Patient-specific gene co-expression networks reveal novel subtypes and predictive biomarkers in lung adenocarcinoma
Abstract Lung adenocarcinoma (LUAD) is a highly heterogenous and aggressive form of non-small cell lung cancer (NSCLC). The use of genome-wide gene co-expression networks (GCNs) has been paramount to describe changes in the transcriptional regulatory programs found between diseased and healthy state...
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| Format: | Article |
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Nature Portfolio
2025-05-01
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| Series: | npj Systems Biology and Applications |
| Online Access: | https://doi.org/10.1038/s41540-025-00522-0 |
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| author | Patricio López-Sánchez Federico Ávila-Moreno Enrique Hernández-Lemus Marieke L. Kuijjer Jesús Espinal-Enríquez |
| author_facet | Patricio López-Sánchez Federico Ávila-Moreno Enrique Hernández-Lemus Marieke L. Kuijjer Jesús Espinal-Enríquez |
| author_sort | Patricio López-Sánchez |
| collection | DOAJ |
| description | Abstract Lung adenocarcinoma (LUAD) is a highly heterogenous and aggressive form of non-small cell lung cancer (NSCLC). The use of genome-wide gene co-expression networks (GCNs) has been paramount to describe changes in the transcriptional regulatory programs found between diseased and healthy states of LUAD. Recently, studies have shown that multiple cancerous phenotypes share a distinct GCN architecture, suggesting that network topology holds promise for understanding disease pathology. However, conventional GCN inference methods struggle to capture the inherent context-specificity within a patient population, thus flattening its heterogeneity. To address this issue, the use of single-sample network (SSN) modelling has emerged as a promising solution into studying heterogeneous traits of cancer through network-based approaches. Here, we reconstructed patient-specific GCNs (n=334) using the LIONESS equation and mutual information as the network inference method. Unsupervised analysis revealed six novel LUAD subtypes based on inter-patient network similarity, each with distinct network motifs reflecting unique biological programs. Supervised analysis, employing regularized Cox regression, identified 12 genes (CHRDL2, SPP2, VAC14, IRF5, GUCY1B1, NCS1, RRM2B, EIF5A2, CCDC62, CTCFL, XG, and TP53INP2) whose weighted degree in SSNs is predictive of patient survival in LUAD. These findings suggest that topological features of SSNs offer valuable insights into the context-specific nature of LUAD malignancy, highlighting the potential of SSN-based approaches for further research. |
| format | Article |
| id | doaj-art-b0fc1842f1604d0484a63d4bb4a63996 |
| institution | OA Journals |
| issn | 2056-7189 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Systems Biology and Applications |
| spelling | doaj-art-b0fc1842f1604d0484a63d4bb4a639962025-08-20T01:49:42ZengNature Portfolionpj Systems Biology and Applications2056-71892025-05-0111111410.1038/s41540-025-00522-0Patient-specific gene co-expression networks reveal novel subtypes and predictive biomarkers in lung adenocarcinomaPatricio López-Sánchez0Federico Ávila-Moreno1Enrique Hernández-Lemus2Marieke L. Kuijjer3Jesús Espinal-Enríquez4Computational Genomics Division, National Institute of Genomic MedicineFacultad de Estudios Superiores-Iztacala (FES-Iztacala), Universidad Nacional Autónoma de México (UNAM)Computational Genomics Division, National Institute of Genomic MedicineCentre for Molecular Medicine Norway (NCMM), University of OsloComputational Genomics Division, National Institute of Genomic MedicineAbstract Lung adenocarcinoma (LUAD) is a highly heterogenous and aggressive form of non-small cell lung cancer (NSCLC). The use of genome-wide gene co-expression networks (GCNs) has been paramount to describe changes in the transcriptional regulatory programs found between diseased and healthy states of LUAD. Recently, studies have shown that multiple cancerous phenotypes share a distinct GCN architecture, suggesting that network topology holds promise for understanding disease pathology. However, conventional GCN inference methods struggle to capture the inherent context-specificity within a patient population, thus flattening its heterogeneity. To address this issue, the use of single-sample network (SSN) modelling has emerged as a promising solution into studying heterogeneous traits of cancer through network-based approaches. Here, we reconstructed patient-specific GCNs (n=334) using the LIONESS equation and mutual information as the network inference method. Unsupervised analysis revealed six novel LUAD subtypes based on inter-patient network similarity, each with distinct network motifs reflecting unique biological programs. Supervised analysis, employing regularized Cox regression, identified 12 genes (CHRDL2, SPP2, VAC14, IRF5, GUCY1B1, NCS1, RRM2B, EIF5A2, CCDC62, CTCFL, XG, and TP53INP2) whose weighted degree in SSNs is predictive of patient survival in LUAD. These findings suggest that topological features of SSNs offer valuable insights into the context-specific nature of LUAD malignancy, highlighting the potential of SSN-based approaches for further research.https://doi.org/10.1038/s41540-025-00522-0 |
| spellingShingle | Patricio López-Sánchez Federico Ávila-Moreno Enrique Hernández-Lemus Marieke L. Kuijjer Jesús Espinal-Enríquez Patient-specific gene co-expression networks reveal novel subtypes and predictive biomarkers in lung adenocarcinoma npj Systems Biology and Applications |
| title | Patient-specific gene co-expression networks reveal novel subtypes and predictive biomarkers in lung adenocarcinoma |
| title_full | Patient-specific gene co-expression networks reveal novel subtypes and predictive biomarkers in lung adenocarcinoma |
| title_fullStr | Patient-specific gene co-expression networks reveal novel subtypes and predictive biomarkers in lung adenocarcinoma |
| title_full_unstemmed | Patient-specific gene co-expression networks reveal novel subtypes and predictive biomarkers in lung adenocarcinoma |
| title_short | Patient-specific gene co-expression networks reveal novel subtypes and predictive biomarkers in lung adenocarcinoma |
| title_sort | patient specific gene co expression networks reveal novel subtypes and predictive biomarkers in lung adenocarcinoma |
| url | https://doi.org/10.1038/s41540-025-00522-0 |
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