Identifying the prognosis implication, immunotherapy response prediction value, and potential targeted compound inhibitors of integrin subunit α3 (ITGA3) in human cancers

Gui, Jiawei; Yang, Lufei; Liu, Junzhe; Li, Yishuang; Zou, Mi; Sun, Chengpeng; Huang, Le; Zhu, Xingen; Huang, Kai

doi:10.1016/j.heliyon.2024.e24236

Heliyon

2024

DOI: 10.1016/j.heliyon.2024.e24236

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Identifying the prognosis implication, immunotherapy response prediction value, and potential targeted compound inhibitors of integrin subunit α3 (ITGA3) in human cancers

Jiawei Gui,

Lufei Yang,

Junzhe Liu

et al.

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A Machine Learning-Based Investigation of Integrin Expression Patterns in Cancer and Metastasis

Shadman,

Gomrok,

Cheng

et al. 2024

Preprint

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Background Integrins, a family of transmembrane receptor proteins, play complex roles in cancer development and metastasis. These roles could be better delineated through machine learning of transcriptomic data to reveal relationships between integrin expression patterns and cancer. Methods We collected publicly available RNA-Seq integrin expression from 8 healthy tissues and their corresponding tumors, along with data from metastatic breast cancer. We then used machine learning methods, including t-SNE visualization and Random Forest classification, to investigate changes in integrin expression patterns. Results Integrin expression varied across tissues and cancers, and between healthy and cancer samples from the same tissue, enabling the creation of models that classify samples by tissue or disease status. The integrins whose expression was important to these classifiers were identified. For example, ITGA7 was key to classification of breast samples by disease status. Analysis in breast tissue revealed that cancer rewires co-expression for most integrins, but the co-expression relationships of some integrins remain unchanged in healthy and cancer samples. Integrin expression in primary breast tumors differed from their metastases, with liver metastasis notably having reduced expression. Conclusions Integrin expression patterns vary widely across tissues and are greatly impacted by cancer. Machine learning of these patterns can effectively distinguish samples by tissue or disease status.

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A Machine Learning-Based Investigation of Integrin Expression Patterns in Cancer and Metastasis

Shadman,

Gomrok,

Cheng

et al. 2024

Preprint

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Identifying the prognosis implication, immunotherapy response prediction value, and potential targeted compound inhibitors of integrin subunit α3 (ITGA3) in human cancers

Cited by 1 publication

References 60 publications

A Machine Learning-Based Investigation of Integrin Expression Patterns in Cancer and Metastasis

A Machine Learning-Based Investigation of Integrin Expression Patterns in Cancer and Metastasis

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