<i>FSTL1</i>基因在胶质瘤发展中作用的研究

doi:10.3877/cma.j.issn.2095-9141.2023.04.003

目的

采用生物信息学分析FSTL1基因在胶质瘤发展中的潜在作用。

方法

从中国胶质瘤基因组图谱计划（CGGA）数据库中下载胶质瘤基因表达量及临床数据，按照FSTL1表达水平将胶质瘤患者分为高表达组（FSTL1表达量≥29.1 reads）和低表达组（FSTL1表达量<29.1 reads）。寻找高低组间差异基因进行基因本体论（GO）分析和基因集富集分析（GSEA）。采用最小绝对收缩和选择算法分析构建预后模型，并在胶质瘤纵向分析（GLASS）数据集中进行验证。

结果

FSTL1的高表达与较差的临床特征有关，如：1p19q的非联合缺失、IDH野生型、较高的WHO级别等。低表达组患者的预后均明显优于高表达组患者，差异有统计学意义（P<0.05）。GO分析结果显示，差异基因主要集中在细胞外基质组织、免疫相关、脉管系统发育等。基因富集分析发现的通路主要有：ECM_RECEPTOR_INTERACTION、G2M_CHECKPOINT、L6_JAK_STAT3_SIGNALING、TNFA_SIGNALING_VIA_NFKB等。与低表达组相比，高表达组显示出较高的免疫和基质评分，但纯度评分较低（均P<0.001），构建风险评分的基因有：ALDOC、GLC1、LINC00634、TGIF1、TPM4、TRAM2。Kaplan-Meier生存曲线显示高风险评分的胶质瘤患者的预后较差。构建生存预测诺模图，受试者工作特征、校正曲线均显示该模型有较好的预测能力。

结论

FSTL1与胶质瘤的临床和分子特征有关，FSTL1的表达量与肿瘤恶性程度有关，是胶质瘤患者的潜在治疗靶点和独立的预后因素。

关键词: 胶质瘤, 中国胶质瘤基因组图谱计划, 免疫浸润, 卵泡抑素样蛋白1

Objective

Utilizing bioinformatics analysis to investigate the potential role of the FSTL1 gene in the development of gliomas.

Methods

The glioma gene expression levels and clinical data were downloaded from the Chinese Glioma Genome Atlas Project (CGGA) database. Glioma patients were categorized into high expression group (FSTL1 expression level ≥ 29.1 reads) and low expression group (FSTL1 expression level<29.1 reads) based on FSTL1 expression level. Differential gene analysis was conducted between the high and low expression groups, and perform gene ontology (GO) and gene set enrichment analysis (GSEA). The least absolute shrinkage and selection operator (LASSO) algorithm was applied to construct a prognostic model. It was also verified in the Glioma Longitudinal Analysis (GLASS) dataset.

Results

High expression of FSTL1 is associated with poor clinical features, such as non-codelof 1p19q, IDH wildtype, higher grade (WHO Ⅲ-Ⅳ), etc. The prognosis of patients in the low expression group was significantly better than that in the high expression group, and the difference was statistically significant (P<0.05). GO analysis showed that DEGs is mainly concentrated in extracellular matrix tissue, immune-related, vascular system development, etc. The main pathways identified by gene enrichment analysis (GSEA) were: ECM_RECEPTOR_INTERACTION, G2M_CHECKPOINT, L6_JAK_STAT3_SIGNALING, and TNFA_SIGNALING_VIA_NFKB, etc. Compared with the low expression group, the high expression group showed higher immune and matrix scores, but lower purity scores (all P<0.001). Genes used for constructing the risk score were: ALDOC, GLC1, LINC00634, TGIF1, TPM4, and TRAM2. The Kaplan-Meier survival curve showed a poor prognosis of the high-risk score. Construct a survival prediction nomogram, and the subject operating characteristics and the correction curve all showed that the model had a good predictive ability.

Conclusion

FSTL1 is related to the clinical and molecular characteristics of glioma, and its expression is related to the degree of tumor malignancy, being a potential therapeutic target and an independent prognostic factor in glioma patients.

Key words: Glioma, China Glioma Genome Atlas Project, Immune infiltration, Follistatin like protein 1

表1 CGGA及GLASS数据集临床信息

Tab.1 Clinical information on CGGA and GLASS datasets

项目	CGGA数据库（n=325）	GLASS数据库（n=378）
性别
男	203	143
女	122	235
年龄（岁）
≥42	175	102
<42	150	276
WHO级别
Ⅱ级	103	38
Ⅲ级	79	31
Ⅳ级	139	307
NA	4	2
IDH状态
突变	175	100
野生	149	256
NA	1	22
1p/19q状态
非联合缺失	250	29
联合缺失	67	314
NA	8	35
MGMT启动子状态
非甲基化	157	82
甲基化	149	99
NA	19	197

