MetaDE Package Research Articles

An Integrated Bioinformatics Approach to Identify Network-Derived Hub Genes in Starving Zebrafish

Simple SummaryIn this study, in order to provide a better understanding and new knowledge about biochemical phenomena and gene interactions in the zebrafish transcriptome, the notion of integrative bioinformatics was used. To do so, we used co-expression and protein–protein interaction (PPI) networks followed by Bayesian network (BN) construction. We provide an integrative bioinformatics pipeline to explore crucial transcriptional insights from zebrafish starvation-induced transcriptome data in a user-friendly way with minimal programming skill requirements. In this way, many genes, e.g., skp1, atp5h, ndufb10, rpl5b, zgc:193613, zgc:123327, zgc:123178, wu:fc58f10, zgc:111986, wu:fc37b12, taldo1, wu:fb62f08, zgc:64133 and acp5a, were identified as hub genes affecting gene expression in the liver of starving zebrafish. These results can be applied in many different areas of zebrafish genomic studies.The present study was aimed at identifying causative hub genes within modules formed by co-expression and protein–protein interaction (PPI) networks, followed by Bayesian network (BN) construction in the liver transcriptome of starved zebrafish. To this end, the GSE11107 and GSE112272 datasets from the GEO databases were downloaded and meta-analyzed using the MetaDE package, an add-on R package. Differentially expressed genes (DEGs) were identified based upon expression intensity N(µ = 0.2, σ2 = 0.4). Reconstruction of BNs was performed by the bnlearn R package on genes within modules using STRINGdb and CEMiTool. ndufs5 (shared among PPI, BN and COEX), rps26, rpl10, sdhc (shared between PPI and BN), ndufa6, ndufa10, ndufb8 (shared between PPI and COEX), skp1, atp5h, ndufb10, rpl5b, zgc:193613, zgc:123327, zgc:123178, wu:fc58f10, zgc:111986, wu:fc37b12, taldo1, wu:fb62f08, zgc:64133 and acp5a (shared between COEX and BN) were identified as causative hub genes affecting gene expression in the liver of starving zebrafish. Future work will shed light on using integrative analyses of miRNA and DNA microarrays simultaneously, and performing in silico and experimental validation of these hub-causative (CST) genes affecting starvation in zebrafish.

Bioinformatics analysis to screen for genes related to myocardial infarction.

Myocardial infarction (MI) is an acute and persistent myocardial ischemia caused by coronary artery disease. This study screened potential genes related to MI. Three gene expression datasets related to MI were downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were screened using the MetaDE package. Afterward, the modules and genes closely related to MI were screened and a gene co-expression network was constructed. A support vector machine (SVM) classification model was then constructed based on the GSE61145 dataset using the e1071 package in R. A total of 98 DEGs were identified in the MI samples. Next, three modules associated with MI were screened and an SVM classification model involving seven genes was constructed. Among them, BCL6, CEACAM8, and CUGBP2 showed co-interactions in the gene co-expression network. Therefore, ACOX1, BCL6, CEACAM8, and CUGBP2, in addition to GPX7, might be feature genes related to MI.

Signaling factors potentially associated to the pathogenesis of Adult T-cell leukemia /lymphoma: A network-analysis and novel findings assessment

Adult T-cell leukemia/lymphoma (ATLL) is a human T-cell leukemia virus (HTLV) type 1-associated disease of TCD4+ cell transformation. Despite extensive studies on ATLL development and progression, the fundamental processes of HTLV-1 oncogenicity are yet to be understood. This study aimed to integrate high-throughput microarray datasets to find novel genes involved in the mechanism of ATLL progression. For this purpose, five microarray datasets were downloaded from the Gene Expression Omnibus database and then profoundly analyzed. Differentially expressed genes and miRNAs were determined using the MetaDE package in the R software and the GEO2R web tool. The STRING database was utilized to construct the protein-protein interaction network and explore hub genes. Gene ontology and pathway enrichment analysis were carried out by employing the EnrichR web tool. Furthermore, flow cytometry was employed to assess the CD4/CD8 ratio, and quantitative reverse transcription-polymerase chain reaction (qRT-PCR) was used to confirm the high-throughput data analysis results. Four miRNAs, including hsa-mir-146, hsa-mir-451, hsa-mir-31, and hsa-mir-125, were among the statistically significant differentially expressed miRNAs between healthy individuals and ATLL patients. Moreover, 924 differentially expressed genes were identified between normal and ATLL samples. Further network analysis highlighted 59 hub genes mainly regulating pathways implicated in viral interferences, immunological processes, cancer, and apoptosis pathways. Among the identified hub genes, RhoA and PRKACB were most considerable in the high-throughput analysis and were further validated by qRT-PCR. The RhoA and PRKACB expression were significantly down-regulated in ATLL patients compared to asymptomatic carriers (p<0.0001 and p=0.004) and healthy subjects (p=0.043 and p=0.002). Therefore, these corresponding miRNAs and proteins could be targeted for diagnosis purposes and designing effective treatments.

