Effect of Rhizoma alismatis on the expression of hub genes in the treatment of gastric cancer

AbstractGastric cancer (GC) is one of the most common types of malignancy. Its potential molecular mechanism has not been clarified. In this study, we aimed to explore potential biomarkers and prognosis-related hub genes associated with GC. The gene chip dataset GSE79973 was downloaded from the GEO datasets and limma package was used to identify the differentially expressed genes (DEGs). A total of 1269 up-regulated and 330 down-regulated genes were identified. The protein-protein interactions (PPI) network of DEGs was constructed by STRING V11 database, and 11 hub genes were selected through intersection of 11 topological analysis methods of CytoHubba in Cytoscape plug-in. All the 11 selected hub genes were found in the module with the highest score from PPI network of all DEGs by the molecular complex detection (MCODE) clustering algorithm. In order to explore the role of the 11 hub genes, we performed GO function and KEGG pathway analysis for them and found that the genes were enriched in a variety of functions and pathways among which cellular senescence, cell cycle, viral carcinogenesis and p53 signaling pathway were the most associated with GC. Kaplan-Meier analysis revealed that 10 out of the 11 hub genes were related to the overall survival of GC patients. Further, seven of the 11 selected hub genes were verified significantly correlated with GC by uni- or multivariable Cox model and LASSO regression analysis including C3, CDK1, FN1, CCNB1, CDC20, BUB1B and MAD2L1. C3, CDK1, FN1, CCNB1, CDC20, BUB1B and MAD2L1 may serve as potential prognostic biomarkers and therapeutic targets for GC.

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Identification of key genes related to macrophage infiltration in gastric cancer based on WGCNA

10.21203/rs.3.rs-56342/v1 ◽

2020 ◽

Author(s):

Haiyan Chen ◽

Cangang Zhang ◽

Shuai Cao ◽

Meng Cao ◽

Nana Zhang ◽

...

Keyword(s):

Gastric Cancer ◽

Immune Cell ◽

Accurate Diagnosis ◽

Marker Genes ◽

Hub Genes ◽

Normal Tissues ◽

Tumor Tissues ◽

New Strategy ◽

Tumor Environment ◽

Advanced Stages

Abstract Background: Gastric cancer (GC) is rampant around the world. Most of the GC cases are detected in advanced stages with poor prognosis. The identification of marker genes for early diagnosis is of great significance. Studying the tumor environment is helpful to acknowledge the process of tumorigenesis, development, and metastasis.Methods: In GEO, 22 kinds of immune cell infiltration were calculated by CIBERSORT. Macrophages were discovered remarkably infiltrated higher in GC compared with normal tissues. WGCNA was utilized to construct the network and then identify key modules and genes related to macrophages in TCGA.Results: Finally, 18 hub genes were verified. In the PPI bar chart, the top 3 genes were chosen as hub genes involved in most pathways. On the TIMER and THPA websites, it is verified that the expression levels of CYBB, CD86 and C3AR1 genes in tumor tissues were higher than those in normal tissues.Conclusion: These genes may work as biomarkers or targets for accurate diagnosis and treatment of GC in the future. Our findings may be a new strategy for the treatment of GC.

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A Tumor Progression Related 7-Gene Signature Indicates Prognosis and Tumor Immune Characteristics of Gastric Cancer

Frontiers in Oncology ◽

10.3389/fonc.2021.690129 ◽

2021 ◽

Vol 11 ◽

Author(s):

Fen Liu ◽

Zongcheng Yang ◽

Lixin Zheng ◽

Wei Shao ◽

Xiujie Cui ◽

...

Keyword(s):

