HDAC6-mediated deacetylation of FLOT2 maintains stability and tumorigenic function of FLOT2 in nasopharyngeal carcinoma. / HDAC6通过介导FLOT2åŽ»ä¹™é °åŒ–ç»´æŒå ¶åœ¨é¼»å’½ç™Œä¸­çš„ç¨³å®šåŠä¿ƒç˜¤ä½œç”¨.

OBJECTIVES: Flotillin-2 (FLOT2) is a prototypical oncogenic and a potential target for cancer therapy. However, strategies for targeting FLOT2 remain undefined. Post-translational modifications are crucial for regulating protein stability, function, and localization. Understanding the mechanisms and roles of post-translational modifications is key to developing targeted therapies. This study aims to investigate the regulation and function of lysine acetylation of FLOT2 in nasopharyngeal carcinoma, providing new insights for targeting FLOT2 in cancer intervention. METHODS: The PhosphoSitePlus database was used to analyze the lysine acetylation sites of FLOT2, and a lysine acetylation site mutation of FLOT2 [FLOT2 (K211R)] was constructed. Nasopharyngeal carcinoma cells were treated with histone deacetylase (HDAC) inhibitor trichostatin A (TSA) and Sirt family deacetylase inhibitor nicotinamide (NAM). TSA-treated human embryonic kidney (HEK)-293T were transfected with FLOT2 mutant plasmids. Co-immunoprecipitation (Co-IP) was used to detect total acetylation levels of FLOT2 and the effects of specific lysine (K) site mutations on FLOT2 acetylation. Western blotting was used to detect FLOT2/FLAG-FLOT2 protein expression in TSA-treated nasopharyngeal carcinoma cells transfected with FLOT mutant plasmids, and real-time reverse transcription PCR (real-time RT-PCR) was used to detect FLOT2 mRNA expression. Nasopharyngeal carcinoma cells were treated with TSA combined with MG132 or chloroquine (CQ) to analyze FLOT2 protein expression. Cycloheximide (CHX) was used to treat HEK-293T cells transfected with FLAG-FLOT2 (WT) or FLAG-FLOT2(K211R) plasmids to assess protein degradation rates. The BioGrid database was used to identify potential interactions between FLOT2 and HDAC6, which were validated by Co-IP. HEK-293T cells were co-transfected with FLAG-FLOT2 (WT)/FLAG-FLOT2 (K211R) and Vector/HDAC6 plasmids, and grouped into FLAG-FLOT2 (WT)+Vector, FLAG-FLOT2 (WT)+HDAC6, FLAG-FLOT2 (K211R)+Vector, and FLAG-FLOT2 (K211R)+HDAC6 to analyze the impact of K211R mutation on total lysine acetylation levels. In 6-0B cells, overexpression of FLOT2 (WT) and FLOT2 (K211R) was performed, and the biological functions of FLOT2 acetylation site mutants were assessed using cell counting kit-8 (CCK-8), colony formation, and Transwell invasion assays. RESULTS: The PhosphoSitePlus database indicated that FLOT2 has an acetylation modification at the K211 site. Co-IP confirmed significant acetylation of FLOT2, with TSA significantly increasing overall FLOT2 acetylation levels, while NAM had no effect. Mutation at the K211 site significantly reduced overall FLOT2 acetylation, unaffected by TSA. TSA decreased FLOT2 protein expression in nasopharyngeal carcinoma cells without affecting FLOT2 mRNA levels or FLOT2 (K211R) protein expression in transfected cells. The degradation rate of FLOT2 (K211R) protein was significantly slower than that of FLOT2 (WT). The proteasome inhibitor MG132 prevented TSA-induced FLOT2 degradation, while the lysosome inhibitor CQ did not. BioGrid data suggested a potential interaction between FLOT2 and HDAC6, confirmed by Co-IP. Knockdown of HDAC6 in nasopharyngeal carcinoma cells significantly increased FLOT2 acetylation; co-transfection of HDAC6 and FLAG-FLOT2 (WT) significantly decreased total lysine acetylation levels, whereas co-transfection of HDAC6 and FLAG-FLOT2 (K211R) had no effect. Knockdown of HDAC6 significantly reduced FLOT2 protein levels without affecting mRNA levels. MG132 prevented HDAC6-knockdown-induced FLOT2 degradation. Knockdown of HDAC6 significantly accelerated FLOT2 degradation. Nasopharyngeal carcinoma cells transfected with FLOT2 (K211R) showed significantly higher proliferation and invasion than those transfected with FLOT2 (WT). CONCLUSIONS: The K211 site of FLOT2 undergoes acetylation modification, and HDAC6 mediates deacetylation at this site, inhibiting proteasomal degradation of FLOT2 and maintaining its stability and tumor-promoting function in nasopharyngeal carcinoma.

CT-based radiomics for predicting radio-chemotherapy response and overall survival in nonsurgical esophageal carcinoma.

