scholarly journals Identification and analysis of small nucleolar RNAs (snoRNAs) associated co-expression and ceRNA regulatory networks in esophageal carcinoma

2020 ◽  
Author(s):  
Haigen Jiang ◽  
Jing Zhuang ◽  
Xi Yang ◽  
Wei Wu ◽  
Siqi Dai ◽  
...  
Keyword(s):  
Survival Analysis ◽  
Esophageal Carcinoma ◽  
Trend Analysis ◽  
Signaling Pathway ◽  
Enrichment Analysis ◽  
Receptor Interaction ◽  
Small Nucleolar Rnas ◽  
Regulation Network ◽  
Nucleolar Rnas ◽  
Normal Controls

Abstract Background: This study aimed to investigate the role of small nucleolar RNAs (snoRNAs) and their host genes (SNHGs) in tumorigenesis and progression of esophageal carcinoma (EC). Methods: RNA-seq data and clinical information for EC patients were obtained from The Cancer Genome Atlas database. Differentially expressed snoRNAs (DE-snoRNAs) and SNHGs (DE-SNHGs) between EC samples and normal controls were screened with the edgeR package, followed by the trend analysis with STEM. Survival analysis was performed using the Kaplan–Meier method with the log-rank test. Identification of co-expressed genes and functional enrichment analyses were performed with Pearson’s correlation analysis and enrichment analysis, respectively. Lastly, the co-expression and ceRNA networks were constructed. Results: A total of 19 DE-snoRNAs and nine DE-SNHGs were identified between EC samples and normal controls. Moreover, two snoRNA clusters related to the clinic stage were identified through trend analysis. Survival analysis results demonstrated that SNORA70D was significantly associated with the overall survival of EC patients (P=0.034). In addition, a co-regulation network that included six snoRNAs and 29 mRNAs was constructed. A ceRNA network comprised of five SNHGs, 11 mRNAs, and 38 miRNAs was constructed based on the top 50 relationship pairs. KEGG pathway enrichment analysis revealed that SNORA14B, SNORA47, SNORA71C, SNORD12B, and SNORD14E in the co-expression network were mainly enriched in the NOD−like receptor signaling pathway, and neuroactive ligand−receptor interaction pathway. SNHG23, SNHG3, SNHG17, SNHG4, and SNHG8 in the ceRNA regulation network were mainly enriched in calcium signaling pathway, cytokine−cytokine receptor interaction, and the extracellular matrix−receptor interaction pathway. Conclusion: The data revealed a series of snoRNAs, SNHGs, and pathways involved in EC carcinogenesis, which will provide a basis for understanding the underlying molecular mechanism of EC development.

scholarly journals Analysis of small nucleolar RNAs (snoRNAs) associated co-expression and construction of ceRNA regulatory networks in esophageal carcinoma

2020 ◽  
Author(s):  
Haigen Jiang ◽  
Jing Zhuang ◽  
Xi Yang ◽  
Wei Wu ◽  
Siqi Dai ◽  
...  
Keyword(s):  
Survival Analysis ◽  
Esophageal Carcinoma ◽  
Trend Analysis ◽  
Signaling Pathway ◽  
Enrichment Analysis ◽  
Receptor Interaction ◽  
Small Nucleolar Rnas ◽  
Regulation Network ◽  
Nucleolar Rnas ◽  
Normal Controls

Abstract Purpose: This study aimed to investigate the role of small nucleolar RNAs (snoRNAs) and their host genes (SNHGs) in tumorigenesis and progression of esophageal carcinoma (EC).Methods: RNA-seq data and clinical information for EC patients were obtained from The Cancer Genome Atlas database. Differentially expressed snoRNAs (DE-snoRNAs) and SNHGs (DE-SNHGs) between EC samples and normal controls were screened with the edgeR package, followed by the trend analysis with STEM. Survival analysis was performed using the Kaplan–Meier method with the log-rank test. Screening of co-expressed genes and functional enrichment analyses were performed with Pearson’s correlation analysis and enrichment analysis, respectively. Lastly, the co-expression and ceRNA networks were constructed.Results: A total of 19 DE-snoRNAs and nine DE-SNHGs were screened between EC samples and normal controls. Moreover, two snoRNA clusters related to the clinic stage were analyzed through trend analysis. Survival analysis results demonstrated that SNORA70D was significantly associated with the overall survival of EC patients (P=0.034). In addition, a co-regulation network that included six snoRNAs and 29 mRNAs was constructed. A ceRNA network comprised of five SNHGs, 11 mRNAs, and 38 miRNAs was constructed based on the top 50 relationship pairs. KEGG pathway enrichment analysis revealed that SNORA14B, SNORA47, SNORA71C, SNORD12B, and SNORD14E in the co-expression network were mainly enriched in the NOD−like receptor signaling pathway, and neuroactive ligand−receptor interaction pathway. SNHG23, SNHG3, SNHG17, SNHG4, and SNHG8 in the ceRNA regulation network were mainly enriched in calcium signaling pathway, cytokine−cytokine receptor interaction, and the extracellular matrix−receptor interaction pathway.Conclusion: The data revealed a series of snoRNAs, SNHGs, and pathways involved in EC carcinogenesis, which will provide a basis for understanding the underlying molecular mechanism of EC development.

