Identification of key genes and pathways in castrate-resistant prostate cancer by integrated bioinformatics analysis

ObjectiveTo identify hub genes and pathways involved in castrate-resistant prostate cancer (CRPC).MethodsThe gene expression profiles of GSE70768 were downloaded from Gene Expression Omnibus (GEO) datasets. A total of 13 CRPC samples and 110 tumor samples were identified. The differentially expressed genes (DEGs) were identified, and the gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) enrichment analysis was performed. Protein-protein interaction (PPI) network module analysis was constructed and performed in Cytoscape software. Weighted correlation network analysis (WGCNA) was conducted to determine hub genes involved in the development and progression of CRPC. The gene expression profiles of GSE80609 were used for validation.ResultsA total of 1738 DEGs were identified, consisting of 962 significantly down-regulated DEGs and 776 significantly upregulated DEGs for the subsequent analysis. GO term enrichment analysis suggested that DEGs were mainly enriched in the extracellular matrix organization, extracellular exosome, extracellular matrix, and extracellular space. KEGG pathway analysis found DEGs significantly enriched in the focal adhesion pathway. PPI network demonstrated that the top 10 hub genes were ALB, ACACB, KLK3, CDH1, IL10, ALDH1A3, KLK2, ALDH3B2, HBA1, COL1A1. Also, WGCNA identified the top 5 hub genes in the turquoise module, including MBD4, BLZF1, PIP5K2B, ZNF486, LRRC37B2. Plus, the Venn diagram demonstrated that HBA1 was the key gene in both GSE70768 and GSE80609 datasets.ConclusionsThese newly identified genes and pathways could help urologists understand the differences in the mechanism between CRPC and PCa. Besides, it might be promising targets for the treatment of CRPC.     

Identification of four novel prognosis biomarkers and potential therapeutic drugs for human colorectal cancer by bioinformatics analysis

Colorectal cancer(CRC)is one of the most deadly cancers in the world with few reliable biomarkers that have been selected into clinical guidelines for prognosis of CRC patients.In this study,mRNA microarray datasets GSE113513,GSE21510,GSE44076,and GSE32323 were obtained from the Gene Expression Omnibus(GEO)and analyzed with bioinformatics to identify hub genes in CRC development.Differentially expressed genes(DEGs)were analyzed using the GEO2 R tool.Gene ontology(GO)and KEGG analyses were performed through the DAVID database.STRING database and Cytoscape software were used to construct a protein-protein interaction(PPI)network and identify key modules and hub genes.Survival analyses of the DEGs were performed on GEPIA database.The Connectivity Map database was used to screen potential drugs.A total of 865 DEGs were identified,including 374 upregulated and 491 downregulated genes.These DEGs were mainly associated with metabolic pathways,pathways in cancer,cell cycle and so on.The PPI network was identified with 863 nodes and 5817 edges.Survival analysis revealed that HMMR,PAICS,ETFDH,and SCG2 were significantly associated with overall survival of CRC patients.And blebbistatin and sulconazole were identified as candidate drugs.In conclusion,our study found four hub genes involved in CRC,which may provide novel potential biomarkers for CRC prognosis,and two potential candidate drugs for CRC.     

Identification of the key pathways and genes involved in HER2-positive breast cancer with brain metastasis

