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DGMP: Identifying Cancer driver genes by Jointing DGCN and MLP from Multi-omics Genomic Data

Genomics, Proteomics & Bioinformatics 2022年第5期20卷 928-938页

作者： Shao-Wu Zhang Jing-Yu Xu Tong Zhang MOE Key Laboratory of Information Fusion Technology School of AutomationNorthwestern Polytechnical UniversityXi’an 710072China

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://***/NWPU-903PR/DGMP.

关键词： driver gene Directed graph convolutional network Multilayer perceptron gene regulatory network Multi-omics data

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Analysis of Commonly and Specifically Dysregulated Pathways in Three Women Cancers

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Chinese Journal of Electronics 2018年第5期27卷 1043-1049页

作者： LIU Xiyang AN Yingying YU Baoguo QIN Guimin School of Software Xidian University School of Computer Science and Technology Xidian University State Key Laboratory of Satellite Navigation System and Equipment Technology

Breast, ovarian and endometrial cancer are three most prevalent gynaecological malignancies. Identifying their common and specific biomarkers is significant for cancer prediction and therapy in females. We propose a method to identify dysregulated pathways in cancer through scoring pathways based on the molecular interaction data and genomic data. Commonly and specifically dysregulated pathways are analyzed across the above three female cancers, which have not been studied as a whole to the best of our knowledge. Our results demonstrate that all the three cancers have close relationships with Type II diabetes and cell cycle-related biology processes. Breast cancer is specifically related to immune system while ovarian cancer and endometrial cancer are associated with blood vascular-related systems such as renin-angiotensin system and coagulation system. In addition, dysregulated pathways are used to predict potential driver genes effectively according to their topological structure and biological information.

关键词： Dysregulated pathway Female cancers driver gene

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T790M mutation in stage IV EGFR-mutated NSCLC patient with acquired resistance reverted to original 19Del mutation after administration of a series of precision treatments:a case report

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Precision Clinical Medicine 2018年第3期1卷 129-133页

作者： Jialong Han Ya Wang Lili Zhong Huijie Zhou Min Yu Yanying Li You Lu Yan Wang Jiang Zhu West China Hospital and West China Medical Center of Sichuan University Department of Thoracic Oncology West China HospitalSichaun University Department of IVF West China Second HospitalSichuan University

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.

关键词： NSCLC EGFR mutation acquired resistance driver gene

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