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Sichuan Da Xue Xue Bao Yi Xue Ban. 2023 Sep 20; 54(5): 899–907.
PMCID: PMC10579086

Language: Chinese | English

GEO数据库联合机器学习策略识别骨关节炎特征性lncRNA分子标志物及实验验证

Identification of Characteristic lncRNA Molecular Markers in Osteoarthritis by Integrating GEO Database and Machine Learning Strategies and Experimental Validation

巧 周 , 1, 2, 3 健 刘 , 1, 3, Δ 凌 忻 , 1, 3 妍妍 方 , 1, 3 亚军 齐 , 3, 4 and 月迪 胡 3, 4

巧 周

安徽中医药大学第一附属医院 (合肥 230031), The First Affiliated Hospital, Anhui University of Chinese Medicine, Hefei 230031, China 安徽中医药大学第二附属医院 (合肥 230061), The Second Affiliated Hospital, Anhui University of Chinese Medicine, Hefei 230061, China 安徽省中医药科学院风湿病研究所 (合肥 230031), Institute of Rheumatism Prevention and Treatment of Traditional Chinese Medicine, Anhui Academy of Chinese Medicine Sciences, Hefei 230031, China

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健 刘

安徽中医药大学第一附属医院 (合肥 230031), The First Affiliated Hospital, Anhui University of Chinese Medicine, Hefei 230031, China 安徽中医药大学第二附属医院 (合肥 230061), The Second Affiliated Hospital, Anhui University of Chinese Medicine, Hefei 230061, China

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凌 忻

安徽中医药大学第一附属医院 (合肥 230031), The First Affiliated Hospital, Anhui University of Chinese Medicine, Hefei 230031, China 安徽中医药大学第二附属医院 (合肥 230061), The Second Affiliated Hospital, Anhui University of Chinese Medicine, Hefei 230061, China

Find articles by 凌 忻

妍妍 方

安徽中医药大学第一附属医院 (合肥 230031), The First Affiliated Hospital, Anhui University of Chinese Medicine, Hefei 230031, China 安徽中医药大学第二附属医院 (合肥 230061), The Second Affiliated Hospital, Anhui University of Chinese Medicine, Hefei 230061, China

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亚军 齐

安徽中医药大学第一附属医院 (合肥 230031), The First Affiliated Hospital, Anhui University of Chinese Medicine, Hefei 230031, China 安徽中医药大学第二附属医院 (合肥 230061), The Second Affiliated Hospital, Anhui University of Chinese Medicine, Hefei 230061, China

Find articles by 亚军 齐

月迪 胡

安徽中医药大学第一附属医院 (合肥 230031), The First Affiliated Hospital, Anhui University of Chinese Medicine, Hefei 230031, China 安徽中医药大学第二附属医院 (合肥 230061), The Second Affiliated Hospital, Anhui University of Chinese Medicine, Hefei 230061, China 安徽中医药大学第一附属医院 (合肥 230031), The First Affiliated Hospital, Anhui University of Chinese Medicine, Hefei 230031, China 安徽中医药大学第二附属医院 (合肥 230061), The Second Affiliated Hospital, Anhui University of Chinese Medicine, Hefei 230061, China 安徽省中医药科学院风湿病研究所 (合肥 230031), Institute of Rheumatism Prevention and Treatment of Traditional Chinese Medicine, Anhui Academy of Chinese Medicine Sciences, Hefei 230031, China 安徽中医药大学 (合肥 230012), Anhui University of Chinese Medicine, Hefei 230012, China

1.4. 临床标本收集

本研究选取安徽中医药大学第一附属医院风湿免疫科于2022年11月–2023年2月确诊的30例未接受药物治疗的OA患者。诊断标准参考2018年中华医学会风湿病学分会修订的《骨关节炎诊断及治疗指南》 [ 20 ] 。X线检查根据Kellgren-Lawrence标准 [ 21 ] 符合1、2、3级的患者。其中11名男性,19名女性,平均年龄为(56.63±12.06)岁。此外,还招募了同期体检中心健康个体作为对照组,排除标准:①合并骨关节炎、类风湿关节炎等风湿病的患者;②合并循环系统、呼吸系统、造血系统等疾病的患者;③孕妇或哺乳期女性的患者;④精神病患者。对年龄、性别进行一对一倾向性匹配,纳入15例作为对照组,其中8例男性,12例女性,平均年龄为(53.60±7.72)岁。两组一般资料差异无统计学意义。安徽中医药大学附属第一医院伦理委员会对这项工作进行了评审和批准(伦理号:No. 2022MCZQ01)。

