Transcriptome Analysis of Bovine Ovarian Follicles at Predeviation and Onset of Deviation Stages of a Follicular Wave

For two libraries (PDF1 and ODF1) using Illumina sequencing 44,082,301 and 43,708,132 clean reads were obtained, respectively. After being mapped to the bovine RefSeq database, 15,533 genes were identified to be expressed in both types of follicles (cut-off RPKM > 0.5), of which 719 were highly expressed in bovine follicles (cut-off RPKM > 100). Furthermore, 83 genes were identified as being differentially expressed in ODF1 versus PDF1, where 42 genes were upregulated and 41 genes were downregulated. KEGG pathway analysis revealed two upregulated genes in ODF1 versus PDF1, CYP11A1, and CYP19A1, which are important genes in the steroid hormone biosynthesis pathway. This study represents the first investigation of transcriptome of bovine follicles at predeviation and onset of deviation stages and provides a foundation for future investigation of the regulatory mechanisms involved in follicular development in cattle.

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Unraveling proteome changes and potential regulatory proteins of bovine follicular Granulosa cells by mass spectrometry and multi-omics analysis

Proteome Science ◽

10.1186/s12953-019-0152-1 ◽

2019 ◽

Vol 17 (1) ◽

Cited By ~ 1

Author(s):

Shuning Hou ◽

Qingling Hao ◽

Zhiwei Zhu ◽

Dongmei Xu ◽

Wenzhong Liu ◽

...

Keyword(s):

Mass Spectrometry ◽

Data Analysis ◽

Pathway Analysis ◽

Kegg Pathway ◽

Follicular Development ◽

Regulatory Proteins ◽

Differentially Expressed ◽

Omics Data ◽

Kegg Pathway Analysis ◽

Omics Data Analysis

Abstract Background In previous study, we performed next-gene sequencing to investigate the differentially expressed transcripts of bovine follicular granulosa cells (GCs) at dominant follicle (DF) and subordinate follicle (SF) stages during first follicular wave. Present study is designed to further identify the key regulatory proteins and signaling pathways associated with follicular development using label-free liquid chromatography-tandem mass spectrometry (LC-MS/MS) and multi-omics data analysis approach. Methods DF and SF from three cattle were collected by daily ultrasonography. The GCs were isolated from each follicle, total proteins were digested by trypsin, and then proteomic analyzed via LC-MS/MS, respectively. Proteins identified were retrieved from Uniprot-COW fasta database, and differentially expressed proteins were used to functional enrichment and KEGG pathway analysis. Proteome data and transcriptome data obtained from previous studies were integrated. Results Total 3409 proteins were identified from 30,321 peptides (FDR ≤0.01) obtained from LC-MS/MS analysis and 259 of them were found to be differentially expressed at different stage of follicular development (fold Change > 2, P < 0.05). KEGG pathway analysis of proteome data revealed important signaling pathways associated with follicular development, multi-omics data analysis results showed 13 proteins were identified as being differentially expressed in DF versus SF. Conclusions This study represents the first investigation of transcriptome and proteome of bovine follicles and offers essential information for future investigation of DF and SF in cattle. It also will enrich the theory of animal follicular development.

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Gene-expression profile and postpartum transition of bovine endometrial side population cells†

Biology of Reproduction ◽

10.1093/biolre/ioab004 ◽

2021 ◽

Author(s):

Ryoki Tatebayashi ◽

Sho Nakamura ◽

Shiori Minabe ◽

Tadashi Furusawa ◽

Ryoya Abe ◽

...

Keyword(s):