The role of IDH1 mutation on gene expression in glioblastoma

Glioblastoma multiforme (GBM) is the most malignant primary brain tumor found in humans. Mutations in isocitrate dehydrogenases 1 (IDH1R132H) was observed in GBM. GBMs with IDH1R132H have longer patient survival compared to GBMs with wild-type IDH1 and identifying the mechanism of this can contribute to the development of a new treatment for GBM. To investigate the role of IDH1R132H on gene expression, we incorporated three expression arrays from GEO including GSE36245, GSE131837, and GSE122586, and one expression array from TCGA which in total investigate gene expression differences between 293 wild type and 41 mutant IDH1 GBM samples. Meta-DE package in R software was applied to screen the differentially expressed genes (DEGs) between IDH1 wild type and IDH1R132H GBMs. DEGs were used to construct co-expression networks by WGCNA package for TCGA data and genes of the detected module were used to build protein-protein interaction (PPI) network. Each of the mentioned expression arrays was analyzed independently using limma package by cutoff |logFC| > 1 and P.value < 0.05, and 13 downregulated and 26 upregulated common genes were identified. By merging the results of limma package and core genes of the PPI network, 10 hub genes including ARAP3, ARHGAP11B, BDNF, CFAP45, CXCL8, MMP9, RHOBTB1, SHH, SYNJ2, and VEGFA were identified. Based on the TCGA database, we tested the prognostic significance of the detected genes using Kaplan-Meier survival analysis. In conclusion, the results suggested that the hub genes may play role in GBM pathogeny.

MiR-574-5P, miR-1827, and miR-4429 as Potential Biomarkers for Schizophrenia.

Schizophrenia is a severe chronic debilitating disorder with millions of affected individuals. Diagnosis is based on clinical presentations, which are made when the progressive disease has appeared. Early diagnosis may help improve the clinical outcomes and response to treatments. Lack of a reliable molecular diagnostic invokes the identification of novel biomarkers. To elucidate the molecular basis of the disease, in this study we used two mRNA expression arrays, including GSE93987 and GSE38485, and one miRNA array, GSE54914, and meta-analysis was conducted for evaluation of mRNA expression arrays via metaDE package. Using WGCNA package, we performed network analysis for both mRNA expression arrays separately. Then, we constructed protein-protein interaction network for significant modules. Limma package was employed to analyze the miRNA array for identification of dysregulated miRNAs (DEMs). Using genes of significant modules and DEMs, a mRNA-miRNA network was constructed and hub genes and miRNAs were identified. To confirm the dysregulated genes, expression values were evaluated through available datasets including GSE62333, GSE93987, and GSE38485. The ability of the detected hub miRNAs to discriminate schizophrenia from healthy controls was evaluated by assessing the receiver-operating curve. Finally, the expression levels of genes and miRNAs were evaluated in 40 schizophrenia patients compared with healthy controls via Real-Time PCR. The results confirmed dysregulation of hsa-miR-574-5P, hsa-miR-1827, hsa-miR-4429, CREBRF, ARPP19, TGFBR2, and YWHAZ in blood samples of schizophrenia patients. In conclusion, three miRNAs including hsa-miR-574-5P, hsa-miR-1827, and hsa-miR-4429 are suggested as potential biomarkers for diagnosis of schizophrenia.

Signature Panel of 11 Methylated mRNAs and 3 Methylated lncRNAs for Prediction of Recurrence-Free Survival in Prostate Cancer Patients.