表1 CGGA及GLASS数据集临床信息

Tab.1 Clinical information on CGGA and GLASS datasets

项目	CGGA数据库（n=325）	GLASS数据库（n=378）
性别
男	203	143
女	122	235
年龄（岁）
≥42	175	102
<42	150	276
WHO级别
Ⅱ级	103	38
Ⅲ级	79	31
Ⅳ级	139	307
NA	4	2
IDH状态
突变	175	100
野生	149	256
NA	1	22
1p/19q状态
非联合缺失	250	29
联合缺失	67	314
NA	8	35
MGMT启动子状态
非甲基化	157	82
甲基化	149	99
NA	19	197

图1 FSTL1表达量高低与患者预后的关系A：CGGA数据集；B：GLASS数据集

Fig.1 Relationship between FSTL1 expression levels and patient prognosis

表2 FSTL1高低表达组患者临床特征比较（CGGA数据集）

Tab.2 Comparison of clinical characteristics between FSTL1 high and low expression groups (CGGA dataset)

项目	高表达组（n=162）	低表达组（n=163）	χ²/t值	P值
年龄（岁，±s）	45.78±12.11	35.28±10.85	5.062	0.031
性别（男/女）	106/56	96/67	1.482	0.224
MGMT甲基化（是/否）	75/80	81/70	0.863	0.358
1p19q联合缺失（是/否）	7/150	59/100	50.962	<0.001
IDH（突变/野生）	45/117	129/33	87.592	<0.001
WHO级别（Ⅱ/Ⅲ/Ⅳ级）	14/38/106	89/40/34	91.630	<0.001

表2 FSTL1高低表达组患者临床特征比较（CGGA数据集）

Tab.2 Comparison of clinical characteristics between FSTL1 high and low expression groups (CGGA dataset)

项目	高表达组（n=162）	低表达组（n=163）	χ²/t值	P值
年龄（岁，±s）	45.78±12.11	35.28±10.85	5.062	0.031
性别（男/女）	106/56	96/67	1.482	0.224
MGMT甲基化（是/否）	75/80	81/70	0.863	0.358
1p19q联合缺失（是/否）	7/150	59/100	50.962	<0.001
IDH（突变/野生）	45/117	129/33	87.592	<0.001
WHO级别（Ⅱ/Ⅲ/Ⅳ级）	14/38/106	89/40/34	91.630	<0.001

表3 FSTL1高低表达组患者临床特征比较（GLASS数据集）

Tab.3 Comparison of clinical characteristics between FSTL1 high and low expression groups (GLASS dataset)

项目	高表达组（n=189）	低表达组（n=189）	χ²/t值	P值
年龄（岁，±s）	48.09±14.37	47.80±14.39	0.184	0.854
性别（男/女）	124/65	111/78	1.901	0.168
MGMT甲基化（是/否）	36/53	63/29	14.340	<0.001
1p19q联合缺失（是/否）	4/159	25/155	12.790	<0.001
IDH（突变/野生）	11/161	89/95	77.540	<0.001
WHO级别（Ⅱ/Ⅲ/Ⅳ级）	1/3/184	37/28/124	65.950	<0.001

表3 FSTL1高低表达组患者临床特征比较（GLASS数据集）

Tab.3 Comparison of clinical characteristics between FSTL1 high and low expression groups (GLASS dataset)

项目	高表达组（n=189）	低表达组（n=189）	χ²/t值	P值
年龄（岁，±s）	48.09±14.37	47.80±14.39	0.184	0.854
性别（男/女）	124/65	111/78	1.901	0.168
MGMT甲基化（是/否）	36/53	63/29	14.340	<0.001
1p19q联合缺失（是/否）	4/159	25/155	12.790	<0.001
IDH（突变/野生）	11/161	89/95	77.540	<0.001
WHO级别（Ⅱ/Ⅲ/Ⅳ级）	1/3/184	37/28/124	65.950	<0.001

图2 差异基因的分布A~B：CGGA数据集（A：差异基因热图；B：差异基因火山图）；C~D：GLASS数据集（C：差异基因热图；D：差异基因火山图）

Fig.2 Distribution of differential genes

图3 差异基因的GO及GSEA分析A，B：差异基因GO分析（A：CGGA数据集；B：GLASS数据集）；C，D：差异基因GSEA分析（C：CGGA数据集；D：GLASS数据集）

Fig.3 GO and GSEA of differential genes.

