NCI Office of Data Sharing (ODS) Data Jamboree (Abstract Submissions): Submission #25

Presenter Information
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First Name: Rawan









Middle Initial: {Empty}









Last Name: Shraim









Degree(s): Ph.D.









Position/Title/Career Status: Bioinformatics Scientist









Organization: Children's Hospital of Philadelphia









Organization Address:
Philadelphia










Email: shraimr@chop.edu









Other (Please Specify): {Empty}













Abstract Information
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Abstract Category: Data integration 









Abstract Keywords: Proteomics, RNA, protein-RNA correlations, data integration









Abstract Title: Deciphering RNA-Protein Relationships Across Cancer and Healthy Tissues 









Abstract Summary:
While prior studies have explored correlations between proteomics and RNA sequencing (RNA-seq) data, significant gaps remain in understanding the biological characteristics of correlating proteins and the mechanisms underlying these relationships. A deeper understanding of transcriptomic-proteomic correlations is critical for improving multi-omic data interpretation and for maximizing the utility of both transcriptomic and proteomic datasets. 
Comprehensive analyses of these relationships have historically been limited by sparse proteomic coverage and the scarcity of datasets with matched proteomic and transcriptomic data. However, recent advances in mass spectrometry and the generation of large-scale, multi-omic datasets now enable more detailed investigation. Our objective in this study is to systematically characterize RNA-protein correlations across multiple datasets, assess how these correlations vary based on protein subcellular localization, protein function and cancer phenotype, and determine whether patterns differ across hematologic malignancies, solid tumors, and healthy tissues.
Beyond global proteomic and transcriptomic comparisons, we are also interested in leveraging the rich multi-omic features now available in these datasets to better understand the variance between RNA and protein levels —including, but not limited to, phosphoproteomics, metabolomics, glycosylation, methylation, and other post-translational modifications—to further dissect the molecular features that contribute to concordant and discordant RNA-protein relationships.
All analyses will be conducted in R/Rstudio and datasets that are accessible have been downloaded/access has been requested to those that are not automatically available through their publications. This work aims to advance our understanding of protein regulation in cancer and normal tissues and to inform best practices for integrative analysis of multi-omic datasets. Insights gained could support biomarker discovery, improve our interpretation of transcriptomics-only studies, and guide the development of therapeutic targets based on protein-level dysregulation.










Upload Abstract: https://events.cancer.gov/sites/default/files/webform/nci_office_of_data_sharing_abstr/145154/NCIDataJamboree_06.20.2025.docx