Immunotherapy has emerged as a promising approach in the management of cancer. However, the suboptimal efficacy of immunotherapy monotherapy underscores the need to develop more effective combination strategies. In this study, we focused on PSMD1 to investigate its role and the molecular pathways by which it regulates the response to immunotherapy in hepatocellular carcinoma (HCC). In HCC, elevated PSMD1 levels are linked to associated with poor prognosis. PSMD1 was predominantly expressed in malignant epithelial cells. Tissue microarray results showed that PSMD1 was highly expressed in tumor tissues. Silencing PSMD1 suppressed HCC cell proliferation and promoted apoptosis in both in vitro and in vivo models. Additionally, PSMD1 suppression decreased PD-L1 expression, thereby enhancing the therapeutic efficacy of anti-PD-1 therapy. Mechanistically, publicly available single-cell RNA sequencing (scRNA-seq) datasets indicated that PSMD1 positively regulates β-catenin signaling. Silencing of PSMD1 decreased the expression of β-catenin pathway-associated proteins. Further analysis via mass spectrometry revealed that PSMD1 interacts with Rhotekin (RTKN) and suppresses its ubiquitination. Subsequent experiments revealed that RTKN enhances β-catenin expression through AKT phosphorylation, thereby increasing PD-L1 transcription. In summary, our findings demonstrate that PSMD1 regulates RTKN protein expression, whereas RTKN facilitates β-catenin expression via AKT phosphorylation. This mechanism contributes to HCC progression and the effectiveness of immunotherapy. The PSMD1/RTKN/β-catenin axis could serve as a promising therapeutic target for HCC.

BACKGROUND IQGAP1 has been identified as a key regulator of tumor progression in multiple malignancies, although its specific role and molecular mechanism in cholangiocarcinoma (CCA), the second most common primary hepatobiliary malignancy, remain largely uncharacterized. The Nrf2/KEAP1 pathway is the core axis regulating cellular oxidative stress, which is closely associated with CCA development and therapeutic resistance. AIM To define the oncogenic role and mechanism of IQGAP1 in CCA. METHODS We screened differentially expressed genes in CCA via bioinformatic analysis of four public datasets (The Cancer Genome Atlas, GSE107943, GSE26566, GSE76297). A series of in vitro functional assays were performed to evaluate IQGAP1’s biological function in CCA cells. Co-IP-MS, molecular docking, ubiquitination assays and Western blot explored underlying molecular mechanisms. In vivo nude mouse xenograft models validated the oncogenic role of IQGAP1/MCM3/Nrf2 axis. RESULTS IQGAP1 was significantly upregulated in CCA tissues and cell lines, and its high expression promoted the proliferation, migration, and anti-apoptotic ability of CCA cells in vitro , as well as tumor growth in vivo . Mechanistically, IQGAP1 recruited the deubiquitinase ubiquitin-specific peptidase 28 to directly interact with MCM3, reducing K48-linked ubiquitination and degradation of MCM3 protein, thereby stabilizing MCM3 expression. Upregulated MCM3 competitively bound to KEAP1, blocking the interaction between KEAP1 and Nrf2, which in turn inhibited Nrf2 ubiquitination, activated the Nrf2 antioxidant pathway, alleviated intracellular oxidative stress, and suppressed CCA cell apoptosis. Rescue experiments confirmed that MCM3 knockdown completely reversed the oncogenic phenotypes induced by IQGAP1 overexpression. CONCLUSION We first demonstrate IQGAP1 drives CCA progression via post-translationally stabilizing MCM3/Nrf2 axis, serving as a promising prognostic biomarker and therapeutic target with novel mechanistic insights into CCA pathogenesis.

