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。     

基于生物信息学筛选和鉴定结直肠癌关键的生物标志物

结直肠癌是世界范围内最为常见的恶性肿瘤之一,目前,关于结直肠癌的分子机制仍在不断的探索中。本文通过生物信息学方法筛选和鉴定结直肠癌关键的生物标志物。从基因表达数据库(GEO)选择了3个数据集[GSE21510(148个样本)、GSE32323(44个样本)、GSE15781(42个样本)],对差异基因的表达以及功能富集进行分析。通过建立蛋白互作网络,运用STRING和Cytoscape对分子进行分析。筛选出472个差异基因,其中上调基因212个,下调基因260个。差异基因的富集及其通路主要包括调节细胞增殖、识别受体信号通路、过氧化物酶体增殖物激活受体(PPAR)信号通路等。其中15个核心基因主要富集在受体蛋白信号通路、细胞表面受体信号和趋化因子信号通路上。生存分析表明,AGT、CXCL2可能参与致癌,促进癌症的转移,影响预后。通过对472个差异基因和15个核心基因的筛选识别,促癌基因AGT和CXCL2可能被视为结直肠癌的生物标志物,为结直肠癌的诊断、治疗和研究提供新的分子靶标。     

浆液性卵巢癌铁死亡关键基因的筛选及[]生物信息学分析

利用生物信息学方法筛选浆液性卵巢癌相关铁死亡关键基因,并预测其生物学功能。从GEO数据库中获得有关浆液性卵巢癌的数据集GSE54388和GSE12470,采用R语言中的“Limma”包分析挑选浆液性卵巢癌上皮组织与正常卵巢上皮组织中差异表达基因,绘制火山图、热图。利用Venn软件在线工具绘制GSE54388,GSE12470,FerrDb三个数据集韦恩图。对相关基因进行功能富集分析、蛋白互作分析、生存分析,对关键基因绘制ROC曲线进行诊断分析。采用GEPIA2 数据库对筛选基因进行验证,并进行免疫浸润分析。结果发现:从GSE54388中筛选出2458个差异基因,其中上调1309个,下调1149个。从GSE12470中筛选出3534个差异基因,其中上调1 837个,下调1 697个。与铁死亡基因数据集取交集,共得到16个差异基因,蛋白互作网络筛选出7个基因构建的关键模块,绘制生存曲线发现浆液性卵巢癌患者中5个基因与患者总生存率不良相关,其中NRAS,PSAT1,CDKN2A,GDF15这4个基因高表达,CAV1低表达。ROC曲线显示这5个基因中CAV1,NRAS,PSAT1的AUC诊断曲线面积大于0.95,有较高的诊断价值。GEPIA2 数据库验证发现5个基因的表达情况与预测相符,仅NRAS基因表达在浆液性卵巢癌患者Ⅱ期、Ⅲ期、Ⅳ期有显著差异(P<0.05)。免疫浸润分析发现CDKN2A表达与aDC细胞浸润水平呈正相关(P<0.05,spearman相关系数0.353);CAV1表达与Mast细胞浸润正向关(P<0.05,spearman相关系数0.327);NRAS与T helper细胞浸呈正向关(P<0.05,spearman相关系数0.362)。通过生物信息学方法筛选出与浆液性卵巢癌铁死亡相关的5个基因CAV1,NRAS,PSAT1,CDKN2A,GDF15,可能在浆液性卵巢癌的发生发展中起重要作用,有望成为该病诊断、治疗和预后的潜在分子生物标志物。     

