Sepsis is a type of systemic inflammatory response caused by infection. The present study aimed to identify novel targets for the treatment of sepsis. We conducted bioinformatic analysis of the microarray Gene Expression Omnibus dataset GSE12624, which includes data on 34 patients with sepsis and 36 healthy individuals without sepsis. Differentially expressed genes (DEGs) in sepsis patients were identified using Bayesian methods included in the limma package in R. Correlations among the expression values of DEGs were analyzed using the weighted gene co‑expression network analysis (WGCNA) to construct a co‑expression network. Subsequently, the generated co‑expression network was visualized using Cytoscape 3.3 software. Additionally, a protein‑protein interaction (PPI) network was constructed based on all the DEGs using STRING. Finally, the integrated regulatory network was constructed based on DEGs, microRNAs (miRNAs) and transcription factors (TFs). A total of 407 DEGs were identified in the sepsis samples, including 227 upregulated DEGs and 180 downregulated DEGs. WGCNA grouped the DEGs into 13 co‑expressed modules. Additionally, MAP3K8 and RPS6KA5 in the MEyellow module were enriched in the MAPK and TNF signaling pathways. In addition, the PPI network comprised 48 nodes and 112 edges, which included the pairs MAP3K8‑RPS6KA5, MAP3K8‑IL10, RPS6KA5‑EXOSC4 and EXOSC4‑EXOSC5. Lastly, the TF‑miRNA‑target DEG regulatory network was constructed based on eight TFs (NF‑κB), seven miRNAs (miR152, miR‑148A/B), and 52 TF‑miRNA‑target gene triplets (17 upregulated genes, including MAP3K8, and 10 downregulated genes, including RPS6KA5). Our analysis showed that the members of the miR‑148 family (miR‑148A/B and miR‑152) are candidate biomarkers for sepsis.