Cancer stem cells (CSCs) play a key role in the posthepatectomy recurrence of hepatocellular carcinoma (HCC). CD133+ HCC cells exhibit liver CSC–like properties, and CSC differentiation–inducing therapy may lead these cells to lose their self-renewal ability and may induce terminal differentiation, which may in turn allow their malignant potential to be controlled. Because arsenic trioxide (As2O3) increases remission rates and prolongs survival among patients with acute promyelocytic leukemia by inducing differentiation and apoptosis of leukemic cells, we hypothesized that As2O3 might also inhibit HCC recurrence and prolong survival time after hepatectomy by inducing differentiation of HCC CSCs.

Background: Chemo-resistance in hepatocellular carcinoma (HCC) is a major problem, and acquired drug resistance prevents cancer therapies from achieving complete responses. Molecular targeting therapy presents an opportunity to impede tumor through combination or sequential therapy, while the accurate effect is vague. Methods: The efficacy of combinations between oxaliplatin and anti-cancer molecular targeting drugs was screened. Strangely, the combined chemotherapy with oxaliplatin and saracatinib induced significantly antagonistic effects. Then the antitumor effects of combined treatment with saracatinib and oxaliplatin were confirmed in wide type HCC as well as in saracatinib- and oxaliplatin-resistant HCC. RNA sequencing was used to explore the resistance mechanism, and the roles of ATP-binding cassette transporter G1 (ABCG1) and Wnt signaling in oxaliplatin resistance were confirmed. Results: Chemotherapy with oxaliplatin and saracatinib individually induced strong anti-HCC effects, while combined or sequential treatment of HCC cells with these two drugs exhibited reduced efficacy compared to treatment with the single drugs. And it was saracatinib treatment caused oxaliplatin resistance. RNA sequencing revealed 458 genes that were altered by treatment with saracatinib and oxaliplatin. Of these, the gene encoding ABCG1 and Wnt-associated genes were significantly upregulated. Upregulation of ABCG1 and oxaliplatin resistance were associated with activation of Wnt signaling. Interference with ABCG1 expression or inhibition of Wnt signaling resulted in reversal of the saracatinib-induced oxaliplatin resistance in HCC. Conclusions: These studies demonstrated that combined or sequential chemotherapy with oxaliplatin and saracatinib reduced antitumor efficacy, and this antagonism was attributed to the activation of Wnt signaling and upregulation of ABCG1 by saracatinib.

Purpose: To construct a 2.5-dimensional (2.5D) CT radiomics-based deep learning (DL) model to predict early postoperative recurrence of hepatocellular carcinoma (HCC). Patients and Methods: We retrospectively analyzed the data of patients who underwent HCC resection at 2 centers. The 232 patients from center 1 were randomly divided into the training (162 patients) and internal validation cohorts (70 patients); 91 patients from center 2 formed the external validation cohort. We developed a 2.5D DL model based on a central 2D image with the maximum tumor cross-section and adjacent slices. Multiple views (transverse, sagittal, and coronal) and phases (arterial, plain, and portal) were incorporated. Multi-instance learning techniques were applied to the extracted data; the resulting comprehensive feature set was modeled using Logistic Regression, RandomForest, ExtraTrees, XGBoost, and LightGBM, with 5-fold cross validation and hyperparameter optimization with Grid-search. Receiver operating characteristic curves, calibration curves, DeLong test, and decision curve analysis were used to evaluate model performance. Results: The 2.5D DL model performed well in the training (AUC: 0.920), internal validation (AUC: 0.825), and external validation cohorts (AUC: 0.795). The 3D DL model performed well in the training cohort and poorly in the internal and external validation cohorts (AUCs: 0.751, 0.666, and 0.567, respectively), indicating overfitting. The combined model (2.5D DL+clinical) performed well in all cohorts (AUCs: 0.921, 0.835, 0.804). The Hosmer-Lemeshow test, DeLong test, and decision curve analysis confirmed the superiority of the combined model over the other signatures. Conclusion: The combined model integrating 2.5D DL and clinical features accurately predicts early postoperative HCC recurrence.

