scholarly journals COVID-19 Transcriptomic Atlas: A Comprehensive Analysis of COVID-19 Related Transcriptomics Datasets

2021 ◽  
Vol 12 ◽  
Author(s):  
Fatma Alqutami ◽  
Abiola Senok ◽  
Mahmood Hachim
Keyword(s):  
Gene Expression ◽  
Molecular Basis ◽  
Research Output ◽  
Gene Expression Omnibus ◽  
Clinical Settings ◽  
Unified Approach ◽  
Transcriptomic Data ◽  
The Common ◽  
Differential Expressed Genes ◽  
Geo Database

Background: To develop anti-viral drugs and vaccines, it is crucial to understand the molecular basis and pathology of COVID-19. An increase in research output is required to generate data and results at a faster rate, therefore bioinformatics plays a crucial role in COVID-19 research. There is an abundance of transcriptomic data from studies carried out on COVID-19, however, their use is limited by the confounding factors pertaining to each study. The reanalysis of all these datasets in a unified approach should help in understanding the molecular basis of COVID-19. This should allow for the identification of COVID-19 biomarkers expressed in patients and the presence of markers specific to disease severity and condition.Aim: In this study, we aim to use the multiple publicly available transcriptomic datasets retrieved from the Gene Expression Omnibus (GEO) database to identify consistently differential expressed genes in different tissues and clinical settings.Materials and Methods: A list of datasets was generated from NCBI’s GEO using the GEOmetadb package through R software. Search keywords included SARS-COV-2 and COVID-19. Datasets in human tissues containing more than ten samples were selected for this study. Differentially expressed genes (DEGs) in each dataset were identified. Then the common DEGs between different datasets, conditions, tissues and clinical settings were shortlisted.Results: Using a unified approach, we were able to identify common DEGs based on the disease conditions, samples source and clinical settings. For each indication, a different set of genes have been identified, revealing that a multitude of factors play a role in the level of gene expression.Conclusion: Unified reanalysis of publically available transcriptomic data showed promising potential in identifying core targets that can explain the molecular pathology and be used as biomarkers for COVID-19.

scholarly journals Comparative Bioinformatics Characteristic of Bladder Cancer Stage 2 from Stage 4 Expression Profile: A Network-Based Study

2018 ◽  
Vol 7 ◽  
pp. e1279
Author(s):  
Mona Zamanian Azodi ◽  
Mostafa Rezaei-Tavirani ◽  
Mohammad Rostami-Nejad ◽  
Majid Rezaei-Tavirani
Keyword(s):  
Gene Expression ◽  
Bladder Cancer ◽  
Gene Expression Omnibus ◽  
Expression Data ◽  
Molecular Feature ◽  
Online Tool ◽  
Therapeutic Protocol ◽  
Stage 4 ◽  
Differential Network ◽  
Geo Database

Background: Bladder cancer (BC) has remained as one of the most challenging issues in medicine. The aim of this study was to investigate the differential network analysis of stages 2 and 4 of BC to better understand the molecular pathology of these states. Materials and Methods: We chose gene expression data of GSE52519 from Gene Expression Omnibus (GEO) database analyzed by the GEO2R online tool. Cytoscape version 3.6.1 and its algorithms are the methods applied for the network construction and investigation of differentially expressed genes (DEG) in these states. Result: Our result revealed that the analysis DEGs provides useful information about a common molecular feature of stages 2 and 4 of BC. Conclusion: Consequently, the network finding revealed that more investigation about stage 2 is required to achieve an effective therapeutic protocol to block the transition from stage 2 to stage 4.[GMJ.2018;7:e1279] 

scholarly journals mRNAsi Index: Machine Learning in Mining Lung Adenocarcinoma Stem Cell Biomarkers

