生物信息学方法研究高脂饮食诱导肥胖对雄性SD大鼠的影响

目的应用生物信息学方法探寻高脂饮食诱导性肥胖雄性SD大鼠生理代谢改变及对生育力的影响。方法利用NCBI中的GEO基因芯片公共数据库进行芯片数据搜索,最终选择芯片数据(GSE8700)作为分析对象,使用bioconductor包中R工具的函数及Limma程序包识别差异性表达基因,应用DAVID数据库对差异表达基因进行GO富集分析和KEGG通路分析,选用String在线数据库构建差异表达基因的PPI网络。结果通过对GSE8700进行分析,得到1 014个表达差异基因,其中上调基因544个,下调基因470个;上调差异基因GO条目全部富集于生物过程(BP),主要为氧化还原、轴突生成、对肽类激素反应、对糖皮质激素反应过程;下调差异基因GO富集于生物过程(BP),主要为女性怀孕、对类固醇激素的反应、甘油三酯代谢等过程;富集于细胞组成(CC),主要为细胞外间质,细胞质,血液微球等组成;富集于分子功能(MF),主要为丝氨酸肽链内切酶活性、脂肪酸结合、磷脂质结合等功能;上调差异基因并未富集到任何KEGG通路,而下调差异基因富集到3条通路,分别为PPAR信号通路(过氧化物酶体增殖物激活型受体)、脂肪的消化和吸收通路、胰腺分泌通路,其中重要的节点基因为热休克蛋白90AB1(Hsp90ab1)、细胞外钙敏感受体(Casr)及趋化因子9(Ccl9)等。结论高脂饮食诱导肥胖雄性SD大鼠脂质代谢发生了紊乱,大鼠生殖功能可能受到影响,类固醇激素、肽类激素代谢异常可能是其影响途径。     

肝癌相关基因表达谱分析及TOP2A表达的临床意义

[摘 要] 目的 探讨肝细胞性肝癌（HCC）相关基因的差异表达、富集通路和蛋白互作网络,分析差异表达基因与HCC预后的关系。方法分析TCGA数据库中HCC相关RNAseq数据,筛选差异表达基因,明确基因富集的GO和KEGG通路,构建蛋白互作网络,找到核心蛋白,通过生存分析,明确核心基因与HCC预后的关系。结果 通过纳入标准筛选268个差异表达基因,显著富集在GO：mitotic nuclear division （P＜0.001）,cell division（P＜0.001）和negative regulation of growth（P＜0.001）及KEGG通路（hsa04110:Cell cycle） （P＜0.001）,均与细胞分裂与增殖密切相关。通过构建蛋白互作网络,筛选核心基因TOP2A,并在临床样本中得到验证（P＜0.001）。生存分析显示,TOP2A的表达量与总体生存时间显著负相关（P=0.002）。结论TCGA高通量数据分析是筛选肿瘤预后靶点的有效途径,TOP2A的高表达是肝细胞性肝癌预后的不良因素。     

Global gene expression profiling of blast lung injury of goats exposed to shock wave

Purpose: Blast lung injury (BLI) is the most common damage resulted from explosion-derived shock wave in military, terrorism and industrial accidents. However, the molecular mechanisms underlying BLI induced by shock wave are still unclear. Methods: In this study, a goat BLI model was established by a fuel air explosive power. The key genes involved in were identified. The goats of the experimental group were fixed on the edge of the explosion cloud, while the goats of the control group were 3 km far away from the explosive environment. After successful modeling for 24 h, all the goats were sacrificed and the lung tissue was harvested for histopathological observation and RNA sequencing. Gene ontology (GO) and kyoto encyclopedia of genes and genomes (KEGG) analysis were performed to identify the main enriched biological functions of differentially expressed genes (DEGs). Quantitative real-time polymerase chain reaction (qRT-PCR) was used to verify the consistency of gene expression. Results: Of the sampled goat lungs, 895 genes were identified to be significantly differentially expressed, and they were involved in 52 significantly enriched GO categories. KEGG analysis revealed that DEGs were highly enriched in 26 pathways, such as cytokine-cytokine receptor interaction, antifolate resistance, arachidonic acid metabolism, amoebiasis and bile secretion, JAK-STAT, and IL-17 signaling pathway. Furthermore, 15 key DEGs involved in the biological processes of BLI were confirmed by qRTPCR, and the results were consistent with RNA sequencing. Conclusion: Gene expression profiling provide a better understanding of the molecular mechanisms of BLI, which will help to set strategy for treating lung injury and preventing secondary lung injury induced by shock wave.     

