UCN-Centric Prognostic Model for Predicting Overall Survival and Immune Response in Colorectal Cancer

Colorectal cancer (CRC), a prevalent malignancy, ranks third in global incidence and second in mortality rates. Despite advances in screening methods such as colonoscopy, the accurate diagnosis of CRC remains challenging due to the absence of reliable biomarkers. This study aimed to develop a robust prognostic model for precise CRC outcome prediction. Employing weighted co-expression network analysis (WGCNA) and Cox regression analysis on data from The Cancer Genome Atlas (TCGA), we identified a panel of 12 genes strongly associated with patient survival. This gene panel facilitated accurate CRC outcome predictions, which is also validated via the external validation cohort GSE17536. We conducted further investigations into the key gene, <i>urocortin</i> (<i>UCN</i>), using single-cell transcriptomic data and immune infiltration analysis in CRC patients. Our results revealed a significant correlation between high <i>UCN</i> expression and the reduced prevalence of key immune cells, including B cells, CD4+ cytotoxic T cells, CD8+ T cells, and NKT cells. Functional experiments showed that <i>UCN</i> gene interference in the CRC cell lines significantly decreased cancer cell proliferation, underscoring <i>UCN</i>'s role in intestinal immunity modulation. The <i>UCN</i>-centric prognostic model developed enhances prognosis prediction accuracy and offers critical insights for CRC diagnosis and therapeutic interventions.

Authors

Institutions

Publication Details

Journal
Genes
Published
2024-08-29
DOI
https://doi.org/10.3390/genes15091139
Citations
2
Primary Topic
Cancer Immunotherapy and Biomarkers
Type
article
Field-Weighted Citation Impact
0.57
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

UCN-Centric Prognostic Model for Predicting Overall Survival and Immune Response in Colorectal Cancer

Feiliang Zhong, Jia Liu, Yue Chen
2 citations
Genes
Cancer Immunotherapy and Biomarkers
0.57
article

UCN-Centric Prognostic Model for Predicting Overall Survival and Immune Response in Colorectal Cancer

Feiliang Zhong, Jia Liu, Yue Chen
article en
2 citations

Abstract

Colorectal cancer (CRC), a prevalent malignancy, ranks third in global incidence and second in mortality rates. Despite advances in screening methods such as colonoscopy, the accurate diagnosis of CRC remains challenging due to the absence of reliable biomarkers. This study aimed to develop a robust prognostic model for precise CRC outcome prediction. Employing weighted co-expression network analysis (WGCNA) and Cox regression analysis on data from The Cancer Genome Atlas (TCGA), we identified a panel of 12 genes strongly associated with patient survival. This gene panel facilitated accurate CRC outcome predictions, which is also validated via the external validation cohort GSE17536. We conducted further investigations into the key gene, <i>urocortin</i> (<i>UCN</i>), using single-cell transcriptomic data and immune infiltration analysis in CRC patients. Our results revealed a significant correlation between high <i>UCN</i> expression and the reduced prevalence of key immune cells, including B cells, CD4+ cytotoxic T cells, CD8+ T cells, and NKT cells. Functional experiments showed that <i>UCN</i> gene interference in the CRC cell lines significantly decreased cancer cell proliferation, underscoring <i>UCN</i>'s role in intestinal immunity modulation. The <i>UCN</i>-centric prognostic model developed enhances prognosis prediction accuracy and offers critical insights for CRC diagnosis and therapeutic interventions.

GenesVol. 15(9)
Tianjin University of Science and Technology (CN), Nankai University (CN)
Good health and well-being
Openalex Percentile: Top 28%
Cancer Immunotherapy and Biomarkers
0.57
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.