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Article

TCox: Correlation-Based Regularization Applied to Colorectal Cancer Survival Data

1
INESC-ID, Instituto Superior Técnico, Universidade de Lisboa, Rua Alves Redol 9, 1000-029 Lisboa, Portugal
2
NOVA Laboratory for Computer Science and Informatics (NOVA LINCS), FCT, UNL, 2829-516 Caparica, Portugal
3
Centro de Matemática e Aplicações (CMA), FCT, UNL, 2829-516 Caparica, Portugal
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Instituto de Medicina Molecular-João Lobo Antunes, Faculdade de Medicina, Universidade de Lisboa, Avenida Professor Egas Moniz, 1649-028 Lisboa, Portugal
5
Oncology Division, Hospital de Santa Maria, Centro Hospitalar Lisboa Norte, 1649-028 Lisboa, Portugal
*
Author to whom correspondence should be addressed.
Biomedicines 2020, 8(11), 488; https://doi.org/10.3390/biomedicines8110488
Received: 17 September 2020 / Revised: 26 October 2020 / Accepted: 6 November 2020 / Published: 10 November 2020
Colorectal cancer (CRC) is one of the leading causes of mortality and morbidity in the world. Being a heterogeneous disease, cancer therapy and prognosis represent a significant challenge to medical care. The molecular information improves the accuracy with which patients are classified and treated since similar pathologies may show different clinical outcomes and other responses to treatment. However, the high dimensionality of gene expression data makes the selection of novel genes a problematic task. We propose TCox, a novel penalization function for Cox models, which promotes the selection of genes that have distinct correlation patterns in normal vs. tumor tissues. We compare TCox to other regularized survival models, Elastic Net, HubCox, and OrphanCox. Gene expression and clinical data of CRC and normal (TCGA) patients are used for model evaluation. Each model is tested 100 times. Within a specific run, eighteen of the features selected by TCox are also selected by the other survival regression models tested, therefore undoubtedly being crucial players in the survival of colorectal cancer patients. Moreover, the TCox model exclusively selects genes able to categorize patients into significant risk groups. Our work demonstrates the ability of the proposed weighted regularizer TCox to disclose novel molecular drivers in CRC survival by accounting for correlation-based network information from both tumor and normal tissue. The results presented support the relevance of network information for biomarker identification in high-dimensional gene expression data and foster new directions for the development of network-based feature selection methods in precision oncology. View Full-Text
Keywords: regularized optimization; Cox regression; survival analysis; TCGA data; RNA-seq data regularized optimization; Cox regression; survival analysis; TCGA data; RNA-seq data
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MDPI and ACS Style

Peixoto, C.; Lopes, M.B.; Martins, M.; Costa, L.; Vinga, S. TCox: Correlation-Based Regularization Applied to Colorectal Cancer Survival Data. Biomedicines 2020, 8, 488. https://doi.org/10.3390/biomedicines8110488

AMA Style

Peixoto C, Lopes MB, Martins M, Costa L, Vinga S. TCox: Correlation-Based Regularization Applied to Colorectal Cancer Survival Data. Biomedicines. 2020; 8(11):488. https://doi.org/10.3390/biomedicines8110488

Chicago/Turabian Style

Peixoto, Carolina, Marta B. Lopes, Marta Martins, Luís Costa, and Susana Vinga. 2020. "TCox: Correlation-Based Regularization Applied to Colorectal Cancer Survival Data" Biomedicines 8, no. 11: 488. https://doi.org/10.3390/biomedicines8110488

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