Single-cell and spatial transcriptomics reveal metastasis mechanism and microenvironment remodeling of lymph node in osteosarcoma.

BACKGROUND: Osteosarcoma (OS) is the most common primary malignant bone tumor and is highly prone to metastasis. OS can metastasize to the lymph node (LN) through the lymphatics, and the metastasis of tumor cells reestablishes the immune landscape of the LN, which is conducive to the growth of tumor cells. However, the mechanism of LN metastasis of osteosarcoma and remodeling of the metastatic lymph node (MLN) microenvironment is not clear. METHODS: Single-cell RNA sequencing of 18 samples from paracancerous, primary tumor, and lymph nodes was performed. Then, new signaling axes closely related to metastasis were identified using bioinformatics, in vitro experiments, and immunohistochemistry. The mechanism of remodeling of the LN microenvironment in tumor cells was investigated by integrating single-cell and spatial transcriptomics. RESULTS: From 18 single-cell sequencing samples, we obtained 117,964 cells. The pseudotime analysis revealed that osteoblast(OB) cells may follow a differentiation path from paracancerous tissue (PC) → primary tumor (PT) → MLN or from PC → PT, during the process of LN metastasis. Next, in combination of bioinformatics, in vitro and in vivo experiments, and immunohistochemistry, we determined that ETS2/IBSP, a new signal axis, might promote LN metastasis. Finally, single-cell and spatial dissection uncovered that OS cells could reshape the microenvironment of LN by interacting with various cell components, such as myeloid, cancer-associated fibroblasts (CAFs), and NK/T cells. CONCLUSIONS: Collectively, our research revealed a new molecular mechanism of LN metastasis and clarified how OS cells influenced the LN microenvironment, which might provide new insight for blocking LN metastasis.

Integrating artificial intelligence in osteosarcoma prognosis: the prognostic significance of SERPINE2 and CPT1B biomarkers.

The principal aim of this investigation is to identify pivotal biomarkers linked to the prognosis of osteosarcoma (OS) through the application of artificial intelligence (AI), with an ultimate goal to enhance prognostic prediction. Expression profiles from 88 OS cases and 396 normal samples were procured from accessible public databases. Prognostic models were established using univariate COX regression analysis and an array of AI methodologies including the XGB method, RF method, GLM method, SVM method, and LASSO regression analysis. Multivariate COX regression analysis was also employed. Immune cell variations in OS were examined using the CIBERSORT software, and a differential analysis was conducted. Routine blood data from 20,679 normal samples and 437 OS cases were analyzed to validate lymphocyte disparity. Histological assessments of the study's postulates were performed through immunohistochemistry and hematoxylin and eosin (HE) staining. AI facilitated the identification of differentially expressed genes, which were utilized to construct a prognostic model. This model discerned that the survival rate in the high-risk category was significantly inferior compared to the low-risk cohort (p < 0.05). SERPINE2 was found to be positively associated with memory B cells, while CPT1B correlated positively with CD8 T cells. Immunohistochemical assessments indicated that SERPINE2 was more prominently expressed in OS tissues relative to adjacent non-tumorous tissues. Conversely, CPT1B expression was elevated in the adjacent non-tumorous tissues compared to OS tissues. Lymphocyte counts from routine blood evaluations exhibited marked differences between normal and OS groups (p < 0.001). The study highlights SERPINE2 and CPT1B as crucial biomarkers for OS prognosis and suggests that dysregulation of lymphocytes plays a significant role in OS pathogenesis. Both SERPINE2 and CPT1B have potential utility as prognostic biomarkers for OS.

Predictive Factors for Seizures after Revascularization in Patients with Moyamoya Disease.

