Results of test training set. (A) The predicted results of the GC subtypes by using the NN model. (B) The predicted results of the GC subtypes by using LR model. The x axis represents real category labels, with the values of the four GC subtypes determined as 0, 0.33, 0.66 and 1, respectively. The y axis represents predicted category labels. GC, gastric cancer; NN, neural network; LR, logistic regression. 

Results of test training set. (A) The predicted results of the GC subtypes by using the NN model. (B) The predicted results of the GC subtypes by using LR model. The x axis represents real category labels, with the values of the four GC subtypes determined as 0, 0.33, 0.66 and 1, respectively. The y axis represents predicted category labels. GC, gastric cancer; NN, neural network; LR, logistic regression. 

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The present study aimed to classify gastric cancer (GC) into subtypes and to screen the subtype‑specific genes, their targeted microRNAs (miRNAs) and enriched pathways to explore the putative mechanism of each GC subtypes. The GSE13861 data set was downloaded from the Gene Expression Omnibus and used to screen differential expression genes (DEGs) i...

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... pylori infection rate in each GC subtype. H. pylori infection rate in each GC subtype was analyzed as aforementioned. The NN model was a more accurate method to distinguish the four GC subtypes compared with the LR model ( Fig. 4A and B, respectively); the NN model was therefore used to predict the GC subtypes for all samples (Table III), and all the GC samples were divided into the four testing-set. Subsequently, the four testing-set was used to predict the subtype of the 100 GC samples in the PMID:24816253 data set. Notably, the H. pylori infection rate in ...

