Linear Models For Microarray Data Research Articles

Drug repositioning based on gene expression data for human HER2-positive breast cancer

Human epidermal growth factor receptor 2 (HER2)-positive breast cancer represents approximately 15–30% of all invasive breast cancers. Despite the recent advances in therapeutic practices of HER2 subtype, drug resistance and tumor recurrence still have remained as major problems. Drug discovery is a long and difficult process, so the aim of this study is to find potential new application for existing therapeutic agents. Gene expression data for breast invasive carcinoma were retrieved from The Cancer Genome Atlas (TCGA) database. The normal and tumor samples were analyzed using Linear Models for Microarray Data (LIMMA) R package in order to find the differentially expressed genes (DEGs). These genes were used as entry for the library of integrated network-based cellular signatures (LINCS) L1000CDS2 software and suggested 24 repurposed drugs. According to the obtained results, some of these drugs including vorinostat, mocetinostat, alvocidib, CGP-60474, BMS-387032, AT-7519, and curcumin have significant functional similarity and structural correlation with FDA-approved breast cancer drugs. Based on the drug-target network, which consisted of the repurposed drugs and their target genes, the aforementioned drugs had the highest degrees. Moreover, the experimental approach verified curcumin as an effective therapeutic agent for HER2 positive breast cancer. Hence, our work suggested that some repurposed drugs based on gene expression data can be noticed as potential drugs for the treatment of HER2-positive breast cancer.

Clustering of genes from microarray data using hierarchical projective adaptive resonance theory: a case study of tuberculosis

We propose the hierarchical Projective Adaptive Resonance Theory (PART) algorithm for classification of gene expression data. This algorithm is realized by combing transposed quasi-supervised PART and unsupervised PART. We develop the corresponding validation statistics for each process and compare it with other clustering algorithms in a case study of tuberculosis (TB). First, we use sample-based transposed quasi-supervised PART to obtain optimal clustering results of samples distinguished by time post-infection and the representative genes for each cluster including up-regulated, down-regulated and stable genes. The up- and down-regulated genes show more than 90% similarity to the result derived from Linear Models for Microarray Data and are verified by weighted k-nearest neighbor model on TB projection. Second, we use gene-based unsupervised PART algorithm to cluster these representative genes where functional enrichment analysis is conducted in each cluster. We further confirm the main immune response of human macrophage-like THP-1 cells against TB within 2 days is type I interferon-mediated innate immunity. This study demonstrates how hierarchical PART algorithm analyzes microarray data. The sample-based quasi-supervised PART extracts representative genes and narrows down the shortlist of disease-relevant genes and gene-based unsupervised PART classifies representative genes that help to interpret immune response against TB.

ProteoSign v2: a faster and evolved user-friendly online tool for statistical analyses of differential proteomics.

Bottom-up proteomics analyses have been proved over the last years to be a powerful tool in the characterization of the proteome and are crucial for understanding cellular and organism behaviour. Through differential proteomic analysis researchers can shed light on groups of proteins or individual proteins that play key roles in certain, normal or pathological conditions. However, several tools for the analysis of such complex datasets are powerful, but hard-to-use with steep learning curves. In addition, some other tools are easy to use, but are weak in terms of analytical power. Previously, we have introduced ProteoSign, a powerful, yet user-friendly open-source online platform for protein differential expression/abundance analysis designed with the end-proteomics user in mind. Part of Proteosign's power stems from the utilization of the well-established Linear Models For Microarray Data (LIMMA) methodology. Here, we present a substantial upgrade of this computational resource, called ProteoSign v2, where we introduce major improvements, also based on user feedback. The new version offers more plot options, supports additional experimental designs, analyzes updated input datasets and performs a gene enrichment analysis of the differentially expressed proteins. We also introduce the deployment of the Docker technology and significantly increase the speed of a full analysis. ProteoSign v2 is available at http://bioinformatics.med.uoc.gr/ProteoSign.

Moderated t-tests for group-level fMRI analysis

In recent years, there has been significant criticism of functional magnetic resonance imaging (fMRI) studies with small sample sizes. The argument is that such studies have low statistical power, as well as reduced likelihood for statistically significant results to be true effects. The prevalence of these studies has led to a situation where a large number of published results are not replicable and likely false. Despite this growing body of evidence, small sample fMRI studies continue to be regularly performed; likely due to the high cost of scanning. In this report we investigate the use of a moderated t-statistic for performing group-level fMRI analysis to help alleviate problems related to small sample sizes. The proposed approach, implemented in the popular R-package LIMMA (linear models for microarray data), has found wide usage in the genomics literature for dealing with similar issues. Utilizing task-based fMRI data from the Human Connectome Project (HCP), we compare the performance of the moderated t-statistic with the standard t-statistic, as well as the pseudo t-statistic commonly used in non-parametric fMRI analysis. We find that the moderated t-test significantly outperforms both alternative approaches for studies with sample sizes less than 40 subjects. Further, we find that the results were consistent both when using voxel-based and cluster-based thresholding. We also introduce an R-package, LIMMI (linear models for medical images), that provides a quick and convenient way to apply the method to fMRI data.

