A bimodal feature fusion convolutional neural network for detecting obstructive sleep apnea/hypopnea from nasal airflow and oximetry signals.

The most prevalent sleep-disordered breathing condition is Obstructive Sleep Apnea (OSA), which has been linked to various health consequences, including cardiovascular disease (CVD) and even sudden death. Therefore, early detection of OSA can effectively help patients prevent the diseases induced by it. However, many existing methods have low accuracy in detecting hypopnea events or even ignore them altogether. According to the guidelines provided by the American Academy of Sleep Medicine (AASM), two modal signals, namely nasal pressure airflow and pulse oxygen saturation (SpO2), offer significant advantages in detecting OSA, particularly hypopnea events. Inspired by this notion, we propose a bimodal feature fusion CNN model that primarily comprises of a dual-branch CNN module and a feature fusion module for the classification of 10-second-long segments of nasal pressure airflow and SpO2. Additionally, an Efficient Channel Attention mechanism (ECA) is incorporated into the second module to adaptively weight feature map of each channel for improving classification accuracy. Furthermore, we design an OSA Severity Assessment Framework (OSAF) to aid physicians in effectively diagnosing OSA severity. The performance of both the bimodal feature fusion CNN model and OSAF is demonstrated to be excellent through per-segment and per-patient experimental results, based on the evaluation of our method using two real-world datasets consisting of polysomnography (PSG) recordings from 450 subjects.

Research and application of deep learning-based sleep staging: Data, modeling, validation, and clinical practice.

Over the past few decades, researchers have attempted to simplify and accelerate the process of sleep stage classification through various approaches; however, only a few such approaches have gained widespread acceptance. Artificial intelligence technology, particularly deep learning, is promising for earning the trust of the sleep medicine community in automated sleep-staging systems, thus facilitating its application in clinical practice and integration into daily life. We aimed to comprehensively review the latest methods that are applying deep learning for enhancing sleep staging efficiency and accuracy. Starting from the requisite "data" for constructing deep learning algorithms, we elucidated the current landscape of this domain and summarized the fundamental modeling process, encompassing signal selection, data pre-processing, model architecture, classification tasks, and performance metrics. Furthermore, we reviewed the applications of automated sleep staging in scenarios such as sleep-disorder screening, diagnostic procedures, and health monitoring and management. Finally, we conducted an in-depth analysis and discussion of the challenges and future in intelligent sleep staging, particularly focusing on large-scale sleep datasets, interdisciplinary collaborations, and human-computer interactions.

Sleep apnea detection from single-lead electrocardiogram signals using effective deep-shallow fusion network.

Objective.Explore a network architecture that can efficiently perform single-lead electrocardiogram (ECG) sleep apnea (SA) detection by utilizing the beneficial information of extended ECG segments and reducing the impact of their noisy information.Approach.We propose an effective deep-shallow fusion network (EDSFnet). The deeper residual network is used to extract high-level features with stronger semantics and less noise from the original ECG segments. The shallower convolutional neural network is used to extract lower-level features with higher resolution containing more detailed neighborhood information from the extended ECG segments. These two types of features are then fused using Effective Channel Attention, implementing automatic weight assignment to take advantage of their complementary nature.Main results.The performance of EDSFnet is evaluated on the Apnea-ECG dataset and the FAH-ECG dataset. In the Apnea-ECG dataset with 35 subjects as the training set and 35 subjects as the test set, the accuracy of EDSFnet was 92.6% and 100% for per-segment and per-recording test, respectively. In the FAH-ECG dataset with 348 subjects as the training set and 88 subjects as the test set, the accuracy of EDSFnet was 89.0% and 93.2% for per-segment and per-recording test, respectively. EDSFnet has achieved state-of-the-art results in both experiments using the publicly available Apnea-ECG dataset and subject-independent experiments using the FAH-ECG clinical dataset.Significance.The success of EDSFnet in handling SA detection underlines its robustness and adaptability. By achieving superior results across different datasets, EDSFnet offers promise in advancing the cost-effective and efficient detection of SA through single-lead ECG, reducing the burden on patients and healthcare systems alike.

A Dual-Scale Convolutional Neural Network for Sleep Apnea Detection with Time-Delayed SpO2 Signals.

