scholarly journals Identification of Condition-Specific Biomarker Systems in Uterine Cancer

2021 ◽  
Author(s):  
Allison R Hickman ◽  
Yuqing Hang ◽  
Rini Pauly ◽  
Frank A Feltus
Keyword(s):  
Biological Networks ◽  
Target Genes ◽  
Uterine Cancer ◽  
Differential Expression Analysis ◽  
Gaussian Mixture ◽  
Functional Enrichment ◽  
Specific Gene ◽  
Uterine Carcinosarcoma ◽  
Uterine Tissue ◽  
Clinical Biomarkers

Abstract Uterine cancer is the fourth most common cancer among women, projected to affect 66,000 US women in 2021. Uterine cancer often arises in the inner lining of the uterus, known as the endometrium, but can present as several different types of cancer, including endometrioid cancer, serous adenocarcinoma, and uterine carcinosarcoma. Previous studies have analyzed the genetic changes between normal and cancerous uterine tissue to identify specific genes of interest, including TP53 and PTEN. Here we used Gaussian Mixture Models to build condition-specific gene co-expression networks for endometrial cancer, uterine carcinosarcoma, and normal uterine tissue. We then incorporated uterine regulatory edges and investigated potential co-regulation relationships. These networks were further validated using differential expression analysis, functional enrichment, and a statistical analysis comparing the expression of transcription factors and their target genes across cancerous and normal uterine samples. These networks allow for a more comprehensive look into the biological networks and pathways affected in uterine cancer compared to previous singular gene analyses. We hope this study can be incorporated into existing knowledge surrounding the genetics of uterine cancer and soon become clinical biomarkers as a tool for better prognosis and treatment.

scholarly journals Identification of Potentially Therapeutic Target Genes of Hepatocellular Carcinoma

2020 ◽  
Vol 17 (3) ◽  
pp. 1053
Author(s):  
Chengzhang Li ◽  
Jiucheng Xu
Keyword(s):  
Gene Expression ◽  
Target Genes ◽  
Expression Profiles ◽  
Differential Expression Analysis ◽  
Gene Expression Profiles ◽  
Functional Enrichment ◽  
Ppi Network ◽  
Hub Genes ◽  
Gene Differential Expression ◽  
Oncomine Database

Background: Hepatocellular carcinoma (HCC) is a major threat to public health. However, few effective therapeutic strategies exist. We aimed to identify potentially therapeutic target genes of HCC by analyzing three gene expression profiles. Methods: The gene expression profiles were analyzed with GEO2R, an interactive web tool for gene differential expression analysis, to identify common differentially expressed genes (DEGs). Functional enrichment analyses were then conducted followed by a protein-protein interaction (PPI) network construction with the common DEGs. The PPI network was employed to identify hub genes, and the expression level of the hub genes was validated via data mining the Oncomine database. Survival analysis was carried out to assess the prognosis of hub genes in HCC patients. Results: A total of 51 common up-regulated DEGs and 201 down-regulated DEGs were obtained after gene differential expression analysis of the profiles. Functional enrichment analyses indicated that these common DEGs are linked to a series of cancer events. We finally identified 10 hub genes, six of which (OIP5, ASPM, NUSAP1, UBE2C, CCNA2, and KIF20A) are reported as novel HCC hub genes. Data mining the Oncomine database validated that the hub genes have a significant high level of expression in HCC samples compared normal samples (t-test, p < 0.05). Survival analysis indicated that overexpression of the hub genes is associated with a significant reduction (p < 0.05) in survival time in HCC patients. Conclusions: We identified six novel HCC hub genes that might be therapeutic targets for the development of drugs for some HCC patients.

scholarly journals Identification and characterization of Piwi-interacting RNAs in human placentas of preeclampsia

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jie He ◽  
Miaomiao Chen ◽  
Jiacheng Xu ◽  
Jie Fang ◽  
Zheng Liu ◽  
...  
Keyword(s):  
Expression Pattern ◽  
Human Placenta ◽  
Target Genes ◽  
Expression Profiles ◽  
Differential Expression Analysis ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Small Rna Sequencing ◽  
Common Disease

