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.
METHODS: RNA was isolated from peripheral whole blood samples (2 x 10 ml) collected from NPC patients/controls (EDTA vacutainer). Gene expression patterns from 99 samples (66 NPC; 33 controls) were assessed using the Affymetrix array. We also collected expression data from 447 patients with other cancers (201 patients) and non-cancer conditions (246 patients). Multivariate logistic regression analysis was used to obtain biomarker signatures differentiating NPC samples from controls and other diseases. Differences were also analysed within a subset (n=28) of a pre-intervention case cohort of patients whom we followed post-treatment.
RESULTS: A blood-based gene expression signature composed of three genes - LDLRAP1, PHF20, and LUC7L3 - is able to differentiate NPC from various other diseases and from unaffected controls with significant accuracy (area under the receiver operating characteristic curve of over 0.90). By subdividing our NPC cohort according to the degree of patient response to treatment we have been able to identify a blood gene signature that may be able to guide the selection of treatment.
CONCLUSION: We have identified a blood-based gene signature that accurately distinguished NPC patients from controls and from patients with other diseases. The genes in the signature, LDLRAP1, PHF20, and LUC7L3, are known to be involved in carcinoma of the head and neck, tumour-associated antigens, and/or cellular signalling. We have also identified blood-based biomarkers that are (potentially) able to predict those patients who are more likely to respond to treatment for NPC. These findings have significant clinical implications for optimizing NPC therapy.
MATERIALS AND METHODS: A total of 17 patients were selected fulfilling one of the Bethesda criteria: colorectal cancer diagnosed in a patient aged less than 50 years old, having synchronous and metachronous colorectal cancer or with a strong family history. Immunohistochemical staining was performed on paraffin embedded tumour tissue samples using four antibodies: MLH1, MSH2, MSH6 and PMS2.
RESULTS: Twelve out of 17 patients (70.6%) were noted to have a family history. A total of 41% (n=7) of the patients had abnormal immunohistochemical staining with one or more of the four antibodies. Loss of expression were noted in 13 tumour tissues with a negative staining score <4. Of 13 tumour tissues, four showed loss expression of MLH1. For PMS2, loss of expression were noted in five cases. Both MSH2 and MSH6 showed loss of expression in two tumour tissues respectively.
CONCLUSIONS: Revised Bethesda criteria and immunohistochemical analysis constituted a convenient approach and is recommended to be a first-line screening for Lynch syndrome in Malay cohorts.
Materials and Methods: All the variants' information was retrieved from the Ensembl genome database, and then different variation prediction analyses were performed. UTRScan was used to predict UTR variants while MaxEntScan was used to predict splice site variants. Meta-analysis by PredictSNP2 was used to predict sSNPs. Parallel prediction analyses by five different software packages including SIFT, PolyPhen-2, Mutation Assessor, I-Mutant2.0 and SNPs&GO were used to predict the effects of nsSNPs. The level of evolutionary conservation of deleterious nsSNPs was further analyzed using ConSurf server. Mutant protein structures of deleterious nsSNPs were modelled and refined using SPARKS-X and ModRefiner for structural comparison.
Results: A total of 56 deleterious variants were identified in this study, including 12 UTR variants, 18 splice site variants, eight sSNPs and 18 nsSNPs. Among these 56 deleterious variants, seven variants were also identified in the Alzheimer's Disease Sequencing Project (ADSP), Alzheimer's Disease Neuroimaging Initiative (ADNI) and Mount Sinai Brain Bank (MSBB) studies.
Discussion: The 56 deleterious variants were predicted to affect the regulation of gene expression, or have functional impacts on these three endocytosis genes and their gene products. The deleterious variants in these genes are expected to affect their cellular function in endocytosis and may be implicated in the pathogenesis of AD as well. The biological consequences of these deleterious variants and their potential impacts on the disease risks could be further validated experimentally and may be useful for gene-disease association study.
OBJECTIVE: In the present review, we highlight the mammalian Hippo pathway, role of its core members, its upstream regulators, downstream effectors and the resistance cases in lung cancers.
RESULTS: Specific interaction of Mer with cell surface hyaluronan receptor CD44 is vital in cell contact inhibition, thereby activating Hippo pathway. Both transcription co-activators YAP and TAZ (also known as WWTR1, being homologs of Drosophila Yki) are important regulators of proliferation and apoptosis, and serve as major downstream effectors of the Hippo pathway. Mutation of NF2, the upstream regulator of Hippo pathway is linked to the cancers.
CONCLUSION: Targeting YAP and TAZ may be important for future drug delivery and treatment.