FAT10 induces immune suppression by upregulating PD-L1 expression in hepatocellular carcinoma.

The upregulation of programmed death ligand 1 (PD-L1) plays a crucial role in facilitating cancer cells to evade immune surveillance through immunosuppression. However, the precise regulatory mechanisms of PD-L1 in hepatocellular carcinoma (HCC) remain undefined. The correlation between PD-L1 and ubiquitin-like molecules (UBLs) was studied using sequencing data from 20 HCC patients in our center, combined with TCGA data. Specifically, the association between FAT10 and PD-L1 was further validated at both the protein and mRNA levels in HCC tissues from our center. Subsequently, the effect of FAT10 on tumor progression and immune suppression was examined through both in vivo and in vitro experiments. Utilizing sequencing data, qPCR, and Western blotting assays, we confirmed that FAT10 was highly expressed in HCC tissues and positively correlated with PD-L1 expression. Additionally, in vitro experiments demonstrated that the overexpression of FAT10 fostered the proliferation, migration, and invasion of HCC cells. Furthermore, the overexpression of FAT10 in HCC cells led to an increase in PD-L1 expression, resulting in the inhibition of T cell proliferation and the enhancement of HCC cell resistance to T cell-mediated cytotoxicity. Moreover, in vivo experiments utilizing the C57BL/6 mouse model revealed that overexpression of FAT10 effectively suppressed the infiltration of CD8 + GZMB + and CD8 + Ki67 + T cells, as well as reduced serum levels of TNF-α and IFN-γ. Mechanistically, we further identified that FAT10 upregulates PD-L1 expression via activating the PI3K/AKT/mTOR pathway, but not in a ubiquitin-like modification. In conclusion, our findings indicate that FAT10 promotes immune evasion of HCC via upregulating PD-L1 expression, suggesting its potential as a novel target to enhance the efficiency of immunotherapy in HCC.

Prognostic value and association of the age-adjusted Charlson Comorbidity Index with sarcopenia within patients with gastric cancer after radical resection.

PURPOSE: The association between the age-adjusted Charlson Comorbidity Index (ACCI) and sarcopenia in patients with gastric cancer (GC) remains ambiguous. This study aimed to investigate the association between the ACCI and sarcopenia and the prognostic value in patients with GC after radical resection. In addition, this study aimed to develop a novel prognostic scoring system based on these factors. METHODS: Univariate and multivariate Cox regression analyses were used to determine prognostic factors in patients undergoing radical GC resection. Based on the ACCI and sarcopenia, a new prognostic score (age-adjusted Charlson Comorbidity Index and Sarcopenia [ACCIS]) was established, and its prognostic value was assessed. RESULTS: This study included 1068 patients with GC. Multivariate analysis revealed that the ACCI and sarcopenia were independent risk factors during the prognosis of GC (P = 0.001 and P < 0.001, respectively). A higher ACCI score independently predicted sarcopenia (P = 0.014). A high ACCIS score was associated with a greater American Society of Anesthesiologists score, higher pathologic TNM (pTNM) stage, and larger tumor size (all P < 0.05). Multivariate analysis demonstrated that the ACCIS independently predicted the prognosis for patients with GC (P < 0.001). By incorporating the ACCIS score into a prognostic model with sex, pTNM stage, tumor size, and tumor differentiation, we constructed a nomogram to predict the prognosis accurately (concordance index of 0.741). CONCLUSION: The ACCI score and sarcopenia are significantly correlated in patients with GC. The integration of the ACCI score and sarcopenia markedly enhances the accuracy of prognostic predictions in patients with GC.

CircNUP54 promotes hepatocellular carcinoma progression via facilitating HuR cytoplasmic export and stabilizing BIRC3 mRNA.

Circular RNAs (circRNAs) have been implicated in tumorigenesis and progression of various cancers. However, the underlying mechanisms of circRNAs in hepatocellular carcinoma (HCC) have not been fully elucidated. Herein, a new oncogenic circRNA, hsa_circ_0070039 (circNUP54), was identified to be significantly upregulated in HCC through circRNA sequencing. As verified in 68 HCC samples, circNUP54 overexpression was correlated with aggressive cancerous behaviors and poor outcomes. Moreover, the function experiments showed that knockdown of circNUP54 inhibited the malignant progression of HCC in vitro and in vivo, whereas overexpression of circNUP54 had the opposite role. Mechanistic investigations carried out by RNA pull-down, RNA immunoprecipitation, and immunofluorescence revealed that circNUP54 interacted with the RNA-binding protein Hu-antigen R (HuR) and promoted its cytoplasmic export. The cytoplasmic accumulation of HuR stabilized the downstream BIRC3 mRNA through its binding to the 3' UTR region. Consequently, the encoded protein of BIRC3, cellular inhibitor of apoptosis 2 (cIAP2), proceeded to activate the NF-κB signal pathway and ultimately contributed to HCC progression. In addition, depletion of BIRC3 rescued the pro-tumorigenic effect of circNUP54 on HCC cells. Overall, this study demonstrated that circNUP54 facilitates HCC progression via regulating the HuR/BIRC3/NF-κB axis, which may serve as a promising therapeutic target for HCC treatment.

Pretreatment CT-based machine learning radiomics model predicts response in unresectable hepatocellular carcinoma treated with lenvatinib plus PD-1 inhibitors and interventional therapy.

