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Background <p>Immunotherapy resistance represents a current research hotspot. Previous studies found that neuroendocrine differentiation (NED) might significantly contribute to the initiation and development of non-small cell lung cancer (NSCLC). However, the interplay between immunotherapy resistance and NED in NSCLC remains unclear. This study explored the relationships between NED-related genes (NEDRGs) and immunotherapy resistance-related genes in NSCLC.</p> Methods <p>Control and NSCLC samples, including NSCLC samples exposed to PD-1 blockade, were selected from public databases to identify differentially expressed genes (DEGs). Then, univariate Cox regression analysis and the proportional hazards assumption test were performed to identify prognostic genes based on the DEGs and NEDRGs, and a risk model was developed and validated. Thereafter, a nomogram was established. Subsequently, gene set enrichment analysis (GSEA), immune analysis, and drug sensitivity analysis were conducted. The key cells were ascertained in a single-cell RNA sequencing dataset. We collected specimens from six patients at Tianjin Medical University General Hospital and validated them using quantitative real-time polymerase chain reaction (RT-qPCR) and immunohistochemistry (IHC).</p> Results <p>Three prognostic genes (<i>RRM2</i>,<i> WDR76</i>, and <i>PLEKHH2</i>) were identified, and the developed risk model could predict the survival of patients with NSCLC. The nomogram had good predictive ability concerning survival rates in NSCLC. Based on the GSEA results, a notable pathway in the two risk groups included cell cycle pathways, and prognostic genes were all enriched in these pathways. Furthermore, activated memory CD4<sup>+</sup> T cells might contribute significantly to survival, and 109 drugs, including AZD6738, could be candidate treatments for NSCLC. Epithelial cells were identified as the key population in which the expression of prognostic genes was altered significantly across different developmental stages. RT-qPCR and IHC confirmed that <i>RRM2 and WDR76</i> expression was higher in cancer tissues than in normal tissues, increasing with disease progression, whereas PLEKHH2 exhibited the opposite trend.</p> Conclusion <p>The developed risk model based on three prognostic genes (<i>RRM2</i>,<i> WDR76</i>, and <i>PLEKHH2</i>) exhibited superior predictive accuracy in NSCLC. These results may provide new insights for investigating novel therapeutic targets in NSCLC.</p>

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Prognostic Significance and Immune Landscape of Neuroendocrine Differentiation-Related Genes in Non-Small Cell Lung Cancer

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月13日

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  • Tong Li,
  • Meidan Luo,
  • Sibo Peng,
  • Jianguo Zhong,
  • Fan Ren,
  • Jingliang Gan,
  • Huangsheng Xie,
  • Jiayue Qiao,
  • Chicheng Liu,
  • Jia Li,
  • Song Xu

摘要

Background

Immunotherapy resistance represents a current research hotspot. Previous studies found that neuroendocrine differentiation (NED) might significantly contribute to the initiation and development of non-small cell lung cancer (NSCLC). However, the interplay between immunotherapy resistance and NED in NSCLC remains unclear. This study explored the relationships between NED-related genes (NEDRGs) and immunotherapy resistance-related genes in NSCLC.

Methods

Control and NSCLC samples, including NSCLC samples exposed to PD-1 blockade, were selected from public databases to identify differentially expressed genes (DEGs). Then, univariate Cox regression analysis and the proportional hazards assumption test were performed to identify prognostic genes based on the DEGs and NEDRGs, and a risk model was developed and validated. Thereafter, a nomogram was established. Subsequently, gene set enrichment analysis (GSEA), immune analysis, and drug sensitivity analysis were conducted. The key cells were ascertained in a single-cell RNA sequencing dataset. We collected specimens from six patients at Tianjin Medical University General Hospital and validated them using quantitative real-time polymerase chain reaction (RT-qPCR) and immunohistochemistry (IHC).

Results

Three prognostic genes (RRM2, WDR76, and PLEKHH2) were identified, and the developed risk model could predict the survival of patients with NSCLC. The nomogram had good predictive ability concerning survival rates in NSCLC. Based on the GSEA results, a notable pathway in the two risk groups included cell cycle pathways, and prognostic genes were all enriched in these pathways. Furthermore, activated memory CD4+ T cells might contribute significantly to survival, and 109 drugs, including AZD6738, could be candidate treatments for NSCLC. Epithelial cells were identified as the key population in which the expression of prognostic genes was altered significantly across different developmental stages. RT-qPCR and IHC confirmed that RRM2 and WDR76 expression was higher in cancer tissues than in normal tissues, increasing with disease progression, whereas PLEKHH2 exhibited the opposite trend.

Conclusion

The developed risk model based on three prognostic genes (RRM2, WDR76, and PLEKHH2) exhibited superior predictive accuracy in NSCLC. These results may provide new insights for investigating novel therapeutic targets in NSCLC.