Identification of mRNA Signature for Predicting Prognosis Risk of Rectal Adenocarcinoma - Jiang, Linlin; Wang, Peng; Su, Mu; Yang, Lili; Wang, Qingbo (Frontiers in Genetics, 2022)
Background: The immune system plays a crucial role in rectal adenocarcinoma (READ). Immune-related genes may help predict READ prognoses. Methods: The Cancer Genome Atlas dataset and GSE56699 were used as training and validation data sets, respectively, and differentially expressed genes (DEGs) were identified. The optimal DEGs combination was determined, and the prognostic risk model was constructed. The correlation between optimal DEGs and immune infiltrating cells was evaluated. Results: Nine DEGs were selected for analysis. Moreover, ADAMDEC1 showed a positive correlation with six immune infiltrates, most notably with B cells and dendritic cells. F13A1 was also positively correlated with six immune infiltrates, particularly macrophage and dendritic cells, whereas LGALS9C was negatively correlated with all immune infiltrates except B cells. Additionally, the prognostic risk model was strongly correlated with the actual situation. We retained only three prognosis risk factors: age, pathologic stage, and prognostic risk model. The stratified analysis revealed that lower ages and pathologic stages have a better prognosis with READ. Age and mRNA prognostic factors were the most important factors in determining the possibility of three- and five-year survival. Conclusions: In summary, we identified a nine-gene prognosis risk model that is applicable to the treatment of READ. Altogether, characteristics such as the gene signature and age have a strong predictive value for prognosis risk.