Evidence map›Paper›PMID 34140640›Full record

ArticleOncogene2021

Cell-type deconvolution analysis identifies cancer-associated myofibroblast component as a poor prognostic factor in multiple cancer types.

Bingrui Li, Guangsheng Pei, Jun Yao, Qingqing Ding, Peilin Jia, Zhongming Zhao

Abstract read
PubMed Publisher
In one paragraph

Article in Oncogene, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.

0numbers the graph read from it
0cells of the map it votes in
35citing papers in PubMed
3.1field-weighted citation impact, top 7% 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

35 citing papers in PubMed, 50 citations in OpenAlex.

  1. Article
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  9. Review
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  11. Review
  12. The fibroinflammatory response in cancer.Nature reviews. Cancer · 2025
    Review
  13. Review
  14. Review
  15. Article
  16. Article
  17. Bioengineered Hydrogels Recapitulate Fibroblast Heterogeneity in Cancer.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024
    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

6 authors at 3 institutions in 1 country.

Bingrui LiCenter for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA.ORCID 0000-0002-4807-1452
Guangsheng PeiCenter for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA.ORCID 0000-0003-1804-7598
Jun YaoDepartment of Molecular and Cellular Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA.
Qingqing DingDepartment of Pathology, University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA.
Peilin JiaCenter for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA. peilin.jia@gmail.com.ORCID 0000-0003-4523-4153
Zhongming ZhaoCenter for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA. zhongming.zhao@uth.tmc.edu.ORCID 0000-0002-3477-0914
The University of Texas MD Anderson Cancer Center · USThe University of Texas Health Science Center at Houston · USThe University of Texas Health Science Center · US

Funding

Transforming dbGaP genetic and genomic data to FAIR-ready by artificial intelligence and machine learning algorithmsR01LM012806 · NLM · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI Zhongming Zhao · 2017 to 2026
$3.7M
U.S. Department of Health & Human Services | National Institutes of Health (NIH) R01LM012806
6 · The paper itself

Abstract

Cancer-associated fibroblasts (CAFs) constitute a prominent component of the tumor microenvironment and play critical roles in cancer progression and drug resistance. Although recent studies indicate CAFs may consist of several CAF subtypes, the breadth of CAF heterogeneity and functional roles of CAF subtypes in cancer progression remain unclear. In this study, we implemented a cell-type deconvolutional approach to comprehensively characterize cell-type alternations across 18 cancer types from The Cancer Genome Atlas (TCGA). Pan-cancer survival analysis using deconvoluted CAF subtypes revealed myofibroblastic CAF (myCAF) composition as a poor prognostic factor in nine cancer types. Patients with higher myCAF compositions tend to have worse response to six antineoplastic drugs predicted by a lncRNA-based Elastic Net prediction model (LENP). In addition, integrative mutational analysis identified 14 and 413 genes associated with the differentiation degree of myCAF and inflammatory CAF (iCAF), respectively, with significant enrichment of genes involved in fibroblast and extracellular matrix (ECM)-related pathways. In summary, our findings systematically illustrated the complex roles of CAF subtypes in patient prognosis and drug response, and identified putative driver genes in CAF-subtype differentiation. These results provided novel therapeutic perspectives for targeting CAF subtypes in tumor microenvironment and arranging treatment scheme based on the CAF compositions in different cancer types.

Indexed as

Cancer-Associated FibroblastsMyofibroblastsNeoplasmsTumor MicroenvironmentBiomarkers, TumorGene Expression Regulation, NeoplasticHumansPrognosisBiomarkers, Tumor

Identifiers

PMID34140640
OpenAlexW3171280400

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.