Gene Expression ◽

Progenitor Cells ◽

Pathway Analysis ◽

Kegg Pathway ◽

Side Population ◽

Gene Set Enrichment Analysis ◽

Stem Cell Marker ◽

Endometrial Cells ◽

Kegg Pathway Analysis ◽

Upregulated Genes

Abstract The mechanism of bovine endometrial regeneration after parturition remains unclear. Here, we hypothesized that bovine endometrial stem/progenitor cells participate in the postpartum regeneration of the endometrium. Flow cytometry analysis identified the presence of side population (SP) cells among endometrial stromal cells. Endometrial SP cells were shown to differentiate into osteoblasts and adipocytes. RNA-seq data showed that the gene expression pattern was different between bovine endometrial SP cells and main population cells. Gene Set Enrichment Analysis identified the enrichment of stemness genes in SP cells. Significantly (false discovery rate < 0.01) upregulated genes in SP cells contained several stem cell marker genes. Gene ontology (GO) analysis of the upregulated genes in SP cells showed enrichment of terms related to RNA metabolic process and transcription. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis of upregulated genes in SP cells revealed enrichment of signaling pathways associated with maintenance and differentiation of stem/progenitor cells. The terms involved in TCA cycles were enriched in GO and KEGG pathway analysis of downregulated genes in SP cells. These results support the assumption that bovine endometrial SP cells exhibit characteristics of somatic stem/progenitor cells. The ratio of SP cells to endometrial cells was lowest on days 9–11 after parturition, which gradually increased thereafter. SP cells were shown to differentiate into epithelial cells. Collectively, these results suggest that bovine endometrial SP cells were temporarily reduced immediately after calving possibly due to their differentiation to provide new endometrial cells.

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Identification of mRNAs and lncRNAs Involved in the Regulation of Follicle Development in Goat

Frontiers in Genetics ◽

10.3389/fgene.2020.589076 ◽

2020 ◽

Vol 11 ◽

Author(s):

Zhifeng Zhao ◽

Xian Zou ◽

Tingting Lu ◽

Ming Deng ◽

Yaokun Li ◽

...

Keyword(s):

Genetic Basis ◽

Molecular Mechanisms ◽

Kegg Pathway ◽

Follicular Development ◽

Regulatory Mechanisms ◽

Follicle Development ◽

Rna Seq ◽

Follicular Maturation ◽

Kegg Pathway Analysis ◽

Non Coding Rnas

Follicular development and maturation has a significant impact on goat reproductive performance, and it is therefore important to understand the molecular basis of this process. The importance of long non-coding RNAs (lncRNAs) in mammalian reproduction has been established, but little is known about the roles of lncRNAs in different follicular stages, especially in goats. In this study, RNA sequencing (RNA-seq) of large follicles (>10 mm) and small follicles (<3 mm) of Chuanzhong black goats was performed to investigate the regulatory mechanisms of lncRNAs and mRNAs in follicular development and maturation. A total of 8 differentially expressed lncRNAs (DElncRNAs) and 1,799 DEmRNAs were identified, and the majority of these were upregulated in small follicles. MRO, TC2N, CDO1, and NTRK1 were potentially associated with follicular maturation. KEGG pathway analysis showed that the DEmRNAs involved in ovarian steroidogenesis (BMP6, CYP11A1, CYP19A1, 3BHSD, STAR, LHCGR, and CYP51A1) and cAMP signaling play roles in regulating follicular maturation and developmental inhibition respectively. Five target pairs of DElncRNA-DEmRNA, namely, ENSCHIT00000001255-OTX2, ENSCHIT00000006005-PEG3, ENSCHIT00000009455-PIWIL3, ENSCHIT00000007977-POMP, and ENSCHIT00000000834-ACTR3 in co-expression analysis provide a clue in follicular development and maturation of lncRNA-mRNA interaction. Our findings provide a valuable resource for lncRNA studies, and could potentially provide a deeper understanding of the genetic basis and molecular mechanisms of goat follicular development and maturation.

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POS0399 TMT-BASED QUANTITATIVE PROTEOMICS ANALYSIS OF SYNOVIAL FLUID-DERIVED EXOSOMES IN RHEUMATOID ARTHRITIS, AXIAL SPONDYLOARTHRITIS, GOUT AND OSTEOARTHRITIS

Annals of the Rheumatic Diseases ◽

10.1136/annrheumdis-2021-eular.3607 ◽

2021 ◽

Vol 80 (Suppl 1) ◽

pp. 428.3-429

Author(s):

Y. Liu ◽

Y. Huang ◽

Q. Huang ◽

Z. Huang ◽

Z. Li ◽

...

Keyword(s):

