Targeting TUBB3 Suppresses Anoikis Resistance and Bone Metastasis in Prostate Cancer.

Bone metastases occur in more than 70% of advanced prostate cancer (PCa) patients, leading to a poor prognosis. Resistance to detachment-induced apoptosis, also known as anoikis, plays a crucial role in the onset of tumor metastasis. Targeting anoikis resistance is of immense therapeutic significance in repression of metastatic spread. In this study, based on an anoikis-related prognostic risk model of PCa, this study identifies TUBB3 as a key anoikis-related prognostic gene that is highly expressed in bone metastatic PCa. TUBB3 expression is increased in anoikis-resistant PCa cells, and TUBB3 depletion significantly reverses anoikis resistance during extracellular matrix (ECM) detachment and inhibits anoikis-resistance-induced PCa cell invasion and migration as well as epithelial-mesenchymal transition (EMT) process. TUBB3 knockdown significantly reduces αvß3/FAK/Src axis activation, blocking its downstream oncogenic signaling. In addition, this work develops bone-targeting lipid nanoparticles (BT-LNP) based on bisphosphonate-modified ionizable lipid for systemic delivery of siRNA targeting TUBB3 (siTUBB3). BT-LNP-delivered siTUBB3 therapy with localization in the bone microenvironment significantly attenuate PCa bone metastasis progression in vivo upon intravenous administration. These findings pinpoint that TUBB3, as a key regulator of anoikis resistance, is an effective therapeutic target in bone metastatic PCa and that BT-LNP-mediated systemic delivery of siTUBB3 can be developed as a novel therapeutic strategy for this disease.

Establishment and validation of an interactive artificial intelligence platform to predict postoperative ambulatory status for patients with metastatic spinal disease: a multicenter analysis.

BACKGROUND: Identification of patients with high-risk of experiencing inability to walk after surgery is important for surgeons to make therapeutic strategies for patients with metastatic spinal disease. However, there is a lack of clinical tool to assess postoperative ambulatory status for those patients. The emergence of artificial intelligence (AI) brings a promising opportunity to develop accurate prediction models. METHODS: This study collected 455 patients with metastatic spinal disease who underwent posterior decompressive surgery at three tertiary medical institutions. Of these, 220 patients were collected from one medical institution to form the model derivation cohort, while 89 and 146 patients were collected from two other medical institutions to form the external validation cohorts 1 and 2, respectively. Patients in the model derivation cohort were used to develop and internally validate models. To establish the interactive AI platform, machine learning techniques were used to develop prediction models, including logistic regression (LR), decision tree (DT), random forest (RF), extreme gradient boosting machine (eXGBM), support vector machine (SVM), and neural network (NN). Furthermore, to enhance the resilience of the study's model, an ensemble machine learning approach was employed using a soft-voting method by combining the results of the above six algorithms. A scoring system incorporating 10 evaluation metrics was used to comprehensively assess the prediction performance of the developed models. The scoring system had a total score of 0 to 60, with higher scores denoting better prediction performance. An interactive AI platform was further deployed via Streamlit. The prediction performance was compared between medical experts and the AI platform in assessing the risk of experiencing postoperative inability to walk among patients with metastatic spinal disease. RESULTS: Among all developed models, the ensemble model outperformed the six other models with the highest score of 57, followed by the eXGBM model (54), SVM model (50), and NN model (50). The ensemble model had the best performance in accuracy and calibration slope, and the second-best performance in precise, recall, specificity, area under the curve (AUC), Brier score, and log loss. The scores of the LR model, RF model, and DT model were 39, 46, and 26, respectively. External validation demonstrated that the ensemble model had an AUC value of 0.873 (95% CI: 0.809-0.936) in the external validation cohort 1 and 0.924 (95% CI: 0.890-0.959) in the external validation cohort 2. In the new ensemble machine learning model excluding the feature of the number of comorbidities, the AUC value was still as high as 0.916 (95% CI: 0.863-0.969). In addition, the AUC values of the new model were 0.880 (95% CI: 0.819-0.940) in the external validation cohort 1 and 0.922 (95% CI: 0.887-0.958) in the external validation cohort 2, indicating favorable generalization of the model. The interactive AI platform was further deployed online based on the final machine learning model, and it was available at https://postoperativeambulatory-izpdr6gsxxwhitr8fubutd.streamlit.app/ . By using the AI platform, researchers were able to obtain the individual predicted risk of postoperative inability to walk, gain insights into the key factors influencing the outcome, and find the stratified therapeutic recommendations. The AUC value obtained from the AI platform was significantly higher than the average AUC value achieved by the medical experts ( P <0.001), denoting that the AI platform obviously outperformed the individual medical experts. CONCLUSIONS: The study successfully develops and validates an interactive AI platform for evaluating the risk of postoperative loss of ambulatory ability in patients with metastatic spinal disease. This AI platform has the potential to serve as a valuable model for guiding healthcare professionals in implementing surgical plans and ultimately enhancing patient outcomes.

