Project description:Background & Aims. The current staging system for colorectal cancer (CRC) based on TNM classification allows prediction of potential recurrence. However, it does not necessarily make reliable personalized prediction of prognosis. In this paper we describe combination of clinicopathological data and gene signature of dissected tumor specimen with stage II and III CRC patients would improve the situation.. Methods. A total of 1978 CRC were collected over 5 years, and then 371 stage II and 322 stage III of them with more than 45.9 months records were subjected to clinicopathological feature analyses. Out of this collection, 129 stage II and III CRC cases were selected for analyses of gene expression profiles with resected specimen. The gene signatures were analyzed by repeated random divisions of the samples into training and test sets to extract discriminator genes. After testing the applicability of this discriminator set, it was subjected to validation using a newly obtained set of 69 samples. Results. The pathological factors in solo or in combinations could not make personalized recurrence prediction, except for partial success with stage II patients. The gene signature, on the other hand, was capable of producing a set of discriminator genes, though the accuracy was yet to be improved. We observed that the best result was obtained when discriminators were selected from stage II CRC samples and used for prognosis of stage II CRC. When stage III cases were included in the process of discriminator extraction or in the process to validate samples, the results were poorer. Finally, we examined 31 independent stage II samples with a set of 30 such discriminators and were able to obtain results with 78 % accuracy, 90 % negative predictive value (NPV), and 55% positive predictive value (PPV). Conclusions. Independent clinicopathological variables were not able to predict prognosis of individual patient, unless the factors are combined. On the other hand, gene signatures allowed accurate prediction of prognosis for individuals, especially with stage II CRC, suggesting its potential use for selection of best treatment option for individual patients. The accuracy of discriminator prediction will be further improved when we take the evolution of CRC into consideration. Of 198 samples, 129 represented the discovery phase and 69 represented the validation phase.

Project description:Background & Aims. The current staging system for colorectal cancer (CRC) based on TNM classification allows prediction of potential recurrence. However, it does not necessarily make reliable personalized prediction of prognosis. In this paper we describe combination of clinicopathological data and gene signature of dissected tumor specimen with stage II and III CRC patients would improve the situation.. Methods. A total of 1978 CRC were collected over 5 years, and then 371 stage II and 322 stage III of them with more than 45.9 months records were subjected to clinicopathological feature analyses. Out of this collection, 129 stage II and III CRC cases were selected for analyses of gene expression profiles with resected specimen. The gene signatures were analyzed by repeated random divisions of the samples into training and test sets to extract discriminator genes. After testing the applicability of this discriminator set, it was subjected to validation using a newly obtained set of 69 samples. Results. The pathological factors in solo or in combinations could not make personalized recurrence prediction, except for partial success with stage II patients. The gene signature, on the other hand, was capable of producing a set of discriminator genes, though the accuracy was yet to be improved. We observed that the best result was obtained when discriminators were selected from stage II CRC samples and used for prognosis of stage II CRC. When stage III cases were included in the process of discriminator extraction or in the process to validate samples, the results were poorer. Finally, we examined 31 independent stage II samples with a set of 30 such discriminators and were able to obtain results with 78 % accuracy, 90 % negative predictive value (NPV), and 55% positive predictive value (PPV). Conclusions. Independent clinicopathological variables were not able to predict prognosis of individual patient, unless the factors are combined. On the other hand, gene signatures allowed accurate prediction of prognosis for individuals, especially with stage II CRC, suggesting its potential use for selection of best treatment option for individual patients. The accuracy of discriminator prediction will be further improved when we take the evolution of CRC into consideration.

Project description:Distant metastasis is the major causes of death in colorectal cancer (CRC) patients. In order to identify genes influencing the prognosis of patients with CRC, we compared gene expression in primary tumors with and without distant metastasis using an oligonucleotide microarray. We also examined the expression of the candidate gene in 100 CRC patients by quantitative real-time reverse transcription PCR and studied the relationship between its expression and the prognosis of patients with CRC. As a result, we identified MUC12 as a candidate gene involved in metastasis processes by microarray analysis. Quantitative real-time reverse transcription PCR showed that MUC12 expression was significantly lower in cancer tissues than in adjacent normal tissues (P < 0.001). In stage II and stage III CRC, patients with low expression showed worse disease-free survival (P = 0.038). Multivariate analysis disclosed that MUC12 expression status was an independent prognostic factor in stage II and stage III CRC (relative risk, 9.532; 95% confidence interval, 2.303-41.905; P = 0.002). This study revealed the prognostic value of MUC12 expression in CRC patients. Moreover, our result suggests MUC12 expression is a possible candidate gene for assessing postoperative adjuvant therapy for CRC patients. Total of 111 microarray datasets (77 for LCM samples, and 17 pairs for homogenized samples from tumor and adjacent tissues) were normalized using robust multi-array average (RMA) method under R 2.6.2 statistical software together with BioConductor package, as described previously. Then, the gene expression levels were log2-transformed, and 62 control probe sets were removed for further analysis. In order to identify a set of genes associated with development of metastatic recurrence, we performed Wilcoxon rank-sum test for gene expression differences of 54,613 probe sets between recurrence and non-recurrence groups. Similarly, Wilcoxon singed-rank test was conducted to select genes which showed significant expression difference between tumor and adjacent tissue. Then, we selected a set of genes that satisfied both of above two criteria.

Project description:Purpose: This study aimed to identify a novel biomarker or a target of treatment for colorectal cancer (CRC). Experimental Design: The expression profiles of cancer cells in 104 patients with CRC were examined using laser microdissection and oligonucleotide microarray analysis. Overexpression in CRC cells especially in patients with distant metastasis was a prerequisite to select candidate genes. The mRNA expression of candidate gene was investigated by quantitative reversetranscription polymerase chain reaction (RT-PCR) in 77 patients as a validation study. We analyzed the protein expression and localization of the candidate gene by immunohistochemical study, and investigated the relationship between the expression and the clinicopathological feature in 274 CRC patients. Results: We identified 6 genes as candidates related to distant metastasis in CRC patients by microarray analysis. Among these genes, Osteoprotegerin (OPG) is known to have an association with aggressiveness in several cancers through inhibiting apoptosis by neutralizing the function of tumor necrosis factor related apoptosis inducing ligand (TRAIL). The mRNA expression of OPG in cancer tissues was significantly higher in patients with distant metastasis than those without metastasis. The overexpression of OPG protein was significantly associated with worse overall survival and relapse free survival (RFS). Moreover, the overexpression of OPG protein was an independent risk factor for recurrence of CRC. Conclusion: The overexpression of OPG would be a predictive biomarker of recurrence and a target of the treatment for CRC. Total of 148 microarray datasets obtained from LCM and homogenized tissues of colorectal cancer patients were normalized using robust multi-array average (RMA) method under R 2.6.2 statistical software with affy package from BioConductor. Normalization was separately performed for LCM dataset and homogenized tissue dataset. The normalized gene expression levels were presented as log2-transformed values by RMA.

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Genome-wide DNA methylation analysis in early-stage colorectal cancer

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