A smoking-associated 7-gene signature for lung cancer diagnosis and prognosis

lncRNA CRNDE Affects Th17/IL-17A and Inhibits Epithelial-Mesenchymal Transition in Lung Epithelial Cells Reducing Asthma Signs

Background

Asthma treatment is difficult due to disease heterogeneity and comorbidities. In addition, the development of drugs targeting the underlying mechanisms of asthma remains slow. We planned to identify the most upregulated differentially expressed long noncoding RNA in asthma to explore its regulatory patterns and pathways in asthma.

Methods

We sensitized mice using a mixture of ovalbumin, house dust mites, and lipopolysaccharide to establish an asthma mouse model. We also sensitized asthma cells with TGF-β1 in an in vitro model. We performed a microarray analysis to identify the lncRNA with the differential expression level in model mice. We applied hematoxylin and eosin and Masson's trichrome stainings to mouse tissues to quantify the tissue damage extent. Next, we assess the levels of lncRNA CRNDE, miR-29a-3p, TGF-β1, MCL-1, E-cadherin, vimentin, and snail. We counted the percentages of Th17 cells using flow cytometry. Finally, we performed a dual-luciferase reporter assay to assess the association between lncRNA CRNDE and miR-29a-3p.

Results

We successfully established asthma mouse/cell models and selected the lncRNA CRNDE for our study. Transfection of si-CRNDE reduced the degree of injury and inflammation in the mouse model and reversed the TGF-β1-induced epithelial-mesenchymal transition (EMT) in the cell model. Moreover, the E-cadherin level was upregulated, and the levels of IL-17A, vimentin, snail, and α-SMA were downregulated. We also discovered that lncRNA CRNDE negatively regulated miR-29a-3p and that this one in turn inhibited MCL-1 in mice. After lncRNA CRNDE expression downregulation, the level of miR-29a-3p was increased, and we detected reduced levels of MCL-1 and EMTs.

Conclusions

lncRNA CRNDE expression downregulation led to reduced inflammation and reduced lung damage in mice with induced asthma, it inhibited the EMTs of lung epithelial cells via the miR-29a-3p/MCL-1 pathway, and it reduced the levels of Th17/IL-17A cells to reduce asthma signs.

Potential Impact of Cancer Susceptibility Genes on Lung Cancer Metastasis

Background. Studies of prognosis-related molecular markers are an important tool to uncover the mechanism of tumour metastasis. Cancer susceptibility gene testing is an important tool for genetic counselling of cancer risk. However, the impact of lung cancer susceptibility genes (LCSGs) on lung cancer metastasis and prognosis has not been well studied. Methods. The list of lung cancer susceptibility genes was retrospectively analysed and updated. After expression profiling and functional analysis, LCSG-based signatures for prognosis were identified by Cox regression and LASSO regression analyses. For translational purposes, nomograms integrating LCSGs and clinical characteristics were constructed. Results. A total of 301 LCSGs were employed for modelling. For lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC), 10-gene and 7-gene signatures were created and independently validated. The LCSG-based risk score could stratify LUAD survival (univariate: hazard ratio $\left(\text{HR}\right)=1.076$ , 95% confidence interval $\left(\text{CI}\right)=1.049$ –1.103, $P<0.001$ ; multivariate: $\text{HR}=1.066$ , 95% $\text{CI}=1.037$ –1.095, $P<0.001$ ) and LUSC survival (univariate: $\text{HR}=1.149$ , 95% $\text{CI}=1.066$ −1.239, $P<0.001$ ; multivariate: $\text{HR}=1.129$ , 95% $\text{CI}=1.038$ −1.228, $P=0.005$ ). One of the processes affected by differentially expressed genes in both LUAD and LUSC was the negative regulation of epithelial cell differentiation. Conclusions. Overall, novel LCSG-based gene signatures for LUAD and LUSC were constructed. These findings could expand the understanding of the impact of LCSG expression on cancer metastasis and prognosis.

Immune-Omics Networks of CD27, PD1, and PDL1 in Non-Small Cell Lung Cancer

To date, there are no prognostic/predictive biomarkers to select chemotherapy, immunotherapy, and radiotherapy in individual non-small cell lung cancer (NSCLC) patients. Major immune-checkpoint inhibitors (ICIs) have more DNA copy number variations (CNV) than mutations in The Cancer Genome Atlas (TCGA) NSCLC tumors. Nevertheless, CNV-mediated dysregulated gene expression in NSCLC is not well understood. Integrated CNV and transcriptional profiles in NSCLC tumors (n = 371) were analyzed using Boolean implication networks for the identification of a multi-omics CD27, PD1, and PDL1 network, containing novel prognostic genes and proliferation genes. A 5-gene (EIF2AK3, F2RL3, FOSL1, SLC25A26, and SPP1) prognostic model was developed and validated for patient stratification (p < 0.02, Kaplan-Meier analyses) in NSCLC tumors (n = 1163). A total of 13 genes (COPA, CSE1L, EIF2B3, LSM3, MCM5, PMPCB, POLR1B, POLR2F, PSMC3, PSMD11, RPL32, RPS18, and SNRPE) had a significant impact on proliferation in 100% of the NSCLC cell lines in both CRISPR-Cas9 (n = 78) and RNA interference (RNAi) assays (n = 92). Multiple identified genes were associated with chemoresponse and radiotherapy response in NSCLC cell lines (n = 117) and patient tumors (n = 966). Repurposing drugs were discovered based on this immune-omics network to improve NSCLC treatment.

