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Sci Rep. 2017 Feb 22;7:43353. doi: 10.1038/srep43353.

A Comprehensive Analysis of Metabolomics and Transcriptomics in Cervical Cancer.

Author information

1
Department of Epidemiology and Biostatistics, School of Public Health, Harbin Medical University, Harbin, 150086, P.R. China.
2
Department of Gynecology Oncology, the Tumor Hospital, Harbin Medical University, Harbin, 150086, P.R. China.
3
State Key Laboratory of Natural Products, Jiangsu Key Laboratory of TCM Evaluation; Translational Research Department of Complex Prescription of TCM, Pharmaceutical University, 639 Longmian Road, Nanjing 211198, P.R. China.
4
School of Basic Medical Sciences, Heilongjiang University of Chinese Medicine, Harbin, Heilongjiang 150040, P.R. China.
5
Harbin Medical University, Harbin, 150086, P.R. China.
6
Key Laboratory of Cardiovascular Medicine Research, Harbin Medical University, Ministry of Education, Harbin, 150086, P.R. China.

Abstract

Cervical cancer (CC) still remains a common and deadly malignancy among females in developing countries. More accurate and reliable diagnostic methods/biomarkers should be discovered. In this study, we performed a comprehensive analysis of metabolomics (285 samples) and transcriptomics (52 samples) on the potential diagnostic implication and metabolic characteristic description in cervical cancer. Sixty-two metabolites were different between CC and normal controls (NOR), in which 5 metabolites (bilirubin, LysoPC(17:0), n-oleoyl threonine, 12-hydroxydodecanoic acid and tetracosahexaenoic acid) were selected as candidate biomarkers for CC. The AUC value, sensitivity (SE), and specificity (SP) of these 5 biomarkers were 0.99, 0.98 and 0.99, respectively. We further analysed the genes in 7 significantly enriched pathways, of which 117 genes, that were expressed differentially, were mainly involved in catalytic activity. Finally, a fully connected network of metabolites and genes in these pathways was built, which can increase the credibility of our selected metabolites. In conclusion, our biomarkers from metabolomics could set a path for CC diagnosis and screening. Our results also showed that variables of both transcriptomics and metabolomics were associated with CC.

PMID:
28225065
PMCID:
PMC5320559
DOI:
10.1038/srep43353
[Indexed for MEDLINE]
Free PMC Article

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