Development and validation of a web-based artificial intelligence prediction model to assess massive intraoperative blood loss for metastatic spinal disease using machine learning techniques.

BACKGROUND CONTEXT: Intraoperative blood loss is a significant concern in patients with metastatic spinal disease. Early identification of patients at high risk of experiencing massive intraoperative blood loss is crucial as it allows for the development of appropriate surgical plans and facilitates timely interventions. However, accurate prediction of intraoperative blood loss remains limited based on prior studies. PURPOSE: The purpose of this study was to develop and validate a web-based artificial intelligence (AI) model to predict massive intraoperative blood loss during surgery for metastatic spinal disease. STUDY DESIGN/SETTING: An observational cohort study. PATIENT SAMPLE: Two hundred seventy-six patients with metastatic spinal tumors undergoing decompressive surgery from two hospitals were included for analysis. Of these, 200 patients were assigned to the derivation cohort for model development and internal validation, while the remaining 76 were allocated to the external validation cohort. OUTCOME MEASURES: The primary outcome was massive intraoperative blood loss defined as an estimated blood loss of 2,500 cc or more. METHODS: Data on patients' demographics, tumor conditions, oncological therapies, surgical strategies, and laboratory examinations were collected in the derivation cohort. SMOTETomek resampling (which is a combination of Synthetic Minority Oversampling Technique and Tomek Links Undersampling) was performed to balance the classes of the dataset and obtain an expanded dataset. The patients were randomly divided into two groups in a proportion of 7:3, with the most used for model development and the remaining for internal validation. External validation was performed in another cohort of 76 patients with metastatic spinal tumors undergoing decompressive surgery from a teaching hospital. The logistic regression (LR) model, and five machine learning models, including K-Nearest Neighbor (KNN), Decision Tree (DT), XGBoosting Machine (XGBM), Random Forest (RF), and Support Vector Machine (SVM), were used to develop prediction models. Model prediction performance was evaluated using area under the curve (AUC), recall, specificity, F1 score, Brier score, and log loss. A scoring system incorporating 10 evaluation metrics was developed to comprehensively evaluate the prediction performance. RESULTS: The incidence of massive intraoperative blood loss was 23.50% (47/200). The model features were comprised of five clinical variables, including tumor type, smoking status, Eastern Cooperative Oncology Group (ECOG) score, surgical process, and preoperative platelet level. The XGBM model performed the best in AUC (0.857 [95% CI: 0.827, 0.877]), accuracy (0.771), recall (0.854), F1 score (0.787), Brier score (0.150), and log loss (0.461), and the RF model ranked second in AUC (0.826 [95% CI: 0.793, 0.861]) and precise (0.705), whereas the AUC of the LR model was only 0.710 (95% CI: 0.665, 0.771), the accuracy was 0.627, the recall was 0.610, and the F1 score was 0.617. According to the scoring system, the XGBM model obtained the highest total score of 55, which signifies the best predictive performance among the evaluated models. External validation showed that the AUC of the XGBM model was also up to 0.809 (95% CI: 0.778, 0.860) and the accuracy was 0.733. The XGBM model, was further deployed online, and can be freely accessed at https://starxueshu-massivebloodloss-main-iudy71.streamlit.app/. CONCLUSIONS: The XGBM model may be a useful AI tool to assess the risk of intraoperative blood loss in patients with metastatic spinal disease undergoing decompressive surgery.

A de novo homozygous missense mutation of the GUSB gene leads to mucopolysaccharidosis type VII identification in a family with twice adverse pregnancy outcomes due to non-immune hydrops fetalis.

Non-immune hydrops fetalis (NIHF) is a common and severe manifestation of many genetic disorders. The ultrasound is an ideal method for diagnosing hydrops fetalis during pregnancy. Since most NIHFs do not have an identifiable cause, determining the underlying etiology remains a challenge for prenatal counseling. Due to advancements in exome sequencing, the diagnostic rates of NIHF have recently increased. As reported here, DNA was extracted from the amniotic fluid of a pregnant woman who was prenatally diagnosed with a NIHF type of unclear origin. Amniocentesis sampling demonstrated a normal female karyotype and copy number variation(CNVs) without alterations. Tri-whole exome sequencing (WES) was conducted to identify possible causative variants. In the fetus, a de novo genetic mutation was identified as a homozygous form. The mutation was located on the glucuronidase beta (GUSB) gene: NM_000181.3: c.1324G > A; p. Ala442Thr; Chr7:65439349, which leads to mucopolysaccharidosis type VII. This mutation was inherited from the parents and was first reported to be related to NIHF. We conclude that the use of WES is beneficial for NIHF cases whose prognosis has not been explained by standard genetic testing.

