Individualized assessment predictive models for risk and overall survival in elderly patients of primary kidney cancer with bone metastases: A large population-based study

BackgroundElderly people are at high risk of metastatic kidney cancer (KC), and, the bone is one of the most common metastatic sites for metastatic KC. However, studies on diagnostic and prognostic prediction models for bone metastases (BM) in elderly KC patients are still vacant. Therefore, it is necessary to establish new diagnostic and prognostic nomograms.MethodsWe downloaded the data of all KC patients aged more than 65 years during 2010–2015 from the Surveillance, Epidemiology, and End Results (SEER) database. Univariate and multivariate logistic regression analyses were used to study independent risk factors of BM in elderly KC patients. Univariate and multivariate Cox regression analysis for the study of independent prognostic factors in elderly KCBM patients. Survival differences were studied using Kaplan–Meier (K–M) survival analysis. The predictive efficacy and clinical utility of nomograms were assessed by receiver operating characteristic (ROC) curve, the area under curve (AUC), calibration curve, and decision curve analysis (DCA).ResultsA final total of 17,404 elderly KC patients (training set: n = 12,184, validation set: n = 5,220) were included to study the risk of BM. 394 elderly KCBM patients (training set: n = 278, validation set: n = 116) were included to study the overall survival (OS). Age, histological type, tumor size, grade, T/N stage and brain/liver/lung metastasis were identified as independent risk factors for developing BM in elderly KC patients. Surgery, lung/liver metastasis and T stage were identified as independent prognostic factors in elderly KCBM patients. The diagnostic nomogram had AUCs of 0.859 and 0.850 in the training and validation sets, respectively. The AUCs of the prognostic nomogram in predicting OS at 12, 24 and 36 months were: training set (0.742, 0.775, 0.787), and validation set (0.721, 0.827, 0.799), respectively. The calibration curve and DCA also showed excellent clinical utility of the two nomograms.ConclusionTwo new nomograms were constructed and validated to predict the risk of developing BM in elderly KC patients and 12-, 24-, and 36-months OS in elderly KCBM patients. These models can help surgeons provide more comprehensive and personalized clinical management programs for this population

IRF4 suppresses osteogenic differentiation of BM-MSCs by transcriptionally activating miR-636/DOCK9 axis

Objectives: Osteoblasts are derived from Bone Marrow-derived Mesenchymal Stem Cells (BM-MSCs), which play an indispensable role in bone formation. In this study, the authors aim to investigate the role of IRF4 in the osteogenic differentiation of BM-MSCs and its potential molecular mechanism. Methods: The authors used lentivirus infection to overexpress IRF4 in BM-MSCs. The expression of IRF4 and osteogenesis-related genes were detected by qRT-PCR and western blot analysis. The osteogenic differentiation of BM-MSCs was evaluated by Alkaline Phosphatase (ALP) activity, Alizarin red staining, and Alkaline Phosphatase (ALP) staining. Chromatin Immunoprecipitation (ChIP), Dual-Luciferase reporter assay and RNA Immunoprecipitation Assay were applied to confirm the regulatory mechanism between IRF4, miR-636 and DOCK9. Results: The authors found IRF4 was down-regulated during the osteogenic differentiation of BM-MSCs, and IRF4 overexpression could decrease the osteogenic differentiation of BM-MSCs by specifically promoting the reduction of Alkaline Phosphatase (ALP) activity and down-regulating osteogenic indicators, including OCN, OPN, Runx2 and CollA1. Mechanistically, IRF4 activated microRNA-636 (miR-636) expression via binding to its promoter region, and Dedicator of Cytokinesis 9 (DOCK9) was identified as the target of miR-636 in BM-MSCs. Moreover, the damage in the capacity of osteogenic differentiation of BM-MSCs induced by IRF4 overexpression could be rescued by miR-636 inhibition. Conclusions: In summary, this paper proposed that IRF4/miR-636/DOCK9 may be considered as targets for the treatment of osteoporosis (OP)

A visualized model for identifying optimal candidates for aggressive locoregional surgical treatment in patients with bone metastases from breast cancer

