Identification of MYH6 as the potential gene for human ischaemic cardiomyopathy

The present study aimed to explore the potential hub genes and pathways of ischaemic cardiomyopathy (ICM) and to investigate the possible associated mechanisms. Two microarray data sets ( GSE5406 and GSE57338 ) were downloaded from the Gene Expression Omnibus (GEO) database. The limma package was used to analyse the differentially expressed genes (DEGs). Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment, Disease Ontology (DO) and Gene Ontology (GO) annotation analyses were performed. A protein-protein interaction (PPI) network was set up using Cytoscape software. Significant modules and hub genes were identified by the Molecular Complex Detection (MCODE) app. Then, further functional validation of hub genes in other microarrays and survival analysis were performed to judge the prognosis. A total of 1065 genes were matched, with an adjusted p < 0.05, and 17 were upregulated and 25 were downregulated with|log₂ (fold change)|≥1.2. After removing the lengthy entries, GO identified 12 items, and 8 pathways were enriched at adjusted p < 0.05 (false discovery rate, FDR set at <0.05). Three modules with a score >8 after MCODE analysis and MYH6 were ultimately identified. When validated in GSE23561 , MYH6 expression was lower in patients with CAD than in healthy controls (p < 0.05). GSE60993 data suggested that MYH6 expression was also lower in AMI patients (p < 0.05). In the GSE59867 data set, MYH6 expression was lower in CAD patients than in AMI patients and lower in heart failure (HF) patients than in non-HF patients. However, there was no difference at different periods within half a year, and HF was increased when MYH6 expression was low (p < 0.05–0.01). We performed an integrated analysis and validation and found that MYH6 expression was closely related to ICM and HF. However, whether this marker can be used as a predictor in blood samples needs further experimental verification.     

Identification of differentially expressed genes and biological characteristics of colorectal cancer by integrated bioinformatics analysis

Colorectal cancer (CRC) ranks as one of the most common malignant tumors worldwide. Its mortality rate has remained high in recent years. Therefore, the aim of this study was to identify significant differentially expressed genes (DEGs) involved in its pathogenesis, which may be used as novel biomarkers or potential therapeutic targets for CRC. The gene expression profiles of GSE21510, GSE32323, GSE89076, and GSE113513 were downloaded from the Gene Expression Omnibus (GEO) database. After screening DEGs in each GEO data set, we further used the robust rank aggregation method to identify 494 significant DEGs including 212 upregulated and 282 downregulated genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed by DAVID and the KOBAS online database, respectively. These DEGs were shown to be significantly enriched in different cancer-related functions and pathways. Then, the STRING database was used to construct the protein–protein interaction network. The module analysis was performed by the MCODE plug-in of Cytoscape based on the whole network. We finally filtered out seven hub genes by the cytoHubba plug-in, including PPBP, CCL28, CXCL12, INSL5, CXCL3, CXCL10, and CXCL11. The expression validation and survival analysis of these hub genes were analyzed based on The Cancer Genome Atlas database. In conclusion, the robust DEGs associated with the carcinogenesis of CRC were screened through the GEO database, and integrated bioinformatics analysis was conducted. Our study provides reliable molecular biomarkers for screening and diagnosis, prognosis as well as novel therapeutic targets for CRC.     

Identification of hub genes and pathways in adrenocortical carcinoma by integrated bioinformatic analysis

Adrenocortical carcinoma (ACC), a rare malignant neoplasm originating from adrenal cortical cells, has high malignancy and few treatments. Therefore, it is necessary to explore the molecular mechanism of tumorigenesis, screen and verify potential biomarkers, which will provide new clues for the treatment and diagnosis of ACC. In this paper, three gene expression profiles (GSE10927, GSE12368 and GSE90713) were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were obtained using the Limma package. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were enriched by DAVID. Protein‐protein interaction (PPI) network was evaluated by STRING database, and PPI network was constructed by Cytoscape. Finally, GEPIA was used to validate hub genes’ expression. Compared with normal adrenal tissues, 74 up‐regulated DEGs and 126 down‐regulated DEGs were found in ACC samples; GO analysis showed that up‐regulated DEGs were enriched in organelle fission, nuclear division, spindle, et al, while down‐regulated DEGs were enriched in angiogenesis, proteinaceous extracellular matrix and growth factor activity; KEGG pathway analysis showed that up‐regulated DEGs were significantly enriched in cell cycle, cellular senescence and progesterone‐mediated oocyte maturation; Nine hub genes (CCNB1, CDK1, TOP2A, CCNA2, CDKN3, MAD2L1, RACGAP1, BUB1 and CCNB2) were identified by PPI network; ACC patients with high expression of 9 hub genes were all associated with worse overall survival (OS). These hub genes and pathways might be involved in the tumorigenesis, which will offer the opportunities to develop the new therapeutic targets of ACC.     

