Sökning: L773:0010 4825 OR L773:1879 0534 >
Reiterative modelin...
Reiterative modeling of combined transcriptomic and proteomic features refines and improves the prediction of early recurrence in squamous cell carcinoma of head and neck
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- Salehi, Amir M. (författare)
- Umeå universitet,Patologi
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- Wang, Lixiao, 1975- (författare)
- Umeå universitet,Patologi
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- Coates, Philip J. (författare)
- Research Centre for Applied Molecular Oncology, Masaryk Memorial Cancer Institute, Brno, Czech Republic
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- Norberg-Spaak, Lena (författare)
- Umeå universitet,Patologi
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- Gu, Xiaolian, 1976- (författare)
- Umeå universitet,Patologi
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- Sgaramella, Nicola (författare)
- Umeå universitet,Patologi
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- Nylander, Karin (författare)
- Umeå universitet,Patologi
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(creator_code:org_t)
- Elsevier, 2022
- 2022
- Engelska.
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Ingår i: Computers in Biology and Medicine. - : Elsevier. - 0010-4825 .- 1879-0534. ; 149
- Relaterad länk:
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https://doi.org/10.1...
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https://umu.diva-por... (primary) (Raw object)
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- Background: Patients with squamous cell carcinoma of the head and neck (SCCHN) have a high-risk of recurrence. We aimed to develop machine learning methods to identify transcriptomic and proteomic features that provide accurate classification models for predicting risk of early recurrence in SCCHN patients.Methods: Clinical, genomic, transcriptomic and proteomic features distinguishing recurrence risk were examined in SCCHN patients from The Cancer Genome Atlas (TCGA). Recurrence within one year after treatment was classified as high-risk and no recurrence as low-risk.Results: No significant differences in individual clinicopathological characteristics, mutation profiles or mRNA expression patterns were seen between the groups using conventional statistical analysis. Using the machine learning algorithm, extreme gradient boosting (XGBoost), ten proteins (RAD50, 4E-BP1, MYH11, MAP2K1, BECN1, NF2, RAB25, ERRFI1, KDR, SERPINE1) and five mRNAs (PLAUR, DKK1, AXIN2, ANG and VEGFA) made the greatest contribution to classification. These features were used to build improved models in XGBoost, achieving the best discrimination performance when combining transcriptomic and proteomic data, providing an accuracy of 0.939 and an Area Under the ROC Curve (AUC) of 0.951.Conclusions: This study highlights machine learning to identify transcriptomic and proteomic factors that play important roles in predicting risk of recurrence in patients with SCCHN and to develop such models by iterative cycles to enhance their accuracy, thereby aiding the introduction of personalized treatment regimens.
Ämnesord
- MEDICIN OCH HÄLSOVETENSKAP -- Medicinska och farmaceutiska grundvetenskaper -- Cell- och molekylärbiologi (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Basic Medicine -- Cell and Molecular Biology (hsv//eng)
Nyckelord
- Early recurrence
- Machine learning
- Multi-omics
- SCCHN
- XGBoost
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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