多生物标志物分析可以通过减少遗传和环境差异引起的误差来提高单生物标志物分析的准确性。因此，多生物标志物分析在早期和精准诊断方面显示出更高的准确性。然而，传统的分析方法由于预处理时间长、结果不一致、样本量大等原因，在多生物标志物分析中存在局限性。为了解决这些问题，通过使用一滴血进行多生物标志物分析，引入了一种快速准确的肺癌精密诊断方法。为此，采用表面增强拉曼光谱免疫分析 (SERSIA) 对生物标志物进行准确、快速且可靠的定量。然后，检查多生物标志物的统计关系，以区分健康对照和肺癌患者。这种方法已被证明是有效的；使用 20 µL 血清，诊断肺癌的准确率达到 92%。它还可以准确识别癌症类型和阶段，分别为 87% 和 85%。这些结果表明多生物标志物分析在克服单一生物标志物诊断带来的挑战方面的重要性。此外，它显着改进了基于多生物标志物的分析方法，说明了其对临床诊断的重要影响。© 2024 Wiley‐VCH GmbH。

Multi-biomarker analysis can enhance the accuracy of the single-biomarker analysis by reducing the errors caused by genetic and environmental differences. For this reason, multi-biomarker analysis shows higher accuracy in early and precision diagnosis. However, conventional analysis methods have limitations for multi-biomarker analysis because of their long pre-processing times, inconsistent results, and large sample requirements. To solve these, a fast and accurate precision diagnostic method is introduced for lung cancer by multi-biomarker profiling using a single drop of blood. For this, surface-enhanced Raman spectroscopic immunoassay (SERSIA) is employed for the accurate, quick, and reliable quantification of biomarkers. Then, it is checked the statistical relation of the multi-biomarkers to differentiate between healthy controls and lung cancer patients. This approach has proven effective; with 20 µL of blood serum, lung cancer is diagnosed with 92% accuracy. It also accurately identifies the type and stage of cancer with 87% and 85%, respectively. These results show the importance of multi-biomarker analysis in overcoming the challenges posed by single-biomarker diagnostics. Furthermore, it markedly improves multi-biomarker-based analysis methods, illustrating its important impact on clinical diagnostics.© 2024 Wiley‐VCH GmbH.