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Updated: May 21, 2025

Predictive Immune Modeling of Solid Tumors
Published on: February 25, 2020
MACC1 is a potential prognostic biomarker for cancer immunotherapy in lung adenocarcinoma
Changqie Pan1, Zhiyuan Zhou2, Jun Cao1
1Thoracic Medicine Department 1, Hunan Cancer Hospital/The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, No. 283, Tongzipo Road, Yuelu District, Changsha, Hunan 410013, China.
Abstract:
Our team previously reported that MACC1 levels are closely related to a variety of tumors and the efficacy of immune checkpoint blockade (ICB) therapy. However, the predictive value of MACC1 levels for lung adenocarcinoma (LUAD) immunotherapy has not been studied. This study aimed to investigate the predictive effect of the oncogene MACC1 on ICB reactivity in patients with LUAD. First, the expression patterns and clinical features of MACC1 in The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases were comprehensively evaluated using R packages. We subsequently assessed the correlations between MACC1 and immunological characteristics in the LUAD tumor microenvironment (TME) using the CIBERSORT algorithm. The results revealed that MACC1 overexpression was significantly correlated with 3 immune checkpoints, 14 tumor-infiltrating immune cells (TIICs), 9 immunomodulators, 5 anticancer immune process activities, and 3 effector genes of TIICs in LUAD. Additionally, on the basis of the prognostic genes from LASSO analysis, we developed the MACC1-related Risk Score (MRRS), which can accurately predict the prognosis and response to cancer immunotherapy in LUAD patients (HR = 3.50, AUC at 1, 2, and 3 years = 0.737, 0.744, and 0.724, respectively). Finally, in vivo experiments revealed that the combination of MACC1 silencing and PD-L1 inhibitors significantly inhibits tumor progression. These findings increase our understanding of MACC1 as a potential prognostic biomarker and potential therapeutic target for cancer immunotherapy. The MRRS may play a critical role in predicting the response of LUAD patients to ICB therapy.
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