Evidence map›Paper›PMID 38928396›Full record

ArticleInternational journal of molecular sciences2024

PCAS: An Integrated Tool for Multi-Dimensional Cancer Research Utilizing Clinical Proteomic Tumor Analysis Consortium Data.

Jin Wang, Xiangrong Song, Meidan Wei, Lexin Qin, Qingyun Zhu, Shujie Wang, Tingting Liang, Wentao Hu, Xinyu Zhu, Jianxiang Li

Abstract read
In one paragraph

Article in International journal of molecular sciences, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing 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

15 citing papers in PubMed.

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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

10 authors.

Jin WangSchool of Public Health, Suzhou Medical College of Soochow University, Suzhou 215123, China.
Xiangrong SongSchool of Public Health, Suzhou Medical College of Soochow University, Suzhou 215123, China.
Meidan WeiSchool of Public Health, Suzhou Medical College of Soochow University, Suzhou 215123, China.
Lexin QinSchool of Public Health, Suzhou Medical College of Soochow University, Suzhou 215123, China.
Qingyun ZhuSchool of Public Health, Suzhou Medical College of Soochow University, Suzhou 215123, China.
Shujie WangSchool of Public Health, Suzhou Medical College of Soochow University, Suzhou 215123, China.
Tingting LiangSchool of Public Health, Suzhou Medical College of Soochow University, Suzhou 215123, China.
Wentao HuSchool of Public Health, Suzhou Medical College of Soochow University, Suzhou 215123, China.
Xinyu ZhuSchool of Public Health, Suzhou Medical College of Soochow University, Suzhou 215123, China.
Jianxiang LiSchool of Public Health, Suzhou Medical College of Soochow University, Suzhou 215123, China.ORCID 0000-0001-6674-7703

Funding

China Postdoctoral Science Foundation 2023M732527Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases NANational Natural Science Foundation of China 82304184National Natural Science Foundation of China 82373613Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD) NA
6 · The paper itself

Abstract

Proteomics offers a robust method for quantifying proteins and elucidating their roles in cellular functions, surpassing the insights provided by transcriptomics. The Clinical Proteomic Tumor Analysis Consortium database, enriched with comprehensive cancer proteomics data including phosphorylation and ubiquitination profiles, alongside transcriptomics data from the Genomic Data Commons, allow for integrative molecular studies of cancer. The ProteoCancer Analysis Suite (PCAS), our newly developed R package and Shinyapp, leverages these resources to facilitate in-depth analyses of proteomics, phosphoproteomics, and transcriptomics, enhancing our understanding of the tumor microenvironment through features like immune infiltration and drug sensitivity analysis. This tool aids in identifying critical signaling pathways and therapeutic targets, particularly through its detailed phosphoproteomic analysis. To demonstrate the functionality of the PCAS, we conducted an analysis of GAPDH across multiple cancer types, revealing a significant upregulation of protein levels, which is consistent with its important biological and clinical significance in tumors, as indicated in our prior research. Further experiments were used to validate the findings performed using the tool. In conclusion, the PCAS is a powerful and valuable tool for conducting comprehensive proteomic analyses, significantly enhancing our ability to uncover oncogenic mechanisms and identify potential therapeutic targets in cancer research.

Indexed as

NeoplasmsProteomicsComputational BiologyHumansProteomeSoftwareTumor MicroenvironmentProteomeclinical proteomic tumor analysis consortium (CPTAC)integrated cancer analysisphosphoproteomicsproteomicsR packageshiny application

Identifiers

PMID38928396
PMCPMC11203781

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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.