Development and validation of a 14-gene signature for prognosis prediction in hepatocellular carcinoma.

Worldwide, hepatocellular carcinoma (HCC) remains a crucial medical problem. Precise and concise prognostic models are urgently needed because of the intricate gene variations among liver cancer cells. We conducted this study to identify a prognostic gene signature with biological significance. We applied two algorithms to generate differentially expressed genes (DEGs) between HCC and normal specimens in The Cancer Genome Atlas cohort (training set included) and performed enrichment analyses to expound on their biological significance. A protein-protein interactions network was established based on the STRING online tool. We then used Cytoscape to screen hub genes in crucial modules. A multigene signature was constructed by Cox regression analysis of hub genes to stratify the prognoses of HCC patients in the training set. The prognostic value of the multigene signature was externally validated in two other sets from Gene Expression Omnibus (GSE14520 and GSE76427), and its role in recurrence prediction was also investigated. A total of 2000 DEGs were obtained, including 1542 upregulated genes and 458 downregulated genes. Subsequently, we constructed a 14-gene signature on the basis of 56 hub genes, which was a good predictor of overall survival. The prognostic signature could be replicated in GSE14520 and GSE76427. Moreover, the 14-gene signature could be applied for recurrence prediction in the training set and GSE14520. In summary, the 14-gene signature extracted from hub genes was involved in some of the HCC-related signalling pathways; it not only served as a predictive signature for HCC outcome but could also be used to predict HCC recurrence.

Standard liver weight model in adult deceased donors with fatty liver: A prospective cohort study.

BACKGROUND: Standard liver weight (SLW) is frequently used in deceased donor liver transplantation to avoid size mismatches with the recipient. However, some deceased donors (DDs) have fatty liver (FL). A few studies have reported that FL could impact liver size. To the best of our knowledge, there are no relevant SLW models for predicting liver size. AIM: To demonstrate the relationship between FL and total liver weight (TLW) in detail and present a related SLW formula. METHODS: We prospectively enrolled 212 adult DDs from West China Hospital of Sichuan University from June 2019 to February 2021, recorded their basic information, such as sex, age, body height (BH) and body weight (BW), and performed abdominal ultrasound (US) and pathological biopsy (PB). The chi-square test and kappa consistency score were used to assess the consistency in terms of FL diagnosed by US relative to PB. Simple linear regression analysis was used to explore the variables related to TLW. Multiple linear regression analysis was used to formulate SLW models, and the root mean standard error and interclass correlation coefficient were used to test the fitting efficiency and accuracy of the model, respectively. Furthermore, the optimal formula was compared with previous formulas. RESULTS: Approximately 28.8% of DDs had FL. US had a high diagnostic ability (sensitivity and specificity were 86.2% and 92.9%, respectively; kappa value was 0.70, P < 0.001) for livers with more than a 5% fatty change. Simple linear regression analysis showed that sex (R2, 0.226; P < 0.001), BH (R2, 0.241; P < 0.001), BW (R2, 0.441; P < 0.001), BMI (R2, 0.224; P < 0.001), BSA (R2, 0.454; P < 0.001) and FL (R2, 0.130; P < 0.001) significantly impacted TLW. In addition, multiple linear regression analysis showed that there was no significant difference in liver weight between the DDs with no steatosis and those with steatosis within 5%. Furthermore, in the context of hepatic steatosis, TLW increased positively (non-linear); compared with the TLW of the non-FL group, the TLW of the groups with hepatic steatosis within 5%, between 5% and 20% and more than 20% increased by 0 g, 90 g, and 340 g, respectively. A novel formula, namely, -348.6 + (110.7 x Sex [0 = Female, 1 = Male]) + 958.0 x BSA + (179.8 x FLUS [0 = No, 1 = Yes]), where FL was diagnosed by US, was more convenient and accurate than any other formula for predicting SLW. CONCLUSION: FL is positively correlated with TLW. The novel formula deduced using sex, BSA and FLUS is the optimal formula for predicting SLW in adult DDs.

Gamma-glutamyl transpeptidase-to-platelet ratio and the fibrosis-4 index in predicting hepatitis B virus-related hepatocellular carcinoma development in elderly chronic hepatitis B patients in China: A single-center retrospective study.

Gamma-glutamyl transpeptidase-to-platelet ratio (GPR) and fibrosis-4 (FIB-4) index have been reported to be useful predictors in predicting hepatocellular carcinoma (HCC) development in chronic hepatitis B (CHB) patients. However, their predictive performances on HCC development have not been validated in elderly patients. Thus, the aim of this study was to evaluate the predictive values of the GPR and FIB-4 index on HCC in elderly CHB patients with in China.Between January 2007 and December 2016, 1011 CHB patients older than 60 years were enrolled in the study, and their data were retrospectively analyzed. Receiver-operating characteristic (ROC) curve analysis was used to determine the optimal cutoff points of GPR and the FIB-4 index. Cumulative HCC incidence rates were calculated by the Kaplan-Meier method and compared by the log-rank test. Univariate and multivariate analyses were performed to detect risk factors for HCC development. The prediction performances of GPR and FIB-4 index were compared based on time-dependent ROC analyses.After a median follow-up of 6.8 (interquartile range 3.9-8.4) years, 39 (3.9%) patients developed HCC. The ROC analysis of GPR and the FIB-4 index at the 5-year time point revealed that the optimal cutoff point was 0.23 for GPR and 4.15 for the FIB-4 index. When stratified by low and high GPR values and FIB-4 indices, the patients' subgroups showed significantly different cumulative incidences of HCC. The multivariate analysis revealed that high GPR (hazard ratio [HR] 4.224; 95% confidence interval [CI] 1.891-9.434, P < .001) was an independent risk factor for HCC development, whereas a high FIB-4 index was not (HR 0.470; 95% CI 0.212-1.043; P = .063). In the time-dependent ROC analysis, GPR showed higher area under curve (AUC) values than the FIB-4 index did at all time points and reached statistical significance at the 5-, 7-, and 10-year time points (GPR vs FIB-4 index, AUC 0.725 vs 0.549 at 5 years, P = .005; GPR vs FIB-4 index, AUC 0.733 vs 0.578 at 7 years, P = .001; GPR vs FIB-4 index, AUC 0.837 vs 0.475 at 10 years, P < .001).In conclusion, our study suggests GPR is superior to the FIB-4 index in predicting HCC development in elderly CHB patients in China.