Integrative analysis of histone acetyltransferase KAT2A in human cancer.

Abstract

The high incidence of mutations and the crucial roles of KAT2A in cancer development have received increased attention. Nevertheless, a systematic comparison of the heterogeneity and dynamics across different cancer types has not been conducted. Hence, a deep analysis using public databases was performed to clarify the contributions of KAT2A and its correlation with tumorigenesis. The raw data regarding KAT2A expression in cancer patients and healthy controls were obtained from The Cancer Genome Atlas (TCGA). Sexually dimorphic manner, genomic alterations, and expression pattern of KAT2A, as well as the association of the KAT2A with survival, were retrieved from UALCAN, cBioportal, and TISIDB databases. Additionally, the Protein-Protein Interaction (PPI) analysis was conducted using the STRING database. The human protein atlas was used to obtain the staining results of protein levels in cancer and normal samples. The correlation between KAT2A and its potential target drugs was determined using TISIDB and HISTome2. Compared to the normal tissues, CHOL and TGCT tumors presented significantly high KAT2A expression, which was positively correlated with BLCA, BRCA, CESC, CHOL, COAD, ESCA, HNSC, KICH, KIRP, LIHC, LUAD, LUSC, READ, STAD, and THCA. However, no significant difference was detected between normal and tumor tissues for the sex difference pattern of KAT2A expression. The PPI analysis indicated that TADA3, CCDC101, TRRAP, SUPT3H, MYC, TADA2A, and USP22 levels were positively correlated with KAT2A expression, while TADA2B and ATXN7 were negatively correlated. A positive link of KAT2A with cancer isotypes and significant connections of the KAT2A expression to poor overall and disease-free survival were also observed. Further validation was conducted using immunohistochemistry (IHC) staining, qPCR, and Western blot. Some potential HAT inhibitory drugs of KAT2A were also determined, but more work and clinical trials are required before their application.

• Authors: Hua Li, Chun Li, Lu-Zong Yang, Ji Liu
• Journal: Cancer biomarkers : section A of Disease markers (Cancer Biomark) (Oct 24 2023) ISSN: 1875-8592 [Electronic] Netherlands
• PMID: 38007639 (Publication Type: Journal Article)