AURKA, TOP2A and MELK are the key genes identified by WGCNA for the pathogenesis of lung adenocarcinoma

Exploration of telomere-related biomarkers for lung adenocarcinoma and targeted drug prediction

AIM

Bioinformatics analyses were performed to identify telomere biomarkers to develop a diagnostic model for lung adenocarcinoma (LUAD) and to predict potential target drugs for patients with LUAD.

BACKGROUND

Telomeres function crucially in maintaining genome stability and chromosome integrity, and telomere-related genes (TRGs) serve as potential prognostic markers in a variety of cancers. However, studies focusing on TRGs in LUAD are limited.

OBJECTIVE

To screen key telomere-related markers for LUAD and to evaluate their potential impact on the occurrence and development of LUAD.

METHODS

LUAD samples were collected from University of California Santa Cruz (UCSC) Xena and 2093 telomere-related genes (TRGs) were obtained from TelNet database. Hub genes were screened using "WGCNA" package. Differentially expressed genes (DEGs) between tumor and control samples were filtered using "DESeq" package. Protein-protein interaction (PPI) network analysis was performed to select candidate genes, from which telomere-related biomarkers were identified by machine learning and used to develop a nomogram. Functional enrichment pathways of the biomarkers were analyzed using "clusterProfiler" package. Correlation between immune cell infiltration and the biomarkers was examined by Spearman method. Targeted drugs were predicted and molecular docking models were developed using AutoDockTools. Finally, the screened biomarkers were validated by performing in vitro cellular assays.

RESULTS

A total of 259 hub genes, 2848 DEGs, and 48 differentially expressed TRGs in LUAD were screened. Subsequently, 13 candidate genes were obtained by PPI network analysis. LASSO and support vector machine-recursive feature elimination (SVM-RFE) algorithms further reduced the number of telomere-related biomarkers to four (CCNB1, CDC20, PLK1, and TOP2A). A nomogram with a strong predictive performance was created. These four biomarkers were mainly enriched in the mitogenic pathways and exhibited a strong correlation with immune cell infiltration. Three drugs (Lucanthone, Fulvestrant, and Myricetin) targeting the four biomarkers were predicted to be able to treat LUAD. Finally, in vitro cellular experiments demonstrated that CCNB1 and PLK1 have potential effects on proliferation, migration, invasion and AKT/mTOR signaling pathway in LUAD cells.

CONCLUSION

This study provided novel diagnostic biomarkers, therapeutic targets, and potential drugs for LUAD.

Knockdown of KIF23 alleviates the progression of asthma by inhibiting pyroptosis

BACKGROUND

Asthma is a chronic disease affecting the lower respiratory tract, which can lead to death in severe cases. The cause of asthma is not fully known, so exploring its potential mechanism is necessary for the targeted therapy of asthma.

METHOD

Asthma mouse model was established with ovalbumin (OVA). H&E staining, immunohistochemistry and ELISA were used to detect the inflammatory response in asthma. Transcriptome sequencing was performed to screen differentially expressed genes (DEGs). The role of KIF23 silencing in cell viability, proliferation and apoptosis was explored by cell counting kit-8, EdU assay and flow cytometry. Effects of KIF23 knockdown on inflammation, oxidative stress and pyroptosis were detected by ELISA and western blot. After screening KIF23-related signalling pathways, the effect of KIF23 on p53 signalling pathway was explored by western blot.

RESULTS

In the asthma model, the levels of caspase-3, IgG in serum and inflammatory factors (interleukin (IL)-1β, KC and tumour necrosis factor (TNF)-α) in serum and bronchoalveolar lavage fluid were increased. Transcriptome sequencing showed that there were 352 DEGs in the asthma model, and 7 hub genes including KIF23 were identified. Knockdown of KIF23 increased cell proliferation and inhibited apoptosis, inflammation and pyroptosis of BEAS-2B cells induced by IL-13 in vitro. In vivo experiments verified that knockdown of KIF23 inhibited oxidative stress, inflammation and pyroptosis to alleviate OVA-induced asthma mice. In addition, p53 signalling pathway was suppressed by KIF23 knockdown.

CONCLUSION

Knockdown of KIF23 alleviated the progression of asthma by suppressing pyroptosis and inhibited p53 signalling pathway.

Identification of LGR4 as a prognostic biomarker in KRAS-mutant lung adenocarcinoma: Evidence from integrated bioinformatics analysis

Globally, lung cancer is the leading cause of cancer-related deaths, primarily non-small cell lung cancer. Kirsten Rat Sarcoma Oncogene Homolog (KRAS) mutations are common in non-small cell lung cancer and linked to a poor prognosis. Covalent inhibitors targeting KRAS-G12C mutation have improved treatment for some patients, but most KRAS-mutant lung adenocarcinoma (KRAS-MT LUAD) cases lack targeted therapies. This gap in treatment options underscores a significant challenge in the field. Our study aimed to identify hub/key genes specifically associated with KRAS-MT LUAD. These hub genes hold the potential to serve as therapeutic targets or biomarkers, providing insights into the pathogenesis and prognosis of lung cancer. We performed a comprehensive analysis on KRAS-MT LUAD samples using diverse data sources. This included TCGA project data for RNA-seq, clinical information, and somatic mutations, along with RNA-seq data for adjacent normal tissues. DESeq2 identified differentially expressed genes (DEGs), while weighted gene co-expression network analysis revealed co-expression modules. Overlapping genes between DEGs and co-expression module with the highest significance were analyzed using gene set enrichment analysis and protein-protein interaction network analysis. Hub genes were identified with the Maximal Clique Centrality algorithm in Cytoscape. Prognostic significance was assessed through survival analysis and validated using the GSE72094 dataset from Gene Expression Omnibus (GEO) database. In KRAS-MT LUAD, 3122 DEGs were found (2131 up-regulated, 985 down-regulated). The blue module, among 25 co-expression modules from weighted gene co-expression network analysis, had the strongest correlation. 804 genes overlapped between DEGs and the blue module. Among 20 hub genes in the blue module, leucine-rich repeats containing G protein-coupled receptor 4 (LGR4) overexpression correlated with worse overall survival. The prognostic significance of LGR4 was confirmed using GSE72094, but surprisingly, the direction of the association was opposite to what was expected. LGR4 stands as a promising biomarker in KRAS-MT LUAD prognosis. Contrasting associations in TCGA and GSE72094 datasets reveal the intricate nature of KRAS-MT LUAD. Additional explorations are imperative to grasp the precise involvement of LGR4 in lung adenocarcinoma prognosis, particularly concerning KRAS mutations. These insights could potentially pave the way for targeted therapeutic interventions, addressing the existing unmet demands in this specific subgroup.