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Background <p>Colorectal cancer (CRC) is a malignant tumor marked by high prevalence and a challenging early detection landscape. While tools such as colonoscopy and serum biomarkers enhance screening efficacy, their invasive nature and inadequate sensitivity and specificity hamper their broad adoption. There is a pressing need for non-invasive, precise biomarkers for early diagnosis. The B9D2 gene, which is essential for ciliary function, has been rarely explored in CRC. This study is the first to investigate the diagnostic potential of B9D2 in CRC, using bioinformatics and machine learning to uncover its novel role in early detection, with implications for clinical translation.</p> Methods <p>Gene expression data from whole blood samples obtained from the GEO database were analyzed to identify differentially expressed genes (DEGs) associated with CRC, using a adjusted p-value threshold of &lt; 0.05 and an absolute logFC &gt; 0.5. The biological functions of these genes were investigated through Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), the Human Protein Atlas (HPA), and Gene Set Enrichment Analysis (GSEA). Additionally, three machine learning methods—Random Forest (RF), LASSO regression, and Support Vector Machine Recursive Feature Elimination (SVM-RFE)—were employed for feature selection and to evaluate the robustness and predictive power of the selected features, with diagnostic efficacy evaluated through Receiver Operating Characteristic (ROC) curves.</p> Results <p>Through this analysis, we identified five key genes—B9D2, CR2, DNMT3B, FOS, and PTGS2—from the GSE203024 dataset. Four of these genes have been previously linked to CRC, typically in tissue samples. Our study strengthens their significance as biomarkers by showing their expression in peripheral blood, a non-invasive source, and using multiple analytical methods. Notably, no previous studies have connected B9D2 to CRC, making this discovery a novel contribution. B9D2 expression was significantly upregulated in CRC patients, with an AUC of 0.797 in ROC analysis. This finding was further validated in the GSE47756 dataset, with an AUC of 0.756, confirming its potential as a reliable diagnostic biomarker for CRC. Further IHC staining showed significant different expression of B9D2 between normal and CRC tissue.</p> Conclusion <p>This study highlights the diagnostic potential of the B9D2 gene in CRC, marking the first time it has been proposed as a biomarker for early detection in CRC. This provides a foundation for its potential application in non-invasive diagnostic methods, such as liquid biopsy. Further experimental and clinical studies are needed to validate B9D2 as a reliable biomarker for early CRC detection and screening.</p>

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Diagnostic potential of the B9D2 gene in colorectal cancer based on whole blood gene expression data and machine learning

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年8月14日

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  • Zhaorui Wang,
  • Yongcheng Fu,
  • Haozhe Zhang,
  • Na Liu,
  • Ningjing Lei

摘要

Background

Colorectal cancer (CRC) is a malignant tumor marked by high prevalence and a challenging early detection landscape. While tools such as colonoscopy and serum biomarkers enhance screening efficacy, their invasive nature and inadequate sensitivity and specificity hamper their broad adoption. There is a pressing need for non-invasive, precise biomarkers for early diagnosis. The B9D2 gene, which is essential for ciliary function, has been rarely explored in CRC. This study is the first to investigate the diagnostic potential of B9D2 in CRC, using bioinformatics and machine learning to uncover its novel role in early detection, with implications for clinical translation.

Methods

Gene expression data from whole blood samples obtained from the GEO database were analyzed to identify differentially expressed genes (DEGs) associated with CRC, using a adjusted p-value threshold of < 0.05 and an absolute logFC > 0.5. The biological functions of these genes were investigated through Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), the Human Protein Atlas (HPA), and Gene Set Enrichment Analysis (GSEA). Additionally, three machine learning methods—Random Forest (RF), LASSO regression, and Support Vector Machine Recursive Feature Elimination (SVM-RFE)—were employed for feature selection and to evaluate the robustness and predictive power of the selected features, with diagnostic efficacy evaluated through Receiver Operating Characteristic (ROC) curves.

Results

Through this analysis, we identified five key genes—B9D2, CR2, DNMT3B, FOS, and PTGS2—from the GSE203024 dataset. Four of these genes have been previously linked to CRC, typically in tissue samples. Our study strengthens their significance as biomarkers by showing their expression in peripheral blood, a non-invasive source, and using multiple analytical methods. Notably, no previous studies have connected B9D2 to CRC, making this discovery a novel contribution. B9D2 expression was significantly upregulated in CRC patients, with an AUC of 0.797 in ROC analysis. This finding was further validated in the GSE47756 dataset, with an AUC of 0.756, confirming its potential as a reliable diagnostic biomarker for CRC. Further IHC staining showed significant different expression of B9D2 between normal and CRC tissue.

Conclusion

This study highlights the diagnostic potential of the B9D2 gene in CRC, marking the first time it has been proposed as a biomarker for early detection in CRC. This provides a foundation for its potential application in non-invasive diagnostic methods, such as liquid biopsy. Further experimental and clinical studies are needed to validate B9D2 as a reliable biomarker for early CRC detection and screening.