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Identification and validation of an ECM organization-related gene signature as a prognostic biomarker and therapeutic target for glioma patients

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Abstract

Background

Glioma is the most common and devastating form of malignant brain tumor, with a poor prognosis. Extracellular matrix (ECM) organization is a crucial determinant of glioma invasion and progression. However, the clinical significance of ECM organization in glioma patients remains unclear.

Objective

To evaluate the prognostic value of ECM organization-related genes in glioma patients and identify potential therapeutic targets.

Methods

Bulk RNA-sequencing and corresponding clinical data for patients with glioma were downloaded from TCGA and GEO databases. Differentially expressed ECM organization genes were identified, and an ECM organization-related gene prognostic model was then generated. Furthermore, the prognostic model has validated in the Chinese Glioma Genome Atlas (CGGA) dataset. The role of TIMP1 in glioma cells by using various functional assays revealed their underlying mechanism in vitro.

Results

We identified and validated a nine-gene signature (TIMP1, SERPINE1, PTX3, POSTN, PLOD3, PDPN, LOXL1, ITGA2, and COL8A1) related to ECM organization as a robust prognostic biomarker for glioma. Time-dependent ROC curve analysis confirmed the specificity and sensitivity of the signature. The signature was closely related to an immunosuppressive phenotype, and its combination with immune checkpoints served as a good predictor for patients' clinical outcomes. Notably, single-cell RNA sequencing analysis revealed high expression of TIMP1 in astrocytes and oligodendrocyte progenitor cells in glioma patients. Last, we show that TIMP1 regulates glioma cell growth and invasion via the AKT/GSK3β signaling pathway.

Conclusion

This study provides promising insights into predicting glioma prognosis and identifying a potential therapeutic target in TIMP1.

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Data availability

The datasets generated and analyzed during the current study are available in the TCGA repository database (https://xenabrowser.net/datapages/), CGGA data portal (http://www.cgga.org.cn/index.jsp), and GEO database (https://www.ncbi.nlm.nih.gov/geo/). For microarray analysis, GSE34152, GSE35493, GSE50161, GSE66354, GSE7696, and GSE60184 were used in this study. For scRNA-seq analysis, the data acquisition GSE138794 was used.

Abbreviations

ECM:

Extracellular matrix

PCA:

Principal component analysis

GEO:

Gene Expression Omnibus

TCGA:

Cancer Genome Atlas

CGGA:

Chinese Glioma Genome Atlas

LASSO:

Absolute shrinkage and selection operator

ROC:

Receiver operating characteristic

AUC:

Areas under the curve

EPIC:

Estimate the Proportion of Immune and Cancer cells

t-SNE:

T-Distributed Stochastic Neighbor Embedding

OS:

Overall survival

DCA:

Decision curve analysis

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Acknowledgements

This work is supported by the Jiangxi Province Focus on Research and Development Plan 20203BBGL73204 (to Q.Z.).

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Authors and Affiliations

  1. Department of Oncology, Ganzhou People’s Hospital, Ganzhou, Jiangxi, China

    Qiong Zhong, Qiuxia Zhong, Xiaolong Cai & Renrui Wu

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  1. Qiong Zhong
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  2. Qiuxia Zhong
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  3. Xiaolong Cai
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Contributions

Q.Z. performed data analysis and all experiments, and drafted the manuscript. Q-X.Z., X.C., and R.W. revised the manuscript. All authors read and approved the final manuscript.

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Correspondence to Qiong Zhong.

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Zhong, Q., Zhong, Q., Cai, X. et al. Identification and validation of an ECM organization-related gene signature as a prognostic biomarker and therapeutic target for glioma patients. Genes Genom 45, 1211–1226 (2023). https://doi.org/10.1007/s13258-023-01413-6

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