microarray analysis

This study aimed to find lncRNAs and mRNAs that were expressed differently by combining microarray datasets from different studies. This was done to find important target genes in gastric cancer for anti-cancer therapy.

Gastric cancer (GC) is the fourth most frequent and second-most deadly malignancy worldwide. Thus, genetic diagnosis and treatment should focus on genetic and epigenetic variables. Based on several studies, disordered expression of non-coding RNAs (ncRNAs), such as lncRNAs, regulate gastric cancer invasion and metastasis. Besides, lncRNAs cooperatively regulate gene expression and GC progression.

We obtained differentially expressed mRNAs (DEmRNAs) and lncRNAs (DElncRNAs) from three GC tissue microarray datasets by meta-analysis and screened genes using the "Limma" package. Then, using the RNAInter database, we allocated DEmRNAs to each DElncRNA. ClusterProfiler and GOplot programs were used to analyze function enrichment pathways and gene ontologies for final DEmRNAs.

A total of 9 differentially expressed lncRNAs (DElncRNAs) (5 up-regulated and 4 down-regulated), and 856 DEmRNAs (451 up-regulated and 405 down-regulated) between tumor and adjacent normal samples were found. Finally, 117 differentially expressed mRNAs were predicted as interactors of six DElncRNAs (H19, WT1-AS, EMX2OS, HOTAIR, ZEB1-AS1, and LINC00261).

In order to promote cancer therapeutics and give knowledge on the process of carcinogenesis, our study projected a network of drug-gene interactions for discovered genes and presented relevant prospective biomarkers for the prognosis of patients with stomach cancer.

In microarray datasets, hundreds and thousands of genes are measured in a small number of samples, and sometimes due to problems that occur during the experiment, the expression value of some genes is recorded as missing. It is a difficult task to determine the genes that cause disease or cancer from a large number of genes. This study aimed to find effective genes in pancreatic cancer (PC). First, the K-nearest neighbor (KNN) imputation method was used to solve the problem of missing values (MVs) of gene expression. Then, the random forest algorithm was used to identify the genes associated with PC.

In this retrospective study, 24 samples from the GSE14245 dataset were examined. Twelve samples were from patients with PC, and 12 samples were from healthy control. After preprocessing and applying the fold-change technique, 29482 genes were used. We used the KNN imputation method to impute when a particular gene had MVs. Then, the genes most strongly associated with PC were selected using the random forest algorithm. We classified the dataset using support vector machine (SVM) and naïve bayes (NB) classifiers, and F-score and Jaccard indices were reported.

Out of the 29482 genes, 1185 genes with fold-changes greater than 3 were selected. After selecting the most associated genes, 21 genes with the most important value were identified. S100P and GPX3 had the highest and lowest importance values, respectively. The F-score and Jaccard value of the SVM and NB classifiers were 95.5, 93, 92, and 92 percent, respectively.

This study is based on the application of the fold change technique, imputation method, and random forest algorithm and could find the most associated genes that were not identified in many studies. We therefore suggest researchers use the random forest algorithm to detect the related genes within the disease of interest.

