A potential role for metastasis-associated in colon cancer 1 (MACC1) as a pan-cancer prognostic and immunological biomarker

Immune Response and Metastasis-Links between the Metastasis Driver MACC1 and Cancer Immune Escape Strategies

Metastasis remains the most critical factor limiting patient survival and the most challenging part of cancer-targeted therapy. Identifying the causal drivers of metastasis and characterizing their properties in various key aspects of cancer biology is essential for the development of novel metastasis-targeting approaches. Metastasis-associated in colon cancer 1 (MACC1) is a prognostic and predictive biomarker that is now recognized in more than 20 cancer entities. Although MACC1 can already be linked with many hallmarks of cancer, one key process-the facilitation of immune evasion-remains poorly understood. In this review, we explore the direct and indirect links between MACC1 and the mechanisms of immune escape. Therein, we highlight the signaling pathways and secreted factors influenced by MACC1 as well as their effects on the infiltration and anti-tumor function of immune cells.

Curcumin inhibits malignant behavior of colorectal cancer cells by regulating M2 polarization of tumor-associated macrophages and metastasis associated in colon cancer 1 (MACC1) expression

The present study was to investigate the underlying mechanism of the antitumor effect of curcumin in colorectal cancer cells, focusing on the M2 polarization of tumor-associated macrophages (TAMs). The effect of curcumin on the malignant behavior of colorectal cancer cells was investigated by WST assay for cell growth, and Transwell assay for cell migration/invasion. THP-1 cells were differentiated into macrophages and coculture with colorectal cancer cells to study the influence of curcumin on M2 polarization, presenting as the levels of ARG1 mRNA, IL-10, and CD163-positive cells. GEO database was searched for the shared altered gene of curcumin in colorectal cells and human monocytes. Molecular docking was used to visualize the binding between curcumin and MACC1. Curcumin restricted the proliferation, apoptosis, and migration/invasion of HCT 116 and SW620 cells. Curcumin attenuated levels of the M2 macrophage markers, CD163 + cells, IL-10 secretion, and ARG1 mRNA. MACC1 was a target of curcumin in colorectal cancer cells, relating to macrophage. Rescue experiments showed that MACC1 overexpression can reverse the antitumor effect of curcumin in colorectal cancer cells and M2 polarization of TAMs. Curcumin's antiproliferative and anti-migratory effects in colorectal cancer cells may be mediated by MACC1 and inhibition of M2 polarization of TAMs.

MACC1 as a Potential Target for the Treatment and Prevention of Breast Cancer

Metastasis associated in colon cancer 1 (MACC1) is an oncogene first identified in colon cancer. MACC1 has been identified in more than 20 different types of solid cancers. It is a key prognostic biomarker in clinical practice and is involved in recurrence, metastasis, and survival in many types of human cancers. MACC1 is significantly associated with the primary tumor, lymph node metastasis, distant metastasis classification, and clinical staging in patients with breast cancer (BC), and MACC1 overexpression is associated with reduced recurrence-free survival (RFS) and worse overall survival (OS) in patients. In addition, MACC1 is involved in BC progression in multiple ways. MACC1 promotes the immune escape of BC cells by affecting the infiltration of immune cells in the tumor microenvironment. Since the FGD5AS1/miR-497/MACC1 axis inhibits the apoptotic pathway in radiation-resistant BC tissues and cell lines, the MACC1 gene may play an important role in BC resistance to radiation. Since MACC1 is involved in numerous biological processes inside and outside BC cells, it is a key player in the tumor microenvironment. Focusing on MACC1, this article briefly discusses its biological effects, emphasizes its molecular mechanisms and pathways of action, and describes its use in the treatment and prevention of breast cancer.

MACC1 Correlates with Tumor Progression and Immune Cell Infiltration of Colon Adenocarcinoma and is Regulated by the lncRNA ZFAS1/miR-642a-5p Axis

Colon adenocarcinoma (COAD) is the most common pathologic type of colon cancer. Metastasis is responsible for the high mortality rate of patients with COAD. The gene, metastasis-associated in colon cancer 1 (MACC1), is a biomarker predictive of both metastatic and metastasis-free survival in patients with colon cancer and other solid tumors. However, the underlying mechanism by which MACC1 affect COAD progression and metastasis remains unknown. In this study, we analyzed the expression level and prognostic value of MACC1, as well as their correlation, in patients with various types of cancer included in The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases. High MACC1 expression was found to be significantly associated with poor prognosis in patients with COAD. Analysis of the potential upstream miRNA of MACC1 showed that miR-642a-5p was downregulated in COAD and was negatively correlated with MACC1 expression. Analysis of the upstream regulators of miR-642a-5p showed that the long non-coding RNA (lncRNA) ZFAS1was the most likely upstream regulator of miR-642a-5p. In addition, the expression of MACC1 correlated positively with tumor immune cell infiltration, as well as with the levels of biomarkers of five kinds of immune cells. In summary, these findings suggest that MACC1 contributes to COAD progression and immune cell infiltration via the ZFAS1/miR-642a-5p/MACC1 axis.

Downregulation of MACC1 facilitates the reversal effect of verapamil on the chemoresistance to active metabolite of irinotecan in human colon cancer cells

The aim of this study is to investigate the reversal effect of verapamil (VER) on chemoresistance to irinotecan (CPT-11) in human colon cancer cells and relevant mechanisms. Cell counting kit-8 (CCK-8) test and colony-forming unit (CFU) experiment results show that VER strengthens the sensitivity of human colon cancer cell line HT29 to CPT-11 but has a small effect on SW480 cells. High-throughput transcriptome sequencing, RT-PCR, and Western blot results show that the inhibition of metastasis-associated in colon cancer-1 (MACC1) expression by VER is the key factor for reversal effect on chemoresistance to CPT-11. Transfection experiments further show that VER can reverse the resistance of human colon cancer cells to SN-38, the active metabolite of CPT-11, when MACC1 is overexpressed. The nude mouse transplantation tumor experiment provides an in vivo proof that VER can strengthen sensitivity to CPT-11 in drug-resistant human colon cancer cells, and the effect might be related to the inhibited expression of MACC1. In summary, VER might strengthen the reversal effect of VER on chemoresistance to CPT-11 in human colon cancer cells and facilitate the apoptosis of human colon cancer cells by downregulating MACC1 expression.

Special issue: informatics & data-driven medicine-2021

Modern medical diagnosis, treatment, or rehabilitation problems of the patient reach completely different levels due to the rapid development of artificial intelligence tools. Methods of machine learning and optimization based on the intersection of historical data of various volumes provide significant support to physicians in the form of accurate and fast solutions of automated diagnostic systems. It significantly improves the quality of medical services. This special issue deals with the problems of medical diagnosis and prognosis in the case of short datasets. The problem is not new, but existing machine learning methods do not always demonstrate the adequacy of prediction or classification models, especially in the case of limited data to implement the training procedures. That is why the improvement of existing and development of new artificial intelligence tools that will be able to solve it effectively is an urgent task. The special issue contains the latest achievements in medical diagnostics based on the processing of small numerical and image-based datasets. Described methods have a strong theoretical basis, and numerous experimental studies confirm the high efficiency of their application in various applied fields of Medicine.