Background: The role of mitochondrial dynamics, encompassing fission, fusion, and mitophagy, in cancer progression has been extensively studied. However, the specific impact of mitochondrial dynamics on hepatocellular carcinoma (HCC) is still under investigation.Methods: In this study, mitochondrial dynamic genes were obtained from the MitoCarta 3.0 database, and gene expression data were collected from The Cancer Genome Atlas (TCGA) database. Based on the expression of these dynamic genes and differentially expressed genes (DEGs), patients were stratified into two clusters. Subsequently, a prognostic model was constructed using univariate COX regression and the least absolute shrinkage and selection operator (LASSO) regression, and the prognostic signature was evaluated. We analyzed the interaction between these model genes and dynamic genes to identify hub genes and reveal mitochondrial status. Furthermore, we assessed immune infiltration, tumor mutational burden (TMB), tumor stemness indices (TSI), and the response to immune checkpoint block (ICB) therapy using the TIDE algorithm and risk scores. Additionally, transmission electron microscopy (TEM), hematoxylin-eosin (H&E) staining, immunohistochemistry (IHC), western blotting (WB), and immunofluorescence (IF) were conducted to afford detailed visualization of the morphology of the mitochondria and the expression patterns of fission-associated proteins.Results: Patients in Cluster 2 exhibited heightened mitochondrial fission and had a worse prognosis. The up-regulated dynamic genes in Cluster 2 were identified as fission genes. GO/KEGG analyses reconfirmed the connection of Cluster 2 to augmented mitochondrial fission activities. Subsequently, a ten-gene prognostic signature based on the differentially expressed genes between the two clusters was generated, with all ten genes being up-regulated in the high-risk group. Moreover, the potential links between these ten signature genes and mitochondrial dynamics were explored, suggesting their involvement in mediating mitochondrial fission through interaction with MTFR2. Further investigation revealed that the high-risk group had an unfavorable prognosis, with a higher mutation frequency of TP53, increased immune checkpoint expression, a higher TIS score, and a lower TIDE score. The mitochondrial imbalance characterized by increased fission and upregulated MTFR2 and DNM1L expression was substantiated in both HCC specimens and cell lines.Conclusions: In conclusion, we developed a novel MTFR2-related prognostic signature comprising ten mitochondrial dynamics genes. These genes play crucial roles in mitochondrial fission and have the potential to serve as important predictors and therapeutic targets for HCC.

Background: Therapeutic interventions such as synthetic drugs and microRNA (miR) modulators have created opportunities for mitigating hepatic ischemia/reperfusion injury (HIRI) by alleviating mitochondrial dysfunction. However, delivering multi-therapeutic ingredients with low toxicity to hepatocytes still lags behind its development.

Background and Aims: The aims of the present study were to characterize the PANoptosis signature in intrahepatic cholangiocarcinoma (ICC) patients, construct a novel model to guide clinical diagnosis and treatment, and further explore the associated molecular mechanisms of drug resistance. Approach and Results: In total, 85 PANoptosis-related genes that possess both PANoptosis and multi-omics features were, respectively, screened from transcriptomic data from the OEP001105 public cohort and from transcriptomic and proteomic sequencing data from The First Affiliated Hospital of Chongqing Medical University. A novel framework integrating Cox regression analysis and 5 machine learning algorithms was developed to identify the 5 hub genes (POSTN, SFN, MYOF, HOGA1, and PECR). The subsequently constructed PANoptosis risk score demonstrates outstanding performance in predicting prognosis and clinical translation across multicenter cohorts with multi-omics profiling. Bulk and single-cell transcriptome profiling were used to investigate the tumor microenvironment, emphasizing the crucial role of macrophages in the tumor microenvironment of ICCs. Moreover, a positive spatial correlation of cancer-associated fibroblasts–derived POSTN expression with tumor-associated macrophages infiltration and PD-L1/PD-L2 expression in ICC patients was observed, suggesting that overexpression of POSTN may lead to resistance to immune checkpoint blockade therapy in ICC patients. Conclusions: The present study identified a precise prognostic and treatment strategy for ICC patients prone to PANoptosis, investigated the molecular mechanisms of PANoptosis in ICC cells, and highlighted the potential clinical relevance of the PANoptosis risk score in predicting prognosis and therapy response. These findings will help guide clinical treatment strategies for ICC.

