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FANCA D1359Y mutation in a patient with gastric polyposis and cancer susceptibility: A case report and review of literature
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作者 Jeffrey Peng huang Johnson Lin +6 位作者 Chi-Yuan Tzen wen-yu huang Chia-Chi Tsai Chih-Jen Chen Yen-Jung Lu Kuei-Fang Chou Ying-Wen Su 《World Journal of Gastroenterology》 SCIE CAS 2018年第38期4412-4418,共7页
Gastric polyposis is a rare disease. Not all polyps progress to cancer. Monoallelic mutation in Fanconi anemia(FA) genes, unlike biallelic gene mutations that causes typical FA phenotype, can increase risks of cancers... Gastric polyposis is a rare disease. Not all polyps progress to cancer. Monoallelic mutation in Fanconi anemia(FA) genes, unlike biallelic gene mutations that causes typical FA phenotype, can increase risks of cancers in a sporadic manner. Aberrations in the FA pathway were reported in all molecular subtypes of gastric cancer. We studied a patient with synchronous gastric cancer from gastric polyposis by conducting a 13-year long-term follow up. Via pathway-driven massive parallel genomic sequencing, a germline mutation at FANCA D1359Y was identified. We identified several recurrent mutations in DNA methylation(TET1, V873I), the β-catenin pathway(CTNNB1, S45F) and RHO signaling pathway(PLEKHG5, R203C) by comparing the genetic events between benign and malignant gastric polyps. Furthermore, we revealed gastric polyposis susceptible genes and genetic events promoting malignant transformation using pathway-driven targeted gene sequencing. 展开更多
关键词 GASTRIC POLYPOSIS GASTRIC cancer ADENOCARCINOMA Fanconi’s ANEMIA MALIGNANT transformation
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Analysis of alkaline phosphatase and γ-glutamyltransferase after radiofrequency ablation of primary liver cancer: A retrospective study
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作者 wen-yu huang Sheng Zheng +7 位作者 Dan Zhu Ying-Lang Zeng Juan Yang Xue-Li Zeng Pei Liu Shun-Ling Zhang Ming Yuan Zhi-Xia Wang 《World Journal of Gastrointestinal Surgery》 SCIE 2024年第9期2860-2869,共10页
BACKGROUND Changes in alkaline phosphatase(ALP)andγ-glutamyltransferase(GGT)levels in patients with primary liver cancer(PLC)after radiofrequency ablation(RFA).Hepatocellular carcinoma is a malignant tumor with high ... BACKGROUND Changes in alkaline phosphatase(ALP)andγ-glutamyltransferase(GGT)levels in patients with primary liver cancer(PLC)after radiofrequency ablation(RFA).Hepatocellular carcinoma is a malignant tumor with high incidence worldwide.As a common local treatment,RFA has attracted much attention for its efficacy and influence on liver function.AIM To investigate the effect of serum ALP and GGT levels on the prognosis of patients with PLC treated by RFA.METHODS The preoperative clinical data of 165 patients who were pathologically or clinically diagnosed with PLC and who received RFA in our hospital between October 2018 and June 2023 were collected.The chi-square test was used to compare the data between groups.The Kaplan-Meier method and Cox regression were used to analyze the associ-ations between serum ALP and GGT levels and overall survival,progression-free survival(PFS)and clinical characteristics of patients before treatment.RESULTS The 1-year survival rates of patients with normal(≤135 U/L)and abnormal(>135 U/L)serum ALP before treatment were 91%and 79%,respectively;the 2-year survival rates were 90%and 68%,respectively;and the 5-year survival rates were 35%and 18%,respectively.The difference between the two groups was statistically significant(P=0.01).Before treatment,the 1-year survival rates of patients with normal serum GGT levels(≤45 U/L)and abnormal serum GGT levels(>45 U/L)were 95%and 87%,the 2-year survival rates were 85%and 71%,and the 5-year survival rates were 37%and 21%,respectively.The difference between the two groups was statist-ically significant(P<0.001).Serum ALP[hazard ratio(HR)=1.766,95%confidence interval(95%CI):1.068-2.921,P=0.027]and GGT(HR=2.312,95%CI:1.367-3.912,P=0.002)is closely related to the overall survival of PLC patients after RF ablation and is an independent prognostic factor.The 1-year PFS rates were 72%and 50%,the 2-year PFS rates were 52%and 21%,and the 5-year PFS rates were 14%and 3%,respectively.The difference between the two groups was statistically significant(P<0001).The 1-year PFS rates were 81%and 56%in patients with normal and abnormal serum GGT levels before treatment,respectively;the 2-year PFS rates were 62%and 35%,respectively;and the 5-year PFS rates were 18%and 7%,respectively,with statistical significance between the two groups(P<0.001).The serum ALP concentration(HR=1.653,95%CI:1.001-2.729,P=0.049)and GGT(HR=1.949,95%CI:1.296-2.930,P=0.001)was closely associated with PFS after RFA in patients with PLC.The proportion of male patients with abnormal ALP levels is high,the Child-Pugh grade of liver function is poor,and the incidence of ascites is high.Among GGT-abnormal patients,the Child-Pugh grade of liver function was poor,the tumor stage was late,the proportion of patients with tumors≥5 cm was high,and the incidence of hepatic encephalopathy was high.CONCLUSION Serum ALP and GGT levels before treatment can be used to predict the prognosis of patients with PLC after RFA,and they have certain guiding significance for the long-term survival of patients with PLC after radiofrequency therapy. 展开更多
关键词 Alkaline phosphatase γ-glutamyltransferase Radiofrequency ablation Primary liver cancer Retrospective study
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Incremental Multi-Label Learning with Active Queries 被引量:3
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作者 Sheng-Jun huang Guo-Xiang Li +1 位作者 wen-yu huang Shao-Yuan Li 《Journal of Computer Science & Technology》 SCIE EI CSCD 2020年第2期234-246,共13页
In multi-label learning,it is rather expensive to label instances since they are simultaneously associated with multiple labels.Therefore,active learning,which reduces the labeling cost by actively querying the labels... In multi-label learning,it is rather expensive to label instances since they are simultaneously associated with multiple labels.Therefore,active learning,which reduces the labeling cost by actively querying the labels of the most valuable data,becomes particularly important for multi-label learning.A good multi-label active learning algorithm usually consists of two crucial elements:a reasonable criterion to evaluate the gain of querying the label for an instance,and an effective classification model,based on whose prediction the criterion can be accurately computed.In this paper,we first introduce an effective multi-label classification model by combining label ranking with threshold learning,which is incrementally trained to avoid retraining from scratch after every query.Based on this model,we then propose to exploit both uncertainty and diversity in the instance space as well as the label space,and actively query the instance-label pairs which can improve the classification model most.Extensive experiments on 20 datasets demonstrate the superiority of the proposed approach to state-of-the-art methods. 展开更多
关键词 ACTIVE LEARNING MULTI-LABEL LEARNING uncertainty DIVERSITY
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