Objective:To screen out the potential key genes of sepsis-associated acute kidney injury (AKI), and provide theoretical and experimental evidence for the treatment of sepsis-associated AKI.Methods:① Bioinformatics analysis: two gene expression datasets (GSE30718 and GSE53773) were downloaded for bioinformatics analysis from the Gene Expression Omnibus (GEO). These two datasets recorded mRNA microarray data from kidney biopsies before and after kidney transplantation, and a subset of patients developed AKI after kidney transplantation. Differential analysis was conducted, and the genes with the same differential expression and a higher area under the receiver operator characteristic curve (AUC) in both databases were used as the target gene for subsequent cell experiments. ② Cell validation experiment: human proximal renal tubular cells HK2 were cultured in vitro, and lipopolysaccharide (LPS) was used for establishing LPS-HK2 cell model (LPS 10 mg/L for 6 hours, LPS model group), and the blank control group was set. Then, small interfering RNA (siRNA) technology was used to knock down the target gene obtained by bioinformatics analysis in LPS-HK2 cells (gene knockdown group), and a gene negative control group was set. The real-time fluorescent quantitative reverse transcription-polymerase chain reaction (RT-qPCR) technique was used to detect the expression of the target gene in HK2 cells. Enzyme-linked immunosorbent assay (ELISA) was used to detect the levels of inflammatory factors in the cell supernatants. Western blotting was used to detect the expressions of key apoptosis proteins. Results:① Results of bioinformatics analysis: 325 genes in the two datasets showed the same expression trend, of which 144 were significantly down-regulated and 181 were significantly up-regulated, while the expression difference of secretory leukocyte protease inhibitor (SLPI) in the two datasets was both statistically significant. Further receiver operator characteristic curve (ROC curve) analysis confirmed that the SLPI expression in GSE30718 and GSE53773 datasets had a high diagnostic efficiency for AKI, with AUC of 0.83 and 0.92, respectively. Therefore, SLPI was selected as the target gene for subsequent cell validation experiment. ② Cell validation experiment: the RT-qPCR analysis showed that the expression of SLPI in LPS-HK2 cells of the LPS model group was significantly higher than that of the blank control group (2 -ΔΔCT: 1.80±0.14 vs. 1.00±0.11, P < 0.01), and the change trend was the same with the results of bioinformatics analysis. Furthermore, knockdown SLPI gene analysis showed that the levels of inflammatory factors in LPS-HK2 cells supernatants in the gene knockdown group were significantly higher than those in the negative control group [Interleukin-6 (IL-6, ng/L): 509.58±27.08 vs. 253.87±75.83, IL-1β (ng/L): 490.99±49.52 vs. 239.67±26.97, tumor necrosis factor-α (TNF-α, ng/L): 755.22±48.66 vs. 502.06±10.92, all P < 0.01]. The above results indicated that SLPI could inhibit the inflammatory response of HK2 cells induced by LPS. The expressions of key apoptosis proteins Bax and caspase-3 in LPS-HK2 cells in the gene knockdown group were significantly higher than those in the negative control group [Bax protein (Bax/GAPDH): 1.38±0.12 vs. 1.00±0.10, caspase-3 protein (caspase-3/GAPDH): 1.44±0.15 vs. 1.00±0.11, both P < 0.05], and Bcl-2 expression was significantly decreased (Bcl-2/GAPDH: 0.83±0.08 vs. 1.00±0.05, P < 0.05), the above results indicated that SLPI could inhibit the apoptosis of cells in the inflammatory response. Conclusion:SLPI can inhibit the inflammatory response and apoptosis of HK2 cells induced by LPS, which may be involved in the protective mechanism of renal tubular cells in the response to sepsis, and is a potential target for the treatment of sepsis-associated AKI.