scholarly journals The Role of Circular RNA CDR1as/ciRS-7 in Regulating Tumor Microenvironment: A Pan-Cancer Analysis

Biomolecules ◽  
2019 ◽  
Vol 9 (9) ◽  
pp. 429 ◽  
Author(s):  
Zou ◽  
Zheng ◽  
Deng ◽  
Yang ◽  
Xie ◽  
...  
Keyword(s):  
Tumor Microenvironment ◽  
Signaling Pathway ◽  
Malignant Tumors ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Circular Rna ◽  
Gene Set Enrichment Analysis ◽  
Functional Enrichment ◽  
Receptor Interaction

Circular RNA CDR1as/ciRS-7 functions as an oncogenic regulator in various cancers. However, there has been a lack of systematic and comprehensive analysis to further elucidate its underlying role in cancer. In the current study, we firstly performed a bioinformatics analysis of CDR1as among 868 cancer samples by using RNA-seq datasets of the MiOncoCirc database. Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), gene set enrichment analysis (GSEA), CIBERSORT, Estimating the Proportion of Immune and Cancer cells (EPIC), and the MAlignant Tumors using Expression data (ESTIMATE) algorithm were applied to investigate the underlying functions and pathways. Functional enrichment analysis suggested that CDR1as has roles associated with angiogenesis, extracellular matrix (ECM) organization, integrin binding, and collagen binding. Moreover, pathway analysis indicated that it may regulate the TGF-β signaling pathway and ECM-receptor interaction. Therefore, we used CIBERSORT, EPIC, and the ESTIMATE algorithm to investigate the association between CDR1as expression and the tumor microenvironment. Our data strongly suggest that CDR1as may play a specific role in immune and stromal cell infiltration in tumor tissue, especially those of CD8+ T cells, activated NK cells, M2 macrophages, cancer-associated fibroblasts (CAFs) and endothelial cells. Generally, systematic and comprehensive analyses of CDR1as were conducted to shed light on its underlying pro-cancerous mechanism. CDR1as regulates the TGF-β signaling pathway and ECM-receptor interaction to serve as a mediator in alteration of the tumor microenvironment.

scholarly journals Uncovering the Mechanisms and Molecular Targets of Weibing Formula 1 against Gastritis: Coupling Network Pharmacology with GEO Database

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Ke Chen ◽  
Luojian Zhang ◽  
Zhen Qu ◽  
Feng Wan ◽  
Jia Li ◽  
...  
Keyword(s):  
Real Time ◽  
Signaling Pathway ◽  
Quantitative Pcr ◽  
Enrichment Analysis ◽  
Mapk Signaling ◽  
Gene Expression Omnibus ◽  
Network Pharmacology ◽  
Receptor Interaction ◽  
Pathway Enrichment Analysis ◽  
Real Time Quantitative Pcr

