bioRxivCancer BiologyPreprint
Transcriptomic Profiling of Old Age Sarcoma Patients using TCGA RNA-seq data
Vidhyavathy Nagarajan, Shreya S. Karandikar, Mary S.J. Dhevanayagam
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Abstract
Sarcoma is a rare malignancy with poor prognosis, especially in older patients ([≥] 65 years) as seen in our preliminary analysis and some previous studies. Moreover, these patients have limited treatment options due to therapy-associated adverse effects and altered tumor micro-environment, which could be associated with their lower prognosis. Studying the underlying biology that drives cancer progression in these patients will help design personalized therapy and improve outcomes for them. This study aims to analyze TCGA-SARC RNA-seq data for characterizing the transcriptomic profile of older age (OA: [≥] 65 years) compared to younger age (YA: 18-65 years) sarcoma patients. RNA-seq and clinical data of sarcoma patients were acquired from TCGA, and the samples were grouped as OA ([≥] 65 years) and YA (18-65 years) patients. Differential gene expression analysis, pathway analysis, transcription factor enrichment analysis, gene-specific survival analysis and network analysis were performed. When comparing the gene expression profiles of the 108 OA and 154 YA patients, significant differentially regulated genes (n=733), transcription factors (n=10), hub genes (n=10) and the pathways that characterize the former were identified. Furthermore, 16 dysregulated genes were found that were significantly associated with a poor prognosis in OA sarcoma patients. In accordance with existing evidence of an altered tumor microenvironment in older-age cancer patients, the identified significant genes are associated with the regulation of certain important tumorigenic pathways such as EMT (epithelial-to-mesenchymal transition), calcium signaling, angiogenesis, ECM (extracellular matrix) degradation, Wnt/{beta}-catenin pathways, suggesting the potential cause for lower prognosis in the OA patients. Thus, these findings pave the way to characterize the OA sarcoma patients which can be validated by multi-omics analysis and clinical studies in the future, in turn providing improved treatment options and survival for the same.
- organism: human
- assay: bulk RNA-seq
- research focus: differential expression
- approach: statistical model
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