Surgical Efficacy and Prognosis of 54 Cases of Spinal Metastases from Breast Cancer

OBJECTIVE To analyze the efficacy and complications of spinal metastasis surgery for breast cancer; to understand the survival and the influencing factors; and to verify the predictive ability of the currently used spinal metastasis cancer survival prediction scoring system on the 1-year postoperative survival rate. METHODS A retrospective study was conducted on 54 patients with spinal metastases from breast cancer who underwent open surgery after multidisciplinary consultation in our hospital from January 2017 to October 2020. Patient demographic-related variables, breast cancer-related variables, spinal disorders-related variables, and treatment-related variables were collected. Survival curves were plotted using the Kaplan-Meier method, one-way tests were performed using the Log-rank method for factors that might affect prognosis, and candidate variables were included in the Cox model for multifactor analysis. The Tomita score, modified Tokuhashi score, modified Bauer score, modified Katagiri score were examined by plotting the subject operating characteristic curve (ROC) and calculating the area under curve (AUC) The AUC was used to test the predictive ability of the SORG (Skeletal Oncology Research Group) original version, SORG line graph version, and SORG web version for 1-year postoperative survival in patients with spinal metastases from breast cancer. RESULTS The average age was 51.3±8.6 years in 54 patients. Twenty-one patients underwent vertebral body debulking surgery, 32 patients underwent palliative canal decompression, and 1 patient underwent vertebral en bloc resection, with an operative time of 229.3 ± 87.6 minutes and intraoperative bleeding of 1018.1 ± 931.1 ml. Postoperatively, the patient experienced significant pain relief and gradual recovery from nerve injury. Major surgical complications included cerebrospinal fluid leakage, secondary spinal cord injury, spinal tumor progression, and broken of fixation. The mean survival time was 32.2 months, including a 6-month survival rate of 90.7%, a 1-year survival rate of 77.8%, and a 2-year survival rate of 60.3%. Univariate analysis showed that pre-operation with neurological deficits, hormone-insensitive type, with brain metastases were potential risk factors for poor prognosis. Multifactorial analysis showed that hormone-insensitive type and concomitant brain metastasis were independent risk factors associated with poor prognosis. The SORG web version had good ability to predict 1-year postoperative survival in patients with spinal metastases from breast cancer. Conclusion Spinal metastasis from breast cancer has good surgical efficacy, low postoperative recurrence rate, and relatively long survival time after surgery. Patients with hormone-insensitive type, with brain metastasis have poor prognosis, and SORG web version can predict patients' 1-year survival more accurately.

Combination of C-Reactive Protein and Neutrophil-to-Lymphocyte Ratio as a Novel Prognostic Index in Patients With Bladder Cancer After Radical Cystectomy

BackgroundInflammation is widely considered an important hallmark of cancer and associated with poor postoperative survival. The objective of this study is to assess the significance of preoperative C-NLR, a new inflammation-based index that includes preoperative C-reactive protein (CRP) and neutrophil-to-lymphocyte ratio (NLR), on therapeutic outcomes for bladder cancer (BC) patients after radical cystectomy (RC).Materials and MethodsBC patients who underwent RC between 2010 and 2019 were retrospectively analyzed from our medical center. The predictive effect of CRP, NLR, and C-NLR on the survival of BC patients were analyzed by the receiver operating characteristic (ROC) curves. The relationship between C-NLR and postoperative survival was investigated by Cox regression. The corresponding nomograms were built based on the Cox regression results of overall survival (OS) and disease-free survival (DFS), which were further validated by ROC curves, decision curve analysis (DCA) curves, and calibration curves.ResultsOf the 199 eligible patients, 83 (41.70%) were classified as high C-NLR group and the remaining 116 (58.30%) were classified as low C-NLR group. ROC analysis showed that C-NLR had the largest area under curve (AUC) compared to CRP and NLR. Multivariate analysis revealed that T-stage and C-NLR [high C-NLR vs. low C-NLR, hazard ratio (HR) = 2.478, 95% confidence interval (CI), 1.538–3.993, p < 0.001] were independent predictors of OS, whereas T-stage, M-stage, and C-NLR (high C-NLR vs. low C-NLR, HR = 2.817, 95% CI, 1.667–4.762, p < 0.001) were independent predictors of DFS. ROC and DCA analysis demonstrated better accuracy and discrimination of 3- and 5-year OS and DFS with C-NLR-based nomogram compared to TNM stage. The calibration curve reconfirmed the accurate predicting performance of nomograms.ConclusionC-NLR is a reliable predictor of long-term prognosis of BC patients after RC and will contribute to the optimization of individual therapy for BC patients.

