An explorative study for leveraging transcriptomic data of embryonic stem cells in mining cancer stemness genes, regulators, and networks

Jihong Yang; Hao Xu; Congshu Li; Zhenhao Li; Zhe Hu; Jihong Yang; Hao Xu; Congshu Li; Zhenhao Li; Zhe Hu

doi:10.3934/mbe.2022650

Mathematical Biosciences and Engineering

2022, Volume 19, Issue 12: 13949-13966. doi: 10.3934/mbe.2022650

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Research article

An explorative study for leveraging transcriptomic data of embryonic stem cells in mining cancer stemness genes, regulators, and networks

1.
College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
2.
BoYu Intelligent Health Innovation Laboratory, Hangzhou 311121, China
3.
Department of Anesthesiology, Affiliated Hospital of Guangdong Medical University, Guangdong 524001, China

Academic Editor: Ulf Schmitz

Received: 11 August 2022 Revised: 07 September 2022 Accepted: 12 September 2022 Published: 22 September 2022

Due to the exquisite ability of cancer stemness to facilitate tumor initiation, metastasis, and cancer therapy resistance, targeting cancer stemness is expected to have clinical implications for cancer treatment. Genes are fundamental for forming and maintaining stemness. Considering shared genetic programs and pathways between embryonic stem cells and cancer stem cells, we conducted a study analyzing transcriptomic data of embryonic stem cells for mining potential cancer stemness genes. Firstly, we integrated co-expression and regression models and predicted 820 stemness genes. Results of gene enrichment analysis confirmed the good prediction performance for enriched signatures in cancer stem cells. Secondly, we provided an application case using the predicted stemness genes to construct a breast cancer stemness network. Mining on the network identified CD44, SOX2, TWIST1, and DLG4 as potential regulators of breast cancer stemness. Thirdly, using the signature of 31,028 chemical perturbations and their correlation with stemness marker genes, we predicted 67 stemness inhibitors with reasonable accuracy of 78%. Two drugs, namely Rigosertib and Proscillaridin A, were first identified as potential stemness inhibitors for melanoma and colon cancer, respectively. Overall, mining embryonic stem cell data provides a valuable way to identify cancer stemness regulators.
- cancer,
- cancer stem cell,
- stemness gene,
- embryonic stem cell,
- stemness inhibitor
Citation: Jihong Yang, Hao Xu, Congshu Li, Zhenhao Li, Zhe Hu. An explorative study for leveraging transcriptomic data of embryonic stem cells in mining cancer stemness genes, regulators, and networks[J]. Mathematical Biosciences and Engineering, 2022, 19(12): 13949-13966. doi: 10.3934/mbe.2022650

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Abstract

Due to the exquisite ability of cancer stemness to facilitate tumor initiation, metastasis, and cancer therapy resistance, targeting cancer stemness is expected to have clinical implications for cancer treatment. Genes are fundamental for forming and maintaining stemness. Considering shared genetic programs and pathways between embryonic stem cells and cancer stem cells, we conducted a study analyzing transcriptomic data of embryonic stem cells for mining potential cancer stemness genes. Firstly, we integrated co-expression and regression models and predicted 820 stemness genes. Results of gene enrichment analysis confirmed the good prediction performance for enriched signatures in cancer stem cells. Secondly, we provided an application case using the predicted stemness genes to construct a breast cancer stemness network. Mining on the network identified CD44, SOX2, TWIST1, and DLG4 as potential regulators of breast cancer stemness. Thirdly, using the signature of 31,028 chemical perturbations and their correlation with stemness marker genes, we predicted 67 stemness inhibitors with reasonable accuracy of 78%. Two drugs, namely Rigosertib and Proscillaridin A, were first identified as potential stemness inhibitors for melanoma and colon cancer, respectively. Overall, mining embryonic stem cell data provides a valuable way to identify cancer stemness regulators.

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