Mechanistic insights into 125I seed implantation therapy for Cholangiocarcinoma: focus on ROS-Mediated apoptosis and the role of GPX2.

OBJECTIVES: Cholangiocarcinoma (CCA) is a rare tumor with a poor prognosis and poses significant therapeutic challenges. Herein, we investigated the mechanism of efficacy of 125I seed implantation therapy in CCA, focusing on the induction of reactive oxygen species (ROS)-mediated apoptosis and the involvement of glutathione peroxidase 2 (GPX2). MATERIALS AND METHODS: Human cholangiocarcinoma cell lines QBC939 and RBE were purchased for in vitro studies. In vivo studies were performed using a rabbit VX2 CCA model. Apoptosis and proliferation were detected by TUNEL staining and clone formation, respectively. ROS generation was detected by dihydroethidium staining. Histological evaluation was performed by hematoxylin and eosin staining. Protein expression was determined by Western blotting and immunohistochemistry. RESULTS: Our results demonstrate that 125I seeds effectively inhibited tumor growth in the rabbit VX2 tumor model and promoted the apoptosis of CCA cells in vitro in a dose-dependent manner. Molecular analyses indicate a marked increase in reactive oxygen species (ROS) levels following treatment with 125I seeds, suggesting the involvement of ROS-mediated apoptosis in the therapeutic mechanism. Furthermore, the downregulation of glutathione peroxidase 2 (GPX2) was observed, indicating its potential role in modulating ROS-mediated apoptosis in CCA. CONCLUSION: 125I seed implantation therapy exerts therapeutic effects on CCA by inducing ROS-mediated apoptosis. The downregulation of GPX2 may contribute to enhanced ROS accumulation and apoptotic cell death. These findings provide mechanistic insights into the therapeutic potential of 125I seed implantation for CCA and highlight ROS-mediated apoptosis and GPX2 regulation as promising targets for further investigation and therapeutic intervention in this malignancy.

XCR1: A promising prognostic marker that pinpoints targeted and immune-based therapy in hepatocellular carcinoma.

Objectives: The lymphotactin receptor X-C motif chemokine receptor 1 (XCR1) is an essential member of the chemokine receptor family and is related to tumor development and progression. Nevertheless, further investigation is required to explore its expression patterns, prognostic values, and functions related to target or immune therapies in patients with hepatocellular carcinoma (HCC). Materials and methods: The differential expression patterns of XCR1 and its prognostic influences were performed through The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) databases. Subsequently, immunohistochemistry (IHC) staining and univariate and multivariate Cox regressions were performed to validate the prognostic values in different subgroups. Furthermore, the potential roles of XCR1 in predicting target and immune therapeutic responses were also investigated. Results: Increased expression level of XCR1 was associated with favorable overall survival (OS) and recurrence-free survival (RFS). Subgroup analysis revealed that a high expression level of XCR1 or positive immune cell proportion score (iCPS) were associated with favorable OS in the HCC patients with favorable tumor characteristics. In addition, the enhanced XCR1 expression was associated with the tumor environment scores, immune cell infiltration levels, and the expression levels of immune checkpoint genes. Further analysis revealed that improved expression of XCR1 was linked to better OS and RFS in HCC patients who received sorafenib. Conclusion: This study identified that XCR1 is a valuable prognostic biomarker in the HCC population, especially in those with favorable tumor characteristics. The combination of iCPS status and BCLC status has a synergistic effect on stratifying patients' OS and RFS. Further analyses showed that XCR1 has the potential ability to predict treatment responses to sorafenib and immune-based therapies.

A Transformer-Based microvascular invasion classifier enhances prognostic stratification in HCC following radiofrequency ablation.