Sleep apnea (SA) is a common breathing disease, with clinical manifestations of sleep snoring at night with apnea and daytime sleepiness. It could lead to ischemic heart disease, stroke, or even sudden death. SpO2 signal is highly related to SA, and many automatic SA detection methods have been proposed. However, extant work focuses on small datasets with relatively few subjects (less than 100) and is unaware of SA syndromes occurring about 5 seconds prior to the SpO2 change. This study proposes an automatic SA detector called DSCNN using a single-lead SpO2 signal with a dual-scale convolutional neural network. To solve the time-delayed problem of SpO2 changes, we enlarge the target SpO2 segment information by combining its subsequent segment information. To utilize neighbouring segments information and further facilitate the SA detection performance, a dual-scale neural network with the fusing information of the prolonged target segment and its two surrounding segments is proposed. Three datasets from multiple centres are employed to verify the generic performance of DSCNN. Here, we must point out that we use two datasets as external datasets, and one of them is collected from the First Affiliated Hospital of Sun Yat-sen University with a large sample size (450 subjects). Extensive experiment results show that DSCNN can achieve promising results which are superior to the existing state-of-the-art methods.

Real-time detection of laryngopharyngeal cancer using an artificial intelligence-assisted system with multimodal data.

BACKGROUND: Laryngopharyngeal cancer (LPC) includes laryngeal and hypopharyngeal cancer, whose early diagnosis can significantly improve the prognosis and quality of life of patients. Pathological biopsy of suspicious cancerous tissue under the guidance of laryngoscopy is the gold standard for diagnosing LPC. However, this subjective examination largely depends on the skills and experience of laryngologists, which increases the possibility of missed diagnoses and repeated unnecessary biopsies. We aimed to develop and validate a deep convolutional neural network-based Laryngopharyngeal Artificial Intelligence Diagnostic System (LPAIDS) for real-time automatically identifying LPC in both laryngoscopy white-light imaging (WLI) and narrow-band imaging (NBI) images to improve the diagnostic accuracy of LPC by reducing diagnostic variation among on-expert laryngologists. METHODS: All 31,543 laryngoscopic images from 2382 patients were categorised into training, verification, and test sets to develop, validate, and internal test LPAIDS. Another 25,063 images from five other hospitals were used as external tests. Overall, 551 videos were used to evaluate the real-time performance of the system, and 200 randomly selected videos were used to compare the diagnostic performance of the LPAIDS with that of laryngologists. Two deep-learning models using either WLI (model W) or NBI (model N) images were constructed to compare with LPAIDS. RESULTS: LPAIDS had a higher diagnostic performance than models W and N, with accuracies of 0·956 and 0·949 in the internal image and video tests, respectively. The robustness and stability of LPAIDS were validated in external sets with the area under the receiver operating characteristic curve values of 0·965-0·987. In the laryngologist-machine competition, LPAIDS achieved an accuracy of 0·940, which was comparable to expert laryngologists and outperformed other laryngologists with varying qualifications. CONCLUSIONS: LPAIDS provided high accuracy and stability in detecting LPC in real-time, which showed great potential for using LPAIDS to improve the diagnostic accuracy of LPC by reducing diagnostic variation among on-expert laryngologists.

Salvage transoral laser microsurgery for early local recurrence of glottic squamous cell cancer.

BACKGROUND: For recurrent laryngeal cancer, the feasibility of salvage transoral laser microsurgery (TLM) remains controversial. This study compared the efficacy of TLM and open partial laryngectomy (OPL) for treatment of early local recurrence of glottic squamous cell cancer (GSCC) and confirm the effectiveness of salvage TLM as a treatment option. METHODS: This retrospective study involved 55 patients with early local recurrent GSCC treated with TLM, and the oncologic outcomes, functional outcomes, hospitalization time and complications were compared with a group of 40 recurrent GSCC patients matched for clinical variables of TLM group, treated by OPL by the same team of surgeons. RESULTS: The 5-year overall survival and disease-specific survival rates were 65.8% and 91.5%, respectively, for 55 patients with rTis-rT2 stage treated by TLM and 77.1% and 94.7%, respectively, for 40 patients with rTis-rT2 stage treated by OPL (OPL group). In the TLM and OPL groups, the local control rates after 5 years were 77.5% and 79.3%, respectively, and the laryngeal preservation rates were 94.4% and 83.6%, respectively (p > 0.05). Compared with the OPL group, the complication rate (1.82%) and hospitalization duration (5.42 ± 2.26 days) were significantly lower in the TLM group (p < 0.05). Compared with the OPL group, postsurgical health-related quality of life and quality of voice were significantly better in the TLM group (p < 0.001). CONCLUSION: Salvage TLM can be used as an effective treatment option for suitable patients after a full, comprehensive, and careful assessment of the characteristics of early locally recurrent glottic carcinoma.

