Identification of MYBL2 as a Novel Prognostic and Therapeutic Target in Hepatocellular Carcinoma Using Integrated Machine Learning
Hepatocellular carcinoma causes great suffering; new markers are needed
Hepatocellular carcinoma (HCC) is challenging to diagnose, prognosticate, and treat, imposing substantial burden on patients and families. Existing biomarkers lack sufficient accuracy, leading to uncertainty in therapeutic selection.
AI and experiments converge to uncover MYBL2
We applied LASSO (Least Absolute Shrinkage and Selection Operator)–based machine learning to TCGA and GEO datasets, incorporating single‑cell transcriptomics and CRISPR dependency screening. MYBL2 distinguished tumor from normal liver with near‑perfect accuracy (AUC = 0.968), and high expression correlated with adverse clinical features such as higher grade, microvascular invasion, HBV positivity, and non‑response to TACE.
Impact extends to therapy and immunity
MYBL2 showed strong correlation with proliferation markers such as AFP, MKI67, PCNA, and BIRC5, and CRISPR knockout suppressed growth in the majority of HCC cell lines. Drug‑sensitivity analysis revealed that high MYBL2 expression is associated with enhanced response to sorafenib and linked to an immunosuppressive microenvironment.
Future significance and outlook
Precision diagnostic kits based on MYBL2 and novel therapeutics targeting MYBL2 are now feasible. Personalized treatment enabled by these advances is expected to markedly improve survival rates.
BACKGROUND: Hepatocellular carcinoma (HCC) presents with poor treatment outcomes, creating an urgent need for novel biomarkers to improve diagnosis, prognosis, and precision medicine. While the MYB family of oncogenes is implicated in cancer, the role and regulatory mechanisms of its member, particularly MYB proto‑oncogene like 2 (MYBL2), remain underexplored in HCC. Therefore, this study aimed to systematically validate the clinical significance of MYBL2, elucidate its functional role in tumor progression and drug sensitivity, and identify its upstream regulatory mechanisms using an integrative machine learning and experimental framework.
METHODS: We applied an integrative pipeline combining LASSO‑based feature selection on TCGA and GEO cohorts, single‑cell transcriptomics, pharmacogenomic surveys, and CRISPR dependency screens. These computational approaches were complemented by experimental validation.
RESULTS: MYBL2 robustly discriminated tumor from normal liver (AUC = 0.968), and high expression was associated with adverse features, including higher grade, microvascular invasion, HBV positivity, non‑response to TACE, and worse survival. A nomogram combining MYBL2 with AJCC stage improved 1‑, 3‑, and 5‑year AUCs versus stage alone. MYBL2 correlated with proliferative biomarkers (AFP, MKI67, PCNA, BIRC5) and CRISPR knockout inhibited growth in most HCC lines. High MYBL2 expression was associated with greater sensitivity to sorafenib in pharmacogenomic screens and was linked to an immunosuppressive microenvironment and higher MSI. Mechanistically, miR‑29a was shown to suppress MYBL2 translation by directly binding to its 3'UTR.
CONCLUSIONS: MYBL2 is a potent diagnostic and prognostic biomarker in HCC that also predicts sorafenib sensitivity. Our findings establish a clear regulatory link where MYBL2 is a direct and functionally important target of the tumor‑suppressive miR‑29a. This positions MYBL2 as a tractable target for miR‑29a‑based therapeutic strategies, warranting clinical validation.
Patients with hepatocellular carcinoma face low survival because accurate prognostic prediction and effective therapy selection are challenging. Leveraging MYBL2 as a novel biomarker and therapeutic target enables personalized treatment, improving quality of life and life expectancy.