BackgroundAlthough pituitary adenoma is a malignant tumor, it can present as invasive growth in some cases. MicroRNA (miR)-26a has been found to be abnormally highly expressed in pituitary adenoma, indicating possible involvement in pathogenesis. As a known target gene of miR-26a, PLAG1 has abnormally low expression in pituitary adenoma. The correlation between miR-26a or PLAG1 expressional abnormality and occurrence of pituitary adenoma is still unknown, as is its association with invasiveness of pituitary adenoma.Material/MethodsPituitary adenoma tissues, including both invasive and non-invasive subtypes, were collected from our Neurosurgery Department, in parallel with normal pituitary tissues from postmortem autopsy. qRT-PCR was used to detect mRNA expression of miR-26a and PLAG1, while Western blotting was used to test PLAG1 protein expression. The correlation between miR-26a and PLAG1, and with pathological features, were analyzed. ROC analysis revealed the utility of miR-26a and PLAG1 in differential diagnosis of invasive/non-invasive pituitary tumors and in analyzing their effects on patient prognosis.ResultsMiR-26a was remarkably upregulated in pituitary tumors, while PLAG1 was downregulated, especially in invasive pituitary tumors. miR-26a and PLAG1 had higher diagnostic values for differentiating between invasive and non-invasive pituitary tumors (AUC=0.889 and 0.818, respectively). Those patients with miR-26 overexpression and PLAG1 downregulation had unfavorable prognosis. miR-26 and PLAG1 are independent factors affecting patient diagnosis.ConclusionsMiR-26a can facilitate occurrence of pituitary tumor and invasiveness, probably via inhibiting PLAG1 expression.

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BackgroundAlong with the development of precision medicine, individual heterogeneity is attracting more and more attentions in clinical research and application. Although the biomolecular reaction seems to be some various when different individuals suffer a same disease (e.g. virus infection), the final pathogen outcomes of individuals always can be mainly described by two categories in clinics, i.e. symptomatic and asymptomatic. Thus, it is still a great challenge to characterize the individual specific intrinsic regulatory convergence during dynamic gene regulation and expression. Except for individual heterogeneity, the sampling time also increase the expression diversity, so that, the capture of similar steady biological state is a key to characterize individual dynamic biological processes.ResultsAssuming the similar biological functions (e.g. pathways) should be suitable to detect consistent functions rather than chaotic genes, we design and implement a new computational framework (ABP: Attractor analysis of Boolean network of Pathway). ABP aims to identify the dynamic phenotype associated pathways in a state-transition manner, using the network attractor to model and quantify the steady pathway states characterizing the final steady biological sate of individuals (e.g. normal or disease). By analyzing multiple temporal gene expression datasets of virus infections, ABP has shown its effectiveness on identifying key pathways associated with phenotype change; inferring the consensus functional cascade among key pathways; and grouping pathway activity states corresponding to disease states.ConclusionsCollectively, ABP can detect key pathways and infer their consensus functional cascade during dynamical process (e.g. virus infection), and can also categorize individuals with disease state well, which is helpful for disease classification and prediction.

Type 2 diabetes (T2D) is known as a disease caused by gene alterations characterized by insulin resistance, thus the insulin-responsive tissues are of great interest for T2D study. It’s of great relevance to systematically investigate commonalities and specificities of T2D among those tissues. Here we establish a multi-level comparative framework across three insulin target tissues (white adipose, skeletal muscle, and liver) to provide a better understanding of T2D. Starting from the ranks of gene expression, we constructed the ‘disease network’ through detecting diverse interactions to provide a well-characterization for disease affected tissues. Then, we applied random walk with restart algorithm to the disease network to prioritize its nodes and edges according to their association with T2D. Finally, we identified a merged core module by combining the clustering coefficient and Jaccard index, which can provide elaborate and visible illumination of the common and specific features for different tissues at network level. Taken together, our network-, gene-, and module-level characterization across different tissues of T2D hold the promise to provide a broader and deeper understanding for T2D mechanism.

BACKGROUND: Persistent postural-perceptual dizziness (PPPD) is a chronic dizziness, its pathogenesis is unknown by now. OBJECTIVE: To study the relationship between the DRD2 gene TaqIA polymorphisms and PPPD, and further to explore the molecular mechanism underlying this disease. METHODS: 43 patients diagnosed with PPPD and 45 randomly selected cases (matched by age and sex) were included in the study and control group, respectively. DRD2 gene TaqIA polymorphisms were detected in all participants by polymerase chain reaction (PCR)combined with the restriction fragment length polymorphism (RFLP) method. RESULTS: In the study group, frequencies of the A1 and A2 TaqIA alleles (65.1% and 34.9%, respectively) were significantly different to those in the control group (46.7% and 53.3%, respectively; P < 0.05). The allele frequency in the study group for the A1/A1 genotype was 34.9%, for A1/A2 was 60.5%, and for A2/A2 was 4.6%, all of which were significantly higher than the control group (24.4%, 44.5%. and 31.1%, respectively; P < 0.01). CONCLUSIONS: Our findings indicate that the DRD2 TaqIA A1 allele is possibly the susceptibility polymorphism for PPPD, and that the A2/A2 genotype has a potentially protective role for PPPD. However, larger independent studies are required for further validation.