Depending on the source of the blastophore, there are various subtypes of laryngeal cancer, each with a unique metastatic risk and prognosis. The forecasting of their prognosis is a pressing issue that needs to be resolved. This study comprised 5953 patients with glottic carcinoma and 4465 individuals with non-glottic type (supraglottic and subglottic). Five clinicopathological characteristics of glottic and non-glottic carcinoma were screened using univariate and multivariate regression for CoxPH (Cox proportional hazards); for other models, 10 (glottic) and 11 (non-glottic) clinicopathological characteristics were selected using least absolute shrinkage and selection operator (LASSO) regression analysis, respectively; the corresponding survival models were established; and the best model was evaluated. We discovered that RSF (Random survival forest) was a superior model for both glottic and non-glottic carcinoma, with a projected concordance index (C-index) of 0.687 for glottic and 0.657 for non-glottic, respectively. The integrated Brier score (IBS) of their 1-year, 3-year, and 5-year time points is, respectively, 0.116, 0.182, 0.195 (glottic), and 0.130, 0.215, 0.220 (non-glottic), demonstrating the model's effective correction. We represented significant variables in a Shapley Additive Explanations (SHAP) plot. The two models are then combined to predict the prognosis for two distinct individuals, which has some effectiveness in predicting prognosis. For our investigation, we established separate models for glottic carcinoma and non-glottic carcinoma that were most effective at predicting survival. RSF is used to evaluate both glottic and non-glottic cancer, and it has a considerable impact on patient prognosis and risk factor prediction.

Background The incidence of oropharyngeal squamous cell carcinomas (OPSCC) has increased in recent decades, and human papillomavirus (HPV) infection is the main cause of OPSCC. The data regarding causes of death (CODs) are vitally important in informing follow-up strategies and revising treatment strategies to deal with any possible preventable treatment-related COD. However, limited studies have assessed the competing COD by HPV status in patients with OPSCC. Objective We aimed to analyze the distribution of the competing COD according to HPV status in OPSCC. Methods We retrospectively included stage I-IVB patients with OPSCC from the Surveillance, Epidemiology, and End Results database between 2010 and 2015. The association between HPV status and head and neck cancer–specific mortality (HNCSM), second primary cancer mortality (SPCM), and noncancer-caused mortality (NCCM) were analyzed. The chi-square test, Kaplan-Meier analysis, and Fine and Gray model were used for statistical analysis. Results We included 5852 patients in this study and 73.2% (n=4283) of them had HPV-related tumors. A total of 1537 (26.3%) patients died, including 789 (51.3%), 333 (21.7%), and 415 (27%) patients who died from head and neck cancer, second cancer, and noncancer causes, respectively. The 5-year HNCSM, SPCM, NCCM, and overall mortality were 14.7%, 6.5%, 7.7%, and 26.4%, respectively. Those with HPV-positive disease had a lower cumulative incidence of HNCSM (subdistribution hazard ratio [sHR] 0.362, 95% CI 0.315-0.417; P<.001), SPCM (sHR 0.400, 95% CI 0.321-0.496; P<.001), and NCCM (sHR 0.460, 95% CI 0.378-0.560; P<.001) than those with HPV-negative disease. The 5-year risk of HNCSM was 26.9% and 10.7% in those with HPV-negative and HPV-positive disease, respectively (P<.001). The 5-year risk of SPCM was 12.4% and 4.6% in those with HPV-negative and HPV-positive disease, respectively (P<.001). The 5-year risk of NCCM of death was 13.7% and 5.8% in those with HPV-negative and HPV-positive disease, respectively (P<.001). Using the Fine and Gray competing-risks model, our results show that those with HPV-negative tumors had a significantly higher risk of HNCSM (P<.001), SPCM (P<.001), and NCCM (P<.001) than those with HPV-negative tumors. Conclusions HPV-positive OPSCC has a lower NCSM, SPCM, and NCCM as compared to those with HPV-negative OPSCC. HPV positivity is a favorable prognostic factor in the context of overcoming cancer as well as in terms of reducing the risk of other CODs in OPSCC. Our finding supports the need to tailor patient follow-up based on the HPV status of patients with OPSCC.

