Objective This study aimed to uncover a regulatory network comprised of long noncoding RNAs (lncRNAs), microRNAs (miRNAs), and messenger RNAs (mRNAs) in laryngeal squamous cell carcinoma (LSCC), to explore its underlying mechanisms and development, and to identify key genetic biomarkers for the prognosis of LSCC. Methods Here, we compared mRNA, lncRNA, and miRNA expression profiles between 111 LSCC and 12 adjacent normal tissues using RNA sequencing (RNA‐Seq) data from the Cancer Genome Atlas (TCGA) database. Based on the interaction information obtained from miRcode, TargetScan, miRTarBase, and miRDB, a lncRNA–miRNA–mRNA competing endogenous RNA (ceRNA) network was constructed using differentially expressed lncRNAs (DElncRNA), miRNAs (DEmiRNA), and mRNAs (DEmRNA). By assessing the functional enrichment of DEmRNAs in this network, the potential underlying mechanisms were explored. In addition, Kaplan–Meier survival analysis was used to assess genetic biomarkers related to the prognosis of LSCC patients. Results Upon comparing LSCC and control tissues, 1640 DElncRNAs, 75 DEmiRNAs, and 3217 DEmRNAs were identified. Based on the prediction between lncRNA–miRNA and miRNA–mRNA relationships, we constructed a ceRNA network comprised of 93 lncRNAs, nine miRNAs, and nine mRNAs. This network predicted that two lncRNAs (AC016773.1 and C00299), two mRNAs (DIO1 and STC2), and two miRNAs (hsa‐mir‐137 and hsa‐mir‐210) were significant biomarkers of LSCC prognosis according to thorough topological and survival analyses (P < .05). Conclusion Through a ceRNA network analysis, our study identifies new lncRNAs, miRNAs, and mRNAs, which can be used as potential biomarkers of LSCC and as therapeutic targets for treating LSCC, thus laying a foundation for future clinical studies.

Laryngeal squamous cell carcinoma (LSCC) is the most common malignant tumor, which occurs in the head and neck. Current treatments for LSCC are all largely weakened by increasing drug resistance. Our study aimed to investigate the effects of long noncoding RNA (lncRNA) H19 on drug resistance in LSCC. In our study, we found that the level of H19 was sharply upregulated in LSCC tissues and drug‐resistant cells compared with the control. Besides, the expression of high‐mobility group B1 (HMGB1) was elevated, and microRNA107 (miR‐107) was suppressed in drug‐resistant cells compared with the control. Further study revealed that the interference of H19 by short hairpin RNA (shRNA) effectively suppressed high autophagy level and obvious drug resistance in drug‐resistant cells. Besides that, miR‐107 was predicted as a target of H19 and inhibiting effects of H19 shRNA on autophagy and drug resistance were both reversed by miR‐107 inhibitor. Moreover, HMGB1 was predicted as a target of miR‐107 in LSCC cells and knockdown of HMGB1 was able to suppress autophagy and drug resistance in LSCC cells. In addition, our investigation demonstrated that H19 shRNA exerted an inhibiting effect on autophagy and drug resistance by downregulating HMGB1 by targeting miR‐107. Finally, the in vivo experiment revealed that LV‐H19 shRNA strongly suppressed drug resistance compared with the usage of cisplatin individually. Taken together, our research indicated an H19–miR‐107–HMGB1 axis in regulating the autophagy‐induced drug resistance in LSCC in vitro and in vivo, providing novel targets for molecular‐targeted therapy and broadening the research for LSCC.

Abstract. Agricultural drought mainly stems from reduced soil moisture and precipitation, and it causes adverse impacts on the growth of crops and vegetation, thereby affecting agricultural production and food security. In order to develop drought mitigation measures, reliable agricultural drought forecasting is essential. In this study, we developed an agricultural drought forecasting model based on canonical vine copulas in three dimensions (3C-vine model) in which antecedent meteorological drought and agricultural drought persistence were utilized as predictors. Furthermore, a meta-Gaussian (MG) model was selected as a reference to evaluate the forecast skill. The agricultural drought in China in August of 2018 was selected as a typical case study, and the spatial patterns of 1- to 3-month lead forecasts of agricultural drought utilizing the 3C-vine model resembled the corresponding observations, indicating the good predictive ability of the model. The performance metrics – the Nash–Sutcliffe efficiency (NSE), the coefficient of determination (R2), and the root-mean-square error (RMSE) – showed that the 3C-vine model outperformed the MG model with respect to forecasting agricultural drought in August for diverse lead times. Moreover, the 3C-vine model exhibited excellent forecast skill with respect to capturing the extreme agricultural drought over different selected typical regions. This study may help to guide drought early warning, drought mitigation, and water resource scheduling.

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