IBPGNET: lung adenocarcinoma recurrence prediction based on neural network interpretability.

Xu, Zhanyu; Liao, Haibo; Huang, Liuliu; Chen, Qingfeng; Lan, Wei; Li, Shikang

Xu, Zhanyu; Liao, Haibo; Huang, Liuliu; Chen, Qingfeng; Lan, Wei; Li, Shikang.

Afiliación

Xu Z; Department of Thoracic and Cardiovascular Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region 530021, China.
Liao H; School of computer, Electronic and Information, Guangxi University, Nanning, Guangxi Zhuang Autonomous Region 530021, China.
Huang L; Department of Thoracic and Cardiovascular Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region 530021, China.
Chen Q; School of computer, Electronic and Information, Guangxi University, Nanning, Guangxi Zhuang Autonomous Region 530021, China.
Lan W; School of computer, Electronic and Information, Guangxi University, Nanning, Guangxi Zhuang Autonomous Region 530021, China.
Li S; Department of Thoracic and Cardiovascular Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region 530021, China.

Brief Bioinform ; 25(3)2024 Mar 27.

Article en En | MEDLINE | ID: mdl-38557672

ABSTRACT

ABSTRACT

Lung adenocarcinoma (LUAD) is the most common histologic subtype of lung cancer. Early-stage patients have a 30-50% probability of metastatic recurrence after surgical treatment. Here, we propose a new computational framework, Interpretable Biological Pathway Graph Neural Networks (IBPGNET), based on pathway hierarchy relationships to predict LUAD recurrence and explore the internal regulatory mechanisms of LUAD. IBPGNET can integrate different omics data efficiently and provide global interpretability. In addition, our experimental results show that IBPGNET outperforms other classification methods in 5-fold cross-validation. IBPGNET identified PSMC1 and PSMD11 as genes associated with LUAD recurrence, and their expression levels were significantly higher in LUAD cells than in normal cells. The knockdown of PSMC1 and PSMD11 in LUAD cells increased their sensitivity to afatinib and decreased cell migration, invasion and proliferation. In addition, the cells showed significantly lower EGFR expression, indicating that PSMC1 and PSMD11 may mediate therapeutic sensitivity through EGFR expression.

Asunto(s)

Adenocarcinoma del Pulmón; Neoplasias Pulmonares; Humanos; Adenocarcinoma del Pulmón/genética; Adenocarcinoma del Pulmón/metabolismo; Neoplasias Pulmonares/metabolismo; Línea Celular Tumoral; Biomarcadores de Tumor/genética; Biomarcadores de Tumor/metabolismo; Regulación Neoplásica de la Expresión Génica; Receptores ErbB/genética; Proliferación Celular

Palabras clave

PSMC1 and PSMD11; lung adenocarcinoma; multi-omics data; neural network interpretability; recurrence prediction

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Adenocarcinoma del Pulmón / Neoplasias Pulmonares Límite: Humans Idioma: En Revista: Brief Bioinform Asunto de la revista: BIOLOGIA / INFORMATICA MEDICA Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Reino Unido

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