MATERIALS AND METHODS: Total RNA was extracted from three formalin-fixed paraffin-embedded (FFPE) samples each of normal cervix, HPV-infected low-grade squamous intraepithelial lesion (LSIL), high-grade SIL (HSIL) and squamous cell carcinoma (SCC). Transcriptomic profiling by microarrays was conducted followed by downstream Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses.
RESULTS: We examined the difference in GOs enriched for each transition stage from normal cervix to LSIL, HSIL, and SCC, and found 307 genes to be differentially expressed. In the transition from normal cervix to LSIL, the extracellular matrix (ECM) genes were significantly downregulated. The MHC class II genes were significantly upregulated in the LSIL to HSIL transition. In the final transition from HSIL to SCC, the immunoglobulin heavy locus genes were significantly upregulated and the ECM pathway was implicated.
CONCLUSION: Deregulation of the immune-related genes including MHC II and immunoglobulin heavy chain genes were involved in the transitions from LSIL to HSIL and SCC, suggesting immune escape from host anti-tumour response. The extracellular matrix plays an important role during the early and late stages of cervical carcinogenesis.
METHODS: We measured levels of 30 chemokines and cytokines in serum and cerebrospinal fluid (CSF) samples from Malaysian children hospitalized with EV71 infection (n = 88), comprising uncomplicated HFMD (n = 47), meningitis (n = 8), acute flaccid paralysis (n = 1), encephalitis (n = 21), and encephalitis with cardiorespiratory compromise (n = 11). Four of the latter patients died.
RESULTS: Both pro-inflammatory and anti-inflammatory mediator levels were elevated, with different patterns of mediator abundance in the CSF and vascular compartments. Serum concentrations of interleukin 1β (IL-1β), interleukin 1 receptor antagonist (IL-1Ra), and granulocyte colony-stimulating factor (G-CSF) were raised significantly in patients who developed cardio-respiratory compromise (P = .013, P = .004, and P < .001, respectively). Serum IL-1Ra and G-CSF levels were also significantly elevated in patients who died, with a serum G-CSF to interleukin 5 ratio of >100 at admission being the most accurate prognostic marker for death (P < .001; accuracy, 85.5%; sensitivity, 100%; specificity, 84.7%).
CONCLUSIONS: Given that IL-1β has a negative inotropic action on the heart, and that both its natural antagonist, IL-1Ra, and G-CSF are being assessed as treatments for acute cardiac impairment, the findings suggest we have identified functional markers of EV71-related cardiac dysfunction and potential treatment options.
RESULTS: In this study, we propose the Context Based Dependency Network (CBDN), a method that is able to infer gene regulatory networks with the regulatory directions from gene expression data only. To determine the regulatory direction, CBDN computes the influence of source to target by evaluating the magnitude changes of expression dependencies between the target gene and the others with conditioning on the source gene. CBDN extends the data processing inequality by involving the dependency direction to distinguish between direct and transitive relationship between genes. We also define two types of important regulators which can influence a majority of the genes in the network directly or indirectly. CBDN can detect both of these two types of important regulators by averaging the influence functions of candidate regulator to the other genes. In our experiments with simulated and real data, even with the regulatory direction taken into account, CBDN outperforms the state-of-the-art approaches for inferring gene regulatory network. CBDN identifies the important regulators in the predicted network: 1. TYROBP influences a batch of genes that are related to Alzheimer's disease; 2. ZNF329 and RB1 significantly regulate those 'mesenchymal' gene expression signature genes for brain tumors.
CONCLUSION: By merely leveraging gene expression data, CBDN can efficiently infer the existence of gene-gene interactions as well as their regulatory directions. The constructed networks are helpful in the identification of important regulators for complex diseases.