Evidence map›Paper›PMID 37936457›Full record

ArticleCurrent medicinal chemistry2024

Comprehensive scRNA-seq Analysis and Identification of CD8_+T Cell Related Gene Markers for Predicting Prognosis and Drug Resistance of Hepatocellular Carcinoma.

Lu Cao, Muqi Liu, Xiaoqian Ma, Pengfei Rong, Juan Zhang, Wei Wang

Abstract read
PubMed Publisher
In one paragraph

Article in Current medicinal chemistry, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

  1. Review
  2. Article
  3. Review
  4. Article
  5. Significance of Malic Enzyme 1 in Cancer: A Review.Current issues in molecular biology · 2025
    Review
  6. Review
  7. 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

6 authors.

Lu CaoThe Institute for Cell Transplantation and Gene Therapy, the Third Xiangya Hospital, Central South University, Changsha, 410005, China.
Muqi LiuThe Institute for Cell Transplantation and Gene Therapy, the Third Xiangya Hospital, Central South University, Changsha, 410005, China.
Xiaoqian MaThe Institute for Cell Transplantation and Gene Therapy, the Third Xiangya Hospital, Central South University, Changsha, 410005, China.
Pengfei RongThe Institute for Cell Transplantation and Gene Therapy, the Third Xiangya Hospital, Central South University, Changsha, 410005, China.
Juan ZhangThe Institute for Cell Transplantation and Gene Therapy, the Third Xiangya Hospital, Central South University, Changsha, 410005, China.
Wei WangThe Institute for Cell Transplantation and Gene Therapy, the Third Xiangya Hospital, Central South University, Changsha, 410005, China.

Funding

National Natural Science Foundation of China 81971721, 82372071
6 · The paper itself

Abstract

backgroundTumor heterogeneity of immune infiltration of cells plays a decisive role in hepatocellular carcinoma (HCC) therapy response and prognosis. This study investigated the effect of different subtypes of CD8+T cells on the HCC tumor microenvironment about its prognosis.

methodsSingle-cell RNA sequencing, transcriptome, and single-nucleotide variant data from LUAD patients were obtained based on the GEO, TCGA, and HCCD18 databases. CD8+ T cells-associated subtypes were identified by consensus clustering analysis, and genes with the highest correlation with prognostic CD8+ T cell subtypes were identified using WGCNA. The ssGSEA and ESTIMATE algorithms were used to calculate pathway enrichment scores and immune cell infiltration levels between different subtypes. Finally, the TIDE algorithm, CYT score, and tumor responsiveness score were utilized to predict patient response to immunotherapy.

resultsWe defined 3 CD8+T cell clusters (CD8_0, CD8_1, CD8_2) based on the scRNA- seq dataset (GSE149614). Among, CD8_2 was prognosis-related risk factor with HCC. We screened 30 prognosis genes from CD8_2, and identified 3 molecular subtypes (clust1, clust2, clust3). Clust1 had better survival outcomes, higher gene mutation, and enhanced immune infiltration. Furthermore, we identified a 12 genes signature (including CYP7A1, SPP1, MSC, CXCL8, CXCL1, GCNT3, TMEM45A, SPP2, ME1, TSPAN13, S100A9, and NQO1) with excellent prediction performance for HCC prognosis. In addition, High-score patients with higher immune infiltration benefited less from immunotherapy. The sensitivity of low-score patients to multiple drugs including Parthenolide and Shikonin was significantly higher than that of high-score patients. Moreover, high-score patients had increased oxidative stress pathways scores, and the RiskScore was closely associated with oxidative stress pathways scores. And the nomogram had good clinical utility.

conclusionTo predict the survival outcome and immunotherapy response for HCC, we developed a 12-gene signature based on the heterogeneity of the CD8+ T cells.

Indexed as

Biomarkers, TumorCarcinoma, HepatocellularCD8-Positive T-LymphocytesDrug Resistance, NeoplasmLiver NeoplasmsHumansPrognosisRNA-SeqSingle-Cell AnalysisSingle-Cell Gene Expression AnalysisTumor MicroenvironmentBiomarkers, TumorCD8+T cellshepatocellular carcinomaimmune infiltrationmethylation.parthenolidescRNA-seq

Identifiers

PMID37936457

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.