Gallbladder carcinoma (GBC), which has high invasion and metastasis risks, remains the most common biliary tract malignancy. Surgical resection for GBC is the only effective treatment, but most patients miss the opportunity for curative surgery because of a lack of timely diagnosis. The aim of this study was to identify and verify early candidate diagnostic and prognostic RNA methylation related genes for GBC via integrated transcriptome bioinformatics analysis. Lists of GBC-related genes and methylation-related genes were collected from public databases to screen differentially expressed genes (DEGs) by using the limma package and the RobustRankAggreg (RRA) package. The core genes were collected with batch effects corrected by the RRA algorithm through protein interaction network analysis, signaling pathway enrichment analysis and gene ranking. Four modules obtained from four public microarray datasets were found to be related to GBC, and FGA, F2, HAO1, CFH, PIPOX, ITIH4, GNMT, MAT1A, MTHFD1, HPX, CTH, EPHX2, HSD17B6, AKR1C4, CFHR3, ENNP1, and NAT2 were revealed to be potential hub genes involved in methylation-related pathways and bile metabolism-related pathways. Among these, FGA, CFH, F2, HPX, and PIPOX were predicted to be methylated genes in GBC, but POPIX had no modification sites for RNA methylation. Furthermore, survival analysis of TCGA (the Cancer Genome Atlas) database showed that six genes among the hub genes, FGA, CFH, ENPP1, CFHR3, ITIH4, and NAT2, were highly expressed and significantly correlated with worse prognosis. Gene correlation analysis revealed that the FGA was positively correlated with the ENPP1, NAT2, and CFHR3, while CFH was positively correlated with the NAT2, CFHR3, and FGA. In addition, the results of immunohistochemistry (IHC) showed that the expressions of FGA, F2, CFH, PIPOX, ITIH4, GNMT, MAT1A, MTHFD1, HPX, CFHR3, NAT2, and ENPP1 were higher in GBC tissues than that in control tissues. In conclusion, two genes, FGA and CFH, were identified as RNA methylation-related genes also involved in bile metabolism in GBC, which may be novel biomarkers to early diagnose and evaluate prognosis for GBC.

Background: Clinically relevant postoperative pancreatic fistula (CR-POPF) represents a significant complication after pancreaticoduodenectomy (PD). Therefore, the early prediction of CR-POPF is of paramount importance. Based on above, this study sought to develop a CR-POPF prediction model that amalgamates radiomics and clinical features to predict CR-POPF, utilizing Shapley Additive explanations (SHAP) for visualization.Methods: Extensive radiomics features were extracted from preoperative enhanced Computed Tomography (CT) images of patients scheduled for PD. Subsequently, feature selection was performed using Least Absolute Shrinkage and Selection Operator (Lasso) regression and random forest (RF) algorithm to select pertinent radiomics and clinical features. Last, 15 CR-POPF prediction models were developed using five distinct machine learning (ML) predictors, based on selected radiomics features, selected clinical features, and a combination of both. Model performance was compared using DeLong’s test for the area under the receiver operating characteristic curve (AUC) differences.Results: The CR-POPF prediction model based on the XGBoost predictor with the combination of the radiomics and clinical features selected by Lasso regression and RF exhibited superior performance among these 15 CR-POPF prediction models, achieving an accuracy of 0.85, an AUC of 0.93. DeLong’s test showed statistically significant differences (P < 0.05) when compared to the radiomics-only and clinical-only models, with recall of 0.63, precision of 0.65, and F1 score of 0.64.Conclusion: The proposed CR-POPF prediction model based on the XGBoost predictor with the combination of the radiomics and clinical features selected by Lasso regression and RF can effectively predicting the CR-POPF and may provide strong support for early clinical management of CR-POPF.

Pyroptosis, a novel pro-inflammatory type of programmed cell death, is involved in the tumorigenesis of various cancers. Recent findings have implicated long non-coding RNAs (lncRNAs) in the serial steps of cancer development. However, the expression and prognostic signatures of pyroptosis-related lncRNAs in hepatocellular carcinoma (HCC) remain largely unknown. Therefore, a pyroptosis-related lncRNA prognostic model was constructed for HCC. Thirty-four pyroptosis-related genes were obtained from previous reviews, and gene expression data were collected from The Cancer Genome Atlas (TCGA) database. Spearman's correlation test was used to identify potential pyroptosis-related lncRNAs. Cox and LASSO regression analyses were used to construct a prognostic model. Subsequently, receiver operating characteristic (ROC) curves were constructed to assess the model's predictive ability for the overall survival (OS) of HCC patients. CytoHubba was used to screen out the potential hub gene, whose expression was verified using clinical samples from HCC patients. Finally, nine pyroptosis-related differentially expressed lncRNAs in HCC were identified, and a prognostic model with four pyroptosis-related lncRNAs was constructed with an area under the ROC curve (AUC) of approximately 0.734. Single-sample gene set enrichment analysis and TCGA revealed different immune infiltration and immune checkpoints between the two risk groups. Moreover, these lncRNAs are closely related to the pyroptosis-related gene, NLRP6, which may be considered a hub gene. NLRP6 was lower-expressed in HCC samples, and patients with lower expression of NLRP6 had the longer OS. In conclusion, NLRP6-dependent pyroptosis-related lncRNAs play important roles in tumor immunity and may be potential predictors and therapeutic targets for HCC.