As of now, there is no public database available for this kind of sample to take as a control.
Article Pathway and Network Analysis in Proteomics
http://pathguide.org
What disease you are studying? How you got that data for only the disease part while no control was tested?
You need to search CPTAC thoroughly, I hope you will find something.
One more option could be to use the non-disease stat protein levels from HPA (human proteome atlas), but you need to be careful as how they generated data vs your data, and processing of the data. This can pose significant hurdle.
To gain insights from your proteomic data in the context of pathways:
1. Protein-protein interaction networks: Construct protein-protein interaction (PPI) networks using available databases or tools. These networks represent the physical interactions between proteins and can provide insights into functional relationships and pathway associations. Analyze the network topology, identify highly connected proteins (hubs), and explore protein clusters or modules that may represent enriched pathways.
2. Functional enrichment analysis: Perform functional enrichment analysis using tools such as DAVID, Enrichr, or g:Profiler. These tools allow you to input a list of proteins and assess enrichment of Gene Ontology (GO) terms, biological pathways, or other functional annotations. This analysis can help identify overrepresented functions or pathways in your protein dataset.
3. Cross-referencing with gene-level data: If available, consider integrating your proteomic data with gene-level or transcriptomic data from the same samples or a related study. By mapping proteins to corresponding genes, you can leverage gene-level pathway analysis methods and identify pathways enriched with differentially expressed genes associated with the proteins of interest.
4. Literature-based analysis: Conduct a literature search to explore existing knowledge and studies related to the proteins identified in your proteomic dataset. Look for studies that have investigated the functions, interactions, or pathways associated with these proteins. This qualitative analysis can provide valuable insights into the potential involvement of specific pathways in your disease sample.
5. Pathway databases: Explore curated pathway databases such as Reactome, KEGG, or WikiPathways. These databases provide well-annotated pathways and can serve as a reference to investigate potential connections between your identified proteins and known pathways. Look for proteins within your dataset that are annotated to specific pathways of interest.
Remember that pathway analysis based solely on proteomic data has limitations.
Hope it helps:credit AI
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