Effects of left ventricular assist device on heart failure patients: A bioinformatics analysis

With the acceleration of demographic aging, heart failure has become a global public health issue. Left ventricular assist device (LVAD) provides a therapeutic option serving as a bridge to transplantation or destination treatment for end-stage heart failure. However, neither the molecular mechanism nor the gene expression profile of LVAD pathophysiology is well understood. Microarray dataset ( GSE21610 ) was retrieved from the online database of the gene expression omnibus (GEO). Differentially expressed genes (DEGs) between microarrays obtained before and after LVAD therapy were analyzed using GEO2R. Gene Ontology enrichment and Kyoto Encyclopedia of Genes and Genomes pathway analysis were carried out, followed by protein–protein interaction (PPI) network construction, which was further visualized by the Cytoscape software. Finally, a target gene-microRNA (miRNA) network was built using the NetworkAnalyst to predict potential miRNA interactions. A total of 36 upregulated DEGs and 14 downregulated DEGs were screened out. Five hub genes with the highest degree of connectivity were identified, including CCL2, CX3CR1, CD163, TLR7, and SERPINE1. CCL2 was identified as the most outstanding hub gene which is specially regulated by miR-124, miR-141, and miR-495. Our study indicates that CCL2 is crucial to the LVAD pathophysiology. The identified hub genes may be involved in cardiac inflammatory responses, remodeling, and the chemokine signaling pathway. These DEGs, pathways, hub genes, miRNAs are valuable for further investigations. This study provides a better understanding of the gene expression profile in LVAD pathophysiology.     

基于生物信息学胰腺腺癌关键基因的筛选及支持向量机诊断模型的构建

背景与目的：胰腺癌是一种常见的消化道恶性肿瘤,其主要病理类型为胰腺腺癌（PAAD）,因早期诊断困难且缺乏有效的治疗措施,故预后极差。因此,寻找PAAD的诊治新靶标具有重要意义。本研究通过生物信息学方法筛选与PAAD诊断和预后相关的关键基因,构建分类PAAD样本和正常样本的支持向量机（SVM）模型,以期为PAAD的诊治及机制研究提供依据。 方法：从基因表达数据库（GEO）中下载3个芯片数据（GSE28735、GSE62165、GSE62452）,应用R语言的Limma包筛选出PAAD组织和正常组织间的差异表达基因（DEGs）。利用STRING数据库对DEGs进行GO和KEGG通路富集分析。再以STRING数据库构建DEGs的蛋白互作网络（PPI）,利用Cytoscape软件进行可视化编辑,并通过MCODE插件进行关键子网络分析。使用R语言的survival包筛选PPI和关键子网络中与预后相关的关键节点,将其上传至Metascape进行功能富集分析。利用R语言caret包中递归式特征消除（RFE）算法筛选关键节点中的最优特征基因,在GEPIA数据库中验证最优特征基因的表达差异,随后通过R语言的e1071包构建最优特征基因的SVM模型,并在3个芯片数据中借助R语言的pROC包对该模型进行验证。在TCGA数据库中,用R语言的survminer包筛选出最优特征基因中与PAAD预后相关的基因作为关键基因。 结果：共筛选出257个DEGs,包括168个上调基因和89个下调基因。GO分析结果表明DEGs主要参与细胞外基质的组成、细胞黏附、丝氨酸肽酶活性等生物学过程。KEGG分析显示,DEGs主要富集于蛋白质的消化和吸收、胰腺的分泌、黏着斑、PI3K-Akt信号通路。生存分析筛选出14个关键节点同时在GSE28735和GSE62452中与预后相关（均P<0.05）,这些基因在肿瘤侵犯和肿瘤发生中发挥一定作用。RFE筛选出8个最优特征基因：LAMA3、FN1、ITGA3、MET、PLAU、CENPF、MMP14、OAS2;GEPIA数据库验证发现这8个最优特征基因在PAAD组织中明显上调（均P<0.01）;这些基因构建的SVM模型在3个芯片数据中ROC曲线的AUC依次为0.898、1.000、0.905。TCGA数据库验证发现LAMA3、ITGA3、MET、PLAU、CENPF及OAS2的上调与PAAD预后不良有关（均P<0.05）。 结论：关键基因LAMA3、ITGA3、MET、PLAU、CENPF及OAS2可能成为PAAD诊治的新靶点;基于8个最优特征基因构建的SVM模型可有效诊断PAAD。     

Identification of key genes and construction of microRNA–mRNA regulatory networks in bladder smooth muscle cell response to mechanical stimuli using microarray expression profiles and bioinformatics analysis

Purpose

To identify keys genes and elucidate miRNA–mRNA regulatory networks in Bladder smooth muscle cell (BSMC) response to mechanical stimuli.