Uncovering potential novel biomarkers and immune infiltration characteristics in persistent atrial fibrillation using integrated bioinformatics analysis.

Atrial fibrillation (AF) is the most common cardiac arrhythmia. This study aimed to identify potential novel biomarkers for persistent AF (pAF) using integrated analyses and explore the immune cell infiltration in this pathological process. Three pAF datasets (GSE31821, GSE41177, and GSE79768) from the Gene Expression Omnibus (GEO) database were integrated with the elimination of batch effects. 264 differentially expressed genes (DEGs) were identified using Linear models for microarray data (LIMMA), 12 modules were screened out by weighted gene co-expression network analysis (WGCNA) in pAF compared with normal controls. Subsequently, common genes (CGs) were identified as the intersection of DEGs and genes in the most significant module. Functional enrichment analysis showed that CGs were mainly enriched in the "Calcineurin-NFAT (nuclear factor of activated T-cells)" signaling pathway, particularly regulator of calcineurin 1 (RCAN1), and protein phosphatase 3 regulatory subunit B, alpha (PPP3R1). Ulteriorly, the microRNA-transcription factor-mRNA network revealed that microRNA-34a-5p could target both RCAN1 and PPP3R1 in the pAF pathogenesis. Finally, immune infiltration analysis by CIBERSORT, a versatile computational method, displayed a higher level of monocytes, dendritic cells and neutrophils, as well as a lower level of CD8+ T cells and T cells regulatory (Tregs) in pAF compared with the control group. In conclusion, our present study revealed several novel pAF-associated genes, miRNAs, and pathways, including microRNA-34a-5p, which might target RCAN1 and PPP3R1 to regulate pAF through the calcineurin-NFAT signaling pathway. In addition, there was a difference in immune infiltration between patients with pAF and normal groups and immune cells might interact with specific genes in pAF.

Identification of circulating hub long noncoding RNAs associated with hypertrophic cardiomyopathy using weighted correlation network analysis.

Hypertrophic cardiomyopathy (HCM) is one of the most commonly inherited heart diseases and the leading cause of sudden cardiac death among adolescents and young adults. Circulating long noncoding RNAs (lncRNAs) have demonstrated potential as diagnostic and therapeutic targets in several cardiovascular diseases. However, the circulating extracellular lncRNA expression profile of patients with HCM remains unclear. Plasma lncRNA expression was evaluated in patients with HCM and healthy controls using a human lncRNA microarray. A weighted correlation network analysis (WGCNA) and linear models for microarray data (Limma) were used. GSE68316 data from cardiac tissue in the Gene Expression Omnibus database were analysed for further validation. Using WGCNA, two modules (referred to as the magenta and the light-yellow module) were identified that were positively associated with HCM. Gene Ontology analysis revealed that lncRNAs in the magenta module targeted ‘heart growth’. Using Limma, a total of 290 lncRNAs were differentially expressed (210 upregulated and 80 downregulated) in the plasma of HCM patients, compared with controls. Moreover, combined WGCNA and Limma analysis demonstrated that 27 hub lncRNAs in the magenta module and 13 hub lncRNAs in the light-yellow module were significantly upregulated, compared with the controls. Moreover, of the 40 differentially expressed hub lncRNAs identified in the two modules, three circulating lncRNAs (lnc-P2RY6-1:1, ENST00000488040 and ENST00000588047) were also significantly upregulated in the HCM cardiac tissue validation dataset. These lncRNAs may serve as biomarkers and therapeutic targets for precise diagnosis and treatment of HCM.

Comprehensive analysis reveals novel gene signature in head and neck squamous cell carcinoma: predicting is associated with poor prognosis in patients.

