Dataset Information

SATB2 is a Promising Biomarker for Identifying a Colorectal Origin for Liver Metastatic Adenocarcinomas.

ABSTRACT: SATB2 (Special AT-rich sequence-binding protein 2) has recently been shown to be a specific biomarker of colorectal cancer (CRC). The aim of this study was to investigate the diagnostic potential of SATB2 as a means of detecting a CRC origin for liver metastases. SATB2 expression was examined in a resection cohort of 101 CRC and 273 non-CRC adenocarcinoma samples using immunohistochemistry (IHC). The diagnostic accuracy of CRC origins of liver metastases based on SATB2 and a three marker panel of SATB2, CK20 and CDX2 was evaluated using an independent cohort of 192 liver biopsies. IHC showed 97 of the 101 (96.0%) primary CRC samples were SATB2 positive, compared to only 6 of the 273 (2.1%) samples of other cancer types. The sensitivity, specificity and AUC values of SATB2 expression in resection samples were 97%, 97.1% and 0.977, respectively. Meanwhile, for the liver biopsy samples, the sensitivity, specificity and AUC values of a CRC liver metastases was 92.2%, 97.8% and 0.948 for SATB2, 95.1%, 91.0% and 0.959 for CK20, and 100%, 85.4% and 0.976 for CDX2, respectively. Further analysis demonstrated that all three-marker positivity was detected in 92/103 (89.3%) CRC and 2/89 (2.2%) non-CRC liver metastases sampled by biopsy. Our findings suggest that SATB2, as measured by IHC, could serve as a promising diagnostic biomarker of CRC metastases. Combining evaluation of SATB2 with CK20 and CDX2 to form a three marker panel further improved the detection of metastatic CRCs in liver biopsy tissues.

SUBMITTER: Zhang YJ

PROVIDER: S-EPMC5898029 | biostudies-literature | 2018 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

SATB2 is a Promising Biomarker for Identifying a Colorectal Origin for Liver Metastatic Adenocarcinomas.

Zhang Yi-Jun YJ Chen Jie-Wei JW He Xiao-Sheng XS Zhang Hui-Zhong HZ Ling Yi-Hong YH Wen Jia-Huai JH Deng Wei-Hao WH Li Peng P Yun Jing-Ping JP Xie Dan D Cai Mu-Yan MY

EBioMedicine 20180109

SATB2 (Special AT-rich sequence-binding protein 2) has recently been shown to be a specific biomarker of colorectal cancer (CRC). The aim of this study was to investigate the diagnostic potential of SATB2 as a means of detecting a CRC origin for liver metastases. SATB2 expression was examined in a resection cohort of 101 CRC and 273 non-CRC adenocarcinoma samples using immunohistochemistry (IHC). The diagnostic accuracy of CRC origins of liver metastases based on SATB2 and a three marker panel o ...[more]

PMID: 29396302

Similar Datasets

Project description:The diagnosis of primary lung adenocarcinomas with intestinal or mucinous differentiation (PAIM) remains challenging due to the overlapping histomorphological, immunohistochemical and genetic characteristics with lung metastatic colorectal cancer (lmCRC). This study aimed to explore the protein biomarkers that could distinguish between PAIM and lmCRC. To uncover differences between the two diseases, we used tandem mass tagging (TMT)-based shotgun proteomics to characterize proteomes of formalin-fixed paraffin-embedded (FFPE) tumor samples of PAIM (n = 22) and lmCRC (n = 17). Then three machine learning algorithms, namely support vector machine (SVM), random forest and the Least Absolute Shrinkage and Selection Operator (LASSO), were utilized to select protein features with diagnostic significance. These candidate proteins were further validated in an independent cohort (PAIM, n = 11; lmCRC, n = 19) by immunochemistry (IHC) to confirm their diagnostic performance. In total, 105 proteins out of 7871 proteins were significantly dysregulated between PAIM and lmCRC samples and well-separated two groups by Uniform Manifold Approximation and Projection (UMAP). The upregulated proteins in PAIM were involved in actin cytoskeleton organization, platelet degranulation, and regulation of leukocyte chemotaxis, while downregulated ones were involved in mitochondrial transmembrane transport, vasculature development, and stem cell proliferation. A set of 10 candidate proteins (high-level expression in lmCRC: CDH17, ATP1B3, GLB1, OXNAD1, LYST, FABP1; high-level expression in PAIM: NARR, MLPH, S100A14, CK7) was ultimately selected to distinguish PAIM from lmCRC by machine learning algorithms. We further confirmed using IHC that the five protein biomarkers including CDH17, CK7, MLPH, FABP1 and NARR were effective biomarkers for distinguishing PAIM from lmCRC. Our study depicts PAIM-specific proteomic characteristics and demonstrates the potential utility of new protein biomarkers for the differential diagnosis of PAIM and lmCRC. These findings may contribute to improving the diagnostic accuracy and guide appropriate treatments for these patients.

Project description:Abstract Background Early diagnosis of liver metastasis is of great importance for enhancing the survival of colorectal adenocarcinoma (CAD) patients, and the combined use of a single biomarker in a classier model has shown great improvement in predicting the metastasis of several types of cancers. However, it is little reported for CAD. This study therefore aimed to screen an optimal classier model of CAD with liver metastasis and explore the metastatic mechanisms of genes when applying this classier model. Methods The differentially expressed genes between primary CAD samples and CAD with metastasis samples were screened from the Moffitt Cancer Center (MCC) dataset GSE131418. The classification performances of six selected algorithms, namely, LR, RF, SVM, GBDT, NN, and CatBoost, for classification of CAD with liver metastasis samples were compared using the MCC dataset GSE131418 by detecting their classification test accuracy. In addition, the consortium datasets of GSE131418 and GSE81558 were used as internal and external validation sets to screen the optimal method. Subsequently, functional analyses and a drug?targeted network construction of the feature genes when applying the optimal method were conducted. Results The optimal CatBoost model with the highest accuracy of 99%, and an area under the curve of 1, was screened, which consisted of 33 feature genes. A functional analysis showed that the feature genes were closely associated with a “steroid metabolic process” and “lipoprotein particle receptor binding” (eg APOB and APOC3). In addition, the feature genes were significantly enriched in the “complement and coagulation cascade” pathways (eg FGA, F2, and F9). In a drug?target interaction network, F2 and F9 were predicted as targets of menadione. Conclusion The CatBoost model constructed using 33 feature genes showed the optimal classification performance for identifying CAD with liver metastasis. APOB, APOC3, FGA, F2, F9, and NKX2?3 were potential biomarkers for classification of CAD with liver metastasis. Menadione might be a promising anti?metastatic drug of CAD cells through functioning its role at sites of F2 and F9. CatBoost model constructed by 33 feature genes showed the optimal classification performance for identifying CAD liver metastasis.

Dataset Information

SATB2 is a Promising Biomarker for Identifying a Colorectal Origin for Liver Metastatic Adenocarcinomas.

Publications

SATB2 is a Promising Biomarker for Identifying a Colorectal Origin for Liver Metastatic Adenocarcinomas.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets