Screening of atrial fibrillation diagnostic markers based on a GEO database chip and bioinformatics analysis

BackgroundStudy have shown that atrial fibrillation (AF) is a disease with genetic risk, and its pathogenesis is still unclear. This study sought to screen the gene microarray data of AF patients and to perform a bioinformatics analysis to identify AF signature diagnostic genes.MethodsThe AF gene sets from the Gene Expression Omnibus (GEO) database were screened, and the differentially expressed genes (DEGs) were identified after the normalization of the data set by R software. We conducted a gene set enrichment analysis, a protein-protein interaction (PPI) network analysis, a gene-gene interaction (GGI) network analysis, and an immuno-infiltration analysis. The core genes were identified from the DEGs, and base on receiver operating characteristic, the top 5 core genes in the 2 data sets were selected as diagnostic factors and a nomogram was constructed. The miRNA of the core genes were predicted and an immune cell correlation analysis was performed.ResultsA total of 20 DEGs were identified. The functions of these DEGs were mainly related to muscle contraction, autophagosome, and bone morphogenetic protein (BMP) binding, and focused on the calcium signaling pathway, ferroptosis, the extracellular matrix-receptor interaction, and other pathways. A total of 5 core genes [i.e., GPR22 (G protein-coupled receptor 22), COG5 (component of oligomeric golgi complex 5), GALNT16 (polypeptide N-acetylgalactosaminyltransferase 16), OTOGL (otogelin-like), and MCOLN3 (mucolipin 3)] were identified, and a linear model for risk prediction was constructed, which has good prediction ability. Plasma cells and Macrophages M2 were significantly increased in AF, while T cells follicular helper and Dendritic cells activated were significantly decreased.ConclusionsIn our study, we identified 5 potential diagnostic key genes (i.e., GPR22, COG5, GALNT16, OTOGL, and MCOLN3). Our findings may provide a theoretical basis for susceptibility analyses and target drug development in AF.     

Increased PYCR1 mRNA predicts poor prognosis in kidney adenocarcinoma: A study based on TCGA database

The pyrroline-5-carboxylate reductase 1 (PYCR1) plays important roles in cancers, but its contribution to adenocarcinoma of the kidney (AK) and the potential mechanism remain to be clarified. In this study, we aimed to demonstrate the relationship between PYCR1 mRNA and AK based on The Cancer Genome Atlas database.PYCR1 mRNA in AK and normal tissues was compared using Wilcoxon rank sum test. The relationship between PYCR1 mRNA and clinicopathological characters was evaluated using logistic regression. The association between PYCR1 mRNA and survival rate was evaluated using Kaplan-Meier test and Cox regression of univariate and multivariate analysis. Additionally, Gene Set Enrichment Analysis was conducted to annotate the biological function of PYCR1 mRNA.Increased PYCR1 mRNA was found in AK tissues. Increased PYCR1 mRNA was related to high histologic grade, clinical stage, and lymph node and distant metastasis. Kaplan-Meier survival analysis and univariate analysis showed that AK patients with increased PYCR1 mRNA had worse prognosis than those without. PYCR1 mRNA remained independently associated with overall survival (HR: 1.34; 95% CI: 1.07–1.66; P = .009) in multivariate analysis. The Gene Set Enrichment Analysis suggested that ribosome, proteasome, inhibition of p53 signaling pathway, extracellular matrix receptor interaction, and homologous recombination were differentially enriched in increased PYCR1 mRNA phenotype.Increased PYCR1 mRNA is a potential marker in patients with AK. More importantly, p53 pathway, ribosome, proteasome, extracellular matrix receptor interaction, and homologous are differentially enriched in AK patients with increased PYCR1 mRNA.     

