CELL INTELLIGENCE · 肿瘤细胞治疗研究
肿瘤细胞治疗研究
英文原题：Identification of ROCK1 as a novel biomarker for postmenopausal osteoporosis and pan-cancer analysis.
Identification of ROCK1 as a novel biomarker for postmenopausal osteoporosis and pan-cancer analysis.
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ROCK1 与 PMOP 的发病机制和免疫浸润显著相关，并影响癌症的发生、发展和预后，为 PMOP 和肿瘤提供了潜在的治疗靶点。然而，在将 ROCK1 作为治疗靶点应用于临床之前，还需要进一步的实验室和临床证据。
绝经后骨质疏松症（PMOP）是一种普遍存在的骨骼疾病，具有重大的全球影响。老年女性骨质疏松性骨折风险升高，给个人和社会带来了沉重负担。遗憾的是，目前缺乏可靠的诊断标志物和精准的治疗靶点，这仍然是PMOP面临的一大挑战。
从GEO数据库下载了PMOP相关数据集GSE7429、GSE56814、GSE56815和GSE147287。通过"limma"包鉴定DEGs。使用WGCNA和机器学习选择与PMOP高度相关的关键模块基因。对所有DEGs和选定的关键枢纽基因进行GSEA、DO、GO和KEGG富集分析。通过GeneMANIA数据库构建PPI网络。ROC曲线和AUC值在训练和验证数据集中验证了枢纽基因的诊断价值。xCell免疫浸润和单细胞分析确定了枢纽基因在PMOP免疫反应中的功能。泛癌分析揭示了枢纽基因在癌症中的作用。
在PMOP患者和健康对照之间共鉴定出1278个DEGs。紫色模块和青色模块被选为关键模块，结合DEGs和模块基因后筛选出112个共有基因。五种机器学习算法筛选出三个hub基因（KCNJ2、HIPK1和ROCK1），并针对hub基因构建了PPI网络。ROC曲线在PMOP的训练数据集（AUC = 0.73）和验证数据集（AUC = 0.81）中均验证了ROCK1的诊断价值。对低ROCK1患者进行了GSEA，富集最显著的领域包括蛋白质结合和免疫反应。DCs和NKT细胞在PMOP中高表达。泛癌分析显示ROCK1低表达与SKCM以及肾脏肿瘤（KIRP、KICH和KIRC）之间存在相关性。
Postmenopausal osteoporosis (PMOP) is a prevalent bone disorder with significant global impact. The elevated risk of osteoporotic fracture in elderly women poses a substantial burden on individuals and society. Unfortunately, the current lack of dependable diagnostic markers and precise therapeutic targets for PMOP remains a major challenge.
PMOP-related datasets GSE7429, GSE56814, GSE56815, and GSE147287, were downloaded from the GEO database. The DEGs were identified by "limma" packages. WGCNA and Machine Learning were used to choose key module genes highly related to PMOP. GSEA, DO, GO, and KEGG enrichment analysis was performed on all DEGs and the selected key hub genes. The PPI network was constructed through the GeneMANIA database. ROC curves and AUC values validated the diagnostic values of the hub genes in both training and validation datasets. xCell immune infiltration and single-cell analysis identified the hub genes' function on immune reaction in PMOP. Pan-cancer analysis revealed the role of the hub genes in cancers.
A total of 1278 DEGs were identified between PMOP patients and the healthy controls. The purple module and cyan module were selected as the key modules and 112 common genes were selected after combining the DEGs and module genes. Five Machine Learning algorithms screened three hub genes (KCNJ2, HIPK1, and ROCK1), and a PPI network was constructed for the hub genes. ROC curves validate the diagnostic values of ROCK1 in both the training (AUC = 0.73) and validation datasets of PMOP (AUC = 0.81). GSEA was performed for the low-ROCK1 patients, and the top enriched field included protein binding and immune reaction. DCs and NKT cells were highly expressed in PMOP. Pan-cancer analysis showed a correlation between low ROCK1 expression and SKCM as well as renal tumors (KIRP, KICH, and KIRC).
ROCK1 was significantly associated with the pathogenesis and immune infiltration of PMOP, and influenced cancer development, progression, and prognosis, which provided a potential therapy target for PMOP and tumors. However, further laboratory and clinical evidence is required before the clinical application of ROCK1 as a therapeutic target.
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