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 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.     

Identification of a five-gene signature with prognostic value in colorectal cancer

Colorectal cancer (CRC) ranks as one of the most commonly diagnosed malignancies worldwide. Although mortality rates have been decreasing, the prognosis of CRC patients is still highly dependent on the individual. Therefore, identifying and understanding novel biomarkers for CRC prognosis remains crucial. The gene expression profiles of five-gene expression omnibus (GEO) data sets of CRC were first downloaded. A total of 352 consistent differentially expressed genes (DEGs) were identified for CRC and paired with normal tissues. Functional analysis including gene ontology and Kyoto encyclopedia of genes and genomes pathway enrichment revealed that these DEGs were related to metabolic pathways, tight junctions, and the cell cycle. Ten hub DEGs were identified based on the search tool for the retrieval of interacting genes database and protein–protein interaction networks. By using univariate Cox proportional hazard regression analysis, we found 11 survival-related genes among these DEGs. We finally established a five-gene signature (kinesin family member 15, N-acetyltransferase 2, glutathione peroxidase 3, secretogranin II, and chloride channel accessory 1) with prognostic value in CRC by step multivariate Cox regression analysis. Based on this risk scoring system, patients in the high-risk group had significantly poorer survival results compared with those in the low-risk group (log-rank test, p < 0.0001). Finally, we validated our gene signature scoring system in two independent GEO cohorts (GSE17536 and GSE33113). We found all five of the signature genes to be DEGs in The Cancer Genome Atlas database. In conclusion, our findings suggest that our five DEG-based signature can provide a novel biomarker with useful applications in CRC prognosis.     

Identification and validation of key genes associated with non-small-cell lung cancer

Non-small-cell lung cancer (NSCLC) is one of the main causes of death induced by cancer globally. However, the molecular aberrations in NSCLC patients remain unclearly. In the present study, four messenger RNA microarray datasets (GSE18842, GSE40275, GSE43458, and GSE102287) were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) between NSCLC tissues and adjacent lung tissues were obtained from GEO2R and the overlapping DEGs were identified. Moreover, functional and pathway enrichment were performed by Funrich, while the protein–protein interaction (PPI) network construction were obtained from STRING and hub genes were visualized and identified by Cytoscape software. Furthermore, validation, overall survival (OS) and tumor staging analysis of selected hub genes were performed by GEPIA. A total of 367 DEGs (95 upregulated and 272 downregulated) were obtained through gene integration analysis. The PPI network consisted of 94 nodes and 1036 edges in the upregulated DEGs and 272 nodes and 464 edges in the downregulated DEGs, respectively. The PPI network identified 46 upregulated and 27 downregulated hub genes among the DEGs, and six (such as CENPE, NCAPH, MYH11, LRRK2, HSD17B6, and A2M) of that have not been identified to be associated with NSCLC so far. Moreover, the expression differences of the mentioned hub genes were consistent with that in lung adenocarcinoma and lung squamous cell carcinoma in the TCGA database. Further analysis showed that all the six hub genes were associated with tumor staging except MYH11, while only the upregulated DEG CENPE was associated with the worse OS of patients with NSCLC. In conclusion, the current study showed that CENPE, NCAPH, MYH11, LRRK2, HSD17B6, and A2M might be the key genes contributed to tumorigenesis or tumor progression in NSCLC, further functional study is needed to explore the involved mechanisms.     

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.

     

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 a six-gene prognostic signature for oral squamous cell carcinoma

Oral squamous cell carcinoma (OSCC) is one of the most common types of malignancies worldwide, and its morbidity and mortality have increased in the near term. Consequently, the purpose of the present study was to identify the notable differentially expressed genes (DEGs) involved in their pathogenesis to obtain new biomarkers or potential therapeutic targets for OSCC. The gene expression profiles of the microarray datasets GSE85195, GSE23558, and GSE10121 were obtained from the Gene Expression Omnibus (GEO) database. After screening the DEGs in each GEO dataset, 249 DEGs in OSCC tissues were obtained. Kyoto Encyclopedia of Genes and Genomes and Gene Ontology pathway enrichment analysis was employed to explore the biological functions and pathways of the above DEGs. A protein–protein interaction network was constructed to obtain a central gene. The corresponding total survival information was analyzed in patients with oral cancer from The Cancer Genome Atlas (TCGA). A total of six candidate genes (CXCL10, OAS2, IFIT1, CCL5, LRRK2, and PLAUR) closely related to the survival rate of patients with oral cancer were identified, and expression verification and overall survival analysis of six genes were performed based on TCGA database. Time-dependent receiver operating characteristic curve analysis yields predictive accuracy of the patient's overall survival. At the same time, the six genes were further verified by quantitative real-time polymerase chain reaction using samples obtained from the patients recruited to the present study. In conclusion, the present study identified the prognostic signature of six genes in OSCC for the first time via comprehensive bioinformatics analysis, which could become potential prognostic markers for OCSS and may provide potential therapeutic targets for tumors.     