家族性高胆固醇血症HDL-miRNAs调节血单核细胞功能机制

本研究通过生物信息学方法分析家族性高胆固醇血症患者外周血单核细胞差异表达基因、HDL载体差异表达miRNA及其生物学功能,研究差异HDL-miRNA与单核细胞差异基因的相关性,探讨HDL-miRNA调控外周血单核细胞功能机制,寻找动脉粥样硬化防治新靶点。运用R语言分析GEO数据库共享平台家族性高胆固醇血症外周血单核细胞基因及HDL-miRNA探针芯片得到差异基因及差异miRNA,利用miRwalk2. 0预测miRNA靶基因,并利用STRING进行蛋白互作分析,构建差异miRNA与差异基因之间的调控网络。运用GO及KEGG分析研究基因功能。利用GEO数据(GSE6054)筛选出834个差异表达基因,利用GEO数据(GSE25108)筛选出HDL上差异miRNA28个。交叉匹配得到由19个差异miRNA和56个差异基因组配对的74对miRNA-靶基因。GO富集分析56个差异基因主要富集于肾上腺素受体信号等分子功能。KEGG分析56个差异基因主要富集于造血谱系通路上。家族性高胆固醇血症差异HDL-miRNA与外周血单核细胞差异mRNA具有相关性,HDL-miRNA有通过调控血单核细胞功能的可能性,可能参与高胆固醇血症导致动脉粥样硬化过程。     

加权ceRNA网络筛选乳腺癌生物标志物

竞争性内源RNA (competing endogenous RNA, ceRNA)作为生物标志物和潜在的治疗靶点,在探究肿瘤的发病机制中,表现出了巨大的研究价值和临床应用前景。本文对乳腺癌ceRNA网络进行了系统的分析,首先通过差异分析获得差异miRNA、mRNA和lncRNA,其次利用网络的边聚集系数(edge clustering coefficient,ECC)和皮尔逊相关系数(Pearson correlation coefficient, PCC)计算ceRNA网络节点的权重,最后采用基于随机森林的逐步特征选择(stepwise feature selection based on random forest, SFS-RF)方法筛选出一组可作为乳腺癌生物标志物的RNA——LINC00466、CHL1-AS2和LINC00337,并利用GEO数据库验证了该组RNA对乳腺癌样本的识别情况。此外,通过GO和KEGG通路富集分析探索了该组RNA在乳腺癌中的生物学功能。结果显示:这些RNA作为生物标志物,在识别乳腺癌样本方面具有高精度和高效率等特性,在乳腺肿瘤细胞的增殖及遗传物质的合成等过程中具有重要生物学意义。     

脱发相关差异表达基因的生物信息学分析

利用生物信息学方法分析脱发相关差异表达基因,有望帮助了解脱发发生发展的分子机制。本研究从NCBI的子数据库GEO中选择基因表达谱GSE45512和GSE45513数据集,利用R语言limma工具包,筛选出两个物种斑秃样本与正常样本的共同显著差异表达基因。对这部分基因进行功能注释和蛋白互作网络分析,同时对全部差异表达基因进行基因集富集分析。结果发现,人头皮斑秃样本共筛选出225个差异表达基因;C3H/HeJ小鼠自发斑秃皮肤样本共筛选出337个差异表达基因;两个物种的共同显著差异表达基因有23个。GO功能富集分析和蛋白互作网络分析显示,这部分差异基因显著富集于免疫相关功能,并且彼此间存在蛋白互作关系。基因集富集分析显示两个物种的差异基因都能显著富集到趋化因子信号通路、细胞因子受体相互作用、金葡菌感染及抗原加工与呈递通路;而且人的下调差异基因不仅映射到了人类表型数据库的脱发表型,也映射到皮肤附属物病理相关表型。综上所述,本研究通过生物信息方法分析脱发皮肤组织与正常皮肤组织的差异表达基因,最终筛选出23个在人和小鼠中共同存在的显著差异表达基因;此外,分析发现脱发与免疫过程及皮肤附属物病变密切相关,这些结果为脱发的诊断和治疗提供了新思路。     

