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Received — 27 May 2026 ⏭

Disentangled Double Machine Learning for Accurate Causal Effect Estimation

✇cs.AI, q-bio.NC updates on arXiv.org
By: Guodu Xiang · Kui Yu · Yujie Wang · Richang Hong · Fuyuan Cao · Jiye Liang
26 May 2026 at 12:00
arXiv:2605.24808v1 Announce Type: cross Abstract: Confounding bias is a key challenge in causal effect estimation from observational data. Double Machine Learning (DML) addresses this issue by estimating treatment and outcome nuisance functions, constructing treatment and outcome residuals, and estimating causal effects from the residuals. However, DML often produces biased and unstable estimates in highdimensional or finite-sample scenarios. One reason is that DML estimates nuisance functions using all covariates without disentangling distinct latent factors, resulting in unreliable nuisance function estimation. Another is that imprecise nuisance estimation further introduces residual dependence between the treatment residual and the remaining outcome error, undermining the accuracy of causal effect estimates. To address these issues, in this paper, we propose Disentangled Double Machine Learning (DDML), a novel algorithm that integrates two key strategies. First, a causal role disentanglement strategy decomposes covariates into confounders, treatment-specific factors, and outcomespecific factors for enabling reliable nuisance function estimation. And second, a residual dependence orthogonalization strategy mitigates residual dependence caused by nuisance estimation errors for enhancing the precision of causal effect estimates. Experimental results on synthetic, semi-synthetic, and real-world datasets demonstrate that DDML significantly outperforms 13 state-of-the-art baseline algorithms in both MAE and RMSE.

PageLLM: A Multi-Grained Reward Framework for Whole-Page Optimization with Large Language Models

✇cs.AI, q-bio.NC updates on arXiv.org
By: Xinyuan Wang · Liang Wu · Dongjie Wang · Yanjie Fu
26 May 2026 at 12:00
arXiv:2506.09084v2 Announce Type: replace-cross Abstract: Whole-page optimization (WPO) decides how search and recommendation results are surfaced to users, and large language models (LLMs) open a new route to it by treating page generation as sequence generation. Adapting LLMs to web-scale WPO, however, remains bottlenecked by the need for costly human annotations and by the mismatched granularity between page-level coherence and item-level placement. In this work we show that these two challenges are coupled: implicit user feedback alone suffices for alignment, provided the reward signal is decoupled into two complementary granularities. We propose PageLLM, a reward-based fine-tuning framework that (i) turns implicit feedback into four contrastive preference-pair families covering relevance, ranking, diversity, and redundancy, (ii) learns a coarse page-level reward and a fine item-level reward that captures engagement-sensitive position swaps, and (iii) combines both rewards in PPO-based RLHF over a pre-trained LLM. Extensive experiments on seven Amazon categories against eleven baselines show that neither reward alone is sufficient -- dropping the page-level or item-level signal reduces NDCG@100 by 17.8% and 15.2% respectively, whereas the joint reward improves NDCG@100 by up to 46.8%. Deployed in a 10M-user online A/B test, PageLLM raises GMV by 0.44% and click-through rate by 0.14%, confirming that multi-grained rewards from implicit feedback scale to production WPO. Code and data are available at an anonymized repository.

ChunkLLM: A Lightweight Pluggable Framework for Accelerating LLMs Inference

✇cs.AI, q-bio.NC updates on arXiv.org
By: Haojie Ouyang · Jianwei Lv · Lei Ren · Chen Wei · Xiaojie Wang · Fangxiang Feng
26 May 2026 at 12:00
arXiv:2510.02361v2 Announce Type: replace-cross Abstract: Transformer-based large models excel in natural language processing and computer vision, but face severe computational inefficiencies due to the self-attention's quadratic complexity with input tokens. Recently, researchers have proposed a series of methods based on block selection and compression to alleviate this problem, but they either have issues with semantic incompleteness or poor training-inference efficiency. To comprehensively address these challenges, we propose ChunkLLM, a lightweight and pluggable training framework. Specifically, we introduce two components: QK Adapter (Q-Adapter and K-Adapter) and Chunk Adapter. The former is attached to each Transformer layer, serving dual purposes of feature compression and chunk attention acquisition. The latter operates at the bottommost layer of the model, functioning to detect chunk boundaries by leveraging contextual semantic information. During the training phase, the parameters of the backbone remain frozen, with only the QK Adapter and Chunk Adapter undergoing training. Notably, we design an attention distillation method for training the QK Adapter, which enhances the recall rate of key chunks. During the inference phase, chunk selection is triggered exclusively when the current token is detected as a chunk boundary, thereby accelerating model inference. Experimental evaluations are conducted on a diverse set of long-text and short-text benchmark datasets spanning multiple tasks. ChunkLLM not only attains comparable performance on short-text benchmarks but also maintains 98.64% of the performance on long-context benchmarks while preserving a 48.58% key-value cache retention rate. Particularly, ChunkLLM attains a maximum speedup of 4.48x in comparison to the vanilla Transformer in the processing of 120K long texts.

