Integrated single-cell and bulk RNA sequencing identifies POSTN as a potential biomarker and therapeutic target for rheumatoid arthritis.

BACKGROUND: This study aimed to integrate single-cell RNA sequencing (scRNA-seq) and bulk RNA-seq data to identify potential biomarkers and therapeutic targets for rheumatoid arthritis (RA). METHOD: Firstly, we obtained the synovial scRNA-seq data from the Immport database and bulk RNA-seq data from the Gene Expression Omnibus (GEO) database. Then, we used weighted gene correlation network analysis (WGCNA) to screen for module genes most relevant to RA and intersected them with the differentially expressed genes (DEGs) obtained from scRNA-seq and bulk RNA-seq to obtain intersecting genes. Next, we constructed a protein-protein interaction (PPI) network of hub genes using the STRING database and Cytoscape software and validated its expression using external validation cohorts. Finally, we performed immune cell infiltration analysis using CIBERSORT and explored the expression and drug binding activity of key gene using clinical samples and molecular docking, respectively. RESULT: We identified six cellular subgroups through dimensionality reduction and clustering, and fibroblasts may be the most important cell cluster in RA based on pseudotime and cell-cell communication analyses. Subsequently, we intersected module genes with DEGs obtained from scRNA-seq and bulk RNA-seq and constructed a PPI network of hub genes (BGN, COL11A1, COL1A1, GUCY1A1, POSTN). In external validation cohorts, POSTN was highly expressed and demonstrated the highest diagnostic performance (AUC = 0.716). In subsequent analyses, we defined POSTN as a key gene and found that its expression level was positively correlated with M2 macrophages in immune cell infiltration analysis. Additionally, POSTN was upregulated in clinical samples and exhibited favorable binding activity with nine anti-rheumatoid arthritis drugs (affinity ≤ -6.0 kcal/mol). CONCLUSION: Through bioinformatics analysis, clinical sample validation, and molecular docking, we found that POSTN was highly expressed in RA and stably bound to common anti-rheumatoid arthritis drugs, which will provide new insights into potential biomarkers and therapeutic targets for RA.

Reliability and accuracy of intraoperative fluoroscopy assessment of acetabular cup anteversion in supine direct anterior approach total hip arthroplasty.

Poor implantation positioning of hip prostheses is considered the primary factor affecting postoperative joint wear. Cup anteversion in direct anterior approach (DAA) total hip arthroplasty (THA) is often excessive. Intraoperative fluoroscopy (IF) are effective for improving implant placement accuracy. This study aimed to analyze IF's reliability and accuracy in assessing intraoperative anteversion. Sixty-two consecutive hips underwent primary THA utilizing DAA alongside IF for cup placement. Intraoperative anteversion was measured using IF images, while postoperative CT and standard anteroposterior (AP) radiographs were used to calculate true anteversion component angles. Differences and correlations between intraoperative and true anteversions were analyzed, and intraclass correlation coefficients (ICC) determined the inter- and intra-observer reliabilities. Excellent intra- and inter-observer reliabilities were observed for all radiographic and CT methods (ICC > 0.9). Strong correlations (PCC > 0.6) existed between anteversion measured on IF image and postoperative CT and AP pelvic measurements. Intraoperative anteversion measured on IF images (16.8 ± 3.2°) was smaller than anteversion measured postoperatively on AP X-rays (21.3 ± 4.7°, P < 0.001) and CT (22.0 ± 4.9°, P < 0.001), with average differences of 4.5°and 5.3°, respectively. Under several influencing factors, the accuracy of IF in assessing cup anteversion in DAA-THA may be limited. However, this still requires large-sample experiments for verification.

Development and validation of multiple linear regression models for predicting total hip arthroplasty acetabular prosthesis.

PURPOSE: To establish a multivariate linear equation to predict the diameter (outer diameter) of the acetabular prosthesis used in total hip arthroplasty. METHODS: A cohort of 258 individuals who underwent THA at our medical facility were included in this study. The independent variables encompassed the patients' height, weight, foot length, gender, age, and surgical access. The dependent variable in this study was the diameter of the acetabular prosthesis utilized during the surgical procedure. The entire cohort dataset was randomly partitioned into a training cohort and a validation cohort, with a ratio of 7:3, employing the SPSS 26.0 software. Pearson correlation analysis was conducted to examine the relationships between the patients' height, weight, foot length, gender, age, surgical access, and the diameter of the acetabular prosthesis in the training cohort. Additionally, a multiple linear regression equation was developed using the independent variables from the training cohort and the diameter of the acetabular prosthesis as the dependent variable. This equation aimed to predict the diameter of the acetabular prosthesis based on the patients' characteristics. The accuracy of the equation was evaluated by substituting the data of the validation cohort into the multiple linear equation. The predicted acetabular prosthesis diameters were then compared with the actual diameters used in the operation. RESULTS: The correlation analysis conducted on the training cohort revealed that surgical access (r = 0.054) and age (r = -0.120) exhibited no significant correlation with the diameter of the acetabular prosthesis utilized during the intraoperative procedure. Conversely, height (r = 0.687), weight (r = 0.654), foot length (r = 0.687), and sex (r = 0.354) demonstrated a significant correlation with the diameter of the acetabular prosthesis used intraoperatively. Furthermore, a predictive equation, denoted as Y (acetabular prosthesis diameter in mm) = 20.592 + 0.548 × foot length (cm) + 0.083 × height (cm) + 0.077 × weight (kg), was derived. This equation accurately predicted the diameter within one size with an accuracy rate of 64.94% and within two sizes with an accuracy rate of 94.81%. CONCLUSION: Anthropometric data can accurately predict the diameter of acetabular prosthesis during total hip arthroplasty.

Development and validation of a prognostic nomogram for bone metastasis from lung cancer: A large population-based study.

Background: Bone is one of the most common metastatic sites of advanced lung cancer, and the median survival time is significantly shorter than that of patients without metastasis. This study aimed to identify prognostic factors associated with survival and construct a practical nomogram to predict overall survival (OS) in lung cancer patients with bone metastasis (BM). Methods: We extracted the patients with BM from lung cancer between 2011 and 2015 from the Surveillance, Epidemiology, and End Result (SEER) database. Univariate and multivariate Cox regressions were performed to identify independent prognostic factors for OS. The variables screened by multivariate Cox regression analysis were used to construct the prognostic nomogram. The performance of the nomogram was assessed by receiver operating characteristic (ROC) curve, concordance index (C-index), and calibration curves, and decision curve analysis (DCA) was used to assess its clinical applicability. Results: A total of 7861 patients were included in this study and were randomly divided into training (n=5505) and validation (n=2356) cohorts using R software in a ratio of 7:3. Cox regression analysis showed that age, sex, race, grade, tumor size, histological type, T stage, N stage, surgery, brain metastasis, liver metastasis, chemotherapy and radiotherapy were independent prognostic factors for OS. The C-index was 0.723 (95% CI: 0.697-0.749) in the training cohorts and 0.738 (95% CI: 0.698-0.778) in the validation cohorts. The AUC of both the training cohorts and the validation cohorts at 3-month (0.842 vs 0.859), 6-month (0.793 vs 0.814), and 1-year (0.776 vs 0.788) showed good predictive performance, and the calibration curves also demonstrated the reliability and stability of the model. Conclusions: The nomogram associated with the prognosis of BM from lung cancer was a reliable and practical tool, which could provide risk assessment and clinical decision-making for individualized treatment of patients.