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

TitleGenetic Variations in the Transforming Growth Factor-β1 Pathway May Improve Predictive Power for Overall Survival in Non-small Cell Lung Cancer
Authors
Keywordsmachine learning
single nuclear polymorphism
overall survival
non-small cell lung cancer
TGF-β1
Issue Date2021
PublisherFrontiers Research Foundation. The Journal's web site is located at http://www.frontiersin.org/oncology
Citation
Frontiers in Oncology, 2021, v. 11, p. article no. 599719 How to Cite?
AbstractPurpose: 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.
Persistent Identifierhttp://hdl.handle.net/10722/301944
ISSN
2020 Impact Factor: 6.244
2020 SCImago Journal Rankings: 1.834
PubMed Central ID

 

DC FieldValueLanguage
dc.contributor.authorZhsng, H-
dc.contributor.authorWang, W-
dc.contributor.authorPi, W-
dc.contributor.authorBi, N-
dc.contributor.authorDesrosiers, C-
dc.contributor.authorKong, F-
dc.contributor.authorCheung, M-
dc.contributor.authorYang, L-
dc.contributor.authorLautenschlaeger, T-
dc.contributor.authorJolly, S-
dc.contributor.authorJin, J-
dc.contributor.authorKong, FMS-
dc.date.accessioned2021-08-21T03:29:16Z-
dc.date.available2021-08-21T03:29:16Z-
dc.date.issued2021-
dc.identifier.citationFrontiers in Oncology, 2021, v. 11, p. article no. 599719-
dc.identifier.issn2234-943X-
dc.identifier.urihttp://hdl.handle.net/10722/301944-
dc.description.abstractPurpose: 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.-
dc.languageeng-
dc.publisherFrontiers Research Foundation. The Journal's web site is located at http://www.frontiersin.org/oncology-
dc.relation.ispartofFrontiers in Oncology-
dc.rightsThis Document is Protected by copyright and was first published by Frontiers. All rights reserved. It is reproduced with permission.-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.subjectmachine learning-
dc.subjectsingle nuclear polymorphism-
dc.subjectoverall survival-
dc.subjectnon-small cell lung cancer-
dc.subjectTGF-β1-
dc.titleGenetic Variations in the Transforming Growth Factor-β1 Pathway May Improve Predictive Power for Overall Survival in Non-small Cell Lung Cancer-
dc.typeArticle-
dc.identifier.emailKong, FMS: kong0001@hku.hk-
dc.identifier.authorityKong, FMS=rp02508-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.3389/fonc.2021.599719-
dc.identifier.pmid34307117-
dc.identifier.pmcidPMC8294034-
dc.identifier.scopuseid_2-s2.0-85111072555-
dc.identifier.hkuros324249-
dc.identifier.volume11-
dc.identifier.spagearticle no. 599719-
dc.identifier.epagearticle no. 599719-
dc.publisher.placeSwitzerland-

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