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Type 2 diabetes mellitus combined with metabolic dysfunction-associated steatotic liver disease (MASLD) leads to an increasing incidence of liver injury year by year, and patients are at a significantly higher risk of developing cirrhosis or even liver failure. No drugs have emerged to specifically treat this disease. The aim of this study is to investigate the mechanisms and causative hub genes of type 2 diabetes combined with MASLD. The data were obtained through the GEO platform for bioinformatics analysis and validated by in vitro experiments to find the causative targets of type 2 diabetes mellitus combined with MASLD, which will provide some theoretical basis for the development of future therapeutic drugs. GSE23343 and GSE49541 were downloaded from the Gene Expression Omnibus (GEO) database to identify differentially expressed genes (DEGs) in type 2 diabetes mellitus combined with MASLD for functional enrichment analysis. And STRING database and Cytoscape software were used to construct Protein–Protein Interaction (PPI) and hub gene networks. And GO (gene ontology, GO) analysis and KEGG (Kyoto encyclopedia of genes and genomes, KEGG) enrichment analysis were performed on target genes. A total of 185 co-expressed DEGs were obtained by differential analysis, and 20 key genes involved in the development and progression of type 2 diabetes were finally screened. These 20 key genes were involved in 529 GO enrichment results and 20 KEGG enrichment results, and were mainly associated with ECM–receptor interaction, Focal adhesion, Human papillomavirus infection, PI3K-Akt signaling pathway, and the Toll-like receptor signaling pathway. A total of two target genes (SPP1, collagen IV) were found to be highly correlated with type 2 diabetes mellitus combined with MASLD. Real time PCR results showed that there was a significant difference in SPP1 and collagen IV mRNA expression among the three groups (P < 0.05). SPP1 and Collagen IV may be candidate biomarkers for type 2 diabetes mellitus combined with MASLD, as verified by bioinformatics screening and in vitro experiments. Our findings provide new targets for the treatment of type 2 diabetes combined with MASLD. [ABSTRACT FROM AUTHOR]