An integrated prognostic model of nuclear-encoded mitochondrial gene signature and clinical information for hepatocellular carcinoma / 上海交通大学学报（医学版）

Objective·To establish a prognostic model for the overall survival(OS)of hepatocellular carcinoma(HCC)based on mitochondrial genes and clinical information.Methods·The gene expression and the clinical data of 369 HCC patients and 50 controls with normal liver were downloaded from The Cancer Genome Atlas(TCGA)database.The nuclear-encoded mitochondrial genes(NEMGs)were obtained from the MitoCarta3.0 database.The"DESeq2"R package and univariate Cox analysis were used to select NEMGs[ubiquinol cytochrome C reductase hinge protein(UQCRH),ATP citrate lyase(ACLY),phosphoenolpyruvate carboxykinase 2(PCK2),Bcl-2 homologous antagonist/killer1(BAK1),Bcl-2-associated X protein(BAX)and Bcl-2/adenovirus E1B interacting protein 3-like(BNIP3L)]in HCC that were associated with OS of HCC and participated in dysregulation of oxidative phosphorylation,tricarboxylic acid cycle and cell apoptosis.Multivariate Cox analysis was applied to select independent risk factors for OS of HCC.A comprehensive prognostic model and a prognostic nomogram with 6-NEMG risk characteristics and TNM staging were established.By using the median of prognostic scores as a cut-off,HCC patients were classified into low-risk and high-risk group.Kaplan-Meier survival curve analysis was conducted and log-rank test was performed to evaluate the survival rates between the low-risk and high-risk group.The area under the curve(AUC)values of receiver operating characteristic(ROC)curve were calculated via using the"timeROC"package.The prognostic model for HCC was validated by using the GEO HCC cohort(GSE14520)for 1,3 and 5 years.Finally,the relative expression level of 6-NEMG was validated in 34 clinical samples of HCC from Xinhua Hospital,Shanghai Jiao Tong University School of Medicine by using real-time quantitative polymerase chain reaction(qPCR)method.Results·Compared to 6-NEMG risk signature only(AUCs for 1,3 and 5 years were 0.77,0.66 and 0.65,respectively)or TNM stage only(AUCs for 1,3 and 5 years were 0.66,0.67 and 0.63,respectively),ROC curve analysis showed that this integrated prognostic model displayed better predictive performance for 1-year(AUC,0.78),3-year(AUC,0.73)and 5-year(AUC,0.69)OS of HCC.The Kaplan-Meier survival curve analysis showed that the OS of HCC patients in the high-risk group was significantly worse than that in the low-risk group(P=0.001).In addition,predictive performance of the prognostic model(AUC for 1,3 and 5 years is 0.67,0.66 and 0.74,respectively)and prognostic differences between the high-risk and low-risk group(P=0.001)were further validated in GEO(GSE14520)external cohort,and these results were consistent with the TCGA data.In addition to BNIP3L,dysregulation of five other NEMGs in the clinical HCC cohort was validated.The correlation analysis in GSE14520 and HCC clinical cohort showed a positive correlation between prognosis score and the size and number of tumors.Conclusion·A new prognostic model that combines 6-NEMG risk characteristics with TNM staging for predicting OS in HCC patients was constructed and validated.This model may help improve the prognosis prediction of HCC patients.

Boosting the photocatalytic performance of Cu2O for hydrogen generation by Au nanostructures and rGO nanosheets.

