Epigenetic pathways play a critical role in the initiation, progression, and metastasis of cancer. Over the past few decades, significant progress has been made in the development of targeted epigenetic modulators (e.g., inhibitors). However, epigenetic inhibitors have faced multiple challenges, including limited clinical efficacy, toxicities, lack of subtype selectivity, and drug resistance. As a result, the design of new epigenetic modulators (e.g., degraders) such as PROTACs, molecular glue, and hydrophobic tagging (HyT) degraders has garnered significant attention from both academia and pharmaceutical industry, and numerous epigenetic degraders have been discovered in the past decade. In this review, we aim to provide an in-depth illustration of new degrading strategies (2017-2023) targeting epigenetic proteins for cancer therapy, focusing on the rational design, pharmacodynamics, pharmacokinetics, clinical status, and crystal structure information of these degraders. Importantly, we also provide deep insights into the potential challenges and corresponding remedies of this approach to drug design and development. Overall, we hope this review will offer a better mechanistic understanding and serve as a useful guide for the development of emerging epigenetic-targeting degraders.

Objectives:To investigate the early related factors for hepatic insufficiency after hemihepatectomy and to construct and validate a nomogram model.Methods:This is a retrospective cohort study.There were 207 patients with liver tumor who underwent hemihepatectomy in the Department of Hepatobiliary Surgery, Cancer Hospital, Chinese Academy of Medical Sciences from October 2016 to December 2022. Using the random number method, patients were randomly divided into a model group( n=166) and a validation group( n=41) according to an 4∶1 ratio. There were 118 males and 48 females in the modeling group,with an age ( M(IQR)) of 59.0(13.3) years (range: 22.0 to 81.0 years),42 patients in the group with postoperative liver insufficiency and 124 patients in the group without postoperative liver insufficiency. There were 32 males and 9 females in the validation group, with an age of 54.0(19.0) years (range: 25.0 to 81.0 years). The first results of the peripheral blood test of patients within 24 hours after surgery were collected,and the independent related factors for incomplete postoperative liver function were determined by multivariate Logistic regression analysis,and related factors of postoperative incomplete liver function were screened by best subset selection. A nomogram model of the related factors of postoperative hepatic insufficiency after hemihepatectomy was constructed using R software,validated by internal and external validation of the model. Results:Multivariate logistic regression analysis showed that elevated D-dimer level and decreased antithrombin Ⅲ (AT-Ⅲ) activity within 24 hours after surgery were independent related factors for the development of postoperative hepatic insufficiency in hemihepatectomized patients. The results of the best subset selection showed that ALT, D-dimer, and AT-Ⅲ activity levels within 24 hours postoperatively were the most relevant factors for postoperative hepatic insufficiency. The R software was applied to build a nomogram prediction model based on the above three indicators in the model set, and the receiver operating characteristic(ROC) curve of the model showed an area under the curve of 0.803 and the calibration curve showed a U-index of -0.012 for the model( P=0.977). The results of the clinical decision analysis and the clinical impact curve indicated that the model had good clinical utility. The internal validation results of the Bootstrap method suggested that the model had reasonable consistency. The area under the ROC curve of the validation group model was 0.806, suggesting that the model had a good generalization prediction ability. Conclusions:The levels of ALT, D-dimer, and AT-Ⅲ activity within 24 hours after hemihepatectomy are valuable indicators for predicting liver insufficiency after hemihepatectomy. The nomogram model is reliable and can be used as an indicator for close postoperative monitoring.