BackgroundRadical prostatectomy is the main treatment for prostate cancer (PCa), a common cancer type among men. Recurrence frequently occurs in a proportion of patients. Therefore, there is a great need to early screen those patients to specifically schedule adjuvant therapy to improve the recurrence-free survival (RFS) rate. This study aims to develop a biomarker to predict RFS for patients with PCa based on the data of methylation, an important heritable contributor to carcinogenesis.MethodsMethylation expression data of PCa patients were downloaded from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus database (GSE26126), and the European Bioinformatics Institute (E-MTAB-6131). The stable co-methylation modules were identified by weighted gene co-expression network analysis. The genes in modules were overlapped with differentially methylated RNAs (DMRs) screened by MetaDE package in three datasets, which were used to screen the prognostic genes using least absolute shrinkage and selection operator analyses. The prognostic performance of the prognostic signature was assessed by survival curve analysis.ResultsFive co-methylation modules were considered preserved in three datasets. A total of 192 genes in these 5 modules were overlapped with 985 DMRs, from which a signature panel of 11 methylated messenger RNAs and 3 methylated long non-coding RNAs was identified. This signature panel could independently predict the 5-year RFS of PCa patients, with an area under the receiver operating characteristic curve (AUC) of 0.969 for the training TCGA dataset and 0.811 for the testing E-MTAB-6131 dataset, both of which were higher than the predictive accuracy of Gleason score (AUC = 0.689). Also, the patients with the same Gleason score (6–7 or 8–10) could be further divided into the high-risk group and the low-risk group.ConclusionThese results suggest that our prognostic model may be a promising biomarker for clinical prediction of RFS in PCa patients.

Coexpression Network Analysis Identifies a Novel Nine-RNA Signature to Improve Prognostic Prediction for Prostate Cancer Patients.

Background Prostate cancer (PCa) is the most common malignancy and the leading cause of cancer death in men. Recent studies suggest the molecular signature was more effective than the clinical indicators for the prognostic prediction, but all of the known studies focused on a single RNA type. The present study was to develop a new prognostic signature by integrating long noncoding RNAs (lncRNAs) and messenger RNAs (mRNAs) and evaluate its prognostic performance. Methods The RNA expression data of PCa patients were downloaded from The Cancer Genome Atlas (TCGA) or Gene Expression Omnibus database (GSE17951, GSE7076, and GSE16560). The PCa-driven modules were identified by constructing a weighted gene coexpression network, the corresponding genes of which were overlapped with differentially expressed RNAs (DERs) screened by the MetaDE package. The optimal prognostic signature was screened using the least absolute shrinkage and selection operator analysis. The prognostic performance and functions of the combined prognostic signature was then assessed. Results Twelve PCa-driven modules were identified using TCGA dataset and validated in the GSE17951 and GSE7076 datasets, and six of them were considered to be preserved. A total of 217 genes in these 6 modules were overlapped with 699 DERs, from which a nine-gene prognostic signature was identified (including 3 lncRNAs and 6 mRNAs), and the risk score of each patient was calculated. The overall survival was significantly shortened in patients having the risk score higher than the cut-off, which was demonstrated in TCGA (p = 5.063E − 03) dataset and validated in the GSE16560 (p = 3.268E − 02) dataset. The prediction accuracy of this risk score was higher than that of clinical indicators (the Gleason score and prostate-specific antigen) or the single RNA type, with the area under the receiver operator characteristic curve of 0.945. Besides, some new therapeutic targets and mechanisms (MAGI2-AS3-SPARC/GJA1/CYSLTR1, DLG5-AS1-DEFB1, and RHPN1-AS1-CDC45/ORC) were also revealed. Conclusion The risk score system established in this study may provide a novel reliable method to identify PCa patients at a high risk of death.

Exploring Kawasaki disease-specific hub genes revealing a striking similarity of expression profile to bacterial infections using weighted gene co-expression network analysis (WGCNA) and co-expression modules identification tool (CEMiTool): An integrated bioinformatics and experimental study