Hub genes identification and association of key pathways with hypoxia in cancer cells: A bioinformatics analysis

Three human cancer cell lines (A549, HCT116, and HeLa) were used to investigate the molecular mechanisms and potential prognostic biomarkers associated with hypoxia. We obtained gene expression data from Gene Expression Omnibus (GEO) datasets GSE11704, GSE147384, and GSE38061, which included 5 hypoxic and 8 control samples. Using the GEO2R tool and Venn diagram software, we identified common differentially expressed genes (cDEGs). The cDEGs were then subjected to Gene ontology (GO) and Kyoto Encyclopedia of Gene and Genome (KEGG) pathway analysis by employing DAVID. The hub genes were identified from critical PPI subnetworks through CytoHuba plugin and these genes' prognostic significance and expression were verified using Kaplan-Meier analysis and Gene Expression Profiling Interactive Analysis (GEPIA), respectively. The research showed 676 common DEGs (cDEGs), with 207 upregulated and 469 downregulated genes. The STRING analysis showed 673 nodes and 1446 edges in the PPI network. We identified 4 significant modules and 19 downregulated hub genes. GO analysis revealed all of them were majorly involved in ribosomal large subunit assembly and biogenesis, rRNA processing, ribosome biogenesis, translation, RNA & protein binding frequently at the sites of nucleolus and nucleoplasm while 11 were significantly associated with a better prognosis of hypoxic tumors. Our research sheds light on the molecular mechanisms that underpin hypoxia in human cancer cell lines and identifies potential prognostic biomarkers for hypoxic tumors.     

Identification of potentially critical genes in the development of heart failure after ST-segment elevation myocardial infarction (STEMI)

Heart failure (HF) remains a common complication after acute ST-segment elevation myocardial infarction (STEMI). Here, we aim to identify critical genes related to the developed HF in patients with STEMI using bioinformatics analysis. The microarray data of GSE59867, including peripheral blood samples from nine patients with post-infarct HF and eight patients without post-infarct HF, were downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) between HF and non-HF groups were screened by LIMMA package. Functional enrichment analyses of DEGs were conducted, followed by construction of a protein-protein interaction (PPI) network. The dynamic messenger RNA (mRNA) level of the hub genes during the follow-up was analyzed to further elucidate their role in HF development. A total of 58 upregulated and 75 downregulated DEGs were screen out. They were mainly enriched in biological processes about inflammatory response, extracellular matrix organization, response to cAMP, immune response, and positive regulation of cytosolic calcium ion concentration. Pathway analysis revealed that the DEGs were also involved in hematopoietic cell lineage, pathways in cancer, and extracellular matrix-receptor interaction. In the PPI network consisting of 58 nodes and 72 interactions, CXCL8 (degree = 15), THBS1 (degree = 8), FOS (degree = 7), and ITGA2B (degree = 6) were identified as the hub genes. In the comparison of patients with and without post-infarct HF, the mRNA level of these hub genes were all higher within 30 days but reached similar at 6 months after STEMI. In conclusion, CXCL8, THBS1, FOS, and ITGA2B may play important roles in the development of HF after acute STEMI.     

