Identification of Key Genes and MicroRNAs in Gastric Cancer via miRNA-mRNA Regulatory Network

Abstract Background Gastric cancer (GC) is a malignant tumor with high mortality. MicroRNAs (miRNAs) participate in various biological processes and disease pathogenesis by targeting messenger RNA (mRNA). The purpose of this study was to identify potential prognostic molecular markers of GC and to characterize the molecular mechanisms of GC. Methods A gene expression profiling dataset (GSE54129) and miRNA expression profiling dataset (GSE113486) were downloaded from the Gene Expression Omnibus (GEO) database. A miRNA-mRNA interaction network was established. Functional and pathway enrichment analyses were performed for differentially expressed genes (DEGs) and differentially expressed miRNAs (DEMs) using FunRich, the clusterProfiler package, and DIANA-mirPath. Survival analysis of key molecular markers was performed using the online tool Kaplan-Meier Plotter and the database OncomiR. Finally, experiments were carried out to verify the expression levels and biological functions of a key gene. Results A total of 390 DEMs and 341 DEGs were identified. Ultimately, 45 genes and 31 miRNAs were selected to establish a miRNA-mRNA regulatory network. Four hub genes (ZFPM2, FUT9, NEUROD1 and LIPH) and six miRNAs (hsa-let-7d-5p, hsa-miR-23b-3p, hsa-miR-23a-3p, hsa-miR-133b, hsa-miR-130a-3p and hsa-miR-124-3p) were identified in the network. DEGs and DEMs were associated with ECM-receptor interactions and metabolic pathways. Two genes (ZFPM2 and LIPH) and two miRNAs (hsa-miR-23a-3p and hsa-miR-130a-3p) were observed to be related to the prognosis of GC. ZFPM2 was highly expressed in GC tissues and various GC cell lines and could promote the proliferation, invasion and migration of GC cells. Conclusion The expression levels of ZFPM2, LIPH, hsa-miR-23a-3p and hsa-miR-130a-3p were closely related to the prognosis of GC. ZFPM2 may serve as a potential molecular marker and therapeutic target for GC. ECM receptor interactions and metabolic abnormalities play a critical role in the GC progression.

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Functional Modules Analysis and Hub Gene Prognostic Values Evaluation Based on Co-Expression Network in Gastric Cancer

10.21203/rs.3.rs-600479/v2 ◽

2021 ◽

Author(s):

Yujia Liu ◽

Xiaoping Hu ◽

Zongfu Pan ◽

Yuchen Jiang ◽

Dandan Guo ◽

...

Keyword(s):

Gene Expression ◽

Gastric Cancer ◽

Regulatory Network ◽

Molecular Mechanisms ◽

Transcriptional Regulatory Network ◽

Receptor Interaction ◽

Ppi Network ◽

Hub Gene ◽

Transcriptional Regulatory ◽

Key Nodes

Abstract Background: Gastric cancer is one of the most common fatal disease worldwide, but its mechanism and therapeutic targets are still unclear. In this study, we have analyzed the differences in gene modules and key pathways in gastric cancer patients, then elaborated the mechanism and effective treatment of gastric cancer with microarray data from the gene expression omnibus(GEO) database. Methods: GEO2R tools were used to identify differential expression genes (DEGs), String database was employed to construct a protein-protein interaction (PPI) network. We imported the PPI network into the Cytoscape software to find key nodes, and employed statistical approach of MCODE to cluster genes. After that the ClueGO was used to enrich and annotate the pathways of key modules. To investigate the relationship between the upstream regulator and hub genes, the transcriptional regulatory network was built based on TFCAT database. Results: 63 characteristic genes of gastric cancer are involved in regulation of ECM-receptor interaction, focal adhesion and protein digestion and absorption. SPARC, FN1, BGN and COL1A2 are four key nodes relating to tumor proliferation and metastasis, and their expression were strongly associated with poor survival (p<0.05). 13 transcription factors including PRRX1 have remarkable changes in gastric cancer, which may play a key role in hub gene regulation. Conclusions: The present study defined the gene expression characteristics and transcriptional regulatory network that promote our understanding of the molecular mechanisms underlying the development of gastric cancer, and might provide new insights into targeted therapy and prognostic markers for the personalized treatment of gastric cancer.

