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  • Oxford University Press (OUP)  (17)
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  • Oxford University Press (OUP)  (17)
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  • 1
    Online Resource
    Online Resource
    Subpathway-GM: identification of metabolic subpathways via joint power of interesting genes and metabolites and their topologies within pathways
    Oxford University Press (OUP) ; 2013
    In:  Nucleic Acids Research Vol. 41, No. 9 ( 2013-05-01), p. e101-e101
    Details
    In: Nucleic Acids Research, Oxford University Press (OUP), Vol. 41, No. 9 ( 2013-05-01), p. e101-e101
    Type of Medium: Online Resource
    ISSN: 1362-4962 , 0305-1048
    URL: Article
    DOI: 10.1093/nar/gkt161
    RVK:
    WA 15000
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2013
    detail.hit.zdb_id: 1472175-2
    SSG: 12
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  • 2
    Online Resource
    Online Resource
    A comprehensive overview of oncogenic pathways in human cancer
    Oxford University Press (OUP) ; 2020
    In:  Briefings in Bioinformatics Vol. 21, No. 3 ( 2020-05-21), p. 957-969
    Details
    In: Briefings in Bioinformatics, Oxford University Press (OUP), Vol. 21, No. 3 ( 2020-05-21), p. 957-969
    Abstract: Alterations of biological pathways can lead to oncogenesis. An overview of these oncogenic pathways would be highly valuable for researchers to reveal the pathogenic mechanism and develop novel therapeutic approaches for cancers. Here, we reviewed approximately 8500 literatures and documented experimentally validated cancer-pathway associations as benchmarking data set. This data resource includes 4709 manually curated relationships between 1557 paths and 49 cancers with 2427 upstream regulators in 7 species. Based on this resource, we first summarized the cancer-pathway associations and revealed some commonly deregulated pathways across tumor types. Then, we systematically analyzed these oncogenic pathways by integrating TCGA pan-cancer data sets. Multi-omics analysis showed oncogenic pathways may play different roles across tumor types under different omics contexts. We also charted the survival relevance landscape of oncogenic pathways in 26 tumor types, identified dominant omics features and found survival relevance for oncogenic pathways varied in tumor types and omics levels. Moreover, we predicted upstream regulators and constructed a hierarchical network model to understand the pathogenic mechanism of human cancers underlying oncogenic pathway context. Finally, we developed `CPAD’ (freely available at http://bio-bigdata.hrbmu.edu.cn/CPAD/), an online resource for exploring oncogenic pathways in human cancers, that integrated manually curated cancer-pathway associations, TCGA pan-cancer multi-omics data sets, drug–target data, drug sensitivity and multi-omics data for cancer cell lines. In summary, our study provides a comprehensive characterization of oncogenic pathways and also presents a valuable resource for investigating the pathogenesis of human cancer.
    Type of Medium: Online Resource
    ISSN: 1467-5463 , 1477-4054
    URL: Article
    DOI: 10.1093/bib/bbz046
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2020
    detail.hit.zdb_id: 2036055-1
    SSG: 12
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  • 3
    Online Resource
    Online Resource
    Topologically inferring risk-active pathways toward precise cancer classification by directed random walk
    Oxford University Press (OUP) ; 2013
    In:  Bioinformatics Vol. 29, No. 17 ( 2013-09-01), p. 2169-2177
    Details
    In: Bioinformatics, Oxford University Press (OUP), Vol. 29, No. 17 ( 2013-09-01), p. 2169-2177
    Abstract: Motivation: The accurate prediction of disease status is a central challenge in clinical cancer research. Microarray-based gene biomarkers have been identified to predict outcome and outperform traditional clinical parameters. However, the robustness of the individual gene biomarkers is questioned because of their little reproducibility between different cohorts of patients. Substantial progress in treatment requires advances in methods to identify robust biomarkers. Several methods incorporating pathway information have been proposed to identify robust pathway markers and build classifiers at the level of functional categories rather than of individual genes. However, current methods consider the pathways as simple gene sets but ignore the pathway topological information, which is essential to infer a more robust pathway activity. Results: Here, we propose a directed random walk (DRW)-based method to infer the pathway activity. DRW evaluates the topological importance of each gene by capturing the structure information embedded in the directed pathway network. The strategy of weighting genes by their topological importance greatly improved the reproducibility of pathway activities. Experiments on 18 cancer datasets showed that the proposed method yielded a more accurate and robust overall performance compared with several existing gene-based and pathway-based classification methods. The resulting risk-active pathways are more reliable in guiding therapeutic selection and the development of pathway-specific therapeutic strategies. Availability: DRW is freely available at http://210.46.85.180:8080/DRWPClass/ Contact:  lixia@hrbmu.edu.cn or dm42298@126.com Supplementary information:  Supplementary data are available at Bioinformatics online.
