Evidence map›Paper›PMID 39414868›Full record

ArticleScientific reports2024

Key genes and immune pathways in T-cell mediated rejection post-liver transplantation identified via integrated RNA-seq and machine learning.

Wenhao Shao, Huaxing Ding, Yan Wang, Zhiyong Shi, Hezhao Zhang, Fanxiu Meng, Qingyao Chang, Haojiang Duan, Kairui Lu, Li Zhang and 1 more

Abstract read
In one paragraph

Article in Scientific reports, 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. Article
  3. Understanding Machine Learning Applications in Lung Transplantation: A Narrative Review.Transplant international : official journal of the European Society for Organ Transplantation · 2025
    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

11 authors.

Wenhao Shao *Faculty of Graduate Studies, Shanxi Medical University, Taiyuan, 030000, China.
Huaxing Ding *Faculty of Graduate Studies, Shanxi Medical University, Taiyuan, 030000, China.
Yan WangDepartment of Hepatobiliary and Pancreatic Surgery and Liver Transplant Center, The First Hospital of Shanxi Medical University, Taiyuan, 030001, Shanxi, China.
Zhiyong ShiDepartment of Hepatobiliary and Pancreatic Surgery and Liver Transplant Center, The First Hospital of Shanxi Medical University, Taiyuan, 030001, Shanxi, China.
Hezhao ZhangDepartment of Hepatobiliary and Pancreatic Surgery and Liver Transplant Center, The First Hospital of Shanxi Medical University, Taiyuan, 030001, Shanxi, China.
Fanxiu MengFaculty of Graduate Studies, Shanxi Medical University, Taiyuan, 030000, China.
Qingyao ChangFaculty of Graduate Studies, Shanxi Medical University, Taiyuan, 030000, China.
Haojiang DuanFaculty of Graduate Studies, Shanxi Medical University, Taiyuan, 030000, China.
Kairui LuFaculty of Graduate Studies, Shanxi Medical University, Taiyuan, 030000, China.
Li ZhangDepartment of Hepatobiliary and Pancreatic Surgery and Liver Transplant Center, The First Hospital of Shanxi Medical University, Taiyuan, 030001, Shanxi, China. zhangli3788@163.com.
Jun XuDepartment of Hepatobiliary and Pancreatic Surgery and Liver Transplant Center, The First Hospital of Shanxi Medical University, Taiyuan, 030001, Shanxi, China. junxuty@163.com.

Funding

National Natural Science Foundation of China 82072737
6 · The paper itself

Abstract

Liver transplantation is the definitive treatment for end-stage liver disease, yet T-cell mediated rejection (TCMR) remains a major challenge. This study aims to identify key genes associated with TCMR and their potential biological processes and mechanisms. The GSE145780 dataset was subjected to differential expression analysis, weighted gene co-expression network analysis (WGCNA), and machine learning algorithms to pinpoint key genes associated with TCMR. Gene Set Enrichment Analysis (GSEA), immune infiltration analysis, and regulatory networks were constructed to ascertain the biological relevance of these genes. Expression validation was performed using single-cell RNA-seq (scRNA-seq) data and liver biopsy tissues from patients. We identified 5 key genes (ITGB2, FCER1G, IL-18, GBP1, and CD53) that are associated with immunological functions, such as chemotactic activity, antigen processing, and T cell differentiation. GSEA highlighted enrichment in chemokine signaling and antigen presentation pathways. A lncRNA-miRNA-mRNA network was delineated, and drug target prediction yielded 26 potential drugs. Evaluation of expression levels in non-rejection (NR) and TCMR groups exhibited significant disparities in T cells and myeloid cells. Tissue analyses from patients corroborated the upregulation of GBP1, IL-18, CD53, and FCER1G in TCMR cases. Through comprehensive analysis, this research has identified 4 genes intimately connected with TCMR following liver transplantation, shedding light on the underlying immune activation pathways and suggesting putative targets for therapeutic intervention.

Indexed as

Graft RejectionLiver TransplantationMachine LearningT-LymphocytesGene Expression ProfilingGene Regulatory NetworksHumansInterleukin-18MicroRNAsRNA-SeqIL18 protein, humanInterleukin-18MicroRNAsEnrichment analysisImmune analysisLiver transplant rejectionSingle-cell RNA sequencingT-cell mediated rejection

Identifiers

PMID39414868
PMCPMC11484935

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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.