Distinct Molecular Mechanisms Analysis of Three Lung Cancer Subtypes Based on Gene Expression Profiles

Identification of LOX as a candidate prognostic biomarker in Glioblastoma multiforme

BACKGROUND

Glioblastoma multiforme (GBM) is the most malignant type of glioma. GBM tumors grow rapidly, have a high degree of malignancy, and are characterized by a fast disease progression. Unfortunately, there is a lack of effective treatments. An effective strategy for the treatment of GBM would be to identify key biomarkers correlating with the occurrence and progression of GBM and developing these biomarkers into therapeutic targets.

METHOD AND RESULTS

In this study, using integrated bioinformatics analysis, we identified differentially expressed genes (DEGs), including 130 genes that were upregulated in GBM compared to normal brain tissue, and 128 genes that were downregulated in GBM. Based on Gene Ontology enrichment analysis and Kyoto Encyclopedia of Genes and Genomes pathway analysis, these genes were associated with regulation of tumor cell adhesion, differentiation, morphology in GBM and were mainly enriched in Complement and coagulation cascades pathway. The Search Tool for the Retrieval of Interacting Genes (STRING) database was used to construct a Protein-Protein Interaction network. Ten hub genes were identified, including FN1, CD44, MYC, CDK1, SERPINE1, COL3A1, COL1A2, LOX, POSTN and EZH2, all of which were significantly upregulated in GBM, these results were confirmed by oncomine database exploration. Alteration analysis of hub genes found that patients with alteration in at least one of the hub genes showed shorter median survival times (p = 0.013) and shorter median disease-free survival times (p = 2.488E-3) than patients without alterations in any of the hub genes. Multiple tests for survival analysis showed that among individual hub genes only expression of LOX was correlated with patient survival (P < 0.05).GDS4467 data set was used to analyze the expression of LOX in gliomas with different degrees of malignancy, and it was found that the expression level of LOX was positively correlated with the malignant degree of gliomas.By analyzing GDS 4535 data set showed that the expression level of LOX was positively correlated with the differentiation degree of GBM cells CONCLUSION: This research suggests that FN1, CD44, MYC, CDK1, SERPINE1, COL3A1, COL1A2, LOX, POSTN and EZH2 are key genes in GBM. However, only LOX is correlated with patient survival and promotes glioblastoma cell differentiation and tumor recurrence. LOX may be a candidate prognostic biomarker and potential therapeutic target for GBM.

LncRNA HOXC-AS3 promotes non-small-cell lung cancer growth and metastasis through upregulation of YBX1

NSCLC is common and is the primary cause of cancer-related deaths due to a lack of early diagnosis and its propensity for metastasis. The pathogenesis of NSCLC is still unclear. Here, we explored the molecular mechanisms underlying NSCLC development, focusing on the HOXC-AS3/YBX1/HOXC8 axis. Human NSCLC specimens and cell lines were used. qRT-PCR and western blotting were utilised to examine the levels of HOXC-AS3/YBX1/HOXC8. CCK-8, colony formation, scratch wound healing and Transwell assays were performed to evaluate cancer cell proliferation, migration and invasion. A nude mouse xenograft model was used to examine tumour growth and metastasis in vivo. RNA pull-down, chromatin immunoprecipitation, coimmunoprecipitation and dual-luciferase assays were applied to validate the interactions of HOXC-AS3/YBX1, MDM2/YBX1 and the YBX1/HOXC8 promoter. The levels of HOXC-AS3 and HOXC8 were increased in human NSCLC specimens and cells. Knockdown of HOXC-AS3 suppressed NSCLC cell proliferation, migration and invasion, as well as tumour growth and metastasis in vivo. HOXC-AS3 directly bound to YBX1 to suppress its ubiquitination mediated by MDM2. YBX1 bound to the HOXC8 promoter and enhanced its transcription. Knockdown of HOXC8 inhibited the effects of HOXC-AS3 overexpression on NSCLC. HOXC-AS3 promotes NSCLC growth and metastasis by stabilising YBX1 and thus increasing HOXC8 transcription. Our study indicates that the HOXC-AS3/YBX1/HOXC8 axis could serve as a biomarker for NSCLC diagnosis or as a target for therapy development.