BackgroundThe risk of brain metastasis (BM) in HER2-positive (+) breast cancer (BC) patients is significantly higher than that in HER2-negative (-) BC patients. The high incidence and mortality rate makes it urgent to elucidate the key pathways and genes involved and identify patients who are more at risk of developing BM.Materials and methodsTo identify the target genes in HER2+BC patients with BM, we analyzed the microarray datasets (GSE43837) derived from the Gene Expression Omnibus (GEO) database. The GEO2R tool was used to extract the differentially expressed genes (DEGs) involved in HER2+ primary BC and BC with BM. Bioinformatics methods including Gene Ontology (GO) functional annotation analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed with the screened DEGs. The protein-protein interactions of the DEGs were analyzed using the Search Tool for the Retrieval of Interacting Genes (STRING) database and visualized using Cytoscape software. Finally, GSEA analysis was performed to identify the hub genes and the important pathways.ResultsA total of 751 upregulated and 285 downregulated DEGs were identified. The GO function and KEGG pathway enrichment analyses indicated that the DEGs were all enriched in the protein binding molecular function. The top five hub nodes were screened out, included PHLPP1, UBC, ACACB, TGFB1, and ACTB. The GSEA results demonstrated that the five hub genes are mainly enriched in the ribosomal pathway.ConclusionOur study suggests that the five hub genes (PHLPP1, UBC, ACACB, TGFB1, and ACTB) are associated with HER2+BC with BM. The GSEA analysis revealed that the ribosomal pathway seems to play a very important role in the pathogenesis of HER2+BC with BM.     

Identification of core genes and potential molecular mechanisms in breast cancer using bioinformatics analysis

BackgroundBreast cancer is the most frequently diagnosed cancer in women worldwide. This study aimed to elucidate the potential key candidate genes and pathways in breast cancer.MethodsThe gene expression profile dataset GSE65212 was downloaded from GEO database. Differentially expressed genes (DEGs) were obtained by the R Bioconductor packages. The Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of DEGs were performed using DAVID database. The protein–protein interaction (PPI) network was then established by STRING and visualized by Cytoscape software. Module analysis of the PPI network was performed by the plug-in Molecular Complex Detection (MCODE). Then, the identified genes were verified by Kaplan–Meier plotter online database and quantitative real-time PCR (qPCR) in breast cancer tissue samples.ResultsA total of 857 differential expressed genes were identified, of which, the upregulated genes were mainly enriched in the cell cycle, while the downregulated genes were mainly enriched in PPAR signaling pathway. Moreover, six hub genes with high degree were identified, including TOP2A, PCNA, CCNB1, CDC20, BIRC5 and CCNA2. Lastly, the Kaplan–Meier plotter online database confirmed that higher expression levels of these hub genes were related to lower overall survival. Experimental validation showed that all six hub genes had the same expression trend as predicted.ConclusionThese results identified key genes, which could be used as a new biomarker for breast cancer diagnosis and treatment.     

基于生物信息学对扩张型心肌病与慢性心力衰竭共同生物标志物的预测*

目的：通过生物信息学的方法预测扩张型心肌病（DCM）与慢性心力衰竭（CHF）发病的共同生物标志物, 为临床上2 种疾病的发病及相关性奠定理论基础。方法：从Gene Expression Omnibus（GEO）数据库下载芯片数 据GSE3585,此为DCM和正常对照组原始数据,同时下载芯片数据GSE76701,此为CHF 和对照组原始数据。 通过R软件分析获得DCM和CHF 发病的差异表达基因,并获得2 种疾病发病的共同差异表达基因,进一步对共 同差异表达基因进行GO 和KEGG富集分析,构建差异表达基因的PPI 相互作用网络图,获得扩张型心肌病和心 衰发病的共同关键基因。结果：DCM的差异表达基因有240 个,其中141 个上调基因,99 个下调基因,CHF 的 差异表达基因有654 个,其中355 个上调基因,299 个下调基因。DCM和CHF 共同的差异表达基因有36 个,其中 19 个上调基因,17 个下调基因。GO 分析显示,差异表达基因主要集中在12 种不同的生理、病理过程中,KEGG 分析获得差异表达基因参与的主要信号通路为5 条,预测7 个关键差异表达基因,分别为：CD163、KYVE1、 MRC1、VSIG4、FCER1G、S100A9、F13A1。结论：该研究初步探讨了DCM与CHF 两种疾病发病分子机制, 获得了两种疾病发病的共同差异表达基因,仍需进一步的实验研究对基因的表达和临床病理特征的相关性进行验 证。     

