Exploring COVID-19 Pandemic Disparities with Transcriptomic Meta-analysis from the Perspective of Personalized Medicine.

Infection with SARS-CoV2, which is responsible for COVID-19, can lead to differences in disease development, severity and mortality rates depending on gender, age or the presence of certain diseases. Considering that existing studies ignore these differences, this study aims to uncover potential differences attributable to gender, age and source of sampling as well as viral load using bioinformatics and multi-omics approaches. Differential gene expression analyses were used to analyse the phenotypic differences between SARS-CoV-2 patients and controls at the mRNA level. Pathway enrichment analyses were performed at the gene set level to identify the activated pathways corresponding to the differences in the samples. Drug repurposing analysis was performed at the protein level, focusing on host-mediated drug candidates to uncover potential therapeutic differences. Significant differences (i.e. the number of differentially expressed genes and their characteristics) were observed for COVID-19 at the mRNA level depending on the sample source, gender and age of the samples. The results of the pathway enrichment show that SARS-CoV-2 can be combated more effectively in the respiratory tract than in the blood samples. Taking into account the different sample sources and their characteristics, different drug candidates were identified. Evaluating disease prediction, prevention and/or treatment strategies from a personalised perspective is crucial. In this study, we not only evaluated the differences in COVID-19 from a personalised perspective, but also provided valuable data for further experimental and clinical efforts. Our findings could shed light on potential pandemics.

A 19-Gene Signature of Serous Ovarian Cancer Identified by Machine Learning and Systems Biology: Prospects for Diagnostics and Personalized Medicine.

Ovarian cancer is a major cause of cancer deaths among women. Early diagnosis and precision/personalized medicine are essential to reduce mortality and morbidity of ovarian cancer, as with new molecular targets to accelerate drug discovery. We report here an integrated systems biology and machine learning (ML) approach based on the differential coexpression analysis to identify candidate systems biomarkers (i.e., gene modules) for serous ovarian cancer. Accordingly, four independent transcriptome datasets were statistically analyzed independently and common differentially expressed genes (DEGs) were identified. Using these DEGs, coexpressed gene pairs were unraveled. Subsequently, differential coexpression networks between the coexpressed gene pairs were reconstructed so as to identify the differentially coexpressed gene modules. Based on the established criteria, "SOV-module" was identified as being significant, consisting of 19 genes. Using independent datasets, the diagnostic capacity of the SOV-module was evaluated using principal component analysis (PCA) and ML techniques. PCA showed a sensitivity and specificity of 96.7% and 100%, respectively, and ML analysis showed an accuracy of up to 100% in distinguishing phenotypes in the present study sample. The prognostic capacity of the SOV-module was evaluated using survival and ML analyses. We found that the SOV-module's performance for prognostics was significant (p-value = 1.36 × 10-4) with an accuracy of 63% in discriminating between survival and death using ML techniques. In summary, the reported genomic systems biomarker candidate offers promise for personalized medicine in diagnosis and prognosis of serous ovarian cancer and warrants further experimental and translational clinical studies.

Differential co-expression network analysis elucidated genes associated with sensitivity to farnesyltransferase inhibitor and prognosis of acute myeloid leukemia.

Acute myeloid leukemia (AML) is a heterogeneous disease and the most common form of acute leukemia with a poor prognosis. Due to its complexity, the disease requires the identification of biomarkers for reliable prognosis. To identify potential disease genes that regulate patient prognosis, we used differential co-expression network analysis and transcriptomics data from relapsed, refractory, and previously untreated AML patients based on their response to treatment in the present study. In addition, we combined functional genomics and transcriptomics data to identify novel and therapeutically potential systems biomarkers for patients who do or do not respond to treatment. As a result, we constructed co-expression networks for response and non-response cases and identified a highly interconnected group of genes consisting of SECISBP2L, MAN1A2, PRPF31, VASP, and SNAPC1 in the response network and a group consisting of PHTF2, SLC11A2, PDLIM5, OTUB1, and KLRD1 in the non-response network, both of which showed high prognostic performance with hazard ratios of 4.12 and 3.66, respectively. Remarkably, ETS1, GATA2, AR, YBX1, and FOXP3 were found to be important transcription factors in both networks. The prognostic indicators reported here could be considered as a resource for identifying tumorigenesis and chemoresistance to farnesyltransferase inhibitor. They could help identify important research directions for the development of new prognostic and therapeutic techniques for AML.