High heterogeneity and poor therapeutic response are major challenges in hepatocellular carcinoma (HCC). This study aimed to identify core hepatocyte subsets and key genes driving HCC progression via a multi-omics approach to inform precise diagnosis and treatment. We analyzed public data from GEO (GSE282701), TCGA-LIHC, and IEU-Open-GWAS (bbj-a-158). scRNA-seq, scPagwas, BayesPrism, and WGCNA were used to identify core HCC cell subsets and genes. SF3B4 expression was validated by Western blot and RT-qPCR in HCC tissues and cell lines. Functional impacts on proliferation and migration were assessed using colony formation, Transwell, and wound healing assays in HepG2 and Huh7 cells. Integrated scRNA-seq and scPagwas analysis identified PKHD1⁺ hepatocytes as a core HCC subset, showing significantly higher trait relevance scores versus other subtypes and a positive correlation with HCC (P < 0.05). BayesPrism quantification in the TCGA-LIHC cohort confirmed that high abundance of PKHD1⁺ hepatocytes correlated with poor prognosis (P < 0.05), immune microenvironment remodeling (increased CAFs and MDSCs), and distinct somatic mutation profiles (elevated CTNNB1 and reduced TP53 mutation rates). SF3B4 was identified as the key gene associated with this subset via WGCNA and differential expression analysis. SF3B4 was upregulated in HCC tissues and cells, and its knockdown suppressed proliferation and migration in HepG2 and Huh7 cells. The PKHD1⁺ hepatocyte subset and SF3B4 represent key regulators of HCC malignancy, offering novel potential targets for prognostic assessment and targeted therapy.

Intrahepatic cholangiocarcinoma (ICC) is a highly aggressive malignancy with limited treatment options, underscoring the need to identify new regulatory molecules. Through high-throughput sequencing of five paired ICC and adjacent normal tissues, we identified a significantly upregulated circular RNA, circ_0057105, which is further validated in ICC cell lines and clinical samples. A correlation between elevated circ_0057105 expression and poor prognosis in ICC patients was observed based on clinical data. Functional in vitro and in vivo assays demonstrated that circ_0057105 promotes ICC cell proliferation, migration, and invasion. Mechanistically, circ_0057105 functions as a competing endogenous RNA (ceRNA) that sponges miR-1290, leading to the upregulation of MDM2. This subsequently enhances MDM2-mediated ubiquitination and degradation of the tumor suppressor p53, thereby inhibiting the p53 signaling pathway. Furthermore, in vivo delivery of small interfering RNA targeting circ_0057105 (si-circ_0057105) encapsulated in lipid nanoparticles (LNPs) effectively suppressed its expression and showed promising therapeutic efficacy. In conclusion, our study demonstrates that circ_0057105 drives ICC progression via the miR-1290-MDM2-p53 axis, establishing it as a valuable prognostic biomarker and a promising therapeutic target. The delivery of siRNA targeting circ_0057105 via lipid nanoparticles represents a potential translational strategy.

Current evidence indicates that circRNAs are involved in the development of multiple malignancies including hepatocellular carcinoma (HCC). However, the specific functions of circRNAs in HCC metabolism and progression and their underlying regulatory mechanisms remain unclear. We have identified a novel circRNA circMFN2, by bioinformatics analysis of circRNA microarray data from the GEO database. The levels of circMFN2 were assessed in HCC cell lines and tissues, and its clinical relevance was assessed. The effect of circMFN2 on HCC cells was evaluated in vitro and in vivo. The effect of ELK1 on glutaminolysis and HCC progression was also explored. Patients with HCC and high circMFN2 expression exhibited worse survival outcomes. Functionally, downregulation of circMFN2 repressed the proliferation, invasion, and migration of HCC cells in vitro, whereas ectopic expression of circMFN2 had the opposite effects. The effects of tumor enhancement by circMFN2 on HCC were confirmed by in vivo experiments. Mechanistically, circMFN2 acted as a sponge for miR-361-3p, leading to the upregulation of its target ELK1, whereas ELK1 was enriched in the MFN2 promoter to enhance the transcription and expression of MFN2, indirectly leading to the upregulation of circMFN2. Additionally, we found that circMFN2 promotes glutaminolysis in HCC by increasing ELK1 phosphorylation. We concluded that circMFN2 facilitates HCC progression via a circMFN2/miR-361-3p/ELK1 feedback loop, which promotes glutaminolysis mediated by the upregulation of phosphorylated ELK1. Therefore, circMFN2 not only serves as a potential prognostic indicator, but it could also serve as a therapeutic target for HCC. Further studies are warranted.

