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.

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.

Immune cell-specific and common molecular signatures in rheumatoid arthritis through molecular network approaches.

Rheumatoid arthritis (RA) is an autoimmune disorder and common symptom of RA is chronic synovial inflammation. The pathogenesis of RA is not fully understood. Therefore, we aimed to identify underlying common and distinct molecular signatures and pathways among ten types of tissue and cells obtained from patients with RA. In this study, transcriptomic data including synovial tissues, macrophages, blood, T cells, CD4+T cells, CD8+T cells, natural killer T (NKT), cells natural killer (NK) cells, neutrophils, and monocyte cells were analyzed with an integrative and comparative network biology perspective. Each dataset yielded a list of differentially expressed genes as well as a reconstruction of the tissue-specific protein-protein interaction (PPI) network. Molecular signatures were identified by a statistical test using the hypergeometric probability density function by employing the interactions of transcriptional regulators and PPI. Reporter metabolites of each dataset were determined by using genome-scale metabolic networks. It was defined as the common hub proteins, novel molecular signatures, and metabolites in two or more tissue types while immune cell-specific molecular signatures were identified, too. Importantly, miR-155-5p is found as a common miRNA in all tissues. Moreover, NCOA3, PRKDC and miR-3160 might be novel molecular signatures for RA. Our results establish a novel approach for identifying immune cell-specific molecular signatures of RA and provide insights into the role of common tissue-specific genes, miRNAs, TFs, receptors, and reporter metabolites. Experimental research should be used to validate the corresponding genes, miRNAs, and metabolites.

Drug Repurposing Analysis for Colorectal Cancer through Network Medicine Framework: Novel Candidate Drugs and Small Molecules.

This study aimed to reveal the drug-repurposing candidates for colorectal cancer (CRC) via drug-repurposing methods and network biology approaches. A novel, differentially co-expressed, highly interconnected, and co-regulated prognostic gene module was identified for CRC. Based on the gene module, polyethylene glycol (PEG), gallic acid, pyrazole, cordycepin, phenothiazine, pantoprazole, cysteamine, indisulam, valinomycin, trametinib, BRD-K81473043, AZD8055, dovitinib, BRD-A17065207, and tyrphostin AG1478 presented as drugs and small molecule candidates previously studied in the CRC. Lornoxicam, suxamethonium, oprelvekin, sirukumab, levetiracetam, sulpiride, NVP-TAE684, AS605240, 480743.cdx, HDAC6 inhibitor ISOX, BRD-K03829970, and L-6307 are proposed as novel drugs and small molecule candidates for CRC.

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.

Disclosing Potential Key Genes, Therapeutic Targets and Agents for Non-Small Cell Lung Cancer: Evidence from Integrative Bioinformatics Analysis.

Non-small-cell lung cancer (NSCLC) is considered as one of the malignant cancers that causes premature death. The present study aimed to identify a few potential novel genes highlighting their functions, pathways, and regulators for diagnosis, prognosis, and therapies of NSCLC by using the integrated bioinformatics approaches. At first, we picked out 1943 DEGs between NSCLC and control samples by using the statistical LIMMA approach. Then we selected 11 DEGs (CDK1, EGFR, FYN, UBC, MYC, CCNB1, FOS, RHOB, CDC6, CDC20, and CHEK1) as the hub-DEGs (potential key genes) by the protein-protein interaction network analysis of DEGs. The DEGs and hub-DEGs regulatory network analysis commonly revealed four transcription factors (FOXC1, GATA2, YY1, and NFIC) and five miRNAs (miR-335-5p, miR-26b-5p, miR-92a-3p, miR-155-5p, and miR-16-5p) as the key transcriptional and post-transcriptional regulators of DEGs as well as hub-DEGs. We also disclosed the pathogenetic processes of NSCLC by investigating the biological processes, molecular function, cellular components, and KEGG pathways of DEGs. The multivariate survival probability curves based on the expression of hub-DEGs in the SurvExpress web-tool and database showed the significant differences between the low- and high-risk groups, which indicates strong prognostic power of hub-DEGs. Then, we explored top-ranked 5-hub-DEGs-guided repurposable drugs based on the Connectivity Map (CMap) database. Out of the selected drugs, we validated six FDA-approved launched drugs (Dinaciclib, Afatinib, Icotinib, Bosutinib, Dasatinib, and TWS-119) by molecular docking interaction analysis with the respective target proteins for the treatment against NSCLC. The detected therapeutic targets and repurposable drugs require further attention by experimental studies to establish them as potential biomarkers for precision medicine in NSCLC treatment.

