Research Center for Drug Safety Evaluation of Hainan, Hainan Medical University, Haikou, Hainan 571199, China.
Jiangsu Food and Pharmaceutical Science College, Huaian, Jiangsu 223023, China.
Aging (Albany NY). 2024 Feb 28;16(5):4563-4578. doi: 10.18632/aging.205611.
Osteoarthritis (OA) is the most common degenerative joint disease worldwide. Further improving the current limited understanding of osteoarthritis has positive clinical value.
OA samples were collected from GEO database and endoplasmic reticulum related genes (ERRGs) were identified. The WGCNA network was further built to identify the crucial gene module. Based on the expression profiles of characteristic ERRGs, LASSO algorithm was used to select key factors according to the minimum λ value. Random forest (RF) algorithm was used to calculate the importance of ERRGs. Subsequently, overlapping genes based on LASSO and RF algorithms were identified as ERRGs-related diagnostic biomarkers. In addition, OA specimens were also collected and performed qRT-PCR quantitative analysis of selected ERRGs.
We identified four ERRGs associated with OA risk assessment through machine learning methods, and verified the abnormal expressions of these screened markers in OA patients through experiments. The influence of selected markers on OA immune infiltration was also evaluated.
Our results provide new evidence for the role of ER stress in the OA progression, as well as new markers and potential intervention targets for OA.
骨关节炎（OA）是全球最常见的退行性关节疾病。进一步提高对骨关节炎的现有有限认识具有积极的临床价值。
从 GEO 数据库中收集 OA 样本，鉴定内质网相关基因（ERRGs）。进一步构建 WGCNA 网络以识别关键基因模块。根据特征 ERRGs 的表达谱，根据最小 λ 值使用 LASSO 算法选择关键因素。随机森林（RF）算法用于计算 ERRGs 的重要性。随后，根据 LASSO 和 RF 算法识别重叠基因作为 ERRGs 相关诊断生物标志物。此外，还收集 OA 标本并对选定的 ERRGs 进行 qRT-PCR 定量分析。
我们通过机器学习方法鉴定了四个与 OA 风险评估相关的 ERRGs，并通过实验验证了这些筛选标记物在 OA 患者中的异常表达。还评估了所选标记物对 OA 免疫浸润的影响。
我们的结果为 ER 应激在 OA 进展中的作用提供了新的证据，并为 OA 提供了新的标记物和潜在的干预靶点。