1.5. 免疫炎症指标测定

采用全自动生化分析仪(日立HITACHI7600)检测全血生化指标,包括免疫球蛋白A(immunoglobulin A, IgA)、C反应蛋白(C-reactive protein, CRP)、白细胞介素6(interleukin 6, IL-6)、补体4(complement 4, C4)、补体3(complement 3, C3)、免疫球蛋白M(immunoglobulin M, IgM)、红细胞沉降率(erythrocyte sedimentation rate, ESR)、免疫球蛋白E(immunoglobulin E, IgE)、免疫球蛋白G(immunoglobulin G, IgG)。

1.6. RT-PCR定量分析

采用密度梯度离心法分离两组样本PBMC。收集细胞沉淀,用Trizol法提取RNA。RNA的数量使用Nano Drop分光光度计(NanoDrop Technologies, Wilmington, NC, USA)测量。RT试剂盒中使用gDNA Eraser(TaKaRa, Shiga, Japan)生成cDNA。 表2 列出了qRT-PCR中使用的引物。内参基因为 GAPDH 。相对表达值均计算为2 −ΔΔCt

表 2

Specific gene primer sequences

特异基因引物序列

Gene Forward primer (5′→3′) Reverse primer (5′→3′)
GAPDH : glyceraldehyde-3-phosphate dehydrogenase; MIR 155 HG : MIR155 host gene; HOTAIR : HOX transcript antisense RNA; H 19: H19 imprinted maternally expressed transcript; NKILA : NF-kappa B interacting lncRNA.
GAPDH TTCCACCCATGGCAAATTCC ATCTCGCTCCTGGAAGATGG
MIR 155 HG GAGTGCTGAAGGCTTGCTGT TTGAACATCCCAGTGACCAG
HOTAIR GGAAAGATCCAAATGGGACC CTAGGAATCAGCACGAAGCA
H 19 TGATGACGGGTGGAGGGGCT TGATGTCGCCCTGTCTGCAC
NKILA CTGTCGGGGACTGGTGTATT AATACACCAGTCCCCGACAG

1.7. 统计学方法

计量资料采用 equation M9 表示,所有样本均用Kolmogorov-Smirnov检验正态性,若符合正态性和方差齐的数据,组间比较采用两独立样本 t 检验,不符合则选取非参数检验。相关性分析采用Spearman或Pearson分析。一对一倾向性匹配法运用SPSS中倾向匹配得分工具实现。 P <0.05为差异有统计学意义。

2. 结果

2.1. 差异表达的lncRNA识别

对5组数据集进行了标准化处理,从 图1A 看出不同的批次之间各自成团,批次之间有比较明显的差别,存在一定的批次效应。 图1B 不同批次的样品重叠在一起,表明批次效应校正成功。使用OriginLab构建整合基因集中差异表达lncRNA的火山图,见 图2 。进一步通过R语言RRA法筛选出105个差异lncRNA,包括30个上调和75个下调的lncRNA。将所得差异表达lncRNA对应统计量进行汇总,按校正后的 P 值即adj. P .Val从小到大进行排序,一般认为adj. P .Val值越小代表差异表达越显著,依据adj. P .Val值选择差异表达最显著的前10个lncRNA,见 表3

An external file that holds a picture, illustration, etc. Object name is scdxxbyxb-54-5-899-1.jpg

Eliminating the batch effect of the data with combat function

Combat函数消除数据的批次效应

A, Five data sets before normalization. B, After normalization of the five data sets.

Volcano plot of differentially expressed lncRNAs in the five datasets

5个数据集差异表达lncRNA火山图

Black represents all differentially expressed lncRNAs, red represents lncRNAs with log 2 FC>0, and green represents lncRNAs with log 2 FC<0.

表 3

Top 10 lncRNAs showing the most significant difference in their expression

差异表达最显著的前10个lncRNA

Index GEO data set Gene log 2 FC P .Value adj. P .Val
1 GSE51588 MIR 155 HG 9.581 4.44E-03 9.05E-02
GSE117999
GSE48556
2 GSE51588 HOTAIR 2.321 6.44E-06 9.90E-04
GSE117999
GSE48556
3 GSE48556 NKILA −3.686 1.46E-05 1.26E-02
GSE169077
4 GSE43270 H 19 2.216 3.05E-05 1.34E-02
GSE51588
GSE117999
GSE48556
5 GSE43270 MEG 3 −3.033 3.01E-05 1.34E-02
GSE51588
GSE117999
GSE48556
6 GSE48556 LINC 00973 2.146 3.76E-05 1.36E-02
7 GSE51588 C 15 orf 54 −2.013 8.44E-05 2.01E-02
GSE117999
GSE48556
8 GSE117999 MEG 9 2.252 1.33E-04 2.43E-02
9 GSE43270 PART 1 2.191 1.73E-03 6.23E-02
GSE51588
GSE117999
GSE48556
10 GSE51588 C 3 orf 79 2.179 2.10E-03 6.67E-02
GSE117999

