Purpose: We aimed to characterize the role of selenium-binding protein 1 (SBP1) in hepatocellular carcinoma (HCC) invasiveness and underlying clinical significance.Experimental Design: SBP1 expression was measured in stepwise metastatic HCC cell lines by Western blotting. The role of SBP1 in HCC was investigated using siRNA. Immunofluorescence analyses were used to detect the interaction between SBP1 and glutathione peroxidase 1 (GPX1). Nineteen fresh tumor tissues and 323 paraffin-embedded samples were used to validate in vitro findings and to detect the prognostic significance of SBP1, respectively.Results: Inhibition of SBP1 effectively increased cell motility, promoted cell proliferation, and inhibited apoptosis only under oxidative stress; it also greatly enhanced GPX1 activity without altering GPX1 expression and downregulated hypoxia-inducible factor-1a (HIF-1a) expression. SBP1 and GPX1 formed nuclear bodies and colocalized under oxidative stress. In freshly isolated clinical HCC tissues, decreased SBP1 was linked with increased GPX1 activity and correlated with vascular invasion. Tumor tissue microarrays indicated that SBP1 was an independent risk factor for overall survival and disease recurrence; patients with lower SBP1 expression experienced shorter overall survival periods and higher rates of disease recurrence (P < 0.001). Further analyses indicated that the predictive power of SBP1 was more significant for patients beyond the Milan criteria than patients within the Milan criteria.Conclusions: Decreased expression of SBP1 could promote tumor invasiveness by increasing GPX1 activity and diminishing HIF-1a expression in HCC; SBP1 could be a novel biomarker for predicting prognosis and guiding personalized therapeutic strategies, especially in patients with advanced HCC.
Although immune checkpoint blockade have demonstrated promising results, their effects on gastric cancer (GC) are under investigation. Understanding the clinical significance of PD1 and its ligands' expression, together with T cell infiltration might provide clues for biomarkers screening in GC immunotherapy. Immunohistochemistry were performed on a tissue microarray including 1,014 GC specimens using PD1, PDL1 and PDL2 antibodies. T cell markers CD3 and CD8 were also stained and quantified by automated image analysis. Correlation with clinical features and outcome were analyzed after controlling for potential confounders including EBV infection, HER2, C-met and PCNA expression. 37.8% of the cases showed membranous PD-L1 expression in tumor cells and 74.9% in infiltrating immune cells. PDL1 expression rate was rather higher in patients without metastasis, in EBV positive group and those with C-met and PCNA expression. GC patients with high level PDL1 expression exhibited better survival. GC Patients with higher T cell infiltration also showed elevated PDL1, PDL2 and PD1 expression and predict favorable outcome, indicating an adaptive immune resistance mechanism may exist. The group of patients infiltrated with lower density CD3+ T cells also without PDL1 expression in tumor cells predict the worst outcome in the subgroup of different PTNM stage, which may suggest an inactive immune status. These results highlights the need to assess both PDL1 expression in all tumor context and the characterization of the GC immune microenvironment.
ObjectiveTumour pathology contains rich information, including tissue structure and cell morphology, that reflects disease progression and patient survival. However, phenotypic information is subtle and complex, making the discovery of prognostic indicators from pathological images challenging.DesignAn interpretable, weakly supervised deep learning framework incorporating prior knowledge was proposed to analyse hepatocellular carcinoma (HCC) and explore new prognostic phenotypes on pathological whole-slide images (WSIs) from the Zhongshan cohort of 1125 HCC patients (2451 WSIs) and TCGA cohort of 320 HCC patients (320 WSIs). A ‘tumour risk score (TRS)’ was established to evaluate patient outcomes, and then risk activation mapping (RAM) was applied to visualise the pathological phenotypes of TRS. The multi-omics data of The Cancer Genome Atlas(TCGA) HCC were used to assess the potential pathogenesis underlying TRS.ResultsSurvival analysis revealed that TRS was an independent prognosticator in both the Zhongshan cohort (p<0.0001) and TCGA cohort (p=0.0003). The predictive ability of TRS was superior to and independent of clinical staging systems, and TRS could evenly stratify patients into up to five groups with significantly different prognoses. Notably, sinusoidal capillarisation, prominent nucleoli and karyotheca, the nucleus/cytoplasm ratio and infiltrating inflammatory cells were identified as the main underlying features of TRS. The multi-omics data of TCGA HCC hint at the relevance of TRS to tumour immune infiltration and genetic alterations such as the FAT3 and RYR2 mutations.ConclusionOur deep learning framework is an effective and labour-saving method for decoding pathological images, providing a valuable means for HCC risk stratification and precise patient treatment.
