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Yuan L, Guo F, Wang L, Zou Q. Prediction of tumor metastasis from sequencing data in the era of genome sequencing. Brief Funct Genomics 2020; 18:412-418. [PMID: 31204784 DOI: 10.1093/bfgp/elz010] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2019] [Revised: 02/22/2019] [Accepted: 04/26/2019] [Indexed: 02/01/2023] Open
Abstract
Tumor metastasis is the key reason for the high mortality rate of tumor. Growing number of scholars have begun to pay attention to the research on tumor metastasis and have achieved satisfactory results in this field. The advent of the era of sequencing has enabled us to study cancer metastasis at the molecular level, which is essential for understanding the molecular mechanism of metastasis, identifying diagnostic markers and therapeutic targets and guiding clinical decision-making. We reviewed the metastasis-related studies using sequencing data, covering detection of metastasis origin sites, determination of metastasis potential and identification of distal metastasis sites. These findings include the discovery of relevant markers and the presentation of prediction tools. Finally, we discussed the challenge of studying metastasis considering the difficulty of obtaining metastatic cancer data, the complexity of tumor heterogeneity and the uncertainty of sample labels.
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Affiliation(s)
- Linlin Yuan
- College of Intelligence and Computing, Tianjin University, Tianjin, China
| | - Fei Guo
- College of Intelligence and Computing, Tianjin University, Tianjin, China
| | - Lei Wang
- College of Computer Engineering & Applied Mathematics, Changsha University, Changsha, China
| | - Quan Zou
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China.,Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
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Wang C, Zhao N, Yuan L, Liu X. Computational Detection of Breast Cancer Invasiveness with DNA Methylation Biomarkers. Cells 2020; 9:E326. [PMID: 32019269 PMCID: PMC7072524 DOI: 10.3390/cells9020326] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2020] [Revised: 01/28/2020] [Accepted: 01/28/2020] [Indexed: 12/14/2022] Open
Abstract
Breast cancer is the most common female malignancy. It has high mortality, primarily due to metastasis and recurrence. Patients with invasive and noninvasive breast cancer require different treatments, so there is an urgent need for predictive tools to guide clinical decision making and avoid overtreatment of noninvasive breast cancer and undertreatment of invasive cases. Here, we divided the sample set based on the genome-wide methylation distance to make full use of metastatic cancer data. Specifically, we implemented two differential methylation analysis methods to identify specific CpG sites. After effective dimensionality reduction, we constructed a methylation-based classifier using the Random Forest algorithm to categorize the primary breast cancer. We took advantage of breast cancer (BRCA) HM450 DNA methylation data and accompanying clinical data from The Cancer Genome Atlas (TCGA) database to validate the performance of the classifier. Overall, this study demonstrates DNA methylation as a potential biomarker to predict breast tumor invasiveness and as a possible parameter that could be included in the studies aiming to predict breast cancer aggressiveness. However, more comparative studies are needed to assess its usability in the clinic. Towards this, we developed a website based on these algorithms to facilitate its use in studies and predictions of breast cancer invasiveness.
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Affiliation(s)
- Chunyu Wang
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150080, China
| | - Ning Zhao
- School of Life Science and Technology, Harbin Institute of Technology, Harbin 150080, China;
| | - Linlin Yuan
- College of Intelligence and Computing, Tianjin University, Tianjin 300350, China;
| | - Xiaoyan Liu
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150080, China
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Liang X, Zhu W, Lv Z, Zou Q. Molecular Computing and Bioinformatics. Molecules 2019; 24:E2358. [PMID: 31247973 PMCID: PMC6651761 DOI: 10.3390/molecules24132358] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2019] [Accepted: 06/25/2019] [Indexed: 02/06/2023] Open
Abstract
Molecular computing and bioinformatics are two important interdisciplinary sciences that study molecules and computers. Molecular computing is a branch of computing that uses DNA, biochemistry, and molecular biology hardware, instead of traditional silicon-based computer technologies. Research and development in this area concerns theory, experiments, and applications of molecular computing. The core advantage of molecular computing is its potential to pack vastly more circuitry onto a microchip than silicon will ever be capable of-and to do it cheaply. Molecules are only a few nanometers in size, making it possible to manufacture chips that contain billions-even trillions-of switches and components. To develop molecular computers, computer scientists must draw on expertise in subjects not usually associated with their field, including organic chemistry, molecular biology, bioengineering, and smart materials. Bioinformatics works on the contrary; bioinformatics researchers develop novel algorithms or software tools for computing or predicting the molecular structure or function. Molecular computing and bioinformatics pay attention to the same object, and have close relationships, but work toward different orientations.
