Development of a Nomogram to Predict Clinically Relevant Postoperative Pancreatic Fistula After Pancreaticoduodenectomy on the Basis of Visceral Fat Area and Magnetic Resonance Imaging.

BACKGROUND: The aim of this study was to develop a nomogram to predict the risk of developing clinically relevant postoperative pancreatic fistula (CR-POPF) after pancreaticoduodenectomy (PD) using preoperative clinical and imaging data. METHODS: The data of 205 patients were retrospectively analyzed, randomly divided into training (n = 125) and testing groups (n = 80). The patients' preoperative laboratory indicators, preoperative clinical baseline data, and preoperative imaging data [enhanced computed tomography (CT), enhanced magnetic resonance imaging (MRI)] were collected. Univariate analyses combined with multivariate logistic regression were used to identify the independent risk factors for CR-POPF. These factors were used to train and validate the model and to develop the risk nomogram. The area under the curve (AUC) was used to measure the predictive ability of the models. The integrated discrimination improvement index (IDI) and decision curve analysis (DCA) were used to assess the clinical feasibility of the nomogram in relation to five other models established in literature. RESULTS: CT visceral fat area (P = 0.014), the pancreatic spleen signal ratio on T1 fat-suppressed MRI sequences (P < 0.001), and CT main pancreatic duct diameter (P = 0.001) were identified as independent prognostic factors and used to develop the model. The final nomogram achieved an AUC of 0.903. The IDI and DCA showed that the nomogram outperformed the other five CR-POPF models in the training and testing cohorts. CONCLUSION: The nomogram achieved a superior predictive ability for CR-POPF following PD than other models described in literature. Clinicians can use this simple model to optimize perioperative planning according to the patient's risk of developing CR-POPF.

Glycolytic and fatty acid oxidation genes affect the treatment and prognosis of liver cancer.

BACKGROUND: Metabolic reprogramming is a feature of tumour cells and is essential to support their rapid proliferation. The glycolytic activity of liver cancer cells is significantly higher than that of normal liver cells, and the rapidly proliferating tumour cells are powered by aerobic glycolysis. Lipid metabolism reprogramming enables tumour cells to meet their needs for highly proliferative growth and is an important driving force for the development of hepatocellular carcinoma (HCC). AIM: To explore the influence of different metabolic subtypes of HCC and analyse their significance in guiding prognosis and treatment based on the molecular mechanism of glycolysis and fatty acid oxidation (FAO). METHODS: By downloading related data from public databases including the Cancer Genome Atlas (TCGA), the Molecular Signatures Database, and International Cancer Genome Consortium, we utilised unsupervised consensus clustering to divide TCGA Liver Hepatocellular Carcinoma samples into four metabolic subgroups and compared single nucleotide polymorphism, copy number variation, tumour microenvironment, and Genomics of Drug Sensitivity in Cancer and Tumour Immune Dysfunction and Exclusion between different metabolites. The differences and causes of survival and the clinical characteristics between them were analysed, and a prognostic model was established based on glycolysis and FAO genes. Combined with the clinical features, a Norman diagram was created to compare the pros and cons of each model. RESULTS: In the four metabolic subgroups, with the increase in glycolytic expression, the median survival of patients showed the worst results, while FAO showed the best. When comparing the follow-up analysis of each group, we considered that the differences between them might be related to reactive oxygen species, somatic copy number variation of key genes, and immune microenvironment. It was also found that the FAO group and the low-risk group had better efficacy and response to immune checkpoint blockade treatment and anti-tumour drugs. CONCLUSION: There are obvious differences in genes, chromosomes, and clinical characteristics between metabolic subgroups. The establishment of a prognostic model could predict patient prognosis and guide clinical treatment.

Identifying glycolysis-related LncRNAs for predicting prognosis in breast cancer patients.

