Aims: The use of circulating cell-free DNA for detection of cancer genetics has been studied extensively. Liquid biopsy often refers to the use of blood as a minimally invasive source of body fluid for detecting circulating tumor DNA (ctDNA). However, urine collection, which is completely noninvasive, has been shown to also have great promise to serve as an alternate body fluid source for ctDNA. In this review article, we focus on the clinical utility of urine for genetic liquid biopsy of nonurological cancers. Conclusion: Although still in early stages as compared with blood-based liquid biopsy, recent studies have demonstrated the value of urine-based liquid biopsies for: nonurological cancer screening; early detection; monitoring for recurrence and metastasis; and therapeutic efficacy. Overall, the completely noninvasive and patient-friendly nature of the urine-based biopsy warrants further development and offers a promising alternative to blood-based biopsies.

Hepatoma Research is an open access journal and focuses on all topics related to hepatoma. The following articles are especially welcome: pathogenesis, clinical examination and early diagnosis of hepatoma, complications of hepatoma, and their preventions and treatments, etc.

Chronic hepatitis B virus (HBV) infection is the major etiology of hepatocellular carcinoma (HCC), frequently with HBV integrating into the host genome. HBV integration, found in 85% of HBV-associated HCC (HBV–HCC) tissue samples, has been suggested to be oncogenic. Here, we investigated the potential of HBV–HCC driver identification via the characterization of recurrently targeted genes (RTGs). A total of 18,596 HBV integration sites from our in-house study and others were analyzed. RTGs were identified by applying three criteria: at least two HCC subjects, reported by at least two studies, and the number of reporting studies. A total of 396 RTGs were identified. Among the 28 most frequent RTGs, defined as affected in at least 10 HCC patients, 23 (82%) were associated with carcinogenesis and 5 (18%) had no known function. Available breakpoint positions from the three most frequent RTGs, TERT, MLL4/KMT2B, and PLEKHG4B, were analyzed. Mutual exclusivity of TERT promoter mutation and HBV integration into TERT was observed. We present an RTG consensus through comprehensive analysis to enable the potential identification and discovery of HCC drivers for drug development and disease management.

Hepatocellular carcinoma is one of the fastest growing cancers in the US and has a low survival rate, partly due to difficulties in early detection. Because of HCC’s high heterogeneity, it has been suggested that multiple biomarkers would be needed to develop a sensitive HCC screening test. This study applied random forest (RF), a machine learning technique, and proposed two novel models, fixed sequential (FS) and two-step (TS), for comparison with two commonly used statistical techniques, logistic regression (LR) and classification and regression trees (CART), in combining multiple urine DNA biomarkers for HCC screening using biomarker values obtained from 137 HCC and 431 non-HCC (224 hepatitis and 207 cirrhosis) subjects. The sensitivity, specificity, area under the receiver operating curve, and variability were estimated through repeated 10-fold cross-validation to compare the models’ performances in accuracy and robustness. We show that RF and TS have higher accuracy and stability; specifically, they reach 90% specificity and 86%/87% sensitivity respectively along with 15% higher sensitivity and 10% higher specificity than LR in cross-validation. The potential of RF and TS to develop a panel of multiple biomarkers and the possibility for self-training, cloud-based models for HCC screening are discussed.