Integrating Single‐Cell Transcriptomics and Machine Learning to Define an ac4C Gene Signature in Lung Adenocarcinoma
ABSTRACT 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 un...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2025-08-01
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| Series: | Thoracic Cancer |
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| Online Access: | https://doi.org/10.1111/1759-7714.70140 |
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| author | Yuan Wang Wei Su Guangyao Zhou Yijie Wang Chunnuan Wu Pengpeng Zhang Lianmin Zhang |
| author_facet | Yuan Wang Wei Su Guangyao Zhou Yijie Wang Chunnuan Wu Pengpeng Zhang Lianmin Zhang |
| author_sort | Yuan Wang |
| collection | DOAJ |
| description | ABSTRACT 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. |
| format | Article |
| id | doaj-art-9c6fc797c88f46609cb12415bb8db900 |
| institution | Kabale University |
| issn | 1759-7706 1759-7714 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Wiley |
| record_format | Article |
| series | Thoracic Cancer |
| spelling | doaj-art-9c6fc797c88f46609cb12415bb8db9002025-08-20T04:01:02ZengWileyThoracic Cancer1759-77061759-77142025-08-011615n/an/a10.1111/1759-7714.70140Integrating Single‐Cell Transcriptomics and Machine Learning to Define an ac4C Gene Signature in Lung AdenocarcinomaYuan Wang0Wei Su1Guangyao Zhou2Yijie Wang3Chunnuan Wu4Pengpeng Zhang5Lianmin Zhang6Key Laboratory of Cancer Prevention and Therapy Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer Tianjin ChinaDepartment of Lung Cancer, Tianjin Lung Cancer Center, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy Tianjin's Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital Tianjin ChinaDepartment of Lung Cancer, Tianjin Lung Cancer Center, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy Tianjin's Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital Tianjin ChinaDepartment of Lung Cancer, Tianjin Lung Cancer Center, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy Tianjin's Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital Tianjin ChinaKey Laboratory of Cancer Prevention and Therapy Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer Tianjin ChinaDepartment of Lung Cancer, Tianjin Lung Cancer Center, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy Tianjin's Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital Tianjin ChinaDepartment of Lung Cancer, Tianjin Lung Cancer Center, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy Tianjin's Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital Tianjin ChinaABSTRACT 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.https://doi.org/10.1111/1759-7714.70140lung cancermachine learningN4‐acetylcytidineprognostic signatureRNA modification |
| spellingShingle | Yuan Wang Wei Su Guangyao Zhou Yijie Wang Chunnuan Wu Pengpeng Zhang Lianmin Zhang Integrating Single‐Cell Transcriptomics and Machine Learning to Define an ac4C Gene Signature in Lung Adenocarcinoma Thoracic Cancer lung cancer machine learning N4‐acetylcytidine prognostic signature RNA modification |
| title | Integrating Single‐Cell Transcriptomics and Machine Learning to Define an ac4C Gene Signature in Lung Adenocarcinoma |
| title_full | Integrating Single‐Cell Transcriptomics and Machine Learning to Define an ac4C Gene Signature in Lung Adenocarcinoma |
| title_fullStr | Integrating Single‐Cell Transcriptomics and Machine Learning to Define an ac4C Gene Signature in Lung Adenocarcinoma |
| title_full_unstemmed | Integrating Single‐Cell Transcriptomics and Machine Learning to Define an ac4C Gene Signature in Lung Adenocarcinoma |
| title_short | Integrating Single‐Cell Transcriptomics and Machine Learning to Define an ac4C Gene Signature in Lung Adenocarcinoma |
| title_sort | integrating single cell transcriptomics and machine learning to define an ac4c gene signature in lung adenocarcinoma |
| topic | lung cancer machine learning N4‐acetylcytidine prognostic signature RNA modification |
| url | https://doi.org/10.1111/1759-7714.70140 |
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