Chung-Chieh Chiao, Yen-Hsi Liu, Nam Nhut Phan, Nu Thuy An Ton, Hoang Dang Khoa Ta, Gangga Anuraga, Do Thi Minh Xuan, Fenny Fitriani, Elvira Mustikawati Putri Hermanto, Muhammad Athoillah, Vivin Andriani, Purity Sabila Ajiningrum, Yung-Fu Wu, Kuen-Haur Lee, Jian-Ying Chuang, Chih-Yang Wang, Tzu-Jen Kao
The complexity of breast cancer includes many interacting biological processes, and proteasome alpha (PSMA) subunits are reported to be involved in many cancerous diseases, although the transcriptomic expression of this gene family in breast cancer still needs to be more thoroughly investigated. Consequently, we used a holistic bioinformatics approach to study the PSMA genes involved in breast cancer by integrating several well-established high-throughput databases and tools, such as cBioPortal, Oncomine, and the Kaplan–Meier plotter. Additionally, correlations of breast cancer patient survival and PSMA messenger RNA expressions were also studied. The results demonstrated that breast cancer tissues had higher expression levels of PSMA genes compared to normal breast tissues. Furthermore, PSMA2, PSMA3, PSMA4, PSMA6, and PSMA7 showed high expression levels, which were correlated with poor survival of breast cancer patients. In contrast, PSMA5 and PSMA8 had high expression levels, which were associated with good prognoses. We also found that PSMA family genes were positively correlated with the cell cycle, ubiquinone metabolism, oxidative stress, and immune response signaling, including antigen presentation by major histocom-patibility class, interferon-gamma, and the cluster of differentiation signaling. Collectively, these findings suggest that PSMA genes have the potential to serve as novel biomarkers and therapeutic targets for breast cancer. Nevertheless, the bioinformatic results from the present study would be strengthened with experimental validation in the future by prospective studies on the underlying biological mechanisms of PSMA genes and breast cancer. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
Ph.D. Program for Cancer Molecular Biology and Drug Discovery, College of Medical Science, Taipei Medical University, Taipei, 11031, Taiwan; Graduate Institute of Cancer Biology and Drug Discovery, College of Medical Science and Technology, Taipei Medical University, Taipei, 11031, Taiwan; NTT Institute of Hi-Technology, Nguyen Tat Thanh University, Ho Chi Minh City, 700000, Viet Nam; Department of Statistics, Faculty of Science and Technology, Universitas PGRI Adi Buana, Surabaya, 60234, Indonesia; Department of Biological Science, Faculty of Science and Technology, Universitas PGRI Adi Buana, Surabaya, 60234, Indonesia; Department of Medical Research, Tri-Service General Hospital, School of Medicine, National Defense Medical Center, Taipei, 11490, Taiwan; Cancer Center, Wan Fang Hospital, Taipei Medical University, Taipei, 11031, Taiwan; TMU Research Center of Cancer Translational Medicine, Taipei Medical University, Taipei, 11031, Taiwan; Ph.D. Program for Neural Regenerative Medicine, Taipei Medical University, Taipei, 11031, Taiwan; Research Center of Neuroscience, Taipei Medical University, Taipei, 11031, Taiwan
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