BackgroundHead and neck squamous cell carcinoma (HNSC) remains an important public health problem, with classic risk factors being smoking and excessive alcohol consumption and usually has a poor prognosis. Therefore, it is important to explore the underlying mechanisms of tumorigenesis and screen the genes and pathways identified from such studies and their role in pathogenesis. The purpose of this study was to identify genes or signal pathways associated with the development of HNSC.MethodsIn this study, we downloaded gene expression profiles of GSE53819 from the Gene Expression Omnibus (GEO) database, including 18 HNSC tissues and 18 normal tissues. The differentially expressed genes (DEGs) were identified using the Linear Models for Microarray Data R package. Adjusted P values <0.01 and |log2 fold change (FC)| ≥2 was regarded as the filter condition. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis of these DEGs were performed on the Database for Annotation, Visualization, and Integrated Discovery (DAVID) online website. Protein-protein interaction (PPI) network was built to visualize the interactions between these DEGs using the STRING online website. Finally, hub genes were identified by The Cancer Genome Atlas (TCGA) database.ResultsA total of 604 DEGs consisting of 159 upregulated genes and 445 downregulated genes were selected. From these DEGs, prognostic related genes could serve as potential biomarkers for the molecular diagnosis and therapeutic intervention of HNSC were identified. Including the known genes, GPR18, CNR2, RSPH4A, ULBP2, TEX101, and STC2. And the novel genes, CCR8, CCDC39, CNTN5, MSLN, and CHGB were strongly implicated in HNSC.ConclusionsIn summary, we indicated genes associated with prognostic in patients, which improve our understanding of HNSC and could be used as new therapeutic targets for HNSC.

Hematopoietic Gene Expression Regulation Through m6A Methylation Predicts Prognosis in Stage III Colorectal Cancer.

BackgroundMethylation of N6 adenosine (m6A) plays important regulatory roles in diverse biological processes. The purpose of this research was to explore the potential mechanism of m6A modification level on the clinical outcome of stage III colorectal cancer (CRC).MethodsGene set variation analysis (GSVA) and gene set enrichment analysis (GSEA) were adopted to reveal the signal pathway which was most likely affected by m6A methylation. The linear models for microarray data (LIMMA) method and the least absolute shrink-age and selection operator (LASSO) Cox regression model were used to identify the signature. The signature can sensitively separate the patients into high and low risk indicating the relapse-free survival (RFS) time based on time-dependent receiver operating characteristic (ROC) analysis. Then, the multi-gene signature was validated in GSE14333 and the Cancer Genome Atlas (TCGA) cohort. The number of the samples in GSE14333 and TCGA cohort are 63 and 150. Finally, two nomograms were set up and validated to predict prognosis of patients with stage III CRC.ResultsThe hematopoietic cell lineage (HCL) signaling pathway was disclosed through GSEA and GSVA. Seven HCL-related genes were determined in the LASSO model to construct signature, with AUC 0.663, 0.708, and 0.703 at 1-, 3-, and 5-year RFS, respectively. Independent datasets analysis and stratification analysis indicated that the HCL-related signature was reliable in distinguishing high- and low-risk stage III CRC patients. Two nomograms incorporating the signature and pathological N stage were set up, which yielded good discrimination and calibration in the predictions of prognosis for stage III CRC patients.ConclusionsA novel HCL-related signature was developed as a predictive model for survival rate of stage III CRC patients. Nomograms based on the signature were advantageous to facilitate personalized counseling and treatment in stage III CRC.

Identifying the mRNAs associated with Bladder cancer recurrence.

To identify the mRNAs associated with bladder cancer (BC) recurrence. The transcription profile of GSE31684 including 39 recurrent BC tumor samples and 54 non-recurrent BC tumor samples as well as transcription profile of GSE13507 including 36 recurrent BC tumor samples and 67 non-recurrent BC tumor samples were downlaoded from the Gene Expression Omnibus. Then, the differentially expressed genes (DEGs) were identified using linear models for microarray data (limma) and the intersections of DEGs from the two datasets were further screened. The weighed gene co-expression network analysis (WGCNA) was used to screen the modules related to BC recurrence. Protein-protein interaction (PPI) network analysis was used to analyze the genes interaction. Their functions were predicted by Gene Ontology and KEGG pathway enrichment. Moreover, The Comparative Toxicogenomics Database 2017 update (CTD) was used to search the BC related pathway. The univariate cox regression analysis was used to identify DEGs associated to the recurrence. Kaplan-Meier plots were used to illustrate recurrence free survival time (RFS). A total of 692 intersections DEGs were screened. WGCNA showed that 7 modules (2279 genes) were stable in both the datasets. A total of 169 intersection DEGs were mapped to the 7 modules. There existed 149 interaction relationships among 81 proteins (18 down-regulated and 63 up-regulated DEGs) in the PPI network. Two KEGG pathways including Focal adhesion and ECM-receptor interaction were enriched which were also found in the CTD. The univariate cox regression analysis showed that 3 DEGs (COL4A1, COL1A2 and COL5A1) were significant related to the prognosis. Multivariate cox regression analysis revealed that pathologic_N (N0-N1 vs N2-N3, p= 0.033) were independent prognostic factors for overall survival in patients with BC. COL4A1, COL1A2 and COL5A1 could be associated with BC recurrence.