Weibing Formula 1, a classic traditional formula, has been widely used clinically to treat gastritis in recent years. However, the potential pharmacological mechanism of Weibing Formula 1 is still unclear to date. A network pharmacology-based strategy was performed to uncover the underlying mechanisms of Weibing Formula 1 against gastritis. Furthermore, we structured the drug-active ingredients-genes–disease network and PPI network of shared targets, and function enrichment analysis of these targets was carried out. Ultimately, Gene Expression Omnibus (GEO) datasets and real-time quantitative PCR were used to verify the related genes. We found 251 potential targets corresponding to 135 bioactive components of Weibing Formula 1. Then, 327 gastritis-related targets were known gastritis-related targets. Among which, 60 common targets were shared between potential targets of Weibing Formula 1 and known gastritis-related targets. The results of pathway enrichment analysis displayed that 60 common targets mostly participated in various pathways related to Toll-like receptor signaling pathway, MAPK signaling pathway, cytokine-cytokine receptor interaction pathway, chemokine signaling pathway, and apoptosis. Based on the GSE60427 dataset, 15 common genes were shared between differentially expressed genes and 60 candidate targets. The verification results of the GSE5081 dataset showed that except for DUOX2 and VCAM1, the other 13 genes were significantly upregulated in gastritis, which was consistent with the results in the GSE60427 dataset. More importantly, real-time quantitative PCR results showed that the expressions of PTGS2, MMP9, CXCL2, and CXCL8 were significantly upregulated and NOS2, EGFR, and IL-10 were downregulated in gastritis patients, while the expressions of PTGS2, MMP9, CXCL2, and CXCL8 were significantly downregulated and NOS2, EGFR, and IL-10 were upregulated after the treatment of Weibing Formula 1. PTGS2, NOS2, EGFR, MMP9, CXCL2, CXCL8, and IL-10 may be the important direct targets of Weibing Formula 1 in gastritis treatment. Our study revealed the mechanism of Weibing Formula 1 in gastritis from an overall and systematic perspective, providing a theoretical basis for further knowing and application of this formula in the future.

scholarly journals Bcl-2 Is Involved in Cardiac Hypertrophy through PI3K-Akt Pathway

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Xianwei Meng ◽  
Jun Cui ◽  
Guibin He
Keyword(s):  
Gene Expression ◽  
Cardiac Hypertrophy ◽  
Signaling Pathway ◽  
Enrichment Analysis ◽  
B Cell Lymphoma ◽  
Akt Signaling ◽  
Receptor Interaction ◽  
Pathway Enrichment Analysis ◽  
Akt Signaling Pathway ◽  
Upregulated Genes

Cardiac hypertrophy (CH) is a common cause of sudden cardiac death and heart failure, resulting in a significant medical burden. The present study is aimed at exploring potential CH-related pathways and the key downstream effectors. The gene expression profile of GSE129090 was obtained from the Gene Expression Omnibus database (GEO), and 1325 differentially expressed genes (DEGs) were identified, including 785 upregulated genes and 540 downregulated genes. Kyoto Encyclopedia of Genes and Genomes (KEGG) and Reactome pathway enrichment analysis of DEGs were then performed. Although there were no pathways enriched by downregulated genes, many CH-related pathways were identified by upregulated genes, including PI3K-Akt signaling pathway, extracellular matrix- (ECM-) receptor interaction, regulation of actin cytoskeleton, and hypertrophic cardiomyopathy (HCM). In the deeper analysis of PI3K-Akt signaling pathway, we found all the signaling transduction pointed to B cell lymphoma-2- (Bcl-2-) mediated cell survival. We then demonstrated that PI3K-Akt signaling pathway was indeed activated in cardiac hypertrophy. Furthermore, no matter LY294002, an inhibitor of the PI3K/AKT signaling pathway, or Venetoclax, a selective Bcl-2 inhibitor, protected against cardiac hypertrophy. In conclusion, these data indicate that Bcl-2 is involved in cardiac hypertrophy as a key downstream effector of PI3K-Akt signaling pathway, suggesting a potential therapeutic target for the clinical management of cardiac hypertrophy.

scholarly journals Identification of potential diagnostic and prognostic biomarkers for LUAD based on TCGA and GEO databases

2021 ◽  
Author(s):  
Qiangqiang Zheng ◽  
Shihui Min ◽  
Qinghua Zhou
Keyword(s):  
Signaling Pathway ◽  
Clinical Features ◽  
Enrichment Analysis ◽  
Expression Level ◽  
Normal Lung ◽  
Receptor Interaction ◽  
Hub Genes ◽  
Kaplan Meier ◽  
Viral Carcinogenesis ◽  
Relationship Of

