micro RNA s (mi RNA s) function as oncogenes or tumor suppressors in human cancers by targeting mRNA s for degradation and/or translational repression. miR‐497 has been proposed as a tumor suppressive mi RNA and its deregulation is observed in human cancers. However, the prognostic value of miR‐497 and its underlying molecular pathways involved in the initiation and development of hepatocellular carcinoma ( HCC ) are poorly investigated. In the present study, we found that the mean level of miR‐497 in HCC tissues was lower than that in adjacent nontumor tissues. Clinical data indicated that low expression of miR‐497 was prominently associated with adverse prognostic features of HCC including high serum alpha‐fetoprotein ( AFP ) level, large tumor size, high Edmondson–Steiner grading and advanced tumor–node–metastasis ( TNM ) stage. Furthermore, miR‐497 was an independent prognostic factor for indicating both 5‐year overall survival and disease‐free survival of HCC patients. Gain‐ and loss‐of‐function studies showed that miR‐497 reduced cell proliferation and induced apoptosis in HCC cells. Yes‐associated protein 1 ( YAP 1) was identified as a direct target of miR‐497 in HCC . An inverse correlation between YAP 1 and miR‐497 expression was observed in HCC tissues. Notably, YAP 1 knockdown abrogated the effects of miR‐497 deletion on HCC cells with decreased cell proliferation and increased apoptosis. In conclusion, we report that miR‐497 is a potent prognostic indicator and may suppress tumor growth of HCC by targeting YAP 1.
Accumulating evidence indicates that microRNAs (miRNAs) play important roles in tumorigenesis and metastasis. Recent research has shown that miR‑196b is implicated in metastasis by regulating the migration and invasion of cancer cells. However, the clinical significance of miR‑196b and its role as well as the underlying mechanisms in hepatocellular carcinoma (HCC) remain largely unknown. Here, we detected miR‑196b expression in HCC and matched non-tumor tissues with qRT‑PCR. We found that miR‑196b displayed higher expression in HCC patient tissues and cells. Clinical analysis revealed that high miR‑196 expression was correlated with venous infiltration, advanced TNM stage and poor prognosis. Functionally, we demonstrated that miR‑196b promoted the migration and invasion of HCC cells in vitro. Moreover, miR‑196b knockdown restrained pulmonary metastasis in vivo. Mechanistically, we confirmed that miR‑196b could directly bind to 3'UTR of forkhead box P2 (FOXP2) mRNA and repress its expression. miR‑196b and FOXP2 showed a negative correlation in HCC tissues. More importantly, upregulation of FOXP2 antagonized miR‑196b‑mediated migration and invasion in Hep3B cells. Furthermore, FOXP2 knockdown partially reversed the anti‑metastatic function of the miR‑196b inhibitor on HCCLM3 cells. Taken together, we demonstrated that miR‑196b may function as a prognostic biomarker and suppressed FOXP2 expression, subsequently leading to the metastasis of HCC. Our findings highlight a novel mechanism of miR‑196b in the progression of HCC and identify miR‑196b/FOXP2 axis as a promising target for HCC.
Abstract. ) is dysregulated in a number of human cancers, where it functions as an oncogenic miRNA. However, the clinical significance of miR-33a and its underlying molecular pathways regarding the progression of hepatocellular carcinoma (HCC) are currently unknown. In the present study, it was observed that the level of miR-33a expression was significantly increased in HCC tissues, relative to adjacent non-tumor tissues. Increased miR-33a expression was significantly correlated with poor prognostic features of HCC, including larger tumor size, higher Edmondson-Steiner grading and higher tumor-node-metastasis tumor stage. Furthermore, high levels of miR-33a expression were associated with decreases in the 5-year overall survival rate and recurrence-free survival of patients with HCC. In addition, functional experiments indicated that overexpression of miR-33a led to increased proliferation and reduced apoptosis of the HCC cell line Huh7, while knockdown of miR-33a decreased proliferation and induced apoptosis in the HCC cell line HepG2. Furthermore, peroxisome proliferator activated receptor alpha (PPARα) was identified as a direct target of miR-33a in HCC. Upregulation of miR-33a was found to reduce the levels of PPARα expression in Huh7 cells, while inhibition of miR-33a lead to a downregulation in PPARα expression in HepG2 cells. Collectively, these results suggest that miR-33a regulates the proliferation and apoptosis of HCC cells, and is a potential prognostic marker of HCC.
At present, the secondary application of electronic medical records is focused on auxiliary medical diagnosis to improve the accuracy of clinical diagnosis. The main research in this article is the prediction method of gestational diabetes based on electronic medical record data. In the original data, the ID number of the medical examiner did not match the medical examination record. In order to ensure the accuracy of the data, this part of the record was removed. First, the preparation stage before building the model is to determine the baseline accuracy of the original data, test the effectiveness of the machine learning algorithm, and then balance the target data set to solve the bias caused by the imbalance between data classes and the illusion of excessive model prediction results. Then, the disease prediction model is constructed by dividing the data set, selecting parameters and algorithms, and visualizing the model. Finally, the effect of predictive model construction is comprehensively judged based on multiple evaluation indicators and control experimental models. In this paper, the RF model can be used to rank the importance of the feature importance of the output feature on the importance of the classification result of the input feature. In order to test the accuracy of regression prediction, the experiment uses absolute mean error and root mean square error to evaluate the accuracy of fasting blood glucose prediction. A logistic regression model is constructed through the training set, and the test set data are brought into the prediction model for prediction. Experimental data show that when the features filtered by WBFS are used, the accuracy, F1 value, and AUC value of logistic regression are 0.809, 0.881, and 0.825, respectively, which is an increase of about 12% compared with when the feature is not used. The results show that the electronic medical record data drive can effectively improve the accuracy of predicting gestational diabetes.
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