Circular RNA circMET contributes to tamoxifen resistance of breast cancer cells by targeting miR-204/AHR signaling.

Breast cancer is a prevalent female malignancy and tamoxifen remains the first-line treatment for breast cancer, but tamoxifen resistance is a frequent clinical problem. Circular RNAs (circRNAs) are a bunch of noncoding RNAs with circular structures and play crucial roles in cancer development. Here, we aimed to explore the unreported function of circMET in the modulation of tamoxifen resistance of breast cancer cells. The expression of circMET was upregulated in tamoxifen-resistant breast cancer cells. The depletion of circMET significantly reduced the cell viability and proliferation in tamoxifen-resistant breast cancer cells and the co-treatment of tamoxifen promoted the effect. Mechanically, the luciferase activity of circMET and was repressed by miR-204-5p and AHR 3'UTR in the cells. The expression of miR-204-5p was elevated by circMET knockdown. The expression of AHR was downregulated by miR-204-5p or circMET depletion, while the miR-204-5p inhibitor reversed the effect of circMET depletion in cells. The overexpression of circMET enhanced the cell viability and proliferation of MCF7-Re and T47D-Re cells but miR-204-5p or AHR depletion blocked the phenotype. Clinically, the expression of circMET and AHR has enhanced in tamoxifen-resistant samples compared with tamoxifen sensitive samples, but miR-204-5p presented a revered expression in the samples. Consequently, we concluded that circular RNA circMET contributed to tamoxifen resistance of breast cancer cells by targeting miR-204-5p/AHR signaling.

Down-regulation of succinate dehydrogenase subunit B and up-regulation of pyruvate dehydrogenase kinase 1 predicts poor prognosis in recurrent nasopharyngeal carcinoma.

Succinate dehydrogenase subunit B (SDHB) and pyruvate dehydrogenase kinase 1 (PDK1) play key roles in the regulation of growth and survival of various cancers. This study aimed to investigate expression of SDHB and PDK1 in recurrent nasopharyngeal carcinoma (rNPC) tissues and analyzed the association of SDHB and PDK1 expression with the clinical significance and potential prognostic implication of rNPC. Immunohistochemistry was performed to determine the expression of SDHB and PDK1 in tissues in primary NPC (pNPC) and rNPC patients. Our results revealed that expression of SDHB in rNPC was significantly lower than that in pNPC, while the expression of PDK1 was higher compared to pNPC. The expression levels of SDHB and PDK1 were associated with T stage, N stage, clinical stage, and metastasis of rNPC. Survival analysis showed that patients with low SDHB expression had a significantly shorter overall survival time than those with high SDHB expression. Patients with high PDK1 expression had a shorter survival time than patients with low PDK1 expression. Multivariate analysis showed that the expression of SDHB and PDK1 was an independent predictor for the survival of patients with rNPC. Our results demonstrated that down-regulation of SDHB and up-regulation of PDK1 may be novel biomarkers for predicting advanced tumor progression and unfavorable prognosis in rNPC patients.

Causal role of immune cell phenotypes in idiopathic sudden sensorineural hearing loss: a bi-directional Mendelian randomization study.