BACKGROUND & AIMS: We aimed to develop a Transformer-based deep learning (DL) network for prognostic stratification in hepatocellular carcinoma (HCC) patients undergoing RFA. METHODS: A Swin Transformer DL network was trained to establish associations between magnetic resonance imaging (MRI) datasets and the ground truth of microvascular invasion (MVI) based on 696 surgical resection (SR) patients with solitary HCC ≤3 cm, and was validated in an external cohort (n = 180). The multiphase MRI-based DL risk outputs using an optimal threshold of .5 was employed as a MVI classifier for prognosis stratification in the RFA cohort (n = 180). RESULTS: Over 90% of all enrolled patients exhibited hepatitis B virus infection. Liver cirrhosis was significantly more prevalent in the RFA cohort compared to the SR cohort (72.2% vs. 44.1%, p < .001). The MVI risk outputs exhibited good performance (area under the curve values = .938 and .883) for predicting MVI in the training and validation cohort, respectively. The RFA patients at high risk of MVI classified by the MVI classifier demonstrated significantly lower recurrence-free survival (RFS) and overall survival rates at 1, 3 and 5 years compared to those classified as low risk (p < .001). Multivariate cox regression modelling of a-fetoprotein > 20 ng/mL [hazard ratio (HR) = 1.53; 95% confidence interval (95% CI): 1.02-2.33, p = .047], high risk of MVI (HR = 3.76; 95% CI: 2.40-5.88, p < .001) and unfavourable tumour location (HR = 2.15; 95% CI: 1.40-3.29, p = .001) yielded a c-index of .731 (bootstrapped 95% CI: .667-.778) for evaluating RFS after RFA. Among the three risk factors, MVI was the most powerful predictor for intrahepatic distance recurrence. CONCLUSIONS: The proposed MVI classifier can serve as a valuable imaging biomarker for prognostic stratification in early-stage HCC patients undergoing RFA.

PGAM1 Inhibition Promotes HCC Ferroptosis and Synergizes with Anti-PD-1 Immunotherapy.

The combination of immunotherapy and molecular targeted therapy exhibits promising therapeutic efficacy in hepatocellular carcinoma (HCC), but the underlying mechanism is still unclear. Here, phosphoglycerate mutase 1 (PGAM1) is identified as a novel immunometabolic target by using a bioinformatic algorithm based on multiple HCC datasets. PGAM1 is highly expressed in HCC and associated with a poor prognosis and a poor response to immunotherapy. In vitro and in vivo experiments indicate that targeting PGAM1 inhibited HCC cell growth and promoted the infiltration of CD8+ T-cells due to decreased enzymatic activity. Mechanistically, inhibition of PGAM1 promotes HCC cell ferroptosis by downregulating Lipocalin (LCN2) by inducing energy stress and ROS-dependent AKT inhibition, which can also downregulate Programmed death 1-ligand 1 (PD-L1). Moreover, an allosteric PGAM1 inhibitor (KH3) exhibits good antitumor effects in patient-derived xenograft (PDX) models and enhanced the efficacy of anti-PD-1 immunotherapy in subcutaneous and orthotopic HCC models. Taken together, the findings demonstrate that PGAM1 inhibition exerts an antitumor effect by promoting ferroptosis and CD8+ T-cell infiltration and can synergize with anti-PD-1 immunotherapy in HCC. Targeting PGAM1 can be a promising new strategy of "killing two birds with one stone" for HCC treatment.

Data-Driven Assisted Decision Making for Surgical Procedure of Hepatocellular Carcinoma Resection and Prognostic Prediction: Development and Validation of Machine Learning Models.

Background: Currently, surgical decisions for hepatocellular carcinoma (HCC) resection are difficult and not sufficiently personalized. We aimed to develop and validate data driven prediction models to assist surgeons in selecting the optimal surgical procedure for patients. Methods: Retrospective data from 361 HCC patients who underwent radical resection in two institutions were included. End-to-end deep learning models were built to automatically segment lesions from the arterial phase (AP) of preoperative dynamic contrast enhanced magnetic resonance imaging (DCE-MRI). Clinical baseline characteristics and radiomic features were rigorously screened. The effectiveness of radiomic features and radiomic-clinical features was also compared. Three ensemble learning models were proposed to perform the surgical procedure decision and the overall survival (OS) and recurrence-free survival (RFS) predictions after taking different solutions, respectively. Results: SegFormer performed best in terms of automatic segmentation, achieving a Mean Intersection over Union (mIoU) of 0.8860. The five-fold cross-validation results showed that inputting radiomic-clinical features outperformed using only radiomic features. The proposed models all outperformed the other mainstream ensemble models. On the external test set, the area under the receiver operating characteristic curve (AUC) of the proposed decision model was 0.7731, and the performance of the prognostic prediction models was also relatively excellent. The application web server based on automatic lesion segmentation was deployed and is available online. Conclusions: In this study, we developed and externally validated the surgical decision-making procedures and prognostic prediction models for HCC for the first time, and the results demonstrated relatively accurate predictions and strong generalizations, which are expected to help clinicians optimize surgical procedures.

