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

Kegg Pathway ◽

Gene Expression Omnibus ◽

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.

Bioinformatics analysis for the identification of differentially expressed genes and related signaling pathways in H. pylori-CagA transfected gastric cancer cells

PeerJ ◽

10.7717/peerj.11203 ◽

2021 ◽

Vol 9 ◽

pp. e11203

Author(s):

Dingyu Chen ◽

Chao Li ◽

Yan Zhao ◽

Jianjiang Zhou ◽

Qinrong Wang ◽

...

Keyword(s):

Gastric Cancer ◽

Helicobacter Pylori ◽

Pathway Analysis ◽

Kegg Pathway ◽

Ppi Network ◽

Ags Cells ◽

Kegg Pathway Analysis ◽

Key Genes

Aim Helicobacter pylori cytotoxin-associated protein A (CagA) is an important virulence factor known to induce gastric cancer development. However, the cause and the underlying molecular events of CagA induction remain unclear. Here, we applied integrated bioinformatics to identify the key genes involved in the process of CagA-induced gastric epithelial cell inflammation and can ceration to comprehend the potential molecular mechanisms involved. Materials and Methods AGS cells were transected with pcDNA3.1 and pcDNA3.1::CagA for 24 h. The transfected cells were subjected to transcriptome sequencing to obtain the expressed genes. Differentially expressed genes (DEG) with adjusted P value < 0.05, — logFC —> 2 were screened, and the R package was applied for gene ontology (GO) enrichment and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. The differential gene protein–protein interaction (PPI) network was constructed using the STRING Cytoscape application, which conducted visual analysis to create the key function networks and identify the key genes. Next, the Kaplan–Meier plotter survival analysis tool was employed to analyze the survival of the key genes derived from the PPI network. Further analysis of the key gene expressions in gastric cancer and normal tissues were performed based on The Cancer Genome Atlas (TCGA) database and RT-qPCR verification. Results After transfection of AGS cells, the cell morphology changes in a hummingbird shape and causes the level of CagA phosphorylation to increase. Transcriptomics identified 6882 DEG, of which 4052 were upregulated and 2830 were downregulated, among which q-value < 0.05, FC > 2, and FC under the condition of ≤2. Accordingly, 1062 DEG were screened, of which 594 were upregulated and 468 were downregulated. The DEG participated in a total of 151 biological processes, 56 cell components, and 40 molecular functions. The KEGG pathway analysis revealed that the DEG were involved in 21 pathways. The PPI network analysis revealed three highly interconnected clusters. In addition, 30 DEG with the highest degree were analyzed in the TCGA database. As a result, 12 DEG were found to be highly expressed in gastric cancer, while seven DEG were related to the poor prognosis of gastric cancer. RT-qPCR verification results showed that Helicobacter pylori CagA caused up-regulation of BPTF, caspase3, CDH1, CTNNB1, and POLR2A expression. Conclusion The current comprehensive analysis provides new insights for exploring the effect of CagA in human gastric cancer, which could help us understand the molecular mechanism underlying the occurrence and development of gastric cancer caused by Helicobacter pylori.

The Screening of Critical Related Genes in Celiac Disease Based on Intraepithelial Lymphocytes Investigation: A Bioinformatics Analysis

10.31661/gmj.v8i0.1407 ◽

2019 ◽

Vol 8 ◽

pp. 1407

Author(s):

Mohammad Rostami-Nejad ◽

Reza Vafaee ◽

Mohammad Javad Ehsani-Ardakani ◽

Nika Aghamohammadi ◽

Aliasghar Keramatinia ◽

...