Comprehensive Construction of a Circular RNA-Associated Competing Endogenous RNA Network Identified Novel Circular RNAs in Hypertrophic Cardiomyopathy by Integrated Analysis.

Hypertrophic cardiomyopathy (HCM), the most common heritable cardiomyopathy, is associated with a high risk of sudden cardiac death. The complexity and behavior of the circular RNA (circRNA)-associated competing endogenous RNA (ceRNA) network in HCM have not been thoroughly elucidated. Plasma circRNA and messenger RNA (mRNA) expression profiles were acquired by using a microarray. Weighted correlation network analysis (WGCNA) and linear models for microarray data (Limma) were used to analyze microarray data. Gene modules, consisting of genes with high correlations, were detected and represented by a designated color. The ceRNA network, including circRNA, microRNA (miRNA), and mRNA, was constructed based on the “ceRNA hypothesis” using an integrated systems biology method. By WGCNA, two modules, namely magenta and red modules, were identified as being positively correlated with HCM. In the combined analysis of WGCNA and Limma, 36 hub circRNAs in the magenta module and 83 hub circRNAs in the red module were significantly upregulated compared with the controls. By coexpression analysis, 270 circRNA–mRNA pairs were identified with a coefficient ≥0.9 and p < 0.05. With Starbase and miRWalk tools, circRNA–miRNA pairs and miRNA–mRNA pairs were predicted. Once these pairs were combined, the ceRNA network with 6 circRNAs, 29 miRNAs, and 6 mRNAs was constructed. Functional analysis demonstrated that these circRNAs in the ceRNA network were associated with calcium-release channel activity and muscle filament sliding. Our study provided a global perspective and systematic analysis of the circRNA-associated ceRNA network in HCM. The identified circRNAs hsa_circ_0043762, hsa_circ_0036248, and hsa_circ_0071269 may be key regulators involved in HCM pathogenesis.

Identification of Differentially Methylated CpG Sites in Fibroblasts from Keloid Scars.

As a part of an abnormal healing process of dermal injuries and irritation, keloid scars arise on the skin as benign fibroproliferative tumors. Although the etiology of keloid scarring remains unsettled, considerable recent evidence suggested that keloidogenesis may be driven by epigenetic changes, particularly, DNA methylation. Therefore, genome-wide scanning of methylated cytosine-phosphoguanine (CpG) sites in extracted DNA from 12 keloid scar fibroblasts (KF) and 12 control skin fibroblasts (CF) (six normal skin fibroblasts and six normotrophic fibroblasts) was conducted using the Illumina Human Methylation 450K BeadChip in two replicates for each sample. Comparing KF and CF used a Linear Models for Microarray Data (Limma) model revealed 100,000 differentially methylated (DM) CpG sites, 20,695 of which were found to be hypomethylated and 79,305 were hypermethylated. The top DM CpG sites were associated with TNKS2, FAM45B, LOC723972, GAS7, RHBDD2 and CAMKK1. Subsequently, the most functionally enriched genes with the top 100 DM CpG sites were significantly (p ≤ 0.05) associated with SH2 domain binding, regulation of transcription, DNA-templated, nucleus, positive regulation of protein targeting to mitochondrion, nucleoplasm, Swr1 complex, histone exchange, and cellular response to organic substance. In addition, NLK, CAMKK1, LPAR2, CASP1, and NHS showed to be the most common regulators in the signaling network analysis. Taken together, these findings shed light on the methylation status of keloids that could be implicated in the underlying mechanism of keloid scars formation and remission.

Integrated analysis of immunocyte infiltration and differential gene expression in tricuspid aortic valve-associated thoracic aortic aneurysms.

