Identification of regulatory SNPs associated with genetic modifications in lung adenocarcinoma

Integrated edge information and pathway topology for drug-disease associations

Drug repurposing is a promising approach to find new therapeutic indications for approved drugs. Many computational approaches have been proposed to prioritize candidate anticancer drugs by gene or pathway level. However, these methods neglect the changes in gene interactions at the edge level. To address the limitation, we develop a computational drug repurposing method (iEdgePathDDA) based on edge information and pathway topology. First, we identify drug-induced and disease-related edges (the changes in gene interactions) within pathways by using the Pearson correlation coefficient. Next, we calculate the inhibition score between drug-induced edges and disease-related edges. Finally, we prioritize drug candidates according to the inhibition score on all disease-related edges. Case studies show that our approach successfully identifies new drug-disease pairs based on CTD database. Compared to the state-of-the-art approaches, the results demonstrate our method has the superior performance in terms of five metrics across colorectal, breast, and lung cancer datasets.

A drug repurposing method based on inhibition effect on gene regulatory network

Numerous computational drug repurposing methods have emerged as efficient alternatives to costly and time-consuming traditional drug discovery approaches. Some of these methods are based on the assumption that the candidate drug should have a reversal effect on disease-associated genes. However, such methods are not applicable in the case that there is limited overlap between disease-related genes and drug-perturbed genes. In this study, we proposed a novel Drug Repurposing method based on the Inhibition Effect on gene regulatory network (DRIE) to identify potential drugs for cancer treatment. DRIE integrated gene expression profile and gene regulatory network to calculate inhibition score by using the shortest path in the disease-specific network. The results on eleven datasets indicated the superior performance of DRIE when compared to other state-of-the-art methods. Case studies showed that our method effectively discovered novel drug-disease associations. Our findings demonstrated that the top-ranked drug candidates had been already validated by CTD database. Additionally, it clearly identified potential agents for three cancers (colorectal, breast, and lung cancer), which was beneficial when annotating drug-disease relationships in the CTD. This study proposed a novel framework for drug repurposing, which would be helpful for drug discovery and development.

Optimal Supervised Reduction of High Dimensional Transcription Data

The plight of navigating high-dimensional transcription datasets remains a persistent problem. This problem is further amplified for complex disorders, such as cancer as these disorders are often multigenic traits with multiple subsets of genes collectively affecting the type, stage, and severity of the trait. We are often faced with a trade off between reducing the dimensionality of our datasets and maintaining the integrity of our data. To accomplish both tasks simultaneously for very high dimensional transcriptome for complex multigenic traits, we propose a new supervised technique, Class Separation Transformation (CST). CST accomplishes both tasks simultaneously by significantly reducing the dimensionality of the input space into a one-dimensional transformed space that provides optimal separation between the differing classes. Furthermore, CST offers an means of explainable ML, as it computes the relative importance of each feature for its contribution to class distinction, which can thus lead to deeper insights and discovery. We compare our method with existing state-of-the-art methods using both real and synthetic datasets, demonstrating that CST is the more accurate, robust, scalable, and computationally advantageous technique relative to existing methods. Code used in this paper is available on https://github.com/richiebailey74/CST.

Toxicogenomics scoring system: TGSS, a novel integrated risk assessment model for chemical carcinogenicity prediction

BACKGROUND

Given the increasing exposure of humans to environmental chemicals and the limitations of conventional toxicity test, there is an urgent need to develop next-generation risk assessment methods.

OBJECTIVES

This study aims to establish a novel computational system named Toxicogenomics Scoring System (TGSS) to predict the carcinogenicity of chemicals coupling chemical-gene interactions with multiple cancer transcriptomic datasets.

METHODS

Chemical-related gene signatures were derived from chemical-gene interaction data from the Comparative Toxicogenomics Database (CTD). For each cancer type in TCGA, genes were ranked by their effects on tumorigenesis, which is based on the differential expression between tumor and normal samples. Next, we developed carcinogenicity scores (C-scores) using pre-ranked GSEA to quantify the correlation between chemical-related gene signatures and ranked gene lists. Then we established TGSS by systematically evaluating the C-scores in multiple chemical-tumor pairs. Furthermore, we examined the performance of our approach by ROC curves or prognostic analyses in TCGA and multiple independent cancer cohorts.