BackgroundThe impact of surgical resection of primary (PTR) on the survival of breast cancer (BC) patients with bone metastasis (BM) has been preliminarily investigated, but it remains unclear which patients are suitable for this procedure. Finally, this study aims to develop a predictive model to screen BC patients with BM who would benefit from local surgery.MethodsBC patients with BM were identified using the Surveillance, Epidemiology, and End Results (SEER) database (2010 and 2015), and 39 patients were obtained for external validation from an Asian medical center. According to the status of local surgery, patients were divided into Surgery and Non-surgery groups. Propensity score matching (PSM) analysis was performed to reduce selection bias. Kaplan-Meier (K-M) survival and Cox regression analyses were conducted before and after PSM to study the survival difference between the two groups. The survival outcome and treatment modality were also investigated in patients with different metastatic patterns. The logistic regression analyses were utilized to determine significant surgery-benefit-related predictors, develop a screening nomogram and its online version, and quantify the beneficial probability of local surgery for BC patients with BM. Receiver operating characteristic (ROC) curves, the area under the curves (AUC), and calibration curves were plotted to evaluate the predictive performance and calibration of this model, whereas decision curve analysis (DCA) was used to assess its clinical usefulness.ResultsThis study included 5,625 eligible patients, of whom 2,133 (37.92%) received surgical resection of primary lesions. K-M survival analysis and Cox regression analysis demonstrated that local surgery was independently associated with better survival. Surgery provided significant survival benefits in most subgroups and metastatic patterns. After PSM, patients who received surgery had a longer survival time (OS: 46 months vs. 32 months, p < 0.001; CSS: 50 months vs. 34 months, p < 0.001). Logistic regression analysis determined six significant surgery-benefit-related variables: T stage, radiotherapy, race, liver metastasis, brain metastasis, and breast subtype. These factors were combined to establish the nomogram and a web probability calculator (https://sunshine1.shinyapps.io/DynNomapp/), with an AUC of 0.673 in the training cohort and an AUC of 0.640 in the validation cohort. The calibration curves exhibited excellent agreement. DCA indicated that the nomogram was clinically useful. Based on this model, surgery patients were assigned into two subsets: estimated sur-non-benefit and estimated sur-benefit. Patients in the estimated sur-benefit subset were associated with longer survival (median OS: 64 months vs. 33 months, P < 0.001). Besides, there was no difference in survival between the estimated sur-non-benefit subset and the non-surgery group.ConclusionOur study further confirmed the significance of local surgery in BC patients with BM and proposed a novel tool to identify optimal surgical candidates

Clinical characteristic–assisted surgical benefit stratification for resection of primary tumor in patients with advanced primary malignant bone neoplasms: a population-based propensity score–matched analysis

BackgroundPrimary tumor resection (PTR) is the standard treatment for patients with primary malignant bone neoplasms (PMBNs). However, it remains unclear whether patients with advanced PMBNs still benefit from PTR. This study aimed to develop a prediction model to estimate the beneficial probability of PTR for this population.MethodsThis study extracted data from patients diagnosed with advanced PMBNs, as recorded in the Surveillance, Epidemiology, and End Results (SEER) database, with the period from 2004 to 2015. The patient cohort was then bifurcated into two groups: those who underwent surgical procedures and the non-surgery group. Propensity score matching (PSM) was utilized to mitigate any confounding factors in the study. The survival rates of patients from both the surgical and non-surgery groups were evaluated using Kaplan–Meier (K-M) curves analysis. Moreover, the study used this method to assess the capacity of the nomogram to distinguish patients likely to derive benefits from surgical intervention. The study was grounded in the hypothesis that patients who underwent PTR and survived beyond the median overall survival (OS) time would potentially benefit from the surgery. Subsequently, logistic regression analysis was performed to ascertain significant predictors, facilitating the development of a nomogram. This nomogram was subjected to both internal and external validation using receiver operating characteristic curves, area under the curve analysis, calibration plots, and decision curve analysis.ResultsThe SEER database provided a total of 839 eligible patients for the study, among which 536 (63.9%) underwent PTR. Following a 2:1 PSM analysis, patients were classified into two groups: 364 patients in the surgery group and 182 patients in the non-surgery group. Both K-M curves and multivariate Cox regression analysis revealed that patients who received PTR had a longer survival duration, observed both before and after PSM. Crucial factors such as age, M stage, and tumor size were identified to be significantly correlated with surgical benefits in patients with advanced PMBNs. Subsequently, a nomogram was developed that uses these independent predictors. The validation of this predictive model confirmed its high accuracy and excellent discrimination ability of the nomogram to distinguish patients who would most likely benefit from surgical intervention.ConclusionIn this study, we devised a user-friendly nomogram to forecast the likehood of surgical benefits for patients diagnosed with advanced PMBNs. This tool facilitates the identification of the most suitable candidates for PTR, thus promoting more discerning and effective use of surgical intervention in this patient population