Effect of PLC-β1/CaM signaling pathway mediated by AT1R on the occurrence and development of hepatocellular carcinoma

Objective To study the roles of AT1R, PLC-β1, CaM and other related signal molecules in the formation and development of hepatocellular carcinoma (HCC) and their correlation. Methods ELISA and immunohistochemistry were used to analyze the expressions of target proteins in serum and liver tissue of HCC patients, and the correlation between AT1R, PLC-β1 and CaM and postoperative survival status of patients was followed up and determined. CCK-8 method was used to screen the doses of Ang II and candesartan sensitive to HepG2 and HCCLM3 cells. Transwell experiment was used to observe the effects of different drugs on the migration and invasion activity of HCC cells. Meanwhile, flow cytometry and Western blot were used to detect the expression levels of AT1R, PLC-β1 and CaM in the cells. Then PLC-β1 siRNA was selected to transfect HCC cells, so as to further clarify the mechanism of the above signal proteins. HepG2 cells were inoculated under the hepatic capsule of mice to induce the formation of HCC in situ. Ang II and candesartan were used to stimulate HCC mice to observe the difference in liver appearance and measure the liver index. Finally, ELISA and immunofluorescence experiments were selected to analyze the levels of target proteins in mouse serum and liver tissue. Results The expression levels of target proteins in serum and liver tissue of HCC patients were significantly increased, and the postoperative survival time of patients with high expression of AT1R, PLC-β1 or CaM was obviously shortened. Ang II and candesartan could significantly promote and inhibit the motility of HCC cells, and had different effects on the levels of AT1R, PLC-β1 and CaM in cells. However, in hepatocellular carcinoma cells transfected with PLC-β1 siRNA, the intervention ability of drugs was obviously weakened. Ang II could significantly promote the formation and progression of mouse HCC, while candesartan had the opposite effect. Meanwhile, medications could affect the expressions of target proteins in mouse serum and liver tissue. Conclusion AT1R, PLC-β1 and CaM may be risk factors affecting the formation and prognosis of HCC, and the PLC-β1/CaM signaling pathway mediated by AT1R is an important way to regulate the migration and invasion activity of HCC cells.

Development and validation a novel preoperative comprehensive prognostic score based on inflammatory and nutritional score, coagulation indicator and tumor marker in esophageal squamous cell carcinoma

We herein propose a novel integrative score based on inflammatory and nutritional score, coagulation indicator and tumor marker, named comprehensive prognostic score (CPS), to predict postoperative survival in resectable esophageal squamous cell carcinoma (ESCC). We also aimed to establish and validate a nomogram based on CPS and other clinical features for individual survival prediction. A total of 490 resectable ESCC patients were randomly divided into either a training or validation cohort at a ratio of 7:3 for retrospective analysis. The CPS, based on squamous cell carcinoma antigen (SCCA), C-reactive protein to albumin ratio (CAR), neutrophil to lymphocyte ratio (NLR), and fibrinogen (FIB), was divided into two models to verify its prognostic value. The predictive model of CPS-based nomogram was established and validated in two cohorts. Patients with CPS low group in model 1 had better 5-year cancer-specific survival (CSS) than those in CPS high group (50.7% vs. 17.8%, P<0.001). For model 2, the 5-year CSS for CPS 0, 1 and 2 were 75.0%, 38.9% and 13.3%, respectively (P<0.001). CPS was confirmed as an independent prognostic score in both models. The CPS-based nomogram can accurately and effectively predict survival in resected ESCC. The CPS is a novel, simple, and effective predictor in resectable ESCC. Moreover, CPS has a potential independent prognostic value in predicting postoperative survival, which can accurately and effectively predict individual survival in resectable ESCC.

