Construction of miRNA-mRNA network for the identification of key biological markers and their associated pathways in IgA nephropathy by employing the integrated bioinformatics analysis

BackgroundAbout half-century ago, Immunoglobulin A nephropathy (IgAN) was discovered as a complicated disease with frequent clinical symptoms. Until now, exact mechanism underlying the pathogenesis of IgAN is poorly known. Therefore, current study was aimed to understand the molecular mechanism of IgAN by identifying the key miRNAs and their targeted hub genes. The key miRNAs might contribute to the diagnosis and therapy of IgAN, and could turn out to be a new star in the field of IgAN.MethodsThe microarray datasets were downloaded from Gene Expresssion Omnibus (GEO) database and analyzed using R package (LIMMA) in order to obtain differential expressed genes (DEGs). Then, the hub genes were identified using cytoHubba plugin of cytoscpae tool and other bioinformatics approaches including protein-protein interaction (PPI) network analysis, module analysis, and miRNA-hub gene network construction was also performed.ResultsA total of 348 DEGs were identified, of which 107 were upregulated genes and 241 were downregulated genes. Subsequently, the 12 overlapped genes were predicted from cytoHubba, and considered as hub genes. Moreover, a network among miRNA-hub genes was created to explore the correlation between the hub genes and their targeted miRNAs. Network construction ultimately lead to the identification of nine gene named FN1, EGR1, FOS, JUN, SERPINE1, MMP2, ATF3, MYC, and IL1B and one novel key miRNA namely, has-miR-144-3p as biomarker for diagnosis and therapy of IgAN.ConclusionThis study updates the information and yield a new perspective in context of understanding the pathogenesis and development of IgAN. In future, key miRNAs might be capable of improving the personalized detection and therapies for IgAN. In vivo and in vitro investigation of miRNAs and pathway interaction is essential to delineate the specific roles of the novel miRNAs, which may help to further reveal the mechanisms underlying IgAN.     

Integrative analyses of genes associated with idiopathic pulmonary fibrosis

Idiopathic pulmonary fibrosis (IPF), characterized by irreversible scarring and progressive destruction of the lung tissue, is one of the most common types of idiopathic interstitial pneumonia worldwide. However, there are no reliable candidates for curative therapies. Hence, elucidation of the mechanisms of IPF genesis and exploration of potential biomarkers and prognostic indicators are essential for accurate diagnosis and treatment of IPF. Recently, efficient microarray and bioinformatics analyses have promoted an understanding of the molecular mechanisms of disease occurrence and development, which is necessary to explore genetic alternations and identify potential diagnostic biomarkers. However, high false-positive rates results have been observed based on single microarray datasets. In the current study, we performed a comprehensive analysis of the differential expression, biological functions, and interactions of IPF-related genes. Three publicly available microarray datasets including 54 IPF samples and 34 normal samples were integrated by performing gene set enrichment analysis and analyzing differentially expressed genes (DEGs). Our results identified 350 DEGs genetically associated with IPF. Gene ontology analyses revealed that the changes in the modules were mostly enriched in the positive regulation of smooth muscle cell proliferation, positive regulation of inflammatory responses, and the extracellular space. Kyoto encyclopedia of genes and genomes enrichment analysis of DEGs revealed that IPF involves the TNF signaling pathway, NOD-like receptor signaling pathway, and PPAR signaling pathway. To identify key genes related to IPF in the protein-protein interaction network, 20 hub genes were screened out with highest scores. Our results provided a framework for developing new pathological molecular networks related to specific diseases in silico.     

Biomarkers of human skin cells identified using DermArray DNA arrays and new bioinformatics methods

Biomarker genes of human skin-derived cells were identified by new simple bioinformatic methods and DNA microarray analysis utilizing in vitro cultures of normal neonatal human epidermal keratinocytes, melanocytes, and dermal fibroblasts. A survey of 4405 human cDNAs was performed using DermArray DNA microarrays. Biomarkers were rank ordered by "likelihood ratio" algorithms and stringent selection criteria that have general applicability for analyzing a minimum of three RNA samples. Signature biomarker genes (up-regulated in one cell type) and anti-signature biomarker genes (down-regulated in one cell type) were determined for the three major skin cell types. Many of the signature genes are known biomarkers for these cell types. In addition, 17 signature genes were identified as ESTs, and 22 anti-signature biomarkers were discovered. Quantitative RT-PCR was used to verify nine signature biomarker genes. A total of 158 biomarkers of normal human skin cells were identified, many of which may be valuable in diagnostic applications and as molecular targets for drug discovery and therapeutic intervention.     