BACKGROUND: Moyamoya disease (MMD) is a rare and complex cerebrovascular disorder that is diagnosed through imaging studies, such as computed tomography or magnetic resonance imagin, which show progressive narrowing of the terminal portion of the internal carotid arteries and the development of compensatory capillary collaterals. The objective of our study was to identify and clarify the predictive factors for seizures in patients with MMD. METHODS: From January 2019 to March 2023, a total of 102 patients with MMD were enrolled in this study. Ten patients with seizures after surgery as the main presentation were included. Patients with epilepsy were compared to those without epilepsy in terms of their clinical characteristics. Multivariable analysis was applied to determine factors linked with postoperative seizures. RESULTS: Ten patients developed seizures after revascularization for MMD. Logistic regression analysis revealed that early seizure (odds ratio [OR], 0.068; 95% CI, 0.014-0.342; P = 0.001), cortical involvement (OR, 9.593; 95% CI, 2.256-40.783; P = 0.002), and postoperative hyperperfusion (OR, 7.417; 95% CI, 1.077-51.093; P = 0.042) were significantly associated with seizures. In a multivariate analysis, it was found that early seizures were significantly associated with a higher likelihood of experiencing seizures (OR, 5.88; 95% CI, 1.01-33.96; P = 0.048), while patients who had seizures were more likely to have cortical involvement (OR, 8.90; 95% CI, 1.55-50.96; P = 0.014) or postoperative hyperperfusion (OR, 12.44; 95% CI, 1.21-127.74; P = 0.034). CONCLUSIONS: Epilepsy in patients with MMD link with several clinical factors. In patients with MMD who undergo bypass surgery, early seizures, cortical involvement, and postoperative hyperperfusion are significant independent predictive factors for the development of epilepsy.

COL3A1 is a potential diagnostic biomarker for synovial chondromatosis and affects the cell cycle and migration of chondrocytes.

BACKGROUND: Synovial chondromatosis (SC) primarily affects the major joints and is characterized by the formation of benign cartilaginous nodules. In the present study, we evaluated the differences in the histology and gene expression of SC and normal cartilages and further elucidated the function of hub genes in SC. METHODS: Histological staining and biochemical analysis were performed to measure collagen and glycosaminoglycan (GAG) contents in SC and normal cartilage samples. Then, microarray analysis was performed using knee joint samples (three normal and three SC samples) to identify the differentially expressed genes (DEGs). Subsequently, bioinformatics analysis was performed to identify the hub genes and explore the mechanisms underlying SC. The intersection of the top 10 upregulated DEGs, top 10 downregulated DEGs, and hub genes was validated in SC tissues. Lastly, in vitro experiments and our clinical cohort were used to determine the potential biological functions and diagnostic value, respectively, of the most significant gene. RESULTS: The GAG and collagen contents were comparable to or higher in SC tissues than in normal tissues. Microarray analysis revealed 143 upregulated and 107 downregulated DEGs in SC. Furthermore, functional enrichment analysis revealed an association between immunity and metabolism-related pathways and SC development. Among 20 hub genes, two intersection genes, namely, collagen type III alpha 1 chain (COL3A1) and HSPA8, were notably expressed in SC tissues, with COL3A1 exhibiting a more significant difference in mRNA expression. Furthermore, COL3A1 can promote chondrocyte migration and cell cycle progression. Additionally, clinical data revealed COL3A1 can be a diagnostic marker for primary SC (AUC = 0.82) and be a positive correlation with neutrophil-to-lymphocyte ratio. CONCLUSIONS: These results suggest that SC tissues contained the abundant GAG and collagen. COL3A1 can affect the function of chondrocytes and be a diagnostic marker of primary SC patients. These findings provide a novel approach and a fundamental contribution for diagnosis and treatment in SC.

Exploring the causal relationship between inflammatory cytokines and inflammatory arthritis: A Mendelian randomization study.

