Genomic Insights into the Role of TOP Gene Family in Soft-Tissue Sarcomas: Implications for Prognosis and Therapy.
Genchun WangXin GanXin ChenQunqian ZengZhuoran ZhangJiantao LiZhou GuoLiang Cai HouJingTing XuHao KangFeng-Jin GuoPublished in: Advanced biology (2024)
This study focuses on the role of topoisomerases (TOPs) in sarcomas (SARCs), highlighting TOPs' influence on sarcoma prognosis through mRNA expression, genetic mutations, immune infiltration, and DNA methylation analysis using transcriptase sequencing and other techniques. The findings indicate that TOP gene mutations correlate with increased inflammation, immune cell infiltration, DNA repair abnormalities, and mitochondrial fusion genes alterations, all of which negatively affect sarcoma prognosis. Abnormal TOP expression may independently affect sarcoma patients' survival. Cutting-edge genomic tools such as Oncomine, gene expression profiling interactive analysis (GEPIA), and cBio Cancer Genomics Portal (cBioPortal) are utilized to explore the TOP gene family (TOP1/1MT/2A/2B/3A/3B) in soft-tissue sarcomas (STSs). This in-depth analysis reveals a notable upregulation of TOP mRNA in STS patients arcoss various SARC subtypes, French Federation Nationale des Centres de Lutte Contre le Cancer classification (FNCLCC) grades, and specific molecular profiles correlating with poorer clinical outcomes. Furthermore, this investigation identifies distinct patterns of immune cell infiltration, genetic mutations, and somatic copy number variations linked to TOP genes that inversely affect patient survival rates. These findings underscore the diagnostic and therapeutic relevance of the TOP gene suite in STSs.
Keyphrases
- copy number
- genome wide
- dna methylation
- mitochondrial dna
- end stage renal disease
- dna repair
- soft tissue
- ejection fraction
- chronic kidney disease
- oxidative stress
- genome wide identification
- poor prognosis
- gene expression
- dna damage
- machine learning
- cell proliferation
- case report
- prognostic factors
- signaling pathway
- deep learning
- single cell
- cell therapy
- transcription factor
- long non coding rna
- free survival
- genome wide analysis