Machine learning reveals diverse cell death patterns in lung adenocarcinoma prognosis and therapy

Shun Wang; Ruohuang Wang; Dingtao Hu; Caoxu Zhang; Peng Cao; Jie Huang

doi:10.1038/s41698-024-00538-5

Machine learning reveals diverse cell death patterns in lung adenocarcinoma prognosis and therapy

NPJ Precis Oncol. 2024 Feb 26;8(1):49. doi: 10.1038/s41698-024-00538-5.

Authors

Shun Wang^#¹, Ruohuang Wang^#², Dingtao Hu^#³, Caoxu Zhang⁴, Peng Cao⁵, Jie Huang⁶

Affiliations

¹ Department of Respiratory Medicine, Shanghai Xuhui Central Hospital, Zhongshan-Xuhui Hospital, Fudan University, Shanghai, 200031, China.
² Department of Otolaryngology, the Second Affiliated Hospital of the Naval Military Medical University (Shanghai Changzheng Hospital), Shanghai, 200003, China.
³ Clinical Cancer Institute, Center for Translational Medicine, Naval Medical University, Shanghai, 200433, China.
⁴ Department of Molecular Diagnostics, The Core Laboratory in Medical Center of Clinical Research, Department of Endocrinology, Shanghai Ninth People's Hospital, State Key Laboratory of Medical Genomics, Shanghai Jiaotong University School of Medicine, Shanghai, 200011, China.
⁵ Department of Interventional Pulmonology, Anhui Chest Hospital, Hefei, Anhui, 230022, China.
⁶ Department of Respiratory Medicine, Shanghai Xuhui Central Hospital, Zhongshan-Xuhui Hospital, Fudan University, Shanghai, 200031, China. syarhuang@163.com.

^# Contributed equally.

Abstract

Cancer cell growth, metastasis, and drug resistance pose significant challenges in the management of lung adenocarcinoma (LUAD). However, there is a deficiency in optimal predictive models capable of accurately forecasting patient prognoses and guiding the selection of targeted treatments. Programmed cell death (PCD) pathways play a pivotal role in the development and progression of various cancers, offering potential as prognostic indicators and drug sensitivity markers for LUAD patients. The development and validation of predictive models were conducted by integrating 13 PCD patterns with comprehensive analysis of bulk RNA, single-cell RNA transcriptomics, and pertinent clinicopathological details derived from TCGA-LUAD and six GEO datasets. Utilizing the machine learning algorithms, we identified ten critical differentially expressed genes associated with PCD in LUAD, namely CHEK2, KRT18, RRM2, GAPDH, MMP1, CHRNA5, TMPRSS4, ITGB4, CD79A, and CTLA4. Subsequently, we conducted a programmed cell death index (PCDI) based on these genes across the aforementioned cohorts and integrated this index with relevant clinical features to develop several prognostic nomograms. Furthermore, we observed a significant correlation between the PCDI and immune features in LUAD, including immune cell infiltration and the expression of immune checkpoint molecules. Additionally, we found that patients with a high PCDI score may exhibit resistance to immunotherapy and standard adjuvant chemotherapy regimens; however, they may benefit from other FDA-supported drugs such as docetaxel and dasatinib. In conclusion, the PCDI holds potential as a prognostic signature and can facilitate personalized treatment for LUAD patients.