Background Long noncoding RNAs (lncRNAs), which have little or no ability to encode proteins, have attracted special attention due to their potential role in cancer disease. We aimed to establish a lncRNA signature and a nomogram incorporating the genomic and clinicopathologic factors to improve the accuracy of survival prediction for laryngeal squamous cell carcinoma (LSCC). Methods A LSCC RNA-sequencing (RNA-seq) dataset and the matched clinicopathologic information were downloaded from The Cancer Genome Atlas (TCGA). Using univariable Cox regression and least absolute shrinkage and selection operator (LASSO) analysis, we developed a thirteen-lncRNA signature related to prognosis. On the basis of multivariable Cox regression analysis results, a nomogram integrating the genomic and clinicopathologic predictors was built. The predictive accuracy and discriminative ability of the inclusive nomogram were confirmed by calibration curve and a concordance index (C-index), and compared with the TNM staging system by C-index and receiver operating characteristic (ROC) analysis. Decision curve analysis (DCA) was conducted to evaluate the clinical value of our nomogram. Results Thirteen overall survival- (OS-) related lncRNAs were identified, and the signature consisting of the selected thirteen lncRNAs could effectively divide patients into high-risk and low-risk subgroups, with area under curves (AUC) of 0.89 (3-year OS) and 0.885 (5-year OS). Independent factors derived from multivariable analysis to predict survival were margin status, tumor status, and lncRNA signature, which were all assembled into the nomogram. The calibration curve for the survival probability showed that the predictions based on the nomogram coincided well with actual observations. The C-index of the nomogram was 0.82 (0.77-0.87), and the area under curve (AUC) of the nomogram in predicting overall survival (OS) was 0.938, both of which were significantly higher than the traditional TNM stage. Decision curve analysis further demonstrated that our nomogram had larger net benefit than TNM stage. Conclusion An inclusive nomogram for patients with LSCC, comprising genomic and clinicopathologic variables, generates more accurate estimations of the survival probability when compared with TNM stage alone, but more data are needed before the nomogram is used in clinical practice.

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BackgroundThis study aimed to investigate the effects of xanthoxyletin, a plant-derived coumarin, on human oral squamous cancer cells in vitro and in mouse xenografts in vivo.Materia/MethodsThe study included SCC-1 human oral cancer cells and EBTr normal embryonic bovine tracheal epithelial cells, which were treated with 0 μM, 5 μM, 10 μM, and 20 μM of xanthoxyletin for 24 hours. The MTT assay assessed cell viability, and autophagy was detected by electron microscopy. Cell apoptosis was investigated using 4′,6-diamidino-2-phenylindole (DAPI), annexin V, and propidium iodide (PI) fluorescence flow cytometry, which was also used to investigate the cell cycle. Protein expression was measured by Western blot. Mouse xenografts were used for the in vivo evaluation of the effects of xanthoxyletin.ResultsXanthoxyletin significantly inhibited the proliferation of oral cancer cells (IC50, 10–30 μM) with lower cytotoxicity for normal cells. Xanthoxyletin treatment was associated with G2/M arrest of the cell cycle and with increased apoptosis and autophagy of SCC-1 cells. Apoptosis and autophagy induced by xanthoxyletin were also associated with changes in expression of the apoptosis-associated proteins, Bax and Bcl-2, and the autophagy-associated proteins, LC3I, LC3II, Beclin 1, p62, and VSp34. Xanthoxyletin inhibited the expression of components of the signaling cascade of the MEK/ERK pathway in the SCC-1 oral cancer cells. The in vivo effects of xanthoxyletin showed inhibition of growth of mouse xenografts.ConclusionsXanthoxyletin inhibited the proliferation of human oral squamous carcinoma cells and induced apoptosis, autophagy, and cell cycle arrest by modulation of the MEK/ERK signaling pathway.

