Evidence map›Paper›PMID 39493875›Full record

ArticleiScience2024

PhosCancer: A comprehensive database for investigating protein phosphorylation in human cancer.

Qun Dong, Danqing Shen, Jiachen Ye, Jiaxin Chen, Jing Li

Abstract read
In one paragraph

Article in iScience, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
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

5 citing papers in PubMed.

  1. Article
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  3. Review
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  5. 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

5 authors.

Qun DongDepartment of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China.
Danqing ShenDepartment of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China.
Jiachen YeDepartment of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China.
Jiaxin ChenDepartment of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China.
Jing LiDepartment of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Protein phosphorylation is a crucial post-translational modification implicated in cancer pathogenesis, offering potential diagnostic and therapeutic targets. Here, we developed PhosCancer, a user-friendly database for extracting biologically and clinically relevant insights from phosphoproteomics data. Leveraging data from the CNHPP and CPTAC, PhosCancer encompasses 174,587 phosphosites from 14 datasets spanning 12 cancer types. Through extensive statistical analyses and integration of annotations from external resources, PhosCancer serves as a convenient one-stop platform facilitating the exploration of phosphorylation profiles across different cancer types. Not only does PhosCancer encompass basic information, 3D structure, functional domains, and upstream kinases, but also provides quantitative associations with nine clinical features, and the relevance with hallmarks in both cancer-specific and pan-cancer views. PhosCancer is a valuable resource for cancer researchers and clinicians, promoting the identification of clinically actionable biomarkers and further facilitating the clinical applications of phosphoproteomic data.

Indexed as

biological databasecancersoftware tool

Identifiers

PMID39493875
PMCPMC11530918

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Registered trials

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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.