Identification and preliminary examination of prognostic genes linked to calcium metabolism in colorectal cancer.

Authors

Chao Liang, Xiao-Jiang Yi, Jia-Li Liu, Hai-Peng Huang, Tian-Ming Jiang, Jing-Fang Diao

Published in

Journal of gastrointestinal oncology. Volume 17. Issue 3. Pages 160. Jun 30, 2026. Epub Apr 30, 2026.

Abstract

Colorectal cancer (CRC) is a substantial global health challenge due to high mortality rates. This study focused on constructing and validating a calcium metabolism-related genes (CAMRGs) signature for CRC outcome prediction.
Transcriptomic and clinical data for CRC were integrated from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. A prognostic risk model was constructed using least absolute shrinkage and selection operator (LASSO)-Cox regression analysis. The predictive performance of the model was validated using an external dataset (GSE17536). Furthermore, the expression levels of the identified prognostic genes were preliminarily assessed via reverse transcription-quantitative polymerase chain reaction (RT-qPCR) in clinical specimens.
PRKCB, ATP2A3, PLCB4, and SLC25A6 were detected as prognostic genes and used to develop risk models. The risk model differentiated samples into high- and low-risk groups with notable variations in overall survival (OS) (P<0.0001), demonstrating favorable predictive performance [area under the curve (AUC) >0.6]. Gene set enrichment analysis (GSEA) reflected marked enrichment of pathways related to metabolism and immune signaling (e.g., oxidative phosphorylation and chemokine signaling) between the two groups (P<0.05). Risk score, age, and pathological stage were defined as independent prognostic factors and the nomogram demonstrated strong predictive performance (AUC >0.8). Finally, RT-qPCR expression assessed the significantly lower expression of PRKCB and ATP2A3 and higher expression of PLCB4 in CRC patients compared to controls (P<0.05).
A CAMRG signature was developed and preliminary examination of CRC prognosis prediction. This model might assist in risk assessment and inform individualized treatment strategies.

PMID:
42434282
Bibliographic data and abstract were imported from PubMed on 11 Jul 2026.

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