Integrative Analysis for Identification of Therapeutic Targets and Prognostic Signatures in Non-Small Cell Lung Cancer.

Differential expressions of certain genes during tumorigenesis may serve to identify novel manageable targets in the clinic. In this work with an integrated bioinformatics approach, we analyzed public microarray datasets from Gene Expression Omnibus (GEO) to explore the key differentially expressed genes (DEGs) in non-small cell lung cancer (NSCLC). We identified a total of 984 common DEGs in 252 healthy and 254 NSCLC gene expression samples. The top 10 DEGs as a result of pathway enrichment and protein-protein interaction analysis were further investigated for their prognostic performances. Among these, we identified high expressions of CDC20, AURKA, CDK1, EZH2, and CDKN2A genes that were associated with significantly poorer overall survival in NSCLC patients. On the contrary, high mRNA expressions of CBL, FYN, LRKK2, and SOCS2 were associated with a significantly better prognosis. Furthermore, our drug target analysis for these hub genes suggests a potential use of Trichostatin A, Pracinostat, TGX-221, PHA-793887, AG-879, and IMD0354 antineoplastic agents to reverse the expression of these DEGs in NSCLC patients.

Drug repurposing for rheumatoid arthritis: Identification of new drug candidates via bioinformatics and text mining analysis.

Rheumatoid arthritis (RA) is an autoimmune disease that results in the destruction of tissue by attacks on the patient by his or her own immune system. Current treatment strategies are not sufficient to overcome RA. In the present study, various transcriptomic data from synovial fluids, synovial fluid-derived macrophages, and blood samples from patients with RA were analysed using bioinformatics approaches to identify tissue-specific repurposing drug candidates for RA. Differentially expressed genes (DEGs) were identified by integrating datasets for each tissue and comparing diseased to healthy samples. Tissue-specific protein-protein interaction (PPI) networks were generated and topologically prominent proteins were selected. Transcription-regulating biomolecules for each tissue type were determined from protein-DNA interaction data. Common DEGs and reporter biomolecules were used to identify drug candidates for repurposing using the hypergeometric test. As a result of bioinformatic analyses, 19 drugs were identified as repurposing candidates for RA, and text mining analyses supported our findings. We hypothesize that the FDA-approved drugs momelotinib, ibrutinib, and sodium butyrate may be promising candidates for RA. In addition, CHEMBL306380, Compound 19a (CHEMBL3116050), ME-344, XL-019, TG100801, JNJ-26483327, and NV-128 were identified as novel repurposing candidates for the treatment of RA. Preclinical and further validation of these drugs may provide new treatment options for RA.

Forecasting Gastric Cancer Diagnosis, Prognosis, and Drug Repurposing with Novel Gene Expression Signatures.

Gastric cancer (GC) is a prevalent disease worldwide with high mortality and poor treatment success. Early diagnosis of GC and forecasting of its prognosis with the use of biomarkers are directly relevant to achieve both personalized/precision medicine and innovation in cancer therapeutics. Gene expression signatures offer one of the promising avenues of research in this regard, as well as guiding drug repurposing analyses in cancers. Using publicly accessible gene expression datasets from the Gene Expression Omnibus and The Cancer Genome Atlas (TCGA), we report here original findings on co-expressed gene modules that are differentially expressed between 133 GC samples and 46 normal tissues, and thus hold potential for novel diagnostic candidates for GC. Furthermore, we found two co-expressed gene modules were significantly associated with poor survival outcomes revealed by survival analysis of the RNA-Seq TCGA datasets. We identified STAT6 (signal transducer and activator of transcription 6) as a key regulator of the identified gene modules. Finally, potential therapeutic drugs that may target and reverse the expression of the identified altered gene modules examined for drug repurposing analyses and the unraveled compounds were further investigated in the literature by the text mining method. Accordingly, we found several repurposed drug candidates, including Trichostatin A, Vorinostat, Parthenolide, Panobinostat, Brefeldin A, Belinostat, and Danusertib. Through text mining analysis and literature search validation, Belinostat and Danusertib were suggested as possible novel drug candidates for GC treatment. These findings collectively inform multiple aspects of GC medical management, including its precision diagnosis, forecasting of possible outcomes, and drug repurposing for innovation in GC medicines in the future.

