Evidence map›Paper›PMID 37284314›Full record

ArticleFrontiers in pharmacology2023

A novel T-cell exhaustion-related feature can accurately predict the prognosis of OC patients.

Kemiao Yuan, Songyun Zhao, Bicheng Ye, Qi Wang, Yuan Liu, Pengpeng Zhang, Jiaheng Xie, Hao Chi, Yu Chen, Chao Cheng and 1 more

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in pharmacology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers.

0numbers the graph read from it
0cells of the map it votes in
39citing papers in PubMed
9.6field-weighted citation impact, top 2% 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

39 citing papers in PubMed, 40 citations in OpenAlex.

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

11 authors at 5 institutions in 1 country.

Kemiao YuanDepartment of Oncology, Traditional Chinese Medicine Hospital of Wuxi, Wuxi, China.
Songyun ZhaoDepartment of Neurosurgery, Wuxi People's Hospital Affiliated to Nanjing Medical University, Wuxi, China.
Bicheng YeSchool of Clinical Medicine, Yangzhou Polytechnic College, Yangzhou, China.
Qi WangDepartment of Gastroenterology, Affiliated Hospital of Jiangsu University, Zhenjiang, China.
Yuan LiuDepartment of General Surgery, Wuxi People's Hospital Affiliated to Nanjing Medical University, Wuxi, China.
Pengpeng ZhangThe First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
Jiaheng XieThe First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
Hao ChiSouthwest Medical University, Luzhou, China.
Yu ChenWuxi Maternal and Child Health Care Hospital, Wuxi, China.
Chao ChengDepartment of Neurosurgery, Wuxi People's Hospital Affiliated to Nanjing Medical University, Wuxi, China.
Jinhui LiuDepartment of Gynecology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
Nanjing Medical University · CNWuxi People's Hospital · CNAffiliated Hospital of Jiangsu University · CNSouthwest Medical University · CNYangzhou Polytechnic Institute · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The phenomenon of T Cell exhaustion (TEX) entails a progressive deterioration in the functionality of T cells within the immune system during prolonged conflicts with chronic infections or tumors. In the context of ovarian cancer immunotherapy, the development, and outcome of treatment are closely linked to T-cell exhaustion. Hence, gaining an in-depth understanding of the features of TEX within the immune microenvironment of ovarian cancer is of paramount importance for the management of OC patients. To this end, we leveraged single-cell RNA data from OC to perform clustering and identify T-cell marker genes utilizing the Unified Modal Approximation and Projection (UMAP) approach. Through GSVA and WGCNA in bulk RNA-seq data, we identified 185 TEX-related genes (TEXRGs). Subsequently, we transformed ten machine learning algorithms into 80 combinations and selected the most optimal one to construct TEX-related prognostic features (TEXRPS) based on the mean C-index of the three OC cohorts. In addition, we explored the disparities in clinicopathological features, mutational status, immune cell infiltration, and immunotherapy efficacy between the high-risk (HR) and low-risk (LR) groups. Upon the integration of clinicopathological features, TEXRPS displayed robust predictive power. Notably, patients in the LR group exhibited a superior prognosis, higher tumor mutational load (TMB), greater immune cell infiltration abundance, and enhanced sensitivity to immunotherapy. Lastly, we verified the differential expression of the model gene CD44 using qRT-PCR. In conclusion, our study offers a valuable tool to guide clinical management and targeted therapy of OC.

Indexed as

immunotherapymachine learningOCScRNA-seqt cell exhaustion

Identifiers

PMID37284314
PMCPMC10239809
OpenAlexW4377292416

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.