Evidence map›Paper›PMID 35774776›Full record

ArticleFrontiers in immunology2022

3D Collagen Fiber Concentration Regulates Treg Cell Infiltration in Triple Negative Breast Cancer.

Huan Gao, Qi Tian, Yan Zhou, Lizhe Zhu, Yinliang Lu, Yingying Ma, Jinteng Feng, Yina Jiang, Bo Wang

Open access · goldAbstract read
In one paragraph

Article in Frontiers in immunology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed
1.7field-weighted citation impact, top 14% 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

16 citing papers in PubMed, 18 citations in OpenAlex.

  1. Review
  2. Review
  3. Review
  4. Review
  5. Review
  6. Review
  7. Article
  8. Review
  9. Article
  10. Review
  11. Review
  12. Review
  13. Article
  14. Article
  15. Review
  16. 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

9 authors at 1 institution in 1 country.

Huan GaoDepartment of Medical Oncology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
Qi TianDepartment of Radiology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
Yan ZhouDepartment of Medical Oncology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
Lizhe ZhuDepartment of Breast Surgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
Yinliang LuDepartment of Radiation Oncology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
Yingying MaDepartment of Medical Oncology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
Jinteng FengDepartment of Medical Oncology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
Yina JiangDepartment of Pathology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
Bo WangCenter for Translational Medicine, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
First Affiliated Hospital of Xi'an Jiaotong University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Triple negative breast cancer (TNBC) is characterized by poor prognosis and a lack of effective therapeutic agents owing to the absence of biomarkers. A high abundance of tumor-infiltrating regulatory T cells (Tregs) was associated with worse prognosis in malignant disease. Exploring the association between Treg cell infiltration and TNBC will provide new insights for understanding TNBC immunosuppression and may pave the way for developing novel immune-based treatments. Materials and Methods: Patients from TCGA were divided into Treg-high (Treg-H) and Treg-low (Treg-L) groups based on the abundance of Tregs according to CIBERSORT analysis. The association between expression level of Tregs and the clinical characteristics as well as prognosis of breast cancer were evaluated. Next, a Treg-related prognostic model was established after survival-dependent univariate Cox and LASSO regression analysis, companied with an external GEO cohort validation. Then, GO, KEGG and GSEA analyses were performed between the Treg-H and Treg-L groups. Masson and Sirius red/Fast Green staining were applied for ECM characterization. Accordingly, Jurkat T cells were encapsulated in 3D collagen to mimic the ECM microenvironment, and the expression levels of CD4, FOXP3 and CD25 were quantified according to immunofluorescence staining. Results: The expression level of Tregs is significantly associated with the clinical characteristics of breast cancer patients, and a high level of Treg cell expression indicates a poor prognosis in TNBC. To further evaluate this, a Treg-related prognostic model was established that accurately predicted outcomes in both TCGA training and GEO validation cohorts of TNBC patients. Subsequently, ECM-associated signaling pathways were identified between the Treg-H and Treg-L groups, indicating the role of ECM in Treg infiltration. Since we found increasing collagen concentrations in TNBC patients with distant migration, we encapsulated Jurkat T cells within a 3D matrix with different collagen concentrations and observed that increasing collagen concentrations promoted the expression of Treg biomarkers, supporting the regulatory role of ECM in Treg infiltration. Conclusion: Our results support the association between Treg expression and breast cancer progression as well as prognosis in the TNBC subtype. Moreover, increasing collagen density may promote Treg infiltration, and thus induce an immunosuppressed TME.

Indexed as

T-Lymphocytes, RegulatoryTriple Negative Breast NeoplasmsBiomarkersCollagenHumansLymphocytes, Tumor-InfiltratingTumor MicroenvironmentBiomarkersCollagenextracellular matrixprognostic modelregulatory T cellstriple negative breast cancertumor-infiltrating lymphocytestumor microenvironment

Identifiers

PMID35774776
PMCPMC9237245
OpenAlexW4282823037

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.