Journal of Cancer · 2026
Integrative bulk and single-cell transcriptomic analysis identify an ac4C-related signature in lung adenocarcinoma.
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Wang Shuo, Yang Changqing, Wang Xingkai, Zhu Zhigang, Xie Yuxin, Wang Rui, Liu Dan, Feng Jing
Аннотация
BACKGROUND: N4-acetylcytidine (ac4C) RNA modification is a critical epitranscriptomic regulator of cancer progression, yet its specific biological functions and regulatory patterns in lung adenocarcinoma (LUAD) remain poorly understood. This study aims to characterize the clinical relevance and potential regulatory patterns of ac4C-related features in the LUAD tumor microenvironment (TME).
METHODS: We integrated bulk RNA-seq data from TCGA and single-cell RNA-seq (scRNA-seq) data from GEO. Weighted gene co-expression network analysis (WGCNA) identified prognosis-related modules. A robust six-gene ac4C-related signature was derived using three consensus machine learning algorithms: LASSO, Random Forest, and SVM-RFE. The signature was further validated using pseudotime trajectory inference, CellChat-based intercellular communication analysis, and in vitro functional assays.
RESULTS: The six-gene signature effectively stratified LUAD patients, identifying a high-risk subgroup characterized by poor survival and frequent TP53 mutations. Single-cell analysis suggested that high ac4C-related signature scores were associated with advanced malignant states and enhanced predicted pro-tumorigenic signaling within the TME, particularly involving the EGF, TGFB, and MIF pathways. Pharmacogenomic modeling identified increased sensitivity to CDK and PLK1 inhibitors in high-risk patients. Experimentally, PLK1 silencing significantly suppressed LUAD cell proliferation and migration.
CONCLUSIONS: This study identifies a potential ac4C-related prognostic signature associated with malignant cell states and predicted communication patterns within the TME, and suggests PLK1 as a candidate therapeutic target in LUAD.
METHODS: We integrated bulk RNA-seq data from TCGA and single-cell RNA-seq (scRNA-seq) data from GEO. Weighted gene co-expression network analysis (WGCNA) identified prognosis-related modules. A robust six-gene ac4C-related signature was derived using three consensus machine learning algorithms: LASSO, Random Forest, and SVM-RFE. The signature was further validated using pseudotime trajectory inference, CellChat-based intercellular communication analysis, and in vitro functional assays.
RESULTS: The six-gene signature effectively stratified LUAD patients, identifying a high-risk subgroup characterized by poor survival and frequent TP53 mutations. Single-cell analysis suggested that high ac4C-related signature scores were associated with advanced malignant states and enhanced predicted pro-tumorigenic signaling within the TME, particularly involving the EGF, TGFB, and MIF pathways. Pharmacogenomic modeling identified increased sensitivity to CDK and PLK1 inhibitors in high-risk patients. Experimentally, PLK1 silencing significantly suppressed LUAD cell proliferation and migration.
CONCLUSIONS: This study identifies a potential ac4C-related prognostic signature associated with malignant cell states and predicted communication patterns within the TME, and suggests PLK1 as a candidate therapeutic target in LUAD.