Ferroptosis is a new type of cell death that has been recognized in recent years and is different from apoptosis, autophagy, and necrosis. It is mainly due to cellular iron homeostasis and lipid peroxidation of iron metabolism caused by large accumulation. There is a close correlation between ferroptosis and hepatocellular carcinoma (HCC). This study shows that the expression of the long noncoding RNA HEPFAL was reduced in HCC tissues. We found that lncRNA HEPFAL can promote ferroptosis by reducing the expression of solute carrier family 7 member 11 (SLC7A11) and increasing the levels of lipid reactive oxygen species (ROS) and iron (two surrogate markers of ferroptosis). In addition, we found that lncRNA HEPFAL increases the sensitivity of erastin-induced ferroptosis, which may be related to mTORC1, and lncRNA HEPFAL can promote the ubiquitination of SLC7A11 and reduce the stability of the SLC7A11 protein, resulting in decreased expression. Understanding these mechanisms indicates that lncRNAs related to ferroptosis are essential for the occurrence and treatment of HCC.

Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer, with an increasing incidence and poor prognosis. In the past decade, artificial intelligence (AI) technology has undergone rapid development in the field of clinical medicine, bringing the advantages of efficient data processing and accurate model construction. Promisingly, AI-based radiomics has played an increasingly important role in the clinical decision-making of HCC patients, providing new technical guarantees for prediction, diagnosis, and prognostication. In this review, we evaluated the current landscape of AI radiomics in the management of HCC, including its diagnosis, individual treatment, and survival prognosis. Furthermore, we discussed remaining challenges and future perspectives regarding the application of AI radiomics in HCC.

Oral, gut, and tumor microbiota have been implicated as important regulators in the carcinogenesis and progression of gastrointestinal malignancies. However, few studies focused on the existence and association of resident microbes within different body regions. Herein, we aim to reveal the durability of the oral-gut-tumor microbiome and its diagnostic performance in hepatocellular carcinoma (HCC). Our study included two cohorts: a retrospective discovery cohort of 364 HBV-HCC patients and 160 controls with oral or fecal samples, a prospective validation cohort of 91 cases, and 124 controls for matching samples, as well as 48 HBV, and 39 HBV-cirrhosis patients for gut microbial patterns examined by 16S rRNA gene sequencing. With the random forest analysis, 10 oral and 9 gut genera that could distinguish HCC from controls in the retrospective cohort were validated among the prospective matching participants, with area under the curve (AUC) values of 0.7971 and 0.8084, respectively. When influential taxa were merged, the AUC of the consistent classifier increased to 0.9405. The performance continued to improve to 0.9811 when combined with serum levels of alpha-fetoprotein (AFP). Specifically, microbial biomarkers represented by Streptococcus displayed a constantly increasing trend during the disease transition. Furthermore, the presence of several dominant microbiota species was confirmed in hepatic tumor and non-tumor tissues with fluorescence in situ hybridization (FISH) and 5 R 16S rRNA gene sequencing. Overall, our findings based on the oral-gut-tumor microbiota provide a reliable approach for the early detection of HCC.

Cholangiocarcinoma (CCA) is a malignant hepatic tumor with a poor prognosis, which needs early diagnosis urgently. The gut microbiota has been shown to play a crucial role in the progression of liver cancer. Here, we explored a gut microbiota model covering genera Burkholderia-Caballeronia-Paraburkholderia, Faecalibacterium, and Ruminococcus_1 (B-F-R) for CCA early diagnosis. A case-control study was conducted to enroll 53 CCA patients, 47 cholelithiasis patients, and 40 healthy controls. The feces samples and clinical information of participants were collected in the same period. The gut microbiota and its diversity of individuals were accessed with 16S rDNA sequencing, and the gut microbiota profile was evaluated according to microbiota diversity. Finally, four enriched genera in the CCA group (genera Bacteroides, Muribaculaceae_unclassified, Muribaculum, and Alistipes) and eight enriched genera in the cholelithiasis group (genera Bifidobacterium, Streptococcus, Agathobacter, Ruminococcus_gnavus_group, Faecalibacterium, Subdoligranulum, Collinsella, Escherichia-Shigella) constitute an overall different microbial community composition (P = 0.001). The B-F-R genera model with better diagnostic value than carbohydrate antigen 19-9 (CA19-9) was identified by random forest and Statistical Analysis of Metagenomic Profiles (STAMP) to distinguish CCA patients from healthy controls [area under the curve (AUC) = 0.973, 95% CI = 0.932–1.0]. Moreover, the correlative analysis found that genera Burkholderia-Caballeronia-Paraburkholderia were positively correlated with body mass index (BMI). The significantly different microbiomes between cholelithiasis and CCA were found via principal coordinates analysis (PCoA) and linear discriminant analysis effect size (LEfSe), and Venn diagram and LEfSe were utilized to identify four genera by comparing microbial compositions among patients with malignant obstructive jaundice (MOJ-Y) or not (MOJ-N). In brief, our findings suggest that gut microbiota vary from benign and malignant hepatobiliary diseases to healthy people and provide evidence supporting gut microbiota to be a non-invasive biomarker for the early diagnosis of CCA.