Diagnostic potential of CDK1 and STAT1 in acute kidney injury associated with gastrointestinal cancers: a bioinformatics-based study.

INTRODUCTION

Patients with gastrointestinal cancers are prone to acute kidney injury (AKI) due to treatment or disease progression, and current diagnostic methods exhibit insufficient sensitivity and specificity. This study aims to evaluate the potential value of CDK1 and STAT1 in the diagnosis of AKI in this patient population.

METHODS

A retrospective analysis was conducted on adjacent tissue, cancerous and the clinical data tissue from 150 gastrointestinal cancer patients treated at our hospital from May 2022 to May 2023. Differentially expressed genes (DEGs) associated with gastrointestinal cancer and kidney injury were identified through bioinformatics analysis. The expression of DEGs proteins in cancerous and adjacent tissues was assessed using immunohistochemical scoring (H scores). Patients were classified into AKI (n = 42) and non-AKI groups (n = 108) according to KDIGO AKI criteria. Univariate and multivariate logistic regression analyses were performed to investigate the influencing factors of AKI occurrence. Spearman correlation analysis was used to explore the relationship between DEGs and AKI biomarkers (Scr, BUN, MAU, and UA). The application value of DEGs in early diagnosis of AKI was evaluated using ROC curves.

RESULTS

Bioinformatics analysis identified CDK1, STAT1, COL1A2, and COL1A1 as DEGs related to AKI in gastrointestinal cancer. Immunohistochemical analysis revealed elevated H scores for CDK1, STAT1, COL1A2, and COL1A1 in tumor tissues. Univariate analysis showed no significant differences in age, sex, marital status, education level, monthly income, disease type, cancer stage, or tumor markers (CEA, CA242, CA50) between AKI and non-AKI groups (P > 0.05). However, the AKI group exhibited significantly higher levels of MAU, UA, and H scores for CDK1, STAT1, COL1A2, and COL1A1 compared to the non-AKI group (P < 0.05). Multivariate logistic regression confirmed that MAU, UA, CDK1, and STAT1 are independent risk factors for AKI in gastrointestinal cancer patients. Correlation analysis indicated a significant positive association between CDK1, STAT1, and AKI biomarker levels (P < 0.05). ROC curve analysis demonstrated that CDK1 and STAT1 possess high diagnostic value for early detection of AKI in patients with gastrointestinal cancer, with enhanced efficacy when used in combination.

CONCLUSION

CDK1 and STAT1 serve as early diagnostic indicators for the occurrence of AKI in gastrointestinal cancer patients.