Department of Histology and Embryology, School of Basic Medicine, Guilin Medical University, Guilin, Guangxi, China.
School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, China.
J Transl Med. 2022 Feb 5;20(1):71. doi: 10.1186/s12967-022-03268-z.
Cancer screening provides the opportunity to detect cancer early, ideally before symptom onset and metastasis, and offers an increased opportunity for a better prognosis. The ideal biomarkers for cancer screening should discriminate individuals who have not developed invasive cancer yet but are destined to do so from healthy subjects. However, most cancers lack effective screening recommendations. Therefore, further studies on novel screening strategies are urgently required.
We used a simple suboptimal inoculation melanoma mouse model to obtain 'pre-diagnostic samples' of mice with macroscopic melanomas. High-throughput sequencing and bioinformatic analysis were employed to identify differentially expressed RNAs in platelet signatures of mice injected with a suboptimal number of melanoma cells (eDEGs) compared with mice with macroscopic melanomas and negative controls. Moreover, 36 genes selected from the eDEGs via bioinformatics analysis were verified in a mouse validation cohort via quantitative real-time PCR. LASSO regression was utilized to generate the prediction models with gene expression signatures as the best predictors for occult tumor progression in mice.
These RNAs identified from eDEGs of mice injected with a suboptimal number of cancer cells were strongly enriched in pathways related to immune response and regulation. The prediction models generated by 36 gene qPCR verification data showed great diagnostic efficacy and predictive value in our murine validation cohort, and could discriminate mice with occult tumors from control group (area under curve (AUC) of 0.935 (training data) and 0.912 (testing data)) (gene signature including Cd19, Cdkn1a, S100a9, Tap1, and Tnfrsf1b) and also from macroscopic tumor group (AUC of 0.920 (training data) and 0.936 (testing data)) (gene signature including Ccr7, Cd4, Kmt2d, and Ly6e).
Our proof-of-concept study provides evidence for potential clinical relevance of blood platelets as a platform for liquid biopsy-based early detection of cancer.
癌症筛查提供了早期发现癌症的机会，理想情况下是在症状出现和转移之前，并有机会获得更好的预后。癌症筛查的理想生物标志物应能区分尚未发展为侵袭性癌症但注定会发展为癌症的个体与健康个体。然而，大多数癌症缺乏有效的筛查建议。因此，迫切需要进一步研究新的筛查策略。
我们使用一种简单的次优接种黑色素瘤小鼠模型，从有宏观黑色素瘤的小鼠中获得“预诊断样本”。我们采用高通量测序和生物信息学分析方法，比较注射少量黑色素瘤细胞的小鼠（eDEGs）与有宏观黑色素瘤和阴性对照的小鼠血小板特征中的差异表达 RNA（eDEGs）。此外，通过生物信息学分析从 eDEGs 中选择 36 个基因，通过定量实时 PCR 在小鼠验证队列中进行验证。LASSO 回归用于生成基于基因表达特征的预测模型，作为小鼠隐匿性肿瘤进展的最佳预测因子。
从注射少量癌细胞的小鼠 eDEGs 中鉴定出的这些 RNA 强烈富集于与免疫反应和调节相关的途径。由 36 个基因 qPCR 验证数据生成的预测模型在我们的小鼠验证队列中显示出良好的诊断效果和预测价值，可以区分隐匿性肿瘤小鼠与对照组（曲线下面积（AUC）为 0.935（训练数据）和 0.912（测试数据））（包括 Cd19、Cdkn1a、S100a9、Tap1 和 Tnfrsf1b 的基因签名），也可以区分宏观肿瘤组（AUC 为 0.920（训练数据）和 0.936（测试数据））（包括 Ccr7、Cd4、Kmt2d 和 Ly6e 的基因签名）。
我们的概念验证研究为血小板作为液体活检早期检测癌症的平台具有潜在临床相关性提供了证据。
