Screening and validation of chemoresistance marker in lung adenocarcinoma based on gene expression profile
10.19405/j.cnki.issn1000-1492.2025.10.006
- VernacularTitle:基于基因表达数据库筛选肺腺癌耐药相关 分子标志物及其临床意义
- Author:
Handong Wei
1
;
Shuxing Chen
2
;
Linting Liu
2
;
Zihan Jing
3
;
Yiting Yang
1
;
Qiong Song
1
;
Wenchu Wang
1
;
Chunlin Zou
1
;
Lihui Wang
1
Author Information
1. Center for Translational Medicine , Key Laboratory of Longevity and Aging⁃Related Diseases , Ministry of Education , Institute of Neuroscience and Guangxi Key Laboratory of Brain Science , School of Basic Medical Sciences , Guangxi Medical University, Nanning 530021
2. The First Clinical Medical College , Guangxi Medical University, Nanning 530021
3. Life Science Institute , Guangxi Medical University, Nanning 530021
- Publication Type:Journal Article
- Keywords:
non small cell lung cancer;
lung adenocarcinoma;
bioinformatics analysis;
differentially expressed genes;
molecular markers;
chemoresistance
- From:
Acta Universitatis Medicinalis Anhui
2025;60(10):1818-1827
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To discover molecular markers associated with lung adenocarcinoma diagnosis/prognosis and drug resistance through screening of differentially expressed genes based on published chip data in gene expression databases using bioinformatics methods.
Methods:Comprehensive analysis was performed in available mRNA microarray datasets including lung adenocarcinoma tissues dataset GSE32863 and lung adenocarcinoma taxane-platin resistance dataset GSE77209 from the gene expression omnibus(GEO) database. Gene ontology enrichment analysis, gene pathway enrichment analysis and protein interaction network analysis were performed based on significantly correlated genes. The expression level of genes was validated in the cancer genome atlas(TCGA) dataset. Survival differences were assessed by the log-rank test in TCGA lung adenocarcinoma dataset. Based on the publications genomics of drug sensitivity in cancer(GDSC) database in CellMiner cross database(CellMiner CDB), Pearson correlation analysis was used to analyze the correlation between differentially expressed genes and the half-maximal inhibitory concentration(IC50) of anticancer drugs.
Results :There were a total of 77 genes which had a different expression in resistance lung adenocarcinoma cells and lung adenocarcinoma cancer tissues. The functional enrichment analysis showed that these co-different expression genes were mainly enriched in microtubule, extracellular exosome, cell cycle and signaling by nuclear receptors. Protein-protein interactions(PPI) network screened 6 most connected genes as molecular complex(MCODE). Among the MCODE, overexpressed ubiquitin conjugating enzyme E2 T(UBE2T), kinesin family member 20A(KIF20A), PCNA clamp associated factor(KIAA0101), pituitary tumor-transforming gene 1(PTTG1) and NIMA related kinase 2(NEK2) were associated with poor outcomes. Survival analysis results showed that these five genes were upregulated in lung adenocarcinoma tissues and drug-resistant cells and were significantly associated with poor prognosis in lung adenocarcinoma patients. Drug sensitivity analysis results suggested that high expression of PTTG1 and UBE2T was significantly associated with sensitivity to multiple anticancer drugs, including paclitaxel and docetaxel. RT-PCR validation showed that PTTG1 andUBE2T were highly expressed in docetaxel-resistant cells A549-TXR and H358-TXR.
Conclusion:PTTG1 andUBE2T holds the potential to be chemoresistance markers in lung adenocarcinoma.
- Full text:202603072116127126基于基因表达数据库筛选肺腺癌耐药相关分子标志物及其临床意义_韦韩东.pdf