RESULTS

Forty-six environmental chemicals were finally included in the study. C-score was calculated for each chemical-tumor pair. The C-scores of IARC Group 3 chemicals were significantly lower than those of chemicals in Group 1 (P-value = 0.02) and Group 2 (P-values = 7.49 ×10^-5). ROC curves analysis indicated that C-score could distinguish "high-risk chemicals" from the other compounds (AUC = 0.67) with a specificity and sensitivity of 0.86 and 0.57. The results of survival analysis were also in line with the assessed carcinogenicity in TGSS for the chemicals in Group 1. Finally, consistent results were further validated in independent cancer cohorts.

CONCLUSION

TGSS highlighted the great potential of integrating chemical-gene interactions with gene-cancer relationships to predict the carcinogenic risk of chemicals, which would be valuable for systems toxicology.

Autoencoder Networks Decipher the Association between Lung Cancer and Alzheimer’s Disease

Lung cancer is the most common malignancy and is responsible for the largest cancer-related mortality worldwide. Alzheimer’s disease is a degenerative neurological disease that burdens healthcare worldwide. While the two diseases are distinct, several transcriptomic studies have demonstrated they are linked. However, no concordant conclusion on how they are associated has been drawn. Since these studies utilized conventional bioinformatics methods, such as the differentially expressed gene (DEG) analysis, it is naturally expected that the proportion of DEGs having either the same or inverse directions in lung cancer and Alzheimer’s disease is substantial. This raises the inconsistency. Therefore, a novel bioinformatics method capable of determining the direction of association is desirable. In this study, the moderated t-tests were first used to identify DEGs that are shared by the two diseases. For the shared DEGs, separate autoencoder (AE) networks were trained to extract a one-dimensional representation (pseudogene) for each disease. Based on these pseudogenes, the association direction between lung cancer and Alzheimer’s disease was inferred. AE networks based on 266 shared DEGs revealed a comorbidity relationship between Alzheimer’s disease and lung cancer. Specifically, Spearman’s correlation coefficient between the predicted values using the two AE networks for the Alzheimer’s disease test set was 0.825 and for the lung cancer test set was 0.316. Novel bioinformatics methods such as an AE network may help decipher how distinct diseases are associated by providing the refined representations of dysregulated genes.

Analysis on methylation and expression of PSMB8 and its correlation with immunity and immunotherapy in lung adenocarcinoma

Aim: To find biomarkers for immunity and immunotherapy in lung adenocarcinoma (LUAD) through multiomics analysis. Materials & methods: The multiomics data of patients with LUAD were downloaded from the TCGA and GEO databases. CIBERSORT, quanTIseq, ESTIMATEScore, k-means clustering, gene set enrichment analysis, gene set variation analysis, immunophenoscore and logistic regression were used in this study. Results: PSMB8 HypoMet-HighExp group patients have more active immune-related pathways, more antitumor immune cells, less protumor immune cells, higher immunophenoscore and longer progression-free survival of immune checkpoint inhibitor therapy than HyperMet-LowExp group. In multivariate analysis, PSMB8 showed an independent value. Conclusion: The combination of DNA methylation and mRNA expression of PSMB8 could independently distinguish types of tumor immune microenvironment and predict programmed cell death protein 1/programmed cell death-ligand 1 inhibitors' effects in patients with LUAD.

Recognition of driver genes with potential prognostic implications in lung adenocarcinoma based on H3K79me2

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The efficacy of H3K79me2 on gene expression regulation is affirmed in LUAD.

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An open-source algorithm for identifying LUAD-related driver genes is presented.

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12 H3K79me2-targeted driver genes with clinical values are verified by qPCR.

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The regions with obvious H3K79me2 signals changes on driver genes are pinpointed.

