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Allele frequency deviation (AFD) as a new prognostic model to predict overall survival in lung adenocarcinoma (LUAD).
Al-Dherasi, Aisha; Liao, Yuwei; Al-Mosaib, Sultan; Hua, Rulin; Wang, Yichen; Yu, Ying; Zhang, Yu; Zhang, Xuehong; Jalayta, Raeda; Mousa, Haithm; Al-Danakh, Abdullah; Alnadari, Fawze; Almoiliqy, Marwan; Baldi, Salem; Shi, Leming; Lv, Dekang; Li, Zhiguang; Liu, Quentin.
Afiliação
  • Al-Dherasi A; Center of Genome and Personalized Medicine, Institute of Cancer Stem Cell, Dalian Medical University, Dalian, 116044, Liaoning, People's Republic of China.
  • Liao Y; Department of Biochemistry, Faculty of Science, Ibb University, Ibb, Yemen.
  • Al-Mosaib S; Yangjiang Key Laboratory of Respiratory Diseases, Yangjiang Peoples Hospital, Yangjiang, Guangdong, People's Republic of China.
  • Hua R; Department of Computer Science and Technology, Sahyadri Science Collage, Kuvempu University, Shimoga district, Karnataka, India.
  • Wang Y; Center of Genome and Personalized Medicine, Institute of Cancer Stem Cell, Dalian Medical University, Dalian, 116044, Liaoning, People's Republic of China.
  • Yu Y; Center of Genome and Personalized Medicine, Institute of Cancer Stem Cell, Dalian Medical University, Dalian, 116044, Liaoning, People's Republic of China.
  • Zhang Y; State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Fudan University, 2005 Songhu Road, Shanghai, 200438, People's Republic of China.
  • Zhang X; Center of Genome and Personalized Medicine, Institute of Cancer Stem Cell, Dalian Medical University, Dalian, 116044, Liaoning, People's Republic of China.
  • Jalayta R; Center of Genome and Personalized Medicine, Institute of Cancer Stem Cell, Dalian Medical University, Dalian, 116044, Liaoning, People's Republic of China.
  • Mousa H; Center of Genome and Personalized Medicine, Institute of Cancer Stem Cell, Dalian Medical University, Dalian, 116044, Liaoning, People's Republic of China.
  • Al-Danakh A; Department of Clinical Biochemistry, College of Laboratory Diagnostic Medicine, Dalian Medical University, Dalian, 116044, Liaoning, People's Republic of China.
  • Alnadari F; Department of Urology, First Affiliated Hospital of Dalian Medical University, Dalian Medical University, Dalian, 116044, Liaoning, People's Republic of China.
  • Almoiliqy M; Department of Food Science and Engineering, College of Food Science and Technology, Nanjing Agricultural University, Nanjing, 210095, Jiangsu, People's Republic of China.
  • Baldi S; Key Lab of Aromatic Plant Resources Exploitation and Utilization in Sichuan Higher Education, Yibin University, Yibin, 644000, Sichuan, China.
  • Shi L; Department of Clinical Biochemistry, College of Laboratory Diagnostic Medicine, Dalian Medical University, Dalian, 116044, Liaoning, People's Republic of China.
  • Lv D; State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Fudan University, 2005 Songhu Road, Shanghai, 200438, People's Republic of China.
  • Li Z; Center of Genome and Personalized Medicine, Institute of Cancer Stem Cell, Dalian Medical University, Dalian, 116044, Liaoning, People's Republic of China. dekanglv@dmu.edu.cn.
  • Liu Q; Center of Genome and Personalized Medicine, Institute of Cancer Stem Cell, Dalian Medical University, Dalian, 116044, Liaoning, People's Republic of China. zhiguangli88@gmail.com.
Cancer Cell Int ; 21(1): 451, 2021 Aug 26.
Article em En | MEDLINE | ID: mdl-34446004
ABSTRACT

BACKGROUND:

Lung adenocarcinoma (LUAD) remains one of the world's most known aggressive malignancies with a high mortality rate. Molecular biological analysis and bioinformatics are of great importance as they have recently occupied a large area in the studies related to the identification of various biomarkers to predict survival for LUAD patients. In our study, we attempted to identify a new prognostic model by developing a new algorithm to calculate the allele frequency deviation (AFD), which in turn may assist in the early diagnosis and prediction of clinical outcomes in LUAD.

METHOD:

First, a new algorithm was developed to calculate AFD using the whole-exome sequencing (WES) dataset. Then, AFD was measured for 102 patients, and the predictive power of AFD was assessed using Kaplan-Meier analysis, receiver operating characteristic (ROC) curves, and area under the curve (AUC). Finally, multivariable cox regression analyses were conducted to evaluate the independence of AFD as an independent prognostic tool.

RESULT:

The Kaplan-Meier analysis showed that AFD effectively segregated patients with LUAD into high-AFD-value and low-AFD-value risk groups (hazard ratio HR = 1.125, 95% confidence interval CI 1.001-1.26, p = 0.04) in the training group. Moreover, the overall survival (OS) of patients who belong to the high-AFD-value group was significantly shorter than that of patients who belong to the low-AFD-value group with 42.8% higher risk and 10% lower risk of death for both groups respectively (HR for death = 1.10; 95% CI 1.01-1.2, p = 0.03) in the training group. Similar results were obtained in the validation group (HR = 4.62, 95% CI 1.22-17.4, p = 0.02) with 41.6%, and 5.5% risk of death for patients who belong to the high and low-AFD-value groups respectively. Univariate and multivariable cox regression analyses demonstrated that AFD is an independent prognostic model for patients with LUAD. The AUC for 5-year survival were 0.712 and 0.86 in the training and validation groups, respectively.

CONCLUSION:

AFD was identified as a new independent prognostic model that could provide a prognostic tool for physicians and contribute to treatment decisions.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Risk_factors_studies / Screening_studies Idioma: En Revista: Cancer Cell Int Ano de publicação: 2021 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Risk_factors_studies / Screening_studies Idioma: En Revista: Cancer Cell Int Ano de publicação: 2021 Tipo de documento: Article