Mini-open compared with the trans-tubular approach in patients with spinal metastases underwent decompression surgery---a retrospective cohort study.

BACKGROUND: This study aimed to evaluate the perioperative safety and efficacy of the Mini-open and trans-tubular approach in patients with spinal metastases who underwent decompression surgery. METHODS: 37 consecutive patients with spinal metastases who underwent decompression surgery through a Mini-open or trans-tubular approach were retrospectively reviewed between June 2017 and June 2022. Thirty-four patients were included in this study. 19 underwent decompression surgery through the Mini-open approach, and 15 underwent the Trans-tubular approach. T-test and chi-square test were used to evaluate the difference between baseline data and primary and secondary outcomes. RESULTS: Baseline characteristics did not differ significantly between Trans-tubular and Mini-open groups except for the Ambulatory status (P < 0.001). There was no significant difference in blood loss between the two groups (P = 0.061). Operative time, intraoperative blood transfusion, intraoperative complication (dural tear), and postoperative hospitalization were comparable in the two groups (P > 0.05). The trans-tubular group had significantly less amount of postoperative drainage (133.5 ± 30.9 ml vs. 364.5 ± 64.2 ml, p = 0.003), and the time of drainage (3.1 ± 0.2 days vs. 4.6 ± 0.5 days, p = 0.019) compared with Mini-open group (P < 0.05). Sub-group analysis showed that for patients with hypo-vascular tumors, the Trans-tubular group had significantly less blood loss than the Mini-open group (951.1 ± 171.7 ml vs. 1599.1 ± 105.7 ml, P = 0.026). CONCLUSIONS: Decompression through Mini-open or Trans-tubular was safe and effective for patients with spinal metastases. The trans-tubular approach might be more suitable for patients with hypo-vascular tumors.

Red blood cell distribution width as a prognostic factor in patients with aplastic anemia treated with cyclosporin A plus androgen or cyclosporine A alone: a retrospective study.

OBJECTIVE: To explore the prognostic value of red blood cell distribution width (RDW) in newly diagnosed aplastic anemia (AA) patients treated with cyclosporine A (CsA) plus androgen or CsA alone. METHODS: We retrospectively analyzed the clinical outcome of 220 patients with AA. According to the baseline level of RDW before treatment, the patients were divided into the high-RDW group (RDW ≥ 15%) and the normal-RDW group (RDW < 15%). RESULTS: The median RDW of non-severe AA (NSAA) and severe AA (SAA) patients was 15.65% and 15.35%, respectively; this were significantly higher than that of very severe AA (VSAA) patients (13.35%). With median follow-up being 46 months, AA patients in the high-RDW group showed better 5-year OS and PFS than the normal-RDW group (93%: 75.3%; 74.3%: 61%). There was a higher ORR in the high-RDW group than the normal-RDW group (68.7%: 52.3%). The ORR of NSAA patients in the high-RDW group was better than that in the normal RDW group (75.8%: 60%). The 5-year OS of SAA/VSAA patients in the high-RDW group was significantly higher than the normal-RDW group (81.8%: 50.8%). CONCLUSION: This is the first documentation on the prognostic value of RDW in AA patients receiving CsA treatment with long-term follow-up, which had shown that high RDW at diagnosis was a better prognostic factor.

Expression and prognostic value of C-reactive protein in adult immune thrombocytopenia (ITP) patients.

The aim of this study was to investigate the effect of C-reactive protein (CRP) on the prognosis of adult patients with Immune thrombocytopenia purpura (ITP). A retrospective study of 628 adult ITP patients, as well as 100 healthy and 100 infected patients, attending the Affiliated Hospital of Xuzhou Medical University from January 2017 to June 2022 was performed. The ITP patients were grouped according to their CRP levels, and the differences in clinical characteristics of each group and the influencing factors of efficacy in newly diagnosed ITP patients were analyzed. CRP levels were significantly higher in the ITP and infected groups compared with healthy controls (P < 0.001), and platelet counts were significantly lower in the ITP group (P < 0.001). Between the CRP normal and elevated group, their age, white blood cell count, neutrophil count, lymphocyte count, red blood cell count, hemoglobin, platelet count, complement C3 and C4, PAIgG, bleeding score, proportion of severe ITP, and proportion of refractory ITP were significantly different (P < 0.05). Patients of severe ITP (P < 0.001), refractory ITP (P = 0.002), and with active bleeding (P < 0.001) had significantly higher CRP levels. Patients with no response after treatment had significantly higher CRP levels than those who achieved CR or R (P < 0.001). Platelet counts (r = - 0.261, P < 0.001) in newly diagnosed ITP patients and treatment outcomes (r = - 0.221, P < 0.001) were negatively correlated with CRP levels, and bleeding score was positively correlated with CRP levels (r = 0.207, P < 0.001). Treatment outcome was positively correlated with decrease in CRP levels (r = 0.313, P = 0.027). A multifactorial regression analysis of the influencing factors of treatment outcomes on newly diagnosed patients found that CRP was an independent risk factor of the prognosis (P = 0.011). In conclusion, CRP can help assess the severity and predict the prognosis of ITP patients.