Cancer Stem Cell Transcriptome Profiling Reveals Seed Genes of Tumorigenesis: New Avenues for Cancer Precision Medicine.

Cancer stem-like cells (CSCs) possess the ability to self-renew and differentiate, and they are among the major factors driving tumorigenesis, metastasis, and resistance to chemotherapy. Therefore, it is critical to understand the molecular substrates of CSC biology so as to discover novel molecular biosignatures that distinguish CSCs and tumor cells. Here, we report new findings and insights by employing four transcriptome datasets associated with CSCs, with CSC and tumor samples from breast, lung, oral, and ovarian tissues. The CSC samples were analyzed to identify differentially expressed genes between CSC and tumor phenotypes. Through comparative profiling of expression levels in different cancer types, we identified 17 "seed genes" that showed a mutual differential expression pattern. We showed that these seed genes were strongly associated with cancer-associated signaling pathways and biological processes, the immune system, and the key cancer hallmarks. Further, the seed genes presented significant changes in their expression profiles in different cancer types and diverse mutation rates, and they also demonstrated high potential as diagnostic and prognostic biomarkers in various cancers. We report a number of seed genes that represent significant potential as "systems biomarkers" for understanding the pathobiology of tumorigenesis. Seed genes offer a new innovation avenue for potential applications toward cancer precision medicine in a broad range of cancers in oncology in the future.

Bioinformatics and machine learning approach identifies potential drug targets and pathways in COVID-19.

Current coronavirus disease-2019 (COVID-19) pandemic has caused massive loss of lives. Clinical trials of vaccines and drugs are currently being conducted around the world; however, till now no effective drug is available for COVID-19. Identification of key genes and perturbed pathways in COVID-19 may uncover potential drug targets and biomarkers. We aimed to identify key gene modules and hub targets involved in COVID-19. We have analyzed SARS-CoV-2 infected peripheral blood mononuclear cell (PBMC) transcriptomic data through gene coexpression analysis. We identified 1520 and 1733 differentially expressed genes (DEGs) from the GSE152418 and CRA002390 PBMC datasets, respectively (FDR < 0.05). We found four key gene modules and hub gene signature based on module membership (MMhub) statistics and protein-protein interaction (PPI) networks (PPIhub). Functional annotation by enrichment analysis of the genes of these modules demonstrated immune and inflammatory response biological processes enriched by the DEGs. The pathway analysis revealed the hub genes were enriched with the IL-17 signaling pathway, cytokine-cytokine receptor interaction pathways. Then, we demonstrated the classification performance of hub genes (PLK1, AURKB, AURKA, CDK1, CDC20, KIF11, CCNB1, KIF2C, DTL and CDC6) with accuracy >0.90 suggesting the biomarker potential of the hub genes. The regulatory network analysis showed transcription factors and microRNAs that target these hub genes. Finally, drug-gene interactions analysis suggests amsacrine, BRD-K68548958, naproxol, palbociclib and teniposide as the top-scored repurposed drugs. The identified biomarkers and pathways might be therapeutic targets to the COVID-19.

Targeting the tumor stroma: integrative analysis reveal GATA2 and TORYAIP1 as novel prognostic targets in breast and ovarian cancer.

Tumor stroma interaction is known to take a crucial role in cancer growth and progression. In the present study, it was performed gene expression analysis of stroma samples with ovarian and breast cancer through an integrative analysis framework to identify common critical biomolecules at multiomics levels. Gene expression datasets were statistically analyzed to identify common differentially expressed genes (DEGs) by comparing tumor stroma and normal stroma samples. The integrative analyses of DEGs indicated that there were 59 common core genes, which might be feasible to be potential marks for cancer stroma targeted strategies. Reporter molecules (i.e. receptor, transcription factors and miRNAs) were determined through a statistical test employing the hypergeometric probability density function. Afterward, the tumor microenvironment protein-protein interaction and the generic network were reconstructed by using identified reporter molecules and common core DEGs. Through a systems medicine approach, it was determined that hub biomolecules, AR, GATA2, miR-124, TOR1AIP1, ESR1, EGFR, STAT1, miR-192, GATA3, COL1A1, in tumor microenvironment generic network. These molecules were also identified as prognostic signatures in breast and ovarian tumor samples via survival analysis. According to literature searching, GATA2 and TORYAIP1 might represent potential biomarkers and candidate drug targets for the stroma targeted cancer therapy applications.

