The synchronous upregulation of a specific protein cluster in the blood predicts both colorectal cancer risk and patient immune status

Abstract

Background: Early detection and treatment of colorectal cancer (CRC) is crucial for improving patient survival rates. This study aims to identify signature molecules associated with CRC, which can serve as valuable indicators for clinical hematological screening.

Method: We have systematically searched the Human Protein Atlas database and the relevant literature for blood protein-coding genes. The CRC dataset from TCGA was used to compare the acquired genes and identify differentially expressed molecules (DEMs). Weighted Gene Co-expression Network Analysis (WGCNA) was employed to identify modules of co-expressed molecules and key molecules within the DEMs. Signature molecules of CRC were then identified from the key molecules using machine learning. These findings were further validated in clinical samples. Finally, Logistic regression was used to create a predictive model that calculated the likelihood of CRC in both healthy individuals and CRC patients. We evaluated the model's sensitivity and specificity using the ROC curve.

Result: By utilizing the CRC dataset, WGCNA analysis, and machine learning, we successfully identified seven signature molecules associated with CRC from 1478 blood protein-coding genes. These markers include S100A11, INHBA, QSOX2, MET, TGFBI, VEGFA and CD44. Analyzing the CRC dataset showed its potential to effectively discriminate between CRC and normal individuals. The up-regulated expression of these markers suggests the existence of an immune evasion mechanism in CRC patients and is strongly correlated with poor prognosis.

Conclusion: The combined detection of the seven signature molecules in CRC can significantly enhance diagnostic efficiency and serve as a novel index for hematological screening of CRC.

Keywords: Colorectal cancer screening; Diagnostic model; Immune status; Machine learning; Serum protein biomarkers.