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PO-046 Gene expression profiling of peripheral blood mononuclear cells in young male trampoline athletes

Exercise Biochemistry Review ◽

10.14428/ebr.v1i3.10113 ◽

2018 ◽

Vol 1 (3) ◽

Author(s):

Li Gao ◽

Yong Jie Yang ◽

En Qi Li ◽

Jia Ning Mao

Keyword(s):

Gene Expression ◽

Differentially Expressed Genes ◽

Bone Health ◽

Expression Profiling ◽

Molecular Mechanisms ◽

Interaction Network ◽

Differentially Expressed ◽

Signal Pathways ◽

Healthy Controls ◽

Mineral Density

Objective Evidence indicates that physical activity influence bone health. However, the molecular mechanisms mediating the beneficial adaptations to exercise are not well understood. The purpose of this study was to examine the differentially expressed genes in PBMC between athletes and healthy controls, and to analyze the important functional genes and signal pathways that cause increased bone mineral density in athletes, in order to further reveal the molecular mechanisms of exercise promoting bone health. Methods Five professional trampoline athletes and five age-matched untrained college students participated in this study. Used the human expression Microarray V4.0 expression profiling chip to detect differentially expressed genes in the two groups, and performed KEGG Pathway analysis and application of STRING database to construct protein interaction Network; Real-Time PCR technology was used to verify the expression of some differential genes. Results Compared with healthy controls, there were significant improvement in lumbar spine bone mineral density, and 236 up-regulated as well as 265 down-regulated in serum samples of athletes. The differentially expressed genes involved 28 signal pathways, such as cell adhesion molecules. Protein interaction network showed that MYC was at the core node position. Real-time PCR results showed that the expression levels of CD40 and ITGα6 genes in the athletes were up-regulated compared with the healthy controls, the detection results were consistent with that of the gene chip. Conclusions The findings highlight that long-term high-intensity trampoline training could induce transcriptional changes in PBMC of the athletes. These data suggest that gene expression fingerprints can serve as a powerful research tool to design novel strategies for monitoring exercise. The findings of the study also provide support for the notion that PBMC could be used as a substitute to study exercise training that affects bone health.

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LINC00184 involved in the regulatory network of ANGPT2 via ceRNA mediated miR-145 inhibition.

Journal of Global Oncology ◽

10.1200/jgo.2019.5.suppl.17 ◽

2019 ◽

Vol 5 (suppl) ◽

pp. 17-17

Author(s):

Haiyan Piao

Keyword(s):

Gene Expression ◽

Gastric Cancer ◽

Regulatory Network ◽

Poor Prognosis ◽

Differentially Expressed ◽

Competing Endogenous Rna ◽

Carcinogenic Effect ◽

Study Methods ◽

Non Coding Rnas ◽

Potential Biomarkers

17 Background: Abnormal gene expression is closely related to the development and poor prognosis of gastric cancer (GC). Since gene does not work alone, we are aimed to elucidate the potential networks between mRNA and non-coding RNAs (ncRNAs) in this study. Methods: Based on the TCGA database, we obtained the differentially expressed RNAs and constructed the competing endogenous RNA (ceRNA) regulatory network. Results: We found a regulatory network based on ANGPT2. We observed ANGPT2 is overexpressed in GC cells and tissues and associated with poor prognosis. ANGPT2 promotes proliferation, invasion, and EMT in GC cells, which could be abolished by miR-145. In addition, LINC00184 can be used as a ceRNA to inhibit the expression of miR-145, thus enhancing the carcinogenic effect of ANGPT2. Conclusions: Our results suggested that LINC00184/miR-145/ANGPT2 axis plays an important role in the occurrence and development of GC and may be potential biomarkers and targets for the treatment of GC.

