Evidence map›Paper›PMID 26286638›Full record

ArticleScientific reports2015

Topologically inferring pathway activity toward precise cancer classification via integrating genomic and metabolomic data: prostate cancer as a case.

Wei Liu, Xuefeng Bai, Yuejuan Liu, Wei Wang, Junwei Han, Qiuyu Wang, Yanjun Xu, Chunlong Zhang, Shihua Zhang, Xuecang Li and 3 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

16 citing papers in PubMed.

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  9. The Translational Potential of Neuroimaging Genomic Analyses To Diagnosis And Treatment In The Mental Disorders.Proceedings of the IEEE. Institute of Electrical and Electronics Engineers · 2019
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors.

Wei LiuDepartment of Mathematics, Heilongjiang Institute of Technology, Harbin, 150050, China.
Xuefeng BaiDepartment of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing, 163319, China.
Yuejuan LiuDepartment of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing, 163319, China.
Wei WangDepartment of Mathematics, Heilongjiang Institute of Technology, Harbin, 150050, China.
Junwei HanCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China.
Qiuyu WangDepartment of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing, 163319, China.
Yanjun XuCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China.
Chunlong ZhangCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China.
Shihua ZhangDepartment of Biostatistics, Anhui Agricultural University, Hefei, 230030, China.
Xuecang LiDepartment of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing, 163319, China.
Zhonggui RenDepartment of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing, 163319, China.
Jian ZhangDepartment of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing, 163319, China.
Chunquan LiDepartment of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing, 163319, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Precise cancer classification is a central challenge in clinical cancer research such as diagnosis, prognosis and metastasis prediction. Most existing cancer classification methods based on gene or metabolite biomarkers were limited to single genomics or metabolomics, and lacked integration and utilization of multiple 'omics' data. The accuracy and robustness of these methods when applied to independent cohorts of patients must be improved. In this study, we propose a directed random walk-based method to evaluate the topological importance of each gene in a reconstructed gene-metabolite graph by integrating information from matched gene expression profiles and metabolomic profiles. The joint use of gene and metabolite information contributes to accurate evaluation of the topological importance of genes and reproducible pathway activities. We constructed classifiers using reproducible pathway activities for precise cancer classification and risk metabolic pathway identification. We applied the proposed method to the classification of prostate cancer. Within-dataset experiments and cross-dataset experiments on three independent datasets demonstrated that the proposed method achieved a more accurate and robust overall performance compared to several existing classification methods. The resulting risk pathways and topologically important differential genes and metabolites provide biologically informative models for prostate cancer prognosis and therapeutic strategies development.

Indexed as

Databases as TopicGenomicsMetabolomicsBiomarkers, TumorBiosynthetic PathwaysHumansMaleMetabolic Networks and PathwaysPrognosisProstatic NeoplasmsReproducibility of ResultsSoftwareBiomarkers, Tumor

Identifiers

PMID26286638
PMCPMC4541321

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.