Background: To predict treatment response and 2 years overall survival (OS) of radio-chemotherapy in patients with esophageal cancer (EC) by radiomics based on the computed tomography (CT) images. Methods: This study retrospectively collected 171 nonsurgical EC patients treated with radio-chemotherapy from Jan 2010 to Jan 2019. 80 patients were randomly divided into training (n=64) and validation (n=16) cohorts to predict the radiochemotherapy response. The models predicting treatment response were established by Lasso and logistic regression. A total of 156 patients were allocated into the training cohort (n=110), validation cohort (n=23) and test set (n=23) to predict 2-year OS. The Lasso Cox model and Cox proportional hazards model established the models predicting 2-year OS. Results: To predict the radiochemotherapy response, WFK as a radiomics feature, and clinical stages and clinical M stages (cM) as clinical features were selected to construct the clinical-radiomics model, achieving 0.78 and 0.75 AUC (area under the curve) in the training and validation sets, respectively. Furthermore, radiomics features called WFI and WGI combined with clinical features (smoking index, pathological types, cM) were the optimal predictors to predict 2-year OS. The AUC values of the clinical-radiomics model were 0.71 and 0.70 in the training set and validation set, respectively. Conclusions: This study demonstrated that planning CT-based radiomics showed the predictability of the radiochemotherapy response and 2-year OS in nonsurgical esophageal carcinoma. The predictive results prior to treatment have the potential to assist physicians in choosing the optimal therapeutic strategy to prolong overall survival.

Corrigendum: Predicting the recurrence and overall survival of patients with glioma based on histopathological images using deep learning.

[This corrects the article DOI: 10.3389/fneur.2023.1100933.].

Predicting the recurrence and overall survival of patients with glioma based on histopathological images using deep learning.

Background: A deep learning (DL) model based on representative biopsy tissues can predict the recurrence and overall survival of patients with glioma, leading to optimized personalized medicine. This research aimed to develop a DL model based on hematoxylin-eosin (HE) stained pathological images and verify its diagnostic accuracy. Methods: Our study retrospectively collected 162 patients with glioma and randomly divided them into a training set (n = 113) and a validation set (n = 49) to build a DL model. The HE-stained slide was segmented into a size of 180 × 180 pixels without overlapping. The patch-level features were extracted by the pre-trained ResNet50 to predict the recurrence and overall survival. Additionally, a light-strategy was introduced where low-size digital biopsy images with clinical information were inputted into the DL model to ensure minimum memory occupation. Results: Our study extracted 512 histopathological features from the HE-stained slides of each glioma patient. We identified 36 and 18 features as significantly related to disease-free survival (DFS) and overall survival (OS), respectively, (P < 0.05) using the univariate Cox proportional-hazards model. Pathomics signature showed a C-index of 0.630 and 0.652 for DFS and OS prediction, respectively. The time-dependent receiver operating characteristic (ROC) curves, along with nomograms, were used to assess the diagnostic accuracy at a fixed time point. In the validation set (n = 49), the area under the curve (AUC) in the 1- and 2-year DFS was 0.955 and 0.904, respectively, and the 2-, 3-, and 5-year OS were 0.969, 0.955, and 0.960, respectively. We stratified the patients into low- and high-risk groups using the median hazard score (0.083 for DFS and-0.177 for OS) and showed significant differences between these groups (P < 0.001). Conclusion: Our results demonstrated that the DL model based on the HE-stained slides showed the predictability of recurrence and survival in patients with glioma. The results can be used to assist oncologists in selecting the optimal treatment strategy in clinical practice.

Development and validation of a chromatin regulator prognostic signature in colon adenocarcinoma.

Aberrant expression of chromatin regulators (CRs) could lead to the development of various diseases including cancer. However, the biological function and prognosis role of CRs in colon adenocarcinoma (COAD) remains unclear. We performed the clustering analyses for expression profiling of COAD downloaded from The Cancer Genome Atlas. We developed a chromatin regulator prognostic model, which was validated in an independent cohort data. Time-intendent receiver operating characteristics curve was used to evaluate predict ability of model. Univariate and multivariate cox regression were used to assess independence of risk score. Nomogram was established to assess individual risk. Gene ontology, and Kyoto Encyclopedia of genes and genomes, gene set variation analysis and gene set enrichment analysis were performed to explore the function of CRs. Immune infiltration and drug sensitivity were also performed to assess effect of CRs on treatment in COAD. COAD can be separated into two subtypes with different clinical characteristics and prognosis. The C2 had elevated immune infiltration levels and low tumor purity. Using 12 chromatin regulators, we developed and validated a prognostic model that can predict the overall survival of COAD patients. We built a risk score that can be an independent prognosis predictor of COAD. The nomogram score system achieved the best predict ability and were also confirmed by decision curve analysis. There were significantly different function and pathway enrichment, immune infiltration levels, and tumor mutation burden between high-risk and low-risk group. The external validation data also indicated that high-risk group had higher stable disease/progressive disease response rate and poorer prognosis than low-risk group. Besides, the signature genes included in the model could cause chemotherapy sensitivity to some small molecular compounds. Our integrative analyses for chromatin regulators could provide new insights for the risk management and individualized treatment in COAD.