OBJECTIVES: Previous studies have reported an association between inflammatory cytokines and inflammatory arthritis, including Ankylosing spondylitis (AS), rheumatoid arthritis (RA), and psoriatic arthritis (PsA). This study aims to explore the causal relationship between inflammatory cytokines and AS, RA, and PsA using Mendelian randomization (MR). METHODS: We conducted a bidirectional two-sample MR analysis using genetic summary data from a publicly available genome-wide association study (GWAS) that included 41 genetic variations of inflammatory cytokines, as well as genetic variant data for AS, RA, and PsA from the FinnGen consortium. The main analysis method used was Inverse variance weighted (IVW) to investigate the causal relationship between exposure and outcome. Additionally, other methods such as MR Egger, weighted median (WM), simple mode, and weighted mode were employed to strengthen the final results. Sensitivity analysis was also performed to ensure the reliability of the findings. RESULTS: The results showed that macrophage colony-stimulating factor (MCSF) was associated with an increased risk of AS (OR = 1.163, 95 % CI = 1.016-1.33, p = 0.028). Conversely, high levels of TRAIL and beta nerve growth factor (ß-NGF) were associated with a decreased risk of AS (OR = 0.892, 95 % CI = 0.81-0.982, p = 0.002; OR = 0.829, 95 % CI = 0.696-0.988, p = 0.036). Four inflammatory cytokines were found to be associated with an increased risk of PsA: vascular endothelial growth factor (VEGF) (OR = 1.161, 95 % CI = 1.057-1.275, p = 0.002); Interleukin 12p70 (IL12p70) (OR = 1.189, 95 % CI = 1.049-1.346, p = 0.007); IL10 (OR = 1.216, 95 % CI = 1.024-1.444, p = 0.026); IL13 (OR = 1.159, 95 % CI = 1.05-1.28, p = 0.004). Interleukin 1 receptor antagonist (IL-1rα) was associated with an increased risk of seropositive RA (OR = 1.181, 95 % CI = 1.044-1.336, p = 0.008). Similarly, genetic susceptibility to inflammatory arthritis was found to be causally associated with multiple inflammatory cytokines. Lastly, the sensitivity analysis supported the robustness of these findings. CONCLUSIONS: This study provides additional insights into the relationship between inflammatory cytokines and inflammatory arthritis, and may offer new clues for the etiology, diagnosis, and treatment of inflammatory arthritis.

Association of survivin positive circulating tumor cell levels with immune escape and prognosis of osteosarcoma.

PURPOSE: Function of survivin protein (encoded by BIRC5) in circulating tumor cells (CTCs) of osteosarcoma (OS) has not been investigated. The goal of this study is to determine whether the expression of survivin protein of CTCs is associated with circulating immune cell infiltration and disease prognosis of OS. METHODS: Blood samples of 20 patients with OS were collected. CanPatrol™ CTC enrichment technology combined with in situ hybridization (ISH) was applied to enrich and test CTCs and survivin protein. Bioinformation analysis combined with data of routine blood test was used to verify the association between survivin and immune cell infiltration in circulatory system. To screen independent prognostic factors, Kaplan-Meier survival curve, univariate and multivariable Cox regression analyses were performed. RESULTS: Bioinformatics analysis showed that BIRC5 was strongly negatively related to lymphocyte, including T cell, NK cell and B cell, which released that BIRC5 played a key role in immune escape via reducing immune cell infiltration in circulatory system. Meanwhile, the number of survivin+ CTCs was significantly negatively connection with lymphocyte count (R = -0.56, p = 0.011), which was consistent with bioinformatics analysis. Kaplan-Meier curve showed that the overall survival rate in high survivin+ CTCs group was significantly lower than low group (88.9% vs 36.4%, p = 0.04). Multivariable Cox regression analyses showed that survivin+ CTCs were an independent prognostic factor (p = 0.019). CONCLUSION: These findings suggested that survivin protein played a key role in immune escape of CTCs and the presence of survivin+ CTCs might be a promising prognostic factor in OS patients.

Predictors of osteoporotic fracture in postmenopausal women: a meta-analysis.

