In our aging world, increasing numbers of people are suffering from calcific aortic valve disease (CAVD). In this study, we used integrated bioinformatics analysis to predict several key genes that are involved in the initiation and progression of CAVD. Expression profiles of 15 calcific and 14 normal human aortic valve samples were generated from two gene expression datasets (GSE12644 and GSE51472). Dataset GSE26953 from the human aortic valve fibrosa-derived endothelial cells cultured under laminar or oscillatory shear stress was also evaluated. Related R packages were used to process the data. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed for functional annotation. Hub genes were identified based on the protein-protein interaction network. CAVD-related gene modules were identified by Weighted Gene Co-expression Network Analysis (WGCNA). The predicted key genes were manually reviewed. In our present work, complex connections among mechano-response, oxidative stress, inflammation and extracellular remodeling pathways in the etiology of CAVD were revealed. The key genes, thus identified, encode a transcription factor KLF2 and phospholipid phosphatase 3 (PLPP3) that are involved in mechano-responses; eNOS involved in oxidative stress; IL-8 involved in inflammation; and collagen triple helix repeat containing 1 (CTHRC1) and secretogranin II (SCG2) involved in extracellular remodeling. These gene products are predicted to play critical roles in CAVD development and progression. The present study provides valuable information for future research and drug development.