Association between cancer antigen 19-9 and diabetes risk: A prospective and Mendelian randomization study

Association of Circulating Carbohydrate Antigen 19-9 Level with Type 2 Diabetic Kidney Disease in Chinese Adults: A Cross-Sectional Study

Objective

Very few and conflicting data are available regarding the correlation between circulating carbohydrate antigen 19-9 (CA19-9) levels and diabetic kidney disease (DKD) and its components including albuminuria and a low estimated glomerular filtration rate (eGFR). This study aimed to examine the association of circulating CA19-9 and DKD in Chinese patients with type 2 diabetes mellitus (T2DM).

Methods

A total of 402 hospitalized T2DM patients between September 2017 and December 2021 were included in this cross-sectional study. There were 224 and 178 subjects in non-DKD and DKD groups, respectively. Serum CA19-9 was measured by chemiluminescence method, and its potential relationship with DKD was evaluated by multivariate logistic regression and correlation analyses, and receiver operating characteristic (ROC) curve analysis.

Results

T2DM patients with DKD had significantly higher serum CA19-9 levels than those without, and serum CA19-9 levels were positively related to urinary albumin-to-creatinine ratio and negatively to eGFR (P<0.01). Multivariate regression analysis revealed that serum CA 19-9 was an independent factor of DKD [odds ratio (OR), 1.018; 95% confidence interval (CI), 1.002-1.035; P<0.05]. Moreover, an increased progressively risk of DKD with an increase in serum CA19-9 quartiles was observed (P for trend <0.001), and T2DM patients in the highest serum CA19-9 quartile were associated with an increased likelihood of DKD when compared to those in the lowest quartile (OR: 2.936, 95% CI 1.129-7.633, P<0.05). Last, the analysis of ROC curves suggested that serum CA 19-9 at a cut of 25.09 U/mL resulted in the highest Youden index with sensitivity 43.8% and 75.4% specificity to predict the presence of DKD.

Conclusion

These results showed that high circulating CA19-9 was related to DKD and may serve as a useful biomarker of DKD in hospitalized Chinese T2DM patients.

Construction and validation of a prognostic risk model for breast cancer based on protein expression

Breast cancer (BRCA) is the primary cause of mortality among females globally. The combination of advanced genomic analysis with proteomics characterization to construct a protein prognostic model will help to screen effective biomarkers and find new therapeutic directions. This study obtained proteomics data from The Cancer Proteome Atlas (TCPA) dataset and clinical data from The Cancer Genome Atlas (TCGA) dataset. Kaplan–Meier and Cox regression analyses were used to construct a prognostic risk model, which was consisted of 6 proteins (CASPASE7CLEAVEDD198, NFKBP65-pS536, PCADHERIN, P27, X4EBP1-pT70, and EIF4G). Based on risk curves, survival curves, receiver operating characteristic curves, and independent prognostic analysis, the protein prognostic model could be viewed as an independent factor to accurately predict the survival time of BRCA patients. We further validated that this prognostic model had good predictive performance in the GSE88770 dataset. The expression of 6 proteins was significantly associated with the overall survival of BRCA patients. The 6 proteins and encoding genes were differentially expressed in normal and primary tumor tissues and in different BRCA stages. In addition, we verified the expression of 3 differential proteins by immunohistochemistry and found that CDH3 and EIF4G1 were significantly higher in breast cancer tissues. Functional enrichment analysis indicated that the 6 genes were mainly related to the HIF-1 signaling pathway and the PI3K-AKT signaling pathway. This study suggested that the prognosis-related proteins might serve as new biomarkers for BRCA diagnosis, and that the risk model could be used to predict the prognosis of BRCA patients.