Purpose: Cardiovascular diseases, the world’s leading cause of death, are linked to changes in tissue mechanical and material properties that affect the signaling of cells in the damaged tissue. It is hard to predict the effect of altered physical cues on cell signaling though, due to the large number of molecules potentially involved. Our goal is to identify genes and molecular networks that mediate cellular response to cardiovascular disease and cardiovascular-related forces. Methods: We used custom computer code, statistics, and bioinformatics tools to meta-analyze PubMed-indexed citations for mentions of genes and proteins. Results: We identified the names and frequencies of genes studied in the context of mechanical cues (shear, strain, stiffness, and pressure) and major diseases (stroke, myocardial infarction, peripheral arterial disease, deep vein thrombosis). Using statistical and bioinformatics analyses of these biomolecules, we identified the cellular functions and molecular gene sets linked to cardiovascular diseases, biophysical cues, and the overlap between these topics. These gene sets formed independent molecular circuits that each related to different biological processes, including inflammation and extracellular matrix remodeling. Conclusion: Computational analysis of cardiovascular and mechanobiology publication data can be used for discovery of evidence-based, data-rich gene networks suitable for future systems biology modeling of mechanosignaling.