SSDAU: Structured Semantic Data Augmentation for Joint Entity and Relation Extraction

✇cs.AI, q-bio.NC updates on arXiv.org
By: Jiawei He · Mengyu Shi · Jiawei Liu · Zhijie Wang · Chunrong Fang · Xikai Yang · Zhenyu Chen
26 May 2026 at 12:00
arXiv:2605.23440v2 Announce Type: replace-cross Abstract: Joint Entity and Relation Extraction (JERE) is highly susceptible to weak generalization due to low-quality training data. Data augmentation is a common strategy to enhance model generalization across different domains. However, existing data augmentation methods often overlook text relevance and may disrupt semantic structures and dependencies, making it difficult to generate effective augmented data for improving model generalization. In this paper, we propose Structured Semantic Data Augmentation (SSDAU), a novel method designed to preserve the semantic structure of text during augmentation. SSDAU segments text based on entity labels and employs an encoder to capture semantic features of entities through context awareness. It then performs entity semantic restructuring to generate augmented data. To distinguish semantically similar entities, SSDAU fuses contextualized embeddings with traditional similarity scores. To mitigate potential topic ambiguity and information loss, we apply the BERTTopic model to filter out irrelevant topics, ensuring topic consistency. We evaluate SSDAU on datasets with different annotation types and compare its performance on five representative JERE models against seven popular data augmentation baselines. Experiments demonstrate that SSDAU generates semantically consistent data with superior robustness against ambiguity (8.26% F1 decrease vs. 31.91% for baselines), significantly outperforming all existing methods across all metrics.

circPARPBP promotes cancer stemness and chemoresistance in triple-negative breast cancer through recruiting SRCAP complex to activate CCL20 transcription

✇Oncogene - Issue - nature.com science feeds
By: Bo Chen · Kang Ma · Wenhuan Zhong · Yunjie Wang · Cheng Long · Xueyan Mao · Hailin Tang · Xudong Zhu · Anli Yang · Peng Liu
21 May 2026 at 08:00

Oncogene, Published online: 21 May 2026; doi:10.1038/s41388-026-03819-4

circPARPBP promotes cancer stemness and chemoresistance in triple-negative breast cancer through recruiting SRCAP complex to activate CCL20 transcription

Integrative multi-omics and experimental validation reveal UBE2C as a central hub gene and prognostic biomarker in hepatocellular carcinoma

✇Omics in Hepatocellular
By: Lihuang Su · Ouyang Zhang · Yujie Wang · Jiaojian Lv · Qingxiu Liu · Mengchen Ma · Liang Xu · Tongtong Pan
19 May 2026 at 18:00

Int Immunopharmacol. 2026 May 19;183:116866. doi: 10.1016/j.intimp.2026.116866. Online ahead of print.

ABSTRACT

Hepatocellular carcinoma (HCC) is a lethal malignancy with a high recurrence rate and limited treatment options. Ubiquitin-conjugating enzyme E2 C (UBE2C) is implicated in various cancers, yet its impact on the HCC immune landscape remains incompletely understood. Herein, hub genes in HCC were identified, by integrating co-expression networks and protein-protein interaction analyses, from the TCGA, GEO, and CPTAC databases. Their expression was analysed using a single-cell transcriptomic database and verified in HCC tissues and cell lines via quantitative reverse transcription-PCR and immunoblotting. Functional roles of UBE2C were assessed using in vitro knockdown experiments and an in vivo subcutaneous tumour model. The tumour immune microenvironment was profiled using spatial transcriptomics, RNA-seq data, and ssGSEA. A prognostic nomogram was constructed based on multivariate Cox regression. UBE2C was identified as a significantly upregulated hub gene in HCC. Single-cell RNA-seq revealed predominant expression of UBE2C in hepatocytes, with dynamic upregulation along differentiation trajectories. UBE2C knockdown suppressed proliferation, induced apoptosis, and inhibited tumour growth. Spatial transcriptomics highlighted UBE2C-high regions within proliferative niches exhibiting immunosuppressive traits-including TGFB1 enrichment, impaired CXCL9-CXCR3 signalling, and exclusion of cytotoxic T cells-which were reduced in immunotherapy responders. UBE2C expression correlated with immune checkpoint genes and specific immune cell subsets. A UBE2C-based nomogram integrating T stage and tumour stage robustly predicted patient survival, and miR-300 and miR-381-3p were identified as potential upstream regulators. These findings establish UBE2C as a key driver of HCC progression and a biomarker for prognosis and immunotherapy stratification.

