AUTHOR=Eid Refaat A. , Mamdouh Farag , Abdulsahib Waleed K. , Alshaya Dalal Sulaiman , Al-Salmi Fawziah A. , Ali Alghamdi Maha , Jafri Ibrahim , Fayad Eman , Alsharif Ghadi , Zaki Mohamed Samir A. , Alshehri Mohammed A. , Noreldin Ahmed E. , Alaa Eldeen Muhammad 

TITLE=ACTL6A: unraveling its prognostic impact and paving the way for targeted therapeutics in carcinogenesis

JOURNAL=Frontiers in Molecular Biosciences

VOLUME=Volume 11 - 2024

YEAR=2024

URL=https://www.frontiersin.org/journals/molecular-biosciences/articles/10.3389/fmolb.2024.1387919

DOI=10.3389/fmolb.2024.1387919

ISSN=2296-889X

ABSTRACT=Increased Actin-like 6A (ACTL6A) expression has been implicated in diverse cancers, yet its comprehensive investigation across various types remains incomplete. Utilizing state-of-the-art bioinformatics tools, we comprehensively analyzed ACTL6A as a potential oncogene and therapeutic target in distinct malignancies. Herein, we concentrated on investigating ACTL6A expression profiles in various human malignancies, focusing on correlations with tumor grade, stage, metastasis, and patient survival. Mutations, amplifications, and other genomic changes were examined in the ACTL6A genetics study. We also examined ACTL6A's epigenetic landscape using rigorous methods, including UALCAN and SMART, to determine its methylation state and cancer impact. Furthermore, we assessed the influence of ACTL6A on immune cell infiltration within the tumor microenvironment using advanced algorithms such as TIMER2, shedding light on its dynamic interplay with the immune response. Molecular docking studies highlighted the interaction of ACTL6A with key cellular pathways, providing insights that were further validated by robust machine learning models predicting the oncogene's structural and functional implications in cancer. We revealed molecular pathways linking ACTL6A to carcinogenesis and essential immunological checkpoints, offering a comprehensive knowledge of its immune system effects. Our advanced machine learning models, including Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), and XGBoost, provided robust insights into the ability of ACTL6A for cancer detection. The outcomes of our investigation show that ACTL6A is elevated in several kinds of tumors and is linked to unfavorable prognoses in numerous human malignancies. Significant prognostic correlations associate high ACTL6A expression with poor tumor grade, stage, metastasis, and patient survival. This study is the first to utilize an integrative bioinformatics approach combined with molecular docking to elucidate ACTL6A's potential as a diagnostic and therapeutic target in cancer. This work comprehensively explains the carcinogenic actions of ACTL6A, highlighting its potential as both a prognostic indication and a target for anti-cancer therapy.