Integrated genomic analysis of recurrence-associated small non-coding RNAs in oesophageal cancer

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Abstract

Objective

Oesophageal squamous cell carcinoma (ESCC) is a heterogeneous disease with variable outcomes that are challenging to predict. A better understanding of the biology of ESCC recurrence is needed to improve patient care. Our goal was to identify small non-coding RNAs (sncRNAs) that could predict the likelihood of recurrence after surgical resection and to uncover potential molecular mechanisms that dictate clinical heterogeneity.

Design

We developed a robust prediction model for recurrence based on the analysis of the expression profile data of sncRNAs from 108 fresh frozen ESCC specimens as a discovery set and assessment of the associations between sncRNAs and recurrence-free survival (RFS). We also evaluated the mechanistic and therapeutic implications of sncRNA obtained through integrated analysis from multiple datasets.

Results

We developed a risk assessment score (RAS) for recurrence with three sncRNAs (microRNA (miR)-223, miR-1269a and nc886) whose expression was significantly associated with RFS in the discovery cohort (n=108). RAS was validated in an independent cohort of 512 patients. In multivariable analysis, RAS was an independent predictor of recurrence (HR, 2.27; 95% CI, 1.26 to 4.09; p=0.007). This signature implies the expression of ΔNp63 and multiple alterations of driver genes like PIK3CA. We suggested therapeutic potentials of immune checkpoint inhibitors in low-risk patients, and Polo-like kinase inhibitors, mammalian target of rapamycin (mTOR) inhibitors, and histone deacetylase inhibitors in high-risk patients.

Conclusion

We developed an easy-to-use prognostic model with three sncRNAs as robust prognostic markers for postoperative recurrence of ESCC. We anticipate that such a stratified and systematic, tumour-specific biological approach will potentially contribute to significant improvement in ESCC treatment.

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