Genes ◽  
2020 ◽  
Vol 11 (3) ◽  
pp. 257 ◽  
Author(s):  
Yitong Zhang ◽  
Joseph Ta-Chien Tseng ◽  
I-Chia Lien ◽  
Fenglan Li ◽  
Wei Wu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Stem Cell ◽  
Lung Adenocarcinoma ◽  
Tumor Necrosis Factor Receptor ◽  
Enrichment Analysis ◽  
Gene Expression Omnibus ◽  
The Cancer Genome Atlas ◽  
Clinical Stages ◽  
Key Genes ◽  
Geo Database

Cancer stem cells (CSCs), characterized by self-renewal and unlimited proliferation, lead to therapeutic resistance in lung cancer. In this study, we aimed to investigate the expressions of stem cell-related genes in lung adenocarcinoma (LUAD). The stemness index based on mRNA expression (mRNAsi) was utilized to analyze LUAD cases in the Cancer Genome Atlas (TCGA). First, mRNAsi was analyzed with differential expressions, survival analysis, clinical stages, and gender in LUADs. Then, the weighted gene co-expression network analysis was performed to discover modules of stemness and key genes. The interplay among the key genes was explored at the transcription and protein levels. The enrichment analysis was performed to annotate the function and pathways of the key genes. The expression levels of key genes were validated in a pan-cancer scale. The pathological stage associated gene expression level and survival probability were also validated. The Gene Expression Omnibus (GEO) database was additionally used for validation. The mRNAsi was significantly upregulated in cancer cases. In general, the mRNAsi score increases according to clinical stages and differs in gender significantly. Lower mRNAsi groups had a better overall survival in major LUADs, within five years. The distinguished modules and key genes were selected according to the correlations to the mRNAsi. Thirteen key genes (CCNB1, BUB1, BUB1B, CDC20, PLK1, TTK, CDC45, ESPL1, CCNA2, MCM6, ORC1, MCM2, and CHEK1) were enriched from the cell cycle Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, relating to cell proliferation Gene Ontology (GO) terms, as well. Eight of the thirteen genes have been reported to be associated with the CSC characteristics. However, all of them have been previously ignored in LUADs. Their expression increased according to the pathological stages of LUAD, and these genes were clearly upregulated in pan-cancers. In the GEO database, only the tumor necrosis factor receptor associated factor-interacting protein (TRAIP) from the blue module was matched with the stemness microarray data. These key genes were found to have strong correlations as a whole, and could be used as therapeutic targets in the treatment of LUAD, by inhibiting the stemness features.

scholarly journals GEDI: an R package for integration of transcriptomic data from multiple high-throughput platforms

2021 ◽  
Author(s):  
Mathias N Stokholm ◽  
Maria B Rabaglino ◽  
Haja N Kadarmideen
Keyword(s):  
Gene Expression ◽  
Data Integration ◽  
Principal Component ◽  
R Package ◽  
Gene Expression Omnibus ◽  
Batch Effect ◽  
Sequencing Data ◽  
Transcriptomic Data ◽  
Ncbi Gene Expression Omnibus ◽  
Forward Stepwise

Transcriptomic data is often expensive and difficult to generate in large cohorts in comparison to genomic data and therefore is often important to integrate multiple transcriptomic datasets from both microarray and next generation sequencing (NGS) based transcriptomic data across similar experiments or clinical trials to improve analytical power and discovery of novel transcripts and genes. However, transcriptomic data integration presents a few challenges including re-annotation and batch effect removal. We developed the Gene Expression Data Integration (GEDI) R package to enable transcriptomic data integration by combining already existing R packages. With just four functions, the GEDI R package makes constructing a transcriptomic data integration pipeline straightforward. Together, the functions overcome the complications in transcriptomic data integration by automatically re-annotating the data and removing the batch effect. The removal of the batch effect is verified with Principal Component Analysis and the data integration is verified using a logistic regression model with forward stepwise feature selection. To demonstrate the functionalities of the GEDI package, we integrated five bovine endometrial transcriptomic datasets from the NCBI Gene Expression Omnibus. The datasets included Affymetrix, Agilent and RNA-sequencing data. Furthermore, we compared the GEDI package to already existing tools and found that GEDI is the only tool that provides a full transcriptomic data integration pipeline including verification of both batch effect removal and data integration.

