Severe acute pancreatitis (SAP) is characterized by excessive inflammatory responses that critically influence disease severity and systemic complications. Although innate immune activation has been extensively studied in SAP, the contribution of adaptive humoral immunity to inflammatory modulation during disease progression remains incompletely understood. In this study, we performed longitudinal single-cell RNA sequencing of peripheral blood mononuclear cells from patients with mild and severe acute pancreatitis to characterize immune alterations across disease stages. We observed dynamic immune remodeling, including increased engagement of B cell–associated humoral immune programs during later stages of severe disease. Among these changes, expression of the endoplasmic reticulum–resident protein MZB1 was prominently associated with plasma cell differentiation and immunoglobulin-related transcriptional activity. To investigate the functional relevance of MZB1, we employed genetic mouse models of experimental pancreatitis. Loss of MZB1 was associated with exacerbated pancreatic inflammation, impaired IgA production, and compromised intestinal barrier integrity. Together, these findings suggest that MZB1-associated humoral immune responses contribute to modulation of inflammatory severity in severe acute pancreatitis by supporting IgA-dependent intestinal barrier integrity and limiting secondary macrophage-driven inflammation.

Severe acute pancreatitis (SAP) is a complex inflammatory disorder with severe immune imbalance. This study investigates the therapeutic potential of extracellular vesicles derived from human adipose mesenchymal stem cells (hADSC-EVs) in modulating Treg differentiation and alleviating SAP. We conducted a phosphoproteomics analysis to evaluate phosphorylation levels, and administered hADSC-EVs in a mouse model of SAP and assessed their impact on Treg differentiation. Phosphoproteomics revealed a significant increase in p-STK3 following hADSC-EVs treatment, restoring Foxp3 level diminished by STK3 knockdown. HADSC-EVs promoted Treg differentiation in a concentration-dependent manner by targeting Foxp3 transcription. In the SAP mouse model, hADSC-EVs improved survival rates and mitigated histopathological alterations. In conclusion, our study revealed that STK3 effectively promotes Treg differentiation and enhances their immunosuppressive capabilities, thereby ameliorating inflammation and attenuating the pathological phenotypes associated with SAP. These findings provide valuable insights into the potential role of hADSC-EVs in regulating immune responses and promoting tissue repair.

Predicting thyroid cancer (TC) and its accompanying metastasis remains challenging because of the complexity of the disease and its diverse pathological subtypes. The complexity and variability of TC necessitate innovative approaches that leverage machine learning (ML) and deep learning (DL) for enhanced accuracy in diagnosis and lymph node metastasis (LNM) prediction. 803 patients were examined using ultrasound for thyroid nodules, inculing 369 benign nodules, and 433 malignant nodules, of which 118 were LNM. All patients were diagnosed by pathologists based on preoperative and postoperative specimens. Transfer learning was used to extract the features of ultrasound two dimensional images. Using images and matched clinical characteristics, we incorporated two-dimensional imaging features and tabular clinical features to predict both malignancy and LNM. We considered several ML models, including a random forest classifier, gradient boost classifier, XGB classifier, Naïve Bayes, and multilayer perceptron. The area under the receiver operating characteristic curve was used to benchmark model performance. The AI model demonstrated notable diagnostic precision, achieving AUROC scores of 0.82 for TC diagnosis and 0.78 for LNM prediction. Crucially, the prediction model demonstrated a synergistic performance when both clinical characteristics and ultrasonographic images were combined, underscoring the complementary strengths of each data type in improving diagnostic accuracy. It should be noted that TI-RADS was significantly correlated with both malignancy and LNM. However, the optimized model was a gradient-boosting model with 12 features and a cross-validation AUROC of 0.82, indicating that imaging features can appreciably summarize the details of the ultrasonographic images for these patients. This study delves into the nuanced integration of imaging and clinical data through the advanced ML, encompassing both traditional ML techniques and DL, to enhance the diagnostic precision and therapeutic strategies for TC.

