An Iron Metabolism-Related Gene Signature for the Prognosis of Colon Cancer.
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ABSTRACT:
SUBMITTER: Yuan J
PROVIDER: S-EPMC8804292 | biostudies-literature | 2021
REPOSITORIES: biostudies-literature
Publications
An Iron Metabolism-Related Gene Signature for the Prognosis of Colon Cancer.
Yuan Jing J Liu Tao T Zhang Yuhong Y
Frontiers in cell and developmental biology 20220118
As an essential microelement, the iron ion is involved in cell proliferation, metabolism, and differentiation. Iron metabolism plays a crucial role in the occurrence and development of colon adenocarcinoma (COAD). In this study, univariate and multivariate Cox regression, and least absolute shrinkage and selection operator analyses were conducted to construct the gene signature, based on a dataset from The Cancer Genome Atlas. We identified the prognostic value of two iron metabolism-related gen ...[more]
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Project description:BackgroundCell metabolism and long noncoding RNA (lncRNA) played crucial roles in cancer development. However, their association in colon adenocarcinoma (COAD) remains unclear.MethodsThe COAD gene expression data and corresponding clinical data were retrieved from The Cancer Genome Atlas (TCGA) database. Differential expression of metabolic genes and lncRNA were identified by comparing tumor and normal colon tissues. Pearson correlation analysis was performed to identify metabolism-associated lncRNA. COAD patients were divided into training cohort and validation cohort by randomization. Then, a univariate Cox regression analysis was introduced to evaluate the correlations between metabolism-related lncRNAs and overall survival (OS) of the patients in the training cohort. The least absolute shrinkage and selection operator (LASSO) method was introduced to determine and establish a prognostic prediction model. Subsequently, survival analysis, receiver operating characteristic (ROC) curve analysis, and Cox regression analysis were generated to estimate the prognostic role of the LncRNA risk score in training, validation, and entire cohorts.ResultsWe identified 152 differentially expressed metabolism-associated lncRNAs (MRLncRNAs). A prognostic prediction model involving four metabolism-related lncRNAs were established using LASSO. In each cohort, COAD patients in the high-risk group had worse OS compared to those in the low-risk group. The ROC analyses demonstrated that the lncRNA signature performed well in predicting OS. Uni- and multivariate analysis indicated that the lncRNA signature as an independent prognostic factor. Furthermore, a correlation analysis demonstrated that LINC01138 was the most closely lncRNA related to metabolic genes. In vitro assays demonstrated that LINC01138 affects tumor progression in COAD.ConclusionsIn summary, we established a metabolism-associated lncRNAs model to predict the prognosis in COAD patients.
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Project description:Background: Pyroptosis is an important component of the tumor microenvironment and associated with the occurrence and progression of cancer. As the expression of pyroptosis-related genes and its impact on the prognosis of colon cancer (CC) remains unclear, we constructed and validated a pyroptosis-related genes signature to predict the prognosis of patients with CC. Methods: Microarray datasets and the follow-up clinical information of CC patients were obtained from the Gene Expression Omnibus (GEO) and the Cancer Genome Atlas (TCGA) databases. Candidate genes were screened out for further analysis. Various methods were combined to construct a robust pyroptosis-related genes signature for predicting the prognosis of patients with CC. Based on the gene signature and clinical features, a decision tree and nomogram were developed to improve risk stratification and quantify risk assessment for individual patients. Results: The pyroptosis-related genes signature successfully discriminated CC patients with high-risk in the training cohorts. The prognostic value of this signature was further confirmed in independent validation cohort. Multivariable Cox regression and stratified survival analysis revealed this signature was an independent prognostic factor for CC patients. The decision tree identified risk subgroups powerfully, and the nomogram incorporating the gene signature and clinical risk factors performed well in the calibration plots. Conclusion: Pyroptosis-related genes signature was an independent prognostic factor, and can be used to predict the prognosis of patients with CC.
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