Evidence map›Paper›PMID 37131778›Full record

ArticleResearch square2023

A Cellular Senescence-Related Signature Predicts Cervical Cancer Patient Outcome and Immunotherapy Sensitivity.

Huijing Shao, Xia Li, Pengfei Wu, Zixi Chen, Caihong Zhang, Hang Gu

Open access · greenAbstract readPreprint
In one paragraph

Article in Research square, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
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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

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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

0 citing papers in PubMed, 0 citations in OpenAlex.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

6 authors at 3 institutions in 1 country.

Huijing ShaoThe First Affiliated Hospital of Naval Medical University: Changhai Hospital.ORCID 0000-0002-6276-0000
Xia LiHuaian First People's Hospital.
Pengfei Wufu dan da xue fu shu yi yuan fu chan ke yi yuan: Obstetrics and Gynecology Hospital of Fudan University.
Zixi ChenPutuo Hospital Affiliated to Shanghai University of Traditional Chinese Medicine: Shanghai PuTuo District Center Hospital.
Caihong ZhangThe First Affiliated Hospital of Naval Medical University: Changhai Hospital.
Hang GuThe First Affiliated Hospital of Naval Medical University: Changhai Hospital.ORCID 0000-0001-7671-5182
Second Military Medical University · CNHuaian First People’s Hospital · CNObstetrics and Gynecology Hospital of Fudan University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cervical cancer (CC) is among the most prevalent gynaecological malignancy. The rate of mortality and morbidity of patients with CC is high. Cellular senescence is involved in tumorigenesis as well as cancer progression. However, the involvement of cellular senescence in CC development is still unclear and requires further investigation. We retrieved data on cellular senescence-related genes (CSRGs) from the "CellAge" Database. We used TCGA-CESC and the CGCI-HTMCP-CC datasets as the training and validation sets, respectively. Eight CSRGs signatures based on the data extracted from these sets were constructed using "univariate" and "Least Absolute Shrinkage and Selection Operator Cox regression analyses". Using this model, we calculated the risk scores of all patients in the training and validation cohort and categorised these patients into the low-risk group (LR-G) and the high-risk group (HR-G). Finally, compared to patients in the HR-G, CC patients in the LR-G demonstrated a more positive clinical prognosis; the expression of senescence-associated secretory phenotype (SASP) markers and immune cell infiltration was higher, and these patients had more active immune responses. In vitro studies showed increased SERPINE1 and IL-1α ((genes included in the signature) expression in CC cells and tissues. The eight-gene prognostic signatures could modulate the expression of SASP factors and the tumour immune micro-environment (TIME). It could be used as a reliable biomarker for predicting the patient's prognosis and response to immunotherapy in CC.

Identifiers

PMID37131778
PMCPMC10153369
OpenAlexW4366548153

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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.