Downregulation of GPX8 in hepatocellular carcinoma: impact on tumor stemness and migration.

PURPOSE: GPX8, which is found in the endoplasmic reticulum lumen, is a member of the Glutathione Peroxidases (GPXs) family. Its role in hepatocellular carcinoma (HCC) is unknown. METHODS: Immunohistochemical staining was used to detect the protein levels of GPX8 in HCC tissue microarrays. A short hairpin RNA lentivirus was used to knock down GPX8, and the main signaling pathways were investigated using transcriptome sequencing and a phosphorylated kinase array. The sphere formation assays, cloning-formation assays and cell migration assays were used to evaluate the stemness and migration ability of HCC cells. Identifying the GPX8-interacting proteins was accomplished through immunoprecipitation and protein mass spectrometry. RESULTS: The GPX8 protein levels were downregulated in HCC patients. Low expression of GPX8 protein was related to early recurrence and poor prognosis in HCC patients. GPX8 knockdown could enhance the stemness and migration ability of HCC cells. Consistently, Based on transcriptome analysis, multiple signaling pathways that include the PI3K-AKT and signaling pathways that regulate the pluripotency of stem cells, were activated after GPX8 knockdown. The downregulation of GPX8 could increase the expression of the tumor stemness markers KLF4, OCT4, and CD133. The in vivo downregulation of GPX8 could also promote the subcutaneous tumor-forming and migration ability of HCC cells. MK-2206, which is a small-molecule inhibitor of AKT, could reverse the tumor-promoting effects both in vivo and in vitro. We discovered that GPX8 and the 71-kDa heat shock cognate protein (Hsc70) have a direct interaction. The phosphorylation of AKT encouraged the translocation of Hsc70 into the nucleus and the expression of the PI3K p110 subunit, thereby increasing the downregulation of GPX8. CONCLUSION: The findings from this study demonstrate the anticancer activity of GPX8 in HCC by inactivating the Hsc70/AKT pathway. The results suggest a possible therapeutic target for HCC.

Development of a deep pathomics score for predicting hepatocellular carcinoma recurrence after liver transplantation.

BACKGROUND AND PURPOSE: Tumor recurrence after liver transplantation (LT) impedes the curative chance for hepatocellular carcinoma (HCC) patients. This study aimed to develop a deep pathomics score (DPS) for predicting tumor recurrence after liver transplantation using deep learning. PATIENTS AND METHODS: Two datasets of 380 HCC patients who underwent LT were enrolled. Residual convolutional neural networks were used to identify six histological structures of HCC. The individual risk score of each structure and DPS were derived by a modified DeepSurv network. Cox regression analysis and Concordance index were used to evaluate the prognostic significance. The cellular exploration of prognostic immune biomarkers was performed by quantitative and spatial proximity analysis according to three panels of 7-color immunofluorescence. RESULTS: The overall classification accuracy of HCC tissue was 97%. At the structural level, immune cells were the most significant tissue category for predicting post-LT recurrence (HR 1.907, 95% CI 1.490-2.440). The C-indices of DPS achieved 0.827 and 0.794 in the training and validation cohorts, respectively. Multivariate analysis for recurrence-free survival (RFS) showed that DPS (HR 4.795, 95% CI 3.017-7.619) was an independent risk factor. Patients in the high-risk subgroup had a shorter RFS, larger tumor diameter and a lower proportion of clear tumor borders. At the cellular level, a higher infiltration of intratumoral NK cells was negatively correlated with recurrence risk. CONCLUSIONS: This study established an effective DPS. Immune cells were the most significant histological structure related to HCC recurrence. DPS performed well in post-LT recurrence prediction and the identification of clinicopathological features.

Low expression of transferrin receptor 2 predict poor prognosis in gastric cancer patients.

This study was to detect the expression level of transferrin receptor 2 (TfR2) in gastric cancer and to analyze its value in predicting the prognosis of gastric cancer patients. Real-time PCR (RT-qPCR) was applied to detect the mRNA expression of TfR2 in gastric cancer tissues and paired adjacent nontumorous tissues. Immunohistochemistry (IHC) and western blotting were used to determine the protein expression of TfR2 in gastric cancer, and the relationship with the prognosis of gastric cancer patients was analyzed. The results were: (1) The mRNA and protein levels of TfR2 in gastric cancer tissues were remarkably lower than those in adjacent nontumorous gastric tissue (P < .05). (2) Clinicopathological analysis showed that the expression level of TfR2 was significantly correlated with TNM stage of patients (P = .047). (3) The results of univariate survival analysis showed that histological grade, T stage, N stage, M stage, Lauren's classification, and TfR2 expression level influenced the overall survival of gastric cancer patients. Multivariate survival analysis showed that low TfR2 expression (HR = 1.671, 95%CI: 1.006-2.774, P = .047), poor differentiation (HR = 2.123, 95%CI: 1.188-3.795, P = .011), and M1 stage (HR = 8.541, 95%CI: 3.416-21.353, P < .001) were independent risk factors affecting the overall survival of gastric cancer patients. In conclusion, TfR2 exhibited low expression in gastric cancer tissue, and is an independent prognostic factor of gastric cancer patients.