Osteoporosis affects more than 200 million women worldwide, with postmenopausal women being particularly susceptible to this condition and its severe sequelae disproportionately, such as osteoporotic fractures. To date, the current focus has been more on symptomatic treatment, rather than preventive measures. To address this, we performed a meta-analysis aiming to identify potential predictors of osteoporotic fractures in postmenopausal women, with the ultimate goal of identifying high-risk patients and exploring potential therapeutic approaches. We searched Embase, MEDLINE and Cochrane with search terms (postmenopausal AND fracture) AND ("risk factor" OR "predictive factor") in May 2022 for cohort and case-control studies on the predictors of osteoporotic fracture in postmenopausal women. Ten studies with 1,287,021 postmenopausal women were found eligible for analyses, in which the sample size ranged from 311 to 1,272,115. The surveyed date spanned from 1993 to 2021. Our results suggested that age, BMI, senior high school and above, parity ≥ 3, history of hypertension, history of diabetes mellitus, history of alcohol intake, age at menarche ≥ 15, age at menopause < 40, age at menopause > 50, estrogen use and vitamin D supplements were significantly associated with osteoporotic fracture in postmenopausal women. Our findings facilitate the early prediction of osteoporotic fracture in postmenopausal women and may contribute to potential therapeutic approaches. By focusing on preventive strategies and identifying high-risk individuals, we can work toward reducing the burden of osteoporosis-related fractures in this vulnerable population.

Using Machine Learning to Predict Surgical Site Infection After Lumbar Spine Surgery.

Objective: The objective of this study was to utilize machine learning techniques to analyze perioperative factors and identify blood glucose levels that can predict the occurrence of surgical site infection following posterior lumbar spinal surgery. Methods: A total of 4019 patients receiving lumbar internal fixation surgery from an institute were enrolled between June 2012 and February 2021. First, the filtered data were randomized into the test and verification groups. Second, in the test group, specific variables were screened using logistic regression analysis, Lasso regression analysis, support vector machine, and random forest. Specific variables obtained using the four methods were intersected, and a dynamic model was constructed. ROC and calibration curves were constructed to assess model performance. Finally, internal model performance was verified in the verification group using ROC and calibration curves. Results: The data from 4019 patients were collected. In total, 1327 eligible cases were selected. By combining logistic regression analysis with three machine learning algorithms, this study identified four predictors associated with SSI, namely Modic changes, sebum thickness, hemoglobin, and glucose. Using this information, a prediction model was developed and visually represented. Then, we constructed ROC and calibration curves using the test group; the area under the ROC curve was 0.988. Further, calibration curve analysis revealed favorable consistency of nomogram-predicted values compared with real measurements. The C-index of our model was 0.986 (95% CI 0.981-0.994). Finally, we used the validation group to validate the model internally; the AUC was 0.987. Calibration curve analysis revealed favorable consistency of nomogram-predicted values compared with real measurements. The C-index was 0.982 (95% CI 0.974-0.999). Conclusion: Logistic regression analysis and machine learning were employed to select four risk factors: Modic changes, sebum thickness, hemoglobin, and glucose. Then, a dynamic prediction model was constructed to help clinicians simplify the monitoring and prevention of SSI.

To infer the probability of cervical ossification of the posterior longitudinal ligament and explore its impact on cervical surgery.

The ossification of the posterior longitudinal ligament (OPLL) in the cervical spine is commonly observed in degenerative changes of the cervical spine. Early detection of cervical OPLL and prevention of postoperative complications are of utmost importance. We gathered data from 775 patients who underwent cervical spine surgery at the First Affiliated Hospital of Guangxi Medical University, collecting a total of 84 variables. Among these patients, 144 had cervical OPLL, while 631 did not. They were randomly divided into a training cohort and a validation cohort. Multiple machine learning (ML) methods were employed to screen the variables and ultimately develop a diagnostic model. Subsequently, we compared the postoperative outcomes of patients with positive and negative cervical OPLL. Initially, we compared the advantages and disadvantages of various ML methods. Seven variables, namely Age, Gender, OPLL, AST, UA, BMI, and CHD, exhibited significant differences and were used to construct a diagnostic nomogram model. The area under the curve (AUC) values of this model in the training and validation groups were 0.76 and 0.728, respectively. Our findings revealed that 69.2% of patients who underwent cervical OPLL surgery eventually required elective anterior surgery, in contrast to 86.8% of patients who did not have cervical OPLL. Patients with cervical OPLL had significantly longer operation times and higher postoperative drainage volumes compared to those without cervical OPLL. Interestingly, preoperative cervical OPLL patients demonstrated significant increases in mean UA, age, and BMI. Furthermore, 27.1% of patients with cervical anterior longitudinal ligament ossification (OALL) also exhibited cervical OPLL, whereas this occurrence was only observed in 6.9% of patients without cervical OALL. We developed a diagnostic model for cervical OPLL using the ML method. Our findings indicate that patients with cervical OPLL are more likely to undergo posterior cervical surgery, and they exhibit elevated UA levels, higher BMI, and increased age. The prevalence of cervical anterior longitudinal ligament ossification was also significantly higher among patients with cervical OPLL.