RAFNet: Restricted attention fusion network for sleep apnea detection.

Sleep apnea (SA) is a common sleep-related breathing disorder, which would lead to damage of multiple systemic organs or even sudden death. In clinical practice, portable device is an important tool to monitor sleep conditions and detect SA events by using physiological signals. However, SA detection performance is still limited due to physiological signals with time-variability and complexity. In this paper, we focus on SA detection with single lead ECG signals, which can be easily collected by a portable device. Under this context, we propose a restricted attention fusion network called RAFNet for sleep apnea detection. Specifically, RR intervals (RRI) and R-peak amplitudes (Rpeak) are generated from ECG signals and divided into one-minute-long segments. To alleviate the problem of insufficient feature information of the target segment, we combine the target segment with two pre- and post-adjacent segments in sequence, (i.e. a five-minute-long segment), as the input. Meanwhile, by leveraging the target segment as the query vector, we propose a new restricted attention mechanism with cascaded morphological and temporal attentions, which can effectively learn the feature information and depress redundant feature information from the adjacent segments with adaptive assigning weight importance. To further improve the SA detection performance, the target and adjacent segment features are fused together with the channel-wise stacking scheme. Experiment results on the public Apnea-ECG dataset and the real clinical FAH-ECG dataset with sleep apnea annotations show that the RAFNet greatly improves SA detection performance and achieves competitive results, which are superior to those achieved by the state-of-the-art baselines.

Validity study of a multiscaled fusion network using single-lead electrocardiogram signals for obstructive sleep apnea diagnosis.

STUDY OBJECTIVES: We evaluated the validity of a squeeze-and-excitation and multiscaled fusion network (SE-MSCNN) using single-lead electrocardiogram (ECG) signals for obstructive sleep apnea detection and classification. METHODS: Overnight polysomnographic data from 436 participants at the Sleep Center of the First Affiliated Hospital of Sun Yat-sen University were used to generate a new FAH-ECG dataset comprising 260, 88, and 88 single-lead ECG signal recordings for training, validation, and testing, respectively. The SE-MSCNN was employed for detection of apnea-hypopnea events from the acquired ECG segments. Sensitivity, specificity, accuracy, and F1 scores were assigned to assess algorithm performance. We also used the SE-MSCNN to estimate the apnea-hypopnea index, classify obstructive sleep apnea severity, and compare the agreement between 2 sleep technicians. RESULTS: The SE-MSCNN's accuracy, sensitivity, specificity, and F1 score on the FAH-ECG dataset were 86.6%, 83.3%, 89.1%, and 0.843, respectively. Although slightly inferior to previously reported results using public datasets, it is superior to state-of-the-art open-source models. Furthermore, the SE-MSCNN had good agreement with manual scoring, such that the Spearman's correlations for the apnea-hypopnea index between the SE-MSCNN and 2 technicians were 0.93 and 0.94, respectively. Cohen's kappa scores in classifying the SE-MSCNN and the 2 sleep technicians were 0.72 and 0.78, respectively. CONCLUSIONS: In this study, we validated the use of the SE-MSCNN in a clinical environment, and despite some limitations the network appeared to meet the performance standards for generalizability. Therefore, updating algorithms based on single-lead ECG signals can facilitate the development of novel wearable devices for efficient obstructive sleep apnea screening. CITATION: Yue H, Li P, Li Y, et al. Validity study of a multiscaled fusion network using single-lead electrocardiogram signals for obstructive sleep apnea diagnosis. J Clin Sleep Med. 2023;19(6):1017-1025.

A safe and effective surgical method for complex pyriform sinus fistula.

