Integrative Analysis of Multi-Omic Data for the Characteristics of Endometrial Cancer.
Tong LiZhijun RuanChunli SongFeng YinTuanjie ZhangLiyun ShiMin ZuoLinlin LuYuhao AnRui WangXiyang YePublished in: ACS omega (2024)
Endometrial cancer (EC) is a frequently diagnosed gynecologic cancer. Identifying reliable prognostic genes for predicting EC onset is crucial for reducing patient morbidity and mortality. Here, a comprehensive strategy with transcriptomic and proteomic data was performed to measure EC's characteristics. Based on the publicly available RNA-seq data, death-associated protein kinase 3, recombination signal-binding protein for the immunoglobulin kappa J region, and myosin light chain 9 were screened out as potential biomarkers that affect the EC patients' prognosis. A linear model was further constructed by multivariate Cox regression for the prediction of the risk of being malignant. From further integrative analysis, exosomes were found to have a highly enriched role that might participate in EC occurrence. The findings were validated by qRT-polymerase chain reaction (PCR) and western blotting. Collectively, we constructed a prognostic-gene-based model for EC prediction and found that exosomes participate in EC incidents, revealing significantly promising support for the diagnosis of EC.
Keyphrases
- endometrial cancer
- rna seq
- binding protein
- single cell
- electronic health record
- mesenchymal stem cells
- stem cells
- big data
- wastewater treatment
- protein kinase
- end stage renal disease
- bone marrow
- data analysis
- ejection fraction
- machine learning
- transcription factor
- gene expression
- patient safety
- nuclear factor
- south africa
- papillary thyroid
- peritoneal dialysis
- dna repair
- lymph node metastasis
- deep learning
- network analysis
- childhood cancer