KAT8/SIRT7-mediated Fascin-K41 acetylation/deacetylation regulates tumor metastasis in esophageal squamous cell carcinoma.

Fascin actin-bundling protein 1 (Fascin) is highly expressed in a variety of cancers, including esophageal squamous cell carcinoma (ESCC), working as an important oncogenic protein and promoting the migration and invasion of cancer cells by bundling F-actin to facilitate the formation of filopodia and invadopodia. However, it is not clear how exactly the function of Fascin is regulated by acetylation in cancer cells. Here, in ESCC cells, the histone acetyltransferase KAT8 catalyzed Fascin lysine 41 (K41) acetylation, to inhibit Fascin-mediated F-actin bundling and the formation of filopodia and invadopodia. Furthermore, NAD-dependent protein deacetylase sirtuin (SIRT) 7-mediated deacetylation of Fascin-K41 enhances the formation of filopodia and invadopodia, which promotes the migration and invasion of ESCC cells. Clinically, the analysis of cancer and adjacent tissue samples from patients with ESCC showed that Fascin-K41 acetylation was lower in the cancer tissue of patients with lymph node metastasis than in that of patients without lymph node metastasis, and low levels of Fascin-K41 acetylation were associated with a poorer prognosis in patients with ESCC. Importantly, K41 acetylation significantly blocked NP-G2-044, one of the Fascin inhibitors currently being clinically evaluated, suggesting that NP-G2-044 may be more suitable for patients with low levels of Fascin-K41 acetylation, but not suitable for patients with high levels of Fascin-K41 acetylation. © 2024 The Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

Targeting USP10 induces degradation of oncogenic ANLN in esophageal squamous cell carcinoma.

Anillin (ANLN) is a mitosis-related protein that promotes contractile ring formation and cytokinesis, but its cell cycle-dependent degradation mechanisms in cancer cells remain unclear. Here, we show that high expression of ANLN promotes cytokinesis and proliferation in esophageal squamous cell carcinoma (ESCC) cells and is associated with poor prognosis in ESCC patients. Furthermore, the findings of the study showed that the deubiquitinating enzyme USP10 interacts with ANLN and positively regulates ANLN protein levels. USP10 removes the K11- and K63-linked ubiquitin chains of ANLN through its deubiquitinase activity and prevents ANLN ubiquitin-mediated degradation. Importantly, USP10 promotes contractile ring assembly at the cytokinetic furrow as well as cytokinesis by stabilizing ANLN. Interestingly, USP10 and the E3 ubiquitin ligase APC/C co-activator Cdh1 formed a functional complex with ANLN in a non-competitive manner to balance ANLN protein levels. In addition, the macrolide compound FW-04-806 (F806), a natural compound with potential for treating ESCC, inhibited the mitosis of ESCC cells by targeting USP10 and promoting ANLN degradation. F806 selectively targeted USP10 and inhibited its catalytic activity but did not affect the binding of Cdh1 to ANLN and alters the balance of the USP10-Cdh1-ANLN complex. Additionally, USP10 expression was positively correlated with ANLN level and poor prognosis of ESCC patients. Overall, targeting the USP10-ANLN axis can effectively inhibit ESCC cell-cycle progression.

P300/CBP-associated factor (PCAF)-mediated acetylation of Fascin at lysine 471 inhibits its actin-bundling activity and tumor metastasis in esophageal cancer.