Increased expression of Cyclin F in liver cancer predicts poor prognosis: A study based on TCGA database

Background:Cyclin F (CCNF) dysfunction has been implicated in various forms of cancer, offering a new avenue for understanding the pathogenic mechanisms underlying hepatocellular carcinoma (HCC). We aimed to evaluate the role of CCNF in HCC using publicly available data from The Cancer Genome Atlas (TCGA).Method:We used TCGA data and Gene Expression Omnibus (GEO) data to analyze the differential expression of CCNF between tumor and adjacent tissues and the relationship between CCNF and clinical characteristics. We compared prognosis of patients with HCC with high and low CCNF expression and constructed receiver operating characteristic (ROC) curves. In addition, we also explored the types of gene mutations in relevant groups and conducted Gene Set Enrichment Analysis (GSEA).Results:The expression of CCNF in liver cancer tissues was significantly increased compared with that in adjacent tissues, and patients with high CCNF expression had a worse prognosis than those with low CCNF expression. Patients with high CCNF expression also had more somatic mutations. High expression of CCNF hampers the prognosis independently. The GSEA showed that the "http://www.gsea-msigdb.org/gsea/msigdb/cards/BIOCARTA_WNT_PATHWAY" Wnt pathway, "http://www.gsea-msigdb.org/gsea/msigdb/cards/BIOCARTA_P53_PATHWAY" P53 pathway, "http://www.gsea-msigdb.org/gsea/msigdb/cards/HALLMARK_PI3K_AKT_MTOR_SIGNALING" PI3K/Akt/mTOR pathway, "http://www.gsea-msigdb.org/gsea/msigdb/cards/HALLMARK_NOTCH_SIGNALING" Notch pathway were enriched in patients with the high CCNF expression phenotype.Conclusion:High CCNF expression can be seen as an independent risk factor for poor survival in HCC. Its expression may serve as a target for the diagnosis and treatment of liver cancer.     

基于TCGA数据库探讨食管癌免疫细胞浸润模式与临床特征和预后的相关性研究

目的 探讨食管癌免疫细胞浸润模式与临床特征和预后的相关性.方法 从公共数据库癌症基因组图谱(TCGA)中下载所有食管癌转录本数据以及临床相关数据,通过Cibersort软件计算22种浸润性免疫细胞类型的相对比例.采用Perl评估食管癌免疫细胞浸润模式与临床特征(年龄、性别、临床分期、肿瘤分级)的相关性,通过Kaplan...     

Identification of candidate biomarkers correlated with the pathogenesis and prognosis of breast cancer via integrated bioinformatics analysis

Background:This study was carried out to identify potential key genes associated with the pathogenesis and prognosis of breast cancer (BC).Methods:Seven GEO datasets ({"type":"entrez-geo","attrs":{"text":"GSE24124","term_id":"24124"}}GSE24124, {"type":"entrez-geo","attrs":{"text":"GSE32641","term_id":"32641"}}GSE32641, {"type":"entrez-geo","attrs":{"text":"GSE36295","term_id":"36295"}}GSE36295, {"type":"entrez-geo","attrs":{"text":"GSE42568","term_id":"42568"}}GSE42568, {"type":"entrez-geo","attrs":{"text":"GSE53752","term_id":"53752"}}GSE53752, {"type":"entrez-geo","attrs":{"text":"GSE70947","term_id":"70947"}}GSE70947, {"type":"entrez-geo","attrs":{"text":"GSE109169","term_id":"109169"}}GSE109169) were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) between BC and normal breast tissue samples were screened by an integrated analysis of multiple gene expression profile datasets. Hub genes related to the pathogenesis and prognosis of BC were verified by employing protein–protein interaction (PPI) network.Results:Ten hub genes with high degree were identified, including CDK1, CDC20, CCNA2, CCNB1, CCNB2, BUB1, BUB1B, CDCA8, KIF11, and TOP2A. Lastly, the Kaplan–Meier plotter (KM plotter) online database demonstrated that higher expression levels of these genes were related to lower overall survival. Experimental validation showed that all 10 hub genes had the same expression trend as predicted.Conclusion:The findings of this research would provide some directive significance for further investigating the diagnostic and prognostic biomarkers to facilitate the molecular targeting therapy of BC, which could be used as a new biomarker for diagnosis and to guide the combination medicine of BC.     