MicroRNAs (miRNAs) can participate in airway remodeling by regulating immune molecule expression. Here, we aimed to identify the differential miRNAs involved in airway remodeling. Airway remodeling was induced by ovalbumin in female BALB/C mice. The differentially expressed miRNAs were screened with microarray. GO (Gene Ontology) and KEGG enrichment analysis was performed. The miRNA target gene network and miRNA target pathway network were constructed. Verification with real-time PCR and Western blot was performed. We identified 63 differentially expressed miRNAs (50 up-regulated and 13 down-regulated) in the lungs of ovalbumin-induced airway remodeling mice. Real-time PCR confirmed that 3 miRNAs (mmu-miR-1931, mmu-miR-712-5p, and mmu-miR-770-5p) were significantly up-regulated, and 4 miRNAs (mmu-miR-128-3p, mmu-miR-182-5p, mmu-miR-130b-3p, and mmu-miR-20b-5p) were significantly down-regulated. The miRNA target gene network analysis identified key mRNAs in the airway remodeling, such as Tnrc6b (trinucleotide repeat containing adaptor 6B), Sesn3 (sestrin 3), Baz2a (bromodomain adjacent to zinc finger domain 2a), and Cux1 (cut like homeobox 1). The miRNA target pathway network showed that the signal pathways such as MAPK (mitogen-activated protein kinase), PI3K/Akt (phosphoinositide 3-Kinase/protein kinase B), p53 (protein 53), and mTOR (mammalian target of rapamycin) were closely related to airway remodeling in asthma. Collectively, differential miRNAs involved in airway remodeling (such as mmu-miR-1931, mmu-miR-712-5p, mmu-miR-770-5p, mmu-miR-128-3p mmu-miR-182-5p, and mmu-miR-130b-3p) as well as their target genes (such as Tnrc6b, Sesn3, Baz2a, and Cux1) and pathways (such as MAPK, PI3K/Akt, p53, mTOR pathways) have been identified. Our findings may help to further understand the pathogenesis of airway remodeling.

Tenascin‑C (TNC) is a large glycoprotein of the extracellular matrix which associated with poor clinical outcomes in several malignancies. TNC over‑expression is repeatedly observed in several cancer tissues and promotes several processes in tumor progression. Until quite recently, more needs to be known about the potential mechanisms of TNC as a key player in cancer progression and metastasis.

In the present study, we performed a bioinformatics analysis of breast and colorectal cancer expression microarray data to survey TNC role and function with holistic view. Gene expression profiles were analyzed to identify differentially expressed genes (DEGs) between normal samples and cancer biopsy samples. The protein‑protein interaction (PPI) networks of the DEGs with CluePedia plugin of Cytoscape software were constructed. Furthermore, after PPI network construction, gene‑regulatory networks analysis was performed to predict long noncoding RNAs and microRNAs associated with TNC and cluster analysis was performed. Using the Clue gene ontology (GO) plugin of Cytoscape software, the GO and pathway enrichment analysis were performed.

PPI and DEGs‑miRNA‑lncRNA regulatory networks showed TNC is a significant node in a huge network, and one of the main gene with high centrality parameters. Furthermore, from the regulatory level perspective, TNC could be significantly impressed by miR‑335‑5p. GO analysis results showed that TNC was significantly enriched in cancer‑related biological processes.

It is important to identify the TNC underlying molecular mechanisms in cancer progression, which may be clinically useful for tumor‑targeting strategies. Bioinformatics analysis provides an insight into the significant roles that TNC plays in cancer progression scenarios.

Aspergillus fumigatus is the most common species causing invasive aspergillosis (IA), a life-threatening infection with more than 80% mortality. Interactions between A. fumigatus and human blood platelets lead to intravascular thrombosis and localized infarcts. To better understand A. fumigatus pathogenesis, we aimed to analyze the genetic basis of interactions between the pathogen and blood platelets.

A bioinformatic pipeline on microarray gene expression dataset, including analysis of differentially expressed genes (DEGs) using Limma R package and their molecular function, as well as biological pathways identification, was conducted to find the effective genes involved in IA. In the wet phase, the gene expression patterns following fungal exposure to blood platelets at 15, 30, 60, and 180 min were evaluated by quantitative reverse transcriptase-PCR analysis.

Three genes encoding aspartic endopeptidases including (Pep1), (Asp f 13), and (β-glucanase) were the standing candidates. The invasion-promoting fungal proteinase-encoding genes were down-regulated after 30 min of hyphal incubation with blood platelets, and then up-regulated at 60 and 180 min, although only Pep1 was greater than the control at the 60and 180 min time points. Also, the same genes were downregulated in more the clinical isolates relative to the standard strain CBS 144.89.