Gastric Cancer ◽

High Risk ◽

Cancer Progression ◽

Risk Score ◽

Risk Model ◽

Cox Regression ◽

Hub Genes ◽

Cox Regression Analysis ◽

High Risk Patients ◽

Risk Patients

BackgroundGastric cancer is a common gastrointestinal malignancy. Since it is often diagnosed in the advanced stage, its mortality rate is high. Traditional therapies (such as continuous chemotherapy) are not satisfactory for advanced gastric cancer, but immunotherapy has shown great therapeutic potential. Gastric cancer has high molecular and phenotypic heterogeneity. New strategies for accurate prognostic evaluation and patient selection for immunotherapy are urgently needed.MethodsWeighted gene coexpression network analysis (WGCNA) was used to identify hub genes related to gastric cancer progression. Based on the hub genes, the samples were divided into two subtypes by consensus clustering analysis. After obtaining the differentially expressed genes between the subtypes, a gastric cancer risk model was constructed through univariate Cox regression, least absolute shrinkage and selection operator (LASSO) regression and multivariate Cox regression analysis. The differences in prognosis, clinical features, tumor microenvironment (TME) components and immune characteristics were compared between subtypes and risk groups, and the connectivity map (CMap) database was applied to identify potential treatments for high-risk patients.ResultsWGCNA and screening revealed nine hub genes closely related to gastric cancer progression. Unsupervised clustering according to hub gene expression grouped gastric cancer patients into two subtypes related to disease progression, and these patients showed significant differences in prognoses, TME immune and stromal scores, and suppressive immune checkpoint expression. Based on the different expression patterns between the subtypes, we constructed a gastric cancer risk model and divided patients into a high-risk group and a low-risk group based on the risk score. High-risk patients had a poorer prognosis, higher TME immune/stromal scores, higher inhibitory immune checkpoint expression, and more immune characteristics suitable for immunotherapy. Multivariate Cox regression analysis including the age, stage and risk score indicated that the risk score can be used as an independent prognostic factor for gastric cancer. On the basis of the risk score, we constructed a nomogram that relatively accurately predicts gastric cancer patient prognoses and screened potential drugs for high-risk patients.ConclusionsOur results suggest that the 7-gene signature related to tumor progression could predict the clinical prognosis and tumor immune characteristics of gastric cancer.

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Identification of hub genes with prognostic values in gastric cancer by bioinformatics analysis

World Journal of Surgical Oncology ◽

10.1186/s12957-018-1409-3 ◽

2018 ◽

Vol 16 (1) ◽

Cited By ~ 15

Author(s):

Ting Li ◽

Xujie Gao ◽

Lei Han ◽

Jinpu Yu ◽

Hui Li

Keyword(s):

Gastric Cancer ◽

Bioinformatics Analysis ◽

Hub Genes

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Bioinformatics Analysis Reveals Biomarkers With Prognostic Benefits in Diffuse Type Gastric Cancer

10.21203/rs.3.rs-36710/v1 ◽

2020 ◽

Author(s):

Sheng Li ◽

Chao Yu ◽

Yuanguang Cheng ◽

Fangchao Du ◽

Gang Wen

Keyword(s):

Gastric Cancer ◽

Pcr Analysis ◽

Diffuse Type ◽

Hub Genes ◽

Screening Criteria ◽

Clinical Prognosis ◽

Qrt Pcr ◽

Ppi Networks ◽

Immunohistochemistry Analysis ◽

Kaplan Meier

Abstract BackgroundGastric cancer (GC) is one of the most common malignancies in digestive system, among which the differentiation of diffuse type GC is relatively poor, the probability of distant metastasis and lymph node metastasis is relatively high, and the clinical prognosis is relatively poor. The purpose of this study is to explore potential signaling pathways and key biomarkers that drive the development of diffuse type GC. Methods Using the “limma” package in R to screen Differentially expressed genes. Screening hub genes by PPI analysis. Immunohistochemistry analysis and qRT-PCR analysis was carried out to detect genes expression. Using Kaplan-Meier Plotter database analyzed the prognostic roles of hub genes.ResultsA total of 355 DEGs consisting of 293 diffuse type DEGs and 62 intestinal type DEGs were selected according to screening criteria, 3 hub genes were chosen from diffuse type DEGs according to the degree of connectivity by using protein-protein interaction (PPI) networks and Cytoscape software including AGT, CXCL12 and ADRB2. Immunohistochemistry analysis and qRT-PCR results showed that the expression of three genes was related to the different GC lauren types. The Kaplan Meier analysis showed that the expression values of these three genes were related to prognosis of diffuse type GC. ConclusionsAGT, CXCL12 and ADRB2 might contribute to the progression of diffuse type GC, which could have potential as biomarkers or therapeutic targets for diffuse type GC.