Drug repurposing for cancer therapy in the era of precision medicine

Drug repurposing refers to the identification of clinically approved drugs, with the known safety profiles and defined pharmacokinetic properties, to new indications. Despite the advances in oncology research, cancers are still associated with the most unmet medical needs. Drug repurposing has emerged as a useful approach for the search for effective and durable cancer treatment. It may also represent a promising strategy to facilitate precision cancer treatment and to overcome drug resistance. The repurposing of non-cancer drugs for precision oncology effectively extends the inventory of actionable molecular targets and thus increases the number of patients who may benefit from precision cancer treatment. In cancer types where genetic heterogeneity is so high that it is not feasible to identify strong repurposed drug candidates for standard treatment, the precision oncology approach offers individual patients access to novel treatment options. For repurposed candidates with low potency, a combination of multiple repurposed drugs may produce a synergistic therapeutic effect. Precautions should be taken when combining repurposed drugs with anticancer agents to avoid detrimental drug-drug interactions and unwanted side effects. New multifactorial data analysis and artificial intelligence methods are needed to untangle the complex association of molecular signatures influencing specific cancer subtypes to facilitate drug repurposing in precision oncology.

Comprehensive analysis of cancer breakpoints reveals signatures of genetic and epigenetic contribution to cancer genome rearrangements

Understanding mechanisms of cancer breakpoint mutagenesis is a difficult task and predictive models of cancer breakpoint formation have to this time failed to achieve even moderate predictive power. Here we take advantage of a machine learning approach that can gather important features from big data and quantify contribution of different factors. We performed comprehensive analysis of almost 630,000 cancer breakpoints and quantified the contribution of genomic and epigenomic features-non-B DNA structures, chromatin organization, transcription factor binding sites and epigenetic markers. The results showed that transcription and formation of non-B DNA structures are two major processes responsible for cancer genome fragility. Epigenetic factors, such as chromatin organization in TADs, open/closed regions, DNA methylation, histone marks are less informative but do make their contribution. As a general trend, individual features inside the groups show a relatively high contribution of G-quadruplexes and repeats and CTCF, GABPA, RXRA, SP1, MAX and NR2F2 transcription factors. Overall, the cancer breakpoint landscape can be represented by well-predicted hotspots and poorly predicted individual breakpoints scattered across genomes. We demonstrated that hotspot mutagenesis has genomic and epigenomic factors, and not all individual cancer breakpoints are just random noise but have a definite mutation signature. Besides we found a long-range action of some features on breakpoint mutagenesis. Combining omics data, cancer-specific individual feature importance and adding the distant to local features, predictive models for cancer breakpoint formation achieved 70-90% ROC AUC for different cancer types; however precision remained low at 2% and the recall did not exceed 50%. On the one hand, the power of models strongly correlates with the size of available cancer breakpoint and epigenomic data, and on the other hand finding strong determinants of cancer breakpoint formation still remains a challenge. The strength of predictive signals of each group and of each feature inside a group can be converted into cancer-specific breakpoint mutation signatures. Overall our results add to the understanding of cancer genome rearrangement processes.

Exploring the mechanism of aidi injection for lung cancer by network pharmacology approach and molecular docking validation

Background. Aidi injection (ADI) is an effective Traditional Chinese medicine preparation widely used for lung cancer. However, the pharmacological mechanisms of ADI on lung cancer remain to be elucidated. Methods. A network pharmacology (NP)-based approach and the molecular docking validation were conducted to explore underlying mechanisms of ADI on lung cancer. The compounds and target genes were screened by Traditional Chinese Medicine Systems Pharmacology (TCMSP) database and Bioinformatics Analysis Tool for Molecular mechANism of Traditional Chinese Medicine (Batman-TCM) database. The STRING database was utilized for protein interaction network construction. The R package clusterProfiler was used for bioinformatics annotation of hub target genes. The gene expression analysis and survival analysis were performed based on The Cancer Genome Atlas (TCGA) database. The Autodock Vina was used for molecular docking validation. Results. A total of five key compounds with 324 putative target genes were screened out, and 14 hub target genes were identified for treating lung cancer. Six hub genes could influence the survival of non-small cell lung cancer (NSCLC) patients. Of these hub genes, the expression pattern of EGFR, MYC, PIK3CA, and SMAD3 were significantly higher in the LUSC, while PIK3CA and RELA expressed lower in the LUAD group and LUSC group, respectively. These six hub genes had good docking affinity with the key compounds of ADI. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis showed that ADI may exert therapeutic effects on lung cancer by regulating critical pathways including the thyroid hormone signaling pathway, MAPK signaling pathway, and PI3K-Akt signaling pathway. Conclusions. The present study explored the potential pharmacological mechanisms of ADI on lung cancer, promoting the clinical application of ADI in treating lung cancer, and providing references for advanced researches.