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Development and Validation of a Novel 13-Genes Prognostic Model for Colon Cancer
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Abstract
Colon cancer is one of the most common malignant tumors in the world. The purpose of this study is to explore the prognostic value of genes in colon cancer. After analyzing gene expression profiles, differential expressed genes between 39 normal tissues and 398 tumor tissues were identified from The Cancer Genome Atlas database. We use Cox and lasso regression to find genes related to prognosis. Through analysis, 13 genes were found to predict the overall survival of colon cancer patients. In addition, the external comparing of gene expression and the single prognostic gene survival analysis were made. Finally, pathway enrichment and mutation status of each gene were also analyzed. After a series of bioinformatics analysis, we select 13 survival-related signature and established a prognostic risk model based on these genes. The prognostic risk model was developed to comprehensively predict the overall survival of colon cancer patients. The prognostic value of the 13-genes (CLDN23,HAND1,IL23A,KLHL35,SIX2,UPK2,HOXC11,KRT6B,SRCIN1,TNNI3,TYRO3,MIR6835,LINC02474) related risk score for each colon cancer patent was calculated to predict the survival. Furthermore, five genes (SIX2 MIR6835 LINC02474 CLDN23 HOXC11) were significantly associated with overall survival (OS). The KEGG pathway enrichment results suggested that most of the pathways are related to the occurrence, metabolism, proliferation and invasion of the tumor cells. It was found that the expression of 13-genes signature can be used as prognostic indicator for colon cancer patients. The 13-genes signature predictive model may help clinicians provide a prognosis and personalized treatment for colon cancer patients.
Title: Development and Validation of a Novel 13-Genes Prognostic Model for Colon Cancer
Description:
Abstract
Colon cancer is one of the most common malignant tumors in the world.
The purpose of this study is to explore the prognostic value of genes in colon cancer.
After analyzing gene expression profiles, differential expressed genes between 39 normal tissues and 398 tumor tissues were identified from The Cancer Genome Atlas database.
We use Cox and lasso regression to find genes related to prognosis.
Through analysis, 13 genes were found to predict the overall survival of colon cancer patients.
In addition, the external comparing of gene expression and the single prognostic gene survival analysis were made.
Finally, pathway enrichment and mutation status of each gene were also analyzed.
After a series of bioinformatics analysis, we select 13 survival-related signature and established a prognostic risk model based on these genes.
The prognostic risk model was developed to comprehensively predict the overall survival of colon cancer patients.
The prognostic value of the 13-genes (CLDN23,HAND1,IL23A,KLHL35,SIX2,UPK2,HOXC11,KRT6B,SRCIN1,TNNI3,TYRO3,MIR6835,LINC02474) related risk score for each colon cancer patent was calculated to predict the survival.
Furthermore, five genes (SIX2 MIR6835 LINC02474 CLDN23 HOXC11) were significantly associated with overall survival (OS).
The KEGG pathway enrichment results suggested that most of the pathways are related to the occurrence, metabolism, proliferation and invasion of the tumor cells.
It was found that the expression of 13-genes signature can be used as prognostic indicator for colon cancer patients.
The 13-genes signature predictive model may help clinicians provide a prognosis and personalized treatment for colon cancer patients.
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