Identification and validation of an immune‑derived multiple programmed cell death index for predicting clinical outcomes, molecular subtyping, and drug sensitivity in lung adenocarcinoma

Chunhong Li; Jiahua Hu; Xiling Jiang; Haiyin Tan; Yiming Mao

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Identification and validation of an immune‑derived multiple programmed cell death index for predicting clinical outcomes, molecular subtyping, and drug sensitivity in lung adenocarcinoma

Chunhong Li ^[3] ; Jiahua Hu ^[3] ; Xiling Jiang ^[1] ; Haiyin Tan ^[1] ; Yiming Mao ^[2]
1. [1] Guilin Medical University
  
  Guilin Medical University
  
  China
2. [2] Shanghai Jiao Tong University
  
  Shanghai Jiao Tong University
  
  China
3. [3] Central Laboratory, The Second Affiliated Hospital of Guilin Medical University, Guilin 541199, Guangxi, China
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Localización: Clinical & translational oncology, ISSN 1699-048X, Vol. 26, Nº. 9, 2024, págs. 2274-2295
Idioma: inglés
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Resumen
- Objectives Comprehensive cross-interaction of multiple programmed cell death (PCD) patterns in the patients with lung adenocarcinoma (LUAD) have not yet been thoroughly investigated.
  
  Methods Here, we collected 19 different PCD patterns, including 1911 PCD-related genes, and developed an immunederived multiple programmed cell death index (MPCDI) based on machine learning methods.
  
  Results Using the median MPCDI scores, we categorized the LUAD patients into two groups: low-MPCDI and high-MPCDI. Our analysis of the TCGA-LUAD training cohort and three external GEO cohorts (GSE37745, GSE30219, and GSE68465) revealed that patients with high-MPCDI experienced a more unfavorable prognosis, whereas those with low-MPCDI had a better prognosis. Furthermore, the results of both univariate and multivariate Cox regression analyses further confirmed that MPCDI serves as a novel independent risk factor. By combining clinical characteristics with the MPCDI, we constructed a nomogram that provides an accurate and reliable quantitative tool for personalized clinical management of LUAD patients. The findings obtained from the analysis of C-index and the decision curve revealed that the nomogram outperformed various clinical variables in terms of net clinical benefit. Encouragingly, the low-MPCDI patients are more sensitive to commonly used chemotherapy drugs, which suggests that MPCDI scores have a guiding role in chemotherapy for LUAD patients.
  
  Conclusion Therefore, MPCDI can be used as a novel clinical diagnostic classifier, providing valuable insights into the clinical management and clinical decision-making for LUAD patients.