Long non-coding RNA and circular RNA and coding RNA profiling of plasma exosomes of osteosarcoma by RNA seq.

Osteosarcoma (OS) is a primary bone tumor with high malignancy and the mechanism of hematogenous metastasis in OS is still not clear. The plasma exosomes derived from osteosarcoma play a key role in the process of tumor metastasis. Here, we established RNA-seq dataset for lncRNAs, circRNAs and mRNAs in plasma exosomes from 10 OS patients and 5 healthy donors. A total of 329.52 Gb of clean data was obtained. Besides, 1754 lincRNAs, 7096 known and 1935 new circRNA was identified. Finally, gene expression profiles and differentially expressed genes (DEGs) were analyzed among these 15 samples. There were 331 DEGs of mRNA, 132 of lincRNA and 489 of circRNA was obtained, respectively. This data set provides a significant resource for relevant researchers to excavate potential dysregulated lncRNAs, circRNAs and mRNAs of plasma exosomes in OS versus normal conditions.

Interaction gene set between osteoclasts and regulatory CD4+ T cells can accurately predict the prognosis of patients with osteosarcoma.

Osteoclasts (OCs) and regulatory CD4+ T cells (CD4+ Tregs) are important components in the tumor microenvironment (TME) of osteosarcoma. In this study, we collected six osteosarcoma samples from our previous study (GSE162454). We also integrated a public database (GSE152048), which included single cell sequencing data of 11 osteosarcoma patients. We obtained 138,192 cells and then successfully identified OCs and CD4+ Tregs. Based on the interaction gene set between OCs and CD4+ Tregs, patients from GSE21257 were distinguished into two clusters by consensus clustering analysis. Both the tumor immune microenvironment and survival prognosis between the two clusters were significantly different. Subsequently, five model genes were identified by protein-protein interaction network based on differentially upregulated genes of cluster 2. Quantitative RT-PCR was used to detect their expression in human osteoblast and osteosarcoma cells. A prognostic model was successfully established using these five genes. Kaplan-Meier survival analysis found that patients in the high-risk group had worse survival (p = 0.029). Therefore, our study first found that cell-cell communication between OCs and CD4+ Tregs significantly alters TME and is connected to poor prognosis of OS. The model we constructed can accurately predict prognosis for osteosarcoma patients.

A novel molecular signature for predicting prognosis and immunotherapy response in osteosarcoma based on tumor-infiltrating cell marker genes.

Background: Tumor infiltrating lymphocytes (TILs), the main component in the tumor microenvironment, play a critical role in the antitumor immune response. Few studies have developed a prognostic model based on TILs in osteosarcoma. Methods: ScRNA-seq data was obtained from our previous research and bulk RNA transcriptome data was from TARGET database. WGCNA was used to obtain the immune-related gene modules. Subsequently, we applied LASSO regression analysis and SVM algorithm to construct a prognostic model based on TILs marker genes. What's more, the prognostic model was verified by external datasets and experiment in vitro. Results: Eleven cell clusters and 2044 TILs marker genes were identified. WGCNA results showed that 545 TILs marker genes were the most strongly related with immune. Subsequently, a risk model including 5 genes was developed. We found that the survival rate was higher in the low-risk group and the risk model could be used as an independent prognostic factor. Meanwhile, high-risk patients had a lower abundance of immune cell infiltration and many immune checkpoint genes were highly expressed in the low-risk group. The prognostic model was also demonstrated to be a good predictive capacity in external datasets. The result of RT-qPCR indicated that these 5 genes have differential expression which accorded with the predicting outcomes. Conclusions: This study developed a new molecular signature based on TILs marker genes, which is very effective in predicting OS prognosis and immunotherapy response.