Network medicine approaches for identification of novel prognostic systems biomarkers and drug candidates for papillary thyroid carcinoma.

Papillary thyroid carcinoma (PTC) is one of the most common endocrine carcinomas worldwide and the aetiology of this cancer is still not well understood. Therefore, it remains important to understand the disease mechanism and find prognostic biomarkers and/or drug candidates for PTC. Compared with approaches based on single-gene assessment, network medicine analysis offers great promise to address this need. Accordingly, in the present study, we performed differential co-expressed network analysis using five transcriptome datasets in patients with PTC and healthy controls. Following meta-analysis of the transcriptome datasets, we uncovered common differentially expressed genes (DEGs) for PTC and, using these genes as proxies, found a highly clustered differentially expressed co-expressed module: a 'PTC-module'. Using independent data, we demonstrated the high prognostic capacity of the PTC-module and designated this module as a prognostic systems biomarker. In addition, using the nodes of the PTC-module, we performed drug repurposing and text mining analyzes to identify novel drug candidates for the disease. We performed molecular docking simulations, and identified: 4-demethoxydaunorubicin hydrochloride, AS605240, BRD-A60245366, ER 27319 maleate, sinensetin, and TWS119 as novel drug candidates whose efficacy was also confirmed by in silico analyzes. Consequently, we have highlighted here the need for differential co-expression analysis to gain a systems-level understanding of a complex disease, and we provide candidate prognostic systems biomarker and novel drugs for PTC.

The Versatility of Plectin in Cancer: A Pan-Cancer Analysis on Potential Diagnostic and Prognostic Impacts of Plectin Isoforms.

Plectin, encoded by PLEC, is a cytoskeletal and scaffold protein with a number of unique isoforms that act on various cellular functions such as cell adhesion, signal transduction, cancer cell invasion, and migration. While plectin has been shown to display high expression and mislocalization in tumor cells, our knowledge of the biological significance of plectin and its isoforms in tumorigenesis remain limited. In this study, we first performed pathway enrichment analysis to identify cancer hallmark proteins associated with plectin. Then, a pan-cancer analysis was performed using RNA-seq data collected from the Cancer Genome Atlas (TCGA) to detect the mRNA expression levels of PLEC and its transcript isoforms, and the prognostic as well as diagnostic significance of the transcript isoforms was evaluated considering cancer stages. We show here that several tissue specific PLEC isoforms are dysregulated in different cancer types and stages but not the expression of PLEC. Among them, PLEC 1d and PLEC 1f are potential biomarker candidates and call for further translational and personalized medicine research. This study makes a contribution as a stride to unravel the molecular mechanisms underpinning plectin isoforms in cancer development and progression by revealing the potent plectin isoforms in different stages of cancer as potential early cancer detection biomarkers. Importantly, uncovering how plectin isoforms guide malignancy and particular cancer types by comprehensive functional studies might open new avenues toward novel cancer therapeutics.

HPV16 status predicts potential protein biomarkers and therapeutics in head and neck squamous cell carcinoma.

Human papillomavirus (HPV) infection, especially HPV16, is one of the causative factors for the development of head and neck squamous cell (HNSC) carcinoma. HPV-positive and HPV-negative HNSC patients differ significantly in their molecular profiles and clinical features, so they should be evaluated differently depending on their HPV status. Given the tremendous variation in HNSC cancers depending on HPV, our goal in this study was to present biomarkers and treatment options tailored to the patient's HPV status. Gene expression levels of HPV16-positive and -negative patients were used as proxies, and the differential interactome algorithm was employed to identify the differential interacting proteins (DIPs). By assessing the prognostic capabilities and druggabilities of DIPs and their interacting partners (DIP-centered modules), we introduce eight modules as potential biomarkers specialized for either positive or negative phenotype. Finally, raloxifene was repositioned for the first time as a drug candidate for the treatment of HPV16-positive HNSC patients.