scholarly journals Integrated bioinformatics analysis for differentially expressed genes and signaling pathways identification in gastric cancer

2019 ◽  
Author(s):  
ChenChen Yang ◽  
Aifeng Gong
Keyword(s):  
Gene Expression ◽  
Gastric Cancer ◽  
Differentially Expressed Genes ◽  
Kegg Pathway ◽  
Gene Expression Omnibus ◽  
Differentially Expressed ◽  
Mrna Levels ◽  
Pcr Assay ◽  
Microarray Datasets ◽  
Geo Database

Abstract Background Gastric cancer (GC) has a high mortality rate in cancer-related deaths worldwide. Here, we identified several vital candidate genes related to gastric cancer development and revealed the potential pathogenic mechanisms using integrated bioinformatics analysis.Methods Two microarray datasets from Gene Expression Omnibus (GEO) database integrated. Limma package was used to analyze differentially expressed genes (DEGs) between GC and matched normal specimens. DAVID was utilized to conduct Gene ontology (GO) and KEGG enrichment analysis. The relative expression of OLFM4, IGF2BP3, CLDN1and MMP1were analyzed based on TCGA database provided by UALCAN. Western blot and quantitative real time PCR assay were performed to determine the protein and mRNA levels of OLFM4, IGF2BP3, CLDN1and MMP1 in GC tissues and cell lines, respectively.Results We downloaded the expression profiles of GSE103236 and GSE118897 from the Gene Expression Omnibus (GEO) database. Two integrated microarray datasets were used to obtain differentially expressed genes (DEGs), and bioinformatics methods were used for in-depth analysis. After gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichments analysis, we identified 61 DEGs in common, of which the expression of 34 genes were elevated and 27 genes were decreased. GO analysis displayed that the biological functions of DEGs mainly focused on negative regulation of growth, fatty acid binding, cellular response to zinc ion and calcium-independent cell-cell adhesion. KEGG pathway analysis demonstrated that these DEGs mainly related to the Wnt and tumor signaling pathway. Interestingly, we found 4 genes were most significantly upregulated in the DEGs, which were OLFM4, IGF2BP3, CLDN1 and MMP1.Then, we confirmed the upregulation of these genes in STAD based on sample types. In the final, western blot and qRT-PCR assay were performed to determine the protein and mRNA levels of OLFM4, IGF2BP3, CLDN1 and MMP1 in GC tissues and cell lines.Conclusion In our study, using integrated bioinformatics to screen DEGs in gastric cancer could benefit us for understanding the pathogenic mechanism underlying gastric cancer progression. Meanwhile, we also identified four significantly upregulated genes in DEGs from both two datasets, which might be used as the biomarkers for early diagnosis and prevention of gastric cancer.

scholarly journals Integrated analysis of cancer stem cells-associated lncRNA-miRNA-mRNA network for ovarian cancer via microarray and Gene Expression Omnibus database

2020 ◽  
Author(s):  
Zheng Li ◽  
Zhijiao Wang ◽  
Yingying Zhou
Keyword(s):  
Gene Expression ◽  
Ovarian Cancer ◽  
Stem Cells ◽  
Stem Cell ◽  
Cancer Stem Cells ◽  
Signaling Pathway ◽  
Gene Expression Omnibus ◽  
Integrated Analysis ◽  
Cerna Network ◽  
Geo Database

