Objective: The present study aimed to summarize and update the evidence regarding the association between periodontitis and psoriasis.Methods: The present systematic review was conducted under the guidelines of Transparent Reporting of Systematic Reviews and Meta‐Analyses (PRISMA) and was recorded in the PROSPERO database, under registration number CRD42017063799. Three databases (MEDLINE, Embase, and Cochrane Library) were searched up to March 2020. Case–control or cohort studies assessing the association between periodontitis and psoriasis were identified. Quantitative synthesis was conducted with meta‐analysis.Results: A total of 13 studies (11 case–control and two cohort studies) assessing the association between periodontitis and psoriasis were included. Of these 13 articles, 9 showed the prevalence of periodontitis or psoriasis. Therefore, meta‐analyses were conducted with data retrieved from the nine studies included. Pooled effect estimate for nine studies showed that patients with periodontitis associated with a higher risk of psoriasis with a pooled OR of 2.87 (95% CI, 1.75–4.69).Conclusions: This systematic review demonstrated a positive association between periodontitis and psoriasis; however, a causal relationship cannot be established. Due to the weak evidence, caution should be taken when interpreting the results regarding periodontal parameters. Well‐designed prospective studies are necessary to evaluate interactions between both diseases.

Most studies reported that high plasma endothelin-1 (ET-1), big ET-1, and C-terminal proET-1 (CT-proET-1) were correlated with poor prognosis of heart failure (HF). However, available evidence remains controversial. To help solve the debate, we collected all the available studies and performed a meta-analysis. We searched the databases covering Embase, PubMed, Ovid, and Web of Science on June 28, 2017. The hazard ratio (HR) or risk ratio (RR) and its 95% confidence intervals (CIs) were collected and calculated by use of a random-effect model. Heterogeneity was assessed by Cochran's Q test, and publication bias was assessed by funnel plots with Egger's and Begg's linear regression test. Thirty-two studies with 18,497 patients were included in the analysis. Results showed that circulating ET-1, big ET-1, and CT-proET-1 were positively correlated with high risk of adverse outcomes, with pooled RRs (95% CIs) of 2.22 (1.82–2.71, P < .001), 2.47 (1.93–3.17, P < .001), and 2.27 (1.57–3.29, P < .001), respectively. In the subgroup of death as primary outcome, the pooled RRs (95% CIs) were 2.13 (1.68–2.70, P < .001), 2.55 (1.82–3.57, P < .001), and 2.02 (1.39–2.92, P < .001) for ET-1, big ET-1, and CT-proET-1, respectively. No significant publication bias was observed in this study. Our meta-analysis provided evidence that increased plasma levels of ET-1, big ET-1, and CT-proET-1 were associated with poor prognosis or mortality for HF populations.

The TNM staging system is often used to predict the prognosis of patients with oral squamous cell carcinoma (OSCC). However, we have found that patients under the same TNM staging may exhibit tremendous differences in survival rates. Therefore, we aimed to investigate the prognosis of postoperative OSCC patients, establish a nomogram survival prediction model, and verify its effectiveness. Operative logs were reviewed for patients who underwent surgical treatment for OSCC at the Peking University School and Hospital of Stomatology. Patient demographic and surgical records were obtained, and they were followed up for overall survival (OS). A total of 432 patients with oral squamous cell carcinoma were included in the study, with a median follow-up time of 47 months. Based on the results of the Cox regression analysis, we constructed and verified the nomogram prediction model, which includes gender, BMI, OPMDs, pain score, SCC grade, and N stage. The C-index value of the 3-year and 5-year prediction models was 0.782 and 0.770, respectively, proving that the model has a certain level of prediction stability. The new nomogram prediction model has potential clinical significance for predicting the postoperative survival of OSCC patients.

