<strong>Background:</strong><span style="font-family:""> This study is aimed towards an exploration of mutant genes in primary liver cancer (PLC) patients by using bioinformatics and d...<strong>Background:</strong><span style="font-family:""> This study is aimed towards an exploration of mutant genes in primary liver cancer (PLC) patients by using bioinformatics and data mining techniques. <b>Methods: </b>Peripheral blood or paraffin-embedded tissues from 8 patients with PLC were analyzed using a 551 cancer-related gene panel on an Illumina NextSeq500 Sequencer (Illumina). Meanwhile, the data of 396 PLC cases were downloaded from The Cancer Genome Atlas (TCGA) database. The common mutated genes were obtained after integrating the mutation information of the above two cohorts, followed by functional enrichment and protein-protein interaction (PPI) analyses. Three well-known databases, including Vogelstein’s list, the Network of Cancer Gene (NCG), and the Catalog of Somatic Mutations in Cancer (COSMIC) database were used to screen driver genes. Furthermore, the Chi-square and logistic analysis were performed to analyze the correlation between the driver genes and clinicopathological characteristics, and Kaplan</span><span style="font-family:"">-</span><span style="font-family:"">Meier (KM) method and multivariate Cox analysis were conducted to evaluate the overall survival outcome. <b>Results:</b> In total, 84 mutation genes were obtained after 8 PLC patients undergoing gene mutation detection with next-generation sequencing (NGS). The top 100 most mutate gene data from PLC patients in TCGA database were downloaded. After integrating the above two cohorts, 17 common mutated genes were identified. Next, 11 driver genes were screened out by analyzing the intersection of the 17 mutation genes and the genes in the three well-known databases. Among them, RB1, TP53, and KRAS gene mutations were connected with clinicopathological characteristics, while all the 11 gene mutations had no relationship with overall survival. <b>Conclusion:</b> This study investigated the mutant genes with significant clinical implications in PLC patients, which may improve the knowledge of gene mutations in PLC molecular pathogenesis.</span>展开更多
AIM To investigate the driver gene mutations associatedwith colorectal cancer （CRC） in the Taiwan Residentspopulation.METHODS： In this study, 103 patients with CRCwere evaluated. The samples consisted of 66 menand ...AIM To investigate the driver gene mutations associatedwith colorectal cancer （CRC） in the Taiwan Residentspopulation.METHODS： In this study, 103 patients with CRCwere evaluated. The samples consisted of 66 menand 37 women with a median age of 59 years and anage range of 26-86 years. We used high-resolutionmelting analysis （HRM） and direct DNA sequencing tocharacterize the mutations in 13 driver genes of CRCrelatedpathways. The HRM assays were conductedusing the LightCycler？ 480 Instrument provided with the software LightCycler 480 Gene Scanning SoftwareVersion 1.5. We also compared the clinicopathologicaldata of CRC patients with the driver gene mutationstatus.RESULTS： Of the 103 patients evaluated, 73.79%had mutations in one of the 13 driver genes. Wediscovered 18 novel mutations in APC , MLH1 , MSH2 ,PMS2 , SMAD4 and TP53 that have not been previouslyreported. Additionally, we found 16 de novo mutationsin APC , BMPR1A , MLH1 , MSH2 , MSH6 , MUTYH andPMS2 in cancerous tissues previously reported in thedbSNP database; however, these mutations couldnot be detected in peripheral blood cells. The APCmutation correlates with lymph node metastasis（34.69% vs 12.96%, P = 0.009） and cancer stage（34.78% vs 14.04%, P = 0.013）. No association wasobserved between other driver gene mutations andclinicopathological features. Furthermore, having twoor more driver gene mutations correlates with thedegree of lymph node metastasis （42.86% vs 24.07%,P = 0.043）.CONCLUSION： Our findings confirm the importanceof 13 CRC-related pathway driver genes in the developmentof CRC in Taiwan Residents patients.展开更多
The identification of tumor driver genes facilitates accurate cancer diagnosis and treatment,playing a key role in precision oncology,along with gene signaling,regulation,and their interaction with protein complexes.T...The identification of tumor driver genes facilitates accurate cancer diagnosis and treatment,playing a key role in precision oncology,along with gene signaling,regulation,and their interaction with protein complexes.To tackle the challenge of distinguishing driver genes from a large number of genomic data,we construct a feature extraction framework for discovering pan-cancer driver genes based on multi-omics data(mutations,gene expression,copy number variants,and DNA methylation)combined with protein–protein interaction(PPI)networks.Using a network propagation algorithm,we mine functional information among nodes in the PPI network,focusing on genes with weak node information to represent specific cancer information.From these functional