Integrated Analysis of N1-Methyladenosine Methylation Regulators-Related lncRNAs in Hepatocellular Carcinoma.

N1-methyladenosine (m1A) and long non-coding RNAs (lncRNAs) play significant roles in tumor progression in hepatocellular carcinoma (HCC). However, their association with HCC is still unclear. In this study, lncRNAs related to m1A were extracted from the mRNA expression matrix in The Cancer Genome Atlas (TCGA) database. Five m1A-related lncRNAs (AL031985.3, NRAV, WAC-AS1, AC026412.3, and AC099850.4) were identified based on lasso Cox regression and they generated a prognostic signature of HCC. The prognostic signature was identified as an independent prognosis factor in HCC patients. Moreover, the prognostic signature achieved better performance than TP53 mutation status or tumor mutational burden (TMB) scores in the stratification of patient survival. The immune landscape indicated that most immune checkpoint genes and immune cells were distributed differently between both risk groups. A higher IC50 of chemotherapeutics (sorafenib, nilotinib, sunitinib, and gefitinib) was observed in the high-risk group, and a lower IC50 of gemcitabine in the low-risk group, suggesting the potential of the prognostic signature in chemosensitivity. In addition, fifty-five potential small molecular drugs were found based on drug sensitivity and NRAV expression. Together, five m1A-related lncRNAs generated a prognostic signature that could be a promising prognostic prediction approach and therapeutic response assessment tool for HCC patients.

Perioperative and oncologic outcomes of laparoscopic versus open liver resection for combined hepatocellular-cholangiocarcinoma: a propensity score matching analysis.

BACKGROUND: Laparoscopic liver resection (LLR) has now been established as a safe and minimally invasive technique that is deemed feasible for treating hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC). However, the role of LLR in treating combined hepatocellular-cholangiocarcinoma (cHCC-CC) patients has been rarely reported. This study aimed to assess the efficacy of LLR when compared with open liver resection (OLR) procedure for patients with cHCC-CC. METHODS: A total of 229 cHCC-CC patients who underwent hepatic resection (34 LLR and 195 OLR patients) from January 2014 to December 2018 in Zhongshan Hospital, Fudan University were enrolled and underwent a 1:2 propensity score matching (PSM) analysis between the LLR and OLR groups to compare perioperative and oncologic outcomes. Overall survival (OS) and recurrence-free survival (RFS) parameters were assessed by the log-rank test and the sensitivity analysis. RESULTS: A total of 34 LLR and 68 OLR patients were included after PSM analysis. The LLR group displayed a shorter postoperative hospital stay (6.61 vs. 8.26 days; p value < 0.001) when compared with the OLR group. No significant differences were observed in the postoperative complications' incidence or a negative surgical margin rate between the two groups (p value = 0.409 and p value = 1.000, respectively). The aspartate aminotransferase (AST), alanine aminotransferase (ALT), and inflammatory indicators in the LLR group were significantly lower than those in the OLR group on the first and third postoperative days. Additionally, OS and RFS were comparable in both the LLR and OLR groups (p value = 0.700 and p value = 0.780, respectively), and similar results were obtained by conducting a sensitivity analysis. CONCLUSION: LLR can impart less liver function damage, better inflammatory response attenuation contributing to a faster recovery, and parallel oncologic outcomes when compared with OLR. Therefore, LLR can be recommended as a safe and effective therapeutic modality for treating selected cHCC-CC patients, especially for those with small tumors in favorable location.

An N6-methyladenosine-associated lncRNA signature for predicting clinical outcome and therapeutic responses in hepatocellular carcinoma.