Hepatocellular carcinoma (HCC), a leading cause of cancer mortality, faces diagnostic and therapeutic challenges due to its histopathological complexity and clinical heterogeneity. Pathomics, an emerging discipline that integrates artificial intelligence (AI) with quantitative pathology image analysis, aims to decode disease heterogeneity by extracting high-dimensional features from histopathological specimens. This review highlights how AI-driven pathomics has revolutionized liver cancer management through automated analysis of whole-slide images. Pathomics integrates deep learning with histopathological features to enable precise tumour classification (e.g., HCC vs cholangiocarcinoma), microvascular invasion (MVI) detection, recurrence risk stratification, and survival prediction. Advanced frameworks such as MVI-AI diagnostic model and CHOWDER demonstrate high accuracy in identifying prognostic biomarkers, whereas multiomics integration links morphometric patterns to molecular signatures (e.g., EZH2 expression and immune infiltration). Despite these breakthroughs, critical bottlenecks persist, including limited multicentre validation studies, "black box" model interpretability, and clinical workflow integration. Future studies should emphasize AI-enhanced multimodal fusion (radiogenomics and liquid biopsy) and standardized platforms to bridge computational pathology and precision oncology, ultimately improving personalized therapeutic strategies for liver malignancies. This synthesis aims to guide research translation and advance personalized therapeutic strategies for liver malignancies.

BACKGROUND:Numerous studies have indicated that circular RNAs (circRNA) are involved in the regulation of various malignant tumors, including intrahepatic cholangiocarcinoma (ICC). However, the exact role of circRNA in the progression of ICC and their underlying regulatory mechanisms remain to be further elucidated. METHODS:We investigated the dysregulation of circRNA expression profiles in five pairs of ICC tissues and adjacent normal tissues through high-throughput sequencing, revealing a significant upregulation of circ_0084927. Next, the levels of circ_0084927 in ICC cell lines and tissues were assessed by qRT-PCR. Then, we analyzed the relationship between circ_0084927 and the prognosis of ICC based on clinical indicators. Then, we investigated the effects by constructing circ_0084927 knockdown and overexpression ICC cell lines in vitro and in vivo. Subsequently, bioinformatics analysis and mechanistic experiments were used to explore the downstream regulatory mechanisms of circ_0084927. Exosomes isolated from gemcitabine-resistant ICC cell lines were used to assess the relationship between exosomal circ_0084927 and gemcitabine resistance. RESULTS:Circ_0084927 is significantly upregulated in ICC cell lines and tissues. High levels of circ_0084927 expression is associated with poorer prognosis in ICC patients. Functionally, circ_0084927 notably promotes ICC proliferation, invasion, and migration while inhibiting apoptosis in vitro and in vivo. Mechanistically, circ_0084927 upregulates its downstream target PDPK1 by competitively binding with miR-4725-5p, thereby activating the downstream AKT/mTOR signaling pathway. Moreover, circ_0084927 can be transferred from gemcitabine-resistant cells to sensitive cells via exosomes, leading to the induction of drug resistance in the recipient cells previously sensitive to gemcitabine. CONCLUSION:Circ_0084927 promotes the expression of PDPK1 by sponging miR-4725-5p, activating the downstream AKT/mTOR signaling pathway. Additionally, circ_0084927 mediates the transfer of gemcitabine resistance in ICC cells through exosomes. Therefore, circ_0084927 not only serves as a promising prognostic indicator but also as a viable therapeutic target for ICC.