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Identification of Hub Genes And Pathways of Gastric Adenocarcinoma Using Bioinformatics Analysis

10.21203/rs.3.rs-455851/v1 ◽

2021 ◽

Author(s):

Yu Di

Keyword(s):

Gastric Cancer ◽

Extracellular Matrix ◽

Gastric Adenocarcinoma ◽

Cellular Response ◽

Receptor Interaction ◽

Signal Pathways ◽

Connectivity Map ◽

Hub Genes ◽

Matrix Organization ◽

Catabolic Process

Abstract Object: Understanding hub genes associated with gastric adenocarcinoma (GC) development could lead to effective advances to diagnose and treat the diseases. In order to discover possible signal pathways and hub genes for the disease, we utilized the bioinformatics tools to analyze its mechanism.Method: The gene chip of GSE7997was download from the GEO Datasets. Expression data of gastric cancer and its adjacent normal tissues were compared and the DEGs were acquired. The clusterProfiler and KEGG.db R packages were used for the analysis of its gene ontology process and KEGG pathways. What’s more, the PPI network was constructed by the STRING website. The hub genes were acquired by the plugin of the Cytohubba in Cytoscape. Finally, these genes were examined by the TCGA datasets and potential drugs were explored by Connectivity map.Results: The up regulated DEGs was mainly associated with the process of an extracellular matrix organization, an extracellular matrix organization, Collagen catabolic process, and multicellular organismal catabolic process. The down regulated DEGs have mainly associated with the process of digestion， cellular response to zinc ion， digestive system process, and organic hydroxy compound metabolic process. The up regulated DEGs was mainly located on PI3K-AKTsignal pathways, human papillomavirus infection，Cytokine-cytokine receptor interaction, and focal adhesion process. The down regulated genes were mainly associated with protein digestion and absorption, mineral absorption, and the pancreatic secretion. Cytohubba had found hub genes of COL1A2, COL5A1, COL4A1, COL5A2, COL6A3, COL11A1, SERPINH1, FN1, and down-regulated COL2A1.These genes were associated with the process of transforming growth factor, extracellular matrix, cell adhesion, wound healing and so on. As examined by the TCGA datasets, these altered genes were associated with overall survivaland no disease progress time (P<0.05). Finally, we got the small molecule drugs of fenofibrate, benzbromarone, semustine, chloroquine, ondansetron, hydroxyachillin, megestrol, ciclopirox and monastrol for gastric cancer by Connectivity map (P<0.05).Conclusion: As mentioned above, we got 9 hub genes and tested by the TCGA datasets of gastric adenocarcinoma. They were associated with overall survival and disease free progressive time in gastric cancer patients. The bioinformatical analysis of the disease may enhance the understanding of the mechanism of disorders.

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Screening and Identification of Key Biomarkers of Gastric Cancer: Three Genes Jointly Predict Gastric Cancer

Frontiers in Oncology ◽

10.3389/fonc.2021.591893 ◽

2021 ◽

Vol 11 ◽

Author(s):

Meng-jie Shan ◽

Ling-bing Meng ◽

Peng Guo ◽

Yuan-meng Zhang ◽

Dexian Kong ◽

...

Keyword(s):

Neural Network ◽

Gene Expression ◽

Gastric Cancer ◽

Network Model ◽

Neural Network Model ◽

Survival Curve ◽

Roc Curves ◽

Online Database ◽

Ppi Network ◽

Hub Genes

BackgroundGastric cancer (GC) is one of the most common cancers all over the world, causing high mortality. Gastric cancer screening is one of the effective strategies used to reduce mortality. We expect that good biomarkers can be discovered to diagnose and treat gastric cancer as early as possible.MethodsWe download four gene expression profiling datasets of gastric cancer (GSE118916, GSE54129, GSE103236, GSE112369), which were obtained from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) between gastric cancer and adjacent normal tissues were detected to explore biomarkers that may play an important role in gastric cancer. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses of overlap genes were conducted by the Metascape online database; the protein-protein interaction (PPI) network was constructed by the STRING online database, and we screened the hub genes of the PPI network using the Cytoscape software. The survival curve analysis was conducted by km-plotter and the stage plots of hub genes were created by the GEPIA online database. PCR, WB, and immunohistochemistry were used to verify the expression of hub genes. A neural network model was established to quantify the predictors of gastric cancer.ResultsThe relative expression level of cadherin-3 (CDH3), lymphoid enhancer-binding factor 1 (LEF1), and matrix metallopeptidase 7 (MMP7) were significantly higher in gastric samples, compared with the normal groups (p<0.05). Receiver operator characteristic (ROC) curves were constructed to determine the effect of the three genes’ expression on gastric cancer, and the AUC was used to determine the degree of confidence: CDH3 (AUC = 0.800, P<0.05, 95% CI =0.857-0.895), LEF1 (AUC=0.620, P<0.05, 95%CI=0.632-0.714), and MMP7 (AUC=0.914, P<0.05, 95%CI=0.714-0.947). The high-risk warning indicator of gastric cancer contained 8<CDH3<15 and 10<expression of LEF1<16.ConclusionsCDH3, LEF1, and MMP7 can be used as candidate biomarkers to construct a neural network model from hub genes, which may be helpful for the early diagnosis of gastric cancer.

