The histone demethylase lysine‐specific demethylase 4A (KDM4A) is reported to be overexpressed and plays a vital in multiple cancers through controlling gene expression by epigenetic regulation of H3K9 or H3K36 methylation marks. However, the biological role and mechanism of KDM4A in prostate cancer (PC) remain unclear. Herein, we reported KDM4A expression was upregulation in phosphatase and tensin homolog knockout mouse prostate tissue. Depletion of KDM4A in PC cells inhibited their proliferation and survival in vivo and vitro. Further studies reveal that USP1 is a deubiquitinase that regulates KDM4A K48‐linked deubiquitin and stability. Interestingly, we found c‐Myc was a key downstream effector of the USP1‐KDM4A/androgen receptor axis in driving PC cell proliferation. Notably, upregulation of KDM4A expression with high USP1 expression was observed in most prostate tumors and inhibition of USP1 promotes PC cells response to therapeutic agent enzalutamide. Our studies propose USP1 could be an anticancer therapeutic target in PC. 相似文献
In the area of large-scale graph data representation and semi-supervised learning, deep graph-based convolutional neural networks have been widely applied. However, typical graph convolutional network (GCN) aggregates information of neighbor nodes based on binary neighborhood similarity (adjacency matrix). It treats all neighbor nodes of one node equally, which does not suppress the influence of dissimilar neighbor nodes. In this paper, we investigate GCN based on similarity matrix instead of adjacency matrix of graph nodes. Gaussian heat kernel similarity in Euclidean space is first adopted, which is named EGCN. Then biologically inspired manifold similarity is trained in reproducing kernel Hilbert space (RKHS), based on which a manifold GCN (named MGCN) is proposed for graph data representation and semi-supervised learning with four different kernel types. The proposed method is evaluated with extensive experiments on four benchmark document citation network datasets. The objective function of manifold similarity learning converges very quickly on different datasets using various kernel functions. Compared with state-of-the-art methods, our method is very competitive in terms of graph node recognition accuracy. In particular, the recognition rates of MGCN (Gaussian kernel) and MGCN (Polynomial Kernel) outperform that of typical GCN about 3.8% on Cora dataset, 3.5% on Citeseer dataset, 1.3% on Pubmed dataset and 4% on Cora_ML dataset, respectively. Although the proposed MGCN is relatively simple and easy to implement, it can discover local manifold structure by manifold similarity learning and suppress the influence of dissimilar neighbor nodes, which shows the effectiveness of the proposed MGCN.
Postoperative morbidity may offset the potential benefits of surgical treatment for spine metastatic disease; hence, risk factors for postoperative complications and reoperations should be taken into considerations during surgical decision-making. In addition, it remains unknown whether complications and reoperations shorten these patients' survival.
Purpose
We aimed to describe and identify factors associated with having a complication within 30 days of index surgery as well as factors associated with having a subsequent reoperation. Furthermore, we assessed the effect of 30-day complications and reoperations on the patients' postoperative survival, as well as described neurologic changes after surgery.
Study Design
Retrospective cohort study.
Patient Sample
We included 647 patients 18 years and older who had surgery for metastatic disease in the spine between January 2002 and January 2014 in one of two affiliated tertiary care centers.
Outcome Measures
Our primary outcomes were complications within 30 days after surgery and reoperations until final follow-up or death.
Methods
We used multivariate logistic regression to identify risk factors for 30-day complications and reoperations. We used the Cox regression analysis to assess the effect of postoperative complications and reoperations on survival.
Results
From 647 included patients, 205 (32%) had a complication within 30 days. The following variables were independently associated with 30-day complications: lower albumin levels (odds ratio [OR]: 0.69, 95% confidence interval [CI]=0.49–0.96, p=.021), additional comorbidities (OR=1.42, 95% CI=1.00–2.01, p=.048), pathologic fracture (OR=1.41, 95% CI=0.97–2.05, p=.031), three or more spine levels operated upon (OR=1.64, 95% CI=1.02–2.64, p=.027), and combined surgical approach (OR=2.44, 95% CI=1.06–5.60, p=.036). One hundred and fifteen patients (18%) had at least one reoperation after the initial surgery; prior radiotherapy (OR=1.56, 95% CI=1.07–2.29, p=.021) to the spinal tumor was independently associated with reoperation. 30-day complications were associated with worse survival (hazard ratio [HR]=1.40, 95% CI=1.17–1.68, p<.001), and reoperation was not significantly associated with worse survival (HR=0.80, 95% CI=0.09–1.00, p=.054). Neurologic status worsened in 42 (6.7%), remained stable in 445 (71%), and improved in 140 (22%) patients after surgery.
Conclusions
Three or more spine levels operated upon and prior radiotherapy should prompt consideration of a preoperative plastic surgery consultation regarding soft tissue coverage. Furthermore, if time allows, aggressive nutritional supplementation should be considered for patient with low preoperative serum albumin levels. Surgeons should be aware of the increase in complications in patients presenting with pathologic fracture, undergoing a combined approach, and with any additional preoperative comorbidities. Importantly, 30-day complications were associated with worsened survival. 相似文献