To develop and validate an MRI-based fusion model for preoperative prediction of perineural invasion (PNI) status in patients with intrahepatic cholangiocarcinoma (ICC). A retrospective collection of 192 ICC patients from three medical centers (training set: n = 147; external test set: n = 45) was performed. Patients were classified into the PNI-positive and PNI-negative groups based on postoperative pathological results. After image preprocessing, a total of 1,197 features were extracted from T2-weighted imaging (T2WI). Feature selection was performed, and a radiomics model was constructed using machine learning algorithms, followed by SHapley Additive exPlanations (SHAP) visualization. Subsequently, a deep learning model was constructed based on the pre-trained ResNet101, with Gradient-weighted Class Activation Mapping (Grad-CAM) used for visualization. Finally, a fusion model incorporating deep learning, radiomics, and clinical features was developed using logistic regression, and visualization was performed with a nomogram. The predictive performance of the model was evaluated based on the area under the curve (AUC), calibration curves, and decision curve analysis (DCA). The fusion model, which integrates deep learning signature, radiomics signature, and two clinical features, demonstrated strong discrimination for PNI status. In the training set, the AUC was 0.905, with an accuracy of 0.823; in the external test set, the AUC was 0.760, with an accuracy of 0.778. Visualization methods provided support for the practical application of the model. The fusion model aids in the preoperative identification of PNI status in patients with ICC, and may help guide clinical decision-making regarding preoperative staging and adjuvant therapy.

BackgroundThis study aims to introduce the concept of habitat subregions and construct an accurate prediction model by analyzing refined medical images, to predict lymph node metastasis (LNM) in patients with intrahepatic cholangiocarcinoma (ICC) before surgery, and to provide personalized support for clinical decision-making.MethodsClinical, radiological, and pathological data from ICC patients were retrospectively collected. Using information from the arterial and venous phases of multisequence CT images, tumor habitat subregions were delineated through the K-means clustering algorithm. Radiomic features were extracted and screened, and prediction models based on different subregions were constructed and compared with traditional intratumoral models. Finally, a lymph node metastasis prediction model was established by integrating the features of several subregional models, and its performance was evaluated.ResultsA total of 164 ICC patients were included in this study, 103 of whom underwent lymph node dissection. The patients were divided into LNM- and LNM + groups on the basis of lymph node status, and significant differences in white blood cell indicators were found between the two groups. Survival analysis revealed that patients with positive lymph nodes had significantly worse prognoses. Through cluster analysis, the optimal number of habitat subregions was determined to be 5, and prediction models based on different subregions were constructed. A comparison of the performance of each model revealed that the Habitat1 and Habitat5 models had excellent performance. The optimal model obtained by fusing the features of the Habitat1 and Habitat5 models had AUC values of 0.923 and 0.913 in the training set and validation set, respectively, demonstrating good predictive ability. Calibration curves and decision curve analysis further validated the superiority and clinical application value of the model.ConclusionsThis study successfully constructed an accurate prediction model based on habitat subregions that can effectively predict the lymph node metastasis of ICC patients preoperatively. This model is expected to provide personalized decision support to clinicians and help to optimize treatment plans and improve patient outcomes.

Colorectal cancer (CRC) progression is driven by diverse molecular mechanisms, underscoring the urgent need for novel therapeutic strategies, especially for liver metastases. Through an integrated analysis of multiple single-cell RNA sequencing databases, zinc finger protein-like 1 (ZFPL1) is identified as a gene specifically enriched in malignant cells from both primary and metastatic CRC. Multi-omics investigations demonstrate that high ZFPL1 expression correlates with aggressive clinicopathological features and poor survival. Functionally, ZFPL1 promotes tumor proliferation, invasion, and migration both in vivo and in vitro. Mechanistically, ZFPL1 directly binds argininosuccinate synthase 1 (ASS1), shielding its K57 residue from tripartite motif containing 33-mediated ubiquitination to prevent proteasomal degradation. This stabilization activates urea cycle metabolism, driving CRC progression. Crucially, ZFPL1 deficiency remodels the tumor microenvironment by reducing immunosuppressive populations-M2 macrophages, and promoting pro-inflammatory M1 polarization. Virtual screening identifies Salvianolic acid B (Sal B) as a ZFPL1 inhibitor, which disrupts ZFPL1-ASS1 binding, triggering ASS1 ubiquitination and degradation. In vivo, Sal B synergized with anti-PD-1 therapy, significantly reducing tumor burden versus monotherapy. These findings establish ZFPL1 as a key regulator of CRC progression through ASS1-dependent urea cycle activation and immunomodulation, nominating the ZFPL1-ASS1 axis as a therapeutic target, with Sal B demonstrating combinatorial potential with immunotherapy.

