Evidence map›Paper›PMID 39924997›Full record

ArticleClinical and molecular hepatology2025

Radiogenomics of intrahepatic cholangiocarcinoma predicts immunochemotherapy response and identifies therapeutic target.

Gu-Wei Ji, Zheng-Gang Xu, Shuo-Chen Liu, Shu-Ya Cao, Chen-Yu Jiao, Ming Lu, Biao Zhang, Yue Yang, Qing Xu, Xiao-Feng Wu and 4 more

Abstract read
In one paragraph

Article in Clinical and molecular hepatology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

15 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Review
  4. Review
  5. Review
  6. Review
  7. Review
  8. Review
  9. Review
  10. Review
  11. Frontiers in oncology · 2026
    Article
  12. Article
  13. Review
  14. Article
  15. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

14 authors.

Gu-Wei JiHepatobiliary Center, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
Zheng-Gang XuHepatobiliary Center, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
Shuo-Chen LiuHepatobiliary Center, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
Shu-Ya CaoHepatobiliary Center, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
Chen-Yu JiaoHepatobiliary Center, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
Ming LuDepartment of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
Biao ZhangDepartment of General Surgery, Yancheng No.1 People's Hospital, Yancheng, China.
Yue YangDepartment of General Surgery, The First People's Hospital of Changzhou, Changzhou, China.
Qing XuDepartment of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
Xiao-Feng WuHepatobiliary Center, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
Ke WangHepatobiliary Center, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
Yong-Xiang XiaHepatobiliary Center, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
Xiang-Cheng LiHepatobiliary Center, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
Xue-Hao WangHepatobiliary Center, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.

Funding

National Natural Science Foundation of China 82102150Natural Science Foundation of Jiangsu Province BK20210968
6 · The paper itself

Abstract

BACKGROUND/

aimsIdentifying patients with intrahepatic cholangiocarcinoma (ICC) likely to benefit from immunochemotherapy, the new front-line treatment, remains challenging. We aimed to unveil a novel radiotranscriptomic signature that can facilitate treatment response prediction by multi-omics integration and multiscale modelling.

methodsWe analyzed bulk, single-cell and spatial transcriptomic data comprising 457 ICC patients to identify an immune-related score (IRS), followed by decoding its spatial immune context. We mapped radiomics profiles onto spatial-specific IRS using machine learning to define a novel radiotranscriptomic signature, followed by multi-scale and multi-cohort validation covering 331 ICC patients. The signature was further explored for the potential therapeutic target from in vitro to in vivo.

resultsWe revealed a novel 3-gene (PLAUR, CD40LG, and FGFR4) IRS whose down-regulation correlated with better survival and improved sensitivity to immunochemotherapy. We highlighted functional IRS-immune interactions within tumor epithelium, rather than stromal compartment, irrespective of geospatial locations. Machine learning pipeline identified the optimal 3-feature radiotranscriptomic signature that was well-validated by immunohistochemical assays in molecular cohort, exhibited favorable external prognostic validity with C-index over 0.64 in resection cohort, and predicted treatment response with an area under the curve of up to 0.84 in immunochemotherapy cohort. We also showed that anti-uPAR/PLAUR alone or in combination with anti-programmed cell death protein 1 therapy remarkably curbed tumor growth, using in vitro ICC cell lines and in vivo humanized ICC patient-derived xenograft mouse models.

conclusionThis proof-of-concept study sheds light on the spatially-resolved radiotranscriptomic signature to improve patient selection for emerging immunochemotherapy and high-order immunotherapy combinations in ICC.

Indexed as

Bile Duct NeoplasmsCholangiocarcinomaImmunotherapyAnimalsCell Line, TumorFemaleHumansMachine LearningMaleMiceMiddle AgedPrognosisTranscriptomeIntrahepatic cholangiocarcinomaMachine learningMulti-omics profilingPrediction modelRadiogenomics

Identifiers

PMID39924997
PMCPMC12260641

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.