Lung adenocarcinoma is a malignancy with a low overall survival and a poor prognosis. Studies have shown that lung adenocarcinoma progression relates to locus-specific/global changes in histone modifications. To explore the relationship between histone modification and gene expression changes, we focused on 11 histone modifications and quantitatively analyzed their influences on gene expression. We found that, among the studied histone modifications, H3K79me2 displayed the greatest impact on gene expression regulation. Based on the Shannon entropy, 867 genes with differential H3K79me2 levels during tumorigenesis were identified. Enrichment analyses showed that these genes were involved in 16 common cancer pathways and 11 tumors and were target-regulated by trans-regulatory elements, such as Tp53 and WT1. Then, an open-source computational framework was presented (https://github.com/zlq-imu/Identification-of-potential-LUND-driver-genes). Twelve potential driver genes were extracted from the genes with differential H3K79me2 levels during tumorigenesis. The expression levels of these potential driver genes were significantly increased/decreased in tumor cells, as assayed by RT–qPCR. A risk score model comprising these driver genes was further constructed, and this model was strongly negatively associated with the overall survival of patients in different datasets. The proportional hazards assumption and outlier test indicated that this model could robustly distinguish patients with different survival rates. Immune analyses and responses to immunotherapeutic and chemotherapeutic agents showed that patients in the high and low-risk groups may have distinct tendencies for clinical selection. Finally, the regions with clear H3K79me2 signal changes on these driver genes were accurately identified. Our research may offer potential molecular biomarkers for lung adenocarcinoma treatment.

Predicting the Lung Adenocarcinoma and Its Biomarkers by Integrating Gene Expression and DNA Methylation Data

The early symptoms of lung adenocarcinoma patients are inapparent, and the clinical diagnosis of lung adenocarcinoma is primarily through X-ray examination and pathological section examination, whereas the discovery of biomarkers points out another direction for the diagnosis of lung adenocarcinoma with the development of bioinformatics technology. However, it is not accurate and trustworthy to diagnose lung adenocarcinoma due to omics data with high-dimension and low-sample size (HDLSS) features or biomarkers produced by utilizing only single omics data. To address the above problems, the feature selection methods of biological analysis are used to reduce the dimension of gene expression data (GSE19188) and DNA methylation data (GSE139032, GSE49996). In addition, the Cartesian product method is used to expand the sample set and integrate gene expression data and DNA methylation data. The classification is built by using a deep neural network and is evaluated on K-fold cross validation. Moreover, gene ontology analysis and literature retrieving are used to analyze the biological relevance of selected genes, TCGA database is used for survival analysis of these potential genes through Kaplan-Meier estimates to discover the detailed molecular mechanism of lung adenocarcinoma. Survival analysis shows that COL5A2 and SERPINB5 are significant for identifying lung adenocarcinoma and are considered biomarkers of lung adenocarcinoma.

Expressional regulation of NKG2DLs is associated with the tumor development and shortened overall survival in lung adenocarcinoma

Natural killer group 2D ligands (NKG2DLs) are expressed on tumor cells as a ligand for Natural killer group 2D (NKG2D) receptors. NKG2DLs interact with NKG2D to induce immune cell-mediated cytotoxicity for eliminating tumors. Studies demonstrated that tumor cells can reduce NKG2DLs' expression to escape from anti-tumor immunity, leading to an aggressive cancer phenotype and poor prognosis in some cancers. However, these studies are limited and there is no comprehensive work on the regulation of NKG2DLs in lung adenocarcinoma (LUAD) which is one of the deadliest cancers worldwide. Here, we conducted an in silico analysis to evaluate the changes in NKG2DLs in LUAD by analyzing The Cancer Genome Atlas and the Gene Expression Omnibus datasets including tumor vs. normal comparisons, TNM stages, survival and infiltrating immune estimation profile. Results indicated that some members of NKG2DL were downregulated in LUAD as compared to normal samples. We determined that MICA (MHC class I polypeptide-related sequence A) was the most and significantly downregulated ligand among others and the results were nearly consistent with the different datasets which we used. Furthermore, survival analysis revealed that down-regulated MICA transcript expression might be one of the prognostic indicators of LUAD. Interestingly, according to the immune cell infiltrating analysis, there wasn't a direct correlation between the MICA transcript expression and immune cell infiltration, while for MICB there was. In addition, in genetic alteration, DNA methylation and miRNA analyses, we did not observe critical outcomes that would clarify the down-regulated MICA expression in detail. Regardless, this study is highly comprehensive and contributes valuable suggestions to further functional studies about the regulation of NKG2DLs and promising immunotherapeutic approaches in LUAD.

