Bioinformatics analysis of the relationship between glioma cell⁃extracellular matrix stiffness and hypoxia

LI Tao; SUN Wenbo; XU Haibo

doi:10.14188/j.1671-8852.2024.0082

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Bioinformatics analysis of the relationship between glioma cell⁃extracellular matrix stiffness and hypoxia

更新时间：2026-11-05

- Bioinformatics analysis of the relationship between glioma cell⁃extracellular matrix stiffness and hypoxia
- Medical Journal of Wuhan University Vol. 46, Issue 3, Pages: 314-320(2025)
- 作者机构：
  
  武汉大学中南医院放射科湖北武汉 430071
- 作者简介：
- 基金信息：
- DOI：10.14188/j.1671-8852.2024.0082
  CLC： R739.41
- Received：31 January 2024，
  
  Published：25 March 2025
- 稿件说明：
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李涛,孙文博,徐海波.胶质瘤细胞外基质刚度与缺氧关系的生物信息学分析[J].武汉大学学报（医学版）,2025,46(03):314-320.

LI Tao,SUN Wenbo,XU Haibo.Bioinformatics analysis of the relationship between glioma cell⁃extracellular matrix stiffness and hypoxia[J].Medical Journal of Wuhan University,2025,46(03):314-320.
李涛,孙文博,徐海波.胶质瘤细胞外基质刚度与缺氧关系的生物信息学分析[J].武汉大学学报（医学版）,2025,46(03):314-320. DOI： 10.14188/j.1671-8852.2024.0082.

LI Tao,SUN Wenbo,XU Haibo.Bioinformatics analysis of the relationship between glioma cell⁃extracellular matrix stiffness and hypoxia[J].Medical Journal of Wuhan University,2025,46(03):314-320. DOI： 10.14188/j.1671-8852.2024.0082.

摘要

目的

探索细胞外基质（ECM）刚度与胶质瘤缺氧微环境之间的关系。通过深入分析两者的相关性，希望为胶质瘤的微环境复杂性提供新的理解，并探索新的治疗策略。

方法

采用生物信息学方法分析GEO和ArrayExpress数据库中的胶质瘤RNA⁃Seq数据集，关注不同ECM刚度和缺氧梯度条件下的基因表达差异。通过差异表达基因（DEGs）分析，识别与ECM刚度相关的关键基因，并进行功能和通路富集分析。利用String数据库构建刚度上调基因的蛋白互作网络，并通过GSE4290数据计算刚度代表基因在肿瘤与正常组织中缺氧ssGSEA富集评分。最后，将U251细胞在不同刚度的聚丙烯酰胺水凝胶上培养，用Western Blot检测缺氧代表基因HIF1α的表达。

结果

在GSE158097数据集中，筛选出215个与ECM刚度相关的DEGs（87个上调，128个下调）。FN1、NDRG1、CHI3L1等基因在软硬基质培养的神经胶质瘤模型中表达差异显著。蛋白互作网络显示FN1为核心基因。通过GSE4290数据计算，两种缺氧评分及FN1基因在肿瘤组织中的表达高于正常组织，且缺氧评分与HIF1α表达水平相关。Western Blot验证了刚度与HIF1α的表达具有相关性。

结论

研究结果表明，在肿瘤的缺氧环境中，ECM刚度相关基因的表达呈现出特定的空间分布模式，并且我们揭示了ECM刚度与缺氧之间具有相关性，这可能是肿瘤进展机制中的一个关键要素。我们的发现为胶质瘤微环境的理解提供了新的视角，并为开发针对胶质瘤微环境的新治疗策略提供了有价值的线索。

Abstract

Objective

To explore the relationship between extracellular matrix (ECM) stiffness and the hypoxic microenvironment in glioma

and by conducting an in⁃depth analysis of their correlation

to provide new insights into the complexity of the glioblastoma microenvironment and explore novel therapeutic strategies.

Methods

We adopted bioinformatics approaches to analyze glioma RNA⁃Seq datasets in the GEO and ArrayExpress databases

focusing on gene expression differences under various ECM stiffness and hypoxia gradient conditions. Through differential expression gene (DEG) analysis

we identified key genes associated with ECM stiffness and performed functional and pathway enrichment analysis. Using the String database

we constructed a protein interaction network for stiffness up⁃regulated genes and calculated the hypoxia ssGSEA enrichment score of stiffness representative genes in tumor versus normal tissues using the GSE4290 dataset. Finally

U251 cells were cultured on polyacrylamide hydrogels with different stiffness

and the expression of the hypoxia representative gene HIF1α was detected by Western Blot.

Results

In the GSE158097 dataset

we identified 215 DEGs related to ECM stiffness (87 upregulated

128 downregulated). Genes such as FN1

NDRG1

and CHI3L1 showed significant expression differences in glioma models cultured on substrates of varying stiffness. The protein interaction network identified FN1 as a core gene. Through analysis of the GSE4290 dataset

both hypoxia scores and FN1 gene expression were found to be higher in tumor tissues compared to normal tissues

and the hypoxia scores were correlated with HIF1α expression levels. Western Blot confirmed the correlation between stiffness and HIF1α expression.

Conclusion

The research results indicate that in the hypoxic environment of tumors

the expression of ECM stiffness⁃related genes exhibits a specific spatial distribution pattern

and we have revealed the correlation between ECM stiffness and hypoxia

which may be a key factor in the mechanism of tumor progression. Our findings provide a new perspective on understanding the glioblastoma microenvironment and offer valuable clues for developing new therapeutic strategies targeting the glioblastoma microenvironment.

关键词

Keywords

references

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