基于GEO数据库对胆管癌潜在分子靶点的筛选及生物信息学分析

背景胆管癌恶性程度高,预后差.靶向治疗是胆管癌的重要研究方向,探索新的分子靶点对于胆管癌靶向治疗至关重要.目的用生物信息学分析方法挖掘胆管癌的枢纽基因,为胆管癌的靶向治疗提供潜在分子靶点.方法从GEO数据库中下载2组胆管癌表达谱芯片数据,采用GEO2R在线分析工具筛选胆管癌肿瘤组织与正常组织差异表达基因,对差异表达基因作GO富集分析、KEGG通路分析、蛋白质相互作用网络分析,利用Cytoscape软件筛选枢纽基因.使用GEPIA数据库对枢纽基因在胆管癌组织中的表达量进行验证.结果共得到共同差异表达基因158个.GO富集分析结果显示,差异基因主要参与细胞对锌离子反应、细胞增殖与粘附、代谢以及蛋白质聚合等生物学过程,主要存在外泌体、胞外区、弹性纤维等区域,主要分子功能与结合肝素、半胱氨酸型内肽酶抑制剂活性、蛋白质同源二聚化、受体结合及磷酸吡哆醛结合等相关.KEGG通路分析结果显示,差异基因主要参与矿物质吸收、代谢、PPAR信号通路及脂肪酸降解等过程.基于String数据库构建蛋白质相互作用网络图,Cytoscape软件CytoHubba插件筛选枢纽基因,皆为上调基因.GEPIA数据库验证枢纽基因在胆管癌组织中表达量显著高于正常组织.结论本研究获取了8个与胆管癌相关的枢纽基因,分别是NUSAP1,TOP2A,RAD51AP1,MCM4,KIAA0101,CDCA5,TYMS,ZWINT.这些基因为深入研究胆管癌的靶向治疗提供了新思路,有望成为新的分子治疗靶点.     

BCL7B is a potential novel diagnosis and prognosis biomarker for sarcomas using bioinformatics analysis

BCL7B plays a potential role in the progression of various cancers, while its role in sarcomas is unknown. We aimed to evaluate BCL7B''s diagnostic and prognostic value, and potential BCL7B-related mechanisms in sarcomas based on The Cancer Genome Atlas (TCGA) database. We collected patients with sarcoma from TCGA. Wilcoxon rank sum test was used to compare the expression of BCL7B in sarcoma samples with different clinical-pathologic features. Univariate Cox regression analysis and multivariate Cox regression analysis were used to evaluate prognosis factors for sarcoma. Gene set enrichment analysis (GSEA) was conducted to elucidate the significant functions and pathways associated with BCL7B. BCL7B was a potential biomarker for distinguishing normal and tumor tissues with the analysis of ROC curve (AUC = 0.588). Low BCL7B expression was significantly correlated with tumor multifocal (OR = 0.39 for yes vs no), larger residual tumor (OR = 0.40 for R1,R2 vs RO), male gender (OR = 0.48 for male vs female) and White race (OR = 0.29 for White vs Asian, Black or African American). High BCL7B expression was correlated with leiomyosarcoma histological type (OR = 6.08 for leiomyosarcoma vs dedifferentiated liposarcoma, pleomorphic sarcoma). Univariate and multivariate Cox regression analysis showed that low BCL7B expression was independently associated with poor overall survival (P = .008). GSEA showed that GPCR (G protein-coupled receptors) ligand binding, secreted factors, class A1 rhodopsin-like receptors, extracellular matrix organization, core matrisome, Fc epsilon receptor I mediated NF-κB activation, and WNT signaling pathway were differentially enriched in BCL7B low expression phenotype (|NES| > 1, adjusted P value <.05, and FDR value <0.25). BCL7B may play an important role in sarcoma progression and may be a potential biomarker for prognosis and diagnosis in sarcomas.     

