<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
Colorectal cancer is a common gastrointestinal malignancy, which ranks the 3rd and 2nd in the world in the number of new cases in male and female with fears, and the 4th and 3rd in the number of deaths in male and fem...Colorectal cancer is a common gastrointestinal malignancy, which ranks the 3rd and 2nd in the world in the number of new cases in male and female with fears, and the 4th and 3rd in the number of deaths in male and female. With the change of people's living habits and diet structure, the morbidity and mortality of CRC in China are increasing day by day, and gradually show a younger trend, which seriously endangers the national health. In recent years, with the application of targeted therapy and immunotherapy in the treatment of colorectal cancer, the treatment of advanced colorectal cancer has entered a new stage. This article reviews the current status and research progress of the treatment of metastatic colorectal cancer with different driver genes.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
Accurate identification of mutation-driven cancer driver genes is vital for cancer biology and targeted therapy.To address noise in protein–protein interaction(PPI)networks,we propose DGCL_DWA,a novel method utilizin...Accurate identification of mutation-driven cancer driver genes is vital for cancer biology and targeted therapy.To address noise in protein–protein interaction(PPI)networks,we propose DGCL_DWA,a novel method utilizing graph diffusion and contrastive learning.DGCL_DWA first employs personalized PageRank to generate a diffusion graph,revealing hidden biological connections.Chebyshev graph convolution extracts features from both the PPI and diffusion networks,and neighborhood contrastive learning harmonizes gene representations,reducing noise.The network-specific features are refined via Chebyshev graph convolutions,which are constrained via node classification and link prediction.A dynamic weight adjustment strategy balances task-specific losses during training.Finally,logistic regression is used to predict driver genes.The experimental results demonstrate the superior performance in pan-cancer and specific cancer driver gene identification compared with state-of-the-art methods.Ablation studies confirm the positive impact of the diffusion graph,contrastive learning,and dynamic weight adjustment on predictive accuracy.The source codes are available at https://doi.org/10.57760/sciencedb.31933.展开更多
Objective: This study aims to establish a method for highly parallel multiplexed detection of genetic mutations in Chinese lung cancer samples through Agena i PLEX chemistry and matrix-assisted laser desorption ioniza...Objective: This study aims to establish a method for highly parallel multiplexed detection of genetic mutations in Chinese lung cancer samples through Agena i PLEX chemistry and matrix-assisted laser desorption ionization time-of-flight analysis on Mass ARRAY mass spectrometry platform.Methods: We reviewed the related literature and data on lung cancer treatments. We also identified 99 mutation hot spots in 13 target genes closely related to the pathogenesis, drug resistance, and metastasis of lung cancer. A total of 297 primers, composed of99 paired forward and reverse amplification primers and 99 matched extension primers, were designed using Assay Design software. The detection method was established by analyzing eight cell lines and six lung cancer specimens. The proposed method was then validated through comparisons by using a Lung Carta^(TM) kit. The sensitivity and specificity of the proposed method were evaluated by directly sequencing EGFR and KRAS genes in 100 lung cancer cases.Results: The proposed method was able to detect multiplex genetic mutations in lung cancer cell lines. This finding was consistent with the observations on previously reported mutations. The proposed method can also detect such mutations in clinical lung cancer specimens. This result was consistent with the observations with Lung Carta^(TM) kit. However, an FGFR2 mutation was detected only through the proposed method. The measured sensitivity and specificity were 100% and 96.3%, respectively.Conclusions: The proposed Mass ARRAY technology-based multiplex method can detect genetic mutations in Chinese lung cancer patients. Therefore, the proposed method can be applied to detect mutations in other cancer tissues.展开更多