Background To develop and validate a computed tomography (CT) image-based deep learning radiomics model (DLRAD) for preoperative prediction of MVI in ICC patients, and to validate its relationship with prognosis. Methods A total of 165 ICC patients were recruited from two centers for retrospective study. Based on the radiomics and deep learning features of arterial phase CT images, dozens of models were constructed and compared using four machine learning methods. The incremental value of different sizes of peritumoral regions to the model was also explored. The performance of the model was evaluated using the area under the curve (AUC), calibration curve and decision curve. Kaplan-Meier curve was used to analyze the relationship between the model prediction results and prognosis. Results According to the radiomics features in the intratumoral and 2mm peritumoral regions and the deep learning features in the tumor, the DLRAD model constructed by the LR method showed the best discrimination ability for MVI. The AUC of the internal validation cohort was 0.86. The AUC of the external validation cohort was 0.89. In addition, the MVI predicted by the model was significantly correlated with the overall survival rate of patients (P = 0.005), which was consistent with the actual situation. Conclusion The DLRAD model constructed by radiomics and deep learning technology can effectively predict MVI in ICC patients. This provides clinicians with a powerful tool to help them make more accurate treatment decisions.

BackgroundNormal hepatic functional reserve is the key to avoiding liver failure after liver surgery. This study investigated the assessment of hepatic functional reserve using liver shear wave velocity (LSWV) combined with biochemical indicators, tumor volume, and portal vein diameter.MethodsIn this single-center prospective study, a total of 123 patients with hepatocellular carcinoma (HCC) were divided into a test group (n=92) and a validation group (n=31). All patients were Child-Pugh grade A. The indocyanine green retention rate at 15 min (ICG-R15), liver shear wave velocity (LSWV), portal vein diameter (Dpv), alanine aminotransferase (ALT), aspartate transaminase (AST), alkaline phosphatase (ALP), γ-glutamyl transpeptidase (γ-GGT), albumin (ALB), prothrombin time (PT), and also liver tumor volume (maximum diameter ≤5 cm) were measured. In the test group, multiple parameters were used to evaluate hepatic functional reserve, and the multiparametric model was established. Receiver operating characteristic (ROC) curve analysis was conducted to assess the diagnostic performance of the multiparametric model. In the validation group, the predictive effectiveness of the multiparametric model was analyzed using consistency tests.ResultsIt was revealed that LSWV, ALB, and PT were statistically significant in evaluation of the hepatic functional reserve (P<0.05). The multiparametric model was formulated as follows: Y= -18.954 + 9.726*LSWV-0.397*ALB+2.063*PT. The value of the area under the curve (AUC) for the multiparametric model was 0.913 (95% confidence interval (CI): 0.835-0.962, P< 0.01), with a cutoff value of 16.656 (sensitivity, 0.763; specificity, 0.926). The Kappa value of consistency testing was 0.655 (P<0.01).ConclusionLSWV combined with ALB and PT exhibited a high predictive effectiveness for the assessment of hepatic functional reserve, assisting the clinical diagnosis and management of liver diseases.

BackgroundIntrahepatic cholangiocellular carcinoma (ICC) is one of the most common invasive malignancies. Currently, ICC is treated with radical surgical resection. However, the majority of patients are diagnosed at an advanced stage, making surgery ineligible for them.Case presentationWe present a case of advanced ICC, which could not undergo radical surgery due to tumor invasion of liver blood vessels. The gemcitabine and oxaliplatin (GEMOX) regimen combined with Tislelizumab immunotherapy and Lenvatinib targeted therapy for 8 cycles resulted in significant tumor shrinkage significantly and the vascular invasion disappeared. CA19–9 levels were reduced to normal levels. Partial remission and successful tumor transformation were achieved. The patient underwent a successful radical surgical resection, including cholecystectomy, resection of liver segments IV, V, and VIII, as well as a regional lymphatic dissection procedure, resulting in complete pathological remission.ConclusionTumor-free surgical margins (R0) resection of patients with advanced ICC after combination of immune, targeted and chemotherapy is rare, and there are almost no cases of complete postoperative remission. The GEMOX regimen in combination with Tislelizumab and Lenvatinib has a good antitumor efficacy and safety profile, and may be a feasible and safe translational treatment option for advanced ICC.

