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.
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.
Cholangiocarcinoma (CCA) is a fatal disease with dismal survival rates. Long non-coding RNA (lncRNA) expression profiling as potential prognostic biomarkers play critical roles in tumor initiation, development, and poor prognosis. Identifying specific lncRNA to predict the prognosis of CCA patients in the early stages is very important for improving a patient’s survival. In the current study, we aimed to establish a novel risk-stratification lncRNA signature panel in CCA. The initial lncRNA discovery was identified in The Cancer Genome Atlas database (TCGA cohort). The Cox regression analysis was used to establish the lncRNA prognostic model and the receiver operating characteristic (ROC) curve analysis was performed to assess the specificity and sensitivity of the model. This was followed by independent validation of the lncRNA signature in the CCA patients from the First Affiliated Hospital of Wenzhou Medical University (WMU cohort). Furthermore, by using the Gene Ontology function and Kyoto Encyclopedia Gene and Genome pathway enrichment analysis, we explored the potential function of prognosis lncRNA. Finally, five lncRNA (HULC; AL359715.5; AC006504.8; AC090114.2; AP00943.4) were screened to establish the predictive model that significantly associated with poor overall survival(HR:4.879;95%CI,1.587-14.996; p =0.006). This five-lncRNA signature model showed excellent accuracy in the TCGA cohort (AUC=0.938), and also robustly predicted survival in the validation WMU cohort(AUC=0.816). Functional enrichment analysis suggested prognostic lncRNA was primarily associated with CCA-related biological processes. Our data established a novel lncRNA signature model for CCA risk-stratification and robust identification of CCA patients with poor molecular genotypes. Moreover, it revealed new molecular mechanisms of CCA.
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