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. 2024 Apr 25;10(9):e30253.
doi: 10.1016/j.heliyon.2024.e30253. eCollection 2024 May 15.

Prediction of novel biomarkers for gastric intestinal metaplasia and gastric adenocarcinoma using bioinformatics analysis

Affiliations

Prediction of novel biomarkers for gastric intestinal metaplasia and gastric adenocarcinoma using bioinformatics analysis

Mohammad Reza Eskandarion et al. Heliyon. .

Abstract

Background & aim: The histologic and molecular changes from intestinal metaplasia (IM) to gastric cancer (GC) have not been fully characterized. The present study sought to identify potential alterations in signaling pathways in IM and GC to predict disease progression; these alterations can be considered therapeutic targets.

Materials & methods: Seven gene expression profiles were selected from the GEO database. Discriminate differentially expressed genes (DEGs) were analyzed by EnrichR. The STRING database, Cytoscape, Gene Expression Profiling Interactive Analysis (GEPIA), cBioPortal, NetworkAnalyst, MirWalk database, OncomiR, and bipartite miRNA‒mRNA correlation network was used for downstream analyses of selected module genes.

Results: Analyses revealed that extracellular matrix-receptor interactions (ITGB1, COL1A1, COL1A2, COL4A1, FN1, COL6A3, and THBS2) in GC and PPAR signaling pathway interactions (FABP1, APOC3, APOA1, HMGCS2, and PPARA and PCK1) in IM may play key roles in both the carcinogenesis and progression of underlying GC from intestinal metaplasia. IM enrichment indicated that this is closely related to digestion and absorption. The TF-hub gene regulatory network revealed that AR, TCF4, SALL4, and ESR1 were more important for hub gene expression. It was revealed that the development and prediction of GC may be affected by hsa-miR-29. It was found that PTGR1, C1orf115, CRYL1, ALDOB, and SULT1B1 were downregulated in GC and upregulated in IM. Therefore, they might have tumor suppressor activity in GC progression.

Conclusion: New potential biomarkers and pathways involved in GC and IM were identified that are important for the transformation of GC from IM to adenocarcinoma and can be therapeutic targets for GC.

Keywords: Bioinformatics analysis; Differentially expressed genes; Gastric cancer; Intestinal metaplasia; MicroRNA; TF.

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Conflict of interest statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Fig. 1
Fig. 1
A flow diagram of the study. GC: Gastric Cancer; IM: Intestinal Metaplasia; GEO: Gene Expression Omnibus; DEG: Differentially Expressed Gene; DAVID: Database for Annotation, Visualization, and Integrated Discovery; STRING: Search Tool for the Retrieval of Interacting Genes; KEGG: Kyoto Encyclopedia of Genes and Genomes; MCODE: Molecular Complex Detection; GEPIA: Gene Expression Profiling Interactive Analysis.
Fig. 2
Fig. 2
Analysis of the top genes in GC (a) and IM (b) by KEGG pathway enrichment.
Fig. 3
Fig. 3
Differentially expressed eight hub genes in GC patients (3A) and six hub genes in IM patients (3B) (red) compared to healthy controls (gray) according to the GEPIA online database (P < 0.05). (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
Fig. 4
Fig. 4
The Kaplan-Meier plotter used to determine the prognostic value of eight core genes in GC (4A) and six hub genes in IM (4B) and significantly related to the survival rate (P < 0.05).
Fig. 5
Fig. 5
Transcriptional regulatory networks (TRNs) consisting of 78 edges and 66 nodes were constructed for the hub genes (Fig. 5A). Various hub genes were regulated by TFs with a degree ≥2. The gastric tissue filters used are shown in Fig. 5B.
Fig. 6
Fig. 6
Bipartite miRNA‒mRNA regulatory network of important miRNAs involved in GC progression from IM to adenocarcinoma.

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