Hepatocellular carcinoma (HCC) is one of the most common aggressive tumors in the world. Despite the availability of various treatments, its prognosis remains poor due to the lack of specific diagnostic indicators and the high heterogeneity of HCC cases. CircRNAs are noncoding RNAs with stable and highly specific expression. Extensive research evidence suggests that circRNAs mediate the pathogenesis and progression of HCC through acting as miRNA sponges, protein modulators, and translation templates. Tumor microenvironment (TME) has become a hotspot of immune-related research in recent years due to its effects on metabolism, secretion and immunity of HCC. Accordingly, understanding the role played by circRNAs in TME is important for the study of HCC. This review will discuss the crosstalk between circRNAs and TME in HCC. In addition, we will discuss the current deficiencies and controversies in research on circRNAs and predict future research directions.

Background Intrahepatic cholangiocarcinoma (ICC) is a highly malignant tumor with a poor prognosis. This study aimed to investigate whether Hemoglobin, Albumin, Lymphocytes, and Platelets (HALP) score and Tumor Burden Score (TBS) serves as independent influencing factors following radical resection in patients with ICC. Furthermore, we sought to evaluate the predictive capacity of the combined HALP and TBS grade, referred to as HTS grade, and to develop a prognostic prediction model. Methods Clinical data for ICC patients who underwent radical resection were retrospectively analyzed. Univariate and multivariate Cox regression analyses were first used to find influencing factors of prognosis for ICC. Receiver operating characteristic (ROC) curves were then used to find the optimal cut-off values for HALP score and TBS and to compare the predictive ability of HALP, TBS, and HTS grade using the area under these curves (AUC). Nomogram prediction models were constructed and validated based on the results of the multivariate analysis. Results Among 423 patients, 234 (55.3%) were male and 202 (47.8) were aged ≥ 60 years. The cut-off value of HALP was found to be 37.1 and for TBS to be 6.3. Our univariate results showed that HALP, TBS, and HTS grade were prognostic factors of ICC patients (all P < 0.05), and ROC results showed that HTS had the best predictive value. The Kaplan–Meier curve showed that the prognosis of ICC patients was worse with increasing HTS grade. Additionally, multivariate regression analysis showed that HTS grade, carbohydrate antigen 19–9 (CA19-9), tumor differentiation, and vascular invasion were independent influencing factors for Overall survival (OS) and that HTS grade, CA19-9, CEA, vascular invasion and lymph node invasion were independent influencing factors for recurrence-free survival (RFS) (all P < 0.05). In the first, second, and third years of the training group, the AUCs for OS were 0.867, 0.902, and 0.881, and the AUCs for RFS were 0.849, 0.841, and 0.899, respectively. In the first, second, and third years of the validation group, the AUCs for OS were 0.727, 0.771, and 0.763, and the AUCs for RFS were 0.733, 0.746, and 0.801, respectively. Through the examination of calibration curves and using decision curve analysis (DCA), nomograms based on HTS grade showed excellent predictive performance. Conclusions Our nomograms based on HTS grade had excellent predictive effects and may thus be able to help clinicians provide individualized clinical decision for ICC patients.

