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  • Springer Science and Business Media LLC  (3)
  • Fan, Jian-Bing  (3)
  • Tao, Jinsheng  (3)
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  • Springer Science and Business Media LLC  (3)
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  • 1
    Online Resource
    Online Resource
    Accurate detection of early-stage lung cancer using a panel of circulating cell-free DNA methylation biomarkers
    Springer Science and Business Media LLC ; 2023
    In:  Biomarker Research Vol. 11, No. 1 ( 2023-04-26)
    Details
    In: Biomarker Research, Springer Science and Business Media LLC, Vol. 11, No. 1 ( 2023-04-26)
    Abstract: 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. Methods 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. Results 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). Conclusion The 7 novel DMRs could be promising methylation biomarkers that merits further development as a noninvasive test for early detection of lung cancer. Graphical abstract
    Type of Medium: Online Resource
    ISSN: 2050-7771
    URL: Article
    DOI: 10.1186/s40364-023-00486-5
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2023
    detail.hit.zdb_id: 2699926-2
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    Online Resource
  • 2
    Online Resource
    Online Resource
    Circulating cell-free DNA-based methylation patterns for breast cancer diagnosis
    Springer Science and Business Media LLC ; 2021
    In:  npj Breast Cancer Vol. 7, No. 1 ( 2021-08-16)
    Details
    In: npj Breast Cancer, Springer Science and Business Media LLC, Vol. 7, No. 1 ( 2021-08-16)
    Abstract: Mammography is used to detect breast cancer (BC), but its sensitivity is limited, especially for dense breasts. Circulating cell-free DNA (cfDNA) methylation tests is expected to compensate for the deficiency of mammography. We derived a specific panel of markers based on computational analysis of the DNA methylation profiles from The Cancer Genome Atlas (TCGA). Through training ( n  = 160) and validation set ( n  = 69), we developed a diagnostic prediction model with 26 markers, which yielded a sensitivity of 89.37% and a specificity of 100% for differentiating malignant disease from normal lesions [AUROC = 0.9816 (95% CI: 96.09-100%), and AUPRC = 0.9704 (95% CI: 94.54–99.46%)]. A simplified 4-marker model including cg23035715 , cg16304215 , cg20072171 , and cg21501525 had a similar diagnostic power [AUROC = 0.9796 (95% CI: 95.56–100%), and AUPRC = 0.9220 (95% CI: 91.02–94.37%)]. We found that a single cfDNA methylation marker, cg23035715 , has a high diagnostic power [AUROC = 0.9395 (95% CI: 89.72–99.27%), and AUPRC = 0.9111 (95% CI: 88.45–93.76%)], with a sensitivity of 84.90% and a specificity of 93.88%. In an independent testing dataset ( n  = 104), the obtained diagnostic prediction model discriminated BC patients from normal controls with high accuracy [AUROC = 0.9449 (95% CI: 90.07–98.91%), and AUPRC = 0.8640 (95% CI: 82.82–89.98%)]. We compared the diagnostic power of cfDNA methylation and mammography. Our model yielded a sensitivity of 94.79% (95% CI: 78.72–97.87%) and a specificity of 98.70% (95% CI: 86.36–100%) for differentiating malignant disease from normal lesions [AUROC = 0.9815 (95% CI: 96.75–99.55%), and AUPRC = 0.9800 (95% CI: 96.6–99.4%)] , with better diagnostic power and had better diagnostic power than that of using mammography [AUROC = 0.9315 (95% CI: 89.95–96.34%), and AUPRC = 0.9490 (95% CI: 91.7–98.1%)]. In addition, hypermethylation profiling provided insights into lymph node metastasis stratifications ( p   〈  0.05). In conclusion, we developed and tested a cfDNA methylation model for BC diagnosis with better performance than mammography.
    Type of Medium: Online Resource
    ISSN: 2374-4677
    URL: Article
    DOI: 10.1038/s41523-021-00316-7
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2021
    detail.hit.zdb_id: 2843288-5
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  • 3
    Online Resource
    Online Resource
    Diagnosis of pulmonary nodules by DNA methylation analysis in bronchoalveolar lavage fluids
    Springer Science and Business Media LLC ; 2021
    In:  Clinical Epigenetics Vol. 13, No. 1 ( 2021-12)
    Details
    In: Clinical Epigenetics, Springer Science and Business Media LLC, Vol. 13, No. 1 ( 2021-12)
    Abstract: Lung cancer is the leading cause of cancer-related mortality. The alteration of DNA methylation plays a major role in the development of lung cancer. Methylation biomarkers become a possible method for lung cancer diagnosis. Results We identified eleven lung cancer-specific methylation markers ( CDO1, GSHR, HOXA11, HOXB4-1, HOXB4-2, HOXB4-3, HOXB4-4, LHX9, MIR196A1 , PTGER4-1, and PTGER4-2 ), which could differentiate benign and malignant pulmonary nodules. The methylation levels of these markers are significantly higher in malignant tissues. In bronchoalveolar lavage fluid (BALF) samples, the methylation signals maintain the same differential trend as in tissues. An optimal 5-marker model for pulmonary nodule diagnosis (malignant vs. benign) was developed from all possible combinations of the eleven markers. In the test set (57 tissue and 71 BALF samples), the area under curve (AUC) value achieves 0.93, and the overall sensitivity is 82% at the specificity of 91%. In an independent validation set (111 BALF samples), the AUC is 0.82 with a specificity of 82% and a sensitivity of 70%. Conclusions This model can differentiate pulmonary adenocarcinoma and squamous carcinoma from benign diseases, especially for infection, inflammation, and tuberculosis. The model’s performance is not affected by gender, age, smoking history, or the solid components of nodules.
    Type of Medium: Online Resource
    ISSN: 1868-7075 , 1868-7083
    URL: Article
    DOI: 10.1186/s13148-021-01163-w
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2021
    detail.hit.zdb_id: 2553921-8
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