The efficacy of thoracolumbar interfascial plane block for lumbar spinal surgeries: a systematic review and meta-analysis.

BACKGROUND: The intent of this meta-analysis was to examine the efficacy of thoracolumbar interfascial plane block (TLIP) for pain control after lumbar spinal surgery. METHODS: Randomized controlled trials (RCTs) published on PubMed, CENTRAL, Scopus, Embase, and Web of Science databases up to February 10, 2023, comparing TLIP with no or sham block or wound infiltration for lumbar spinal surgeries were included. Pain scores, total analgesic consumption, and postoperative nausea and vomiting (PONV) were analyzed. RESULTS: Seventeen RCTs were eligible. Comparing TLIP with no block or sham block, the meta-analysis showed a significant decrease of pain scores at rest and movement at 2 h, 8 h, 12 h, and 24 h. Pooled analysis of four studies showed a significant difference in pain scores at rest between TLIP and wound infiltration group at 8 h but not at 2 h, 12 h, and 24 h. Total analgesic consumption was significantly reduced with TLIP block as compared to no block/sham block and wound infiltration. TLIP block also significantly reduced PONV. GRADE assessment of the evidence was moderate. CONCLUSION: Moderate quality evidence indicates that TLIP blocks are effective in pain control after lumbar spinal surgeries. TLIP reduces pain scores at rest and movement for up to 24 h, reduces total analgesic consumption, and the incidence of PONV. However, evidence of its efficacy as compared to wound infiltration of local anesthetics is scarce. Results should be interpreted with caution owing low to moderate quality of the primary studies and marked heterogeneity.

Artificial intelligence reveals dysregulation of osteosarcoma and cuproptosis-related biomarkers, PDHA1, CDKN2A and neutrophils.

At present, the impact of cuproptosis-related genes in the study of osteosarcoma is largely unknown. Genome-wide data of osteosarcoma and controls were downloaded from 3 different databases, and specific diagnostic models associated with cuproptosis in osteosarcoma were constructed by support vector machines with artificial intelligence, random forest trees and LASSO regression. Differential analysis of immune cell infiltration was examined using routine blood data from 25,665 cases. Differential expression was examined using immunohistochemistry and PCR. PDHA1 and CDKN2A were obtained as specific cuproptosis-related biomarkers for osteosarcoma after artificial intelligence analysis. PDHA1, CDKN2A and neutrophils were differentially expressed in OS and control groups. PDHA1 and CDKN2A are significantly dysregulated in OS and are able to serve as biomarkers of OS.

Identification of osteosarcoma m6A-related prognostic biomarkers using artificial intelligence: RBM15.

