ArticleBioMed research international2021
Identification of Biomarkers for Predicting Allograft Rejection following Kidney Transplantation Based on the Weighted Gene Coexpression Network Analysis.
Article in BioMed research international, 2021. 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 It has been retracted, and should not be counted.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 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, 9 citations in OpenAlex.
- Landscape of the immune infiltration and identification of molecular diagnostic markers associated with immune cells in patients with kidney transplantation.Scientific reports · 2024Article
- Retracted: Identification of Biomarkers for Predicting Allograft Rejection following Kidney Transplantation Based on the Weighted Gene Coexpression Network Analysis.BioMed research international · 2024Article
- Monitoring of Serological, Cellular and Genomic Biomarkers in Transplantation, Computational Prediction Models and Role of Cell-Free DNA in Transplant Outcome.International journal of molecular sciences · 2023Review
- SLAMF8 Participates in Acute Renal Transplant RejectionFrontiers in immunology · 2022Article
Corrections and comments
- Retraction · 2024-01-09Compromised Peer Review · Investigation by Journal/Publisher · Investigation by Third Party · Paper Mill · Unreliable Results and/or Conclusions ·
- Retracted
Authors and funding
10 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Kidney transplantation is the promising treatment of choice for chronic kidney disease and end-stage kidney disease and can effectively improve the quality of life and survival rates of patients. However, the allograft rejection following kidney transplantation has a negative impact on transplant success. Therefore, the present study is aimed at screening novel biomarkers for the diagnosis and treatment of allograft rejection following kidney transplantation for improving long-term transplant outcome. In the study, a total of 8 modules and 3065 genes were identified by WGCNA based on the GSE46474 and GSE15296 dataset from the Gene Expression Omnibus (GEO) database. Moreover, the results of Gene Ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis showed that these genes were mainly involved in the immune-related biological processes and pathways. Thus, 317 immune-related genes were selected for further analysis. Finally, 5 genes (including CD200R1, VAV2, FASLG, SH2D1B, and RAP2B) were identified as the candidate biomarkers based on the ROC and difference analysis. Furthermore, we also found that in the 5 biomarkers an interaction might exist among each other in the protein and transcription level. Taken together, our study identified CD200R1, VAV2, FASLG, SH2D1B, and RAP2B as the candidate diagnostic biomarkers, which might contribute to the prevention and treatment of allograft rejection following kidney transplantation.
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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.