Plasma microRNA-15a/16-1-based machine learning for early detection of hepatitis B virus-related hepatocellular carcinoma
10.1016/j.livres.2024.05.003
- Author:
Wei HUAN
1
;
Luo SONGHAO
;
Bi YANHUA
;
Liao CHUNHONG
;
Lian YIFAN
;
Zhang JIAJUN
;
Huang YUEHUA
Author Information
1. Guangdong Provincial Key Laboratory of Liver Disease Research
- Keywords:
Hepatitis B virus-related hepatocellular carcinoma(HBV-HCC);
microRNA-15a;
microRNA-16-1;
Biomarker;
Machine learning;
Pseudotemporal ordering
- From:
Liver Research
2024;8(2):105-117
- CountryChina
- Language:Chinese
-
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.
