Evidence map›Paper›PMID 39031800›Full record

ArticleJournal of cellular and molecular medicine2024

Unravelling infiltrating T-cell heterogeneity in kidney renal clear cell carcinoma: Integrative single-cell and spatial transcriptomic profiling.

Haiqing Chen, Haoyuan Zuo, Jinbang Huang, Jie Liu, Lai Jiang, Chenglu Jiang, Shengke Zhang, Qingwen Hu, Haotian Lai, Bangchao Yin and 4 more

Abstract read
In one paragraph

Article in Journal of cellular and molecular medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.

0numbers the graph read from it
0cells of the map it votes in
24citing 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

24 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. TSPAN4Frontiers in immunology · 2025
    Article
  12. Article
  13. Article
  14. Review
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. 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

14 authors.

Haiqing ChenDepartment of General Surgery (Hepatopancreatobiliary Surgery), The Affiliated Hospital, Southwest Medical University, Luzhou, China.
Haoyuan ZuoDepartment of General Surgery (Hepatopancreatobiliary Surgery), The Affiliated Hospital, Southwest Medical University, Luzhou, China.
Jinbang HuangSchool of Clinical Medicine, The Affiliated Hospital, Southwest Medical University, Luzhou, China.
Jie LiuDepartment of General Surgery (Hepatopancreatobiliary Surgery), The Affiliated Hospital, Southwest Medical University, Luzhou, China.
Lai JiangSchool of Clinical Medicine, The Affiliated Hospital, Southwest Medical University, Luzhou, China.
Chenglu JiangSchool of Clinical Medicine, The Affiliated Hospital, Southwest Medical University, Luzhou, China.
Shengke ZhangSchool of Clinical Medicine, The Affiliated Hospital, Southwest Medical University, Luzhou, China.
Qingwen HuSchool of Clinical Medicine, The Affiliated Hospital, Southwest Medical University, Luzhou, China.
Haotian LaiSchool of Clinical Medicine, The Affiliated Hospital, Southwest Medical University, Luzhou, China.
Bangchao YinDepartment of Pathology, Sixth People's Hospital of Yibin, Yibin, China.
Guanhu YangDepartment of Specialty Medicine, Ohio University, Athens, Ohio, USA.
Gang MaiDepartment of General Surgery (Hepatopancreatobiliary Surgery), The Affiliated Hospital, Southwest Medical University, Luzhou, China.
Bo LiDepartment of General Surgery (Hepatopancreatobiliary Surgery), The Affiliated Hospital, Southwest Medical University, Luzhou, China.
Hao ChiSchool of Clinical Medicine, The Affiliated Hospital, Southwest Medical University, Luzhou, China.ORCID 0000-0002-5210-0770

Funding

Dazhou Science and Technology Bureau project 21ZDYF0025Sichuan Medical Association Project S21048The Sichuan Provincial Science and Technology Department's Program 22ZDYF1898
6 · The paper itself

Abstract

Kidney renal clear cell carcinoma (KIRC) pathogenesis intricately involves immune system dynamics, particularly the role of T cells within the tumour microenvironment. Through a multifaceted approach encompassing single-cell RNA sequencing, spatial transcriptome analysis and bulk transcriptome profiling, we systematically explored the contribution of infiltrating T cells to KIRC heterogeneity. Employing high-density weighted gene co-expression network analysis (hdWGCNA), module scoring and machine learning, we identified a distinct signature of infiltrating T cell-associated genes (ITSGs). Spatial transcriptomic data were analysed using robust cell type decomposition (RCTD) to uncover spatial interactions. Further analyses included enrichment assessments, immune infiltration evaluations and drug susceptibility predictions. Experimental validation involved PCR experiments, CCK-8 assays, plate cloning assays, wound-healing assays and Transwell assays. Six subpopulations of infiltrating and proliferating T cells were identified in KIRC, with notable dynamics observed in mid- to late-stage disease progression. Spatial analysis revealed significant correlations between T cells and epithelial cells across varying distances within the tumour microenvironment. The ITSG-based prognostic model demonstrated robust predictive capabilities, implicating these genes in immune modulation and metabolic pathways and offering prognostic insights into drug sensitivity for 12 KIRC treatment agents. Experimental validation underscored the functional relevance of PPIB in KIRC cell proliferation, invasion and migration. Our study comprehensively characterizes infiltrating T-cell heterogeneity in KIRC using single-cell RNA sequencing and spatial transcriptome data. The stable prognostic model based on ITSGs unveils infiltrating T cells' prognostic potential, shedding light on the immune microenvironment and offering avenues for personalized treatment and immunotherapy.

Indexed as

Carcinoma, Renal CellGene Expression ProfilingGene Expression Regulation, NeoplasticKidney NeoplasmsSingle-Cell AnalysisT-LymphocytesTranscriptomeTumor MicroenvironmentCell Line, TumorCell ProliferationGene Regulatory NetworksHumansLymphocytes, Tumor-InfiltratingPrognosisimmune microenvironmentmachine learningmulti‐omicssingle‐cell analysisspatial transcriptomeT cellstumour heterogeneity

Identifiers

PMID39031800
PMCPMC11190954

What OpenQuestion 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 OpenQuestion graph.