Osteosarcoma has the worst prognosis among malignant bone tumors, and effective biomarkers are lacking. Our study aims to explore m6A-related and immune-related biomarkers. Gene expression profiles of osteosarcoma and healthy controls were downloaded from multiple public databases, and their m6A-based gene expression was utilized for tumor typing using bioinformatics. Subsequently, a prognostic model for osteosarcoma was constructed using the least absolute shrinkage and selection operator and multivariate Cox regression analysis, and its immune cell composition was calculated using the CIBERSORTx algorithm. We also performed drug sensitivity analysis for these two genes. Finally, analysis was validated using immunohistochemistry. We also examined the RBM15 gene by qRT-PCR in an in vitro experiment. We collected routine blood data from 1738 patients diagnosed with osteosarcoma and 24,344 non-osteosarcoma patients and used two independent sample t tests to verify the accuracy of the CIBERSORTx analysis for immune cell differences. The analysis based on m6A gene expression tumor typing was most reliable using the two typing methods. The prognostic model based on the two genes constituting RNA-binding motif protein 15 (RBM15) and YTDC1 had a much lower survival rate for patients in the high-risk group than those in the low-risk group (P < 0.05). CIBERSORTx immune cell component analysis demonstrated that RBM15 showed a negative and positive correlation with T cells gamma delta and activated natural killer cells, respectively. Drug sensitivity analysis showed that these two genes showed varying degrees of correlation with multiple drugs. The results of immunohistochemistry revealed that the expression of these two genes was significantly higher in osteosarcoma than in paraneoplastic tissues. The results of qRT-PCR experiments showed that the expression of RBM15 was significantly higher in both osteosarcomas than in the control cell lines. Absolute lymphocyte value, lymphocyte percentage, hematocrit and erythrocyte count were lower in osteosarcoma than in the control group (P < 0.001). RBM15 and YTHDC1 can serve as potential prognostic biomarkers associated with m6A in osteosarcoma.

Application of machine learning in prediction of bone cement leakage during single-level thoracolumbar percutaneous vertebroplasty.

BACKGROUND: In the elderly, osteoporotic vertebral compression fractures (OVCFs) of the thoracolumbar vertebra are common, and percutaneous vertebroplasty (PVP) is a common surgical method after fracture. Machine learning (ML) was used in this study to assist clinicians in preventing bone cement leakage during PVP surgery. METHODS: The clinical data of 374 patients with thoracolumbar OVCFs who underwent single-level PVP at The First People's Hospital of Chenzhou were chosen. It included 150 patients with bone cement leakage and 224 patients without it. We screened the feature variables using four ML methods and used the intersection to generate the prediction model. In addition, predictive models were used in the validation cohort. RESULTS: The ML method was used to select five factors to create a Nomogram diagnostic model. The nomogram model's AUC was 0.646667, and its C value was 0.647. The calibration curves revealed a consistent relationship between nomogram predictions and actual probabilities. In 91 randomized samples, the AUC of this nomogram model was 0.7555116. CONCLUSION: In this study, we invented a prediction model for bone cement leakage in single-segment PVP surgery, which can help doctors in performing better surgery with reduced risk.

Analysis of scRNA-seq and bulk RNA-seq demonstrates the effects of EVI2B or CD361 on CD8+ T cells in osteosarcoma.

Osteosarcoma (OS) is a common primary malignant tumor of the bone in children and adolescents. The five-year survival rate is estimated to be ~70% based on the currently available treatment modalities. It is well known that tumor-infiltrating immune cells (TIICs) that are the most important components in the tumor microenvironment can exert a killing effect on tumor cells. Therefore, in the present study, 85 RNA-sequencing OS samples were categorized into high- and low-immune score groups with ESTIAMATE. Based on the immune score groups, 474 differentially expressed genes (DEGs) were acquired using the LIMMA package of R language. Subsequently, 86 DEGs were taken through univariate COX regression analysis, of which 14 were screened out by least absolute shrinkage and selection operator regression analysis. Furthermore, multivariate COX regression analysis was performed to obtain 4 DEGs. Finally, ecotropic virus integration site 2B (EVI2B) or CD361 gene was screened out via Kaplan-Meier analysis. In addition, CIBERSORT algorithm was used to evaluate the proportion of 22 kinds of TIICs in OS. Correlation analysis revealed that the high expression level of EVI2B can elevate the infiltrated proportion of CD8+ T cells. Moreover, analysis of single cell RNA-sequencing transcriptome datasets and immunohistochemical staining uncovered that EVI2B was mainly expressed on CD8+ T cells and that EVI2B could promote the expression of granzyme A and K of CD8+ T cells to exhibit a potent killing effect on tumor cells. Therefore, EVI2B was identified as a protective immune-related gene and contributed to good prognosis in OS patients.

