A growing body of studies has demonstrated that long non‐coding RNA (lncRNA) are regarded as the primary section of the ceRNA network. This is thought to be the case owing to its regulation of protein‐coding gene expression by functioning as miRNA sponges. However, functional roles and regulatory mechanisms of lncRNA‐mediated ceRNA in cervical squamous cell carcinoma (CESC), as well as their use for potential prediction of CESC prognosis, remains unknown. The aberrant expression profiles of mRNA, lncRNA, and miRNA of 306 cervical squamous cancer tissues and three adjacent cervical tissues were obtained from the TCGA database. A lncRNA‐mRNA‐miRNA ceRNA network in CESC was constructed. Meanwhile, Gene Ontology (GO) and KEGG pathway analysis were performed using Cytoscape plug‐in BinGo and DAVID database. We identified a total of 493 lncRNA, 70 miRNA, and 1921 mRNA as differentially expressed profiles. An aberrant lncRNA‐mRNA‐miRNA ceRNA network was constructed in CESC, it was composed of 50 DElncRNA, 18 DEmiRNA, and 81 DEmRNA. According to the overall survival analysis, 3 out of 50 lncRNA, 10 out of 81 mRNA, and 1 out of 18 miRNA functioned as prognostic biomarkers for patients with CESC (P value < 0.05). We extracted the sub‐network in the ceRNA network and found that two novel lncRNA were recognized as key genes. These included lncRNA MEG3 and lncRNA ADAMTS9‐AS2. The present study provides a new insight into a better understanding of the lncRNA‐related ceRNA network in CESC, and the novel recognized ceRNA network will help us to improve our understanding of lncRNA‐mediated ceRNA regulatory mechanisms in the pathogenesis of CESC.

Glycosylation changes are key molecular events in tumorigenesis, progression and glycosyltransferases play a vital role in the this process. FUT8 belongs to the fucosyltransferase family and is the key enzyme involved in N-glycan core fucosylation. FUT8 and/or core fucosylated proteins are frequently upregulated in liver, lung, colorectal, pancreas, prostate,breast, oral cavity, oesophagus, and thyroid tumours, diffuse large B-cell lymphoma, ependymoma, medulloblastoma and glioblastoma multiforme and downregulated in gastric cancer. They can be used as markers of cancer diagnosis, occurrence, progression and prognosis. Core fucosylated EGFR, TGFBR, E-cadherin, PD1/PD-L1 and α3β1 integrin are potential targets for tumour therapy. In addition, IGg1 antibody defucosylation can improve antibody affinity, which is another aspect of FUT8 that could be applied to tumour therapy.

Background: Immune infiltration of head and neck cancer (HNC) highly correlated with the patient's prognosis. However, previous studies failed to explain the diversity of different cell types that make up the function of the immune response system. The aim of the study was to uncover the differences in immune phenotypes of the tumor microenvironment (TME) between HNC adjacent tumor tissues and tumor tissues using CIBERSORT method and explore their therapeutic implications.Method: In current work, we employed the CIBERSORT method to evaluate the relative proportions of immune cell profiling in 11 paired HNC and adjacent samples, and analyzed the correlation between immune cell infiltration and clinical information. The tumor-infiltrating immune cells of TCGA HNC cohort was analyzed for the first time. The fractions of LM22 immune cells were imputed to determine the correlation between each immune cell subpopulation and survival and response to chemotherapy. Three types of molecular classification were identified via “CancerSubtypes” R-package. The functional enrichment was analyzed in each subtype.Results: The profiles of immune infiltration in TCGA HNC cohort significantly vary between paired cancer and para-cancerous tissue and the variation could reflect the individual difference. Total Macrophage, Macrophages M0 and NK cells resting were elevated in HNC tissues, while total T cells, total B cells, T cells CD8, B cell navie, T cell follicular helper, NK cells activated, Monocyte and Mast cells resting were decreased when compared to paracancerous tissues. Among each cell immune subtype, T cells regulatory Tregs, B cells naïve, T cells follicular helper, and T cells CD4 memory activated was significantly associated with HNC survival. Three clusters were observed via Cancer Subtypes R-package. Each cancer subtype has a specific molecular classification and subtype-specific immune cell characterization.Conclusions: Our data suggest a difference in immune response may be an important driver of HNC progression and response to treatment. The deconvolution algorithm of gene expression microarray data by CIBERSOFT provides useful information about the immune cell composition of HNC patients.