Screening and identification of hub genes in pancreatic cancer by integrated bioinformatics analysis

Pancreatic cancer (Pa) is a malignant tumor of the digestive tract with high degree of malignancy, this study aimed to obtain the hub genes in the tumorigenesis of Pa. Microarray datasets GSE15471, GSE16515, and GSE62452 were downloaded from Gene Expression Omnibus (GEO) database, GEO2R was conducted to screen the differentially expressed genes (DEGs), and functional enrichment analyses were carried out by Database for Annotation, Visualization and Integrated Discovery (DAVID). The protein-protein interaction (PPI) network was constructed with the Search Tool for the Retrieval of Interacting Genes (STRING), and the hub genes were identified by Cytoscape. Totally 205 DEGs were identified, consisting of 51 downregulated genes and 154 upregulated genes enriched in Gene Ontology terms including extracellular matrix (ECM) organization, collagen binding, cell adhesion, and pathways associated with ECM-receptor interaction, focal adhesion, and protein digestion. Two modules in the PPI were chosen and biological process analyses showed that the module genes were mainly enriched in ECM and cell adhesion. Twenty-four hub genes were confirmed, the survival analyses from the cBioPortal online platform revealed that topoisomerase (DNA) II α (TOP2A), periostin (POSTN), plasminogen activator, urokinase (PLAU), and versican (VCAN) may be involved in the carcinogenesis and progression of Pa, and the receiver-operating characteristic curves indicated their diagnostic value for Pa. Among them, TOP2A, POSTN, and PLAU have been previously reported as biomarkers for Pa, and far too little attention has been paid to VCAN. Analysis from R2 online platform showed that Pa patients with high VCAN expression were more sensitive to gemcitabine than those with low level, suggesting that VCAN may be an indicator to guide the use of the chemotherapeutic drug. In vitro experiments also showed that the sensitivity of the VCAN siRNA group to gemcitabine was lower than that of the control group. In conclusion, this study discerned hub genes and pathways related to the development of Pa, and VCAN was identified as a novel biomarker for the diagnose and therapy of Pa.     

数据挖掘探究肝-肾-肺纤维化的关键基因

当前用于纤维化治疗的方法很少且疗效有限,为进一步了解纤维化的消退机制以发现潜在的治疗靶点。从Gene Expression Omnibus(GEO)数据库中选取了三个具有代表性的小鼠肝、肾、肺纤维化样本的mRNA数据集,使用GEO2R工具和Venn分析识别了差异表达基因(Differentially Expressed Genes, DEGs)。通过Webgestalt在线工具对DEGs进行基因功能富集。蛋白质-蛋白质相互作用(Protein-Protein Interactions, PPI)网络是由STRING数据库生成的。然后利用CytoHubba插件探索了关键基因,分别选取了三器官共有DEG和肝特异性DEG中MCC (Maximal Clique Centrality)得分最高的前10个作为关键基因。研究中整合分析了基于小鼠模型的肝-肾-肺纤维化的数据集,GSE36066和GSE97546用于第一轮的DEG分析,由于研究除了探究三种器官纤维化共有差异基因,也进一步探究了肝纤维化特有关键基因,所以引入另外一个肝纤维化数据集GSE55747用于验证分析。结果识别出58个肝肺肾纤维化...     

Restoration of miR-29b exerts anti-cancer effects on glioblastoma

Background

Methylation plays an important role in the etiology and pathogenesis of colorectal cancer (CRC). This study aimed to identify aberrantly methylated-differentially expressed genes (DEGs) and pathways in CRC by comprehensive bioinformatics analysis.

Methods

Data of gene expression microarrays (GSE68468, GSE44076) and gene methylation microarrays (GSE29490, GSE17648) were downloaded from GEO database. Aberrantly methylated-DEGs were obtained by GEO2R. Functional and enrichment analyses of selected genes were performed using DAVID database. Protein–protein interaction (PPI) network was constructed by STRING and visualized in Cytoscape. MCODE was used for module analysis of the PPI network.

Results

Totally 411 hypomethylation-high expression genes were identified, which were enriched in biological processes of response to wounding or inflammation, cell proliferation and adhesion. Pathway enrichment showed cytokine–cytokine receptor interaction, p53 signaling and cell cycle. The top 5 hub genes of PPI network were CAD, CCND1, ATM, RB1 and MET. Additionally, 239 hypermethylation-low expression genes were identified, which demonstrated enrichment in biological processes including cell–cell signaling, nerve impulse transmission, etc. Pathway analysis indicated enrichment in calcium signaling, maturity onset diabetes of the young, cell adhesion molecules, etc. The top 5 hub genes of PPI network were EGFR, ACTA1, SST, ESR1 and DNM2. After validation in TCGA database, most hub genes still remained significant.

