Plasma microRNA-15a/16-1-based machine learning for early detection of hepatitis B virus-related hepatocellular carcinoma

Abstract

Background and aims: Hepatocellular carcinoma (HCC), which is prevalent worldwide and has a high mortality rate, needs to be effectively diagnosed. We aimed to evaluate the significance of plasma microRNA-15a/16-1 (miR-15a/16) as a biomarker of hepatitis B virus-related HCC (HBV-HCC) using the machine learning model. This study was the first large-scale investigation of these two miRNAs in HCC plasma samples.

Methods: Using quantitative polymerase chain reaction, we measured the plasma miR-15a/16 levels in a total of 766 participants, including 74 healthy controls, 335 with chronic hepatitis B (CHB), 47 with compensated liver cirrhosis, and 310 with HBV-HCC. The diagnostic performance of miR-15a/16 was examined using a machine learning model and compared with that of alpha-fetoprotein (AFP). Lastly, to validate the diagnostic efficiency of miR-15a/16, we performed pseudotemporal sorting of the samples to simulate progression from CHB to HCC.

Results: Plasma miR-15a/16 was significantly decreased in HCC than in all control groups (P < 0.05 for all). In the training cohort, the area under the receiver operating characteristic curve (AUC), sensitivity, and average precision (AP) for the detection of HCC were higher for miR-15a (AUC = 0.80, 67.3%, AP = 0.80) and miR-16 (AUC = 0.83, 79.0%, AP = 0.83) than for AFP (AUC = 0.74, 61.7%, AP = 0.72). Combining miR-15a/16 with AFP increased the AUC to 0.86 (sensitivity 85.9%) and the AP to 0.85 and was significantly superior to the other markers in this study (P < 0.05 for all), as further demonstrated by the detection error tradeoff curves. Moreover, miR-15a/16 impressively showed potent diagnostic power in early-stage, small-tumor, and AFP-negative HCC. A validation cohort confirmed these results. Lastly, the simulated follow-up of patients further validated the diagnostic efficiency of miR-15a/16.

Conclusions: We developed and validated a plasma miR-15a/16-based machine learning model, which exhibited better diagnostic performance for the early diagnosis of HCC compared to that of AFP.

Keywords

Hepatitis B virus-related hepatocellular carcinoma (HBV-HCC) / microRNA-15a / microRNA-16-1 / Biomarker / Machine learning / Pseudotemporal ordering

Data availability statement

The data contained in this manuscript or supplementary ma-terial will be made available upon request.

Authors’ contributions

Huan Wei, Songhao Luo, and Yanhua Bi contributed equally to this paper and should be considered co-first authors. Huan Wei: Data curation, Investigation, Methodology, Visualization, Writing e original draft, Writing e review & editing. Songhao Luo: Formal analysis, Methodology, Software, Validation, Visualization, Writing e original draft, Writing e review & editing. Yanhua Bi: Data curation, Investigation, Methodology, Validation, Writing e original draft. Chunhong Liao: Data curation, Investigation. Yifan Lian: Conceptualization, Funding acquisition, Project administration, Resources, Supervision, Writing e review & editing. Jiajun Zhang: Conceptualization, Funding acquisition, Methodology, Software, Supervision, Writing e review & editing. Yuehua Huang: Conceptualization, Formal analysis, Funding acquisition, Project administration, Resources, Supervision, Writing e review & editing.

Declaration of competing interest

The authors declare that there is no conflict of interest.

Acknowledgements

This study was supported by Research and Development Plan-ned Project in Key Areas of Guangdong Province (No. 2019B110233002); National Natural Science Foundation of China (No. 12171494 and 11931019); Natural Science Foundation of Guangdong Province, China (No. 2022A1515011540); Guangdong Province Key Laboratory of Computational Science at the Sun Yat-sen University (No. 2020B1212060032); Joint Key Projects of City and Hospital of Guangzhou Science and Technology (No. 202201020422); and General Planned Project of Guangzhou Sci-ence and Technology (No. 202201010950).

Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.livres.2024.05.003.

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