Accumulating evidence has demonstrated that gene alterations play a crucial role in LUAD development, progression, and prognosis. The current study aimed to identify the hub genes associated with LUAD. In the present study, we used TCGA database to screen the hub genes. Then, we validated the results by GEO datasets. Finally, we used cBioPortal, UALCAN, qRT-PCR, HPA database, TCGA database, and Kaplan-Meier plotter database to estimate the gene mutation, gene transcription, protein expression, clinical features of hub genes in patients with LUAD. A total of 5,930 DEGs were screened out in TCGA database. Enrichment analysis revealed that DEGs were involved in the transcriptional misregulation in cancer, viral carcinogenesis, cAMP signaling pathway, calcium signaling pathway, and ECM-receptor interaction. The combining results of MCODE and CytoHubba showed that ADCY8, ADRB2, CALCA, GCG, GNGT1, and NPSR1 were hub genes. Then, we verified the above results by GSE118370, GSE136043, and GSE140797 datasets. Compared with normal lung tissues, the expression level of ADCY8 and ADRB2 were lower in LUAD tissues, but the expression level of CALCA, GCG, GNGT1, and NPSR1 were higher. In the prognosis analyses, the low expression of ADCY8 and ADRB2 and the high expression of CALCA, GCG, GNGT1, and NPSR1 were correlated with poor OS and poor PFS. The significant differences in the relationship of the expression of 6 hub genes and clinical features were observed. In conclusion, 6 hub genes will not only contribute to elucidating the pathogenesis of LUAD, and may be potential therapeutic targets for LUAD.

scholarly journals Network Pharmacology-Based Study on the Mechanism of Pinellia ternata in Asthma Treatment

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Yanmin Lyu ◽  
Xiangjing Chen ◽  
Qing Xia ◽  
Shanshan Zhang ◽  
Chengfang Yao
Keyword(s):  
Candidate Genes ◽  
Signaling Pathway ◽  
Expression Profiles ◽  
Animal Experiments ◽  
Enrichment Analysis ◽  
Th2 Cells ◽  
Network Pharmacology ◽  
Receptor Interaction ◽  
Pathway Enrichment Analysis ◽  
Pinellia Ternata

Background. Pinellia ternata (PT), a medicinal plant, has had an extensive application in the treatment of asthma in China, whereas its underlying pharmacological mechanisms remain unclear. Methods. Firstly, a network pharmacology method was adopted to collect activated components of PT from Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP). Targets of PT were assessed by exploiting the PharmMapper website; asthma-related targets were collected from the OMIM website, and target-target interaction networks were built. Secondly, critical nodes exhibiting high possibility were identified as the hub nodes in the network, which were employed to conduct Gene Ontology (GO) comment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) signaling pathway enrichment analysis. Finally, the tissue expression profiles of key candidate genes were identified by the Gene Expression Omnibus (GEO) database, and the therapeutic effect of PT was verified by an animal experiment. Results. 57 achievable targets of PT on asthma were confirmed as hub nodes through using the network pharmacology method. As revealed from the KEGG enrichment analysis, the signaling pathways were notably enriched in pathways of the T-cell receptor signaling pathway, JAK-STAT signaling pathway, and cytokine-cytokine receptor interaction. The expression profiles of candidate genes including Mmp2, Nr3c1, il-10, il-4, il-13, il-17a, il-2, tlr4, tlr9, ccl2, csf2, and vefgα were identified. Moreover, according to transcriptome RNA sequencing data from lung tissues of allergic mice compared to normal mice, the mRNA level of Mmp2 and il-4 was upregulated ( P < 0.001 ). In animal experiments, PT could alleviate the allergic response of mice by inhibiting the activation of T-helper type 2 (TH2) cells and the expression of Mmp2 and il-4. Conclusions. Our study provides candidate genes that may be either used for future studies related to diagnosis/prognosis or as targets for asthma management. Besides, animal experiments showed that PT could treat asthma by regulating the expression of Mmp2 and il-4.

scholarly journals A Network Pharmacology-based Analysis of the Molecular Mechanism of Tanreqing in the Treatment of the Coronavirus Disease 2019 (COVID-19)

2020 ◽  
Author(s):  
Liuliu Yang ◽  
Minyong Wen ◽  
Wenjiang Zheng ◽  
Xiaohong Liu ◽  
yong wang
Keyword(s):  
Signaling Pathway ◽  
Molecular Mechanism ◽  
Chemical Constituents ◽  
Hypoxia Inducible Factor ◽  
Enrichment Analysis ◽  
Clinical Treatment ◽  
Laboratory Research ◽  
Network Pharmacology ◽  
Receptor Interaction ◽  
Protein Protein Interaction

