Background: Cancer-associated fibroblasts (CAFs) are involved in tumor growth, angiogenesis, metastasis, and resistance to therapy. We sought to explore the CAFs characteristics in hepatocellular carcinoma (HCC) and establish a CAF-based risk signature for predicting the prognosis of HCC patients.Methods: The signal-cell RNA sequencing (scRNA-seq) data was obtained from the GEO database. Bulk RNA-seq data and microarray data of HCC were obtained from the TCGA and GEO databases respectively. Seurat R package was applied to process scRNA-seq data and identify CAF clusters according to the CAF markers. Differential expression analysis was performed to screen differentially expressed genes (DEGs) between normal and tumor samples in TCGA dataset. Then Pearson correlation analysis was used to determine the DEGs associated with CAF clusters, followed by the univariate Cox regression analysis to identify CAF-related prognostic genes. Lasso regression was implemented to construct a risk signature based on CAF-related prognostic genes. Finally, a nomogram model based on the risk signature and clinicopathological characteristics was developed.Results: Based on scRNA-seq data, we identified 4 CAF clusters in HCC, 3 of which were associated with prognosis in HCC. A total of 423 genes were identified from 2811 DEGs to be significantly correlated with CAF clusters, and were narrowed down to generate a risk signature with 6 genes. These six genes were primarily connected with 39 pathways, such as angiogenesis, apoptosis, and hypoxia. Meanwhile, the risk signature was significantly associated with stromal and immune scores, as well as some immune cells. Multivariate analysis revealed that risk signature was an independent prognostic factor for HCC, and its value in predicting immunotherapeutic outcomes was confirmed. A novel nomogram integrating the stage and CAF-based risk signature was constructed, which exhibited favorable predictability and reliability in the prognosis prediction of HCC.Conclusion: CAF-based risk signatures can effectively predict the prognosis of HCC, and comprehensive characterization of the CAF signature of HCC may help to interpret the response of HCC to immunotherapy and provide new strategies for cancer treatment.

Extrahepatic cholangiocarcinoma (ECC), a highly malignant type of cancer with increasing incidence, has a poor prognosis due to limited treatment options. Based on genomic analysis of ECC patient samples, here we report that aldo-keto reductase family 1 member C1 (AKR1C1) is highly expressed in human ECC tissues and closely associated with ECC progression and poor prognosis. Intriguingly, we show that inducible AKR1C1 knockdown triggers ECC cells to undergo ferroptosis. Mechanistically, AKR1C1 degrades the protein stability of the cytochrome P450 family member CYP1B1, a newly discovered mediator of ferroptosis, via ubiquitin-proteasomal degradation. Additionally, AKR1C1 decreases CYP1B1 mRNA level through the transcriptional factor aryl-hydrocarbon receptor (AHR). Furthermore, the AKR1C1–CYP1B1 axis modulates ferroptosis in ECC cells via the cAMP–PKA signaling pathway. Finally, in a xenograft mouse model of ECC, AKR1C1 depletion sensitizes cancer cells to ferroptosis and synergizes with ferroptosis inducers to suppress tumor growth. Therefore, the AKR1C1–CYP1B1–cAMP signaling axis is a promising therapeutic target for ECC treatment, especially in combination with ferroptosis inducers.

Background: Differential diagnosis of high-grade dysplastic nodules (HGDN) and well-differentiated hepatocellular carcinoma (WDHCC) represents a challenge to experienced hepatic clinicians, radiologists and hepatopathologists. Methods: The expression profiles of aminoacylase-1 (ACY1), sequestosome-1 (SQSTM1) and glypican-3 (GPC3) in low-grade dysplastic nodules (LGDN), HGDN and WDHCC were assessed by immunohistochemistry. The differential diagnostic performances of these three markers alone and in combination for HGDN and WDHCC were investigated by logistic regression models (HGDN = 21; WDHCC = 32) and validated in an independent test set (HGDN, n = 21; WDHCC n = 24). Postoperative overall survival and time to recurrence were evaluated by univariate and multivariate analyses in an independent set of 500 patients. Results: ACY1, SQSTM1 and GPC3 were differentially expressed in each group. For the differential diagnosis of WDHCC from HGDN, the sensitivity and specificity of the combination of ACY1 + SQSTM1 + GPC3 for detecting WDHCC were 93.8% and 95.2% respectively in the training set, which were higher than any of the three two-marker combinations. The validities of the four diagnostic models were further confirmed in an independent test set, and corresponding good sensitivity and specificity were observed. Interestingly, GPC3 expression in HCC tissues combined with serum α-fetoprotein (AFP) was found to be an independent predictor for overall survival and time to recurrence. Conclusions: ACY1 + SQSTM1 + GPC3 combination represents a potentially valuable biomarker for distinguishing between WDHCC and HGDN using immunohistochemistry. Meanwhile, low GPC3 staining combined with positive serum AFP may play a practical role in predicting poor postoperative outcome and high tumor recurrence risk.

