Prediction of breast cancer proteins involved in immunotherapy, metastasis, and RNA-binding using molecular descriptors and artificial neural networks

Andrés López-Cortés; Alejandro Cabrera-Andrade; José M. Vázquez-Naya; Alejandro Pazos; Humberto González-Díaz; PAZ Y MIÑO CEPEDA, CESAR ANTONIO; Yunierkis Pérez-Castillo; Santiago Guerrero; Eduardo Tejera; Cristian R. Munteanu

doi:10.1038/s41598-020-65584-y

Prediction of breast cancer proteins involved in immunotherapy, metastasis, and RNA-binding using molecular descriptors and artificial neural networks

Journal

Scientific Reports

ISSN

2045-2322

Date Issued

2020-05-22

Author(s)

Andrés López-Cortés

Facultad de Ciencias de la Salud Eugenio Espejo

Alejandro Cabrera-Andrade

José M. Vázquez-Naya

Alejandro Pazos

Humberto González-Díaz

PAZ Y MIÑO CEPEDA, CESAR ANTONIO

Facultad de Ciencias de la Salud Eugenio Espejo

Yunierkis Pérez-Castillo

Santiago Guerrero

Facultad de Ciencias de la Salud Eugenio Espejo

Eduardo Tejera

Cristian R. Munteanu

DOI

10.1038/s41598-020-65584-y

URL

https://cris.ute.edu.ec/handle/123456789/691

Abstract

AbstractBreast cancer (BC) is a heterogeneous disease where genomic alterations, protein expression deregulation, signaling pathway alterations, hormone disruption, ethnicity and environmental determinants are involved. Due to the complexity of BC, the prediction of proteins involved in this disease is a trending topic in drug design. This work is proposing accurate prediction classifier for BC proteins using six sets of protein sequence descriptors and 13 machine-learning methods. After using a univariate feature selection for the mix of five descriptor families, the best classifier was obtained using multilayer perceptron method (artificial neural network) and 300 features. The performance of the model is demonstrated by the area under the receiver operating characteristics (AUROC) of 0.980 ± 0.0037, and accuracy of 0.936 ± 0.0056 (3-fold cross-validation). Regarding the prediction of 4,504 cancer-associated proteins using this model, the best ranked cancer immunotherapy proteins related to BC were RPS27, SUPT4H1, CLPSL2, POLR2K, RPL38, AKT3, CDK3, RPS20, RASL11A and UBTD1; the best ranked metastasis driver proteins related to BC were S100A9, DDA1, TXN, PRNP, RPS27, S100A14, S100A7, MAPK1, AGR3 and NDUFA13; and the best ranked RNA-binding proteins related to BC were S100A9, TXN, RPS27L, RPS27, RPS27A, RPL38, MRPL54, PPAN, RPS20 and CSRP1. This powerful model predicts several BC-related proteins that should be deeply studied to find new biomarkers and better therapeutic targets. Scripts can be downloaded athttps://github.com/muntisa/neural-networks-for-breast-cancer-proteins.