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Establishment of new predictive markers for distant recurrence of colorectal cancer using lectin microarray analysis.

ABSTRACT: We evaluated the clinical benefits of novel predictive markers for distant recurrence with colorectal cancer using lectin microarray analysis of cell surface glycan modifications. Glycoproteins were extracted from formalin-fixed, paraffin-embedded tumor specimens and normal epithelium from 53 consecutive curatively resected stage I-III colorectal cancer cases and then subjected to lectin microarray to obtain lectin-glycan interaction (LGI) values. In addition, clinicopathological factors associated with distant recurrence were identified. LGI values that were associated with distant recurrence were validated with an additional 55 curatively resected stage II colorectal cancer cases. LGI values for Agaricus bisporus (ABA) lectin, prominent in cancer tissues, were statistically associated with distant recurrence. ABA lectin staining exhibited strikingly intense signals in the cytoplasm and apical surfaces of cancer cells, while weak staining was observed in the supranuclear regions of normal epithelium. This ABA tumor/normal LGI ratio may be a new predictive biomarker for distant recurrence of curatively resected colorectal cancer.

SUBMITTER: Nakajima K

PROVIDER: S-EPMC4329012 | biostudies-literature | 2015 Feb

REPOSITORIES: biostudies-literature

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Establishment of new predictive markers for distant recurrence of colorectal cancer using lectin microarray analysis.

Nakajima Kentaro K Inomata Masafumi M Iha Hidekatsu H Hiratsuka Takahiro T Etoh Tsuyoshi T Shiraishi Norio N Kashima Kenji K Kitano Seigo S

Cancer medicine 20141030 2

We evaluated the clinical benefits of novel predictive markers for distant recurrence with colorectal cancer using lectin microarray analysis of cell surface glycan modifications. Glycoproteins were extracted from formalin-fixed, paraffin-embedded tumor specimens and normal epithelium from 53 consecutive curatively resected stage I-III colorectal cancer cases and then subjected to lectin microarray to obtain lectin-glycan interaction (LGI) values. In addition, clinicopathological factors associa ...[more]

PMID: 25355679

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Project description:BackgroundStage II colorectal cancer patients had heterogeneous prognosis, and patients with recurrent events had poor survival. In this study, we aimed to identify stage II colorectal cancer recurrence associated genes by microarray meta-analysis and build predictive models to stratify patients' recurrence-free survival.MethodsWe searched the GEO database to retrieve eligible microarray datasets. The microarray meta-analysis was used to identify universal recurrence associated genes. Total samples were randomly divided into the training set and the test set. Two survival models (lasso Cox model and random survival forest model) were trained in the training set, and AUC values of the time-dependent receiver operating characteristic (ROC) curves were calculated. Survival analysis was performed to determine whether there was significant difference between the predicted high and low risk groups in the test set.ResultsSix datasets containing 651 stage II colorectal cancer patients were included in this study. The microarray meta-analysis identified 479 recurrence associated genes. KEGG and GO enrichment analysis showed that G protein-coupled glutamate receptor binding and Hedgehog signaling were significantly enriched. AUC values of the lasso Cox model and the random survival forest model were 0.815 and 0.993 at 60 months, respectively. In addition, the random survival forest model demonstrated that the effects of gene expression on the recurrence-free survival probability were nonlinear. According to the risk scores computed by the random survival forest model, the high risk group had significantly higher recurrence risk than the low risk group (HR = 1.824, 95% CI: 1.079-3.084, p = 0.025).ConclusionsWe identified 479 stage II colorectal cancer recurrence associated genes by microarray meta-analysis. The random survival forest model which was based on the recurrence associated gene signature could strongly predict the recurrence risk of stage II colorectal cancer patients.

Project description:BackgroundIn recent years, nomogram prediction models have been widely used to evaluate the prognosis of various diseases. However, studies in primary hepatocellular carcinoma (HCC) are limited. This study sought to explore the risk factors of recurrence of patients with primary HCC after surgical resection and establish a nomogram prediction model.MethodsThe data of 424 patients with primary HCC who had been admitted to the Wuhan Third Hospital were retrospectively collected. The patients were followed-up for 5 years after surgery. The patients were divided into the recurrence group (n=189) and control group (n=235) according to whether the cancer recurred after surgery. The differences in the clinical characteristics between the two groups were analyzed. The risk factors of recurrence after surgical resection of primary HCC were also analyzed, and a prediction model was then established using R4.0.3 statistical software.ResultsThere were significant statistical differences between the two groups in terms of the tumor size, systemic immune-inflammation (SII) index, the number of lesions, tumor differentiation degree, ascites, vascular invasion, and portal vein tumor thrombus (P<0.05). The multivariate regression analysis showed that multiple foci, poorly differentiated tumors, ascites, vascular invasion, and portal vein tumor thrombus were risk factors for the recurrence of primary HCC in patients after surgical resection (P<0.05). The data set was randomly divided into a training set and verification set. The sample size of the training set was 297, and the sample size of the verification set was 127. The area under the receiver operating characteristic (ROC) curve of the training set was 0.866 [95% confidence interval (CI): 0.824-0.907], and the area under the ROC curve of the validation set was 0.812 (95% CI: 0.734-0.890). The Hosmer-Lemeshow Goodness-of-Fit Test was used to test the model with the validation set (χ2=11.243, P=0.188), which indicated that the model had high value in predicting the recurrence of primary HCC after surgical resection.ConclusionsThis model had high value in predicting the recurrence of primary HCC in patients after surgical resection. This model could assist clinicians to assess the prognosis of patients. Intensive treatment for high-risk patients might improve the prognosis of patients.

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Establishment of new predictive markers for distant recurrence of colorectal cancer using lectin microarray analysis.

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Establishment of new predictive markers for distant recurrence of colorectal cancer using lectin microarray analysis.

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