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Updated: May 15, 2025
Phosphopeptide Enrichment Coupled with Label-free Quantitative Mass Spectrometry to Investigate the Phosphoproteome in Prostate Cancer
Published on: August 2, 2018
Inferring kinase-phosphosite regulation from phosphoproteome-enriched cancer multi-omics datasets
Haoyang Cheng1,2, Zhuoran Liang1, Yijin Wu1
1State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, 651 Dongfeng East Road, Guangzhou 510060, China.
Abstract:
Phosphorylation in eukaryotic cells plays a key role in regulating cell signaling and disease progression. Despite the ability to detect thousands of phosphosites in a single experiment using high-throughput technologies, the kinases responsible for regulating these sites are largely unidentified. To solve this, we collected the quantitative data at the transcriptional, protein, and phosphorylation levels of 10 159 samples from 23 tumor datasets and 15 adjacent normal tissue datasets. Our analysis aimed to uncover the potential impact and linkage of kinase-phosphosite (KPS) pairs through experimental evidence in publications and prediction tools commonly used. We discovered that both experimentally validated and tool-predicted KPS pairs were enriched in groups where there is a significant correlation between kinase expression/phosphorylation level and the phosphorylation level of phosphosite. This suggested that a quantitative correlation could infer the KPS interconnections. Furthermore, the Spearman's correlation coefficient for these pairs were notably higher in tumor samples, indicating that these regulatory interactions are particularly pronounced in tumors. Consequently, building on the KPS correlations of different datasets as predictive features, we have developed an innovative approach that employed an oversampling method combined with and XGBoost algorithm (SMOTE-XGBoost) to predict potential kinase-specific phosphorylation sites in proteins. Moreover, the computed correlations and predictions of kinase-phosphosite interconnections were integrated into the eKPI database (https://ekpi.omicsbio.info/). In summary, our study could provide helpful information and facilitate further research on the regulatory relationship between kinases and phosphosites.
Insights
Identifying kinase-phosphosite (KPS) pairs is crucial for understanding cell signaling and disease. This study quantifies KPS correlations across tumor and normal tissues, developing a novel SMOTE-XGBoost method to predict kinase-specific phosphorylation sites.
Area of Science:
- Molecular Biology
- Cancer Research
- Bioinformatics
Background:
- Phosphorylation is vital for eukaryotic cell signaling and disease, but identifying responsible kinases for detected phosphosites remains a challenge.
- High-throughput technologies detect numerous phosphosites, yet the specific kinases regulating them are largely unknown, hindering a complete understanding of cellular regulation.
Purpose of the Study:
- To identify and characterize kinase-phosphosite (KPS) pairs by analyzing quantitative data across multiple tumor and normal tissue datasets.
- To develop and validate a computational approach for predicting kinase-specific phosphorylation sites using KPS correlations.
Main Methods:
- Collected and analyzed quantitative data (transcriptional, protein, phosphorylation levels) from 10,159 tumor and 15 adjacent normal tissue samples.
- Investigated KPS pair linkages using experimental evidence and prediction tools, assessing correlations between kinase and phosphosite levels.
- Employed an oversampling method with an XGBoost algorithm (SMOTE-XGBoost) to predict potential kinase-specific phosphorylation sites.
Main Results:
- Found significant correlations between kinase expression/phosphorylation and phosphosite levels, validating KPS interconnections.
- Observed notably higher Spearman's correlation coefficients for KPS pairs in tumor samples compared to normal tissues.
- Successfully developed and applied the SMOTE-XGBoost approach to predict kinase-specific phosphorylation sites, integrating findings into the eKPI database.
Conclusions:
- Quantitative correlation analysis is effective for inferring kinase-phosphosite interconnections.
- Tumor-specific regulatory interactions between kinases and phosphosites are more pronounced than in normal tissues.
- The developed SMOTE-XGBoost method and the eKPI database offer valuable resources for advancing research on kinase-phosphosite regulatory relationships.
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