Identification of novel potential homologous repair deficiency-associated genes in pancreatic adenocarcinoma via WGCNA coexpression network analysis and machine learning

Homologous repair deficiency (HRD) impedes double-strand break repair, which is a common driver of carcinogenesis. Positive HRD status can be used as theranostic markers of response to platinum- and PARP inhibitor-based chemotherapies. Here, we aimed to fully investigate the therapeutic and prognostic potential of HRD in pancreatic adenocarcinoma (PAAD) and identify effective biomarkers related to HRD using comprehensive bioinformatics analysis. The HRD score was defined as the unweighted sum of the LOH, TAI, and LST scores, and it was obtained based on the previous literature. To characterize PAAD immune infiltration subtypes, the “ConsensusClusterPlus” package in R was used to conduct unsupervised clustering. A WGCNA was conducted to elucidate the gene coexpression modules and hub genes in the HRD-related gene module of PAAD. The functional enrichment study was performed using Metascape. LASSO analysis was performed using the “glmnet” package in R, while the random forest algorithm was realized using the “randomForest” package in R. The prognostic variables were evaluated using univariate Cox analysis. The prognostic risk model was built using the LASSO approach. ROC curve and KM survival analyses were performed to assess the prognostic potential of the risk model. The half-maximal inhibitory concentration (IC50) of the PARP inhibitors was estimated using the “pRRophetic” package in R and the Genomics of Drug Sensitivity in Cancer database. The “rms” package in R was used to create the nomogram. A high HRD score indicated a poor prognosis and an advanced clinical process in PAAD patients. PAAD tumors with high HRD levels revealed significant T helper lymphocyte depletion, upregulated levels of cancer stem cells, and increased sensitivity to rucaparib, Olaparib, and veliparib. Using WGCNA, 11 coexpression modules were obtained. The red module and 122 hub genes were identified as the most correlated with HRD in PAAD. Functional enrichment analysis revealed that the 122 hub genes were mainly concentrated in cell cycle pathways. One novel HRD-related gene signature consisting of CKS1B, HJURP, and TPX2 were screened via LASSO analysis and a random forest algorithm, and they were validated using independent validation sets. No direct association between HRD and <i>CKS1B</i>, <i>HJURP</i>, or <i>TPX2</i> has not been reported in the literature so far. Thus, these findings indicated that <i>CKS1B</i>, <i>HJURP</i>, and <i>TPX2</i> have potential as diagnostic and prognostic biomarkers for PAAD. We constructed a novel HRD-related prognostic model that provides new insights into PAAD prognosis and immunotherapy. Based on bioinformatics analysis, we comprehensively explored the therapeutic and prognostic potential of HRD in PAAD. One novel HRD-related gene signature consisting of CKS1B, HJURP, and TPX2 were identified through the combination of WGCNA, LASSO analysis and a random forest algorithm. A novel HRD-related risk model that can predict clinical prognosis and immunotherapeutic response in PAAD patients was constructed.

同源重组修复缺陷（Homologous Repair Deficiency, HRD）会阻碍双链断裂修复，是致癌作用的常见驱动因素。HRD阳性状态可作为铂类及多聚ADP核糖聚合酶（PARP）抑制剂化疗响应的诊疗标志物。本研究旨在通过全面的生物信息学分析，系统探究HRD在胰腺腺癌（Pancreatic Adenocarcinoma, PAAD）中的治疗及预后潜力，并筛选与HRD相关的有效生物标志物。HRD评分定义为杂合性缺失（Loss of Heterozygosity, LOH）、端粒等位基因失衡（Telomeric Allelic Imbalance, TAI）以及大规模状态转换（Large Scale Transitions, LST）评分的未加权总和，其计算依据既往文献。为刻画PAAD的免疫浸润亚型，本研究使用R语言的`ConsensusClusterPlus`包开展无监督聚类分析。通过加权基因共表达网络分析（Weighted Gene Co-expression Network Analysis, WGCNA），解析PAAD中HRD相关基因模块的基因共表达模式与核心基因。采用Metascape工具进行功能富集分析。使用R语言的`glmnet`包完成最小绝对收缩和选择算子（Least Absolute Shrinkage and Selection Operator, LASSO）分析，借助`randomForest`包实现随机森林算法。通过单因素Cox回归分析评估预后变量，采用LASSO方法构建预后风险模型。使用受试者工作特征（Receiver Operating Characteristic, ROC）曲线及Kaplan-Meier（KM）生存分析评估该风险模型的预后价值。采用R语言的`pRRophetic`包及癌症药物敏感性基因组学（Genomics of Drug Sensitivity in Cancer, GDSC）数据库估算PARP抑制剂的半抑制浓度（Half-Maximal Inhibitory Concentration, IC50）。使用R语言的`rms`包构建列线图。在PAAD患者中，高HRD评分提示不良预后与进展期临床病程。HRD水平较高的PAAD肿瘤呈现显著的辅助T淋巴细胞耗竭、癌症干细胞水平上调，且对芦卡帕利（rucaparib）、奥拉帕利（olaparib）及维利帕利（veliparib）敏感性升高。通过WGCNA分析，共获得11个共表达模块，其中红色模块与122个核心基因被鉴定为与PAAD中HRD相关性最强的模块与基因集。功能富集分析显示，该122个核心基因主要富集于细胞周期通路。通过LASSO分析与随机森林算法，筛选得到由CKS1B、HJURP及TPX2组成的新型HRD相关基因特征标志物，并通过独立验证集完成验证。截至目前，尚无文献报道HRD与CKS1B、HJURP或TPX2之间存在直接关联。因此，上述结果提示CKS1B、HJURP及TPX2有望作为PAAD的诊断与预后生物标志物。本研究构建了新型HRD相关预后模型，为PAAD的预后评估与免疫治疗提供了新的视角。本研究通过生物信息学分析全面探索了HRD在PAAD中的治疗与预后潜力，结合WGCNA、LASSO分析及随机森林算法，鉴定得到由CKS1B、HJURP及TPX2组成的新型HRD相关基因特征标志物，并构建了一款可预测PAAD患者临床预后与免疫治疗响应的新型HRD相关风险模型。