In acute pancreatitis (AP), the release of mitochondrial DNA (mtDNA) from pancreatic acinar cells (PACs) plays a pivotal role in triggering a lethal systemic inflammatory response. Despite the importance of mtDNA release, the regulatory mechanisms upstream of this event remain poorly understood, hindering the development of targeted therapeutic strategies. To address this, we utilized single-cell RNA sequencing, CUT Tag, luciferase reporter assays, and experiments in a PAC-specific knockout mouse model to investigate the transcriptional program governing vesicle transport and mtDNA release in the context of AP. Our analysis revealed that vesicle transport pathways were activated in AP PACs and identified Runx1 as a core transcriptional regulator. We discovered that Runx1 directly binds and activates the Snx9 promoter. This interaction initiates a pathological cascade wherein Runx1-Snx9 signaling drives mitochondrial fragmentation and the biogenesis of intracellular mitochondrial-derived vesicles (MDVs). Under AP conditions, these MDVs are diverted from degradative pathways and routed to the secretory machinery to be released as pathogenic, extracellular mitochondrial-derived vesicles (Ex-MDVs). These Ex-MDVs were confirmed to be highly pathogenic, strongly activating the cGAS-STING pathway in macrophages. Notably, PAC-specific deletion of Runx1 in a mouse model significantly mitigated pancreatic injury and suppressed the systemic inflammatory storm associated with AP. This study is the first to elucidate the Runx1-Snx9 transcriptional axis as the core upstream mechanism responsible for the anomalous generation and secretion of Ex-MDVs from PACs during AP, providing novel insights into AP pathogenesis and identifying this axis as a potential therapeutic target.

Background:In recent years, artificial intelligence (AI) technology has experienced significant growth, leading to the development of advanced tools that assist radiologists in image interpretation and diagnostic decision-making. In the field of medical image processing, object detection and segmentation are crucial research areas. Achieving automatic and rapid segmentation of organs and lesions can significantly enhance physicians' work efficiency. However, most existing networks feature complex architectures and entail high computational complexity. Due to the limited processing power of the devices, achieving real-time segmentation often proves challenging. Therefore, the aim of this study was to design a lightweight segmentation algorithm that reduces resource consumption and enhances real-time performance. Methods:Firstly, we propose a compact U-shaped network called the weak redundancy U-Net (WRU-Net) specifically designed for real-time segmentation tasks in medical imaging. By reducing the number of channels, feature redundancy across all scales is reduced, compelling the network to utilize resources efficiently at every level. Furthermore, we propose "auxiliary information flows" to facilitate the propagation of phased results, thereby enhancing the decoder. Secondly, this paper introduces a novel visual prompt mode that differs from the standard object detection mode. We refer to this as "object prompt", which means visualizing the position of the target object in an image to guide the viewer. Unlike standard object detection tasks that provide bounding boxes, this paper achieves the aforementioned effect in the form of heatmaps. Correspondingly, we propose a network for heatmap prediction, which further simplifies task complexity and achieves semantic detection of the target object. Results:We conducted experiments using the datasets of the thyroid nodule, chest, placental vessel, brain tumors, heart, liver, and spleen, which include medical images of X-ray, computed tomography (CT), and ultrasound. Furthermore, we conducted comparative experiments using several state-of-the-art networks. The segmentation performance of the networks was evaluated using metrics such as the Dice similarity coefficient (DSC), intersection over union (IoU), precision, and recall. The average DSC, IoU, precision, and recall of our model across each dataset were 88.53%, 82.58%, 86.34%, and 84.85%, respectively. Regarding efficiency, our WRU-Net achieved 130.67 KB model size and 488 frames per second (FPS), outperforming larger models. For heatmap prediction, our network exhibited a similarly efficient profile with a parameter size of only 24.75 KB and a speed of 9,862.13 FPS on graphics processing unit (GPU) and 107.93 FPS on central processing unit (CPU). Conclusions:In scenarios where the calculation ability of the equipment is constrained or real-time performance requirements are stringent, the design of lightweight networks serves as a fundamental approach to achieving high-speed data stream processing functionality. For instance, this applies to embedded devices and real-time ultrasonic scanning applications. The method proposed in this paper significantly reduces the number of parameters, thereby decreasing computational resource consumption and enhancing speed. This improvement holds substantial significance for optimizing computational efficiency in practical applications.

