Integrated analysis reveals prognostic value and progression-related role of AMIGO2 in prostate cancer

Role of AMIGO2 in cancer progression: Novel insights (Review)

Adhesion molecule with IgG-like domain 2 (AMIGO2) is a novel scaffold protein initially identified in cerebellar granule neurons, and inhibits apoptosis of neurons. It is also widely expressed in various malignant tumors, including gastric cancer, colorectal carcinoma, ovarian cancer, prostate cancer and melanoma. During the past decades, it has been revealed that AMIGO2 can act as an oncogene, participating in tumor occurrence and development, for example by inhibiting apoptosis, accelerating cell proliferation, migration and adhesion, and promoting tumor metastasis and drug resistance. The present review discusses the recent advancements regarding AMIGO2 in the field of cancer, emphasizing its related molecular mechanisms to identify novel therapeutic strategies targeting AMIGO2 for cancer treatment in the future.

AMIGO2 is involved in the spread of peritoneal metastasis in serous ovarian cancer via promoting adhesion to the peritoneal mesothelial cells

BACKGROUND

Amphoterin-induced gene and open reading frame 2 (AMIGO2) have been reported to be related to the prognosis of colorectal, gastric, and cervical cancer. However, their association with ovarian cancer remains unclear. This study aimed to elucidate the role of AMIGO2 in ovarian cancer.

METHODS

AMIGO2 expression was evaluated using immunohistochemistry in patients with ovarian serous carcinoma. We validated in vitro studies using four serous ovarian cancer cell lines and in vivo studies using a murine model.

RESULTS

The AMIGO2-high group had significantly shorter progression-free survival (PFS) than the AMIGO2-low group. The predictive index of the AMIGO2-high group was considerably higher than that of the AMIGO2-low group. The rate of complete cytoreductive surgery was lower in the AMIGO2-high group than in the AMIGO2-low group. Moreover, in vitro studies revealed that four serous ovarian cancer cell lines exhibited AMIGO2 expression and adhesion to mesothelial cells. Adhesion to mesothelial cells was attenuated by AMIGO2 knockdown in SKOV3 and SHIN3 cells. Furthermore, AMIGO2 downregulation in SKOV3 cells significantly suppressed peritoneal dissemination in the murine model.

CONCLUSION

These results suggest that high AMIGO2 expression in serous ovarian carcinoma cells contributes to a poor prognosis by promoting peritoneal metastasis through enhanced cell adhesion to mesothelial cells.

Machine learning-based approach for efficient prediction of diagnosis, prognosis and lymph node metastasis of papillary thyroid carcinoma using adhesion signature selection

The association between adhesion function and papillary thyroid carcinoma (PTC) is increasingly recognized; however, the precise role of adhesion function in the pathogenesis and prognosis of PTC remains unclear. In this study, we employed the robust rank aggregation algorithm to identify 64 stable adhesion-related differentially expressed genes (ARDGs). Subsequently, using univariate Cox regression analysis, we identified 16 prognostic ARDGs. To construct PTC survival risk scoring models, we employed Lasso Cox and multivariate + stepwise Cox regression methods. Comparative analysis of these models revealed that the Lasso Cox regression model (LPSRSM) displayed superior performance. Further analyses identified age and LPSRSM as independent prognostic factors for PTC. Notably, patients classified as low-risk by LPSRSM exhibited significantly better prognosis, as demonstrated by Kaplan-Meier survival analyses. Additionally, we investigated the potential impact of adhesion feature on energy metabolism and inflammatory responses. Furthermore, leveraging the CMAP database, we screened 10 drugs that may improve prognosis. Finally, using Lasso regression analysis, we identified four genes for a diagnostic model of lymph node metastasis and three genes for a diagnostic model of tumor. These gene models hold promise for prognosis and disease diagnosis in PTC.