A novel HIF1α-STIL-FOXM1 axis regulates tumor metastasis

BACKGROUND

Metastasis is the major cause of morbidity and mortality in cancer that involves in multiple steps including epithelial-mesenchymal transition (EMT) process. Centrosome is an organelle that functions as the major microtubule organizing center (MTOC), and centrosome abnormalities are commonly correlated with tumor aggressiveness. However, the conclusive mechanisms indicating specific centrosomal proteins participated in tumor progression and metastasis remain largely unknown.

METHODS

The expression levels of centriolar/centrosomal genes in various types of cancers were first examined by in silico analysis of the data derived from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), and European Bioinformatics Institute (EBI) datasets. The expression of STIL (SCL/TAL1-interrupting locus) protein in clinical specimens was further assessed by Immunohistochemistry (IHC) analysis and the oncogenic roles of STIL in tumorigenesis were analyzed using in vitro and in vivo assays, including cell migration, invasion, xenograft tumor formation, and metastasis assays. The transcriptome differences between low- and high-STIL expression cells were analyzed by RNA-seq to uncover candidate genes involved in oncogenic pathways. The quantitative polymerase chain reaction (qPCR) and reporter assays were performed to confirm the results. The chromatin immunoprecipitation (ChIP)-qPCR assay was applied to demonstrate the binding of transcriptional factors to the promoter.

RESULTS

The expression of STIL shows the most significant increase in lung and various other types of cancers, and is highly associated with patients' survival rate. Depletion of STIL inhibits tumor growth and metastasis. Interestingly, excess STIL activates the EMT pathway, and subsequently enhances cancer cell migration and invasion. Importantly, we reveal an unexpected role of STIL in tumor metastasis. A subset of STIL translocate into nucleus and associate with FOXM1 (Forkhead box protein M1) to promote tumor metastasis and stemness via FOXM1-mediated downstream target genes. Furthermore, we demonstrate that hypoxia-inducible factor 1α (HIF1α) directly binds to the STIL promoter and upregulates STIL expression under hypoxic condition.

CONCLUSIONS

Our findings indicate that STIL promotes tumor metastasis through the HIF1α-STIL-FOXM1 axis, and highlight the importance of STIL as a promising therapeutic target for lung cancer treatment.

Comparative Study of Classification Algorithms for Various DNA Microarray Data

Microarrays are applications of electrical engineering and technology in biology that allow simultaneous measurement of expression of numerous genes, and they can be used to analyze specific diseases. This study undertakes classification analyses of various microarrays to compare the performances of classification algorithms over different data traits. The datasets were classified into test and control groups based on five utilized machine learning methods, including MultiLayer Perceptron (MLP), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), and k-Nearest Neighbors (KNN), and the resulting accuracies were compared. k-fold cross-validation was used in evaluating the performance and the result was analyzed by comparing the performances of the five machine learning methods. Through the experiments, it was observed that the two tree-based methods, DT and RF, showed similar trends in results and the remaining three methods, MLP, SVM, and DT, showed similar trends. DT and RF generally showed worse performance than other methods except for one dataset. This suggests that, for the effective classification of microarray data, selecting a classification algorithm that is suitable for data traits is crucial to ensure optimum performance.

A novel epithelial-mesenchymal transition-related gene signature for prognosis prediction in patients with lung adenocarcinoma

Traditional pathological diagnoses and clinical methods are insufficient to accurately predict the prognosis of lung adenocarcinoma (LUAD). Epithelial-mesenchymal transition (EMT) process is closely related to tumor cell migration. However, the prognostic value of EMT-related genes in LUAD is still unclear. In this study, we collected bulk RNA-sequencing (RNA-seq) and microarray data of LUAD patients from public databases and identified different expressed EMT-related genes in tumor and normal tissues. Then, we used the least absolute shrinkage and selection operator Cox regression model to develop a multigene signature in the cancer genome atlas (TCGA) cohort and validated the model in the OncoSG (Singapore Oncology Data Portal) cohort as well as other datasets. Finally, we constructed a 12-gene signature to divide LUAD patients into high-risk and low-risk groups of overall survival (OS), which has a better stability and accuracy in predicating the OS of patients compared with some other published signatures of LUAD. In addition, evaluation of the risk model using the time-related receiver operating characteristic (ROC) curve confirmed the predictive ability of the model. Functional analysis showed that these genes are related to immunity. CD8 T cell and CD4 T cell types were significantly negatively correlated with the risk score in the analysis of immune infiltration. In general, our model provides useful information that may help clinicians better predict the prognosis of LUAD patients and provides potential targets for immunotherapy of LUAD.