BACKGROUND:Thyroid cancer is one of the most common endocrine tumors worldwide, especially among women and the metastatic mechanism of papillary thyroid carcinoma remains poorly understood. METHODS:Thyroid cancer tissue samples were obtained for single-cell RNA-sequencing and spatial transcriptomics, aiming to intratumoral and antimetastatic heterogeneity of advanced PTC. The functions of APOE in PTC cell proliferation and invasion were confirmed through in vivo and in vitro assays. Pseudotime analysis and CellChat were performed to explore the the molecular mechanisms of the APOE in PTC progression. RESULTS:We identified a subpopulation of tumor cells with lower expression levels of APOE, associated with advanced stages of PTC and cervical metastasis. APOE overexpression significantly reduced tumor cell proliferation and invasion, both in vitro and in vivo, by activating the ABCA1-LXR axis. APOE- tumor cells may promote tumor growth by interacting with dendritic cells and CD4+ T cells via CD99- rather than CD6-regulated signaling. We established a machine learning-based scRNA-seq data, 13-gene signature predictive of lymph node metastasis. CONCLUSIONS:We identified a distinct APOE- tumor cell population associated with cervical metastasis and poor prognosis. Our results and models have potential clinical, prognostic, and therapeutic implications for advanced PTC. KEY POINTS:A subpopulation of tumor cells with lower expression levels of APOE was strongly associated with more advanced stages and metastasis of PTC. APOE-negative (APOE-) cellsoverall exhibited weaker interactions with immune cells. A machine-learning bioinformatics model based on scRNA-seq data of in-situ thyroid cancer tissue was established to predict lymph node metastasis.

Effective early prediction of acute pancreatitis (AP) severity remains an unmet clinical need due to limited molecular characterization of systemic immune responses. We performed integrated single-cell RNA sequencing with T- and B-cell receptor profiling on peripheral blood mononuclear cells from AP patients (n = 7) at days 1, 3, and 7 after admission. Immune landscape analysis revealed marked inter-patient heterogeneity, with a distinct expansion of MZB1-expressing plasma cells that were strongly associated with complicated AP and recovery. Functional validation in an independent cohort (n = 14) confirmed disease-associated plasma cell markers, alongside altered serum immunoglobulin and cytokine profiles (n = 32). From these findings, we established a nine-gene B-cell-derived transcriptomic signature (S100A8, DUSP1, JUN, HBA2, FOS, CYBA, JUNB, S100A9, and WDR83OS) predictive of AP severity. This model demonstrated high discriminative performance in internal validation (n = 114; AUROC > 0.95, superior to standard clinical scoring systems), and sustained accuracy in external validation cohorts of AP (n = 87) and AP combined with non-AP sepsis (n = 174) for predicting persistent organ failure. Our study identifies a mechanistic and predictive role for MZB1⁺ plasma cells in AP pathogenesis, offering a novel immune-based stratification strategy with potential for precision clinical management.

Acute respiratory distress syndrome (ARDS) is a clinical syndrome of acute hypoxic respiratory failure caused by diffuse lung inflammation and edema. ARDS can be precipitated by intrapulmonary factors or extrapulmonary factors, which can lead to severe hypoxemia. Patients suffering from ARDS have high mortality rates, including a 28-day mortality rate of 34.8% and an overall in-hospital mortality rate of 40.0%. The pathophysiology of ARDS is complex and involves the activation and dysregulation of multiple overlapping and interacting pathways of systemic inflammation and coagulation, including the respiratory system, circulatory system, and immune system. In general, the treatment of inflammatory injuries is a coordinated process that involves the downregulation of proinflammatory pathways and the upregulation of anti-inflammatory pathways. Given the complexity of the underlying disease, treatment needs to be tailored to the problem. Hence, we discuss the pathogenesis and treatment methods of affected organs, including 2019 coronavirus disease (COVID-19)-related pneumonia, drowning, trauma, blood transfusion, severe acute pancreatitis, and sepsis. This review is intended to provide a new perspective concerning ARDS and offer novel insight into future therapeutic interventions.