Objectives:To investigate the early related factors for hepatic insufficiency after hemihepatectomy and to construct and validate a nomogram model.Methods:This is a retrospective cohort study.There were 207 patients with liver tumor who underwent hemihepatectomy in the Department of Hepatobiliary Surgery, Cancer Hospital, Chinese Academy of Medical Sciences from October 2016 to December 2022. Using the random number method, patients were randomly divided into a model group( n=166) and a validation group( n=41) according to an 4∶1 ratio. There were 118 males and 48 females in the modeling group,with an age ( M(IQR)) of 59.0(13.3) years (range: 22.0 to 81.0 years),42 patients in the group with postoperative liver insufficiency and 124 patients in the group without postoperative liver insufficiency. There were 32 males and 9 females in the validation group, with an age of 54.0(19.0) years (range: 25.0 to 81.0 years). The first results of the peripheral blood test of patients within 24 hours after surgery were collected,and the independent related factors for incomplete postoperative liver function were determined by multivariate Logistic regression analysis,and related factors of postoperative incomplete liver function were screened by best subset selection. A nomogram model of the related factors of postoperative hepatic insufficiency after hemihepatectomy was constructed using R software,validated by internal and external validation of the model. Results:Multivariate logistic regression analysis showed that elevated D-dimer level and decreased antithrombin Ⅲ (AT-Ⅲ) activity within 24 hours after surgery were independent related factors for the development of postoperative hepatic insufficiency in hemihepatectomized patients. The results of the best subset selection showed that ALT, D-dimer, and AT-Ⅲ activity levels within 24 hours postoperatively were the most relevant factors for postoperative hepatic insufficiency. The R software was applied to build a nomogram prediction model based on the above three indicators in the model set, and the receiver operating characteristic(ROC) curve of the model showed an area under the curve of 0.803 and the calibration curve showed a U-index of -0.012 for the model( P=0.977). The results of the clinical decision analysis and the clinical impact curve indicated that the model had good clinical utility. The internal validation results of the Bootstrap method suggested that the model had reasonable consistency. The area under the ROC curve of the validation group model was 0.806, suggesting that the model had a good generalization prediction ability. Conclusions:The levels of ALT, D-dimer, and AT-Ⅲ activity within 24 hours after hemihepatectomy are valuable indicators for predicting liver insufficiency after hemihepatectomy. The nomogram model is reliable and can be used as an indicator for close postoperative monitoring.

Objective:To explore the best treatment for local ablation of colon cancer liver metastases (CRLM) by meta-analysis.Methods:The electronic databases of PubMed, Web of Science, Embase, CNKI and the Cochrane Library were searched from the establishment to August 22, 2022, and studies that report outcomes with comparison between microwave ablation (WMA) and radiofrequency ablation (RFA) in CRLM treatment were selected by inclusion and exclusion criteria. Furthermore, the perioperative and survival data were statistically summarized and analyzed by Review Manager 5.3 software.Results:A total of 5 retrospective studies were included with a total sample size of 648 cases, including 316 cases (48.8%) in the WMA group and 332 cases (51.2%) in the RFA group. The results of meta-analysis showed that locoregional recurrence rate in WMA group was significantly lower than that in RFA group. The 1-year and 2-year disease-free survival (DFS) of the WMA group was significantly better than that of the RFA group with HR of 1.77 ( P=0.04, 95% CI: 1.04-3.02) and 1.60 ( P=0.02, 95% CI: 1.09-2.35), respectively. Conclusion:The local control rate and 1-year and 2-year DFS of WMA were superior to RFA.

The avian influenza A (H7N9) virus is a zoonotic virus that is closely associated with live poultry markets. It has caused infections in humans in China since 2013. Five waves of the H7N9 influenza epidemic occurred in China between March 2013 and September 2017. H7N9 with low-pathogenicity dominated in the first four waves, whereas highly pathogenic H7N9 influenza emerged in poultry and spread to humans during the fifth wave, causing wide concern. Specialists and officials from China and other countries responded quickly, controlled the epidemic well thus far, and characterized the virus by using new technologies and surveillance tools that were made possible by their preparedness efforts. Here, we review the characteristics of the H7N9 viruses that were identified while controlling the spread of the disease. It was summarized and discussed from the perspectives of molecular epidemiology, clinical features, virulence and pathogenesis, receptor binding, T-cell responses, monoclonal antibody development, vaccine development, and disease burden. These data provide tools for minimizing the future threat of H7N9 and other emerging and re-emerging viruses, such as SARS-CoV-2.