Development of a web-based calculator to predict three-month mortality among patients with bone metastases from cancer of unknown primary: An internally and externally validated study using machine-learning techniques.

Background: Individualized therapeutic strategies can be carried out under the guidance of expected lifespan, hence survival prediction is important. Nonetheless, reliable survival estimation in individuals with bone metastases from cancer of unknown primary (CUP) is still scarce. The objective of the study is to construct a model as well as a web-based calculator to predict three-month mortality among bone metastasis patients with CUP using machine learning-based techniques. Methods: This study enrolled 1010 patients from a large oncological database, the Surveillance, Epidemiology, and End Results (SEER) database, in the United States between 2010 and 2018. The entire patient population was classified into two cohorts at random: a training cohort (n=600, 60%) and a validation cohort (410, 40%). Patients from the validation cohort were used to validate models after they had been developed using the four machine learning approaches of random forest, gradient boosting machine, decision tree, and eXGBoosting machine on patients from the training cohort. In addition, 101 patients from two large teaching hospital were served as an external validation cohort. To evaluate each model's ability to predict the outcome, prediction measures such as area under the receiver operating characteristic (AUROC) curves, accuracy, and Youden index were generated. The study's risk stratification was done using the best cut-off value. The Streamlit software was used to establish a web-based calculator. Results: The three-month mortality was 72.38% (731/1010) in the entire cohort. The multivariate analysis revealed that older age (P=0.031), lung metastasis (P=0.012), and liver metastasis (P=0.008) were risk contributors for three-month mortality, while radiation (P=0.002) and chemotherapy (P<0.001) were protective factors. The random forest model showed the highest area under curve (AUC) value (0.796, 95% CI: 0.746-0.847), the second-highest precision (0.876) and accuracy (0.778), and the highest Youden index (1.486), in comparison to the other three machine learning approaches. The AUC value was 0.748 (95% CI: 0.653-0.843) and the accuracy was 0.745, according to the external validation cohort. Based on the random forest model, a web calculator was established: https://starxueshu-codeok-main-8jv2ws.streamlitapp.com/. When compared to patients in the low-risk groups, patients in the high-risk groups had a 1.99 times higher chance of dying within three months in the internal validation cohort and a 2.37 times higher chance in the external validation cohort (Both P<0.001). Conclusions: The random forest model has promising performance with favorable discrimination and calibration. This study suggests a web-based calculator based on the random forest model to estimate the three-month mortality among bone metastases from CUP, and it may be a helpful tool to direct clinical decision-making, inform patients about their prognosis, and facilitate therapeutic communication between patients and physicians.

Manual Homogenization Improves the Sensitivity of Microbiological Culture for Patients with Pyogenic Spondylitis.

Objective: This study aimed to investigate the effects of manual homogenization on the sensitivity of microbiological culture for patients with pyogenic spondylitis. Methods: From October 2018 to March 2021, patients undergoing fluoroscopy-guided biopsy or open debridement due to pyogenic spondylitis were recruited. Their demographic data and baseline characteristics were recorded. Tissue samples were obtained through fluoroscopy-guided biopsy or open debridement. Tissue samples were divided into three parts: manual homogenization (MH), manual mixture (MM), and pathological examination. Sterile normal saline was set as the negative control to exclude false-positive culture results. The Chi-square test was used to detect the difference of microbiological culture results. Results: Twenty-four consecutive patients (33 tissue cultures) with pyogenic spondylitis treated in our department between October 2018 and March 2021 were recruited in this study. The average age was 61.7±3.2 years old and 10 patients were female. The MH group had a significantly higher positive rate compared with the MM group in aerobic conditions: 78.8% (26 isolates) vs 54.5% (18 isolates), P=0.037 and anaerobic condition: 63.6% (21 isolates) vs 39.4% (13 isolates), P=0.049. The results of subgroup analyses showed that MH could improve the culture sensitivity for patients with previous antibiotics use and without paravertebral abscesses but not reach a significant level on statistics. Conclusion: Based on the present study, manual homogenization could improve the sensitivity of microbiological cultures for patients with pyogenic spondylitis.

Machine learning approaches for prediction of early death among lung cancer patients with bone metastases using routine clinical characteristics: An analysis of 19,887 patients.