BackgroundProgressive dilatation is responsible for significant mortality and morbidity in patients with thoracic aortic aneurysms (TAAs). Studies have shown that the development and progression of TAAs are closely related to immune regulatory pathways and genes. Therefore, it is important to understand the immune regulatory mechanisms and biomarkers of TAA dilatation.MethodsSystematic bioinformatics analysis was applied, including linear models for microarray data (LIMMA) differential expression analyses, principal component analysis (PCA), immunocyte identification, and genetic function enrichment analysis.ResultsOur results showed that both aortic intima-media (AMed) and outer aortic adventitia (AAdv) tissues were closely associated with T cell activation during the process of tricuspid aortic valve (TAV)-associated TAA dilation. Additionally, the degree of infiltration of resting memory CD4+ T cells was linked to both AAdv and AMed vascular dilation. The core regulators PPTRC, IL1B, CD4, CD3G, and IL2RA were also identified and are closely related to resting memory CD4+ T cell infiltration in this pathological process.ConclusionsThe candidate genes PPTRC, IL1B, CD4, CD3G, and IL2RA were involved in the regulation of resting memory CD4 T cell tissue infiltration, which is closely related to the process of AAdv and AMed vascular dilation in TAV patients.

Serum Protein Changes in Pediatric Sepsis Patients Identified With an Aptamer-Based Multiplexed Proteomic Approach.

Sepsis, a life-threatening organ dysfunction caused by a dysregulated host response to infection, is a leading cause of death and disability among children worldwide. Identifying sepsis in pediatric patients is difficult and can lead to treatment delay. Our objective was to assess the host proteomic response to infection utilizing an aptamer-based multiplexed proteomics approach to identify novel serum protein changes that might help distinguish between pediatric sepsis and infection-negative systemic inflammation and hence can potentially improve sensitivity and specificity of the diagnosis of sepsis over current clinical criteria approaches. Retrospective, observational cohort study. PICU and cardiac ICU, Seattle Children's Hospital, Seattle, WA. A cohort of 40 children with clinically overt sepsis and 30 children immediately postcardiopulmonary bypass surgery (infection-negative systemic inflammation control subjects) was recruited. Children with sepsis had a confirmed or suspected infection, two or more systemic inflammatory response syndrome criteria, and at least cardiovascular and/or pulmonary organ dysfunction. None. Serum samples from 35 of the sepsis and 28 of the bypass surgery subjects were available for screening with an aptamer-based proteomic platform that measures 1,305 proteins to search for large-scale serum protein expression pattern changes in sepsis. A total of 111 proteins were significantly differentially expressed between the sepsis and control groups, using the linear models for microarray data (linear modeling) and Boruta (decision trees) R packages, with 55 being previously identified in sepsis patients. Weighted gene correlation network analysis helped identify 76 proteins that correlated highly with clinical sepsis traits, 27 of which had not been previously reported in sepsis. The serum protein changes identified with the aptamer-based multiplexed proteomics approach used in this study can be useful to distinguish between sepsis and noninfectious systemic inflammation.

Integrative systematic review meta-analysis and bioinformatics identifies MicroRNA-21 and its target genes as biomarkers for colorectal adenocarcinoma.

Advanced colorectal has poor survival and are difficult to treat. Therefore, there is an urgent need for biomarkers to diagnose this cancer at earlier manageable stages. Micro-RNAs (miRNAs) are amongst the most significant biomarkers that have shown promise in improving management and early detection of different types of cancers. However, since MiRNAs are non-coding, the main limitation of using them as biomarkers is that they do not have associated phenotype and therefore difficult to validate using other techniques. This makes it difficult to understand the mechanism of miRNA is disease initiation and progression, therefore any methodology that can provide semantics to miRNA expression would enhance the understanding of the role of miRNA in disease. Here we report an integrative meta-analysis and bioinformatics methodology that showed microRNA-21 and its associated target mRNA to be the most significant predictive biomarkers for colorectal adenoma and adenocarcinoma. After drawing key inferences by meta-analysis, the authors then developed a bioinformatics method to identify mir-21 gene targeting in a specific tissue using two different bioinformatics approaches; absolute GSEA (Gene Set Enrichment Analysis) and LIMMA (Linear Models for MicroArray data) to identify differentially expressed genes of miRNA-21. Results from GSEA intersection with mir-21 gene targets was a subset of longer gene list that was obtained from the GEO2R intersect. In our study, both of longer GEO2R gene target list and the more focused GSEA list established the fact that mir-21 target numerous functional pathways that are mostly interconnected. Our three steps bioinformatics approach identified ABCB1, HPGD, BCL2, TIAM1, TLR3, and PDCD4 as common targets for mir-21 in both of adenoma as well as adenocarcinoma suggesting they are biomarkers for early CRC. The approach in this study proposed combining the big data from the scientific literature together with novel bioinformatics to bring about a methodology that can be used to first identify which microRNAs are involved in a specific disease, and then to identify a panel of biomarkers derived from the microRNAs target genes, and from these target genes the functional significance of these microRNAs can be inferred providing better clinical value for the surgeon.

