BackgroundHepatocellular carcinoma (HCC) is a common malignant tumor with high mortality and poor prognoses around the world. Ferroptosis is a new form of cell death, and some studies have found that it is related to cancer immunotherapy. The aim of our research was to find immunity- and ferroptosis-related biomarkers to improve the treatment and prognosis of HCC by bioinformatics analysis.MethodsFirst, we obtained the original RNA sequencing (RNA-seq) expression data and corresponding clinical data of HCC from The Cancer Genome Atlas (TGCA) database and performed differential analysis. Second, we used immunity- and ferroptosis-related differentially expressed genes (DEGs) to perform a computational difference algorithm and Cox regression analysis. Third, we explored the potential molecular mechanisms and properties of immunity- and ferroptosis-related DEGs by computational biology and performed a new prognostic index based on immunity- and ferroptosis-related DEGs by multivariable Cox analysis. Finally, we used HCC data from International Cancer Genome Consortium (ICGC) data to perform validation.ResultsWe obtained 31 immunity (p < 0.001)- and 14 ferroptosis (p < 0.05)-related DEGs correlated with overall survival (OS) in the univariate Cox regression analysis. Then, we screened five immunity- and two ferroptosis-related DEGs (HSPA4, ISG20L2, NRAS, IL17D, NDRG1, ACSL4, and G6PD) to establish a predictive model by multivariate Cox regression analysis. Receiver operating characteristic (ROC) and Kaplan–Meier (K–M) analyses demonstrated a good performance of the seven-biomarker signature. Functional enrichment analysis including Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) revealed that the seven-biomarker signature was mainly associated with HCC-related biological processes such as nuclear division and the cell cycle, and the immune status was different between the two risk groups.ConclusionOur results suggest that this specific seven-biomarker signature may be clinically useful in the prediction of HCC prognoses beyond conventional clinicopathological factors. Moreover, it also brings us new insights into the molecular mechanisms of HCC.

Background Hepatocellular carcinoma (HCC) is a kind of tumor with high invasiveness, and patients with advanced HCC have a higher risk of early death. The aim of the present study was to identify the risk factors of early death in patients with advanced HCC and establish predictive nomograms. Methods Death that occurred within 3 months of initial diagnosis is defined as early death. Patients diagnosed with stage IV HCC between 2010 and 2015 were collected from the Surveillance, Epidemiology, and End Results database for model establishment and verification. Univariable and multivariable logistic regression analyses were used to identify the risk factors. Predictive nomograms were constructed and an internal validation was performed. Decision curve analysis (DCA) was used to verify the true clinical application value of the models. Results Of 6603 patients (57% age > 60, 81% male, 70% white, 46% married), 21% and 79% had stage IVA and IVB, respectively. On the multivariable analyses, risk factors for early deaths in patients with stage IVA were age, tumor size, histological grade, alpha-fetoprotein (AFP), fibrosis score, tumor stage (T stage), surgery, radiotherapy, and chemotherapy, and that in stage IVB were age, histological grade, AFP, T stage, node stage (N stage), bone metastasis, lung metastasis, surgery, radiotherapy, and chemotherapy. The areas under the curves (AUCs) were 0.830 (95% CI 0.809–0.851) and 0.789 (95% CI 0.768–0.810) in stage IVA and IVB, respectively. Nomograms comprising risk factors with the concordance indexes (C-indexes) were 0.820 (95% CI 0.799–0.841) in stage IVA and 0.785 (95% CI 0.764–0.0.806) in stage IVB for internal validation (Bootstrapping, 1000re-samplings). The calibration plots of the nomograms show that the predicted early death was consistent with the actual value. The results of the DCA analysis show that the nomograms had a good clinical application. Conclusion The nomograms can be beneficial for clinicians in identifying the risk factors for early death of patients with advanced HCC and predicting the probability of early death, so as to allow for individualized treatment plans to be accurately selected.

