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Abstract Relative fat mass (RFM) is a novel indicator for measuring body fat. This cross-section study aims to explore the association between RFM and periodontitis and to investigate possible effect modifiers in U.S. adults based on the National Health and Nutrition Examination Survey 2009–2014. The category of periodontitis was defined by the CDC/AAP. Mean clinical attachment loss and mean pocket probing depth (PPD) were calculated. The RFM formula is: 64 − (20 × height/WC) + (12 × sex), with sex coded as 1 for female and 0 for male. Natural cubic spline and weighted multivariable regression analyses were conducted to investigate the relationship between RFM and periodontal status. Subgroup and interaction analyses were also employed to assess the moderating roles of age, gender, and race. A total of 10,307 participants were included in our study. Compared to the lowest quartiles, individuals in the highest quartiles of RFM levels were more likely to have moderate/severe periodontitis (ORQ4vs1 = 1.64, 95% CI 1.30–2.06) and had a higher mean PPD (βQ4vs1 = 0.15, 95% CI 0.09–0.22). This association was particularly stronger in populations under the age of 60, with significant interactions. Taken together, RFM is positively associated with periodontitis, particularly in those under 60 years old.

Abstract Background Exposure to ethylene oxide (EO) induces inflammation and oxidative stress, which are the main mechanisms of periodontitis. However, the effect of EO on periodontal health is not unclear. In this study, we aimed to explore the relationship between EO exposure and the risk of periodontitis in general US adults. Methods Data used in our study from the National Health and Nutritional Examination Survey (NHANES) 2013–2014. The EO biomarker, hemoglobin adduct of EO (HbEO), was measured in blood samples utilizing high-performance liquid chromatography-tandem mass spectrometry. Periodontitis category was defined by the CDC/AAP according to clinical periodontal parameters. Natural cubic spline, weight multivariable logistic regression analyses and subgroup analysis were used to explore the association between EO exposure and the risk of periodontitis. Results A total of 1497 participants over the age of 30 were included in our study. A non-linear positive association with periodontitis was identified for HbEO levels. Participants in the highest tertile of HbEO levels were more likely to have poorer periodontal health compared to the lowest tertile (ORtertile3vs1 = 2.80, 95% CI: 1.85–4.24). Similar results were also found in different subgroups. Conclusions HbEO levels are positively associated with poor periodontal health in US adults. Additional longitudinal studies are necessary to further enhance our comprehension of the impact of exposure to EO on periodontal status.

Abstract Background Inflammation and oxidative stress are two hallmarks of periodontitis. Retinol is an antioxidant and suppresses expression of pro-inflammatory factors. However, the evidence for an association between retinol intake and periodontitis is limited. Thus, the aim of this study is to assess the association between retinol intake and periodontal health. Methods Data used in this cross-sectional study from the National Health and Nutrition Examination Survey (NHANES) 2009–2014 (n = 9081). Dietary intake of retinol was measured based on two 24-h dietary recall interviews. The category of periodontitis was defined by the CDC/AAP according to clinical periodontal parameters. Univariate and multivariate logistic regression analyses were applied to investigate the relationship between retinol intake and the risk of periodontitis. Results Compared with the lowest tertile, individuals in the highest tertile of retinol intake were less likely to be periodontitis (ORtertile3vs1 = 0.79, 95% CI: 0.65–0.96). The association was still significant in populations who were less than 60 years old (ORtertile3vs1 = 0.80, 95% CI: 0.65–0.97), non-Hispanic black (ORtertile3vs1 = 0.62, 95% CI: 0.42–0.94), PI ≤ 1.3 (ORtertile3vs1 = 0.72, 95% CI: 0.55–0.93), 1.3

