Background Aberrant DNA methylation may offer opportunities in revolutionizing cancer screening and diagnosis. We sought to identify a non-invasive DNA methylation-based screening approach using cell-free DNA (cfDNA) for early detection of hepatocellular carcinoma (HCC). Methods Differentially, DNA methylation blocks were determined by comparing methylation profiles of biopsy-proven HCC, liver cirrhosis, and normal tissue samples with high throughput DNA bisulfite sequencing. A multi-layer HCC screening model was subsequently constructed based on tissue-derived differentially methylated blocks (DMBs). This model was tested in a cohort consisting of 120 HCC, 92 liver cirrhotic, and 290 healthy plasma samples including 65 hepatitis B surface antigen-seropositive (HBsAg+) samples, independently validated in a cohort consisting of 67 HCC, 111 liver cirrhotic, and 242 healthy plasma samples including 56 HBsAg+ samples. Results Based on methylation profiling of tissue samples, 2321 DMBs were identified, which were subsequently used to construct a cfDNA-based HCC screening model, achieved a sensitivity of 86% and specificity of 98% in the training cohort and a sensitivity of 84% and specificity of 96% in the independent validation cohort. This model obtained a sensitivity of 76% in 37 early-stage HCC (Barcelona clinical liver cancer [BCLC] stage 0-A) patients. The screening model can effectively discriminate HCC patients from non-HCC controls, including liver cirrhotic patients, asymptomatic HBsAg+ and healthy individuals, achieving an AUC of 0.957(95% CI 0.939–0.975), whereas serum α-fetoprotein (AFP) only achieved an AUC of 0.803 (95% CI 0.758–0.847). Besides detecting patients with early-stage HCC from non-HCC controls, this model showed high capacity for distinguishing early-stage HCC from a high risk population (AUC=0.934; 95% CI 0.905–0.963), also significantly outperforming AFP. Furthermore, our model also showed superior performance in distinguishing HCC with normal AFP (< 20ng ml−1) from high risk population (AUC=0.93; 95% CI 0.892–0.969). Conclusions We have developed a sensitive blood-based non-invasive HCC screening model which can effectively distinguish early-stage HCC patients from high risk population and demonstrated its performance through an independent validation cohort. Trial registration The study was approved by the ethic committee of The Second Xiangya Hospital of Central South University (KYLL2018072) and Chongqing University Cancer Hospital (2019167). The study is registered at ClinicalTrials.gov(#NCT04383353).

Purpose: To construct a competing endogenous RNA (ceRNA) topology network of RNAseq data and micro RNA-seq (miRNA-seq) data to identify key prognostic long non-coding RNA (lncRNAs) in luminal breast cancer, and validate the results by human luminal breast cancer samples. Materials and Methods: The RNA-seq data and miRNA-seq data of luminal A breast cancer in the The Cancer Genome Atlas (TCGA) database were downloaded and compared with those in the miRcode database to obtain lncRNA-miRNA relationship pairs. Final target genes were predicted by all three databases (miRDB, miRTarBase, and TargetScan), thereby obtaining the miRNA-messenger RNA (miRNA-mRNA) relationship pairs and a ceRNA topology network was constructed, then mRNA enrichment analysis, ceRNA topological and stability analysis, univariate and multivariate Cox regression analysis were performed. Overall survival (OS) was evaluated and the key prognostic RNAs were identified. The expression difference between normal and tumor, as well as the correlation of high expression in tumor with pathological parameters (Ki-67, Grade, tumor diameter) were validated by human breast cancer specimens. Results: A ceRNA topology network was constructed and six lncRNAs were finally identified (The higher expression of PART1, IGF2.AS, WT1.AS, OIP5.AS1, and SLC25A5. AS1 was associated with poor prognosis while AL035706.1 was adverse) and the poor prognostic ones were higher expressed in tumor tissue and correlated with a higher Ki-67 (>10%), tumor grades (II, III) and tumor diameters (>1.5 cm). Using six lncRNAs, we constructed a prognostic model, which performed well for the classification of prognosis in the module. Conclusion: We identified and verified six biomarkers (OS-predicting) in luminal breast cancer, which significantly enriched the prediction and potential targets of this subtype.

Objective: Hepatocellular carcinoma(HCC),the most prevalent form of liver cancer, owns high morbidity and mortality. The radical surgery is the preference. It is of great clinical significance to predict the postoperative survival. Methods: All clinical characteristics of 1187 patients participants from multicenter were collected. We identified several indicators significantly associated with HCC survival through logistic analysis to develop the prediction model. Further analysis revealed the independent predictive capacity of the predictive model. A nomogram comprising the predictive model was established. The decision curve analysis(DCA), receiver operating characteristic (ROC) curve analysis and Kaplan-Meier analysis confirmed the good performance of the predictive model. Results: As a result, we identified several clinical indicators that were significantly associated with HCC survival through univariate analysis and multivariate analysis. The predictive model was consist of clinical features and tumor characteristics readily obtained after surgery. All the factors above were incorporated into the nomogram and the application of the nomogram gave good discrimination and good calibration. Calibration curves showed a favorable consistency between the predicted probabilities. ROC curve analysis showed that the nomogram had good discrimination both in the training group and validation group, respectively. Moreover, decision curve analysis has been implemented to evaluate and compare prediction nomogram. Kaplan-Meier analysis showed significant differences in prognosis among different risk groups. Conclusion: The study provides a novel model for predicting HCC patients undergone radical surgery.