ObjectiveThe lack of highly sensitive and specific diagnostic biomarkers is a major contributor to the poor outcomes of patients with hepatocellular carcinoma (HCC). We sought to develop a non-invasive diagnostic approach using circulating cell-free DNA (cfDNA) for the early detection of HCC.DesignApplying the 5hmC-Seal technique, we obtained genome-wide 5-hydroxymethylcytosines (5hmC) in cfDNA samples from 2554 Chinese subjects: 1204 patients with HCC, 392 patients with chronic hepatitis B virus infection (CHB) or liver cirrhosis (LC) and 958 healthy individuals and patients with benign liver lesions. A diagnostic model for early HCC was developed through case-control analyses using the elastic net regularisation for feature selection.ResultsThe 5hmC-Seal data from patients with HCC showed a genome-wide distribution enriched with liver-derived enhancer marks. We developed a 32-gene diagnostic model that accurately distinguished early HCC (stage 0/A) based on the Barcelona Clinic Liver Cancer staging system from non-HCC (validation set: area under curve (AUC)=88.4%; (95% CI 85.8% to 91.1%)), showing superior performance over α-fetoprotein (AFP). Besides detecting patients with early stage or small tumours (eg, ≤2.0 cm) from non-HCC, the 5hmC model showed high capacity for distinguishing early HCC from high risk subjects with CHB or LC history (validation set: AUC=84.6%; (95% CI 80.6% to 88.7%)), also significantly outperforming AFP. Furthermore, the 5hmC diagnostic model appeared to be independent from potential confounders (eg, smoking/alcohol intake history).ConclusionWe have developed and validated a non-invasive approach with clinical application potential for the early detection of HCC that are still surgically resectable in high risk individuals.

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The axillary lymph node status remains the most valuable prognostic factor for breast cancer patients. However, approximately 20-30% of node-positive patients remain free of distant metastases within 15-30 years. It is important to develop molecular markers that are able to predict for the risk of distant metastasis and to develop patient-tailored therapy strategies. We hypothesize that the lymph node metastases may represent the most metastatic fraction of the primary cancers. Therefore, we sought to identify the differentially expressed genes by microarray between the primary tumors and their paired lymph node metastases samples collected from 26 patients. A set of 79 differentially expressed genes between primary cancers and metastasis samples was identified to correctly separate most of primary cancers from lymph node metastases. And decreased expression of matrix metalloproteinase 2, fibronectin, osteoblast specific factor 2, collagen type XI alpha 1 in lymph node metastases were further confirmed by real-time RT-PCR performed on 30 specimen pairs. This set of genes also classified 35 primary cancers into two groups with different prognosis: "high risk group" and "low risk group." Patients in "high risk group" had a 4.65-fold hazard ratio (95% CI 1.02-21.13, P = 0.047) to develop a distant metastasis within 43 months comparing with the "low risk group." This suggested that the gene signature consisting of 79 differentially expressed genes between primary cancers and lymph node metastases could also predict clinical outcome of node-positive patients, and that the molecular classification based on the gene signature could guide patient-tailored therapy.

• Based on radiomics features, a signature is established to differentiate adenocarcinoma in situ and minimally invasive adenocarcinoma from invasive lung adenocarcinoma.