Drug repositioning via host-pathogen protein-protein interactions for the treatment of cervical cancer.

Introduction: Integrating interaction data with biological knowledge can be a critical approach for drug development or drug repurposing. In this context, host-pathogen-protein-protein interaction (HP-PPI) networks are useful instrument to uncover the phenomena underlying therapeutic effects in infectious diseases, including cervical cancer, which is almost exclusively due to human papillomavirus (HPV) infections. Cervical cancer is one of the second leading causes of death, and HPV16 and HPV18 are the most common subtypes worldwide. Given the limitations of traditionally used virus-directed drug therapies for infectious diseases and, at the same time, recent cancer statistics for cervical cancer cases, the need for innovative treatments becomes clear. Methods: Accordingly, in this study, we emphasize the potential of host proteins as drug targets and identify promising host protein candidates for cervical cancer by considering potential differences between HPV subtypes (i.e., HPV16 and HPV18) within a novel bioinformatics framework that we have developed. Subsequently, subtype-specific HP-PPI networks were constructed to obtain host proteins. Using this framework, we next selected biologically significant host proteins. Using these prominent host proteins, we performed drug repurposing analysis. Finally, by following our framework we identify the most promising host-oriented drug candidates for cervical cancer. Results: As a result of this framework, we discovered both previously associated and novel drug candidates, including interferon alfacon-1, pimecrolimus, and hyaluronan specifically for HPV16 and HPV18 subtypes, respectively. Discussion: Consequently, with this study, we have provided valuable data for further experimental and clinical efforts and presented a novel bioinformatics framework that can be applied to any infectious disease.

Bioinformatics Prediction and Machine Learning on Gene Expression Data Identifies Novel Gene Candidates in Gastric Cancer.

Gastric cancer (GC) is one of the five most common cancers in the world and unfortunately has a high mortality rate. To date, the pathogenesis and disease genes of GC are unclear, so the need for new diagnostic and prognostic strategies for GC is undeniable. Despite particular findings in this regard, a holistic approach encompassing molecular data from different biological levels for GC has been lacking. To translate Big Data into system-level biomarkers, in this study, we integrated three different GC gene expression data with three different biological networks for the first time and captured biologically significant (i.e., reporter) transcripts, hub proteins, transcription factors, and receptor molecules of GC. We analyzed the revealed biomolecules with independent RNA-seq data for their diagnostic and prognostic capabilities. While this holistic approach uncovered biomolecules already associated with GC, it also revealed novel system biomarker candidates for GC. Classification performances of novel candidate biomarkers with machine learning approaches were investigated. With this study, AES, CEBPZ, GRK6, HPGDS, SKIL, and SP3 were identified for the first time as diagnostic and/or prognostic biomarker candidates for GC. Consequently, we have provided valuable data for further experimental and clinical efforts that may be useful for the diagnosis and/or prognosis of GC.

A Pan-Cancer Atlas of Differentially Interacting Hallmarks of Cancer Proteins.

Cancer hallmark genes and proteins orchestrate and drive carcinogenesis to a large extent, therefore, it is important to study these features in different cancer types to understand the process of tumorigenesis and discover measurable indicators. We performed a pan-cancer analysis to map differentially interacting hallmarks of cancer proteins (DIHCP). The TCGA transcriptome data associated with 12 common cancers were analyzed and the differential interactome algorithm was applied to determine DIHCPs and DIHCP-centric modules (i.e., DIHCPs and their interacting partners) that exhibit significant changes in their interaction patterns between the tumor and control phenotypes. The diagnostic and prognostic capabilities of the identified modules were assessed to determine the ability of the modules to function as system biomarkers. In addition, the druggability of the prognostic and diagnostic DIHCPs was investigated. As a result, we found a total of 30 DIHCP-centric modules that showed high diagnostic or prognostic performance in any of the 12 cancer types. Furthermore, from the 16 DIHCP-centric modules examined, 29% of these were druggable. Our study presents candidate systems' biomarkers that may be valuable for understanding the process of tumorigenesis and improving personalized treatment strategies for various cancers, with a focus on their ten hallmark characteristics.

Human oncogenic viruses: an overview of protein biomarkers in viral cancers and their potential use in clinics.

