ArticleFrontiers in oncology2022
Circulating Glycan Monosaccharide Composite-Based Biomarker Diagnoses Colorectal Cancer at Early Stages and Predicts Prognosis.
Article in Frontiers in oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed, 8 citations in OpenAlex.
- Dynamic monitoring of immunoglobulin G as a prognostic indicator after curative resection in high-risk stage II-III colorectal cancer: a retrospective cohort study.Frontiers in oncology · 2026Article
- A hot and cold tumor‑related prognostic signature for stage II colorectal cancer.Oncology letters · 2024Article
- HPLC for simultaneous quantification of free mannose and glucose concentrations in serum: use in detection of ovarian cancer.Frontiers in chemistry · 2023Article
- Determination of monosaccharide composition in human serum by an improved HPLC method and its application as candidate biomarkers for endometrial cancer.Frontiers in oncology · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
16 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: Early diagnosis could lead to a cure of colorectal cancer (CRC). Since CRC is related to aging and lifestyles, we tested if the environmental information-enriched monosaccharide composite (MC) of circulating glycans could serve as an early diagnostic biomarker for CRC. Meanwhile, we evaluated its role in predicting prognosis. Methods: HPAEC-PAD was used to quantify glycan monosaccharide compositions from a total of 467 serum samples including CRC patients, colorectal adenoma (CRA) patients and healthy individuals. Two diagnostic model was constructed by logistic regression analysis. The diagnostic performance of the two models was verified in the retrospective validation group and the prospective validation group. The prognostic performance of the model was assessed by survival analysis. Results: The concentrations of monosaccharides in serum were significantly higher in CRA and CRC patients than in healthy individuals. Two diagnostic models were constructed: MC1 was used to distinguish between healthy individuals and CRC; MC2 was used to distinguish between healthy individuals and CRA. Area under receptor operating characteristic curve (AUC) of MC2 and MC1 was 0.8025 and 0.9403 respectively. However, the AUC of CEA between healthy individuals and CRC was 0.7384. Moreover, in early stage of CRC (without lymph node metastasis), the positive rates of CEA and MC1 were 28% and 80%, respectively. The follow-up data showed that the increased MC1 value was associated with poor survival in patients with CRC ( Discussion: The MC1 model is superior to CEA in the diagnosis of CRC, especially in the early diagnosis. MC1 can be used for predicting prognosis of CRC patients, and elevated MC1 values indicate poor survival.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.