Non-small cell lung cancer (NSCLC) is the most frequent cause of cancer-related death worldwide. Although many molecular-targeted drugs for NSCLC have been developed in recent years, the 5-year survival rate of patients with NSCLC remains low. Therefore, an improved understanding of the molecular mechanisms underlying the biology of NSCLC is essential for developing novel therapeutic strategies for the treatment of NSCLC. In this study, we examined the role of
miR-130b
in NSCLC. Our results showed that high expression of
miR-130b
in clinical specimens was significantly associated with poor overall survival in patients with NSCLC. Moreover,
miR-130b
expression was significantly increased in NSCLC clinical specimens from patients with vascular and lymphatic invasion. Consistent with this, overexpression of
miR-130b
promoted invasion and matrix metalloproteinase-2 (MMP-2) activity in A549 cells. Argonaute2 immunoprecipitation and gene array analysis identified tissue inhibitor of metalloproteinase-2 (TIMP-2) as a target of
miR-130b
. Invasion activity promoted by
miR-130b
was attenuated by TIMP-2 overexpression in A549 cells. Furthermore, TIMP-2 concentrations in serum were inversely correlated with relative
miR-130b
expression in tumor tissues from the same patients with NSCLC. Overall,
miR-130b
was found to act as an oncomiR, promoting metastasis by downregulating TIMP-2 and invasion activities in NSCLC cells.
Comprehenders’ perception of the world is mediated by the mental models they construct. During discourse processing, incoming information allows comprehenders to update their model of the events being described. At the same time, comprehenders use these models to generate expectations about who or what will be mentioned next. The temporal dynamics of this interdependence between language processing and mental event representation has been difficult to disentangle. The present visual world eye-tracking experiment measures listeners’ coreference expectations during an intersentential pause between a sentence about a transfer-of-possession event and a continuation mentioning either its Source or Goal. We found a temporally dispersed but sustained preference for fixating the Goal that was significantly greater when the event was described as completed rather than incomplete (passed versus was passing). This aligns with reported offline sensitivity to event structure, as conveyed via verb aspect, and provides new evidence that our mental model of an event leads to early and, crucially, proactive expectations about subsequent mention in the upcoming discourse.
Objective: To examine whether the machine-learning approach using 18F-FDG-PET-based radiomic and deep-learning features is useful for predicting the pathological risk subtypes of thymic epithelial tumors (TETs). Methods: This retrospective study included 79 TET [27 low-risk thymomas (types A, AB and B1), 31 high-risk thymomas (types B2 and B3) and 21 thymic carcinomas] patients who underwent pre-therapeutic 18F-FDG-PET/CT. High-risk TETs (high-risk thymomas and thymic carcinomas) were 52 patients. The 107 PET-based radiomic features, including SUV-related parameters [maximum SUV (SUVmax), metabolic tumor volume (MTV), and total lesion glycolysis (TLG)] and 1024 deep-learning features extracted from the convolutional neural network were used to predict the pathological risk subtypes of TETs using six different machine-learning algorithms. The AUCs were calculated to compare the predictive performances. Results: SUV-related parameters yielded the following AUCs for predicting thymic carcinomas: SUVmax 0.713, MTV 0.442, and TLG 0.479 or high-risk TETs: SUVmax 0.673, MTV 0.533, and TLG 0.539. The best-performing algorithm was the logistic regression model for predicting thymic carcinomas (AUC 0.900, accuracy 81.0%), and the random forest (RF) model for high-risk TETs (AUC 0.744, accuracy 72.2%). The AUC was significantly higher in the logistic regression model than 3 SUV-related parameters for predicting thymic carcinomas, and in the RF model than MTV and TLG for predicting high-risk TETs (each; p < 0.05). Conclusions: 18F-FDG-PET-based radiomic analysis using a machine-learning approach may be useful for predicting the pathological risk subtypes of TETs. Advances in knowledge: Machine-learning approach using 18F-FDG-PET-based radiomic features has the potential to predict the pathological risk subtypes of TETs.
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