PMID:42155390 | DOI:10.1016/j.intimp.2026.116866

Received — 13 April 2026 ⏭

Clinical application of base editing for treating β-thalassaemia

✇Nature - Issue - nature.com science feeds
By: Yongrong Lai · Rongrong Liu · Lijie Wang · Xu-Kai Ma · Yaliang Li · Gaohui Yang · Lingling Shi · Yi-Lin Guo · Zhenbin Wei · Xuemei Zhou · Wenchao Xu · Yaofeng Hou · Annarita Miccio · Bei Yang · Xiaodun Mou · Li Yang · Jia Chen
8 April 2026 at 08:00

Nature, Published online: 08 April 2026; doi:10.1038/s41586-026-10342-9

A clinical phase 1 trial of a single infusion of CS-101, CD34+ cells modified using a transformer base editor to reactivate fetal haemoglobin production, led to early and enduring transfusion independence in patients with β-thalassaemia.

Asymmetric selection of a rice immune module and rebuild of disease resistance

✇Nature - Issue - nature.com science feeds
By: Hui Lin · Fudan Chen · Guanyun Cheng · Bingxiao Yan · Meng Yuan · Jie Qiu · Yiduo Lu · Mingzhe Suo · Ying Chen · Yijie Wang · Kaixuan Cui · Xiangyu Gong · Shasha Liu · Bofan Liu · Jiyun Liu · Jianjun Wang · Rongbai Li · Bizeng Mao · Jianlong Xu · Jong-Seong Jeon · Xuehui Huang · Bin Han · Dong-Lei Yang · Qifei Gao · Haiming Xu · Yiwen Deng · Gongyou Chen · Zuhua He
8 April 2026 at 08:00

Nature, Published online: 08 April 2026; doi:10.1038/s41586-026-10361-6

Stacking XA48-mediated effector-triggered immunity with XA21-mediated pattern-triggered immunity in Oryza sativa japonica reconstitutes the broad-spectrum resistance from wild rice.
Received — 8 April 2026 ⏭

Agile Deliberation: Concept Deliberation for Subjective Visual Classification

✇cs.AI, q-bio.NC updates on arXiv.org
By: Leijie Wang · Otilia Stretcu · Wei Qiao · Thomas Denby · Krishnamurthy Viswanathan · Enming Luo · Chun-Ta Lu · Tushar Dogra · Ranjay Krishna · Ariel Fuxman
7 April 2026 at 12:00
arXiv:2512.10821v2 Announce Type: replace Abstract: From content moderation to content curation, applications requiring vision classifiers for visual concepts are rapidly expanding. Existing human-in-the-loop approaches typically assume users begin with a clear, stable concept understanding to be able to provide high-quality supervision. In reality, users often start with a vague idea and must iteratively refine it through "concept deliberation", a practice we uncovered through structured interviews with content moderation experts. We operationalize the common strategies in deliberation used by real content moderators into a human-in-the-loop framework called "Agile Deliberation" that explicitly supports evolving and subjective concepts. The system supports users in defining the concept for themselves by exposing them to borderline cases. The system does this with two deliberation stages: (1) concept scoping, which decomposes the initial concept into a structured hierarchy of sub-concepts, and (2) concept iteration, which surfaces semantically borderline examples for user reflection and feedback to iteratively align an image classifier with the user's evolving intent. Since concept deliberation is inherently subjective and interactive, we painstakingly evaluate the framework through 18 user sessions, each 1.5h long, rather than standard benchmarking datasets. We find that Agile Deliberation achieves 7.5% higher F1 scores than automated decomposition baselines and more than 3% higher than manual deliberation, while participants reported clearer conceptual understanding and lower cognitive effort.
Received — 4 April 2026 ⏭

