The characterization of circulating tumor cells (CTCs) by liquid biopsy has a great potential for precision medicine in oncology. Here, a universal and tandem logic-based strategy is developed by combining multiple nanomaterials and nanopore sensing for the determination of mucin 1 protein (MUC1) and breast cancer CTCs in real samples. The strategy consists of analyte-triggered signal conversion, cascaded amplification via nanomaterials including copper sulfide nanoparticles (CuS NPs), silver nanoparticles (Ag NPs), and biomaterials including DNA hydrogel and DNAzyme, and single-molecule-level detection by nanopore sensing. The amplification of the non-DNA nanomaterial gives this method considerable stability, significantly lowers the limit of detection (LOD), and enhances the anti-interference performance for complicated samples. As a result, the ultrasensitive detection of MUC1 could be achieved in the range of 0.0005–0.5 pg/mL, with an LOD of 0.1 fg/mL. Moreover, we further tested MUC1 as a biomarker for the clinical diagnosis of breast cancer CTCs under double-blind conditions on the basis of this strategy, and MCF-7 cells could be accurately detected in the range from 5 to 2000 cells/mL, with an LOD of 2 cells/mL within 6 h. The detection results of the 19 clinical samples were highly consistent with those of the clinical pathological sections, nuclear magnetic resonance imaging, and color ultrasound. These results demonstrate the validity and reliability of our method and further proved the feasibility of MUC1 as a clinical diagnostic biomarker for CTCs.

Background miR-124-3p can inhibit integrin β3 (ITGB3) expression to suppress the migration and invasion of gastric cancer (GC), and in the process lncRNA HOXA11-AS may act as a molecular sponge. Methods Luciferase reporter assay was conducted to verify the binding of miR-124-3p and HOXA11-AS. RT-PCR and western blot were performed to detect the expression of HOXA11-AS, miR-124-3p and ITGB3 in GC tissues and cells. Gene silence and overexpression experiments as well as cell migration and invasion assays on GC cell lines were performed to determine the regulation of molecular pathways, HOXA11-AS/miR-124-3p/ITGB3. Furthermore, the role of HOXA11-AS in GC was confirmed in mice models. Results We found HOXA11-AS is up-regulated in GC tissues and can bind with miR-124-3p. Through overexpression/knockdown experiments and function tests in vitro, we demonstrated HOXA11-AS can promote ITGB3 expression by sponging miR-124-3p, consequently enhance the proliferation, migration, and invasion of GC cells. Meanwhile, we validated that HOXA11-AS promotes migration and invasion of GC cells via down-regulating miR-124-3p and up-regulating ITGB3 in vivo. Conclusions We demonstrated that lncRNA HOXA11-AS can increase ITGB3 expression to promote the migration and invasion of gastric cancer by sponging miR-124-3p. Our results suggested that HOXA11-AS may reasonably serve as a promising diagnostic biomarker and a potential therapeutic target of GC.

Microbiome research has extended into the cancer area in the past decades. The microbes can affect oncogenesis, progression and treatment response through various mechanisms, including direct regulation and indirect impacts. Microbiota-associated detections and agents have been developed to facilitate cancer diagnosis and therapy. Additionally, the cancer microbiome has been recently re-defined. Identifications of intra-tumoral microbes and cancer-related circulating microbial DNA (cmDNA) promoted novel research directions in cancer-microbiome areas. In this review, we define the human system of commensal microbes and cancer microbiome from a brand-new perspective and emphasize the potential values of cmDNA as a promising biomarker in cancer liquid biopsy. We outlined all existing studies on the relationship between cmDNA and cancer, and outlooks for potential preclinical and clinical applications of cmDNA in cancer precision medicine and critical problems to be overcome for this burgeoning field.

No abstract

BackgroundThe non-invasive preoperative diagnosis of microvascular invasion (MVI) in hepatocellular carcinoma (HCC) is vital for precise surgical decision-making and patient prognosis. Herein, we aimed to develop an MVI prediction model with valid performance and clinical interpretability.MethodsA total of 2160 patients with HCC without macroscopic invasion who underwent hepatectomy for the first time in West China Hospital from January 2015 to June 2019 were retrospectively included, and randomly divided into training and a validation cohort at a ratio of 8:2. Preoperative demographic features, imaging characteristics, and laboratory indexes of the patients were collected. Five machine learning algorithms were used: logistic regression, random forest, support vector machine, extreme gradient boosting (XGBoost), and multilayer perception. Performance was evaluated using the area under the receiver operating characteristic curve (AUC). We also determined the Shapley Additive exPlanation value to explain the influence of each feature on the MVI prediction model.ResultsThe top six important preoperative factors associated with MVI were the maximum image diameter, protein induced by vitamin K absence or antagonist-II, α-fetoprotein level, satellite nodules, alanine aminotransferase (AST)/aspartate aminotransferase (ALT) ratio, and AST level, according to the XGBoost model. The XGBoost model for preoperative prediction of MVI exhibited a better AUC (0.8, 95% confidence interval: 0.74–0.83) than the other prediction models. Furthermore, to facilitate use of the model in clinical settings, we developed a user-friendly online calculator for MVI risk prediction based on the XGBoost model.ConclusionsThe XGBoost model achieved outstanding performance for non-invasive preoperative prediction of MVI based on big data. Moreover, the MVI risk calculator would assist clinicians in conveniently determining the optimal therapeutic remedy and ameliorating the prognosis of patients with HCC.