Aberrant Functional and Causal Connectivity in Acute Tinnitus With Sensorineural Hearing Loss

Purpose: The neural bases in acute tinnitus remains largely undetected. The objective of this study was to identify the alteration of the brain network involved in patients with acute tinnitus and hearing loss. Methods: Acute tinnitus patients (n = 24) with hearing loss and age-, sex-, education-matched healthy controls (n = 21) participated in the current study and underwent resting-state functional magnetic resonance imaging (fMRI) scanning. Regional homogeneity and amplitude of low-frequency fluctuation were used to investigate the local spontaneous neural activity and functional connectivity (FC), and Granger causality analysis (GCA) was used to analyze the undirected and directed connectivity of brain regions. Results: Compared with healthy subjects, acute tinnitus patients had a general reduction in FC between auditory and non-auditory brain regions. Based on FC analysis, the superior temporal gyrus (STG) revealed reduced undirected connectivity with non-auditory brain regions including the amygdala (AMYG), nucleus accumbens (NAc), the cerebellum, and postcentral gyrus (PoCG). Using the GCA algorithm, increased effective connectivity from the right AMYG to the right STG, and reduced connectivity from the right PoCG to the left NAc was observed in acute tinnitus patients with hearing loss. The pure-tone threshold was positively correlated with FC between the AMYG and STG, and negatively correlated with FC between the left NAc and the right PoCG. In addition, a negative association between the GCA value from the right PoCG to the left NAc and the THI scores was observed. Conclusion: Acute tinnitus patients have aberrant FC strength and causal connectivity in both the auditory and non-auditory cortex, especially in the STG, AMYG, and NAc. The current findings will provide a new perspective for understanding the neuropathophysiological mechanism in acute tinnitus

A Pan-cancer analysis reveals high-frequency genetic alterations in mediators of signaling by the tgf-β superfamily

We present an integromic analysis of gene alterations that modulate transforming growth factor β (TGF-β)-Smad-mediated signaling in 9,125 tumor samples across 33 cancer types in The Cancer Genome Atlas (TCGA). Focusing on genes that encode mediators and regulators of TGF-β signaling, we found at least one genomic alteration (mutation, homozygous deletion, or amplification) in 39% of samples, with highest frequencies in gastrointestinal cancers. We identified mutation hotspots in genes that encode TGF-β ligands (BMP5), receptors (TGFBR2, AVCR2A, and BMPR2), and Smads (SMAD2 and SMAD4). Alterations in the TGF-β superfamily correlated positively with expression of metastasis-associated genes and with decreased survival. Correlation analyses showed the contributions of mutation, amplification, deletion, DNA methylation, and miRNA expression to transcriptional activity of TGF-β signaling in each cancer type. This study provides a broad molecular perspective relevant for future functional and therapeutic studies of the diverse cancer pathways mediated by the TGF-β superfamily

Diagnosis of COVID-19 Pneumonia Based on Graph Convolutional Network

10.3389/fmed.2020.612962Frontiers in Medicine761296

An Artificial Intelligence Model for Predicting 1-Year Survival of Bone Metastases in Non-Small-Cell Lung Cancer Patients Based on XGBoost Algorithm

