From the ‡Department of Ophthalmology, Johns Hopkins School of Medicine, Baltimore, Maryland 21205.
§Department of Pharmacology and Molecular Sciences, Johns Hopkins School of Medicine, Baltimore, Maryland 21205.
Mol Cell Proteomics. 2017 Dec;16(12):2069-2078. doi: 10.1074/mcp.RA117.000212. Epub 2017 Oct 11.
Lung cancer (LC) remains the leading cause of mortality from malignant tumors worldwide. Currently, a lack of serological biomarkers for early LC diagnosis is a major roadblock for early intervention and prevention of LC. To undertake this challenge, we employed a two-phase strategy to discover and validate a biomarker panel using a protein array-based approach. In Phase I, we obtained serological autoimmune profiles of 80 LC patients and 20 healthy subjects on HuProt arrays, and identified 170 candidate proteins significantly associated with LC. In Phase II, we constructed a LC focused array with the 170 proteins, and profiled a large cohort, comprised of 352 LC patients, 93 healthy individuals, and 101 patients with lung benign lesions (LBL). The comparison of autoimmune profiles between the early stage LC and the combined group of healthy and LBL allowed us to identify and validate a biomarker panel of p53, HRas, and ETHE1 for diagnosis of early stage LC with 50% sensitivity at >90% specificity. Finally, the performance of this biomarker panel was confirmed in ELISA tests. In summary, this study represents one of the most comprehensive proteome-wide surveys with one of the largest ( 1,101 unique samples) and most diverse ( nine disease groups) cohorts, resulting in a biomarker panel with good performance.
肺癌(LC)仍然是全球恶性肿瘤死亡的主要原因。目前,缺乏用于早期 LC 诊断的血清生物标志物是早期干预和预防 LC 的主要障碍。为了应对这一挑战,我们采用了两阶段策略,使用基于蛋白质阵列的方法来发现和验证生物标志物组合。在第一阶段,我们在 HuProt 阵列上获得了 80 名 LC 患者和 20 名健康受试者的血清自身免疫图谱,并鉴定出 170 种与 LC 显著相关的候选蛋白。在第二阶段,我们用这 170 种蛋白质构建了一个 LC 聚焦阵列,并对一个由 352 名 LC 患者、93 名健康个体和 101 名肺部良性病变(LBL)患者组成的大队列进行了分析。早期 LC 与健康和 LBL 综合组之间的自身免疫图谱比较使我们能够鉴定和验证一个由 p53、HRas 和 ETHE1 组成的生物标志物组合,其在 >90%特异性时具有 50%的敏感性,用于早期 LC 的诊断。最后,在 ELISA 测试中证实了该生物标志物组合的性能。总之,这项研究代表了最全面的蛋白质组学调查之一,具有最大(1101 个独特样本)和最多样化(9 个疾病组)的队列之一,产生了具有良好性能的生物标志物组合。