Methods

Human BSMCs, seeded on a silicone membrane, were subjected to mechanical stretch or without stretch. Microarray was used to analyze the differential expression of mRNAs and miRNAs between human BSMCs under mechanical stretch and control static control group. Differentially expressed genes(DEGs) and miRNAs (DEMs) in these two groups were identified. Subsequently, differentially expressed DEGs were conducted with functional analysis, and then PPI network was constructed. Finally, miRNA–mRNA regulatory network was visualized using Cytoscape.

Results

1639 significant DEGs and three DEMs were identified between the stretch group and control static group. The PPI network of DEGs was constructed by STRING, which was composed of 1459 nodes and 1481 edges, including 188 upregulated genes and 255 downregulated genes. Moreover, 36 genes in the PPI network were identified as hub genes in BSMCs response to mechanical stretch, e.g. CCNH, CPSF2, TSNAX, ARPC5 and PSMD3 genes. Subsequently, 39 clusters were selected from PPI network using MCODE, and it was shown that the most significant cluster consisted of 14 nodes and 91 edges. Besides, miR-503HG was the most significantly downregulated miRNA and was predicted to target five upregulated genes, including SMAD7, CCND3, WIPI2, NYNRIN and PVRL1.

Conclusions

Our data mining and integration help reveal the mechanotransduction mechanism of BSMCs’ response to mechanical stimulation and contribute to the early diagnosis of bladder outlet obstruction (BOO) as well as the improvement of pathogenesis of BOO treatment.

     

应用生物信息学挖掘肝母细胞瘤发病差异基因

目的筛选肝母细胞瘤(hepatoblastoma)组织与小儿正常肝脏组织中的差异表达基因,探究肝母细胞瘤的发病机制,为其诊断和治疗提供新方向。方法从GEO数据库中检索获取肝母细胞瘤组织和小儿正常肝脏组织的芯片数据,通过R语言软件RSTUDIO筛选芯片中的差异表达基因,使用DAVID数据库对筛选所得的差异表达基因进行功能注释,通过STRING数据库构建蛋白质相互作用网络,并进行中心性分析。结果经筛选共获得肝母细胞瘤组织中290个差异表达基因,其中上调基因99个,下调基因191个(P0.05)。GO(Gene Ontology)功能注释分析显示,上调差异基因主要涉及细胞分裂、细胞外外泌体、金属离子结合等94个功能簇,下调差异基因主要涉及脂蛋白代谢、细胞外外泌体、血红素结合等100个功能簇。蛋白质相互作用网络分析示IMPDH2、AGXT、ALDH1A1、ALDH2、PFAS、SERPINC1、AGXT2、KNG1、APOA1、MAT1A、APOC3和HSD17B6 12个基因为与其他节点关系最密切的核心调控基因。结论通过多种生物信息学方法联合分析三组高通量基因芯片,获得了肝母细胞瘤组织与正常小儿肝脏组织间的差异表达基因,并进一步从不同角度分析肝母细胞瘤异常增殖、转移等恶性生物学过程的发生机制,为肝母细胞瘤的诊断和治疗提供新方向。     

Re-Exploring Biomarkers and Therapeutic Targets in Primary Melanoma Patients: Insights from Network-Based Analysis of Microarray Data

