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Background: Hepatocellular carcinoma (HCC) is one of the most common and lethal malignancies. Early detection of HCC could largely reduce mortalities. Ultrasonography (US) and serum Alpha Fetoprotein (AFP) test are the screening methods that are most frequently applied to high-risk populations. Due to the poor performance of AFP testing, and the highly operator-dependent nature of US, a biomarker for HCC early diagnosis is highly sought after. We developed a method for HCC screening using a 22-gene expression signature.
Methods: Peripheral whole blood of 98 patients were processed through microarrays for the first round of feature selection via two strategies, Minimal Redundancy Maximal Relevance and Least Absolute Shrinkage and Selection Operator combined with Support Vector Machine (SVM). Candidate genes were combined for further validation through qPCR in an enlarged population with 316 samples with 104 chronic hepatitis, 112 liver cirrhosis (LC), and 100 HCC.
Results: A 22-gene signature was established in classifying HCC and non-cancer samples with good performance. The area under curve reached 0.94 in all of the samples and 0.93 in the AFP -negative samples.
Conclusions: We have established a blood mRNA signature with high performance for HCC screening. Our results show transcriptome of peripheral blood could be valuable source for biomarkers.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7154425 | PMC |
| http://dx.doi.org/10.21037/atm.2020.01.93 | DOI Listing |
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