Clinical-transcriptomic classification of lumbar disc degeneration enhanced by machine learning.

Background: Lumbar disc degeneration (LDD) displays considerable heterogeneity in terms of clinical features and pathological changes. However, researchers have not clearly determined whether the transcriptome variations in LDD could be used to identify or interpret the causes of heterogeneity in clinical features. This study aimed to identify the transcriptomic classification of degenerated discs in LDD patients and whether the molecular subtypes of LDD could be accurately predicted using clinical features.

Methods: One hundred and twenty-two nucleus pulposus (NP) tissues from 108 patients were consecutively collected for bulk RNA sequencing (RNA-seq). An unsupervised clustering method was employed to analyze the bulk RNA matrix. Differential analysis was performed to characterize the transcriptional signatures and subtype-specific extracellular matrix (ECM) dysregulation. The cell subpopulation states of each subtype were inferred by integrating bulk and single-cell sequencing datasets. Transwell and dual-luciferase reporter gene assays were employed to investigate possible molecular mechanisms involved. Machine learning algorithm diagnostic prediction models were developed to correlate molecular classification with clinical features.

Results: LDD was classified into 4 subtypes with distinct molecular signatures and ECM remodeling: C1 with collagenesis, C2 with ossification, C3 with low chondrogenesis, and C4 with fibrogenesis. Chond1-3 in C1 dominated disc collagenesis via the activation of the mechanosensors TRPV4 and PIEZO1; NP progenitor cells in C2 exhibited chondrogenic and osteogenic phenotypes; Chond1 in C3 was linked to a disrupted hypoxic microenvironment leading to reduced chondrogenesis; Macrophages in C4 played a crucial role in disc fibrogenesis via the secretion of tumor necrosis factor-α (TNF-α). Furthermore, the random forest diagnostic prediction model was proven to have a robust performance [area under the receiver operating characteristic (ROC) curve: 0.9312; accuracy: 0.84] in stratifying the molecular subtypes of LDD based on 12 clinical features.

Conclusions: Our study delineates 4 distinct molecular subtypes of LDD that can be accurately stratified on the basis of clinical features. The identification of these subtypes would facilitate precise diagnostics and guide the development of personalized treatment strategies for LDD.