Rheumatoid Arthritis ◽

Cluster Analysis ◽

Synovial Fluid ◽

Pathway Analysis ◽

Quantitative Proteomics ◽

Axial Spondyloarthritis ◽

Kegg Pathway ◽

Hierarchical Cluster ◽

Kegg Pathway Analysis ◽

Differential Proteins

Background:The pathogeneses of the joint diseases rheumatoid arthritis (RA), axial spondyloarthritis (axSpA), gout, and osteoarthritis (OA) are still not fully elucidated. Exosomes in synovial fluid (SF) has a critical role in the pathogenesis of arthritis. None of study has compared the proteomics of SF-derived exosomes in RA, axSpA, gout and OA.Objectives:To compare the proteomics of SF-derived exosomes in RA, axSpA, gout and OA based on tandem mass tags (TMT) labeled quantitative proteomics technique.Methods:SF-derived exosomes was isolated from RA, axSpA, gout and OA patients by the Exoquick kit combined ultracentrifugation method. TMT labeled quantitative proteomics technique was used to compare the proteomics of SF-derived exosomes. Volcano plot, hierarchical cluster, Gene Ontologies (GO), Encyclopedia of Genes and Genomes (KEGG) pathway analysis were conducted.Results:A total of 1678 credible proteins were detected. With the cut off criteria of |log2 (fold-change)| ≥1.2 and p-value <0.05, 267 (140 up-regulated and 127 down-regulated)differential proteins were found in OA vs gout, 291 (179 and 112) in axSpA vs OA, 515 (109 and 406) in RA vs axSpA, 298 (191 and 107) in axSpA vs gout, 462 (160 and 302) in RA vs gout, 536 (170 and 366) in RA vs OA. GO analysis showed that the biological progress of differential proteins were mainly enriched in the “immune response”. Regarding the molecular function, the differential proteins mainly mediated “antigen binding”. GO analysis of the cellular components indicated that most proteins were annotated as “extracellular exosomes”. KEGG pathway analysis demonstrated differential proteins were significantly enriched in “complement and coagulation cascades”. The hierarchical cluster analysis of the differential proteins in the four groups showed that Lysozyme C and Keratin were more abundant in gout, Hemoglobin and Actin-related protein 2/3 complex subunit 3 in OA, Sodium/potassium-transporting ATPase subunit alpha-1 and Immunoglobulin heavy constant delta in axSpA, Pregnancy zone protein and Stromelysin-1 in RA.Conclusion:The protein profiles of SF-derived exosomes in RA, axSpA, gout and OA patients were different. The differential proteins were the potential biomarkers of RA, axSpA, gout and OA.References:[1]Cretu D, Diamandis E P, Chandran V. Delineating the synovial fluid proteome: recent advancements and ongoing challenges in biomarker research.[J]. Critical reviews in clinical laboratory sciences, 2013,50(2):51-63.[2]McArdle A J, Menikou S. What is proteomics?[J]. Archives of disease in childhood. Education and practice edition, 2020.Figure 1.The hierarchical cluster analysis of differential proteins in axSpA, OA, Gout and RA.Disclosure of Interests:None declared

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Identification of Pathways and Key Genes in Venous Remodeling After Arteriovenous Fistula by Bioinformatics Analysis

Frontiers in Physiology ◽

10.3389/fphys.2020.565240 ◽

2020 ◽

Vol 11 ◽

Author(s):

Kong Jie ◽

Wang Feng ◽

Zhao Boxiang ◽

Gong Maofeng ◽

Zhang Jianbin ◽

...

Keyword(s):

Arteriovenous Fistula ◽

Pathway Analysis ◽

Kegg Pathway ◽

Function Analysis ◽

Gene Set Enrichment Analysis ◽

Control Group ◽

Hub Genes ◽

Molting Cycle ◽

Kegg Pathway Analysis ◽

First Choice

The arteriovenous fistula (AVF) is the first choice for vascular access for hemodialysis of renal failure patients. Venous remodeling after exposure to high fistula flow is important for AVF to mature but the mechanism underlying remodeling is still unknown. The objective of this study is to identify the molecular mechanisms that contribute to venous remodeling after AVF. To screen and identify the differentially expressed genes (DEGs) that may involve venous remodeling after AVF, we used bioinformatics to download the public microarray data (GSE39488) from the Gene Expression Omnibus (GEO) and screen for DEGs. We then performed gene ontology (GO) function analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, and gene set enrichment analysis (GSEA) for the functional annotation of DEGs. The protein-protein interaction (PPI) network was constructed and the hub genes were carried out. Finally, we harvested 12 normal vein samples and 12 AVF vein samples which were used to confirm the expressions of the hub genes by immunohistochemistry. A total of 45 DEGs were detected, including 32 upregulated and 13 downregulated DEGs. The biological process (BP) of the GO analysis were enriched in the extrinsic apoptotic signaling pathway, cGMP-mediated pathway signaling, and molting cycle. The KEGG pathway analysis showed that the upregulated DEGs were enriched in glycosaminoglycan biosynthesis and purine metabolism, while the downregulated DEGs were mainly enriched in pathways of glycosaminoglycan biosynthesis, antifolate resistance, and ABC transporters. The GSEA analysis result showed that the top three involved pathways were oxidative phosphorylation, TNFA signaling via NF-K B, and the inflammatory response. The PPI was constructed and the hub genes found through the method of DMNC showed that INHBA and NR4A2 might play an important role in venous remodeling after AVF. The integrated optical density (DOI) examined by immunohistochemistry staining showed that the expression of both INHBA and NR4A2 increased in AVF compared to the control group. Our research contributes to the understanding of the molecular mechanism of venous remodeling after exposure to high fistula flow, which may be useful in treating AVF failure.