BACKGROUND:Drug-induced hearing loss (DIHL) is very common, and seriously affects people's happiness in life. RG108 is a small molecule inhibitor. RG108 is protective against DIHL. Our purpose is to probe the incidence of RG108 on cisplatin-induced ototoxicity.MATERIALS AND METHODS:In our research, the ototoxicity of RG108 was investigated in HEI-OC1. We observed under the microscope whether RG108 had an effect on cisplatin-induced cochlear hair cells. RNA-seq experiments were further performed to explore possible gene ontology (GO) and pathways. ROS assay was applied to supervisory the effect of RG108 on oxidative harm of auditory cells. In auditory cells, RG108 was tested for its effects on apoptosis-related proteins by Western blotting (WB).RESULTS:GO analysis showed that RG108 associated with apoptosis. KEGG analysis shows RG108 may act on PI3K-AKT signaling pathway (PASP) in hearing loss. BIOCARTA analysis showed that RG108 may affect oxidative stress by activating NRF2 pathway. ROS ascerted that RG108 could rescue oxidative harm in HEI-OC1. RG108 rescued cisplatin-induced significant increase in Bax and significant decrease in BCL2. RG108 attenuates cisplatin-induced cochlear apoptosis through upregulated phosphorylated PI3K and phosphorylated AKT and down-regulated caspase3. MTT experiments showed that both PI3K and AKT inhibitors could significantly rescue the damage caused by cisplatin to HEI-OC1. RG108 significantly increases the level of NRF2/HO-1/NQO1 in cisplatin-induced cells.CONCLUSION:Overall, these results provide evidence that NRF2/PI3K-AKT axis may mediate RG108 in the treatment of DIHL, which provide a broader outlook on drug-induced deafness treatment.

Background Cisplatin is a commonly used chemotherapeutic drug in clinics, and long-term application will lead to hearing impairment. LLY-283, an inhibitor of PRMT5, has not been reported in deafness. Our study aimed to explore the mechanism of LLY-283 in hearing impairment. Materials and Methods First, we performed RNA-seq (cisplatin in the experimental group and DMSO in the control group) to obtain the biological processes mainly involved in differentially expressed genes (DEGs). CCK-8 and LDH experiments were used to observe the effect of LLY-283 on cisplatin-induced auditory cell injury. ROS experiment was used to monitor the impact of LLY-283 on oxidative damage of auditory cells. Effect of LLY-283 on apoptosis of auditory cells detected by TUNEL experiment. PCR and Western blotting were used to detect the expression of genes and proteins related to auditory cell apoptosis in LLY-283 cells. Meanwhile, we explored the effect of LLY-283 on the expression of PRMT5 in cisplatin-induced hearing impaired cells at RNA and protein levels. Results Biological process analysis showed that DEGs were mainly enriched in the apoptotic process involved in morphogenesis (-Log(10) P = 3.71). CCK-8 and LDH experiments confirmed that LLY-283 could save cisplatin-induced auditory cell injury. ROS experiments confirmed that LLY-283 could rescue cisplatin-induced oxidative damage to auditory cells. TUNEL experiments confirmed that LLY-283 could protect cisplatin-induced apoptosis of auditory cells. Meanwhile, LLY-283 could inhibit the expression of PRMT5 in auditory cells induced by cisplatin. Conclusion LLY-283 can rescue cisplatin-induced auditory cell apoptosis injury. LLY-283 can inhibit the increase in PRMT5 expression induced by cisplatin.