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 Meier Plotter在线分析工具对核心基因与患者生存情况的相关性进行验证.通过上述方法筛选出309个DEGs,其中上调基因94个,下调基因215个.差异基因功能分析显示上调的DEGs主要参与细胞周期和卵母细胞减数分裂通路等途径,下调的DEGs则在补体和凝血级联、代谢途径以及咖啡因代谢途径富集.筛选出15个具有高度关联性的核心基因(BUB1,BUB1B,BIRC5,CCNB1,CCNB2,CDC20,CDK1,KIF-20A,MAD2L1,NCAPG,ZWINT,PBK,BTL,TTK和NUSAP1),它们与肝癌患者的总体生存率具有明显相关性,并为其构建了miRNA调控网络.本研究通过生物信息学方法有效分析了肝细胞癌发生、发展相关的差异表达基因,筛选出15个核心基因,分析其生物学相关功能,以期探索肝细胞癌发病机制,并为临床诊断标志物的改进以及筛选提供一定的理论基础.     

SARS-CoV-2病毒感染潜在关键分子生物标志物及免疫浸润特征分析

应用生物信息学方法筛选新型冠状病毒肺炎（corona virus disease 2019,COVID-19）感染的潜在关键分子生物标志物并分析其免疫浸润特征。从GEO数据库下载GSE152418数据集,其中COVID-19患者17例,健康对照17例。用加权基因共表达网络分析（weighted gene co-expression network analysis,WGCNA）方法筛选出COVID-19最相关的模块基因。与差异基因取交集得到共同基因,进行功能及信号通路富集分析,构建蛋白互作网络筛选关键基因,构建关键基因的miRNA-TF-mRNA调控网络,用CIBERSORT算法预测样本免疫细胞浸润特征。差异分析得到2 049个差异基因。WGCNA分析7个模块中“土耳其蓝色”模块与COVID-19相关性最高（r=0.91,P<0.001）。模块中基因显著性和模块隶属度呈显著正相关（r=0.96,P<0.001）。得到共同基因766个,主要参与有丝分裂、微管结合、阳离子通道活性及卵母细胞减数分裂、细胞衰老等。蛋白互作网络筛选到前10位关键基因分别为CDK1、BUB1、CCNA2、CDC20、KIF11、BUB1B、CDCA8、TOP2A、CCNB2、KIF20A,构建的miRNA-TF-mRNA网络包含51个miRNA、5个TF、10个mRNA。COVID-19患者较健康对照组幼稚B细胞、嗜酸性粒细胞浸润水平显著降低（P<0.05）,浆细胞、活化肥大细胞浸润水平显著升高（P<0.05）。通过WGCNA及蛋白互作网络分析筛选出10个关键基因,并预测到调控关键基因的5个TF及51个miRNA,且COVID-19患者与健康对照的免疫浸润特征存在统计学差异,这些与免疫细胞相关的分子标志物可能作为COVID-19免疫治疗的潜在靶标。     

斑马鱼急性无机砷暴露后肝脏差异表达基因的生物信息学分析

砷是一种致癌物,是心血管、外周血管疾病、神经疾病、糖尿病和各种癌症的致病因素。目的:利用GO数据库和KEGG数据库等生物信息学方法对GEO数据库数据中的差异表达基因进行评价。利用生物信息学分析软件对差异基因进行功能富集、功能注释分析和生存分析。利用Cytoscape上的蛋白-蛋白相互作用网络(Protein-protein interaction network, PPI)软件对179个差异基因进行筛选和分析。结果发现126个基因作用于蛋白靶点,其中有10个基因为关键基因分别为:PSMB3、HSP701、HSPE1、STIP1、HSPD1、HSP70、DNAJB1B、HSP90AA1.1、HSPA9H和TCP1。核心基因主要作用于内质网中的蛋白质加工通路。这可能会为砷对肝脏损伤的潜在生物标志物和生物学机制提供新的思路。     