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Identification of Prognostic Markers by Gene Expression Profiling In Myelodysplastic Syndrome Hematopoietic Stem Cells

Blood ◽

10.1182/blood.v116.21.298.298 ◽

2010 ◽

Vol 116 (21) ◽

pp. 298-298 ◽

Cited By ~ 1

Author(s):

Andrea Pellagatti ◽

Mario Cazzola ◽

Aristoteles Giagounidis ◽

Janet Perry ◽

Luca Malcovati ◽

...

Keyword(s):

Gene Expression ◽

Stem Cells ◽

Bone Marrow ◽

Molecular Markers ◽

Principal Components ◽

Expression Profiling ◽

Training Set ◽

Test Set ◽

Hematopoietic Stem ◽

Expression Levels

Abstract Abstract 298 The myelodysplastic syndromes (MDS) are a heterogeneous group of clonal hematopoietic stem cell malignancies that are characterized by ineffective hematopoiesis resulting in peripheral cytopenias and a hypercellular bone marrow. Approximately 40% of patients with MDS will develop an acute myeloid leukemia. It is important to establish prognosis of MDS patients since the treatment options vary from supportive care to bone marrow transplantation. In order to determine the relationship of gene expression levels to prognosis and so identify new molecular markers, we have used gene expression profiling to study the transcriptome of the hematopoietic stem cells of 125 MDS patients with a minimum 12 month follow up. The CD34+ cells obtained from MDS patients and healthy individuals were analyzed using Affymetrix U133 Plus2.0 arrays. The patients were split randomly in a training set (n=84) and a test set (n=41). Supervised principal components analysis was used to identify genes correlated with survival. Using the 84 patients in the training set, the Cox scores were computed for each gene, and the principal components calculated on the genes with the highest Cox scores. The first of the principal components was then used to generate a regression model to predict the survival in the test set. Finally, for each probe set an importance score was calculated equal to its correlation with the supervised principal component predictor. This approach returned a list of 150 top ranked probe sets correlated with survival. Patients in the training set were split into tertiles based on the predictor (low, medium and high score) and patients in the test set were assigned to their predicted class, and Kaplan-Meier plots were generated for both training and test set. The differences in survival for both training and test set were statistically significant (Figure 1). Top ranked genes showing lower expression levels in patients with shorter survival include CDH1, LEF1 and AKAP12/Gravin. Top ranked genes showing higher expression levels in patients with shorter survival include IL23A, WT1 and PTHR2. Figure 2 shows survival of patients divided into tertiles of expression for the individual genes CDH1, LEF1 and WT1. It is probable that the genes identified in this study will become the first validated molecular markers for MDS prognosis. Multivariate analysis is currently being performed. Figure 1 Figure 1. Figure 2 Figure 2. Disclosure: No relevant conflicts of interest to declare.

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Meta-analysis based gene expression profiling reveals functional genes in ovarian cancer

Bioscience Reports ◽

10.1042/bsr20202911 ◽

2020 ◽

Vol 40 (11) ◽

Author(s):

Lin Zhao ◽

Yuhui Li ◽

Zhen Zhang ◽

Jing Zou ◽

Jianfu Li ◽

...

Keyword(s):