Development of an Immune-Related Risk Signature for Predicting Prognosis in Lung Squamous Cell Carcinoma

Lung squamous cell carcinoma (LSCC) is the most common subtype of non-small cell lung cancer. Immunotherapy has become an effective treatment in recent years, while patients showed different responses to the current treatment. It is vital to identify the potential immunogenomic signatures to predict patient' prognosis. The expression profiles of LSCC patients with the clinical information were downloaded from TCGA database. Differentially expressed immune-related genes (IRGs) were extracted using edgeR algorithm, and functional enrichment analysis showed that these IRGs were primarily enriched in inflammatory- and immune-related processes. "Cytokine-cytokine receptor interaction" and "PI3K-AKT signaling pathway" were the most enriched KEGG pathways. 27 differentially expressed IRGs were significantly correlated with the overall survival (OS) of patients using univariate Cox regression analysis. A prognostic risk signature that comprises seven IRGs (GCCR, FGF8, CLEC4M, PTH, SLC10A2, NPPC, and FGF4) was developed with effective predictive performance by multivariable Cox stepwise regression analysis. Most importantly, the signature could be an independent prognostic predictor after adjusting for clinicopathological parameters, and also validated in two independent LSCC cohorts (GSE4573 and GSE17710). Potential molecular mechanisms and tumor immune landscape of these IRGs were investigated through computational biology. Analysis of tumor infiltrating lymphocytes and immune checkpoint molecules revealed distinct immune landscape in high- and low-risk group. The study was the first time to construct IRG-based immune signature in the recognition of disease progression and prognosis of LSCC patients.

The antiangiogenic action of cisplatin on endothelial cells is mediated through the release of tissue inhibitor of matrix metalloproteinases-1 from lung cancer cells

In addition to suppressing cancer cell proliferation and tumor growth, cisplatin has been shown to inhibit tumor angiogenesis. However, the underlying mechanism remains a matter of debate. The present study addressed the impact of cisplatin on potential tumor-to-endothelial cell communication conferring an antiangiogenic effect. For this purpose, migration and tube formation of human umbilical vein endothelial cells (HUVECs) exposed to conditioned media (CM) from vehicle- or cisplatin-treated A549 and H358 lung cancer cells were quantified. Cancer cells were exposed to non-toxic concentrations of cisplatin to mimic low-dose treatment conditions. CM from cancer cells exposed to cisplatin at concentrations of 0.01 to 1 µM elicited a concentration-dependent decrease in HUVEC migration and tube formation as compared with CM from vehicle-treated cells. The viability of HUVECs was virtually unaltered under these conditions. siRNA approaches revealed cisplatin-induced expression and subsequent release of tissue inhibitor of matrix metalloproteinases-1 (TIMP-1) by lung cancer cells to be causally linked to a decrease in HUVEC migration and tube formation. Moreover, TIMP-1 upregulation and consequent inhibition of HUVEC migration by cisplatin was shown to be dependent on activation of p38 and p42/44 mitogen-activated protein kinases. Inhibition of angiogenic features was not observed when HUVECs were directly exposed to cisplatin. Similarly, antiangiogenic effects were not detectable in HUVECs exposed to CM from the cisplatin-challenged bronchial non-cancer cell line BEAS-2B. Collectively, the present data suggest a pivotal role of cisplatin-induced TIMP-1 release from lung cancer cells in tumor-to-endothelial cell communication resulting in a reduced cancer-associated angiogenic impact on endothelial cells.

Network-based identification of biomarkers coexpressed with multiple pathways

Unraveling complex molecular interactions and networks and incorporating clinical information in modeling will present a paradigm shift in molecular medicine. Embedding biological relevance via modeling molecular networks and pathways has become increasingly important for biomarker identification in cancer susceptibility and metastasis studies. Here, we give a comprehensive overview of computational methods used for biomarker identification, and provide a performance comparison of several network models used in studies of cancer susceptibility, disease progression, and prognostication. Specifically, we evaluated implication networks, Boolean networks, Bayesian networks, and Pearson’s correlation networks in constructing gene coexpression networks for identifying lung cancer diagnostic and prognostic biomarkers. The results show that implication networks, implemented in Genet package, identified sets of biomarkers that generated an accurate prediction of lung cancer risk and metastases; meanwhile, implication networks revealed more biologically relevant molecular interactions than Boolean networks, Bayesian networks, and Pearson’s correlation networks when evaluated with MSigDB database.