The meniscus is a complex tissue and is integral to knee joint health and function. Although the meniscus has been studied for years, a relatively large amount of basic science data on meniscal health and disease are unavailable. Genomic and proteomic analyses of meniscal pathology could greatly improve our understanding of etiopathogenesis and the progression of meniscal disease, yet these analyses are lacking in the current literature. Therefore, the objective of this study was to use microarray and proteomic analyses to compare aged-normal and pathologic meniscal tissues. Meniscal tissue was collected from the knees of five patient groups (n = 3/group). Cohorts included patients undergoing meniscectomy with or without articular cartilage pathology, patients undergoing total knee arthroplasty with mild or moderate-severe osteoarthritis, and aged-normal controls from organ donors. Tissue sections were collected from the white/white and white/red zones of posterior medial menisci. Expression levels were compared between pathologic and control menisci to identify genes of interest (at least a ×1.5 fold change in expression levels between two or more groups) using microarray analysis. Proteomics analysis was performed using mass spectrometry to identify proteins of interest (those with possible trends identified between the aged-normal and pathologic groups). The microarray identified 157 genes of interest. Genes were categorized into the following subgroups: (1) synthesis, (2) vascularity, (3) degradation and antidegradation, and (4) signaling pathways. Mass spectrometry identified 173 proteins of interest. Proteins were further divided into the following categories: (1) extracellular matrix (ECM) proteins; (2) proteins associated with vascularity; (3) degradation and antidegradation proteins; (4) cytoskeleton proteins; (5) glycolysis pathway proteins; and (6) signaling proteins. These data provide novel molecular and biochemical information for the investigation of meniscal pathology. Further evaluation of these disease indicators will help researchers develop algorithms for diagnostic, therapeutic, and prognostic strategies related to meniscal disorders.