Aberrant ARMCX1 Expression Is an Independent Predictor of Poor Prognosis in Gastric Cancer

J Oncol. 2022 May 5:2022:9348917. doi: 10.1155/2022/9348917. eCollection 2022.

Abstract

ARMCX1 (Armadillo repeat containing X-linked 1) is identified to be the novel tumor suppressor gene related to multiple tumor types. Nonetheless, its effect on gastric cancer (GC) is still poorly understood. The present work determined ARMCX1 level within GC and the relation with clinicopathological characteristics. This work also collected relevant information in The Cancer Genome Atlas (TCGA) database for investigating associations of ARMCX1 with clinicopathologic variables and then validated in our GC cohort. Receiver operating characteristic (ROC) curves were plotted for assessing whether ARMCX1 expression was significant in diagnosing GC. Kaplan-Meier (KM) and Cox regression analyses were conducted for assessing clinicopathological characteristics associated with overall survival (OS) of GC cases. The data from the Human Protein Atlas (HPA) and Gene Expression Omnibus (GEO) databases was also analyzed for further validation, and biological processes (BPs) were identified by gene set enrichment analysis (GSEA). GC tissues showed markedly decreased ARMCX1 level relative to healthy counterparts (P < 0.001). Interestingly, ARMCX1 upregulation predicted low differentiation, poor OS, increased invasion, and late tumor stage. In addition, the area under ROC curve (AUC) and P value were 0.747 and <0.001, separately. Cases showing ARMCX1 upregulation showed significantly poor prognostic outcome compared with patients showing downregulation (P = 0.007). Furthermore, multivariate analysis showed that ARMCX1 upregulation independently predicted the risk of OS (P = 0.0017, hazard ratio, 1.089). GSEA analysis identified that several cancer-related pathways, such as focal adhesion, ECM receptor interaction, JAK/STAT, melanoma, WNT, and cancer, were enriched in GCs. We conclude that ARMCX1 serves as the possibly independent biomarker to diagnose and predict GC prognostic outcome.