RESUMEN
Ovarian cancer is the fourth leading cause of gynecological cancer death among women in the United States. Early detection is a critical prerequisite to initiating effective cancer therapy. Gene microarray technology and proteomics have provided much of the biomarkers with potential use for diagnosis. However, more research is needed to fully understand disease onset and progression. To this end, we have performed microarray analysis with the goal of identifying molecular interaction networks defining tumor growth. Microarray analysis was performed on a limited set of ovarian tissues with various pathological diagnoses using Human Genome Focus Array (HGFA) for the detection of approximately 8500 human transcripts. Hierarchical clustering identified groups of ovarian tissues reflective of low malignant potential/early cancer onset and possible pre-cancerous stages involving small molecule, cytokine and/or hormone-dependent feed-back responses specific to the pelvic reproductive system and a priori initiated tumor suppression mechanisms. ANOVA followed by post hoc Scheffe confirmed our hypotheses. Moreover, we established a protein/protein interaction database associated with HGFA probe sets. This database was used to build and visualize molecular networks integrating small but significant changes in gene expression. In conclusion, we were able for the first time to delineate an intersecting genetic pattern linking ovarian tissues reflective of low potential malignancy/early cancer onset stages via long distance signaling between tissues of gynecological origin.