Identification of cancer-related gene network in hepatocellular carcinoma by combined bioinformatic approach and experimental validation

HCC (hepatocellular carcinoma) is a highly aggressive malignancy that cause a mass of deaths world widely. We chose gene expression datasets of GSE27635 and GSE28248 from GEO database to find out key genes and their interaction network during the progression and metastasis of HCC. GEO2R online tool was used to screen differentially expressed genes (DEGs) between tumor and peri-tumor tissues based on these two datasets. The identified differentially expressed genes were prepared for further analysis such as GO function, KEGG pathway, PPI network analysis using Database for Annotation, Visualization and Integrated Discovery (DAVID) and Retrieval of Interacting Genes (STRING). Two modules were constructed by MOCDE plugin in Cytoscape and 21 genes were selected as hub genes during this analysis. The expression heatmap and GO function of hub genes were performed using R pheatmap package and BiNGO plugin in Cytoscape respectively. Six hub genes including CDC25 A, CDK1, HMMR, MYBL2, TOP2A were recollected for survival analysis and their expression was validated using Kaplan Meier-plotter and GEPIA website. We also investigated the DEGs between metastasis and non-metastasis tissues and two genes (NQO1 and PTHLH) are highly associated with the metastasis in HCC. Further verification using woundhealing and transwell assay confirmed their ability to mediate cell migration and invasion. In summary, our results obtained by bioinformatic analysis and experimental validation revealed the dominant genes and their interaction networks that are associated with the progression and metastasis of HCC and might serve as potential targets for HCC therapy and diagnosis.     

基于RNA-seq的db/db小鼠大脑皮质的转录组学分析

目的通过对db/db和野生型(WT)小鼠大脑皮质组织全转录组学分析,探索参与调节2型糖尿病诱导的脑功能障碍的差异表达基因(DEGs)及相关通路和网络。方法取雄性野生型WT和db/db小鼠各9只,在第8和24周检测小鼠的体质量和血糖,之后收集动物大脑皮质进行全转录组测序(RNA-seq),并进行DEGs,GO、KEGG及蛋白互作网络分析。结果与WT组相比,db/db组大脑皮质发生变化的306个转录本中有178个表达上调,128个表达下调。DEGs中,43个上调(如Clcnka和Trim17),59个下调(如Arih1和Nectin-3)。蛋白互作网络图中的13个枢纽基因均下调,且大多属于线粒体编码家族。同时,db/db小鼠在多项GO富集类别中具有显著差异,如细胞过程、细胞部分等。此外,KEGG功能富集结果显示DEGs在代谢、帕金森病(PD)、阿尔茨海默病(AD)等相关通路中高度富集,且这些富集通路中的DEGs主要影响了线粒体氧化磷酸化过程。结论揭示了2型糖尿病与中枢神经系统损伤之间的关系及潜在的相关基因、通路及网络。     

Comparison of the gene expression profiles between gallstones and gallbladder polyps

Background: Gallstones and gallbladder polyps (GPs) are two major types of gallbladder diseases that share multiple common symptoms. However, their pathological mechanism remains largely unknown. The aim of our study is to identify gallstones and GPs related-genes and gain an insight into the underlying genetic basis of these diseases. Methods: We enrolled 7 patients with gallstones and 2 patients with GP for RNA-Seq and we conducted functional enrichment analysis and protein-protein interaction (PPI) networks analysis for identified differentially expressed genes (DEGs). Results: RNA-Seq produced 41.7 million in gallstones and 32.1 million pairs in GPs. A total of 147 DEGs was identified between gallstones and GPs. We found GO terms for molecular functions significantly enriched in antigen binding (GO:0003823, P=5.9E-11), while for biological processes, the enriched GO terms were immune response (GO:0006955, P=2.6E-15), and for cellular component, the enriched GO terms were extracellular region (GO:0005576, P=2.7E-15). To further evaluate the biological significance for the DEGs, we also performed the KEGG pathway enrichment analysis. The most significant pathway in our KEGG analysis was Cytokine-cytokine receptor interaction (P=7.5E-06). PPI network analysis indicated that the significant hub proteins containing S100A9 (S100 calcium binding protein A9, Degree=94) and CR2 (complement component receptor 2, Degree=8). Conclusion: This present study suggests some promising genes and may provide a clue to the role of these genes playing in the development of gallstones and GPs.     