INTRODUCTION: Although the idea that carcinogenesis might be caused by viruses was first voiced about 100 years ago, today's data disappointingly show that we have not made much progress in preventing and/or treating viral cancers in a century. According to recent studies, infections are responsible for approximately 13% of cancer development in the world. Today, it is accepted and proven by many authorities that Epstein-Barr virus (EBV), Hepatitis B virus (HBV), Hepatitis C virus (HCV), Human Herpesvirus 8 (HHV8), Human T-cell Lymphotropic virus 1 (HTLV1) and highly oncogenic Human Papillomaviruses (HPVs) cause or/and contribute to cancer development in humans. AREAS COVERED: Considering the insufficient prevention and/or treatment strategies for viral cancers, in this review we present the current knowledge on protein biomarkers of oncogenic viruses. In addition, we aimed to decipher their potential for clinical use by evaluating whether the proposed biomarkers are expressed in body fluids, are druggable, and act as tumor suppressors or oncoproteins. EXPERT OPINION: Consequently, we believe that this review will shed light on researchers and provide a guide to find remarkable solutions for the prevention and/or treatment of viral cancers.

Repositioning of Anti-Inflammatory Drugs for the Treatment of Cervical Cancer Sub-Types.

Cervical cancer is the fourth most commonly diagnosed cancer worldwide and, in almost all cases is caused by infection with highly oncogenic Human Papillomaviruses (HPVs). On the other hand, inflammation is one of the hallmarks of cancer research. Here, we focused on inflammatory proteins that classify cervical cancer patients by considering individual differences between cancer patients in contrast to conventional treatments. We repurposed anti-inflammatory drugs for therapy of HPV-16 and HPV-18 infected groups, separately. In this study, we employed systems biology approaches to unveil the diagnostic and treatment options from a precision medicine perspective by delineating differential inflammation-associated biomarkers associated with carcinogenesis for both subtypes. We performed a meta-analysis of cervical cancer-associated transcriptomic datasets considering subtype differences of samples and identified the differentially expressed genes (DEGs). Using gene signature reversal on HPV-16 and HPV-18, we performed both signature- and network-based drug reversal to identify anti-inflammatory drug candidates against inflammation-associated nodes. The anti-inflammatory drug candidates were evaluated using molecular docking to determine the potential of physical interactions between the anti-inflammatory drug and inflammation-associated nodes as drug targets. We proposed 4 novels anti-inflammatory drugs (AS-601245, betamethasone, narciclasin, and methylprednisolone) for the treatment of HPV-16, 3 novel drugs for the treatment of HPV-18 (daphnetin, phenylbutazone, and tiaprofenoic acid), and 5 novel drugs (aldosterone, BMS-345541, etodolac, hydrocortisone, and prednisolone) for the treatment of both subtypes. We proposed anti-inflammatory drug candidates that have the potential to be therapeutic agents for the prevention and/or treatment of cervical cancer.

Acute Myeloid Leukemia: New Multiomics Molecular Signatures and Implications for Systems Medicine Diagnostics and Therapeutics Innovation.

Acute myeloid leukemia (AML) is a common, complex, and multifactorial malignancy of the hematopoietic system. AML diagnosis and treatment outcomes display marked heterogeneity and patient-to-patient variations. To date, AML-related biomarker discovery research has employed single omics inquiries. Multiomics analyses that reconcile and integrate the data streams from multiple levels of the cellular hierarchy, from genes to proteins to metabolites, offer much promise for innovation in AML diagnostics and therapeutics. We report, in this study, a systems medicine and multiomics approach to integrate the AML transcriptome data and reporter biomolecules at the RNA, protein, and metabolite levels using genome-scale biological networks. We utilized two independent transcriptome datasets (GSE5122, GSE8970) in the Gene Expression Omnibus database. We identified new multiomics molecular signatures of relevance to AML: miRNAs (e.g., mir-484 and miR-519d-3p), receptors (ACVR1 and PTPRG), transcription factors (PRDM14 and GATA3), and metabolites (in particular, amino acid derivatives). The differential expression profiles of all reporter biomolecules were crossvalidated in independent RNA-Seq and miRNA-Seq datasets. Notably, we found that PTPRG holds important prognostication potential as evaluated by Kaplan-Meier survival analyses. The multiomics relationships unraveled in this analysis point toward the genomic pathogenesis of AML. These multiomics molecular leads warrant further research and development as potential diagnostic and therapeutic targets.