龋病感染牙髓相关基因的筛选与生物信息学分析

目的:综合应用生物信息学技术,从分子水平对龋坏牙髓与正常牙髓的差异基因进行筛选分析,初步探讨其作用机制。方法:从GEO基因表达数据库中下载龋坏牙髓相关芯片数据集,采用MORPHEUS在线筛选差异表达基因,结合DAIVID、STRING等在线分析工具对差异表达基因进行GO功能富集分析及KEGG通路分析,后用Cytoscape软件构建蛋白质相互作用网络。结果:共筛选出375个差异表达基因,其中表达上调253个、下调122个,主要涉及免疫应答、炎症反应、细胞因子应答和生物矿化组织发育等生物过程,以及抗原加工提呈和NF-κB信号等生物通路。通过蛋白质互作网络构建分析发现,MMP9、IL-8、PTPRC、CXCR4等10个基因处于核心节点位置。结论:借助生物信息学方法能得到可靠的相关差异基因信息,能够有效指导进一步的研究。得到的差异基因可以作为龋病诊断的指示因子和机制研究的候选基因。     

Identification of circRNA and mRNA expression profiles and functional networks of vascular tissue in lipopolysaccharide‐induced sepsis

Sepsis is the most common cause of death in intensive care units. This study investigated the circular RNA (circRNA) and mRNA expression profiles and functional networks of the aortic tissue in sepsis. We established a lipopolysaccharide (LPS)‐induced rat sepsis model. High‐throughput sequencing was performed on the aorta tissue to identify differentially expressed (DE) circRNAs and mRNAs, which were validated by real‐time quantitative polymerase chain reaction (RT‐qPCR). Bioinformatic analysis was carried out and coding and non‐coding co‐expression (CNC) and competing endogenous RNA (ceRNA) regulatory networks were constructed to investigate the mechanisms. In total, 373 up‐regulated and 428 down‐regulated circRNAs and 2063 up‐regulated and 2903 down‐regulated mRNAs were identified. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses of mRNAs showed that the down‐regulated genes were mainly enriched in the process of energy generation. CNC and ceRNA regulatory networks were constructed with seven DE circRNAs. The results of functional enrichment analysis of CNC target genes revealed the important role of circRNAs in inflammatory response. The ceRNA network also highlighted the significant enrichment in calcium signalling pathway. Significant alterations in circRNAs and mRNAs were observed in the aortic tissue of septic rats. In addition, CNC and ceRNA networks were established.     

The exploration of new therapeutic targets for HPV-negative head and neck squamous cell cancer through the construction of a ceRNA network and immune microenvironment analysis

Previous studies have shown that human papillomavirus (HPV)-negative patients with head and neck squamous cell cancer (HNSCC) suffer from an unsatisfactory prognosis. Long noncoding RNAs (lncRNAs) have been verified to participate in many biological processes, including regulating gene expression as competing endogenous RNAs (ceRNAs), while few studies focused the ceRNA network regulation mechanism in patients with HPV-negative HNSCC tumor. Meanwhile, the immune microenvironment may be critical in the development and prognosis of HPV-negative tumors. Our study aimed to further investigate the pathogenesis and potential biomarkers for the diagnosis, therapy and prognosis of HPV-negative HNSCC through a ceRNA network. Comprehensively analyzing the sequencing data of lncRNAs, microRNAs (miRNAs), and messenger RNAs (mRNAs) in The Cancer Genome Atlas HNSCC dataset, we constructed a differentially expressed ceRNA network containing 131 lncRNAs, 35 miRNAs and 162 mRNAs. Then, survival analysis in the network was cited to explore the prognostic biomarkers. Eight mRNAs, nine lncRNAs, and one miRNA were identified to be associated with prognosis. Neuropilin (NRP) binding function, retinoid X receptor (RXR) binding, and the vascular endothelial growth factor (VEGF) signaling pathway were associated with the enrichment analysis, and they also related to the immune microenvironment. Combined with the analysis of the immune microenvironment differences, we obtained new targeted therapies using an RXR agonist, or a combination of the VEGF monoclonal antibody and an NRP antagonist, which may provide a promising future for HPV-negative HNSCC patients.     

