RESUMEN
Few robust biomarkers are available for distant metastatic colorectal cancer (CRC) patients. Aberrant high expression of CDH3 has been reported in advanced CRC patients, but the value of CDH3 as a biomarker for the diagnosis and prognosis of distant metastatic CRC patients remains to be evaluated. In this study, we explored the serum levels of CDH3 in different stages of CRC patients and sought to determine whether serum CDH3 serves as an independent biomarker for distant metastatic CRC patients. We analyzed the serum CDH3 levels by ELISA in a cohort of CRCs (n=96) and normal controls (n=28). We compared the serum CDH3 levels between normal controls and different stages of CRCs. As a potential diagnostic marker of distant metastatic CRC, the specificity and sensitivity of serum CDH3 were evaluated. Multivariate analysis was also performed to determine whether serum CDH3 was an independent risk factor. Moreover, the changes of serum CDH3 levels were monitored and analyzed before and after palliative chemotherapy. Serum levels of CDH3, CA24-2, CA19-9, CA72-4, and CEA were significantly elevated in distant metastatic CRCs. CA24-2 (r=0.24, P=0.01), CA19-9 (r=0.20, P=0.03), CA72-4 (r=0.64, P<0.0001), and CEA (r=0.31, P=0.0012) all had a certain correlation with CDH3. After three cycles of palliative chemotherapy, levels of CDH3, CA24-2, CA19-9, CA72-4, and CEA of partial response CRCs were reduced to 38.8% (95% confidence interval [CI]: 30.95%-53.77%), 57.73% (95% CI: 2.085%-73.83%), 50.33% (95% CI: 9.935%-79.42%), 74.74% (95% CI: 25.21%-88.00%), and 59.16% (95% CI: 12.65%-83.56%) of baseline, respectively. The areas under the receiver operating characteristic curves of CDH3, CA24-2, CA19-9, CA72-4, and CEA with chemotherapy response were 0.900, 0.597, 0.635, 0.608, and 0.507, respectively. Serum CDH3 is an effective serum biomarker for the diagnosis of distant metastatic CRCs and monitoring response to palliative chemotherapy in distant metastatic CRCs.
RESUMEN
BACKGROUND: Lipidomics and metabolomics are closely related to tumor phenotypes, and serum lipoprotein subclasses and small-molecule metabolites are considered as promising biomarkers for breast cancer (BC) diagnosis. This study aimed to explore potential biomarker models based on lipidomic and metabolomic analysis that could distinguish BC from healthy controls (HCs) and triple-negative BC (TNBC) from non-TNBC. METHODS: Blood samples were collected from 114 patients with BC and 75 HCs. A total of 112 types of lipoprotein subclasses and 30 types of small-molecule metabolites in the serum were detected by 1 H-NMR. All lipoprotein subclasses and small-molecule metabolites were subjected to a three-step screening process in the order of significance (p < 0.05), univariate regression (p < 0.1), and lasso regression (nonzero coefficient). Discriminant models of BC versus HCs and TNBC versus non-TNBC were established using binary logistic regression. RESULTS: We developed a valid discriminant model based on three-biomarker panel (formic acid, TPA2, and L6TG) that could distinguish patients with BC from HCs. The area under the receiver operating characteristic curve (AUC) was 0.999 (95% confidence interval [CI]: 0.995-1.000) and 0.990 (95% CI: 0.959-1.000) in the training and validation sets, respectively. Based on the panel (D-dimer, CA15-3, CEA, L5CH, glutamine, and ornithine), a discriminant model was established to differentiate between TNBC and non-TNBC, with AUC of 0.892 (95% CI: 0.778-0.967) and 0.905 (95% CI: 0.754-0.987) in the training and validation sets, respectively. CONCLUSION: This study revealed lipidomic and metabolomic differences between BC versus HCs and TNBC versus non-TNBC. Two validated discriminatory models established against lipidomic and metabolomic differences can accurately distinguish BC from HCs and TNBC from non-TNBC. IMPACT: Two validated discriminatory models can be used for early BC screening and help BC patients avoid time-consuming, expensive, and dangerous BC screening.