Conclusion

In summary, our study indicated possible aberrantly methylated-differentially expressed genes and pathways in CRC by bioinformatics analysis, which may provide novel insights for unraveling pathogenesis of CRC. Hub genes including CAD, CCND1, ATM, RB1, MET, EGFR, ACTA1, SST, ESR1 and DNM2 might serve as aberrantly methylation-based biomarkers for precise diagnosis and treatment of CRC in the future.

     

应用GSEA和WGCNA方法分析细菌性败血症宿主关键差异基因及意义

【目的】采用生物信息学方法分析公共数据库来源的细菌性败血症患者全血转录组学表达谱,探讨细菌败血症相关的宿主关键差异基因及意义。【方法】基于GEO数据库中GSE80496和GSE72829全血转录组基因数据集,采用GEO2R、基因集富集分析(GSEA)联用加权基因共表达网络分析(WGCNA)筛选细菌性败血症患者相比健康人群显著改变的差异基因,通过R软件对交集基因进行GO功能分析和KEGG富集分析。同时,通过String 11.0和Cytoscape分析枢纽基因,验证枢纽基因在数据集GSE72809(Health组52例,Definedsepsis组52例)全血标本中的表达情况,并探讨婴儿性别、月(胎)龄、出生体重、是否接触抗生素等因素与靶基因表达谱间的关系。【结果】分析GSE80496和GSE72829数据集分别筛选得到932个基因和319个基因,联合WGCNA枢纽模块交集得到与细菌性败血症发病相关的10个枢纽基因(MMP9、ITGAM、CSTD、GAPDH、PGLYRP1、FOLR3、OSCAR、TLR5、IL1RN和TIMP1);GSEA分析获得关键通路(氨基酸糖类-核糖代谢、PPAR信号通路、聚糖生物合成通路、自噬调控通路、补体、凝血因子级联反应、尼古丁和烟酰胺代谢、不饱和脂肪酸生物合成和阿尔兹海默症通路)及生物学过程(类固醇激素分泌、腺苷酸环化酶的激活、细胞外基质降解和金属离子运输)。【结论】本项研究通过GEO2R、GSEA联用WGCNA分析,筛选出与细菌性败血症发病相关的2个枢纽模块、10个枢纽基因以及一些关键信号通路和生物学过程,可为后续深入研究细菌性败血症致病机制奠定理论依据。     

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。     

Identification of key genes and pathways of thyroid cancer by integrated bioinformatics analysis

Thyroid cancer is a common endocrine malignancy with a rapidly increasing incidence worldwide. Although its mortality is steady or declining because of earlier diagnoses, its survival rate varies because of different tumour types. Thus, the aim of this study was to identify key biomarkers and novel therapeutic targets in thyroid cancer. The expression profiles of GSE3467, GSE5364, GSE29265 and GSE53157 were downloaded from the Gene Expression Omnibus database, which included a total of 97 thyroid cancer and 48 normal samples. After screening significant differentially expressed genes (DEGs) in each data set, we used the robust rank aggregation method to identify 358 robust DEGs, including 135 upregulated and 224 downregulated genes, in four datasets. Gene Ontology and Kyoto Encyclopaedia of Genes and Genomes pathway enrichment analyses of DEGs were performed by DAVID and the KOBAS online database, respectively. The results showed that these DEGs were significantly enriched in various cancer-related functions and pathways. Then, the STRING database was used to construct the protein–protein interaction network, and modules analysis was performed. Finally, we filtered out five hub genes, including LPAR5, NMU, FN1, NPY1R, and CXCL12, from the whole network. Expression validation and survival analysis of these hub genes based on the The Cancer Genome Atlas database suggested the robustness of the above results. In conclusion, these results provided novel and reliable biomarkers for thyroid cancer, which will be useful for further clinical applications in thyroid cancer diagnosis, prognosis and targeted therapy.     