Keyword(s):

Celiac Disease ◽

Bioinformatics Analysis ◽

Intraepithelial Lymphocytes ◽

Gene Expression Omnibus ◽

T Cell Differentiation ◽

Protein Protein Interaction ◽

Critical Nodes ◽

Geo Database

Background: Celiac disease (CD) is an immunological intestinal disorder, which is characterized by response to gluten. In addition to the environmental factors and dysbiosis of the gut microbiota, genetic susceptibility has an important role in the pathogenesis of this multifactorial disorder. Therefore, this study aims to present the crucial involved genes in CD pathogenesis. Materials and Methods: In this bioinformatics analysis study, significant differentially expressed genes of intraepithelial lymphocytes (IELs) samples of celiac patients versus normal patients from Gene Expression Omnibus (GEO) database were screened via the protein-protein interaction (PPI) network. The critical nodes based on degree values, betweenness centrality, and fold changes were determined and enriched by ClueGO to find relative biological terms. Results: According to the network analysis, five central nodes including IL2, PIK3CA, PRDM10, AKT1, and SRC and eight significant differentially expressed genes (DEGs) were determined as the critical genes related to CD. Also, CD4+, CD25+, alpha-beta regulatory T cell differentiation are identified as prominent biological terms in the celiac disease patients. Conclusion: There is a possible biomarker panel related to CD that can be used as a therapeutic or diagnostic tool to manage the disease. [GMJ.2019;8:e1407]

FCN3 Is a Prognostic Biomarker Correlated with Immune Response in Liver Cancer

10.21203/rs.3.rs-90864/v1 ◽

2020 ◽

Author(s):

Zhongxiao Lu ◽

Jian Wu ◽

Yi-ming Li ◽

Wen-xiang Chen ◽

Qiang-feng Yu ◽

...

Keyword(s):

Gene Expression ◽

Immune Response ◽

Liver Cancer ◽

Kegg Pathway ◽

Ppi Network ◽

Hub Genes ◽

R Software ◽

Hub Gene

Abstract AimLiver cancer is a common malignant tumor whose molecular pathogenesis remains unclear. This study attempts to identify key genes related to liver cancer by bioinformatics analysis and analyze their biological functions.MethodsThe gene expression data of the microarray were downloaded from the Gene Expression Omnibus(GEO) database. The differentially expressed genes (DEGs) were then identified by the R software package “limma” and were subjected to gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses using DAVID. The protein-protein interaction (PPI) network was constructed via String, and the results were visualized in Cytoscape. Modules and hub genes were identified using the MCODE plugin, while the expression of hub genes and its effects were analyzed by GEPIA2. Additionally, the co-expression of the hub gene was explored in String, while the GO results were visualized using the R software. Finally, the targets of the hub gene were predicted through an online website. ResultsIn total, 43 differentially expressed genes were obtained. The GO analysis was mainly concentrated in the redox process and nuclear mitosis, while the KEGG pathway analysis was mainly enriched in retinol metabolism and the cell cycle. Moreover, four hub genes were identified in the PPI network, however, the Kaplan-Meier risk curve showed that only ECT2 and FCN3 affected the survival of liver cancer. ECT2 was found to be high expressed in liver cancer, carrying out signal transduction and targeting hsa-miR-27a-3p. FCN3 was observed to be lowly expressed in liver cancer and related to the immune response, targeting hsa-miR132-5p.ConclusionThe obtained findings suggest that two genes are significantly related to the prognosis of liver cancer, and the analysis of their biological function provided novel insight into the pathogenesis of liver cancer. Furthermore, FCN3 may serve as a promising biomarker for patients with liver cancer.

Combined Analysis of Expression Profiles in a Mouse Model and Patients Identified BHMT2 as a New Regulator of Lipid Metabolism in Metabolic-Associated Fatty Liver Disease

Frontiers in Cell and Developmental Biology ◽

10.3389/fcell.2021.741710 ◽

2021 ◽

Vol 9 ◽

Author(s):

Yongqiang Ma ◽

Zhi Tan ◽

Qiang Li ◽

Wenling Fan ◽

Guangshun Chen ◽

...

