Evidence map›Paper›PMID 38349474›Full record

ArticleCancer immunology, immunotherapy : CII2024

Identification macrophage signatures in prostate cancer by single-cell sequencing and machine learning.

Zhen Kang, Yu-Xuan Zhao, Ren Shun Qian Qiu, Dong-Ning Chen, Qing-Shui Zheng, Xue-Yi Xue, Ning Xu, Yong Wei

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

Article in Cancer immunology, immunotherapy : CII, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed, 1 pooled it
3.1field-weighted citation impact, top 7% 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

8 citing papers in PubMed, 1 synthesis or guideline pooled it, 11 citations in OpenAlex.

  1. Pooled it
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  4. Review
  5. Review
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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

8 authors at 2 institutions in 1 country.

Zhen Kang *Department of Urology, Urology Research Institute, The First Affiliated Hospital, Fujian Medical University, Fuzhou, 350212, China.
Yu-Xuan Zhao *Department of Urology, Urology Research Institute, The First Affiliated Hospital, Fujian Medical University, Fuzhou, 350212, China.
Ren Shun Qian Qiu *Department of Urology, Urology Research Institute, The First Affiliated Hospital, Fujian Medical University, Fuzhou, 350212, China.
Dong-Ning ChenDepartment of Urology, Urology Research Institute, The First Affiliated Hospital, Fujian Medical University, Fuzhou, 350212, China.
Qing-Shui ZhengDepartment of Urology, Urology Research Institute, The First Affiliated Hospital, Fujian Medical University, Fuzhou, 350212, China.
Xue-Yi XueDepartment of Urology, Urology Research Institute, The First Affiliated Hospital, Fujian Medical University, Fuzhou, 350212, China.
Ning XuDepartment of Urology, Urology Research Institute, The First Affiliated Hospital, Fujian Medical University, Fuzhou, 350212, China. drxun@fjmu.edu.cn.
Yong WeiDepartment of Urology, Urology Research Institute, The First Affiliated Hospital, Fujian Medical University, Fuzhou, 350212, China. weiyong2017@fjmu.edu.cn.
First Affiliated Hospital of Fujian Medical University · CNFujian Medical University · CN

Funding

Science and Technology Innovation Joint Fund project of Fujian province 2021Y9126
6 · The paper itself

Abstract

backgroundThe tumor microenvironment (TME) encompasses a variety of cells that influence immune responses and tumor growth, with tumor-associated macrophages (TAM) being a crucial component of the TME. TAM can guide prostate cancer in different directions in response to various external stimuli.

methodsFirst, we downloaded prostate cancer single-cell sequencing data and second-generation sequencing data from multiple public databases. From these data, we identified characteristic genes associated with TAM clusters. We then employed machine learning techniques to select the most accurate TAM gene set and developed a TAM-related risk label for prostate cancer. We analyzed the tumor-relatedness of the TAM-related risk label and different risk groups within the population. Finally, we validated the accuracy of the prognostic label using single-cell sequencing data, qPCR, and WB assays, among other methods.

resultsIn this study, the TAM_2 cell cluster has been identified as promoting the progression of prostate cancer, possibly representing M2 macrophages. The 9 TAM feature genes selected through ten machine learning methods and demonstrated their effectiveness in predicting the progression of prostate cancer patients. Additionally, we have linked these TAM feature genes to clinical pathological characteristics, allowing us to construct a nomogram. This nomogram provides clinical practitioners with a quantitative tool for assessing the prognosis of prostate cancer patients.

conclusionThis study has analyzed the potential relationship between TAM and PCa and established a TAM-related prognostic model. It holds promise as a valuable tool for the management and treatment of PCa patients.

Indexed as

MacrophagesProstatic NeoplasmsHumansMachine LearningMaleNomogramsTumor-Associated MacrophagesTumor MicroenvironmentCancer subtypeMachine learningProstate cancerSingle-cell RNA-seqTumor-associated macrophage

Identifiers

PMID38349474
PMCPMC10864475
OpenAlexW4391778997

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.