Identification of Diagnostic Biomarkers Associated with Stromal and Immune Cell Infiltration in Fatty Infiltration After Rotator Cuff Tear by Integrating Bioinformatic Analysis and Machine-Learning.

Abstract

PurposeThe present study aimed to explore potential diagnostic biomarkers for fatty infiltration (FI) of the rotator cuff muscles after rotator cuff tear (RCT) and investigate the influence of stromal and immune cell infiltration on this pathology.MethodsThe GSE130447 and GSE103266 datasets were downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were identified, and gene set enrichment analyses were performed by R software. Two machine learning algorithms, random forest and multiple support vector machine recursive feature elimination (mSVM-RFE), were used to screen candidate biomarkers. The diagnostic value of the screened biomarkers was further validated by the area under the ROC curve (AUC) in the GSE103266 dataset. Murine microenvironment cell population counter (mMCP-counter) method was employed to estimate stromal and immune cell infiltration of FI. The correlation between biomarkers and infiltrated immune and stromal cell subsets was further analyzed.ResultsA total of 2123 DEGs were identified. The identified DEGs were predominantly linked to immune system process, extracellular matrix organization and PPAR signalling pathway. FABP5 (AUC = 0.958) and MGP (AUC = 1) were screened as diagnostic biomarkers of FI. Stromal and immune cell infiltration analysis showed that monocytes, mast cells, vessels, endothelial cells and fibroblasts may be related to the process of FI. FABP5 and MGP were positively correlated with vessels whereas negatively correlated with monocytes and mast cells.ConclusionFABP5 and MGP can serve as diagnostic biomarkers of FI after RCT, and stromal and immune cell infiltration may play a crucial role in this pathology.