Total Differentially Expressed Genes Research Articles

Comparative transcriptome analysis revealed gene expression differences in fruits between two Actinidia chinensis cultivars

The kiwifruit JD (Jindun No. 1) and HY (Hongyang) are two species of Actinidia chinensis, a major horticultural crop growing worldwide. Fruity texture and taste of HY are better compared with that of JD. However, the fruit transcriptome differences at the ripening stage between JD and HY are poorly understood. In this study, we identified a total of 2,565 differentially expressed genes (DEGs), including 1,336 upregulated and 1,229 downregulated DEGs between the two cultivars. Among the total DEGs, 2,329 matched at least one database. Meanwhile, 1,915 could be aligned to the GO (Gene Ontology), 551 could be aligned to the KEGG (Kyoto Encyclopedia of Genes and Genomes Pathway), and 2,187 could be aligned to SWISS-PROT (SWISS-PROT Protein Sequence) database, respectively. To analyze the DEGs using functional categorization, some regulated processes were significantly changed, and 52 TF (transcription factor) families were detected. This study revealed the change in gene expression level at maturity through the analysis of DEGs data, and these data can provide valuable clues for exploring the molecular mechanism between two kiwifruit varieties, and will be helpful for molecular breeding in Actinidia plants.

Identification of potential hub genes for the diagnosis and therapy of dilated cardiomyopathy with heart failure through bioinformatics analysis

Dilated cardiomyopathy (DCM) is a common cause of heart failure. However, genetic-level treatments are not available for this condition. In this study, we searched for biological markers and therapeutic targets for DCM from a genetic perspective. We chose microarray datasets of idiopathic DCM with heart failure tissues and normal function (NF) heart tissues, which were downloaded from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were analyzed by the GEO2R tool. Gene ontology (GO) and gene set enrichment analysis were used to analyze the functions of DEGs and the pathways in which they are involved. Next, protein-protein interaction networks were built to filter out the hub genes from DEGs. The expression of hub gene was validated by other GEO datasets. Receiver operating characteristic (ROC) curves were plotted to verify the accuracy of the genetic diagnosis. In the end, the mRNA-miRNA-lncRNA network was built to find potentially correlative genes. Twenty-eight common DEGs in total were screened, and GO analysis showed that DEGs were mainly associated with neutrophil degranulation and activation, regulation of Wnt signaling pathway and the development of cardiac cell and tissue. Five hub genes (asporin [ASPN], osteoglycin [OGN], secreted frizzled-related protein 4 [SFRP4], membrane metalloendopeptidase [MME], and natriuretic peptide gene [NPPA]) were shown to be highly expressed in the validation sets and accurate in distinguish between DCM and NF by ROC curves. miRNA prediction of the hub genes revealed that hsa-mir-28b-5p was associated with SFRP4, ASPN, and MME. All of them may serve as biological diagnostic indicators and provide direction for treatment at the genetic level.

Abstract 1442: Discovery of recurrence and prognosis associated genes in prostate cancer