ObjectiveTo identify novel biomarkers and therapeutic targets for primary melanoma using network-based microarray data analysis.MethodsEligible microarray datasets from the Gene Expression Omnibus (GEO) database were used to identify differentially expressed genes (DEGs). The protein-protein interaction (PPI) network, Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed to identify hub genes and pathways that might affect the survival of melanoma patients. Immunohistochemistry results obtained from the Human Protein Atlas (HPA) database confirmed the protein expression levels of hub genes. The Cancer Genome Atlas (TCGA) database was used to further verify the gene expression levels and conduct survival analysis.ResultsThree microarray datasets (GSE3189, GSE15605, and GSE46517) containing 122 melanoma and 30 normal skin tissue samples were included. A total of 262 common differentially expressed genes (cDEGs) were identified based on three statistical approaches (Fisher's method, the random effects model (REM), and vote counting) with strict criteria. Of these, two upregulated genes, centromere protein F (CENPF) and pituitary tumor-transforming gene 1 (PTTG1), were selected as hub genes. HPA and TCGA database analyses confirmed that CENPF and PTTG1 were overexpressed in melanoma. Survival analysis showed that high expression levels of CENPF were significantly correlated with decreased overall survival (OS) (P=0.028).ConclusionThe expression level of CENPF was significantly upregulated in melanoma and correlated with decreased OS. Thus, CENPF may represent a novel biomarker and therapeutic target for melanoma patients.     

肾移植术后BK病毒相关性肾病核心基因及免疫微环境的生物信息学分析

目的通过生物信息学方法分析肾移植术后BK病毒相关性肾病(BKVAN)的核心基因及其与浸润的免疫细胞相关性。 方法从美国国立生物技术信息中心基因表达综合数据库下载BKVAN相关数据集GSE75693和GSE72925,BK病毒(BKV)血症相关数据集GSE47199。合并GSE75693和GSE72925后筛选差异表达基因(DEGs),然后进行基因本体生物过程(GOBP)以及京都基因与基因组百科全书(KEGG)通路分析,并通过蛋白-蛋白相互作用(PPI)网络进一步筛选核心基因。使用CIBERSORT进行免疫浸润分析,然后计算差异的免疫细胞和核心基因的相关性。最后,在GSE47199数据集筛选BKV血症和BKVAN共同的核心基因,使用基因集富集分析(GSEA)鉴定共同的核心基因分别在BKVAN和BKV血症中的生物过程。所有统计分析及可视化均基于R语言(4.0.2)。P<0.05为差异有统计学意义。 结果在合并数据中共筛选出175个上调及70个下调DEGs。在PPI网络中,通过5种方法交集得到9个核心基因,核心基因主要富集在免疫细胞活化与功能相关的进程;在KEGG分析中,核心基因主要富集在病毒蛋白与细胞因子和细胞因子受体间相互作用、细胞因子-细胞因子受体间相互作用以及趋化因子信号通路等。免疫浸润分析表明PTPRC、CCL5、TYROBP、CXCL10、CD2和CXCL9与BKVAN中浸润的免疫细胞相关。CD2是BKVAN和BKV血症的共同核心基因。 结论通过生物信息学方法筛选出BKVAN的核心基因,其中PTPRC、CCL5、TYROBP、CXCL10、CD2和CXCL9与BKVAN中浸润的免疫细胞相关,CD2是BKVAN和BKV血症的共同核心基因,这些标志物为肾移植术后BKVAN的诊治提供依据。     

基于GEO数据库的胰腺癌循环肿瘤细胞代谢通路及关键基因研究

目的探讨胰腺癌循环肿瘤细胞（CTCs）中起关键作用的代谢通路及关键基因。方法 从GEO数据库筛选胰腺癌CTCs相关数据集,利用R studio软件筛选差异基因,与KEGG数据库的代谢相关基因比对,寻找代谢相关差异基因。对差异基因进行KEGG通路富集,使用STRING、Cytoscape进行蛋白相互作用网络分析和可视化。最后,对比两数据集富集的代谢通路,获得关键通路及基因,并利用TCGA和TIMER数据库分析关键基因与临床特征和免疫浸润的关系。结果 分别从数据集GSE118556和GSE18670筛选出834个和1119个差异基因。前者基因主要富集到半胱氨酸和蛋氨酸代谢、一碳代谢和辅酶因子合成等代谢通路,后者基因主要富集到一碳代谢、嘌呤代谢和甘油磷脂代谢通路。其中转酮醇酶（TKT）在两个数据集的一碳代谢中均显著上调。TKT与总体生存期、肿瘤分期、组织学分级相关（P<0.05）。同样编码转酮醇酶同工酶的TKTL1和TKTL2与免疫浸润相关（P<0.05）。结论 通过对胰腺癌CTCs数据集的生物信息学分析,发现一碳代谢和TKT可能在CTCs的形成和维持中起关键作用,为进一步研究胰...     