BACKGROUND: Fascin is crucial for cancer cell filopodium formation and tumor metastasis, and is functionally regulated by post-translational modifications. However, whether and how Fascin is regulated by acetylation remains unclear. This study explored the regulation of Fascin acetylation and its corresponding roles in filopodium formation and tumor metastasis. METHODS: Immunoprecipitation and glutathione-S-transferase pull-down assays were performed to examine the interaction between Fascin and acetyltransferase P300/CBP-associated factor (PCAF), and immunofluorescence was used to investigate their colocalization. An in vitro acetylation assay was performed to identify Fascin acetylation sites by using mass spectrometry. A specific antibody against acetylated Fascin was generated and used to detect the PCAF-mediated Fascin acetylation in esophageal squamous cell carcinoma (ESCC) cells using Western blotting by overexpressing and knocking down PCAF expression. An in vitro cell migration assay was performed, and a xenograft model was established to study in vivo tumor metastasis. Live-cell imaging and fluorescence recovery after photobleaching were used to evaluate the function and dynamics of acetylated Fascin in filopodium formation. The clinical significance of acetylated Fascin and PCAF in ESCC was evaluated using immunohistochemistry. RESULTS: Fascin directly interacted and colocalized with PCAF in the cytoplasm and was acetylated at lysine 471 (K471) by PCAF. Using the specific anti-AcK471-Fascin antibody, Fascin was found to be acetylated in ESCC cells, and the acetylation level was consequently increased after PCAF overexpression and decreased after PCAF knockdown. Functionally, Fascin-K471 acetylation markedly suppressed in vitro ESCC cell migration and in vivo tumor metastasis, whereas Fascin-K471 deacetylation exhibited a potent oncogenic function. Moreover, Fascin-K471 acetylation reduced filopodial length and density, and lifespan of ESCC cells, while its deacetylation produced the opposite effect. In the filipodium shaft, K471-acetylated Fascin displayed rapid dynamic exchange, suggesting that it remained in its monomeric form owing to its weakened actin-bundling activity. Clinically, high levels of AcK471-Fascin in ESCC tissues were strongly associated with prolonged overall survival and disease-free survival of ESCC patients. CONCLUSIONS: Fascin interacts directly with PCAF and is acetylated at lysine 471 in ESCC cells. Fascin-K471 acetylation suppressed ESCC cell migration and tumor metastasis by reducing filopodium formation through the impairment of its actin-bundling activity.

Preoperative identification of microvascular invasion in hepatocellular carcinoma by XGBoost and deep learning.

PURPOSE: Microvascular invasion (MVI) is a valuable predictor of survival in hepatocellular carcinoma (HCC) patients. This study developed predictive models using eXtreme Gradient Boosting (XGBoost) and deep learning based on CT images to predict MVI preoperatively. METHODS: In total, 405 patients were included. A total of 7302 radiomic features and 17 radiological features were extracted by a radiomics feature extraction package and radiologists, respectively. We developed a XGBoost model based on radiomics features, radiological features and clinical variables and a three-dimensional convolutional neural network (3D-CNN) to predict MVI status. Next, we compared the efficacy of the two models. RESULTS: Of the 405 patients, 220 (54.3%) were MVI positive, and 185 (45.7%) were MVI negative. The areas under the receiver operating characteristic curves (AUROCs) of the Radiomics-Radiological-Clinical (RRC) Model and 3D-CNN Model in the training set were 0.952 (95% confidence interval (CI) 0.923-0.973) and 0.980 (95% CI 0.959-0.993), respectively (p = 0.14). The AUROCs of the RRC Model and 3D-CNN Model in the validation set were 0.887 (95% CI 0.797-0.947) and 0.906 (95% CI 0.821-0.960), respectively (p = 0.83). Based on the MVI status predicted by the RRC and 3D-CNN Models, the mean recurrence-free survival (RFS) was significantly better in the predicted MVI-negative group than that in the predicted MVI-positive group (RRC Model: 69.95 vs. 24.80 months, p < 0.001; 3D-CNN Model: 64.06 vs. 31.05 months, p = 0.027). CONCLUSION: The RRC Model and 3D-CNN models showed considerable efficacy in identifying MVI preoperatively. These machine learning models may facilitate decision-making in HCC treatment but requires further validation.

Multiphase convolutional dense network for the classification of focal liver lesions on dynamic contrast-enhanced computed tomography.

