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.

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.

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.

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.

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.

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.