Objective: This article aims to propose a new surgical method for the treatment of pyriform fistula, especially for the complex pyriform fistula. Methods: A total of 36 patients with pyriform fistula underwent the procedure between August 2017 to October 2020. Surgery was performed by the senior authors using the same technique at the same clinical center for all patients. The median follow-up time was 33 months. Meantime, we collected information on patients with pyriform fistula using traditional surgical methods in our hospital from April 2015 to November 2018 for comparison. Results: The surgery was successfully completed in 36 patients. In all, 32 patients had a history of multiple incisions and drainage, 16 patients had a history of surgical resections, and two patients had a history of cauterization of the internal fistula. Compared with traditional surgical methods, our new surgical method greatly shortens the length of the surgical incision (4.3 vs. 5.5, p < 0.0001), reduces the operation time (8.1 vs. 27.1, p < 0.0001), and reduces the blood loss (103.2 vs. 196.8, p < 0.0001). None of the 36 patients in this study had complications such as pharyngeal fistula, recurrent laryngeal nerve paralysis, or hypothyroidism. The mean follow-up duration after the excision of the lesion was 34.1 months. To date, no patients have relapsed. Conclusion: Our experience showed that this surgical technique could be used to completely remove the fistula, and it was easier to perform than the conventional strategies. These treatment options result in less trauma and reliable results, especially for complex pyriform fistulas. Level of evidence: IV.

Bacteriological analysis based on disease severity and clinical characteristics in patients with deep neck space abscess.

BACKGROUND: Deep neck space abscess (DNSA) is a serious infection in the head and neck. Antibiotic therapy is an important treatment in patients with DNSA. However, the results of bacterial culture need at least 48 h, and the positive rate is only 30-50%, indicating that the use of empiric antibiotic treatment for most patients with DNSA should at least 48 h or even throughout the whole course of treatment. Thus, how to use empiric antibiotics has always been a problem for clinicians. This study analyzed the distribution of bacteria based on disease severity and clinical characteristics of DNSA patients, and provides bacteriological guidance for the empiric use of antibiotics. METHODS: We analyzed 433 patients with DNSA who were diagnosed and treated at nine medical centers in Guangdong Province between January 1, 2015, and December 31, 2020. A nomogram for disease severity (mild/severe) was constructed using least absolute shrinkage and selection operator-logistic regression analysis. Clinical characteristics for the Gram reaction of the strain were identified using multivariate analyses. RESULTS: 92 (21.2%) patients developed life-threatening complications. The nomogram for disease severity comprised of seven predictors. The area under the receiver operating characteristic curves of the nomogram in the training and validation cohorts were 0.951 and 0.931, respectively. In the mild cases, 43.2% (101/234) had positive culture results (49% for Gram-positive and 51% for Gram-negative strains). The positive rate of cultures in the patients with severe disease was 63% (58/92, 37.9% for Gram-positive, and 62.1% for Gram-negative strains). Diabetes mellitus was an independent predictor of Gram-negative strains in the mild disease group, whereas gas formation and trismus were independent predictors of Gram-positive strains in the severe disease group. The positivity rate of multidrug-resistant strains was higher in the severe disease group (12.1%) than in the mild disease group (1.0%) (P < 0.001). Metagenomic sequencing was helpful for the bacteriological diagnosis of DNSA by identifying anaerobic strains (83.3%). CONCLUSION: We established a DNSA clinical severity prediction model and found some predictors for the type of Gram-staining strains in different disease severity cases. These results can help clinicians in effectively choosing an empiric antibiotic treatment.

Low expression of lncRNA SBF2-AS1 regulates the miR-302b-3p/TGFBR2 axis, promoting metastasis in laryngeal cancer.