Identifying cancer driver genes has paramount significance in elucidating the intricate mechanisms underlying cancer development,progression,and therapeutic interventions.Abundant omics data and interactome networks p...Identifying cancer driver genes has paramount significance in elucidating the intricate mechanisms underlying cancer development,progression,and therapeutic interventions.Abundant omics data and interactome networks provided by numerous extensive databases enable the application of graph deep learning techniques that incorporate network structures into the deep learning framework.However,most existing models primarily focus on individual network,inevitably neglecting the incompleteness and noise of interactions.Moreover,samples with imbalanced classes in driver gene identification hamper the performance of models.To address this,we propose a novel deep learning framework MMGN,which integrates multiplex networks and pan-cancer multiomics data using graph neural networks combined with negative sample inference to discover cancer driver genes,which not only enhances gene feature learning based on the mutual information and the consensus regularizer,but also achieves balanced class of positive and negative samples for model training.The reliability of MMGN has been verified by the Area Under the Receiver Operating Characteristic curves(AUROC)and the Area Under the Precision-Recall Curves(AUPRC).We believe MMGN has the potential to provide new prospects in precision oncology and may find broader applications in predicting biomarkers for other intricate diseases.展开更多
With the rapid development of lung cancer molecular detection and precisiontherapy, targeted therapy has covered the entire process of diagnosis andtreatment of nonsmall cell lung cancer patients. Overall mortality fr...With the rapid development of lung cancer molecular detection and precisiontherapy, targeted therapy has covered the entire process of diagnosis andtreatment of nonsmall cell lung cancer patients. Overall mortality from lungcancer has decreased significantly over the past 20 years, especially since theintroduction of targeted drugs in 2013. In 2022, targeted therapy for lungcancer has developed rapidly. The optimization of treatment modes and theexploration of new target drugs such as antibody‐drug conjugates will broadenthe selection range of nonsmall cell lung cancer patients with positive drivergenes. This article reviews the latest advances in targeted therapy for drivergene‐positive lung cancer in 2022.展开更多
Existing studies have yet to elucidate clearly the mechanisms of secondary resistance to third generation epidermal growth factor receptor(EGFR)tyrosine kinase inhibitors(TKIs),neither is there any established standar...Existing studies have yet to elucidate clearly the mechanisms of secondary resistance to third generation epidermal growth factor receptor(EGFR)tyrosine kinase inhibitors(TKIs),neither is there any established standard therapy for patients resistant to third generation EGFR-TKIs.This case report demonstrates a rare mutation pattern in a male patient with a pathologic diagnosis of non-small cell lung cancer(NSCLC)harboring an EGFR exon 19 deletion(19Del)mutation,who then acquired an EGFR-T790M mutation after developing resistance to the first generation EGFR-TKI(gefitinib).The mutation reverted to the original EGFR-19Del mutation after the patient developed secondary resistance against the third generation TKI(osimertinib).This patient eventually achieved partial response(PR)with second generation TKI(afatinib)as a fourth-line treatment.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>
文摘Colorectal cancer is a common gastrointestinal malignancy, which ranks the 3rd and 2nd in the world in the number of new cases in male and female with fears, and the 4th and 3rd in the number of deaths in male and female. With the change of people's living habits and diet structure, the morbidity and mortality of CRC in China are increasing day by day, and gradually show a younger trend, which seriously endangers the national health. In recent years, with the application of targeted therapy and immunotherapy in the treatment of colorectal cancer, the treatment of advanced colorectal cancer has entered a new stage. This article reviews the current status and research progress of the treatment of metastatic colorectal cancer with different driver genes.
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 in part by the National Natural Science Foundation of China under Grant Nos.62472202 and 61972185the Yunnan Ten Thousand Talents Plan for Young Professionals.
文摘Accurate identification of mutation-driven cancer driver genes is vital for cancer biology and targeted therapy.To address noise in protein–protein interaction(PPI)networks,we propose DGCL_DWA,a novel method utilizing graph diffusion and contrastive learning.DGCL_DWA first employs personalized PageRank to generate a diffusion graph,revealing hidden biological connections.Chebyshev graph convolution extracts features from both the PPI and diffusion networks,and neighborhood contrastive learning harmonizes gene representations,reducing noise.The network-specific features are refined via Chebyshev graph convolutions,which are constrained via node classification and link prediction.A dynamic weight adjustment strategy balances task-specific losses during training.Finally,logistic regression is used to predict driver genes.The experimental results demonstrate the superior performance in pan-cancer and specific cancer driver gene identification compared with state-of-the-art methods.Ablation studies confirm the positive impact of the diffusion graph,contrastive learning,and dynamic weight adjustment on predictive accuracy.The source codes are available at https://doi.org/10.57760/sciencedb.31933.