Gene Expression ◽

Ovarian Cancer ◽

Gene Expression Profiling ◽

Differentially Expressed Genes ◽

Expression Profiling ◽

Molecular Mechanisms ◽

Meta Analysis ◽

Differentially Expressed ◽

Functional Genes ◽

P Value

Abstract Background: Ovarian cancer causes high mortality rate worldwide, and despite numerous attempts, the outcome for patients with ovarian cancer are still not well improved. Microarray-based gene expressional analysis provides with valuable information for discriminating functional genes in ovarian cancer development and progression. However, due to the differences in experimental design, the results varied significantly across individual datasets. Methods: In the present study, the data of gene expression in ovarian cancer were downloaded from Gene Expression Omnibus (GEO) and 16 studies were included. A meta-analysis based gene expression analysis was performed to identify differentially expressed genes (DEGs). The most differentially expressed genes in our meta-analysis were selected for gene expression and gene function validation. Results: A total of 972 DEGs with P-value < 0.001 were identified in ovarian cancer, including 541 up-regulated genes and 431 down-regulated genes, among which 92 additional DEGs were found as gained DEGs. Top five up- and down-regulated genes were selected for the validation of gene expression profiling. Among these genes, up-regulated CD24 molecule (CD24), SRY (sex determining region Y)-box transcription factor 17 (SOX17), WFDC2, epithelial cell adhesion molecule (EPCAM), innate immunity activator (INAVA), and down-regulated aldehyde oxidase 1 (AOX1) were revealed to be with consistent expressional patterns in clinical patient samples of ovarian cancer. Gene functional analysis demonstrated that up-regulated WFDC2 and INAVA promoted ovarian cancer cell migration, WFDC2 enhanced cell proliferation, while down-regulated AOX1 was functional in inducing cell apoptosis of ovarian cancer. Conclusion: Our study shed light on the molecular mechanisms underlying the development of ovarian cancer, and facilitated the understanding of novel diagnostic and therapeutic targets in ovarian cancer.

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Gene Expression Profiling of B-Lymphocyte and Plasma Cell Populations from Waldenström’s Macroglobulinemia. Comparison with Expression Patterns of the Same Cell-Counterparts from Other B-Cell Neoplasms.

Blood ◽

10.1182/blood.v106.11.503.503 ◽

2005 ◽

Vol 106 (11) ◽

pp. 503-503

Author(s):

Norma C. Gutierrez ◽

Enrique M. Ocio ◽

Patricia Maiso ◽

Encarna Ferminan ◽

Manuel Delgado ◽

...

Keyword(s):

Gene Expression ◽

B Cell ◽

Gene Expression Profiling ◽

Expression Profiling ◽

Expression Patterns ◽

Lymphocytic Leukemia ◽

Differentially Expressed ◽

Multiparameter Flow Cytometry ◽

Cell Populations ◽

Expression Levels

Abstract The tumoral clone of Waldenström’s macroglobulinemia (WM) shows a wide morphological heterogeneity which ranges from B lymphocytes (BL) to plasma cells (PC), including a lymphoplasmacytic population that defines the disease. The differences between these cell compartments and their cell-counterpart in other lymphoproliferative disorders have not yet been sufficiently explored. We compared the gene expression profiling (GEP) of BL and PC from patients diagnosed with WM, with clonal BL and PC from patients with chronic lymphocytic leukemia (CLL) and multiple myeloma (MM) respectively. Bone marrow samples from 10 WM and 10 MM patients and peripheral blood from 10 CLL patients were used for the analysis. The isolation of the different cell populations was carried out by multiparameter flow cytometry sorting with the following monoclonal antibodies combination: Kappa or Lambda-FITC, CD10-PE, CD38-PerCP-Cy5.5, CD19-PE-Cy7, CD34-APC and CD45-APC-Cy7. Total RNA (100–500 ng) was amplified and labeled using the “GeneChip Two-Cycle cDNA Synthesis Kit” and hybridized to “Human Genome U133A” microarray (Affymetrix). Processing of genechip data was carried out using the Robust Multi-chip Average (RMA) and the Affymetrix Microarray Suite v.5 (MAS5) gene expression algorithms. Two-way hierarchical cluster analysis showed that GEP was able to classify PC from WM and PC from MM in two different groups. In a similar way, BL from WM and CLL were grouped in different clusters. The Significance Analysis of Microarrays (SAM) algorithm identified gene expression changes in a total of 163 genes (103 up and 60 down-regulated) when MW PC were compared with MM PC. Some of these genes were related to regulation of PC development: PAX5 was overexpressed and IRF4 infraexpressed in WM PC. PC from WM displayed high expression levels of MYB and DEK oncogenes while MM PC showed an elevated expression level of v-MAF oncogene. Regarding gene functional categories, “immune response” and “signal transduction” were the biological process more active in WM PC. Comparison between BL from WM and BL from CLL revealed that 31 genes were differentially expressed: IL4 receptor, LEF1 (WNT/b-catenin pathway), fibromodulin (modulator of TGF-b activity) and FGR oncogene (tyrosine kinase protein) showed very low expression levels in WM BL compared to CLL BL. In contrast, the growth factor IL6 was over-expressed in WM BL. These results indicate that both PC and BL from WM are genetically different from the MM and CLL cell-counterpart. The differentially expressed genes have important functions in the B-cell differentiation and oncogenesis. Supported by Spanish Myeloma Network (G03/136) and “Ministerio de Ciencia y Tecnología” (SAF04/06587) and “Junta de Castilla y León” grants (SA032/04)

