Cardiorenal syndrome type 3 (CRS-3) is damage to the heart following acute kidney injury (AKI). Although many experiments have found that inflammation, oxidative stress, and cardiomyocyte death are involved in cardiomyocyte pathophysiological alterations during CRS-3, they lack a non-bias analysis to figure out the primary mediator of cardiac dysfunction. Herein proteomic analysis was operated in CRS-3 and growth factor receptor-bound protein 2 (Grb2) was identified as a regulator involving AKI-related myocardial damage. Increased Grb2 was associated with cardiac diastolic dysfunction and mitochondrial bioenergetics impairment; these pathological changes could be reversed through the administration of a Grb2-specific inhibitor during AKI. Molecular investigation illustrated that augmented Grb2 promoted cardiomyocyte mitochondrial metabolism disorder through inhibiting the Akt/mTOR signaling pathway. Besides that, Mouse Inflammation Array Q1 further identified IL-6 as the upstream stimulator of Grb2 upregulation after AKI. Exogenous administration of IL-6 induced cardiomyocyte damage and mitochondrial bioenergetics impairment, whereas these effects were nullified in cardiomyocytes pretreated with Grb2 inhibitor. Our results altogether identify CRS-3 to be caused by the upregulations of IL-6/Grb2 which contribute to cardiac dysfunction through inhibiting the Akt/mTOR signaling pathway and inducing cardiomyocyte mitochondrial bioenergetics impairment. This finding provides a potential target for the clinical treatment of patients with CRS-3.

Background: Acute kidney injury (AKI) is a common and severe disease, which poses a global health burden with high morbidity and mortality. In recent years, ferroptosis has been recognized as being deeply related to Acute kidney injury. Our aim is to develop a diagnostic signature for Acute kidney injury based on ferroptosis-related genes (FRGs) through integrated bioinformatics analysis and machine learning. Methods: Our previously uploaded mouse Acute kidney injury dataset GSE192883 and another dataset, GSE153625, were downloaded to identify commonly expressed differentially expressed genes (coDEGs) through bioinformatic analysis. The FRGs were then overlapped with the coDEGs to identify differentially expressed FRGs (deFRGs). Immune cell infiltration was used to investigate immune cell dysregulation in Acute kidney injury. Functional enrichment analysis and protein-protein interaction network analysis were applied to identify candidate hub genes for Acute kidney injury. Then, receiver operator characteristic curve analysis and machine learning analysis (Lasso) were used to screen for diagnostic markers in two human datasets. Finally, these potential biomarkers were validated by quantitative real-time PCR in an Acute kidney injury model and across multiple datasets. Results: A total of 885 coDEGs and 33 deFRGs were commonly identified as differentially expressed in both GSE192883 and GSE153625 datasets. In cluster 1 of the coDEGs PPI network, we found a group of 20 genes clustered together with deFRGs, resulting in a total of 48 upregulated hub genes being identified. After ROC analysis, we discovered that 25 hub genes had an area under the curve (AUC) greater than 0.7; Lcn2, Plin2, and Atf3 all had AUCs over than this threshold in both human datasets GSE217427 and GSE139061. Through Lasso analysis, four hub genes (Lcn2, Atf3, Pir, and Mcm3) were screened for building a nomogram and evaluating diagnostic value. Finally, the expression of these four genes was validated in Acute kidney injury datasets and laboratory investigations, revealing that they may serve as ideal ferroptosis markers for Acute kidney injury. Conclusion: Four hub genes (Lcn2, Atf3, Pir, and Mcm3) were identified. After verification, the signature's versatility was confirmed and a nomogram model based on these four genes effectively distinguished Acute kidney injury samples. Our findings provide critical insight into the progression of Acute kidney injury and can guide individualized diagnosis and treatment.