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Integrating Machine Learning and Mendelian Randomization Determined a Functional Neurotrophin-Related Gene Signature in Patients with Lower-Grade Glioma

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Abstract

Recent researches reported that neurotrophins can promote glioma growth/invasion but the relevant model for predicting patients’ survival in Lower-Grade Gliomas (LGGs) lacked. In this study, we adopted univariate Cox analysis, LASSO regression, and multivariate Cox analysis to determine a signature including five neurotrophin-related genes (NTGs), CLIC1, SULF2, TGIF1, TTF2, and WEE1. Two-sample Mendelian Randomization (MR) further explored whether these prognostic-related genes were genetic variants that increase the risk of glioma. A total of 1306 patients have been included in this study, and the results obtained from the training set can be verified by four independent validation sets. The low-risk subgroup had longer overall survival in five datasets, and its AUC values all reached above 0.7. The risk groups divided by the NTGs signature exhibited a distinct difference in targeted therapies from the copy-number variation, somatic mutation, LGG’s surrounding microenvironment, and drug response. MR corroborated that TGIF1 was a potential causal target for increasing the risk of glioma. Our study identified a five-NTGs signature that presented an excellent survival prediction and potential biological function, providing new insight for the selection of LGGs therapy.

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Availability of Data and Materials

The datasets generated and analyzed during the current study are available in the Cancer Genome Atlas (TCGA) repository, (https://portal.gdc.cancer.gov/), the GSEA database (http://software.broadinstitute.org/gsea/msigdb/index.jsp), ArrayExpress database (https://www.ebi.ac.uk/arrayexpress/), and the Chinese Glioma Genome Atlas (CGGA) repository, (http://www.cgga.org.cn/).

Abbreviations

LGGs:

Lower-Grade Gliomas

NTGs:

Neurotrophin-related genes

TME:

Tumor microenvironment

NGF:

Nerve growth factor

BDNF:

Brain-derived neurotrophic factor

NT-3:

Neurotrophin-3

p75NTR:

P75 neurotrophin receptor

TCGA:

The Cancer Genome Atlas

CGGA:

Chinese Glioma Genome Atlas

TPM:

Transcript per million

LASSO:

Least absolute shrinkage and selection operator

KM:

Kaplan–Meier

ssGSEA:

Single-sample gene set enrichment

TMB:

Tumor mutational burden

DEGs:

Differentially expressed genes

ICIs:

Immune checkpoint inhibitors

GEP:

Gene expression signature

MIAS:

MHC I association immunoscore

GDSC:

Genomics of Drug Sensitivity in Cancer

GSA:

Gene set analysis

PCA:

Principal component analysis

IC50:

The half-maximal inhibitory concentration

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Acknowledgements

We are very grateful to the TCGA, CGGA, GSEA, and CIBERSOR database and other public resources for providing us with a research foundation.

Funding

This research was funded by (National Youth Science Foundation Project), grant number [82204159]) and Science and Technology Research Program of Chongqing Municipal Education Commission (Grant No.KJQN202300423).

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Author notes

  1. Cong Zhang, Guichuan Lai and Jielian Deng have contributed equally to this work.

Authors and Affiliations

  1. Department of Epidemiology and Health Statistics, School of Public Health, Chongqing Medical University, Yixue Road, Chongqing, 400016, China

    Cong Zhang, Guichuan Lai, Jielian Deng, Kangjie Li, Xiaoni Zhong & Biao Xie

  2. The Fifth People’s Hospital of Chongqing, Renji Road, Chongqing, 400062, China

    Liuyi Chen

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  1. Cong Zhang
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Contributions

Conceptualization, CZ; methodology, CZ and JD; software, CZ and KL; validation, CZ and JD; formal analysis, CZ and LC; resources, GL; data curation, GL; writing—original draft preparation, CZ; writing—review and editing, XZ and BX; visualization, CZ; supervision, BX; project administration, XZ; funding acquisition, BX. All authors have read and agreed to the published version of the manuscript.

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Correspondence to Xiaoni Zhong or Biao Xie.

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Zhang, C., Lai, G., Deng, J. et al. Integrating Machine Learning and Mendelian Randomization Determined a Functional Neurotrophin-Related Gene Signature in Patients with Lower-Grade Glioma. Mol Biotechnol (2024). https://doi.org/10.1007/s12033-023-01045-x

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