图4 高表达组和低表达组的免疫、基质、纯度评分及CIBERSORT分析结果A~B：免疫、基质及纯度评分分析（A：CGGA数据集；B：GLASS数据集）；C~D：CIBERSORT分析（C：CGGA数据集；D：GLASS数据集）；与低表达组比较，^aP<0.05

Fig.4 Immunological, matrix, purity scores, and CIBERSORT analysis results of the high expression and low expression groups

图5 预后模型的构建A：比例风险模型中调整参数选择的交叉验证；B：候选基因的单变量分析；C~F：CGGA数据集；G~J：GLASS数据集；C，G：风险评分与生存状况的关系；D，H：基于风险评分的Kaplan-Meier生存曲线；E，I：预后模型预测1、3、5年生存率的ROC曲线；F，J：风险评分与临床特征的关系

Fig.5 Construction of prognostic models

表4 CGGA队列临床病理特征的单因素和多因素Cox回归分析

Tab.4 Univariate and multivariate Cox regression analysis of clinical pathological characteristics in CGGA cohort

临床病理特征	单因素分析			多因素分析
临床病理特征	HR	95%CI	P值	HR	95%CI	P值
风险评分	2.985	2.289~3.894	<0.001	2.167	1.146~4.098	<0.001
性别	1.269	1.007~1.600	<0.001	0.888	0.630~1.251	0.496
诊断时年龄	1.035	1.026~1.044	<0.001	1.015	0.998~1.032	0.095
WHO级别	3.671	2.782~4.845	<0.001	2.967	1.822~4.831	<0.001
MGMT甲基化	0.447	0.322~0.621	<0.001	0.704	0.477~1.040	0.078
IDH状态	0.275	0.203~0.037	<0.001	1.796	0.938~3.438	0.077
1p19q状态	0.328	0.202~0.534	<0.001	0.397	0.181~0.869	<0.001

表4 CGGA队列临床病理特征的单因素和多因素Cox回归分析

Tab.4 Univariate and multivariate Cox regression analysis of clinical pathological characteristics in CGGA cohort

临床病理特征	单因素分析			多因素分析
临床病理特征	HR	95%CI	P值	HR	95%CI	P值
风险评分	2.985	2.289~3.894	<0.001	2.167	1.146~4.098	<0.001
性别	1.269	1.007~1.600	<0.001	0.888	0.630~1.251	0.496
诊断时年龄	1.035	1.026~1.044	<0.001	1.015	0.998~1.032	0.095
WHO级别	3.671	2.782~4.845	<0.001	2.967	1.822~4.831	<0.001
MGMT甲基化	0.447	0.322~0.621	<0.001	0.704	0.477~1.040	0.078
IDH状态	0.275	0.203~0.037	<0.001	1.796	0.938~3.438	0.077
1p19q状态	0.328	0.202~0.534	<0.001	0.397	0.181~0.869	<0.001

表5 GLASS队列临床病理特征的单因素和多因素Cox回归分析

Tab.5 Univariate and multivariate Cox regression analysis of clinicalpathological characteristics in GLASS cohort

临床病理特征	单因素分析			多因素分析
临床病理特征	HR	95%CI	P值	HR	95%CI	P值
风险评分	15.172	9.974~23.079	<0.001	3.217	1.842~5.617	<0.001
性别	0.941	0.716~1.236	0.660	1.423	0.965~1.652	0.857
诊断时年龄	1.033	1.020~1.046	<0.001	1.014	1.001~1.027	0.031
WHO级别	2.912	2.417~3.508	<0.001	1.749	1.398~2.188	<0.001
MGMT甲基化	0.830	0.632~1.088	0.177	0.704	0.477~1.040	0.078
IDH状态	2.821	2.137~3.724	<0.001	1.147	0.803~1.638	0.451
1p19q状态	0.170	0.104~0.277	<0.001	0.326	0.190~0.559	<0.001

表5 GLASS队列临床病理特征的单因素和多因素Cox回归分析

Tab.5 Univariate and multivariate Cox regression analysis of clinicalpathological characteristics in GLASS cohort

临床病理特征	单因素分析			多因素分析
临床病理特征	HR	95%CI	P值	HR	95%CI	P值
风险评分	15.172	9.974~23.079	<0.001	3.217	1.842~5.617	<0.001
性别	0.941	0.716~1.236	0.660	1.423	0.965~1.652	0.857
诊断时年龄	1.033	1.020~1.046	<0.001	1.014	1.001~1.027	0.031
WHO级别	2.912	2.417~3.508	<0.001	1.749	1.398~2.188	<0.001
MGMT甲基化	0.830	0.632~1.088	0.177	0.704	0.477~1.040	0.078
IDH状态	2.821	2.137~3.724	<0.001	1.147	0.803~1.638	0.451
1p19q状态	0.170	0.104~0.277	<0.001	0.326	0.190~0.559	<0.001

图6 生存预测诺模图的构建及校准曲线A：CGGA数据集；B：GLASS数据集

Fig.6 Construction and calibration curve of survival prediction nomogram

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Study of the role of the FSTL1 gene in glioma development

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Study of the role of the FSTL1 gene in glioma development