Identification of a Ten-Gene Signature of DNA Damage Response Pathways with Prognostic Value in Esophageal Squamous Cell Carcinoma.
Weitao ZhuangXiaosong BenZihao ZhouYu DingYong TangShujie HuangCheng DengYuchen LiaoQiaoxia ZhouJing ZhaoGuoqiang WangYu XuXiaofang WenYuzi ZhangShangli CaiRixin ChenGuibin QiaoPublished in: Journal of oncology (2021)
Molecular prognostic signatures are critical for treatment decision-making in esophageal squamous cell cancer (ESCC), but the robustness of these signatures is limited. The aberrant DNA damage response (DDR) pathway may lead to the accumulation of mutations and thus accelerate tumor progression in ESCC. Given this, we applied the LASSO Cox regression to the transcriptomic data of DDR genes, and a prognostic DDR-related gene expression signature (DRGS) consisting of ten genes was constructed, including PARP3, POLB, XRCC5, MLH1, DMC1, GTF2H3, PER1, SMC5, TCEA1, and HERC2 . The DRGS was independently associated with overall survival in both training and validation cohorts. The DRGS achieved higher accuracy than six previously reported multigene signatures for the prediction of prognosis in comparable cohorts. Furtherly, a nomogram incorporating DRGS and clinicopathological features showed improved predicting performance. Taken together, the DRGS was identified as a novel, robust, and effective prognostic indicator, which may refine the scheme of risk stratification and management in ESCC patients.
Keyphrases
- dna damage response
- genome wide
- dna repair
- squamous cell
- dna methylation
- gene expression
- dna damage
- genome wide identification
- bioinformatics analysis
- end stage renal disease
- ejection fraction
- newly diagnosed
- copy number
- chronic kidney disease
- prognostic factors
- papillary thyroid
- poor prognosis
- wastewater treatment
- genome wide analysis
- big data
- lymph node metastasis
- rna seq
- squamous cell carcinoma
- electronic health record
- combination therapy
- single molecule
- transcription factor
- free survival
- replacement therapy
- young adults
- data analysis