Abstract Background Long noncoding RNAs (lncRNAs) play a critical role in innate and adaptive immune responses. Thus, we aimed to identify ideal subtypes for head and neck squamous cell carcinoma (HNSCC) based on immune-related lncRNAs. Methods TCGA HNSCC cohort was divided into two datasets (training and validation dataset), and 960 previously characterized immune-related lncRNAs were extracted for non-negative matrix factorization analysis. We characterized our HNSCC subtypes based on biological behaviors, immune landscape and response to immunotherapy in both training and validation cohort. A lncRNA-signature was generated to predict our HNSCC subtypes, and essential lncRNAs involved in tumor microenvironment (TME) were identified. Results We developed and validated two HNSCC subtypes (C1 and C2) based on the 70 lncRNAs in the training and validation cohort. C2 subtype displayed good prognosis, high immune cell infiltration, immune-related genes expression and sensitivity to PD-1 blockade. C1 subtype was associated with high activity of mTORC1 signaling and glycolysis as well as high fraction of inactive immune cells. Finally, we generated a 31-lncRNA signature that could predict our above subtypes with high accurate. Additionally, TRG-AS1 was identified as the essential lncRNA involving TME formation. Knockdown of TRG-AS1 inhibited the expression of HLA-A, HLA-B, HLA-C, CXCL9, CXCL10 and CXCL11. High expression of TRG-AS1 indicated a favorable prognosis in HNSCC and anti-PD-L1 cohort (IMvigor210). Conclusions Our study establishes a novel HNSCC classification on the basis of 31-lncRNA, helping to identify beneficiaries for anti-PD-1 treatment. In addition, a critical lncRNA TRG-AS1 is identified as a new potential prognosis biomarker as well as therapeutic target.

Abstract SAM pointed domain containing E26 transformation-specific transcription factor (SPDEF) plays dual roles in the initiation and development of human malignancies. However, the biological role of SPDEF in head and neck squamous cell carcinoma (HNSCC) remains unclear. In this study, the expression level of SPDEF and its correlation with the clinical parameters of patients with HNSCC were determined using TCGA-HNSC, GSE65858, and our own clinical cohorts. CCK8, colony formation, cell cycle analysis, and a xenograft tumor growth model were used to determine the molecular functions of SPDEF in HNSCC. ChIP-qPCR, dual luciferase reporter assay, and rescue experiments were conducted to explore the potential molecular mechanism of SPDEF in HNSCC. Compared with normal epithelial tissues, SPDEF was significantly downregulated in HNSCC tissues. Patients with HNSCC with low SPDEF mRNA levels exhibited poor clinical outcomes. Restoring SPDEF inhibited HNSCC cell viability and colony formation and induced G0/G1 cell cycle arrest, while silencing SPDEF promoted cell proliferation in vitro. The xenograft tumor growth model showed that tumors with SPDEF overexpression had slower growth rates, smaller volumes, and lower weights. SPDEF could directly bind to the promoter region of NR4A1 and promoted its transcription, inducing the suppression of AKT, MAPK, and NF-κB signaling pathways. Moreover, silencing NR4A1 blocked the suppressive effect of SPDEF in HNSCC cells. Here, we demonstrate that SPDEF acts as a tumor suppressor by transcriptionally activating NR4A1 in HNSCC. Our findings provide novel insights into the molecular mechanism of SPDEF in tumorigenesis and a novel potential therapeutic target for HNSCC.

Protein arginine methyltransferase 5 (PRMT5) is an important type II arginine methyltransferase that can play roles in cancers in a highly tissue-specific manner, but its role in the carcinogenesis and metastasis of head and neck squamous cell carcinoma (HNSCC) remains unclear. Here, we detected PRMT5 expression in HNSCC tissues and performed series of in vivo and in vitro assays to investigate the function and mechanism of PRMT5 in HNSCC. We found that PRMT5 was overexpressed in dysplastic and cancer tissues, and associated with lymph node metastasis and worse patient survival. PRMT5 knockdown repressed the malignant phenotype of HNSCC cells in vitro and in vivo. PRMT5 specific inhibitor blocked the formation of precancerous lesion and HNSCC in 4NQO-induced tongue carcinogenesis model, prevented lymph node metastasis in tongue orthotopic xenograft model and inhibited cancer development in subcutaneous xenograft model and Patient-Derived tumor Xenograft (PDX) model. Mechanistically, PRMT5-catalyzed H3R2me2s promotes the enrichment of H3K4me3 in the Twist1 promoter region by recruiting WDR5, and subsequently activates the transcription of Twist1. The rescue experiments indicated that overexpressed Twist1 abrogated the inhibition of cell invasion induced by PRMT5 inhibitor. In summary, this study elucidates that PRMT5 inhibition could reduce H3K4me3-mediated Twist1 transcription and retard the carcinogenesis and metastasis of HNSCC.