基于生物信息学筛选炎症相关结直肠癌基因及其特征分析

目的通过对炎症性肠病差异基因的筛选以及结直肠癌队列生存特征和表达模式的探究,为炎症相关结直肠癌的发生与发展的后续研究提供候选基因。 方法从GEO数据库中选择RNA测序表达谱数据集GSE95473和GSE107597,通过常规转录组表达谱差异分析,筛选出炎症性肠病差异表达基因（DEG）,利用GO数据库获取DEG的功能注释,并利用KEGG数据库进行通路富集分析。同时基于TCGA数据库进一步在结直肠癌数据集中筛选具有预后意义的基因,并评价其在结直肠肿瘤中的表达特征。 结果两个数据集筛选到了共有DEGs 100个,通过主成分分析证实这些基因能够对结直肠癌肿瘤和黏膜区分良好。进一步筛选,获得ALDOB,SPINK4,REG4,IL1B,C2CD4A,CXCL8,NOS2,CXCL3等候选基因。这些基因在结直肠肿瘤中高表达,并且这些基因的高表达往往提示患者预后较好。 结论ALDOB,SPINK4,REG4,IL1B,C2CD4A,CXCL8,NOS2,CXCL3可能在炎症相关结直肠癌的发生发展中发挥重要作用,有待于后续炎癌转化相关功能验证和机制探究。     

基于TCGA和GEO数据挖掘分析NAT1在结肠癌中的表达及预后意义

目的探讨结肠癌癌组织中N-乙酰化转移酶1（NAT1）的表达及其对结肠癌患者预后的影响。 方法通过肿瘤免疫评估资源（TIMER）数据库分析NAT1 mRNA在33种肿瘤中的表达情况,并用人类蛋白质图谱（HPA）数据库的免疫组化结果验证NAT1蛋白在结肠癌中的表达。通过肿瘤基因图谱（TCGA）和基因表达综合（GEO）数据库获得NAT1在结肠癌中的表达数据及相关临床特征参数,分析NAT1 mRNA表达水平与结肠癌患者的临床特征和总生存期（OS）的关系,并构建预后模型。采用基因集富集分析（GSEA）预测NAT1相关的基因通路。采用CIBERSORT分析NAT1与免疫浸润的关系。 结果TIMER数据库分析结果显示,在13种肿瘤组织中NAT1 mRNA表达水平低于正常对照组织。TCGA数据库结果提示,结肠癌组织中NAT1 mRNA表达水平均明显低于正常对照组织或癌旁正常组织,差异均有统计学意义（均P＜0.01）,并在GSE44076、GSE44861和GSE73360中得到验证。HPA数据库的免疫组化结果提示,NAT1蛋白在结肠癌组织中呈低表达。TCGA数据库分析结果提示,NAT1 mRNA表达水平与结肠癌患者的N分期、M分期和stage分期均有关（均P＜0.01）。NAT1高表达组患者OS均好于低表达组（均P＜0.05）。单因素Cox分析表明,NAT1 mRNA表达水平是影响结肠癌患者OS的危险因素（P＜0.05）,并和其他危险因素构建列线图,同时使用校准曲线和ROC评估了预后模型的特异性和敏感性。选取本院确诊的35例结肠癌患者肿瘤组织作为肿瘤组,选取其癌旁正常组织作为对照组。采用实时荧光定量PCR（qRT-PCR）法检测NAT1表达水平,结果与数据库结果一致（P＜0.05）。GSEA结果提示NAT1高表达样本上调抗坏血酸和醛糖酸盐代谢、戊糖和葡萄糖醛酸的相互转化、淀粉和蔗糖代谢、卟啉和叶绿素代谢途径、视黄醇代谢、药物代谢相关酶等基因集,而下调糖胺聚糖生物合成途径、Hedgehog信号通路、基底细胞癌、ECM受体作用通路、神经活性配体-受体相互作用途径、Wnt信号通路等基因集。使用CIBERSORT计算每个样品中的免疫细胞浸润,高NAT1组免疫细胞中原始B细胞、静息记忆CD4 T细胞、静息树突状细胞、活化树突状细胞显著增加,而M0巨噬细胞显著减少（均P＜0.05）。 结论结肠癌中NAT1 mRNA表达水平下调,NAT1低表达提示结肠癌患者的预后差,可作为结肠癌患者治疗的潜在靶点。     

