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

Lv, Mengmeng; Jiao, Yunjuan; Yang, Bicheng; Ye, Mengchen; Di, Wenyu; Su, Wei; Zhong, Jiateng

doi:10.3390/biology12030455

Cited by 4 publications

(3 citation statements)

References 102 publications

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“…NaV1.7 is encoded by SCN9A . In the study by Xia et al (2016) , the expression of SCN9A correlated with the expression of the oncoprotein metastasis-associated in colon cancer-1 (MACC1), which significantly influences the development, invasion, and metastasis of various malignant cancers ( Lv et al, 2023 ). The members of the FXYD gene family are small ion transport regulators that interact with Na+/K+-ATPase.…”

Section: Discussionmentioning

confidence: 99%

SVM-DO: identification of tumor-discriminating mRNA signatures via support vectormachines supported by disease ontology

ÖZER,

ÖZBEK SARICA,

ARĞA

2023

Turkish Journal of Biology

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Background/aim The complicated nature of tumor formation makes it difficult to identify discriminatory genes. Recently, transcriptome-based supervised classification methods using support vector machines (SVMs) have become popular in this field. However, the inclusion of less significant variables in the construction of classification models can lead to misclassification. To improve model performance, feature selection methods such as enrichment analysis can be used to extract useful variable sets. The detection of genes that can discriminate between normal and tumor samples in the association of cancer and disease remains an area of limited information. We therefore aimed to discover novel and practical sets of discriminatory biomarkers by utilizing the association of cancer and disease. Materials and methods In this study, we employed an SVM classification method for differentially expressed genes enriched by Disease Ontology and filtered nondiscriminatory features using Wilk’s lambda criterion prior to classification. Our approach uses the discovery of disease-associated genes as a viable strategy to identify gene sets that discriminate between tumor and normal states. We analyzed the performance of our algorithm using comprehensive RNA-Seq data for adenocarcinoma of the colon, squamous cell carcinoma of the lung, and adenocarcinoma of the lung. The classification performance of the obtained gene sets was analyzed by comparison with different expression datasets and previous studies using the same datasets. Results It was found that our algorithm extracts stable small gene sets that provide high accuracy in predicting cancer status. In addition, the gene sets generated by our method perform well in survival analyses, indicating their potential for prognosis. Conclusion By combining gene sets for both diagnosis and prognosis, our method can improve clinical applications in cancer research. Our algorithm is available as an R package with a graphical user interface in Bioconductor ( https://doi.org/10.18129/B9.bioc.SVMDO ) and GitHub ( https://github.com/robogeno/SVMDO ).

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Section: Discussionmentioning

confidence: 99%

SVM-DO: identification of tumor-discriminating mRNA signatures via support vectormachines supported by disease ontology

ÖZER,

ÖZBEK SARICA,

ARĞA

2023

Turkish Journal of Biology

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“…Additionally, one of the functional outcomes of HGF/c-Met stimulation is the MACC1mediated induction of angiogenesis through increasing the production and secretion of VEGF [23,24]. VEGF, in turn, can directly influence the recruitment, differentiation, and activity of cells belonging to the innate as well as adaptive immune system.…”

Section: Macc1 Influences Immune Cell Infiltration and Tumor-immunity...mentioning

confidence: 99%

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

Torke,

Walther,

Stein

2024

Cancers

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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.

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“…MACC1 then binds to the promoter of the MET gene to increase the transcription and generate more MET protein as a receptor for HGF. Thus, the HGF-MET-MACC1 axis results in abnormal cell proliferation and an increased ability of cancer migration, invasion, and metastasis [18][19][20]. MACC1 overexpression could contribute to colorectal cancer progression and metastasis [18].…”

Section: Introductionmentioning

confidence: 99%

The impacts of MACC1 gene polymorphisms on urothelial cell carcinoma susceptibility and clinicopathologic characteristics in Taiwan

Chang,

Hung,

Chou

et al. 2023

J. Cancer

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Urothelial cell carcinoma (UCC) is a common malignancy of the urinary tract in Taiwan. Metastasis-Associated in Colon Cancer 1 (MACC1), a newly identified oncogene and regulator of the HGF/Met signaling pathway, has been shown to play a critical role in the development and progression of several types of cancer. Our study aims to investigate the impact of MACC1 gene polymorphisms on the clinicopathological features of patients with UCC. In this study, we included a total of 719 patients with UCC and 719 healthy controls. The genotyping of five MACC1 gene polymorphisms (rs1990172, rs975263, rs3095007, rs4721888, and rs3735615) was performed using real-time PCR with TaqMan assays. Our findings indicate that urothelial cancer patients with MACC1 rs3095007 A allele had a decreased risk of >T2 stage [Odds ratio (OR)=0.619, 95% CI=0.394-0.971, p=0.036] and lymph node invasion (OR=0.448, 95% CI=0.201-0.998, p=0.044). Additionally, these individuals were associated with longer relapse-free survival (p=0.007) and overall survival (p=0.028). In conclusion, our findings demonstrate that urothelial cancer patients with MACC1 (rs3095007) CA and AA genotypes have a lower risk of advanced T stage and lymph node metastasis. Additionally, these genotypes were associated with longer relapse-free survival and overall survival, highlighting the potential of these biomarkers as predictors of UCC prognosis.

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

Cited by 4 publications

References 102 publications

SVM-DO: identification of tumor-discriminating mRNA signatures via support vectormachines supported by disease ontology

SVM-DO: identification of tumor-discriminating mRNA signatures via support vectormachines supported by disease ontology

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

The impacts of MACC1 gene polymorphisms on urothelial cell carcinoma susceptibility and clinicopathologic characteristics in Taiwan

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