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

Expression and clinical significance of miR-8078 in patients with congenital heart disease-associated pulmonary arterial hypertension

OBJECTIVES

This study aimed to analyze the plasma levels of miR-8078 in patients with congenital heart disease-associated pulmonary arterial hypertension (CHD-PAH) and to explore the diagnostic value and potential mechanisms of miR-8078 in CHD-PAH.

METHODS

Plasma samples were collected from 110 patients with congenital heart disease. Subsequently, based on the mean pulmonary artery pressure (PAPm) measured via right heart catheterization, the patients were divided into three groups: no-PAH group (Group W, PAPm < 25 mmHg), mild group (Group M, 25 mmHg ≤ PAPm < 35 mmHg), and moderate-to-severe group (Group H, PAPm ≥ 35 mmHg). The study also involved a control group (Group C) comprised of 40 healthy individuals. The miR-8078 expression levels were determined by means of reverse transcription-polymerase chain reaction (RT-PCR). The target genes and biological functions of miR-8078 were predicted using TargetScan, PicTar, and miRDB software. Statistical analysis was performed to evaluate the correlation between miR-8078 and hemodynamic parameters in CHD-PAH, in addition to its diagnostic value.

RESULTS

The plasma miR-8078 expression levels were significantly higher in the moderate-to-severe group when compared with the control group, no-PAH group, and mild group (p < 0.05). Furthermore, the mild group and no-PAH group showed significantly higher miR-8078 expression levels when compared with the control group (p < 0.05). Both results were consistent with the high-throughput sequencing results. KEGG pathway analysis of the miR-8078 target genes revealed associations with morphine addiction, ubiquitin-mediated proteolysis, and parathyroid hormone synthesis and secretion. GO enrichment analysis indicated the involvement of miR-8078 in the regulation of transcription by RNA polymerase II, the positive regulation of stress-activated MAPK cascade, the transmembrane transport of CI- and K+ ions, chromatin organization, and atrioventricular valve morphogenesis. Correlation analysis showed that the miR-8078 expression levels were positively correlated with the pulmonary artery systolic pressure, mean pulmonary artery pressure, and pulmonary vascular resistance (correlation coefficients of 0.404, 0.397, and 0.283, respectively; all p < 0.05). Univariate and multivariate regression analyses revealed plasma miR-8078 (odds ratio: 1.475, 95 % confidence interval: 1.053-2.065, p < 0.05) to be an independent risk factor for CHD-PAH. Receiver operating characteristic curve analysis revealed that the area under the curve (AUC) for miR-8078 alone and for B-type natriuretic peptide alone in diagnosing CHD-PAH was 0.686 and 0.851, respectively, while the AUC for a combined diagnosis was 0.874, which was higher than that associated with the individual diagnoses (p < 0.05).

CONCLUSION

The findings of this study suggest that miR-8078 is upregulated in CHD-PAH, while the results of the bioinformatics analysis indicate its involvement in the pathogenesis of CHD-PAH, suggesting it to be a potential therapeutic target or biomarker.

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.

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.

Epigenetic drug development for autoimmune and inflammatory diseases

ES-62 is the major secreted protein of the parasitic filarial nematode, Acanthocheilonema viteae. The molecule exists as a large tetramer (MW, ~240kD), which possesses immunomodulatory properties by virtue of multiple phosphorylcholine (PC) moieties attached to N-type glycans. By suppressing inflammatory immune responses, ES-62 can prevent disease development in certain mouse models of allergic and autoimmune conditions, including joint pathology in collagen-induced arthritis (CIA), a model of rheumatoid arthritis (RA). Such protection is associated with functional suppression of "pathogenic" hyper-responsive synovial fibroblasts (SFs), which exhibit an aggressive inflammatory and bone-damaging phenotype induced by their epigenetic rewiring in response to the inflammatory microenvironment of the arthritic joint. Critically, exposure to ES-62 in vivo induces a stably-imprinted CIA-SF phenotype that exhibits functional responses more typical of healthy, Naïve-SFs. Consistent with this, ES-62 "rewiring" of SFs away from the hyper-responsive phenotype is associated with suppression of ERK activation, STAT3 activation and miR-155 upregulation, signals widely associated with SF pathogenesis. Surprisingly however, DNA methylome analysis of Naïve-, CIA- and ES-62-CIA-SF cohorts reveals that rather than simply preventing pathogenic rewiring of SFs, ES-62 induces further changes in DNA methylation under the inflammatory conditions pertaining in the inflamed joint, including targeting genes associated with ciliogenesis, to programme a novel "resolving" CIA-SF phenotype. In addition to introducing a previously unsuspected aspect of ES-62's mechanism of action, such unique behaviour signposts the potential for developing DNA methylation signatures predictive of pathogenesis and its resolution and hence, candidate mechanisms by which novel therapeutic interventions could prevent SFs from perpetuating joint inflammation and destruction in RA. Pertinent to these translational aspects of ES-62-behavior, small molecule analogues (SMAs) based on ES-62's active PC-moieties mimic the rewiring of SFs as well as the protection against joint disease in CIA afforded by the parasitic worm product.

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.

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.