PDF(Pubmed)
Abstract：
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.
摘要：
尽管免疫检查点抑制剂和靶向治疗取得了进展，在美国，皮肤癌仍然是一个重大的公共卫生问题。疾病的错综复杂，包括遗传学，免疫反应，和外部因素，呼吁采取全面的方法。系统遗传学技术，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被证明是有价值的。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精确定位诊断标志物方面的功效。强调系统遗传学在推进创新疗法方面的潜力。虽然存在某些限制，例如外部因素的概括性和情境化，人工智能技术的持续进步为克服这些挑战提供了希望。通过提供协议和一个涉及Braf的实际例子，我们的目标是激励早期职业实验皮肤科医生采用这些工具，并将这些技术无缝地整合到他们的皮肤癌研究中，将他们定位在抗击这种毁灭性疾病的创新方法的最前沿。