As a narrow band-gap semiconductor, cuprous oxide (Cu2O) has a relatively high conduction band that can exhibit high driving force for the photocatalytic generation of hydrogen under visible light. Besides, its adjustable morphologies and abundant source also make it possible to be employed as a theoretically optimal photocatalyst. However, the low charge migration and poor stability commonly limit its practical application, and various strategies have been explored in previous studies. In this study, we have novelly utilized Au nanorod (NR) and nanobipyramid (NBP) nanocrystallites as well as rGO nanosheets to boost the photocatalytic activity of Cu2O over hydrogen generation. The ternary rGO wrapped Au@Cu2O with a yolk-shelled structure (y-Au@Cu2O/rGO) was synthesized by a handy and controllable method. When excited by solar light (λ > 400 nm), it was found that the H2 yields of Cu2O/rGO, y-Au nanoparticle@Cu2O/rGO, y-Au NR@Cu2O/rGO, and y-Au NBP@Cu2O/rGO were increased in the order of 248, 702, 1582 and 1894 µmol g-1 in 4 h. The outstanding photocatalytic performances of y-Au NR@Cu2O/rGO and y-Au NBP@Cu2O/rGO could be attributed to the combination function of quick electron transfer of rGO and abundant near-infrared-light-driven hot carriers on Au NRs and NBPs that could inject into Cu2O and then a quick transfer to rGO to participate in H2 reduction. Besides the above results, it was also found that Cu2O maintained good stability after several cycling photocatalysis tests, which could be ascribed to the migration of holes from Cu2O to Au that prevented the photooxidation of Cu2O. This study may give a guide to fabricating controllable and effective photocatalysts based on plasmonic metals, semiconductors, or two-dimensional nanosheets, which possess full-solar-light-driven photocatalytic activities in the future.

Deep learning for differential diagnosis of malignant hepatic tumors based on multi-phase contrast-enhanced CT and clinical data.

BACKGROUND: Liver cancer remains the leading cause of cancer death globally, and the treatment strategies are distinct for each type of malignant hepatic tumors. However, the differential diagnosis before surgery is challenging and subjective. This study aims to build an automatic diagnostic model for differentiating malignant hepatic tumors based on patients' multimodal medical data including multi-phase contrast-enhanced computed tomography and clinical features. METHODS: Our study consisted of 723 patients from two centers, who were pathologically diagnosed with HCC, ICC or metastatic liver cancer. The training set and the test set consisted of 499 and 113 patients from center 1, respectively. The external test set consisted of 111 patients from center 2. We proposed a deep learning model with the modular design of SpatialExtractor-TemporalEncoder-Integration-Classifier (STIC), which take the advantage of deep CNN and gated RNN to effectively extract and integrate the diagnosis-related radiological and clinical features of patients. The code is publicly available at https://github.com/ruitian-olivia/STIC-model . RESULTS: The STIC model achieved an accuracy of 86.2% and AUC of 0.893 for classifying HCC and ICC on the test set. When extended to differential diagnosis of malignant hepatic tumors, the STIC model achieved an accuracy of 72.6% on the test set, comparable with the diagnostic level of doctors' consensus (70.8%). With the assistance of the STIC model, doctors achieved better performance than doctors' consensus diagnosis, with an increase of 8.3% in accuracy and 26.9% in sensitivity for ICC diagnosis on average. On the external test set from center 2, the STIC model achieved an accuracy of 82.9%, which verify the model's generalization ability. CONCLUSIONS: We incorporated deep CNN and gated RNN in the STIC model design for differentiating malignant hepatic tumors based on multi-phase CECT and clinical features. Our model can assist doctors to achieve better diagnostic performance, which is expected to serve as an AI assistance system and promote the precise treatment of liver cancer.

Circular RNAs and Hepatocellular Carcinoma: New Epigenetic Players With Diagnostic and Prognostic Roles.

Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related death worldwide. Due to the lack of potent diagnosis and prognosis biomarkers and effective therapeutic targets, the overall prognosis of survival is poor in HCC patients. Circular RNAs (circRNAs) are a class of novel endogenous non-coding RNAs with covalently closed loop structures and implicated in diverse physiological processes and pathological diseases. Recent studies have demonstrated the involvement of circRNAs in HCC diagnosis, prognosis, development, and drug resistance, suggesting that circRNAs may be a class of novel targets for improving HCC diagnosis, prognosis, and treatments. In fact, some artificial circRNAs have been engineered and showed their therapeutic potential in treating HCV infection and gastric cancer. In this review, we introduce the potential of circRNAs as biomarkers for HCC diagnosis and prognosis, as therapeutic targets for HCC treatments and discuss the challenges in circRNA research and chances of circRNA application.