    Type of Medium: Online Resource
    ISSN: 1367-4811 , 1367-4803
    URL: Article
    DOI: 10.1093/bioinformatics/btt373
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2013
    detail.hit.zdb_id: 1468345-3
    SSG: 12
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  • 4
    Online Resource
    Online Resource
    SurvivalMeth: a web server to investigate the effect of DNA methylation-related functional elements on prognosis
    Oxford University Press (OUP) ; 2021
    In:  Briefings in Bioinformatics Vol. 22, No. 3 ( 2021-05-20)
    Details
    In: Briefings in Bioinformatics, Oxford University Press (OUP), Vol. 22, No. 3 ( 2021-05-20)
    Abstract: Aberrant DNA methylation is a fundamental characterization of epigenetics for carcinogenesis. Abnormality of DNA methylation-related functional elements (DMFEs) may lead to dysfunction of regulatory genes in the progression of cancers, contributing to prognosis of many cancers. There is an urgent need to construct a tool to comprehensively assess the impact of DMFEs on prognosis. Therefore, we developed SurvivalMeth (http://bio-bigdata.hrbmu.edu.cn/survivalmeth) to explore the prognosis-related DMFEs, which documented many kinds of DMFEs, including 309,465 CpG island-related elements, 104,748 transcript-related elements, 77,634 repeat elements, as well as cell-type specific 1,689,653 super enhancers (SE) and 1,304,902 CTCF binding regions for analysis. SurvivalMeth is a convenient tool which collected DNA methylation profiles of 36 cancers and allowed users to query their genes of interest in different datasets for prognosis. Furthermore, SurvivalMeth not only integrated different combinations, including single DMFE, multiple DMFEs, SEs and clinical data, to perform survival analysis on preupload data but also allowed for uploading customized DNA methylation profile of DMFEs from various diseases to analyze. SurvivalMeth provided a comprehensive resource and automated analysis for prognostic DMFEs, including DMFE methylation level, correlation analysis, clinical analysis, differential analysis, DMFE annotation, survival-related detailed result and visualization of survival analysis. In summary, we believe that SurvivalMeth will facilitate prognostic research of DMFEs in diverse cancers.
    Type of Medium: Online Resource
    ISSN: 1467-5463 , 1477-4054
    URL: Article
    DOI: 10.1093/bib/bbaa162
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2021
    detail.hit.zdb_id: 2036055-1
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  • 5
    Online Resource
    Online Resource
    RNAactDrug: a comprehensive database of RNAs associated with drug sensitivity from multi-omics data
    Oxford University Press (OUP) ; 2020
    In:  Briefings in Bioinformatics Vol. 21, No. 6 ( 2020-12-01), p. 2167-2174
    Details
    In: Briefings in Bioinformatics, Oxford University Press (OUP), Vol. 21, No. 6 ( 2020-12-01), p. 2167-2174
    Abstract: Drug sensitivity has always been at the core of individualized cancer chemotherapy. However, we have been overwhelmed by large-scale pharmacogenomic data in the era of next-generation sequencing technology, which makes it increasingly challenging for researchers, especially those without bioinformatic experience, to perform data integration, exploration and analysis. To bridge this gap, we developed RNAactDrug, a comprehensive database of RNAs associated with drug sensitivity from multi-omics data, which allows users to explore drug sensitivity and RNA molecule associations directly. It provides association data between drug sensitivity and RNA molecules including mRNAs, long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) at four molecular levels (expression, copy number variation, mutation and methylation) from integrated analysis of three large-scale pharmacogenomic databases (GDSC, CellMiner and CCLE). RNAactDrug currently stores more than 4 924 200 associations of RNA molecules and drug sensitivity at four molecular levels covering more than 19 770 mRNAs, 11 119 lncRNAs, 438 miRNAs and 4155 drugs. A user-friendly interface enriched with various browsing sections augmented with advance search facility for querying the database is offered for users retrieving. RNAactDrug provides a comprehensive resource for RNA molecules acting in drug sensitivity, and it could be used to prioritize drug sensitivity–related RNA molecules, further promoting the identification of clinically actionable biomarkers in drug sensitivity and drug development more cost-efficiently by making this knowledge accessible to both basic researchers and clinical practitioners. Database URL: http://bio-bigdata.hrbmu.edu.cn/RNAactDrug.
