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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| Format: | Article |
| Language: | English |
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BMC
2025-02-01
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| Series: | Respiratory Research |
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| Online Access: | https://doi.org/10.1186/s12931-025-03127-7 |
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| author | Shaoqiang Li Yangqing Zhan Yan Wang Weilong Li Xidong Wang Haoru Wang Wenjun Sun Xuefang Cao Zhengtu Li Feng Ye |
| author_facet | Shaoqiang Li Yangqing Zhan Yan Wang Weilong Li Xidong Wang Haoru Wang Wenjun Sun Xuefang Cao Zhengtu Li Feng Ye |
| author_sort | Shaoqiang Li |
| collection | DOAJ |
| description | 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 |
| format | Article |
| id | doaj-art-cf1c6f2384414f3dab1d24a53e8bbdab |
| institution | Kabale University |
| issn | 1465-993X |
| language | English |
| publishDate | 2025-02-01 |
| publisher | BMC |
| record_format | Article |
| series | Respiratory Research |
| spelling | doaj-art-cf1c6f2384414f3dab1d24a53e8bbdab2025-02-09T12:51:24ZengBMCRespiratory Research1465-993X2025-02-0126111410.1186/s12931-025-03127-7One-step diagnosis of infection and lung cancer using metagenomic sequencingShaoqiang Li0Yangqing Zhan1Yan Wang2Weilong Li3Xidong Wang4Haoru Wang5Wenjun Sun6Xuefang Cao7Zhengtu Li8Feng Ye9State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, National Center for Respiratory Medicine, Department of Respiratory, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical UniversityState Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, National Center for Respiratory Medicine, Department of Respiratory, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical UniversityState Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, National Center for Respiratory Medicine, Department of Respiratory, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical UniversityState Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, National Center for Respiratory Medicine, Department of Respiratory, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical UniversityState Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, National Center for Respiratory Medicine, Department of Respiratory, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical UniversityState Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, National Center for Respiratory Medicine, Department of Respiratory, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical UniversityState Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, National Center for Respiratory Medicine, Department of Respiratory, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical UniversityMatriDx Biotechnology Co., LtdState Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, National Center for Respiratory Medicine, Department of Respiratory, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical UniversityState Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, National Center for Respiratory Medicine, Department of Respiratory, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical UniversityAbstract 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 Abstracthttps://doi.org/10.1186/s12931-025-03127-7Onco-mNGSInfection diagnosisLung cancer diagnosisClinical performanceBronchial lavage fluid |
| spellingShingle | Shaoqiang Li Yangqing Zhan Yan Wang Weilong Li Xidong Wang Haoru Wang Wenjun Sun Xuefang Cao Zhengtu Li Feng Ye One-step diagnosis of infection and lung cancer using metagenomic sequencing Respiratory Research Onco-mNGS Infection diagnosis Lung cancer diagnosis Clinical performance Bronchial lavage fluid |
| title | One-step diagnosis of infection and lung cancer using metagenomic sequencing |
| title_full | One-step diagnosis of infection and lung cancer using metagenomic sequencing |
| title_fullStr | One-step diagnosis of infection and lung cancer using metagenomic sequencing |
| title_full_unstemmed | One-step diagnosis of infection and lung cancer using metagenomic sequencing |
| title_short | One-step diagnosis of infection and lung cancer using metagenomic sequencing |
| title_sort | one step diagnosis of infection and lung cancer using metagenomic sequencing |
| topic | Onco-mNGS Infection diagnosis Lung cancer diagnosis Clinical performance Bronchial lavage fluid |
| url | https://doi.org/10.1186/s12931-025-03127-7 |
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