Acute pancreatitis (AP) is a severe inflammatory disorder associated with metabolic reprogramming and mitochondrial dysfunction. This study investigated central carbon metabolism alterations in pancreatic acinar cells during AP, elucidated the molecular mechanisms of tricarboxylic acid (TCA) cycle disorders, and explored the role of protein hypersuccinylation in AP pathogenesis. Using in vitro and in vivo AP models, targeted metabolomics and bioinformatics analyses revealed TCA cycle dysregulation characterized by elevated succinyl-CoA and decreased succinate levels. Colorimetric assays, mass spectrometry, and site-directed mutagenesis demonstrated that SIRT5 downregulation led to SUCLA2 hypersuccinylation at K118, inhibiting succinyl-CoA synthetase activity and triggering a vicious cycle of succinyl-CoA accumulation and SUCLA2 succinylation. Adenovirus-mediated SIRT5 overexpression and SUCLA2 knockdown clarified the SIRT5-SUCLA2 pathway's role in regulating TCA cycle disorders. Protein succinylation levels positively correlated with pancreatic tissue damage and mitochondrial injury severity. Succinylome analysis identified cytochrome c1 (CYC1) as a key hypersuccinylated protein, and the SIRT5-SUCLA2 pathway regulated its succinylation level and electron transport chain complex III activity. Hypersuccinylation induced mitochondrial DNA release, activating the cGAS-STING pathway, contributing to multiple organ dysfunction syndrome. Modulating the SIRT5-SUCLA2 axis attenuated TCA cycle dysregulation, protein hypersuccinylation, mitochondrial damage, and inflammatory responses in AP. These findings reveal novel mechanisms linking the SIRT5-SUCLA2 axis, TCA cycle dysfunction, and protein hypersuccinylation in AP pathogenesis, providing potential therapeutic targets for AP treatment.

AIM:Tracheal diverticulum, an air-filled sac typically located on the right posterolateral aspect of the trachea, has an unclear etiology. This study evaluates the clinical management and outcomes of incidentally detected tracheal diverticula in patients with papillary thyroid carcinoma, emphasizing the need for preoperative diagnosis and selective treatment. CASE PRESENTATION:Within our multi-center thyroid surgery cohort, seven cases of tracheal diverticula were incidentally discovered during thyroidectomy, with preoperative diagnosis achieved in only a subset of patients. Some tracheal diverticula were surgically excised, allowing for histopathological examination, whereas others were left in situ. All patients recovered without postoperative complications. RESULTS:Histopathological examination of the resected tracheal diverticula confirmed benign pathology. All patients, including those with untreated tracheal diverticula, remained asymptomatic during follow-up, with no tracheal abnormalities or complications observed. High-resolution computed tomography and three-dimensional reconstruction technology proved effective for the preoperative diagnosis of tracheal diverticulum. CONCLUSIONS:Routine surgical treatment is unnecessary for asymptomatic patients, with resection of tracheal diverticulum reserved for symptomatic cases. Diagnostic approaches such as high-resolution computed tomography and three-dimensional reconstruction serve as essential preoperative assessments before thyroidectomy, enabling accurate diagnosis of tracheal diverticulum.

Supplementary Data S1. STROBE-checklist.

Supplementary Figure S1. Supplementary Figure S1. Mutation profiling in all malignant samples tested by the NGS-28 genes. Clinicopathological features included gender, age, tumor subtype, lymph node metastases (LNM), tumor size and tumor stage. PTC: papillary thyroid cancer; FTC: follicular thyroid cancer; PDTC: poorly differentiated thyroid cancer; ATC: anaplastic thyroid cancer; MTC: medullary thyroid cancer.

Para-tracheal capsular dissection, conducted adjacent to the trachea, is postulated to mitigate the risk of parathyroid gland injury. This study compares postoperative parathyroid function following para-tracheal and conventional capsular dissection in thyroidectomy procedures. A retrospective analysis was performed on the medical records of 142 patients who underwent bilateral thyroidectomy with capsular dissection. In this randomized trial, 76 patients received para-tracheal capsular dissection, while 66 underwent the conventional approach. All patients were administered carbon nanoparticles as a lymphatic tracer, and their serum parathyroid hormone (PTH) and calcium (Ca2+) levels were evaluated postoperatively. Those presenting with serum PTH and/or Ca2+ levels below the normal range on the first postoperative day received management and follow-up. On the first postoperative day, the para-tracheal group exhibited significantly higher serum PTH levels (2.26 pmol/L, interquartile range [IQR] 1.46-3.53 pmol/L) compared to the conventional group (2.00 pmol/L, IQR 0.93-2.95 pmol/L, P = 0.04). Additionally, the para-tracheal group demonstrated a lesser reduction in PTH levels (47% vs. 58% from the pre-surgical level, P = 0.06). The proportion of patients with low PTH levels on the first postoperative day was significantly lower in the para-tracheal group (21/76, 27.6%) than in the conventional group (29/66, 43.9%; P < 0.05). There was no significant difference in serum Ca2+ levels between the groups on the first postoperative day, indicating that calcium supplement prophylaxis could prevent hypocalcemia. Follow-up results showed a significant reduction in transient hypoparathyroidism in the para-tracheal group (0/76) compared to the conventional group (4/66, P = 0.04, Fisher's exact test). The incidence of permanent hypoparathyroidism was also lower in the para-tracheal group (0/76) compared to the conventional group (2/66, P = 0.21, Fisher's exact test). Para-tracheal capsular dissection appears to offer superior protection of the parathyroid glands compared to conventional dissection, potentially reducing the risk of both transient and permanent postoperative hypoparathyroidism.

