Hollow mesoporous organosilica nanotheranostics incorporating formimidoyltransferase cyclodeaminase (FTCD) plasmids for magnetic resonance imaging and tetrahydrofolate metabolism fission on hepatocellular carcinoma

Crosstalk of non-apoptotic RCD panel in hepatocellular carcinoma reveals the prognostic and therapeutic optimization

Non-apoptotic regulated cell death (RCD) of tumor cells profoundly affects tumor progression and plays critical roles in determining response to immune checkpoint inhibitors (ICIs). Prognosis-distinctive HCC subtypes were identified by consensus cluster analysis based on the expressions of 507 non-apoptotic RCD genes obtained from databases and literature. Meanwhile, a set of bioinformatic tools was integrated to analyze the differences of the tumor immune microenvironment infiltration, genetic mutation, copy number variation, and epigenetics alternations within two subtypes. Finally, a non-apoptotic RCDRS signature was constructed and its reliability was evaluated in HCC patients' tissues. The high-RCDRS HCC subgroup showed a significantly lower overall survival and less sensitivity to ICIs compared to low-RCDRS subgroup, but higher sensitivity to cisplatin, paclitaxel, and sorafenib. Overall, we established an RCDRS panel consisting of four non-apoptotic RCD genes, which might be a promising predictor for evaluating HCC prognosis, guiding therapeutic decision-making, and ultimately improving patient outcomes.

Mesoporous Nanoparticles for Diagnosis and Treatment of Liver Cancer in the Era of Precise Medicine

Primary liver cancer is the seventh-most-common cancer worldwide and the fourth-leading cause of cancer mortality. In the current era of precision medicine, the diagnosis and management of liver cancer are full of challenges and prospects. Mesoporous nanoparticles are often designed as specific carriers of drugs and imaging agents because of their special morphology and physical and chemical properties. In recent years, the design of the elemental composition and morphology of mesoporous nanoparticles have greatly improved their drug-loading efficiency, biocompatibility and biodegradability. Especially in the field of primary liver cancer, mesoporous nanoparticles have been modified as highly tumor-specific imaging contrast agents and targeting therapeutic medicine. Various generations of complexes and structures have been determined for the complicated clinical management requirements. In this review, we summarize these advanced mesoporous designs in the different diagnostic and therapeutic fields of liver cancer and discuss the relevant advantages and disadvantages of transforming applications. By comparing the material properties, drug-delivery characteristics and application methods of different kinds of mesoporous materials in liver cancer, we try to help determine the most suitable drug carriers and information media for future clinical trials. We hope to improve the fabrication of biomedical mesoporous nanoparticles and provide direct evidence for specific cancer management.

A metabolism-associated gene signature for prognosis prediction of hepatocellular carcinoma

Hepatocellular carcinoma (HCC), the most frequently occurring type of cancer, is strongly associated with metabolic disorders. In this study, we aimed to characterize the metabolic features of HCC and normal tissue adjacent to the tumor (NAT). By using samples from The Cancer Genome Atlas (TCGA) liver cancer cohort and comparing 85 well-defined metabolic pathways obtained from the Kyoto Encyclopedia of Genes and Genomes (KEGG), 70 and 7 pathways were found to be significantly downregulated and upregulated, respectively, in HCC, revealing that tumor tissue lacks the ability to maintain normal metabolic levels. Through unsupervised hierarchical clustering of metabolic pathways, we found that metabolic heterogeneity correlated with prognosis in HCC samples. Thus, using the least absolute shrinkage and selection operator (LASSO) and filtering independent prognostic genes by the Cox proportional hazards model, a six-gene-based metabolic score model was constructed to enable HCC classification. This model showed that high expression of LDHA and CHAC2 was associated with an unfavorable prognosis but that high ADPGK, GOT2, MTHFS, and FTCD expression was associated with a favorable prognosis. Patients with higher metabolic scores had poor prognoses (p value = 2.19e-11, hazard ratio = 3.767, 95% CI = 2.555-5.555). By associating the score level with clinical features and genomic alterations, it was found that NAT had the lowest metabolic score and HCC with tumor stage III/IV the highest. qRT‒PCR results for HCC patients also revealed that tumor samples had higher score levels than NAT. Regarding genetic alterations, patients with higher metabolic scores had more TP53 gene mutations than those with lower metabolic scores (p value = 8.383e-05). Validation of this metabolic score model was performed using another two independent HCC cohorts from the Gene Expression Omnibus (GEO) repository and other TCGA datasets and achieved good performance, suggesting that this model may be used as a reliable tool for predicting the prognosis of HCC patients.