Evidence map›Paper›PMID 38657119›Full record

ArticleAsian journal of andrology2024

Single-cell analysis revealing the metabolic landscape of prostate cancer.

Jing Wang, He-Kang Ding, Han-Jiang Xu, De-Kai Hu, William Hankey, Li Chen, Jun Xiao, Chao-Zhao Liang, Bing Zhao, Ling-Fan Xu

Open access · goldAbstract read
In one paragraph

Article in Asian journal of andrology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
2.5field-weighted citation impact, top 11% 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

4 citing papers in PubMed, 6 citations in OpenAlex.

  1. Review
  2. Review
  3. Article
  4. 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

10 authors at 4 institutions in 2 countries.

Jing WangDepartment of Urologic Oncology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230031, China.
He-Kang DingDepartment of Urology, The First Affiliated Hospital of Anhui Medical University, Hefei 230001, China.
Han-Jiang XuDepartment of Urology, The First Affiliated Hospital of Anhui Medical University, Hefei 230001, China.
De-Kai HuDepartment of Urology, The First Affiliated Hospital of Anhui Medical University, Hefei 230001, China.
William HankeyDepartment of Genetics, University of North Carolina, Chapel Hill, NC 27599, USA.
Li ChenDepartment of Geriatrics, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230001, China.
Jun XiaoDepartment of Urology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230001, China.
Chao-Zhao LiangDepartment of Urology, The First Affiliated Hospital of Anhui Medical University, Hefei 230001, China.
Bing ZhaoDepartment of Geriatrics, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230001, China.
Ling-Fan XuDepartment of Urology, The First Affiliated Hospital of Anhui Medical University, Hefei 230001, China.
Anhui Medical University · CNUniversity of Science and Technology of China · CNFirst Affiliated Hospital of Anhui Medical University · CNUniversity of North Carolina at Chapel Hill · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

abstractTumor metabolic reprogramming is a hallmark of cancer development, and targeting metabolic vulnerabilities has been proven to be an effective approach for castration-resistant prostate cancer (CRPC) treatment. Nevertheless, treatment failure inevitably occurs, largely due to cellular heterogeneity, which cannot be deciphered by traditional bulk sequencing techniques. By employing computational pipelines for single-cell RNA sequencing, we demonstrated that epithelial cells within the prostate are more metabolically active and plastic than stromal cells. Moreover, we identified that neuroendocrine (NE) cells tend to have high metabolic rates, which might explain the high demand for nutrients and energy exhibited by neuroendocrine prostate cancer (NEPC), one of the most lethal variants of prostate cancer (PCa). Additionally, we demonstrated through computational and experimental approaches that variation in mitochondrial activity is the greatest contributor to metabolic heterogeneity among both tumor cells and nontumor cells. These results establish a detailed metabolic landscape of PCa, highlight a potential mechanism of disease progression, and emphasize the importance of future studies on tumor heterogeneity and the tumor microenvironment from a metabolic perspective.

Indexed as

Prostatic NeoplasmsSingle-Cell AnalysisEpithelial CellsHumansMaleMitochondriaProstateProstatic Neoplasms, Castration-ResistantTumor Microenvironment

Identifiers

PMID38657119
PMCPMC11449408
OpenAlexW4395042103

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-SA
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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.