PURPOSE: Functions associated with glycolysis could serve as targets or biomarkers for therapy cancer. Our purpose was to establish a prognostic model that could evaluate the importance of Glycolysis-related lncRNAs in breast cancer. METHODS: Gene expressions were evaluated for breast cancer through The Cancer Genome Atlas (TCGA) database, and we calculated Pearson correlations to discover potential related lncRNAs. Differentially expressed genes were identified via criteria of FDR < 0.05 and |FC|> 2. Total samples were separated into training and validating sets randomly. Univariate Cox regression identified 14 prognostic lncRNAs in training set. A prognostic model was constructed to evaluate the accuracy in predicting prognosis. The univariate and multivariate Cox analysis were performed to verify whether lncRNA signature could be an independent prognostic factor The signature was validated in validating set. Immune infiltration levels were assessed. RESULTS: Eighty-nine differentially expressed lncRNAs were identified from 420 Glycolysis-related lncRNAs. 14 lncRNAs were correlated with prognosis in training set and were selected to establish the prognostic model. Low risk group had better prognosis in both training (p= 9.025 e -10) and validating (p= 4.272 e -3) sets. The univariate and multivariate Cox analysis revealed that risk score of glycolysis-related lncRNAs (P< 0.001) was an independent prognostic factor in both training and validating sets. The neutrophils (p= 4.214 e -13, r=-0.223), CD4+ T cells (p= 1.833 e -20, r=-0.283), CD8+ T cells (p= 7.641 e -12, r=-0.211), B cells (p= 2.502 e -10, r=-0.195) and dendritic cells (p= 5.14 e -18, r=-0.265) were negatively correlated with risk score of prognostic model. The Macrophage (p= 0.016, r= 0.0755) was positively correlated with the risk score. CONCLUSION: Our study indicated that glycolysis-related lncRNAs had a significant role to facilitate the individualized survival prediction in breast cancer patients, which would be a potential therapeutic target.

Cisplatin-resistant HepG2 cell-derived exosomes transfer cisplatin resistance to cisplatin-sensitive cells in HCC.

BACKGROUNDS: Cancer cell resistance to chemotherapy drugs such as Gemcitabine, Oxaliplatin, Cisplatin, Doxorubicin, and 5-fluorouracil account for the main reason of chemotherapy failure for HCC patients, especially for those with advanced HCC or metastasis patients. This emerging resistance limits the effectiveness and clinical application of these chemotherapy drugs. Previous studies reported that drug-resistant tumor cell-derived exosomes could transfer their resistance property to tumor sensitive cells in some cancer, including lung and gastric cancer. This study sought to explore whether HepG2/DDP cell-derived exosomes transmit cisplatin (DDP) resistance to HepG2 and other HCC sensitive cells, and provide considerable guidance for HCC nursing with Cisplatin DDP clinically. METHODS: The HepG2 DDP-resistant cell line (HepG2/DDP) was established, and the exosomes from both HepG2/DDP and HepG2 cells were isolated and named ES-2, ES-1, respectively. HepG2 or SMMC-7721 or Huh7 cells were treated with DDP or DDP + ES-2, and HepG2/DDP cells were treated with ES-1. Then, the activation of drug resistance via HepG2/DDP exosomes transfer to HepG2, SMMC-7721 and Huh7 cells were assessed by cell viability assay and ROS formation. Moreover, the relative expression of P-glycoprotein (P-gp) was measured by western blot analysis. RESULTS: HepG2/DDP cell-derived exosomes were successfully isolated from cisplatin-resistant HepG2 cells, and named ES-2. Cell viability of HepG2 or SMMC-7721 or Huh7 cells treated with DDP + ES-2 was enhanced compared with that of DDP treatment group. Also, the concentration of ROS generated in cells under DDP or DDP + ES-2 treatment was strongly increased compared with that of control, although the concentration of ROS was clearly smaller in DDP + ES-2 treatment group compared with DDP treatment. At the same time, the expression of P-gp was upregulated on the ES-2 surface. CONCLUSION: The results mentioned above clarified that HepG2/DDP cell-derived exosomes conferred cisplatin resistance to HepG2 and other HCC cell lines, and provided a new significance for improving the effectiveness of DDP in treating HCC.