Background: Hepatocellular carcinoma (HCC) is the leading cause of tumor-related mortality worldwide. N6-methyladenosine (m6A) and long noncoding RNAs (lncRNAs) have been reported to play significant roles in prognosis assessment and decision-making strategies for HCC. This study aimed to investigate the significance of prognosis and treatment response assessment of m6A-related lncRNAs in HCC. Methods: We used Pearson's correlation coefficient (r) to identify m6A-associated lncRNAs. We then performed univariate, least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analyses on the screened m6A-related lncRNAs to build a prognostic risk model for patients with HCC. The prognostic values and predictive performance of the model were then analyzed through Kaplan-Meier curve, receiver operating characteristic (ROC) curve, and nomogram. In addition, the potential value of this model for assessing sorafenib or immunotherapeutic responses was investigated based on the R package "pRRophetic" and immunophenoscore (IPS), respectively. Results: Fourteen m6A-related lncRNAs were identified to construct the predictive model (P<0.05). Patients with high risk showed poorer survival than those with low risk. The risk score may serve as an independent predictor for the prognosis of patients with HCC even in the subgroup analysis. Moreover, our predictive model outperformed TP53 mutation status or tumor mutation burden (TMB) scores in the stratification of patient survival. Notably, high- and low-risk patients were shown to have different estimated responses for sorafenib and immunotherapies. Conclusions: This study identified that a novel 14-m6A-related lncRNA signature could be a promising predictor for patient survival, and it might provide a vista for treatment response assessment of chemotherapy and immunotherapy.

A predictive model integrating deep and radiomics features based on gadobenate dimeglumine-enhanced MRI for postoperative early recurrence of hepatocellular carcinoma.

PURPOSE: Hepatocellular carcinoma (HCC) is the most common liver cancer worldwide, and early recurrence of HCC after curative hepatic resection is indicative of poor prognoses. We aim to develop a predictive model for postoperative early recurrence of HCC based on deep and radiomics features from multi-phasic magnetic resonance imaging (MRI). MATERIALS AND METHODS: A total of 472 HCC patients were included and divided into the training (n = 378) and validation (n = 94) cohorts in the retrospective study. We separately extracted radiomics features and deep features from eight phases of gadoxetic acid-enhanced MRI and utilized the least absolute shrinkage and selection operator logistic regression algorithm for feature selection and model construction. We integrated the selected two types of features into a combined model and established a radiomics model as well as a deep learning (DL) model for comparison. RESULTS: In the training and validation cohorts, the combined model demonstrated better performance for stratifying patients at high risk of early recurrence (AUC of 0.911 and 0.840, accuracy of 0.779 and 0.777, sensitivity of 0.927 and 0.769, specificity 0.720 and 0.779) than the radiomics model (AUC of 0.740 and 0.780) and the DL model (AUC of 0.887 and 0.813). CONCLUSION: The combined model integrating deep and radiomics features from multi-phasic MRI is efficient for noninvasively stratifying patients at high risk of early HCC recurrence after resection.

A Multiparametric Fusion Deep Learning Model Based on DCE-MRI for Preoperative Prediction of Microvascular Invasion in Intrahepatic Cholangiocarcinoma.

BACKGROUND: Assessment of microvascular invasion (MVI) in intrahepatic cholangiocarcinoma (ICC) by using a noninvasive method is an unresolved issue. Deep learning (DL) methods based on multiparametric fusion of MR images have the potential of preoperative assessment of MVI. PURPOSE: To investigate whether a multiparametric fusion DL model based on MR images can be used for preoperative assessment of MVI in ICC. STUDY TYPE: Retrospective. POPULATION: A total of 519 patients (200 females and 319 males) with a single ICC were categorized as a training (n = 361), validation (n = 90), and an external test cohort (n = 68). FIELD STRENGTH/SEQUENCE: A 1.5 T and 3.0 T; axial T2-weighted turbo spin-echo sequence, diffusion-weighted imaging with a single-shot spin-echo planar sequence, and dynamic contrast-enhanced (DCE) imaging with T1-weighted three-dimensional quick spoiled gradient echo sequence. ASSESSMENT: DL models of multiparametric fusion convolutional neural network (CNN) and late fusion CNN were both constructed for evaluating MVI in ICC. Gradient-weighted class activation mapping was used for visual interpretation of MVI status in ICC. STATISTICAL TESTS: The DL model performance was assessed through the receiver operating characteristic curve (ROC) analysis, and the area under the ROC curve (AUC) with the accuracy, sensitivity, and specificity were measured. P value < 0.05 was considered as statistical significance. RESULTS: In the external test cohort, the proposed multiparametric fusion DL model achieved an AUC of 0.888 with an accuracy of 86.8%, sensitivity of 85.7%, and specificity of 87.0% for evaluating MVI in ICC, and the positive predictive value and negative predictive value were 63.2% and 95.9%, respectively. The late fusion DL model achieved a lower AUC of 0.866, with an accuracy of 83.8%, sensitivity of 78.6%, specificity of 85.2% for evaluating MVI in ICC. DATA CONCLUSION: Our DL model based on multiparametric fusion of MRI achieved a good diagnostic performance in the evaluation of MVI in ICC. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY: Stage 2.