Identification of potential drugs for diffuse large b-cell lymphoma based on bioinformatics and Connectivity Map database

Diffuse large B-cell lymphoma (DLBCL) is the most main subtype in non-Hodgkin lymphoma. After chemotherapy, about 30% of patients with DLBCL develop resistance and relapse. This study was to identify potential therapeutic drugs for DLBCL using the bioinformatics method. The differentially expressed genes (DEGs) between DLBCL and non-cancer samples were downloaded from the Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO). Gene ontology enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis of DEGs were analyzed using the Database for Annotation, Visualization, and Integrated Discovery. The R software package (SubpathwayMiner) was used to perform pathway analysis on DEGs affected by drugs found in the Connectivity Map (CMap) database. Protein–protein interaction (PPI) networks of DEGs were constructed using the Search Tool for the Retrieval of Interacting Genes online database and Cytoscape software. In order to identify potential novel drugs for DLBCL, the DLBCL-related pathways and drug-affected pathways were integrated. The results showed that 1927 DEGs were identified from TCGA and GEO. We found 54 significant pathways of DLBCL using KEGG pathway analysis. By integrating pathways, we identified five overlapping pathways and 47 drugs that affected these pathways. The PPI network analysis results showed that the CDK2 is closely associated with three overlapping pathways (cell cycle, p53 signaling pathway, and small cell lung cancer). The further literature verification results showed that etoposide, rinotecan, methotrexate, resveratrol, and irinotecan have been used as classic clinical drugs for DLBCL. Anisomycin, naproxen, gossypol, vorinostat, emetine, mycophenolic acid and daunorubicin also act on DLBCL. It was found through bioinformatics analysis that paclitaxel in the drug-pathway network can be used as a potential novel drug for DLBCL.     

Brief Exercises Affect Gene Expression in Circulating Monocytes

We aimed to give a systematic hypothesis on the functions of exercise on circulating monocytes by identifying a discrete set of genes in circulating monocytes that were altered by exercise. The microarray expression profile of GSE51835 was downloaded from gene expression omnibus (GEO) database for the identification of differentially expressed genes (DEGs) using limma and affy packages in R language. Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed for DEGs, followed by the construction of co‐expression network and protein–protein interaction (PPI) network. The top 10 nodes in PPI network were screened, and subnetwork was constructed for the key genes identification. Totally, 35 DEGs, including 2 upregulated genes and 33 downregulated genes, were identified. The enriched GO terms were mainly linked to immune response and defence response, and the enriched KEGG pathways were mainly associated with natural killer cell‐mediated cytotoxicity and graft‐versus‐host disease. Dual‐specificity phosphatase 2 (DUSP2) was identified as a key node in the co‐expression network. In the PPI network, CD247 module (CD247), chemokine (C‐X‐C motif) receptor 4 (CXCR4), granzyme B (GZMB) and perforin 1 (PRF1) were identified as key nodes. An important interaction, GZMB/PRF1, was detected. Five key genes, including DUSP2, CD247, CXCR4, GZMB and PRF1, and an interaction of GZMB/PRF1, were significant factors in the immune processes of circulating monocytes, which might be regulated by brief exercises, leading to the enhancement of immune function.     

