原发性骨髓纤维化（PM）是骨髓增生性肿瘤之一，其来源于干细胞的克隆性肿瘤已被注意到。该疾病的诊断基于：体格检查、外周血结果、骨髓形态学、细胞遗传学和分子标记物。然而，PM的分子标记物——JAK2V617F基因突变也在其他骨髓增生性肿瘤中观察到，例如：红细胞增多症和原发性血小板增多症。因此，有必要寻找能提供独特于PM并允许更高准确性的PM诊断和治疗的方法。在本研究中，我们拟采用拉曼光谱和主成分分析（PCA）、偏最小二乘（PLS）分析作为PM的有益诊断工具。因此，我们使用从PM患者收集的血清进行分类，且这些患者是根据PM的临床参数进行分类的，如：PM的动态国际预后评分系统（DIPSS）加分、JAK2V617F突变、脾脏大小、骨髓网状纤维化程度和使用羟基脲药物特征。拉曼光谱显示PM患者的C-H、C-C和C-C/C-N含量较健康患者高，并且酰胺Ⅱ含量较高，酰胺Ⅰ和CH3基的振动较低。此外，还观察到PM患者中酰胺Ⅱ和酰胺Ⅰ的振动有偏移。机器学习方法在分析拉曼区域时表现出：（i）800 cm-1和1800 cm-1、（ii）1600 cm-1-1700 cm-1和（iii）2700 cm-1-3000 cm-1呈现出100%的准确性、敏感性和特异性。拉曼光谱动态的差异显示，酰胺Ⅱ和酰胺Ⅰ的差异在区分PM和健康患者方面是最显著的。值得注意的是，迄今为止，拉曼光谱在通过拉曼光谱与PM临床预后评分之间的相关性进行PM疾病的临床诊断方面的有效性尚未建立。此外，我们的结果显示，拉曼信号与骨髓纤维化以及JAKV617F之间存在相关性。因此，结果揭示了拉曼光谱在医学实验室诊断中同时定量多个生物标志物的高潜力，尤其是在选择的拉曼区域内。版权所有 © 2023. Elsevier Inc. 发布。

Primary myelofibrosis (PM) is one of the myeloproliferative neoplasm, where stem cell derived clonal neoplasms was noticed. Diagnosis of this disease is based on: physical examination, peripheral blood findings, bone marrow morphology, cytogenetics, and molecular markers. However, molecular marker of PM, which is mutation in JAK2V617F gene, was observed also in other myeloproliferative neoplasm such as: polycythemia vera and essential thrombocythemia. Therefore, there is a need to find methods which provide a marker unique to PM and allowing for higher accuracy of PM diagnosis and consequently the treatment of the disease. Continuing, in this study we purposed Raman spectroscopy and Principal Components Analysis (PCA), Partial Least Squares (PLS) analysis as a helpful diagnostic tool of PM. Consequently, we used serum collected from PM patients, which were classified using clinical parameters of PM such as: the dynamic international prognostic scoring system for primary myelofibrosis (DIPSS) plus score, the JAK2V617F mutation, spleen size, bone marrow reticulin fibrosis degree and use of hydroxyurea drug features. Raman spectra showed higher amount of C-H, C-C and C-C/C-N and amide II and lower amount of amide I and vibrations of CH3 groups in PM patients than in healthy one. Furthermore, shifts of amides II and I vibrations in PM patients were noticed. Machine learning methods were used for analyze of Raman regions: (i) 800 cm-1 and 1800 cm-1, (ii) 1600 cm-1-1700 cm-1, and (iii) 2700 cm-1-3000 cm-1 showed 100 % of accuracy, sensitivity, and specificity. Differences in the spectra dynamic showed, that differences in the amide II and amide I were the most significant in differentiation PM and healthy subjects. Importantly, until now, efficacy of Raman spectroscopy has not been established in clinical diagnostics of PM disease using the correlation between Raman spectra and PM clinical prognostic scoring. Continuing, our results showed correlation between Raman signals and bone marrow fibrosis, as well as JAKV617F. Consequently, the results reveal, that Raman spectroscopy has a high potential for use in medical laboratory diagnostics to quantify multiple biomarkers simultaneously, especially in the selected Raman regions.Copyright © 2023. Published by Elsevier Inc.