Purpose: Bone is one of the most common sites for the spread of malignant tumors. Patients with bone metastases whose prognosis was shorter than 3 months (early death) were considered as surgical contraindications. However, the information currently available in the literature limits our capacity to assess the risk likelihood of 3 month mortality. As a result, the study's objective is to create an accurate prediction model utilizing machine-learning techniques to predict 3 month mortality specifically among lung cancer patients with bone metastases according to easily available clinical data. Methods: This study enrolled 19,887 lung cancer patients with bone metastases between 2010 and 2018 from a large oncologic database in the United States. According to a ratio of 8:2, the entire patient cohort was randomly assigned to a training (n = 15881, 80%) and validation (n = 4,006, 20%) group. In the training group, prediction models were trained and optimized using six approaches, including logistic regression, XGBoosting machine, random forest, neural network, gradient boosting machine, and decision tree. There were 13 metrics, including the Brier score, calibration slope, intercept-in-large, area under the curve (AUC), and sensitivity, used to assess the model's prediction performance in the validation group. In each metric, the best prediction effectiveness was assigned six points, while the worst was given one point. The model with the highest sum score of the 13 measures was optimal. The model's explainability was performed using the local interpretable model-agnostic explanation (LIME) according to the optimal model. Predictor importance was assessed using H2O automatic machine learning. Risk stratification was also evaluated based on the optimal threshold. Results: Among all recruited patients, the 3 month mortality was 48.5%. Twelve variables, including age, primary site, histology, race, sex, tumor (T) stage, node (N) stage, brain metastasis, liver metastasis, cancer-directed surgery, radiation, and chemotherapy, were significantly associated with 3 month mortality based on multivariate analysis, and these variables were included for developing prediction models. With the highest sum score of all the measurements, the gradient boosting machine approach outperformed all the other models (62 points), followed by the XGBooting machine approach (59 points) and logistic regression (53). The area under the curve (AUC) was 0.820 (95% confident interval [CI]: 0.807-0.833), 0.820 (95% CI: 0.807-0.833), and 0.815 (95% CI: 0.801-0.828), respectively, calibration slope was 0.97, 0.95, and 0.96, respectively, and accuracy was all 0.772. Explainability of models was conducted to rank the predictors and visualize their contributions to an individual's mortality outcome. The top four important predictors in the population according to H2O automatic machine learning were chemotherapy, followed by liver metastasis, radiation, and brain metastasis. Compared to patients in the low-risk group, patients in the high-risk group were more than three times the odds of dying within 3 months (P < 0.001). Conclusions: Using machine learning techniques, this study offers a number of models, and the optimal model is found after thoroughly assessing and contrasting the prediction performance of each model. The optimal model can be a pragmatic risk prediction tool and is capable of identifying lung cancer patients with bone metastases who are at high risk for 3 month mortality, informing risk counseling, and aiding clinical treatment decision-making. It is better advised for patients in the high-risk group to have radiotherapy alone, the best supportive care, or minimally invasive procedures like cementoplasty.

Machine learning in predicting cardiac surgery-associated acute kidney injury: A systemic review and meta-analysis.

Background: Cardiac surgery-associated acute kidney injury (CSA-AKI) is a common complication following cardiac surgery. Early prediction of CSA-AKI is of great significance for improving patients' prognoses. The aim of this study is to systematically evaluate the predictive performance of machine learning models for CSA-AKI. Methods: Cochrane Library, PubMed, EMBASE, and Web of Science were searched from inception to 18 March 2022. Risk of bias assessment was performed using PROBAST. Rsoftware (version 4.1.1) was used to calculate the accuracy and C-index of CSA-AKI prediction. The importance of CSA-AKI prediction was defined according to the frequency of related factors in the models. Results: There were 38 eligible studies included, with a total of 255,943 patients and 60 machine learning models. The models mainly included Logistic Regression (n = 34), Neural Net (n = 6), Support Vector Machine (n = 4), Random Forest (n = 6), Extreme Gradient Boosting (n = 3), Decision Tree (n = 3), Gradient Boosted Machine (n = 1), COX regression (n = 1), κNeural Net (n = 1), and Naïve Bayes (n = 1), of which 51 models with intact recording in the training set and 17 in the validating set. Variables with the highest predicting frequency included Logistic Regression, Neural Net, Support Vector Machine, and Random Forest. The C-index and accuracy wer 0.76 (0.740, 0.780) and 0.72 (0.70, 0.73), respectively, in the training set, and 0.79 (0.75, 0.83) and 0.73 (0.71, 0.74), respectively, in the test set. Conclusion: The machine learning-based model is effective for the early prediction of CSA-AKI. More machine learning methods based on noninvasive or minimally invasive predictive indicators are needed to improve the predictive performance and make accurate predictions of CSA-AKI. Logistic regression remains currently the most commonly applied model in CSA-AKI prediction, although it is not the one with the best performance. There are other models that would be more effective, such as NNET and XGBoost. Systematic review registration: https://www.crd.york.ac.uk/; review registration ID: CRD42022345259.