Long non-coding RNAs (lncRNAs) which have little or no protein-coding capacity, due to their potential roles in the cancer disease, caught a particular interest. Our study aims to develop an lncRNAs-based classifier and a nomogram incorporating the lncRNAs classifier and clinicopathologic factors to help to improve the accuracy of recurrence prediction for head and neck squamous cell carcinoma (HNSCC) patients. The HNSCC lncRNAs profiling data and the corresponding clinicopathologic information were downloaded from TANRIC database and cBioPortal. Using univariable Cox regression and Least absolute shrinkage and selection operator (LASSO) analysis, we developed 15-lncRNAs-based classifier related to recurrence. On the basis of multivariable Cox regression analysis results, a nomogram integrating the genomic and clinicopathologic predictors was built. The predictive accuracy and discriminative ability of the inclusive nomogram were confirmed by calibration curve and a concordance index (C-index), and compared with TNM stage system by C-index, receiver operating characteristic (ROC) analysis. Decision curve analysis (DCA) was conducted to evaluate clinical value of our nomogram. Consequently, fifteen recurrence-free survival (RFS) -related lncRNAs were identified, and the classifier consisting of the established 15 lncRNAs could effectively divide patients into high-risk and low-risk subgroup. The prediction ability of the 15-lncRNAs-based classifier for predicting 3- year and 5-year RFS were 0.833 and 0.771. Independent factors derived from multivariable analysis to predict recurrence were number of positive LNs, margin status, mutation count and lncRNAs classifier, which were all embedded into the nomogram. The calibration curve for the recurrence probability showed that the predictions based on the nomogram were in good coincide with practical observations. The C-index of the nomogram was 0.76 (0.72–0.79), and the area under curve (AUC) of nomogram in predicting RFS was 0.809, which were significantly higher than traditional TNM stage and 15-lncRNAs-based classifier. Decision curve analysis further demonstrated that our nomogram had larger net benefit than TNM stage and 15-lncRNAs-based classifier. The results were confirmed externally. In summary, a visually inclusive nomogram for patients with HNSCC, comprising genomic and clinicopathologic variables, generates more accurate prediction of the recurrence probability when compared TNM stage alone, but more additional data remains needed before being used in clinical practice.

Long non-coding RNAs (lncRNAs), which have little or no ability to encode proteins, have attracted special attention due to their potential role in cancer disease. We aimed to establish a lncRNAs signature and a nomogram incorporating the genomic and clinicopathologic factors to improve the accuracy of survival prediction for laryngeal squamous cell carcinoma (LSCC). LSCC RNA sequencing (RNA-seq) data set and the matched clinicopathologic information were downloaded from the Cancer Genome Atlas (TCGA). Using univariable Cox regression and Least absolute shrinkage and selection operator (LASSO) analysis, we developed a thirteen lncRNAs signature related to prognosis. On the basis of multivariable Cox regression analysis results, a nomogram integrating the genomic and clinicopathologic predictors was built. The predictive accuracy and discriminative ability of the inclusive nomogram were confirmed by calibration curve and a concordance index (C-index), and compared with TNM stage system by C-index, receiver operating characteristic (ROC) analysis. Decision curve analysis (DCA) was conducted to assess clinical value of our nomogram. Consequently, thirteen overall survival (OS) -related lncRNAs were identified, and the signature consisting of the selected thirteen lncRNAs could effectively divide patients into high-risk and low-risk subgroup, with the area under curve (AUC) of 0.89 (3-year OS) and AUC of 0.885(5-year OS). Independent factors derived from multivariable analysis to predict survival were margin status, tumor status and lncRNAs signature, which were all assembled into the nomogram. The calibration curve for the survival probability showed that the predictions based on the nomogram were in good coincide with actual observations. The C-index of the nomogram was 0.82 (0.77-0.87), and the area under curve (AUC) of nomogram in predicting overall survival (OS) was 0.938, which were significantly higher than traditional TNM stage. Decision curve analysis further demonstrated that our nomogram had larger net benefit than TNM stage. In summary, an inclusive nomogram for patients with LSCC, comprising genomic and clinicopathologic variables, generates more accurate estimations of the survival probability when compared TNM stage alone, but more additional data remains needed before being used in clinical practice.

Background: Tumour repopulation initiated by residual tumour cells in response to cytotoxic therapy has been described clinically and biologically, but the mechanisms are unclear. Here, we aimed to investigate the mechanisms for the tumour-promoting effect in dying cells and for tumour repopulation in surviving tongue cancer cells.Methods: Tumour repopulation in vitro and in vivo was represented by luciferase activities. The differentially expressed cytokines in the conditioned medium (CM) were identified using a cytokine array. Gain or loss of function was investigated using inhibitors, neutralising antibodies, shRNAs and ectopic overexpression strategies. Results: We found that dying tumour cells undergoing cytotoxic therapy increase the growth of living tongue cancer cells in vitro and in vivo. Dying tumour cells create amphiregulin (AREG)-and basic fibroblast growth factor (bFGF)-based extracellular environments via cytotoxic treatment-induced endoplasmic reticulum stress. This environment stimulates growth by activating lysine acetyltransferase 6B (KAT6B)-dependent nuclear factor-kappa B (NF-κB) signalling in living tumour cells. As direct targets of NF-κB, miR-22 targets KAT6B to repress its expression, but long noncoding RNAs (lncRNAs) Xiaoting Jia, Ge Wang and Lihong Wu contributed equally to the work.

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