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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.

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Department of Epidemiology and Biostatistics, School of Public Health, Harbin Medical University, Harbin, 150086, P.R. China.
Department of Gynecology Oncology, the Tumor Hospital, Harbin Medical University, Harbin, 150086, P.R. China.
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.
School of Basic Medical Sciences, Heilongjiang University of Chinese Medicine, Harbin, Heilongjiang 150040, P.R. China.
Harbin Medical University, Harbin, 150086, P.R. China.
Key Laboratory of Cardiovascular Medicine Research, Harbin Medical University, Ministry of Education, Harbin, 150086, P.R. China.


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.

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