Key Prognostic Value of Lysosomal Protein Transmembrane 5 in Kidney Renal Clear Cell Carcinoma.

Objective: To investigate the significance of lysosomal protein transmembrane 5 (LAPTM5) in kidney renal clear cell carcinoma (KIRC). Methods: Bioinformatics analysis as an efficient and accurate method was employed to explore the expression levels, prognostic significance, and regulatory pathways of LAPTM5 in KIRC. Finally, the association of LAPTM5 with tumor immune infiltrates was initially investigated. Results: High LAPTM5 expression was observed in KIRC, and its mRNA expression was correlated with gender, stage, and grade (all P < 0.05) but regardless of age. Besides, high LAPTM5 mRNA expression predicted poor overall survival (OS) of KIRC patients (P < 0.01). Further, Cox regression analysis revealed the independent prognostic value of LAPTM5 for OS in KIRC patients (P < 0.001). In addition, the genetic alteration frequency of LAPTM5 was low and had no significant impact on KIRC patient prognosis. However, the low methylation levels of the two methylated sites in the LAPTM5 gene was closely linked to poor OS (all P < 0.05). Kyoto Encyclopedia of Genes and Genomes (KEGG) and gene set enrichment analysis (GSEA) results showed that the common regulatory pathway was immune- and inflammatory-related pathway. Moreover, LAPTM5 was also associated with tumor immune infiltrates (all P < 0.001). Conclusion: LAPTM5 served as an independent prognostic factor for KIRC patients. LAPTM5 might affect the OS of KIRC patients through the involvement of the immune-related pathway. Therefore, LAPTM5 served as a potential biomarker for OS of KIRC patients.

Risk factors for predicting cement leakage in percutaneous vertebroplasty for spinal metastases.

BACKGROUND: The study aimed to identify the risk factors of cement leakage following percutaneous vertebroplasty for spinal metastases. METHODS: 230 consecutive patients with 530 vertebrae were retrospectively reviewed. Characteristics including age, primary cancer, location, pathological fracture, the integrity of the posterior wall, and the volume of bone cement were considered as potential risk factors. Cement leakage was evaluated by postoperative imaging examination and classified into three subtypes with different potential sequelae: spinal canal leakage, intravascular leakage around vertebrae, intradiscal and paravertebral leakage. Univariate and multivariate analyses were used to assess the risk factors. RESULTS: Leakage was detected in 185 vertebrae (34.9%), 18.3% for intradiscal and paravertebral, 13.2% for intravascular around vertebrae, and 7.0% for spinal canal. Multivariate analysis showed that incomplete posterior wall (P = 0.001) and breast cancer (P = 0.015) were strong predictive factors for spinal canal leakage, incomplete posterior wall (P = 0.024) was for intravascular leakage around vertebrae, thoracic (P = 0.010) and pathological fracture (P = 0.000) were for intradiscal and paravertebral leakage. CONCLUSIONS: Our findings suggest that cement leakage is common following percutaneous vertebroplasty for spinal metastases. The incomplete posterior wall is an unfavourable factor for intravascular leakage around vertebrae. Vertebrae with incomplete posterior wall and breast cancer metastases are more likely to develop spinal canal leakage.

SPP1 Promotes Enzalutamide Resistance and Epithelial-Mesenchymal-Transition Activation in Castration-Resistant Prostate Cancer via PI3K/AKT and ERK1/2 Pathways.

The bottleneck arising from castration-resistant prostate cancer (CRPC) treatment is its high metastasis potential and antiandrogen drug resistance, which severely affects survival time of prostate cancer (PCa) patients. Secreted phosphoprotein 1 (SPP1) is a cardinal mediator of tumor-associated inflammation and facilitates metastasis. In our previous study, we firstly revealed SPP1 was a potential hub signature for predicting metastatic CRPC (mCRPC) development. Herein, we integrated multiple databases to explore the association of SPP1 expression with prognosis, survival, and metastatic levels in CRPC progression and investigated SPP1 expression in PCa tissues and cell lines. Next, PCa cell lines with overexpression or depletion of SPP1 were established to study the effect of SPP1 on enzalutamide sensitivity and adhesion and migration of prostate cancer cell lines and further explore the underlying regulatory mechanisms. Bioinformatics analysis, polymerase chain reaction (PCR), immunohistochemical staining, and western blot results suggested SPP1 upregulation had strong relationship with the malignant progression of CRPC and enzalutamide resistance. SPP1 knockdown enhanced enzalutamide sensitivity and repressed invasion and migration of prostate cancer cells. Importantly, upregulating SPP1 promoted, while silencing SPP1 attenuated epithelial-mesenchymal-transition (EMT). Our results further demonstrated that SPP1 overexpression maintains the activation of PI3K/AKT and ERK1/2 signaling pathways. Overall, our findings unraveled the functional role and clinical significance of SPP1 in PCa progression and help to discover new potential targets against mCRPC.