Integrated bioinformatics analysis for differentially expressed genes and signaling pathways identification in gastric cancer

Background: Gastric cancer (GC) has a high mortality rate in cancer-related deaths worldwide. Currently, the pathogenesis of gastric cancer progression remains unclear. Here, we identified several vital candidate genes related to gastric cancer development and revealed the potential pathogenic mechanisms using integrated bioinformatics analysis.Methods: Two microarray datasets from Gene Expression Omnibus (GEO) database integrated. Limma package was used to analyze differentially expressed genes (DEGs) between GC and matched normal specimens. DAVID was utilized to conduct Gene ontology (GO) and KEGG enrichment analysis. The relative expression of OLFM4, IGF2BP3, CLDN1 and MMP1were analyzed based on TCGA database provided by UALCAN. Western blot and quantitative real time PCR assay were performed to determine the protein and mRNA levels of OLFM4, IGF2BP3, CLDN1 and MMP1 in GC tissues and cell lines, respectively.Results: We downloaded the expression profiles of {"type":"entrez-geo","attrs":{"text":"GSE103236","term_id":"103236"}}GSE103236 and {"type":"entrez-geo","attrs":{"text":"GSE118897","term_id":"118897"}}GSE118897 from the Gene Expression Omnibus (GEO) database. Two integrated microarray datasets were used to obtain differentially expressed genes (DEGs), and bioinformatics methods were used for in-depth analysis. After gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichments analysis, we identified 61 DEGs in common, of which the expression of 34 genes were elevated and 27 genes were decreased. GO analysis displayed that the biological functions of DEGs mainly focused on negative regulation of growth, fatty acid binding, cellular response to zinc ion and calcium-independent cell-cell adhesion. KEGG pathway analysis demonstrated that these DEGs mainly related to the Wnt and tumor signaling pathway. Interestingly, we found 4 genes were most significantly upregulated in the DEGs, which were OLFM4, IGF2BP3, CLDN1 and MMP1. Then, we confirmed the upregulation of these genes in STAD based on sample types. In the final, western blot and qRT-PCR assay were performed to determine the protein and mRNA levels of OLFM4, IGF2BP3, CLDN1 and MMP1 in GC tissues and cell lines.Conclusion: In our study, using integrated bioinformatics to screen DEGs in gastric cancer could benefit us for understanding the pathogenic mechanism underlying gastric cancer progression. Meanwhile, we also identified four significantly upregulated genes in DEGs from both two datasets, which might be used as the biomarkers for early diagnosis and prevention of gastric cancer.     

Identification of DNA methylation-regulated differentially-expressed genes and related pathways using Illumina 450K BeadChip and bioinformatic analysis in gastric cancer

In the current research, we aimed to identify and analyze methylation-regulated differentially-expressed genes (MeDEGs) and related pathways using bioinformatic methods. We downloaded RNA-seq, Illumina Human Methylation 450 K BeadChip and clinical information of gastric cancer (GC) from The Cancer Genome Atlas (TCGA) project. Differentially-expressed genes (DEGs) were identified using the edgeR package. Then, we performed Spearman’s correlation analysis between DEG expression levels and methylation levels. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed in the DAVID database. We then conducted Kaplan–Meier survival analysis to explore the relationship between methylation, expression and prognosis. The protein–protein interaction networks were further analyzed using the STRING database. A total of 204 down-regulated DEGs and 164 up-regulated DEGs were identified as MeDEGs. GO and KEGG pathway analyses showed that MeDEGs were enriched in multiple cancer-related terms. Kaplan–Meier survival analysis showed that eight up-regulated MeDEGs (CAMKV, COMP, FGF3, FGF19, FOXL2, IGF2BP1, IGFBP1 and NPPB) and five down-regulated MeDEGs (ALDH3B2, CALML3, FLRT1, G6PC and HRASLS2) were associated with prognosis of GC patients. In addition, PPI networks and KEGG pathway analyses further confirmed the critical role of prognosis-related MeDEGs. In conclusion, methylation plays a critical role in GC progression. Multiple MeDEGs are related to prognosis, suggesting that they may be potential targets in tumor treatment.     

