ABSTRACT. We identified disturbed pathways in heart failure (HF) based on Gibbs sampling combined with pathway enrichment analysis. A total of 396 Markov chains (MCs) (gene count >5) were obtained. After Gibbs sampling, six differentially expressed molecular functions (DEMFs) (possibility ≥0.8) were obtained. As statistical analysis was performed on the number of individual differentially expressed genes (DEGs), we found that there were 137 DEGs with frequency of occurrence ≥2 in the DEMFs. Pathway enrichment analysis showed that these 137 DEGs were enriched in eight significant pathways under the condition of P < 0.001. The five most significant pathways were: the calcium signaling pathway (P = 9.08E-19), arrhythmogenic right ventricular cardiomyopathy (P = 5.66E-13), cardiac muscle contraction (P = 8.04E-13), hypertrophic cardiomyopathy (P = 2.55E-12), and dilated cardiomyopathy (P = 7.30E-12). In conclusion, this novel method for identifying significant pathways in HF based on Gibbs sampling combined with pathway enrichment analysis was suitable. We predict that several altered pathways (such as the calcium signaling pathway and dilated cardiomyopathy) may play important roles in HF and are potentially novel predictive and prognostic markers for HF.
Medulloblastoma (MB) is the most common malignant brain tumor in infants and children. Four molecular subtypes of MB are recognized: WNT, SHH, Group 3 (G3), and Group 4 (G4). Compared with WNT and SHH subtypes, G3 MBs exhibit significantly worse outcomes and higher metastatic rates, and there is no effective treatment yet. Moreover, G3 and G4 MBs are much more common in boys than girls, i.e., sex bias, which also plays important roles in cancer prognosis and drug response. However, the molecular mechanism of G3 remains unclear, and there are no well-identified biomarker genes associated with these phenotypes, i.e., worse survival rate, higher metastasis rate, and sex bias. In this exploratory study, we aim to identify potential biomarkers associated with the three phenotypes using integrative analysis of gene expression, methylation and copy number variation datasets. In the results, we identified a set of biomarker genes and linked them into a network signature. The network signature showed better performance in the separation of G3 MB patients into subtypes with a significant difference in terms of the three phenotypes. To identify potentially effective drugs for G3 MBs, a set of drugs with diverse targets were prioritized, which can potentially inhibit the network signature. These drugs or combinations thereof might be effective for G3 treatment.
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