Knockdown of carboxypeptidase A4 (CPA4) inhibits gastric cancer cell progression via cell cycle arrest and apoptosis.

Background: Gastric cancer is one of the most prevalent cancers, with a low survival rate at the later stages. Carboxypeptidase A4 (CPA4) is associated with the aggressiveness and growth in cancer. However, its regulatory role in gastric cancer remains unknown. Therefore, we investigated the role of CPA4 in gastric cancer progression in vitro. Methods: The human gastric adenocarcinoma cell line (AGS cell line) was used in the present study. CPA4 knockdown lentiviruses were constructed. Western blot analysis was performed to evaluate the protein expression levels of epithelial-mesenchymal transition (EMT) transcription factors, EMT biomarkers, and proteins involved in the Wnt signaling pathway, mitogen-activated protein kinase (MAPK) signaling pathway, and phosphoinositide 3-kinase (PI3K)/protein kinase B (AKT)/mammalian target of rapamycin (mTOR) signaling pathway. Quantitative real-time polymerase chain reaction (qRT-PCR) analysis was carried out to evaluate the mRNA expression level of CPA4. The String database was employed for protein-protein interaction (PPI) network analysis. Cell colony formation, proliferation, migration, invasion, apoptosis, and cell cycle analyses were performed using corresponding kits. Results: CPA4 is highly expressed in gastric cancer cell lines. Overexpressed CPA4 was associated with the induction of EMT. Knockdown of CPA4 inhibited cell colony formation, proliferation, migration, and invasion of gastric cancer cells. Knockdown of CPA4 also promoted cell apoptosis of gastric cancer cells. Conclusions: Knockdown of CPA4 inhibited cell progression via arresting the cell cycle and inducing EMT in gastric cancer.

Newly detected liver nodules with a history of colorectal cancer: are they metastatic? Review of 2,632 cases in a single center.

BACKGROUND: The diagnosis of newly detected liver nodules in patients with colorectal cancer (CRC) is crucial for determining prognosis and treatment. Accurate identification of benign nodules can help avoid unnecessary therapy. The aim of our study was to retrospectively review patients with CRC who underwent liver resection for benign liver nodules misdiagnosed as CRC metastasis (CRLM) in our institution. METHODS: We reviewed all patients with a history of CRC who underwent liver resection from January 2012 to December 2019 in our institution. We specifically focused on nodules pathologically confirmed as benign. The pathology was rechecked by an independent pathologist. The clinicopathological characteristics of these patients were collected. Preoperative imaging examinations, including ultrasound (US), magnetic resonance imaging (MRI), and positron emission tomography-computed tomography (PET-CT) were reviewed. RESULTS: From 2012 to 2019, a total of 2,632 patients with CRC who were preoperatively diagnosed CRLM received liver resection, among which 2,584 (98.2%) cases were proven to be malignant, and 48 (1.8%) cases were benign. Among these 48 cases, 24 were pathologically confirmed as focal nodular hyperplasia (FNH), 9 were peliosis, 10 were inflammatory lesions, and 5 were hemangioma. At least one preoperative imaging examination indicated CRLM, with a median size of 2.0 cm (range, 0.4-8.0 cm). Before liver resection, ten patients received chemotherapy after the discovery of liver nodules. CONCLUSIONS: It should be noted that newly detected liver nodules in patients with a history of CRC could be benign. Accurate diagnosis of liver nodules in CRC is necessary to avoid overtreatment and to identify cost-effective medication.

A High-Accuracy Model Based on Plasma miRNAs Diagnoses Intrahepatic Cholangiocarcinoma: A Single Center with 1001 Samples.

