17 research outputs found

    Prognostic Significance and Gene Expression Profiles of p53 Mutations in Microsatellite-Stable Stage III Colorectal Adenocarcinomas

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    Although the prognostic value of p53 abnormalities in Stage III microsatellite stable (MSS) colorectal cancers (CRCs) is known, the gene expression profiles specific to the p53 status in the MSS background are not known. Therefore, the current investigation has focused on identification and validation of the gene expression profiles associated with p53 mutant phenotypes in MSS Stage III CRCs. Genomic DNA extracted from 135 formalin-fixed paraffin-embedded tissues, was analyzed for microsatellite instability (MSI) and p53 mutations. Further, mRNA samples extracted from five p53-mutant and five p53-wild-type MSS-CRC snap-frozen tissues were profiled for differential gene expression by Affymetrix Human Genome U133 Plus 2.0 arrays. Differentially expressed genes were further validated by the high-throughput quantitative nuclease protection assay (qNPA), and confirmed by quantitative real-time polymerase chain reaction (qRT-PCR) and by immunohistochemistry (IHC). Survival rates were estimated by Kaplan-Meier and Cox regression analyses. A higher incidence of p53 mutations was found in MSS (58%) than in MSI (30%) phenotypes. Both univariate (log-rank, P = 0.025) and multivariate (hazard ratio, 2.52; 95% confidence interval, 1.25–5.08) analyses have demonstrated that patients with MSS-p53 mutant phenotypes had poor CRC-specific survival when compared to MSS-p53 wild-type phenotypes. Gene expression analyses identified 84 differentially expressed genes. Of 49 down-regulated genes, LPAR6, PDLIM3, and PLAT, and, of 35 up-regulated genes, TRIM29, FUT3, IQGAP3, and SLC6A8 were confirmed by qNPA, qRT-PCR, and IHC platforms. p53 mutations are associated with poor survival of patients with Stage III MSS CRCs and p53-mutant and wild-type phenotypes have distinct gene expression profiles that might be helpful in identifying aggressive subsets

    Evaluation of lymph node numbers for adequate staging of Stage II and III colon cancer

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    <p>Abstract</p> <p>Background</p> <p>Although evaluation of at least 12 lymph nodes (LNs) is recommended as the minimum number of nodes required for accurate staging of colon cancer patients, there is disagreement on what constitutes an adequate identification of such LNs.</p> <p>Methods</p> <p>To evaluate the minimum number of LNs for adequate staging of Stage II and III colon cancer, 490 patients were categorized into groups based on 1-6, 7-11, 12-19, and ≥ 20 LNs collected.</p> <p>Results</p> <p>For patients with Stage II or III disease, examination of 12 LNs was not significantly associated with recurrence or mortality. For Stage II (HR = 0.33; 95% CI, 0.12-0.91), but not for Stage III patients (HR = 1.59; 95% CI, 0.54-4.64), examination of ≥20 LNs was associated with a reduced risk of recurrence within 2 years. However, examination of ≥20 LNs had a 55% (Stage II, HR = 0.45; 95% CI, 0.23-0.87) and a 31% (Stage III, HR = 0.69; 95% CI, 0.38-1.26) decreased risk of mortality, respectively. For each six additional LNs examined from Stage III patients, there was a 19% increased probability of finding a positive LN (parameter estimate = 0.18510, p < 0.0001). For Stage II and III colon cancers, there was improved survival and a decreased risk of recurrence with an increased number of LNs examined, regardless of the cutoff-points. Examination of ≥7 or ≥12 LNs had similar outcomes, but there were significant outcome benefits at the ≥20 cutoff-point only for Stage II patients. For Stage III patients, examination of 6 additional LNs detected one additional positive LN.</p> <p>Conclusions</p> <p>Thus, the 12 LN cut-off point cannot be supported as requisite in determining adequate staging of colon cancer based on current data. However, a minimum of 6 LNs should be examined for adequate staging of Stage II and III colon cancer patients.</p

    Development of combination tapered fiber-optic biosensor dip probe for quantitative estimation of interleukin-6 in serum samples

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    A combination tapered fiber-optic biosensor (CTFOB) dip probe for rapid and cost-effective quantification of proteins in serum samples has been developed. This device relies on diode laser excitation and a charged-coupled device spectrometer and functions on a technique of sandwich immunoassay. As a proof of principle, this technique was applied in a quantitative estimation of interleukin IL-6. The probes detected IL-6 at picomolar levels in serum samples obtained from a patient with lupus, an autoimmune disease, and a patient with lymphoma. The estimated concentration of IL-6 in the lupus sample was 5.9 ± 0.6 pM, and in the lymphoma sample, it was below the detection limit. These concentrations were verified by a procedure involving bead-based xMAP technology. A similar trend in the concentrations was observed. The specificity of the CTFOB dip probes was assessed by analysis with receiver operating characteristics. This analysis suggests that the dip probes can detect 5-pM or higher concentration of IL-6 in these samples with specificities of 100%. The results provide information for guiding further studies in the utilization of these probes to quantify other analytes in body fluids with high specificity and sensitivity

    Prognostic Significance of p53

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    Comparison of Predicted Probabilities of Proportional Hazards Regression and Linear Discriminant Analysis Methods Using a Colorectal Cancer Molecular Biomarker Database

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    Background: Although a majority of studies in cancer biomarker discovery claim to use proportional hazards regression (PHREG) to the study the ability of a biomarker to predict survival, few studies use the predicted probabilities obtained from the model to test the quality of the model. In this paper, we compared the quality of predictions by a PHREG model to that of a linear discriminant analysis (LDA) in both training and test set settings. Methods: The PHREG and LDA models were built on a 491 colorectal cancer (CRC) patient dataset comprised of demographic and clinicopathologic variables, and phenotypic expression of p53 and Bcl-2. Two variable selection methods, stepwise discriminant analysis and the backward selection, were used to identify the final models. The endpoint of prediction in these models was five-year post-surgery survival. We also used linear regression model to examine the effect of bin size in the training set on the accuracy of prediction in the test set.Results: The two variable selection techniques resulted in different models when stage was included in the list of variables available for selection. However, the proportion of survivors and non-survivors correctly identified was identical in both of these models. When stage was excluded from the variable list, the error rate for the LDA model was 42% as compared to an error rate of 34% for the PHREG model.Conclusions: This study suggests that a PHREG model can perform as well or better than a traditional classifier such as LDA to classify patients into prognostic classes. Also, this study suggests that in the absence of the tumor stage as a variable, Bcl-2 expression is a strong prognostic molecular marker of CRC
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