The 5-year survival rate of laryngeal cancer continues to decline, and the laryngeal particularity of the anatomy adversely affects the patient's quality of life. Emerging evidence suggests that long noncoding RNAs (lncRNAs) are closely correlated to key steps in the malignant progression of cancer cells. In this study, we report the role of lncRNA SBF2-AS1/miR-302b-3p/TGFBR2 interactions in the metastasis of laryngeal squamous cell carcinoma (LSCC). We verified that SBF2-AS1 was significantly downregulated in LSCC tissues and cell lines using qRT-PCR analysis. Its low expression was correlated to lymph node metastasis and an advanced clinical stage. More importantly, LSCC patients with low expression of SBF2-AS1 tended to have a poor prognosis. Based on this, we performed gain-of-function and loss-of-function experiments in LSCC cell lines. The results confirmed that knocking down SBF2-AS1 can promote the metastasis of LSCC cells and enhance epithelial-mesenchymal transition phenotype, while the upregulation of SBF2-AS1 expression resulted in the opposite. Our in vivo model verified that SBF2-AS1 overexpression could inhibit LSCC cell metastasis. Subsequent mechanistic studies revealed that SBF2-AS1 acted as a competing endogenous RNA that upregulated the expression of TGFBR2 by endogenous sponging for miR-302b-3p in LSCC cell lines. Moreover, miR-302b-3p overexpression reversed the inhibitory effects on LSCC metastasis induced by upregulation of SBF2-AS1 expression, and inhibition of TGFBR2 expression reversed the effect of SBF2-AS1 on metastasis. Our study proposes SBF2-AS1 as a biomarker to predict the prognosis of LSCC patients and a novel potential therapeutic target.

A novel approach to diagnose sleep apnea using enhanced frequency extraction network.

Sleep apnea-hypopnea syndrome (SAHS), as a widespread respiratory sleep disorder, if left untreated, can lead to a series of pathological changes. By using Polysomnography (PSG), traditional SAHS diagnosis tends to be complex and costly. Nasal airflow (NA) is the most direct reflection of the severity of SAHS. Therefore, we try to take advantage of NA signals that can be easily recorded by wearable devices. In this paper, we present an automatic detection approach of SAH events based on single-channel signal. Through this approach, an enhanced frequency extraction network is designed, which factorizes the mixed feature maps by their frequencies. And the spatial resolution of low-frequency components is reduced so as to save spending. Besides, in our research, the vanilla convolution block of the high-frequency components are replaced by residual blocks and smaller groups of filters with bigger size kernels. And we use the spatial attention module to facilitate feature extraction. Compared with state-of-the-art networks in this field, the promising results reveal that the proposed network for SAH events multiclass classification shows outstanding performance with accuracy of 91.23%, sensitivity of 90.81% and specificity of 90.59%. Thus, we believe that our approach, as a low-cost and high-efficiency solution, shows a great potential for detecting SAH events.

A novel risk score for the prediction of airway management in patients with deep neck space abscess: a multicenter retrospective cohort study.

BACKGROUND: Airway management, including noninvasive endotracheal intubation or invasive tracheostomy, is an essential treatment strategy for patients with deep neck space abscess (DNSA) to reverse acute hypoxia, which aids in avoiding acute cerebral hypoxia and cardiac arrest. This study aimed to develop and validate a novel risk score to predict the need for airway management in patients with DNSA. METHODS: Patients with DNSA admitted to 9 hospitals in Guangdong Province between January 1, 2015, and December 31, 2020, were included. The cohort was divided into the training and validation cohorts. The risk score was developed using the least absolute shrinkage and selection operator (LASSO) and logistic regression models in the training cohort. The external validity and diagnostic ability were assessed in the validation cohort. RESULTS: A total of 440 DNSA patients were included, of which 363 (60 required airway management) entered into the training cohort and 77 (13 required airway management) entered into the validation cohort. The risk score included 7 independent predictors (p < 0.05): multispace involvement (odd ratio [OR] 6.42, 95% confidence interval [CI] 1.79-23.07, p < 0.001), gas formation (OR 4.95, 95% CI 2.04-12.00, p < 0.001), dyspnea (OR 10.35, 95% CI 3.47-30.89, p < 0.001), primary region of infection, neutrophil percentage (OR 1.10, 95% CI 1.02-1.18, p = 0.015), platelet count to lymphocyte count ratio (OR 1.01, 95% CI 1.00-1.01, p = 0.010), and albumin level (OR 0.86, 95% CI 0.80-0.92, p < 0.001). Internal validation showed good discrimination, with an area under the curve (AUC) of 0.951 (95% CI 0.924-0.971), and good calibration (Hosmer-Lemeshow [HL] test, p = 0.821). Application of the clinical risk score in the validation cohort also revealed good discrimination (AUC 0.947, 95% CI 0.871-0.985) and calibration (HL test, p = 0.618). Decision curve analyses in both cohorts demonstrated that patients could benefit from this risk score. The score has been transformed into an online calculator that is freely available to the public. CONCLUSIONS: The risk score may help predict a patient's risk of requiring airway management, thus advancing patient safety and supporting appropriate treatment.