基于机器学习的胰腺癌特征基因筛选初步研究

背景与目的 胰腺癌是一种难治的癌症,90%以上的患者在诊断后1年内死亡。胰腺癌病变组织和正常组织之间存在差异表达基因（DEGs）可能与胰腺癌的发生和发展密切相关。本研究运用机器学习方法对胰腺癌DEGs进行筛选,以期为研究该病的发生机制提供依据。方法 从公共基因GEO数据库中筛选胰腺癌基因表达谱,使用线性回归模型软件包Limma对不同组的芯片进行差异性计算,归一化;使用R语言获得DEGs,对筛选出来的DEGs特征选择方法进一步进行筛选;基于获得的核心DEGs,采用AdaBoost和Bagging算法分别构建胰腺癌预测模型。用DAVID 网站对核心DEGs进行GO功能分析和KEGG通路富集分析,再用STRING网站及Cytscape软件对核心DEGs进行蛋白-蛋白相互作用（PPI）网络分析,最后用GEPIA网站对预后相关的核心DEGs行生存分析。结果 通过特征筛选,得到了18个关键的DEGs;以该18个DEGs建立特征子集,结合AdaBoost算法建立了预测模型,预报准确率可以达到92.3%。通过对DEGs的GO和KEGG分析,发现CDK1、CCNA2和CCNB1有间接作用,对胰腺癌的形成和发展有一定的作用。生存分析显示,CDK1（P=0.000 8）、CCNB1（P=0.012）、CSK2（P=0.023）、CKS1B（P=0.001 3）的表达量与患者总生存期（OS）有相关性,它们的表达量越高,患者OS越短。结论 机器学习方法可较好地对胰腺癌特征基因进行筛选,对胰腺癌的诊治及相关的药物开发具有一定意义。     

Transplantation KCNMA1 modified bone marrow‐mesenchymal stem cell therapy for diabetes mellitus‐induced erectile dysfunction

This study assessed the effect of KCNMA1 transfected bone marrow‐mesenchymal stem cells (BM‐MSCs) on the improvement of erectile function in diabetic rats. Sixty male Sprague–Dawley rats were injected with streptozotocin (STZ) and screened with apomorphine (APO) to establish diabetes mellitus‐induced erectile dysfunction (DMED). DMED rats were randomly divided into four groups: rats in each group underwent intracavernous injection with either phosphate buffer solution (DMED+PBS), nontransfected MSCs (DMED+MSCs), empty vector transfected MSCs (DMED+null‐MSCs) or KCNMA1 transfected MSCs (DMED+KCNMA1‐MSCs). Before injection, high levels of KCNMA1 expression were confirmed in KCNMA1‐MSCs using RT‐PCR and Western blotting. The lentivirus transfected MSCs maintained their potential for multidirectional differentiation. Four weeks after injection, erectile function was ascertained by measuring intracavernous pressure (ICP). Penile tissues were collected for immunohistochemical analysis. The expression of KCNMA1 in the corpus cavernosum was increased, and the DMED+KCNMA1‐MSCs group displayed a significant improvement of erectile function. We concluded that KCNMA1 was able to enhance the positive effect of MSCs in the treatment of diabetes‐associated erectile dysfunction.     

结直肠癌奥沙利铂耐药关键基因的生物信息学分析及意义

目的筛选与结直肠癌（CRC）中奥沙利铂（OXA）耐药性相关的基因和通路。 方法首先通过GEO数据库分析GSE76092的基因表达谱,筛选出CRC的OXA敏感和OXA耐药细胞系之间的差异表达基因（DEGs）。利用DAVID数据库进行基因本体论（Go）分析和京都基因和基因组百科全书（KEGG）通路分析。通过STRING工具构建蛋白质-蛋白质相互作用（PPI）网络。经MCODE插件选择关键基因,并利用GEPIA工具进行生存分析。最后使用miRWalk数据库预测相关的miRNA。 结果通过数据分析总共获得474个DEGs,并筛选了相关的信号通路和PPI网络。筛选出15个中心基因,其中7个显著参与NF-κB和趋化因子信号等通路。对7个关键基因的生存分析表明,CXCL8、IL-1β和PTGS2表达水平与CRC患者的总体生存相关。预测hsa-miR-6893-5p、hsa-miR-7851-3p和hsa-miR-96-3p是OXA耐药相关核心miRNA。 结论基于生物信息学筛选出来的OXA耐药关键基因和信号通路,为CRC中OXA耐药的潜在机制提供更深入的了解。