Identification of Significant Genes in Lung Cancer of Nonsmoking Women via Bioinformatics Analysis

Background

The aim of this study was to identify potential key genes, proteins, and associated interaction networks for the development of lung cancer in nonsmoking women through a bioinformatics approach.

Methods

We used the GSE19804 dataset, which includes 60 lung cancer and corresponding paracancerous tissue samples from nonsmoking women, to perform the work. The GSE19804 microarray was downloaded from the GEO database and differentially expressed genes were identified using the limma package analysis in R software, with the screening criteria of p value < 0.01 and ∣log₂ fold change (FC) | >2.

Results

A total of 169 DEGs including 130 upregulated genes and 39 downregulated were selected. Gene Ontology and KEGG pathway analysis were performed using the DAVID website, and protein-protein interaction (PPI) networks were constructed and the hub gene module was screened through STING and Cytoscape.

Conclusions

We obtained five key genes such as GREM1, MMP11, SPP1, FOSB, and IL33 which were strongly associated with lung cancer in nonsmoking women, which improved understanding and could serve as new therapeutic targets, but their functionality needs further experimental verification.

Linking genotype to phenotype in multi-omics data of small sample

BACKGROUND

Genome-wide association studies (GWAS) that link genotype to phenotype represent an effective means to associate an individual genetic background with a disease or trait. However, single-omics data only provide limited information on biological mechanisms, and it is necessary to improve the accuracy for predicting the biological association between genotype and phenotype by integrating multi-omics data. Typically, gene expression data are integrated to analyze the effect of single nucleotide polymorphisms (SNPs) on phenotype. Such multi-omics data integration mainly follows two approaches: multi-staged analysis and meta-dimensional analysis, which respectively ignore intra-omics and inter-omics associations. Moreover, both approaches require omics data from a single sample set, and the large feature set of SNPs necessitates a large sample size for model establishment, but it is difficult to obtain multi-omics data from a single, large sample set.

RESULTS

To address this problem, we propose a method of genotype-phenotype association based on multi-omics data from small samples. The workflow of this method includes clustering genes using a protein-protein interaction network and gene expression data, screening gene clusters with group lasso, obtaining SNP clusters corresponding to the selected gene clusters through expression quantitative trait locus data, integrating SNP clusters and corresponding gene clusters and phenotypes into three-layer network blocks, analyzing and predicting based on each block, and obtaining the final prediction by taking the average.

CONCLUSIONS

We compare this method to others using two datasets and find that our method shows better results in both cases. Our method can effectively solve the prediction problem in multi-omics data of small sample, and provide valuable resources for further studies on the fusion of more omics data.

GEO Data Mining Identifies OLR1 as a Potential Biomarker in NSCLC Immunotherapy

Non-small cell lung cancer (NSCLC) is the most common type of lung cancer. The tumor immune microenvironment (TME) in NSCLC is closely correlated to tumor initiation, progression, and prognosis. TME failure impedes the generation of an effective antitumor immune response. In this study, we attempted to explore TME and identify a potential biomarker for NSCLC immunotherapy. 48 potential immune-related genes were identified from 11 eligible Gene Expression Omnibus (GEO) data sets. We applied the CIBERSORT computational approach to quantify bulk gene expression profiles and thereby infer the proportions of 22 subsets of tumor-infiltrating immune cells (TICs); 16 kinds of TICs showed differential distributions between the tumor and control tissue samples. Multiple linear regression analysis was used to determine the correlation between TICs and 48 potential immune-related genes. Nine differential immune-related genes showed statistical significance. We analyzed the influence of nine differential immune-related genes on NSCLC immunotherapy, and OLR1 exhibited the strongest correlation with four well-recognized biomarkers (PD-L1, CD8A, GZMB, and NOS2) of immunotherapy. Differential expression of OLR1 showed its considerable potential to divide TICs distribution, as determined by non-linear dimensionality reduction analysis. In immunotherapy prediction analysis with the comparatively reliable tool TIDE, patients with higher OLR1 expression were predicted to have better immunotherapy outcomes, and OLR1 expression was potentially highly correlated with PD-L1 expression, the average of CD8A and CD8B, IFNG, and Merck18 expression, T cell dysfunction and exclusion potential, and other significant immunotherapy predictors. These findings contribute to the current understanding of TME with immunotherapy. OLR1 also shows potential as a predictor or a regulator in NSCLC immunotherapy.