Keyword(s):

Gene Expression ◽

Lipid Metabolism ◽

Liver Disease ◽

Fatty Liver ◽

Fatty Liver Disease ◽

Target Genes ◽

Overlapping Genes ◽

Geo Database

Metabolic associated fatty liver disease (MAFLD) is associated with obesity, type 2 diabetes mellitus, and other metabolic syndromes. Farnesoid X receptor (FXR, NR1H4) plays a prominent role in hepatic lipid metabolism. This study combined the expression of liver genes in FXR knockout (KO) mice and MAFLD patients to identify new pathogenic pathways for MAFLD based on genome-wide transcriptional profiling. In addition, the roles of new target genes in the MAFLD pathogenic pathway were also explored. Two groups of differentially expressed genes were obtained from FXR-KO mice and MAFLD patients by transcriptional analysis of liver tissue samples. The similarities and differences between the two groups of differentially expressed genes were analyzed to identify novel pathogenic pathways and target genes. After the integration analysis of differentially expressed genes, we identified 134 overlapping genes, many of which have been reported to play an important role in lipid metabolism. Our unique analysis method of comparing differential gene expression between FXR-KO mice and patients with MAFLD is useful to identify target genes and pathways that may be strongly implicated in the pathogenesis of MAFLD. The overlapping genes with high specificity were screened using the Gene Expression Omnibus (GEO) database. Through comparison and analysis with the GEO database, we determined that BHMT2 and PKLR could be highly correlated with MAFLD. Clinical data analysis and RNA interference testing in vitro confirmed that BHMT2 may a new regulator of lipid metabolism in MAFLD pathogenesis. These results may provide new ideas for understanding the pathogenesis of MAFLD and thus provide new targets for the treatment of MAFLD.

Identification of key methylation differentially expressed genes in posterior fossa ependymoma based on epigenomic and transcriptome analysis

Journal of Translational Medicine ◽

10.1186/s12967-021-02834-1 ◽

2021 ◽

Vol 19 (1) ◽

Author(s):

Guanyi Wang ◽

Yibin Jia ◽

Yuqin Ye ◽

Enming Kang ◽

Huijun Chen ◽

...

Keyword(s):

Gene Expression ◽

Posterior Fossa ◽

Poor Prognosis ◽

Cpg Island ◽

Adaptive Immune Response ◽

R Package ◽

Gene Expression Omnibus ◽

Hub Genes

Abstract Background Posterior fossa ependymoma (EPN-PF) can be classified into Group A posterior fossa ependymoma (EPN-PFA) and Group B posterior fossa ependymoma (EPN-PFB) according to DNA CpG island methylation profile status and gene expression. EPN-PFA usually occurs in children younger than 5 years and has a poor prognosis. Methods Using epigenome and transcriptome microarray data, a multi-component weighted gene co-expression network analysis (WGCNA) was used to systematically identify the hub genes of EPN-PF. We downloaded two microarray datasets (GSE66354 and GSE114523) from the Gene Expression Omnibus (GEO) database. The Limma R package was used to identify differentially expressed genes (DEGs), and ChAMP R was used to analyze the differential methylation genes (DMGs) between EPN-PFA and EPN-PFB. GO and KEGG enrichment analyses were performed using the Metascape database. Results GO analysis showed that enriched genes were significantly enriched in the extracellular matrix organization, adaptive immune response, membrane raft, focal adhesion, NF-kappa B pathway, and axon guidance, as suggested by KEGG analysis. Through WGCNA, we found that MEblue had a significant correlation with EPN-PF (R = 0.69, P = 1 × 10–08) and selected the 180 hub genes in the blue module. By comparing the DEGs, DMGs, and hub genes in the co-expression network, we identified five hypermethylated, lower expressed genes in EPN-PFA (ATP4B, CCDC151, DMKN, SCN4B, and TUBA4B), and three of them were confirmed by IHC. Conclusion ssGSEA and GSVA analysis indicated that these five hub genes could lead to poor prognosis by inducing hypoxia, PI3K-Akt-mTOR, and TNFα-NFKB pathways. Further study of these dysmethylated hub genes in EPN-PF and the pathways they participate in may provides new ideas for EPN-PF treatment.

