背景:肺腺癌(LUAD)是严重影响人类健康的最常见的恶性肿瘤。先前的研究表明,糖基化水平的异常促进了肺癌的进展和不良预后。因此,本研究旨在探讨与糖基转移酶(GTs)相关的LUAD预后特征。
方法:从癌症基因组图谱(TCGA)数据库获得基因表达谱,和GTs从GlycomeDB数据库获得。使用边缘包装和维恩图鉴定差异表达的GTs相关基因(DGTs)。基因本体论(GO),京都基因和基因组百科全书(KEGG),并采用通路分析(IPA)方法研究了DGTs的生物过程。随后,进行Cox和最小绝对收缩和选择算子(LASSO)回归分析以构建LUAD的预后模型。采用Kaplan-Meier(K-M)分析探讨LUAD患者的总生存期(OS)。通过受试者工作特征分析(ROC)评估预后模型的准确性和特异性。此外,采用单样本基因集富集分析(ssGSEA)算法对肿瘤环境中浸润的免疫细胞进行分析。
结果:共有48个DGT主要富集在糖基化过程中,糖蛋白生物合成过程,鞘糖脂生物合成-乳酸和新奥拉托系列,和细胞介导的免疫反应。此外,B3GNT3,MFNG,GYLTL1B,ALG3和GALNT13被筛选为预后基因,以构建LUAD的风险模型,将LUAD患者分为高危组和低危组。K-M曲线表明,高风险评分的患者的OS比低风险评分的患者短。ROC分析表明,风险模型可以有效地诊断LUAD。此外,高危组浸润性aDCs(p<0.05)和Tgds(p<0.01)比例高于低危组。Spearman的相关分析表明,预后基因(MFNG和ALG3)与浸润免疫细胞显着相关。
结论:总之,本研究建立了LUAD患者预后的GTs相关风险模型,为LUAD提供新的治疗靶点。然而,糖基化相关基因在LUAD中的生物学作用有待进一步探讨。
BACKGROUND: Lung adenocarcinoma (LUAD) is the most common malignant tumor that seriously affects human health. Previous studies have indicated that abnormal levels of glycosylation promote progression and poor prognosis of lung cancer. Thus, the present study aimed to explore the prognostic signature related to glycosyltransferases (GTs) for LUAD.
METHODS: The gene expression profiles were obtained from The Cancer Genome Atlas (TCGA) database, and GTs were obtained from the GlycomeDB database. Differentially expressed GTs-related genes (DGTs) were identified using edge package and Venn diagram. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and ingenuity pathway analysis (IPA) methods were used to investigate the biological processes of DGTs. Subsequently, Cox and Least Absolute Shrinkage and Selection Operator (LASSO) regression analyses were performed to construct a prognostic model for LUAD. Kaplan-Meier (K-M) analysis was adopted to explore the overall survival (OS) of LUAD patients. The accuracy and specificity of the prognostic model were evaluated by receiver operating characteristic analysis (ROC). In addition, single-sample gene set enrichment analysis (ssGSEA) algorithm was used to analyze the infiltrating immune cells in the tumor environment.
RESULTS: A total of 48 DGTs were mainly enriched in the processes of glycosylation, glycoprotein biosynthetic process, glycosphingolipid biosynthesis-lacto and neolacto series, and cell-mediated immune response. Furthermore, B3GNT3, MFNG, GYLTL1B, ALG3, and GALNT13 were screened as prognostic genes to construct a risk model for LUAD, and the LUAD patients were divided into high- and low-risk groups. K-M curve suggested that patients with a high-risk score had shorter OS than those with a low-risk score. The ROC analysis demonstrated that the risk model efficiently diagnoses LUAD. Additionally, the proportion of infiltrating aDCs (p < 0.05) and Tgds (p < 0.01) was higher in the high-risk group than in the low-risk group. Spearman\'s correlation analysis manifested that the prognostic genes (MFNG and ALG3) were significantly correlated with infiltrating immune cells.
CONCLUSIONS: In summary, this study established a novel GTs-related risk model for the prognosis of LUAD patients, providing new therapeutic targets for LUAD. However, the biological role of glycosylation-related genes in LUAD needs to be explored further.