Machine learning-based identification of disulfidptosis-associated signature for improving outcomes and immunotherapy responses in patients with adrenocortical carcinoma

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Introduction: Disulfidptosis, a newly discovered type of cell death, has been found to be closely associated with the onset and progression of tumors. Adrenocortical carcinoma (ACC) is a rare but aggressive malignancy originating from the adrenal cortex.

Aim(s): To explore disulfidptosis related regulators in ACC progression and their prognositic values.

Materials and methods: Four public microarray ACC cohorts were collected and used in the present study. A novel computational framework and 10 machine learning algorithms (101 combinations) were applied to construct a disulfidptosi-related prognostic index (DRPI). Survival analysis was performed to measure the survival difference across DRPI-risk groups. Spearman correlation analysis was used to the relevance assessment. Wilcox test was used to measure the expression level difference.

Conference:

Presenting Author:

Authors: Liu S,

Keywords: disulfidptosis, machine learning, immunotherapy, adrenocortical carcinoma, prognosis, drugs,