The Pan-Cancer Crosstalk Between the EFNA Family and Tumor Microenvironment for Prognosis and Immunotherapy of Gastric Cancer

Background: EFNA1–5 have important physiological functions in regulating tumorigenesis and metastasis. However, correlating EFNA genes in the tumor immune microenvironment (TIME), and the prognosis of patients with gastric cancer remains to be determined. Methods: Using public databases, the expression of EFNA1-5 in pan-cancer and gastric cancer was comprehensively analyzed using UCSC Xena, the Oncomine dataset and UALCAN. We further completed survival analysis by Kaplan-Meier plotter to evaluate the prognosis of the high and low expression groups of the EFNAs gene in patients with gastric cancer. The TIMER tool was used to reveal the correlation between immune cell infiltration and genes of interest. Spearman correlation was used to find an association between the EFNA genes and tumor stem cells, TIME, microsatellite instability (MSI) or tumor mutational burden (TMB). We also used cBioportal, GeneMANIA and STRINGS to explore the types of changes in these genes and the protein interactions. Finally, we described the TIME based on QUANTISEQ algorithm, predicted the relationship between the EFNA genes and half-maximal inhibitory concentration (IC(50)), and analyzed the relationship between the EFNA family genes and immune checkpoints. Results: The expression of EFNA1, EFNA3, EFNA4, and EFNA5 was elevated in pan-cancer. Compared with normal adjacent tissues, EFNA1, EFNA3, and EFNA4 were up-regulated in gastric cancer. In terms of the influence on the survival of patients, the expression of EFNA3 and EFNA4 were related to overall survival (OS) and disease-free survival (DFS) for patients with gastric cancer. High expression of EFNA5 often predicted poor OS and DFS. In gastric cancer, the expression of EFNA3 and EFNA4 showed a significant negative correlation with B cells. The higher the expression of EFNA5, the higher the abundance of B cells, CD4+T cells and macrophages. CD8+T cells, dendritic cells infiltration and EFNA1-4 expression were negatively correlated. The infiltration of CD4+T cells, macrophages and neutrophils was negatively correlated with the expression of EFNA1, EFNA3, and EFNA4. TMB and MSI were positively correlated with EFNA3/EFNA4 expression. In the tumor microenvironment and drug sensitivity, EFNA3/4/5 also showed a significant correlation. In addition, we explored the relationship between the EFNA family genes and the immune microenvironment (B cells, M2 macrophages, monocytes, CD8(+) T cells, regulatory T cells, myeloid dendritic cells, natural killer cells, non-regulatory CD4(+) T cells), immune checkpoint (PDCD1, PDCD1LG2, CD274, CTLA4), and IC(50) of common chemotherapeutic drugs for gastric cancer (5-fluorouracil, cisplatin, docetaxel and gemcitabine). Conclusions: Our study provides new ideas for tumor treatment and prognosis from the perspective of TIME, and nominates EFNA1–5 to become potential therapeutic targets for gastric cancer.