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.

     

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。     

Epigenetic profiling and mRNA expression reveal candidate genes as biomarkers for colorectal cancer

Colorectal cancer (CRC) is characterized by DNA methylation, which is associated with genomic instability and tumor initiation. As an important epigenetic regulation, DNA methylation can be used as a potential therapeutic target for CRC. In our study, we downloaded DNA methylation profiles (GSE17648 and GSE29490) and RNA sequencing microarray data (GSE25070 and GSE32323) from the Gene Expression Omnibus (GEO) database. As a result, 14 aberrantly methylated differentially expressed genes (DEGs) were screened according to the different criteria. We further validated these DEGs in The Cancer Genome Atlas (TCGA) database and obtained Pearson's correlation coefficient (COR) for the relationship between gene expression and DNA methylation. Three candidate genes (SOX9, TCN1, and TGFBI) with COR greater than 0.3 were screened out as Hub genes. The receiver operating characteristic result indicated that SOX9 and TGFBI effectively serve as biomarkers for the early diagnosis of CRC. Furthermore, the potential prognosis of the Hub genes for CRC patients was evaluated. Only TGFBI, which is regulated by methylation, can predict patient disease-free survival. Additionally, we examined the methylation level of the Hub genes in CRC cells in the Cancer Cell Line Encyclopedia database. Considering that methylation status tends to be highly modified on CpG islands in tumorigenesis, we screened the CpG island methylation of TGFBI based on the TCGA database and verified its diagnostic value in the GEO database. Our result revealed two Hub genes (TCN1 and TGFBI) whose aberrant expressions were regulated by DNA methylation. Additionally, we uncovered the hypermethylation of TGFBI on CpG islands and its clinical value in the diagnosis of CRC.     

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.     

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个肝肺肾纤维化共有DEG和 85个肝纤维化特异性DEG。功能富集分析表明,共有DEG主要在免疫反应和炎症反应过程中富集。肝纤维化特有DEG与脂质和脂肪酸代谢过程有关。之后通过PPI网络选择了20个关键基因。为了评估这些关键基因成为潜在靶点的可能性,进一步分析了它们的成药性(Druggability)。得出以下结论:TYROBP,FCGR3B,ALOX5AP和CD14或可成为纤维化治疗的潜在靶点,CYP8B1和UGT2A3则可能与非酒精性脂肪性肝病(Non-alcoholic Fatty Liver Disease, NAFLD)或非酒精性脂肪性肝炎(Non-Alcoholic Steato-Hepatitis , NASH)相关。对于基因FCGR1B,C1QB,LY86和CD53,目前没有直接的研究证据表明其与纤维化相关,需要进一步验证它们在纤维化发生过程中的功能。     

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.     

Bioinformatic analysis of key pathways and genes involved in pediatric atopic dermatitis

The initiation of atopic dermatitis (AD) typically happens very early in life, but most of our understanding of AD is derived from studies on AD patients in adult. The aim of the present study was to identify gene signature speficic to pediatric AD comapred with adult AD. The gene expression profiles of four datasets ({"type":"entrez-geo","attrs":{"text":"GSE32924","term_id":"32924"}}GSE32924, {"type":"entrez-geo","attrs":{"text":"GSE36842","term_id":"36842"}}GSE36842, {"type":"entrez-geo","attrs":{"text":"GSE58558","term_id":"58558"}}GSE58558, and {"type":"entrez-geo","attrs":{"text":"GSE107361","term_id":"107361"}}GSE107361) were downloaded from the GEO database. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) enrichment analyses were performed, and protein–protein interaction (PPI) network was constructed by Cytoscape software. Total 654 differentially expressed genes (DEGs) (394 up-regulated and 260 down-regulated) were identified in pediatric AD samples with adult AD samples as control. The up-regulated DEGs were significantly enriched in the migration and chemotaxis of granulocyte and neutrophil, while down-regulated DEGs were significantly enriched in biological adhesion. KEGG pathway analysis showed that up-regulated DEGs participated in chemokine signaling pathway while down-regulated DEGs participated in adherens junction, focal adhesion, and regulation of actin cytoskeleton. The top 10 hub genes GAPDH, EGFR, ACTB, ESR1, CDK1, CXCL8, CD44, KRAS, PTGS2, and SMC3 were involved in chemokine signaling pathway, cytokine–cytokine receptor interaction, interleukin-17 signaling pathway, and regulation of actin cytoskeleton. In conclusion, we identified DEGs and hub genes involved in pediatric AD, which might be used as therapeutic targets and diagnostic biomarkers for pediatric AD.     