features,we extract distribution features of pan-cancer data,pan-cancer TOPSIS features of functional features using the ideal solution method,and SetExpan features of pan-cancer data from the gene functional features,a method to rank pan-cancer data based on the average inverse rank.These features represent the common message of pan-cancer.Finally,we use the lightGBM classification algorithm for gene prediction.Experimental results show that our method outperforms existing methods in terms of the area under the check precision-recall curve(AUPRC)and demonstrates better performance across different PPI networks.This indicates our framework’s effectiveness in predicting potential cancer genes,offering valuable insights for the diagnosis and treatment of tumors.展开更多
Identification of cancer driver genes plays an important role in precision oncology research,which is helpful to understand cancer initiation and progression.However,most existing computational methods mainly used the...Identification of cancer driver genes plays an important role in precision oncology research,which is helpful to understand cancer initiation and progression.However,most existing computational methods mainly used the protein–protein interaction(PPI)networks,or treated the directed gene regulatory networks(GRNs)as the undirected gene–gene association networks to identify the cancer driver genes,which will lose the unique structure regulatory information in the directed GRNs,and then affect the outcome of the cancer driver gene identification.Here,based on the multi-omics pan-cancer data(i.e.,gene expression,mutation,copy number variation,and DNA methylation),we propose a novel method(called DGMP)to identify cancer driver genes by jointing directed graph convolutional network(DGCN)and multilayer perceptron(MLP).DGMP learns the multi-omics features of genes as well as the topological structure features in GRN with the DGCN model and uses MLP to weigh more on gene features for mitigating the bias toward the graph topological features in the DGCN learning process.The results on three GRNs show that DGMP outperforms other existing state-of-the-art methods.The ablation experimental results on the Dawn Net network indicate that introducing MLP into DGCN can offset the performance degradation of DGCN,and jointing MLP and DGCN can effectively improve the performance of identifying cancer driver genes.DGMP can identify not only the highly mutated cancer driver genes but also the driver genes harboring other kinds of alterations(e.g.,differential expression and aberrant DNA methylation)or genes involved in GRNs with other cancer genes.The source code of DGMP can be freely downloaded from https://github.com/NWPU-903PR/DGMP.展开更多
Next-generation sequencing has allowed identification of millions of somatic mutations in human cancer cells.A key challenge in interpreting cancer genomes is to distinguish drivers of cancer development among availab...Next-generation sequencing has allowed identification of millions of somatic mutations in human cancer cells.A key challenge in interpreting cancer genomes is to distinguish drivers of cancer development among available genetic mutations.To address this issue,we present the first webbased application,consensus cancer driver gene caller(C^3),to identify the consensus driver genes using six different complementary strategies,i.e.,frequency-based,machine learning-based,functional bias-based,clustering-based,statistics model-based,and network-based strategies.This application allows users to specify customized operations when calling driver genes,and provides solid statistical evaluations and interpretable visualizations on the integration results.C^3 is implemented in Python and is freely available for public use at http://drivergene.rwebox.com/c3.展开更多
目的比较伴或不伴基因突变的原发性肺腺癌在体积倍增时间(volume doubling time,VDT)和质量倍增时间(mass doubling time,MDT)间的差异。方法选取2019年1月—2020年12月在同济大学附属上海市肺科医院进行手术治疗且术前至少进行2次胸部...目的比较伴或不伴基因突变的原发性肺腺癌在体积倍增时间(volume doubling time,VDT)和质量倍增时间(mass doubling time,MDT)间的差异。方法选取2019年1月—2020年12月在同济大学附属上海市肺科医院进行手术治疗且术前至少进行2次胸部非增强CT扫描的患者为研究对象。根据放射科医师手工分割的三维掩模计算VDT和MDT。采用Bland-Altman方法进行观察者内变异性评估。采用Mann-Whitney U检验比较驱动基因有无突变肿瘤的VDT和MDT差异。采用Kruskal-Wallis检验比较不同EGFR突变位点VDT和MDT的差异。结果共计279例患者(男性99例,女性280例),平均年龄(62.15±8.90)岁,共287个结节。根据驱动基因状态分为突变组72例,野生组215例,基因突变发生率为74.9%(215/287)。突变组和野生组MDT在驱动基因状态上的差异有统计学意义(537 d vs 824 d,P=0.004),VDT的差异无统计学意义(767 d vs 593 d)。EGFR阳性腺癌的MDT比EGFR阴性腺癌长,但VDT差异并不显著(VDT,758 d vs 593 d,P=0.382;MDT,824 d vs 537 d,P=0.004)。在生长结节中,驱动基因阳性腺癌的VDT和MDT均比野生型腺癌长(VDT,759 d vs 592 d,P=0.048;MDT,749 d vs 499 d,P<0.001;VDT,768 d vs 593 d,P=0.081,MDT,737 d vs 518 d,P=0.001)。结论在原发性浸润性肺腺癌中,驱动基因阳性(尤其是EGFR阳性)的肿瘤倍增时间更长,且在生长中的结节中更为显著。展开更多
