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Background <p>Lung cancer, especially small-cell lung cancer (SCLC), is a widespread and deadly disease often detected at advanced stages, resulting in low five-year survival rates. This study aims to identify new genetic targets to enhance understanding of the genetic drivers of SCLC progression.</p> Methods <p>Data from 215 samples (82 normal, 133 tumor) across four datasets were retrieved from the GEO database. Using R software, we normalized and analyzed the data to assess correlations between differentially expressed genes (DEGs) and SCLC. Techniques included differential expression, expression quantitative trait loci (eQTL), and Mendelian randomization (MR) analyses. Functional and pathway analyses utilized Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Machine learning was applied to develop predictive models for disease diagnosis and progression.</p> Results <p>Analysis of 129 samples revealed 369 upregulated and 529 downregulated genes. Six genes with shared regions were significantly linked to SCLC. GO and KEGG analyses highlighted their roles in vital processes like organic hydroxy compound biosynthesis. CIBERSORT analysis emphasized immune cell variations in SCLC patients. Machine learning identified key genes, with survival analysis showing significant differences for COLEC12 and MUC1, validated by GSEA and qPCR.</p> Conclusion <p>COLEC12 and MUC1 are novel diagnostic markers and therapeutic targets for SCLC, offering potential for targeted treatments and future research.</p>

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Public Transcriptomic Data Mining for SCLC: From Candidate Ma rkers to Therapeutic Exploration

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月21日

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  • Hailin Liu,
  • Fangyuan Qu,
  • Guangyao Zhou,
  • Yuechen Cui,
  • Bo Yan,
  • Lianmin Zhang,
  • Chenguang Li,
  • Zhenfa Zhang,
  • Tingting Qin,
  • Qiangzhe Zhang

摘要

Background

Lung cancer, especially small-cell lung cancer (SCLC), is a widespread and deadly disease often detected at advanced stages, resulting in low five-year survival rates. This study aims to identify new genetic targets to enhance understanding of the genetic drivers of SCLC progression.

Methods

Data from 215 samples (82 normal, 133 tumor) across four datasets were retrieved from the GEO database. Using R software, we normalized and analyzed the data to assess correlations between differentially expressed genes (DEGs) and SCLC. Techniques included differential expression, expression quantitative trait loci (eQTL), and Mendelian randomization (MR) analyses. Functional and pathway analyses utilized Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Machine learning was applied to develop predictive models for disease diagnosis and progression.

Results

Analysis of 129 samples revealed 369 upregulated and 529 downregulated genes. Six genes with shared regions were significantly linked to SCLC. GO and KEGG analyses highlighted their roles in vital processes like organic hydroxy compound biosynthesis. CIBERSORT analysis emphasized immune cell variations in SCLC patients. Machine learning identified key genes, with survival analysis showing significant differences for COLEC12 and MUC1, validated by GSEA and qPCR.

Conclusion

COLEC12 and MUC1 are novel diagnostic markers and therapeutic targets for SCLC, offering potential for targeted treatments and future research.