Our findings delineate the possible induction of fungal-encoded proteinases by blood platelets. This provides a new research line into A. fumigatus’ molecular pathogenesis. Such insight into IA pathogenesis might also guide researchers toward novel platelet-based therapies that involve molecular interventions, especially in IA patients.

Microarray technology is an accurate method for recognition of disease association gene alterations. However, there still is not an effective approach for the evaluation of gene expression in ovarian cancer. A reliable approach is described to identify genes associated with ovarian cancer. Microarray gene expression data analysis was applied to correct systematic differences through four different normalization methods; LOESS, 3D LOESS, and neural network (NN3, NN4). Then, three different clustering methods of K-means, fuzzy C-means, and hierarchical methods were examined on corrected gene expression values. The proposed approach was tested on a reliable source of genes’ information, where the entropy of genes in samples and Euclidean distance were used for gene selection. Our findings revealed that a neural-network-based normalization method could better control the effects of non-biological variations from microarray data. Moreover, the hierarchical clustering was more effective compared to other methods, and resulted in the identification of three genes, including BC029410, DUSP2, and ILDR1, as candidates for disease-association genes. According to the finding of the present study, hierarchical clustering with nonlinear-based normalization could have the ability to prioritize genes for ovarian cancer

Tumor stage is one of the most reliable prognostic factors in the clinical characterization of colorectal cancer. The identification of genes associated with tumor staging may facilitate the personalized molecular diagnosis and treatment along with better risk stratification in colorectal cancer. The study aimed to identify genetic signatures associated with tumor staging and patients’ survival in colorectal cancer and recognize the patients' risk category for clinical outcomes based on transcriptomic data. In this retrospective cohort study, two available transcriptomic datasets, including 232 patients with colorectal cancer under accession number GSE17537 and GSE17536 were used as discovery and validation sets, respectively. A Bayesian sparse group selection method in the discovery set was applied to identify the associated genes with the tumor staging. Then further screening was performed using survival analysis, and significant genes were used to develop a gene signature model. Finally, the robust performance of the signature model was assessed in the validation set. A total of 56 genes were significantly associated with the tumor staging in colorectal cancer. Survival analysis resulted in a shortlist of 19 genes, including ADH1B (P = 0.012), AHI (P = 0.006), AKAP12 (P = 0.018), BNIP3 (P = 0.015), CLDN11 (P = 0.015), CST9L (P = 0.028), DPP10 (P = 0.029), FBXO33 (P = 0.036), HEBP (P = 0.025), INTS4 (P = 0.003), LIPJ (P = 0.001), MMP21 (P = 0.006), NGRN (P = 0.014), PAFAH1B2 (P = 0.035), PCOLCE2 (P = 0.009), PIM1 (P = 0.007), TBKBP1 (P = 0.003), TCEB3B (P = 0.001), and TIPARP (P = 0.018), developing the signature model and validation. In both discovery and validation sets, the discrimination ability of the signature model to categorize patients with colorectal cancer into low- and high-risk subgroups for mortality and recurrence at 3- and 5-years showed good discrimination performances, with the area under the receiver operating characteristic curve (ROC) ranging from 0.64 to 0.88. It also had good sensitivity (discovery set 63.1%, validation set 61.7%) and specificity (discovery set 75.0%, validation set 59.3%) to discriminate between early- and late-stage groups. We identified a 19-gene signature associated with tumor staging and survival of colorectal cancer, which may represent potential diagnosis and prognosis markers, and help to classify patients with colorectal cancer into low- or high-risk subgroups.

