Show simple item record

dc.contributor.authorZhang, Hong
dc.contributor.authorWang, Weili
dc.contributor.authorPi, Wenhu
dc.contributor.authorBi, Nan
dc.contributor.authorDesRosiers, Colleen
dc.contributor.authorKong, Fengchong
dc.contributor.authorCheng, Monica
dc.contributor.authorYang, Li
dc.contributor.authorLautenschlaeger, Tim
dc.contributor.authorJolly, Shruti
dc.contributor.authorJin, Jianyue
dc.contributor.authorKong, Feng-Ming Spring
dc.date.accessioned2021-07-28T15:23:59Z
dc.date.available2021-07-28T15:23:59Z
dc.date.issued2021-07-07
dc.identifier.urihttp://hdl.handle.net/10713/16254
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.en_US
dc.description.urihttps://doi.org/10.3389/fonc.2021.599719en_US
dc.description.urihttp://www.ncbi.nlm.nih.gov/pmc/articles/pmc8294034/en_US
dc.language.isoenen_US
dc.publisherFrontiers Media S.A.en_US
dc.relation.ispartofFrontiers in Oncologyen_US
dc.rightsCopyright © 2021 Zhang, Wang, Pi, Bi, DesRosiers, Kong, Cheng, Yang, Lautenschlaeger, Jolly, Jin and Kong.en_US
dc.subjectTGF-β1en_US
dc.subjectmachine learningen_US
dc.subjectnon-small cell lung canceren_US
dc.subjectoverall survivalen_US
dc.subjectsingle nuclear polymorphismen_US
dc.titleGenetic Variations in the Transforming Growth Factor-β1 Pathway May Improve Predictive Power for Overall Survival in Non-small Cell Lung Canceren_US
dc.typeArticleen_US
dc.identifier.doi10.3389/fonc.2021.599719
dc.identifier.pmid34307117
dc.source.volume11
dc.source.beginpage599719
dc.source.endpage
dc.source.countrySwitzerland


This item appears in the following Collection(s)

Show simple item record