Evidence map›Paper›PMID 40750772›Full record

ArticleSignal transduction and targeted therapy2025

Targeting AKR1B1 inhibits metabolic reprogramming to reverse systemic therapy resistance in hepatocellular carcinoma.

Qi Wang, Juan Liu, Ming Yang, Jun Zhou, Yaxuan Li, Jingjing Zheng, Hao Jia, Shuhua Yue, Yinpeng Le, Yuxin Su and 4 more

Abstract read
In one paragraph

Article in Signal transduction and targeted therapy, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed
–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

18 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Review
  6. Review
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Review
  14. Article
  15. Review
  16. Review
  17. Article
  18. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

14 authors.

Qi Wang *Hepato-Pancreato-Biliary Center, Beijing Tsinghua Changgung Hospital, Key Laboratory of Digital Intelligence Hepatology, Ministry of Education, School of Clinical Medicine, Tsinghua Medicine, Tsinghua University, Beijing, China.
Juan Liu *Hepato-Pancreato-Biliary Center, Beijing Tsinghua Changgung Hospital, Key Laboratory of Digital Intelligence Hepatology, Ministry of Education, School of Clinical Medicine, Tsinghua Medicine, Tsinghua University, Beijing, China. lja02720@btch.edu.cn.ORCID 0000-0003-1399-6719
Ming YangHepato-Pancreato-Biliary Center, Beijing Tsinghua Changgung Hospital, Key Laboratory of Digital Intelligence Hepatology, Ministry of Education, School of Clinical Medicine, Tsinghua Medicine, Tsinghua University, Beijing, China.
Jun ZhouHepato-Pancreato-Biliary Center, Beijing Tsinghua Changgung Hospital, Key Laboratory of Digital Intelligence Hepatology, Ministry of Education, School of Clinical Medicine, Tsinghua Medicine, Tsinghua University, Beijing, China.
Yaxuan LiHepato-Pancreato-Biliary Center, Beijing Tsinghua Changgung Hospital, Key Laboratory of Digital Intelligence Hepatology, Ministry of Education, School of Clinical Medicine, Tsinghua Medicine, Tsinghua University, Beijing, China.
Jingjing ZhengDepartment of Hepatobiliary and Pancreatic Surgery, The First Hospital of Jilin University, Jilin University, Changchun, China.
Hao JiaKey Laboratory of Biomechanics and Mechanobiology (Beihang University), Ministry of Education, Institute of Medical Photonics, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, China.
Shuhua YueKey Laboratory of Biomechanics and Mechanobiology (Beihang University), Ministry of Education, Institute of Medical Photonics, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, China.ORCID 0000-0003-1830-008X
Yinpeng LeHepato-Pancreato-Biliary Center, Beijing Tsinghua Changgung Hospital, Key Laboratory of Digital Intelligence Hepatology, Ministry of Education, School of Clinical Medicine, Tsinghua Medicine, Tsinghua University, Beijing, China.
Yuxin SuHepato-Pancreato-Biliary Center, Beijing Tsinghua Changgung Hospital, Key Laboratory of Digital Intelligence Hepatology, Ministry of Education, School of Clinical Medicine, Tsinghua Medicine, Tsinghua University, Beijing, China.
Wenrui MaClinical Translational Science Center, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua Medicine, Tsinghua University, Beijing, China.
Ni AnClinical Translational Science Center, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua Medicine, Tsinghua University, Beijing, China.
Yunfang WangHepato-Pancreato-Biliary Center, Beijing Tsinghua Changgung Hospital, Key Laboratory of Digital Intelligence Hepatology, Ministry of Education, School of Clinical Medicine, Tsinghua Medicine, Tsinghua University, Beijing, China. wangyf008@mail.tsinghua.edu.cn.
Jiahong DongHepato-Pancreato-Biliary Center, Beijing Tsinghua Changgung Hospital, Key Laboratory of Digital Intelligence Hepatology, Ministry of Education, School of Clinical Medicine, Tsinghua Medicine, Tsinghua University, Beijing, China. dongjiahong@mail.tsinghua.edu.cn.

Funding

National Natural Science Foundation of China (National Science Foundation of China) 32371477National Natural Science Foundation of China (National Science Foundation of China) 92168207
6 · The paper itself

Abstract

Hepatocellular carcinoma (HCC) is a leading cause of cancer-related mortality, and resistance to systemic therapies remains a significant clinical challenge. This study investigated the mechanisms by which metabolic reprogramming contributes to systemic treatment resistance in HCC. We established HCC cell lines with multidrug resistance characteristics and observed enhanced metabolic activity in these cells. Integrated multiomics analyses revealed hyperactive glucose‒lipid and glutathione metabolic pathways that play critical roles in supporting tumor cell proliferation and survival. We constructed a metabolic reprogramming atlas for HCC-resistant cells and identified aldo-keto reductase (Aldo-keto reductase family 1 Member B1, AKR1B1) as a key regulator of this reprogramming, which sustains drug resistance by regulating energy metabolism and enhancing stress tolerance. Importantly, AKR1B1 expression levels are closely associated with drug resistance and poor prognosis in HCC patients. The secretory nature of AKR1B1 not only underscores its predictive value but also facilitates the intercellular transmission of drug resistance. In terms of overcoming resistance, the AKR1B1 inhibitor epalrestat significantly mitigated drug resistance when it was used in combination with standard therapies. These findings underscore the importance of metabolic reprogramming in the development of HCC resistance. AKR1B1, a key enzyme that regulates metabolic reprogramming, has been identified as a potential biomarker and therapeutic target, providing new insights into overcoming resistance in HCC treatment.

Indexed as

Aldehyde ReductaseCarcinoma, HepatocellularDrug Resistance, NeoplasmLiver NeoplasmsCell Line, TumorCell ProliferationGene Expression Regulation, NeoplasticHumansMetabolic ReprogrammingAKR1B1 protein, humanAldehyde Reductase

Identifiers

PMID40750772
PMCPMC12317016

What Socratic holds

Textmetadata
LicenceCC BY
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 Socratic graph.