Kawasaki disease (KD) has been declared a rare idiopathic condition for a long time. The children age less than five years, as the most susceptible group, are at risk of this disease.Since the cause of the disease is unknown, this study was designed to investigate the cause of KD. We applied metaDE and WGCNA packages in order to perform a meta-analysis and identify network modules of co-expressed genes, respectively, on three expression array datasets and also CEMiTool package to confirm detected modules by WGCNA. Using the Pearson correlation coefficient, the resemblance of KD to other symptomatic-similar diseases, including bacterial infections, viral infections, JIA (juvenile idiopathic arthritis), HSP (Henoch-Schönlein purpura), GAS (group A streptococcal), and HAdV (adenovirus) was accurately estimated. In addition to validation by more three expression array datasets, serum samples of 16 patients and eight control participants have undergone the Real-Time PCR assay so as to evaluate produced bioinformatic results. WGCNA showed 3840 differentially expressed genes (DEGs) in KD in comparison with other diseases accompanying resembling clinical manifestations. Through further bioinformatic analysis and validation, 42 out of DEGs were introduced as hub genes, which the results of Real-Time PCR assay subsequently attested to the majority of them. The DEGs possessed a remarkable commonality with those of bacterial conditions. According to our exhaustive results, the origin of KD has been revealed pertinent to bacterial infections. Another interesting finding in this study is introducing IVIG in combination with particular antibiotics as a novel therapeutic approach, which supported by a score of authentic research studies to overcome KD.

Screening the underlying molecular mechanisms involved in the development of heart failure

Heart failure (HF) causes a great socioeconomic and medical burden in our societies. We aimed to screen the molecular mechanisms related to the development of HF that may lead to new therapeutic targets. We incorporated four expression datasets (GSE5406, GSE1869, GSE29819, and GSE42955) of dilated cardiomyopathy (DCM) from GEO repository to identify differentially expressed genes (DEGs) between 33 controls and 111 patients. WGCNA package, MetaQC, MetaDE, and Metapath packages of Meta-omics, affy, and limma R packages have been used in the bioinformatics analysis for the assessment of quality control, differentially expressed genes and enrichment of detected genes. Search Tool for the Retrieval Interacting Genes (STRING) was used as well for the analysis of protein-protein interaction (PPI) network. Using the false discovery rate (FDR) cut-off of 0.0001, 508 genes (229 up-regulated and 279 down-regulated), were differentially expressed by the Fisher's method. The most enrichment Gene Ontology (GO) terms contain genes involved in oxidative phosphorylation (OXPHOS), proteasome complex, and protein folding related pathways. Finally, to increase the significance of our study, the identified DEGs by meta-analysis cross-checked by the results of the DEGs analysis of patients with right ventricular dysfunction (GSE29819, that contains 6 controls and 7 patients samples). Overall, our bioinformatic-based study provided a preliminary molecular profile which is worth to be further investigated as possible new therapeutic targets.

Bioinformatics Analysis of Microarray Datasets to Identify Prognostic Factors in Lung Adenocarcinoma.

Most patients with lung adenocarcinoma (LUAD) present high recurrence rate and poor prognosis after therapy. Therefore, the purpose of this study was to identify prognostic factors involved in LUAD. Five microarray datasets (including GSE75037, GSE63459, GSE43458, GSE32863, and GSE10072) were downloaded. After data preprocessing and quality control, meta-analysis was performed to screen differentially expressed genes (DEGs) using the MetaDE.ES method in MetaDE package. Subsequently, network construction and module identification were conducted by the Weighted Gene Co-expression Network Analysis method. Moreover, survival-associated genes were identified using the univariate and multivariate Cox regression method in survival package. The risk score model was constructed by prognosis associated genes, followed by the Kaplan-Meier survival analysis. Oncomine expressions analysis of several prognosis associated genes was conducted. The expression levels of key genes were detected using quantitative real-time PCR experiments. A total of 1434 DEGs between LUAD and normal samples were identified. Nine disease-associated modules were identified, in which M8 module was most correlated with LAUD phenotype. A total of 89 indicators (including T stage, M stage, and ADIPOR2) were significantly associated with LAUD prognosis, while only T stage and 9 DEGs (e.g., ARHGEF3, GTSE1, RBM15 and CD52) were retained as the potential prognostic factors following multivariate COX regression analysis. The upregulated adiponectin receptor 2 (ADIPOR2), rho guanine nucleotide exchange factor 3 (ARHGEF3), and CD52 molecule (CD52), and downregulated GTSE1 were validated in LAUD samples of Oncomine database. Importantly, ADIPOR2 and ARHGEF3 were confirmed to be down-regulated in LUAD tissues. ADIPOR2, ARHGEF3, G2 and S-phase expressed 1 (GTSE1) and CD52 might be promising prognostic factors in LUAD.