Identification of key gene contributing to vitiligo by immune infiltration

Background: A deeper understanding of new prognostic and diagnostic biomarkers for vitiligo, an autoimmune disease, is needed. The purpose of this study is to identify the underlying long noncoding RNAs (lncRNAs) and immune infiltration related to the cause of vitiligo. Methods: The microarray data ({"type":"entrez-geo","attrs":{"text":"GSE75819","term_id":"75819"}}GSE75819) were available to be downloaded from NCBI-GEO. Eight hub genes were identified from the Protein-protein interaction (PPI) network by the dissection of differentially expressed genes (DEG), Kyoto Gene and Genomic Encyclopedia (KEGG) expansion pathway, and Gene Ontology (GO). Further analysis based on the immune infiltration as well as the correlation between DEGs and immune cells was performed. Our conclusions were verified by using the GSE534 eventually. Results: According to our analysis, we obtained a total of 666 DEGs and 8 hub genes that include ECT2, CCT8, VRK1, UQCRH, EBNA1BP2, CRY2, IFIH1, and BCCIP, which may play an important role in vitiligo. Moreover, the immune infiltration profiles varied significantly between normal and vitiligo tissues. Compared with normal tissues, vitiligo tissues contained a greater proportion of mast cells (P<0.05). The analysis revealed that T cells regulatory (Tregs) have a negative correlation with the VRK1 expression (R=-0:77, P<0.001), whereas the mast cells resting have a positive correlation with the VRK1 expression (R=0:72, P<0.001) in vitiligo. Conclusion: The gene expression profile of vitiligo was realized by a bioinformatics method. The expressions of 8 hub genes and 22 immune cells were found, as the same as CRY2 and VRK1 have a special correlation with immune cells, which may be a significant cause of the pathogenesis of vitiligo. This provides a new idea for the diagnosis and treatment of vitiligo.     

利什曼原虫感染树突状细胞早期基因表达差异分析及关键基因筛选

目的:分析利什曼原虫感染树突状细胞(DCs)早期的基因表达与信号通路变化,探究DCs感染后应答,寻找利什曼原虫感染后基于DCs的免疫治疗方法。方法:GEO数据库下载利什曼原虫感染前后DCs基因芯片数据,RStudio软件筛选差异表达基因(DEGs),STRING构建DEGs蛋白质相互作用网络(PPI),Cytoscape筛选差异表达蛋白质的核心模块,RStudio软件对DEGs进行GO和KEGG富集分析。结果:共筛选出DEGs 129个,其中IL12B与CXCL10差异最为显著,GO分析共富集23个过程,主要涉及病毒感染过程相关细胞反应及Ⅰ-IFN相关免疫反应;KEGG分析共富集3条信号通路,分别为甲型流感、麻疹及DNA复制信号通路。结论:利什曼原虫感染DCs前后Ⅰ-IFN信号通路和TLR4/NF-κB信号通路激活,影响IL12表达,提示Ⅰ-IFN/IL12信号通路与TLR4/NF-κB/IL12信号通路可作为利什曼原虫感染治疗的靶点,CXCL10也有望成为潜在的治疗靶点;利什曼原虫感染后,出现类似病毒感染现象,推测抗病毒免疫疗法可能在对抗利什曼原虫感染中具有一定疗效。     

Identification of Key Genes and Pathways associated with Endometriosis by Weighted Gene Co-expression Network Analysis