Abstract BackgroundHearing loss (HL) is a public health event, which seriously affects the happiness of people’s life. Hsa-miR-124-5p has not been reported in HL. This study aimed to construct a miRNA-mRNA network associated with HL.MethodsSubjects were screened through clinical audiology experiments. MiRNA-seq was performed on the peripheral blood mononuclear cells (PBMC), and the differentially expressed miRNAs (DEMs, P-Value < 0.05, ∣log FC | ≥1) were obtained by analysis. We selected the most significantly up-regulated DEMs for research. We predicted the downstream target genes of the most significantly up-regulated DEMs through miRDB, mirDIP, mirtarbase and TargetScan databases, and four database overlapping genes were considered as downstream target genes. Enrichment analysis was performed on overlapping genes. Then, a protein-protein interaction (PPI) network of overlapping genes was performed through STRING database, aimed to pick out hub genes. Finally, we construct the miRNA-mRNA network.ResultsThere were 6 clinical participants, 3 hearing loss patients and 3 healthy subjects.There are 29 DEMs, of which hsa-miR-124-5p is the most significantly up-regulated DEMs. 13 downstream target genes corresponding to hsa-miR-124-5p were screened, which were significantly enriched in cell cycle and metabolism of lipids. PPI analysis was performed on 13 target genes, and two hub genes (CSTF2/ TXNRD1) were finally obtained. Most importantly, miRNA-mRNA networks containing hsa-miR-124-5p/CSTF2 and hsa-miR-124-5p/TXNRD1 were successfully constructed.ConclusionsIn summary, hsa-miR-124-5p may be a novel biomarker for hearing loss patients and play a important role in hearing loss patients by targeting CSTF2 and TXNRD1. Hsa-miR-124-5p may participate in cell cycle and metabolism of lipids biological process in hearing loss patients.

BACKGROUND:Hearing loss is becoming more and more general. It may occur at all age and affect the language learning ability of children and trigger serious social problems. METHODS:The hearing loss differentially expressed genes (HL-DEGs) were recognized through a comparison with healthy subjects. The Gene Ontology (GO) analysis was executed by DAVID. The reactome analysis of HL-DEGs was performed by Clue-GO. Next, we used STRING, an online website, to identify crucial protein-protein interactions among HL-DEGs. Cytoscape software was employed to construct a protein-protein interaction network. MCODE, a plug-in of the Cytoscape software, was used for module analysis. Finally, we used DGIdb database to ascertain the targeted drugs for MCODE genes. RESULTS:Four hundred four HL-DEGs were identified, among which the most up-regulated 10 genes were AL008707.1, SDR42E1P5, BX005040.1, AL671883.2, MT1XP1, AC016957.1, U2AF1L5, XIST, DAAM2, and ADAMTS2, and the most down-regulated 10 genes were ALOX15, PRSS33, IL5RA, SMPD3, IGHV1-2, IGLV3-9, RHOXF1P1, CACNG6, MYOM2, and RSAD2. Through STRING database and MCODE analysis, we finally got 16 MCODE genes. These genes can be regarded as hearing loss related genes. Through biological analysis, it is found that these genes are enriched in pathways related to apoptosis such as tumor necrosis factor. Among them, MMP8, LTF, ORM2, FOLR3, and TCN1 have corresponding targeted drugs. Foremost, MCODE genes should be investigated for its usefulness as a new biomarker for diagnosis and treatment. CONCLUSION:In summary, our study produced a sixteen-gene signature and associated drugs that could be diagnosis and treatment of hearing loss patients.

Objective:To explore the relationship between benign paroxysmal positional vertigo （BPPV） and sleep disorders through the analysis of subjective and objective sleep conditions. Methods:Forty-five patients with BPPV and fifty controls who met the inclusion and exclusion criteria were selected for Pittsburgh sleep quality index （PSQI） questionnaire survey and polysomnography （PSG） check, and SPSS 23.0 was used to compare and analyze the results. Results:Compared with the control group, patients with BPPV had changes in sleep structure, high apnea hypopnea index （AHI） and significantly decreased subjective sleep quality （P<0.05）, and there were significant differences in the PQSI scores of patients with BPPV before and after treatment （P<0.05）.Binary logistic regression analysis of BPPV and AHI showed that for every 1 increase in AHI index, the probability of BPPV increased by 1.8 times （OR=2.80, 95%CI=2.25-3.66）.After grouping AHI and performing regression analysis, it was found that the risk of BPPV in patients with AHI≥5 was 3.94 times that of patients with AHI <5（OR=3.94，95%CI=1.63-9.48）. Conclusion:Patients with BPPV have decreased sleep quality and altered sleep structure, and in this study, AHI is found to be a risk indicator for BPPV.