AbstractPreeclampsia is a common disease of pregnancy that poses a serious threat to the safety of pregnant women and the fetus; however, the etiology of preeclampsia is inconclusive. Piwi-interacting RNAs (piRNAs) are novel non-coding RNAs that are present at high levels in germ cells and are associated with spermatogenesis. Emerging evidence demonstrated that piRNA is expressed in a variety of human tissues and is closely associated with tumorigenesis. However, changes in the piRNA expression profile in the placenta have not been investigated. In this study, we used small RNA sequencing to evaluate the differences in piRNA expression profiles between preeclampsia and control patients and potential functions. Differential expression analysis found 41 up-regulated and 36 down-regulated piRNAs in preeclamptic samples. In addition, the functional enrichment analysis of piRNAs target genes indicated that they were related to the extracellular matrix (ECM) formation and tissue-specific. Finally, we examined the expression pattern of the PIWL family proteins in the placenta, and PIWL3 and PIWIL4 were the primary subtypes in the human placenta. In summary, this study first summarized the changes in the expression pattern of piRNA in preeclampsia and provided new clues for the regulatory role of piRNA in the human placenta.

scholarly journals Self-Organizing Map Methodology for Sorting Differential Expression Data of MMP-9 Inhibition

2019 ◽  
Author(s):  
Rachel St. Clair ◽  
Michael Teti ◽  
Ania Knapinska ◽  
Gregg Fields ◽  
William Hahn ◽  
...  
Keyword(s):  
Data Analysis ◽  
Differential Expression ◽  
Target Genes ◽  
Differential Expression Analysis ◽  
Enrichment Analysis ◽  
Functional Enrichment ◽  
Similar Data ◽  
Self Organizing Map ◽  
Expression Data ◽  
Self Organizing

AbstractAn unsupervised machine-learning model, based on a self-organizing map (SOM), was employed to extract suggested target genes from DESeq2 differential expression analysis data. Such methodology was tested on matrixmetalloproteinase 9 (MMP-9) inhibitors. The model generated information on several novel gene hits that may be regulated by MMP-9, suggesting the self-organizing map method may serve as a useful analytic tool in degradomics research for further differential expression data analysis. Original data was generated from a previous study, which consisted of quantitative measures in changes of levels of gene expression from 32,000 genes in four different conditions of stimulated T-cells treated with an MMP-9 inhibitor. Since intracellular target of MMP-9 are not yet well characterized, the functional enrichment analysis program, WebGestalt, was used for validation of the SOM identified regulated genes. The proposed data analysis method indicated MMP-9’s prominent role in biological regulatory and metabolic processes as major categories of regulation of the predicted genes. Both fields suggest extensive intracellular targets for MMP-9-triggered regulation, which are new interests in MMP-9 research. The methodology presented here is useful for similar knowledge and discovery from quantitative datasets and a proposed extension of DESeq2 or similar data analysis.

Identification of Candidate Genetic Markers and a Novel 4-genes Diagnostic Model in Osteoarthritis through Integrating Multiple Microarray Data

2020 ◽  
Vol 23 (8) ◽  
pp. 805-813
Author(s):  
Ai Jiang ◽  
Peng Xu ◽  
Zhenda Zhao ◽  
Qizhao Tan ◽  
Shang Sun ◽  
...  
Keyword(s):  
Signaling Pathway ◽  
Microarray Data ◽  
Differential Expression Analysis ◽  
Enrichment Analysis ◽  
Mapk Signaling ◽  
Functional Enrichment ◽  
Joint Disease ◽  
Support Vector ◽  
Diagnostic Model ◽  
Data Sets

Background: Osteoarthritis (OA) is a joint disease that leads to a high disability rate and a low quality of life. With the development of modern molecular biology techniques, some key genes and diagnostic markers have been reported. However, the etiology and pathogenesis of OA are still unknown. Objective: To develop a gene signature in OA. Method: In this study, five microarray data sets were integrated to conduct a comprehensive network and pathway analysis of the biological functions of OA related genes, which can provide valuable information and further explore the etiology and pathogenesis of OA. Results and Discussion: Differential expression analysis identified 180 genes with significantly expressed expression in OA. Functional enrichment analysis showed that the up-regulated genes were associated with rheumatoid arthritis (p < 0.01). Down-regulated genes regulate the biological processes of negative regulation of kinase activity and some signaling pathways such as MAPK signaling pathway (p < 0.001) and IL-17 signaling pathway (p < 0.001). In addition, the OA specific protein-protein interaction (PPI) network was constructed based on the differentially expressed genes. The analysis of network topological attributes showed that differentially upregulated VEGFA, MYC, ATF3 and JUN genes were hub genes of the network, which may influence the occurrence and development of OA through regulating cell cycle or apoptosis, and were potential biomarkers of OA. Finally, the support vector machine (SVM) method was used to establish the diagnosis model of OA, which not only had excellent predictive power in internal and external data sets (AUC > 0.9), but also had high predictive performance in different chip platforms (AUC > 0.9) and also had effective ability in blood samples (AUC > 0.8). Conclusion: The 4-genes diagnostic model may be of great help to the early diagnosis and prediction of OA.

