基于转录组学和机器学习算法的肺结核铁死亡相关关键基因的研究

doi:10.19982/j.issn.1000-6621.20230273

摘要/Abstract

摘要：

目的：运用转录组学和机器学习方法探索铁死亡关键基因与肺结核发病机制的相关性。方法：以“pulmonary tuberculosis”为关键词,以测序类型(转录组学)和物种(HOMO sapiens)等为条件从公共存储库NCBI GEO(http://www.ncbi.nlm.nih.gov/geo)中进行搜索和筛选,得到GSE153326和GSE67589两套转录组数据集。将GSE153326作为训练组数据[包含健康人群血液样本8份,结核分枝杆菌(Mycobacterium tuberculosis, MTB)阳性血液样本52份]、GSE67589作为验证组数据(包含健康人群血液样本30份,MTB阳性27份);使用R脚本对数据进行矫正和注释,对两转录组的差异表达基因鉴定后,筛选出GSE153326训练组差异基因中与铁死亡相关基因(TBFerDEG)的表达情况,并对TBFerDEG的基因本体(GO)及京都基因和基因组百科全书(KEGG)进行富集分析;利用LASSO回归分析和SVM算法,从TBFerDEG中获得与铁死亡有明显差异的关键基因,并对其进行ROC分析,探索与关键基因相关的药物调控网络;最后将关键基因导入GSE67589验证组中,以验证训练组筛选出的铁死亡关键基因的诊断意义。结果：通过生物信息学分析,共筛选得到416个TBFerDEG,剔除不符合入选要求的基因,最终得到56个TBFerDEG差异基因。GO富集分析发现,肺结核中与铁死亡相关的生物学进程有细胞对化学应激的反应自噬调节、线粒体自噬、线粒体解体等;参与的通路有AMPK信号通路和铁死亡等。通过LASSO回归分析和SVM算法,最终得到5个与铁死亡相关的关键基因,分别为BID、AR、STK11、ALOX12和SRC,AUC分别为0.807、0.858、0.734、0.840和0.880;验证关键基因结果表明,AR和SRC基因在验证组(GSE67589)中MTB阳性与健康人群中的表达差异均有统计学意义(P值分别为0.004和0.017)。结论：AR和SRC是肺结核中与铁死亡相关的关键基因,可能为今后的基础研究奠定一定的借鉴和参考。

关键词: 结核,肺, 铁死亡, 基因表达, 机器学习

Abstract:

Objective: To investigate the correlation between key genes associated with ferroptosis and the pathogenic mechanism of pulmonary tuberculosis using transcriptomics and machine learning methodologies. Methods: Two transcriptomic datasets were obtained, named as GSE153326 and GSE67589, through searching the NCBI GEO public repository (http://www.ncbi.nlm.nih.gov/geo) using keywords “pulmonary tuberculosis” and specific criteria such as sequencing type (transcriptomics) and species (HOMO sapiens). GSE153326 served as a training dataset, comprising eight blood samples from healthy individuals and 52 blood samples with positive Mycobacterium tuberculosis (MTB). GSE67589 served as the validation dataset, including 30 blood samples from healthy individuals and 27 MTB-positive samples. Data refinement and annotation were performed using R scripts. After identification of differentially expressed genes in the two transcriptomic datasets, gene expression related to ferroptosis (TBFerDEG) in the training dataset GSE153326 were obtained. Enrichment analysis of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways for TBFerDEG was conducted. LASSO regression analysis and the SVM algorithm were applied to extract key genes associated with ferroptosis from TBFerDEG. ROC analysis was performed to explore the drug regulatory network related to these key genes. Finally, the key genes were incorporated into the validation dataset GSE67589 to validate the diagnostic performance of the ferroptosis-associated key genes identified in the training dataset. Results: Through bioinformatics analysis, a total of 416 TBFerDEGs were identified and 56 differentially expressed genes were obtained after filtering of non-matched genes. GO enrichment analysis revealed that biological processes related to ferroptosis in pulmonary tuberculosis include cellular response to chemical stress by autophagy regulation, mitochondrial autophagy, and mitochondrial disintegration. The implicated pathways encompassed the AMPK signaling pathway and ferroptosis. Through LASSO regression analysis and the SVM algorithm, five key genes associated with ferroptosis were ultimately identified, BID, AR, STK11, ALOX12, and SRC, with AUCs of 0.807, 0.858, 0.734, 0.840, and 0.880, respectively. Expression of AR (P=0.004) and SRC (P=0.017) in MTB-positive group were significantly different compared to the control group in the validation dataset. Conclusion: AR and SRC are key genes associated with ferroptosis in pulmonary tuberculosis, providing valuable insights for future basic research in this field.

