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Backgrounds <p>Non-small cell lung cancer (NSCLC) is an aggressive malignant tumor characterized by early recurrence and poor prognosis. Homotypic cell-in-cell (HoCIC) are significantly associated with adverse outcomes in multiple tumors, serving as valuable indicators for patient outcome assessment. However, current HoCIC diagnosis methods rely primarily on manual microscopic observation and lack standardized detection biomarkers and methodologies, which may introduce bias into research findings. Therefore, this study aims to identify diagnostic markers for HoCIC in NSCLC, laying the foundation for further research into the biological roles and mechanisms of HoCIC.</p> Methods <p>Kaplan‒Meier curves and log-rank tests were used to investigate the relationship between HoCIC and prognosis. Bioinformatics analysis of NSCLC gene expression data related to HoCIC from the Gene Expression Omnibus (GEO) dataset revealed differentially HoCIC expressed genes ( HoCICDEGs) between tumor tissues and normal tissues. We identified an overlapping HoCIC hub gene, BECN1, among the HoCICDEGs and autophagy-related genes (ARGs). The expression and biological functions of BECN1 were analysed via The Cancer Genome Atlas (TCGA) database. The Kaplan‒Meier, TIMER2.0, cBioPortal, and GSCA public databases were subsequently used to investigate the prognosis, immune infiltration, genetic alterations, and drug sensitivity associated with BECN1. Finally, clinical NSCLC samples were collected for immunohistochemical experiments to validate BECN1 expression and its diagnostic value for HoCIC.</p> Results <p>HoCIC was significantly correlated with poor overall survival (OS) and disease-free survival (DFS). We identified BECN1 as a core gene associated with HoCIC in NSCLC, which is highly expressed in tumor tissues and is correlated with unfavourable prognosis. BECN1 is correlated with the mitotic spindle, G2M checkpoint, and MYC pathways, suppresses immune cell infiltration, and is sensitive to most anticancer drugs. In our validated NSCLC cohort, BECN1 protein was highly expressed in tumor tissues and demonstrated a significant association with HoCIC, serving as an independent risk factor for HoCIC. The HoCIC prediction model constructed on the basis of BECN1 demonstrated favourable diagnostic capability, discriminatory power, and clinical benefit.</p> Conclusions <p>In summary, this study identified BECN1 as a diagnostic biomarker associated with HoCIC in NSCLC, providing a strong foundation for improving diagnostic and research strategies related to this phenomenon.</p>

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A comprehensive analysis identified BECN1 as potential diagnostic biomarker for homotypic cell-in-cell in non-small cell lung cancer through integrated bioinformatics and clinical validation approaches

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月31日

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  • Xiaona Liu,
  • Rui Guo,
  • Lei Qiang,
  • Xiaozhong Huang,
  • Ya’nan Wang,
  • Dongxuan Li,
  • Jun Yang

摘要

Backgrounds

Non-small cell lung cancer (NSCLC) is an aggressive malignant tumor characterized by early recurrence and poor prognosis. Homotypic cell-in-cell (HoCIC) are significantly associated with adverse outcomes in multiple tumors, serving as valuable indicators for patient outcome assessment. However, current HoCIC diagnosis methods rely primarily on manual microscopic observation and lack standardized detection biomarkers and methodologies, which may introduce bias into research findings. Therefore, this study aims to identify diagnostic markers for HoCIC in NSCLC, laying the foundation for further research into the biological roles and mechanisms of HoCIC.

Methods

Kaplan‒Meier curves and log-rank tests were used to investigate the relationship between HoCIC and prognosis. Bioinformatics analysis of NSCLC gene expression data related to HoCIC from the Gene Expression Omnibus (GEO) dataset revealed differentially HoCIC expressed genes ( HoCICDEGs) between tumor tissues and normal tissues. We identified an overlapping HoCIC hub gene, BECN1, among the HoCICDEGs and autophagy-related genes (ARGs). The expression and biological functions of BECN1 were analysed via The Cancer Genome Atlas (TCGA) database. The Kaplan‒Meier, TIMER2.0, cBioPortal, and GSCA public databases were subsequently used to investigate the prognosis, immune infiltration, genetic alterations, and drug sensitivity associated with BECN1. Finally, clinical NSCLC samples were collected for immunohistochemical experiments to validate BECN1 expression and its diagnostic value for HoCIC.

Results

HoCIC was significantly correlated with poor overall survival (OS) and disease-free survival (DFS). We identified BECN1 as a core gene associated with HoCIC in NSCLC, which is highly expressed in tumor tissues and is correlated with unfavourable prognosis. BECN1 is correlated with the mitotic spindle, G2M checkpoint, and MYC pathways, suppresses immune cell infiltration, and is sensitive to most anticancer drugs. In our validated NSCLC cohort, BECN1 protein was highly expressed in tumor tissues and demonstrated a significant association with HoCIC, serving as an independent risk factor for HoCIC. The HoCIC prediction model constructed on the basis of BECN1 demonstrated favourable diagnostic capability, discriminatory power, and clinical benefit.

Conclusions

In summary, this study identified BECN1 as a diagnostic biomarker associated with HoCIC in NSCLC, providing a strong foundation for improving diagnostic and research strategies related to this phenomenon.