Animals ; COVID-19 ; China/epidemiology* ; Humans ; Influenza A Virus, H7N9 Subtype ; Influenza in Birds/epidemiology* ; Influenza, Human/prevention & control* ; Poultry ; SARS-CoV-2

Objective:To study the nutritional risk and nutritional support in patients with liver cancer during perioperative period.Methods:In Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical, the clinical data of 507 liver cancer patients who underwent surgery College from January 2019 to January 2020 were retrospectively analyzed. The perioperative nutrition was assessed by nutritional risk screening 2002 (NRS 2002), ≥3 scores was diagnosed nutritional risk, and the nutritional support was counted.Results:Among 507 patients, 82 cases (16.2%) had nutritional risk. There was no statistical difference in rate of nutritional risk between male and female: 15.3% (58/379) vs. 18.8% (24/128), χ2 = 0.838, P>0.05. There was no statistical difference in rate of nutritional risk between primary liver cancer patients and secondary liver cancer patients: 18.0% (63/350) vs. 12.1% (19/157), χ2 = 2.781, P>0.05. The rate of nutritional risk in ≥ 60 years old patients was significantly higher than that in <60 years old patients: 25.9% (62/239) vs. 7.5% (20/268), and there was statistical difference ( χ2 = 31.819, P<0.01). The age, incidence of dystrophy and rate of nutritional support before surgery in patients with nutritional risk were significantly higher than those in patients without nutritional risk: (65.3 ± 12.7) years old vs. (55.9 ± 8.9) years old, 13.4% (11/82) vs. 0 and 24.4% (20/82) vs. 2.6% (11/425), and there were statistical differences ( P<0.01); there were no statistical differences in sex composition, tumor origin, rate of nutritional support after surgery and albumin between patients with nutritional risk and patients without nutritional risk ( P>0.05). Among 31 nutritional support patients before surgery, parenteral nutrition (PN) was in 1 case, enteral nutrition (EN) was in 30 cases; among 453 nutritional support patients after surgery, PN was in 297 cases, EN was in 27 cases, and PN + EN was in 129 cases. Conclusions:The incidence of nutritional risk in patients with liver cancer during perioperative period is high, and especially elderly patients should pay attention to nutritional support. NRS 2002 is a powerful tool and should be recommended to use at patients with liver cancer, and provide the evidence of nutritional therapy.

Objective:To investigate the definition and influencing factors of early recurrence after resection for synchronous colorectal cancer liver metastases (sCRLM).Methods:Patients with sCRLM in Department of Hepatobiliary Surgery, Cancer Hospital, Chinese Academy of Medical Sciences from December 2008 to December 2016 were included. Restricted cubic spline was used to determine the correlations between the time of recurrence and the long-term prognosis. The univariable and multivariable Cox was performed to measure the feasibility of recurrence within 6 months as the early recurrence. Then apply logistic regression, support vector machine, decision tree, random forest, artificial neural network and XGBoost, these machine learning algorithm to comprehensively rank the importance of every clinicopathological variable to early recurrence, and according to the comprehensively ranks, we introduced variables into the multivariable logistic regression model and observed the receiver operating characteristic curve (ROC) of the logistic regression model, based on the ROC area under curve, Akaike information criterion, and Bayesian information criterion, we identified the best performed variable combination and introduced them into the multivariate logistic regression analysis to confirm the independent risk factors for early recurrence. Subsequently, inverse probability weighting (IPTW) was performed on the therapy-associated independent risk factor to evaluate and validate its influence on the early recurrence of sCRLM patients after reducing the standardized mean difference of all covariates.Results:A total of 228 sCRLM patients who received resection were enrolled and followed up from 2.10 to 108.57 months. There were 142 males and 86 females, aged (55.89±0.67) years old. In 170 (74.6%) patients with recurrence, restricted cube analysis determined that the hazard ratio (HR) of disease free survival (DFS) and