Comparison of Minimally Invasive Tubular Surgery with Conventional Surgery in the Treatment of Thoracolumbar Metastasis.

BACKGROUND: This study aimed to evaluate the perioperative safety and efficacy of minimally invasive tubular surgery for patients with spinal metastasis. METHODS: A total of 161 consecutive patients with spinal metastasis between June 2017 and June 2020 were retrospectively reviewed. A total of 36 patients were included in this study, 14 patients underwent minimally invasive tubular surgery (M), and 22 patients underwent conventional surgery (C). T-test and chi-square tests were used to evaluate demographic and perioperative data differences between the two groups. RESULTS: Baseline characteristics did not differ significantly between M and C groups except for the SINS (p=0.002) and preoperative Alb (p=0.026). There was no significant difference in operative time and complications between M and C groups (p<0.05). The M group had less mean blood loss than the C group (1275 vs 718mL, p=0.045). Blood transfusion was comparable between the two groups (p<0.05). The mean amount and drainage time were lower than the C group (141 vs 873mL, p<0.001; 3.1 vs 7.0 days, P<0.001). The mean postoperative hospitalization of the M group was 8.8 days, which was lower than the C group (11.3 days, p=0.045). Sub-analysis showed that for patients with hyper-vascular tumor, the M group had less mean amount and time of drainage compared with the C group (p<0.05); for patients with hypo-vascular tumor, the mean blood loss and amount of blood transfusion were also reduced in M group (p<0.05). The mean blood loss and drainage time of patients with hypo-vascular tumors were less than patients with hyper-vascular tumors in the M group (p<0.05). CONCLUSION: In selected cases, minimally invasive tubular surgery is safe and effective for patients with spinal metastasis. Patients with hypo-vascular tumors were more suitable for this technique with less blood loss, fewer blood transfusions, minor drainage, and shorter postoperative hospitalization.

Risk factors of total blood loss in the posterior surgery for patients with thoracolumbar metastasis.

BACKGROUND: Blood loss in posterior surgery patients with thoracolumbar metastasis posed a significant challenge to surgeons. This study aimed to explore the risk factors of blood loss in posterior surgery for patients with thoracolumbar metastasis. METHODS: One hundred forty-two patients were retrospectively reviewed. Their baseline characteristics were recorded. The Gross equation was used to calculate blood loss on a surgical day. Multivariate linear regression was used to analyze the risk factors. RESULTS: Mean blood loss of 142 patients were 2055 ± 94 ml. Hypervascular primary tumor (kidney, thyroid and liver) (P = 0.017), wide or marginal excision (en-bloc: P = 0.001), metastasis at the lumbar spine (P = 0.033), and the presence of extraosseous tumor mass (P = 0.012) were independent risk factors of blood loss in the posterior surgery. Sub-analysis showed that wide or marginal excision (en-bloc: P < 0.001) and metastasis at lumbar spine (P = 0.007) were associated with blood loss for patients with non-hyper vascular primary tumors. Wide or marginal excision (piece-meal: P = 0.014) and the presence of an extraosseous tumor mass (P = 0.034) were associated with blood loss for patients with hypervascular primary tumors. CONCLUSION: Hypervascular primary tumor (kidney, thyroid, and liver) was an independent risk factor of blood loss in the posterior surgery. The presence of extraosseous tumor mass and wide or marginal excision (piece-meal) were independent risk factors for patients with hypervascular primary tumors. Metastasis at the lumbar spine and wide or marginal excision (en-bloc) were independent risk factors for patients with non-hyper vascular primary tumors.

A Nomogram to Predict Intra-Spinal Canal Cement Leakage Among Elderly Patients with Spine Metastases: An Internal-Validated Model.