Identification of key biomarkers for thyroid cancer by integrative gene expression profiles

Experimental Biology and Medicine ◽

10.1177/15353702211008809 ◽

2021 ◽

pp. 153537022110088

Author(s):

Jinyi Tian ◽

Yizhou Bai ◽

Anyang Liu ◽

Bin Luo

Keyword(s):

Gene Expression ◽

Thyroid Cancer ◽

Molecular Mechanisms ◽

Expression Profiles ◽

Thyroid Tissue ◽

Gene Signature ◽

Gene Expression Omnibus ◽

Tissue Samples

Thyroid cancer is a frequently diagnosed malignancy and the incidence has been increased rapidly in recent years. Despite the favorable prognosis of most thyroid cancer patients, advanced patients with metastasis and recurrence still have poor prognosis. Therefore, the molecular mechanisms of progression and targeted biomarkers were investigated for developing effective targets for treating thyroid cancer. Eight chip datasets from the gene expression omnibus database were selected and the inSilicoDb and inSilicoMerging R/Bioconductor packages were used to integrate and normalize them across platforms. After merging the eight gene expression omnibus datasets, we obtained one dataset that contained the expression profiles of 319 samples (188 tumor samples plus 131 normal thyroid tissue samples). After screening, we identified 594 significantly differentially expressed genes (277 up-regulated genes plus 317 down-regulated genes) between the tumor and normal tissue samples. The differentially expressed genes exhibited enrichment in multiple signaling pathways, such as p53 signaling. By building a protein–protein interaction network and module analysis, we confirmed seven hub genes, and they were all differentially expressed at all the clinical stages of thyroid cancer. A diagnostic seven-gene signature was established using a logistic regression model with the area under the receiver operating characteristic curve (AUC) of 0.967. Seven robust candidate biomarkers predictive of thyroid cancer were identified, and the obtained seven-gene signature may serve as a useful marker for thyroid cancer diagnosis and prognosis.

INTEGRATED BIOINFORMATICS ANALYSIS OF DIFFERENTIALLY EXPRESSED GENES (DEGS) OF ALZHEIMER'S DISEASE (AD) DATASETS FROM GENE EXPRESSION OMNIBUS (GEO)

Alzheimer s & Dementia ◽

10.1016/j.jalz.2017.06.1270 ◽

2017 ◽

Vol 13 (7) ◽

pp. P953 ◽

Cited By ~ 3

Author(s):

Ashwani Kumar ◽

Ankush Bansal

Keyword(s):

Gene Expression ◽

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Bioinformatics Analysis ◽

Gene Expression Omnibus ◽

Differentially Expressed

High SLC7A11 Expression Is Correlated With Poor Prognosis and Associated With Ferroptosis in Ovarian Cancer

10.21203/rs.3.rs-233820/v1 ◽

2021 ◽

Author(s):

Mengqi Deng ◽

Yanqin Zhang ◽

Xiangyu Chang ◽

Di Wu ◽

Chunyu Xu ◽

...

Keyword(s):

Gene Expression ◽

Ovarian Cancer ◽

Protein Expression ◽

Poor Prognosis ◽

Bioinformatic Analysis ◽

Gene Expression Omnibus ◽

Normal Tissues ◽

And Control

Abstract The current treatments of ovarian cancer (OC) do not yield satisfactory outcomes. Hence, it is necessary to find new treatment targets for OC. In this study, a comprehensive bioinformatic analysis was conducted to identify differentially expressed genes (DEGs) between OC and control tissues. Five datasets were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were screened by comparing gene expression between OC and control tissues. Module analysis of DEGs was performed on the STRING database and GEPIA. Kaplan Meier plotter and GEPIA database analysis the overall survival. Finally, SLC7A11 was found to be is the hubgene. And we confirm that the protein expression of SLC7A11 was increased in OC tissues. Analysis of a variety of tumor gene databases showed that SLC7A11 gene regulated the processes of OC. The low mutation rate of the gene (which were of amplified type) and high mRNA expression were associated with poor prognosis of OC patients.Using erastin-treated ovarian cancer (OC) cell lines, we examined the relationship between ferroptosis and OC. Results showed that OC tissues contained higher malondialdehyde (MDA) levels than normal tissues. Unlike normal ovarian epithelial cells which are not sensitive to erastin, the OC cell line, ES-2 is very sensitive to erastin. Here, we found that ferrostatin-1 treatment increased levels of reactive oxygen species (ROS), malondialdehyde, and SLC7A11 protein expression. These results provide an important theoretical basis for further studies into the role of SLC7A11, the effective biomarker and potential drug target, in the occurrence and development of OC.

