Jiang Linlin, Wang Peng, Su Mu, Yang Lili, Wang Qingbo
Department of Chemotherapy, The Second Hospital of Nanjing, Nanjing University of Chinese Medicine, Nanjing, China.
Department of General Surgery, The Second Hospital of Nanjing, Nanjing University of Chinese Medicine, Nanjing, China.
Front Genet. 2022 May 18;13:880945. doi: 10.3389/fgene.2022.880945. eCollection 2022.
The immune system plays a crucial role in rectal adenocarcinoma (READ). Immune-related genes may help predict READ prognoses. The Cancer Genome Atlas dataset and GSE56699 were used as the training and validation datasets, respectively, and differentially expressed genes (DEGs) were identified. The optimal DEG combination was determined, and the prognostic risk model was constructed. The correlation between optimal DEGs and immune infiltrating cells was evaluated. Nine DEGs were selected for analysis. Moreover, showed a positive correlation with six immune infiltrates, most notably with B cells and dendritic cells. was also positively correlated with six immune infiltrates, particularly macrophage and dendritic cells, whereas 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 3- and 5-year survival. 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.
免疫系统在直肠腺癌（READ）中起着至关重要的作用。免疫相关基因可能有助于预测READ的预后。分别将癌症基因组图谱数据集和GSE56699用作训练和验证数据集，并鉴定差异表达基因（DEG）。确定最佳DEG组合，并构建预后风险模型。评估最佳DEG与免疫浸润细胞之间的相关性。选择了9个DEG进行分析。此外， 与六种免疫浸润呈正相关，最显著的是与B细胞和树突状细胞。 也与六种免疫浸润呈正相关，特别是巨噬细胞和树突状细胞，而 除了B细胞外与所有免疫浸润呈负相关。此外，预后风险模型与实际情况密切相关。我们仅保留了三个预后风险因素：年龄、病理分期和预后风险模型。分层分析显示，较低的年龄和病理分期对READ有较好的预后。年龄和mRNA预后因素是决定3年和5年生存率可能性的最重要因素。总之，我们确定了一个适用于READ治疗的九基因预后风险模型。总体而言，基因特征和年龄等特征对预后风险具有很强的预测价值。
