Supervised Machine-Based Learning and Computational Analysis to Reveal Unique Molecular Signatures Associated with Wound Healing and Fibrotic Outcomes to Lens Injury

Authors

Catherine Lalman, Thomas Jefferson UniversityFollow
Kylie R. Stabler, Thomas Jefferson UniversityFollow
Yimin Yang
Janice L Walker, Thomas Jefferson UniversityFollow

Document Type

Article

Publication Date

8-1-2025

Comments

This article is the author's final published version in International Journal of Molecular Sciences, Volume 26, Issue 15, 2025, Article number 7422.

The published version is available at https://doi.org/10.3390/ijms26157422. Copyright © 2025 by the authors.

Abstract

Posterior capsule opacification (PCO), a frequent complication of cataract surgery, arises from dysregulated wound healing and fibrotic transformation of residual lens epithelial cells. While transcriptomic and machine learning (ML) approaches have elucidated fibrosis-related pathways in other tissues, the molecular divergence between regenerative and fibrotic outcomes in the lens remains unclear. Here, we used an ex vivo chick lens injury model to simulate post-surgical conditions, collecting RNA from lenses undergoing either regenerative wound healing or fibrosis between days 1–3 post-injury. Bulk RNA sequencing data were normalized, log-transformed, and subjected to univariate filtering prior to training LASSO, SVM, and RF ML models to identify discriminatory gene signatures. Each model was independently validated using a held-out test set. Distinct gene sets were identified, including fibrosis-associated genes (VGLL3, CEBPD, MXRA7, LMNA, gga-miR-143, RF00072) and wound-healing-associated genes (HS3ST2, ID1), with several achieving perfect classification. Gene Set Enrichment Analysis revealed divergent pathway activation, including extracellular matrix remodeling, DNA replication, and spliceosome associated with fibrosis. RT-PCR in independent explants confirmed key differential expression levels. These findings demonstrate the utility of supervised ML for discovering lens-specific fibrotic and regenerative gene features and nominate biomarkers for targeted intervention to mitigate PCO. © 2025 by the authors.

Recommended Citation

Lalman, Catherine; Stabler, Kylie R.; Yang, Yimin; and Walker, Janice L, "Supervised Machine-Based Learning and Computational Analysis to Reveal Unique Molecular Signatures Associated with Wound Healing and Fibrotic Outcomes to Lens Injury" (2025). Department of Pathology, Anatomy, and Cell Biology Faculty Papers. Paper 451.
https://jdc.jefferson.edu/pacbfp/451

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 License.

PubMed ID

40806551

Language

English