Abnormal spindle-like microcephaly-associated protein enhances cell invasion through Wnt/β-catenin-dependent regulation of epithelial-mesenchymal transition in non-small cell lung cancer cells

Background

Lung cancer is one of the most common cancer worldwide, invasion and metastasis are still the bottleneck in the clinical setting. More diagnostic markers and drug targets need to be clarified. Therefore, we screened abnormal spindle-like microcephaly-associated protein (ASPM) as our candidate gene, which is associated with the poor prognosis. The aim of the present study was to understand the roles of ASPM in cell invasion in non-small cell lung cancer (NSCLC).

Methods

Gene Expression Omnibus (GEO) datamining was used to identify ASPM. Transwell invasion assay, quantitative reverse transcription polymerase chain reaction (qRT-PCR), and Western blot analysis were performed to discover the molecular functions of ASPM. Overexpression and small interfering mediated knockdown techniques have been used to study the cell invasion hallmarks of cancer.

Results

ASPM stood out among all the candidate genes from GEO datamining. ASPM in lung cancer tissues has been associated with poor overall survival rate. The protein levels of ASPM has been validated using lung cancer patients’ tissues, which upregulation of ASPM expression has been found in lung cancer patients. Silencing of ASPM decreased the cell invasion reflected by epithelial-mesenchymal transition (EMT) biomarkers: downregulation of vimentin and upregulation of E-cadherin. Matrix metalloproteinase (MMP) 2/9 protein levels were also affected upon transient knockdown of ASPM. Furthermore, the suppression of ASPM markedly inhibited the Wnt/β-catenin signaling pathway in vitro. The ectopic expression of ASPM had the opposite effect. The inhibition of β-catenin in ASPM-overexpressing lung cancer cells reduced the expression of EMT markers. The inhibitory effects on the Wnt/β-catenin signaling pathway were attenuated in cancer cells when ASPM was silenced. These findings demonstrated that the silencing of ASPM strongly reduced cell invasion in lung cancer.

Conclusions

ASPM promoted NSCLC invasion through EMT and by affecting the MMP family of proteins. The Wnt/β-catenin signaling pathway played an indispensable role in the ASPM-mediated NSCLC EMT-invasion cascade.

Expression of SARS-COV-2 cell receptor gene ACE2 is associated with immunosuppression and metabolic reprogramming in lung adenocarcinoma based on bioinformatics analyses of gene expression profiles

The aberrant expression level of SARS-CoV-2 cell receptor gene ACE2 was reported in lung adenocarcinoma (LUAD) comorbidity of COVID-19. However, the association of ACE2 expression levels with immunosuppression and metabolic reprogramming in LUAD remains lacking. We investigated the expression level of ACE2, an association of ACE2 expression level with various types of immune signatures, immune ratios, and pathways. We employed a weighted gene co-expression network analysis (WGCNA) R package to identify the gene modules and investigated prognostic roles of hub genes in LUAD. Overexpression of ACE2 level was found in LUAD and ACE2 expression was negatively associated with various types of immune signatures including CD8⁺ T cells, CD4⁺ regulatory T cells, NK cells, and T cell activation. Besides, ACE2 upregulation was not only associated with CD8⁺ T cell/CD4+ regulatory T cell ratios but also linked with downregulation of immune-markers including CD8A, KLRC1, GZMA, GZMB, NKG7, CCL4, and IFNG. Moreover, the ACE2 expression level was found to be associated with the enrichment level of various metabolic pathways and it was also found that the metabolic pathways are directly positively correlated with the increased expression levels of ACE2, indicating that the overexpression of ACE2 is associated with metabolic reprogramming in LUAD. Furthermore, WGCNA based analysis revealed the gene modules in the high-ACE2-expression-level group of LUAD and identified GCLC and SLC7A11 hub genes which are not only highly expressed in lung adenocarcinoma but also correlated with the poor survival prognosis. Our analysis of ACE2 in LUAD tissues suggests that ACE2 is not only a receptor but is also associated with immunosuppression and metabolic reprogramming. This study underlines the clue for understanding the clinical significance of ACE2 in COVID-19 patients with LUAD comorbidity.