scholarly journals Integrated network analysis identifying potential novel drug candidates and targets for Parkinson's disease

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Pusheng Quan ◽  
Kai Wang ◽  
Shi Yan ◽  
Shirong Wen ◽  
Chengqun Wei ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Drug Target ◽  
Target Genes ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Functional Enrichment ◽  
Control Group ◽  
Drug Candidates ◽  
Novel Drug

AbstractThis study aimed to identify potential novel drug candidates and targets for Parkinson’s disease. First, 970 genes that have been reported to be related to PD were collected from five databases, and functional enrichment analysis of these genes was conducted to investigate their potential mechanisms. Then, we collected drugs and related targets from DrugBank, narrowed the list by proximity scores and Inverted Gene Set Enrichment analysis of drug targets, and identified potential drug candidates for PD treatment. Finally, we compared the expression distribution of the candidate drug-target genes between the PD group and the control group in the public dataset with the largest sample size (GSE99039) in Gene Expression Omnibus. Ten drugs with an FDR < 0.1 and their corresponding targets were identified. Some target genes of the ten drugs significantly overlapped with PD-related genes or already known therapeutic targets for PD. Nine differentially expressed drug-target genes with p < 0.05 were screened. This work will facilitate further research into the possible efficacy of new drugs for PD and will provide valuable clues for drug design.

scholarly journals Emerin Represses STAT3 Signaling through Nuclear Membrane-Based Spatial Control

2021 ◽  
Vol 22 (13) ◽  
pp. 6669
Author(s):  
Byongsun Lee ◽  
Seungjae Lee ◽  
Younggwang Lee ◽  
Yongjin Park ◽  
Jaekyung Shim
Keyword(s):  
Muscular Dystrophy ◽  
Nuclear Membrane ◽  
Target Genes ◽  
Janus Kinase ◽  
C2c12 Cells ◽  
Specific Gene ◽  
Lamin A ◽  
Stat3 Signaling ◽  
Exact Function ◽  
Muscle Homeostasis

Emerin is the inner nuclear membrane protein involved in maintaining the mechanical integrity of the nuclear membrane. Mutations in EMD encoding emerin cause Emery-Dreifuss muscular dystrophy (EDMD). There has been accumulating evidence that emerin regulation of specific gene expression is associated with this disease, but the exact function of emerin has still less revealing. Here, we have shown that emerin downregulates signal transducers and activators of transcription 3 (STAT3) signaling, activated exclusively by Janus-kinase (JAK). Deletion mutation experiments showed that the lamin-binding domain of emerin is essential for the inhibition of STAT3 signaling. Emerin interacted directly and co-localized with STAT3 in the nuclear membrane. Emerin knockdown induced STAT3 target genes Bcl2 and Survivin to increase cell survival signals and suppress hydrogen peroxide-induced cell death in HeLa cells. Specifically, downregulation of BAF or lamin A/C increases STAT3 signaling, suggesting that correct-localized emerin by assembling with BAF and lamin A/C acts as an intrinsic inhibitor against STAT3 signaling. In C2C12 cells, emerin knockdown induced STAT3 target gene, Pax7, and activated abnormal myoblast proliferation associated with muscle wasting in skeletal muscle homeostasis. Our results indicate that emerin downregulates STAT3 signaling by inducing retention of STAT3 and delaying STAT3 signaling in the nuclear membrane. This mechanism provides clues to the etiology of emerin-related muscular dystrophy and could be a new therapeutic target for treatment.

scholarly journals Identification of Tumorigenic and Prognostic Biomarkers in Colorectal Cancer Based on microRNA Expression Profiles

