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    Genetic Variations in the Transforming Growth Factor-β1 Pathway May Improve Predictive Power for Overall Survival in Non-small Cell Lung Cancer

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    Author
    Zhang, Hong
    Wang, Weili
    Pi, Wenhu
    Bi, Nan
    DesRosiers, Colleen
    Kong, Fengchong
    Cheng, Monica
    Yang, Li
    Lautenschlaeger, Tim
    Jolly, Shruti
    Jin, Jianyue
    Kong, Feng-Ming Spring
    Show allShow less

    Date
    2021-07-07
    Journal
    Frontiers in Oncology
    Publisher
    Frontiers Media S.A.
    Type
    Article
    
    Metadata
    Show full item record
    See at
    https://doi.org/10.3389/fonc.2021.599719
    http://www.ncbi.nlm.nih.gov/pmc/articles/pmc8294034/
    Abstract
    Purpose: Transforming growth factor-β1 (TGF-β1), a known immune suppressor, plays an important role in tumor progression and overall survival (OS) in many types of cancers. We hypothesized that genetic variations of single nucleotide polymorphisms (SNPs) in the TGF-β1 pathway can predict survival in patients with non-small cell lung cancer (NSCLC) after radiation therapy. Materials and Methods: Fourteen functional SNPs in the TGF-β1 pathway were measured in 166 patients with NSCLC enrolled in a multi-center clinical trial. Clinical factors, including age, gender, ethnicity, smoking status, stage group, histology, Karnofsky Performance Status, equivalent dose at 2 Gy fractions (EQD2), and the use of chemotherapy, were first tested under the univariate Cox's proportional hazards model. All significant clinical predictors were combined as a group of predictors named "Clinical." The significant SNPs under the Cox proportional hazards model were combined as a group of predictors named "SNP." The predictive powers of models using Clinical and Clinical + SNP were compared with the cross-validation concordance index (C-index) of random forest models. Results: Age, gender, stage group, smoking, histology, and EQD2 were identified as significant clinical predictors: Clinical. Among 14 SNPs, BMP2:rs235756 (HR = 0.63; 95% CI:0.42-0.93; p = 0.022), SMAD9:rs7333607 (HR = 2.79; 95% CI 1.22-6.41; p = 0.015), SMAD3:rs12102171 (HR = 0.68; 95% CI: 0.46-1.00; p = 0.050), and SMAD4: rs12456284 (HR = 0.63; 95% CI: 0.43-0.92; p = 0.016) were identified as powerful predictors of SNP. After adding SNP, the C-index of the model increased from 84.1 to 87.6% at 24 months and from 79.4 to 84.4% at 36 months. Conclusion: Genetic variations in the TGF-β1 pathway have the potential to improve the prediction accuracy for OS in patients with NSCLC.
    Rights/Terms
    Copyright © 2021 Zhang, Wang, Pi, Bi, DesRosiers, Kong, Cheng, Yang, Lautenschlaeger, Jolly, Jin and Kong.
    Keyword
    TGF-β1
    machine learning
    non-small cell lung cancer
    overall survival
    single nuclear polymorphism
    Identifier to cite or link to this item
    http://hdl.handle.net/10713/16254
    ae974a485f413a2113503eed53cd6c53
    10.3389/fonc.2021.599719
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