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The expression level of ACE2 is negatively associated with the immune signatures.

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ACE2 upregulation also linked with the downregulation of immune-markers.

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The ACE2 expression level is positively associated with the metabolic pathways.

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WGCNA based analysis revealed the gene modules in a high-ACE2-expression-level group.

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Highly expressed GCLC and SLC7A11 hub genes are correlated with the poor prognosis.

Myc linked to dysregulation of cholesterol transport and storage in nonsmall cell lung cancer

Nonsmall cell lung cancer (NSCLC) is a leading cause of cancer-related deaths. While mutations in Kras and overexpression of Myc are commonly found in patients, the role of altered lipid metabolism in lung cancer and its interplay with oncogenic Myc is poorly understood. Here we use a transgenic mouse model of Kras-driven lung adenocarcinoma with reversible activation of Myc combined with surface analysis lipid profiling of lung tumors and transcriptomics to study the effect of Myc activity on cholesterol homeostasis. Our findings reveal that the activation of Myc leads to the accumulation of cholesteryl esters (CEs) stored in lipid droplets. Subsequent Myc deactivation leads to further increases in CEs, in contrast to tumors in which Myc was never activated. Gene expression analysis linked cholesterol transport and storage pathways to Myc activity. Our results suggest that increased Myc activity is associated with increased cholesterol influx, reduced efflux, and accumulation of CE-rich lipid droplets in lung tumors. Targeting cholesterol homeostasis is proposed as a promising avenue to explore for novel treatments of lung cancer, with diagnostic and stratification potential in human NSCLC.

CYP3A5 suppresses metastasis of lung adenocarcinoma through ATOH8/Smad1 axis

Cytochrome P450 3A5 (CYP3A5) maintains primary roles in toxic metabolism, catalyzes redox reaction, and contributes to chemotherapeutic resistance. However, the mechanism of CYP3A5 in carcinogenesis remains largely undefined. Here, we investigated a novel role of CYP3A5 inhibiting the metastasis in lung adenocarcinoma (LUAD) via ATOH8/Smad1 axis. We found that CYP3A5 was generally down-regulated in LUAD by RT-PCR, western blot and immunohistochmeistry (IHC) in tissues and cell lines. Low expression of CYP3A5 was significantly associated with poor prognosis of LUAD patients. Functionally, ectopic expression of CYP3A5 could substantially inhibit the migration and invasion in vitro. Consistently, up-regulation of CYP3A5 dramatically suppressed metastatic ability in vivo. Mechanistically, high-throughput phosphorylation chip indicated that CYP3A5 significantly decreased the phosphorylation of Smad1, resulting in suppression of metastasis. Furthermore, bioinformatics analysis and co-immunoprecipitation (Co-IP) experiments uncovered that CYP3A5 interacted with ATOH8, and the interaction, in turn, mediated in-activation in the Smad1 pathway. The combined IHC panel, including CYP3A5 and phosphorylation of Smad1, exhibited a better prognostic value for LUAD patients than any of these components individually. Taken together, CYP3A5 repressed activation of Smad1 to inhibit LUAD metastasis via interacting with ATOH8, indicating a novel potential mechanism of CYP3A5 in LUAD progression.