Abstract Background: Cancer stem cells (CSCs) are associated with the recurrence, metastasis and chemoresistance of epithelial ovarian cancer. Competing endogenous RNAs (CeRNAs) play an important role in maintenance of ovarian cancer stem cell-like cells (OCSCs) characteristics. To construct a ceRNA regulatory network for OCSCs, microarray technology and Gene Expression Omnibus (GEO) database had been used. Human serous epithelial ovarian carcinoma cell line COC1 cells were treated with cisplatin and paclitaxel then maintained in stem cell conditions for 6 days to obtain CD117+/CD133+ cells (OCSCs). We identified the differentially expressed miRNAs (DEMs), lncRNA (DELs) and mRNA (DEGs) between OCSCs and COC1 by microarray and combined them with representative microarray profiles in GEO Database. Results: According to the combination, 28 DEMs were identified at first, and 452 DEGs were obtained combining with the predicted targets of these miRNAs and our mRNA microarray results. Up-regulated DEGs of them were significantly enriched in ‘p53 signaling pathway’, ‘FoxO signaling pathway’ and ‘MicroRNAs in cancer’, whereas down-regulated DEGs were significantly enriched in ‘Adherens junction’ and ‘Hepatitis C’ pathway. 29 transcripts of 17 lncRNAs should be the ceRNAs of 10 of these miRNAs according to bioinformatics predicted results and lncRNA microarray. Finally, we obtained ceRNA network with 10 DEMs, 21 DEGs, and 25 transcripts of 13 DELs which should play an important role in maintenance of OCSCs characteristics. LINC00665-miR-146a-5p-NRP2 should be one of ceRNA pathways of the network. The qPCR results indicated that the expression of miR-146a-5p in OCSCs was lower than that in COC1, and LINC00665 shows the opposite trend. These results were consistent with the results of microarray partially. When LINC00665 expression was up-regulated in COC1, the cell proliferation ability enhanced, apoptosis rate reduced, and the percentage of G2/M phase cells increased. Conclusions: The ceRNA network we constructed may be involved in the stem cell characteristics maintenance of OCSCs and provide directions for further OCSCs research in the future, so as to assist the development and treatment of ovarian cancer.

Discovering the signature of a lupus-related microRNA profile in the Gene Expression Omnibus repository

Lupus ◽  
2020 ◽  
Vol 29 (11) ◽  
pp. 1321-1335
Author(s):  
Forouzan Omidi ◽  
Sayed Abdolhakim Hosseini ◽  
Abbas Ahmadi ◽  
Kambiz Hassanzadeh ◽  
Shima Rajaei ◽  
...  
Keyword(s):  
Gene Expression ◽  
Autoimmune Diseases ◽  
Current Knowledge ◽  
Gene Expression Omnibus ◽  
Systemic Autoimmune Diseases ◽  
Diagnostic Biomarkers ◽  
Novel Mirna ◽  
Microrna Profile ◽  
Potential Biomarkers ◽  
Geo Database

Lupus is one of the most prevalent systemic autoimmune diseases. It is a multifactorial disease in which genetic, epigenetic and environmental factors play significant roles. The pathogenesis of lupus is not yet well understood. However, deregulation of microRNAs (miRNAs) – one of the post-transcriptional regulators of genes – can contribute to the development of autoimmune diseases. Over the last two decades, advances in the profiling of miRNA using microarray have received much attention, and it has been demonstrated that miRNAs play a regulatory role in the pathogenesis of lupus. Therefore, dysregulated miRNAs can be considered as promising diagnostic biomarkers for lupus. This article is an overview of lupus-related miRNA profiling studies and arrays in the Gene Expression Omnibus (GEO) database. The aims of our study were to widen current knowledge of known dysregulated miRNAs as potential biomarkers of SLE and to introduce a bioinformatics approach to using microarray data and finding novel miRNA and gene candidates for further study. We identified hsa-miR-4709-5p, hsa-miR-140, hsa-miR-145, hsa-miR-659, hsa-miR-134, hsa-miR-150, hsa-miR-584, hsa-miR-409 and hsa-miR-152 as potential biomarkers by integrated bioinformatics analysis.

scholarly journals Identification of Potential core genes in Sevoflurance induced Myocardial Energy Metabolism in Patients Undergoing Off-pump Coronary Artery Bypass Graft Surgery using Bioinformatics analysis

