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2000
Volume 27, Issue 8
  • ISSN: 1386-2073
  • E-ISSN: 1875-5402

Abstract

Background: The involvement of aberrantly expressed miR-301b-3p has been discovered in diverse human tumors. Our study was primarily centered around the role of miR-301b-3p in diagnosing lung adenocarcinoma (LUAD). Method: We used the TCGA database to download the TCGA-LUAD dataset and selected miR- 301b-3p as the object of our study by differential expression analysis of miRNAs combined with previous studies. The LUAD diagnostic model was constructed utilizing machine learning based on miR-301b-3p expression. The predictive performance of the diagnostic model was found to be excellent by ROC curves combined with the clinical information of the dataset samples. GSEA, GO, and KEGG enrichment analyses demonstrated that miR-301b-3p may mediate the cell cycle by regulating the expression of hormones. Subsequently, combined with tumor immunity and mutation analysis, it was found that patients in the low-expression group had better immune infiltration, indicating that their response rate to immunotherapy may be relatively high. Finally, a mouse xenograft model was constructed to verify how miR-301b-3p affected LUAD progression in mice. Result: The results illustrated that overexpressed miR-301b-3p could cause faster tumor growth in mice. On the contrary, the growth of LUAD could be impeded by the downregulated miR-301b-3p expression. It was suggested that miR-301b-3p had a crucial part in LUAD progression. Conclusion: Overall, the diagnostic performance of the LUAD diagnostic model constructed based on miR-301b-3p is great, and the model can be used as a potential diagnostic marker for LUAD to provide new ideas for clinical diagnosis.

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/content/journals/cchts/10.2174/1386207326666230821112230
2024-05-01
2025-04-05
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  • Article Type:
    Research Article
Keyword(s): biomarkers; IHC; lung adenocarcinoma; machine learning; miR-301b-3p; patients
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