Multi-omics profiling and digital image analysis reveal the potential prognostic and immunotherapeutic properties of CD93 in stomach adenocarcinoma

Abstract

Background: Recent evidence highlights the fact that immunotherapy has significantly improved patient outcomes. CD93, as a type I transmembrane glycoprotein, was correlated with tumor-associated angiogenesis; however, how CD93 correlates with immunotherapy in stomach adenocarcinoma (STAD) remains unclear.

Methods: TCGA, GTEx, GEO, TIMER2.0, HPA, TISIDB, TCIA, cBioPortal, LinkedOmics, and ImmuCellAI public databases were used to elucidate CD93 in STAD. Visualization and statistical analysis of data were performed by R (Version 4.1.3), GraphPad (Version 8.0.1), and QuPath (Version 0.3.2).

Results: CD93 was highly expressed in STAD compared with adjacent normal tissues. The overexpression of CD93 was significantly correlated with a poor prognosis in STAD. There was a negative correlation between CD93 expression levels with CD93 mutation and methylation in STAD. Our results revealed that CD93 expression was positively associated with most immunosuppressive genes (including PD-1, PD-L1, CTLA-4, and LAG3), immunostimulatory genes, HLA, chemokine, and chemokine receptor proteins in STAD. Furthermore, in STAD, CD93 was noticeably associated with the abundance of multiple immune cell infiltration levels. Functional HALLMARK and KEGG term enhancement analysis of CD93 through Gene Set Enrichment Analysis was correlated with the process of the angiogenesis pathway. Subsequently, digital image analysis results by QuPath revealed that the properties of CD93⁺ cells were statistically significant in different regions of stomach cancer and normal stomach tissue. Finally, we utilized external databases, including GEO, TISIDB, ImmuCellAI, and TCIA, to validate that CD93 plays a key role in the immunotherapy of STAD.

Conclusion: Our study reveals that CD93 is a potential oncogene and is an indicative biomarker of a worse prognosis and exerts its immunomodulatory properties and potential possibilities for immunotherapy in STAD.

Keywords: CD93; digital image analysis; immunotherapy; multi-omics; stomach adenocarcinoma.

Figure 1

(A) CD93 expression difference between stomach adenocarcinoma (STAD) and adjacent normal tissue in The Cancer Genome Atlas (TCGA) database. (B) CD93 expression difference between STAD and adjacent normal tissue in the integrated database of TCGA and GTEx. ***p < 0.001.

Figure 2

(A) CD93 mutation frequency in stomach adenocarcinoma (STAD) according to the cBioportal database. (B) CD93 expression level in different mutation types in STAD according to the cBioportal database. (C) CD93 expression level in different copy number variation types in STAD according to the cBioportal database. (D) Correlation between CD93 expression and CD93 methylation in STAD (Spearman, r = -0.387, p < 0.001).

Figure 3

Forest plot showing the univariate (A) and multivariate (B) Cox regression analysis results in stomach adenocarcinoma (STAD). (C) Kaplan–Meier curves showing that the high CD93 expression was correlated with poor prognosis in STAD.

Figure 4

Expression correlations between CD93 and immune-related genes according to TIMER2.0 database: (A) chemokine receptor genes, (B) HLA, (C) immunosuppressive genes, (D) chemokine genes, and (E) immunostimulatory genes in stomach adenocarcinoma.

Figure 5

(A) Correlation of CD93 expression with stromal score (Spearman, r = 0.62, p = 8.3e-43), immune score (Spearman, r = 0.34, p = 3.2e-12), and ESTIMATE score (Spearman, r = 0.52, p = 2.4e-28) in stomach adenocarcinoma (STAD). (B) Correlations of CD93 expression and immune cell infiltration level in STAD.

Figure 6

Result of Gene Set Enrichment Analysis (GSEA). GSEA (HALLMARK (A, B) and Kyoto Encyclopedia of Genes and Genomes (C, D) between CD93 high- and low-expression groups in stomach adenocarcinoma.

Figure 7

Human Protein Atlas immunohistochemical staining image analysis results by QuPath. (A) Results of tissue segmentation and cell recognition of stomach cancer and normal stomach tissue. (B) The integral optical density and average optical density of CD93⁺ cells in stomach cancer and normal stomach tissue were statistically significant. The CD93⁺ cell number (C), positive rate (D), and density (E) were statistically significant in different regions of stomach cancer and normal stomach tissue. (F) Contrast results of CD93⁺ cell number and optical density values of CD93⁺ cells in different regions of stomach cancer. Student’s t-test (meet the normal distribution) and Mann–Whitney U-tests (do not meet the normal distribution). *p < 0.05. ns, no significance.

Figure 8

(A) Kaplan–Meier curves showing overall survival in 432 stomach adenocarcinoma patients from GEO database (GSE26253) [log-rank test, HR = 0.70 (0.52–0.95), p = 0.0220]. (B) CD93 expression difference between immunotherapy responders and non-responders according to TISIDB database. (C) Predicted results of immune checkpoint blockade therapy response based on ImmuCellAI database. (D) The immunophenotype scores in low-CD93 group and high-CD93 group. *p < 0.05, ****p < 0.0001. ns, no significance.