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.

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/Aims: Kidney renal clear cell carcinoma (KIRC) is one of the most fatal malignancies due to late diagnosis and poor treatment. To improve its prognosis, a screening for molecular biomarkers of KIRC is urgently needed. Long non-coding RNAs (lncRNAs) play important roles in tumorigenesis and prognosis of cancers. However, it is not clear whether lncRNAs can be used as molecular biomarkers in predicting the survival of KIRC patients. Methods: In this study, our aim was to identify lncRNAs/mRNAs signatures and their prognostic values in KIRC. The aberrant expression profile of mRNAs and lncRNAs in 529 KIRC tissues and 72 adjacent non-tumor pancreatic tissues were obtained from the Cancer Genome Atlas (TCGA). A weighted gene co-expression network analysis (WGCNA) of two key lncRNAs was constructed. We constructed an aberrant lncRNA-mRNA-miRNA ceRNA network in CESC. In addition, Gene Ontology (GO) and KEGG pathway analysis were performed. Results: Using lncRNA/mRNA expression profiling data, the overall analysis revealed that two novel lncRNA signatures (DNM1P35 and MIR155HG) and several mRNAs were found to be significantly correlated with KIRC patient’s overall analysis. Based on the target gene of the two lncRNA in co-expression network, the GO and KEGG analysis were also performed. A dysregulated lncRNA-related ceRNA network was also observed. Conclusion: These results suggested that the two novel lncRNAs signatures may act as independent prognostic biomarkers for predicting the survival of KIRC patient.

Head and neck cancer (HNC) remains one of the most malignant tumors with a significantly high mortality. DNA methylation exerts a vital role in the prognosis of HNC. In this study, we try to screen abnormal differential methylation genes (DMGs) and pathways in Head–Neck Squamous Cell Carcinoma via integral bioinformatics analysis. Data of gene expression microarrays and gene methylation microarrays were obtained from the Cancer Genome Atlas database. Aberrant DMGs were identified by the R Limma package. We conducted the Cox regression analysis to select the prognostic aberrant DMGs and site‐specific methylation. Five aberrant DMGs were recognized that significantly correlated with overall survival. The prognostic model was constructed based on five DMGs (PAX9, STK33, GPR150, INSM1, and EPHX3). The five DMG models acted as prognostic biomarkers for HNC. The area under the curve based on the five DMGs predicting 5‐year survival is 0.665. Moreover, the correlation between the DMGs/site‐specific methylation and gene expression was also explored. The findings demonstrated that the five DMGs can be used as independent prognostic biomarkers for predicting the prognosis of patients with HNC. Our study might lay the groundwork for further mechanism exploration in HNC and may help identify diagnostic biomarkers for early stage HNC.

Many primary and secondary studies reported the association between Toll-like receptor 4 (TLR4) polymorphism and periodontitis susceptibility, which mainly focused on TLR4–299A>G or TLR4–399C>T of Caucasian, however, these studies had different conclusions. The aim of this study was to reassess relative studies about TLR4 polymorphism and periodontitis susceptibility, and update meta-analysis. We searched the electronic database including CNKI (Chinese National Knowledge Infrastructure), PubMed, Embase, and hand searched relative studies until January 4, 2016. Two authors selected studies according to inclusion and exclusion criteria, assessed studies using Newcastle-Ottawa Scale case control study (NOS), and calculated the combined effect size using STATA software, version 12.0. This meta-analysis included 18 studies, containing 2453 healthy participants and 2987 patients with chronic periodontitis (CP) and 462 patients with aggressive periodontitis (AP). There was a significance between TLR4C>G (rs7873784) allele and CP in Asian, and its recessive model was also significant (for C vs G: odds ratio [OR] = 0.72, 95% confidence interval [CI] = 0.54–0.95, I2 = 0%; for CC + CG vs GG: OR = 0.66, 95% CI = 0.49–0.89, I2 = 0%). However, we did not detect any significant relevance between other TLR4 polymorphism and periodontitis susceptibility in overall and subgroup analyses. The sensitive analysis showed that dropping any single studies did not affect the pooled-analysis results. Publication bias was not detected. The meta-analysis found association between TLR4C>G (rs7873784) allele and CP in Asian and it may passed on to offsprings in the form of recessiveness. However, further studies about the association between TLR4C>G (rs7873784) and CP is warranted to confirm.