OBJECTIVES: Intrahepatic cholangiocarcinoma (iCCA) is a highly malignant cancer. More than 70% of patients are diagnosed at an advanced stage. The aim of this study was to evaluate the diagnostic value of plasma miR-21, miR-122, and CA19-9, hoping to establish a novel model to improve the accuracy for diagnosing iCCA. MATERIALS AND METHODS: Plasma miR-21 and miR-122 were detected in 359 iCCA patients and 642 controls (healthy, benign liver lesions, other malignant liver tumors). All 1001 samples were allocated to training cohort (n = 668) and validation cohort (n = 333) in a chronological order. A logistic regression model was applied to combine these markers. Area under the receiver operating characteristic curve (AUC) was used as an accuracy index to evaluate the diagnostic performance. RESULTS: Plasma miR-21 and miR-122 were significantly higher in iCCA patients than those in controls. Higher plasma miR-21 level was significantly correlated with larger tumor size (p = 0.030). A three-marker model was constructed by using miR-21, miR-122 and CA19-9, which showed an AUC of 0.853 (95% CI: 0.824-0.879; sensitivity: 73.0%, specificity: 87.4%) to differentiate iCCA from controls. These results were subsequently confirmed in the validation cohort with an AUC of 0.866 (0.825-0.901). The results were similar for diagnosing early (stages 0-I) iCCA patients (AUC: 0.848) and CA19-9negative iCCA patients (AUC: 0.795). CONCLUSIONS: We established a novel three-marker model with a high accuracy based on a large number of participants to differentiate iCCA from controls. This model showed a great clinical value especially for the diagnosis of early iCCA and CA19-9negative iCCA.

Using deep learning to predict microvascular invasion in hepatocellular carcinoma based on dynamic contrast-enhanced MRI combined with clinical parameters.

PURPOSE: Microvascular invasion (MVI) is a critical determinant of the early recurrence and poor prognosis of patients with hepatocellular carcinoma (HCC). Prediction of MVI status is clinically significant for the decision of treatment strategies and the assessment of patient's prognosis. A deep learning (DL) model was developed to predict the MVI status and grade in HCC patients based on preoperative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and clinical parameters. METHODS: HCC patients with pathologically confirmed MVI status from January to December 2016 were enrolled and preoperative DCE-MRI of these patients were collected in this study. Then they were randomly divided into the training and testing cohorts. A DL model with eight conventional neural network (CNN) branches for eight MRI sequences was built to predict the presence of MVI, and further combined with clinical parameters for better prediction. RESULTS: Among 601 HCC patients, 376 patients were pathologically MVI absent, and 225 patients were MVI present. To predict the presence of MVI, the DL model based only on images achieved an area under curve (AUC) of 0.915 in the testing cohort as compared to the radiomics model with an AUC of 0.731. The DL combined with clinical parameters (DLC) model yielded the best predictive performance with an AUC of 0.931. For the MVI-grade stratification, the DLC models achieved an overall accuracy of 0.793. Survival analysis demonstrated that the patients with DLC-predicted MVI status were associated with the poor overall survival (OS) and recurrence-free survival (RFS). Further investigation showed that hepatectomy with the wide resection margin contributes to better OS and RFS in the DLC-predicted MVI present patients. CONCLUSION: The proposed DLC model can provide a non-invasive approach to evaluate MVI before surgery, which can help surgeons make decisions of surgical strategies and assess patient's prognosis.

PD-1/PD-L1 expression profiles within intrahepatic cholangiocarcinoma predict clinical outcome.

OBJECTIVE: Immunotherapy targeting the programmed cell death protein-1 (PD-1)/programmed cell death protein ligand 1 (PD-L1) pathway has been observed to be efficient in several solid tumors. We aim to investigate the prognostic significance of PD-1/PD-L1 expression profile in intrahepatic cholangiocarcinoma (ICC). MATERIALS AND METHODS: We investigated the expression of PD-1, PD-L1, CD8+ T cells, and CD68+ macrophages in paired tumor and adjacent normal tissues from 322 ICC patients using tyramide signal amplification (TSA)-based multiplexed immunohistochemistry. RESULTS: We found that high proportion of tumor-infiltrating CD8+ PD-1High within CD8+ PD-1+ T cells significantly correlated with advanced TNM stage (P = 0.035). ICC patients with high proportion of CD8+ PD-1High in CD8+ PD-1+ had worse postoperative survival than low proportion patients (P = 0.0037), which was an independently prognostic factor for OS (P = 0.025,). The density of CD68+ PD-L1+ significantly and positively correlated with the density of CD8+ PD-1High (P < 0.0001, r = 0.5927). The proportion of CD68+ PD-L1+ within CD68+ ICC was the risk factor for OS and TTR but not an independently factor for prognosis. The CD68+ PD-L1+ macrophages and CD8+ PD-1High T cells may cooperatively play a role in inhibiting anti-tumor immunity. CONCLUSION: CD68+ PD-L1+ macrophages and CD8+ PD-1High T cells predict unfavorable prognosis, which could also bring new progress about immune target therapy in ICC research.