Key words: Tuberculosis, pulmonary, Ferroptosis, Gene expression, Machine learning

中图分类号:

R52
R446

叶江娥, 方雪晖, 熊延军, 刘盛盛. 基于转录组学和机器学习算法的肺结核铁死亡相关关键基因的研究[J]. 中国防痨杂志, 2024, 46(1): 92-99. doi: 10.19982/j.issn.1000-6621.20230273

Ye Jiang’e, Fang Xuehui, Xiong Yanjun, Liu Shengsheng. Transcriptomics and machine learning algorithm-based characterization of ferroptosis-related genes in tuberculosis[J]. Chinese Journal of Antituberculosis, 2024, 46(1): 92-99. doi: 10.19982/j.issn.1000-6621.20230273

图/表 6

表1

表2

训练组中56个TBFerDEG的GO富集分析结果

项目	名称	中文名称	调整后P值
生物学过程	cellular response to chemical stress	细胞对化学应激的反应	<0.001
生物学过程	regulation of autophagy	自噬的调节	<0.001
生物学过程	autophagy of mitochondrion	线粒体自噬	<0.001
生物学过程	mitochondrion disassembly	线粒体分解	<0.001
生物学过程	regulation of apoptotic signaling pathway	细胞凋亡信号通路的调控	<0.001
生物学过程	negative regulation of apoptotic signaling pathway	细胞凋亡信号途径的负调控	<0.001
生物学过程	response to metal ion	对金属离子的反应	<0.001
生物学过程	organelle disassembly	细胞器解体	<0.001
生物学过程	cellular component disassembly	细胞成分分解	<0.001
生物学过程	cellular response to oxidative stress	细胞对氧化应激的反应	<0.001
细胞组分	mitochondrial outer membrane	线粒体外膜	0.018
细胞组分	autophagosome membrane	自噬体膜	0.018
细胞组分	extrinsic component of organelle membrane	细胞器膜的外在成分	0.018
细胞组分	organelle outer membrane	细胞器外膜	0.018
细胞组分	outer membrane	外膜	0.018
细胞组分	cell leading edge	细胞前缘	0.032
细胞组分	lamellipodium membrane	薄层膜	0.042
细胞组分	extrinsic component of membrane	膜外成分	0.042
细胞组分	basolateral plasma membrane	基底侧质膜	0.061
细胞组分	autophagosome	自噬体	0.061
分子功能	RNA polymerase Ⅱ-specific DNA-binding transcription factor binding	RNA聚合酶Ⅱ特异性DNA结合转录因子结合	<0.001
分子功能	DNA-binding transcription factor binding	DNA结合型转录因子结合	<0.001
分子功能	protein serine/threonine/tyrosine kinase activity	蛋白丝氨酸/苏氨酸/酪氨酸激酶活性	<0.001
分子功能	DNA-binding transcription activator activity, RNA polymerase Ⅱ-specific	DNA结合型转录激活剂活性,RNA聚合酶Ⅱ特异性	<0.001
分子功能	DNA-binding transcription activator activity	DNA结合型转录激活剂活性	<0.001
分子功能	protein serine kinase activity	蛋白丝氨酸激酶活性	<0.001
分子功能	ubiquitin protein ligase binding	泛素蛋白连接酶结合	0.009
分子功能	protein N-terminus binding	蛋白质N端结合	0.009
分子功能	protein serine/threonine kinase activity	蛋白丝氨酸/苏氨酸激酶活性	0.009
分子功能	transcription coregulator binding	转录核心调节因子结合	0.009