Abstract The molecular pathogenesis of Prostate cancer (PCa) is poorly understood, which limits the diagnosis and treatment options. PCa is one of the leading malignant tumors in US men. This research aims to investigate various molecular regulatory pathways triggered by differentially expressed genes (DEGs) to discover Hub genes as diagnostic or therapeutic targets to improve PCa prognosis. We used eight PCa Microarray datasets (GSE46602, GSE38241, GSE69223, GSE3325, GSE32571, GSE55945, GSE104749, and GSE26126) from the NCBI/GEO. We identified DEGs from datasets by comparing PCa (n=247) and control prostate tissues (n=221) using GEO2R with the criteria of |log2FC| (fold change) ≥ 1 and P-value < 0.05. We implemented volcano plot analysis and generated Venn diagrams to identify overlapping genes. We then applied DAVID.6.8, Gene Ontology (GO) and KEGG pathway analysis to potentially associate DEGs' with biological functions and pathways in PCa pathogenesis. The protein-protein interaction (PPI) networks of the recognized DEGs and the significant nodes were constructed by STRING and visualized by Cytoscape and GeneMANIA. Finally, module analysis of the PPI network was performed by MCODE and CytoHubba to identify the Hub genes. The eight GEO datasets include total DEGs = 11595, upregulated = 3795, and downregulated genes = 3548. We identified 472 DEGs overlappings (the Key Genes) among the eight datasets with 357 downregulated and 115 upregulated genes. The GO and KEGG analysis for genes showed that they were principally involved in cell adhesion, focal adhesion, cell proliferation, calcium signaling pathway, extracellular exosome, and cancer pathways. The top significant (P-Value <1.20E-03) transcriptional factors (TFs) connected with downregulated (BACH1, AP1, BACH2, LYF1, SRF, and NF1) and upregulated (MYOD, NFKAPPAB, MSX1, ROAZ, PAX5, and MYCMAX) genes. The PPI networks and the significance analysis were performed (STRING local clustering coefficient of 0.37, average node degree 4.53 and PPI enrichment p-value < 1.0E-16), GeneMANIA maximum resultant genes = 20, and maximum resultant attributes=10. MCODE scores > 7, degree cutoff = 2, node score cutoff = 0.1, Max depth = 100 and k-score = 2). CytoHubba included two topological analysis methods (DNMC and MCC). Results discovered eleven Hub genes (BDNF, CCK, GRIA2, NTRK2, SNAP25, SYN1, SYT1, ACTG2, ANXA2, ANXA6, and MFGE8). The 11 Hub genes among 472 DEGs directly correlate to the recurrence and prognosis of PCa. The discovered Hub genes and pathways may be potentially involved in PCa etiology in different patients. Recognizing these Hub genes may further assist in understanding molecular pathology to develop diagnostic and treatment regimens for a better prognosis of PCa patients. Citation Format: Diaaidden Alwadi, Alok Deoraj, Quentin Felty, Deodutta Roy. Discovery of recurrence and prognosis associated genes in prostate cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1442.

Identification of crucial genes associated with lung adenocarcinoma by bioinformatic analysis.

Lung cancer is the world's most common malignancies and ranks first among all cancer-related deaths. Lung adenocarcinoma (LUAD) is the most frequent histological type in lung cancer. Its pathogenesis has not yet been fully elucidated, so it is of great significance to explore related genes for elucidating the molecular mechanism involved in occurrence and development of LUAD.To explore the crucial genes associated with LUAD development and progression, microarray datasets GSE7670, GSE10072, and GSE31547 were acquired from the Gene Expression Omnibus (GEO) database. R language Limma package was adopted to screen the differentially expressed genes (DEGs). The clusterProfiler package was used for enrichment analysis and annotation of the Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genome (KEGG) pathways for DEGs. The Search Tool for the Retrieval of Interacting Genes database (STRING) was used to construct the protein interaction network for DEGs, while Cytoscape was adopted to visualize it. The functional module was screened with Cytoscape's MCODE (The Molecular Complex Detection) plugin. The crucial genes associated with LUAD were identified by cytoHubba plugin. Kaplan–Meier plotter online tool was used to perform survival analysis of the hub gene.Three hundred twenty-one DEGs in total were screened, of which 105 were upregulated and 216 were downregulated. It was found that some GO terms and pathways (e.g., collagen trimer, extracellular structure organization, heparin binding, complement and coagulation cascades, malaria, protein digestion and absorption, and PPAR signaling pathway) were considerably enriched in DEGs. UBE2C, TOP2A, RRM2, CDC20, CCNB2, KIAA0101, BUB1B, TPX2, PRC1, and CDK1 were identified as crucial genes. Survival analysis showed that the overexpression of UBE2C, TOP2A, RRM2, CDC20, CCNB2, KIAA0101, BUB1B, TPX2, and PRC1 significantly reduced the overall survival of LUAD patients. One of the crucial genes: UBE2C was validated by immunohistochemistry to be upregulated in LUAD tissues.This study screened out potential biomarkers of LUAD, providing a theoretical basis for elucidating the pathogenesis and evaluating the prognosis of LUAD.

