Objective:Constructing a risk prediction model to assess the impact of various factors on the need for auxiliary bone grafting with implant placement following alveolar ridge preservation (ARP) in posterior teeth.Methods:According to the sample size calculation formula, the sample size was calculated using the pmsampsize package of R 4.1.3 software, based on inclusion and exclusion criteria, a total of 110 posterior teeth in 98 patients who underwent ARP at the Department of Periodontology, School and Hospital of Stomatology, China Medical University, from January 2018 to May 2024 were conducted. Teeth were randomly divided into modeling group and validation group with 7∶3 ratio according to the random number table. The modeling group was divided into direct implantation group and auxiliary bone grafting group on the basis of whether auxiliary bone grafting was performed 6 months after ARP. Univariate and multivariate analyses were conducted to identify factors influencing auxiliary bone grafting with implant placement following ARP. Nomogram was constructed using R software. Receiver operator characteristic (ROC) curve and calibration curve were drawn to evaluate model differentiation and consistency. The decision curve analysis (DCA) was used to assess the clinical application value of the model.Results:Age ( OR=1.06, P=0.001), maximum attachment loss (AL) ( OR=1.75, P<0.001), reason of tooth extraction ( OR=12.73, P<0.001), smoking [<10 cigarettes/d ( OR=7.59, P<0.001);≥10 cigarettes/d ( OR=28.12, P<0.001)] and stage of periodontitis [stage Ⅱ ( OR=2.57, P=0.430); stage Ⅲ ( OR=21.00, P=0.007); stage Ⅳ ( OR=76.50, P<0.001)] influenced the necessity for auxiliary bone grafting with implant placement after ARP. After multivariate analysis of the above influencing factors, it was found that smoking [<10 cigarettes/d ( OR=7.02, P=0.009);≥10 cigarettes/d ( OR=10.27, P=0.026)] was an independent risk factor for the need of auxiliary bone grafting with implant placement after ARP. The area under the ROC curve for internal verification was 0.90 (95 %CI: 0.84-0.97), and the H-L goodness of fit test results were χ 2=4.79, P=0.780, indicating a good agreement. The area under the externally verified ROC curve was 0.97 (95 %CI: 0.92-1.00), suggesting that the fitting effect was slightly lower than that of the modeling group, and the predicted value of the model was slightly lower than the true value, which might underestimate the risk of additional surgery in patients. Results:of H-L goodness of fit test were χ 2=5.03, P=0.754. The DCA curve showed that when the probability of high-risk threshold was between 0.06 and 0.93, the clinical application value of the prediction model was higher. Conclusions:Age, smoking, reason of tooth extraction, stage of periodontitis, and maximum AL of the affected teeth were related to the necessity for auxiliary bone grafting with implant placement 6 months after ARP. Smoking was an independent risk factor for auxiliary bone grafting surgery. The constructed nomogram model had good discrimination and consistency.

Objective:Constructing a risk prediction model to assess the impact of various factors on the need for auxiliary bone grafting with implant placement following alveolar ridge preservation (ARP) in posterior teeth.Methods:According to the sample size calculation formula, the sample size was calculated using the pmsampsize package of R 4.1.3 software, based on inclusion and exclusion criteria, a total of 110 posterior teeth in 98 patients who underwent ARP at the Department of Periodontology, School and Hospital of Stomatology, China Medical University, from January 2018 to May 2024 were conducted. Teeth were randomly divided into modeling group and validation group with 7∶3 ratio according to the random number table. The modeling group was divided into direct implantation group and auxiliary bone grafting group on the basis of whether auxiliary bone grafting was performed 6 months after ARP. Univariate and multivariate analyses were conducted to identify factors influencing auxiliary bone grafting with implant placement following ARP. Nomogram was constructed using R software. Receiver operator characteristic (ROC) curve and calibration curve were drawn to evaluate model differentiation and consistency. The decision curve analysis (DCA) was used to assess the clinical application value of the model.Results:Age ( OR=1.06, P=0.001), maximum attachment loss (AL) ( OR=1.75, P<0.001), reason of tooth extraction ( OR=12.73, P<0.001), smoking [<10 cigarettes/d ( OR=7.59, P<0.001);≥10 cigarettes/d ( OR=28.12, P<0.001)] and stage of periodontitis [stage Ⅱ ( OR=2.57, P=0.430); stage Ⅲ ( OR=21.00, P=0.007); stage Ⅳ ( OR=76.50, P<0.001)] influenced the necessity for auxiliary bone grafting with implant placement after ARP. After multivariate analysis of the above influencing factors, it was found that smoking [<10 cigarettes/d ( OR=7.02, P=0.009);≥10 cigarettes/d ( OR=10.27, P=0.026)] was an independent risk factor for the need of auxiliary bone grafting with implant placement after ARP. The area under the ROC curve for internal verification was 0.90 (95 %CI: 0.84-0.97), and the H-L goodness of fit test results were χ 2=4.79, P=0.780, indicating a good agreement. The area under the externally verified ROC curve was 0.97 (95 %CI: 0.92-1.00), suggesting that the fitting effect was slightly lower than that of the modeling group, and the predicted value of the model was slightly lower than the true value, which might underestimate the risk of additional surgery in patients. Results:of H-L goodness of fit test were χ 2=5.03, P=0.754. The DCA curve showed that when the probability of high-risk threshold was between 0.06 and 0.93, the clinical application value of the prediction model was higher. Conclusions:Age, smoking, reason of tooth extraction, stage of periodontitis, and maximum AL of the affected teeth were related to the necessity for auxiliary bone grafting with implant placement 6 months after ARP. Smoking was an independent risk factor for auxiliary bone grafting surgery. The constructed nomogram model had good discrimination and consistency.

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.
Carcinogenesis ; Cell Proliferation ; Head and Neck Neoplasms ; Humans ; Nuclear Receptor Subfamily 4, Group A, Member 1 ; Proto-Oncogene Proteins c-ets ; Squamous Cell Carcinoma of Head and Neck ; Transcription Factors