A novel microRNA signature predicts survival in liver hepatocellular carcinoma after hepatectomy

Liver hepatocellular carcinoma (LIHC) is the most common type of primary liver cancer. In the current study, genome-wide miRNA-Seq and mRNA profiles in 318 LIHC patients derived from The Cancer Genome Atlas (TCGA) were analysed to identify miRNA-based signatures for LIHC prognosis with survival analysis and a semi-supervised principal components (SPC) method. A seven-miRNA signature was confirmed for overall survival (OS) prediction by comparing miRNA profiles in paired primary tumour and solid tumour normal tissues. Thereafter, a linear prognostic model that consisted of seven miRNAs was established and used to divide patients into high- and low-risk groups according to prognostic scores. Subsequent Kaplan-Meier analysis revealed that the seven-miRNA signature correlated with a good predictive clinical outcome for 5-year survival in LIHC patients. Additionally, this miRNA-based prognostic model could also be used for OS prognosis of LIHC patients in early stages, which could guide the future therapy of those patients and promote the OS rate. Moreover, the seven-miRNA signature was an independent prognostic factor. In conclusion, this signature may serve as a prognostic biomarker and guide LIHC therapy, and it could even be used as an LIHC therapeutic target in the future

Droplet digital PCR-based circulating microRNA detection serve as a promising diagnostic method for gastric cancer

Abstract Background Novel non-invasive biomarkers for gastric cancer (GC) are needed, because the present diagnostic methods for GC are either invasive or insensitive and non-specific in clinic. The presence of stable circulating microRNAs (miRNAs) in plasma suggested a promising role as GC biomarkers. Methods Based on the quantitative droplet digital PCR (ddPCR), four miRNAs (miR-21, miR-93, miR-106a and miR-106b) related to the presence of GC were identified in plasma from a training cohort of 147 participants and a validation cohort of 28 participants. Results All circulating miRNA levels were significantly higher in the plasma of GC patients compared to healthy controls (P < 0.05). Through a combination of four miRNAs by logistic regression model, receiver operating characteristic (ROC) analyses yielded the highest AUC value of 0.887 in discriminating GC patients from healthy volunteers. Furthermore, miR-21, miR-93 and miR-106b levels were significantly related to an advanced TNM stage in GC patients. ROC analyses of the combined miRNA panel also showed the highest AUC value of 0.809 in discriminating GC patients with TNM stage I and II from stage III and IV. Through combining four miRNAs and clinical parameters, a classical random forest model was established in the training stage. In the validation cohort, it correctly discriminated 23 out of 28 samples in the blinded phase (false rate, 17.8%). Conclusions Using the ddPCR technique, circulating miR-21, miR-93, miR-106a and miR-106b could be used as diagnostic plasma biomarkers in gastric cancer patients

Additional file 1: of Droplet digital PCR-based circulating microRNA detection serve as a promising diagnostic method for gastric cancer

Figure S1. A random forest model for discriminating healthy volunteers, gastric cancer patients with low TNM stage (stage I and II) and high TNM stage (stage III and IV) G0 represents healthy volunteers; G1 represents GC patients with TNM stage I and II; G2 represents GC patients with TNM stage III and IV. (TIF 624 kb