    Type of Medium: Online Resource
    ISSN: 1467-5463 , 1477-4054
    URL: Article
    DOI: 10.1093/bib/bbz142
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2020
    detail.hit.zdb_id: 2036055-1
    SSG: 12
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  • 6
    Online Resource
    Online Resource
    Identification and comprehensive characterization of lncRNAs with copy number variations and their driving transcriptional perturbed subpathways reveal functional significance for cancer
    Oxford University Press (OUP) ; 2020
    In:  Briefings in Bioinformatics Vol. 21, No. 6 ( 2020-12-01), p. 2153-2166
    Details
    In: Briefings in Bioinformatics, Oxford University Press (OUP), Vol. 21, No. 6 ( 2020-12-01), p. 2153-2166
    Abstract: Numerous studies have shown that copy number variation (CNV) in lncRNA regions play critical roles in the initiation and progression of cancer. However, our knowledge about their functionalities is still limited. Here, we firstly provided a computational method to identify lncRNAs with copy number variation (lncRNAs-CNV) and their driving transcriptional perturbed subpathways by integrating multidimensional omics data of cancer. The high reliability and accuracy of our method have been demonstrated. Then, the method was applied to 14 cancer types, and a comprehensive characterization and analysis was performed. LncRNAs-CNV had high specificity in cancers, and those with high CNV level may perturb broad biological functions. Some core subpathways and cancer hallmarks widely perturbed by lncRNAs-CNV were revealed. Moreover, subpathways highlighted the functional diversity of lncRNAs-CNV in various cancers. Survival analysis indicated that functional lncRNAs-CNV could be candidate prognostic biomarkers for clinical applications, such as ST7-AS1, CDKN2B-AS1 and EGFR-AS1. In addition, cascade responses and a functional crosstalk model among lncRNAs-CNV, impacted genes, driving subpathways and cancer hallmarks were proposed for understanding the driving mechanism of lncRNAs-CNV. Finally, we developed a user-friendly web interface-LncCASE (http://bio-bigdata.hrbmu.edu.cn/LncCASE/) for exploring lncRNAs-CNV and their driving subpathways in various cancer types. Our study identified and systematically characterized lncRNAs-CNV and their driving subpathways and presented valuable resources for investigating the functionalities of non-coding variations and the mechanisms of tumorigenesis.
    Type of Medium: Online Resource
    ISSN: 1467-5463 , 1477-4054
    URL: Article
    DOI: 10.1093/bib/bbz113
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2020
    detail.hit.zdb_id: 2036055-1
    SSG: 12
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  • 7
    Online Resource
    Online Resource
    Reduction of the canonical function of a glycolytic enzyme enolase triggers immune responses that further affect metabolism and growth in Arabidopsis
    Oxford University Press (OUP) ; 2022
    In:  The Plant Cell Vol. 34, No. 5 ( 2022-04-26), p. 1745-1767
    Details
    In: The Plant Cell, Oxford University Press (OUP), Vol. 34, No. 5 ( 2022-04-26), p. 1745-1767
    Abstract: Primary metabolism provides energy for growth and development as well as secondary metabolites for diverse environmental responses. Here we describe an unexpected consequence of disruption of a glycolytic enzyme enolase named LOW EXPRESSION OF OSMOTICALLY RESPONSIVE GENE 2 (LOS2) in causing constitutive defense responses or autoimmunity in Arabidopsis thaliana. The autoimmunity in the los2 mutant is accompanied by a higher expression of about one-quarter of intracellular immune receptor nucleotide-binding leucine-rich repeat (NLR) genes in the genome and is partially dependent on one of these NLR genes. The LOS2 gene was hypothesized to produce an alternatively translated protein c-Myc Binding Protein (MBP-1) that functions as a transcriptional repressor. Complementation tests show that LOS2 executes its function in growth and immunity regulation through the canonical enolase activity but not the production of MBP-1. In addition, the autoimmunity in the los2 mutants leads to a higher accumulation of sugars and organic acids and a depletion of glycolytic metabolites. These findings indicate that LOS2 does not exert its function in immune responses through an alternatively translated protein MBP-1. Rather, they show that a perturbation of glycolysis from the reduction of the enolase activity results in activation of NLR-involved immune responses which further influences primary metabolism and plant growth, highlighting the complex interaction between primary metabolism and plant immunity.