2.2. 候选的lncRNA分子标志物的筛选与验证

运用3种算法对候选的105个差异lncRNA进行识别,其中LASSO算法得出14个关键的生物标志物( 图3 )。SVM-RFE算法确定了6个基因作为必要的生物标志物( 图4 ), 图4A 是5倍交叉验证后曲线变化的错误率,图中6-0.173表示筛选出的6个特征性基因的错误率是0.173,越接近0,表明错误率越低; 图4B 是5倍交叉验证后曲线变化的准确率,图中6-0.827表示筛选出6个特征性基因的准确率是0.827,越接近1,表明准确率越高。除此之外,RF算法认为24个基因是重要的生物标志物( 图5A 5B )。Venn图绘制3种算法的重叠基因( 图6A ),最终得到4个lncRNA,其中 HOTAIR H 19、 MIR 155 HG 上调(log 2 FC≥1), NKILA 下调( log 2 FC<−1)。ROC曲线显示,它们可能是有价值的生物标志物, AUC 分别为0.9415〔95%置信区间(confidence interval, CI ):0.9010~0.9820〕( HOTAIR )、0.8016(95% CI :0.7244~0.8788)( H 19)、0.9751(95% CI :0.9475~0.9991)( MIR 155 HG )、0.7957(95% CI :0.7165~0.8748)( NKILA )( 图6B )。

LASSO algorithm was used to screen out 14 lncRNAs

LASSO算法筛选14个lncRNA

A, Each curve in the figure represents the change trajectory of each independent variable coefficient, the vertical coordinate is the value of the coefficient, the lower horizontal coordinate is log ( λ ), and the upper horizontal coordinate is the number of non-zero coefficients in the model at this time. B, The vertical coordinate is Binomial Deviance (dichotomous anomaly), which can be interpreted as the magnitude of the error of the model. There are two dashed lines of values in the figure, the left is the line with the lowest error and the right is the line with fewer features.

Support vector machine recursive feature elimination (SVM-RFE) algorithm was used to screen out 6 key lncRNAs

SVM-RFE算法筛选出6个关键lncRNA

Graph A is SVM error and graph B is SVM accuracy. 5×CV represents 5-fold cross-validation. The number 6-0.173 in Fig 4A indicates that the error rate for the six trait genes screened out was 0.173. The number 6-0.827 in Fig 4B indicates that the accuracy rate of the six trait genes screened out was 0.827.

Random forest (RF) algorithm was used to screen out 24 feature lncRNAs

RF算法筛选24个特征lncRNA

A, The dynamics of the random forest prediction error versus the number of random trees, with the vertical axis of error representing the error; the horizontal axis of trees representing the tree number. The black, red, and green lines show how the false positive rate varies with the number of decision trees for all samples, samples from osteoarthritis patients, and samples from normal healthy people in the five datasets, respectively. B, The 24 genes sorted by importance.

Screening and validation of key lncRNAs

关键lncRNA的筛选及验证

A, Venn diagram was used to screen for overlapping genes identified by the three algorithms. B, ROC curves for validating diagnostic efficacy after fitting key lncRNA to one variable.

2.3. 临床患者免疫炎症指标变化

与正常对照组相比,OA患者中炎症指标(ESR、CRP、IL-6)、免疫球蛋白(IgA、IgE)、补体C4升高,差异有统计学意义( P <0.05)。见 表4

表 4

Changes in immunoinflammatory indicators in the two groups

两组免疫炎症指标的变化

Indicator NC group ( n =15) OA group ( n =30)
ESR: erythrocyte sedimentation rate; CRP: C-reactive protein; IgA: immunoglobulin A; IgM: immunoglobulin M; IgG: immunoglobulin G; IgE: immunoglobulin E; C3: complement 3; C4: complement 4; IL-6: interleukin 6. The other abbreviations are explained in the notes to Table 1.
ESR/(mm/1 h) 3.45±1.34 15.6±7.34 <0.001
CRP/(mg/L) 0.73±0.56 8.3±4.24 <0.001
IgA/(g/L) 1.68±0.22 3.73±1.25 <0.001
IgM/(g/L) 1.04±0.12 1.25±0.65 0.654
IgG/(g/L) 11.47±3.45 13.79±6.44 0.545
IgE/(IU/mL) 19.49±9.45 70.56±15.56 0.013
C3/(g/L) 0.63±0.12 0.84±0.32 0.576
C4/(g/L) 0.11±0.11 0.76±0.89 0.021
IL-6/(pg/mL) 2.38±1.45 13.09±3.56 0.011

