Integrating Single-Cell Transcriptomics and Machine Learning to Define an ac4C Gene Signature in Lung Adenocarcinoma.

Introduction: Lung adenocarcinoma, the most common subtype of non-small cell lung cancer, faces challenges such as drug resistance and tumor heterogeneity. N4-acetylcytidine (ac4C) is an important RNA modification involved in cancer progression, but its role in lung adenocarcinoma remains unclear.

Methods: This study analyzed transcriptomic and single-cell RNA sequencing data from public databases to investigate the expression and clinical significance of ac4C-related genes in lung adenocarcinoma. Ten machine learning algorithms were applied to develop and validate an ac4C-related gene signature (ARGSig) for prognosis prediction across multiple independent cohorts.

Results: Cells with high ac4C activity showed increased intercellular communication and activation of tumor-associated pathways. The ARGSig model effectively stratified patients by survival outcomes and predicted sensitivity to immune checkpoint inhibitors and chemotherapy agents.

Conclusion: ac4C modification and its related genes play a critical role in lung adenocarcinoma development. The ARGSig model provides a promising molecular tool for prognosis evaluation and personalized treatment guidance in lung adenocarcinoma patients.