Genome-wide identification of cold responsive transcription factors in Brassica napus L

BackgroundCold stress is one of the primary environmental factors that affect plant growth and productivity, especially for crops like Brassica napus that live through cold seasons. Till recently, although a number of genes and pathways involved in B. napus cold response have been revealed by independent studies, a genome-wide identification of the key regulators and the regulatory networks is still lack. In this study, we investigated the transcriptomes of cold stressed semi-winter and winter type rapeseeds in short day condition, mainly with the purpose to systematically identify the functional conserved transcription factors (TFs) in cold response of B. napus.ResultsGlobal modulation of gene expression was observed in both the semi-winter type line (158A) and the winter type line (SGDH284) rapeseeds, in response to a seven-day chilling stress in short-day condition. Function analysis of differentially expressed genes (DEGs) revealed enhanced stresses response mechanisms and inhibited photosynthesis in both lines, as well as a more extensive inhibition of some primary biological processes in the semi-winter type line. Over 400 TFs were differentially expressed in response to cold stress, including 56 of them showed high similarity to the known cold response TFs and were consistently regulated in 158A and SGDH284, as well as 25 TFs which targets were over-represented in the total DEGs. A further investigation based on their interactions indicated the critical roles of several TFs in cold response of B. napus.ConclusionIn summary, our results revealed the alteration of gene expression in cold stressed semi-winter and winter ecotype B. napus lines and provided a valuable collection of candidate key regulators involved in B. napus response to cold stress, which could expand our understanding of plant stress response and benefit the future improvement of the breed of rapeseeds.

Identification of key pathways and genes in PTEN mutation prostate cancer by bioinformatics analysis

BackgroundProstate cancer (Pca) remains one of the leading adult malignancies. PTEN (Phosphatase and Tensin Homolog) mutant is the top common mutated genes in prostate cancer, which makes it a promising biomarker in future individualized treatment.MethodsWe obtained gene expression data of prostate cancer from TCGA (The Cancer Genome Atlas) database for analysis. We analyzed the DEGs (differentially expressed genes), and used online tools or software to analyze Gene ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene set enrichment analysis (GSEA), Search Tool for the Retrieval of Interacting Genes/Proteins, and Molecular Complex Detection.ResultsLatest TCGA data showed PTEN mutation in about 22% patients. 1736 DEGs in total were identified. Results of gene functional enrichment analyses showed that muscle contraction, negative regulation of growth and multiple metabolic progression were significantly enriched. GNG13, ACTN2, POTEE, ACTA1, MYH6, MYH3, MYH7, MYL1, TNNC1 and TNNC2 were the top ten hub genes. Patients with PTEN mutation showed relatively decreased mRNA expression level of PTEN. Survival analysis indicated the risk of disease recurrence in patients with PTEN mutation.ConclusionsOur findings suggested that PTEN mutation in prostate cancer may induce changes in a variety of genes and pathways and affect disease progression, suggesting the significance of PTEN mutation in individualized treatment of prostate cancer.

Identification of Differentially Expressed Genes and Signaling Pathways in Acute Myocardial Infarction Based on Integrated Bioinformatics Analysis.

Background Acute myocardial infarction (AMI) is a common disease with high morbidity and mortality around the world. The aim of this research was to determine the differentially expressed genes (DEGs), which may serve as potential therapeutic targets or new biomarkers in AMI. Methods From the Gene Expression Omnibus (GEO) database, three gene expression profiles (GSE775, GSE19322, and GSE97494) were downloaded. To identify the DEGs, integrated bioinformatics analysis and robust rank aggregation (RRA) method were applied. These DEGs were performed through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses by using Clusterprofiler package. In order to explore the correlation between these DEGs, the interaction network of protein-protein internet (PPI) was constructed using the STRING database. Utilizing the MCODE plug-in of Cytoscape, the module analysis was performed. Utilizing the cytoHubba plug-in, the hub genes were screened out. Results 57 DEGs in total were identified, including 2 down- and 55 upregulated genes. These DEGs were mainly enriched in cytokine-cytokine receptor interaction, chemokine signaling pathway, TNF signaling pathway, and so on. The module analysis filtered out 18 key genes, including Cxcl5, Arg1, Cxcl1, Spp1, Selp, Ptx3, Tnfaip6, Mmp8, Serpine1, Ptgs2, Il6, Il1r2, Il1b, Ccl3, Ccr1, Hmox1, Cxcl2, and Ccl2. Ccr1 was the most fundamental gene in PPI network. 4 hub genes in total were identified, including Cxcl1, Cxcl2, Cxcl5, and Mmp8. Conclusion This study may provide credible molecular biomarkers in terms of screening, diagnosis, and prognosis for AMI. Meanwhile, it also serves as a basis for exploring new therapeutic target for AMI.