Transcriptomic Profile of Bone Marrow-Derived Mesenchymal Stromal Cells of Pediatric Pre-B Acute Lymphoblastic Leukemia Patients and Healthy Donors after Interaction with Leukemic Cells

Background: Acute lymphoblastic leukemia (ALL) is a hematological malignancy characterized by the abnormal expansion of clonal lymphoblasts in the bone marrow (BM). The behavior of malignant lymphoblasts is not only dependent on their genomic abnormalities but also, on their complex and reciprocal relationships with their local niche. BM microenvironment is a dynamic network of growth factors, cytokines, and stromal cells, providing a permissive environment for leukemogenesis and progression. Despite some evidence about the implication of the bidirectional interaction between mesenchymal stromal cells (MSCs) and leukemic cell on growth and survival, it is still largely unknown. Although an increasing number of studies are reporting on the expression of specific genes in leukemic cells-interacting MSCs, gene expression profiling in cocultured MSCs (with respect to mono culture conditions) has not been done on a transcriptome-wide basis. Taking all this into consideration, we have established cocultures between BM-derived MSCs and the leukemic mononuclear cells and performed gene expression profile (GEP) studies on the MSC population to determine those deregulated genes due to the coculture condition with respect to MSCs in mono culture. Methods: Primary MSCs from BM samples of healthy donors (n=4) and pediatric pre-B ALL patients (n=4) were isolated and expanded and mononuclear cells were, also, isolated from patients peripheral blood. At passage 2, healthy/patient (h/p) MSCs were cocultured with the leukemic cells for 72 hours and subsequently subjected to GEP analyses. RNA isolated from h/pMSCs and biotinylated amplified RNA was synthesized. For the hybridization HG-U133 Plus, 2.0 GeneChip oligonucleotide arrays (Affymetrix) was used. Raw microarray data were processed into expression values through the Affy package in R. RMA (robust multi-array average) algorithm was used for preprocessing. The linear regression model package LIMMA (Linear Models for Microarray Data) was used to identify significant differential expression of genes (DEGs) compared to h/pMSCs after coculture with MSCs from the same origin in mono culture. Minimum 1.5 fold change and p-value &lt; 0.001 were defined as the threshold. Functional enrichment, gene network and pathway analysis, were performed on selected sets of genes after differential expression analyses using two bioinformatics tools: DAVID Bioinformatics Resources 6.8 and WebGestalt 2019. Results: We identified 293 DEGs when analyzing pMSC samples vs. hMSC in coculture condition and 853 DEGs of pMSC vs. hMSC analyze in mono culture condition and only 15 DEGs common in both group. According to the result of functional enrichment analysis of pMSC vs. hMSC in coculture condition, 29 gene ontology (GO) term biological process including (top five) apoptotic process, axon guidance, regulation of JNK cascade, cell-cell adhesion and protein phosphorylation and 4 KEGG pathways of DEGs including axon guidance, FoxO signalling pathway, Wnt signalling pathway and signalling pathways regulating pluripotency of stem cells were determined. Comparing pMSC vs. hMSC in mono culture condition, 292 GO term biological process including extracellular matrix organization, skeletal system development, angiogenesis and signal transduction and 43 KEGG pathways of DEGs including pathways in cancer, TGFß signalling pathway, ECM-receptor interaction, p53 signalling pathway and PI3-Akt signalling pathway were determined.Five genes with upregulated expression after coculture (COL18A1, EFNA5, MAP3K8, CASP1, IGF1R) and after mono culture (MMP1, CXCL1, IL1B, PCDH10, CXCL6) were identified. Conclusions: Deregulated genes especially after coculture condition in axon guidance and ephrin receptor-mediated signalling pathway of which the importance is not yet understood in cancer microenvironment including EPHB1, ephrinA5, ephrinB3, p21 activated kinase 2 (PAK2), and ROBO3 showed highly differential expression pattern between pMSCs and hMSCs. This is the first report indicating the importance of axon guidance genes as a result of interaction between MSCs and leukemic cells. The elucidation of deregulated mechanisms and pathways provides new molecular insights to the contribution of these cells to ALL pathophysiology and understanding cross talk between these cells may lead to found new therapeutic targets. Disclosures No relevant conflicts of interest to declare.

PTHR1 May Be Involved in Progression of Osteosarcoma by Regulating miR-124-3p-AR-Tgfb1i1, miR-27a-3p-PPARG-Abca1, and miR-103/590-3p-AXIN2 Axes.