Background: A growing body of evidence suggests that immunological processes have a significant role in developing idiopathic sudden sensorineural hearing loss (SSHL). However, few studies have examined the association between immune cell phenotype and SSHL using Mendelian Randomization (MR). Methods: The online genome-wide association studies (GWAS) database was used to compile data from GWAS covering 731 immunophenotypes and SSHL. Inverse variance weighted (IVW) analysis was primarily used for MR study, and single nucleotide polymorphisms (SNPs) associated with immunophenotypes served as dependent variables. A sensitivity study and the false discovery rate (FDR) correction were used to examine the MR hypothesis. In addition, the possibility of reverse causality between immunophenotype and SSHL was validated by reverse MR. Reverse MR was analyzed in a manner consistent with forward MR. Results: After FDR correction and sensitivity analysis, we screened 7 immunophenotypes, including IgD+ CD38dim %lymphocyte (95% CI: 1.0019, 1.0742, p = 3.87 × 10-2, FDR = 1.15 × 10-2); Unsw mem AC (95% CI: 1.004, 1.2522, p = 4.23 × 10-2, FDR = 2.25 × 10-2); CD86+ myeloid DC AC (95% CI: 1.0083, 1.1147, p = 2.24 × 10-2, FDR = 4.27 × 10-2); CD33dim HLA DR- AC (95% CI: 1.0046, 1.0583, p = 2.12 × 10-2, FDR = 4.69 × 10-2); SSC-A on CD8br (95% CI: 1.0028, 1.1461, p = 4.12 × 10-2, FDR = 4.71 × 10-2); CD45RA- CD4+ %T cell (95% CI: 1.0036, 1.0503, p = 2.32 × 10-2, FDR = 4.82 × 10-2); DP (CD4+CD8+) AC (95% CI: 1.011, 1.2091, p = 2.78 × 10-2, FDR = 4.97 × 10-2). There was a strong causal relationship with SSHL onset, and the reliability of the results was verified. Furthermore, the immunological cell profile and SSHL did not appear to be closely associated, as shown by reverse MR analysis. Conclusion: Our study provides more support for the current hypothesis that immunophenotypes and the pathophysiology of SSHL are closely associated. Further validation is needed to assess the role of these immunophenotypes in SSHL.

Applications of Machine Learning in Meniere's Disease Assessment Based on Pure-Tone Audiometry.

OBJECTIVE: To apply machine learning models based on air conduction thresholds of pure-tone audiometry for automatic diagnosis of Meniere's disease (MD) and prediction of endolymphatic hydrops (EH). STUDY DESIGN: Retrospective study. SETTING: Tertiary medical center. METHODS: Gadolinium-enhanced magnetic resonance imaging sequences and pure-tone audiometry data were collected. Subsequently, basic and multiple analytical features were engineered based on the air conduction thresholds of pure-tone audiometry. Later, 5 classical machine learning models were trained to diagnose MD using the engineered features. The models demonstrating excellent performance were also selected to predict EH. The model's effectiveness in MD diagnosis was compared with experienced otolaryngologists. RESULTS: First, the winning light gradient boosting (LGB) machine learning model trained by multiple features demonstrates a remarkable performance on the diagnosis of MD, achieving an accuracy rate of 87%, sensitivity of 83%, specificity of 90%, and a robust area under the receiver operating characteristic curve of 0.95, which compares favorably with experienced clinicians. Second, the LGB model, with an accuracy of 78% on EH prediction, outperformed the other 3 machine learning models. Finally, a feature importance analysis reveals a pivotal role of the specific pure-tone audiometry features that are essential for both MD diagnosis and EH prediction. Highlighted features include standard deviation and mean of the whole-frequency hearing, the peak of the audiogram, and hearing at low frequencies, notably at 250 Hz. CONCLUSION: An efficient machine learning model based on pure-tone audiometry features was produced to diagnose MD, which also showed the potential to predict the subtypes of EH. The innovative approach demonstrated a game-changing strategy for MD screening and promising cost-effective benefits for the health care enterprise.

Statistical neural network (SNN) for predicting signal-to-noise ratio (SNR) from static parameters and its validation in 16-bit, 125-MSPS analog-to-digital converters (ADCs).

In the analog-to-digital converter (ADC) test process, the static and dynamic performance parameters are the most important, and the tests for these parameters account for the bulk of the ADC test cost. These two types of parameters follow certain relationships, which are incorporated into the ADC test to reduce the cost. In this paper, we focus on the signal-to-noise ratio (SNR), a key indicator of the dynamic performances of ADCs. A statistical neural network (SNN) with two hidden layers was constructed to predict the SNR from the feature variables, which were extracted from the static parameters. A 16-bit, 125-MSPS ADC was used to evaluate the proposed prediction model. Compared to the measured SNR obtained by traditional fast Fourier transform based test methods, the predicted value had a mean average error of only 0.75 dB. In addition, the Shapley additive explanations interpreter was adopted to analyze the feature dependences of the SNN model, and the results demonstrated that the deterioration of the integral nonlinearity-curve-related features could significantly decrease the SNR, which is consistent with previous research results. The reported results demonstrated that, at the cost of a slight loss of accuracy, the proposed SNN can significantly reduce the test complexity, avoid dynamic parameter measurements, and reduce the total test time by about 4%.