Efficient Reasoning with Balanced Thinking

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yulin Li · Tengyao Tu · Li Ding · Junjie Wang · Huiling Zhen · Yixin Chen · Yong Li · Zhuotao Tian
3 April 2026 at 12:00
arXiv:2603.12372v3 Announce Type: replace Abstract: Large Reasoning Models (LRMs) have shown remarkable reasoning capabilities, yet they often suffer from overthinking, expending redundant computational steps on simple problems, or underthinking, failing to explore sufficient reasoning paths despite inherent capabilities. These issues lead to inefficiencies and potential inaccuracies, limiting practical deployment in resource-constrained settings. Existing methods to mitigate overthinking, such as suppressing reflective keywords or adjusting reasoning length, may inadvertently induce underthinking, compromising accuracy. Therefore, we propose ReBalance, a training-free framework that achieves efficient reasoning with balanced thinking. ReBalance leverages confidence as a continuous indicator of reasoning dynamics, identifying overthinking through high confidence variance and underthinking via consistent overconfidence. By aggregating hidden states from a small-scale dataset into reasoning mode prototypes, we compute a steering vector to guide LRMs' reasoning trajectories. A dynamic control function modulates this vector's strength and direction based on real-time confidence, pruning redundancy during overthinking, and promoting exploration during underthinking. Extensive experiments conducted on four models ranging from 0.5B to 32B, and across nine benchmarks in math reasoning, general question answering, and coding tasks demonstrate that ReBalance effectively reduces output redundancy while improving accuracy, offering a general, training-free, and plug-and-play strategy for efficient and robust LRM deployment. Project page and code are available at https://rebalance-ai.github.io .

DIVER: A Multi-Stage Approach for Reasoning-intensive Information Retrieval

✇cs.AI, q-bio.NC updates on arXiv.org
By: Duolin Sun · Meixiu Long · Dan Yang · Junjie Wang · Yecheng Luo · Yue Shen · Jian Wang · Hualei Zhou · Chunxiao Guo · Peng Wei · Jiahai Wang · Jinjie Gu
3 April 2026 at 12:00
arXiv:2508.07995v5 Announce Type: replace-cross Abstract: Retrieval-augmented generation has achieved strong performance on knowledge-intensive tasks where query-document relevance can be identified through direct lexical or semantic matches. However, many real-world queries involve abstract reasoning, analogical thinking, or multi-step inference, which existing retrievers often struggle to capture. To address this challenge, we present DIVER, a retrieval pipeline designed for reasoning-intensive information retrieval. It consists of four components. The document preprocessing stage enhances readability and preserves content by cleaning noisy texts and segmenting long documents. The query expansion stage leverages large language models to iteratively refine user queries with explicit reasoning and evidence from retrieved documents. The retrieval stage employs a model fine-tuned on synthetic data spanning medical and mathematical domains, along with hard negatives, enabling effective handling of reasoning-intensive queries. Finally, the reranking stage combines pointwise and listwise strategies to produce both fine-grained and globally consistent rankings. On the BRIGHT benchmark, DIVER achieves state-of-the-art nDCG@10 scores of 46.8 overall and 31.9 on original queries, consistently outperforming competitive reasoning-aware models. These results demonstrate the effectiveness of reasoning-aware retrieval strategies in complex real-world tasks.

Contextual Distributionally Robust Optimization with Causal and Continuous Structure: An Interpretable and Tractable Approach

✇cs.AI, q-bio.NC updates on arXiv.org
By: Fenglin Zhang · Jie Wang
3 April 2026 at 12:00
arXiv:2601.11016v2 Announce Type: replace-cross Abstract: In this paper, we introduce a framework for contextual distributionally robust optimization (DRO) that considers the causal and continuous structure of the underlying distribution by developing interpretable and tractable decision rules that prescribe decisions using covariates. We first introduce the causal Sinkhorn discrepancy (CSD), an entropy-regularized causal Wasserstein distance that encourages continuous transport plans while preserving the causal consistency. We then formulate a contextual DRO model with a CSD-based ambiguity set, termed Causal Sinkhorn DRO (Causal-SDRO), and derive its strong dual reformulation where the worst-case distribution is characterized as a mixture of Gibbs distributions. To solve the corresponding infinite-dimensional policy optimization, we propose the Soft Regression Forest (SRF) decision rule, which approximates optimal policies within arbitrary measurable function spaces. The SRF preserves the interpretability of classical decision trees while being fully parametric, differentiable, and Lipschitz smooth, enabling intrinsic interpretation from both global and local perspectives. To solve the Causal-SDRO with parametric decision rules, we develop an efficient stochastic compositional gradient algorithm that converges to an $\varepsilon$-stationary point at a rate of $O(\varepsilon^{-4})$, matching the convergence rate of standard stochastic gradient descent. Finally, we validate our method through numerical experiments on synthetic and real-world datasets, demonstrating its superior performance and interpretability.
Received — 2 April 2026 ⏭