Combination therapy such as photodynamic therapy (PDT)-enhanced chemotherapy is regarded as a promising strategy for cancer treatment. Boron-dipyrromethene (BODIPY), as close relatives of porphyrins, was widely used in PDT. However, poor water solubility, rapid metabolism by the body and lack of targeting limits its clinical application. Lenvatinib, as the first-line drug for molecular-targeted therapy of liver cancer, restricted its clinical application for its side effects. Herein, to achieve the synergy between PDT and chemotherapy, we synthesized two halogenated BODIPY, BDPBr 2 and BDPCl 2 , which were prepared into self-assembly nanoparticles with lenvatinib, and were encapsulated with Pluronic F127 through the nanoprecipitation method, namely, LBPNPs (LBBr 2 NPs and LBCl 2 NPs). The fluorescence quantum yields of LBPNPs were 0.73 and 0.71, respectively. The calculated loading rates of lenvatinib for LBBr 2 NPs and LBCl 2 NPs were 11.8 and 10.2%, respectively. LBPNPs can be hydrolyzed under weakly acidic conditions (pH 5.0) to generate reactive oxygen species (ROS), and the release rate of lenvatinib reached 88.5 and 82.4%. Additionally, LBPNPs can be effectively taken up by Hep3B and Huh7 liver cancer cells, releasing halogenated BODIPY and lenvatinib in the acidic environment of tumor cells to enhance the targeting performance of chemotherapeutics. Compared with free lenvatinib and separate halogenated BODIPY, LBPNPs can inhibit tumor growth more effectively through pH-responsive chemo/photodynamic synergistic therapy and significantly promote the cascade of caspase apoptotic protease. This study shows that LBPNPs can be a promising nanotheranostic agent for synergetic chemo/photodynamic liver cancer therapy.

Hepatocellular carcinoma (HCC) has long been a major global clinical problem as one of the most common malignant tumours with a high rate of recurrence and mortality. Although potentially curative therapies are available for the early and intermediate stages, the treatment of patients with advanced HCC remains to be resolved. Fortunately, the past few years have shown the emergence of successful systemic therapies to treat HCC. At the molecular level, HCC is a heterogeneous disease, and current research on the molecular characteristics of HCC has revealed numerous therapeutic targets. Targeted agents based on signalling molecules have been successfully supported in clinical trials, and molecular targeted therapy has already become a milestone for disease management in patients with HCC. Immunotherapy, a viable approach for the treatment of HCC, recognizes the antigens expressed by the tumour and treats the tumour using the immune system of the host, making it both selective and specific. In addition, the pipeline for HCC is evolving towards combination therapies with promising clinical outcomes. More drugs designed to focus on specific pathways and immune checkpoints are being developed in the clinic. It has been demonstrated that some drugs can improve the prognosis of patients with HCC in first- or second-line settings, and these drugs have been approved by the Food and Drug Administration or are nearing approval. This review describes targeting pathways and systemic treatment strategies in HCC and summarizes effective targeted and immune-based drugs for patients with HCC and the problems encountered.

BackgroundThe liver is the most common organ for distant metastasis of pancreatic cancer, and patients with pancreatic cancer liver metastases (PCLM) often die in a short period of time. As such, the establishment of an effective nomogram to predict the probability of early death (survival time ≤3 months) in PCLM patients is of considerable significance.MethodsPatients diagnosed with PCLM in the Surveillance, Epidemiology, and End Result (SEER) database between 2010 and 2015 were included for model construction and internal validation. A data set was obtained from the Chinese population for external validation. Risk factors that contributed to all-cause and cancer-specific early death were determined by means of univariable and multivariable logistic regression. The accuracy of the nomogram was verified by means of receiver operating characteristic (ROC) curves, and the true consistency of the model was assessed by calibration curves. The clinical applicability of the model was evaluated by means of decision curve analysis (DCA).ResultsA total of 12,955 patients were included in the present study, of whom 7,219 (55.7%) experienced early death and 6,973 (53.8%) patients died of PCLM. Through multivariable logistic regression analysis, 11 risk factors associated with all-cause early death and 12 risk factors associated with cancer-specific early death were identified. The area under the curves (AUCs) for all-cause and cancer-specific early death were 0.806 (95% CI: 0.785- 0.827) and 0.808 (95% CI: 0.787- 0.829), respectively. Internal validation showed that the C-indexes of all-cause and cancer-specific early death after bootstrapping (5,000 re-samplings) were 0.805 (95% CI: 0.784-0.826) and 0.807 (95% CI: 0.786-0.828), respectively. As revealed by the calibration curves, the constructed nomograms exhibited good consistency. The decision curve analysis (DCA) indicated the nomograms had significant clinical applicability.ConclusionIn the present study, reliable nomograms were developed for predicting the early death probability in patients with PCLM. Such tools can help clinicians identify high-risk patients and develop individualized treatment plans as early as possible.