DNA methylation biomarkers for head and neck squamous cell carcinoma

DNA methylation plays an important role in the etiology and pathogenesis of head and neck squamous cell carcinoma (HNSCC). The current study aimed to identify aberrantly methylated-differentially expressed genes (DEGs) by a comprehensive bioinformatics analysis. In addition, we screened for DEGs affected by DNA methylation modification and further investigated their prognostic values for HNSCC. We included microarray data of DNA methylation (GSE25093 and GSE33202) and gene expression (GSE23036 and GSE58911) from Gene Expression Omnibus. Aberrantly methylated-DEGs were analyzed with R software. The Cancer Genome Atlas (TCGA) RNA sequencing and DNA methylation (Illumina HumanMethylation450) databases were utilized for validation. In total, 27 aberrantly methylated genes accompanied by altered expression were identified. After confirmation by The Cancer Genome Atlas (TCGA) database, 2 hypermethylated-low-expression genes (FAM135B and ZNF610) and 2 hypomethylated-high-expression genes (HOXA9 and DCC) were identified. A receiver operating characteristic (ROC) curve confirmed the diagnostic value of these four methylated genes for HNSCC. Multivariate Cox proportional hazards analysis showed that FAM135B methylation was a favorable independent prognostic biomarker for overall survival of HNSCC patients.     

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.     

Analysis of dysregulation of immune system in pancreatic cancer based on gene expression profile

The aim of this study was to explore the dysregulated expression of the immune system in pancreatic cancer and clarify the pathogenesis of pancreatic cancer. The Dataset GSE15471 was downloaded from GEO database, Student’s t test was used to screen differentially expressed genes (DEGs) between the pancreatic cancer group and the normal control group. Kyoto Encyclopedia of Genes and Genomes (KEGG) provides functional annotation was employed to explore the significant DEGs involved in biological functions. We got 988 significantly DEGs, including 832 up-regulated genes and 156 down-regulated genes. The ratio of up-regulated genes and down-regulated genes was 5.3. Total 13 biological pathways which were significant enriched with DEGs by KEGG pathway enrichment analysis. Finally, we constructed a overall network of the immune system in pancreatic cancer with these biological pathways information. Our study reveals that dysregulated pathways in pancreatic cancer associated with the immune system. Besides, we also identify some important molecular biomarkers of the pancreatic cancer, including CXCR4 and CD4. Dysfunctional pathways and important molecular biomarkers of pancreatic cancer will provide useful information for potential treatment of pancreatic cancer.     

A Model-Based Joint Identification of Differentially Expressed Genes and Phenotype-Associated Genes

Over the last decade, many analytical methods and tools have been developed for microarray data. The detection of differentially expressed genes (DEGs) among different treatment groups is often a primary purpose of microarray data analysis. In addition, association studies investigating the relationship between genes and a phenotype of interest such as survival time are also popular in microarray data analysis. Phenotype association analysis provides a list of phenotype-associated genes (PAGs). However, it is sometimes necessary to identify genes that are both DEGs and PAGs. We consider the joint identification of DEGs and PAGs in microarray data analyses. The first approach we used was a naïve approach that detects DEGs and PAGs separately and then identifies the genes in an intersection of the list of PAGs and DEGs. The second approach we considered was a hierarchical approach that detects DEGs first and then chooses PAGs from among the DEGs or vice versa. In this study, we propose a new model-based approach for the joint identification of DEGs and PAGs. Unlike the previous two-step approaches, the proposed method identifies genes simultaneously that are DEGs and PAGs. This method uses standard regression models but adopts different null hypothesis from ordinary regression models, which allows us to perform joint identification in one-step. The proposed model-based methods were evaluated using experimental data and simulation studies. The proposed methods were used to analyze a microarray experiment in which the main interest lies in detecting genes that are both DEGs and PAGs, where DEGs are identified between two diet groups and PAGs are associated with four phenotypes reflecting the expression of leptin, adiponectin, insulin-like growth factor 1, and insulin. Model-based approaches provided a larger number of genes, which are both DEGs and PAGs, than other methods. Simulation studies showed that they have more power than other methods. Through analysis of data from experimental microarrays and simulation studies, the proposed model-based approach was shown to provide a more powerful result than the naïve approach and the hierarchical approach. Since our approach is model-based, it is very flexible and can easily handle different types of covariates.     