Non-small-cell lung cancer (NSCLC) patients often develop bone metastases (BM), and the overall survival for these patients is usually perishing. However, a model with high accuracy for predicting the survival of NSCLC with BM is still lacking. Here, we aimed to establish a model based on artificial intelligence for predicting the 1-year survival rate of NSCLC with BM by using extreme gradient boosting (XGBoost), a large-scale machine learning algorithm. We selected NSCLC patients with BM between 2010 and 2015 from the Surveillance, Epidemiology, and End Results database. In total, 5973 cases were enrolled and divided into the training (n=4183) and validation (n=1790) sets. XGBoost, random forest, support vector machine, and logistic algorithms were used to generate predictive models. Receiver operating characteristic curves were used to evaluate and compare the predictive performance of each model. The parameters including tumor size, age, race, sex, primary site, histological subtype, grade, laterality, T stage, N stage, surgery, radiotherapy, chemotherapy, distant metastases to other sites (lung, brain, and liver), and marital status were selected to construct all predictive models. The XGBoost model had a better performance in both training and validation sets as compared with other models in terms of accuracy. Our data suggested that the XGBoost model is the most precise and personalized tool for predicting the 1-year survival rate for NSCLC patients with BM. This model can help the clinicians to design more rational and effective therapeutic strategies

Construction and Validation of a Convenient Clinical Nomogram to Predict the Risk of Brain Metastasis in Renal Cell Carcinoma Patients

Brain metastasis (BM) is a typical type of metastasis in renal cell carcinoma (RCC) patients. The early detection of BM is likely a crucial step for RCC patients to receive appropriate treatment and prolong their overall survival. The aim of this study was to identify the independent predictors of BM and construct a nomogram to predict the risk of BM. Demographic and clinicopathological data were obtained from the Surveillance, Epidemiology, and End Results (SEER) database for RCC patients between 2010 and 2015. Univariate and multivariate logistic regression analyses were performed to identify the independent risk factors, and then, a visual nomogram was constructed. Multiple parameters were used to evaluate the discrimination and clinical value. We finally included 42577 RCC patients. Multivariate logistic regression analysis showed that histological type, tumor size, bone metastatic status, and lung metastatic status were independent BM-associated risk factors for RCC. We developed a nomogram to predict the risk of BM in patients with RCC, which showed favorable calibration with a C-index of 0.924 (0.903-0.945) in the training cohort and 0.911 (0.871-0.952) in the validation cohort. The calibration curves and decision curve analysis (DCA) also demonstrated the reliability and accuracy of the clinical prediction model. The nomogram was shown to be a practical, precise, and personalized clinical tool for identifying the RCC patients with a high risk of BM, which not only will contribute to the more reasonable allocation of medical resources but will also enable a further improvements in the prognosis and quality of life of RCC patients

Table_1_A visualized model for identifying optimal candidates for aggressive locoregional surgical treatment in patients with bone metastases from breast cancer.docx

BackgroundThe impact of surgical resection of primary (PTR) on the survival of breast cancer (BC) patients with bone metastasis (BM) has been preliminarily investigated, but it remains unclear which patients are suitable for this procedure. Finally, this study aims to develop a predictive model to screen BC patients with BM who would benefit from local surgery.MethodsBC patients with BM were identified using the Surveillance, Epidemiology, and End Results (SEER) database (2010 and 2015), and 39 patients were obtained for external validation from an Asian medical center. According to the status of local surgery, patients were divided into Surgery and Non-surgery groups. Propensity score matching (PSM) analysis was performed to reduce selection bias. Kaplan-Meier (K-M) survival and Cox regression analyses were conducted before and after PSM to study the survival difference between the two groups. The survival outcome and treatment modality were also investigated in patients with different metastatic patterns. The logistic regression analyses were utilized to determine significant surgery-benefit-related predictors, develop a screening nomogram and its online version, and quantify the beneficial probability of local surgery for BC patients with BM. Receiver operating characteristic (ROC) curves, the area under the curves (AUC), and calibration curves were plotted to evaluate the predictive performance and calibration of this model, whereas decision curve analysis (DCA) was used to assess its clinical usefulness.ResultsThis study included 5,625 eligible patients, of whom 2,133 (37.92%) received surgical resection of primary lesions. K-M survival analysis and Cox regression analysis demonstrated that local surgery was independently associated with better survival. Surgery provided significant survival benefits in most subgroups and metastatic patterns. After PSM, patients who received surgery had a longer survival time (OS: 46 months vs. 32 months, p ConclusionOur study further confirmed the significance of local surgery in BC patients with BM and proposed a novel tool to identify optimal surgical candidates.</p