Detection of pan-cancer surface protein biomarkers via a network-based approach on transcriptomics data

Area of Science:

• Oncology
• Bioinformatics
• Systems Biology

Background:

• Cell surface proteins are crucial in cancer research as diagnostic markers and therapeutic targets.
• These proteins act as signaling hubs, regulating cell responses and gene expression.
• Network analysis of transcriptomic data can reveal cell surface protein activity, as demonstrated in breast cancer.

Purpose of the Study:

• To demonstrate the utility of the SURFACER method for uncovering knowledge within and across cancer datasets.
• To showcase the ability of SURFACER to stratify different cancers into surface-activity-specific groups.
• To identify potential cancer-wide markers for targeted therapies and diagnostic approaches.

Main Methods:

• Utilized SURFACER, a publicly available network-based analysis tool.
• Applied integrated network analysis to transcriptomic data.
• Focused on the activity of curated cell surface proteins to identify surface signaling hubs.

Main Results:

• SURFACER effectively discovers relevant biological insights within and across diverse cancer datasets.
• The method successfully stratifies various cancer types into distinct groups based on surface protein activity.
• Identified key surface proteins driving significant phenotypic changes at a network level.

Conclusions:

• SURFACER is a powerful tool for analyzing cell surface protein activity and identifying signaling hubs in cancer.
• This network-based approach facilitates the discovery of cancer-wide biomarkers.
• The findings support the development of targeted anticancer therapies and improved diagnostic strategies.