Comparative Transcriptomic Analysis of Biological Process and Key Pathway in Three Cotton (Gossypium spp.) Species Under Drought Stress.

Drought is one of the most important abiotic stresses that seriously affects cotton growth, development, and production worldwide. However, the molecular mechanism, key pathway, and responsible genes for drought tolerance incotton have not been stated clearly. In this research, high-throughput next generation sequencing technique was utilized to investigate gene expression profiles of three cotton species (Gossypium hirsutum, Gossypium arboreum, and Gossypium barbadense L.) under drought stress. A total of 6968 differentially expressed genes (DEGs) were identified, where 2053, 742, and 4173 genes were tested as statistically significant; 648, 320, and 1998 genes were up-regulated, and 1405, 422, and 2175 were down-regulated in TM-1, Zhongmian-16, and Pima4-S, respectively. Total DEGs were annotated and classified into functional groups under gene ontology analysis. The biological process was present only in tolerant species(TM-1), indicating drought tolerance condition. The Kyoto encyclopedia of genes and genomes showed the involvement of plant hormone signal transduction and metabolic pathways enrichment under drought stress. Several transcription factors associated with ethylene-responsive genes (ICE1, MYB44, FAMA, etc.) were identified as playing key roles in acclimatizing to drought stress. Drought also caused significant changes in the expression of certain functional genes linked to abscisic acid (ABA) responses (NCED, PYL, PP2C, and SRK2E), reactive oxygen species (ROS) related in small heat shock protein and 18.1 kDa I heat shock protein, YLS3, and ODORANT1 genes. These results will provide deeper insights into the molecular mechanisms of drought stress adaptation in cotton.

Analysis of dynamic molecular networks for pancreatic ductal adenocarcinoma progression

BackgroundPancreatic ductal adenocarcinoma (PDAC) is one of the deadliest solid tumors. The rapid progression of PDAC results in an advanced stage of patients when diagnosed. However, the dynamic molecular mechanism underlying PDAC progression remains far from clear.MethodsThe microarray GSE62165 containing PDAC staging samples was obtained from Gene Expression Omnibus and the differentially expressed genes (DEGs) between normal tissue and PDAC of different stages were profiled using R software, respectively. The software program Short Time-series Expression Miner was applied to cluster, compare, and visualize gene expression differences between PDAC stages. Then, function annotation and pathway enrichment of DEGs were conducted by Database for Annotation Visualization and Integrated Discovery. Further, the Cytoscape plugin DyNetViewer was applied to construct the dynamic protein–protein interaction networks and to analyze different topological variation of nodes and clusters over time. The phosphosite markers of stage-specific protein kinases were predicted by PhosphoSitePlus database. Moreover, survival analysis of candidate genes and pathways was performed by Kaplan–Meier plotter. Finally, candidate genes were validated by immunohistochemistry in PDAC tissues.ResultsCompared with normal tissues, the total DEGs number for each PDAC stage were 994 (stage I), 967 (stage IIa), 965 (stage IIb), 1027 (stage III), 925 (stage IV), respectively. The stage-course gene expression analysis showed that 30 distinct expressional models were clustered. Kyoto Encyclopedia of Genes and Genomes analysis indicated that the up-regulated DEGs were commonly enriched in five fundamental pathways throughout five stages, including pathways in cancer, small cell lung cancer, ECM-receptor interaction, amoebiasis, focal adhesion. Except for amoebiasis, these pathways were associated with poor PDAC overall survival. Meanwhile, LAMA3, LAMB3, LAMC2, COL4A1 and FN1 were commonly shared by these five pathways and were unfavorable factors for prognosis. Furthermore, by constructing the stage-course dynamic protein interaction network, 45 functional molecular modules and 19 nodes were identified as featured regulators for all PDAC stages, among which the collagen family and integrins were considered as two main regulators for facilitating aggressive progression. Additionally, the clinical relevance analysis suggested that the stage IV featured nodes MLF1IP and ITGB4 were significantly correlated with shorter overall survival. Moreover, 15 stage-specific protein kinases were identified from the dynamic network and CHEK1 was particularly activated at stage IV. Experimental validation showed that MLF1IP, LAMA3 and LAMB3 were progressively increased from tumor initiation to progression.ConclusionsOur study provided a view for a better understanding of the dynamic landscape of molecular interaction networks during PDAC progression and offered potential targets for therapeutic intervention.