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Yuntao Shi ◽  
Yingying Zhuang ◽  
Jialing Zhang ◽  
Mengxue Chen ◽  
Shangnong Wu
Keyword(s):  
Target Genes ◽  
Cox Regression ◽  
Expression Profiles ◽  
Survival Rates ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Risk Groups ◽  
Normal Mucosa ◽  
Microrna Expression ◽  
Functional Enrichment

Objective. Although noncoding RNAs, especially the microRNAs, have been found to play key roles in CRC development in intestinal tissue, the specific mechanism of these microRNAs has not been fully understood. Methods. GEO and TCGA database were used to explore the microRNA expression profiles of normal mucosa, adenoma, and carcinoma. And the differential expression genes were selected. Computationally, we built the SVM model and multivariable Cox regression model to evaluate the performance of tumorigenic microRNAs in discriminating the adenomas from normal tissues and risk prediction. Results. In this study, we identified 20 miRNA biomarkers dysregulated in the colon adenomas. The functional enrichment analysis showed that MAPK activity and MAPK cascade were highly enriched by these tumorigenic microRNAs. We also investigated the target genes of the tumorigenic microRNAs. Eleven genes, including PIGF, TPI1, KLF4, RARS, PCBP2, EIF5A, HK2, RAVER2, HMGN1, MAPK6, and NDUFA2, were identified to be frequently targeted by the tumorigenic microRNAs. The high AUC value and distinct overall survival rates between the two risk groups suggested that these tumorigenic microRNAs had the potential of diagnostic and prognostic value in CRC. Conclusions. The present study revealed possible mechanisms and pathways that may contribute to tumorigenesis of CRC, which could not only be used as CRC early detection biomarkers, but also be useful for tumorigenesis mechanism studies.

scholarly journals Screening Driving Transcription Factors in the Processing of Gastric Cancer

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Guangzhong Xu ◽  
Kai Li ◽  
Nengwei Zhang ◽  
Bin Zhu ◽  
Guosheng Feng
Keyword(s):  
Gastric Cancer ◽  
Transcription Factors ◽  
Regulatory Network ◽  
Target Genes ◽  
Transcriptional Regulatory Network ◽  
Expression Profiles ◽  
Functional Enrichment ◽  
Regulatory Mechanisms ◽  
Integrated Analysis ◽  
Transcriptional Regulatory

Background. Construction of the transcriptional regulatory network can provide additional clues on the regulatory mechanisms and therapeutic applications in gastric cancer.Methods. Gene expression profiles of gastric cancer were downloaded from GEO database for integrated analysis. All of DEGs were analyzed by GO enrichment and KEGG pathway enrichment. Transcription factors were further identified and then a global transcriptional regulatory network was constructed.Results. By integrated analysis of the six eligible datasets (340 cases and 43 controls), a bunch of 2327 DEGs were identified, including 2100 upregulated and 227 downregulated DEGs. Functional enrichment analysis of DEGs showed that digestion was a significantly enriched GO term for biological process. Moreover, there were two important enriched KEGG pathways: cell cycle and homologous recombination. Furthermore, a total of 70 differentially expressed TFs were identified and the transcriptional regulatory network was constructed, which consisted of 566 TF-target interactions. The top ten TFs regulating most downstream target genes were BRCA1, ARID3A, EHF, SOX10, ZNF263, FOXL1, FEV, GATA3, FOXC1, and FOXD1. Most of them were involved in the carcinogenesis of gastric cancer.Conclusion. The transcriptional regulatory network can help researchers to further clarify the underlying regulatory mechanisms of gastric cancer tumorigenesis.

A Six CT83-Related Genes Based Prognostic Signature for Lung Adenocarcinoma

2021 ◽  
Vol 24 ◽  
Author(s):  
Yongmei Wang ◽  
Guimin Zhang ◽  
Ruixian Wang
Keyword(s):  
Regression Analysis ◽  
Lung Adenocarcinoma ◽  
Differential Expression ◽  
Risk Score ◽  
Prognostic Model ◽  
Cox Regression ◽  
Differential Expression Analysis ◽  
Functional Enrichment ◽  
Risk Scores ◽  
Cox Regression Analysis

