Evidence map›Paper›PMID 39654899›Full record

ArticleFrontiers in immunology2024

Integrating multi-omics techniques and

Yang Li, Yuan Chen, Yang Zhang, Yunsheng Fang, Ling Wu, Ying Zhao, Danqiong Wang, Xiaoyuan Qiao

Abstract read
In one paragraph

Article in Frontiers in immunology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Review
  2. Review
  3. 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.

Yang LiDepartment of General Medicine, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Third Hospital of Shanxi Medical University, Tongji Shanxi Hospital, Taiyuan, China.
Yuan ChenDepartment of General Medicine, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Third Hospital of Shanxi Medical University, Tongji Shanxi Hospital, Taiyuan, China.
Yang ZhangSchool of Mechanical Engineering, Taiyuan University of Science and Technology, Taiyuan, China.
Yunsheng FangThe Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China.
Ling WuTumor Center, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Third Hospital of Shanxi Medical University, Tongji Shanxi Hospital, Taiyuan, China.
Ying ZhaoDepartment of General Medicine, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Third Hospital of Shanxi Medical University, Tongji Shanxi Hospital, Taiyuan, China.
Danqiong WangDepartment of General Medicine, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Third Hospital of Shanxi Medical University, Tongji Shanxi Hospital, Taiyuan, China.
Xiaoyuan QiaoDepartment of Comprehensive Medicine, Shanxi Province Cancer Hospital/Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences/Cancer Hospital Affiliated to Shanxi Medical University, Taiyuan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Hepatocellular carcinoma (HCC) is a common malignancy worldwide, and its development is closely related to abnormalities in iron metabolism. This study aims to systematically analyze changes in iron metabolism in the tumor microenvironment of HCC using single-cell sequencing technology, and investigate the potential mechanisms by which iron metabolism regulation affects the survival of liver cancer patients. Materials and methods: Single-cell sequencing data from hepatocellular carcinoma patients were obtained from the GEO database. By iron metabolism genomic scoring, we assessed differences in iron metabolism levels in hepatocellular carcinoma samples. By cell communication analysis as well as GO and KEGG enrichment analysis, we determined the functional role of iron metabolism in different cell types. We used survival analysis and Kaplan-Meier curves to assess the impact of iron metabolism levels on patient prognosis. In addition, we identified and analyzed the expression profile of the GLRX3 gene, investigated its key regulatory role in iron metabolism, and validated its clinical value as a prognostic marker. Finally, we explored the effect of GLRX3 on hepatocellular carcinoma phenotype by Results: Bioinformatics results and experimental validation confirmed the dysregulation of iron metabolism in the development of hepatocellular carcinoma, revealing iron's regulatory influence across various cell types. Additionally, GLRX3 was identified as a key regulatory factor in iron metabolism, and the mechanism by which GLRX3 regulates tumor cell proliferation and immune evasion was determined. Furthermore, experiments verified GLRX3's role in facilitating tumor cell proliferation and invasion. Conclusion: This study highlights the critical role of iron metabolism in the progression of hepatocellular carcinoma, particularly the regulatory mechanism of the GLRX3 gene in tumor cell proliferation and immune evasion. Iron metabolism abnormalities are not only drivers of liver cancer development but also key indicators of patient prognosis.

Indexed as

Carcinoma, HepatocellularCell ProliferationGene Expression Regulation, NeoplasticIronLiver NeoplasmsTumor MicroenvironmentBiomarkers, TumorCell Line, TumorComputational BiologyHumansMultiomicsNeoplasm InvasivenessPrognosisRepressor ProteinsBiomarkers, TumorIronRepressor ProteinsGLRX3immune signaturesimmunotherapyiron metabolismmulti-omics analysispersonalized therapyprecision medicine

Identifiers

PMID39654899
PMCPMC11625766

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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.