因表达谱的变化。然后,对呈现差异表达的基因进行GO和Pathway富集分析,并对部分重要的候选基因进行定量RT-PCR验证。基因芯片结果表明,采用siRNA有效抑制H1299细胞中PRR11表达后,共有550个基因的mRNA水平出现明显变化,其中139个基因表达上调,411个基因表达下调。生物信息学分析结果表明,上述差异表达的基因显著富集于细胞周期和MAPK通路。定量RT-PCR验证分析结果表明,PRR11表达抑制后确实可导致多个与细胞周期和肿瘤发生发展密切相关的基因(包括DHRS2、EPB41L3、CCNA1、MAP4K4、RRM1、NFIB)呈现显著的表达变化。这些结果提示,PRR11可能通过上述通路和/或基因的表达变化参与肺癌的发生发展过程。     

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。     

系统型硬化症尿液lncRNA-mRNA差异表达基因筛选及生物信息学分析

为探讨系统性硬化症(SSc)患者尿液样本中的长链非编码RNA(lncRNA)、信使RNA(mRNA)的表达谱和生物学功能。选取6名SSc患者和3名健康对照者(HC),采集样本为中段晨尿,应用mRNA和lncRNA微阵列检测总RNA表达变异,SSc组与HC组相比。检测尿液lncRNA和mRNA表达,Gene ontology (GO)分析Kyoto Encyclopedia of Genes and Genomes (KEGG)信号通路分析差异表达的lncRNA功能分布;STRING在线网站和Cytoscape软件网络应用分析构建蛋白质相互作用网络(PPI)并筛选出核心基因(Hub Gene)。结果发现：与HC相比,SSc患者尿液中共有645个(上调546,下调99)mRNA和1 888个(上调1 647,下调241)lncRNA差异表达(Fold Change绝对值≥2,且P≤0.05)。KEGG通路结果显示富集TGF-β信号通路、氧化磷酸化、磷酸戊糖通路。SSc的GO分析显示与转录调控、DNA去甲基化、白介素6反应等相关;PPI网络分析表明主要富集在氧化磷酸化、细胞凋亡、自噬途径通路...     

三棱内酯B调控人冠状动脉内皮细胞基因表达谱的生物信息学分析

目的:分析三棱内酯B在人冠状动脉内皮细胞中的表达谱数据集,寻找三棱内酯B调控血管内皮功能的关键作用靶点。方法:基于GEO公共数据库,下载原始表达谱数据集(GSE44598),经过差异基因筛选,功能注释,通路富集,信号通路网络以及基因互作网络分析,找出三棱内酯B对人冠状动脉内皮细胞基因表达谱产生影响的关键基因和信号通路。结果:同对照组相比,三棱内酯B给药组共有5224个基因有显著性差异,包括2628个上调基因和2596个下调基因。基因功能注释和信号通路富集分析表明,差异基因主要参与了细胞周期过程。网络分析显示,MAPK信号通路、细胞周期通路以及PLCG2,PRKACA和ADCY4等为关键信号通路和基因。结论:三棱内酯B通过影响PLCG2,PRKACA,ADCY4等基因的表达,参与MAPK和细胞周期等信号通路,从而调节人冠状内皮细胞的功能。这些关键基因和信号通路是三棱内酯B在心血管疾病治疗应用中潜在的作用靶点。     

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。     

伴HBV感染性肝癌调控枢纽基因筛选与初步鉴定

慢性乙型肝炎病毒(Hepatitis B virus,HBV)感染引起的原发性肝癌涉及多种基因、转录本和蛋白质的相互作用及调控。从单个基因的角度来看,某个基因的表达量的改变只能对肝癌发生发展的局部作出解释而无法从整体行为进行深入和全面的探索,无法满足高度复杂性的调控研究需要。筛选乙肝相关性肝癌的基因芯片数据获取差异表达基因后,应用加权基因共表达网络分析算法构建基因共表达网络,识别与肝癌发生相关的模块,利用可视化筛选枢纽基因,并针对枢纽基因进行基因本体富集分析和初步验证。富集分析和文献挖掘一致发现,某些枢纽基因确实与多种癌症的发生与发展存在显著的关联。权重基因共表达网络分析方法被证明是一个高效的系统生物学方法,应用该方法发现了新的HBV相关性肝癌枢纽基因。经实验验证,发现枢纽基因SHARPIN促进细胞迁移。该研究对肝癌发生的调控机制以及发现HBV慢性感染导致肝癌的新型诊断标志物和(或)药物作用靶点提供了新的视野。     