Deep Learning for Diagnosis and Classification of Obstructive Sleep Apnea: A Nasal Airflow-Based Multi-Resolution Residual Network.

PURPOSE: This study evaluated a novel approach for diagnosis and classification of obstructive sleep apnea (OSA), called Obstructive Sleep Apnea Smart System (OSASS), using residual networks and single-channel nasal pressure airflow signals. METHODS: Data were collected from the sleep center of the First Affiliated Hospital, Sun Yat-sen University, and the Integrative Department of Guangdong Province Traditional Chinese Medical Hospital. We developed a new model called the multi-resolution residual network (Mr-ResNet) based on a residual network to detect nasal pressure airflow signals recorded by polysomnography (PSG) automatically. The performance of the model was assessed by its sensitivity, specificity, accuracy, and F1-score. We built OSASS based on Mr-ResNet to estimate the apnea‒hypopnea index (AHI) and to classify the severity of OSA, and compared the agreement between OSASS output and the registered polysomnographic technologist (RPSGT) score, assessed by two technologists. RESULTS: In the primary test set, the sensitivity, specificity, accuracy, and F1-score of Mr-ResNet were 90.8%, 90.5%, 91.2%, and 90.5%, respectively. In the independent test set, the Spearman correlation for AHI between OSASS and the RPSGT score determined by two technologists was 0.94 (p < 0.001) and 0.96 (p < 0.001), respectively. Cohen's Kappa scores for classification between OSASS and the two technologists' scores were 0.81 and 0.84, respectively. CONCLUSION: Our results indicated that OSASS can automatically diagnose and classify OSA using signals from a single-channel nasal pressure airflow, which is consistent with polysomnographic technologists' findings. Thus, OSASS holds promise for clinical application.

Using RNA sequencing to identify a putative lncRNA-associated ceRNA network in laryngeal squamous cell carcinoma.

Accumulating evidence indicates that lncRNAs can interact with miRNAs to regulate target mRNAs through competitive interactions. However, this mechanism remains largely unexplored in laryngeal squamous cell carcinoma (LSCC). In this study, transcriptome-wide RNA sequencing was performed on 3 pairs of LSCC tissues and adjacent normal tissues to investigate the expression profiles of lncRNAs, miRNAs and mRNAs, with differential expression of 171 lncRNAs, 36 miRNAs and 1709 mRNAs detected. Seven lncRNAs, eight mRNAs and three miRNAs were identified to be dysregulated in patients' tissues by using qRT-PCR. GO and KEGG pathway enrichment analyses were performed to elucidate the potential functions of these differentially expressed genes in LSCC. Subsequently, a ceRNA (lncRNA-miRNA-mRNA) network including 4631 ceRNA pairs was constructed based on predicted miRNAs shared by lncRNAs and mRNAs. Cis- and transregulatory lncRNAs were analysed by bioinformatics-based methods. Importantly, mRNA-related ceRNA networks (mRCNs) were further obtained based on potential cancer-related coding genes. Coexpression between lncRNAs and downstream mRNAs was used as a criterion for the validation of mRCNs, with the ZNF561-AS1-miR217-WNT5A and SATB1-AS1-miR1299-SAV1/CCNG2/SH3 KBP1/JADE1/HIPK2 ceRNA regulatory interactions determined, followed by experimental validation after siRNA transfection. Moreover, ceRNA activity analysis revealed that different activities of ceRNA modules existing in specific pathological environments may contribute to the tumorigenesis of LSCC. Consistently, both downregulated SATB1-AS1 and ZNF561-AS1 significantly promoted laryngeal cancer cell migration and invasion, indicating their important roles in LSCC via a ceRNA regulatory mechanism. Taken together, the results of this investigation uncovered and systemically characterized a lncRNA-related ceRNA regulatory network that may be valuable for the diagnosis and treatment of LSCC.