2.4. RT-PCR法检测lncRNA分子标志物的表达和相关性分析

RT-PCR结果显示,与正常人相比,OA患者PBMC中 HOTAIR H 19、 MIR 155 HG 相对表达量升高( P <0.01), NKILA 相对表达量下降( P <0.01),与生物信息学分析结果一致( 图7 )。Pearson相关性分析表明, H 19与IgA( r =0.439, P =0.018)呈正相关, MIR 155 HG 与CRP( r =0.785, P <0.001)、IgM( r =0.454, P =0.008)、IL-6( r =0.610, P <0.001)呈正相关, NKILA 与ESR( r =−0.425, P =0.021)呈负相关,与IL-6( r =0.650, P <0.001)呈正相关, HOTAIR 与CRP( r =0.589, P =0.001)、IL-6( r =0.492, P =0.006)和IgE( r =0.445, P =0.014)呈正相关。表明筛选出的4个特征性lncRNA与免疫炎症指标存在相关性( 表5 )。

An external file that holds a picture, illustration, etc. Object name is scdxxbyxb-54-5-899-7.jpg

RT-PCR to detect the expression of lncRNAs molecular markers

RT-PCR检测lncRNA分子标志物的表达

表 5

Pearson analysis of lncRNA molecular markers and immunoinflammatory indicators

lncRNA分子标志物与免疫炎症指标的Pearson分析

Indicator H 19 MIR 155 HG NKILA HOTAIR
ESR, CRP, IgA, IgM, IgG, IgE, C3, C4 and IL-6 denote the same as those in Table 4. H 19, MIR 155 HG , NKILA and HOTAIR denote the same as those in Table 2.
ESR/(mm/1 h) 0.044 0.816 0.355 0.052 −0.425 0.021 0.345 0.054
CRP/(mg/L) 0.014 0.941 0.785 <0.001 −0.308 0.064 0.589 0.001
IgA/(g/L) 0.439 0.018 0.220 0.243 −0.312 0.056 0.212 0.260
IgM/(g/L) 0.298 0.110 0.454 0.008 −0.063 0.742 0.040 0.834
IgG/(g/L) 0.090 0.637 0.119 0.531 −0.122 0.522 0.095 0.618
IgE/(IU/mL) 0.358 0.051 0.008 0.968 −0.183 0.333 0.445 0.014
C3/(g/L) 0.035 0.856 0.212 0.260 −0.194 0.304 0.214 0.247
C4/(g/L) 0.028 0.883 0.010 0.960 −0.007 0.972 0.221 0.214
IL-6/(pg/mL) 0.061 0.749 0.610 <0.001 0.650 <0.001 0.492 0.006

3. 讨论

OA的发生发展涉及复杂的生物学反应 [ 22 ] 。由于缺乏早期检测和评估治疗结果的有效方法,目前,生物标志物的发现成为辅助疾病监测的前瞻性方法。本研究基于GEO测序数据集,结合机器学习策略,寻找OA新的免疫炎症相关的分子标志物,并在OA患者外周血标本中进行验证,为OA的早期诊断和治疗提供新的思路和研究方向。

在本研究中,通过筛选5个OA软骨细胞全基因组基因表达谱,整合差异表达的lncRNA,确定了105个差异lncRNA,包括30个上调和75个下调的lncRNA。根据adj. P .Val值从小到大排序, 表3 列出前10的lncRNA,包括下调的 NKILA MEG 3和 C 15 orf 54(log 2 FC<-1),上调的 MIR 155 HG HOTAIR H 19、 LINC 00973、 MEG 9、 PART 1和 C 3 orf 79(log 2 FC≥1)。 还有一些lncRNA值得关注,如上调的 XIST [ 23 ] TUG 1 [ 24 ] DANCR [ 25 ] MIAT [ 26 ] ,下调的 MEG 3 [ 27 ] THRIL [ 28 ] GAS 5 [ 8 ] ATB [ 29 ] 。这些lncRNA可能与OA的发病机制有关。

