Objective: Thyroid disease has been reported to associate with gut microbiota, but the effects of thyroid cancer and thyroid nodules on the oral microbiota are still largely unknown. This study aimed to identify the variation in salivary microbiota and their potential association with thyroid cancer and thyroid nodules.Methods: We used 16S rRNA high-throughput sequencing to examine the salivary microbiota of thyroid cancer patients (n = 14), thyroid nodules patients (n = 9), and healthy controls (n = 15).Results: The alpha-diversity indices Chao1 and ACE were found to be relatively higher in patients with thyroid cancer and thyroid nodules compared to healthy controls. The beta diversity in both the thyroid cancer and thyroid nodules groups was divergent from the healthy control group. The genera Alloprevotella, Anaeroglobus, Acinetobacter, unclassified Bacteroidales, and unclassified Cyanobacteriales were significantly enriched in the thyroid cancer group compared with the healthy control group. In contrast, the microbiome of the healthy controls was mainly composed of the genera Haemophilus, Lautropia, Allorhizobium Neorhizobium Pararhizobium Rhizobium, Escherichia Shigella, and unclassified Rhodobacteraceae. The thyroid nodules group was dominated by genre uncultured Candidatus Saccharibacteria bacterium, unclassified Clostridiales bacterium feline oral taxon 148, Treponema, unclassified Prevotellaceae, Mobiluncus, and Acholeplasma. In contrast, the genera unclassified Rhodobacteraceae and Aggregatibacter dominated the healthy control group. The study also found that clinical indicators were correlated with the saliva microbiome.Conclusion: The salivary microbiota variation may be connected with thyroid cancer and thyroid nodules.

The association between tumor necrosis factor-alpha (TNF-α-308G/A, -238G/A, -863C/A, -1031T/C, and -857C/T) polymorphism and either chronic (CP) or aggressive (AgP) periodontitis susceptibility was conflicting. This meta-analysis aimed to quantitatively estimate the association. A total of 52 studies involving 5519 patients and 7260 controls were identified through a search of multiple electronic databases. Odds ratios (ORs) and their 95% confidence intervals using allele, homozygous, heterozygous, dominant, and recessive genetic models were computed to assess the strength of the association. The TNF-α-308G/A polymorphism was significantly associated with decreased risks of CP (GG vs AA: OR = 0.353, P < .001; GG+GA vs AA: OR = 0.480, P < .001) and AgP (G vs A: OR = 0.651, P < .001; GG vs AA: OR = 0.306, P < .001; GG+GA vs AA: OR = 0.384, P < .001) in Asians. There were no associations between TNF-α-238G/A, -863C/A, -1031T/C, -857C/T polymorphism and susceptibility to AgP. No associations were also found between CP susceptibility and TNF-α-238G/A, -857C/T polymorphism. These findings supported that TNF-α-308G/A polymorphism might be the protective factors of CP and AgP in Asians, and TNF-α-238G/A, -863C/A, -1031T/C, -857C/T polymorphism is not linked to AgP susceptibility.

Objective. To screen for potential endoplasmic reticulum stress- (ERS-) related biomarkers of periodontitis using machine learning methods and explore their relationship with immune cells. Methods. Three datasets of periodontitis (GSE10334, GES16134, and GES23586) were obtained from the Gene Expression Omnibus (GEO), and the samples were randomly assigned to the training set or the validation set. ERS-related differentially expressed genes (DEGs) between periodontitis and healthy periodontal tissues were screened and analyzed for GO, KEGG, and DO enrichment. Key DEGs were screened by two machine learning algorithms, LASSO regression and support vector machine-recursive feature elimination (SVM-RFE); then, the potential biomarkers were identified through validation. The infiltration of immune cells of periodontitis was calculated using the CIBERSORT algorithm, and the correlation between immune cells and potential biomarkers was specifically analyzed through the Spearman method. Results. We obtained 36 ERS-related DEGs of periodontitis from the training set, from which 11 key DEGs were screened by further machine learning. SERPINA1, ERLEC1, and VWF showed high diagnostic values ( ), so they were considered as potential biomarkers for periodontitis. According to the results of the immune cell infiltration analysis, these three potential biomarkers showed marked correlations with plasma cells, neutrophils, resting dendritic cells, resting mast cells, and follicular helper T cells. Conclusions. Three ERS-related genes, SERPINA1, ERLEC1, and VWF, showed valuable biomarker potential for periodontitis, which provide a target base for future studies on early diagnosis and treatment of periodontitis.

