Comparison of tumor related signaling pathways with known compounds to determine potential agents for lung adenocarcinoma

Ciclopirox Olamine Exerts Tumor-Suppressor Effects via Topoisomerase II Alpha in Lung Adenocarcinoma

Background

Globally, lung cancer is one of the most malignant tumors, of which lung adenocarcinoma (LUAD) is the most common subtype, with a particularly poor prognosis. Ciclopirox olamine (CPX) is an antifungal drug and was recently identified as a potential antitumor agent. However, how CPX and its mechanism of action function during LUAD remain unclear.

Methods

The effects of CPX on cell proliferation, cell cycle, reactive oxygen species (ROS) levels, and apoptosis were assessed using the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl-2H-tetrazolium bromide (MTT) assay, colony formation, western blotting, flow cytometry assays, and immunohistochemistry. Global gene expression levels were compared between control and CPX-treated LUAD cells. A LUAD xenograft mouse model was used to evaluate the potential in vivo effects of CPX.

Results

We observed that CPX displayed strong antitumorigenic properties in LUAD cells, inhibited LUAD proliferation, induced ROS production, caused DNA damage, and activated the ATR-CHK1-P53 pathway. Topoisomerase II alpha (TOP2A) is overexpressed in LUAD and associated with a poor prognosis. By analyzing differentially expressed genes (DEGs), TOP2A was significantly down-regulated in CPX-treated LUAD cells. Furthermore, CPX treatment substantially inhibited in vivo LUAD xenograft growth without toxicity or side effects to the hematological system and internal organs.

Conclusions

Collectively, for the first time, we showed that CPX exerted tumor-suppressor effects in LUAD via TOP2A, suggesting CPX could potentially function as a promising chemotherapeutic for LUAD treatment.

CHEK1: a hub gene related to poor prognosis for lung adenocarcinoma

Aim: The study aims to pinpoint hub genes and investigate their functions in order to gain insightful understandings of lung adenocarcinoma (LUAD). Methods: Bioinformatic approaches were adopted to investigate genes in databases including Gene Expression Omnibus, WebGestalt, STRING and Cytoscape, GEPIA2, Oncomine, Human Protein Atlas, TIMER2.0, UALCAN, cBioPortal, TargetScanHuman, OncomiR, ENCORI, Kaplan-Meier plotter, UCSC Xena, European Molecular Biology Laboratory - European Bioinformatics Institute Single Cell Expression Atlas and CancerSEA. Results: Five hub genes were ascertained. CHEK1 was overexpressed in a range of cancers, including LUAD. Promoter methylation, amplification and miRNA regulation might trigger CHEK1 upregulation, signaling poor prognosis. CHEK1 with its coexpressed genes were enriched in the cell cycle pathway. Intratumor heterogeneity of CHEK1 expression could be observed. Cell clusters with CHEK1 expression were more prone to metastasis and epithelial-to-mesenchymal transition. Conclusion: CHEK1 might potentially act as a prognostic biomarker for LUAD.

High DSCC1 Level Predicts Poor Prognosis of Lung Adenocarcinoma

Purpose

To evaluate the role of DSCC1 in LUAD.

Patients and Methods

Based on TCGA and GTEx, the Wilcoxon rank-sum test was used to compare the expression differences of DSCC1 between the normal samples of GTEx combined TCGA and the unpaired tumor samples of TCGA, and to compare DSCC1 expression values between tumor tissues and paired normal LUAD tissues in the TCGA cohort. Kruskal–Wallis rank-sum test, Wilcoxon rank-sum test, and logistics regression were used to compare the relationship between the expression of DSCC1 and the clinicopathological parameters. The biological function of DSCC1 was annotated by GSEA and ssGSEA, while Kaplan–Meier and Cox regression analysis were used to evaluate the prognostic value of DSCC1. Furthermore, the time-dependent ROC curve was used to analyze the diagnostic efficacy of DSCC1 in LUAD.

