Table1_Construction of a prognostic model via WGCNA combined with the LASSO algorithm for stomach adenocarcinoma patients.docx

ObjectiveThis study aimed to identify prognostic signatures to predict the prognosis of patients with stomach adenocarcinoma (STAD), which is necessary to improve poor prognosis and offer possible treatment strategies for STAD patients.
MethodsThe overlapping genes between the key model genes that were screened by the weighted gene co-expression network analysis (WGCNA) and differentially expressed genes (DEGs) whose expression was different with significance between normal and tumor tissues were extracted to serve as co-expression genes. Then, enrichment analysis was performed on these genes. Furthermore, the least absolute shrinkage and selection operator (LASSO) regression was performed to screen the hub genes among overlapping genes. Finally, we constructed a model to explore the influence of polygenic risk scores on the survival probability of patients with STAD, and interaction effect and mediating analyses were also performed.
ResultsDEGs included 2,899 upregulated genes and 2,896 downregulated genes. After crossing the DEGs and light-yellow module genes that were obtained by WGCNA, a total of 39 overlapping genes were extracted. The gene enrichment analysis revealed that these genes were enriched in the prion diseases, biosynthesis of unsaturated fatty acids, RNA metabolic process, hydrolase activity, etc. PIP5K1P1, PTTG3P, and SNORD15B were determined by LASSO-Cox. The prognostic prediction of the three-gene model was established. The Cox regression analysis showed that the comprehensive risk score for three genes was an independent prognosis factor.
ConclusionPIP5K1P1, PTTG3P, and SNORD15B are related to the prognosis and overall survival of patients. The three-gene risk model constructed has independent prognosis predictive ability for STAD.

研究目的 本研究旨在识别可用于预测胃腺癌（stomach adenocarcinoma, STAD）患者预后的预后特征，以期改善该类患者的不良预后并为其提供潜在治疗策略。
研究方法 本研究首先通过加权基因共表达网络分析（weighted gene co-expression network analysis, WGCNA）筛选关键模型基因，并提取正常组织与肿瘤组织间存在显著表达差异的差异表达基因（differentially expressed genes, DEGs），取二者的重叠基因作为共表达基因。随后对上述基因开展富集分析。进一步采用最小绝对收缩和选择算子（least absolute shrinkage and selection operator, LASSO）回归从重叠基因中筛选核心基因。最后构建多基因风险评分模型，以探究其对胃腺癌患者生存概率的影响，并同步开展交互效应分析与中介分析。
研究结果 本次分析共筛选得到2899个上调差异表达基因与2896个下调差异表达基因。将差异表达基因与加权基因共表达网络分析得到的亮黄色模块基因取交集后，共提取得到39个重叠基因。基因富集分析结果显示，上述基因主要富集于朊病毒病、不饱和脂肪酸生物合成、RNA代谢过程、水解酶活性等通路及功能条目。通过LASSO-Cox分析筛选得到PIP5K1P1、PTTG3P及SNORD15B三个基因，并构建了基于该三基因的预后预测模型。Cox回归分析结果表明，该三基因综合风险评分可作为胃腺癌患者预后的独立危险因素。
研究结论 PIP5K1P1、PTTG3P与SNORD15B均与胃腺癌患者的预后及总生存期密切相关。本研究构建的三基因风险模型对胃腺癌具有独立的预后预测能力。