Background: Endometriosis is a common gynecological disorder with high rates of infertility and pelvic pain. However, its pathogenesis and diagnostic biomarkers remain unclear. This study aimed to elucidate potential hub genes and key pathways associated with endometriosis in ectopic endometrium (EC) and eutopic endometrium (EU).Material and Method: EC and EU-associated microarray datasets were obtained from the gene expression omnibus (GEO) database. Gene set enrichment analysis was performed to obtain further biological insight into the EU and EC-associated genes. Weighted gene co-expression network analysis (WGCNA) was performed to find clinically significant modules of highly-correlated genes. The hub genes that belong to both the weighted gene co-expression network and protein-protein interaction (PPI) network were identified using a Venn diagram.Results: We obtained EC and EU-associated microarray datasets {"type":"entrez-geo","attrs":{"text":"GSE7305","term_id":"7305"}}GSE7305 and {"type":"entrez-geo","attrs":{"text":"GSE120103","term_id":"120103"}}GSE120103. Genes in the EC were mainly enriched in the immune response and immune cell trafficking, and genes in the EU were mainly enriched in stress response and steroid hormone biosynthesis. PPI networks and weighted gene co-expression networks were constructed. An EC-associated blue module and an EU-associated magenta module were identified, and their function annotations revealed that hormone receptor signaling or inflammatory microenvironments may promote EU passing through the oviducts and migrating to the ovarian surfaces, and adhesion and immune correlated genes may induce the successful ectopic implantation of the endometrium (EC). Twelve hub genes in the EC and sixteen hub genes in the EU were recognized and further validated in independent datasets.Conclusion: Our study identified, for the first time, the hub genes and enrichment pathways in the EC and EU using WGCNA, which may provide a comprehensive understanding of the pathogenesis of endometriosis and have important clinical implications for the treatment and diagnosis of endometriosis.     

去分化脂肪肉瘤潜在核心基因的生物信息学预测及分析

目的 探讨去分化脂肪肉瘤的潜在核心基因在其恶性生物学行为中的作用.方法 获取基因表达数据库(gene expres-sion omnibus,GEO)数据库中GSE21122和GSE52390的芯片数据,通过GEO2R筛选差异表达基因,对差异表达基因进行GO功能、KEGG通路富集分析和蛋白互作分析,并用Cytoscap...     

Integrated bioinformatics analysis reveals novel key biomarkers and potential candidate small molecule drugs in gastric cancer

Background and objective

The underlying molecular mechanisms of gastric cancer (GC) have yet not been investigated clearly. In this study, we aimed to identify hub genes involved in the pathogenesis and prognosis of GC.

Integrated analysis of the molecular mechanisms in idiopathic pulmonary fibrosis

Rationale: Idiopathic pulmonary fibrosis (IPF) is one of the most aggressive forms of idiopathic interstitial pneumonia. Some miRNAs may be associated with IPF and may affect the occurrence and development of IPF in various pathways. Many miRNAs and genes that may be involved in the development of IPF have been discovered using chip and high throughput technologies.Methods: We analyzed one miRNA and four mRNA databases. We identified hub genes and pathways related to IPF using GO, KEGG enrichment analysis, gene set variation analysis (GSVA), PPI network construction, and hub gene analysis. A comprehensive analysis of differentially expressed miRNAs (DEMs), predicted miRNA target genes, and differentially expressed genes (DEGs) led to the creation of a miRNA-mRNA regulatory network in IPF.Results: We found 203 DEGs and 165 DEMs that were associated with IPF. The findings of enrichment analyses showed that these DEGs were mainly involved in antimicrobial humoral response, antimicrobial humoral immune response mediated by antimicrobial peptide, extracellular matrix organization, cell killing, and organ or tissue specific immune response. The VEGFA, CDH5, and WNT3A genes overlapped between hub genes and the miRNA-mRNA regulatory network. The miRNAs including miR-199b-5p, miR-140-5p, miR-199a-5p, miR-125A-5p, and miR-107 that we predicted would regulate the VEGFA, CDH5, and WNT3A genes, which were also associated with IPF or other fibrosis-related diseases. GSVA indicated that metabolic processes of UTP and IMP, immune response, regulation of Th2 cell cytokine production, and positive regulation of NK cell-mediated immunity are associated with the pathogenesis and treatment of IPF. These pathways also interact with VEGFA, CDH5, and WNT3A.Conclusion: These findings provide a new research direction for the diagnosis and treatment of IPF.