BackgroundBasement membranes (BMs) have recently emerged as significant players in cancer progression and metastasis, rendering them promising targets for potential anti-cancer therapies. Here, we aimed to develop a novel signature of basement membrane-related genes (BMRGs) for the prediction of clinical prognosis and tumor microenvironment in hepatocellular carcinoma (HCC).MethodsThe differentially expressed BMRGs were subjected to univariate Cox regression analysis to identify BMRGs with prognostic significance. A six-BMRGs risk score model was constructed using Least Absolute Shrinkage Selection Operator (LASSO) Cox regression. Furthermore, a nomogram incorporating the BMRGs score and other clinicopathological features was developed for accurate prediction of survival rate in patients with HCC.ResultsA total of 121 differentially expressed BMRGs were screened from the TCGA HCC cohort. The functions of these BMRGs were significantly enriched in the extracellular matrix structure and signal transduction. The six-BMRGs risk score, comprising CD151, CTSA, MMP1, ROBO3, ADAMTS5 and MEP1A, was established for the prediction of clinical prognosis, tumor microenvironment characteristics, and immunotherapy response in HCC. Kaplan-Meier analysis revealed that the BMRGs score-high group showed a significantly shorter overall survival than BMRGs score-low group. A nomogram showed that the BMRGs score could be used as a new effective clinical predictor and can be combined with other clinical variables to improve the prognosis of patients with HCC. Furthermore, the high BMRGs score subgroup exhibited an immunosuppressive state characterized by infiltration of macrophages and T-regulatory cells, elevated tumor immune dysfunction and exclusion (TIDE) score, as well as enhanced expression of immune checkpoints including PD-1, PD-L1, CTLA4, PD-L2, HAVCR2, and TIGIT. Finally, a multi-step analysis was conducted to identify two pivotal hub genes, PKM and ITGA3, in the high-scoring group of BMRGs, which exhibited significant associations with an unfavorable prognosis in HCC.ConclusionOur study suggests that the BMRGs score can serve as a robust biomarker for predicting clinical outcomes and evaluating the tumor microenvironment in patients with HCC, thereby facilitating more effective clinical implementation of immunotherapy.

Abstract Background Tumor morphology, immune function, inflammatory levels, and nutritional status play critical roles in the progression of intrahepatic cholangiocarcinoma (ICC). This multicenter study aimed to investigate the association between markers related to tumor morphology, immune function, inflammatory levels, and nutritional status with the prognosis of ICC patients. Additionally, a novel tumor morphology immune inflammatory nutritional score (TIIN score), integrating these factors was constructed. Methods A retrospective analysis was performed on 418 patients who underwent radical surgical resection and had postoperative pathological confirmation of ICC between January 2016 and January 2020 at three medical centers. The cohort was divided into a training set (n = 272) and a validation set (n = 146). The prognostic significance of 16 relevant markers was assessed, and the TIIN score was derived using LASSO regression. Subsequently, the TIIN-nomogram models for OS and RFS were developed based on the TIIN score and the results of multivariate analysis. The predictive performance of the TIIN-nomogram models was evaluated using ROC survival curves, calibration curves, and clinical decision curve analysis (DCA). Results The TIIN score, derived from albumin-to-alkaline phosphatase ratio (AAPR), albumin–globulin ratio (AGR), monocyte-to-lymphocyte ratio (MLR), and tumor burden score (TBS), effectively categorized patients into high-risk and low-risk groups using the optimal cutoff value. Compared to individual metrics, the TIIN score demonstrated superior predictive value for both OS and RFS. Furthermore, the TIIN score exhibited strong associations with clinical indicators including obstructive jaundice, CEA, CA19-9, Child–pugh grade, perineural invasion, and 8th edition AJCC N stage. Univariate and multivariate analysis confirmed the TIIN score as an independent risk factor for postoperative OS and RFS in ICC patients (p < 0.05). Notably, the TIIN-nomogram models for OS and RFS, constructed based on the multivariate analysis and incorporating the TIIN score, demonstrated excellent predictive ability for postoperative survival in ICC patients. Conclusion The development and validation of the TIIN score, a comprehensive composite index incorporating tumor morphology, immune function, inflammatory level, and nutritional status, significantly contribute to the prognostic assessment of ICC patients. Furthermore, the successful application of the TIIN-nomogram prediction model underscores its potential as a valuable tool in guiding individualized treatment strategies for ICC patients. These findings emphasize the importance of personalized approaches in improving the clinical management and outcomes of ICC.