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Functional Modules Analysis and Hub Gene Prognostic Values Evaluation Based on Co-Expression Network in Gastric Cancer

10.21203/rs.3.rs-600479/v1 ◽

2021 ◽

Author(s):

Yujia Liu ◽

Xiaoping Hu ◽

Zongfu Pan ◽

Yuchen Jiang ◽

Dandan Guo ◽

...

Keyword(s):

Gene Expression ◽

Gastric Cancer ◽

Regulatory Network ◽

Molecular Mechanisms ◽

Transcriptional Regulatory Network ◽

Receptor Interaction ◽

Ppi Network ◽

Hub Gene ◽

Transcriptional Regulatory ◽

Key Nodes

Abstract Background: Gastric cancer is one of the most common fatal disease worldwide, but its mechanism and therapeutic targets are still unclear. In this study, we have analyzed the differences in gene modules and key pathways in gastric cancer patients, then elaborated the mechanism and effective treatment of gastric cancer with microarray data from the gene expression omnibus(GEO) database.Methods: GEO2R tools were used to identify differential expression genes (DEGs), String database was employed to construct a protein-protein interaction (PPI) network. We imported the PPI network into the Cytoscape software to find key nodes, and employed statistical approach of MCODE to cluster genes. After that the ClueGO was used to enrich and annotate the pathways of key modules. To investigate the relationship between the upstream regulator and hub genes, the transcriptional regulatory network was built based on TFCAT database.Results: 63 characteristic genes of gastric cancer are involved in regulation of ECM-receptor interaction, focal adhesion and protein digestion and absorption. SPARC, FN1, BGN and COL1A2 are four key nodes relating to tumor proliferation and metastasis, and their expression were strongly associated with poor survival (p<0.05). 13 transcription factors including PRRX1 have remarkable changes in gastric cancer, which may play a key role in hub gene regulation.Conclusions: The present study defined the gene expression characteristics and transcriptional regulatory network that promote our understanding of the molecular mechanisms underlying the development of gastric cancer, and might provide new insights into targeted therapy and prognostic markers for the personalized treatment of gastric cancer.

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A Sparse and Low-Rank Regression Model for Identifying the Relationships Between DNA Methylation and Gene Expression Levels in Gastric Cancer and the Prediction of Prognosis

Genes ◽

10.3390/genes12060854 ◽

2021 ◽

Vol 12 (6) ◽

pp. 854

Author(s):

Yishu Wang ◽

Lingyun Xu ◽

Dongmei Ai

Keyword(s):

Gene Expression ◽

Gastric Cancer ◽

Dna Methylation ◽

Regression Model ◽

The Cancer Genome Atlas ◽

Low Rank ◽

Expression Levels ◽

Rank Regression ◽

Prediction Of Prognosis ◽

Gene Expression Levels

DNA methylation is an important regulator of gene expression that can influence tumor heterogeneity and shows weak and varying expression levels among different genes. Gastric cancer (GC) is a highly heterogeneous cancer of the digestive system with a high mortality rate worldwide. The heterogeneous subtypes of GC lead to different prognoses. In this study, we explored the relationships between DNA methylation and gene expression levels by introducing a sparse low-rank regression model based on a GC dataset with 375 tumor samples and 32 normal samples from The Cancer Genome Atlas database. Differences in the DNA methylation levels and sites were found to be associated with differences in the expressed genes related to GC development. Overall, 29 methylation-driven genes were found to be related to the GC subtypes, and in the prognostic model, we explored five prognoses related to the methylation sites. Finally, based on a low-rank matrix, seven subgroups were identified with different methylation statuses. These specific classifications based on DNA methylation levels may help to account for heterogeneity and aid in personalized treatments.

