Multi-bioinformatics revealed potential biomarkers and repurposed drugs for gastric adenocarcinoma-related gastric intestinal metaplasia

Abstract

Biomarkers associated with the progression from gastric intestinal metaplasia (GIM) to gastric adenocarcinoma (GA), i.e., GA-related GIM, could provide valuable insights into identifying patients with increased risk for GA. The aim of this study was to utilize multi-bioinformatics to reveal potential biomarkers for the GA-related GIM and predict potential drug repurposing for GA prevention in patients. The multi-bioinformatics included gene expression matrix (GEM) by microarray gene expression (MGE), ScType (a fully automated and ultra-fast cell-type identification based solely on a given scRNA-seq data), Ingenuity Pathway Analysis, PageRank centrality, GO and MSigDB enrichments, Cytoscape, Human Protein Atlas and molecular docking analysis in combination with immunohistochemistry. To identify GA-related GIM, paired surgical biopsies were collected from 16 GIM-GA patients who underwent gastrectomy, yielding 64 samples (4 biopsies per stomach x 16 patients) for MGE. Co-analysis was performed by including scRNAseq and immunohistochemistry datasets of endoscopic biopsies of 37 patients. The results of the present study showed potential biomarkers for GA-related GIM, including GEM of individual patients, individual genes (such as RBP2 and CD44), signaling pathways, network of molecules, and network of signaling pathways with key topological nodes. Accordingly, potential treatment targets with repurposed drugs were identified including epidermal growth factor receptor, proto-oncogene tyrosine-protein kinase Src, paxillin, transcription factor Jun, breast cancer type 1 susceptibility protein, cellular tumor antigen p53, mouse double minute 2, and CD44.

Fig. 1. GEM.

Of note, the gene expression heatmaps annotated according to histology, GHAI and location of lesions as well as single cell atlas in individual patients (a) and in GIM, normal and GA (b). Patient numbers (1-16, except 6) and sample numbers below heatmaps were included.

Fig. 2. UMAP plots.

Of note, cell type clusters (a) and annotations according to the pathological appearances (b, c) and low expression of the RBP2 gene in GA (d) but high expression in GIM (e). CAG chronic atrophic gastritis, EGA early gastric adenocarcinoma, IMS severe intestinal metaplasia, IMM mild intestinal metaplasia, and NAG non-atrophic gastritis.

Fig. 3. Study design.

Canonical pathway activation and inhibition in GIM (a) and GA (b). Note: Pathways with obtained z-scores included [z-score > 0: activation (red); z-score < 0: inhibition (green); z-scores from −4 to 6 in (a) and from −6 to 6 in (b)]. Pearsons correlation with linear regression between GIM and GA (c). r = -0.5677 and p < 0.001 in (c).

Fig. 4

Network of molecules highlighting 23 proteins/genes/metabolites characteristic to GA-related GIM, particularly in connection to stem cell-related Wnt-HIPPO genes. Note: sizes of nodes and genes reflect numbers of interactions.

Fig. 5. Network of signaling pathway characterized as ‘Team’ for GA-related GIM.

Note: sizes of nodes reflect percentages of team-related genes per pathway and links presents the connections between pathways (a); percentages of gene/team (b) (for detailed percentages of each team, see Table S3).

Fig. 6. Protein-protein interactions.

Note: Hub proteins in GIM-GA “niche” (indicated in dashed line) and links with the repurposed drugs (Rx, indicated in orange) revealed by IPA analysis.

Fig. 7. Representative immunohistochemistry of 9 hub proteins.

Note: tissue microarray at Human Protein Atlas (a) and immunofluorescent staining (b) in which CD44 is indicated in green and Ki67 in red, and CD44 on goblet cells in (arrows). Bar = 100 μm.

Fig. 8

Study design showing multi-bioinformatics used for analysis of surgical biopsies (4 samples per stomach = total 60 samples from 16 patients), single-cell RNA sequencing from 13 patients, immunohistochemistry from 19 patients.