Identification of prognostic alternative splicing events in sarcoma

Area of Science:

• Oncology
• Molecular Biology
• Genomics

Background:

• Sarcoma is a rare cancer with poor prognosis.
• Aberrant alternative splicing (AS) events are implicated in cancer development.
• Identifying prognostic biomarkers for sarcoma is critical.

Purpose of the Study:

• To identify alternative splicing (AS)-related survival genes as potential sarcoma biomarkers.
• To investigate the functional roles of AS events in sarcoma pathogenesis.
• To develop a predictive model for sarcoma patient survival.

Main Methods:

• Downloaded RNA-sequencing and AS-event data from The Cancer Genome Atlas (TCGA) sarcoma cohort and TCGA SpliceSeq.
• Utilized univariate and multivariate Cox regression analyses to identify survival-related AS events and construct a predictive gene signature.
• Employed KOBAS 3.0 and Cytoscape for functional annotation and network interaction analysis of AS-related genes.

Main Results:

• Detected 9674 AS events across 40,184 genes in 236 sarcoma samples.
• Identified and validated ten key survival-related genes (TUBB3, TRIM69, ZNFX1, VAV1, KCNN2, VGLL3, AK7, ARMC4, LRRC1, and CRIP1) involved in sarcoma.
• Developed a multivariate survival model using these ten genes that accurately predicts patient outcomes.

Conclusions:

• Alternative splicing events play a significant role in sarcoma.
• The identified ten-gene signature based on AS events shows promise as a prognostic predictor for sarcoma patients.
• This study enhances understanding of AS in sarcoma and offers a potential tool for survival prediction.