Cyclin-dependent kinase inhibitor 3 is overexpressed in hepatocellular carcinoma and promotes tumor cell proliferation

Cyclin-dependent kinase inhibitor 3 (CDKN3) belongs to the protein phosphatases family and has a dual function in cell cycling. The function of this gene has been studied in several kinds of cancers, but its role in human hepatocellular carcinoma (HCC) remains to be elucidated. In this study, we found that CDKN3 was frequently overexpressed in both HCC cell lines and clinical samples, and this overexpression was correlated with poor tumor differentiation and advanced tumor stage. Functional studies showed that overexpression of CDKN3 could promote cell proliferation by stimulating G1-S transition but has no impact on cell apoptosis and invasion. Microarray-based co-expression analysis identified a total of 61 genes co-expressed with CDKN3, with most of them involved in cell proliferation, and BIRC5 was located at the center of CDKN3 co-expression network. These results suggest that CDKN3 acts as an oncogene in human hepatocellular carcinoma and antagonism of CDKN3 may be of interest for the treatment of HCC.     

Discovery of differentially expressed genes related to histological subtype of hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is one of the most common human malignancies in the world. To identify the histological subtype-specific genes of HCC, we analyzed the gene expression profile of 10 HCC patients by means of cDNA microarray. We proposed a systematic approach for determining the discriminatory genes and revealing the biological phenomena of HCC with cDNA microarray data. First, normalization of cDNA microarray data was performed to reduce or minimize systematic variations. On the basis of the suitably normalized data, we identified specific genes involved in histological subtype of HCC. Two classification methods, Fisher's discriminant analysis (FDA) and support vector machine (SVM), were used to evaluate the reliability of the selected genes and discriminate the histological subtypes of HCC. This study may provide a clue for the needs of different chemotherapy and the reason for heterogeneity of the clinical responses according to histological subtypes.     

Intratumoral immune heterogeneity of prostate cancer characterized by typing and hub genes

Discordant abundances of different immune cell subtypes is regarded to be an essential feature of tumour tissue. Direct studies in Prostate cancer (PC) of intratumoral immune heterogeneity characterized by immune cell subtype, are still lacking. Using the single sample gene set enrichment analysis (ssGSEA) algorithm, the abundance of 28 immune cells infiltration (ICI) were determined for PC. A NMF was performed to determine tumour-sample clustering based on the abundance of ICI and PFS information. Hub genes of clusters were identified via weighted gene co-expression network analysis (WGCNA). The multivariate dimensionality reduction analysis of hub genes expression matrix was carried out via principal component analysis (PCA) to obtain immune score (IS). We analysed the correlation between clustering, IS and clinical phenotype. We divided the 495 patients into clusterA (n = 193) and clusterB (n = 302) on the basis of ICI and PFS via NMF. The progression-free survival (PFS) were better for clusterA than for clusterB (p < 0.001). Each immune cell subtypes was more abundant in clusterA than in clusterB (p < 0.001). The expression levels of CTAL-4 and PD-L1 were lower in clusterB than in clusterA (p < 0.001 and p = 0.006). We obtained 103 hub genes via WGCNA. In the training and validation cohorts, the prognosis of high IS group was worse than that of the low IS group (p < 0.05). IS had good predictive effect on 5-year PFS. The expression of immune checkpoint genes was higher in the low IS group than in the high IS group (p < 0.01). Patients with low IS and receiving hormone therapy had better prognosis than other groups. The combination of IS and clinical characteristics including lymph node metastasis and gleason score can better differentiate patient outcomes than using it alone. IS was a practical algorithm to predict the prognosis of patients. Advanced PC patients with low IS may be more sensitive to hormone therapy. CXCL10, CXCL5, MMP1, CXCL12, CXCL11, CXCL2, STAT1, IL-6 and TLR2 were hub genes, which may drive the homing of immune cells in tumours and promote immune cell differentiation.     