表2

表3

图1

表4

图2~6

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序号	基因名	序号	基因名	序号	基因名	序号	基因名
1	RPL8	15	PEBP1	29	YTHDC2	43	ARF6
2	EMC2	16	MAPK14	30	TRIM26	44	AR
3	G6PD	17	ATF3	31	PIEZO1	45	MTF1
4	VDAC2	18	FBXW7	32	SRC	46	PARP8
5	PIK3CA	19	AQP3	33	STAT3	47	PARP10
6	FLT3	20	CYP4F8	34	CISD2	48	PDSS2
7	TFRC	21	PEX3	35	CD44	49	OIP5-AS1
8	SLC38A1	22	MAP3K11	36	JUN	50	CREB1
9	ATG7	23	GSK3B	37	AIFM2	51	FABP4
10	ALOX12	24	MDM2	38	CHMP6	52	TMSB4Y
11	ULK1	25	MYCN	39	SREBF1	53	CAMKK2
12	MAP1LC3A	26	SNX5	40	FXN	54	EZH2
13	BID	27	MICU1	41	STK11	55	PTPN18
14	CDKN2A	28	HOTAIR	42	BRD3	56	TERT

项目	名称	中文名称	调整后P值
信号通路	FoxO signaling pathway	FoxO信号通路	0.004
信号通路	EGFR tyrosine kinase inhibitor resistance	表皮生长因子受体酪氨酸激酶抑制剂的抗药性	0.004
信号通路	Cellular senescence	细胞衰老	0.022
信号通路	Inflammatory mediator regulation of TRP channels	炎症介质对TRP通道的调控	0.008
信号通路	T cell receptor signaling pathway	T细胞受体信号通路	0.009
信号通路	TNF signaling pathway	TNF信号通路	0.012
信号通路	mTOR signaling pathway	mTOR信号通路	0.024
信号通路	B cell receptor signaling pathway	B细胞受体信号通路	0.024
信号通路	PI3K-Akt signaling pathway	PI3K-Akt信号通路	0.024
信号通路	Necroptosis	坏死	0.024
信号通路	Tuberculosis	结核病	0.032
信号通路	NOD-like receptor signaling pathway	NOD样受体信号通路	0.034
信号通路	Toll-like receptor signaling pathway	Toll样受体信号通路	0.038
信号通路	Th17 cell differentiation	Th17细胞分化	0.040
信号通路	Apoptosis	细胞凋亡	0.064

基因	药物	作用关系	基因	药物	作用关系
AR	十一酸睾酮	兴奋剂	SRC	伊洛司替布	抑制剂
AR	氟氧甲基甾酮	兴奋剂	SRC	萨拉卡替尼	抑制剂
AR	丙甾酮	兴奋剂	SRC	博苏替尼	抑制剂
AR	睾酮	兴奋剂	SRC	kx2-391	抑制剂
AR	丙酸睾丸素	兴奋剂	SRC	普纳替尼	抑制剂
AR	尼罗酰胺	拮抗剂	SRC	azm-475271	抑制剂
AR	羟基氟他胺	拮抗剂	SRC	enmd-981693	抑制剂
AR	屈螺酮	拮抗剂	SRC	enmd-2076	抑制剂
AR	半乳甾酮	拮抗剂	SRC	橙皮苷	抑制剂
AR	比卡鲁胺	拮抗剂	SRC	azd-0424	抑制剂
SRC	万得他尼	抑制剂	SRC	tg100-801	抑制剂
SRC	pd-0166285	抑制剂	STK11	陶扎色替	抑制剂
SRC	尼达尼布	抑制剂	SRC	达沙替尼	多靶点抑制剂
SRC	kx2-361	抑制剂	AR	gsk2881078	调节剂
SRC	xl-228	抑制剂