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... In the miR-338/CCL21/NF-κB pathway, miR-338 may increase apoptosis of GC cells by targeting CCL21 leading to NF-κB activation [31]. In GC patients, there has been an association between overexpression of CCL21 in tumor tissues and metastatic features as well as a suboptimal clinical outcome [32]. ...
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... In previous studies, extensive analysis on H.pylori infection and GC datasets has been performed and the link between H.pylori infection and GC has been reported with the involvement of similar genes and pathways [13][14][15] . However, comparative analysis on H.pylori infection and GC samples from multi-population studies have not been performed previously by the integration of microarray and RNA-seq gene expression profiling. ...
... Previously, scientists have performed differential expression analysis on H.pylori infection and GC datasets including microarray and RNA-seq analysis [13][14][15] . However, these studies have generated, analyzed and explored data of a targeted population to understand the genetic variations in H.pylori infection and GC. ...
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... Recently, miR-338/CCL21/NF-kB signaling, miR-146b/ PSMD3/proteasome, miR-34a/VCL/focal adhesión and miR-34c/VCL/focal adhesion subtype-specific subpaths were identified and might have a role in GC development and classification, although data on their prognostic value have not been published (40). ...
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... miR-338 inhibition of the NF-κB signaling pathway. Li et al (61) screened differentially expressed genes from 90 samples in the Gene Expression Omnibus database and classified gastric cancer into 4 subtypes based on different target miRNAs. The most important feature of the second subtype was the miR-338/C-C motif chemokine ligand (CCL)21/NF-κB signaling pathway. ...
... miR-198/protein inhibitor of activated STAT4/NF-κB and miR-370/CCL21/NF-κB also belonged to this subtype. It was indicated that miR-338 is able to inhibit the proliferation and migration of gastric cancer cells (61). miR-7 inhibition of the NF-κB signaling pathway. ...
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... Gastric cancer (GC) is one of the most common gastrointestinal malignancies in the clinic; in 2015, 1,313,000 people were diagnosed with GC, and 813,000 people died from it [1][2][3][4][5][6][7][8][9]. GC displays high heterogeneity with respect to histopathological and epidemiological characteristics [1,10] and can be divided into proximal nondiffuse, diffuse, and distal nondiffuse subtypes [1,11]. Gastric adenocarcinoma (GA) is the primary pathological type associated with environmental factors, including a high-salt diet, infectious agents, and smoking, and this form of GC is characterized by ease of invasion and metastasis and a very low early diagnosis rate [5,12]. ...
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Gastric cancer (GC) is the most common malignancy of the stomach. This study was aimed at elucidating the regulatory network of circRNA-miRNA-mRNA and identifying the precise inflammation-related targets in GC. The expression profiles of GSE83521, GSE78091, and GSE33651 were obtained from the GEO database. Interactions between miRNAs and circRNAs were investigated by the Circular RNA Interactome, and targets of miRNAs were predicted with miRTarBase. Then, a circRNA/miRNA/mRNA regulatory network was constructed. Also, functional enrichment analysis of selected differentially expressed genes (DEGs) was performed. The inflammation-/GC-related targets were collected in the GeneCards and GenLiP3 database, respectively. And a protein-protein interaction (PPI) network of DE mRNAs was constructed with STRING and Cytoscape to identify hub genes. The genetic alterations, neighboring gene networks, expression levels, and the poor prognosis of hub genes were investigated in cBioPortal, Oncomine, and Human Protein Atlas databases and Kaplan-Meier plotter, respectively. A total of 10 DE miRNAs and 33 DEGs were identified. The regulatory network contained 26 circRNAs, 10 miRNAs, and 1459 mRNAs. Functional enrichment analysis revealed that the selected 33 DEGs were involved in negative regulation of fat cell differentiation, response to wounding, extracellular matrix- (ECM-) receptor interaction, and regulation of cell growth pathways. THBS1, FN1, CALM1, COL4A1, CTGF, and IGFBP5 were selected as inflammation-related hub genes of GC in the PPI network. The genetic alterations in these hub genes were related to amplification and missense mutations. Furthermore, the genes RYR2, ERBB2, PI3KCA, and HELZ2 were connected to hub genes in this study. The hub gene levels in clinical specimens were markedly upregulated in GC tissues and correlated with poor overall survival (OS). Our results suggest that THBS1, FN1, CALM1, COL4A1, CTGF, and IGFBP5 were associated with the pathogenesis of gastric carcinogenesis and may serve as biomarkers and inflammation-related targets for GC. 1. Introduction Gastric cancer (GC) is one of the most common gastrointestinal malignancies in the clinic; in 2015, 1,313,000 people were diagnosed with GC, and 813,000 people died from it [1–9]. GC displays high heterogeneity with respect to histopathological and epidemiological characteristics [1, 10] and can be divided into proximal nondiffuse, diffuse, and distal nondiffuse subtypes [1, 11]. Gastric adenocarcinoma (GA) is the primary pathological type associated with environmental factors, including a high-salt diet, infectious agents, and smoking, and this form of GC is characterized by ease of invasion and metastasis and a very low early diagnosis rate [5, 12]. Despite the tremendous efforts that have been paid in the diagnosis of GC, together with improvements in surgical techniques and targeted chemotherapy in recent years, the prognosis of patients with GC remains unsatisfactory due to the prevalence of diagnosis of the disease at advanced stages that are often accompanied by lymphatic metastasis, which limits successful therapeutic strategies [7]. Additionally, cancer-related systemic inflammation is recognized as a flag sign of cancer-related cachexia development and progression, which results in an inappropriate systemic reaction, including fevers, weight loss, and sweats [13]. Therefore, further exploration of appropriate molecular biomarkers for early diagnosis and identification of potential inflammation-related targets of GC are urgently needed to expand the promising strategies to enhance the therapeutic efficacy and clinical prognosis in patients with GA. Circular RNA (circRNA) is a novel type of endogenous, noncoding RNA, which is also widely expressed in different species and has been demonstrated to belong to a class of RNAs with tissue-/developmental-stage specificity, making them potential diagnostic and prognostic biomarkers [3, 14]. MicroRNAs (miRNAs) are a special type of small noncoding RNAs comprising approximately 21 nucleotides that bind to the 3-untranslated regions (3-UTRs) of target messenger RNAs (mRNAs) and leading to mRNA degradation or blocking of translation, which contribute to the central regulation of cell proliferation, differentiation, and apoptosis [9, 15, 16]. Recent studies have revealed that the abundance and evolutionary conservation of circRNAs play significant effects in regulating cancer progression-related processes such as metastasis, apoptosis, and invasion and that circRNAs primarily function as miRNA sponges, thereby relieving miRNA-mediated target repression. For instance, circRNA-ZFR serves as a sponge of miR-130a/miR-107 and modulates PTEN expression, resulting in the suppression of GC cell proliferation and the promotion of apoptosis [17]. Another report by Zhang et al. showed that circRNA-LARP4 inhibits cell proliferation and invasion in GC by sequestering miR-424-5p and regulating LATS1 expression [18]. Besides, it has been reported that knockdown of ciRS-133 reduced cancer cachexia by activating PRDM16 and suppressing miR-133 in tumor-implanted mice [19]. Although several circRNAs have been identified to participate in the pathogenesis of GC, it is still necessary to conduct a more comprehensive analysis of circRNA-miRNA-mRNA regulatory networks and to identify potential inflammation-related targets in GC. In this study, we performed an integrated analysis of circRNA expression profiles in GC from the Gene Expression Omnibus (GEO) database. miRNAs and mRNAs from GEO datasets were employed to distinguish the circRNA-related dysregulated miRNAs and the miRNA-related dysregulated mRNAs, and then, a circRNA/miRNA/mRNA network was constructed to elucidate the relationships among differentially expressed (DE) circRNAs, miRNAs, and mRNAs. >GO (Gene Ontology) and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway enrichment analysis revealed the potential biological functions of selected miRNA target genes (DEGs). Besides, the inflammation-/GC-related targets were collected in the GeneCards and GenLiP3 database, respectively. Then, a PPI network of the selected DE mRNAs was constructed to identify the inflammation-related hub genes based on the value of the overall degree, node betweenness, and closeness through network topology calculations. Furthermore, genetic alterations and neighboring gene networks were investigated by cBioPortal, and the expression transcriptional level of hub genes was explored using Oncomine databases with subsequent validation using the Human Protein Atlas. Finally, survival analysis of hub genes was performed using the Kaplan-Meier plotter. These findings may enable us to identify novel diagnostic or prognostic biomarkers and suggested that THBS1, FN1, CALM1, COL4A1, CTGF, and IGFBP5 might be inflammation-related candidate targets for GC. The workflow of the current study is shown in Figure 1.