Multiomics Data Integration Identifies New Molecular Signatures for Abdominal Aortic Aneurysm and Aortic Occlusive Disease: Implications for Early Diagnosis, Prognosis, and Therapeutic Targets.

Cardiovascular disease (CVD) is the leading cause of death among adults in developed countries. Among CVDs, abdominal aortic aneurysm (AAA) and aortic occlusive disease (AOD) are of great public health importance because of the high mortality rate in the elderly population. Despite significant molecular insights into AAA and AOD, the molecular mechanisms of these diseases remain unclear, and the current lack of robust diagnostic and prognostic biomarkers requires novel approaches to biomarker discovery and molecular targeting. In this study, we performed a comparative analysis of genome-wide expression data from patients with large AAA (n = 29), small AAA (n = 20), AOD (n = 9), and controls (n = 10). Specifically, we identified the differentially expressed genes and associated molecular pathways and biological processes (BPs) in each disease. Using a systems science approach, these data were linked to comprehensive human biological networks (i.e., protein-protein interaction, transcriptional regulatory, and metabolic networks) to identify molecular signatures of the salient mechanisms of AAA and AOD. Significant alterations in lipid metabolism and valine, leucine, and isoleucine metabolism, as well as neurodegenerative diseases and sex differences in the pathogenesis of AAA and AOD were identified. In the presence of aneurysm, size-dependent changes in lipid metabolism were observed. In addition, molecules and signaling pathways related to immunity, inflammation, infectious disease, and oxidative phosphorylation were identified in common. The results of the comparative and integrative analyzes revealed important clues to disease mechanisms and reporter molecules at various levels that warrant future development as potential prognostic biomarkers and putative therapeutic targets.

Systems-level biomarkers identification and drug repositioning in colorectal cancer.

Colorectal cancer (CRC) is the most commonly diagnosed fatal cancer in both women and men worldwide. CRC ranked second in mortality and third in incidence in 2020. It is difficult to diagnose CRC at an early stage as there are no clinical symptoms. Despite advances in molecular biology, only a limited number of biomarkers have been translated into routine clinical practice to predict risk, prognosis and response to treatment. In the last decades, systems biology approaches at the omics level have gained importance. Over the years, several biomarkers for CRC have been discovered in terms of disease diagnosis and prognosis. On the other hand, a few drugs are being developed and used in clinics for the treatment of CRC. However, the development of new drugs is very costly and time-consuming as the research and development takes about 10 years and more than $1 billion. Therefore, drug repositioning (DR) could save time and money by establishing new indications for existing drugs. In this review, we aim to provide an overview of biomarkers for the diagnosis and prognosis of CRC from the systems biology perspective and insights into DR approaches for the prevention or treatment of CRC.

The Repertoire of Glycan Alterations and Glycoproteins in Human Cancers.

Cancer as the leading cause of death worldwide has many issues that still need to be addressed. Since the alterations on the glycan compositions or/and structures (i.e., glycosylation, sialylation, and fucosylation) are common features of tumorigenesis, glycomics becomes an emerging field examining the structure and function of glycans. In the past, cancer studies heavily relied on genomics and transcriptomics with relatively little exploration of the glycan alterations and glycoprotein biomarkers among individuals and populations. Since glycosylation of proteins increases their structural complexity by several orders of magnitude, glycome studies resulted in highly dynamic biomarkers that can be evaluated for cancer diagnosis, prognosis, and therapy. Glycome not only integrates our genetic background with past and present environmental factors but also offers a promise of more efficient patient stratification compared with genetic variations. Therefore, studying glycans holds great potential for better diagnostic markers as well as developing more efficient treatment strategies in human cancers. While recent developments in glycomics and associated technologies now offer new possibilities to achieve a high-throughput profiling of glycan diversity, we aim to give an overview of the current status of glycan research and the potential applications of the glycans in the scope of the personalized medicine strategies for cancer.

