Allele frequency deviation (AFD) as a new prognostic model to predict overall survival in lung adenocarcinoma (LUAD)

Allele frequency deviation (AFD) as a new prognostic model to predict overall survival in lung adenocarcinoma (LUAD)

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 o...

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Published in:Cancer cell international Vol. 21; no. 1; pp. 1 - 451
Main Authors: 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
Format: Journal Article
Language:English
Published: London BioMed Central 26-08-2021
BMC
Subjects:
Adenocarcinoma
Algorithms
Allele frequency deviation (AFD)
Alleles
Bioinformatics
Biomarkers
Cancer therapies
Death
Gene expression
Gene frequency
Genomes
Lung Adenocarcinoma (LUAD)
Lung cancer
Medical prognosis
Mutation
Overall survival
Patients
Primary Research
Risk groups
Software
Survival
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Summary: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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ISSN:1475-2867
1475-2867
DOI:10.1186/s12935-021-02127-z