Background and Objective Periodontitis is a multifactorial disease that can lead to the progressive destruction of dental support tissue. However, the detailed mechanisms and specific biomarkers involved in periodontitis remain to be further studied. Recently, long non‐coding RNAs (lncRNAs) have been found to play a more important role than other types of RNAs. In our study, we analysed the expression of lncRNAs in periodontitis by analysing GSE16134. Material and Methods We identified highly correlated genes by analysing GSE16134 with weighted gene co‐expression network analysis (WGCNA) and identified 50 hub lncRNAs that were dysregulated. Then, we used the Linear Models for Microarray Data (Limma) package to identify the hub lncRNAs that were differentially expressed (DElncRNAs). The ceRNA co‐expression network data were obtained from several sites, including miRcode, and were used to assess the potential WGCNA function of hub DElncRNAs in periodontitis. Besides, we divided the samples into LBX2‐AS1 high and low expression group by the expression level of LBX2‐AS1 and calculated DEG by Limma package. Furthermore, we performed GO function, KEGG pathway and GSEA enrichment of DEGs. Results In the analysis, we identified 50 hub lncRNAs that may play important roles in periodontitis. Then, we used the Limma package to identify 3 hub DElncRNAs (LINC00687, LBX2‐AS1 and LINC01566). We elucidated the potential function of the hub DElncRNA LBX2‐AS1 in periodontitis by constructing a co‐expression network of lncRNA‐miRNA‐mRNA interactions. Totally, 573 DEGs (354 up‐ and 219 downregulated) in periodontitis samples were identified. DEGs were enriched in different GO terms and pathways, such as neutrophil degranulation, neutrophil activation, neutrophil activation involved in immune response, neutrophil‐mediated immunity, antigen processing and presentation, JAK‐STAT signalling pathway, natural killer cell‐mediated cytotoxicity, EGFR tyrosine kinase inhibitor resistance, phosphatidylinositol signalling system and Vascular Endothelial Growth Factor (VEGF) signalling pathway. Conclusion In our study, we found that 3 hub DElncRNAs (LINC00687, LBX2‐AS1 and LINC01566) may be involved in the pathogenesis of periodontitis based on WGCNA and Limma analysis. Our study aimed to elucidate the mechanisms involved in periodontitis at the genetic and epigenetic levels by constructing a ceRNA network associated with lncRNA. Besides, identification DEGs of differential LBX2‐AS1 and functional annotation showed that LBX2‐AS1 might be associated with periodontitis.

Background Previous studies have shown that alternative splicing (AS) plays a key role in carcinogenesis and prognosis of cancer. However, systematic profiles of AS signatures in head and neck cancer (HNC) have not yet been reported. Methods In this study, AS data, RNA‐Seq data, and corresponding clinicopathological information of 489 HNC patients were downloaded from The Cancer Genome Atlas. Univariate and multivariate Cox regression analyses were performed to screen for survival‐associated AS events. Functional and pathway enrichment analysis was also performed. The prognostic models and splicing networks were constructed using integrated bioinformatics analysis tools. Results Among the 42,849 alternating splicing events identified in 10,121 genes, 5,165 survival‐associated AS events in 2,419 genes were observed in univariate Cox regression analysis. Among the seven types, alternate terminator events were the most powerful prognostic factors. Multivariate Cox analysis was then used to screen for the AS genes with prognostic value. Four candidate genes (TPM1, CLASRP, PRRC1, and DNASE1L1) were found to be independent prognostic factors for HNC patients. A prognostic prediction model was built based on the four genes. The area under the receiver operating characteristic risk score curve for predicting the survival status of HNC patients was 0.704. In addition, splicing interaction network indicated that the splicing factors have significant functions in HNC. Conclusion A comprehensive analysis of AS events in HNC was performed. A powerful prognostic predictor for HNC patients was established based on AS events could.