Comprehensive analysis of anoikis-related genes in diagnosis osteoarthritis: based on machine learning and single-cell RNA sequencing data

Abstract

Osteoarthritis (OA) is a degenerative disease closely associated with Anoikis. The objective of this work was to discover novel transcriptome-based anoikis-related biomarkers and pathways for OA progression.The microarray datasets GSE114007 and GSE89408 were downloaded using the Gene Expression Omnibus (GEO) database. A collection of genes linked to anoikis has been collected from the GeneCards database. The intersection genes of the differential anoikis-related genes (DEARGs) were identified using a Venn diagram. Infiltration analyses were used to identify and study the differentially expressed genes (DEGs). Anoikis clustering was used to identify the DEGs. By using gene clustering, two OA subgroups were formed using the DEGs. GSE152805 was used to analyse OA cartilage on a single cell level. 10 DEARGs were identified by lasso analysis, and two Anoikis subtypes were constructed. MEgreen module was found in disease WGCNA analysis, and MEturquoise module was most significant in gene clusters WGCNA. The XGB, SVM, RF, and GLM models identified five hub genes (CDH2, SHCBP1, SCG2, C10orf10, P FKFB3), and the diagnostic model built using these five genes performed well in the training and validation cohorts. analysing single-cell RNA sequencing data from GSE152805, including 25,852 cells of 6 OA cartilage.

Keywords:

• Osteoarthritis
• anoikis
• immunity
• machine learning
• single-cell analysis

Author contributions

JunSong Zhang had substantial contributions to the conception or design of the study. RunSang Pan, GuoLu Li, JianXiang Teng, and HongBo Zhao were involved in the acquisition, analysis or interpretation of data for the work. ChangHua Zhou, JiSheng Zhu, Hao Zhen, and XiaoBin Tian drafted the work and revised it critically for important intellectual content and finally approved the version to be published. All authors agree to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The GSE114007, GSE89408, GSE55235, and GSE152805 datasets were publicly available and obtained from Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/geo/).

Additional information

Funding

The study was funded by Science and Technology Foundation of Guizhou Province (Grant No. GZKJ-2021-072).