overall survival (OS) satisfies a linear relationship ( P<0.05), and Cox analysis indicated that 6 months as the time cutoff for defining early recurrence was feasible ( HR=3.405, 95% CI: 2.098-5.526, P<0.05). Early recurrence was occurred in 93 (40.79%) patients. The survival rate of patients in early recurrence group was significantly lower than that in the late recurrence group ( HR=3.405, 95% CI: 2.098-5.526, P<0.05, and the 5-year survival rate was 14.0% vs 52.0%). Comprehensive analysis of 6 machine learning algorithms identified that the total number of lymph node dissection >22 ( OR=0.258, 95% CI: 0.132-0.506, P<0.05) is an independent protective factor for early recurrence, while the number of liver metastases>3 ( OR=4.715, 95% CI: 2.467-9.011, P<0.05) and postoperative complications ( OR=2.334, 95% CI: 1.269-4.291, P<0.05) are independent risk factors. Finally, the IPTW analysis fully reduced the influence of covariate confounding influence via causal inference to prove lymph node dissection associated with early recurrence (IPTW OR=0.29, P<0.05), benefiting the DFS (IPTW HR=0.4887, P<0.05), but without influence on OS (IPTW HR=0.6951, P>0.05). Conclusion:Six months after sCRLM as the definition of early recurrence, it has significant feasibility. The long-term survival of patients with early recurrence is poor. The independent influencing factors of early recurrence after sCRLM are the total number of lymph node dissection, the number of liver metastases and postoperative complications disease.

Objective:To investigate the application value of machine learning algorithms and COX nomogram in the survival prediction of hepatocellular carcinoma (HCC) after resection.Methods:The retrospective and descriptive study was conducted. The clinicopathological data of 375 patients with HCC who underwent radical resection in the Cancer Hospital of Chinese Academy of Medical Sciences and Peking Union Medical College from January 2012 to January 2017 were collected. There were 304 males and 71 females, aged from 21 to 79 years, with a median age of 57 years. According to the random numbers showed in the computer, 375 patients were divided into training dataset consisting of 300 patients and validation dataset consisting of 75 patients, with a ratio of 8∶2. Machine learning algorithms including logistic regression (LR), supporting vector machine (SVM), decision tree (DT), random forest (RF), and artificial neural network (ANN) were used to construct survival prediction models for HCC after resection, so as to identify the optimal machine learning algorithm prediction model. A COX nomogram prediction model for predicting postoperative survival in patients with HCC was also constructed. Comparison of performance for predicting postoperative survival of HCC patients was conducted between the optimal machine learning algorithm prediction model and the COX nomogram prediction model. Observation indicators: (1) analysis of clinicopathological data of patients in the training dataset and validation dataset; (2) follow-up and survival of patients in the training dataset and validation dataset; (3) construction and evaluation of machine learning algorithm prediction models; (4) construction and evaluation of COX nomogram prediction model; (5) evaluation of prediction performance between RF machine learning algorithm prediction model and COX nomogram prediction model. Follow-up was performed using outpatient examination or telephone interview to detect survival of patients up to December 2019 or death. Measurement data with normal distribution were expressed as Mean± SD, and comparison between groups was analyzed by the paired t test. Measurement data with skewed distribution were expressed as M ( P25, P75) or M (range), and comparison between groups was analyzed by the Mann-Whitney U test. Count data were represented as absolute numbers. Comparison between groups was performed using the chi-square test when Tmin ≥5 and N ≥40, using the calibration chi-square test when 1≤ Tmin ≤5 and N ≥40, and using Fisher exact probability when Tmin <1 or N <40. The Kaplan-Meier method was used to calculate survival rate and draw survival curve. The COX proportional hazard model was used for univariate analysis, and variables with P<0.2 were included for the Lasso regression analysis. According to the lambda value, variables affecting