Abstract Background: This paper discusses the molecular mechanism of Tanreqing (TRQ) in the treatment of the coronavirus disease 2019 (COVID-19) using the network pharmacology approach. Our study provides new ideas on the laboratory research and clinical treatment of the disease. Method: Information on the chemical constituents of TRQ and the genes targeted by the disease was collected. The common gene targets of the drug and the disease were input into the Search Tool for the Retrieval of Interacting Genes/Proteins（STRING）database to understand the interaction among target proteins. The protein–protein interaction (PPI) network and a network of the chemical constituents of TRQ and their targets were constructed using Cytoscape. Gene ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed using the R program and other relevant software packages. Results: Twenty-eight active chemical constituents and 365 gene targets were identified for TRQ. Out of these genes, 113 were also found to be involved in the pathogenesis of the disease. Enrichment analysis revealed the therapeutic role that TRQ could have played in the treatment of COVID-19, via the regulation of important pathways such as the renin–angiotension system, neuroactive ligand–receptor interaction, phospholipase D (PLD) signaling pathway, calcium signaling pathway, and the hypoxia-inducible factor 1 (HIF-1) signaling pathway. Conclusions: This study attempts to predict the molecular mechanism of TRQ in the treatment of COVID-19, and suggests TRQ intervention through multiple targets and pathways in processes including inflammatory response, immune regulation, and apoptosis during the treatment of the disease. This study indicates the potential rational application of TRQ in the clinical treatment of COVID-19.

scholarly journals Identification of therapeutic targets and prognostic biomarkers among STAT family in glioblastoma

2020 ◽  
Author(s):  
Chenglin Li ◽  
Yanfei Zhou ◽  
Hanshun Deng ◽  
Yuanshen Ye ◽  
Shuizhen Zhao ◽  
...  
Keyword(s):  
Immune Response ◽  
Signaling Pathway ◽  
Web Applications ◽  
Therapeutic Targets ◽  
Prognostic Biomarkers ◽  
Receptor Interaction ◽  
Immune Gene ◽  
Kegg Pathways ◽  
Incidence And Mortality ◽  
Regulation Network

Abstract Background: Glioblastoma (GBM) is the most common and aggressive primary brain malignancies with high incidence and mortality. The aberrant activation of STAT signaling was confirmed to result in tumor pathogenesis and progress by regulating cell cycle, cell survival, and immune response. Methods: The clinical significance of and regulation network of STAT family in GBM were explored with several web applications or database. Results: The level of STAT1/3/5A/5B/6 were increased in GBM while STAT4 level was decreased. GBM patients with high expression of STAT1/2/3/5A/6 and low expression of STAT4/5B had a worse overall survival. Among the STAT family, STAT 4 and STAT6 were the top two frequently mutated genes. Correlation suggested a low to moderate correlation among STAT family. STAT family were also involved in the activation or inhibition of the famous cancer related pathways. Immune infiltrates analysis suggested that STAT5A level showed significantly correlated with the abundance of immune cells and the level of immune gene biomarkers. GO functions and KEGG pathways analysis revealed that STAT5A was involved in immune response-regulating signaling pathway, neutrophil and lymphocyte mediated immunity, single-stranded DNA binding, cytokine-cytokine receptor interaction, NOD-like receptor signaling pathway, NF-kappa B signaling pathway, and TNF signaling pathway. Moreover, we also identified several Kinase and transcription factor targets of STAT5A in GBM. Conclusions: Our results revealed the therapeutic targets, prognostic biomarkers and regulation network of STAT family in GBM, laying the foundation for further studies about STAT family in therapy and prognosis of GBM.

scholarly journals Analysis of Potential Genes and Pathways Involved in the Pathogenesis of Acne by Bioinformatics

2019 ◽  
Vol 2019 ◽  
pp. 1-8 ◽  
Author(s):  
Biao Chen ◽  
Yan Zheng ◽  
Yanhua Liang
Keyword(s):  
Signaling Pathway ◽  
Normal Skin ◽  
Enrichment Analysis ◽  
Bioinformatic Analysis ◽  
Cytokine Receptor ◽  
Receptor Interaction ◽  
Ppi Network ◽  
Pathway Enrichment ◽  
Chemokine Signaling ◽  
Chemokine Signaling Pathway