Estrogen receptor-positive (ER-positive) breast cancer is a subgroup of breast tumors that is more likely to respond to hormone therapy. ER-positive and ER- negative breast cancers tend to show different patterns of metastasis because of different signaling cascade and genes that are activated by estrogen response. Genetic factors can contribute to high rates of metastasis in ER-positive breast cancer. Fucosyltransferase 8 (FUT8) is a member of fucosyltransferases family and plays an important role in α-1,6 linkage to the first GlcNAc residue of N-glycans chain. In this study, for the first time, we predicted FUT8 by bioinformatics tools as a novel therapeutic target for ER-positive breast cancer. Microarray gene expression data of 9 patients with ER+ve and 10 individuals with ER-ve breast cancer was extracted from Geodatasets. Gene expression of two ER+ and ER- patients was compared with logfc and then sorted by their p-values. Moreover, the most related pathway, protein interaction, and function of this gene were identified with GeneCard and DAVID databases. FUT8 was highly expressed in patients with ER+ve breast cancer that may be associated with the metastasis. FUT8 encodes an enzyme that belongs to fucosyltransferases family. The expression of this gene may contribute to the malignancy features of cancer cells and their invasive and metastatic capabilities. Having in mind FUT8 hyperexpression, its function in malignancy, and its pathways, it can be concluded that FUT8 can be used as a therapeutic target in ER+ve breast cancer.

Human herpes viruses, as common viruses, not only affect mainly the skin, mucosa, and nervous tissue, but also can cause a variety of serious diseases in children.
The aim of this study was to determine the sensitivity and specificity of multiplex PCR-based DNA microarray technology in comparison with PCR method and IgM ELISA.
A total of 108 blood samples from children with viral infections were collected and analyzed by multiplex PCR-based DNA microarray technology, PCR method, and IgM ELISA.
Of 108 specimens, 16 were positive which gave a positive rate of 14.8%. Most of the patients were infected with EBV and HCMV. The sensitivity and specificity of this technology for detecting human herpes viruses were 100% when compared to PCR method. The crude agreement between multiplex PCR-based DNA microarray technology and IgM ELISA for detecting human herpes viruses was 95.4%.
The results indicated that multiplex PCR-based DNA microarray technology is a rapid auxiliary diagnostic method for simultaneous detection of the seven common herpes viruses with high sensitivity and specificity.

Despite the huge efforts, chronic kidney disease (CKD) remains as an unsolved problem in medicine. Many studies have shown a central role for transforming growth factor beta-1 (TGFβ-1) and its downstream signaling cascades in the pathogenesis of CKD. In this study, we have reanalyzed a microarray dataset to recognize critical signaling pathways controlled by TGFβ-1.
This study is a bioinformatics reanalysis for a microarray data. The GSE23338 dataset was downloaded from the gene expression omnibus (GEO) database which assesses the mRNA expression profile of TGFβ-1 treated human kidney cells after 24 and 48 hours incubation. The protein interaction networks for differentially expressed (DE) genes in both time points were constructed and enriched. In addition, by network topology analysis, genes with high centrality were identified and then pathway enrichment analysis was performed with either the total network genes or with the central nodes.
We found 110 and 170 genes differentially expressed in the time points 24 and 48 hours, respectively. As the genes in each time point had few interactions, the networks were enriched by adding previously known genes interacting with the differentially expressed ones. In terms of degree, betweenness, and closeness centrality parameters 62 and 60 nodes were considered to be central in the enriched networks of 24 hours and 48 hours treatment, respectively. Pathway enrichment analysis with the central nodes was more informative than those with all network nodes or even initial DE genes, revealing key signaling pathways.
We introduced a method for the analysis of microarray data that integrates the expression pattern of genes with their topological properties in protein interaction networks. This holistic novel approach allows extracting knowledge from raw bulk omics data.

Diffuse large B-cell lymphoma is the most common type of non-Hodgkin lymphoma. MicroRNAs (miRNAs) are endogenous small RNA, which can regulate gene expression at the post-transcriptional level. MiRNA profiling has shown a great potential as novel diagnostic and prognostic biomarkers. The present study was performed at the Nemazee Teaching Hospital (Shiraz, Iran) from 2011 to 2013.The aim of this study was to assess the deregulation of miRNAs profiles in DLBL against hyperplasic reactive lymph node as a normal. This could serve as a biomarker for DLBL. The miRCURY LNATM microarray was used on the total RNA, which was extracted from formalin-fixed paraffin-embedded tissue of 24 de novo diffuse large B-cell lymphoma patients and 14 normal lymph nodes. The greatest changes were detected in miR-4284 and miR-4484 level in patient’s lymphoma samples. These miRNAs can act as a diagnostic biomarker for DLBL.

