Evidence map›Paper›PMID 35614988›Full record

ArticleMolecular therapy. Nucleic acids2022

Immunogenomic landscape analyses of immune molecule signature-based risk panel for patients with triple-negative breast cancer.

Cun Liu, Ye Li, Xiaoming Xing, Jing Zhuang, Jigang Wang, Chunyan Wang, Lujun Zhang, Lijuan Liu, Fubin Feng, Huayao Li and 4 more

Abstract read
In one paragraph

Article in Molecular therapy. Nucleic acids, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed, 1 pooled it
–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

16 citing papers in PubMed, 1 synthesis or guideline pooled it.

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

Cun LiuFirst School of Clinical Medicine, Shandong University of Traditional Chinese Medicine, Jinan 250000, China.
Ye LiFirst School of Clinical Medicine, Shandong University of Traditional Chinese Medicine, Jinan 250000, China.
Xiaoming XingDepartment of Pathology, The Affiliated Hospital of Qingdao University, Qingdao 266000, China.
Jing ZhuangDepartment of Oncology, Weifang Traditional Chinese Hospital, Weifang 261000, China.
Jigang WangDepartment of Pathology, The Affiliated Hospital of Qingdao University, Qingdao 266000, China.
Chunyan WangDepartment of Physics and Optoelectronic Engineering, Weifang University, Weifang 261000, China.
Lujun ZhangDepartment of Physics and Optoelectronic Engineering, Weifang University, Weifang 261000, China.
Lijuan LiuDepartment of Special Medicine, School of Basic Medicine, Qingdao University, Qingdao 266000, China.
Fubin FengDepartment of Oncology, Weifang Traditional Chinese Hospital, Weifang 261000, China.
Huayao LiCollege of Traditional Chinese Medicine, Shandong University of Traditional Chinese Medicine, Jinan 250000, China.
Chundi GaoFirst School of Clinical Medicine, Shandong University of Traditional Chinese Medicine, Jinan 250000, China.
Yang YuCollege of Traditional Chinese Medicine, Shandong University of Traditional Chinese Medicine, Jinan 250000, China.
Jingyang LiuFirst School of Clinical Medicine, Shandong University of Traditional Chinese Medicine, Jinan 250000, China.
Changgang SunDepartment of Oncology, Weifang Traditional Chinese Hospital, Weifang 261000, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Triple-negative breast cancer (TNBC) presented as high heterogeneous immunogenicity that lacks useful clinical signatures to risk-stratify immune-benefit subtypes. We hypothesized that molecular-based phenotypic characterization of TNBC tumors and their immunity may overcome these challenges. We enrolled 1,145 patients with TNBC for analysis. Through combining algorithm integration analysis and TNBC datasets, a tumor immune risk score (TIRS) panel consisting of 8 potential biomarkers was identified. The TIRS panel represented excellent effectiveness as an independent predictor. High- and low risk stratification of patients was further achieved by TIRS, and significant survival and immune-infiltration pattern differences were found in each cohort, both at the transcriptome and protein levels. Non-negative matrix factorization clustering further identified four different tumor immune microenvironment types (TIMTs), among which TIMT-II was associated with the best prognosis and immune status, whereas TIMT-IV had the opposite effect, TIMT-III was associated with highly unstable genomes, and TIMT-I displayed stem-cell-related characteristics along with high stromal scores and may have extensive enrichment of tumor-associated fibroblasts and vascular cells. In conclusion, our TIRS panel could serve as a robust prognostic signature and provide therapeutic benefits for immunotherapy. Additionally, coordinating four TIMTs may be helpful for clinical decision-making in TNBC patients.

Indexed as

immune risk signaturerisk stratificationtreatment strategytriple-negative breast cancertumor immune microenvironment types

Identifiers

PMID35614988
PMCPMC9123090

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.