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Bioinformatic Analysis of Circular RNA-Associated ceRNA Network Associated with Hepatocellular Carcinoma

BioMed Research International ◽

10.1155/2019/8308694 ◽

2019 ◽

Vol 2019 ◽

pp. 1-14 ◽

Cited By ~ 1

Author(s):

Jiacheng Wu ◽

Shui Liu ◽

Yien Xiang ◽

Xianzhi Qu ◽

Yingjun Xie ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Pathway Analysis ◽

Target Genes ◽

Kegg Pathway ◽

Interaction Network ◽

Bioinformatic Analysis ◽

Differentially Expressed ◽

Qrt Pcr ◽

Cerna Network ◽

Kegg Pathway Analysis

Hepatocellular carcinoma (HCC) is the sixth most common cancer worldwide and is associated with a high mortality rate and poor treatment efficacy. In an attempt to investigate the mechanisms involved in the pathogenesis of HCC, bioinformatic analysis and validation by qRT-PCR were performed. Three circRNA GEO datasets and one miRNA GEO dataset were selected for this purpose. Upon combined biological prediction, a total of 11 differentially expressed circRNAs, 15 differentially expressed miRNAs, and 560 target genes were screened to construct a circRNA-related ceRNA network. GO analysis and KEGG pathway analysis were performed for the 560 target genes. To further screen key genes, a protein-protein interaction network of the target genes was constructed using STRING, and the genes and modules with higher degree were identified by MCODE and CytoHubba plugins of Cytoscape. Subsequently, a module was screened out and subjected to GO enrichment analysis and KEGG pathway analysis. This module included eight genes, which were further screened using TCGA. Finally, UBE2L3 was selected as a key gene and the hsa_circ_0009910–miR-1261–UBE2L3 regulatory axis was established. The relative expression of the regulatory axis members was confirmed by qRT-PCR in 30 pairs of samples, including HCC tissues and adjacent nontumor tissues. The results suggested that hsa_circ_0009910, which was upregulated in HCC tissues, participates in the pathogenesis of HCC by acting as a sponge of miR-1261 to regulate the expression of UBE2L3. Overall, this study provides support for the possible mechanisms of progression in HCC.

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Screening and Functional Prediction of Key Candidate Genes in Hepatitis B Virus-Associated Hepatocellular Carcinoma

BioMed Research International ◽

10.1155/2020/7653506 ◽

2020 ◽

Vol 2020 ◽

pp. 1-13

Author(s):

Xia Chen ◽

Ling Liao ◽

Yuwei Li ◽

Hengliu Huang ◽

Qing Huang ◽

...

Keyword(s):

Gene Expression ◽

Hepatocellular Carcinoma ◽

Hepatitis B Virus ◽

Hepatitis B ◽

Pathway Analysis ◽

Kegg Pathway ◽

Metabolic Process ◽

Hub Genes ◽

Kegg Pathway Analysis ◽

B Virus

Background. The molecular mechanism by which hepatitis B virus (HBV) induces hepatocellular carcinoma (HCC) is still unknown. The genomic expression profile and bioinformatics methods were used to investigate the potential pathogenesis and therapeutic targets for HBV-associated HCC (HBV-HCC). Methods. The microarray dataset GSE55092 was downloaded from the Gene Expression Omnibus (GEO) database. The data was analyzed by the bioinformatics software to find differentially expressed genes (DEGs). Gene Ontology (GO) enrichment analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, ingenuity pathway analysis (IPA), and protein-protein interaction (PPI) network analysis were then performed on DEGs. The hub genes were identified using Centiscape2.2 and Molecular Complex Detection (MCODE) in the Cytoscape software (Cytoscape_v3.7.2). The survival data of these hub genes was downloaded from the Gene Expression Profiling Interactive Analysis (GEPIA). Results. A total of 2264 mRNA transcripts were differentially expressed, including 764 upregulated and 1500 downregulated in tumor tissues. GO analysis revealed that these DEGs were related to the small-molecule metabolic process, xenobiotic metabolic process, and cellular nitrogen compound metabolic process. KEGG pathway analysis revealed that metabolic pathways, complement and coagulation cascades, and chemical carcinogenesis were involved. Diseases and biofunctions showed that DEGs were mainly associated with the following diseases or biological function abnormalities: cancer, organismal injury and abnormalities, gastrointestinal disease, and hepatic system disease. The top 10 upstream regulators were predicted to be activated or inhibited by Z-score and identified 25 networks. The 10 genes with the highest degree of connectivity were defined as the hub genes. Cox regression revealed that all the 10 genes (CDC20, BUB1B, KIF11, TTK, EZH2, ZWINT, NDC80, TPX2, MELK, and KIF20A) were related to the overall survival. Conclusion. Our study provided a registry of genes that play important roles in regulating the development of HBV-HCC, assisting us in understanding the molecular mechanisms that underlie the carcinogenesis and progression of HCC.