Background: Lactylation is closely involved in cancer progression, but its role in hepatocellular carcinoma (HCC) is unclear. The present work set out to develop a lactylation‐related gene (LRG) signature for HCC. Methods: The lactylation score of tumor and normal groups was calculated using the gene set variation analysis (GSVA) package. The single‐cell RNA sequencing (scRNA‐seq) analysis of HCC was performed in the “Seurat” package. Prognostic LRGs were selected by performing univariate and least absolute shrinkage and selection operator (LASSO) Cox regression analyses to develop and validate a Riskscore model. Functional enrichment analysis was conducted by gene set enrichment analysis (GSEA) using the “clusterProfiler” package. Genomic characteristics between different risk groups were compared, and tumor mutational burden (TMB) was calculated by the “Maftools” package. Immune cell infiltration was assessed by algorithms of cell‐type identification by estimating relative subsets of RNA transcript (CIBERSORT), microenvironment cell populations‐counter (MCP‐counter), estimating the proportions of immune and cancer cells (EPIC), tumor immune estimation resource (TIMER), and single‐sample gene set enrichment analysis (ssGSEA). Immunotherapy response was predicted by the tumor immune dysfunction and exclusion (TIDE) algorithm. Drug sensitivity was analyzed using the “pRRophetic” package. A nomogram was established using the “rms” package. The expressions of the prognostic LRGs in HCC cells were verified by in vitro test, and cell counting kit‐8 (CCK‐8), wound healing, and transwell assays were carried out to measure the viability, migration, and invasion of HCC cells. Results: The lactylation score, which was higher in the tumor group than in the normal group, has been confirmed as an independent factor for the prognostic evaluation in HCC. Six prognostic LRGs, including two protective genes ( FTCD and APCS ) and four risk genes ( LGALS3 , C1orf43 , TALDO1 , and CCT5 ), were identified to develop a Riskscore model with a strong prognostic prediction performance in HCC. The scRNA‐seq analysis revealed that LGALS3 was largely expressed in myeloid cells, while APCS , FTCD , TALDO1 , CCT5 , and C1orf43 were mainly expressed in hepatocytes. The high‐risk group was primarily enriched in the pathways involved in tumor occurrence and development, with higher T cell infiltration. Moreover, the high‐risk group was found to be less responsive to immunotherapy but was more sensitive to chemotherapeutic drugs. By integrating Riskscore and clinical features, a nomogram with a high predictive accuracy was developed. Additionally, C1orf43 , CCT5 , TALDO1 , and LGALS3 were highly expressed in HCC cells. Silencing CCT5 inhibited the viability, migration, and invasion of HCC cells. Conclusion: The present work developed a novel LRG gene signature that could be considered a promising therapeutic target and biomarker for HCC.

The consistent use of metformin has been linked to a reduced incidence of neoplastic diseases among diabetic populations. As a preventive intervention, metformin may offer a more favorable risk-benefit profile. Here, we explored the efficacy of metformin in the primary prevention of cholangiofibrosis, which can precede the carcinogen-induced development of cholangiocarcinoma (CCA). Our objective was to assess the potential of metformin to act as an intervention prior to the onset of these conditions. A rat model of thioacetamide (TAA)-induced cholangiofibrosis was utilized to assess the impact of metformin on the induction process of cholangiocarcinoma (CCA). Liver tissues were harvested and analyzed histologically using light microscopy, complemented by a deep-learning convolutional neural network for enhanced evaluation. Additionally, RNA sequencing (RNA-seq) was performed to investigate the genetic alterations associated with metformin treatment in this TAA-induced cholangiofibrosis model. In the rat model, the TAA control group exhibited an increased incidence and average count of cholangiofibrosis cases in the liver, with rates of 100 % and an average of 12.0, compared to the metformin-treated group, which showed an incidence of 70 % and an average of 3.3. Notably, the progression from normal cholangioles to cholangiofibrosis was associated with the upregulation of several proteins critical for metabolic processes and the tumor microenvironment. These alterations were significantly mitigated by metformin treatment. Long-term metformin use may offer protective benefits against cholangiofibrosis, partially by regulating metabolic processes and improving the tumor microenvironment.