Identification of key microRNAs and hub genes in non-small-cell lung cancer using integrative bioinformatics and functional analyses

Non-small-cell lung cancer (NSCLC) is an extremely debilitating respiratory malignancy. However, the pathogenesis of NSCLC has not been fully clarified. The main objective of our study was to identify potential microRNAs (miRNAs) and their regulatory mechanism in NSCLC. Using a systematic review, two NSCLC-associated miRNA data sets (GSE102286 and GSE56036) were obtained from Gene Expression Omnibus, and the differentially expressed miRNAs (DE-miRNAs) were accessed by GEO2R. Survival analysis of candidate DE-miRNAs was conducted using the Kaplan-Meier plotter database. To further illustrate the roles of DE-miRNAs in NSCLC, their potential target genes were predicted by miRNet and were annotated by the Database for Annotation, Visualization and Integrated Discovery (DAVID) program. Moreover, the protein-protein interaction (PPI) and miRNA-hub gene regulatory network were established using the STRING database and Cytoscape software. The function of DE-miRNAs in NSCLC cells was evaluated by transwell assay. Compared with normal tissues, a total of eight DE-miRNAs was commonly changed in two data sets. The survival analysis showed that six miRNAs (miR-21-5p, miR-31-5p, miR-708-5p, miR-30a-5p, miR-451a, and miR-126-3p) were significantly correlated with overall survival. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis indicated that target genes of upregulated miRNAs were enriched in pathways in cancer, microRNAs in cancer and proteoglycans in cancer, while the target genes of downregulated miRNAs were mainly associated with pathways in cancer, the PI3K-Akt signaling pathway and HTLV-I infection. Based on the miRNA-hub gene network and expression analysis, PTEN, EGFR, STAT3, RHOA, VEGFA, TP53, CTNNB1, and KRAS were identified as potential target genes. Furthermore, all six miRNAs exhibited significant effects on NSCLC cell invasion. These findings indicate that six DE-miRNAs and their target genes may play important roles in the pathogenesis of NSCLC, which will provide novel information for NSCLC treatments.     

Identification of core genes and outcomes in hepatocellular carcinoma by bioinformatics analysis

Hepatocellular carcinoma (HCC) is the most common malignant liver disease in the world. However, the mechanistic relationships among various genes and signaling pathways are still largely unclear. In this study, we aimed to elucidate potential core candidate genes and pathways in HCC. The expression profiles GSE14520, GSE25097, GSE29721, and GSE62232, which cover 606 tumor and 550 nontumour samples, were downloaded from the Gene Expression Omnibus (GEO) database. Furthermore, HCC RNA-seq datasets were also downloaded from the Cancer Genome Atlas (TCGA) database. The differentially expressed genes (DEGs) were filtered using R software, and we performed gene ontology (GO) and Kyoto Encyclopedia of Gene and Genome (KEGG) pathway analysis using the online databases DAVID 6.8 and KOBAS 3.0. Furthermore, the protein-protein interaction (PPI) network complex of these DEGs was constructed by Cytoscape software, the molecular complex detection (MCODE) plug-in and the online database STRING. First, a total of 173 DEGs (41 upregulated and 132 downregulated) were identified that were aberrantly expressed in both the GEO and TCGA datasets. Second, GO analysis revealed that most of the DEGs were significantly enriched in extracellular exosomes, cytosol, extracellular region, and extracellular space. Signaling pathway analysis indicated that the DEGs had common pathways in metabolism-related pathways, cell cycle, and biological oxidations. Third, 146 nodes were identified from the DEG PPI network complex, and two important modules with a high degree were detected using the MCODE plug-in. In addition, 10 core genes were identified, TOP2A, NDC80, FOXM1, HMMR, KNTC1, PTTG1, FEN1, RFC4, SMC4, and PRC1. Finally, Kaplan-Meier analysis of overall survival and correlation analysis were applied to these genes. The abovementioned findings indicate that the identified core genes and pathways in this bioinformatics analysis could significantly enrich our understanding of the development and recurrence of HCC; furthermore, these candidate genes and pathways could be therapeutic targets for HCC treatment.     

Screening of hub genes and prediction of putative drugs in arsenic-related bladder carcinoma: An in silico study