Objective.Ultrasound is the predominant modality in medical practice for evaluating thyroid nodules. Currently, diagnosis is typically based on textural information. This study aims to develop an automated texture classification approach to aid physicians in interpreting ultrasound images of thyroid nodules. However, there is currently a scarcity of pixel-level labeled datasets for the texture classes of thyroid nodules. The labeling of such datasets relies on professional and experienced doctors, requiring a significant amount of manpower. Therefore, the objective of this study is to develop an unsupervised method for classifying nodule textures.Approach.Firstly, we develop a spatial mapping network to transform the one-dimensional pixel value space into a high-dimensional space to extract comprehensive feature information. Subsequently, we outline the principles of feature selection that are suitable for clustering. Then we propose a pixel-level clustering algorithm with a region growth pattern, and a distance evaluation method for texture sets among different nodules is established.Main results.Our algorithm achieves a pixel-level classification accuracy of 0.931 for the cystic and solid region, 0.870 for the hypoechoic region, 0.959 for the isoechoic region, and 0.961 for the hyperechoic region. The efficacy of our algorithm and its concordance with human observation have been demonstrated. Furthermore, we conduct calculations and visualize the distribution of different textures in benign and malignant nodules.Significance.This method can be used for the automatic generation of pixel-level labels of thyroid nodule texture, aiding in the construction of texture datasets, and offering image analysis information for medical professionals.

Due to the absence of definitive diagnostic criteria, there remains a lack of consensus regarding the risk assessment of central lymph node metastasis (CLNM) and the necessity for prophylactic lymph node surgery in ultrasound-diagnosed thyroid cancer. The localization of thyroid nodules is a recognized predictor of CLNM; however, quantifying this relationship is challenging due to variable measurements. In this study, we developed a differential isomorphism-based alignment method combined with a graph transformer to accurately extract localization and morphological information of thyroid nodules, thereby predicting CLNM. We collected 88,796 ultrasound images from 48,969 patients who underwent central lymph node (CLN) surgery and utilized these images to train our predictive model, ACE-Net. Furthermore, we employed an interpretable methodology to explore the factors influencing CLNM and generated a risk heatmap to visually represent the distribution of CLNM risk across different thyroid regions. ACE-Net demonstrated superior performance in 6 external multicenter tests (AUC = 0.826), surpassing the predictive accuracy of human experts (accuracy = 0.561). The risk heatmap enabled the identification of high-risk areas for CLNM, likely correlating with lymphatic metastatic pathways. Additionally, it was observed that the likelihood of metastasis exceeded 80% when the nodal margin’s minimum distance from the thyroid capsule was less than 1.25 mm. ACE-Net’s capacity to effectively predict CLNM and provide interpretable disease-related insights can importantly reduce unnecessary lymph node dissections by 37.9%, without missing positive cases, thus offering a valuable tool for clinical decision-making.

The extraction of contrast-filled vessels from X-ray coronary angiography (XCA) image sequence has important clinical significance for intuitively diagnosis and therapy. In this study, the XCA image sequence is regarded as a 3D tensor input, the vessel layer is regarded as a sparse tensor, and the background layer is regarded as a low-rank tensor. Using tensor nuclear norm (TNN) minimization, a novel method for vessel layer extraction based on tensor robust principal component analysis (TRPCA) is proposed. Furthermore, considering the irregular movement of vessels and the low-frequency dynamic disturbance of surrounding irrelevant tissues, the total variation (TV) regularized spatial-temporal constraint is introduced to smooth the foreground layer. Subsequently, for vessel layer images with uneven contrast distribution, a two-stage region growing (TSRG) method is utilized for vessel enhancement and segmentation. A global threshold method is used as the preprocessing to obtain main branches, and the Radon-Like features (RLF) filter is used to enhance and connect broken minor segments, the final binary vessel mask is constructed by combining the two intermediate results. The visibility of TV-TRPCA algorithm for foreground extraction is evaluated on clinical XCA image sequences and third-party dataset, which can effectively improve the performance of commonly used vessel segmentation algorithms. Based on TV-TRPCA, the accuracy of TSRG algorithm for vessel segmentation is further evaluated. Both qualitative and quantitative results validate the superiority of the proposed method over existing state-of-the-art approaches.