通过集成3种不同的算法,得到3种算法的重叠基因,分别是 HOTAIR H 19、 MIR 155 HG NKILA 。ROC曲线结果也表明,4个生物标志物 AUC 均大于0.7,表明预测结果具有较强的准确性。 HOTAIR 在OA组织中高表达,通过抑制软骨细胞增殖,促进细胞凋亡和细胞外基质降解导致软骨细胞的功能障碍 [ 30 ] 。lncRNA NKILA 通过与核因子κB/核因子κB的抑制蛋白(nuclear factor kappa-B/inhibitor of NF-κB, NF-κB/IκB)复合物结合来覆盖IκB的磷酸化位点,从而抑制NF-κB的过度活化 [ 31 ] 。lncRNA MIR 155 HG 也称为B细胞整合簇。 MIR 155 HG 的表达与免疫细胞、分子和免疫检查点分子的浸润水平显著相关 [ 32 ] MIR 155 HG 参与破骨细胞的调节。在骨质疏松小鼠中, MIR 155 HG 通过AMP依赖的蛋白激酶(AMP-activated protein kinase, AMPK)途径靶向瘦素受体基因来抑制破骨细胞活化 [ 33 ] MIR 155 HG 上调可能是OA炎症过程的重要因素。lncRNA H 19在OA中高度表达,可能通过白介素(Interleukin,IL)-38和IL-36之间的相互作用促进OA中的炎症反应 [ 5 ] 。综上所述,基于3种算法的机器学习组合模型和文献调研,均表明 HOTAIR H 19、 MIR 155 HG NKILA 与OA中免疫炎症存在相关性,可作为特征性诊断的标志物,但其诊断意义还需要大量的实验来验证。

临床样本验证结果显示,与正常健康组相比, HOTAIR H 19、 MIR 155 HG 相对表达量升高, NKILA 相对表达量下降,结果与生物信息学预测结果相一致,表明本研究整合预测策略的可行性。相关性分析显示4个特征性lncRNA与免疫炎症指标的相关性。IL-6在软骨病理的发展中起着关键作用,OA患者血清或滑液中IL-6水平的增加与疾病的严重程度相关;然而,IL-6也增加了抗分解代谢因子的表达,具有保护作用;表明IL-6在OA中起双重作用,可能是由IL-6经典与反式信号传导的不同效应引起的 [ 34 ] 。IgE介导的肥大细胞通过高亲和力IgE受体(FcεRI),导致促炎细胞和降解介质(包括类胰蛋白酶)的释放,导致组织损伤、炎症和肥大细胞活化,导致OA的发展和进展 [ 35 ] 。CRP和ESR可作为炎症反应的非特异性指标 [ 36 ] 。研究发现CRP是影响OA诊断和严重程度分级的独立因素 [ 37 ] 。免疫球蛋白亚型在OA患者血清中表达升高,参与了OA发病过程 [ 38 ] 。例如,IgA [ 39 ] 和 IgM [ 40 ] 作为机体内高亲和力抗体,参与免疫应答反应。相关性分析也表明, MIR 155 HG NKILA HOTAIR 与炎症指标(CRP、ESR和IL-6)、免疫指标(IgE、IgA和IgM)存在相关性,表明特征性的lncRNA参与了OA的免疫炎症反应,在OA临床患者应用中有重要意义。

本研究预测的结果来源于GEO数据库多个实验测序数据的组合,多个算法整合机器学习策略来识别特征性基因,信息利用度方面较单个数据集和小样本的实验验证更全面和完善。然而,本研究存在一定的局限性:样本量相对较小,软骨标本难获取,因此选择外周血标本进行验证。此外,OA患者之间的个体差异,包括社会经济地位、疾病严重程度和疾病持续时间,可能会影响结果的准确性。因此,进一步大样本的体内外实验验证特征性的lncRNA在OA免疫炎症中的确切机制将是我们下一步研究的目标。

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作者贡献声明 刘健负责论文构思,忻凌负责正式分析,周巧和齐亚军负责调查研究,周巧、方妍妍和胡月迪负责可视化,周巧负责初稿写作和审读与编辑写作。所有作者已经同意将文章提交给本刊,且对将要发表的版本进行最终定稿,并同意对工作的所有方面负责。

利益冲突 所有作者均声明不存在利益冲突

Funding Statement

安徽省高等学校科学研究项目(自然科学类)重点项目(No. 2022AH050449)、安徽省第12批“115”创新团队(皖人才办〔2019〕1号)、安徽省名中医刘健工作室建设项目(中医药发展秘〔2018〕11号)和安徽省中医药领军人才项目(中医药发展秘〔2018〕23号)资助

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