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Construction and clinical significance of prognostic risk markers based on cancer driver genes in lung adenocarcinoma.
Su, Yazhou; Huo, Tingting; Wang, Yanan; Li, Jingyan.
Afiliación
  • Su Y; Department of Thoracic Surgery, The First Affiliated Hospital of Xinxiang Medical University, 88 Jiankang Road, Weihui, 453100, Henan province, China. suyazhousu@163.com.
  • Huo T; Department of Oncology, The First Affiliated Hospital of Xinxiang Medical University, Weihui, 453100, Henan province, China.
  • Wang Y; Department of Oncology, The First Affiliated Hospital of Xinxiang Medical University, Weihui, 453100, Henan province, China.
  • Li J; Department of Oncology, The First Affiliated Hospital of Xinxiang Medical University, Weihui, 453100, Henan province, China.
Clin Transl Oncol ; 2024 Sep 18.
Article en En | MEDLINE | ID: mdl-39292390
ABSTRACT

BACKGROUND:

Cancer driver genes (CDGs) have been reported as key factors influencing the progression of lung adenocarcinoma (LUAD). However, the role of CDGs in LUAD prognosis has not been fully elucidated.

METHODS:

LUAD transcriptome data and CDG-related data were obtained from public databases and literature. Differentially expressed CDGs (DE-CDGs) greatly associated with LUAD survival (P < 0.05) were identified to establish a prognostic model. In addition, immune analysis of high-risk (HR) and low-risk (LR) groups was conducted by utilizing the CIBERSORT and single sample gene set enrichment analysis (ssGSEA) algorithms to assess immune differences. Subsequently, mutation analysis was conducted using maftools. Finally, candidate drugs were identified using the CellMiner database.

RESULTS:

40 DE-CDGs significantly associated with LUAD survival and 11 DE-CDGs associated with prognosis were identified through screening. Regression analysis revealed that risk score can independently predict LUAD prognosis (P < 0.05). Immune landscape analysis revealed that compared to the HR group, the LR group had higher immune scores and high infiltration of various immune cells such as follicular helper B cells and T cells. Mutation landscape analysis demonstrated that missense mutation was the most common mutation type in both risk groups. Drug prediction analysis revealed strong correlations of fulvestrant, S-63845, sapacitabine, lomustine, BLU-667, SR16157, motesanib, AZD-9496, XK-469, dimethylfasudil, P-529, and imatinib with the model genes, suggesting their potential as candidate drugs targeting the model genes.

CONCLUSION:

This study identified 11 effective biomarkers, DE-CDGs, which can predict LUAD prognosis and explored the biological significance of CDGs in LUAD prognosis, immunotherapy, and treatment.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Clin Transl Oncol Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Italia
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Clin Transl Oncol Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Italia