IMPACT: Influence Modeling for Open-Set Time Series Anomaly Detection

✇cs.AI, q-bio.NC updates on arXiv.org
By: Xiaohui Zhou · Yijie Wang · Hongzuo Xu · Weixuan Liang · Xiaoli Li · Guansong Pang
1 April 2026 at 12:00
arXiv:2603.29183v1 Announce Type: cross Abstract: Open-set anomaly detection (OSAD) is an emerging paradigm designed to utilize limited labeled data from anomaly classes seen in training to identify both seen and unseen anomalies during testing. Current approaches rely on simple augmentation methods to generate pseudo anomalies that replicate unseen anomalies. Despite being promising in image data, these methods are found to be ineffective in time series data due to the failure to preserve its sequential nature, resulting in trivial or unrealistic anomaly patterns. They are further plagued when the training data is contaminated with unlabeled anomalies. This work introduces $\textbf{IMPACT}$, a novel framework that leverages $\underline{\textbf{i}}$nfluence $\underline{\textbf{m}}$odeling for o$\underline{\textbf{p}}$en-set time series $\underline{\textbf{a}}$nomaly dete$\underline{\textbf{ct}}$ion, to tackle these challenges. The key insight is to $\textbf{i)}$ learn an influence function that can accurately estimate the impact of individual training samples on the modeling, and then $\textbf{ii)}$ leverage these influence scores to generate semantically divergent yet realistic unseen anomalies for time series while repurposing high-influential samples as supervised anomalies for anomaly decontamination. Extensive experiments show that IMPACT significantly outperforms existing state-of-the-art methods, showing superior accuracy under varying OSAD settings and contamination rates.

UniAI-GraphRAG: Synergizing Ontology-Guided Extraction, Multi-Dimensional Clustering, and Dual-Channel Fusion for Robust Multi-Hop Reasoning

✇cs.AI, q-bio.NC updates on arXiv.org
By: Jie Wang · Honghua Huang · Xi Ge · Jianhui Su · Wen Liu · Shiguo Lian
1 April 2026 at 12:00
arXiv:2603.25152v2 Announce Type: replace Abstract: Retrieval-Augmented Generation (RAG) systems face significant challenges in complex reasoning, multi-hop queries, and domain-specific QA. While existing GraphRAG frameworks have made progress in structural knowledge organization, they still have limitations in cross-industry adaptability, community report integrity, and retrieval performance. This paper proposes UniAI-GraphRAG, an enhanced framework built upon open-source GraphRAG. The framework introduces three core innovations: (1) Ontology-Guided Knowledge Extraction that uses predefined Schema to guide LLMs in accurately identifying domain-specific entities and relations; (2) Multi-Dimensional Community Clustering Strategy that improves community completeness through alignment completion, attribute-based clustering, and multi-hop relationship clustering; (3) Dual-Channel Graph Retrieval Fusion that balances QA accuracy and performance through hybrid graph and community retrieval. Evaluation results on MultiHopRAG benchmark show that UniAI-GraphRAG outperforms mainstream open source solutions (e.g.LightRAG) in comprehensive F1 scores, particularly in inference and temporal queries. The code is available at https://github.com/UnicomAI/wanwu/tree/main/rag/rag_open_source/rag_core/graph.

Correction: The multifunctional RNA helicase DDX39A drives glioblastoma progression by modulating WISP1 alternative splicing that induces an immunosuppressive macrophage polarization

✇Oncogene - Issue - nature.com science feeds
By: Yan Zhang · Zhiwei Xue · Naibin Zhang · Yuehua Zhu · YanZhao Wu · Meilin Lv · Zhihan Zhang · Feiyu Mu · Wenchen Xing · Ziyi Tang · Chunjie Wang · Zhiyi Xue · Wenjing Zhou · Xiaofei Liu · Xingang Li · Rolf Bjerkvig · Bin Huang · Mingzhi Han · Jian Wang · Donghai Wang
1 April 2026 at 08:00

Oncogene, Published online: 01 April 2026; doi:10.1038/s41388-026-03756-2

Correction: The multifunctional RNA helicase DDX39A drives glioblastoma progression by modulating WISP1 alternative splicing that induces an immunosuppressive macrophage polarization

