Development of 14‐gene signature prognostic model based on metastasis for colorectal cancer. Issue 1 (16th December 2022)

• Record Type: Journal Article
• Authors: Li, Tong; Yu, Qian; Liu, Te; Yang, Wenjing; Chen, Wei; Jin, Anli; Wang, Hao; Ding, Lin; Zhang, Chunyan; Pan, Baishen; Wang, Beili; Guo, Wei
• Is Part Of: Journal of clinical laboratory analysis. Volume 37:Issue 1(2023)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-12-16
• Subjects: GEO -- immune infiltration -- lasso regression analysis -- prognostic signature -- TCGA; Diagnosis, Laboratory -- Periodicals; Medical laboratory technology -- Periodicals; 616
• DOI: 10.1002/jcla.24800
• Languages: English
• ISSNs: 0887-8013

Abstract

Abstract: Background: Metastasis is the main cause of death of colorectal tumors, in our study a prognosis model was built by analyzing the differentially expressed genes between metastatic and non‐metastatic colorectal cancer (CRC). We used this feature to predict CRC patient prognosis and explore the causes of colorectal tumor metastasis by characterizing the immune status alteration. Methods: CRC patient data were obtained from TCGA and GEO databases. We constructed a risk prognostic model by using Cox regression and the least absolute shrinkage and selection operator (LASSO) based on CRC metastasis‐related genes. We also obtained a nomogram to predict the prognosis of CRC patients. Finally, we explored the underlying mechanism of these metastasis‐related genes and CRC prognosis using immune infiltration analysis and experimental verification. Results: According to our prognostic model, in TCGA, the area under the curve (AUC) values of the training and test sets were 0.72 and 0.76, respectively, and 0.68 for the GEO external data set. This suggested that the treatment and prognosis of patients could be effectively determined. At the same time, we found that the B and T cells in both tissues and peripheral blood of high MR‐risk score patients were mostly in immune static or inactivated states compared with those of low MR‐risk score patients. Conclusions: MR‐risk score has a direct correlation with CRC patient prognosis. It is useful for predicting the prognosis and patient immune status for these patients. Abstract : A 14‐gene signature prognostic model was constructed by analyzing genes specifically expressed in metastatic colorectal tumors. This model can be used to predict the survival rates of CRC patients. We also preliminarily examined the immunology‐based mechanisms controlling colorectal tumor metastasis. Poor prognosis may be affected by the loss of T and B cell functions and a maintained naïve state. These data may help guide CRC clinical practice and individualized treatment.