Our previous study has indicated that the parathyroid hormone type 1 receptor (PTHR1) may play important roles in development and progression of osteosarcoma (OS) by regulating Wnt, angiogenesis, and inflammation pathway genes. The goal of this study was to further illuminate the roles of PTHR1 in OS by investigating upstream regulation mechanisms (including microRNA [miRNA] and transcription factors [TFs]) of crucial genes. The microarray dataset GSE46861 was downloaded from the Gene Expression Omnibus database, in which six tumors with short hairpin RNA (shRNA) PTHR1 knockdown (PTHR1.358) and six tumors with shRNA control knockdown (Ren.1309) were collected from mice. Differentially expressed genes (DEGs) between PTHR1.358 and Ren.1309 were identified using the linear models for microarray data (LIMMA) method, and then the miRNA-TF-mRNA regulatory network was constructed using data from corresponding databases, followed by module analysis, to screen crucial regulatory relationships. OS-related human miRNAs were extracted from the curated Osteosarcoma Database. Gene ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were enriched using the Database for Annotation, Visualization, and Integrated Discovery (DAVID) tool. As a result, the miRNA-TF-mRNA regulatory network, including 1049 nodes (516 miRNA, 25 TFs, and 508 DEGs) and 15942 edges (interaction relationships, such as Pparg-Abca1 and miR-590-3p-AXIN2), was constructed, from which three significant modules were extracted and modules 2 and 3 contained interactions between miRNAs/TFs and DEGs such as miR-103-3p-AXIN2, miR-124-3p-AR-Tgfb1i1, and miR-27a-3p-PPARG-Abca1. miR-27a-3p was a known miRNA associated with OS. Abca1, AR, and miR-124-3p were hub genes in the miRNA-TF-mRNA network. Tgfb1i1 was involved in cell proliferation, Abca1 participated in the cholesterol metabolic process, and AXIN2 was associated with the canonical Wnt signaling pathway. Furthermore, we also confirmed upregulation of miR-590-3p and downregulation of AXIN2 in the mouse OS cell line K7M2-WT transfected with PTHR1 shRNA. In conclusion, PTHR1 may play important roles in progression of OS by activating miR-124-3p-AR-Tgfb1i1, miR-27a-3p-PPARG-Abca1, and miR-103/590-3p-AXIN2 axes.

Identification of novel key lncRNAs involved in periodontitis by weighted gene co-expression network analysis.

Periodontitis is a multifactorial disease that can lead to the progressive destruction of dental support tissue. However, the detailed mechanisms and specific biomarkers involved in periodontitis remain to be further studied. Recently, long non-coding RNAs (lncRNAs) have been found to play a more important role than other types of RNAs. In our study, we analysed the expression of lncRNAs in periodontitis by analysing GSE16134. We identified highly correlated genes by analysing GSE16134 with weighted gene co-expression network analysis (WGCNA) and identified 50 hub lncRNAs that were dysregulated. Then, we used the Linear Models for Microarray Data (Limma) package to identify the hub lncRNAs that were differentially expressed (DElncRNAs). The ceRNA co-expression network data were obtained from several sites, including miRcode, and were used to assess the potential WGCNA function of hub DElncRNAs in periodontitis. Besides, we divided the samples into LBX2-AS1 high and low expression group by the expression level of LBX2-AS1 and calculated DEG by Limma package. Furthermore, we performed GO function, KEGG pathway and GSEA enrichment of DEGs. In the analysis, we identified 50 hub lncRNAs that may play important roles in periodontitis. Then, we used the Limma package to identify 3 hub DElncRNAs (LINC00687, LBX2-AS1 and LINC01566). We elucidated the potential function of the hub DElncRNA LBX2-AS1 in periodontitis by constructing a co-expression network of lncRNA-miRNA-mRNA interactions. Totally, 573 DEGs (354 up- and 219 downregulated) in periodontitis samples were identified. DEGs were enriched in different GO terms and pathways, such as neutrophil degranulation, neutrophil activation, neutrophil activation involved in immune response, neutrophil-mediated immunity, antigen processing and presentation, JAK-STAT signalling pathway, natural killer cell-mediated cytotoxicity, EGFR tyrosine kinase inhibitor resistance, phosphatidylinositol signalling system and Vascular Endothelial Growth Factor (VEGF) signalling pathway. In our study, we found that 3 hub DElncRNAs (LINC00687, LBX2-AS1 and LINC01566) may be involved in the pathogenesis of periodontitis based on WGCNA and Limma analysis. Our study aimed to elucidate the mechanisms involved in periodontitis at the genetic and epigenetic levels by constructing a ceRNA network associated with lncRNA. Besides, identification DEGs of differential LBX2-AS1 and functional annotation showed that LBX2-AS1 might be associated with periodontitis.

