Significance of PRO2000/ANCCA expression, a novel proliferation-associated protein in hepatocellular carcinoma

BACKGROUND: PRO2000/ANCCA may be an important candidate gene which located within a region of chromosome 8q in hepatocellular carcinoma (HCC). However, its significance remains unclear. The aim of this study was to explore the clinical significance of PRO2000/ANCCA expression in HCC. METHODS: The correlations of PRO2000/ANCCA expression with clinicopathological factors and prognosis of HCC patients were analyzed. Expression of PRO2000/ANCCA, ki-67, cyclinD1, p53 and p21 was detected in HCCs from 107 patients along with corresponding non-tumor tissues by immunohistochemistry. RESULTS: PRO2000/ANCCA expression was present in 66 of 107 (64.94%) HCC specimens in which 36 of 76 (47.37%) in well differentiated tumors and 30 of 31 (96.77%) in poorly differentiated tumors respectively, while 8 (7.48%) in adjacent non-tumor tissues with scattered positive cells. PRO2000/ANCCA expression was associated with clinicopathological features such as histological differentiation, number of tumor nodules, TNM stage, tumor microsatellite, portal vein tumor thrombus and recurrence, but not with gender, age, tumor size, cirrhosis, HBV infection and serum fetoprotein (AFP) level. There was a close relationship between PRO2000/ANCCA and ki-67 and cyclinD1 in HCC. PRO2000/ANCCA immunopositivity was independent of p53 and p21(WAF1/Cip1). CONCLUSIONS: Increased expression of PRO2000/ANCCA is associated with adverse outcome in patients with HCC and is a predictor of poor prognosis for HCC. PRO2000/ANCCA may be involved in the development of HCC and might promote cell proliferation through a p53/ P21(WAF1/Cip1)-independent pathway

Predicting the PSQA results of volumetric modulated arc therapy based on dosiomics features: a multi-center study

Backgroud and objectivesThe implementation of patient-specific quality assurance (PSQA) has become a crucial aspect of the radiation therapy process. Machine learning models have demonstrated their potential as virtual QA tools, accurately predicting the gamma passing rate (GPR) of volumetric modulated arc therapy (VMAT)plans, thereby ensuring safe and efficient treatment for patients. However, there is limited multi-center research dedicated to predicting the GPR. In this study, a dosiomics-based machine learning approach was employed to construct a prediction model for classifying GPR in multiple radiotherapy institutions. Additionally, the model’s performance was compared by evaluating the impact of two distinct feature selection methods.MethodsA retrospective data collection was conducted on 572 VMAT patients across three radiotherapy institutions. Utilizing a three-dimensional dose verification technique grounded in real-time measurements, γ analysis was conducted according to the criteria of 3%/2 mm and 2%/2 mm, employing a dose threshold of 10% along with absolute dose and global normalization mode. Dosiomics features were extracted from the dose files, and distinct subsets of features were selected as inputs for the model using the random forest (RF) and RF combined with SHapley Additive exPlanations (SHAP) methods. The data underwent training using the extreme gradient boosting (XGBoost) algorithm, and the model’s classification performance was assessed through F1-score and area under the curve (AUC) values.ResultsThe model exhibited optimal performance under the 3%/2 mm criteria, utilizing a subset of 20 features and attaining an AUC value of 0.88 and an F1-score of 0.89. Similarly, under the 2%/2 mm criteria, the model demonstrated superior performance with a subset of 10 features, resulting in an AUC value of 0.91 and an F1-score of 0.89. The feature selection methods of RF and RF + SHAP have achieved good model performance by selecting as few features as possible.ConclusionBased on the multi-center PSQA results, it is possible to utilize dosiomics features extracted from dose files to construct a machine learning predictive model. This model demonstrates excellent discriminative abilities, thus promoting the progress of gamma passing rate prognostic models in clinical application and implementation. Furthermore, it holds potential in providing patients with secure and efficient personalized QA management, while also reducing the workload of medical physicists

Additional file 1 of Novel KMT2B gene mutation in MUC4 positive low-grade fibromyxoid sarcoma

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