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High heterogeneity undermines generalization of differential expression results in RNA-Seq analysis

Human Genomics ◽

10.1186/s40246-021-00308-5 ◽

2021 ◽

Vol 15 (1) ◽

Author(s):

Weitong Cui ◽

Huaru Xue ◽

Lei Wei ◽

Jinghua Jin ◽

Xuewen Tian ◽

...

Keyword(s):

Gene Expression ◽

Differential Expression ◽

Small Sample ◽

Differentially Expressed ◽

Cancer Type ◽

Rna Seq ◽

Sample Sizes ◽

Large Sample ◽

Expression Levels ◽

Gene Expression Levels

Abstract Background RNA sequencing (RNA-Seq) has been widely applied in oncology for monitoring transcriptome changes. However, the emerging problem that high variation of gene expression levels caused by tumor heterogeneity may affect the reproducibility of differential expression (DE) results has rarely been studied. Here, we investigated the reproducibility of DE results for any given number of biological replicates between 3 and 24 and explored why a great many differentially expressed genes (DEGs) were not reproducible. Results Our findings demonstrate that poor reproducibility of DE results exists not only for small sample sizes, but also for relatively large sample sizes. Quite a few of the DEGs detected are specific to the samples in use, rather than genuinely differentially expressed under different conditions. Poor reproducibility of DE results is mainly caused by high variation of gene expression levels for the same gene in different samples. Even though biological variation may account for much of the high variation of gene expression levels, the effect of outlier count data also needs to be treated seriously, as outlier data severely interfere with DE analysis. Conclusions High heterogeneity exists not only in tumor tissue samples of each cancer type studied, but also in normal samples. High heterogeneity leads to poor reproducibility of DEGs, undermining generalization of differential expression results. Therefore, it is necessary to use large sample sizes (at least 10 if possible) in RNA-Seq experimental designs to reduce the impact of biological variability and DE results should be interpreted cautiously unless soundly validated.

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Endurance Training Regulates Expression of Some Angiogenesis-Related Genes in Cardiac Tissue of Experimentally Induced Diabetic Rats

Biomolecules ◽

10.3390/biom11040498 ◽

2021 ◽

Vol 11 (4) ◽

pp. 498

Author(s):

Mojdeh Khajehlandi ◽

Lotfali Bolboli ◽

Marefat Siahkuhian ◽

Mohammad Rami ◽

Mohammadreza Tabandeh ◽

...

Keyword(s):

Gene Expression ◽

Endurance Training ◽

Molecular Mechanisms ◽

Cardiac Tissue ◽

Critical Role ◽

Diabetic Rats ◽

Moderate Intensity ◽

Pcr Analysis ◽

Cardiac Tissues ◽

The Impact

Exercise can ameliorate cardiovascular dysfunctions in the diabetes condition, but its precise molecular mechanisms have not been entirely understood. The aim of the present study was to determine the impact of endurance training on expression of angiogenesis-related genes in cardiac tissue of diabetic rats. Thirty adults male Wistar rats were randomly divided into three groups (N = 10) including diabetic training (DT), sedentary diabetes (SD), and sedentary healthy (SH), in which diabetes was induced by a single dose of streptozotocin (50 mg/kg). Endurance training (ET) with moderate-intensity was performed on a motorized treadmill for six weeks. Training duration and treadmill speed were increased during five weeks, but they were kept constant at the final week, and slope was zero at all stages. Real-time polymerase chain reaction (RT-PCR) analysis was used to measure the expression of myocyte enhancer factor-2C (MEF2C), histone deacetylase-4 (HDAC4) and Calmodulin-dependent protein kinase II (CaMKII) in cardiac tissues of the rats. Our results demonstrated that six weeks of ET increased gene expression of MEF2C significantly (p < 0.05), and caused a significant reduction in HDAC4 and CaMKII gene expression in the DT rats compared to the SD rats (p < 0.05). We concluded that moderate-intensity ET could play a critical role in ameliorating cardiovascular dysfunction in a diabetes condition by regulating the expression of some angiogenesis-related genes in cardiac tissues.

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