Establishment of a prognostic signature for lung adenocarcinoma by integration of 7 pyroptosis-related genes and cross-validation between the TCGA and GEO cohorts: A comprehensive bioinformatics analysis

Pyroptosis-related genes (PRGs) have been reported to be associated with prognosis of lung adenocarcinoma (LUAD). Until now, the relationship of PRGs to the prognosis of LUAD patients and its underlying mechanisms have been poorly elucidated. Using The Cancer Genome Atlas (TCGA) LUAD cohort, a prior bioinformatics analysis constructed a prognostic signature incorporating 5 PRGs (NLRP7, NLRP1, NLRP2, NOD1, and CASP6) for predicting prognosis of LUAD patients. However, it has not been validated by the Gene Expression Omnibus (GEO) LUAD cohort yet. We implemented a modified bioinformatics analysis to, respectively, construct one prognostic signature with the TCGA cohort and with the GEO cohort and attempted to perform cross-validations by the GEO cohort and the TCGA cohort alternately in turn. Univariate and multivariate Cox regression analysis screened PRGs and constructed 2 prognostic signatures with the TCGA and GEO cohorts. All LUAD samples were classified into high- and low-risk groups according to the median risk score that was generated by regression formula. Kaplan-Meier survival analysis compared the overall survival rate between the 2 risk groups, and receiver operating characteristic curve analysis evaluated predictive performance of the 2 signatures. Additionally, risk score, combined with clinicopathological features, was subjected to multivariate Cox regression analysis, to evaluate independent prognostic value of the 2 signatures. Finally, the 2 signatures received cross-validations by the GEO and TCGA cohorts, alternately. The TCGA cohort yielded a 3-gene signature (PYCARD, NLRP1, and NLRC4), whereas the GEO cohort built a 7-gene signature (SCAF11, NOD1, NLRP2, NLRP1, GPX4, CASP8, and AIM2) for predicting the prognosis of LUAD patients. Multivariate analysis proved independent prognostic value of risk score in the TCGA cohort (hazard ratio, = 1.939,; P = 8.43 × 10⁻⁴) and the GEO cohort (hazard ratio, = 2.291,; P = 4.34 × 10⁻⁹). Cross-validations confirmed prognostic value for the 7-gene signature from the GEO cohort by the TCGA cohort but not for the 3-gene signature from the TCGA cohort by the GEO cohort. We develop and validate a 7-gene prognostic signature (SCAF11, NOD1, NLRP2, NLRP1, GPX4, CASP8, and AIM2) with independent prognostic value for patients with LUAD.     

Identification of potential biomarkers for colorectal cancer by clinical database analysis and Kaplan–Meier curves analysis

