Background: In traditional Chinese medicine (TCM) clinical practice, TCM syndromes help to understand human homeostasis and guide individualized treatment. However, the TCM syndrome changes with disease progression, of which the scientific basis and mechanism remain unclear. Methods: To demonstrate the underlying mechanism of dynamic changes in the TCM syndrome, we applied a dynamic network biomarker (DNB) algorithm to obtain the DNBs of changes in the TCM syndrome, based on the transcriptomic data of patients with chronic hepatitis B and typical TCM syndromes, including healthy controls and patients with liver-gallbladder dampness-heat syndrome (LGDHS), liver-depression spleen-deficiency syndrome (LDSDS), and liver-kidney yin-deficiency syndrome (LKYDS). The DNB model exploits collective fluctuations and correlations of the observed genes, then diagnoses the critical state. Results: Our results showed that the DNBs of TCM syndromes were comprised of 52 genes and the tipping point occurred at the LDSDS stage. Meanwhile, there were numerous differentially expressed genes between LGDHS and LKYDS, which highlighted the drastic changes before and after the tipping point, implying the 52 DNBs could serve as early-warning signals of the upcoming change in the TCM syndrome. Next, we validated DNBs by cytokine profiling and isobaric tags for relative and absolute quantitation (iTRAQ). The results showed that PLG (plasminogen) and coagulation factor XII (F12) were significantly expressed during the progression of TCM syndrome from LGDHS to LKYDS. Conclusions: This study provides a scientific understanding of changes in the TCM syndrome. During this process, the cytokine system was involved all the time. The DNBs PLG and F12 were confirmed to significantly change during TCM-syndrome progression and indicated a potential value of DNBs in auxiliary diagnosis of TCM syndrome in CHB. Trial registration Identifier: NCT03189992. Registered on June 4, 2017. Retrospectively registered (http://www.clinicaltrials.gov)

Liver cirrhosis is one of the most common non-neoplastic causes of mortality worldwide. Chronic hepatitis B (CHB) is a major cause of liver cirrhosis in China. To find biomarkers for the diagnosis of CHB caused cirrhosis (HBC), we examined the transcriptional profiling of CHB and HBC. The leukocyte samples of CHB (n = 5) and HBC (n = 5) were analyzed by microarray. The results showed that 2128 mapped genes were differentially expressed between CHB and HBC (fold change ≥ 2.0, p < 0.05). Gene ontology (GO) analysis indicated that these 2128 differentially expressed genes (DEGs) were enriched for immune response and cell formation functions mostly. Moreover, co-expression networks using the k-core algorithm were established to determine the core genes, which may play important roles in the progression of CHB to HBC. There were markedly different gene co-expression patterns in CHB and HBC. We validated the five core genes, CASP1, TGFBI, IFI30, HLA-DMA and PAG1 in CHB (n = 60) and HBC (n = 60) by quantitative RT-PCR. The expression of the five genes were consistent with microarray, and there were statistically significant co-expression patterns of TGFβ1, PAG1 and HLA-DMA mRNA (Pearson correlation coefficient >0.6). Furthermore, we constructed an mRNA panel of TGFBI, IFI30, HLA-DMA and PAG1 (TIPH HBCtest) by means of a logistic regression model, and evaluated the TIPH HBCtest for HBC diagnosis by area under the receiver operating characteristic curve (AUC) analysis, which showed a higher accuracy (AUC = 0.903). This study suggested that there are particular transcriptional profiles, gene co-expression patterns and core genes in CHB and HBC. The TIPH HBC test may be useful in the diagnosis of HBC from CHB.

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.