Prediction of breast cancer proteins using molecular descriptors and artificial neural networks: a focus on cancer immunotherapy proteins, metastasis driver proteins, and RNA-binding proteins

Abstract

BackgroundBreast cancer (BC) is a heterogeneous disease characterized by an intricate interplay between different biological aspects such as ethnicity, genomic alterations, gene expression deregulation, hormone disruption, signaling pathway alterations and environmental determinants. Due to the complexity of BC, the prediction of proteins involved in this disease is a trending topic in drug design.MethodsThis 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.ResultsThe 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 4504 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.ConclusionsThis powerful model predicts several BC-related proteins which should be deeply studied to find new biomarkers and better therapeutic targets. The script and the results are available as a free repository at https://github.com/muntisa/neural-networks-for-breast-cancer-proteins.

Integrated Transcriptome Analysis of Radioresistant Cells Revealed Genes and Pathways Predictive Of Tumor Response to Radiotherapy and Chemotherapy in Breast Cancer

“…[30][31][32][33][34][35] Regarding the top 10 hub genes identified in the gene co-expression networks, we highlighted classical oncogenes in cancer as JUN, STAT1, and EGFR genes in luminal breast cancer [34][35][36] and RPS27A, MAPK1, C3, TNF, and GNB2L1 genes in TNBC tumors. [37][38][39][40][41][42][43] All these genes Figure 6. Hub genes expression in luminal and TNBC tumors of breast cancer patients with pCR or pPR/pNR after chemotherapy.…”

In silico analyses reveal new putative Breast Cancer RNA-binding proteins

“…As key regulators of post-transcription, RNA binding proteins (RBPs) are particularly relevant due to their implication in every post-transcriptional step of gene expression: RNA splicing, transport, stability, translation, and localization. As a result, genomic alterations of these proteins lead to dysfunctional cellular processes; indeed, RBPs are emerging as critical regulators of tumorigenesis but only few have well-defined roles in BC 18–26 . To date, 1,393 RBPs have been experimentally identified from human RNA interactomes 27 .…”