BACKGROUND: Lenvatinib plus PD-1 inhibitors and interventional (LPI) therapy have demonstrated promising treatment effects in unresectable hepatocellular carcinoma (HCC). However, biomarkers for predicting the response to LPI therapy remain to be further explored. We aimed to develop a radiomics model to noninvasively predict the efficacy of LPI therapy. METHODS: Clinical data of patients with HCC receiving LPI therapy were collected in our institution. The clinical model was built with clinical information. Nine machine learning classifiers were tested and the multilayer perceptron classifier with optimal performance was used as the radiomics model. The clinical-radiomics model was constructed by integrating clinical and radiomics scores through logistic regression analysis. RESULTS: 151 patients were enrolled in this study (2:1 randomization, 101 and 50 in the training and validation cohorts), of which three achieved complete response, 69 showed partial response, 46 showed stable disease, and 33 showed progressive disease. The objective response rate, disease control rate, and conversion resection rates were 47.7, 78.1 and 23.2%. 14 features were selected from the initially extracted 1223 for radiomics model construction. The area under the curves of the radiomics model (0.900 for training and 0.893 for validation) were comparable to that of the clinical-radiomics model (0.912 for training and 0.892 for validation), and both were superior to the clinical model (0.669 for training and 0.585 for validation). Meanwhile, the radiomics model can categorize participants into high-risk and low-risk groups for progression-free survival (PFS) and overall survival (OS) in the training (HR 1.913, 95% CI 1.121 to 3.265, p=0.016 for PFS; HR 4.252, 95% CI 2.051 to 8.816, p=0.001 for OS) and validation sets (HR 2.347, 95% CI 1.095 to 5.031, p=0.012 for PFS; HR 2.592, 95% CI 1.050 to 6.394, p=0.019 for OS). CONCLUSION: The promising machine learning radiomics model was developed and validated to predict the efficacy of LPI therapy for patients with HCC and perform risk stratification, with comparable performance to clinical-radiomics model.

A novel risk score system based on immune subtypes for identifying optimal mRNA vaccination population in hepatocellular carcinoma.

PURPOSE: Although mRNA vaccines have shown certain clinical benefits in multiple malignancies, their therapeutic efficacies against hepatocellular carcinoma (HCC) remains uncertain. This study focused on establishing a novel risk score system based on immune subtypes so as to identify optimal HCC mRNA vaccination population. METHODS: GEPIA, cBioPortal and TIMER databases were utilized to identify candidate genes for mRNA vaccination in HCC. Subsequently, immune subtypes were constructed based on the candidate genes. According to the differential expressed genes among various immune subtypes, a risk score system was established using machine learning algorithm. Besides, multi-color immunofluorescence of tumor tissues from 72 HCC patients were applied to validate the feasibility and efficiency of the risk score system. RESULTS: Twelve overexpressed and mutated genes associated with poor survival and APCs infiltration were identified as potential candidate targets for mRNA vaccination. Three immune subtypes (e.g. IS1, IS2 and IS3) with distinct clinicopathological and molecular profiles were constructed according to the 12 candidate genes. Based on the immune subtype, a risk score system was developed, and according to the risk score from low to high, HCC patients were classified into four subgroups on average (e.g. RS1, RS2, RS3 and RS4). RS4 mainly overlapped with IS3, RS1 with IS2, and RS2+RS3 with IS1. ROC analysis also suggested the significant capacity of the risk score to distinguish between the three immune subtypes. Higher risk score exhibited robustly predictive ability for worse survival, which was further independently proved by multi-color immunofluorescence of HCC samples. Notably, RS4 tumors exhibited an increased immunosuppressive phenotype, higher expression of the twelve potential candidate targets and increased genome altered fraction, and therefore might benefit more from vaccination. CONCLUSIONS: This novel risk score system based on immune subtypes enabled the identification of RS4 tumor that, due to its highly immunosuppressive microenvironment, may benefit from HCC mRNA vaccination.

Toripalimab Plus Bevacizumab as First-line Treatment for Advanced Hepatocellular Carcinoma: A Prospective, Multicenter, Single-Arm, Phase II Trial.

PURPOSE: This phase II, multicenter, prospective, single-arm study aimed to evaluate the efficacy and safety of toripalimab plus bevacizumab for treating advanced hepatocellular carcinoma (HCC). PATIENTS AND METHODS: Treatment-naïve patients with advanced HCC received toripalimab 240 mg plus bevacizumab 15 mg/kg every 3 weeks. The primary endpoints included safety and tolerability and objective response rate (ORR) assessed by the investigator per Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1. RESULTS: Fifty-four patients were enrolled between April 17, 2020, and December 11, 2020. As assessed by the investigator according to RECIST v1.1, the ORR was 31.5% [95% confidence interval (CI), 19.5-45.6] and the lower bound of the 95% CI was above the prespecified boundary of 10%. The independent review committee (IRC) assessed ORR according to the modified RECIST (mRECIST), which was 46.3% (95% CI, 32.6-60.4). The median progression-free survival was 8.5 (95% CI, 5.5-11.0) and 9.8 months (95% CI, 5.6 to not evaluable) as assessed by the investigator according to RECIST v1.1 and IRC according to mRECIST criteria, respectively. The median overall survival (OS) was not reached, and the 12- and 24-month OS rates were 77.3% and 63.5%, respectively. Grade 3 or higher treatment-emergent adverse events (TEAEs) occurred in 27 patients (50.0%). The most common TEAEs were proteinuria (59.3%), hypertension (38.9%), increased aspartate aminotransferase (33.3%), increased amylase (29.6%), decreased platelet count (27.8%), and increased bilirubin levels (27.8%). CONCLUSIONS: Toripalimab plus bevacizumab showed a favorable efficacy and safety profile, supporting further studies on this combination regimen as a first-line treatment for advanced HCC.