Results

We downloaded the RNA-Seq data of 513 LUAD cases. The expression of DSCC1 was significantly correlated with T stage (OR = 1.04(1.02–1.07), P = 0.002), pathological stage (OR=1.03 (1.01–1.05), P = 0.008) and TP53 status (OR=1.10 (1.07–1.14), P < 0.001). The high expression of DSCC1 was significantly correlated with DSS (HR=1.56 (1.07–2.26), P = 0.021) and OS (HR=1.53 (1.14–2.05), P = 0.004). Moreover, ROC curve analysis (AUC=0.845, CI (0.820-0.870)) indicated DSCC1 as a potential diagnostic molecule for LUAD. In the group with high DSCC1 expression phenotype, down-regulation of EGFR signal, reduction of IL-6 deprivation, cell cycle, and p53 signal pathway were significantly abundant. Spearman correlation analysis showed that the expression of DSCC1 was positively correlated with the infiltration of Th2 cells, T Helper cells.

Conclusion

Our results suggest that DSCC1 may be an important biomarker for the treatment of LUAD.

Connectivity Mapping: Methods and Applications

Connectivity mapping resources consist of signatures representing changes in cellular state following systematic small-molecule, disease, gene, or other form of perturbations. Such resources enable the characterization of signatures from novel perturbations based on similarity; provide a global view of the space of many themed perturbations; and allow the ability to predict cellular, tissue, and organismal phenotypes for perturbagens. A signature search engine enables hypothesis generation by finding connections between query signatures and the database of signatures. This framework has been used to identify connections between small molecules and their targets, to discover cell-specific responses to perturbations and ways to reverse disease expression states with small molecules, and to predict small-molecule mimickers for existing drugs. This review provides a historical perspective and the current state of connectivity mapping resources with a focus on both methodology and community implementations.

Dysbiosis of the Salivary Microbiome Is Associated With Non-smoking Female Lung Cancer and Correlated With Immunocytochemistry Markers

Background: Association between oral bacteria and increased risk of lung cancer have been reported in several previous studies, however, the potential association between salivary microbiome and lung cancer in non-smoking women have not been evaluated. There is also no report on the relationship between immunocytochemistry markers and salivary microbiota.

Method: In this study, we assessed the salivary microbiome of 75 non-smoking female lung cancer patients and 172 matched healthy individuals using 16S rRNA gene amplicon sequencing. We also calculated the Spearman's rank correlation coefficient between salivary microbiota and three immunohistochemical markers (TTF-1, Napsin A and CK7).

Result: We analyzed the salivary microbiota of 247 subjects and found that non-smoking female lung cancer patients exhibited oral microbial dysbiosis. There was significantly lower microbial diversity and richness in lung cancer patients when compared to the control group (Shannon index, P < 0.01; Ace index, P < 0.0001). Based on the analysis of similarities, the composition of the microbiota in lung cancer patients also differed from that of the control group (r = 0.454, P < 0.001, unweighted UniFrac; r = 0.113, P < 0.01, weighted UniFrac). The bacterial genera Sphingomonas (P < 0.05) and Blastomonas (P < 0.0001) were relatively higher in non-smoking female lung cancer patients, whereas Acinetobacter (P < 0.001) and Streptococcus (P < 0.01) were higher in controls. Based on Spearman's correlation analysis, a significantly positive correlation can be observed between CK7 and Enterobacteriaceae (r = 0.223, P < 0.05). At the same time, Napsin A was positively associated with genera Blastomonas (r = 0.251, P < 0.05). TTF-1 exhibited a significantly positive correlation with Enterobacteriaceae (r = 0.262, P < 0.05). Functional analysis from inferred metagenomes indicated that oral microbiome in non-smoking female lung cancer patients were related to cancer pathways, p53 signaling pathway, apoptosis and tuberculosis.

Conclusions: The study identified distinct salivary microbiome profiles in non-smoking female lung cancer patients, revealed potential correlations between salivary microbiome and immunocytochemistry markers used in clinical diagnostics, and provided proof that salivary microbiota can be an informative source for discovering non-invasive lung cancer biomarkers.