The peroxidase family of peroxiredoxins (PRDXs) plays a vital role in maintaining the intracellular balance of ROS. However, their function in head and neck squamous cell carcinoma (HNSCC) has not been investigated. We therefore explored the value of PRDXs in HNSCC. We found that the expression of PRDX1, PRDX4, and PRDX5 in HNSCC increased while the expression of PRDX2 decreased. Moreover, the high expression of PRDX4/5/6 indicated a poor prognosis. Lower expression of PRDX1/5 was linked to more immune cell infiltration, higher expression of immune-related molecules and a more likely response to anti-PD-1 treatment. Moreover, PRDX5 knockdown inhibited HNSCC cell proliferation, invasion and metastasis and it might promote apoptosis through its antioxidant property. Taken together, our study highlights the potential role of PRDXs in HNSCC. The function of PRDX5 in the development of HNSCC and the formation of the immune microenvironment makes it a promising potential therapeutic target.

ObjectiveTo develop and validate a simple-to-use prognostic scoring model based on clinical and pathological features which can predict overall survival (OS) of patients with oral squamous cell carcinoma (OSCC) and facilitate personalized treatment planning.Materials and MethodsOSCC patients (n = 404) from a public hospital were divided into a training cohort (n = 282) and an internal validation cohort (n = 122). A total of 12 clinical and pathological features were included in Kaplan–Meier analysis to identify the factors associated with OS. Multivariable Cox proportional hazards regression analysis was performed to further identify important variables and establish prognostic models. Nomogram was generated to predict the individual’s 1-, 3- and 5-year OS rates. The performance of the prognostic scoring model was compared with that of the pathological one and the AJCC TNM staging system by the receiver operating characteristic curve (ROC), concordance index (C-index), calibration curve, and decision curve analysis (DCA). Patients were classified into high- and low-risk groups according to the risk scores of the nomogram. The nomogram-illustrated model was independently tested in an external validation cohort of 95 patients.ResultsFour significant variables (physical examination-tumor size, imaging examination-tumor size, pathological nodal involvement stage, and histologic grade) were included into the nomogram-illustrated model (clinical–pathological model). The area under the ROC curve (AUC) of the clinical–pathological model was 0.687, 0.719, and 0.722 for 1-, 3- and 5-year survival, respectively, which was superior to that of the pathological model (AUC = 0.649, 0.707, 0.717, respectively) and AJCC TNM staging system (AUC = 0.628, 0.668, 0.677, respectively). The clinical–pathological model exhibited improved discriminative power compared with pathological model and AJCC TNM staging system (C-index = 0.755, 0.702, 0.642, respectively) in the external validation cohort. The calibration curves and DCA also displayed excellent predictive performances.ConclusionThis clinical and pathological feature based prognostic scoring model showed better predictive ability compared with the pathological one, which would be a useful tool of personalized accurate risk stratification and precision therapy planning for OSCC patients.

TP53INP2 plays an important role in regulating gene transcription and starvation-induced autophagy, however, its function in head and neck squamous cell carcinoma (HNSCC) remains unclear. Therefore, we assessed the expression and prognostic value of TP53INP2. In addition, RNAseq, miRNAseq, copy number variation, and mutation profiles from The Cancer Genome Atlas (TCGA) dataset were applied to evaluate the distinctive genomic patterns related to TP53INP2 expression. We found that TP53INP2 expression was lower in HNSCC compared with normal controls. Patients with higher TP53INP2 expression had longer survival time. Knockdown of TP53INP2 promoted cell viability. Functional analysis exhibited that TP53INP2 was linked to DNA replication, DNA repair, cell cycle, and multiple metabolic pathways. Moreover, TP53INP2 might affect the expression of multiple genes via enhancing the transcriptional activity of nuclear hormone receptors. A competing endogenous RNA (ceRNA) network consisting of 33 lncRNAs, eight miRNAs, and 13 mRNAs was constructed based on the expression of TP53INP2. Taken together, our study highlights the potential value of TP53INP2 in predicting the survival of HNSCC and its important role in the genesis and development of HNSCC.