STAT3-mediated transactivation of NOVA2 promotes lung adenocarcinoma metastasis by splicing SMAD4

✇Oncogene - Issue - nature.com science feeds
By: Shengjie Wang · Xin Tong · Runfeng Sun · Xinyi Xia · Donglai Chen · Zhao Wang · Hao Shi · Chenzhuo Wu · Xiaoxiao Guo · Die Hu · Ersuo Jin · Hong-Tao Zhang
1 April 2026 at 08:00

Oncogene, Published online: 01 April 2026; doi:10.1038/s41388-026-03752-6

STAT3-mediated transactivation of NOVA2 promotes lung adenocarcinoma metastasis by splicing SMAD4

Nanoscale transfer-printed full-colour ultrahigh-resolution quantum dot LEDs

✇Nature - Issue - nature.com science feeds
By: Lihua Lin · Jie Wang · Hailong Hu · Haolin Luo · Yanbin Liu · Xingjie Yang · Jingnan Su · De’er Li · Zhongwei Xu · Chengyu Luo · Yongshen Yu · Tailiang Guo · Fushan Li
1 April 2026 at 08:00

Nature, Published online: 01 April 2026; doi:10.1038/s41586-026-10333-w

A dual-action force dynamics strategy using a hard silicon template as a nanoimprinting stamp combined with inverted transfer printing is described for the manufacture of high-performance full-colour ultrahigh-resolution quantum dot light-emitting diodes (LEDs) for active-matrix displays, while revealing electric-field reconstruction in nanoscale arrays and introducing dielectric matching to mitigate field concentration and performance degradation.
  • ✇MRD
  • Circulating Tumor DNA in Cholangiocarcinoma: A Precision Oncology Roadmap Haixing Wei · Jie Wang · Qing Wu · Mengbin Qin
    Cancer Manag Res. 2026 Feb 6;18:574678. doi: 10.2147/CMAR.S574678. eCollection 2026.ABSTRACTCholangiocarcinoma (CCA) is a rare but aggressive malignancy with a rising global incidence and few therapeutic options for advanced disease. In recent decades, precision oncology for CCA has advanced rapidly, particularly through the development of targeted therapies for patients with actionable genetic alterations. These therapies have markedly prolonged survival and improved other clinical outcomes amo
     
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Circulating Tumor DNA in Cholangiocarcinoma: A Precision Oncology Roadmap

✇MRD
By: Haixing Wei · Jie Wang · Qing Wu · Mengbin Qin
26 March 2026 at 18:00

Cancer Manag Res. 2026 Feb 6;18:574678. doi: 10.2147/CMAR.S574678. eCollection 2026.

ABSTRACT

Cholangiocarcinoma (CCA) is a rare but aggressive malignancy with a rising global incidence and few therapeutic options for advanced disease. In recent decades, precision oncology for CCA has advanced rapidly, particularly through the development of targeted therapies for patients with actionable genetic alterations. These therapies have markedly prolonged survival and improved other clinical outcomes among patients with unresectable, advanced CCA. The implementation of precision oncology largely depends on detecting genetic mutations to guide patient selection and treatment, using tumor tissue biopsies or liquid biopsies, including circulating tumor DNA (ctDNA) from blood or bile. As a minimally invasive biomarker, ctDNA shows great promise for transforming the clinical management of CCA. This review provides a comprehensive overview of the roles of ctDNA in CCA, including early detection, prognostic stratification, minimal residual disease assessment, recurrence monitoring, therapeutic target identification, and treatment response evaluation. A synthesis of existing studies indicates that bile-derived ctDNA shows superior sensitivity compared with blood-based ctDNA in capturing the genetic profiles and heterogeneity of CCA. We also propose an integrative framework that illustrates how ctDNA profiling can inform diagnosis, treatment, and surveillance across the disease continuum. Because research on ctDNA in CCA remains in its infancy, we discuss current challenges and outline future directions for translating these findings into clinical practice. Collectively, the evidence positions ctDNA-particularly bile-derived ctDNA-as a dynamic tool for real-time genomic profiling, sensitive residual disease detection, and therapy monitoring. This integrative framework provides a roadmap for translating these capabilities into clinical practice, with the potential to enable earlier, more personalized interventions and improve outcomes for patients with CCA.

PMID:41883993 | PMC:PMC13012645 | DOI:10.2147/CMAR.S574678

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