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

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

doi:10.1007/s12094-024-03439-y

Clin Transl Oncol

2024

DOI: 10.1007/s12094-024-03439-y

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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,

Jiahua Hu,

Xiling Jiang

et al.

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2024

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Cited by 1 publication

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Integrating machine learning and multi-omics analysis to develop an immune-derived multiple programmed cell death signature for predicting clinical outcomes in gastric cancer

Li,

Hu,

et al. 2024

Oncologie

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Objectives Metastasis of tumor cells is the leading reason for mortality among patients diagnosed with gastric cancer (GC). Emerging evidence indicated a strong correlation between programmed cell death (PCD) and the invasion and metastasis of tumor cells. Therefore, we aimed to develop a programmed cell death signature to assess the prognosis and therapeutic efficacy in GC patients. Methods Here, we collected 1911 PCD-related genes from 19 different PCD patterns, and developed an immune-derived multiple programmed cell death index (MPCDI) using the integrating machine learning and multi-omics analysis, and systematically dissected heterogeneity in GC patients. Subsequently, we divided GC patients into two categories, namely high-MPCDI group and low-MPCDI group, using the median MPCDI as the threshold. We performed a comprehensive analysis of the clinical characteristics, somatic mutations, immune infiltration, drug sensitivity, and immunotherapeutic efficacy of the two groups. Results Survival and immunotherapy response analyses indicated that the high-MPCDI patients experienced a poorer overall survival (p=0.018) and were more resistant to commonly used chemotherapeutic drugs but benefited from immunotherapy compared to the low-MPCDI patients. In addition, MPCDI was confirmed as a standalone risk factor for overall survival, and nomograms can provide a precise tool for the clinical diagnosis of GC patients. Conclusions Taken together, the MPCDI can serve as a robust clinical diagnostic classifier to guide medication administration and improve outcomes in GC patients.

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Integrating machine learning and multi-omics analysis to develop an immune-derived multiple programmed cell death signature for predicting clinical outcomes in gastric cancer

Li,

Hu,

et al. 2024

Oncologie

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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

Cited by 1 publication

References 66 publications

Integrating machine learning and multi-omics analysis to develop an immune-derived multiple programmed cell death signature for predicting clinical outcomes in gastric cancer

Integrating machine learning and multi-omics analysis to develop an immune-derived multiple programmed cell death signature for predicting clinical outcomes in gastric cancer

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