Introduction: Schizophrenia (SCZ), a severe neuropsychiatric disorder with high genetic susceptibility, has high rates of misdiagnosis due to the unavoidably subjective factors and heterogeneous clinical presentations. Hypoxia has been identified as an importantly risk factor that participates in the development of SCZ. Therefore, development of a hypoxia-related biomarker for SCZ diagnosis is promising. Therefore, we dedicated to develop a biomarker that could contribute to distinguishing healthy controls and SCZ patients. Methods: GSE17612, GSE21935 and GSE53987 datasets, consist of 97 control samples and 99 SCZ samples, were involved in our study. The hypoxia score was calculated based on the single-sample gene-set enrichment analysis (ssGSEA) using the hypoxia-related differentially expressed genes to quantify the expression levels of these genes for each SCZ patients. Patients in high-score groups were defined if their hypoxia score was in the upper half of all hypoxia scores, and patients in low-score groups if their hypoxia score were in the lower half. GSEA was applied to detect the functional pathway of these differently expressed genes. CIBERSORT algorithm was utilized to evaluate the tumor-infiltrating immune cells of SCZ patients. Results: In this study, we developed and validated a biomarker consisting of 12 hypoxia-related genes that could distinguish healthy controls and SCZ patients robustly. We found that the metabolism reprogramming might be activated in patient with high hypoxia score. Finally, CIBERSORT analysis illustrated that lower composition of naive B cells and higher composition of memory B cells might be observed in low-score groups of SCZ patients. Conclusion: These findings revealed that the hypoxia-related signature was acceptable as a detector for SCZ, providing further insight into effective diagnosis and treatment strategies for SCZ.


中文翻译：

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基于全基因组基因表达鉴定缺氧相关特征作为精神分裂症的候选检测器

简介：精神分裂症 (SCZ) 是一种具有高度遗传易感性的严重神经精神疾病，由于不可避免的主观因素和异质性临床表现，误诊率很高。缺氧已被确定为参与 SCZ 发展的重要危险因素。因此，开发用于 SCZ 诊断的缺氧相关生物标志物是有希望的。因此，我们致力于开发一种有助于区分健康对照和 SCZ 患者的生物标志物。方法：我们的研究涉及 GSE17612、GSE21935 和 GSE53987 数据集，包括 97 个对照样本和 99 个 SCZ 样本。基于单样本基因集富集分析 (ssGSEA) 计算缺氧评分，使用与缺氧相关的差异表达基因来量化每个 SCZ 患者这些基因的表达水平。如果缺氧评分在所有缺氧评分的上半部分，则定义高分组患者，如果缺氧评分在下半部分，则定义低分组患者。GSEA 用于检测这些差异表达基因的功能通路。CIBERSORT 算法用于评估 SCZ 患者的肿瘤浸润免疫细胞。结果：在这项研究中，我们开发并验证了一种生物标志物，该生物标志物由 12 个缺氧相关基因组成，可以有效区分健康对照和 SCZ 患者。我们发现代谢重编程可能在缺氧评分高的患者中被激活。最后，CIBERSORT 分析表明，在低分 SCZ 患者组中可能观察到较低的幼稚 B 细胞组成和较高的记忆 B 细胞组成。结论：这些发现表明缺氧相关特征作为 SCZ 的检测器是可以接受的，从而进一步深入了解 SCZ 的有效诊断和治疗策略。