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CXCR4 negatively correlates with NKT cell infiltration during gastric precancerous lesions progression to early gastric cancer

10.21203/rs.3.rs-79316/v1 ◽

2020 ◽

Author(s):

Xiaotao Jiang ◽

Kunhai Zhuang ◽

Kailin Jiang ◽

Yi Wen ◽

Linling Xie ◽

...

Keyword(s):

Gastric Cancer ◽

Immune Cells ◽

Immune Cell ◽

Precancerous Lesions ◽

Clinical Information ◽

Cell Infiltration ◽

Hub Genes ◽

Nkt Cell ◽

Hub Gene ◽

Correlation Test

Abstract Background: With the coming of immunotherapy era, immunotherapy is gradually playing a vital role in the treatment of gastric cancer (GC). However, immune microenvironment in gastric precancerous lesions (GPL) and early gastric cancer (EGC) still remain largely unknown. Methods: From the Gene Expression Omnibus (GEO), data of three GPL-related gene expression profiles (GSE55696, GSE87666 and GSE130823) and three GC data sets with clinical information (GSE66229, GSE15459 and GSE34942) were downloaded. Three GC data were consolidated as a GC meta-GEO cohort. RNA sequencing data of 375 stomach adenocarcinoma (STAD) samples with clinical information from The Cancer Genome Atlas (TCGA) and 175 stomach normal controls (NC) from Genotype-Tissue Expression (GTEx) datasets were obtained from the UCSC Xena browser, which were merged as a STAD TCGA-GTEx cohort. The abundance of immune cells in above datasets were estimated using Immune Cell Abundance Identifier (ImmuCellAI) algorithm. Firstly, key immune cells associated with GPL progression to EGC were identified using one‐way analysis of variance (ANOVA) test as well as Spearman’s correlation test in two GPL and EGC related datasets (GSE55696 and GSE87666). Then, weighted gene co-expression analysis (WGCNA) and pathway enrichment were adopted to identify hub gene co-expression network. Candidate hub genes were identified based on network parameters. Combining expression comparison and prognosis analysis in STAD TCGA-GTEx and GC meta-GEO cohort, Genes with significant difference between GC and NC and prognostic significance were identified as real hub genes. Correlation between real hub genes and key immune cells was evaluated using Pearson’s correlation test. The pattern of key immune cells infiltration and hub genes expression as well as their correlation during GPL progression to EGC were validated in an independent cohort GSE130823. The correlation was also verified in the GC datasets (STAD TCGA-GTEx and GC meta-GEO cohort).Results: Combining with GSE55696 and GSE87666 cohorts, NKT cell was found gradually decreased with GPL progression and negatively correlated with tumorigenesis significantly. It was identified as the key immune cell associated with GPL progression to EGC based on one-way ANOVA test and Spearman’s correlation test. Further verification indicated that it was significantly downregulated in GC in meta-GEO cohort and STAD TCGA-GTEx cohort. According to the results of WGCNA and KEGG pathway enrichment, green modules in GSE55696 and GSE87666 cohorts were considered as hub modules as they were negatively associated with NKT cell infiltration at a significant level and their overlapping genes were significantly enriched in immune-related pathways. In further screening, CXCR4 was found to be significantly upregulated in GC and had a poor prognosis, which was determined as the real hub gene. CXCR4 expression was found increased with GPL progression, positively correlated with tumorigenesis and negatively correlated with NKT cell infiltration significantly. The pattern of NKT cell infiltration and CXCR4 expression as well as their relationship stay consistent in the independent GPL cohort GSE130823. The negative correlation of CXCR4 with NKT cell infiltration was also confirmed in GC datasets (GC meta-GEO cohort and STAD TCGA-GTEx cohort).Conclusion: CXCR4 and NKT cell are possible to serve as biomarkers in monitoring GPL progression to EGC. Besides, CXCR4 may be involved in regulating NKT cell infiltration during GPL progression to EGC, which may provide a new immunotherapeutic target.