    Type of Medium: Online Resource
    ISSN: 1040-4651 , 1532-298X
    URL: Article
    DOI: 10.1093/plcell/koab283
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2022
    detail.hit.zdb_id: 623171-8
    detail.hit.zdb_id: 2004373-9
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  • 8
    Online Resource
    Online Resource
    Prognostic Value of ctDNA Detection in Patients With Locally Advanced Rectal Cancer Undergoing Neoadjuvant Chemoradiotherapy: A Systematic Review and Meta-analysis
    Oxford University Press (OUP) ; 2023
    In:  The Oncologist ( 2023-06-09)
    Details
    In: The Oncologist, Oxford University Press (OUP), ( 2023-06-09)
    Abstract: Circulating tumor DNA (ctDNA) is increasingly used as a biomarker for metastatic rectal cancer and has recently shown promising results in the early detection of recurrence risk. Methods We conducted a systematic review and meta-analysis to explore the prognostic value of ctDNA detection in LARC patients undergoing neoadjuvant chemoradiotherapy (nCRT). We systematically searched electronic databases for observational or interventional studies that included LARC patients undergoing nCRT. Study selection according to the PRISMA guidelines and quality assessment of the REMARK tool for biomarker studies. The primary endpoint was the impact of ctDNA detection at different time points (baseline, post-nCRT, post-surgery) on relapse-free survival (RFS) and overall survival (OS). The secondary endpoint was to study the association between ctDNA detection and pathological complete response(pCR) at different time points. Results After further review and analysis of the 625 articles initially retrieved, we finally included 10 eligible studies. We found no significant correlation between ctDNA detection at baseline and long-term survival outcomes or the probability of achieving a pCR. However, the presence of ctDNA at post-nCRT was associated with worse RFS (HR = 9.16, 95% CI, 5.48-15.32), worse OS (HR = 8.49, 95% CI, 2.20-32.72), and worse pCR results (OR = 0.40, 95%CI, 0.18-0.89). The correlation between the presence of ctDNA at post-surgery and worse RFS was more obvious (HR = 14.94; 95% CI, 7.48-9.83). Conclusions Our results suggest that ctDNA detection is a promising biomarker for the evaluation of response and prognosis in LARC patients undergoing nCRT, which merits further evaluation in the following prospective trials.
    Type of Medium: Online Resource
    ISSN: 1083-7159 , 1549-490X
    URL: Article
    DOI: 10.1093/oncolo/oyad151
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2023
    detail.hit.zdb_id: 2023829-0
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  • 9
    Online Resource
    Online Resource
    Apple ALMT9 Requires a Conserved C-Terminal Domain for Malate Transport Underlying Fruit Acidity
    Oxford University Press (OUP) ; 2020
    In:  Plant Physiology Vol. 182, No. 2 ( 2020-02), p. 992-1006
    Details
    In: Plant Physiology, Oxford University Press (OUP), Vol. 182, No. 2 ( 2020-02), p. 992-1006
    Type of Medium: Online Resource
    ISSN: 0032-0889 , 1532-2548
    URL: Article
    DOI: 10.1104/pp.19.01300
    RVK:
    WA 15000
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2020
    detail.hit.zdb_id: 2004346-6
    detail.hit.zdb_id: 208914-2
    SSG: 12
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  • 10
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    Multi-modality artificial intelligence in digital pathology
    Oxford University Press (OUP) ; 2022
    In:  Briefings in Bioinformatics Vol. 23, No. 6 ( 2022-11-19)
    Details
    In: Briefings in Bioinformatics, Oxford University Press (OUP), Vol. 23, No. 6 ( 2022-11-19)
    Abstract: In common medical procedures, the time-consuming and expensive nature of obtaining test results plagues doctors and patients. Digital pathology research allows using computational technologies to manage data, presenting an opportunity to improve the efficiency of diagnosis and treatment. Artificial intelligence (AI) has a great advantage in the data analytics phase. Extensive research has shown that AI algorithms can produce more up-to-date and standardized conclusions for whole slide images. In conjunction with the development of high-throughput sequencing technologies, algorithms can integrate and analyze data from multiple modalities to explore the correspondence between morphological features and gene expression. This review investigates using the most popular image data, hematoxylin–eosin stained tissue slide images, to find a strategic solution for the imbalance of healthcare resources. The article focuses on the role that the development of deep learning technology has in assisting doctors’ work and discusses the opportunities and challenges of AI.
    Type of Medium: Online Resource
    ISSN: 1467-5463 , 1477-4054
    URL: Article
    DOI: 10.1093/bib/bbac367
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2022
    detail.hit.zdb_id: 2036055-1
    SSG: 12
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