Detection and Independent Validation of Model-Based Quantitative Transcriptional Regulation Relationships Altered in Lung Cancers

Differential expressions of genes are widely evaluated for the diagnosis and prognosis correlations with diseases. But limited studies investigate how transcriptional regulations are quantitatively altered in diseases. This study proposes a novel model-based quantitative measurement of transcriptional regulatory relationships between mRNA genes and Transcription Factor (TF) genes (mqTrans features). This study didn't consider the regulatory relationships between TF genes, so the mRNA genes were the protein-coding genes excluding the TF genes. The models are trained in the control samples in a lung cancer dataset and evaluated in two independent datasets and the hold-out testing samples from the third dataset. Twenty-nine mRNA genes are detected with transcriptional regulations quantitatively altered in lung cancers. The transcriptional modification technologies like RNA interference (RNAi) may be utilized to restore the altered transcriptional regulations in lung cancers.

Genetic variants in the histone methylation and acetylation pathway and their risks in eight types of cancers

OBJECTIVES

The histone methylation and acetylation pathway genes regulate cell growth and survival. Aberrations in this pathway are implicated in a variety of cancers. This study aimed to identify germline genetic variants in histone methylation and acetylation pathway genes that may contribute to risk in eight types of cancers and to explore the relation between the whole pathway and their risks in these types of cancers.

METHODS

Germline genetic variants in 89 genes in the histone methylation and acetylation pathway were explored. Gene-based and pathway-based associations with eight types of cancers were analyzed using logistic regression models and the permutation-based adaptive rank-truncated product method, respectively.

RESULTS

Gene-level associations revealed that genetic variants in 45 genes were significantly associated with the risk of cancer. The total histone methylation and acetylation pathway was significantly associated with the risk of esophageal squamous cell carcinoma (P = 0.0492) and prostate (P = 0.0038), lung (P = 0.00015), and bladder cancer (P = 0.00135), but not with breast (P = 0.182), pancreatic (P = 0.336) and gastric cancer (P = 0.347) and renal cell carcinoma (P =0.828).

CONCLUSIONS

Our study suggested there is an association between germline genetic variation at the overall histone methylation and acetylation pathway level and some individual genes with cancer risk. Further studies are needed to validate these relations and to explore relative mechanisms.

[Diffuse and calcified nodular opacities]

Pulmonary adenocarcinoma is difficult to identify right away with respect to anamnestic and even to radiological data. We here report the case of a woman with dyspnea. Radiological examination showed disseminated micronodular opacity confluent in both lung fields with calcifications in certain locations. Histological examination of transbronchial biopsies allowed the diagnosis. The prognosis was poor with the death of the patient.

Gene-set Analysis with CGI Information for Differential DNA Methylation Profiling

DNA methylation is a well-established epigenetic biomarker for many diseases. Studying the relationships among a group of genes and their methylations may help to unravel the etiology of diseases. Since CpG-islands (CGIs) play a crucial role in the regulation of transcription during methylation, including them in the analysis may provide further information in understanding the pathogenesis of cancers. Such CGI information, however, has usually been overlooked in existing gene-set analyses. Here we aimed to include both pathway information and CGI status to rank competing gene-sets and identify among them the genes most likely contributing to DNA methylation changes. To accomplish this, we devised a Bayesian model for matched case-control studies with parameters for CGI status and pathway associations, while incorporating intra-gene-set information. Three cancer studies with candidate pathways were analyzed to illustrate this approach. The strength of association for each candidate pathway and the influence of each gene were evaluated. Results show that, based on probabilities, the importance of pathways and genes can be determined. The findings confirm that some of these genes are cancer-related and may hold the potential to be targeted in drug development.

Sequential model selection-based segmentation to detect DNA copy number variation

Array-based CGH experiments are designed to detect genomic aberrations or regions of DNA copy-number variation that are associated with an outcome, typically a state of disease. Most of the existing statistical methods target on detecting DNA copy number variations in a single sample or array. We focus on the detection of group effect variation, through simultaneous study of multiple samples from multiple groups. Rather than using direct segmentation or smoothing techniques, as commonly seen in existing detection methods, we develop a sequential model selection procedure that is guided by a modified Bayesian information criterion. This approach improves detection accuracy by accumulatively utilizing information across contiguous clones, and has computational advantage over the existing popular detection methods. Our empirical investigation suggests that the performance of the proposed method is superior to that of the existing detection methods, in particular, in detecting small segments or separating neighboring segments with differential degrees of copy-number variation.