Prediction of Postoperative Survival Level of Esophageal Cancer Patients Based on Kaplan-Meier(K-M) Survival Analysis and Gray Wolf Optimization (GWO)-BP Model

Background: Esophageal squamous cell carcinoma (ESCC) is a global safety problem, especially the low 5-year survival rate of patients after surgery, and their healthy life after surgery is directly threatened.Methods: Kaplan-Meier(K-M) survival analysis is used to screen the blood indexes of patients with ESCC. The gray wolf algorithm (GWO) is introduced to optimize the weight threshold of back-propagation (BP) neural network, and a prediction model based on K-M-GWO-BP is established.Results: According to the influencing factors of postoperative survival, the postoperative survival level of patients is predicted. K-M survival analysis is used to analyze the relevant risk factors, the redundant variables are eliminated, and the whole structure of the neural network is simplified. The initial weight of BP neural network is optimized by GWO.Conclusions: BP neural network model, PSO-BP, GA-BP, SSA-BP, GWO-BP, K-M-BP, K-M-PSO-BP, K-MGA-BP, K-M-SSA-BP and K-M-GWO-BP are compared, the prediction accuracy of K-M-GWO-BP neural network model is the best.

A Hybrid Method to Predict Postoperative Survival of Lung Cancer Using Improved SMOTE and Adaptive SVM

Predicting postoperative survival of lung cancer patients (LCPs) is an important problem of medical decision-making. However, the imbalanced distribution of patient survival in the dataset increases the difficulty of prediction. Although the synthetic minority oversampling technique (SMOTE) can be used to deal with imbalanced data, it cannot identify data noise. On the other hand, many studies use a support vector machine (SVM) combined with resampling technology to deal with imbalanced data. However, most studies require manual setting of SVM parameters, which makes it difficult to obtain the best performance. In this paper, a hybrid improved SMOTE and adaptive SVM method is proposed for imbalance data to predict the postoperative survival of LCPs. The proposed method is divided into two stages: in the first stage, the cross-validated committees filter (CVCF) is used to remove noise samples to improve the performance of SMOTE. In the second stage, we propose an adaptive SVM, which uses fuzzy self-tuning particle swarm optimization (FPSO) to optimize the parameters of SVM. Compared with other advanced algorithms, our proposed method obtains the best performance with 95.11% accuracy, 95.10% G -mean, 95.02% F1, and 95.10% area under the curve (AUC) for predicting postoperative survival of LCPs.

Changes in CRP and CA19-9 during Preoperative Oncological Therapy Predict Postoperative Survival in Pancreatic Ductal Adenocarcinoma

<b><i>Introduction:</i></b> Tumor and systemic inflammatory markers predict survival. This retrospective study aimed to explore the changes in CRP, CA19-9, and other routine laboratory tests during preoperative oncological therapy as prognostic factors in pancreatic ductal adenocarcinoma (PDAC). <b><i>Methods:</i></b> Between 2000 and 2016, 68 borderline resectable PDAC patients received preoperative oncological therapy and underwent subsequent surgery at Helsinki University Hospital, Finland. We investigated changes in CRP, CA19-9, CEA, albumin, leukocytes, bilirubin, and platelets and examined the impact on survival. <b><i>Results:</i></b> In the multivariate analysis, CRP remaining at ≥3 mg/L after preoperative oncological therapy predicted a poorer postoperative outcome when compared to CRP decreasing to or remaining at &#x3c;3 mg/L (hazard ratio [HR] 2.766, 95% confidence interval [CI]: 1.300–5.885, <i>p</i> = 0.008). Furthermore, a CA19-9 decrease &#x3e;90% during preoperative treatment predicted a favorable postoperative outcome (HR 0.297, 95% CI: 0.124–0.708, <i>p</i> = 0.006). In the Kaplan-Meier analysis, the median survival for patients with CRP remaining at &#x3c;3 mg/L was longer than among patients with an increased CRP level at ≥3 mg/L (42 months vs. 24 months, <i>p</i> = 0.001). Patients with a CA19-9 decrease &#x3e;90% or level normalization (to ≤37 kU/L) during preoperative treatment exhibited a median survival of 47 months; those with a 50–90% decrease, 15 months; and those with a &#x3c;50% decrease, 17 months (<i>p</i> &#x3c; 0.001). <b><i>Conclusions:</i></b> Changes in CRP and CA19-9 during preoperative oncological therapy predict postoperative survival.