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Distant Metastasis in Colorectal Cancer Patients-Do We Have New Predicting Clinicopathological and Molecular Biomarkers? A Comprehensive Review.

ABSTRACT: Colorectal cancer (CRC) remains a serious health problem worldwide. Approximately half of patients will develop distant metastasis after CRC resection, usually with very poor prognosis afterwards. Because patient performance after distant metastasis surgery remains very heterogeneous, ranging from death within 2 years to a long-term cure, there is a clinical need for a precise risk stratification of patients to aid pre- and post-operative decisions. Furthermore, around 20% of identified CRC cases are at IV stage disease, known as a metastatic CRC (mCRC). In this review, we overview possible molecular and clinicopathological biomarkers that may provide prognostic and predictive information for patients with distant metastasis. These may comprise sidedness of the tumor, molecular profile and epigenetic characteristics of the primary tumor and arising metastatic CRC, and early markers reflecting cancer cell resistance in mCRC and biomarkers identified from transcriptome. This review discusses current stage in employment of these biomarkers in clinical practice as well as summarizes current experience in identifying predictive biomarkers in mCRC treatment.

SUBMITTER: Filip S

PROVIDER: S-EPMC7432613 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

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Distant Metastasis in Colorectal Cancer Patients-Do We Have New Predicting Clinicopathological and Molecular Biomarkers? A Comprehensive Review.

Filip Stanislav S Vymetalkova Veronika V Petera Jiri J Vodickova Ludmila L Kubecek Ondrej O John Stanislav S Cecka Filip F Krupova Marketa M Manethova Monika M Cervena Klara K Vodicka Pavel P

International journal of molecular sciences 20200724 15

Colorectal cancer (CRC) remains a serious health problem worldwide. Approximately half of patients will develop distant metastasis after CRC resection, usually with very poor prognosis afterwards. Because patient performance after distant metastasis surgery remains very heterogeneous, ranging from death within 2 years to a long-term cure, there is a clinical need for a precise risk stratification of patients to aid pre- and post-operative decisions. Furthermore, around 20% of identified CRC case ...[more]

PMID: 32722130

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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

Distant Metastasis in Colorectal Cancer Patients-Do We Have New Predicting Clinicopathological and Molecular Biomarkers? A Comprehensive Review.

Publications

Distant Metastasis in Colorectal Cancer Patients-Do We Have New Predicting Clinicopathological and Molecular Biomarkers? A Comprehensive Review.

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