Exosomal miR-17-5p promotes angiogenesis in nasopharyngeal carcinoma via targeting BAMBI.

Objective: The purpose of our study is to investigate the role of miR-17-5p in angiogenesis of nasopharyngeal carcinoma and the crosstalk between HUVECs and CNE-2 via exosomes. Methods: Firstly, flow cytometry, cell viability assay, transwell assay, and tube formation were used to explore the role of miR-17-5p in angiogenesis. Then zebrafish model was used to confirm effects of miR-17-5p on angiogenesis. qRT-PCR analysis and Immunofluorescence assay were used to explore the expression of miR-17-5p in NPC tissues and cells compared to the normal control. Besides, in vitro assays were used to analyze the biological functions of miR-17-5p in NPC. What's more, in vitro and in vivo assays were used to detect the function of exosomal miR-17-5p in angiogenesis. Finally, luciferase reporter assay and western bolt were used to determine the relationship between miR-17-5p and BAMBI. Results: We observed that high expression of miR-17-5p promoted angiogenesis in NPC. Also, high expression of miR-17-5p promoted the NPC cells proliferation and migration. To know whether there's any communication between HUVECs and NPC cells, exosomes derived from CNE-2 cells were collected. Further results showed that exosomal miR-17-5p secreted from NPC promoted the angiogenesis. What's more, in vitro assays revealed that miR-17-5p targets BAMBI and regulates AKT/VEGF-A signaling. Conclusions: Our study showed that exosomal miR-17-5p derived from NPC cells promotes angiogenesis via targeting BAMBI and regulates AKT/VEGF-A signaling.

Long Noncoding RNA GAS5 Acts As A Tumor Suppressor In Laryngeal Squamous Cell Carcinoma Via miR-21.

PURPOSE: Long noncoding RNAs (lncRNAs) have been identified as an important class of noncoding RNAs that are deeply involved in multiple biological processes in tumorigenesis. This study is to investigate the critical roles and biological function of lncRNA growth arrest-specific 5 (GAS5) in tumorigenesis of laryngeal squamous cell carcinoma (LSCC). PATIENTS AND METHODS: A total of 59 samples of LSCC and paired adjacent tissue, as well as corresponding clinicopathological information were collected. GAS5 expression in both LSCC tissues and human SUN1076 and SNU899 cell lines were analyzed by Real-time quantitative RT-PCR method. Ectopic expression of GAS5 by vector transfection in LSCC cell lines and followed by in vitro experiments was to investigate the critical roles and function of GAS5 in LSCC. Cell Counting Kit 8 (CCK8) assay and PE/7AAD Annexin V Apoptosis analysis was to evaluate cell proliferation ability and cell apoptosis. Co-transfection of GAS5 and miR-21 was to explore the interaction between GAS5 and miR-21 in LSCC. BAX and CDK6 protein level were analyzed by western blot method. RESULTS: This study demonstrated that GAS5 was significantly downregulated in LSCC tissue and human LSCC cell lines. GAS5 levels were correlated with the clinicopathological features of LSCC patients. In addition, the ectopic expression of GAS5 significantly inhibited cell proliferation and promoted apoptosis. Co-expression analyses indicated that GAS5 is negatively correlated with miR-21 in LSCC tissues. Overexpression of miR-21 eliminated GAS5-mediated cell apoptosis and proliferation suppression. Furthermore, GAS5, which upregulated BAX mRNA expression and downregulated CDK6 mRNA expression, was reversed by ectopic expression of miR-21. CONCLUSION: GAS5 suppresses LSCC progression through the negative regulation of miR-21 and its targets involved in cell proliferation and apoptosis, indicating that GAS5 may serve as a biomarker and potential target for LSCC therapy.

Metastasis-associated miR-23a from nasopharyngeal carcinoma-derived exosomes mediates angiogenesis by repressing a novel target gene TSGA10.

Benefiting from more precise imaging and radiotherapy, patients with locoregionally nasopharyngeal carcinoma (NPC) have a significantly higher survival rate. Nonetheless, distant metastasis is still the predominant mode of failure. Advances in cancer research have highlighted that pathological angiogenesis is necessary for tumor metastasis by offering oxygen, nutrients, or cell metastatic conduits. MicroRNAs (miRNAs), a class of small noncoding RNAs, are increasingly implicated in modulation of angiogenesis in physiological and pathological conditions. Currently, we detected that miR-23a was highly enriched in NPC tissues at the metastatic or premetastatic stage, and its levels in NPC were associated with microvessel density. Subsequently, we proved that alteration of miR-23a expression modulated the growth, migration, and tube formation of HUVECs in vitro and affected the blood vessel outgrowth in the zebrafish model. Considering the possibility that extracellular miR-23a was horizontally transferred from CNE2 cells to HUVECs, we analyzed miR-23a encapsulated in exosomes, showing that overexpression of exosomal miR-23a in NPC promoted angiogenesis both in vitro and in vivo. Moreover, we provided evidences that miR-23a regulated angiogenesis by directly targeting testis-specific gene antigen (TSGA10). Taken together, our findings revealed that metastasis-associated miR-23a from NPC-derived exosomes plays an important role in mediating angiogenesis by targeting TSGA10.

A disintegrin and metalloprotease 10-containing exosomes derived from nasal polyps promote angiogenesis and vascular permeability.

Abnormal angiogenesis and vascular permeability is important for the formation of nasal polyps (NPs). Increasing evidence has indicated that exosomes serve a vital role in modulating angiogenesis and vascular permeability. A disintegrin and metalloprotease 10 (ADAM10), an important type of proteinase that is overexpressed in various diseases, can influence angiogenesis and vascular permeability and has been observed in healthy nasal exosomes. To the best of our knowledge, the expression levels and the function of ADAM10 in NLF­derived exosomes from NPs has not been demonstrated previously. In order to determine the influence of exosomes derived from nasal lavage fluid (NLF) on angiogenesis and vascular permeability, 25 nasal polyp patients and 15 healthy volunteers were enrolled in the present study. NLF was collected from all of the subjects. Exosomes were isolated from NLF, visualized under transmission electron microscope and identified using western blot analysis. The effect of exosomes on human umbilical vein endothelial cells (HUVECs) was measured by tube formation and permeability assays in vitro. The expression of exosomal ADAM10 was also analyzed by western blotting. NLF­derived exosomes from NPs influenced proliferation, tube formation and the permeability of HUVECs. ADAM10 was highly expressed in NLF­derived exosomes from NPs when compared with healthy volunteers. Thus, NLF­derived exosomes from NPs promoted angiogenesis and vascular permeability, which may be associated with abundant ADAM10 in NP exosomes.

Achaete-scute complex homologue-1 promotes development of laryngocarcinoma via facilitating the epithelial-mesenchymal transformation.

Laryngeal cancer is one of the most common fatal cancers among head and neck carcinomas, whose mechanism, however, remains unclear. The proneural basic-helix-loop-helix protein achaete-scute complex homologue-1, a member of the basic helix-loop-helix family, plays a very important role in many cancers. This study aims to explore the clinical value and mechanism of achaete-scute complex homologue-1 in laryngeal cancer. Methods including Cell Counting Kit-8, flow cytometry, Transwell invasion assays, and scratch assay were adopted to further explore the bio-function of achaete-scute complex homologue-1, whose expression was examined in fresh and paraffin chip of laryngeal carcinoma tissues by means of western blot and immunohistochemistry, after the interference of achaete-scute complex homologue-1; achaete-scute complex homologue-1, an overexpression in laryngeal carcinoma whose carcinogenicity potential was confirmed via western blot, was correlative with T classification (p = 0.002), histological differentiation (p = 0.000), lymph node metastasis (p = 0.000), and poor survival (p = 0.000). Multivariate analysis shows that achaete-scute complex homologue-1 overexpression is an independent prognostic factor unfavorable to laryngeal carcinoma patients (p = 0.000). Moreover, knocking down achaete-scute complex homologue-1 expression could significantly suppress the proliferation, migration, and invasion of laryngeal carcinoma cell in vitro and disorder epithelial-mesenchymal transformation-associated protein expression. Achaete-scute complex homologue-1 plays an important role in the genesis and progression of laryngeal carcinoma and may act as a potential biomarker for therapeutic target and prognostic prediction.