What question did this study set out to answer?

This research aims to identify piRNAs as potential biomarkers for colorectal cancer (CRC).

January 14, 2026Journal of Clinical Oncology0 citations

Characterization of piwi-RNA signatures in colorectal cancer.

YFYuanyuan FuSouth China Agricultural University

Key Points

This research aims to identify piRNAs as potential biomarkers for colorectal cancer (CRC).
Analyzed small RNA sequencing data from Gene Expression Omnibus (GEO) to identify differentially expressed piRNAs.
Constructed diagnostic models using machine learning algorithms, validated with TCGA-COAD dataset.
Conducted prognostic modeling using LASSO Cox regression, comparing benchmarks against published prognostic signatures.
Analyzed predicted target genes of candidate piRNAs for pathway enrichment.
Support vector machine achieved the best diagnostic performance (AUC = 0.932).
A 10-piRNA prognostic signature was significantly associated with poor survival (HR = 2.76), especially in advanced disease stages.
The prognostic model outperformed 109 existing signatures.
Identified piR-36011 as a common biomarker in both diagnostic and prognostic models.

Abstract

227 Background: Although incidence and mortality rates have declined, colorectal cancer (CRC) remains a leading cause of cancer-related deaths worldwide. Identifying novel diagnostic and prognostic biomarkers, as well as therapeutic targets, is critical for improving patient outcomes. This study evaluates PIWI-interacting RNAs (piRNAs) as potential biomarkers for CRC. Methods: Colorectal Cancer small RNA sequencing data from the Gene Expression Omnibus (GEO) were analyzed to identify differentially expressed piRNAs. Diagnostic models were constructed using multiple machine learning algorithms, with validation performed in The Cancer Genome Atlas Colon Adenocarcinoma (TCGA-COAD) dataset. Prognostic modeling was conducted with LASSO Cox regression in TCGA-COAD and benchmarked against 109 published prognostic signatures. Predicted target genes of candidate piRNAs were further analyzed for pathway enrichment. Results: Among four machine learning classifiers, the support vector machine achieved the best diagnostic performance (AUC = 0.932). A 10-piRNA prognostic signature was significantly associated with poor survival p < 0.001, HR = 2.76 (1.68–4.5), with risk further increased in advanced disease Stage III: p < 0.05, HR = 3.22 (1.12–9.2); Stage IV: p < 0.05, HR = 8.28 (2.88–23.8). The 10-piRNA signature retained prognostic significance in younger patients (40–60 years) and those with early-stage disease (Stage I–II). When applied to TCGA-COAD, this model outperformed 109 previously published signatures. Notably, piR-36011 was identified in both diagnostic and prognostic models, with predicted targets involved in oncogenic pathways such as Netrin-1 signaling, MAPK, BRAF, and RAF1. Conclusions: Differentially expressed piRNAs show strong diagnostic and prognostic utility in CRC. The piRNA-based prognostic model outperformed existing signatures, suggesting that piRNAs may represent more reliable biomarkers. In particular, piR-36011 emerges as a promising dual-purpose biomarker linked to colorectal cancer oncogenesis.

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