Identification of novel biomarkers in ischemic stroke: a genome-wide integrated analysis

BackgroundIschemic Stroke (IS) is the most common neurological emergency disease and has become the second most frequent cause of death after coronary artery disease in 2015. Owing to its high fatality rate and narrow therapeutic time window, early identification and prevention of potential stroke is becoming increasingly important.MethodsWe used meta-analysis and bioinformatics mining to explore disease-related pathways and regulatory networks after combining messengerRNA (mRNA) and miRNA expression analyses. The purpose of our study was to screen for candidate target genes and microRNA(miRNA) for early diagnosis of potential stroke.ResultsFive datasets were collected from the Gene Expression Omnibus (GEO) database by systematical retrieval, which contained three mRNA datasets (102 peripheral blood samples in total) and two miRNA dataset (59 peripheral blood samples). Approximately 221 different expression(DE) mRNAs (155 upregulated and 66 downregulated mRNAs) and 185 DE miRNAs were obtained using the metaDE package and GEO2R tools. Further functional enrichments of DE-mRNA, DE-miRNA and protein-protein interaction (PPI) were performed and visualized using Cytoscape.ConclusionOur study identified six core mRNAs and two regulated miRNAs in the pathogenesis of stroke, and we elaborated the intrinsic role of systemic lupus erythematosus (SLE) and atypical infections in stroke, which may aid in the development of precision medicine for treating ischemic stroke. However, the role of these novel biomarkers and the underlying molecular mechanisms in IS require further fundamental experiments and further clinical evidence.

A six‑gene support vector machine classifier contributes to the diagnosis of pediatric septic shock

Septic shock is induced by an uncontrolled inflammatory immune response to pathogens and the survival rate of patients with pediatric septic shock (PSS) is particularly low, with a mortality rate of 25–50%. The present study explored the mechanisms of PSS using four microarray datasets (GSE26378, GSE26440, GSE13904 and GSE4607) that were obtained from the Gene Expression Omnibus database. Based on the MetaDE package, the consistently differentially expressed genes (DEGs) in the four datasets were screened. Using the WGCNA package, the disease-associated modules and genes were identified. Subsequently, the optimal feature genes were further selected using the caret package. Finally, a support vector machine (SVM) classifier based on the optimal feature genes was built using the e1071 package. Initially, there were 2,699 consistent DEGs across the four datasets. From the 10 significantly stable modules across the datasets, four stable modules (including the magenta, purple, turquoise and yellow modules), in which the consistent DEGs were significantly enriched (P<0.05), were further screened. Subsequently, six optimal feature genes (including cysteine rich transmembrane module containing 1, S100 calcium binding protein A9, solute carrier family 2 member 14, stomatin, uridine phosphorylase 1 and utrophin) were selected from the genes in the four stable modules. Additionally, an effective SVM classifier was constructed based on the six optimal genes. The SVM classifier based on the six optimal genes has the potential to be applied for PSS diagnosis. This may improve the accuracy of early PSS diagnosis and suggest possible molecular targets for interventions.

MetaDE-Based Analysis of circRNA Expression Profiles Involved in Gastric Cancer.

Circular RNAs (circRNAs) could play carcinogenic roles in gastric cancer (GC) and have potential to be biomarkers for GC early diagnosis, which needs to be further excavated and supported by more evidence. The study aims to identify more authentic circRNA expression profiles that could function as potential biomarkers in GC. circRNA expression data in three microarrays were downloaded from Gene Expression Omnibus datasets. A systematic meta-analysis based on an integrated dataset pre-processed from the three microarrays was conducted to identify a panel of differentially expressed circRNAs (DEcircs) by using the metaDE package. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes term enrichment were used to note the corresponding functions of DEcircs. Quantitative real-time polymerase chain reaction was applied to verify the DEcircs expression in cancer tissues and adjacent paracancerous tissues. A GC risk-related circRNAs-miRNAs-mRNAs network was further constructed and analyzed. MetaDE analysis suggested 64 DEcircs between cancer tissues and adjacent normal tissues. GO and KEGG analysis showed that the parental genes of these DEcircs were mainly associated with histone methylation, Wnt signalosome and histone methylation activity. Hsa_circ_0005927 and hsa_circ_0067934 were verified in GC tissues, and a GC risk-related network was constructed. MetaDE-based circRNA expression profiles revealed a series of potential biomarkers involved in GC. Two circRNAs, hsa_circ_0005927 and hsa_circ_0067934, could be more authentic biomarkers for GC screening. The GC risk-related network of hsa_circ_0005927/hsa_circ_0067934 and their downstream targets will provide new genetic insights for GC research.

