Combining novel lncRNA-derived microproteins with canonical proteins in clinical models enhanced hepatocellular carcinoma recurrence prediction, increasing the AUC by up to 0.085.
Observational
Integrating novel lncRNA-derived microproteins into clinical models enhances the prediction of hepatocellular carcinoma recurrence.
Effect estimate: AUC increase 0.005 to 0.085
Microproteins (i.e., peptides) are increasingly recognized for their functions in versatile biological contexts but their clinical relevance and utility remain largely unexplored.Proteogenomic approaches can accelerate microprotein discovery in clinical samples by integrating proteomic data with genomics and transcriptomics evidence.However, long noncoding RNA (lncRNA)-derived microproteins (lncPeps) remain largely unidentified, resulting in unmatchable MS/MS spectra.To solve this problem, we have used high-quality Ribo-seq translatomic datasets to generate an extensive database of human liver lncRNA-derived open reading frames (lncORFs), which we subsequently applied to proteomics data of tumor-adjacent normal tissue pairs from hepatocellular carcinoma (HCC) patients.Using the new database, we discovered 104 novel lncPeps including 46 lncPeps differentially expressed between tumor and non-tumor tissues, and 13 lncPeps with significant correlation with prognosis.Remarkably, combining the expression of lncPeps with canonical proteins in a LASSO regression model improved predictive performance for recurrence, increasing the AUC by 0.005 to 0.085 across three recurrence time points.These findings suggest that lncPeps discovery contributes to our understanding of the molecular heterogeneity and progression of HCC, and broadens the range of potential biomarker candidates or treatment targets J o u r n a l P r e -p r o o f Discovery of lncPeps in liver cancer for the disease. J o u r n a l P r e -p r o o fIntegrated Ribo-seq and mass spectrometry mapping of the liver cancer "dark proteome" reveals 104 novel microproteins translated from lncRNA open reading frames (lncPeps).This proteogenomic analysis identifies 46 tumor-specific and 13 prognostic lncPeps, whose inclusion in clinical models enhances HCC recurrence prediction by up to 0.085 AUC.These findings establish a robust discovery workflow for noncanonical microproteins, characterizing them as a critical, functional layer of the human proteome with substantial clinical utility for patient management.
Bingwu et al. (2026) conducted an observational in Hepatocellular carcinoma (HCC). LASSO regression model including lncPeps and canonical proteins was evaluated on Predictive performance for recurrence (AUC increase 0.005 to 0.085). Combining novel lncRNA-derived microproteins with canonical proteins in clinical models enhanced hepatocellular carcinoma recurrence prediction, increasing the AUC by up to 0.085.
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