Identifying MRPS10 as a Diagnostic Biomarker for MDD via Machine Learning

Major depressive disorder (MDD) is a serious psychological disorder, which can lead to a high disability and mortality rate. Two GEO datasets were merged to create the analysis dataset. A total of 17,598 differentially expressed genes (DEGs) were utilized for Gene Set Enrichment Analysis (GSEA) to identify pathways distinguishing the MDD group from the control group. Notably, the gene MRPS10 emerged as a prominent candidate for a diagnostic biomarker of MDD, particularly indicating the late-stage condition, as identified by Least Absolute Shrinkage and Selection Operator (LASSO) and Support Vector Machine Recursive Feature Elimination (SVM-RFE). Receiver Operating Characteristic (ROC) curves for MRPS10 were employed to demonstrate its discriminatory power. The CIBERSORT algorithm was utilized to evaluate the distribution of tissue-infiltrating immune cells in both the MDD and control groups. The diagnostic biomarker, the gene MRPS10, demonstrated a positive correlation with resting dendritic cells and M1 macrophages, and a negative correlation with monocytes and activated NK cells. This underscores the significant role of this gene in immune cell infiltration. To bolster the reliability of our findings, we conducted an additional analysis on the expression of the gene MRPS10 within single-cell transcriptome data, which revealed significant differences in expression levels between the main excitatory neuron (EX) and inhibitory neuron (IN) types EX/L2/4, EX/L4/6, and IN/VIP. In summary, our research has pinpointed the gene MRPS10 as a biomarker, thereby enhancing the knowledge repository pertinent to clinical diagnostics and pharmaceutical development for MDD.

搜集汇总

背景与挑战

背景概述

该数据集通过整合两个GEO数据集和机器学习方法（如LASSO和SVM-RFE），识别出MRPS10基因作为重度抑郁症（MDD）的诊断生物标志物，特别适用于晚期阶段，并通过ROC曲线和免疫细胞浸润分析验证了其判别能力和生物学意义。数据集还包含单细胞转录组验证，支持MDD临床诊断和药物开发研究，属于计算机科学与技术领域。