<i>SERPINH1</i> as a Novel Biomarker for Colon Cancer Bone Metastasis with Machine Learning and Immunohistochemistry Validation

<b><i>Background:</i></b> Bone metastasis (BM) is a serious clinical symptom of advanced colorectal cancer. However, there is a lack of effective biomarkers for early diagnosis and treatment. <b><i>Method:</i></b> RNA-seq data from public databases (GSE49355, GSE101607) were collected and normalized and batch effects were removed using the combat package. Differential expression analysis was performed to identify significant genes. Robust Rank Aggregation and machine learning algorithms were used to pinpoint candidate biomarkers. These biomarkers were validated using immunohistochemistry and further analyzed for survival rates. Enrichment analysis was conducted to explore biological mechanisms. Additionally, drug sensitivity and immune infiltration analyses were performed to provide insights into potential therapeutic targets. <b><i>Results:</i></b> Analysis results revealed 386 genes elevated in primary versus normal tissues and 26 genes varying between primary and BM. Serpin Protease Inhibitor Clade H1 (<i>SERPINH1)</i> as a novel biomarker for colon cancer metastasis. High <i>SERPINH1</i> expression correlates with poor survival outcomes and is linked to high lymphatic invasion and advanced cancer stages. Additionally, <i>SERPINH1</i> expression influences immune infiltration and is not predictive of chemotherapy response, but potential new drugs are suggested for high-expression cases. The gene also enriches classical cancer pathways such as Hedgehog and transforming growth factor-β. <b><i>Conclusions:</i></b> We identified novel colon cancer BM markers, including <i>SERPINH1</i>, using machine learning algorithms combined with traditional transcriptomic data and validated their expression through immunohistochemistry. This biomarker could significantly assist clinicians in making more precise treatment decisions.