DNA methylation biomarkers for diagnosis of primary liver cancer and distinguishing hepatocellular carcinoma from intrahepatic cholangiocarcinoma

Abstract

Hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC) are the two most common pathology subtypes of primary liver cancer (PLC). Identifying DNA methylation biomarkers for diagnosis of PLC and further distinguishing HCC from ICC plays a vital role in subsequent treatment options selection. To obtain potential diagnostic DNA methylation sites for PLC, differentially methylated CpG (DMC) sites were first screened by comparing the methylation data between normal liver samples and PLC samples (ICC samples and HCC samples). A random forest algorithm was then used to select specific DMC sites with top Gini value. To avoid overfitting, another cohort was taken as an external validation for evaluating the area under curves (AUCs) of different DMC sites combination. A similar model construction strategy was applied to distinguish HCC from ICC. In addition, we identified DNA Methylation-Driven Genes in HCC and ICC via MethylMix method and performed pathway analysis by utilizing MetaCore. Finally, we not only performed methylator phenotype based on independent prognostic sites but also analyzed the correlations between methylator phenotype and clinical factors in HCC and ICC, respectively. To diagnose PLC, we developed a model based on three PLC-specific methylation sites (cg24035245, cg21072795, and cg00261162), whose sensitivity and specificity achieved 98.8%,94.8% in training set and 97.3%,81% in validation set. Then, to further divide the PLC samples into HCC and ICC, we established another mode through three methylation sites (cg17769836, cg17591574, and cg07823562), HCC accuracy and ICC accuracy achieved 95.8%, 89.8% in the training set and 96.8%,85.4% in the validation set. In HCC, the enrichment pathways were mainly related to protein folding, oxidative stress, and glutathione metabolism. While in ICC, immune response, embryonic hepatocyte maturation were the top pathways. Both in HCC and ICC, methylator phenotype correlated well with overall survival time and clinical factors involved in tumor progression. In summary, our study provides the biomarkers based on methylation sites not only for the diagnosis of PLC but also for distinguishing HCC from ICC.

A Novel Immune and Inflammatory Related Gene Signature in Hepatocellular Carcinoma

“…However, there are no relevant reports on the immune and in ammation-related markers in serum. At present, the prediction of liver cancer models related to iron death, DNA methylation, m6A methylation, long non-coding RNA, endoplasmic reticulum stress and glycolysis has good predictive effects [31][32][33][34][35][36] .The prognostic model constructed by immune and in ammation-related genes screened in this study has an AUC of 77.9% (Figure2D), which also has a good predictive effect.…”

Liquid Biopsy Using Cell-Free or Circulating Tumor DNA in the Management of Hepatocellular Carcinoma

“…Models based on cfDNA methylation for HCC detection could achieve a typical sensitivity of 91%–97% and specificity of 85%–92%. 42 , 60 , 61 Combining methylation markers such as HOXA1, EMX1, and TSPYL5 with other biomarkers such as AFP or AFP-L3 can improve the performance for early stage HCC detection. 62 , 63 , 64 …”

Epigenetic Changes Affecting the Development of Hepatocellular Carcinoma

“…An integrated bioinformatic analysis may also help to identify diagnostic biomarkers to differentiate between different types of liver cancer. For instance, Bai et al described methylation sites typical for HCC but not cholangiocarcinoma [46]. Future research may allow the estimation of the prognostic and diagnostic value of integrated bioinformatic data from HCC cells and tissues.…”