BACKGROUND: New tumor markers and markers of tumor progression are needed for improved staging and for better assessment of treatment of many cancers. Gene expression profiling techniques offer the opportunity to discover such markers. We investigated the feasibility of sample pooling strategy in combination with a novel analysis algorithm to identify markers. METHODS: Total RNA from human colon tumors (n = 60) of multiple stages (adenomas; cancers with modified Astler Collier stages B, C, and D; and liver metastases) were pooled within stages and compared with pooled normal mucosal specimens (n = 10) by using oligonucleotide expression arrays. Genes that showed consistent increases or decreases in their expression through tumor progression were identified. Northern blot analysis was used to validate the findings. All statistical tests were two-sided. RESULTS: More than 300 candidate tumor markers and more than 100 markers of tumor progression were identified. Northern analysis of 11 candidate tumor markers confirmed the gene expression changes. The gene for the secreted integrin-binding protein osteopontin was most consistently differentially expressed in conjunction with tumor progression. Its potential as a progression marker was validated (Spearman's rho = 0.903; P<.001) with northern blot analysis using RNA from an independent set of 10 normal and 43 tumor samples representing all stages. Moreover, a statistically significant correlation between osteopontin protein expression and advancing tumor stage was identified with the use of 303 additional specimens (human cancer = 185, adenomas = 67, and normal mucosal specimens = 51) (Spearman's rho = 0.667; P<.001). CONCLUSIONS: Sample pooling can be a powerful, cost-effective, and rapid means of identifying the most common changes in a gene expression profile. We identified osteopontin as a clinically useful marker of tumor progression by use of gene expression profiling on pooled samples.
BACKGROUND: New tumor markers and markers of tumor progression are needed for improved staging and for better assessment of treatment of many cancers. Gene expression profiling techniques offer the opportunity to discover such markers. We investigated the feasibility of sample pooling strategy in combination with a novel analysis algorithm to identify markers. METHODS: Total RNA from humancolon tumors (n = 60) of multiple stages (adenomas; cancers with modified Astler Collier stages B, C, and D; and liver metastases) were pooled within stages and compared with pooled normal mucosal specimens (n = 10) by using oligonucleotide expression arrays. Genes that showed consistent increases or decreases in their expression through tumor progression were identified. Northern blot analysis was used to validate the findings. All statistical tests were two-sided. RESULTS: More than 300 candidate tumor markers and more than 100 markers of tumor progression were identified. Northern analysis of 11 candidate tumor markers confirmed the gene expression changes. The gene for the secreted integrin-binding protein osteopontin was most consistently differentially expressed in conjunction with tumor progression. Its potential as a progression marker was validated (Spearman's rho = 0.903; P<.001) with northern blot analysis using RNA from an independent set of 10 normal and 43 tumor samples representing all stages. Moreover, a statistically significant correlation between osteopontin protein expression and advancing tumor stage was identified with the use of 303 additional specimens (humancancer = 185, adenomas = 67, and normal mucosal specimens = 51) (Spearman's rho = 0.667; P<.001). CONCLUSIONS: Sample pooling can be a powerful, cost-effective, and rapid means of identifying the most common changes in a gene expression profile. We identified osteopontin as a clinically useful marker of tumor progression by use of gene expression profiling on pooled samples.
Authors: Karyn G Robinson; Ting Nie; Aaron D Baldwin; Elaine C Yang; Kristi L Kiick; Robert E Akins Journal: J Biomed Mater Res A Date: 2012-02-28 Impact factor: 4.396
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