Evidence map›Paper›PMID 37735483›Full record

ArticleJournal of translational medicine2023

Characterization of tumor microenvironment and tumor immunology based on the double-stranded RNA-binding protein related genes in cervical cancer.

Jin Li, Chong Wan, Xiaoqi Li, Chenlian Quan, Xiaoqiu Li, Xiaohua Wu

Open access · goldAbstract read
In one paragraph

Article in Journal of translational medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 2 pooled it
1.4field-weighted citation impact, top 17% 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

5 citing papers in PubMed, 2 syntheses or guidelines pooled it, 6 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Article
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  5. 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 2 institutions in 1 country.

Jin Li *Department of Gynecologic Oncology, Fudan University Shanghai Cancer Center, Fudan University, No. 270 Dong'an Road, Shanghai, 200032, China.
Chong Wan *Precision Medicine Center, Yangtze Delta Region Institute of Tsinghua University, Jiaxing, China.
Xiaoqi LiDepartment of Gynecologic Oncology, Fudan University Shanghai Cancer Center, Fudan University, No. 270 Dong'an Road, Shanghai, 200032, China.
Chenlian QuanDepartment of Gynecologic Oncology, Fudan University Shanghai Cancer Center, Fudan University, No. 270 Dong'an Road, Shanghai, 200032, China.
Xiaoqiu LiDepartment of Pathology, Fudan University Shanghai Cancer Center, Fudan University, Shanghai, 200032, China.
Xiaohua WuDepartment of Gynecologic Oncology, Fudan University Shanghai Cancer Center, Fudan University, No. 270 Dong'an Road, Shanghai, 200032, China. wu.xh@fudan.edu.cn.ORCID http://orcid.org/0009-0003-9674-8389
Shanghai Medical College of Fudan University · CNTsinghua University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundCervical cancer is one of the most common gynecological cancers threatening women's health worldwide. Double-stranded RNA-binding proteins (dsRBPs) regulate innate immunity and are therefore believed to be involved in virus-related malignancies, however, their role in cervical cancer is not well known.

methodsWe performed RNA-seq of tumor samples from cervical cancer patients in local cohort and also assessed the RNA-seq and clinical data derived from public datasets. By using single sample Gene Set Enrichment Analysis (ssGSEA) and univariate Cox analysis, patients were stratified into distinct dsRBP clusters. Stepwise Cox and CoxBoost were performed to construct a risk model based on optimal dsRBPs clusters-related differentially expressed genes (DEGs), and GSE44001 and CGCI-HTMCP-CC were employed as two external validation cohorts. Single cell RNA sequencing data from GSE168652 and Scissor algorithm were applied to evaluated the signature-related cell population.

resultsThe expression of dsRBP features was found to be associated with HPV infection and carcinogenesis in CESC. However, only Adenosine deaminases acting on RNA (ADAR) and Dicer, Drosha, and Argonautes (DDR) exhibited significant correlations with the overall survival (OS) of CESC patients. Based on these findings, CESC patients were divided into three dsRBP clusters. Cluster 3 showed superior OS but lower levels of ADAR and DDR. Additionally, Cluster 3 demonstrated enhanced innate immunity, with significantly higher activity in cancer immunity cycles, immune scores, and levels of tumor-infiltrating immune cells, particularly CD8+ T cells. Furthermore, a risk model based on nine dsRBP cluster-related DEGs was established. The accuracy of survival prediction for 1 to 5 years was consistently above 0.78, and this model's robust predictive capacity was confirmed by two external validation sets. The low-risk group exhibited significantly higher levels of immune checkpoints, such as PDCD1 and CTLA4, as well as a higher abundance of CD8+ T cells. Analysis of single-cell sequencing data revealed a significant association between the dsRBP signature and glycolysis. Importantly, low-risk patients showed improved OS and a higher response rate to immunotherapy, along with enduring clinical benefits from concurrent chemoradiotherapy.

conclusionsdsRBP played a crucial role in the regulation of prognosis and tumor immunology in cervical cancer, and its prognostic signature provides a strategy for risk stratification and immunotherapy evaluation.

Indexed as

Uterine Cervical NeoplasmsAlgorithmsCarcinogenesisCD8-Positive T-LymphocytesFemaleHumansTumor MicroenvironmentCervical cancerChemotherapydsRBPImmunotherapyRisk modelTumor microenvironment

Identifiers

PMID37735483
PMCPMC10515034
OpenAlexW4386927848

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.