BackgroundThis study was performed to investigate the role of nucleotide-binding oligomerization domain (NOD)-like receptor X1 (NLRX1) in regulating hepatocellular carcinoma (HCC) progression.MethodsExpression levels of NLRX1 in clinical specimens and cell lines were determined by reverse transcription-polymerase chain reaction (RT-PCR) and western blot (WB). Transwell assays were conducted to evaluate the effect of NLRX1 on cell invasion, and flow cytometry was used to assess apoptosis. Expression patterns of key molecules in the phosphoinositide 3-kinase (PI3K)-AKT pathways were determined via WB. The effect of NLRX1 on cell senescence was evaluated with β-galactosidase assays. Kaplan-Meier analyses and Cox regression models were used for prognostic evaluation.ResultsNLRX1 was downregulated in tumor tissue compared with adjacent normal liver tissue. Low tumor NLRX1 expression was identified as an independent indicator for HCC prognosis (recurrence: hazard ratio [HR] 1.87, 95% confidence interval [CI] 1.26–2.76, overall survival [OS] 2.26, 95% CI 1.44–3.56). NLRX1 over-expression (OE) significantly inhibited invasiveness ability and induced apoptosis in HCC cells. In vivo experiments showed that NLRX1 knock-down (KD) significantly promoted HCC growth. Mechanistically, NLRX1 exhibited a suppressor function by decreasing phosphorylation of AKT and thus downregulating Snail1 expression, which inhibited epithelial-mesenchymal-transition (EMT) in HCC cells. Moreover, NLRX1 OE could induce cell senescence via an AKT-P21-dependent manner.ConclusionsNLRX1 acted as a tumor suppressor in HCC by inducing apoptosis, promoting senescence, and decreasing invasiveness by repressing PI3K-AKT signaling pathway. Future investigations will focus on restoring expression of NLRX1 to provide new insights into HCC treatment.Electronic supplementary materialThe online version of this article (10.1186/s13045-018-0573-9) contains supplementary material, which is available to authorized users.
BackgroundMicrovascular invasion (MVI) is recognized as a prognostic factor associated with poor outcome in hepatocellular carcinoma (HCC) patients after curative resection. It remains unclear, however, whether MVI can provide prognostic information for patients at a specific tumor stage.MethodsConsecutive HCC patients who underwent curative resection in years of 2007 and 2008 (discovery cohort) were enrolled in this retrospective study. Patients were stratified by the Barcelona Clinic Liver Cancer (BCLC) staging system. The prognostic significance of MVI for overall survival (OS) and recurrence-free survival (RFS) was studied in each subgroup. The clinical significance of MVI was validated in another cohort of patients underwent curative surgery in the year of 2006 (validation cohort).ResultsOf the 1540 patients in the discovery cohort, 389 (25.3%) patients had detectable MVI. Occurrence rates of MVI in the BCLC stage 0, A, and B subgroups were 12.4, 26.2, and 34.4%, respectively. In univariate analysis, MVI was associated with poor OS and RFS (P < 0.001 for both) in HCC patients at stage A, with poor OS in patients at stage 0 (P = 0.028), and with poor RFS at stage B (P = 0.039). In multivariate analysis, MVI was an independent risk factor for OS (HR = 1.431, 95% CI, 1.163–1.761, P < 0.001) and RFS (HR = 1.400, 95% CI, 1.150–1.705, P = 0.001) in patients at stage A; and an independent risk factor for RFS (P = 0.043) in patients at stage B. A similar clinical significance of MVI was found in the validation cohort.ConclusionsMVI has limited prognostic value for HCC patients at BCLC stages 0 and B. For those at stage A, MVI was associated with patient survival and may help to select patients with high risk of disease recurrence.Electronic supplementary materialThe online version of this article (doi:10.1186/s12885-017-3050-x) contains supplementary material, which is available to authorized users.
Background: Alpha-fetoprotein (AFP) is a widely used biomarker for hepatocellular carcinoma (HCC) early detection. However, low sensitivity and false negativity of AFP raise the requirement of more effective early diagnostic approaches for HCC. Methods: We employed a three-phase strategy to identify serum autoantibody (AAb) signature for HCC early diagnosis using protein array-based approach. A total of 1253 serum samples from HCC, liver cirrhosis, and healthy controls were prospectively collected from three liver cancer centers in China. The Human Proteome Microarray, comprising 21,154 unique proteins, was first applied to identify AAb candidates in discovery phase (n = 100) and to further fabricate HCCfocused arrays. Then, an artificial neural network (ANN) model was used to discover AAbs for HCC detection in a test phase (n = 576) and a validation phase (n = 577), respectively. Results: Using HCC-focused array, we identified and validated a novel 7-AAb panel containing CIAPIN1, EGFR, MAS1, SLC44A3, ASAH1, UBL7, and ZNF428 for effective HCC detection. The ANN model of this panel showed improvement of sensitivity (61.6-77.7%) compared to AFP (cutoff 400 ng/mL, 28.4-30.7%). Notably, it was able to detect AFP-negative HCC with AUC values of 0.841-0.948. For early-stage HCC (BCLC 0/A) detection, it outperformed AFP (cutoff 400 ng/mL) with approximately 10% increase in AUC. Conclusions: The 7-AAb panel provides potentially clinical value for non-invasive early detection of HCC, and brings new clues on understanding the immune response against hepatocarcinogenesis.
This study aimed to identify serum biomarkers for microvascular invasion (MVI) in hepatocellular carcinoma (HCC). MVI is a histological sign of micrometastasis in the liver and is considered as one of the most powerful prognostic factors in HCC. The serum of HCC patients with different vascular invasion statuses was examined by iTRAQ-based proteomic profiling. The expression levels of 24 proteins were associated with the extent of vascular invasion in the pooled samples of 45 HCC cases. Western blot analyses in 90 HCC cases confirmed the correlation of the expression level of paraoxonase 1 (PON1) with the extent of vascular invasion. ELISA assays demonstrated the diagnostic utility of the PON1 level, with the area under curve values of 0.847 and 0.889 for the MVI and gross vascular invasion, respectively, relative to the patients without vascular invasion, in a cohort of 387 additional HCC cases. Immunohistochemistry revealed that PON1 expression in tumor cells was inversely correlated with the extent of vascular invasion in 200 additional HCC cases. In conclusion, using a proteomic approach, we found that serum PON1 was a novel diagnostic biomarker for MVI. The prognostic values of serum PON1 and its possible therapeutic applications are worth further investigation.
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