Role of MicroRNA-214 in Dishevelled1-Modulated ß-catenin Signalling in Non-Small Cell Lung Cancer Progression.

Background: The mortality of patients with non-small cell lung cancer (NSCLC) is rather high. This is largely because of the lack of specific targets and understanding of the molecular mechanism for early diagnosis. Dishevelled (Dvl) dysregulation leads to malignant progression. We confirmed that Dvl1 expression is associated with a poor prognosis of patients with NSCLC. However, how Dvl1 transmits signals through the Wnt/ß-catenin pathway remains unknown. Methods: In this study, the expression levels of Dvl1 and ß-catenin in resected NSCLC samples were immunohistochemically analysed. Dvl1 cDNA and small interfering RNA against ß-catenin were transfected into NSCLC cells, and their effects on canonical Wnt signalling and biological behaviour of NSCLC cells were analysed. Using bioinformatics analyses, an interaction between microRNA (miR)-214 and ß-catenin was identified; miR-214 expression was determined in NSCLC tissues using quantitative real-time polymerase chain reaction. An exogenous miR-214 (mimic) was used to analyse the biological behaviour of NSCLC cells and the effect of Dvl1 on canonical Wnt activation. Results: Dvl1 overexpression in NSCLC tissues as well as Dvl1 and ß-catenin nuclear coexpression were significantly associated with poor prognosis of NSCLC (P < 0.05). Additionally, Dvl1 promoted Wnt/ß-catenin signalling to enhance the malignant phenotype of NSCLC cells. Moreover, miR-214 directly targeted the 3' untranslated region of ß-catenin to inhibit the activation of canonical Wnt signalling induced by Dvl1. Conclusions: Our results suggest that Dvl1 is a potential therapeutic target for NSCLC and that miR-214 plays an inhibitory role in Dvl1-mediated activation of Wnt/ß-catenin signalling in NSCLC cells, which could affect NSCLC progression.

Case report: Intraoperative frozen section analysis of Thyroid paraganglioma.

Paraganglioma (PGL) is a neuroendocrine tumor that arises from the sympathetic or parasympathetic paraganglia. Primary thyroid PGL is extremely rare. PGL may be difficult to diagnose on frozen sections because its histopathological features, such as polygonal tumor cells with eosinophilic cytoplasm arranged irregularly, overlap with those of thyroid follicular adenoma. We present a case of thyroid PGL in a female patient and provide a detailed description of the patient's clinicopathologic characteristics. Cervical computed tomography showed a left thyroid mass with uneven density. Intraoperative frozen section analysis showed an uneven fibrous septa and rich networks of delicate vessels surrounding tumor cell nests. The tumor cells were polygonal or epithelioid with eosinophilic cytoplasm, arranged in a nest, trabecular, or organoid pattern were and diagnosed as thyroid follicular adenoma. However, in postoperative immunohistochemistry, these were diagnosed as thyroid PGL. The postoperative recovery was uneventful. The patient showed no signs of tumor recurrence or metastasis until 16 months of follow-up. Herein, we summarize the characteristic features of thyroid PGL based on frozen section analysis. In the appropriate clinical context, its proper use as diagnostic and differential diagnostic management strategies is recommended.

Analysis of Factors of Productivity of Tight Conglomerate Reservoirs Based on Random Forest Algorithm.

The tight conglomerate reservoir of Baikouquan formation in the MA 131 well block in the Junggar basin abounds with petroleum reserves, yet the vertical wells in this reservoir have achieved a limited development effect. The tight conglomerate reservoirs have become an important target for exploration and exploitation. The high-efficiency development scheme of a small well spacing three-dimensional (3D) staggered well pattern has been determined by a series of field tests on well pattern and well spacing development. Multistage fracturing with a horizontal well has been demonstrated as the primary development technology. The horizontal wells in the MA 131 small well spacing demonstration area have achieved significantly different development effects, and the major controlling factors for high and stable production of a single well remain unclear. In this study, we proposed an evaluation model of major productivity controlling factors of the tight conglomerate reservoir to provide a reference for oil recovery based on a random forest (RF) machine-learning algorithm. The productivity factors were investigated from two aspects: petrophysical facies that are capable of indicating the genetic mechanism of geological dessert and engineering dessert parameters forming complex fracture networks. Resultantly, the reservoir in the MA 131 well block can be classified into 12 petrophysical facies according to the sedimentary characteristics and diagenesis analysis. The mercury injection curves of a variety of petrophysical facies can be classified into four reservoir quality types. The RF model was trained on 80% of the data to predict the oil well class using the selected features as primary inputs while the remaining 20% of the data were set to test the model performance. The results indicated that the RF model produced excellent results with only 12 misclassifications across the entire data set of 627 samples that represent <2% error. The important evaluation score of the random forest algorithm model showed that the reservoir type, oil saturation, horizontal stress difference, and gravel content are the most important four indicators, with each value exceeding 15%. Brittleness and maximum horizontal stress are considered the least important indexes, with values of less than 5%. Reservoir quality and oil saturation were confirmed as the major controlling factors and material foundation for oil wells' high and stable production. As indicated in this study, stress difference and gravel content are the major controlling factors in the formation of a complex fracture network.