Evidence map›Paper›PMID 37434092›Full record

ArticleJournal of cancer research and clinical oncology2023

Cuproptosis-related signature for clinical prognosis and immunotherapy sensitivity in hepatocellular carcinoma.

Shaohua Xu, Kexin Dong, Ruihuan Gao, Ying Yang, Yidan Zhou, Chunhua Luo, Wei Chen, Song-Mei Liu

Open access · greenAbstract read
In one paragraph

Article in Journal of cancer research and clinical oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
3.1field-weighted citation impact, top 8% of its field
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

10 citing papers in PubMed, 12 citations in OpenAlex.

  1. Review
  2. Article
  3. Article
  4. Targeting cuproptosis in liver cancer: Molecular mechanisms and therapeutic implications.Apoptosis : an international journal on programmed cell death · 2025
    Review
  5. Article
  6. Article
  7. Review
  8. Review
  9. Article
  10. Article
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

8 authors at 3 institutions in 1 country.

Shaohua Xu *Department of Clinical Laboratory, Center for Gene Diagnosis and Program of Clinical Laboratory, Zhongnan Hospital of Wuhan University, Wuhan, 430071, China.
Kexin Dong *Department of Clinical Laboratory, Center for Gene Diagnosis and Program of Clinical Laboratory, Zhongnan Hospital of Wuhan University, Wuhan, 430071, China.
Ruihuan GaoDepartment of Clinical Laboratory, Center for Gene Diagnosis and Program of Clinical Laboratory, Zhongnan Hospital of Wuhan University, Wuhan, 430071, China.
Ying YangDepartment of Clinical Laboratory, Center for Gene Diagnosis and Program of Clinical Laboratory, Zhongnan Hospital of Wuhan University, Wuhan, 430071, China.
Yidan ZhouDepartment of Clinical Laboratory, Center for Gene Diagnosis and Program of Clinical Laboratory, Zhongnan Hospital of Wuhan University, Wuhan, 430071, China.
Chunhua LuoThe First College of Clinical Medical Science, Three Gorges University, Hubei, 443000, China.
Wei ChenDepartment of Clinical Laboratory, Center for Gene Diagnosis and Program of Clinical Laboratory, Zhongnan Hospital of Wuhan University, Wuhan, 430071, China. 602400856@qq.com.
Song-Mei LiuDepartment of Clinical Laboratory, Center for Gene Diagnosis and Program of Clinical Laboratory, Zhongnan Hospital of Wuhan University, Wuhan, 430071, China. smliu@whu.edu.cn.
Zhongnan Hospital of Wuhan University · CNWuhan University · CNChina Three Gorges University · CN

Funding

Hubei Provincial Natural Science Fund for Creative Research Groups 2019CFA018the National Natural Science Foundation of China 81772276
6 · The paper itself

Abstract

backgroundCopper homeostasis imbalance has been implicated in tumor progression, aggressiveness, and treatment response. However, the precise roles of cuproptosis-related genes (CRGs) in hepatocellular carcinoma (HCC) remain poorly understood.

methodsIn this study, we employed a consensus clustering algorithm to identify distinct molecular subtypes. We then performed Kaplan-Meier analysis and univariate Cox regression analysis to identify prognostic differentially expressed genes. The expression of these genes was subsequently validated using qPCR on fresh-frozen tissues obtained from HCC patients. Moreover, leveraging the TCGA-HCC cohort, we constructed a CRGs-related risk prediction model using the LASSO and multivariate Cox regression analysis.

resultsBy analyzing the data, we successfully established a CRGs risk prognostic model for HCC patients, comprising five differential genes (CAD, SGCB, TXNRD1, KDR, and MTND4P20). Cox regression analysis revealed that the CRGs risk score could serve as an independent prognostic factor for overall survival (hazard ratio [HR] = 1.308, 95% confidence interval [CI] = 1.200 - 1.426, P < 0.001). The area under the curve (AUC) values of the CRGs-score for predicting 1-year, 3-year, and 5-year survival rates were 0.785, 0.724, and 0.723, respectively. Notably, the expression levels of immune checkpoints (including PD-1, PD-L1, and CTLA4) significantly differed between the low- and high-risk score groups. Furthermore, the low-risk score group displayed increased sensitivity to sorafenib, cisplatin, cyclopamine, nilotinib, salubrinal, and gemcitabine, whereas the high-risk score group exhibited heightened sensitivity to lapatinib, erlotinib, and gefitinib.

conclusionsOur findings highlight the potential of the CRGs risk score as an independent and promising biomarker for clinical prognosis and immunotherapy sensitivity in HCC patients.

Indexed as

Carcinoma, HepatocellularLiver NeoplasmsApoptosisHumansImmunotherapyPrognosisSorafenibTumor MicroenvironmentSorafenibChemotherapy sensitivityCuproptosisHepatocellular carcinomaPrognostic modelTumor microenvironment

Identifiers

PMID37434092
PMCPMC11796695
OpenAlexW4383903960

What OpenQuestion holds

Textmetadata
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.