2019 ◽  
Author(s):  
Hua Lin
Keyword(s):  
Gene Expression ◽  
Energy Metabolism ◽  
Artery Bypass ◽  
Bypass Graft ◽  
Gene Expression Omnibus ◽  
Hub Genes ◽  
Myocardial Energy Metabolism ◽  
Off Pump ◽  
Pump Coronary Artery Bypass ◽  
Geo Database

Abstract Background: Myocardial ischemia-reperfusion injury always happened after Off-pump coronary artery bypass graft(OPCABG), and this can not be avoided altogether. In this study, we tried to detect potential genes of sevoflurane-induced myocardial energy metabolism in patients undergoing OPCABG using bioinformatics analysis. Methods: We download and analyze the gene expression profile data from the Gene Expression Omnibus(GEO) database using bioinformatics methods. We downloded the gene expression data from the Gene Expression Omnibus(GEO) database using bioinformatics methods. Gene Ontology(GO) functional annotation analysis and Kyoto Encyclopedia of Genes and Genomes(KEGG) pathway enrichment analysis were used to analysis the screened differentially expressed genes(DEGs). Then, we established a protein–protein interaction (PPI) network to find hub genes associated with myocardial energy metabolism. Results: Through PPI network, we find ten hub genes, including JUN, EGR1, ATF3, FOSB, JUNB, DUSP1, EGR2, NR4A1, BTG2, NR4A2. Conclusions: In conclusion, the proteins encoded by EGR1，ATF3，c-Fos，Btg2，JunB，DUSP1，NR4A1，BTG2 and NR4A2 were related to cardiac function. ATF3, FOSB, JUNB, DUSP1, NR4A1, NR4A2 are related to apoptosis of cardiomyocytes. The protein encoded by BTG2 is related to hypertrophy. Sevoflurane regulates cell transcription, inflammatory and apoptosis through those hub genes to protect myocardial.

scholarly journals Prognostic Value of Genes Related with Tumor Microenvironment in Ovarian Cancer

2020 ◽  
Author(s):  
Shimei Li ◽  
Jiyi Yao ◽  
Shen Zhang ◽  
Xinchuan Zhou ◽  
Xinbao Zhao ◽  
...  
Keyword(s):  
Gene Expression ◽  
Ovarian Cancer ◽  
Tumor Microenvironment ◽  
Poor Prognosis ◽  
External Validation ◽  
Gene Expression Omnibus ◽  
The Cancer Genome Atlas ◽  
Gene Expression Signatures ◽  
Poor Outcomes ◽  
Geo Database

Abstract Background Ovarian cancer (OV) is the fifth leading cause of cancer death among females. Growing evidence supports a key role of tumor microenvironment in growth, progress, and metastasis of OV. However, the impacts of gene expression signatures related with OV microenvironment on prognosis have not been well-established . This study aimed to apply ESTIMATE algorithm to extract genes related with tumor microenvironment that predicted poor outcomes in OV patients. Methods The gene expression profile of OV samples were downloaded from The Cancer Genome Atlas (TCGA) database. The immune scores and stromal scores of 469 OV samples were available based on the ESTIMATE algorithm. To better understand impacts of gene expression signatures related with OV microenvironment on prognosis, these samples were categorized based on their ESTIMATE scores into high and low score groups. A different OV cohort from the Gene Expression Omnibus (GEO) database was used for external validation. Results The molecular subtypes in OV patients were correlated with stromal scores, in which the mesenchymal subtype had the highest stromal scores (p < 0.0001). Poor prognosis were found in patients (especially for patients with overall survivals (OS) < 5 years) with higher stromal score (p = 0.0376). 449 differentially expressed genes (DEGs) in stromal scores group were identified and 26 DEGs were significantly associated with poor prognosis in OV patients (p < 0.05). Eventually, 6 genes have further validated to be significantly associated with poor outcomes in 40 patients from a different OV cohort of GEO database (p < 0.05). Conclusion In this study, several genes related with tumor microenvironment that predicted poor prognosis in OV patients were extracted. In addition, some previously overlooked genes could be potential prognostic biomarkers for OV.

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