Construction of a paclitaxel-related competitive endogenous RNA network and identification of a potential regulatory axis in pancreatic cancer

BackgroundIncreasing numbers of studies have elucidated the role of competitive endogenous RNA (ceRNA) networks in carcinogenesis. However, the potential role of the paclitaxel-related ceRNA network in the innate mechanism and prognosis of pancreatic cancer has not been identified.MethodsComprehensive bioinformatics analyses were performed to identify drug-related miRNAs (DRmiRNAs), drug-related mRNAs (DRmRNAs) and drug-related lncRNAs (DRlncRNAs) and construct a ceRNA network. The ssGSEA and CIBERSORT algorithms were utilized for immune cell infiltration analysis. Additionally, we validated our paclitaxel-related ceRNA regulatory axis at the gene expression level; functional experiments were conducted to explore the biological functions of the key genes.ResultsA total of 182 mRNAs, 13 miRNAs, and 53 lncRNAs were confirmed in the paclitaxel-related ceRNA network. In total, 6 mRNAs, 4 miRNAs, and 6 lncRNAs were identified to establish a risk signature and exhibited optimal prognostic effects. The mRNA signature can predict the abundance of immune cell infiltration and the sensitivity of different chemotherapeutic drugs and may also have a guiding effect in immune checkpoint therapy. A potential PART1/hsa-mir-21/SCRN1 axis was confirmed according to the ceRNA theory and was verified by qPCR. The results indicated that PART1 knockdown markedly increased hsa-mir-21 expression but inhibited SCRN1 expression, weakening the proliferation and migration abilities.ConclusionsWe hypothesized that the paclitaxel-related ceRNA network strongly influences the innate mechanism, prognosis, and immune infiltration of pancreatic cancer. Our risk signatures can accurately predict survival outcomes and provide a clinical basis.     

ABCC9, NKAPL,and TMEM132C are potential diagnostic and prognostic markers in triple‐negative breast cancer

Triple‐negative breast cancer (TNBC) is a highly heterogeneous disease. The aim of this study is to identify the diagnostic and poor prognostic signatures in TNBC by exploring the aberrant DNA methylation and gene expression. Differential expression and methylation analysis of the TNBC and paracancer samples from The Cancer Genome Atlas were performed. Gene set enrichment and protein–protein interaction (PPI) network analysis was used to explore the mechanisms of TNBC. Methylation‐gene expression correlation analysis was performed, and multivariate Cox analysis and receiver operating characteristics analysis were used to further screen the hub genes for TNBC. We identified 1,525 differentially expressed genes and 150 differentially methylated genes between TNBC and paracancer samples. About 96.64% of the methylation sites were located on the CpG island. A total of 17 Gene Ontology biological process terms and 18 signal pathways were significantly enriched. GNG4, GNG11, PENK, MAOA, and AOX1 were identified as the core genes of the PPI network. Methylation‐expression correlations revealed that ABCC9 (cg06951626), NKAPL (cg18675097, cg01031101, and cg17384889), and TMEM132C (cg03530754) showed promise as diagnostic and prognostic markers in TNBC. ABCC9 (cg06951626), NKAPL (cg18675097, cg01031101, and cg17384889), and TMEM132C (cg03530754) were potential diagnostic and prognostic markers in TNBC.     

Identification and development of long non‐coding RNA‐associated regulatory network in colorectal cancer