Background: This study aims to explore the prognostic values of CT83 and CT83-related genes in lung adenocarcinoma (LUAD). Methods: We downloaded the mRNA profiles of 513 LUAD patients (RNA sequencing data) and 246 NSCLC patients (Affymetrix Human Genome U133 Plus 2.0 Array) from TCGA and GEO databases. According to the median expression of CT83, the TCGA samples were divided into high and low expression groups, and differential expression analysis between them was performed. Functional enrichment analysis of differential expression genes (DEGs) was conducted. Univariate Cox regression analysis and LASSO Cox regression analysis were performed to screen the optimal prognostic DEGs. Then we established the prognostic model. A Nomogram model was constructed to predict the overall survival (OS) probability of LUAD patients. Results: CT83 expression was significantly correlated to the prognosis of LUAD patients. A total of 59 DEGs were identified, and a predictive model was constructed based on six optimal CT83-related DEGs, including CPS1, RHOV, TNNT1, FAM83A, IGF2BP1, and GRIN2A, could effectively predict the prognosis of LUAD patients. The nomogram could reliably predict the OS of LUAD patients. Moreover, the six important immune checkpoints (CTLA4, PD1, IDO1, TDO2, LAG3, and TIGIT) were closely correlated with the Risk Score, which was also differentially expressed between the LUAD samples with high and low-Risk Scores, suggesting that the poor prognosis of LUAD patients with high-Risk Score might be due to the immunosuppressive microenvironments. Conclusion: A prognostic model based on six optimal CT83 related genes could effectively predict the prognosis of LUAD patients.

Abstract P625: Non-coding Rna Regulation Of Gene Expression In Angiotensin Ii-induced Vascular Damage

Hypertension ◽  
2015 ◽  
Vol 66 (suppl_1) ◽  
Author(s):  
Kugeng Huo ◽  
Tlili Barhoumi ◽  
Júlio C Fraulob-Aquino ◽  
Chantal Richer ◽  
Mathieu Lajoie ◽  
...  
Keyword(s):  
Gene Expression ◽  
Differential Expression Analysis ◽  
Regulation Of Gene Expression ◽  
Vascular Damage ◽  
Mesenteric Arteries ◽  
Functional Enrichment ◽  
Molecular Networks ◽  
Mirna Cluster ◽  
Ang Ii ◽  
Illumina Hiseq

Introduction: Non-coding RNAs (ncRNAs), including long ncRNAs (lncRNAs) and microRNAs (miRs), account for ~98% of the transcribed RNAs. They have been shown to play a role in cardiovascular disease. Vascular damage is an early manifestation and a cause of end-organ damage in hypertension. However, it is unknown whether ncRNAs are involved in the development of vascular injury in hypertension. We hypothesize that ncRNA regulation participates in mechanisms of vascular remodeling and plays an important role in the pathophysiology of hypertension. Methods and Results: Ten-week old male C57BL/6 mice were infused or not with angiotensin (Ang) II for 14 days. Systolic blood pressure (BP) determined by telemetry was increased by Ang II infusion compared to control (146±8 vs 113±5 mmHg, P<0.001). Total RNA was extracted from mesenteric arteries for total and small RNA deep sequencing using Illumina HiSeq-2500. Sequences were aligned to the mm10 genome with STAR, annotated and counted using HTSeq-count or miRDeep2. Differential expression analysis was done in R. Differentially expressed (DE) mRNAs (550 up & 266 down), lncRNAs (7 up & 42 down), miRs (23 up & 12 down) were identified in the Ang II-treated group (1.5 fold change, q<0.05). Targetscan was used to predict interactions between DE miRs and the inversely correlated DE mRNAs or DE lncRNAs. MEME Suite was used to predict DE transcription factor binding sites in the promoter region of genes encoding DE mRNAs, lncRNAs and miRs. Cytoscape was used to construct molecular networks integrating the above interactions and the gene expression profile and to perform functional enrichment analysis, which revealed enrichment of extracellular matrix and developmental processes in DE miR-targeting DE mRNAs (q<1E-20). Ten DE miRNAs whose expression levels correlated (P<0.05) with BP were identified, 9 of which are located in a single miRNA cluster that is conserved in humans. Conclusions: We have identified a conserved miRNA cluster that may play a pivotal role in the regulation of vascular damage in hypertension. A sub-network of genes that participates in the interaction between the miRNA cluster and other BP-correlated RNAs was selected for future investigation to identify therapeutic targets.

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