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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Bibliographic Details
Published in:Proceedings / IEEE International Symposium on Computer-Based Medical Systems pp. 234 - 239
Main Authors: Cadenas, Marc Rios, Lopez-Fernandez, Aurelio, Gomez-Vela, Francisco A., Ortega, Juan A., Perez, Inmaculada Rincon
Format: Conference Proceeding
Language:English
Published: IEEE 18.06.2025
Subjects:
Bioinformatics
Biomarkers
Computational modeling
Gene co-expression network
High performance computing
Malignant tumors
Medical diagnostic imaging
Neoplasms
Network analyzers
Regulation
Sarcoma
Scalability
ISSN:2372-9198
Online Access:Get full text
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Summary: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.
ISSN:2372-9198
DOI:10.1109/CBMS65348.2025.00055