MEG3 alleviates ankylosing spondylitis by suppressing osteogenic differentiation of mesenchymal stem cells through regulating microRNA-125a-5p-mediated TNFAIP3.

Osteoblasts are important regulators of bone formation, but their roles in ankylosing spondylitis (AS) remain unclear. This study aims to explore the role of long non-coding RNA (lncRNA) maternally expressed 3 (MEG3) MEG3 in AS. Serum from AS patients as well as AS mesenchymal stem cells (ASMSCs) and healthy donors mesenchymal stem cells (HDMSCs) was collected. Accordingly, poorly expressed MEG3 and TNF alpha induced protein 3 (TNFAIP3) as well as overexpressed microRNA-125a-5p (miR-125a-5p) were noted in the serum of AS patients and in ASMSCs during the osteogenic induction process. Meanwhile, the interaction among MEG3, miR-125a-5p, and TNFAIP3 was determined and their effect on osteoblast activity was examined in vitro and in vivo. Overexpression of MEG3 and TNFAIP3 or inhibition of miR-125a-5p was found to inactivate the Wnt/ß-catenin pathway, thus suppressing osteogenic differentiation of MSCs. MEG3 competitively bound to miR-125a-5p to increase TNFAIP3 expression, thereby inactivating the Wnt/ß-catenin pathway and repressing the osteogenic differentiation of MSCs. In proteoglycan (PG)-induced AS mouse models, MEG3 also reduced osteogenic activity of MSCs to inhibit AS progression through the miR-125a-5p/TNFAIP3/Wnt/ß-catenin axis. Therefore, up-regulation of MEG3 or depletion of miR-125a-5p holds potential of alleviating AS, which sheds light on a new therapeutic strategy for AS treatment.

Corrigendum: Single-cell transcriptomics reveals the complexity of the tumor microenvironment of treatment-naive osteosarcoma.

[This corrects the article DOI: 10.3389/fonc.2021.709210.].

LncRNA PVT1 regulates biological function of osteoarthritis cells by regulating miR-497/AKT3 axis.

Growing evidence indicates that lncRNAs are involved in the progression of several diseases, including osteoarthritis (OA). However, the role of the lncRNA PVT1 in OA is still unclear. The present study was aimed at exploring the impact of PVT1 on OA progression, along with potential underlying mechanisms. PVT1 expression levels in articular cartilage tissue of OA patients and non-OA patients were evaluated. To assess the proliferation and apoptosis of chondrocytes subject to treatment, PVT1, miR-497, and AKT3 were either knocked down or upregulated in IL-1ß-induced chondrocytes. The variables detected were changes in levels of AKT3 and extracellular matrix (ECM)-related factors (including aggrecan, collagen Type II, and MMP-9). Elevated PVT1 levels were found in cartilage tissue of OA patients and IL-1ß-induced chondrocytes. It was also observed that PVT1 knockdown and miR-497 upregulation led to enhanced cell proliferation and suppressed apoptosis. In addition, a decrease in aggrecan and collagen type II levels and an increase in MMP-9 levels were observed in IL-1ß-induced chondrocytes. A dual luciferase reporter assay was performed to identify the factors that interacted with miR-497, PVT1, and AKT3. It was observed through rescue experiments that enhancing AKT3 expression or knocking down miR-497 could reverse the impacts of PVT1 knockdown in IL-1ß-induced chondrocytes. An upregulation of PVT1 is observed in OA patients. On the other hand, PVT1 knockdown can decrease the effects of IL-1ß on the proliferation, apoptosis, and expression of ECM-related proteins of chondrocytes through the regulation of the miR-497/AKT3 axis. PVT1 levels are elevated in the cartilage tissue of OA patients and IL-1ß-induced chondrocytes. PVT1 knockdown alleviates the effects of IL-1ß treatment on the proliferation and apoptosis of chondrocytes and ECM degradation in chondrocytes by regulating the miR-497/AKT3 axis.