Machine-Learning-Based Clinical Biomarker Using Cell-Free DNA for Hepatocellular Carcinoma (HCC)

Taehee Lee, Piper A. Rawding, Jiyoon Bu, Sunghee Hyun, Woosun Rou, Hongjae Jeon, Seokhyun Kim, Byungseok Lee, Luke J. Kubiatowicz, Dawon Kim, Seungpyo Hong, Hyuksoo Eun

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)

Abstract

(1) Background: Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related death worldwide. Although various serum enzymes have been utilized for the diagnosis and prognosis of HCC, the currently available biomarkers lack the sensitivity needed to detect HCC at early stages and accurately predict treatment responses. (2) Methods: We utilized our highly sensitive cell-free DNA (cfDNA) detection system, in combination with a machine learning algorithm, to provide a platform for improved diagnosis and prognosis of HCC. (3) Results: cfDNA, specifi-cally alpha-fetoprotein (AFP) expression in captured cfDNA, demonstrated the highest accuracy for diagnosing malignancies among the serum/plasma biomarkers used in this study, including AFP, aspartate aminotransferase, alanine aminotransferase, albumin, alkaline phosphatase, and bilirubin. The diagnostic/prognostic capability of cfDNA was further improved by establishing a cfDNA score (cfDHCC), which integrated the total plasma cfDNA levels and cfAFP-DNA expression into a single score using machine learning algorithms. (4) Conclusion: The cfDHCC score demonstrated significantly improved accuracy in determining the pathological features of HCC and predicting patients’ survival outcomes compared to the other biomarkers. The results presented herein reveal that our cfDNA capture/analysis platform is a promising approach to effectively utilize cfDNA as a biomarker for the diagnosis and prognosis of HCC.

Original languageEnglish
Article number2061
JournalCancers
Volume14
Issue number9
DOIs
Publication statusPublished - 2022 May 1

Bibliographical note

Funding Information:
Funding: This study was supported by the National Science Foundation (NSF) # DMR-1808251, NIAMS/NIH # 1R01AR069541, NIBIB/NIH # 1R21EB022374, the Wisconsin Head & Neck Cancer SPORE Grant, the Falk Medical Research Trust—Catalyst Awards Program, Milton J. Henrichs Endowed Chair fund, Chungnam National University Sejong Hospital Research Fund 2021, NRF under grant # NRF-2020R1A2C4001856/#NRF-2019M3E5D1A02068557, and KHIDI under grant # HR20C0025040021. This work was supported by the National Research Foundation of Korea Grant funded by the Korean Government (MOE). This work was supported by Inha University.

Publisher Copyright:
© 2022 by the authorsLicensee MDPI, Basel, Switzerland.

All Science Journal Classification (ASJC) codes

  • Oncology
  • Cancer Research

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