Title:MiR-301b-3p can be used as a Potential Marker for the Diagnosis of Lung
Adenocarcinoma
Volume: 27
Issue: 8
Author(s): Weibo Qi*, Niu Niu, Junjie Zhao, Haitao Liu and Fan Yang
Affiliation:
- Department of Cardiothoracic Surgery, The First Hospital of Jiaxing, Affiliated Hospital of Jiaxing University, Jiaxing, Zhejiang, 31400, China.
Keywords:
miR-301b-3p, lung adenocarcinoma, biomarkers, machine learning, IHC, patients.
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.