Compressed sensing based sub-Nyquist sampling of multiple sinusoids with dual rate channels.

In this paper, we propose a reconstruction approach for a multiple-sinusoidal signal. The signal reconstruction requires a small number of samples and is based on a sub-Nyquist sampling scheme with dual rate channels. In the proposed sampling scheme, the samples are grouped into multiple cosets. To obtain enough different cosets to reconstruct a signal, the sampling rates of channels are required to be relative coprime. For each coset, the Whittaker-Shannon interpolation formula is employed to construct the relation between the sub-Nyquist samples and the original signal, which is used to construct the measurement matrix. Since the multiple-sinusoidal signal is sparse in the frequency domain, compressed sensing theory can be adopted to reconstruct the signal. Simulation results are reported to demonstrate the feasibility and effectiveness of the proposed approach.

Frequent overexpression of PDK1 in primary nasopharyngeal carcinoma is associated with poor prognosis.

OBJECTIVE: Pyruvate dehydrogenase kinase 1 (PDK1) is reported to be up-regulated and play multiple functions in several cancers. Herein, we investigated its roles in primary nasopharyngeal carcinoma. METHODS: Expression of PDK1 protein was examined by immunohistochemical staining in 102 samples of primary nasopharyngeal carcinoma (pNPC) and 31 samples of chronic nasopharyngitis. The relationships of PDK1 expression levels with clinical features and prognosis in NPC patients were analyzed. RESULTS: PDK1 protein expression was significantly greater in pNPC tissues than in the chronic nasopharyngitis tissues (P<0.01). In addition, overexpression of PDK1 was associated with advanced clinical stage (P<0.01), advanced N classification (P=0.024), and distant metastasis (P=0.048). Kaplan-Meier survival analysis demonstrated that patients with higher PDK1 expression had significantly shorter overall survival (P=0.006), disease-free survival (P=0.015), loco-regional relapse-free survival (P=0.008), and distant metastasis-free survival (P=0.017). Furthermore, multi-variate analysis showed that PDK1 expression was an independent prognostic factor in pNPC patients. CONCLUSION: PDK1 is frequently upregulated in pNPC and may serve as a prognostic marker.

[Low expression of succinate dehydrogenase subunit B in locally recurrent nasopharyngeal carcinoma and implication for prognosis].

OBJECTIVE: To study the expression of succinate dehydrogenase subunit B (SDHB) in the tissues of locally recurrent nasopharyngeal carcinoma (rNPC) and the correlation with the clinicopathological factors and prognosis of rNPC. METHODS: Immunohistochemistry was used to detect the expression of SDHB in the tissues of primary and locally recurrent nasopharyngeal carcinoma. The relationship between SDHB expression and clinicopathological features was analyzed using the Chi-square test, and Kaplan-Meier method and Log-rank test were used for survival analysis. The independent prognostic factors of rNPC were analyzed by Cox regression model. RESULTS: Low SDHB expression was showed in 76.5% (39/51) of the patients with rNPC, significantly higher than 57.1% (24/42) of primary nasopharyngeal carcinoma (χ(2) = 4.098, P < 0.05). Low expression of SDHB strongly was correlated with T classification, clinical stage and cranial nerve palsy. Patients with low SDHB expression had a shorter survival time and a lower 3 or 5 year survival rate compared to the patients with high SDHB expression. Multivariate analysis showed that low SDHB expression was an independent predictor for overall survival of patients with rNPC (P < 0.05). CONCLUSION: The low SDHB expression is an independent indicator for poor prognosis of rNPC and may play an important role in the recurrence of rNPC.