Identification of Crucial Genes and Signaling Pathways in Alectinib-Resistant Lung Adenocarcinoma Using Bioinformatic
Zhilong Li1, Yafeng Fan2, Yong Ma3
1Department of Thoracic Surgery, Shanxi Province Cancer Hospital/Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences/Cancer Hospital Affiliated to Shanxi Medical University, Taiyuan, 030013, China.
Abstract:
Alectinib, a second-generation anaplastic lymphoma kinase (ALK) inhibitor, has been shown to be effective for patients with ALK-positive non-small cell lung cancer (NSCLC). However, alectinib resistance is a serious problem worldwide. To the best of our knowledge, little information is available on its molecular mechanisms using the Gene Expression Omnibus (GEO) database. In this study, the differentially expressed genes (DEGs) were selected from the gene expression profile GSE73167 between parental and alectinib-resistant human lung adenocarcinoma (LUAD) cell samples. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene Ontology (GO) annotation enrichment analyses were conducted using Database for Annotation, Visualization and Integrated Discovery (DAVID). The construction of protein-protein interaction (PPI) network was performed to visualize DEGs. The hub genes were extracted based on the analysis of the PPI network using plug-in cytoHubba of Cytoscape software. The functional roles of the key genes were investigated using Gene Expression Profiling Interactive Analysis (GEPIA), University of Alabama at Birmingham Cancer (UALCAN), Gene Set Enrichment Analysis (GSEA), and Tumor Immune Estimation Resource (TIMER) analysis. The networks of kinase, miRNA, and transcription-factor targets of SFTPD were explored using LinkedOmics. The drug sensitivity analysis of SFTPD was analyzed using the RNAactDrug database. Results showed a total of 144 DEGs were identified. Five hub genes were extracted, including mucin 5B (MUC5B), surfactant protein D (SFTPD), deleted in malignant brain tumors 1 (DMBT1), surfactant protein A2 (SFTPA2), and trefoil factor 3 (TFF3). The survival analysis using GEPIA displayed that low expression of SFTPD had a significantly negative effect on the prognosis of patients with LUAD. GSEA revealed that low expression of SFTPD was positively correlated with the pathways associated with drug resistance, such as DNA replication, cell cycle, drug metabolism, and DNA damage repair, including mismatch repair (MMR), base excision repair (BER), homologous recombination (HR), and nucleotide excision repair (NER). The SFTPD expression was negatively correlated with the drug sensitivity of alectinib according to RNAactDrug database. The expression of SFTPD was further validated in parental H3122 cells and alectinib-resistant H3122 cells by quantitative reverse transcription PCR (RT-qPCR). In conclusion, our study found that the five hub genes, especially low expression of SFTPD, are closely related to alectinib resistance in patients with LUAD.
Insights
This study identifies five key genes, particularly surfactant protein D (SFTPD), linked to alectinib resistance in lung cancer. Low SFTPD expression correlates with poorer prognosis and increased resistance to alectinib treatment.
Area of Science:
- Oncology
- Molecular Biology
- Bioinformatics
Background:
- Alectinib is a crucial treatment for anaplastic lymphoma kinase-positive non-small cell lung cancer (NSCLC).
- Alectinib resistance poses a significant global challenge, necessitating research into its underlying molecular mechanisms.
- Limited information exists on the molecular basis of alectinib resistance, particularly utilizing public gene expression data.
Purpose of the Study:
- To identify differentially expressed genes (DEGs) associated with alectinib resistance in human lung adenocarcinoma (LUAD) cells.
- To investigate the functional roles of key genes and their association with alectinib resistance and patient prognosis.
- To explore the potential of surfactant protein D (SFTPD) as a biomarker for alectinib resistance.
Main Methods:
- Analysis of gene expression profile GSE73167 from the Gene Expression Omnibus (GEO) database.
- Identification of differentially expressed genes (DEGs) and construction of a protein-protein interaction (PPI) network.
- Utilized bioinformatics tools (DAVID, Cytoscape, GEPIA, UALCAN, GSEA, TIMER, LinkedOmics, RNAactDrug) for gene analysis, pathway enrichment, and drug sensitivity assessment.
- Validated SFTPD expression using quantitative reverse transcription PCR (RT-qPCR).
Main Results:
- Identified 144 DEGs and five hub genes: MUC5B, SFTPD, DMBT1, SFTPA2, and TFF3.
- Low SFTPD expression was significantly associated with poor prognosis in LUAD patients and correlated with pathways promoting drug resistance (DNA replication, cell cycle, drug metabolism, DNA repair).
- SFTPD expression negatively correlated with alectinib sensitivity.
Conclusions:
- The identified five hub genes, especially SFTPD, are critically involved in alectinib resistance in LUAD.
- Low SFTPD expression is a potential indicator of alectinib resistance and poor patient outcomes.
- Further research into SFTPD's role could lead to novel therapeutic strategies for overcoming alectinib resistance.
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