Colorectal cancer (CRC) is one of the leading causes of cancer‐associated death globally. Long non‐coding RNAs (lncRNAs) have been identified as micro RNA (miRNA) sponges in a competing endogenous RNA (ceRNA) network and are involved in the regulation of mRNA expression. This study aims to construct a lncRNA‐associated ceRNA network and investigate the prognostic biomarkers in CRC. A total of 38 differentially expressed (DE) lncRNAs, 23 DEmiRNAs and 27 DEmRNAs were identified by analysing the expression profiles of CRC obtained from The Cancer Genome Atlas (TCGA). These RNAs were chosen to develop a ceRNA regulatory network of CRC, which comprised 125 edges. Survival analysis showed that four lncRNAs, six miRNAs and five mRNAs were significantly associated with overall survival. A potential regulatory axis of ADAMTS9‐AS2/miR‐32/PHLPP2 was identified from the network. Experimental validation was performed using clinical samples by quantitative real‐time PCR (qRT‐PCR), which showed that expression of the genes in the axis was associated with clinicopathological features and the correlation among them perfectly conformed to the ‘ceRNA theory’. Overexpression of ADAMTS9‐AS2 in colon cancer cell lines significantly inhibited the miR‐32 expression and promoted PHLPP2 expression, while ADAMTS9‐AS2 knockdown had the opposite effects. The constructed novel ceRNA network may provide a comprehensive understanding of the mechanisms of CRC carcinogenesis. The ADAMTS9‐AS2/miR‐32/PHLPP2 regulatory axis may serve as a potential therapeutic target for CRC.     

Identification and validation of HOXD3 and UNC5C as molecular signatures in keloid based on weighted gene co-expression network analysis

BackgroundKeloid is a benign proliferative disease characterized by excessive deposition of extracellular matrix collagen during skin wound healing. The mechanisms of keloid formation have not been fully elucidated, and the current treatment methods are not effective for all keloid patients. Therefore, there is an urgent need to find more effective therapies, and our research focused on identifying characteristic molecular signatures of keloid to explore potential therapeutic targets.MethodsGene expression profiles of keloid and control group samples were retrieved from the GEO database. Taking the GSE113619 dataset as the training set, the dataset collected skin tissues from non-lesion sites of healthy and keloid-prone individuals, denoted as Day0. The second sampling was performed 42 days later at the original sampling site of control and keloid groups, denoted as Day42.The 'limma' package and Venn diagram identified differentially expressed genes (DEGs) specific to keloid day42 versus day0 samples. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Reactome pathway functional enrichment, and annotation of the characteristic genes were conducted on the Metascape website. Ingenuity canonical pathways, disease & function enrichment analysis and gene interaction network were performed and predicted in Ingenuity Pathway Analysis (IPA) software. Key module genes related to keloid were filtered out by Weighted Gene Co-expression Network Analysis (WGCNA). We utilized the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm to screen the characteristic genes in keloid by the ‘glmnet’ package. The area under the curve (AUC) of receiver operating characteristic (ROC) was utilized to determine the effectiveness of potential signatures in discriminating keloid samples from normal samples and performed by using the ‘pROC’ package. The enrich scores of 24 immune cells in each sample were calculated by the single-sample gene set enrichment analysis (ssGSEA) algorithm, and then the Gene Set Variation Analysis (GSVA) was performed. Finally, RNA from 4 normal and 6 keloid samples was extracted, and RT-qPCR and Western Blot validated the expression of characteristic genes.ResultsA total of 640 DEGs specific to keloid day42 versus day0 samples were detected. 69 key module genes were uncovered and implicated in ‘NCAM signaling for neurite out-growth’, ‘oncogenic MAPK signaling’, ‘transmission across chemical synapses’ pathways, and the mitotic cell cycle-related processes. Five characteristic genes (MTUS1, UNC5C, CEP57, NAA35, and HOXD3) of keloid were identified by LASSO, and among which UNC5C and HOXD3 were validated by ROC plot in external dataset, RT-qPCR and Western Blot in validation samples. The result of ssGSEA indicated that the infiltration of neutrophils showed a relatively higher abundance and natural killer cells with relatively low enrichment in the keloid group compared to the control group. UNC5C was correlated with more immune cells compared with other characteristic genes.ConclusionIn this study, characteristic genes associated with keloid were identified by bioinformatic approaches and verified in clinical validation samples, providing potential targets for the diagnosis and treatment of keloid.