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Genetic alterations in tumor cells often lead to the emergence of growth-stimulatory autocrine and paracrine signals, involving overexpression of secreted peptide growth factors, cytokines, and hormones. Increased levels of these soluble proteins may be exploited for cancer diagnosis and management or as points of therapeutic intervention. Here, we combined the use of controlled vocabulary terms and sequence-based algorithms to predict genes encoding secreted proteins from among ≈12,500 sequences represented on oligonucleotide microarrays. Expression of these genes was queried in 150 carcinomas from 10 anatomic sites of origin and compared with 46 normal tissues derived from the corresponding sites of tumor origin and other body tissues and organs. Of 74 different genes identified as overexpressed in cancer tissues, several encode proteins with demonstrated clinical diagnostic application, such as α-fetoprotein in liver carcinoma, and kallikreins 6 and 10 in ovarian cancer, or therapeutic utility, such as gastrin-releasing peptide/bombesin in lung carcinomas. We show that several of the other candidate genes encode proteins with high levels of tumor-associated expression by immunohistochemistry on tissue microarrays and further demonstrate significantly elevated levels of another novel candidate protein, macrophage inhibitory cytokine 1, a distant member of the tranforming growth factor-β superfamily, in the serum of patients with metastatic prostate, breast, and colorectal carcinomas. Our results suggest that the combination of annotation/protein sequence analysis, transcript profiling, immunohistochemistry, and immunoassay is a powerful approach for delineating candidate biomarkers with potential clinical significance and may be broadly applicable to other human diseases.