Background:The degree of inflammation and immune status is widely recognized to be associated with intrahepatic cholangiocarcinoma (ICC) and is closely linked to poor postoperative survival. The purpose of this study was to evaluate whether the systemic immune-inflammatory index (SII) and the albumin bilirubin (ALBI) grade together exhibit better predictive strength compared to SII and ALBI separately in patients with ICC undergoing curative surgical resection.Methods:A retrospective analysis was performed on a cohort of 374 patients with histologically confirmed ICC who underwent curative surgical resection from January 2016 to January 2020 at three medical centers. The cohort was divided into a training set comprising 258 patients and a validation set consisting of 116 patients. Subsequently, the prognostic predictive abilities of three indicators, namely SII, ALBI, and SII+ALBI grade, were evaluated. Independent risk factors were identified through univariate and multivariate analyses. The identified independent risk factors were then utilized to construct a nomogram prediction model, and the predictive strength of the nomogram prediction model was assessed through Receiver Operating Characteristic (ROC) survival curves and calibration curves.Results:Univariate analysis of the training set, consisting of 258 eligible patients with ICC, revealed that SII, ALBI, and SII+ALBI grade were significant prognostic factors for overall survival (OS) and recurrence-free survival (RFS) (p < 0.05). Multivariate analysis revealed the independent significance of SII+ALBI grade as a risk factor for postoperative OS and RFS (p < 0.05). Furthermore, we conducted an analysis of the correlation between SII, ALBI, SII+ALBI grade, and clinical features, indicating that SII+ALBI grade exhibited stronger associations with clinical and pathological characteristics compared to SII and ALBI. We constructed a predictive model for postoperative survival in ICC based on SII+ALBI grade, as determined by the results of multivariate analysis. Evaluation of the model's predictive strength was performed through ROC survival curves and calibration curves in the training set and validation set, revealing favorable predictive performance.Conclusion:The SII+ALBI grade, a novel classification based on inflammatory and immune status, serves as a reliable prognostic indicator for postoperative OS and RFS in patients with ICC.

Objective:A predictive nomogram model for the prognosis of intrahepatic cholangiocarcinoma (ICC) patients after curative resection was constructed based on the albumin-bilirubin score and tumor burden score (ATS) grade, and the predictive performance of the nomogram model was evaluated.Methods:Retrospective analysis of clinical data was made, from ICC patients who underwent curative resection at Zhengzhou University People's Hospital and Zhengzhou University Cancer Hospital from January 2016 to January 2020. A total of 258 patients were included in the study, with 140 males and 118 females, with an average age of (56.5±9.5) years. The 258 ICC patients were randomly divided into a training set ( n=174) and a testing set ( n=84) in a 7∶3 ratio. Single-factor and multi-factor Cox regression analyses were performed to identify prognostic factors for ICC patients of the training set, and then a nomogram model was constructed. The performance of the nomogram model was evaluated by using the concordance index (C-index), calibration curve, and risky decision curve analysis. Results:In the training set, univariate Cox regression analysis indicated that albumin-bilirubin (ALBI), tumor burden score (TBS), carcinoembryonic antigen (CEA), tumor differentitation, lymphvascular invasion and ATS significantly influenced overall survival after radical resection for ICC (all P<0.05). Multifactorial Cox regression analysis revealed that ATS grade, CEA, tumor differentiation, lymphovascular invasion, and AJCC N stage are independent risk factors for the prognosis of ICC patients after curative resection (all P<0.05). Assessment of the postoperative survival prediction model based on multifactorial Cox regression yielded a C-index of 0.775(95% CI: 0.747-0.841) for the training set and 0.731(95% CI: 0.668-0.828) for the testing set. The calibration curves for both the training and testing sets indicated strong predictive capability of the model. Additionally, the risk decision curve also suggested high net benefit of the model. Conclusions:The preoperative ATS grade is an independent factor affecting the survival after ICC radical resection. The nomogram model constructed based on ATS grade demonstrates excellent predictive value for postoperative prognosis in ICC patients.