Levels and clinical significance of serum C1QC and VCAM-1 in patients with colorectal cancer or colorectal polyps/adenomas.
[BACKGROUND] Colorectal cancer (CRC) screening is crucial for prevention.
- p-value P < .05
APA
Yang C, Zhang B, et al. (2026). Levels and clinical significance of serum C1QC and VCAM-1 in patients with colorectal cancer or colorectal polyps/adenomas.. Postgraduate medical journal. https://doi.org/10.1093/postmj/qgaf237
MLA
Yang C, et al.. "Levels and clinical significance of serum C1QC and VCAM-1 in patients with colorectal cancer or colorectal polyps/adenomas.." Postgraduate medical journal, 2026.
PMID
41510953
Abstract
[BACKGROUND] Colorectal cancer (CRC) screening is crucial for prevention. This study explored serum complement component 1q (C1QC) and vascular cell adhesion molecule-1 (VCAM-1) expression in CRC/polyp patients and their clinical significance for CRC diagnosis/staging.
[METHODS] Serum samples from 135 CRC patients, 135 polyp/adenoma patients, and 135 healthy controls (collected between 1 January 2023 and October 30 2023) were analyzed retrospectively. Data-independent acquisition proteomics identified differentially expressed proteins. C1QC and VCAM-1 levels were quantified via enzyme-linked immunosorbent assay. Diagnostic performance was evaluated via receiver operating characteristic curves and the area under the curve. Statistical analysis (SPSS 27.0, GraphPad Prism 9.5.1) included analysis of variance, Pearson correlation, and logistic regression (P < .05 was considered significant).
[RESULTS] C1QC and VCAM-1 levels were significantly greater in the CRC patient group than in the healthy/polyp group (P < .05), with no difference between the polyp and healthy groups (P > .05). Receiver operating characteristic analysis revealed that C1QC (cutoff: 52.34 μg/dl) and VCAM-1 (cutoff: 431.215 ng/ml) had 78.2% and 66.8% diagnostic accuracy, respectively. Combined detection achieved 80.2% accuracy, surpassing that of carcinoembryonic antigen/carbohydrate antigen 199. Both biomarkers increased with disease progression (P < .05) and aided staging assessment.
[CONCLUSION] Serum C1QC and VCAM-1 demonstrate high diagnostic efficacy in CRC, correlate with pathological features, and hold promise as novel serological screening biomarkers.
[METHODS] Serum samples from 135 CRC patients, 135 polyp/adenoma patients, and 135 healthy controls (collected between 1 January 2023 and October 30 2023) were analyzed retrospectively. Data-independent acquisition proteomics identified differentially expressed proteins. C1QC and VCAM-1 levels were quantified via enzyme-linked immunosorbent assay. Diagnostic performance was evaluated via receiver operating characteristic curves and the area under the curve. Statistical analysis (SPSS 27.0, GraphPad Prism 9.5.1) included analysis of variance, Pearson correlation, and logistic regression (P < .05 was considered significant).
[RESULTS] C1QC and VCAM-1 levels were significantly greater in the CRC patient group than in the healthy/polyp group (P < .05), with no difference between the polyp and healthy groups (P > .05). Receiver operating characteristic analysis revealed that C1QC (cutoff: 52.34 μg/dl) and VCAM-1 (cutoff: 431.215 ng/ml) had 78.2% and 66.8% diagnostic accuracy, respectively. Combined detection achieved 80.2% accuracy, surpassing that of carcinoembryonic antigen/carbohydrate antigen 199. Both biomarkers increased with disease progression (P < .05) and aided staging assessment.
[CONCLUSION] Serum C1QC and VCAM-1 demonstrate high diagnostic efficacy in CRC, correlate with pathological features, and hold promise as novel serological screening biomarkers.
같은 제1저자의 인용 많은 논문 (5)
- Estimating major pathological response in non-small cell lung cancer patients with post-neoadjuvant therapy using MMT-net.
- Long-term fine particulate air pollution exposure and risk of gastric cancer mortality in Taiwan.
- A trispecific antibody engaging T cells with tumour and myeloid cells augments antitumour immunity.
- Lactylation as a metabolic-epigenetic switch in cancer: dual roles in cell death resistance and therapeutic vulnerability.
- Development of a machine learning model for preoperative prediction of spread through air spaces in resectable non-small cell lung cancer: A single-center retrospective study.