One-step diagnosis of infection and lung cancer using metagenomic sequencing
Abstract Background Traditional detection methods face challenges in meeting the diverse clinical needs for diagnosing both lung cancer and infections within a single test. Onco-mNGS has emerged as a promising solution capable of accurately identifying infectious pathogens and tumors simultaneously....
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| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
BMC
2025-02-01
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| Series: | Respiratory Research |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12931-025-03127-7 |
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| Summary: | Abstract Background Traditional detection methods face challenges in meeting the diverse clinical needs for diagnosing both lung cancer and infections within a single test. Onco-mNGS has emerged as a promising solution capable of accurately identifying infectious pathogens and tumors simultaneously. However, critical evidence is still lacking regarding its diagnostic performance in distinguishing between pulmonary infections, tumors, and non-infectious, non-tumor conditions in real clinical settings. Methods In this study, data were gathered from 223 participants presenting symptoms of lung infection or tumor who underwent Onco-mNGS testing. Patients were categorized into four groups based on clinical diagnoses: infection, tumor, tumor with infection, and non-infection-non-tumor. Comparisons were made across different groups, subtypes, and stages of lung cancer regarding copy number variation (CNV) patterns, microbiome compositions, and clinical detection indices. Results Compared to conventional infection testing methods, Onco-mNGS demonstrates superior infection detection performance, boasting a sensitivity of 81.82%, specificity of 72.55%, and an overall accuracy of 77.58%. In lung cancer diagnosis, Onco-mNGS showcases excellent diagnostic capabilities with sensitivity, specificity, accuracy, positive predictive value, and negative predictive value reaching 88.46%, 100%, 91.41%, 100%, and 90.98%, respectively. In bronchoalveolar lavage fluid (BALF) samples, these values stand at 87.5%, 100%, 94.74%, 100%, and 91.67%, respectively. Notably, more abundant CNV mutation types and higher mutation rates were observed in adenocarcinoma (ADC) compared to squamous cell carcinoma (SCC). Concurrently, onco-mNGS data revealed specific enrichment of Capnocytophaga sputigeria in the ADC group and Candida parapsilosis in the SCC group. These species exhibited significant correlations with C reaction protein (CRP) and CA153 values. Furthermore, Haemophilus influenzae was enriched in the early-stage SCC group and significantly associated with CRP values. Conclusions Onco-mNGS has exhibited exceptional efficiencies in the detection and differentiation of infection and lung cancer. This study provides a novel technological option for achieving single-step precise and swift detection of lung cancer. Graphical Abstract |
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| ISSN: | 1465-993X |