Gene expression analysis in Kawasaki disease; bioinformatics and experimental approach

Abstract Kawasaki disease (KD) is an inflammatory condition in children, which has unknown etiology with an insufficiently described genetic mechanism. There is no accurate molecular diagnostic test for KD, but some genetic factors have been proposed in previous studies. In this study, we investigated the underlying molecular alterations based on both in silico and wet lab analysis to candidate transcriptional biosignature and biomarker. metaQC, metaDE, and metapath packages have been used in the bioinformatic analysis for the assessment of quality control, investigation of differentially expressed genes, and enrichment of detected genes, respectively. In the next step, miRNA array was analyzed by biobase, GEOquery, and limma packages. All bioinformatic studies were conducted with the R software platform. Finally, Real-Time PCR was performed on patient samples for the evaluation of the bioinformatic results. The results of bioinformatic analysis led to the introduction of 28 genes with the highest difference in gene expression, and 14 miRNAs with the highest difference in expression after microRNA array analysis. Real-time PCR results validated candidate genes and miRNAs as KD transcriptional biosignatures and biomarkers, respectively. Our studies have shown MyD88, KREMEN1, TLR5, ALPK1, IRAK4, PFKFB3, HK3, CREB, CR1, SLC2A14, and FPR1 as the most important genes involved in KD. Also, hsa-miR-575, hsa-miR-483-5p, hsa-miR-4271, hsa-miR-4327 as the most likely miRNAs to interfere with KD. The altered expression levels of the aforementioned genes and miRNAs can be studied further for therapeutic targets.

Identification of key genes and pathways in calcific aortic valve disease by bioinformatics analysis.

Calcific aortic valve disease (CAVD) is the most common type of valvular heart disease in the elderly. This study is aimed to explore molecular mechanism of CAVD via bioinformatics analysis. The gene expression profiles of GSE51472 (including 5 normal aortic valve and 5 calcified aortic valve) and GSE83453 (including 8 normal aortic valve and 19 calcified aortic valve) were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were screened using the MetaDE package in R software. Functional and pathway enrichment analysis were performed based on Gene ontology (GO) and KEGG pathway database. Then, STRING database, Cytoscape and MCODE were applied to construct the protein-protein interaction (PPI) network and screen hub genes. Pathway enrichment analysis was further performed for hub genes and gene clusters identified via module analysis. A total of 107 DEGs were identified in CAVD (53 up-regulated genes, and 54 down-regulated genes), and they were mainly enriched in the terms of immune response, extracellular matrix organization, leukocyte transendothelial migration, cell adhesion molecules (CAMs), and fatty acid metabolism. Five hub genes including VCAM1, MMP9, ITGB2, RAC2, and vWF were identified via PPI network, which were mainly enriched in terms of leukocyte transendothelial migration and cell adhesion. An independently down-regulated protein cluster containing ALDH2, HIBCH, ACADVL, ECHDC2, VAT1L, and MAOA was also identified via PPI network. The present study identified VCAM1, MMP9, ITGB2, RAC2, vWF and ALDH2 as key genes in the progression of CAVD. Immune cells infiltration might play a key role in the progression of CAVD, while ALDH2-mediated detoxification effect might play a protective role in CAVD. Further studies are needed to elucidate the pathogenesis of CAVD.

Regulatory Network Analysis to Reveal Important miRNAs and Genes in Non-Small Cell Lung Cancer.

Objective Lung cancer has high incidence and mortality rate, and non-small cell lung cancer (NSCLC) takes up approximately 85% of lung cancer cases. This study is aimed to reveal miRNAs and genes involved in the mechanisms of NSCLC. Materials and Methods In this retrospective study, GSE21933 (21 NSCLC samples and 21 normal samples), GSE27262 (25 NSCLC samples and 25 normal samples), GSE43458 (40 NSCLC samples and 30 normal samples) and GSE74706 (18 NSCLC samples and 18 normal samples) were searched from gene expression omnibus (GEO) database. The differentially expressed genes (DEGs) were screened from the four microarray datasets using MetaDE package, and then conducted with functional annotation using DAVID tool. Afterwards, protein-protein interaction (PPI) network and module analyses were carried out using Cytoscape software. Based on miR2Disease and Mirwalk2 databases, microRNAs (miRNAs)-DEG pairs were selected. Finally, Cytoscape software was applied to construct miRNA-DEG regulatory network. Results Totally, 727 DEGs (382 up-regulated and 345 down-regulated) had the same expression trends in all of the four microarray datasets. In the PPI network, TP53 and FOS could interact with each other and they were among the top 10 nodes. Besides, five network modules were found. After construction of the miRNA-gene network, top 10 miRNAs (such as hsa-miR-16-5p, hsa-let-7b-5p, hsa-miR-15a-5p, hsa-miR-15b-5p, hsa-let-7a-5p and hsa-miR-34a- 5p) and genes (such as HMGA1, BTG2, SOD2 and TP53) were selected. ConclusionThese miRNAs and genes might contribute to the pathogenesis of NSCLC.