Abstract Background Glycemic control is vital in the care of type 2 diabetes mellitus (T2DM) and is significantly associated with the incidence of clinical complications. This Bayesian network analysis was conducted with an aim of evaluating the efficacy of scaling and root planning (SRP) and SRP + adjuvant treatments in improving glycemic control in chronic periodontitis (CP) and T2DM patients, and to guide clinical practice. Methods We searched the Pubmed, Embase, Cochrane Library and Web of Science databases up to 4 May 2018 for randomized controlled trials (RCTs). This was at least three months of the duration of study that involved patients with periodontitis and T2DM without other systemic diseases given SRP. Patients in the control group did not receive treatment or SRP combination with adjuvant therapy. Outcomes were given as HbA1c% and levels fasting plasma glucose (FPG). Random-effects meta-analysis and Bayesian network meta-analysis were conducted to pool RCT data. Cochrane’s risk of bias tool was used to assess the risk of bias. Results Fourteen RCTs were included. Most were unclear or with high risk of bias. Compared to patients who did not receive treatment, patients who received periodontal treatments showed improved HbA1c% level, including SRP (the mean difference (MD) -0.399 95% CrI 0.088 to 0.79), SRP + antibiotic (MD 0.62, 95% CrI 0.18 to 1.11), SRP + photodynamic therapy (aPDT) + doxycycline (Doxy) (MD 1.082 95% CrI 0.13 to 2.077) and SRP + laser (MD 0.66 95% CrI 0.1037, 1.33). Among the different treatments, SRP + aPDT + Doxy ranked best. Regarding fasting plasma glucose (FPG), SRP did not show advantage over no treatment (MD 4.91 95% CI − 1.95 to 11.78) and SRP with adjuvant treatments were not better than SRP alone (MD -0.28 95% CI -8.66, 8.11). Conclusion The results of this meta-analysis seem to support that periodontal treatment with aPDT + Doxy possesses the best efficacy in lowering HbA1c% of non-smoking CP without severe T2DM complications. However, longer-term well-executed, multi-center trails are required to corroborate the results.

Abstract Background Studies indicate locally-delivered statins offer additional benefits to scaling and root planning (SRP), however, it is still hard to say which type of statins is better. This network meta-analysis aimed to assess the effect of locally-delivered statins and rank the most efficacious statin for treating chronic periodontitis (CP) in combination with SRP. Methods We screened four literature databases (Pubmed, Embase, Cochrane Library, and Web of Science) for randomized controlled clinical trials (RCTs) published up to June 2018 that compared different statins in the treatment of chronic periodontitis. The outcomes analyzed were changes in intrabony defect depth (IBD), pocket depth (PD), and clinical attachment level (CAL). We carried out Bayesian network meta-analysis of CP without systemic diseases. Traditional and Bayesian network meta-analyses were conducted using random-effects models. Results Greater filling of IBD, reduction in PD, and gain in CAL were observed for SRP treated in combination with statins when compared to SRP alone for treating CP without systemic diseases. Specifically, SRP+ Atorvastatin (ATV) (mean difference [MD]: 1.5 mm, 1.4 mm, 1.8 mm, respectively), SRP + Rosuvastatin (RSV) (MD: 1.8 mm, 2.0 mm, 2.1 mm, respectively), and SRP + Simvastatin (SMV) (MD: 1.1 mm, 2.2 mm, 2.1 mm, respectively) were identified. However, no difference was found among the statins tested. In CP patients with type 2 diabetic (T2DM) or in smokers, additional benefits were observed from locally delivered statins. Conclusion Local statin use adjunctive to SRP confers additional benefits in treating CP by SRP, even in T2DM and smokers. RSV may be the best one to fill in IBD. However, considering the limitations of this study, clinicians must use cautious when applying the results and further studies are required to explore the efficacy of statins in CP with or without the risk factors (T2DM comorbidity or smoking history).

