Purpose: This study aimed to describe the use of a novel 4-lncRNA signature to predict prognosis in patients with laryngeal cancer and to explore its possible mechanisms. Methods: We identified lncRNAs that were differentially expressed between 111 tumor tissue samples and 12 matched normal tissue samples from The Cancer Genome Atlas Database (TCGA). We used Cox regression analysis to identify lncRNAs that were correlated with prognosis. A 4-lncRNA signature was developed to predict the prognosis of patients with laryngeal cancer. The receiver operating characteristic (ROC) curves and area under the curve (AUC) were used to verify the validity of this Cox regression model, and an independent prognosis analysis was used to confirm that the 4-lncRNA signature was an independent prognostic factor. Furthermore, the function of these lncRNAs was inferred using related gene prediction and Gene ontology (GO) enrichment analysis in order to clarify the possible mechanisms underlying their predictive ability. Results: In total, 214 differentially expressed lncRNAs were identified, and a 4-lncRNA signature was constructed using Cox survival analysis. The risk coefficients in the multivariate Cox analysis revealed that LINC02154 and MNX1-AS1 are risk factors for laryngeal cancer, whereas MYHAS and LINC01281 appear to be protective factors. The results of a functional annotation analysis suggested that the mechanisms by which these lncRNAs influence prognosis in laryngeal cancer may involve the extracellular exosome, the Notch signaling pathway, voltage-gated calcium channels, and the Wnt signaling pathway. Conclusion: We identified a novel 4-lncRNA signature that can predict the prognosis of patients with laryngeal cancer and that may influence the prognosis of laryngeal cancer by regulating immunity, tumor apoptosis, metastasis, invasion, and other characteristics through the Notch signaling pathway, voltage-gated calcium channels, and the Wnt signaling pathway.

Object: Glioma is a common malignant tumours in the central nervous system (CNS), that exhibits high morbidity, a low cure rate, and a high recurrence rate. Currently, immune cells are increasingly known to play roles in the suppression of tumourigenesis, progression and tumour growth in many tumours. Therefore, given this increasing evidence, we explored the levels of some immune cell genes for predicting the prognosis of patients with glioma. Methods: We extracted glioma data from The Cancer Genome Atlas (TCGA). Using the Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) algorithm, the relative proportions of 22 types of infiltrating immune cells were determined. In addition, the relationships between the scales of some immune cells and sex/age were also calculated by a series of analyses. A P-value was derived for the deconvolution of each sample, providing credibility for the data analysis (P < 0.05). All analyses were conducted using R version 3.5.2. Fiveyear overall survival (OS) also showed the effectiveness and prognostic value of each proportion of immune cells in glioma; a bar plot, correlation-based heatmap (corheatmap), and heatmap were used to represent the proportions of immune cells in each glioma sample. Results: In total, 703 transcriptomes from a clinical dataset of glioma patients were drawn from the TCGA database. The relative proportions of 22 types of infiltrating immune cells are presented in a bar plot and heatmap. In addition, we identified the levels of immune cells related to prognosis in patients with glioma. Activated dendritic cells (DCs), eosinophils, activated mast cells, monocytes and activated natural killer (NK) cells were positively related to prognosis in the patients with glioma; however, resting NK cells, CD8 + T cells, T follicular helper cells, gamma delta T cells and M0 macrophages were negatively related to prognosis in the patients with glioma. Specifically, the proportions of several immune cells were significantly related to patient age and sex. Furthermore, the level of M0 macrophages was significant in regard to interactions with other immune cells, including monocytes and gamma delta T cells, in glioma tissues through sample data analysis. Conclusion: We performed a novel gene expression-based study of the levels of immune cell subtypes and prognosis in glioma, which has potential clinical prognostic value for patients with glioma.

The objectives of this study are to investigate the expressions of matrix metalloproteinase inducing factor (CD147), matrix metalloproteinase-2 (MMP-2), matrix metalloproteinase-9 (MMP-9) in laryngeal tumor tissues and its significant correlation with tumor infiltration, metastasis and prognosis. Laryngeal tumor tissue from 48 laryngeal cancer patients with complete clinical information was collected. The laryngitis tissue from 15 patients were collected as control group. Immunohistochemical analysis for CD147, MMP-2 and MMP-9 was performed for all the tissue. The results showed the expression rates of CD147, MMP-2 and MMP-9 in laryngeal carcinoma were 87.5 %, 75.0 % and 79.2 % respectively, significantly higher than those (26.7 %, 6.7 %, and 33.3 % respectively) in the control group are (P < 0.01). High expression of CD147, MMP-2 and MMP-9 related to the clinical stages and lymphatic metastasis of laryngeal carcinoma. Univariate survival analysis showed that the 5-year survival of laryngeal carcinoma patients with low expression of CD147, MMP-2 and MMP-9was 83.3 %, 83.3 % and 90 % respectively, while the patients with high expression had 5-year survival at 25 %, 7.7 % and 18.2 % respectively (P < 0.05). Multiple regression analysis showed that high expression of MMP-9 was independently associated with poor prognosis (P < 0.05). High expression of CD147, MMP-2 and MMP-9 were related with laryngeal carcinoma invasion and metastasis. High expressions of CD147, MMP-2 and MMP-9 were all predictive factors of poor prognosis of laryngeal carcinoma.

In this study, we purpose to investigate a novel five‐gene signature for predicting the prognosis of patients with laryngeal cancer. The laryngeal cancer datasets were obtained from The Cancer Genome Atlas (TCGA). Both univariate and multivariate Cox regression analysis was applied to screening for prognostic differential expressed genes (DEGs), and a novel gene signature was obtained. The performance of this Cox regression model was tested by receiver operating characteristic (ROC) curves and area under the curve (AUC). Further survival analysis for each of the five genes was carried out through the Kaplan‐Meier curve and Log‐rank test. Totally, 622 DEGs were screened from the TCGA datasets in this study. We construct a five‐gene signature through Cox survival analysis. Patients were divided into low‐ and high‐risk groups depending on the median risk score, and a significant difference of the 5‐year overall survival was found between these two groups (P < .05). ROC curves verified that this five‐gene signature had good performance to predict the prognosis of laryngeal cancer (AUC = 0.862, P < .05). In conclusion, the five‐gene signature consist of EMP1, HOXB9, DPY19L2P1, MMP1, and KLHDC7B might be applied as an independent prognosis predictor of laryngeal cancer.