Long noncoding RNA expression profiles in sub-lethal heat-treated hepatoma carcinoma cells.

BACKGROUND: Sub-lethal heat treatment characterizes a transition zone of radiofrequency ablation (RFA) which explains hepatocellular carcinoma (HCC) residual cancer occurrence in this area after RFA treatment. The biochemistry of residual cancer cell recurrence is poorly understood, but long noncoding RNAs (lncRNAs) may have aberrant expression that is associated with diverse cancers. Thus, we measured lncRNA gene expression in sub-lethally heat-treated HCC cells using microarray. METHOD: Differentially expressed lncRNA and mRNA were measured with an Agilent Human lncRNA + mRNA Array V4.0 (4 × 180 K format) containing 41,000 lncRNAs and 34,000 mRNAs. Bioinformatics analysis was used to assess differentially expressed lncRNA and mRNA. Seven lncRNA and seven mRNA were validated by qRT-PCR analysis in HCC cells. RESULTS: Genome-wide lncRNA and mRNA expression data in sub-lethal heat-treated SMMC-7721 HCC cells 558 lncRNA and 250 mRNA were significantly up-regulated and 224 lncRNA and 1031 mRNA down-regulated compared to normal cultured SMMC-7721 cells. We demonstrated for the first time that ENST00000570843.1, ENST00000567668.1, ENST00000582249.1, ENST00000450304.1, TCONS_00015544, ENST00000602478.1, TCONS_00001266 and ARC, IL12RB1, HSPA6 were upregulated, whereas STAT3, PRPSAP1, MCU, URB2 were down-regulated in sub-lethally heat-treated HCC cells. CONCLUSIONS: lncRNA expression data in sub-lethally heat-treated HCC cells will provide important insights about lncRNAs' contribution to HCC recurrence after RFA treatment.

A radiomics model based on preoperative gadoxetic acid-enhanced magnetic resonance imaging for predicting post-hepatectomy liver failure in patients with hepatocellular carcinoma.

Background: Post-hepatectomy liver failure (PHLF) is a fatal complication after liver resection in patients with hepatocellular carcinoma (HCC). It is of clinical importance to estimate the risk of PHLF preoperatively. Aims: This study aimed to develop and validate a prediction model based on preoperative gadoxetic acid-enhanced magnetic resonance imaging to estimate the risk of PHLF in patients with HCC. Methods: A total of 276 patients were retrospectively included and randomly divided into training and test cohorts (194:82). Clinicopathological variables were assessed to identify significant indicators for PHLF prediction. Radiomics features were extracted from the normal liver parenchyma at the hepatobiliary phase and the reproducible, robust and non-redundant ones were filtered for modeling. Prediction models were developed using clinicopathological variables (Clin-model), radiomics features (Rad-model), and their combination. Results: The PHLF incidence rate was 24% in the whole cohort. The combined model, consisting of albumin-bilirubin (ALBI) score, indocyanine green retention test at 15 min (ICG-R15), and Rad-score (derived from 16 radiomics features) outperformed the Clin-model and the Rad-model. It yielded an area under the receiver operating characteristic curve (AUC) of 0.84 (95% confidence interval (CI): 0.77-0.90) in the training cohort and 0.82 (95% CI: 0.72-0.91) in the test cohort. The model demonstrated a good consistency by the Hosmer-Lemeshow test and the calibration curve. The combined model was visualized as a nomogram for estimating individual risk of PHLF. Conclusion: A model combining clinicopathological risk factors and radiomics signature can be applied to identify patients with high risk of PHLF and serve as a decision aid when planning surgery treatment in patients with HCC.