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Serum miR-16 as a potential biomarker for human cancer diagnosis: results from a large-scale population

  • Original Article – Clinical Oncology
  • Published:
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Abstract

Background

Cancer is a serious public health problem worldwide, and difficulty in early diagnosis has been the chief obstacle to improve the prognosis of patients. Recently, microRNAs (miRNAs) were widely studied to be potential biomarkers for cancer detection. miR-16 is a prevalent but sophisticated one. In the current study, we aimed to assess the diagnostic value of serum miR-16 for cancer detection.

Methods

A total of 1458 cancer patients, containing ten types of cancers, and 1457 non-cancer controls were recruited in this study. qRT-PCR was used for the amplification of miRNAs. In addition, a meta-analysis of reported studies was performed to confirm our findings systematically.

Results

Consequently, miR-16 was down-regulated in ESCC, GCA and GNCA patients compared with NCs (all P < 0.001), while up-regulated in PDAC patients (P = 0.001), LAC, LSCC and EEC patients (all P < 0.001). But no significant differences were observed in CRC, EOC and TC patients when compared to NCs (P = 0.747, 0.235 and 0.268, respectively). The areas under the receiver operating characteristic (ROC) curve of miR-16 in GCA, ESCC, LAC, LSCC, GNCA, PDAC and EEC were 0.881, 0.780, 0.757, 0.693, 0.602, 0.614 and 0.681, respectively. Results of meta-analysis showed that miR-16 achieved an overall pooled sensitivity of 0.72, specificity of 0.79, and AUC of 0.85, suggesting that miR-16 was a promising biomarker in cancer detection.

Conclusions

We provided a comprehensive view of the diagnostic value of serum miR-16 in cancer diagnosis, and confirmed that circulating miR-16 could play an important role in cancer detection.

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Acknowledgements

We thank Ping Liu and Wei Zhu (Department of Oncology, the First Affiliated Hospital of Nanjing Medical University) for constructive suggestion.

Funding

Supported by grants: (1) Wuxi Health and Family Planning Commission Project; Grant number: Q201724. (2) Kunshan Social Development Science and Technology Project; Grant number: KS1643.

Author information

Author notes

  1. Zebo Huang, Wenjiao Chen and Yiping Du contributed equally to this work.

Authors and Affiliations

  1. Department of Oncology, The Affiliated Hospital of Jiangnan University, 200 Huihe Road, Wuxi, 214062, China

    Zebo Huang, Yong Mao & Dong Hua

  2. Department of Oncology, The Affiliated Yixing Hospital of Jiangsu University, 75 Tongzhenguan Road, Wuxi, 214200, China

    Wenjiao Chen

  3. Department of Oncology, The First People’s Hospital of Kunshan Affiliated with Jiangsu University, Suzhou, 215300, China

    Yiping Du

  4. Department of Clinical Laboratory, The Affiliated Hospital of Jiangnan University, 200 Huihe Road, Wuxi, 214062, China

    Qin Guo

  5. Department of Oncology, The First Affiliated Hospital of Nanjing Medical University, 300 Guangzhou Road, Nanjing, 210029, China

    Xin Zhou

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  1. Zebo Huang
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Contributions

HZB, CWJ and DYP performed experiments; HZB conducted the meta-analysis; GQ and SD reviewed eligible articles; WXH and MY provided the serum samples of patients and normal controls; HZB and ZX drafted the manuscript; ZX and HD designed the study, critically interpreted results; All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Xin Zhou or Dong Hua.

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Conflict of interest

All authors declare that they have no conflict of interest.

Ethics approval

All the procedures were approved by the Ethics Committee of the First Affiliated Hospital of Nanjing Medical University and the Affiliated Hospital of Jiangnan University in compliance with the Declaration of Helsinki, and the written informed consent was obtained from each participant.

Informed consent

Informed consent was obtained from all individual participants included in the study.

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Supplementary material 1 (DOC 728 KB)

Figure S1 Risk of bias and applicability concerns graph a review of authors’ judgments about each domain presented as percentages across included studies.

Figure S2 Forest plots of pooled A: positive likelihood ratio (PLR); B: negative likelihood ratio (NLR); C: diagnostic odds ratio (DOR) for miR-16 in cancer diagnosis.

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Huang, Z., Chen, W., Du, Y. et al. Serum miR-16 as a potential biomarker for human cancer diagnosis: results from a large-scale population. J Cancer Res Clin Oncol 145, 787–796 (2019). https://doi.org/10.1007/s00432-019-02849-8

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  • DOI: https://doi.org/10.1007/s00432-019-02849-8

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