BackgroundAlternative splicing (AS) plays an essential role in tumorigenesis and progression. This study aimed to develop a novel prognostic model based on the AS events to obtain more accurate survival prediction and search for potential therapeutic targets in oral squamous cell carcinoma (OSCC).MethodsSeven types of AS events in 326 OSCC patients with RNA-seq were obtained from the TCGA SpliceSeq tool and the TCGA database. Cox analysis, the least absolute shrinkage and selection operator Cox regression and random forest were employed to establish prognostic models. Genomics of Drug Sensitivity in Cancer (GDSC) was adopted to estimate the possible drug sensiticity. Prognostic splicing factor (SF)-AS network was constructed by Cytoscape.ResultsThe final model included 12 AS events, showing satisfactory performance. The area under the curve for 3- and 5-year survival in the training cohort was 0.83 and 0.82, respectively while that in internal validation was 0.83 and 0.82 accordingly. The calibration curve also indicated a satisfactory agreement between the observation and the predictive values. Low-risk patients stratified by the final model presented higher sensitivity to three chemo drugs. Besides, the prognostic SF-AS regulatory network contained five key SFs and 62 AS events.ConclusionsWe developed a powerful prognostic AS signature for OSCC and deepened the understanding of SF-AS network regulatory mechanisms. Low-risk patients tended to be more sensitive to the three chemo drugs while five key SFs including CELF2, TIA1, HNRNPC, HNRNPK, and SRSF9 were identified as potential prognostic biomarkers, which may offer new prospects for effective therapies of OSCC.

Background Oral squamous cell carcinoma (OSCC) is the most common type of head and neck squamous cell carcinoma with an unsatisfactory prognosis. The aim of this study was to identify potential prognostic mRNA biomarkers of OSCC based on analysis of The Cancer Genome Atlas (TCGA). Methods Expression profiles and clinical data of OSCC patients were collected from TCGA database. Univariate Cox analysis and the least absolute shrinkage and selection operator Cox (LASSO Cox) regression were used to primarily screen prognostic biomarkers. Then multivariate Cox analysis was performed to build a prognostic model based on the selected prognostic mRNAs. Nomograms were generated to predict the individual’s overall survival at 3 and 5 years. The model performance was assessed by the time-dependent receiver operating characteristic (ROC) curve and calibration plot in both training cohort and validation cohort (GSE41613 from NCBI GEO databases). In addition, machine learning was used to assess the importance of risk factors of OSCC. Finally, in order to explore the potential mechanisms of OSCC, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis was completed. Results Three mRNAs (CLEC3B, C6 and CLCN1) were finally identified as a prognostic biomarker pattern. The risk score was imputed as: (−0.38602 × expression level of CLEC3B) + (−0.20632 × expression level of CLCN1) + (0.31541 × expression level of C6). In the TCGA training cohort, the area under the curve (AUC) was 0.705 and 0.711 for 3- and 5-year survival, respectively. In the validation cohort, AUC was 0.718 and 0.717 for 3- and 5-year survival. A satisfactory agreement between predictive values and observation values was demonstrated by the calibration curve in the probabilities of 3- and 5- year survival in both cohorts. Furthermore, machine learning identified the 3-mRNA signature as the most important risk factor to survival of OSCC. Neuroactive ligand-receptor interaction was most enriched mostly in KEGG pathway analysis. Conclusion A 3-mRNA signature (CLEC3B, C6 and CLCN1) successfully predicted the survival of OSCC patients in both training and test cohort. In addition, this signature was an independent and the most important risk factor of OSCC.

Background Increasing evidence has demonstrated that long non-coding RNAs (lncRNAs) play an important role in the competitive endogenous RNA (ceRNA) networks in that they regulate protein-coding gene expression by sponging microRNAs (miRNAs). However, the understanding of the ceRNA network in tongue squamous cell carcinoma (TSCC) remains limited. Methods Expression profile data regarding mRNAs, miRNAs and lncRNAs as well as clinical information on 122 TSCC tissues and 15 normal controls from The Cancer Genome Atlas (TCGA) database were collected. We used the edgR package to identify differentially expressed mRNAs (DEmRNAs), lncRNAs (DElncRNAs) and miRNAs (DEmiRNAs) between TSCC samples and normal samples. In order to explore the functions of DEmRNAs, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis was performed. Subsequently, a ceRNA network was established based on the identified DElncRNAs–DEmiRNAs and DEmiRNAs–DEmRNAs interactions. The RNAs within the ceRNA network were analyzed for their correlation with overall disease survival. Finally, lncRNAs were specifically analyzed for their correlation with clinical features in the included TSCC patient samples. Results A total of 1867 mRNAs, 828 lncRNAs and 81 miRNAs were identified as differentially expressed in TSCC tissues (—log 2fold change— ≥ 2; adjusted P value