DCK is a promising prognostic biomarker and correlated with immune infiltrates in hepatocellular carcinoma.

BACKGROUND: Deoxycytidine kinase (DCK), an enzyme in the nucleoside biosynthetic pathway, can affect the development of immune cells. However, the relationships between the expression of DCK, patient prognosis, and tumor-infiltrating immune cells (TIICs) in hepatocellular carcinoma (HCC) are still unclear. METHODS: The expression of DCK in HCC was analyzed through the Oncomine and Tumor Immune Estimation Resource (TIMER) databases. The impact of DCK on clinical prognosis was investigated via the Kaplan-Meier plotter and verified in the Gene Expression Profiling Interactive Analysis (GEPIA) databases. The interrelationships between DCK expression and TIICs in HCC were analyzed by the TIMER database. Additionally, the relationship between DCK expression and immune cell gene markers was calculated through TIMER and GEPIA databases. RESULTS: Compared with the adjacent normal tissues, high expression of DCK was observed in HCC tissues. Also, the higher expression of DCK was correlated to poorer prognosis in HCC patients, and it was associated with decreased survival in those with early stage and grade. Moreover, DCK expression was positively correlated with TIICs, including CD4+ and CD8+ T cells, B cells, monocytes, tumor-associated macrophages (TAMs), M1 and M2 macrophages, neutrophils, natural killer cells, and dendritic cells. Specifically, DCK expression levels were significantly associated with diverse immune gene marker sets, including those of Tregs and exhausted T cells. CONCLUSION: These findings suggest that DCK expression is correlated with patient outcomes and tumor infiltration cell levels in HCC patients. Additionally, the increased level of DCK was associated with marker genes of Tregs and exhaustion-related inhibitory receptors, suggesting the potential role of DCK in immunosuppression and immune escape. These findings suggest that DCK can function as a potential novel prognostic biomarker and reflect the immune infiltration status in HCC patients.

Inhibition of JNK signalling mediates PPARα-dependent protection against intrahepatic cholestasis by fenofibrate.

BACKGROUND AND PURPOSE: Fenofibrate, a PPARα agonist, is the most widely prescribed drug for treating hyperlipidaemia. Although fibrate drugs are reported to be beneficial for cholestasis, their underlying mechanism has not been determined. EXPERIMENTAL APPROACH: Wild-type mice and Pparα-null mice were pretreated orally with fenofibrate for 3 days, following which α-naphthylisothiocyanate (ANIT) was administered to induce cholestasis. The PPARα agonist WY14643 and JNK inhibitor SP600125 were used to determine the role of PPARα and the JNK pathway, respectively, in cholestatic liver injury. The same fenofibrate regimen was applied to investigate its beneficial effects on sclerosing cholangitis in a DDC-induced cholestatic model. KEY RESULTS: Fenofibrate, 25 mg·kg-1 twice a day, totally attenuated ANIT-induced cholestasis and liver injury as indicated by biochemical and histological analyses. This protection occurred in wild-type, but not in Pparα-null, mice. Alterations in bile acid synthesis and transport were found to be an adaptive response rather than a direct effect of fenofibrate. WY14643 attenuated ANIT-induced cholestasis and liver injury coincident with inhibition of JNK signalling. Although SP600125 did not affect cholestasis, it inhibited liver injury in the ANIT model when the dose of fenofibrate used was ineffective. Fenofibrate was also revealed to have a beneficial effect in the sclerosing cholangitis model. CONCLUSIONS AND IMPLICATIONS: These data suggest that the protective effects of fenofibrate against cholestasis-induced hepatic injury are dependent on PPARα and fenofibrate dose, and are mediated through inhibition of JNK signalling. This mechanism of fenofibrate protection against intrahepatic cholestasis may offer additional therapeutic opportunities for cholestatic liver diseases.

