Hu Shuo, Tao Jinsheng, Peng Minhua, Ye Zhujia, Chen Zhiwei, Chen Haisheng, Yu Haifeng, Wang Bo, Fan Jian-Bing, Ni Bin
Department of Thoracic Surgery, The First Affiliated Hospital of Soochow University, Suzhou, China.
Anchordx Medical Co., Ltd, Guangzhou, China.
Biomark Res. 2023 Apr 26;11(1):45. doi: 10.1186/s40364-023-00486-5.
Lung cancer remains the leading cause of cancer mortality worldwide. Early detection of lung cancer helps improve treatment and survival. Numerous aberrant DNA methylations have been reported in early-stage lung cancer. Here, we sought to identify novel DNA methylation biomarkers that could potentially be used for noninvasive early diagnosis of lung cancers.
This prospective-specimen collection and retrospective-blinded-evaluation trial enrolled a total of 317 participants (198 tissues and 119 plasmas) comprising healthy controls, patients with lung cancer and benign disease between January 2020 and December 2021. Tissue and plasma samples were subjected to targeted bisulfite sequencing with a lung cancer specific panel targeting 9,307 differential methylation regions (DMRs). DMRs associated with lung cancer were identified by comparing the methylation profiles of tissue samples from patients with lung cancer and benign disease. Markers were selected with minimum redundancy and maximum relevance algorithm. A prediction model for lung cancer diagnosis was built through logistic regression algorithm and validated independently in tissue samples. Furthermore, the performance of this developed model was evaluated in a set of plasma cell-free DNA (cfDNA) samples.
We identified 7 DMRs corresponding to 7 differentially methylated genes (DMGs) including HOXB4, HOXA7, HOXD8, ITGA4, ZNF808, PTGER4, and B3GNTL1 that were highly associated with lung cancer by comparing the methylation profiles of lung cancer and benign nodule tissue. Based on the 7-DMR biomarker panel, we developed a new diagnostic model in tissue samples, termed "7-DMR model", to distinguish lung cancers from benign diseases, achieving AUCs of 0.97 (95%CI: 0.93-1.00)/0.96 (0.92-1.00), sensitivities of 0.89 (0.82-0.95)/0.92 (0.86-0.98), specificities of 0.94 (0.89-0.99)/1.00 (1.00-1.00), and accuracies of 0.90 (0.84-0.96)/0.94 (0.89-0.99) in the discovery cohort (n = 96) and the independent validation cohort (n = 81), respectively. Furthermore, the 7-DMR model was applied to noninvasive discrimination of lung cancers and non-lung cancers including benign lung diseases and healthy controls in an independent validation cohort of plasma samples (n = 106), yielding an AUC of 0.94 (0.86-1.00), sensitivity of 0.81 (0.73-0.88), specificity of 0.98 (0.95-1.00), and accuracy of 0.93 (0.89-0.98).
The 7 novel DMRs could be promising methylation biomarkers that merits further development as a noninvasive test for early detection of lung cancer.
肺癌仍是全球癌症死亡的主要原因。肺癌的早期检测有助于改善治疗效果和提高生存率。在早期肺癌中已报道了许多异常的DNA甲基化。在此,我们试图鉴定可潜在用于肺癌非侵入性早期诊断的新型DNA甲基化生物标志物。
这项前瞻性样本收集和回顾性盲法评估试验共纳入了317名参与者(198份组织样本和119份血浆样本),包括2020年1月至2021年12月期间的健康对照、肺癌患者和良性疾病患者。组织和血浆样本采用针对9307个差异甲基化区域(DMR)的肺癌特异性面板进行靶向亚硫酸氢盐测序。通过比较肺癌患者和良性疾病患者的组织样本甲基化谱,鉴定与肺癌相关的DMR。采用最小冗余最大相关算法选择标志物。通过逻辑回归算法建立肺癌诊断预测模型,并在组织样本中进行独立验证。此外,在一组血浆游离DNA(cfDNA)样本中评估该模型的性能。
通过比较肺癌和良性结节组织的甲基化谱,我们鉴定出7个与7个差异甲基化基因(DMG)相对应的DMR,包括HOXB4、HOXA7、HOXD8、ITGA4、ZNF808、PTGER4和B3GNTL1,这些基因与肺癌高度相关。基于这7个DMR生物标志物面板,我们在组织样本中开发了一种新的诊断模型,称为“7-DMR模型”,以区分肺癌和良性疾病,在发现队列(n = 96)和独立验证队列(n = 81)中,其曲线下面积(AUC)分别为0.97(95%CI:0.93 - 1.00)/0.96(0.92 - 1.00),灵敏度分别为0.89(0.82 - 0.95)/0.92(0.86 - 0.98),特异性分别为0.94(0.89 - 0.99)/1.00(1.00 - 1.00),准确率分别为0.90(0.84 - 0.96)/0.94(0.89 - 0.99)。此外,7-DMR模型应用于血浆样本独立验证队列(n = 106)中肺癌与非肺癌(包括良性肺部疾病和健康对照)的非侵入性鉴别,AUC为0.94(0.86 - 1.00),灵敏度为0.81(0.7