PURPOSE: This study aimed to assess the risk variables for predicting intra-spinal canal cement leakage, especially among elderly patients with spine metastases after being treated with percutaneous vertebroplasty (PVP). Furthermore, we proposed and validated a nomogram to stratify risks of intra-spinal canal cement leakage. METHODS: We retrospectively analyzed 163 elderly patients (age ≧65 years) with spine metastases who underwent PVP. Patients were randomly divided into a training cohort (n=100) and a validation cohort (n=63). The multivariate logistic regression analysis was used to screen potential risk variables in the training cohort. Significant risk variables were included in the nomogram, and the nomogram was developed according to the estimates of the each included variable. The predictive effectiveness of the nomogram was validated using discrimination and calibration performance. RESULTS: The overall prevalence of intra-spinal canal cement leakage was 9.82% (16/163). In the training cohort, female patients (14.71%, 5/34) showed a higher rate of intra-spinal canal cement leakage as compared with male patients (4.55%, 3/66). The nomogram consisted of sex, cortical osteolytic destruction in posterior wall, and load-bearing lines of spine. The nomogram had acceptable discrimination, with the area under the receiver operating characteristic (AUROC) of 0.75 in the training cohort, 0.64 in the validation cohort, and 0.69 in the entire cohort, and also showed favorable calibration based on the goodness-of-fit test. According to the nomogram, three risk groups were developed: the low risk group had an actual probability of 7.03%, the medium risk group was 11.54%, and high risk group was 44.44%. The difference between the three groups was significant (P ˂ 0.01). CONCLUSION: Intra-spinal canal cement leakage after PVP is not scarce among elderly patients. We proposed and internally validated a nomogram that is capable of calculating the risk of intra-spinal canal cement leakage among elderly patients with spine metastases. Careful surgical plan should be conducted among patients with a high risk of developing intra-spinal canal cement leakage.

Effect of the Timing of Surgery on Neurological Recovery for Patients with Incomplete Paraplegia Caused by Metastatic Spinal Cord Compression.

OBJECTIVE: This study aimed to investigate the effect of timing of surgery on neurological recovery for patients with metastatic spinal cord compression (MSCC). METHODS: According to the timing of surgery, 75 patients with incomplete paraplegia caused by MSCC were assigned to 3 groups: within 3 days (group A), between 4 days and 7 days (group B), and after 7 days (group C). T-test, one-way ANOVA, Mann-Whitney U-test, and Chi-square test were used to evaluate the difference in the improvement of American Spinal Injury Association Impairment Scale (AIS) and ambulatory status, the incidence of perioperative complications, surgical site infection, and the length of hospital stay between 3 groups. RESULTS: Patients with incomplete paraplegia treated in our department had an average of 17.4±1.8 days delayed and most occurred before hospitalization (4.0±0.4 vs 13.2±1.8, P<0.001). There was no significant difference in the AIS improvement between patients with different pre-op AIS. The timing of surgery was significantly correlated with AIS improvement (correlation coefficient=-0.257, P=0.019). Sub-analysis showed that patients who underwent surgery within 7 days (group A and group B) had significantly better AIS improvement compared with group C (improved at least 1 grade, P=0.043; improved more than 1 grade, P=0.039) and the surgery timing was more important for patients with AIS B and C. The timing of surgery was significantly correlated with the length of hospital stay (correlation coefficient=0.335, P=0.003). Patients of group C had the longest length of hospital stay (P=0.002). The incidence of perioperative complications and surgical site infection did not differ significantly between the 3 groups. CONCLUSION: Delay surgery was common in incomplete paraplegia patients with MSCC. Patients with AIS B and C who underwent surgery within 7 days had better AIS improvement.

Prediction of early vascular cement leakage following percutaneous vertebroplasty in spine metastases: the Peking University First Hospital Score (PUFHS).

BACKGROUND: Cement leakage into venous blood posed significant challenge to surgeons. The aim of the study was to create a Peking University First Hospital Score (PUFHS) which could evaluate the probability of vascular cement leakage among spine metastases patients following percutaneous vertebroplasty. METHODS: The study retrospectively enrolled 272 spine metastases patients treated with percutaneous vertebroplasty. We randomly extracted all enrolled patients as the training or validation group and baseline characteristic comparison was assessed between the two groups. Creation of the PUFHS was performed in the training group and validation of the PUFHS was performed in the validation group. RESULTS: Of all the 272 patients, the total number of included vertebrae was 632 and the median treated levels were 2 per patient. Vascular cement leakage occurred in 26.47% (72/272) of patients. The baseline characteristics were comparable between the two groups (P > 0.05). Three risk predictors (primary cancer types, number of treated vertebrae levels, and vertebrae collapse) were included in the PUFHS. The area under the receiver operating characteristic curve (AUROC) of the PUFHS was 0.71 in the training group and 0.69 in the validation group. The corresponding correct classification rates were 73.0 and 70.1%, respectively. The calibration slope was 0.78 (95% confidence interval[CI]: 0.45-1.10) in the training group and 1.10 (95% CI: 0.73-1.46) in the validation group. The corresponding intercepts were 0.06 (95% CI: - 0.04-0.17) and - 0.0079 (95% CI: - 0.11-0.092), respectively. CONCLUSIONS: Vascular cement leakage is common among spine metastases after percutaneous vertebroplasty. The PUFHS can calculate the probability of vascular cement leakage, which can be a useful tool to inform surgeons about vascular cement leakage risk in advance.