基金supported by the Special Fund for Research in the Public Interest from the National Health and Family Planning Commission of PRC (Grant No. 201402031)the Key Lab System Project of the Guangdong Science and Technology Department (Grant No. 2012A061400006)the Special Fund for Research in the Public Interest and Capacity Building from the Guangdong Science and Technology Department (Grant No. 2014A020212225)
文摘Objective: This study aims to establish a method for highly parallel multiplexed detection of genetic mutations in Chinese lung cancer samples through Agena i PLEX chemistry and matrix-assisted laser desorption ionization time-of-flight analysis on Mass ARRAY mass spectrometry platform.Methods: We reviewed the related literature and data on lung cancer treatments. We also identified 99 mutation hot spots in 13 target genes closely related to the pathogenesis, drug resistance, and metastasis of lung cancer. A total of 297 primers, composed of99 paired forward and reverse amplification primers and 99 matched extension primers, were designed using Assay Design software. The detection method was established by analyzing eight cell lines and six lung cancer specimens. The proposed method was then validated through comparisons by using a Lung Carta^(TM) kit. The sensitivity and specificity of the proposed method were evaluated by directly sequencing EGFR and KRAS genes in 100 lung cancer cases.Results: The proposed method was able to detect multiplex genetic mutations in lung cancer cell lines. This finding was consistent with the observations on previously reported mutations. The proposed method can also detect such mutations in clinical lung cancer specimens. This result was consistent with the observations with Lung Carta^(TM) kit. However, an FGFR2 mutation was detected only through the proposed method. The measured sensitivity and specificity were 100% and 96.3%, respectively.Conclusions: The proposed Mass ARRAY technology-based multiplex method can detect genetic mutations in Chinese lung cancer patients. Therefore, the proposed method can be applied to detect mutations in other cancer tissues.
基金supported in part by the National Natural Science Foundation of China(No.62202383)the Guangdong Basic and Applied Basic Research Foundation(No.2024A1515012602)the National Key Research and Development Program of China(No.2022YFD1801200).
文摘Identifying cancer driver genes has paramount significance in elucidating the intricate mechanisms underlying cancer development,progression,and therapeutic interventions.Abundant omics data and interactome networks provided by numerous extensive databases enable the application of graph deep learning techniques that incorporate network structures into the deep learning framework.However,most existing models primarily focus on individual network,inevitably neglecting the incompleteness and noise of interactions.Moreover,samples with imbalanced classes in driver gene identification hamper the performance of models.To address this,we propose a novel deep learning framework MMGN,which integrates multiplex networks and pan-cancer multiomics data using graph neural networks combined with negative sample inference to discover cancer driver genes,which not only enhances gene feature learning based on the mutual information and the consensus regularizer,but also achieves balanced class of positive and negative samples for model training.The reliability of MMGN has been verified by the Area Under the Receiver Operating Characteristic curves(AUROC)and the Area Under the Precision-Recall Curves(AUPRC).We believe MMGN has the potential to provide new prospects in precision oncology and may find broader applications in predicting biomarkers for other intricate diseases.
文摘With the rapid development of lung cancer molecular detection and precisiontherapy, targeted therapy has covered the entire process of diagnosis andtreatment of nonsmall cell lung cancer patients. Overall mortality from lungcancer has decreased significantly over the past 20 years, especially since theintroduction of targeted drugs in 2013. In 2022, targeted therapy for lungcancer has developed rapidly. The optimization of treatment modes and theexploration of new target drugs such as antibody‐drug conjugates will broadenthe selection range of nonsmall cell lung cancer patients with positive drivergenes. This article reviews the latest advances in targeted therapy for drivergene‐positive lung cancer in 2022.
基金supported by the National Natural Science Foundation of China(Grant No.81673032).
文摘Existing studies have yet to elucidate clearly the mechanisms of secondary resistance to third generation epidermal growth factor receptor(EGFR)tyrosine kinase inhibitors(TKIs),neither is there any established standard therapy for patients resistant to third generation EGFR-TKIs.This case report demonstrates a rare mutation pattern in a male patient with a pathologic diagnosis of non-small cell lung cancer(NSCLC)harboring an EGFR exon 19 deletion(19Del)mutation,who then acquired an EGFR-T790M mutation after developing resistance to the first generation EGFR-TKI(gefitinib).The mutation reverted to the original EGFR-19Del mutation after the patient developed secondary resistance against the third generation TKI(osimertinib).This patient eventually achieved partial response(PR)with second generation TKI(afatinib)as a fourth-line treatment.