文摘<strong>Background:</strong><span style="font-family:""> This study is aimed towards an exploration of mutant genes in primary liver cancer (PLC) patients by using bioinformatics and data mining techniques. <b>Methods: </b>Peripheral blood or paraffin-embedded tissues from 8 patients with PLC were analyzed using a 551 cancer-related gene panel on an Illumina NextSeq500 Sequencer (Illumina). Meanwhile, the data of 396 PLC cases were downloaded from The Cancer Genome Atlas (TCGA) database. The common mutated genes were obtained after integrating the mutation information of the above two cohorts, followed by functional enrichment and protein-protein interaction (PPI) analyses. Three well-known databases, including Vogelstein’s list, the Network of Cancer Gene (NCG), and the Catalog of Somatic Mutations in Cancer (COSMIC) database were used to screen driver genes. Furthermore, the Chi-square and logistic analysis were performed to analyze the correlation between the driver genes and clinicopathological characteristics, and Kaplan</span><span style="font-family:"">-</span><span style="font-family:"">Meier (KM) method and multivariate Cox analysis were conducted to evaluate the overall survival outcome. <b>Results:</b> In total, 84 mutation genes were obtained after 8 PLC patients undergoing gene mutation detection with next-generation sequencing (NGS). The top 100 most mutate gene data from PLC patients in TCGA database were downloaded. After integrating the above two cohorts, 17 common mutated genes were identified. Next, 11 driver genes were screened out by analyzing the intersection of the 17 mutation genes and the genes in the three well-known databases. Among them, RB1, TP53, and KRAS gene mutations were connected with clinicopathological characteristics, while all the 11 gene mutations had no relationship with overall survival. <b>Conclusion:</b> This study investigated the mutant genes with significant clinical implications in PLC patients, which may improve the knowledge of gene mutations in PLC molecular pathogenesis.</span>
基金research grant from the China Medical University Hospital,DMR-103-017
文摘AIM To investigate the driver gene mutations associatedwith colorectal cancer （CRC） in the Taiwan Residentspopulation.METHODS： In this study, 103 patients with CRCwere evaluated. The samples consisted of 66 menand 37 women with a median age of 59 years and anage range of 26-86 years. We used high-resolutionmelting analysis （HRM） and direct DNA sequencing tocharacterize the mutations in 13 driver genes of CRCrelatedpathways. The HRM assays were conductedusing the LightCycler？ 480 Instrument provided with the software LightCycler 480 Gene Scanning SoftwareVersion 1.5. We also compared the clinicopathologicaldata of CRC patients with the driver gene mutationstatus.RESULTS： Of the 103 patients evaluated, 73.79%had mutations in one of the 13 driver genes. Wediscovered 18 novel mutations in APC , MLH1 , MSH2 ,PMS2 , SMAD4 and TP53 that have not been previouslyreported. Additionally, we found 16 de novo mutationsin APC , BMPR1A , MLH1 , MSH2 , MSH6 , MUTYH andPMS2 in cancerous tissues previously reported in thedbSNP database; however, these mutations couldnot be detected in peripheral blood cells. The APCmutation correlates with lymph node metastasis（34.69% vs 12.96%, P = 0.009） and cancer stage（34.78% vs 14.04%, P = 0.013）. No association wasobserved between other driver gene mutations andclinicopathological features. Furthermore, having twoor more driver gene mutations correlates with thedegree of lymph node metastasis （42.86% vs 24.07%,P = 0.043）.CONCLUSION： Our findings confirm the importanceof 13 CRC-related pathway driver genes in the developmentof CRC in Taiwan Residents patients.
基金National Natural Science Foundation of China,Grant/Award Numbers:61902215,61902216,61972226。
文摘The identification of tumor driver genes facilitates accurate cancer diagnosis and treatment,playing a key role in precision oncology,along with gene signaling,regulation,and their interaction with protein complexes.To tackle the challenge of distinguishing driver genes from a large number of genomic data,we construct a feature extraction framework for discovering pan-cancer driver genes based on multi-omics data(mutations,gene expression,copy number variants,and DNA methylation)combined with protein–protein interaction(PPI)networks.Using a network propagation algorithm,we mine functional information among nodes in the PPI network,focusing on genes with weak node information to represent specific cancer information.From these functional features,we extract distribution features of pan-cancer data,pan-cancer TOPSIS features of functional features using the ideal solution method,and SetExpan features of pan-cancer data from the gene functional features,a method to rank pan-cancer data based on the average inverse rank.These features represent the common message of pan-cancer.Finally,we use the lightGBM classification algorithm for gene prediction.Experimental results show that our method outperforms existing methods in terms of the area under the check precision-recall curve(AUPRC)and demonstrates better performance across different PPI networks.This indicates our framework’s effectiveness in predicting potential cancer genes,offering valuable insights for the diagnosis and treatment of tumors.
