An effective mixed-model for screening differentially expressed genes of breast cancer based on LR-RF

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Prediction of Breast Cancer Proteins Involved in Immunotherapy, Metastasis, and RNA-Binding Using Molecular Descriptors and Artifcial Neural Networks

[Abstract] Breast 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 classifer 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 fve descriptor families, the best classifer was obtained using multilayer perceptron method (artifcial 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 fnd new biomarkers and better therapeutic targets. Scripts can be downloaded at https://github.com/muntisa/ neural-networks-for-breast-cancer-proteins.This work was supported by a) Universidad UTE (Ecuador), b) the Collaborative Project in Genomic Data Integration (CICLOGEN) PI17/01826 funded by the Carlos III Health Institute from the Spanish National plan for Scientific and Technical Research and Innovation 2013-2016 and the European Regional Development Funds (FEDER) - “A way to build Europe”; c) the General Directorate of Culture, Education and University Management of Xunta de Galicia ED431D 2017/16 and “Drug Discovery Galician Network” Ref. ED431G/01 and the “Galician Network for Colorectal Cancer Research” (Ref. ED431D 2017/23); d) the Spanish Ministry of Economy and Competitiveness for its support through the funding of the unique installation BIOCAI (UNLC08-1E-002, UNLC13-13-3503) and the European Regional Development Funds (FEDER) by the European Union; e) the Consolidation and Structuring of Competitive Research Units - Competitive Reference Groups (ED431C 2018/49), funded by the Ministry of Education, University and Vocational Training of the Xunta de Galicia endowed with EU FEDER funds; f) research grants from Ministry of Economy and Competitiveness, MINECO, Spain (FEDER CTQ2016-74881-P), Basque government (IT1045-16), and kind support of Ikerbasque, Basque Foundation for Science; and, g) Sociedad Latinoamericana de Farmacogenómica y Medicina Personalizada (SOLFAGEM)Xunta de Galicia; ED431D 2017/16Xunta de Galicia; ED431G/01Xunta de Galicia; ED431D 2017/23Xunta de Galicia; ED431C 2018/49Gobierno Vasco; IT1045-1

Doctor of Philosophy

dissertationProgrammed cell death 4 (PDCD4) has been described as a tumor suppressor in multiple cancer cell types. In vitro, exogenous expression of PDCD4 results in decreased anchorage-independent cell growth and invasion. These anticancer phenotypes are attributed to inhibition of the translation initiation factor eIF4A when bound to PDCD4. In this dissertation, I report the discovery of novel interactions with the nuclear pore protein Nup153, the exon junction core protein eIF4AIII, and protein arginine methyltransferase 5 (PRMT5) that may modulate PDCD4 in a cancer context. PDCD4 levels are often suppressed in cancerous cells compared to normal surrounding tissues, and elevated expression in tumors is correlated with better survival outcomes. Despite this, 20-30% of patients with tumors that express high levels of PDCD4 have poor outcomes, indicating that these cancers deactivate PDCD4. Our analyses of transcript expression in breast cancer patients show that simultaneous upregulation of PRMT5 with PDCD4 results in poor survival outcomes. Using an orthotopic tumor model, I demonstrate that simultaneous expression of PDCD4 and PRMT5 in the breast cancer cell line MCF7 causes accelerated tumor growth. This tumor growth phenotype is dependent on PRMT5 enzymatic activity and the PDCD4 Nterminal site that is modified by PRMT5. This demonstrates that PDCD4 tumor suppressor function is radically altered when modified by PRMT5. Furthermore, this provides a mechanism for poor outcomes in patients with tumors that express elevated iv PDCD4. These findings show the utility of tracking both PDCD4 and PRMT5 as biomarkers and reveals PRMT5 as a potential target of chemotherapy. Finally, PDCD4 acts as a tumor suppressor through inhibition of the RNA helicase activity of eIF4A, although the precise mechanism of how this is accomplished has been unknown. In this dissertation, I report that PDCD4 interferes with the ability of eIF4A to interact with RNA, thereby deactivating its RNA helicase function. This provides a clear in vitro mechanism for eIF4A inhibition by PDCD4

A case study of microarray breast cancer classification using machine learning algorithms with grid search cross validation

Breast cancer is one of the leading causes of death and most frequently diagnosed cancer amongst women. Annually, almost half a million women do not survive the disease and die from breast cancer. Machine learning is a subfield of artificial intelligence (AI) and computer science that uses data and algorithms to mimic how humans learn, and gradually improving its accuracy. In this work, simple machine learning methods are used to classify breast cancer microarray data to normal and relapse. The data is from the gene expression omnibus (GEO) website namely GSE45255 and GSE15852. These two datasets are integrated and combined to form a single dataset. The study involved three machine learning algorithms, random forest (RF), extra tree (ET), and support vector machine (SVM). Grid search cross validation (CV) is applied for hyperparameter tuning of the algorithms. The result shows that the tuned SVM is best among the tested algorithms with accuracy of 97.78%. In the future it is recommended to include feature selection method to get the optimal features and better classification accuracies

The cancer secretome: a reservoir of biomarkers

Biomarkers are pivotal for cancer detection, diagnosis, prognosis and therapeutic monitoring. However, currently available cancer biomarkers have the disadvantage of lacking specificity and/or sensitivity. Developing effective cancer biomarkers becomes a pressing and permanent need. The cancer secretome, the totality of proteins released by cancer cells or tissues, provides useful tools for the discovery of novel biomarkers. The focus of this article is to review the recent advances in cancer secretome analysis. We aim to elaborate the approaches currently employed for cancer secretome studies, as well as its applications in the identification of biomarkers and the clarification of carcinogenesis mechanisms. Challenges encountered in this newly emerging field, including sample preparation, in vivo secretome analysis and biomarker validation, are also discussed. Further improvements on strategies and technologies will continue to drive forward cancer secretome research and enable development of a wealth of clinically valuable cancer biomarkers

The pathogenesis of classical Hodgkin lymphoma : investigation of possible viral pathogens and recurrent chromosomal imbalances

Hodgkin lymphoma (HL) is a malignant lymphoma that is diagnosed mostly in young adults, and is the second most common malignancy to affect this age group. This disease is subdivided into two entities with different aetiologies: classical HL (cHL) (~95% of cases) and nodular lymphocyte-predominant HL. In Europe, ~82% of young adults with cHL are non-Epstein-Barr virus associated and epidemiological studies have suggested that a common infectious agent may play a key role in the aetiology of these cases. The molecular biology of HL is not well understood, primarily due to the low number of Hodgkin and Reed-Sternberg (HRS) cells present within these tumours. However, recently developed techniques for the selection and micromanipulation of single HRS cells from tumours, and the development of molecular cytogenetic techniques (i.e. array-comparative genomic hybridisation (CGH) are overcoming these difficulties. To investigate a potential candidate virus, DNA samples from cHL biopsies were screened for the measles virus (MV) and polyomaviruses (PyV), using immunohistochemistry and highly sensitive PCR assays. Chromosomal imbalances in six well-established cHL-derived cell lines and a cHL case were analysed by array-CGH. To obtain sufficient DNA for array-CGH from the cHL case, single HRS cells were isolated using laser microdissection. DNA was extracted then amplified by degenerate oligonucleotide primer polymerase chain reaction. MV and PyV genomes were not detected within cHL biopsies. Recurrent chromosomal imbalances were confirmed within the cHL-derived cell lines and cHL case, in addition to several novel imbalances. This is the first time that a cHL case has been analysed by array-CGH