Elucidating the clinical and immunological value of m6A regulator-mediated methylation modification patterns in adrenocortical carcinoma

Wenhao Xu, Haoming Li, Yasir Hameed, Mostafa A. Abdel-Maksoud, Saeedah Musaed Almutairi, Ayman Mubarak, Mohammed Aufy, Wael Alturaiki, Abdulaziz J. Alshalani, Ayman M. Mahmoud, Chen Li

Veröffentlichungen: Beitrag in FachzeitschriftArtikelPeer Reviewed

Abstract

N6-methyladenosine methylation (m6A) is a common type of epigenetic alteration that prominently affects the prognosis of tumor patients. However, it is unknown how the m6A regulator affects the tumor microenvironment (TME) cell infiltration in adrenocortical carcinoma (ACC) and how it affects the prognosis of ACC patients yet. The m6A alteration patterns of 112 ACC patients were evaluated, furthermore, the association with immune infiltration cell features was investigated. The unsupervised clustering method was applied to typify the m6A alteration patterns of ACC patients. The principal component analysis (PCA) technique was taken to create the m6A score to assess the alteration pattern in specific malignancies. We found two independent patterns of m6A alteration in ACC patients. The TME cell infiltration features were significantly in accordance with phenotypes of tumor immune-inflamed and immune desert in both patterns. The m6Ascore also served as an independent predictive factor in ACC patients. The somatic copy number variation (CNV) and patients prognosis can be predicted by m6A alteration patterns. Moreover, the ACC patients with high m6A scores had better overall survival (OS) and higher efficiency in immune checkpoint blockade therapy. Our work demonstrated the significance of m6A alteration to the ACC patients immunotherapy. The individual m6A alteration patterns analysis might contribute to ACC patients prognosis prediction and immunotherapy choice.

OriginalspracheEnglisch
Seiten (von - bis)819-831
Seitenumfang13
FachzeitschriftOncology Research
Jahrgang31
Ausgabenummer5
DOIs
PublikationsstatusVeröffentlicht - 2023

ÖFOS 2012

  • 106005 Bioinformatik

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