Evidence map›Paper›PMID 35785167›Full record

ArticleFrontiers in oncology2022

Identification of Metabolism-Related Gene-Based Subgroup in Prostate Cancer.

Guopeng Yu, Bo Liang, Keneng Yin, Ming Zhan, Xin Gu, Jiangyi Wang, Shangqing Song, Yushan Liu, Qing Yang, Tianhai Ji and 1 more

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

Article in Frontiers in oncology, 2022. 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
0.3field-weighted citation impact, top 42% 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, 2 citations in OpenAlex.

  1. Article
  2. 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

11 authors at 3 institutions in 1 country.

Guopeng YuDepartment of Urology, Shanghai Ninth People's Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China.
Bo LiangThe Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China.
Keneng Yin174 Clinical College, Anhui Medical University, Hefei, China.
Ming ZhanDepartment of Urology, Shanghai Ninth People's Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China.
Xin GuDepartment of Urology, Shanghai Ninth People's Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China.
Jiangyi WangDepartment of Urology, Shanghai Ninth People's Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China.
Shangqing SongDepartment of Urology, Shanghai Ninth People's Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China.
Yushan LiuDepartment of Urology, Shanghai Ninth People's Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China.
Qing YangDepartment of Urology, Shanghai Ninth People's Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China.
Tianhai Ji174 Clinical College, Anhui Medical University, Hefei, China.
Bin XuDepartment of Urology, Shanghai Ninth People's Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China.
Shanghai Ninth People's Hospital · CNAnhui Medical University · CNSecond Affiliated Hospital of Zhejiang University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Prostate cancer is still the main male health problem in the world. The role of metabolism in the occurrence and development of prostate cancer is becoming more and more obvious, but it is not clear. Here we firstly identified a metabolism-related gene-based subgroup in prostate cancer. We used metabolism-related genes to divide prostate cancer patients from The Cancer Genome Atlas into different clinical benefit populations, which was verified in the International Cancer Genome Consortium. After that, we analyzed the metabolic and immunological mechanisms of clinical beneficiaries from the aspects of functional analysis of differentially expressed genes, gene set variation analysis, tumor purity, tumor microenvironment, copy number variations, single-nucleotide polymorphism, and tumor-specific neoantigens. We identified 56 significant genes for non-negative matrix factorization after survival-related univariate regression analysis and identified three subgroups. Patients in subgroup 2 had better overall survival, disease-free interval, progression-free interval, and disease-specific survival. Functional analysis indicated that differentially expressed genes in subgroup 2 were enriched in the xenobiotic metabolic process and regulation of cell development. Moreover, the metabolism and tumor purity of subgroup 2 were higher than those of subgroup 1 and subgroup 3, whereas the composition of immune cells of subgroup 2 was lower than that of subgroup 1 and subgroup 3. The expression of major immune genes, such as CCL2, CD274, CD276, CD4, CTLA4, CXCR4, IL1A, IL6, LAG3, TGFB1, TNFRSF4, TNFRSF9, and PDCD1LG2, in subgroup 2 was almost significantly lower than that in subgroup 1 and subgroup 3, which is consistent with the results of tumor purity analysis. Finally, we identified that subgroup 2 had lower copy number variations, single-nucleotide polymorphism, and neoantigen mutation. Our systematic study established a metabolism-related gene-based subgroup to predict outcomes of prostate cancer patients, which may contribute to individual prevention and treatment.

Indexed as

immuneInternational Cancer Genome Consortium (ICGC)metabolismnon-negative matrix factorization (NMF)prostate cancerThe Cancer Genome Atlas (TCGA)

Identifiers

PMID35785167
PMCPMC9243363
OpenAlexW4282973007

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.