基金supported in part by the National Natural Science Foundation of China(Grant Nos.62173271 and 61873202 to SWZ)。
文摘Identification of cancer driver genes plays an important role in precision oncology research,which is helpful to understand cancer initiation and progression.However,most existing computational methods mainly used the protein–protein interaction(PPI)networks,or treated the directed gene regulatory networks(GRNs)as the undirected gene–gene association networks to identify the cancer driver genes,which will lose the unique structure regulatory information in the directed GRNs,and then affect the outcome of the cancer driver gene identification.Here,based on the multi-omics pan-cancer data(i.e.,gene expression,mutation,copy number variation,and DNA methylation),we propose a novel method(called DGMP)to identify cancer driver genes by jointing directed graph convolutional network(DGCN)and multilayer perceptron(MLP).DGMP learns the multi-omics features of genes as well as the topological structure features in GRN with the DGCN model and uses MLP to weigh more on gene features for mitigating the bias toward the graph topological features in the DGCN learning process.The results on three GRNs show that DGMP outperforms other existing state-of-the-art methods.The ablation experimental results on the Dawn Net network indicate that introducing MLP into DGCN can offset the performance degradation of DGCN,and jointing MLP and DGCN can effectively improve the performance of identifying cancer driver genes.DGMP can identify not only the highly mutated cancer driver genes but also the driver genes harboring other kinds of alterations(e.g.,differential expression and aberrant DNA methylation)or genes involved in GRNs with other cancer genes.The source code of DGMP can be freely downloaded from https://github.com/NWPU-903PR/DGMP.
基金supported by the National Major Research and Innovation Program of China(Grant Nos.2017YFC0908500and 2016YFC1303205)National Natural Science Foundation of China(Grant No.61572361)+2 种基金Shanghai Rising-Star Program(Grant No.16QA1403900)Shanghai Natural Science Foundation Program(Grant No.17ZR1449400)Fundamental Research Funds for the Central Universities(Grant No.1501219106),China
文摘Next-generation sequencing has allowed identification of millions of somatic mutations in human cancer cells.A key challenge in interpreting cancer genomes is to distinguish drivers of cancer development among available genetic mutations.To address this issue,we present the first webbased application,consensus cancer driver gene caller(C^3),to identify the consensus driver genes using six different complementary strategies,i.e.,frequency-based,machine learning-based,functional bias-based,clustering-based,statistics model-based,and network-based strategies.This application allows users to specify customized operations when calling driver genes,and provides solid statistical evaluations and interpretable visualizations on the integration results.C^3 is implemented in Python and is freely available for public use at http://drivergene.rwebox.com/c3.
文摘目的比较伴或不伴基因突变的原发性肺腺癌在体积倍增时间(volume doubling time,VDT)和质量倍增时间(mass doubling time,MDT)间的差异。方法选取2019年1月—2020年12月在同济大学附属上海市肺科医院进行手术治疗且术前至少进行2次胸部非增强CT扫描的患者为研究对象。根据放射科医师手工分割的三维掩模计算VDT和MDT。采用Bland-Altman方法进行观察者内变异性评估。采用Mann-Whitney U检验比较驱动基因有无突变肿瘤的VDT和MDT差异。采用Kruskal-Wallis检验比较不同EGFR突变位点VDT和MDT的差异。结果共计279例患者(男性99例,女性280例),平均年龄(62.15±8.90)岁,共287个结节。根据驱动基因状态分为突变组72例,野生组215例,基因突变发生率为74.9%(215/287)。突变组和野生组MDT在驱动基因状态上的差异有统计学意义(537 d vs 824 d,P=0.004),VDT的差异无统计学意义(767 d vs 593 d)。EGFR阳性腺癌的MDT比EGFR阴性腺癌长,但VDT差异并不显著(VDT,758 d vs 593 d,P=0.382;MDT,824 d vs 537 d,P=0.004)。在生长结节中,驱动基因阳性腺癌的VDT和MDT均比野生型腺癌长(VDT,759 d vs 592 d,P=0.048;MDT,749 d vs 499 d,P<0.001;VDT,768 d vs 593 d,P=0.081,MDT,737 d vs 518 d,P=0.001)。结论在原发性浸润性肺腺癌中,驱动基因阳性(尤其是EGFR阳性)的肿瘤倍增时间更长,且在生长中的结节中更为显著。
