Purpose: To explore fecal immune-related proteins that can be used for colorectal cancer (CRC) diagnosis.
Patients And Methods: Three independent cohorts were used in present study. In the discovery cohort, which included 14 CRC patients and 6 healthy controls (HCs), label-free proteomics was applied to identify immune-related proteins in stool that could be used for CRC diagnosis. Exploring potential links between gut microbes and immune-related proteins by 16S rRNA sequencing. The abundance of fecal immune-associated proteins was verified by ELISA in two independent validation cohorts and a biomarker panel was constructed that could be used for CRC diagnosis. The validation cohort I included 192 CRC patients and 151 HCs from 6 different hospitals. The validation cohort II included 141 CRC patients, 82 colorectal adenoma (CRA) patients, and 87 HCs from another hospital. Finally, the expression of biomarkers in cancer tissues was verified by immunohistochemistry (IHC).
Results: In the discovery study, 436 plausible fecal proteins were identified. And among 67 differential fecal proteins (|log2 fold change| > 1, P< 0.01) that could be used for CRC diagnosis, 16 immune-related proteins with diagnostic value were identified. The 16S rRNA sequencing results showed a positive correlation between immune-related proteins and the abundance of oncogenic bacteria. In the validation cohort I, a biomarker panel consisting of five fecal immune-related proteins (CAT, LTF, MMP9, RBP4, and SERPINA3) was constructed based on the least absolute shrinkage and selection operator (LASSO) and multivariate logistic regression. The biomarker panel was found to be superior to hemoglobin in the diagnosis of CRC in both validation cohort I and validation cohort II. The IHC result showed that protein expression levels of these five immune-related proteins were significantly higher in CRC tissue than in normal colorectal tissue.
Conclusion: A novel biomarker panel consisting of fecal immune-related proteins can be used for the diagnosis of CRC.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10258350 | PMC |
| http://dx.doi.org/10.3389/fimmu.2023.1126217 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Exploring the relationship between MGAT2 and glioblastoma: A Mendelian Randomization and bioinformatics approach.
Brain Res
January 2025
Department of Neurosurgery, The Affiliated Hospital of Xuzhou Medical University, Xuzhou 221002, China; Institute of Nervous System Diseases, Xuzhou Medical University, Xuzhou 221002, China. Electronic address:
Background: Mannosyl-glycoprotein beta-1,2-N-acetylglucosaminyltransferase 2 (MGAT2) and tumors' relevant research was in full swing recently. Therefore, we employed Mendelian Randomization (MR) alongside bioinformatics to thoroughly investigate the possible relationship between MGAT2 and glioblastoma (GBM).
Methods: We utilized the summary statistics of genome-wide association studies (GWAS) for MGAT2 (N = 35,559 from deCODE) and glioblastoma (N = 379,155 from FinnGen).
Biomarkers.
Alzheimers Dement
December 2024
Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy, University of Gothenburg, Mölndal, Gothenburg, Sweden.
Background: Emerging evidence underscores the importance of neuroinflammation in the progression of Alzheimer's disease (AD) pathophysiology. Recent studies indicate the involvement of the inflammatory mechanisms both in amyloid- β (Aβ) and tau deposition in the brain. Nevertheless, due to the complexity of the immune responses and the intricate interplay between the peripheral and the central nervous systems, identifying biomarkers that reflect the brain´s inflammatory state in AD has been a challenge.
View Article and Find Full Text PDFBiomarkers.
Alzheimers Dement
December 2024
National Center for Geriatrics and Gerontology, Obu, Aichi, Japan.
Background: The detailed mechanism of Alzheimer's disease (AD) has not been fully elucidated, and a comprehensive gene expression analysis of the entire process leading up to the onset of AD has never been conducted on a large scale.
Method: We performed RNA-seq analysis of 1227 blood samples consisting of 424 AD, 543 mild cognitive impairment (MCI), and 260 cognitive normal (CN) subjects, and examined differentially expressed genes (DEGs) between CN and MCI (CN-MCI), and between MCI and AD (MCI-AD). Pathway analysis of DEGs were performed and subsequently retrospective prediction models were constructed using the enriched genes within these pathways.
Background: Nearly all people with Down Syndrome (DS) develop Alzheimer's dementia (AD) by the 7 decade of life. However, whether the alterations in fluid biomarker levels associated with DS follow the same pattern to those observed in other forms of AD is not well understood.
Method: We used mass spectrometry-based proteomics to measure 1116 proteins in cerebrospinal fluid (CSF) across euploid controls (n=130), sporadic late-onset AD (LOAD, n=89), asymptomatic DS (n=117), prodromal DS (n=57), and dementia DS (n=80) cases, and compared the protein changes observed in DS to those in LOAD and to those recently described in autosomal dominant AD (ADAD).
Biomarkers.
Alzheimers Dement
December 2024
Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, Netherlands.
Background: Individuals with mild cognitive impairment (MCI) due to Alzheimer's disease (AD) show variability in cognitive decline. Little is known about the underlying mechanisms, but these are likely to depend on tau levels. Using untargeted proteomics in cerebrospinal fluid (CSF), we studied which processes were associated with cognitive decline in A+T- and A+T+ MCI individuals.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!