Pathways involved in viral oncogenesis: New perspectives from virus-host protein interactomics.

Oncogenic viruses are among the apparent causes of cancer-associated mortality. It was estimated that 12% to 15% of human malignancies are linked to oncoviruses. Although modernist strategies and traditional genetic studies have defined host-pathogen interactions of the oncoviruses, their host functions which are critical for the establishment of infection still remain mysterious. However, over the last few years, it has become clear that infections hijack and modify cellular pathways for their benefit. In this context, we constructed the virus-host protein interaction networks of seven oncoviruses (EBV, HBV, HCV, HTLV-1, HHV8, HPV16, and HPV18), and revealed cellular pathways hijacking as a result of oncogenic virus infection. Several signaling pathways/processes such as TGF-ß signaling, cell cycle, retinoblastoma tumor suppressor protein, and androgen receptor signaling were mutually targeted by viruses to induce oncogenesis. Besides, cellular pathways specific to a certain virus were detected. By this study, we believe that we improve the understanding of the molecular pathogenesis of viral oncogenesis and provide information in setting new targets for treatment, prognosis, and diagnosis.

Novel Genomic Biomarker Candidates for Cervical Cancer As Identified by Differential Co-Expression Network Analysis.

Cervical cancer is the second most common malignancy and the third reason for mortality among women in developing countries. Although infection by the oncogenic human papilloma viruses is a major cause, genomic contributors are still largely unknown. Network analyses, compared with candidate gene studies, offer greater promise to map the interactions among genomic loci contributing to cervical cancer risk. We report here a differential co-expression network analysis in five gene expression datasets (GSE7803, GSE9750, GSE39001, GSE52903, and GSE63514, from the Gene Expression Omnibus) in patients with cervical cancer and healthy controls. Kaplan-Meier Survival and principle component analyses were employed to evaluate prognostic and diagnostic performances of biomarker candidates, respectively. As a result, seven distinct co-expressed gene modules were identified. Among these, five modules (with sizes of 9-45 genes) presented high prognostic and diagnostic capabilities with hazard ratios of 2.28-11.3, and diagnostic odds ratios of 85.2-548.8. Moreover, these modules were associated with several key biological processes such as cell cycle regulation, keratinization, neutrophil degranulation, and the phospholipase D signaling pathway. In addition, transcription factors ETS1 and GATA2 were noted as common regulatory elements. These genomic biomarker candidates identified by differential co-expression network analysis offer new prospects for translational cancer research, not to mention personalized medicine to forecast cervical cancer susceptibility and prognosis. Looking into the future, we also suggest that the search for a molecular basis of common complex diseases should be complemented by differential co-expression analyses to obtain a systems-level understanding of disease phenotype variability.

Potential biomarkers and therapeutic targets in cervical cancer: Insights from the meta-analysis of transcriptomics data within network biomedicine perspective.

The malignant neoplasm of the cervix, cervical cancer, has effects on the reproductive tract. Although infection with oncogenic human papillomavirus is essential for cervical cancer development, it alone is insufficient to explain the development of cervical cancer. Therefore, other risk factors such as host genetic factors should be identified, and their importance in cervical cancer induction should be determined. Although gene expression profiling studies in the last decade have made significant molecular findings about cervical cancer, adequate screening and effective treatment strategies have yet to be achieved. In the current study, meta-analysis was performed on cervical cancer-associated transcriptome data and reporter biomolecules were identified at RNA (mRNA, miRNA), protein (receptor, transcription factor, etc.), and metabolite levels by the integration of gene expression profiles with genome-scale biomolecular networks. This approach revealed already-known biomarkers, tumor suppressors and oncogenes in cervical cancer as well as various receptors (e.g. ephrin receptors EPHA4, EPHA5, and EPHB2; endothelin receptors EDNRA and EDNRB; nuclear receptors NCOA3, NR2C1, and NR2C2), miRNAs (e.g., miR-192-5p, miR-193b-3p, and miR-215-5p), transcription factors (particularly E2F4, ETS1, and CUTL1), other proteins (e.g., KAT2B, PARP1, CDK1, GSK3B, WNK1, and CRYAB), and metabolites (particularly, arachidonic acids) as novel biomarker candidates and potential therapeutic targets. The differential expression profiles of all reporter biomolecules were cross-validated in independent RNA-Seq and miRNA-Seq datasets, and the prognostic power of several reporter biomolecules, including KAT2B, PCNA, CD86, miR-192-5p and miR-215-5p was also demonstrated. In this study, we reported valuable data for further experimental and clinical efforts, because the proposed biomolecules have significant potential as systems biomarkers for screening or therapeutic purposes in cervical carcinoma.