Caveolin-1 identified as a key mediator of acute lung injury using bioinformatics and functional research

Acute lung injury (ALI) is a potentially life-threatening, devastating disease with an extremely high rate of mortality. The underlying mechanism of ALI is currently unclear. In this study, we aimed to confirm the hub genes associated with ALI and explore their functions and molecular mechanisms using bioinformatics methods. Five microarray datasets available in GEO were used to perform Robust Rank Aggregation (RRA) to identify differentially expressed genes (DEGs) and the key genes were identified via the protein-protein interaction (PPI) network. Lipopolysaccharide intraperitoneal injection was administered to establish an ALI model. Overall, 40 robust DEGs, which are mainly involved in the inflammatory response, protein catabolic process, and NF-κB signaling pathway were identified. Among these DEGs, we identified two genes associated with ALI, of which the CAV-1/NF-κB axis was significantly upregulated in ALI, and was identified as one of the most effective targets for ALI prevention. Subsequently, the expression of CAV-1 was knocked down using AAV-shCAV-1 or CAV-1-siRNA to study its effect on the pathogenesis of ALI in vivo and in vitro. The results of this study indicated that CAV-1/NF-κB axis levels were elevated in vivo and in vitro, accompanied by an increase in lung inflammation and autophagy. The knockdown of CAV-1 may improve ALI. Mechanistically, inflammation was reduced mainly by decreasing the expression levels of CD3 and F4/80, and activating autophagy by inhibiting AKT/mTOR and promoting the AMPK signaling pathway. Taken together, this study provides crucial evidence that CAV-1 knockdown inhibits the occurrence of ALI, suggesting that the CAV-1/NF-κB axis may be a promising therapeutic target for ALI treatment.Subject terms: Cell signalling, Respiratory tract diseases     

Bioinformatic analyses of microRNA-targeted genes and microarray-identified genes correlated with Barrett's esophagus

Barrett's esophagus (BE) is defined as a metaplasia condition in the distal esophagus, in which the native squamous epithelium lining is replaced by a columnar epithelium with or without intestinal metaplasia. It is commonly accepted that BE is a precancerous lesion for esophageal adenocarcinoma. The aim of this study was to investigate the aberrant microRNAs (miRNAs) and differentially expressed genes (DEGs) associated with BE based on online microarray datasets. One miRNA and five gene expression profiling datasets were retrieved from the Gene Expression Omnibus Database. Aberrant microRNAs and DEGs were obtained using R/Bioconductor statistical analysis language and software. 23 dysregulated miRNAs and 632 DEGs demonstrating consistent expression tendencies in the five gene microarrays were identified in BE. Moreover, 1962 target genes of aberrant miRNAs were predicted using three bioinformatic tools, namely TargetScan, RNA22-HSA and miRDB. Ultimately, 93 target DEGs were obtained, after which functional annotation was performed on DAVID Bioinformatics Resources. Among Gene Ontology (GO) biological processes, digestive tract development and epithelial cell differentiation have demonstrated significant associations with BE pathogenesis. In addition, analysis of the KEGG pathways has revealed associations with cancer. To enable further study, one miRNA-target DEGs regulatory network was constructed using Cytoscape. 6 target DEGs demonstrated higher-degree distributions in the network, and ROC analysis indicated that FNDC3B may be the best potential biomarker for BE diagnosis. The data presented herein may provide new perspectives for exploring BE pathogenesis and may offer hits with regard to potential biomarkers in BE diagnosis, prediction and therapeutic evaluation.     

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 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.     

Discovery of core gene families associated with liver metastasis in colorectal cancer and regulatory roles in tumor cell immune infiltration

In this study, we aimed to uncover genes that drive the pathogenesis of liver metastasis in colorectal cancer (CRC), and identify effective genes that could serve as potential therapeutic targets for treating with colorectal liver metastasis patients based on two GEO datasets. Several bioinformatics approaches were implemented. First, differential expression analysis screened out key differentially expressed genes (DEGs) across the two GEO datasets. Based on gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses, we identified the enrichment functions and pathways of the DEGs that were associated with liver metastasis in CRC. Second, immune infiltration analysis identified key immune signature gene sets associated with CRC liver metastasis, among which two key immune gene families (CD and CCL) identified as key DEGs were filtered by protein-protein interaction (PPI) network. Some of the members in these gene families were associated with disease free survival (DFS) or overall survival (OS) in two subtypes of CRC, namely COAD and READ. Finally, functional enrichment analysis of the two gene families and their neighboring genes revealed that they were closely associated with cytokine, leukocyte proliferation and chemotaxis. These results are valuable in comprehending the pathogenesis of liver metastasis in CRC, and are of seminal importance in understanding the role of immune tumor infiltration in CRC. Our study also identified potentially effective therapeutic targets for liver metastasis in CRC including CCL20, CCL24 and CD70.