Prognostic Pathways Guide Drug Indications in Pan-Cancers

Area of Science:

• Bioinformatics
• Computational Biology
• Genomics

Background:

• Pathway-level analysis offers a higher-level interpretation of post-genomic data compared to single-molecule analysis.
• Molecular-targeted therapies focusing on signaling pathways represent a paradigm shift in anticancer treatment.
• Current methods for narrowing down extensive biological pathway lists are limited.

Purpose of the Study:

• To propose a novel strategy for in silico Drug Prescription on biological pathways across pan-Cancers (CDP).
• To connect drugs to candidate biological pathways for improved drug discovery and repositioning.
• To construct a heterogeneous network linking traditional Chinese medicines (TCMs), pathways, and cancers.

Main Methods:

• Developed the in silico Drug Prescription (CDP) strategy to link drugs with biological pathways.
• Applied CDP to a dataset of 120 traditional Chinese medicines (TCMs).
• Constructed a heterogeneous network of "TCM-pathways-cancers" across pan-cancers.

Main Results:

• Identified specific "TCM-pathways-cancers" triplets using the CDP strategy.
• The computational prescribing method provided molecular-level insights into TCM efficacy.
• Demonstrated CDP's potential for drug repositioning in Western medicine.

Conclusions:

• The CDP strategy offers a novel approach for drug discovery and understanding drug mechanisms at the pathway level.
• This method can deepen the understanding of traditional Chinese medicine efficacy.
• CDP facilitates drug repositioning, potentially accelerating the development of new cancer therapies.