Identification of Common Pathogenetic Processes between Schizophrenia and Diabetes Mellitus by Systems Biology Analysis.

Schizophrenia (SCZ) is a psychiatric disorder characterized by both positive symptoms (i.e., psychosis) and negative symptoms (such as apathy, anhedonia, and poverty of speech). Epidemiological data show a high likelihood of early onset of type 2 diabetes mellitus (T2DM) in SCZ patients. However, the molecular processes that could explain the epidemiological association between SCZ and T2DM have not yet been characterized. Therefore, in the present study, we aimed to identify underlying common molecular pathogenetic processes and pathways between SCZ and T2DM. To this aim, we analyzed peripheral blood mononuclear cell (PBMC) transcriptomic data from SCZ and T2DM patients, and we detected 28 differentially expressed genes (DEGs) commonly modulated between SCZ and T2DM. Inflammatory-associated processes and membrane trafficking pathways as common biological processes were found to be in common between SCZ and T2DM. Analysis of the putative transcription factors involved in the regulation of the DEGs revealed that STAT1 (Signal Transducer and Activator of Transcription 1), RELA (v-rel reticuloendotheliosis viral oncogene homolog A (avian)), NFKB1 (Nuclear Factor Kappa B Subunit 1), and ERG (ETS-related gene) are involved in the expression of common DEGs in SCZ and T2DM. In conclusion, we provide core molecular signatures and pathways that are shared between SCZ and T2DM, which may contribute to the epidemiological association between them.

Co-expressed functional module-related genes in ovarian cancer stem cells represent novel prognostic biomarkers in ovarian cancer.

Ovarian cancer is the leading cause of death from gynecologic malignancies. Cancer stem cells (CSC) seem to play a crucial role in tumor metastasis, recurrence, and chemoresistance. Therefore, CSCs offer significant potential for developing therapeutic targets and to understand tumor recurrence and chemoresistance mechanisms. In the present study, our aim was the identification of the gene group in ovarian CSCs (O-CSCs) and the potential of the resultant gene group in ovarian cancer prognosis. Two different microarray data sets were analyzed by comparing gene expression levels between O-CSCs and cancer samples. The O-CSC co-expression network was reconstructed and its modules were identified. According to the analysis results, 74 mutual DEGs were identified. The O-CSC-specific co-expression network included 32 nodes and 95 edges (network density: 19%), while the co-expression network in cancer samples was reconstructed with 74 nodes and 1066 edges (network density: 39%). Understanding of the molecular mechanism and signatures of O-CSCs should provide valuable insight into chemotherapy resistance and recurrence of ovarian tumors. A highly connected 12 gene module in O-CSC samples of BAMB1, NFKB12, EZR, TNFAIP3, C1orf86, PMAIP1, GEM, KHDRBS3, FILIP1, FGFR2, TGFBR3 and PEG10, (network density: 67%) was identified. Prognostic performance of these genes was evaluated independently using six ovarian cancer datasets (n = 1933 patient samples) via survival analysis. These co-expressed genes were determined as prognostic targets in ovarian cancer. Through literature search validation, five genes (C1orf86, PMAIP1, FILIP1, NFKB12 and PEG10) suggested as novel molecular targets in ovarian cancer. The presented prognostic biomarkers here provide a resource for the understanding of tumor recurrence and chemoresistance and may facilitate critical research directions and development of new prognostic and therapeutic strategies for ovarian cancer. ABBREVIATIONS: CSCs: cancer stem cells; O-CSCs: ovarian CSCs; FACS: fluorescence-activated cell sorting; SP: side population; MP: main population; TFs: transcription factors.

Mapping the Molecular Basis and Markers of Papillary Thyroid Carcinoma Progression and Metastasis Using Global Transcriptome and microRNA Profiling.