In Iran, invasive nontyphoidal Salmonella (iNTS) disease causes severe bacteremic illness among children The microarray analysis enables identification of the strains that have the 90kb Salmonella typhimurium virulence plasmid, presence or absence of the Salmonella pathogenicity islands (SPIs), adherence factors and other virulence determinants. Twelve isolates of S. typhimurium obtained from faeces of children with gastroenteritis were analyzed by microarray technique.
The virulence plasmid was present in 83.33% of isolates and all the isolates contained the SPI-4 and SPI-5. None of the strains had the cytolethal distending toxin, cdtB. All strains were positive for rck and mig-14. The adherence genes were present in all the strains in the range of 51.55% to 73.20% of the adherence genes interrogated in the microarray. Two strains were the least pathogenic S. typhimurium.
Microarray analysis proved to be a valuable tool in confirmation of serotyping results and genetic characterization of S. Typhimurium.

The microarray technology is in needed of cost-effective, low background noise and stable substrates for successful hybridization and analysis. In this research, we developed a three-dimentional stable and mechanically reliable microarray substrates by coating of two polymeric layers on standard microscope glass slides. For fabrication of these substrates, a thin film of oxidized agarose was prepared on the Poly-L-Lysine (PLL) coated glass slides. Unmodified oligonucleotide probes were spotted and immobilized on these double layered thin films by adsorption on the porous structure of the agarose film. Some of the aldehyde groups of the activated agarose linked covalently to PLL amine groups; on the other side, they bound to amino groups of adsorbed tail of biomolecules. These linkages were fixed by UV irradiation at 254 nm using a CL-1000 UV. These prepared substrates were compared to only agarose-coated and PLL-coated slides. Atomic Force Microscope (AFM) results demonstrated that agarose provided three-dimensional surface which had higher loading and bindig capacity for biomolecules than PLL-coated surface which had two-dimensional surface. The nano-indentation tests demonstrated the prepared double coating was more reliable and flexible for mechanical robotic spotting. In addition, the repeated indentation on different substrates showed uniformity of coatings. The stability of novel coating was sufficient for hybridization process. The signal-to-noise ratio in hybridization reactions performed on the agarose-PLL coated substrates increased two fold and four fold compared to agarose and PLL coated substrates, respectively. Finally, the agarose-PLL microarrays had the highest signal (2920) and lowest background signal (205) in hybridization, suggesting that the prepared slides are suitable in analyzing wide concentration range of analytes.

In the periodontium, the functions of the cell populations regarding the host-mediated tissue destruction in health and disease are not well understood. The purpose of this study was to measure the expression of genes differentially expressed in chronically inflamed periodontal ligament (PDL) cells compared to healthy PDL cells.
We compared the genome-wide gene expressions of chronically inflamed and healthy PDL cells by microarray analysis, and validated the data by real-time RT-PCR to identify the genes that might play distinct roles in chronic periodontal disease in vivo.
The expression rates of 14,239 genes were investigated and 3,165 of them were found differentially expressed by at least two-fold; the expression rates of 1,515 genes were significantly upregulated and the expression rates of 1,650 genes were significantly downregulated in inflamed PDL cells.
We focused on mainly structural components, for example, laminins and integrins, as well as degrading enzymes, for example, MMPs and cathepsins. The molecular composition of the laminin network varies in chronically inflamed compared to healthy PDL cells in vivo. Furthermore, integrin alpha6beta4, together with laminin-332, might be involved in chronic periodontal inflammation. Diverse keratins were upregulated, indicating that the epithelial cell rests of Malassez might also be involved in chronic periodontitis. The microarray analysis has identified a profile of genes potentially involved in chronic periodontal inflammation in vivo.