BACKGROUND: The accurate classification of focal liver lesions (FLLs) is essential to properly guide treatment options and predict prognosis. Dynamic contrast-enhanced computed tomography (DCE-CT) is still the cornerstone in the exact classification of FLLs due to its noninvasive nature, high scanning speed, and high-density resolution. Since their recent development, convolutional neural network-based deep learning techniques has been recognized to have high potential for image recognition tasks. AIM: To develop and evaluate an automated multiphase convolutional dense network (MP-CDN) to classify FLLs on multiphase CT. METHODS: A total of 517 FLLs scanned on a 320-detector CT scanner using a four-phase DCE-CT imaging protocol (including precontrast phase, arterial phase, portal venous phase, and delayed phase) from 2012 to 2017 were retrospectively enrolled. FLLs were classified into four categories: Category A, hepatocellular carcinoma (HCC); category B, liver metastases; category C, benign non-inflammatory FLLs including hemangiomas, focal nodular hyperplasias and adenomas; and category D, hepatic abscesses. Each category was split into a training set and test set in an approximate 8:2 ratio. An MP-CDN classifier with a sequential input of the four-phase CT images was developed to automatically classify FLLs. The classification performance of the model was evaluated on the test set; the accuracy and specificity were calculated from the confusion matrix, and the area under the receiver operating characteristic curve (AUC) was calculated from the SoftMax probability outputted from the last layer of the MP-CDN. RESULTS: A total of 410 FLLs were used for training and 107 FLLs were used for testing. The mean classification accuracy of the test set was 81.3% (87/107). The accuracy/specificity of distinguishing each category from the others were 0.916/0.964, 0.925/0.905, 0.860/0.918, and 0.925/0.963 for HCC, metastases, benign non-inflammatory FLLs, and abscesses on the test set, respectively. The AUC (95% confidence interval) for differentiating each category from the others was 0.92 (0.837-0.992), 0.99 (0.967-1.00), 0.88 (0.795-0.955) and 0.96 (0.914-0.996) for HCC, metastases, benign non-inflammatory FLLs, and abscesses on the test set, respectively. CONCLUSION: MP-CDN accurately classified FLLs detected on four-phase CT as HCC, metastases, benign non-inflammatory FLLs and hepatic abscesses and may assist radiologists in identifying the different types of FLLs.

Noninvasive evaluation of radiation-enhanced glioma cells invasiveness by ultra-high-field (1)H-MRS in vitro.

INTRODUCTION: Glioma is the most common type of the primary CNS tumor. Radiotherapy is an important treatment measure after surgery. However, its highly invasive character is the main reason of postoperative recurrence. The aim of the study was to probe the correlation between the invasion ability and the metabolite characteristics of glioma cells at the cellular level after irradiation by using 14.7T high-resolution nuclear proton magnetic resonance spectroscopy ((1)H-MRS). METHODS: To determine the matrix metalloproteinase-2 (MMP-2) activity and metabolite ratios of glioma cells after irradiation with different doses of X-rays, U87 and C6 glioma cells were exposed to X-ray irradiation of 0, 1, 5, 10, and 15Gy. After 20h, the perchloric acid (PCA) extraction method was used to evaluate water-soluble metabolites [choline (Cho), creatine (Cr), and N-acetylaspartate (NAA)], and (1)H-MRS patterns and changes in metabolite ratios were observed in vitro by 14.7T high resolution (1)H-MRS. Matrigel invasion assays and gelatin zymography were performed to test the invasion ability of U87 and C6 glioma cells. RESULTS: Good MR spectra were obtained from PCA method extracts of U87 and C6 glioma cells. Both radiation-induced MMP-2 activity and the Cho/Cr and Cho/NAA ratios increased after irradiation, and their increase occurred in a dose-dependent manner. The MMP-2 activity and the Cho/Cr and Cho/NAA ratios of glioma cells increased after irradiation up to 10Gy and decreased thereafter. In particular, the Cho/NAA ratio of U87 cells increased from 3.55±0.06 (0Gy) to 9.13±0.30 (10Gy) and then declined to 5.94±0.15 (15Gy). Furthermore, the invasion ability of glioma cells had a strong positive correlation with the Cho/Cr and Cho/NAA ratios. Both the Cho/Cr ratio and the Cho/NAA ratio of U87 glioma cells were highly positively correlated with the number of invading cells in the Matrigel invasion assay. The Pearson's correlation coefficient (r) values of U87 cells were 0.89 (Cho/Cr ratio versus invasion ability) and 0.91 (Cho/NAA ratio versus invasion ability) (P<0.01). C6 cells exhibited similar changes to those of U87 cells. CONCLUSIONS: In vitro high-resolution (1)H-MRS is useful for detecting glioma invasiveness at the cellular level.