Background: Head and neck squamous cell carcinoma (HNSCC) is a highly aggressive malignancy with a poor prognosis, necessitating the discovery of novel and reliable molecular biomarkers for improved clinical management. Traditional bulk transcriptomic analyses often mask the cellular heterogeneity and spatial complexity of the tumor microenvironment, limiting the identification of robust biomarkers. This study aimed to identify and validate key driver genes in HNSCC through a comprehensive multi-omics and machine learning-based approach. Materials and methods: Transcriptomic data from multiple GEO datasets (GSE29330, GSE6631, GSE138206) and the TCGA-HNSC cohort were integrated and analyzed to identify consensus differentially expressed genes (DEGs). A suite of four machine learning algorithms (LASSO, SVM-RFE, XGBoost, Boruta) was employed to screen for core candidate genes. The cellular origins and spatial distribution of these core genes were subsequently dissected using public single-cell (GSE215403) and spatial transcriptomics (GSE252265) data. Finally, the expression of the key gene, SAM and SH3 domain-containing 1 (SASH1), was validated at the protein level via Western blot in HNSCC cell lines, and its clinical and therapeutic value was assessed through survival, clinical correlation, and drug sensitivity analyses. Results: An integrated analysis of bulk transcriptomic data identified 159 consensus DEGs, from which four core genes (COL1A1, EMP1, MYH11, SASH1) were robustly selected by all four machine learning algorithms. Multi-omics validation revealed that SASH1 was specifically downregulated within the malignant cell population and its expression was spatially exclusive from the COL1A1-high fibrotic stromal regions. Western blot confirmed the significant downregulation of SASH1 protein in HNSCC cells compared to controls. Importantly, low SASH1 expression was significantly associated with poorer overall survival in the TCGA cohort ( P < 0.05), a prognostic value not observed for the other core genes. Functional analyses linked SASH1 to critical pathways including cell cycle and adhesion. Furthermore, SASH1 expression levels correlated with sensitivity to multiple targeted drugs, including ATR and Aurora kinase inhibitors. Conclusion: By systematically integrating multi-platform transcriptomics, machine learning, and multi-dimensional validation, this study identifies SASH1 as a robust prognostic biomarker and a potential predictor of therapeutic response in HNSCC. The established multi-omics pipeline provides a meaningful framework for biomarker discovery and highlights SASH1 as a promising target for advancing precision medicine in HNSCC.

Background: Tumors, as intricate ecosystems, comprise oncocytes and the highly dynamic tumor stroma. Tumor stroma, representing the non-cancerous and non-cellular composition of the tumor microenvironment (TME), plays a crucial role in oncogenesis and progression, through its interactions with biological, chemical, and mechanical signals. This review aims to analyze the challenges of stroma mimicry models, and highlight advanced personalized co-culture approaches for recapitulating tumor stroma using patient-derived tumor organoids (PDTOs). Methods: This review synthesizes findings from recent studies on tumor stroma composition, stromal remodeling, and the spatiotemporal heterogeneities of the TME. It explores popular stroma-related models, co-culture systems integrating PDTOs with stromal elements, and advanced techniques to improve stroma mimicry. Results: Stroma remodeling, driven by stromal cells, highlights the dynamism and heterogeneity of the TME. PDTOs, derived from tumor tissues or cancer-specific stem cells, accurately mimic the tissue-specific and genetic features of primary tumors, making them valuable for drug screening. Co-culture models combining PDTOs with stromal elements effectively recreate the dynamic TME, showing promise in personalized anti-cancer therapy. Advanced co-culture techniques and flexible combinations enhance the precision of tumor-stroma recapitulation. Conclusions: PDTO-based co-culture systems offer a promising platform for stroma mimicry and personalized anti-cancer therapy development. This review underscores the importance of refining these models to advance precision medicine and improve therapeutic outcomes.

BACKGROUND: Ossifying fibroma in the jaws is a benign tumor and easily recurs in children, of which the treatment methods and prognosis still remain controversial. In this study, we aimed to review the clinicopathological characteristics, treatment, and prognosis of ossifying fibroma in the jaws of children, and offer recommendations for clinical decision-making.