[Establishment of a prediction model for colorectal cancer immune cell infiltration based on the cancer genome atlas (TCGA) database]

Development and validation of epigenetic modification-related signals for the diagnosis and prognosis of colorectal cancer

BACKGROUND

Colorectal cancer (CRC) is one of the world's most common malignancies. Epigenetics is the study of heritable changes in characteristics beyond the DNA sequence. Epigenetic information is essential for maintaining specific expression patterns of genes and the normal development of individuals, and disorders of epigenetic modifications may alter the expression of oncogenes and tumor suppressor genes and affect the development of cancer. This study elucidates the relationship between epigenetics and the prognosis of CRC patients by developing a predictive model to explore the potential value of epigenetics in the treatment of CRC.

METHODS

Gene expression data of CRC patients' tumor tissue and controls were downloaded from GEO database. Combined with the 720 epigenetic-related genes (ERGs) downloaded from EpiFactors database, prognosis-related epigenetic genes were selected by univariate cox and LASSO analyses. The Kaplan-Meier and ROC curve were used to analyze the accuracy of the model. Data of 238 CRC samples with survival data downloaded from the GSE17538 were used for validation. Finally, the risk model is combined with the clinical characteristics of CRC patients to perform univariate and multivariate cox regression analysis to obtain independent risk factors and draw nomogram. Then we evaluated the accuracy of its prediction by calibration curves.

RESULTS

A total of 2906 differentially expressed genes (DEGs) were identified between CRC and control samples. After overlapping DEGs with 720 ERGs, 56 epigenetic-related DEGs (DEERGs) were identified. Combining univariate and LASSO regression analysis, the 8 epigenetic-related genes-based risk score model of CRC was established. The ROC curves and survival difference of high and low risk groups revealed the good performance of the risk score model based on prognostic biomarkers in both training and validation sets. A nomogram with good performance to predict the survival of CRC patients were established based on age, NM stage and risk score. The calibration curves showed that the prognostic model had good predictive performance.

CONCLUSION

In this study, an epigenetically relevant 8-gene signature was constructed that can effectively predict the prognosis of CRC patients and provide potential directions for targeted therapies for CRC.