This study aimed to explore critical genes as potential biomarkers for the diagnosis and prognosis of colorectal cancer (CRC) for clinical utility. To identify and screen candidate genes involved in CRC carcinogenesis and disease progression, we downloaded microarray datasets {"type":"entrez-geo","attrs":{"text":"GSE89076","term_id":"89076"}}GSE89076, {"type":"entrez-geo","attrs":{"text":"GSE73360","term_id":"73360"}}GSE73360, and {"type":"entrez-geo","attrs":{"text":"GSE32323","term_id":"32323"}}GSE32323 from the GEO database identified differentially expressed genes (DEGs), and performed a functional enrichment analysis. A protein-protein interaction network was constructed, and correlated module analysis was performed using STRING and Cytoscape. The Kaplan–Meier survival curve shows the survival of the hub genes. The expression of cyclin-dependent kinase (CDK1), cyclin B1 (CCNB1), and PCNA in tissues and changes in tumor grade were analyzed. A total of 329 DEGs were identified, including 264 upregulated and 65 downregulated genes. The functions and pathways of DEGs include the mitotic cell cycle, poly(A) RNA binding replication, ATP binding, DNA replication, ribosome biogenesis in eukaryotes, and RNA transport. Forty-seven Hub genes were identified, and biological process analysis showed that these genes were mainly enriched in cell cycle and DNA replication. Patients with mutations in CDK1, PCNA, and CCNB1 had poorer survival rates. CDK1, PCNA, and CCNB1 were significantly overexpressed in the tumor tissues. The expression of CDK1 and CCNB1 gradually decreased with increasing tumor grade. CDK1, CCNB1, and PCNA can be used as potential markers for the diagnosis and prognosis of CRC. These genes are overexpressed in colon cancer tissues and are associated with low survival rates in CRC patients.     

基于TCGA数据库分析肝细胞癌组织UCK2、PRIM1和DNTM1基因水平对患者预后的影响

目的 通过分析TCGA数据库肝细胞癌(HCC)组织基因组数据,分析差异基因,寻找影响肝癌患者预后的分子标志物.方法 搜索癌症基因图谱(TCGA)数据库,查找HCC组织差异基因及临床和病理学资料,进行基因筛选和生存分析.根据差异基因水平,以fdr=0.05和lgFC=1为筛选依据,绘制生存曲线,选择基因集"c2.cp.k...     

Molecular clustering based on gene set expression and its relationship with prognosis in patients with lung adenocarcinoma

BackgroundLung adenocarcinoma (LUAD) is a subtype of lung cancer with high morbidity and mortality. While genotyping is an important determinant for the prognosis of LUAD patients, there is a paucity of studies on gene set-based expression (GSE) typing for LUAD. This current study used GSE methodology to perform gene typing of LUAD patients.MethodsClinical and genomic information of the LUAD patients were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Patients with LUAD were clustered into different molecular subtypes depending on the clinical and gene set expression characteristics. The survival rate and silhouette widths were compared between each molecular subtype. Differences in survival rate between gene sets were analyzed using Kaplan-Meier survival curves. Cox regression and Lasso regression were used to establish the prognostic gene set model based on the TCGA database, and the results were validated using the GEO dataset.ResultsA total of 10 hub genes were finally identified and clustered into 3 subtypes with a mean contour width of 0.96. There were significant differences in survival rates among the 3 subtypes (P<0.05). Gene Ontology (GO) analysis indicated that the related biological processes (BP) were mainly involved in regulation of cell cycle, mitotic cell cycle phase transition, and proteasome-mediated ubiquitin-dependent protein catabolic process. The cellular components (CC) were related to the spindle, chromosomal region, and midbody. Molecular function (MF) mainly focused on ubiquitin-like protein ligase binding, translation regulator activity, and oxidation activity. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis showed that the main pathways included the Epstein Barr virus infection pathway of neurogeneration, the p53 signaling pathway, and the proteome pathways. In addition, the protein-protein interaction network was analyzed using the STRING and Cytospace software, and the top 9 hub genes identified were KIF2C, DLGAP5, KIF20A, PSMC1, PSMD1, PSMB7, SNAI2, FGF13, and BMP2.ConclusionsPatients with LUAD can be clustered into three subtypes based on the expression of gene sets. These findings contribute to understanding the pathogenesis and molecular mechanisms in LUAD, and may lead to potential individualized pharmacogenetic therapy for patients with LUAD.     