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CXCR4 Negatively Correlates with NKT Cell Infiltration During Gastric Precancerous Lesions Progression to Early Gastric Cancer

10.21203/rs.3.rs-88936/v1 ◽

2020 ◽

Author(s):

Xiaotao Jiang ◽

Kunhai Zhuang ◽

Kailin Jiang ◽

Yi Wen ◽

Linling Xie ◽

...

Keyword(s):

Gastric Cancer ◽

Early Gastric Cancer ◽

Immune Cells ◽

Immune Cell ◽

Precancerous Lesions ◽

Expression Profiles ◽

Cell Infiltration ◽

Hub Genes ◽

Nkt Cell ◽

Hub Gene

Abstract Background Immune microenvironment in gastric precancerous lesions (GPL) and early gastric cancer (EGC) still remain largely unknown. This study aims to identify key immune cells and hub genes associated with GPL progression to EGC. Methods Immune Cell Abundance Identifier (ImmuCellAI) algorithm was used to quantify the proportions of immune cells of GPL and GC samples based on gene expression profiles. Key immune cells associated with GPL progression to EGC were identified using one‐way analysis of variance (ANOVA) test and Spearman’s correlation test. Weighted gene co-expression analysis (WGCNA) and pathway enrichment were adopted to identify hub gene co-expression network and hub genes associated with the key immune cells infiltration. The pattern of key immune cells infiltration, hub genes expression and their correlation were verified in an independent GPL-EGC cohort and GC datasets.Results NKT cell was found gradually decreased during GPL progression to EGC and negatively correlated with tumorigenesis. According to WGCNA and hub genes screening, CXCR4, having a poor prognosis, increased with GPL progression, positively correlated with tumorigenesis and negatively correlated with NKT cell infiltration significantly, was identified as the real hub gene. The negative correlation between CXCR4 and NKT cell infiltration was successfully verified in an independent GPL-EGC cohort and GC datasets.Conclusion CXCR4 and NKT cell are possible to serve as biomarkers in monitoring GPL progression to EGC. Besides, CXCR4 may be involved in regulating NKT cell infiltration during GPL progression to EGC, which may provide a new immunotherapeutic target.

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Detecting Hub Genes Associated With Lauren Subtype of Gastric Cancer by Weighted Gene Co-Expression Network Analysis

10.21203/rs.3.rs-61626/v1 ◽

2020 ◽

Author(s):

XU LIU ◽

Li Yao ◽

Jingkun Qu ◽

Lin Liu ◽

XU LIU ◽

...

Keyword(s):

Gastric Cancer ◽

Network Analysis ◽

Cancer Survival ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Functional Enrichment ◽

Diffuse Gastric Cancer ◽

Hub Genes ◽

Gene Modules

Abstract Background Gastric cancer is a rather heterogeneous type of malignant tumor. Among the several classification system, Lauren classification can reflect biological and pathological differences of different gastric cancer.Method to provide systematic biological perspectives, we employ weighted gene co-expression network analysis to reveal transcriptomic characteristics of gastric cancer. GSE15459 and TCGA STAD dataset were downloaded. Co-expressional network was constructed and gene modules were identified. Result Two key modules blue and red were suggested to be associated with diffuse gastric cancer. Functional enrichment analysis of genes from the two modules was performed. Validating in TCGA STAD dataset, we propose 10 genes TNS1, PGM5, CPXM2, LIMS2, AOC3, CRYAB, ANGPTL1, BOC and TOP2A to be hub-genes for diffuse gastric cancer. Finally these ten genes were associated with gastric cancer survival. Conclusion More attention need to be paid and further experimental study is required to elucidate the role of these genes.

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