ArticleContrast media & molecular imaging2022
Analysis of the Relationship between the Expression Level of TTR and APOH and Prognosis in Patients with Colorectal Cancer Metastasis Based on Bioinformatics.
Article in Contrast media & molecular imaging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 6 papers.
What it found
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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
6 citing papers in PubMed, 20 citations in OpenAlex.
- Association of pre-diagnosis plasma proteomic contexture with overall survival of early- and late-stage colon cancer patients.BMC cancer · 2025Article
- Single-cell transcriptomics reveals stage- and side-specificity of gene modules in colorectal cancer.Research square · 2024Article
- Review
- Comparative proteomic profiling of plasma exosomes in lung cancer cases of liver and brain metastasis.Cell & bioscience · 2023Article
- A metabolomic and proteomic study to elucidate the molecular mechanisms of immunotherapy resistance in patients with oesophageal squamous cell carcinoma.Biomedical reports · 2023Article
- Retracted: Analysis of the Relationship between the Expression Level of TTR and APOH and Prognosis in Patients with Colorectal Cancer Metastasis Based on Bioinformatics.Contrast media & molecular imaging · 2023Article
Corrections and comments
- Retraction · 2023-09-27Concerns/Issues about Data · Concerns/Issues about Results and/or Conclusions · Concerns/Issues about Referencing/Attributions · Concerns/Issues about Peer Review · Investigation by Journal/Publisher · Investigation by Third Party · Paper Mill · Computer-Aided Content or Computer-Generated Content · Unreliable Results and/or Conclusions · · See also: https://pubpeer.com/publications/A70D5A85FD296F765266B568BDEDD8
- Retracted
Authors and funding
4 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The expression of TTR and apolipoprotein H (APOH) genes and their relationship with prognosis in patients with colorectal cancer (CRC) metastasis by using bioinformatics analysis techniques are explored. The expression profiles of related genes in patients with CRC metastasis are retrieved from the Gene Expression Omnibus (GEO) database. The core genes transthyretin (TTR) and APOH are screened by constructing protein-protein interaction (PPI) network, and the corresponding patient data of 327 patients are extracted and included in the metastasis group. The TTR and APOH genes of 300 patients without CRC metastasis are screened and included in the control group. The relationship between the expression levels of TTR and APOH and the clinicopathological parameters of patients with CRC metastasis is analyzed. Kaplan-Meier survival curve is drawn to observe the influence of overexpression and low expression of TTR and APOH on the prognosis and survival of patients in the metastatic group. Receiver operating characteristic (ROC) curve is drawn to observe the prognostic efficacy of combined TTR and APOH detection in patients with CRC metastasis. The experimental results show that bioassay can confirm the close relationship between TTR, APOH, and patients with CRC metastasis. Regular detection of serum TTR and APOH expression can effectively assess the patient's condition and take measures to improve the prognosis of the patients.
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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.