Identification of key genes associated with the progression of liver fibrosis to hepatocellular carcinoma based on iTRAQ proteomics and GEO database

Introduction and objectiveLiver fibrosis (LF) often leads to cirrhosis and even hepatocellular carcinoma (HCC), but the molecular mechanism remains unclear. The aims of the present study were to identify potential biomarkers for the progression of LF to HCC and explore the associated molecular mechanisms.Materials and methodsThe isobaric tags for relative and absolute quantitation (iTRAQ) was used to detect changes in the protein expression profiles of liver tissues and to screen the differentially expressed proteins (DEPs). The differentially expressed genes (DEGs) of LF rats and patients were screened by Gene Expression Database (GEO). Subsequently, the clinicopathological analysis of the overlapping genes in different pathological stages in HCC patients based on GEPIA database was conducted.ResultsiTRAQ proteomic analysis revealed 689, 749 and 585 DEPs in the 6W, 8W and 12W groups, respectively. ALDH2, SLC27A5 and ASNS were not only the DEPs found in rats with LF with different stages but were also the DEGs related to the pathological stages and survival in patients with HCC.ConclusionsALDH2, SLC27A5 and ASNS were the potential biomarkers associated with the progression of LF to HCC.     

Identification of key genes and pathways associated with sex differences in rheumatoid arthritis based on bioinformatics analysis

Clinical Rheumatology - Women are more likely than men to develop the chronic, progressive autoimmune disease known as rheumatoid arthritis (RA). Although there may be a complex interplay between...     

Mining TCGA and GEO databases for the prediction of poor prognosis in lung adenocarcinoma based on up-regulated expression of TNS4

To investigate the clinical significance of Tensin4 (TNS4) in human cancers, particularly lung cancer, we mined the Cancer Genome Atlas database for lung adenocarcinoma (TCGA-LUAD) and the Gene Expression Omnibus database to predict poor prognosis based on the up-regulated expression of TNS4 in LUAD. The correlation between the clinical pathologic features of patients and TNS4 gene expression was analyzed using the Wilcoxon signed-rank test. Cox regression analysis was used to evaluate the association of clinicopathologic characteristics with the overall survival (OS) of cancer patients using TCGA data. The relationship between TNS4 expression and cancer patient survival was evaluated with Kaplan–Meier survival curves and meta-analyses. GO and KEGG were also included in the data mining methods. The expression level of TNS4 in LUAD tissue was higher than that in adjacent normal tissue (P < .001). According to the Kaplan–Meier survival curve, LUAD patients with high TNS4 expression had worse prognosis than those with low TNS4 expression (P < .001 for OS; P = .028 for progression-free survival). A positive correlation between TNS4 expression and poor OS was found with both univariate and multivariate analyses. Increased TNS4 expression in LUAD was closely correlated with a higher disease stage (P = .007), positive lymph nodes (P = .005), and larger tumor size (P = .002). Moreover, meta-analysis including seven independent datasets showed LUAD patients with higher TNS4 had poorer OS (combined hazard ratio = 1.27, 95% confidence interval 1.16–1.39). In the high-TNS4 population, regulation of the actin cytoskeleton, extracellular matrix receptor interactions, and focal adhesion were differentially enriched. Integrin α6β4 and laminin-5 genes were also associated with TNS4. TNS4 expression may be a potential biomarker for predicting poor survival in LUAD. Moreover, the correlation between TNS4 and integrin α6β4 may be attributed to the role of TNS4 in LUAD.     