prognosis were screened for COX proportional hazard model to perform multivariate analysis. Results:(1) Analysis of clinicopathological data of patients in the training dataset and validation dataset: cases without microvascular invasion or with microvascular invasion, cases without liver cirrhosis or with liver cirrhosis of the training dataset were 292, 8, 105, 195, respectively, versus 69, 6, 37, 38 of the validation dataset, showing significant differences between the two groups ( χ2=4.749, 5.239, P<0.05). (2) Follow-up and survival of patients in the training dataset and validation dataset: all the 375 patients received follow-up. The 300 patients in the training dataset were followed up for 1.1-85.5 months, with a median follow-up time of 50.3 months. Seventy-five patients in the validation dataset were followed up for 1.0-85.7 months, with a median follow-up time of 46.7 months. The postoperative 1-, 3-year overall survival rates of the 375 patients were 91.7%, 79.5%. The postoperative 1-, 3-year overall survival rates of the training dataset were 92.0%, 79.7%, versus 90.7%, 81.9% of the validation dataset, showing no significant difference in postoperative survival between the two groups ( χ2=0.113, P>0.05). (3) Construction and evaluation of machine learning algorithm prediction models. ① Selection of the optimal machine learning algorithm prediction model: according to information divergence of variables for prediction of 3 years postoperative survival of HCC, five machine learning algorithms were used to comprehensively rank the variables of clinicopathological factors of HCC, including LR, SVM, DT, RF, and ANN. The main predictive factors were screened out, as hepatitis B e antigen (HBeAg), surgical procedure, maximum tumor diameter, perioperative blood transfusion, liver capsule invasion, and liver segment Ⅳ invasion. The rank sequence 3, 6, 9, 12, 15, 18, 21, 24, 27, 29 variables of predictive factors were introduced into 5 machine learning algorithms in turn. The results showed that the area under curve (AUC) of the receiver operating charateristic curve of LR, SVM, DT, and RF machine learning algorithm prediction models tended to be stable when 9 variables are introduced. When more than 12 variables were introduced, the AUC of ANN machine learning algorithm prediction model fluctuated significantly, the stability of AUC of LR and SVM machine learning algorithm prediction models continued to improve, and the AUC of RF machine learning algorithm prediction model was nearly 0.990, suggesting RF machine learning algorithm prediction model as the optimal machine learning algorithm prediction model. ② Optimization and evaluation of RF machine learning algorithm prediction model: 29 variables of predictive factors were sequentially introduced into the RF machine learning algorithm to construct the optimal RF machine learning algorithm prediction model in the training dataset. The results showed that when 10 variables were introduced, results of grid search method showed 4 as the optimal number of nodes in DT, and 1 000 as the optimal number of DT. When the number of introduced variables were not less than 10, the AUC of RF machine learning algorithm prediction model was about 0.990. When 10 variables were introduced, the RF machine learning algorithm prediction model had an AUC of 0.992 for postoperative overall survival of 3 years, a sensitivity of 0.629, a specificity of 0.996 in the training dataset, an AUC of 0.723 for postoperative overall survival of 3 years, a sensitivity of 0.177, a specificity of 0.948 in the validation dataset. (4) Construction and evaluation of COX nomogram prediction model. ① Analysis of postoperative survival factors of HCC patients in the training dataset. Results of univariate analysis showed that HBeAg, alpha fetoprotein (AFP), preoperative blood transfusion, maximum tumor diameter, liver capsule invasion, and degree of tumor differentiation were related factors for postoperative survival of HCC patients [ hazard ratio ( HR)=1.958, 1.878, 2.170, 1.188, 2.052, 0.222, 95% confidence interval ( CI): 1.185-3.235, 1.147-3.076, 1.389-3.393, 1.092-1.291, 1.240-3.395, 0.070-0.703, P<0.05]. Clinico-pathological data with P<0.2 were included for Lasso regression analysis, and the results showed that age, HBeAg, AFP, surgical procedure, perioperative blood transfusion, maximum tumor