Papillary thyroid carcinoma (PTC) is the most common type of thyroid cancer (TC). In a subgroup of patients with PTC, the disease progresses to an invasive stage or in some cases to distant organ metastasis. At present, there is an unmet clinical and diagnostic need for early identification of patients with PTC who are at risk of disease progression or metastasis. In this study, we report several molecular leads and potential biomarker candidates of PTC metastasis for further translational research. The study design was based on comparisons of PTC in three different groups using cross-sectional sampling: Group 1, PTC localized to the thyroid (n = 20); Group 2, PTC with extrathyroidal progression (n = 22); and Group 3, PTC with distant organ metastasis (n = 20). Global transcriptome and microRNAs (miRNA) analyses were conducted using an initial screening set comprising nine formalin-fixed paraffin-embedded PTC samples obtained from three independent patients per study group. The findings were subsequently validated by quantitative real-time polymerase chain reaction (qRT-PCR) using the abovementioned independent patient sample set (n = 62). Comparative analyses of differentially expressed miRNAs showed that miR-193-3p, miR-182-5p, and miR-3607-3p were novel miRNAs associated with PTC metastasis. These potential miRNA biomarkers were associated with TC metastasis and miRNA-target gene associations, which may provide important clinicopathological information on metastasis. Our findings provide new molecular leads for further translational biomarker research, which could facilitate the identification of patients at risk of PTC disease progression or metastasis.

Identification of key biomolecules in rheumatoid arthritis through the reconstruction of comprehensive disease-specific biological networks.

Rheumatoid arthritis (RA) frequently seen chronic synovial inflammation causing joint destruction, chronic disability and reduced life expectancy. The pathogenesis of RA is not completely known. In this study, several gene expression data including synovial tissue and macrophages from synovial tissues were integrated with a holistic perspective and the molecular targets and signatures in RA were determined. Differentially expressed genes (DEGs) were identified from each dataset by comparing diseased and healthy samples. Afterward, the RA-specific protein-protein interaction (PPI) and the transcriptional regulatory network were reconstructed by using several biomolecule interaction data. Key biomolecules were determined through a statistical test employing the hypergeometric probability density function by using the physical interactions of transcriptional regulators and PPI. The integrative analyses of DEGs indicated that there were 110 and 494 common genes between synovial tissues and macrophages related datasets, respectively. Common DEGs of all datasets were identified as 25 genes and these core genes which might be feasible to uncover the mutual biological mechanism insights behind the RA pathogenesis were used for disease specific biological networks reconstruction. It was determined the hub proteins, novel key biomolecules (i.e. receptor, transcription factors and miRNAs) and biomolecules interactions by using the core DEGs. It was identified STAT1, RAC2 and KYNU as hub proteins, PEPD as a receptor, NR4A1, MEOX2, KLF4, IRF1 and MYB as TFs, miR-299, miR-8078, miR-146a, miR-3659 and miR-6882 as key miRNAs. It was determined that biomolecule interaction scenarios using identified key biomolecules and novel biomolecules including RAC2, PEPD, NR4A1, MEOX2, miR-299, miR-8078, miR-3659 and miR-6882 in RA. Our novel findings could be a crucial resource for the understanding of RA molecular mechanism and may be considered as drug targets and development of novel diagnostic strategies. Corresponding genes and miRNAs should be validated via experimental studies.

Identification of molecular signatures and pathways to identify novel therapeutic targets in Alzheimer's disease: Insights from a systems biomedicine perspective.

Alzheimer's disease (AD) is a progressive neurodegenerative disease characterized by the accumulation of amyloid plaques and neurofibrillary tangles in the brain. However, there are no peripheral biomarkers available that can detect AD onset. This study aimed to identify the molecular signatures in AD through an integrative analysis of blood gene expression data. We used two microarray datasets (GSE4226 and GSE4229) comparing peripheral blood transcriptomes of AD patients and controls to identify differentially expressed genes (DEGs). Gene set and protein overrepresentation analysis, protein-protein interaction (PPI), DEGs-Transcription Factors (TFs) interactions, DEGs-microRNAs (miRNAs) interactions, protein-drug interactions, and protein subcellular localizations analyses were performed on DEGs common to the datasets. We identified 25 common DEGs between the two datasets. Integration of genome scale transcriptome datasets with biomolecular networks revealed hub genes (NOL6, ATF3, TUBB, UQCRC1, CASP2, SND1, VCAM1, BTF3, VPS37B), common transcription factors (FOXC1, GATA2, NFIC, PPARG, USF2, YY1) and miRNAs (mir-20a-5p, mir-93-5p, mir-16-5p, let-7b-5p, mir-708-5p, mir-24-3p, mir-26b-5p, mir-17-5p, mir-193-3p, mir-186-5p). Evaluation of histone modifications revealed that hub genes possess several histone modification sites associated with AD. Protein-drug interactions revealed 10 compounds that affect the identified AD candidate biomolecules, including anti-neoplastic agents (Vinorelbine, Vincristine, Vinblastine, Epothilone D, Epothilone B, CYT997, and ZEN-012), a dermatological (Podofilox) and an immunosuppressive agent (Colchicine). The subcellular localization of molecular signatures varied, including nuclear, plasma membrane and cytosolic proteins. In the present study, it was identified blood-cell derived molecular signatures that might be useful as candidate peripheral biomarkers in AD. It was also identified potential drugs and epigenetic data associated with these molecules that may be useful in designing therapeutic approaches to ameliorate AD.