Background Hepatocellular carcinoma (HCC) is the second leading cause of cancer death worldwide. Chemotherapy is an alternative treatment for advanced HCCs, but chemo-resistance prevents cancer therapies from achieving stable and complete responses. Understanding the underlying mechanisms in chemo-resistance is critical to improve the efficacy of HCC. Methods The expression levels of Id-1 and CCN2 were detected in large cohorts of HCCs, and functional analyses of Id-1 and CCN2 were performed both in vitro and in vivo. cDNA microarrays were performed to evaluate the alterations of expression profiling of HCC cells with overexpression of CCN2. Finally, the role of downstream signaling of MAPK/Id-1 signaling pathway in oxaliplatin resistance were also explored. Results The increased expression of Id-1 and CCN2 were closely related to oxaliplatin resistance in HCC. Upregulation of CCN2 and Id-1 was independently associated with shorter survival and increased recurrence in HCC patients, and significantly enhanced oxaliplatin resistance and promoted lung metastasis in vivo, whereas knock-down of their expression significantly reversed the chemo-resistance and inhibited HCC cell stemness. cDNA microarrays and PCR revealed that Id-1 and MAPK pathway were the downstream signaling of CCN2. CCN2 significantly enhanced oxaliplatin resistance by activating the MAPK/Id-1 signaling pathway, and Id-1 could upregulate CCN2 in a positive feedback manner. Conclusions CCN2/MAPK/Id-1 loop feedback amplification is involved in oxaliplatin resistance, and the combination of oxaliplatin with inhibitor of CCN2 or MAPK signaling could provide a promising approach to ameliorating oxaliplatin resistance in HCC.

Hepatocellular carcinoma (HCC) is a malignant tumor associated with a high recurrence rate after hepatectomy. Recently, preoperative inflammatory and liver function reserve indices were found to predict increased risk of recurrence and decreased survival in HCC patients. This study aims to evaluate the ability of the γ-glutamyl transpeptidase-to-albumin ratio (GAR) and aspartate aminotransferase-to-lymphocyte ratio (ALRI), individually and in combination, to predict the prognosis of HCC patients after hepatectomy. We retrospectively reviewed 206 HCC patients who underwent radical resection at the General Hospital of Ningxia Medical University from January 2011 to November 2016. Receiver operating characteristic (ROC) curve analysis was performed to determine the optimal cut-off value for GAR and ALRI. The Pearson Chi-Squared test was used to analyze the correlations between GAR, ALRI and clinicopathological characteristics. Univariate and multivariate analyses were used to determine the predictive value of these factors for disease-free survival (DFS) and overall survival (OS). Survival rates were drawn according to the Kaplan-Meier method and differences between subgroups were compared by the log-rank statistics. GAR and ALRI were significantly correlated with gender, history of smoking, prothrombin time, tumor diameter, T stage and early intrahepatic recurrence by the Pearson Chi-Squared test (all P < .05). Univariate analysis indicated that T stage, GAR and ALRI were significantly correlated with DFS and OS in HCC patients after hepatectomy. Multivariate analysis illustrated that GAR and ALRI were independently related to DFS and OS in HCC patients. Preoperative GAR > 0.946 or ALRI > 18.734 predicted poor prognosis in HCC patients after hepatectomy. Additionally, the predictive scope of GAR combined with ALRI was more sensitive than that of either individual measurement alone. Our data indicate that there is a close association between the clinicopathological characteristics in HCC patients and increased GAR or ALRI. Higher levels of GAR and ALRI could sensitively and specifically predict a poor prognosis in HCC patients after hepatectomy. Furthermore, combined usage of GAR and ALRI could improve the accuracy of this prediction.

Anillin actin-binding protein (ANLN) is crucially involved in cell proliferation and migration. Moreover, ANLN is significantly in tumor progression in several types of human malignant tumors; however, it remains unclear whether ANLN acts through common molecular pathways within different tumor microenvironments, pathogeneses, prognoses and immunotherapy contexts. Therefore, this study aimed to perform bioinformatics analysis to examine the correlation of ANLN with tumor immune infiltration, immune evasion, tumor progression, immunotherapy, and tumor prognosis. We observed increased ANLN expression in multiple tumors, which could be involved in tumor cell proliferation, migration, infiltration, and prognosis. The level of ANLN methylation and genetic alteration was associated with prognosis in numerous tumors. ANLN facilitates tumor immune evasion through different mechanisms, which involve T-cell exclusion in different cancer types and tumor-infiltrating immune cells in colon adenocarcinoma, kidney renal clear cell carcinoma, liver hepatocellular carcinoma, and prostate adenocarcinoma. Additionally, ANLN is correlated with immune or chemotherapeutic outcomes in malignant cancers. Notably, ANLN expression may be a predictive biomarker for the response to immune checkpoint inhibitors. Taken together, our findings suggest that ANLN can be used as an onco-immunological biomarker and could serve as a hallmark for tumor screening, prognosis, individualized treatment design, and follow-up.