Evidence map›Paper›PMID 36203594›Full record

ArticleFrontiers in immunology2022

Identification of cuproptosis -related subtypes, the development of a prognosis model, and characterization of tumor microenvironment infiltration in prostate cancer.

Liang Jin, Wangli Mei, Xiang Liu, Xianchao Sun, Shiyong Xin, Zhen Zhou, Jiaxin Zhang, Bihui Zhang, Ping Chen, Ming Cai and 1 more

Open access · goldAbstract read
In one paragraph

Article in Frontiers in immunology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
23citing papers in PubMed, 2 pooled it
12.2field-weighted citation impact, top 1% 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

23 citing papers in PubMed, 2 syntheses or guidelines pooled it, 43 citations in OpenAlex.

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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 3 institutions in 1 country.

Liang JinDepartment of Urology, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, China.
Wangli MeiDepartment of Urology, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, China.
Xiang LiuDepartment of Urology, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, China.
Xianchao SunDepartment of Urology, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, China.
Shiyong XinDepartment of Urology, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, China.
Zhen ZhouDepartment of Urology, Shanghai Putuo District People's Hospital, School of Medicine, Tongji University, Shanghai, China.
Jiaxin ZhangDepartment of Urology, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Shanghai, China.
Bihui ZhangDepartment of Urology, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Shanghai, China.
Ping ChenSchool of Life Sciences and Technology, Tongji University, Shanghai, China.
Ming CaiDepartment of Orthopaedics, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Shanghai, China.
Lin YeDepartment of Urology, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, China.
Tongji University · CNShanghai East Hospital · CNShanghai Tenth People's Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cuproptosis, Copper Induced Cell Death, is a newly defined type of programmed cell death, involving in the regulation of tricarboxylic acid (TCA) cycle. Dysfunction of cuproptosis induces cytotoxicity and influences the proliferation of multiple tumors. However, the direct prognostic effect of cuproptosis related genes and corresponding regulating mechanisms amid prostate cancer remains unknown. A multi-omics analysis strategy was adopted to explore the role of ten cuproptosis related genes in The Cancer Genome Atlas- Prostate Adenocarcinoma (TCGA-PRAD). Firstly, mRNA expression, Copy Number Variance (CNV), mutation, DNA methylation and prognostic power of the ten genes were illustrated. Based on transcriptomic data, we developed a novel prognostic model named the Cuproptosis-related gene score (CRGScore), Their biological functions were then detected by enrichment analysis and unsupervised cluster analysis. Following that, their correlation with Tumor Immune Microenvironment (TIME), immunotherapy, Biochemical Recurrence (BCR) and chemotherapeutic resistance were elaborated by relevant bioinformatics algorithms. Ten cuproptosis related genes exhibited extensive alteration of CNV and DNA methylation and showed significant influence on the prognosis of prostate cancer patients. These genes mainly enriched in E2F and G2M targets and mitosis pathways, Samples with high CRGScore showed enhancement resulting in the increased infiltration of T cell, B cell, NK cells. They also demonstrated close correlations with the BCR status, expression of eight immune checkpoints and chemotherapeutic resistances in prostate cancer. Our comprehensive analysis of CRGScore revealed an extensive regulatory mechanism by which they affect the tumor-immune-stromal microenvironment, clinicopathological features, and prognosis. We also determined the therapeutic liability of CRGScore in targeted therapy and immunotherapy. These findings highlight the crucial clinical implications of CRGScore and provide new ideas for guiding personalized immunotherapy strategies for patients with Pca.

Indexed as

ApoptosisProstatic NeoplasmsTumor MicroenvironmentCopperHumansMalePrognosisRNA, MessengerTricarboxylic AcidsCopperRNA, MessengerTricarboxylic Acidscuproptosisimmunotherapyprognosisprostate cancertumor immune microenvironment

Identifiers

PMID36203594
PMCPMC9530990
OpenAlexW4297907049

What OpenQuestion holds

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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.