PXD062101 is an
original dataset announced via ProteomeXchange.
Dataset Summary
Title | Mapping the Interactome of KRAS and Its G12C/D/V Mutants by Integrating TurboID Proximity Labeling with Quantitative Proteomics |
Description | RAS is one of the most frequently mutated proto-oncogenes in human malignancies, with mutation sites and subtypes exhibiting tumor-type dependent distribution. Oncogenic RAS mutations will maintain continuously GTP-bound activation states that leading to dysregulation of downstream signaling, ultimately disrupting cellular homeostasis to induce malignant transformation of cells. In this study, we employed TurboID proximity-labeling technology integrated with quantitative proteomics LC-MS/MS to systematically characterize the proximal binding proteins of wild-type KRAS and three high-frequency oncogenic mutant subtypes G12C, G12D and G12V. Through comprehensive bioinformatic analysis of mutation-specific interaction networks and distinct metabolic pathways, we identified significant enrichment of mutant KRAS binding proteins in insulin signaling pathway, reactive oxygen species related pathways, glucose and lipid metabolism and so on. Metabolic reprogramming pathways in KRAS G12 mutations collectively fuel tumor proliferation and immune evasion. In addition, we also comparatively analyzed the proximal binding proteins similarity in three G12 mutants. Notably, we observed that the KRAS E3 ubiquitin ligase adaptor LZTR1 diminished binding with mutations, and the mTORC1 important regulatory protein LAMTOR1 was enhanced recruitment by mutations. This multi-dimensional profiling delineates a comprehensive mapping of KRAS WT and G12 mutant interactomes and unveils the metabolic reprogramming pathways associated with KRAS activating mutations. Our analysis result provides potential therapeutic targets for KRAS-driven tumorigenesis while establishing a mechanistic framework for developing KRAS mutation-specific therapeutic strategies. |
HostingRepository | PRIDE |
AnnounceDate | 2025-06-09 |
AnnouncementXML | Submission_2025-06-08_16:11:32.210.xml |
DigitalObjectIdentifier | |
ReviewLevel | Peer-reviewed dataset |
DatasetOrigin | Original dataset |
RepositorySupport | Unsupported dataset by repository |
PrimarySubmitter | chengzhi wang |
SpeciesList | scientific name: Homo sapiens (Human); NCBI TaxID: 9606; |
ModificationList | No PTMs are included in the dataset |
Instrument | ZenoTOF 7600 |
Dataset History
Revision | Datetime | Status | ChangeLog Entry |
0 | 2025-03-21 01:13:24 | ID requested | |
⏵ 1 | 2025-06-08 16:11:33 | announced | |
Publication List
10.3390/biology14050477; |
Song J, Wang B, Zou M, Zhou H, Ding Y, Ren W, Fang L, Zhang J, Mapping the Interactome of KRAS and Its G12C/D/V Mutants by Integrating TurboID Proximity Labeling with Quantitative Proteomics. Biology (Basel), 14(5):(2025) [pubmed] |
Keyword List
submitter keyword: KRAS interactome |
KRAS G12 mutants |
TurboID proximity-labeling |
Quantitative Proteomics;Metabolic reprogramming |
Contact List
Lei Fang |
contact affiliation | State Key Laboratory of Pharmaceutical Biotechnology, Jiangsu Key Laboratory of Molecular Medicine, Chemistry and Biomedicine Innovation Center, Medical School of Nanjing University, Nanjing 210093, China. |
contact email | njfanglei@nju.edu.cn |
lab head | |
chengzhi wang |
contact affiliation | university |
contact email | 602022350046@smail.nju.edu.cn |
dataset submitter | |
Full Dataset Link List
Dataset FTP location
NOTE: Most web browsers have now discontinued native support for FTP access within the browser window. But you can usually install another FTP app (we recommend FileZilla) and configure your browser to launch the external application when you click on this FTP link. Or otherwise, launch an app that supports FTP (like FileZilla) and use this address: ftp://ftp.pride.ebi.ac.uk/pride/data/archive/2025/06/PXD062101 |
PRIDE project URI |
Repository Record List
[ + ]
[ - ]
- PRIDE
- PXD062101
- Label: PRIDE project
- Name: Mapping the Interactome of KRAS and Its G12C/D/V Mutants by Integrating TurboID Proximity Labeling with Quantitative Proteomics