Neurexophilin and PC-esterase domain family member 4 (NXPE4) and prostate androgen-regulated mucin-like protein 1 (PARM1) as prognostic biomarkers for colorectal cancer

Owing to the high morbidity and mortality, novel biomarkers in the occurrence and development of colorectal cancer (CRC) are needed nowadays. In this study, the CRC-related datasets were downloaded from the Gene Expression Omnibus (GEO) database and The Cancer Genome Atlas (TCGA) database. After screening the differentially expressed genes (DEGs) in R software, a total of 238 upregulated and 199 downregulated DEGs were revealed simultaneously. Then the Kaplan-Meier survival analysis and Cox regression analysis were used to reveal the prognostic function of these DEGs. Neurexophilin and PC-esterase domain family member 4 (NXPE4) and prostate androgen-regulated mucin-like protein 1 (PARM1) were two outstanding independent overall survival (OS) and relapse-free survival (RFS) prognostic genes of CRC in TCGA database. We next verified the expression of NXPE4 and PARM1 messenger RNA (mRNA) levels were significantly lower in CRC tumor tissue than in the adjacent noncancerous tissue in our clinical samples, and NXPE4 mRNA expression level was related to the tumor location and tumor size, while PARM1 was related to tumor location, lymph nodes metastasis, and tumor size. This study demonstrated that NXPE4 and PARM1 might be two potential novel prognostic biomarkers for CRC.     

Identification of IMPA2 as the hub gene associated with colorectal cancer and liver metastasis by integrated bioinformatics analysis

Background and ObjectivesColorectal cancer (CRC) is one of the most common malignant tumors worldwide with high incidence and mortality rate, while colorectal liver metastasis (CRLM) is one of the major causes of cancer-related deaths. Therefore, the present study aims to identify the hub gene associated with CRC carcinogenesis and liver metastasis, and then explore its diagnostic and prognostic value as well as the potential regulation mechanism.MethodsThe overlapping differential co-expression genes among CRC, CRLM, and normal tissues were explored on the GSE49355 and GSE81582 datasets from the Gene Expression Omnibus (GEO) database by integrated bioinformatics analysis. Then, the hub prognostic genes were selected from the overlapping genes by univariate Cox proportional hazard analysis and online database Gene Expression Profiling Interactive Analysis 2 (GEPIA2). Subsequently, the clinical value of the hub genes was evaluated in the TCGA and GSE39582 cohorts. Finally, the underlying mechanisms of the hub gene regulating CRC carcinogenesis and metastasis were explored by Gene function annotation and DNA methylation analysis.ResultsInositol mono-phosphatase 2 (IMPA2) was identified as the hub gene associated with CRC carcinogenesis and liver metastasis. IMPA2 had an excellent diagnostic efficiency, and its expression was significantly decreased in CRC and liver metastasis samples, being positively correlated with poor prognosis. Moreover, its low expression was associated with AJCC stage III+IV, T4, N1+2, and M1. In addition, our results revealed that the potential mechanisms used by IMPA2 to mediate CRC carcinogenesis and metastasis could be associated with lipid metabolism and epithelial mesenchymal transition (EMT). Finally, IMPA2 expression could be regulated by DNA methylation.ConclusionsIMPA2 was identified and reported for the first time as a hub gene biomarker in the diagnosis and prognosis of CRC, which could regulate CRC carcinogenesis and liver metastasis through the regulation of lipid metabolism, EMT, and DNA methylation.     

Identification of key biomarkers in diabetic nephropathy via bioinformatic analysis

Diabetic nephropathy (DN) is a major cause of end-stage renal disease. Although intense efforts have been made to elucidate the pathogenesis, the molecular mechanisms of DN remain to be clarified. To identify the candidate genes in the progression of DN, microarray datasets GSE30122, GSE30528, and GSE47183 were downloaded from the Gene Expression Omnibus database. The differentially expressed genes (DEGs) were identified, and function enrichment analyses were performed. The protein-protein interaction network was constructed and the module analysis was performed using the Search Tool for the Retrieval of Interacting Genes and Cytoscape. A total of 61 DEGs were identified. The enriched functions and pathways of the DEGs included glomerulus development, extracellular exosome, collagen binding, and the PI3K-Akt signaling pathway. Fifteen hub genes were identified and biological process analysis revealed that these genes were mainly enriched in acute inflammatory response, inflammatory response, and blood vessel development. Correlation analysis between unexplored hub genes and clinical features of DN suggested that COL6A3, MS4A6A,PLCE1, TNNC1, TNNI1, TNN2, and VSIG4 may involve in the progression of DN. In conclusion, DEGs and hub genes identified in this study may deepen our understanding of molecular mechanisms underlying the progression of DN, and provide candidate targets for diagnosis and treatment of DN.