University of Tennessee Health Science Center School of Medicine, Memphis, Tennessee, USA.
University of Queensland Medical School, Brisbane, Queensland, Australia.
Exp Dermatol. 2024 Mar;33(3):e15043. doi: 10.1111/exd.15043.
Despite progress made with immune checkpoint inhibitors and targeted therapies, skin cancer remains a significant public health concern in the United States. The intricacies of the disease, encompassing genetics, immune responses, and external factors, call for a comprehensive approach. Techniques in systems genetics, including transcriptional correlation analysis, functional pathway enrichment analysis, and protein-protein interaction network analysis, prove valuable in deciphering intricate molecular mechanisms and identifying potential diagnostic and therapeutic targets for skin cancer. Recent studies demonstrate the efficacy of these techniques in uncovering molecular processes and pinpointing diagnostic markers for various skin cancer types, highlighting the potential of systems genetics in advancing innovative therapies. While certain limitations exist, such as generalizability and contextualization of external factors, the ongoing progress in AI technologies provides hope in overcoming these challenges. By providing protocols and a practical example involving Braf, we aim to inspire early-career experimental dermatologists to adopt these tools and seamlessly integrate these techniques into their skin cancer research, positioning them at the forefront of innovative approaches in combating this devastating disease.
尽管免疫检查点抑制剂和靶向治疗取得了进展，但皮肤癌仍然是美国一个重大的公共卫生关注点。该疾病的复杂性涉及遗传、免疫反应和外部因素，需要采取综合方法。系统遗传学中的技术，包括转录相关分析、功能途径富集分析和蛋白质-蛋白质相互作用网络分析，在破译复杂的分子机制和确定皮肤癌的潜在诊断和治疗靶点方面证明是有价值的。最近的研究表明，这些技术在揭示各种皮肤癌类型的分子过程和确定诊断标志物方面具有疗效，突显了系统遗传学在推进创新疗法方面的潜力。尽管存在一定的局限性，如外部因素的可推广性和情境化，但人工智能技术的持续进展带来了克服这些挑战的希望。通过提供涉及 Braf 的方案和实际示例，我们旨在激励早期职业的实验皮肤科医生采用这些工具，并将这些技术无缝地融入他们的皮肤癌研究中，使他们处于创新方法对抗这种毁灭性疾病的前沿。