Characteristics of Tumor Immune Gene and Immune Cell Infiltration during Keloid Formation

10.21203/rs.3.rs-50554/v1 ◽

2020 ◽

Author(s):

Hao Liu ◽

Mengjie Shan ◽

Youbin Wang ◽

Kexin Song ◽

Shu Liu ◽

...

Keyword(s):

Gene Expression ◽

Immune Cell ◽

Kegg Pathway ◽

Cytokine Receptor ◽

Immune Gene ◽

Immune Gene Expression ◽

M1 Macrophage ◽

Keloid Formation

Abstract Background: Keloids are benign fibroproliferative skin tumors that can cause disfigurement and disability. Although current research has sought to examine keloids from the perspectives of genetics, inflammation, immunity, and tumorigenesis, their pathological mechanisms remain unclear. Methods: In this study, we used three datasets of tumor immune gene expression profiling from the normal skin tissue of keloid patients (N group), inflammation tissue of keloid patients (I group), and keloid tissue of keloid patients (K group) to describe the occurrence and characteristics of keloid development. Tumor immune-related genes were analyzed, and the differentially expressed genes (DEGs) between the three groups were compared. Gene Ontology (GO) categories and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were carried out to determine the main functions of the differentially expressed genes and keloid-related pathways. Results: We identified several genes that may play an important role in keloid development. These genes are CCR1, SELL, CCR7, CD40LG, CD69, CXCL8, ITGAM, ITGAX, CD86, and CXCL9. GO analysis revealed that there were variations in biological processes (BP) between I group and N group, including regulation of lymphocyte activation and T-cell activation. Similar variations were also found between I group and K group, which may play an important role in keloid initiation and formation. Variations in molecular function (MF) were markedly enriched in cytokine receptor binding and receptor ligand activity. Analysis of the KEGG pathway between I group and N group revealed that DEGs were primarily enriched in cytokine−cytokine receptor interaction and viral protein interaction with cytokine and cytokine receptor. We identified a higher proportion of M2 macrophages in N group than in I group, although the difference was not obvious. M1 macrophage production differed significantly between I group and K group. The proportions of CD8+T cells varied significantly between N group and K group. We traced multiple tumor immune-related hub genes from keloid formation and analyzed immune cell subsets in keloid development. Possible molecular mechanisms were described in this study using bioinformatics. Conclusions: These results provide another possibility to elucidate keloid pathogenesis and therapeutic targets in terms of tumor immune gene expression.

Analysis of pathways and networks influencing the differential expression of genes in coronary artery disease

Archives of Biological Sciences ◽

10.2298/abs1403983l ◽

2014 ◽

Vol 66 (3) ◽

pp. 983-988 ◽

Cited By ~ 1

Author(s):

Hui Li ◽

Xiaolan Zhong ◽

Chaomin Li ◽

Lijing Peng ◽

Wei Liu ◽

...

Keyword(s):

Gene Expression ◽

Coronary Artery Disease ◽

Coronary Artery ◽

Molecular Mechanisms ◽

Gene Expression Omnibus ◽

Hub Genes ◽

Network Analyses ◽

Artery Disease

Coronary artery disease (CAD) is the leading cause of death worldwide. Microarray analysis is a practical approach to study gene transcription changes that may reflect signatures that underlie the pathogenesis of CAD. Using gene expression profile data from the Gene Expression Omnibus database, we identified differentially expressed genes that can contribute to the pathology of CAD. Further pathway and network analyses were also implemented to identify pathways and hub genes related to the disease. We observed 466 downregulated and 560 upregulated genes. The ribosome pathway was the most significantly over-represented pathway with differentially expressed genes. Over 35% of the genes in this pathway were downregulated. Hub genes in the network, such as IL7R, FYN, CALM1 ESR1 and PLCG1, may play crucial roles in the pathogenesis of CAD. Our results facilitate the identification of molecular mechanisms that underlie CAD.