A 9‑lncRNA risk score system for predicting the prognosis of patients with hepatitis B virus‑positive hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer, and can be induced by hepatitis B virus (HBV) infection. The aim of the present study was to screen prognosis-associated long noncoding RNAs (lncRNAs) and construct a risk score system for the disease. The RNA-sequencing data of patients with HCC (including 100 HCC samples and 26 normal samples) were extracted from The Cancer Genome Atlas (TCGA) database. In addition, GSE55092, GSE19665 and GSE10186 datasets were downloaded from the Gene Expression Omnibus database. Combined with weighted gene co-expression network analysis, the identification and functional annotation of stable modules was performed. Using the MetaDE package, the consensus differentially expressed RNAs (DE-RNAs) were analyzed. To construct a risk score system, prognosis-associated lncRNAs and the optimal lncRNA combination were separately analyzed by survival and penalized packages. Finally, pathway enrichment analysis for the nodes in an lncRNA-mRNA network was conducted via Gene Set Enrichment Analysis. A total of four stable modules and 3,051 consensus DE-RNAs were identified. The stable modules were significantly associated with the histological grades of HCC, tumor, node and metastasis stage, pathological stage, recurrence and exposure to radiation therapy. A 9-lncRNA optimal combination [DiGeorge syndrome critical region gene 9, glucosidase, β, acid 3 (GBA3), HLA complex group 4, N-acetyltransferase 8B, neighbor of breast cancer 1 gene 2, prostate androgen-regulated transcript 1, ret finger protein like 1 antisense RNA 1, solute carrier family 22 member 18 antisense and T-cell leukemia/lymphoma 6] was selected from the 14 prognosis-associated lncRNAs, and was further supported by the validation dataset, GSE10186. The lncRNA-mRNA co-expression network revealed lncRNA GBA3 as a positive regulator of phosphoenolpyruvate carboxykinase 2, an important enzyme in the metabolic pathway of gluconeogenesis. A risk score system was established based on the optimal 9 lncRNAs, which may be valuable for predicting the prognosis of patients with HBV-positive HCC and improving understanding of mechanisms associated with the pathogenesis of this disease. On the contrary, a larger, independent cohort of patients is required to further validate the risk-score system.

Five-long non-coding RNA risk score system for the effective prediction of gastric cancer patient survival.

The prognosis for patients with gastric cancer (GC) is usually poor, as the majority of patients have reached the advanced stages of disease at the point of diagnosis. Therefore, revealing the mechanisms of GC is necessary for the identification of key biomarkers and the development of effective targeted therapies. The present study aimed to identify long non-coding RNAs (lncRNAs) prominently expressed in patients with GC. The GC dataset (including 384 GC samples) was downloaded from The Cancer Genome Atlas database as the training set. A number of other GC datasets were obtained from the Gene Expression Omnibus database as validation sets. Following data processing, lncRNAs were annotated, followed by co-expression module analysis to identify stable modules, using the weighted gene co-expression network analysis (WGCNA) package. Prognosis-associated lncRNAs were screened using the ‘survival’ package. Following the selection of the optimal lncRNA combinations using the ‘penalized’ package, risk score systems were constructed and assessed. Consensus differentially-expressed RNAs (DE-RNAs) were screened using the MetaDE package, and an lncRNA-mRNA network was constructed. Additionally, pathway enrichment analysis was conducted for the network nodes using gene set enrichment analysis (GSEA). A total of seven modules (blue, brown, green, grey, red, turquoise and yellow) were obtained following WGCNA analysis, among which the green and turquoise modules were stable and associated with the histological grade of GC. A total of 12 prognosis-associated lncRNAs were identified in the two modules. Combined with the optimal lncRNA combinations, risk score systems were constructed. The risk score system based on the green module [including ITPK1 antisense RNA 1 (ITPK1-AS1), KCNQ1 downstream neighbor (KCNQ1DN), long intergenic non-protein coding RNA 167 (LINC00167), LINC00173 and LINC00307] was the more efficient at predicting risk compared with those based on the turquoise, or the green + turquoise modules. A total of 1,105 consensus DE-RNAs were identified; GSEA revealed that LINC00167, LINC00173 and LINC00307 had the same association directions with 4 pathways and the 32 genes involved in those pathways. In conclusion, a risk score system based on the green module may be applied to predict the survival of patients with GC. Furthermore, ITPK1-AS1, KCNQ1DN, LINC00167, LINC00173 and LINC00307 may serve as biomarkers for GC pathogenesis.