Multiomics Analysis of Tumor Microenvironment Reveals Gata2 and miRNA-124-3p as Potential Novel Biomarkers in Ovarian Cancer.

Ovarian cancer is a common and, yet, one of the most deadly human cancers due to its insidious onset and the current lack of robust early diagnostic tests. Tumors are complex tissues comprised of not only malignant cells but also genetically stable stromal cells. Understanding the molecular mechanisms behind epithelial-stromal crosstalk in ovarian cancer is a great challenge in particular. In the present study, we performed comparative analyses of transcriptome data from laser microdissected epithelial, stromal, and ovarian tumor tissues, and identified common and tissue-specific reporter biomolecules-genes, receptors, membrane proteins, transcription factors (TFs), microRNAs (miRNAs), and metabolites-by integration of transcriptome data with genome-scale biomolecular networks. Tissue-specific response maps included common differentially expressed genes (DEGs) and reporter biomolecules were reconstructed and topological analyses were performed. We found that CDK2, EP300, and SRC as receptor-related functions or membrane proteins; Ets1, Ar, Gata2, and Foxp3 as TFs; and miR-16-5p and miR-124-3p as putative biomarkers and warrant further validation research. In addition, we report in this study that Gata2 and miR-124-3p are potential novel reporter biomolecules for ovarian cancer. The study of tissue-specific reporter biomolecules in epithelial cells, stroma, and tumor tissues as exemplified in the present study offers promise in biomarker discovery and diagnostics innovation for common complex human diseases such as ovarian cancer.

RNA-based ovarian cancer research from 'a gene to systems biomedicine' perspective.

Ovarian cancer remains the leading cause of death from a gynecologic malignancy, and treatment of this disease is harder than any other type of female reproductive cancer. Improvements in the diagnosis and development of novel and effective treatment strategies for complex pathophysiologies, such as ovarian cancer, require a better understanding of disease emergence and mechanisms of progression through systems medicine approaches. RNA-level analyses generate new information that can help in understanding the mechanisms behind disease pathogenesis, to identify new biomarkers and therapeutic targets and in new drug discovery. Whole RNA sequencing and coding and non-coding RNA expression array datasets have shed light on the mechanisms underlying disease progression and have identified mRNAs, miRNAs, and lncRNAs involved in ovarian cancer progression. In addition, the results from these analyses indicate that various signalling pathways and biological processes are associated with ovarian cancer. Here, we present a comprehensive literature review on RNA-based ovarian cancer research and highlight the benefits of integrative approaches within the systems biomedicine concept for future ovarian cancer research. We invite the ovarian cancer and systems biomedicine research fields to join forces to achieve the interdisciplinary caliber and rigor required to find real-life solutions to common, devastating, and complex diseases such as ovarian cancer. ABBREVIATIONS: CAF: cancer-associated fibroblasts; COG: Cluster of Orthologous Groups; DEA: disease enrichment analysis; EOC: epithelial ovarian carcinoma; ESCC: oesophageal squamous cell carcinoma; GSI: gamma secretase inhibitor; GO: Gene Ontology; GSEA: gene set enrichment analyzes; HAS: Hungarian Academy of Sciences; lncRNAs: long non-coding RNAs; MAPK/ERK: mitogen-activated protein kinase/extracellular signal-regulated kinases; NGS: next-generation sequencing; ncRNAs: non-coding RNAs; OvC: ovarian cancer; PI3K/Akt/mTOR: phosphatidylinositol-3-kinase/protein kinase B/mammalian target of rapamycin; RT-PCR: real-time polymerase chain reaction; SNP: single nucleotide polymorphism; TF: transcription factor; TGF-ß: transforming growth factor-ß.