Restrepo Juan Carlos, Dueñas Diana, Corredor Zuray, Liscano Yamil
Grupo de Investigación en Salud Integral (GISI), Departamento Facultad de Salud, Universidad Santiago de Cali, Cali 760035, Colombia.
Grupo de Investigaciones en Odontología (GIOD), Facultad de Odontología, Universidad Cooperativa de Colombia, Pasto 520002, Colombia.
Cancers (Basel). 2023 Jul 3;15(13):3474. doi: 10.3390/cancers15133474.
Non-small cell lung cancer (NSCLC) is a significant public health concern with high mortality rates. Recent advancements in genomic data, bioinformatics tools, and the utilization of biomarkers have improved the possibilities for early diagnosis, effective treatment, and follow-up in NSCLC. Biomarkers play a crucial role in precision medicine by providing measurable indicators of disease characteristics, enabling tailored treatment strategies. The integration of big data and artificial intelligence (AI) further enhances the potential for personalized medicine through advanced biomarker analysis. However, challenges remain in the impact of new biomarkers on mortality and treatment efficacy due to limited evidence. Data analysis, interpretation, and the adoption of precision medicine approaches in clinical practice pose additional challenges and emphasize the integration of biomarkers with advanced technologies such as genomic data analysis and artificial intelligence (AI), which enhance the potential of precision medicine in NSCLC. Despite these obstacles, the integration of biomarkers into precision medicine has shown promising results in NSCLC, improving patient outcomes and enabling targeted therapies. Continued research and advancements in biomarker discovery, utilization, and evidence generation are necessary to overcome these challenges and further enhance the efficacy of precision medicine. Addressing these obstacles will contribute to the continued improvement of patient outcomes in non-small cell lung cancer.
非小细胞肺癌(NSCLC)是一个重大的公共卫生问题,死亡率很高。基因组数据、生物信息学工具以及生物标志物的应用方面的最新进展,提高了NSCLC早期诊断、有效治疗和随访的可能性。生物标志物通过提供疾病特征的可测量指标,在精准医学中发挥着关键作用,从而实现量身定制的治疗策略。大数据和人工智能(AI)的整合通过先进的生物标志物分析进一步增强了个性化医疗的潜力。然而,由于证据有限,新生物标志物对死亡率和治疗效果的影响仍然存在挑战。数据分析、解读以及在临床实践中采用精准医学方法带来了额外的挑战,并强调生物标志物与基因组数据分析和人工智能(AI)等先进技术的整合,这增强了NSCLC精准医学的潜力。尽管存在这些障碍,但将生物标志物整合到精准医学中在NSCLC中已显示出有前景的结果,改善了患者预后并实现了靶向治疗。持续开展生物标志物发现、应用和证据生成方面的研究和进展,对于克服这些挑战并进一步提高精准医学的疗效是必要的。解决这些障碍将有助于持续改善非小细胞肺癌患者的预后。