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 newly identified cell death mechanism and potential biomarker for hepatocellular carcinoma (HCC) therapy; however, its clinical relevance and underlying mechanism remain unclear. In this study, transcriptome and methylome data from 374 HCC cases were investigated for 41 ferroptosis‐related genes to identify ferroptosis activity‐associated subtypes. These subtypes were further investigated for associations with clinical and pathological variables, gene mutation landscapes, deregulated pathways and tumour microenvironmental immunity. A gene expression signature and predictive model were developed and validated using an additional 232 HCC cases from another independent cohort. Two distinct ferroptosis phenotypes (Ferroptosis‐H and Ferroptosis‐L) were identified according to ferroptosis gene expression and methylation in the patients with HCC. Patients with the Ferroptosis‐H had worse overall and disease‐specific survival, and the molecular subtypes were significantly associated with different clinical characteristics, mRNA expression patterns, tumour mutation profiles and microenvironmental immune status. Furthermore, a 15‐gene ferroptosis‐related prognostic model (FPM) for HCC was developed and validated which demonstrated accurate risk stratification ability. A nomogram included the FPM risk score, ECOG PS and hepatitis B status was developed for eventual clinical translation. Our results suggest that HCC subtypes defined by ferroptosis gene expression and methylation may be used to stratify patients for clinical decision‐making.

TROP-2, a tumor-associated antigen, has been implicated in the progression of various epithelial tumors. Due to its favorable expression profile, TROP-2 has emerged as a promising target for antibody–drug conjugates (ADCs) based anti-tumor therapies. Although ADCs have shown efficacy in cancer treatment, their application in solid tumors is hindered by their high molecular weight, poor tumor penetration, and release of cytotoxic molecules. Therefore, a recombinant immunotoxin was developed based on a shark-derived variable domain of immunoglobulin new antigen receptor (VNAR) antibody. VNARs are only one-tenth the size of IgG antibodies and possess remarkable tissue penetration capabilities and high stability. In this study, a shark VNAR phage display library was created, leading to the identification of shark VNAR-5G8 that targets TROP-2. VNAR-5G8 exhibited a high affinity and cellular internalization ability towards cells expressing high levels of TROP-2. Epitope analysis revealed that VNAR-5G8 recognizes a hidden epitope consisting of CRD and TY-1 on TROP-2. Subsequently, VNAR-5G8 was fused with a truncated form of Pseudomonas exotoxin (PE38) to create the recombinant immunotoxin (5G8-PE38), which exhibited significant anti-tumor activity in vitro and in vivo. Overall, this study highlights the promise of 5G8-PE38 as a valuable candidate for cancer therapy.

Hepatocellular carcinoma (HCC) is a highly invasive malignancy prone to recurrence, and patients with HCC have a low 5-year survival rate. Long non-coding RNAs (lncRNAs) play a vital role in the occurrence and development of HCC. N6-methyladenosine methylation (m6A) is the most common modification influencing cancer development. Here, we used the transcriptome of m6A regulators and lncRNAs, along with the complete corresponding clinical HCC patient information obtained from The Cancer Genome Atlas (TCGA), to explore the role of m6A regulator-related lncRNA (m6ARlnc) as a prognostic biomarker in patients with HCC. The prognostic m6ARlnc was selected using Pearson correlation and univariate Cox regression analyses. Moreover, three clusters were obtained via consensus clustering analysis and further investigated for differences in immune infiltration, immune microenvironment, and prognosis. Subsequently, nine m6ARlncs were identified with Lasso-Cox regression analysis to construct the prognostic signature m6A-9LPS for patients with HCC in the training cohort (n = 226). Based on m6A-9LPS, the risk score for each case was calculated. Patients were then divided into high- and low-risk subgroups based on the cutoff value set by the X-tile software. m6A-9LPS showed a strong prognosis prediction ability in the validation cohort (n = 116), the whole cohort (n = 342), and even clinicopathological stratified survival analysis. Combining the risk score and clinical characteristics, we established a nomogram for predicting the overall survival (OS) of patients. To further understand the mechanism underlying the m6A-9LPS-based classification of prognosis differences, KEGG and GO enrichment analyses, competitive endogenous RNA (ceRNA) network, chemotherapeutic agent sensibility, and immune checkpoint expression level were assessed. Taken together, m6A-9LPS could be used as a precise prediction model for the prognosis of patients with HCC, which will help in individualized treatment of HCC.

Background: In recent years, immunotherapy has changed the therapeutic landscape of hepatocellular carcinoma (HCC). Since the efficacy of immunotherapy is closely related to the tumor microenvironment (TME), in this study, we constructed a prognostic model based on TME to predict the prognosis and immunotherapy effect of HCC patients.Methods: Transcriptome and follow-up data of 374 HCC patients were acquired from the TCGA Cancer Genome Atlas (TCGA) database. The immune/stromal/estimate scores (TME scores) and tumor purity were calculated using the ESTIMATE algorithm and the module most associated with TME scores were screened by the weighted gene co-expression network analysis (WGCNA). A TME score-related prognostic model was constructed and patients were divided into a high-risk group and a low-risk group. Kaplan-Meier survival curves and receiver operator characteristic curve (ROC) were used to evaluate the performance of the TME risk prognostic model and validated with the external database International Cancer Genome Consortium (ICGC) cohort. Combined with clinicopathologic factors, a prognostic nomogram was established. The nomogram’s ability to predict prognosis was assessed by ROC, calibration curve, and the decision curve analysis (DCA). Gene Set Enrichment Analyses (GSEA) were conducted to explore the underlying biological functions and pathways of this risk signature. Moreover, the possible correlation of risk signature with TME immune cell infiltration, immune checkpoint inhibitor (ICI) treatment response, single-nucleotide polymorphisms (SNPs), and drug sensitivity were assessed. Finally, real-time PCR was used to verify the gene expression levels in normal liver cells and cancer cells.Results: KM survival analysis results indicated that high immune/stromal/estimate score groups were closely associated with a better prognosis, while the tumor purity showed a reverse trend (p < 0.01). WGCNA demonstrated that the yellow module was significantly correlated with the TME score. The 5-genes TME risk signature was built to predict the prognosis of patients with HCC including DAB2, IL18RAP, RAMP3, FCER1G, and LHFPL2. Patients with a low-risk score have higher levels of tumor-infiltrating immune cells and higher expression of immune checkpoints, which may be more sensitive to immunotherapy.Conclusion: It provided a theoretical basis for predicting the prognosis and personalized treatment of patients with HCC.