Evidence map›Paper›PMID 37078136›Full record

ArticleCancer control : journal of the Moffitt Cancer Center

Immune-Related Genes' Prognostic, Therapeutic and Diagnostic Value in Ovarian Cancer Immune-Related Gene Biomarker in Ovarian Cancer.

Bo Ding, Wenjing Yan, Siyuan Shen, Dan Meng, Xue Chen, Shizhi Wang, Yang Shen

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

Article in Cancer control : journal of the Moffitt Cancer Center. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
1.0field-weighted citation impact, top 22% 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

2 citing papers in PubMed, 4 citations in OpenAlex.

  1. Article
  2. 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

7 authors at 2 institutions in 1 country.

Bo DingDepartment of Obstetrics and Gynecology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China.ORCID 0000-0001-8771-6578
Wenjing YanKey Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing, China.
Siyuan ShenKey Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing, China.
Dan MengKey Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing, China.
Xue ChenKey Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing, China.
Shizhi WangKey Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing, China.
Yang ShenDepartment of Obstetrics and Gynecology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China.
Southeast University · CNZhongda Hospital Southeast University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesThe abnormal expression of immune-related genes (IRGs) plays an important role in the occurrence and progression of ovarian cancer (OC), which is the main cause of mortality among gynecological cancer patients. This study aims to establish a prognostic risk model and comprehensively analyze the relationship between OC risk score and prognosis, immune cell infiltration (ICI) and therapeutic sensitivity in OC.

methodsWe retrospectively evaluated the clinicopathological characteristics of consecutive OC patients in the Cancer Genome Atlas (TCGA) database. First, the prognostic risk model was constructed by bioinformatics methods. And then, we systematically assessed model robustness, and correlations between risk score and prognosis, and immune cell infiltration. The ICGC cohort was used to verify the prognostic risk model. Finally, we evaluated their value in the treatment of OC immunotherapy and chemotherapy.

resultsA total of 10 IRGs were identified to construct the prognostic risk model. Survival analysis revealed that patients in the low-risk group had a better prognosis (

conclusionsCollectively, we identified a novel ten IRGs signature that may be applied as a prognostic predictor of OC, thereby benefiting clinical decision-making and personalized treatment of patients.

Indexed as

Ovarian NeoplasmsBiomarkersFemaleHumansImmunotherapyPrognosisRetrospective StudiesBiomarkersdiagnosisimmune-related genesovarian cancerprognostic risk modeltreatment

Identifiers

PMID37078136
PMCPMC10126801
OpenAlexW4366594143

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.