An artificial intelligence network‐guided signature for predicting outcome and immunotherapy response in lung adenocarcinoma patients based on 26 machine learning algorithms. Issue 4 (23rd February 2023)

• Record Type: Journal Article
• Authors: Zhang, Nan; Zhang, Hao; Liu, Zaoqu; Dai, Ziyu; Wu, Wantao; Zhou, Ran; Li, Shuyu; Wang, Zeyu; Liang, Xisong; Wen, Jie; Zhang, Xun; Zhang, Bo; Ouyang, Sirui; Zhang, Jian; Luo, Peng; Li, Xizhe; Cheng, Quan
• Is Part Of: Cell proliferation. Volume 56:Issue 4(2023)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2023-02-23
• Subjects: Cell proliferation -- Periodicals; 571.84
• DOI: 10.1111/cpr.13409
• Languages: English
• ISSNs: 0960-7722

Abstract

Abstract: The immune cells play an increasingly vital role in influencing the proliferation, progression, and metastasis of lung adenocarcinoma (LUAD) cells. However, the potential of immune cells' specific genes‐based model remains largely unknown. In the current study, by analysing single‐cell RNA sequencing (scRNA‐seq) data and bulk RNA sequencing data, the tumour‐infiltrating immune cell (TIIC) associated signature was developed based on a total of 26 machine learning (ML) algorithms. As a result, the TIIC signature score could predict survival outcomes of LUAD patients across five independent datasets. The TIIC signature score showed superior performance to 168 previously established signatures in LUAD. Moreover, the TIIC signature score developed by the immunofluorescence staining of the tissue array of LUAD patients showed a prognostic value. Our research revealed a solid connection between TIIC signature score and tumour immunity as well as metabolism. Additionally, it has been discovered that the TIIC signature score can forecast genomic change, chemotherapeutic drug susceptibility, and—most significantly—immunotherapeutic response. As a newly demonstrated biomarker, the TIIC signature score facilitated the selection of the LUAD population who would benefit from future clinical stratification. Abstract : In this study, by analysing single‐cell RNA sequencing (scRNA‐seq) data and bulk RNA sequencing data, the tumour‐infiltrating immune cell (TIIC) associated signature was developed based on 26 machine learning algorithms. The performance of the TIIC signature score in predicting prognosis and immunotherapy response in LUAD patients was systematically excavated.