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Affiliation(s)
- Xin Liang
- School of Mathematics and Statistics, Hainan Normal University, Haikou 570100, China
| | - Wen Zhu
- School of Mathematics and Statistics, Hainan Normal University, Haikou 570100, China
| | - Zhibin Lv
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 610054, China.
| | - Quan Zou
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 610054, China.
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 611731, China.
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Wang Y, Zhang X, Ding S, Geng Y, Liu J, Zhao Z, Zhang R, Xiao X, Wang J. A graph-based algorithm for estimating clonal haplotypes of tumor sample from sequencing data. BMC Med Genomics 2019; 12:27. [PMID: 30704456 PMCID: PMC6357344 DOI: 10.1186/s12920-018-0457-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
BACKGROUND Haplotype phasing is an important step in many bioinformatics workflows. In cancer genomics, it is suggested that reconstructing the clonal haplotypes of a tumor sample could facilitate a comprehensive understanding of its clonal architecture and further provide valuable reference in clinical diagnosis and treatment. However, the sequencing data is an admixture of reads sampled from different clonal haplotypes, which complicates the computational problem by exponentially increasing the solution-space and leads the existing algorithms to an unacceptable time-/space- complexity. In addition, the evolutionary process among clonal haplotypes further weakens those algorithms by bringing indistinguishable candidate solutions. RESULTS To improve the algorithmic performance of phasing clonal haplotypes, in this article, we propose MixSubHap, which is a graph-based computational pipeline working on cancer sequencing data. To reduce the computation complexity, MixSubHap adopts three bounding strategies to limit the solution space and filter out false positive candidates. It first estimates the global clonal structure by clustering the variant allelic frequencies on sampled point mutations. This offers a priori on the number of clonal haplotypes when copy-number variations are not considered. Then, it utilizes a greedy extension algorithm to approximately find the longest linkage of the locally assembled contigs. Finally, it incorporates a read-depth stripping algorithm to filter out false linkages according to the posterior estimation of tumor purity and the estimated percentage of each sub-clone in the sample. A series of experiments are conducted to verify the performance of the proposed pipeline. CONCLUSIONS The results demonstrate that MixSubHap is able to identify about 90% on average of the preset clonal haplotypes under different simulation configurations. Especially, MixSubHap is robust when decreasing the mutation rates, in which cases the longest assembled contig could reach to 10kbps, while the accuracy of assigning a mutation to its haplotype still keeps more than 60% on average. MixSubHap is considered as a practical algorithm to reconstruct clonal haplotypes from cancer sequencing data. The source codes have been uploaded and maintained at https://github.com/YixuanWang1120/MixSubHap for academic use only.
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Affiliation(s)
- Yixuan Wang
- Department of Computer Science and Technology, School of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an, 710048 China
- Shaanxi Engineering Research Center of Medical and Health Big Data, School of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an, 710048 China
| | - Xuanping Zhang
- Department of Computer Science and Technology, School of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an, 710048 China
- Shaanxi Engineering Research Center of Medical and Health Big Data, School of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an, 710048 China
| | - Shuai Ding
- School of Management, Ministry of Education Key Laboratory of Process Optimization and Intelligent Decision-Making, Hefei University of Technology, Hefei, 23009 China
| | - Yu Geng
- Department of Computer Science and Technology, School of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an, 710048 China
- Shaanxi Engineering Research Center of Medical and Health Big Data, School of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an, 710048 China
| | - Jianye Liu
- Department of Computer Science and Technology, School of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an, 710048 China
- Shaanxi Engineering Research Center of Medical and Health Big Data, School of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an, 710048 China
| | - Zhongmeng Zhao
- Department of Computer Science and Technology, School of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an, 710048 China
- Shaanxi Engineering Research Center of Medical and Health Big Data, School of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an, 710048 China
| | - Rong Zhang
- Department of Computer Science and Technology, School of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an, 710048 China
- Shaanxi Engineering Research Center of Medical and Health Big Data, School of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an, 710048 China
| | - Xiao Xiao
- Shaanxi Engineering Research Center of Medical and Health Big Data, School of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an, 710048 China
- Institute of Health Administration and Policy, School of Public Policy and Administration, Xi’an Jiaotong University, Xi’an, 710048 China
| | - Jiayin Wang
- Department of Computer Science and Technology, School of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an, 710048 China
- Shaanxi Engineering Research Center of Medical and Health Big Data, School of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an, 710048 China
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