Peroxisome Proliferator-Activated Receptor α Activation Suppresses Cytochrome P450 Induction Potential in Mice Treated with Gemfibrozil.

Gemfibrozil, a peroxisome proliferator-activated receptor α (PPARα) agonist, is widely used for hypertriglyceridaemia and mixed hyperlipidaemia. Drug-drug interaction of gemfibrozil and other PPARα agonists has been reported. However, the role of PPARα in cytochrome P450 (CYP) induction by fibrates is not well known. In this study, wild-type mice were first fed gemfibrozil-containing diets (0.375%, 0.75% and 1.5%) for 14 days to establish a dose-response relationship for CYP induction. Then, wild-type mice and Pparα-null mice were treated with a 0.75% gemfibrozil-containing diet for 7 days. CYP3a, CYP2b and CYP2c were induced in a dose-dependent manner by gemfibrozil. In Pparα-null mice, their mRNA level, protein level and activity were induced more than those in wild-type mice. So, gemfibrozil induced CYP, and this action was inhibited by activated PPARα. These data suggested that the induction potential of CYPs was suppressed by activated PPARα, showing a potential role of this receptor in drug-drug interactions and metabolic diseases treated with fibrates.

Dual action of peroxisome proliferator-activated receptor alpha in perfluorodecanoic acid-induced hepatotoxicity.

Perfluorodecanoic acid (PFDA) is widely used in production of many daily necessities based on their surface properties and stability. It was assigned as a Persistent Organic Pollutant in 2009 and became a public concern partly because of its potential for activation of the peroxisome proliferator-activated receptor alpha (PPARα). In this study, wild-type and Ppara-null mice were administered PFDA (80 mg/kg). Blood and liver tissues were collected and subjected to systemic toxicological and mechanistic analysis. UPLC-ESI-QTOFMS-based metabolomics was used to explore the contributing components of the serum metabolome that led to variation between wild-type and Pparα-null mice. Bile acid homeostasis was disrupted, and slight hepatocyte injury in wild-type mice accompanied by adaptive regulation of bile acid synthesis and transport was observed. The serum metabolome in wild-type clustered differently from that in Pparα-null, featured by sharp increases in bile acid components. Differential toxicokinetic tendency was supported by regulation of UDP-glucuronosyltransferases dependent on PPARα, but it did not contribute to the hepatotoxic responses. Increase in Il-10 and activation of the JNK pathway indicated inflammation was induced by disruption of bile acid homeostasis in wild-type mice. Inhibition of p-p65 dependent on PPARα activation by PFDA stopped the inflammatory cascade, as indicated by negative response of Il-6, Tnf-α, and STAT3 signaling. These data suggest disruptive and protective role of PPARα in hepatic responses induced by PFDA.

PPARα-dependent increase of mouse urine output by gemfibrozil and fenofibrate.

While gemfibrozil and fenofibrate are prescribed for anti-dyslipidemia treatment, a rational basis for the use of these drugs for treatment of dyslipidemia with concurrent metabolic syndrome has not been established. In this study, wild-type and Pparα-null mice were fed gemfibrozil- or fenofibrate-containing diets for 14 days. Urine output (24 h) was monitored, and urine, serum, and liver and kidney tissues were subjected to toxicity assessment. A 2-month challenge followed by a 2-week wash-out was performed for gemfibrozil to determine urine output and the potential toxicity. A therapeutically equivalent dose of gemfibrozil was more effective than fenofibrate in increasing urine output. This regulatory effect was not observed in Pparα-null mice. In contrast, hepatomegaly induced by fenofibrate was more pronounced than that of gemfibrozil. No significant toxicity was observed in liver or kidney in the 2-month treatment with gemfibrozil. These data demonstrated PPARα mediates the increased urine output by fibrates. Considering the relative action on hepatomegaly and the regulatory effect on urine output, gemfibrozil may be the preferable drug to increase urine output. These results revealed a new pharmacodynamic effect of clinically prescribed PPARα agonists and suggested the potential value of gemfibrozil in modification of blood pressure.