Influence of batch effect correction methods on drug induced differential gene expression profiles

BackgroundBatch effects were not accounted for in most of the studies of computational drug repositioning based on gene expression signatures. It is unknown how batch effect removal methods impact the results of signature-based drug repositioning. Herein, we conducted differential analyses on the Connectivity Map (CMAP) database using several batch effect correction methods to evaluate the influence of batch effect correction methods on computational drug repositioning using microarray data and compare several batch effect correction methods.ResultsDifferences in average signature size were observed with different methods applied. The gene signatures identified by the Latent Effect Adjustment after Primary Projection (LEAPP) method and the methods fitted with Linear Models for Microarray Data (limma) software demonstrated little agreement. The external validity of the gene signatures was evaluated by connectivity mapping between the CMAP database and the Library of Integrated Network-based Cellular Signatures (LINCS) database. The results of connectivity mapping indicate that the genes identified were not reliable for drugs with total sample size (drug + control samples) smaller than 40, irrespective of the batch effect correction method applied. With total sample size larger than 40, the methods correcting for batch effects produced significantly better results than the method with no batch effect correction. In a simulation study, the power was generally low for simulated data with sample size smaller than 40. We observed best performance when using the limma method correcting for two principal components.ConclusionBatch effect correction methods strongly impact differential gene expression analysis when the sample size is large enough to contain sufficient information and thus the downstream drug repositioning. We recommend including two or three principal components as covariates in fitting models with limma when sample size is sufficient (larger than 40 drug and controls combined).

Identify differential gene expressions in fatty infiltration process in rotator cuff

BackgroundRotator cuff tears are one of the most frequent upper extremity injuries and lead to pain and disability. Recent studies have implicated fatty infiltration in rotator cuff is a key failure element with the higher re-tear rates and poorer functional prognosis. Therefore, we investigated the differential expression of key genes in each stage of rotator cuff tear.MethodsA published expression profile was downloaded from the Gene Expression Omnibus database and analyzed using the Linear Models for Microarray Data (LIMMA) package in R language to identify differentially expressed genes (DEGs) in different stages of injured rotator cuff muscles. Gene ontology (GO) functional and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed to annotate the function of the DEGs. Finally, PPI network and module analysis were used to identify hub genes.ResultsA total of 1089 fatty infiltration-related DEGs were identified, including 733 upregulated and 356 downregulated genes, and GO analyses confirmed that fatty infiltration was strongly associated with inflammatory response, aging, response to lipopolysaccharide, and immune response. Significantly enriched KEGG pathways associated with these DEGs included the phagosome, cell adhesion molecules, tuberculosis, and osteoclast differentiation. Further analyses via a PPI network and module analysis identified a total of 259 hub genes. Among these, Tmprss11d, Ptprc, Itgam, Mmp9, Tlr2, Il1b, Il18, Ccl5, Cxcl10, and Ccr7 were the top ten hub genes.ConclusionsOur findings indicated the potential key genes and pathways involved in fatty degeneration in the development of fatty infiltration and supplied underlying therapeutic targets in the future.

Prediction of key gene function in spinal muscular atrophy using guilt by association method based on network and gene ontology.

Guilt by association (GBA) algorithm has been widely used to predict gene functions statistically, and a network-based approach may increase the confidence and veracity of identifying molecular signatures for diseases. The aim of the present study was to suggest a gene ontology (GO)-based method by integrating the GBA algorithm and network, to identify key gene functions for spinal muscular atrophy (SMA). The inference of predicting key gene functions was comprised of four steps, preparing gene lists and sets; extracting differentially expressed genes (DEGs) using microarray data [linear models for microarray data (limma)] package; constructing a co-expression matrix on gene lists using the Spearman correlation coefficient method; and predicting gene functions by GBA algorithm. Ultimately, key gene functions were predicted according to the area under the curve (AUC) index for GO terms and the GO terms with AUC >0.7 were determined as the optimal gene functions for SMA. A total of 484 DEGs and 466 background GO terms were regarded as gene lists and sets for the subsequent analyses, respectively. The predicted results obtained from the network-based GBA approach showed 141 gene sets had a good classified performance with AUC >0.5. Most significantly, 3 gene sets with AUC >0.7 were denoted as seed gene functions for SMA, including cell morphogenesis, which is involved in differentiation and ossification. In conclusion, we have predicted 3 key gene functions for SMA compared with control utilizing network-based GBA algorithm. The findings may provide great insights to reveal pathological and molecular mechanism underlying SMA.