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Objective <p>This study aimed to identify novel circulating protein biomarkers for hepatocellular carcinoma (HCC) superior to alpha-fetoprotein (AFP) by integrating tumor stemness index and secreted protein screening, and to explore their roles in prognosis, immune microenvironment, and tumor mechanisms.</p> Methods <p>Stemness-associated differentially expressed genes were identified from TCGA (The Cancer Genome Atlas) and GEO (Gene Expression Omnibus) databases after applying ComBat batch effect correction and quantile normalization to harmonize the datasets. WGCNA (weighted gene co-expression network analysis), secreted protein databases, and plasma sequencing were used to select candidate genes. Functional and immune infiltration analyses were performed, alongside multivariable Cox regression modeling and inverse variance weighted (IVW) Mendelian Randomization (MR) for prognostic and causal validation.</p> Results <p>A total of 19 genes significantly associated with tumor stemness and detectable in circulation were identified. After filtering based on clinicopathological features and survival analysis, 10 key genes were selected. The receiver operating characteristic (ROC) curve analysis revealed that, aside from FCN3 (ficolin 3), the diagnostic sensitivity and specificity of CRHBP (corticotropin-releasing hormone binding protein), CLEC3B (C-type lectin domain family 3 member B), TEK (TEK receptor tyrosine kinase), SOGA1 (SOGA family member 1), IL33 (interleukin 33), CXCL12 (C-X-C motif chemokine ligand 12), NENF (neudesin neurotrophic factor), and ITM2B (integral membrane protein 2B) (AUCs [area under the curves] ranging from 0.67 to 0.99) surpassed those of AFP (AUC = 0.64). Cibersort analysis indicated significant associations of CRHBP, CLEC3B, TEK, IL33, NENF, CXCL12, and AFP with various immune cell infiltrates. The gene set enrichment analysis (GSEA) suggested that CRHBP, CLEC3B, TEK, NENF, and AFP were primarily enriched in cell cycle-related pathways, whereas IL33, CXCL12, SOGA1, and ITM2B were involved in MAPK, GNRH, and NOTCH signaling. A multivariable Cox regression model identified CLEC3B, TEK, and NENF for constructing a prognostic signature, which demonstrated strong predictive performance in the TCGA cohort, as evidenced by the concordance index (C-index), time-dependent ROC analysis (1-/3-/5-year AUCs: 0.74/0.72/0.70), and decision curve analysis. IVW MR analysis further confirmed a causal relationship between cis-expression quantitative trait loci (cis-eQTLs) of the NENF gene and HCC risk (<i>P</i> = 0.024), with no significant pleiotropy or heterogeneity detected. Elevated NENF protein expression, validated by proteomic and immunohistochemical analyses of public databases and a tissue microarray cohort, was significantly associated with HCC progression and reduced overall survival (hazard ratio [HR], 2.456; 95% CI, 1.271–4.960; <i>P</i> = 0.012).</p> Conclusion <p>This multi-omics study reveals a panel of eight circulating proteins with diagnostic performance superior to that of AFP and independent prognostic value in HCC, nominating NENF as a causal biomarker for disease progression and survival.</p>

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Identification of novel circulating protein biomarkers for hepatocellular carcinoma superior to alpha-fetoprotein through a stemness index and secretome analysis

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月9日

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  • Cai Gao,
  • Xuebing Li,
  • Xinwei Liu,
  • Sha Yan,
  • Xiaodan Ran,
  • Jingxian Han

摘要

Objective

This study aimed to identify novel circulating protein biomarkers for hepatocellular carcinoma (HCC) superior to alpha-fetoprotein (AFP) by integrating tumor stemness index and secreted protein screening, and to explore their roles in prognosis, immune microenvironment, and tumor mechanisms.

Methods

Stemness-associated differentially expressed genes were identified from TCGA (The Cancer Genome Atlas) and GEO (Gene Expression Omnibus) databases after applying ComBat batch effect correction and quantile normalization to harmonize the datasets. WGCNA (weighted gene co-expression network analysis), secreted protein databases, and plasma sequencing were used to select candidate genes. Functional and immune infiltration analyses were performed, alongside multivariable Cox regression modeling and inverse variance weighted (IVW) Mendelian Randomization (MR) for prognostic and causal validation.

Results

A total of 19 genes significantly associated with tumor stemness and detectable in circulation were identified. After filtering based on clinicopathological features and survival analysis, 10 key genes were selected. The receiver operating characteristic (ROC) curve analysis revealed that, aside from FCN3 (ficolin 3), the diagnostic sensitivity and specificity of CRHBP (corticotropin-releasing hormone binding protein), CLEC3B (C-type lectin domain family 3 member B), TEK (TEK receptor tyrosine kinase), SOGA1 (SOGA family member 1), IL33 (interleukin 33), CXCL12 (C-X-C motif chemokine ligand 12), NENF (neudesin neurotrophic factor), and ITM2B (integral membrane protein 2B) (AUCs [area under the curves] ranging from 0.67 to 0.99) surpassed those of AFP (AUC = 0.64). Cibersort analysis indicated significant associations of CRHBP, CLEC3B, TEK, IL33, NENF, CXCL12, and AFP with various immune cell infiltrates. The gene set enrichment analysis (GSEA) suggested that CRHBP, CLEC3B, TEK, NENF, and AFP were primarily enriched in cell cycle-related pathways, whereas IL33, CXCL12, SOGA1, and ITM2B were involved in MAPK, GNRH, and NOTCH signaling. A multivariable Cox regression model identified CLEC3B, TEK, and NENF for constructing a prognostic signature, which demonstrated strong predictive performance in the TCGA cohort, as evidenced by the concordance index (C-index), time-dependent ROC analysis (1-/3-/5-year AUCs: 0.74/0.72/0.70), and decision curve analysis. IVW MR analysis further confirmed a causal relationship between cis-expression quantitative trait loci (cis-eQTLs) of the NENF gene and HCC risk (P = 0.024), with no significant pleiotropy or heterogeneity detected. Elevated NENF protein expression, validated by proteomic and immunohistochemical analyses of public databases and a tissue microarray cohort, was significantly associated with HCC progression and reduced overall survival (hazard ratio [HR], 2.456; 95% CI, 1.271–4.960; P = 0.012).

Conclusion

This multi-omics study reveals a panel of eight circulating proteins with diagnostic performance superior to that of AFP and independent prognostic value in HCC, nominating NENF as a causal biomarker for disease progression and survival.