Backgrounds: To date, there is still no consensus about the clinical efficacy of non-surgical periodontal therapy in rheumatoid arthritis (RA) patients with periodontitis. Therefore, the aim of this study was to summarize clinical data regarding the efficacy of scaling and root planing (SRP) in patients with RA and periodontitis compared to non-RA periodontitis patients. Methods: We selected randomized controlled trials (RCTs) that compared periodontal clinical data in RA as compared to non-RA periodontitis patients by searching Embase, PubMed and Cochrane Central Register of Controlled Trials and by manually retrieving from the earliest records to March 8, 2021. The overall effect size of plaque index (PI), gingival index (GI), attachment loss (AL), probing depth (PD) and bleeding on probing (BOP) were calculated by either a fixed or random-effect model, and subgroup analyses were conducted according to the different time points of follow-up. Two investigators extracted the data and assess the accuracy of the obtained results with 95% of Confidence Intervals (CI). Cochrane Collaboration's tool was responsible for the evaluation of the literature quality and the inter-study heterogeneity was evaluated by Q test and I² statistic. Sensitivity analyses were applied for results with heterogeneity. Publication bias was determined by Begg's test, Egger's test and the trim-and-fill method. Results: Seven RCTs including 212 patients eventually met the inclusion criteria for the study. As the primary results, the change of PD was not statistically significant and in the secondary results changes of PI, GI, AL and BOP were also not statistically significant in RA patients with periodontitis compared to non-RA periodontitis patients. In subgroup analysis, a larger BOP reduction at 3 months, PI and AL reduction at 6 months were observed in patients with RA and periodontitis group. The results of sensitivity analyses had no significant effect. No evidence of potential publication bias was tested. There were some limitations due to the small number of eligible RCTs. Conclusions: SRP is equally effective in RA as compared to non-RA periodontitis patients. It suggests RA does not affect the clinical efficacy of non-surgical periodontal therapy. These results could serve evidence-based practice.

Background: Disulfidptosis and ferroptosis are forms of programmed cell death that may be associated with the pathogenesis of periodontitis. Our study developed periodontitis-associated biomarkers combining disulfidptosis and ferroptosis, which provides a new perspective on the pathogenesis of periodontitis.Methods: Firstly, we obtained the periodontitis dataset from public databases and found disulfidptosis- and ferroptosis-related differentially expressed transcripts based on the disulfidptosis and ferroptosis transcript sets. After that, transcripts that are tissue biomarkers for periodontitis were found using three machine learning methods. We also generated transcript subclusters from two periodontitis microarray datasets: GSE16134 and GSE23586. Furthermore, three transcripts with the best classification efficiency were further screened. Their expression and classification efficacy were validated using qRT-PCR. Finally, periodontal clinical indicators of 32 clinical patients were collected, and the correlation between three transcripts above and periodontal clinical indicators was analyzed.Results: We identified six transcripts that are tissue biomarkers for periodontitis, the top three transcripts with the best classification, and delineated two expression patterns in periodontitis.Conclusions: Our study found that disulfidptosis and ferroptosis were associated with immune responses and may involve periodontitis genesis.

Bone is a dynamic organ that, once formed, undergoes a constant remodeling process that includes bone resorption and synthesis. Osteoclasts and osteoblasts are primarily responsible for controlling this process. X-box binding protein 1 (XBP1), a transcription factor, affects the metabolism of bones in various ways. In recent years, numerous studies have revealed that XBP1 plays a vital role in bone metabolism, including osteoclast and osteoblast development, as well as in regulating immune cell differentiation that affects the immune microenvironment of bone remodeling. In this review, we highlight the regulatory mechanisms of XBP1 on osteoclasts and osteoblasts, how XBP1 affects the immune microenvironment of bone remodeling by influencing the differentiation of immune cells, and predict the possible future research directions of XBP1 to provide new insights for the treatment of bone-related metabolic diseases.