Acne is the eighth most frequent disease worldwide. Inflammatory response runs through all stages of acne. It is complicated and is involved in innate and adaptive immunity. This study aimed to explore the candidate genes and their relative signaling pathways in inflammatory acne using data mining analysis. Microarray data GSE6475 and GSE53795, including 18 acne lesion tissues and 18 matched normal skin tissues, were obtained. Differentially expressed genes (DEGs) were filtered and subjected to functional and pathway enrichment analyses. Protein–protein interaction (PPI) network and module analyses were also performed based on the DEGs. In this work, 154 common DEGs, including 145 upregulated and 9 downregulated, were obtained from two microarray profiles. Gene Ontology and pathway enrichment of DEGs were clustered using significant enrichment analysis. A PPI network containing 110 nodes/DEGs was constructed, and 31 hub genes were obtained. Four modules in the PPI network, which mainly participated in chemokine signaling pathway, cytokine–cytokine receptor interaction, and Fc gamma R-mediated phagocytosis, were extracted. In conclusion, aberrant DEGs and pathways involved in acne pathogenesis were identified using bioinformatic analysis. The DEGs included FPR2, ITGB2, CXCL8, C3AR1, CXCL1, FCER1G, LILRB2, PTPRC, SAA1, CCR2, ICAM1, and FPR1, and the pathways included chemokine signaling pathway, cytokine–cytokine receptor interaction, and Fc gamma R-mediated phagocytosis. This study could serve as a basis for further understanding the pathogenesis and potential therapeutic targets of inflammatory acne.

scholarly journals A comprehensive analysis of the lncRNAs and genes for the gastric carcinoma

2020 ◽  
Author(s):  
Wei Cheng ◽  
Minzhe Li ◽  
Shaofeng Lin ◽  
Jun Xiao
Keyword(s):  
Signaling Pathway ◽  
Molecular Mechanism ◽  
Risk Model ◽  
Risk Group ◽  
Enrichment Analysis ◽  
Low Risk ◽  
Gene Set Enrichment Analysis ◽  
Receptor Interaction ◽  
Gene Set Enrichment ◽  
Specificity And Sensitivity

Abstract BackgroundGastric carcinoma (GC) in one of the most common malignant tumors in the worldwide. Despite numerous studies, the molecular mechanism of is still unclear and the prognosis of GC remains poor.MethodsThe present study obtained expression data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database. The gene risk signature and lncRNA signature were constructed by performing the univariate cox regression analysis and least absolute shrinkage and selection operator (LASSO) analysis. The receive operator curve (ROC) analysis was applied to evaluated the specificity and sensitivity of risk signature. The potential pathway was performed by using the Gene Set Enrichment Analysis (GSEA).ResultsIn total, 1641 differentially expressed genes (DEGs) and 985 differentially expressed lncRNAs (DElncRNAs) were obtained among GC samples. A 6 prognostic DEGs (DIRC1, IQCM, MATN3, SOX14, C5orf46 and CYP19A1) classifier and 9 prognostic DElncRNAs (AC007126.1, AC011352.1, AL356417.2, AP000695.1, LINC01210, LINC01614, VCAN-AS1, AC005165.1 and AC011586.2) classifier were identified using lasso-penalized multivariate survival modelling with 10 fold cross-validation. According to median risk score, patients were divided into high risk group and low risk group. The overall survival result for patients have a significant divergence between high rosk group and low risk group (p < 0.001). The 6 DEGs signature risk model and 9 DElncRNAs signature risk model was further verified that can served as an independent prognostic biomarkers for GC prediction among the clinical traits (P < 0.001). Moreover, two independent cohort GEO dataset (GSE13911 and GSE70800) was employed to evaluate the specificity and sensitivity of the prognostic gene and prognostic lncRNA. Gene set enrichment analysis (GSEA) result for the risk model revealed that these gene involved in ECM receptor interaction pathway, ERBB signaling pathway, UBIQUITIN mediated proteolysis, cell adhesion molecules CAMS, ECM receptor interaction, focal adhesion, pathways in cancer and TGF beta signaling pathway, meaning that the prognostic gene risk model and lncRNAs risk model play an crucial role and have important prognostic values.ConclusionThese findings may have important significance in understanding the molecular mechanism of GC and potential therapeutic method for the GC patients.

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