基于生物信息学的糖尿病心肌病生物标志物及关键通路的筛选

目的通过对基因表达(GEO)数据库中糖尿病心肌病(DCM)相关的基因芯片进行生物信息学分析,获得DCM的生物标志物及其调控的关键通路。方法从GEO数据库获取DCM的基因表达芯片(GSE26887),并借助DAVID在线分析平台对这些基因进行基因本体论(GO)富集分析和京都基因与基因组百科全书(KEGG)信号通路分析,同时利用生物信息学软件STRING 10.0构建这些基因的蛋白-蛋白相互作用(PPI)网络。结果本研究中所采用的芯片GSE26887共包含7例DCM患者及5名健康对照。共筛选出差异表达基因(DEGs)236个,包括134个上调基因及102个下调基因。其中,差异最大的5个上调基因依次为NPPA、SFRP4、DSC1、NEB及FRZB;差异最大的5个下调基因依次为SERPINE1、SERPINA3、ANKRD2、XRCC4及S100A8。GO和KEGG结果表明,DCM发展过程中的DEGs主要富集在炎症、免疫紊乱、代谢紊乱、线粒体功能障碍等方面。PPI网络揭示连接度最高的15个hub基因依次为IL-6、MYC、ACTA2、SERPINE1、ASPN、SPP1、KIT、TFRC、FMOD、PDE5A、MYH6、FPR1、C3、CDKN1A及SOCS3。结论 DCM患者的DEGs与炎症、免疫紊乱及能量代谢密切相关,本研究所筛选出的差异最大的5个上调基因和5个下调基因有望成为DCM诊断的标志分子,15个hub基因有望成为DCM治疗的靶点。     

基于Oncomine及TCGA数据集分析结肠肿瘤中HMGB1基因的表达及预后价值

目的研究结肠肿瘤中高迁移率族蛋白B1基因（HMGB1）的差异表达及预后价值。 方法从Oncomine及TCGA数据集中筛选出2 191例结肠肿瘤患者HMGB1基因表达数据及临床病理数据,采用Mann-Whitney U检验比较结肠癌与腺瘤、左半结肠癌与右半结肠癌、原位癌与浸润癌、黏液性腺癌与其他病理类型结肠癌、以及发生淋巴结转移与无淋巴结转移、发生远处转移与无远处转移结肠癌组织中HMGB1基因差异表达情况,并绘制Kaplan-Meier生存曲线。 结果HMGB1基因在结肠癌组织和腺瘤组织中均较正常结肠组织高表达（P<0.001）,在结肠癌组织中较结肠腺瘤组织中高表达,在左半结肠癌组织中较右半结肠癌高表达（P<0.05）,在黏液性腺癌组织中较其他病理类型低表达（P<0.05）,在浸润癌组织中较原位癌高表达（P<0.001）。有淋巴结转移及远处转移者较未转移者高表达（P<0.05）。HMGB1基因高表达提示更高的5年生存率（P=0.011）,尤其对于女性结肠癌患者（P=0.006）。 结论HMGB1基因可作为判断结肠癌浸润深度、淋巴转移、远处转移及预后的标志物。     

基于GEO和TCGA数据库食管鳞状细胞癌预后模型构建与验证

目的探讨食管鳞状细胞癌(esophageal squamous cell carcinoma,ESCC)组织与正常组织之间的差异表达基因,构建ESCC的预后相关模型并验证其临床应用价值。方法首先,基于GSE20347、GSE23400、GSE26886、GSE45168、GSE77861数据集确定ESCC的差异表达基因。其次,经通路富集后使用STRING和Cytoscape软件筛选关键基因。随后,Kaplan-Meier Plotter和单变量Cox回归用于生存分析,多因素Cox回归应用于构建预后模型。实时荧光定量PCR用于验证预后相关基因的差异表达情况。此外,我们通过Kaplan-Meier曲线、ROC曲线、一致性指数、灵敏度和特异度验证模型的预后价值。最后,利用基因集富集分析进一步探讨ESCC预后机制。结果本研究发现98个差异表达基因和15个关键基因,其中6个关键基因与预后相关。此外,基于VCAN、ALOX12和ACPP的预后模型经验证临床应用价值较好。结论基于VCAN、ALOX12和ACPP的预后模型可独立预测ESCC的预后。