Identification of Candidate Genes and MicroRNAs for Acute Myocardial Infarction by Weighted Gene Coexpression Network Analysis.

Background Identification of potential molecular targets of acute myocardial infarction is crucial to our comprehensive understanding of the disease mechanism. However, studies of gene coexpression analysis via jointing multiple microarray data of acute myocardial infarction still remain restricted. Methods Microarray data of acute myocardial infarction (GSE48060, GSE66360, GSE97320, and GSE19339) were downloaded from Gene Expression Omnibus database. Three data sets without heterogeneity (GSE48060, GSE66360, and GSE97320) were subjected to differential expression analysis using MetaDE package. Differentially expressed genes having upper 25% variation across samples were imported in weighted gene coexpression network analysis. Functional and pathway enrichment analyses were conducted for genes in the most significant module using DAVID. The predicted microRNAs to regulate target genes in the most significant module were identified using TargetScan. Moreover, subpathway analyses using iSubpathwayMiner package and GenCLiP 2.0 were performed on hub genes with high connective weight in the most significant module. Results A total of 1027 differentially expressed genes and 33 specific modules were screened out between acute myocardial infarction patients and control samples. Ficolin (collagen/fibrinogen domain containing) 1 (FCN1), CD14 molecule (CD14), S100 calcium binding protein A9 (S100A9), and mitochondrial aldehyde dehydrogenase 2 (ALDH2) were identified as critical target molecules; hsa-let-7d, hsa-let-7b, hsa-miR-124-3, and hsa-miR-9-1 were identified as potential regulators of the expression of the key genes in the two biggest modules. Conclusions FCN1, CD14, S100A9, ALDH2, hsa-let-7d, hsa-let-7b, hsa-miR-124-3, and hsa-miR-9-1 were identified as potential candidate regulators in acute myocardial infarction. These findings might provide new comprehension into the underlying molecular mechanism of disease.

Integrated Analysis and Identification of Novel Biomarkers in Parkinson's Disease.

Parkinson’s disease (PD) is a quite common neurodegenerative disorder with a prevalence of approximately 1:800–1,000 in subjects over 60 years old. The aim of our study was to determine the candidate target genes in PD through meta-analysis of multiple gene expression arrays datasets and to further combine mRNA and miRNA expression analyses to identify more convincing biological targets and their regulatory factors. Six included datasets were obtained from the Gene Expression Omnibus database by systematical search, including five mRNA datasets (150 substantia nigra samples in total) and one miRNA dataset containing 32 peripheral blood samples. A chip meta-analysis of five microarray data was conducted by using the metaDE package and 94 differentially expressed (DE) mRNAs were comprehensively obtained. And 19 deregulated DE miRNAs were obtained through the analysis of one miRNAs dataset by Qlucore Omics Explorer software. An interaction network formed by DE mRNAs, DE miRNAs, and important pathways was discovered after we analyzed the functional enrichment, protein–protein interactions, and miRNA targetome prediction analysis. In conclusion, this study suggested that five significantly downregulated mRNAs (MAPK8, CDC42, NDUFS1, COX4I1, and SDHC) and three significantly downregulated miRNAs (miR-126-5p, miR-19-3p, and miR-29a-3p) were potentially useful diagnostic markers in clinic, and lipid metabolism (especially non-alcoholic fatty liver disease pathway) and mitochondrial dysregulation may be the keys to biochemically detectable molecular defects. However, the role of these new biomarkers and molecular mechanisms in PD requires further experiments in vivo and in vitro and further clinical evidence.