Background: The competing endogenous RNAs (ceRNAs) hypothesis has received increasing attention as a novel explanation for tumorigenesis and cancer progression. However, there is still a lack of comprehensive analysis of the circular RNA (circRNA)-long non-coding RNA (lncRNA)-miRNA-mRNA ceRNA network in hepatocellular carcinoma (HCC). Methods: RNA sequencing data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database were employed to identify Differentially Expressed mRNAs (DEmRNAs), DElncRNAs, and DEcircRNAs between HCC and normal tissues. Candidates were identified to construct networks through a comprehensive bioinformatics strategy. A prognostic mRNA signature was established based on data from TCGA database and validated using data from the GEO database. Then, the HCC prognostic circRNA-lncRNA-miRNA-mRNA ceRNA network was established. Finally, the expression and function of an unexplored hub gene, deoxythymidylate kinase (DTYMK), was explored through data mining. The results were examined using clinical samples and in vitro experiments. Results: We constructed a prognostic signature with seven target mRNAs by univariate, lasso and multivariate Cox regression analyses, which yielded 1, 3 and 5-year AUC values of 0.797, 0.733 and 0.721, respectively, indicating its sensitivity and specificity in the prognosis of HCC. Moreover, the prognostic signature could be validated in GSE14520. The prognostic ceRNA network of 21 circRNAs, 15 lncRNAs, 5 miRNAs, and 7 mRNAs was established according to the targeting relationship between 7 hub mRNAs and other RNAs. Our experiment results indicated that the depletion of DTYMK inhibited liver cancer cell proliferation and invasion. Conclusions: The network revealed in this study may help comprehensively elucidate the ceRNA mechanisms driving HCC, and provide novel candidate biomarkers for evaluating the prognosis of HCC.
Background
The aim of this study was to assess the accuracy of actual resected liver volume (ARLV) in anatomical liver resections (ALRs) guided by 3‐dimensional parenchymal staining using fusion indocyanine green fluorescence imaging (IGFI).
Methods
Patients eligible for hepatic resection were enrolled in the current study from January 2016 to November 2017. All patients underwent surgery planning based on Medical Image Three‐Dimensional Visualization System (MI‐3DVS) before the operation, in which predicted resected liver volumes (PRLVs) were calculated. Under 3‐dimensional guidance by fusion IGFI, ALRs were performed and ARLVs were measured. Simple linear regression, intra‐class correlation coefficient (ICC) and Bland‐Altman analysis were used to evaluate the relationship and agreement between PRLV and ARLV.
Results
Of the 27 patients who achieved valid demarcation by fusion IGFI, 12 (44.4%) received hemihepatectomy, while 10 (37.0%) and five (18.5%) underwent sectionectomy and segmentectomy, respectively. The relationship and agreement between PRLV (481.37 ± 189.47 cm³) and ARLV (450.57 ± 205.19 cm³) were then evaluated. The simple regression equation obtained was PRLV = 0.874 × ARLV + 87.75 (R = 0.946;
P = 0.000). Meanwhile, ARLV (ICC = 0.943) achieved an excellent agreement with PRLV (
P < 0.001); 25 of 27 dots were in the range of 95% confidence interval in Bland‐Altman analysis.
Conclusions
In the study, these findings validated the consistency between PRLV calculated by MI‐3DVS and ARLV guided by fusion IGFI, which proved that IGFI can accurately guide anatomical hepatectomy. Generally, fusion IGFI can provide a valid, feasible and accurate demarcation line, which can confer precision to hepatic resection.
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