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.

Identification of Prognostic Biomarker Signatures and Candidate Drugs in Colorectal Cancer: Insights from Systems Biology Analysis.

Colorectal cancer (CRC) is the second most common cause of cancer-related death in the world, but early diagnosis ameliorates the survival of CRC. This report aimed to identify molecular biomarker signatures in CRC. We analyzed two microarray datasets (GSE35279 and GSE21815) from the Gene Expression Omnibus (GEO) to identify mutual differentially expressed genes (DEGs). We integrated DEGs with protein⁻protein interaction and transcriptional/post-transcriptional regulatory networks to identify reporter signaling and regulatory molecules; utilized functional overrepresentation and pathway enrichment analyses to elucidate their roles in biological processes and molecular pathways; performed survival analyses to evaluate their prognostic performance; and applied drug repositioning analyses through Connectivity Map (CMap) and geneXpharma tools to hypothesize possible drug candidates targeting reporter molecules. A total of 727 upregulated and 99 downregulated DEGs were detected. The PI3K/Akt signaling, Wnt signaling, extracellular matrix (ECM) interaction, and cell cycle were identified as significantly enriched pathways. Ten hub proteins (ADNP, CCND1, CD44, CDK4, CEBPB, CENPA, CENPH, CENPN, MYC, and RFC2), 10 transcription factors (ETS1, ESR1, GATA1, GATA2, GATA3, AR, YBX1, FOXP3, E2F4, and PRDM14) and two microRNAs (miRNAs) (miR-193b-3p and miR-615-3p) were detected as reporter molecules. The survival analyses through Kaplan⁻Meier curves indicated remarkable performance of reporter molecules in the estimation of survival probability in CRC patients. In addition, several drug candidates including anti-neoplastic and immunomodulating agents were repositioned. This study presents biomarker signatures at protein and RNA levels with prognostic capability in CRC. We think that the molecular signatures and candidate drugs presented in this study might be useful in future studies indenting the development of accurate diagnostic and/or prognostic biomarker screens and efficient therapeutic strategies in CRC.

Network-based approach to identify molecular signatures and therapeutic agents in Alzheimer's disease.

Alzheimer's disease (AD) is a dynamic degeneration of the brain with progressive dementia. Considering the uncertainties in its molecular mechanism, in the present study, we employed network-based integrative analyses, and aimed to explore the key molecules and their associations with small drugs to identify potential biomarkers and therapeutic agents for the AD. First of all, we studied a transcriptome dataset and identified 1521 differentially expressed genes (DEGs). Integration of transcriptome data with protein-protein and transcriptional regulatory interactions resulted with central (hub) proteins (UBA52, RAC1, CREBBP, AR, RPS11, SMAD3, RPS6, RPL12, RPL15, and UBC), regulatory transcription factors (FOXC1, GATA2, YY1, FOXL1, NFIC, E2F1, USF2, SRF, PPARG, and JUN) and microRNAs (mir-335-5p, mir-26b-5p, mir-93-5p, mir-124-3p, mir-17-5p, mir-16-5p, mir-20a-5p, mir-92a-3p, mir-106b-5p, and mir-192-5p) as key signaling and regulatory molecules associated with transcriptional changes for the AD. Considering these key molecules as potential therapeutic targets and Connectivity Map (CMap) architecture, candidate small molecular agents (such as STOCK1N-35696) were identified as novel potential therapeutics for the AD. This study presents molecular signatures at RNA and protein levels which might be useful in increasing discernment of the molecular mechanisms, and potential drug targets and therapeutics to design effective treatment strategies for the AD.