Background: Nowadays, non-small cell lung cancer (NSCLC) is a common and highly fatal malignancy in worldwide. Therefore, to identify the potential prognostic markers and therapeutic targets is urgent for patients. Objective: This study aims to find hub targets associated with NSCLC using multiple databases. Methods: Differentially expressed genes (DEGs) from Genome Expression Omnibus (GEO) cohorts were employed for the enrichment analyses of Gene Ontology (GO) terms and the Kyoto Encyclopedia of Genes and Genome (KEGG) pathways. Candidate key genes, filtered from the topological parameter 'Degree' and validated using the The Cancer Genome Atlas (TCGA) cohort, were analyzed for their association with clinicopathological features and prognosis of NSCLC. Meanwhile, immunohistochemical cohort analyses and biological verification were further evaluated. Results: A total of 146 DEGs were identified following data preprocessing, and a protein-protein interaction (PPI) systematic network was constructed based on them. The top ten candidate core genes were further extracted from the above PPI network by using 'Degree' value, among which COL1A1 was shown to associate with overall survival (OS) of NSCLC as determined by using the Kaplan-Meier analysis (p=0.028), and could serve as an independent prognostic factor for OS in NSCLC patients (HR, 0.814; 95% CI, 0.665-0.996; p=0.046). We then analyzed the clinical stages, PPI, mutations, potential biological functions and immune regulations of COL1A1 in NSCLC patients using multiple bioinformatics tools, including GEPIA, GeneMANIA, cBioPortal, GESA and TISIDB. Finally, we further experimentally validated the overexpression of COL1A1 in NSCLC samples, and found that inhibition of COL1A1 expression moderately sensitized NSCLC cells to cisplatin. Conclusion: Thus, our results show that COL1A1 may serve as a potential prognostic marker and therapeutic target in NSCLC.

Minichromosome maintenance proteins (MCMs) are considered to be essential factors coupling DNA replication to both cell cycle progression and checkpoint regulation. Previous studies have shown that dysregulation of MCMs are implicated in tumorigenesis of lung cancer. However, the distinct expression/mutation patterns and prognostic values of MCMs in lung cancer have yet to be systematically elucidated. In the present study, we analyzed the transcriptional levels, mutations, and prognostic value of MCM1-10 in non-small cell lung cancer (NSCLC) patients using multiple bioinformatics tools, including ONCOMINE, GEPIA, Kaplan–Meier Plotter, cBioPortal, and GESA. The analysis results from GEPIA dataset showed that MCM2/4/10 was significantly high expressed in both lung adenocarcinoma (LUAD) and squamous cell lung carcinomas (LUSCs). Meanwhile, the expression levels of MCM2/4/6/7/8 were associated with advanced tumor stages. Subsequent survival analysis using the Kaplan–Meier Plotter indicated that high expression levels of MCM1/2/3/4/5/6/7/8/10 were associated with worse overall survival (OS), while high expression level of MCM9 predicted better OS in these patients. Furthermore, we experimentally validated overexpression of MCM2 and MCM4 in NSCLC, thus the results from this study support a view that they may serve as potential prospective biomarkers to identify high-risk subgroups of NSCLC patients.

Danggui Sini Decoction (DSD), a classic Chinese herb medicine (CHM) formula, has been used to treat various diseases in China for centuries. However, it remains challenging to reveal its mechanism of action through conventional pharmacological methods. Here, we first explored the mechanism of action of DSD with the assistance of network pharmacology and bioinformatic analysis tools, and found a potential therapeutic effect of DSD on cancer. Indeed, our in vivo experiment demonstrated that oral administration of DSD could significantly inhibit the growth of xenografted gastric cancer (GC) on mice. The subsequent enrichment analyses for 123 candidate core targets evacuated from the drug/disease-target protein-protein interaction network showed that DSD could affect the key biological processes involving the survival and growth of GC cells, such as apoptosis and cell cycle, and the disturbance of these biological processes is likely attributed to the simultaneous inhibition of multiple signaling pathways, including PI3K/Akt, MAPK, and p53 pathways. Notably, these in silico results were further validated by a series of cellular functional and molecular biological assays in vitro. Moreover, molecular docking analysis suggested an important role of MCM2 in delivering the pharmacological activity of DSD against GC. Together, these results indicate that our network pharmacology and bioinformatics-guided approach is feasible and useful in exploring not only the mechanism of action, but also the “new use” of the old CHM formula.