BackgroundEvidence showed that inorganic arsenic (iAs) can trigger malignant transformation in cells with complex mechanisms. Thus, we aimed to investigate the possible molecules, pathways and therapeutic drugs for iAs-induced bladder cancer (BC) by using bioinformatics approaches.MethodsMicroarray-based data were analyzed to screen the differentially expressed genes (DEGs) between iAs-related BC cells and controls. Then, the roles of DEGs were annotated and the hub genes were screened out by protein-protein interaction network. The key genes were further selected from the hub genes through an assessment of the prognostic values. Afterward, potential drugs were predicted by using CMAP analysis.ResultsAnalysis of a dataset (GSE90023) generated 21 upregulated and 47 downregulated DEGs, which were enriched in various signaling pathways. Among the DEGs, four hub genes including WNT7B, SFRP1, DNAJB2, and ATF3, were identified as the key genes because they might predict poor prognosis in BC patients. Lastly, Cantharidin was predicted to be a potential drug reversing iAs-induced malignant transformation in urinary epithelium cells.ConclusionThe present study found several hub genes involved in iAs-induced malignant transformation in urinary epithelium cells, and predicted several small agents for iAs toxicity prevention or therapy.     

Identifying potential biomarkers in hepatitis B virus infection and its response to the antiviral therapy by integrated bioinformatic analysis

The antiviral treatment efficacy varies among chronic hepatitis B (CHB) patients and the underlying mechanism is unclear. An integrated bioinformatics analysis was performed to investigate the host factors that affect the therapeutic responsiveness in CHB patients. Four GEO data sets (GSE54747, GSE27555, GSE66698 and GSE66699) were downloaded from the Gene Expression Omnibus (GEO) database and analysed to identify differentially expressed genes(DEGs). Enrichment analyses of the DEGs were conducted using the DAVID database. Immune cell infiltration characteristics were analysed by CIBERSORT. Upstream miRNAs and lncRNAs of hub DEGs were identified by miRWalk 3.0 and miRNet in combination with the MNDR platform. As a result, seventy-seven overlapping DEGs and 15 hub genes were identified including CCL5, CXCL9, MYH2, CXCR4, CD74, CCL4, HLA-DRB1, ACTA1, CD69, CXCL10, HLA-DRB5, HLA-DQB1, CXCL13, STAT1 and CKM. The enrichment analyses revealed that the DEGs were mainly enriched in immune response and chemokine signalling pathways. Investigation of immune cell infiltration in liver samples suggested significantly different infiltration between responders and non-responders, mainly characterized by higher proportions of CD8+ T cells and activated NK cells in non-responders. The prediction of upstream miRNAs and lncRNAs led to the identification of a potential mRNA-miRNA-lncRNA regulatory network composed of 2 lncRNAs (H19 and GAS5) and 5 miRNAs (hsa-mir-106b-5p, hsa-mir-17-5p, hsa-mir-20a-5p, hsa-mir-6720-5p and hsa-mir-93-5p) targeting CCL5 mRNA. In conclusion, our study suggested that host genetic factors could affect therapeutic responsiveness in CHB patients. The antiviral process might be associated with the chemokine-mediated immune response and immune cell infiltration in the liver microenvironment.     

Identification of hub genes with diagnostic values in pancreatic cancer by bioinformatics analyses and supervised learning methods

Background

Pancreatic cancer is one of the most lethal tumors with poor prognosis, and lacks of effective biomarkers in diagnosis and treatment. The aim of this investigation was to identify hub genes in pancreatic cancer, which would serve as potential biomarkers for cancer diagnosis and therapy in the future.

Methods

Combination of two expression profiles of GSE16515 and GSE22780 from Gene Expression Omnibus (GEO) database was served as training set. Differentially expressed genes (DEGs) with top 25% variance followed by protein-protein interaction (PPI) network were performed to find candidate genes. Then, hub genes were further screened by survival and cox analyses in The Cancer Genome Atlas (TCGA) database. Finally, hub genes were validated in GSE15471 dataset from GEO by supervised learning methods k-nearest neighbor (kNN) and random forest algorithms.

Results

After quality control and batch effect elimination of training set, 181 DEGs bearing top 25% variance were identified as candidate genes. Then, two hub genes, MMP7 and ITGA2, correlating with diagnosis and prognosis of pancreatic cancer were screened as hub genes according to above-mentioned bioinformatics methods. Finally, hub genes were demonstrated to successfully differ tumor samples from normal tissues with predictive accuracies reached to 93.59 and 81.31% by using kNN and random forest algorithms, respectively.

Conclusions

All the hub genes were associated with the regulation of tumor microenvironment, which implicated in tumor proliferation, progression, migration, and metastasis. Our results provide a novel prospect for diagnosis and treatment of pancreatic cancer, which may have a further application in clinical.