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Refining the plant steroid hormone biosynthesis pathway

Trends in Plant Science ◽

10.1016/j.tplants.2007.07.001 ◽

2007 ◽

Vol 12 (9) ◽

pp. 377-380 ◽

Cited By ~ 26

Author(s):

Gerard J. Bishop

Keyword(s):

Steroid Hormone ◽

Biosynthesis Pathway ◽

Steroid Hormone Biosynthesis ◽

Hormone Biosynthesis

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Identification of key candidate genes and biological pathways in bladder cancer

PeerJ ◽

10.7717/peerj.6036 ◽

2018 ◽

Vol 6 ◽

pp. e6036 ◽

Cited By ~ 22

Author(s):

Xin Gao ◽

Yinyi Chen ◽

Mei Chen ◽

Shunlan Wang ◽

Xiaohong Wen ◽

...

Keyword(s):

Bladder Cancer ◽

Pathway Analysis ◽

Molecular Mechanisms ◽

Kegg Pathway ◽

Aurora Kinase ◽

Smooth Muscle Contraction ◽

Ppi Network ◽

Tissue Samples ◽

Kegg Pathway Analysis ◽

Key Genes

Background Bladder cancer is a malignant tumor in the urinary system with high mortality and recurrence rates. However, the causes and recurrence mechanism of bladder cancer are not fully understood. In this study, we used integrated bioinformatics to screen for key genes associated with the development of bladder cancer and reveal their potential molecular mechanisms. Methods The GSE7476, GSE13507, GSE37815 and GSE65635 expression profiles were downloaded from the Gene Expression Omnibus database, and these datasets contain 304 tissue samples, including 81 normal bladder tissue samples and 223 bladder cancer samples. The RobustRankAggreg (RRA) method was utilized to integrate and analyze the four datasets to obtain integrated differentially expressed genes (DEGs), and the gene ontology (GO) functional annotation and Kyoto encyclopedia of genes and genomes (KEGG) pathway analysis were performed. Protein-protein interaction (PPI) network and module analyses were performed using Cytoscape software. The OncoLnc online tool was utilized to analyze the relationship between the expression of hub genes and the prognosis of bladder cancer. Results In total, 343 DEGs, including 111 upregulated and 232 downregulated genes, were identified from the four datasets. GO analysis showed that the upregulated genes were mainly involved in mitotic nuclear division, the spindle and protein binding. The downregulated genes were mainly involved in cell adhesion, extracellular exosomes and calcium ion binding. The top five enriched pathways obtained in the KEGG pathway analysis were focal adhesion (FA), PI3K-Akt signaling pathway, proteoglycans in cancer, extracellular matrix (ECM)-receptor interaction and vascular smooth muscle contraction. The top 10 hub genes identified from the PPI network were vascular endothelial growth factor A (VEGFA), TOP2A, CCNB1, Cell division cycle 20 (CDC20), aurora kinase B, ACTA2, Aurora kinase A, UBE2C, CEP55 and CCNB2. Survival analysis revealed that the expression levels of ACTA2, CCNB1, CDC20 and VEGFA were related to the prognosis of patients with bladder cancer. In addition, a KEGG pathway analysis of the top 2 modules identified from the PPI network revealed that Module 1 mainly involved the cell cycle and oocyte meiosis, while the analysis in Module 2 mainly involved the complement and coagulation cascades, vascular smooth muscle contraction and FA. Conclusions This study identified key genes and pathways in bladder cancer, which will improve our understanding of the molecular mechanisms underlying the development and progression of bladder cancer. These key genes might be potential therapeutic targets and biomarkers for the treatment of bladder cancer.

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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 ◽

Differentially Expressed Genes ◽

Pathway Analysis ◽

Kegg Pathway ◽

Differentially Expressed ◽

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.

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