diameter, tumor located at liver segment Ⅴ or Ⅷ, liver capsule invasion, and degree of tumor differentiation as high differentiation, moderate-high differentiation, moderate differentiation, moderate-low differentiation were related factors for postoperative survival of HCC patients. The above factors were included for further multivariate COX analysis, and the results showed that HBeAg, surgical procedure, maximum tumor diameter were independent factors affecting postoperative survival of HCC patients ( HR=1.770, 8.799, 1.142, 95% CI: 1.049- 2.987, 1.203-64.342, 1.051-1.242, P<0.05). ② Construction and evaluation of COX nomogram prediction model: the clinicopathological factors of P≤0.1 in the COX multivariate analysis were induced to Rstudio software and rms software package to construct COX nomogram prediction model in the training dataset. The COX nomogram prediction model for predicting postoperative overall survival had an consistency index of 0.723 (se=0.028), an AUC of 0.760 for postoperative overall survival of 3 years in the training dataset, an AUC of 0.795 for postoperative overall survival of 3 years in the validation dataset. The verification of the calibration plot in the training dataset showed that the COX nomogram prediction model had a good prediction performance for postoperative survival. COX nomogram score=0.627 06×HBeAg (normal=0, abnormal=1)+ 0.134 34×maximum tumor diameter (cm)+ 2.107 58×surgical procedure (laparoscopy=0, laparotomy=1)+ 0.545 58×perioperative blood transfusion (without blood transfusion=0, with blood transfusion=1)-1.421 33×high differentiation (non-high differentiation=0, high differentiation=1). The COX nomogram risk scores of all patients were calculated. Xtile software was used to find the optimal threshold of COX nomogram risk scores. Patients with risk scores ≥2.9 were assigned into high risk group, and patients with risk scores <2.9 were assigned into low risk group. Results of Kaplan-Meier overall survival curve showed a significant difference in the postoperative overall survival between low risk group and high risk group of the training dataset ( χ2=33.065, P<0.05). There was a significant difference in the postoperative overall survival between low risk group and high risk group of the validation dataset ( χ2=6.585, P<0.05). Results of further analysis by the decision-making curve showed that COX nomogram prediction model based on the combination of HBeAg, surgical procedure, perioperative blood transfusion, maximum tumor diameter, and degree of tumor differentiation was superior to any of the above individual factors in prediction performance. (5) Evaluation of prediction performance between RF machine learning algorithm prediction model and COX nomogram prediction model: prediction difference between two models was investigated by analyzing maximun tumor diameter (the important variable shared in both models), and by comparing the predictive error curve of both models. The results showed that the postoperative 3-year survival rates predicted by RF machine learning algorithm prediction model and COX nomogram prediction model were 77.17% and 74.77% respectively for tumor with maximum diameter of 2.2 cm ( χ2=0.182, P>0.05), 57.51% and 61.65% for tumor with maximum diameter of 6.3 cm ( χ2=0.394, P>0.05), 51.03% and 27.52% for tumor with maximum diameter of 14.2 cm ( χ2=12.762, P<0.05). With the increase of the maximum tumor diameter, the difference in survival rates predicted between the two models turned larger. In the validation dataset, the AUC for postoperative overall survival of 3 years of RF machine learning algorithm prediction model and COX nomogram prediction model was 0.723 and 0.795, showing a significant difference between the two models ( t=3.353, P<0.05). Resluts of Bootstrap cross-validation for prediction error showed that the integrated Brier scores of RF machine learning algorithm prediction model and COX nomogram prediction model for predicting 3-year survival were 0.139 and 0.134, respectively. The prediction error of COX nomogram prediction model was lower than that of RF machine learning algorithm prediction model. Conclusion:Compared with machine learning algorithm prediction models, the COX nomogram prediction model performs better in predicting 3 years postoperative survival of HCC, with fewer variables, which is easy for clinical use.

