Dynamic edge-based biomarker non-invasively predicts hepatocellular carcinoma with hepatitis B virus infection for individual patients based on blood testing

Abstract

Hepatitis B virus (HBV)-induced hepatocellular carcinoma (HCC) is a major cause of cancer-related deaths in Asia and Africa. Developing effective and non-invasive biomarkers of HCC for individual patients remains an urgent task for early diagnosis and convenient monitoring. Analyzing the transcriptomic profiles of peripheral blood mononuclear cells from both healthy donors and patients with chronic HBV infection in different states (i.e. HBV carrier, chronic hepatitis B, cirrhosis, and HCC), we identified a set of 19 candidate genes according to our algorithm of dynamic network biomarkers. These genes can both characterize different stages during HCC progression and identify cirrhosis as the critical transition stage before carcinogenesis. The interaction effects (i.e. co-expressions) of candidate genes were used to build an accurate prediction model: the so-called edge-based biomarker. Considering the convenience and robustness of biomarkers in clinical applications, we performed functional analysis, validated candidate genes in other independent samples of our collected cohort, and finally selected COL5A1, HLA-DQB1, MMP2, and CDK4 to build edge panel as prediction models. We demonstrated that the edge panel had great performance in both diagnosis and prognosis in terms of precision and specificity for HCC, especially for patients with alpha-fetoprotein-negative HCC. Our study not only provides a novel edge-based biomarker for non-invasive and effective diagnosis of HBV-associated HCC to each individual patient but also introduces a new way to integrate the interaction terms of individual molecules for clinical diagnosis and prognosis from the network and dynamics perspectives.

Chronic hepatitis B: dynamic change in Traditional Chinese Medicine syndrome by dynamic network biomarkers

“…Progression of both TCM syndromes and complex diseases usually have nonlinear transitions [33, 35, 37]. Such a transition plays key roles in biological processes.…”

“…Thus, we introduced a mathematical model, i.e., the DNB method to identify the predictive biomarkers and understand the molecular mechanisms of TCM syndrome dynamic change. The DNB method has been applied successfully to studies in multiple fields (e.g., disease diagnosis and prognosis, therapeutic response, cell differentiation) [35, 38–40].…”

“…The DNB method can identify the critical state of TCM-syndrome progression, based on the correlation and fluctuation of molecules. Unlike the traditional molecular biomarker approach, the DNB method identifies differentially associated networks in a dynamic manner, to determine the corresponding functional DNBs of networks [34, 35].…”

New Blood Biomarkers for the Diagnosis of AFP-Negative Hepatocellular Carcinoma

“…Zhang et al ( 28 ) identified 615 differentially hydroxymethylated regions from ANHC tissues compared to adjacent normal tissues, which were significantly enriched in gene ontology functions, and they found that some hydroxymethylated genes were involved in ANHC development. Lu et al ( 32 ) found that a panel based on four candidate genes (COL5A1, HLA-DQB1, MMP2, and CDK4) was valuable for the diagnosis of ANHC patients [with an area under the receiver operating characteristic curve (AUROC) of 0.768], and Zheng et al ( 33 ) also found that a blood-based 22-gene signature was valuable for ANHC screening (AUROC of 0.93, sensitivity of 91.3%, specificity of 83.2%). Circulating cell-free DNA (cfDNA), microRNAs, and circular RNAs are easily detected genetic biomarkers that are valuable for the diagnosis of HCC, including ANHC.…”

Gene dysregulation analysis builds a mechanistic signature for prognosis and therapeutic benefit in colorectal cancer

“…Therefore, the currently available predictive signatures unavoidably tend to have limited mechanism explanatory power, which has become a common concern in precision medicine ( Robinson et al., 2013 ). There is a general consensus among both clinicians and biologists about the need for signatures with mechanistic interpretability as well as high predictive accuracy for cancer precision medicine ( Robinson et al., 2013 ; Topalian et al., 2016 ; Lu et al., 2019 ). It could also be expected that taking mechanistic interpretation into consideration would further enhance the predictive accuracy and robustness of signatures in clinical application ( Robinson et al., 2013 ).…”