High-Performance Computing-Driven Gene Co-Expression Network Analysis for Biomarkers Discovery in Soft Tissue Sarcomas
Soft tissue sarcomas (STS), such as leiomyosarcoma (LMS) and malignant peripheral nerve sheath tumors (MPNST), are aggressive neoplasms with limited treatment alternatives. Comprehending their biological mechanisms requires the examination of gene expression data, provides insights into transcriptio...
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| Veröffentlicht in: | Proceedings / IEEE International Symposium on Computer-Based Medical Systems S. 234 - 239 |
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18.06.2025
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| ISSN: | 2372-9198 |
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| Abstract | Soft tissue sarcomas (STS), such as leiomyosarcoma (LMS) and malignant peripheral nerve sheath tumors (MPNST), are aggressive neoplasms with limited treatment alternatives. Comprehending their biological mechanisms requires the examination of gene expression data, provides insights into transcriptional activity. Gene co-expression networks (GCNs) are essential for elucidating functional gene linkages within datasets, depicting genes as nodes and their interactions as edges. Such methods are typically employed for the discovery of potential biomarkers. However, the computational performance for analysing huge genomic datasets requires High-Performance Computing (HPC) technologies, such as GPGPU and distributed computing models, to improve scalability and efficiency. This study uses HPC-based GCN techniques to uncover potential biomarkers associated with the aggressiveness of LMS and MPNST. Through the comparison of co-expression networks from malignant tumor tissues and their normal or benign counterparts, we identify genes exhibiting differential expression patterns that may facilitate sarcoma growth. As a result, six potential biomarkers were identified in the study. This holistic approach improves our comprehension of STS biology while providing novel tools for biomarker identification and prospective therapeutic targeting. |
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| AbstractList | Soft tissue sarcomas (STS), such as leiomyosarcoma (LMS) and malignant peripheral nerve sheath tumors (MPNST), are aggressive neoplasms with limited treatment alternatives. Comprehending their biological mechanisms requires the examination of gene expression data, provides insights into transcriptional activity. Gene co-expression networks (GCNs) are essential for elucidating functional gene linkages within datasets, depicting genes as nodes and their interactions as edges. Such methods are typically employed for the discovery of potential biomarkers. However, the computational performance for analysing huge genomic datasets requires High-Performance Computing (HPC) technologies, such as GPGPU and distributed computing models, to improve scalability and efficiency. This study uses HPC-based GCN techniques to uncover potential biomarkers associated with the aggressiveness of LMS and MPNST. Through the comparison of co-expression networks from malignant tumor tissues and their normal or benign counterparts, we identify genes exhibiting differential expression patterns that may facilitate sarcoma growth. As a result, six potential biomarkers were identified in the study. This holistic approach improves our comprehension of STS biology while providing novel tools for biomarker identification and prospective therapeutic targeting. |
| Author | Cadenas, Marc Rios Lopez-Fernandez, Aurelio Gomez-Vela, Francisco A. Perez, Inmaculada Rincon Ortega, Juan A. |
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| Snippet | Soft tissue sarcomas (STS), such as leiomyosarcoma (LMS) and malignant peripheral nerve sheath tumors (MPNST), are aggressive neoplasms with limited treatment... |
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| SubjectTerms | Bioinformatics Biomarkers Computational modeling Gene co-expression network High performance computing Malignant tumors Medical diagnostic imaging Neoplasms Network analyzers Regulation Sarcoma Scalability |
| Title | High-Performance Computing-Driven Gene Co-Expression Network Analysis for Biomarkers Discovery in Soft Tissue Sarcomas |
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