Background Acute pancreatitis (AP) is a clinically common acute abdominal disease, whose pathogenesis remains unclear. The severe patients usually have multiple complications and lack specific drugs, leading to a high mortality and poor outcome. Acinar cells are recognized as the initial site of AP. However, there are no precise single-cell transcriptomic profiles to decipher the landscape of acinar cells during AP, which are the missing pieces of jigsaw we aimed to complete in this study.Methods A single-cell sequencing dataset was used to identify the cell types in pancreas of AP mice and to depict the transcriptomic maps in acinar cells. The pathways' activities were evaluated by gene sets enrichment analysis (GSEA) and single-cell gene sets variation analysis (GSVA). Pseudotime analysis was performed to describe the development trajectories of acinar cells. We also constructed the protein-protein interaction (PPI) network and identified the hub genes. Another independent single-cell sequencing dataset of pancreas samples from AP mice and a bulk RNA sequencing dataset of peripheral blood samples from AP patients were also analyzed.Results In this study, we identified genetic markers of each cell type in the pancreas of AP mice based on single-cell sequencing datasets and analyzed the transcription changes in acinar cells. We found that acinar cells featured acinar-ductal metaplasia (ADM), as well as increased endocytosis and vesicle transport activity during AP. Notably, the endoplasmic reticulum stress (ERS) and ER-associated degradation (ERAD) pathways activated by accumulation of unfolded/misfolded proteins in acinar cells could be pivotal for the development of AP.Conclusion We deciphered the distinct roadmap of acinar cells in the early stage of AP at single-cell level. ERS and ERAD pathways are crucially important for acinar homeostasis and the pathogenesis of AP.

Over the years, pancreatic cancer has experienced a global surge in incidence and mortality rates, largely attributed to the influence of obesity and diabetes mellitus on disease initiation and progression. In this study, we investigated the pathogenesis of pancreatic cancer in mice subjected to a high-fat diet (HFD) and observed an increase in citric acid expenditure. Notably, citrate treatment demonstrates significant efficacy in promoting tumor cell apoptosis, suppressing cell proliferation, and inhibiting tumor growth in vivo. Our investigations revealed that citrate achieved these effects by releasing secreted protein acidic and rich in cysteine (SPARC) proteins, repolarizing M2 macrophages into M1 macrophages, and facilitating tumor cell apoptosis. Overall, our research highlights the critical role of citric acid as a pivotal metabolite in the intricate relationship between obesity and pancreatic cancer. Furthermore, we uncovered the significant metabolic and immune checkpoint function of SPARC in pancreatic cancer, suggesting its potential as both a biomarker and therapeutic target in treating this patient population.

Intrahepatic cholangiocarcinoma (ICCA) is a highly malignant and heterogeneous bile duct malignancy with limited treatment options. Disulfidptosis, a recently discovered form of cell death, known to play a crucial role in tumor progression. However, there is currently no study focusing on the disulfidptosis in ICCA tumor cells and its association with patient prognosis. To gain insights into the role of disulfidptosis in ICCA, we investigated the prognostic implications of disulfidptosis-related genes (DRGs) and their relationship with the tumor microenvironment in ICCA. Gene expression data were collected from the Gene Expression Omnibus (GEO) and European Bioinformatics Institute (EMBL-EBI) databases. Univariate and multivariate Cox regression analyses were used to construct a 4-gene prognostic risk model (MAIP1, CHST4, IQCK, IQGAP2) based on DRGs. Subsequently, 197 ICCA samples were divided into high- and low-risk groups based on the risk score. Kaplan-Meier survival curves revealed a shorter survival time for the high-risk group. Enrichment analysis indicated decreased disulfidptosis in the high-risk group. Further analyses using CIBERSORT and ESTIMATE suggested poorer immune cell infiltration and weaker immune cell function in the high-risk group. Additionally, the high-risk group exhibited low sensitivity to commonly used chemotherapy and targeted therapy agents. Our findings not only enhance our understanding of the role of disulfidptosis in ICCA but also provide an effective prognostic indicator for predicting patient outcomes and guiding treatment decisions, ultimately may improve clinical outcomes and overall survival in ICCA patients.