Network-based analysis of prostate cancer cell lines reveals novel marker gene candidates associated with radioresistance and patient relapse

• Published in: PLoS computational biology 2019-11, Vol.15 (11), p.e1007460-e1007460
• Main Authors: Seifert, Michael, Peitzsch, Claudia, Gorodetska, Ielizaveta, Börner, Caroline, Klink, Barbara, Dubrovska, Anna
• Format: Article
• Language: English
• Subjects: Analysis; Androgens; Apoptosis; Biological markers; Biology and Life Sciences; Biomarkers; Biomarkers, Tumor - genetics; Biotechnology; Cancer cells; Cancer genetics; Cancer therapies; Cancer treatment; Care and treatment; Cell Line, Tumor; Clinical trials; Computational Biology - methods; Computational neuroscience; Computer and Information Sciences; Copy number; Deoxyribonucleic acid; Development and progression; DNA; DNA repair; Gene Dosage - genetics; Gene expression; Gene Expression Profiling - methods; Gene Expression Regulation, Neoplastic - genetics; Gene Regulatory Networks - genetics; Gene Silencing; Genes; Genetic aspects; Genetic engineering; Genetic research; Genomes; Genomics; Health risks; Hospitals; Humans; Kinases; Male; Medical prognosis; Medical research; Medicine; Medicine and Health Sciences; Metastasis; Molecular modelling; Mutation; Neoplasm Recurrence, Local - genetics; Nerve Growth Factors - genetics; Oncology; Parenting; Patients; Prostate cancer; Prostatic Neoplasms - genetics; Radiation; Radiation (Physics); Radiation therapy; Radiation Tolerance - genetics; Radioresistance; Radiosensitivity; Radiosensitization; Radiotherapy; Recurrence; Recurrence (Disease); Regression analysis; Research and analysis methods; Risk groups; RNA, Messenger - genetics; RNA, Small Interfering; Signal Transduction - genetics; siRNA; Steroidogenic acute regulatory protein; Transcriptome - genetics; Tumor cell lines
• ISSN: 1553-7358; 1553-734X; 1553-7358
• DOI: 10.1371/journal.pcbi.1007460

Radiation therapy is an important and effective treatment option for prostate cancer, but high-risk patients are prone to relapse due to radioresistance of cancer cells. Molecular mechanisms that contribute to radioresistance are not fully understood. Novel computational strategies are needed to identify radioresistance driver genes from hundreds of gene copy number alterations. We developed a network-based approach based on lasso regression in combination with network propagation for the analysis of prostate cancer cell lines with acquired radioresistance to identify clinically relevant marker genes associated with radioresistance in prostate cancer patients. We analyzed established radioresistant cell lines of the prostate cancer cell lines DU145 and LNCaP and compared their gene copy number and expression profiles to their radiosensitive parental cells. We found that radioresistant DU145 showed much more gene copy number alterations than LNCaP and their gene expression profiles were highly cell line specific. We learned a genome-wide prostate cancer-specific gene regulatory network and quantified impacts of differentially expressed genes with directly underlying copy number alterations on known radioresistance marker genes. This revealed several potential driver candidates involved in the regulation of cancer-relevant processes. Importantly, we found that ten driver candidates from DU145 (ADAMTS9, AKR1B10, CXXC5, FST, FOXL1, GRPR, ITGA2, SOX17, STARD4, VGF) and four from LNCaP (FHL5, LYPLAL1, PAK7, TDRD6) were able to distinguish irradiated prostate cancer patients into early and late relapse groups. Moreover, in-depth in vitro validations for VGF (Neurosecretory protein VGF) showed that siRNA-mediated gene silencing increased the radiosensitivity of DU145 and LNCaP cells. Our computational approach enabled to predict novel radioresistance driver gene candidates. Additional preclinical and clinical studies are required to further validate the role of VGF and other candidate genes as potential biomarkers for the prediction of radiotherapy responses and as potential targets for radiosensitization of prostate cancer.