Objective:To evaluate the effect of ATPase family AAA-domain containing protein 3A (ATAD3A) gene silencing on the proliferation, invasion and migration of A375 human melanoma cells.Methods:From August to December in 2019, melanoma and paracancerous tissues were collected from 3 patients with pathologically diagnosed melanoma in People′s Hospital of Chongqing Yubei District, and Western blot analysis was performed to measure the protein expression of ATAD3A in the above tissues. Cultured A375 human melanoma cells were divided into 2 groups to be infected with a lentiviral vector carrying shATAD3A (shATAD3A group) and an empty vector (shCtrl group) respectively, and real-time fluorescence-based quantitative PCR (qRT-PCR) and Western blot analysis were performed to verify the interference efficiency. Cell counting kit-8 (CCK8) assay and colony formation assay were performed to compare cell proliferative ability and colony-formation ability respectively between the 2 groups, and Transwell invasion assay and wound healing assay to compare invasive and migratory abilities respectively between the above 2 groups. Western blot analysis was performed to determine the expression of cell self-renewal-related proteins (NANOG, SRY-related high-mobility-group box protein SOX2, octamer-binding protein 4[OCT4]) and invasion- and migration-related proteins (matrix metalloproteinase 2[MMP2], vimentin, zinc-finger transcription factor SLUG) in the 2 groups. Two-independent-sample t test was used to compare the experimental indices between the 2 groups. Results:Western blot analysis showed that ATAD3A was significantly highly expressed in the 3 melanoma tissues compared with the paracancerous tissues ( t = 10.825, P < 0.001) . qRT-PCR and Western blot analysis showed that the mRNA and protein expression of ATAD3A in A375 cells was significantly lower in the shATAD3A group (0.230 ± 0.073, 0.279 ± 0.267, respectively) than in the shCtrl group (1.000 ± 0.244, 0.867 ± 0.115, respectively; t = 9.461, 8.595, respectively; P < 0.001 or = 0.002) , indicating that the ATAD3A gene-silenced A375 cell line was successfully constructed. Colony formation assay revealed that the colony-formation rate was significantly lower in the shATAD3A group than in the shCtrl group (22.667% ± 2.510% vs. 43.667% ± 5.030%, t = 6.464, P = 0.003) , and CCK-8 assay showed that the cellular proliferative activity significantly decreased from day 2 to day 4 in the shATAD3A group compared with the shCtrl group. Wound healing assay showed significantly slower wound healing and decreased wound healing rate from the 12 th hour (32.920% ± 4.642% vs. 49.302% ± 1.448%, t = 5.835, P = 0.004) to the 24 th hour in the shATAD3A group compared with the shCtrl group, and Transwell invasion assay revealed significantly decreased number of invasive cells in the lower Transwell chambers in the shATAD3A group compared with the shCtrl group (68.330 ± 13.050 vs. 234.330 ± 19.139, t = 12.411, P < 0.001) . Western blot analysis showed that the protein expression of NANOG, SOX2, OCT4, MMP2, vimentin, SLUG was significantly lower in the shATAD3A group than in the shCtrl group ( P < 0.05 or 0.001) . Conclusion:ATAD3A is highly expressed in melanoma tissues, and ATAD3A gene silencing can inhibit the proliferation, invasion and migration abilities of melanoma A375 cells.

Neuroendocrine neoplasms (NENs) are relatively rare heterogeneous tumors that originate from peptidergic neurons and neuroendocrine cells and have been referred to as "carcinoids" in the past. Although this type of tumor had been previously considered to be indolent tumor with a low degree of malignancy, with the development of medicine and clinical study, researchers found that NENs had the potential to metastasize. They can occur in any part of the body where neuroendocrine cells are distributed and gastro-entero-pancreatic neuroendocrine neoplasms (GEP-NENs) are the most common type of NENs.Due to the improvement of techniques such as endoscopy and imaging, the incidence of rectal neuroendocrine tumors(R-NENs) and the number of related clinical researches have both increased significantly in recent years. Although researches in Chinese and foreign medical centers are mostly retrospective studies of small samples and the efficacies of different treatment methods are still under debating and lack of sufficient medical evidence to support, the diagnosis and treatment of this disease is gradually becoming standardized according to the proposal of corresponding guidelines. The recent advances in the epidemiology, diagnosis and treatment of rectal neuroendocrine neoplasms are reviewed in this paper.