ArticleAmerican journal of cancer research2012
A multiplex serum protein assay for determining the probability of colorectal cancer.
Article in American journal of cancer research, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 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
11 citing papers in PubMed, 23 citations in OpenAlex.
- Trial
- A proteomic classifier panel for early screening of colorectal cancer: a case control study.Journal of translational medicine · 2024Article
- A robust immune-related gene pairs signature for predicting the overall survival of esophageal cancer.BMC genomics · 2023Article
- Gelsolin: A comprehensive pan-cancer analysis of potential prognosis, diagnostic, and immune biomarkers.Frontiers in genetics · 2023Article
- DEFB4A is a potential prognostic biomarker for colorectal cancer.Oncology letters · 2020Article
- Article
- Blood-Based Protein Signatures for Early Detection of Colorectal Cancer: A Systematic Review.Clinical and translational gastroenterology · 2017Article
- A Timely Shift from Shotgun to Targeted Proteomics and How It Can Be Groundbreaking for Cancer Research.Frontiers in oncology · 2017Review
- Mass spectrometry based biomarker discovery, verification, and validation--quality assurance and control of protein biomarker assays.Molecular oncology · 2014Review
- Proteomics for discovery of candidate colorectal cancer biomarkers.World journal of gastroenterology · 2014Review
- Blood-based testing for colorectal cancer screening.Molecular diagnosis & therapy · 2014Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Our purpose is to develop a serum assay to determine an individual's probability of having colorectal cancer (CRC). We have discovered a protein panel yielding encouraging, clinically significant results. We evaluated 431 serum samples from donors screened for CRC by colonoscopy. We compared the concentration of seven proteins in individuals with CRC versus individuals found to be CRC free. The assay monitored a single peptide from each of seven proteins. Comparing CRC to normal samples in univariate two-sample t-tests, 6 of the 7 proteins yielded a p-value less than 0.01. Logistic regression was used to construct a model for determination of CRC probability. The model was fit on a randomly chosen training set of 321 samples. Using 6 of the 7 proteins (ORM1, GSN, C9, HABP2, SAA2, and C3) and a cut point of 0.4, an independent test set of 110 samples yielded a sensitivity of 93.75%, a specificity of 82.89% and a prevalence-adjusted negative predictive value (NPV) of 99.9775% for the assay. The results demonstrate that the assay has promise as a sensitive, non-invasive diagnostic test to provide individuals with an understanding of their own probability of having CRC.
Indexed as
Identifiers
22957311PMC3433100W1565946679What 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.