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Background <p>Lung cancer is the predominant contributor to cancer-induced mortality, with non-small cell lung cancer (NSCLC) constituting the majority. However, the intricacies of tumorigenesis and progression in this context remain incompletely understood.</p> Methods <p>Utilizing single-cell RNA sequencing data retrieved from the GEO database, we performed high-dimensional weighted gene co-expression network analysis to identify pivotal gene modules exhibiting the highest correlation with the clinical stages of NSCLC patients. Subsequently, we formulated a novel prognostic three-gene signature, subjecting it to thorough analysis using bulk RNA sequencing data obtained from the TCGA-LUAD dataset.</p> Results <p>A tumor-intrinsic prognostic signature, comprising SEC61G, NPTN, and ALDOA, emerged from our investigation. The risk score derived from this gene signature revealed significantly poorer overall survival among patients in the high-risk group. Patients in different risk groups exhibited distinct immune statuses, with those in the low-risk group likely to benefit more from immunotherapy. Furthermore, in vitro experiments demonstrated that SEC61G facilitates tumor proliferation and migration with the activation of the WNT/β-Catenin signaling pathway.</p> Conclusion <p>Our study unveiled a novel tumor-intrinsic gene signature, shedding light on improved prognostication for NSCLC and facilitating risk-stratified management.</p>

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Identification of a novel signature in progression of non-small cell lung cancer based on HdWGCNA and in vitro validation

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月26日

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  • Kunpeng Zhang,
  • Wenqiang Xia,
  • Yi Li,
  • Bowen Shi,
  • Weiwei Yang

摘要

Background

Lung cancer is the predominant contributor to cancer-induced mortality, with non-small cell lung cancer (NSCLC) constituting the majority. However, the intricacies of tumorigenesis and progression in this context remain incompletely understood.

Methods

Utilizing single-cell RNA sequencing data retrieved from the GEO database, we performed high-dimensional weighted gene co-expression network analysis to identify pivotal gene modules exhibiting the highest correlation with the clinical stages of NSCLC patients. Subsequently, we formulated a novel prognostic three-gene signature, subjecting it to thorough analysis using bulk RNA sequencing data obtained from the TCGA-LUAD dataset.

Results

A tumor-intrinsic prognostic signature, comprising SEC61G, NPTN, and ALDOA, emerged from our investigation. The risk score derived from this gene signature revealed significantly poorer overall survival among patients in the high-risk group. Patients in different risk groups exhibited distinct immune statuses, with those in the low-risk group likely to benefit more from immunotherapy. Furthermore, in vitro experiments demonstrated that SEC61G facilitates tumor proliferation and migration with the activation of the WNT/β-Catenin signaling pathway.

Conclusion

Our study unveiled a novel tumor-intrinsic gene signature, shedding light on improved prognostication for NSCLC and facilitating risk-stratified management.