Identifying breast cancer subtypes associated modules and biomarkers by integrated bioinformatics analysis

Breast cancer is the most common form of cancer afflicting women worldwide. Patients with breast cancer of different molecular classifications need varied treatments. Since it is known that the development of breast cancer involves multiple genes and functions, identification of functional gene modules (clusters of the functionally related genes) is indispensable as opposed to isolated genes, in order to investigate their relationship derived from the gene co-expression analysis. In total, 6315 differentially expressed genes (DEGs) were recognized and subjected to the co-expression analysis. Seven modules were screened out. The blue and turquoise modules have been selected from the module trait association analysis since the genes in these two modules are significantly correlated with the breast cancer subtypes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment show that the blue module genes engaged in cell cycle, DNA replication, p53 signaling pathway, and pathway in cancer. According to the connectivity analysis and survival analysis, 8 out of 96 hub genes were filtered and have shown the highest expression in basal-like breast cancer. Furthermore, the hub genes were validated by the external datasets and quantitative real-time PCR (qRT-PCR). In summary, hub genes of Cyclin E1 (CCNE1), Centromere Protein N (CENPN), Checkpoint kinase 1 (CHEK1), Polo-like kinase 1 (PLK1), DNA replication and sister chromatid cohesion 1 (DSCC1), Family with sequence similarity 64, member A (FAM64A), Ubiquitin Conjugating Enzyme E2 C (UBE2C) and Ubiquitin Conjugating Enzyme E2 T (UBE2T) may serve as the prognostic markers for different subtypes of breast cancer.     

Integrated genomic and gene expression profiling identifies two major genomic circuits in urothelial carcinoma

Similar to other malignancies, urothelial carcinoma (UC) is characterized by specific recurrent chromosomal aberrations and gene mutations. However, the interconnection between specific genomic alterations, and how patterns of chromosomal alterations adhere to different molecular subgroups of UC, is less clear. We applied tiling resolution array CGH to 146 cases of UC and identified a number of regions harboring recurrent focal genomic amplifications and deletions. Several potential oncogenes were included in the amplified regions, including known oncogenes like E2F3, CCND1, and CCNE1, as well as new candidate genes, such as SETDB1 (1q21), and BCL2L1 (20q11). We next combined genome profiling with global gene expression, gene mutation, and protein expression data and identified two major genomic circuits operating in urothelial carcinoma. The first circuit was characterized by FGFR3 alterations, overexpression of CCND1, and 9q and CDKN2A deletions. The second circuit was defined by E3F3 amplifications and RB1 deletions, as well as gains of 5p, deletions at PTEN and 2q36, 16q, 20q, and elevated CDKN2A levels. TP53/MDM2 alterations were common for advanced tumors within the two circuits. Our data also suggest a possible RAS/RAF circuit. The tumors with worst prognosis showed a gene expression profile that indicated a keratinized phenotype. Taken together, our integrative approach revealed at least two separate networks of genomic alterations linked to the molecular diversity seen in UC, and that these circuits may reflect distinct pathways of tumor development.     

Development and validation of LRP1B mutation-associated prognostic model for hepatocellular carcinoma

Purpose: To develop a lipoprotein receptor-related protein 1B (LRP1B) gene mutation-based prognostic model for hepatocellular carcinoma (HCC) patients risk prediction. Methods: The LRP1B gene mutation rate was calculated from HCC patient samples. Meanwhile, differentially expressed genes according to LRP1B mutant were screened out for prognostic model establishment. Based on this innovative model, HCC patients were categorized into high- and low-risk groups. The immune status including immune cell infiltration ratio and checkpoints have been explored in two groups. The functions of LRP1B and risk factors in the model were verified using both in vivo and in vitro experiments. Results: It could be demonstrated that LRP1B was a potential negative predictor for HCC patients prognosis with high mutation frequency. The functions of LRP1B were verified with ELISA and Quantitative Real-time PCR method based on clinic-recruited HCC participants. Eleven genes displayed significant differences according to LRP1B status, which could better predict HCC patient prognosis. The functions of these genes were examined using HCC cell line HCCLM3, suggesting they played a pivotal role in determining HCC cell proliferation and apoptosis. From the immune cell infiltration ratio analysis, there was a significant difference in the infiltration degree of seven types of immune cells and two immune checkpoints between high- and low-risk HCC patients. Conclusion: The present study hypothesized a potential prognostic biomarker and developed a novel LRP1B mutation-associated prognostic model for HCC, which provided a systematic reference for future understanding of clinical research.