Network analyses elucidate the role of SMYD3 in esophageal squamous cell carcinoma.

SMYD3 is a member of the SET and myeloid‐Nervy‐DEAF‐1 (MYND) domain‐containing protein family of methyltransferases, which are known to play critical roles in carcinogenesis. Expression of SMYD3 is elevated in various cancers, including esophageal squamous cell carcinoma (ESCC), and is correlated with the survival time of patients with ESCC. Here, we dissect gene expression data, from a previously described KYSE150 ESCC cell line in which SMYD3 had been knocked down, by integration with the protein–protein interaction (PPI) network, to find the new potential biological roles of SMYD3 and subsequent target genes. By construction of a specific PPI network, differentially expressed genes (DEGs), following SMYD3 knockdown, were identified as interacting with thousands of neighboring proteins. Enrichment analyses from the DAVID Functional Annotation Chart found significant Gene Ontology (GO) terms associated with transcription activities, which were closely related to SMYD3 function. For example, YAP1 and GATA3 might be a target gene for SMYD3 to regulate transcription. Enrichment annotation of the total DEG PPI network by GO ‘Biological Process’ generated a connected functional map and found 532 significant terms, including known and potential biological roles of SMYD3 protein, such as expression regulation, signal transduction, cell cycle, cell metastasis, and invasion. Subcellular localization analyses found that DEGs and their interacting proteins were distributed in multiple layers, which might reflect the intricate biological processes at the spatial level. Our analysis of the PPI network has provided important clues for future detection of the biological roles and mechanisms, as well as the target genes of SMYD3.

Systematic identification of novel biomarker signatures associated with acquired erlotinib resistance in cancer cells

Acquired erlotinib resistance (AER) during cancer treatment remains a major clinical challenge that results in the recurrence and metastasis of cancers. Therefore, we sought to identify differentially expressed genes (DEGs) by performing a meta-analysis of AER-related microarray datasets and discover biomarkers by conducting a systemic in-silico analysis. Using the RankProd algorithm, we identified 775 DEGs (536 up-regulated and 239 down-regulated). Functional enrichment analyses of the total DEG s suggested that “cell adhesion” and “cytokine-cytokine receptor interactions” may be closely associated with AER process. Some DEGs shared target sites of the potential micro-RNA including miR-21, miR-200b/c, miR-429 and miR-9. Target sites of FOXJ1, NFAT, FOXO4, and JUN were also significantly enriched. From the proteinprotein interaction network, we clustered four functional modules by p-value and node density and found hub genes with many interacting neighbors. Finally, we identified seven candidate hub DEGs (TIMP3, SPARC, ITGA1, CCNA1, SOX2, KRT14, and PTPRZ1) for AER development.

Gene expression profiling in shoot apical meristem of Gossypium hirsutum

Early maturity is a particularly important agronomic trait for cotton breeding in China and is determined by many morphological and phenological traits. The time of floral initiation is one of most important factors related to early maturation of cotton. The aim of this study was to identify differentially expressed (DE) genes related to floral initiation using the Arabidopsis thaliana GeneChip® system on the shoot apical meristems (SAMs) of an early-maturing cotton cultivar. Compared with SAMs at 10 days, 365 genes were differentially expressed in the SAMs at 20 days. Of these, 210 and 155 transcripts were up- and down- regulated, respectively. The results of Gene Ontology (GO) annotation indicated that most genes fell into the four largest functional groups: metabolism, transposable elements, protein binding or cofactor requirements, and protein fate. These groups constituted 18.9, 11.8, 12.8 and 6.8% of the total DE genes, respectively. Many DE genes were identified, including those encoding the transcription factors SOC1-like floral activator MADS4, B3-domain containing transcription factor, MYB2, MYB85 and GHMADS-1. Our research found that the B3-domain containing transcription factor was similar to Arabidopsis genes encoding auxin response factor 36 and VERNALIZATION 1 (VRN1) and was one of 155 down-regulated ‘Apex-unique’ transcripts. The B3-domain containing transcription factor was 1128 bp long and was named GhV1 (GenBank accession No. GU929695). The induction of the transcripts we identified in the cotton SAM after 10 days of SD revealed that the transition to reproductive development occurred at this particular time point. These results allowed for a detailed description of temporal gene expression changes in the cotton SAM as it undergoes the floral initiation process.