Bioinformatics analyses of the differences between lung adenocarcinoma and squamous cell carcinoma using The Cancer Genome Atlas expression data

Expression Profiles of Hypoxia-Related Genes of Cancers Originating from Anatomically Similar Locations Using TCGA Database Analysis

Background: Hypoxia is a well-recognized characteristic of the tumor microenvironment of solid cancers. This study aimed to analyze hypoxia-related genes shared by groups based on tumor location. Methods: A total of 9 hypoxia-related pathways from the Kyoto Encyclopedia of Genes and Genomes database or the Reactome database were selected, and 850 hypoxia-related genes were analyzed. Based on their anatomical locations, 14 tumor types were categorized into 6 groups. The group-specific genetic risk score was classified as high- or low-risk based on mRNA expression, and survival outcomes were evaluated. Results: The risk scores in the Female Reproductive group and the Lung group were internally and externally validated. In the Female Reproductive group, CDKN2A, FN1, and ITGA5 were identified as hub genes associated with poor prognosis, while IL2RB and LEF1 were associated with favorable prognosis. In the Lung group, ITGB1 and LDHA were associated with poor prognosis, and GLS2 was associated with favorable prognosis. Functional enrichment analysis showed that the Female Reproductive group was enriched in relation to cilia and skin, while the Lung group was enriched in relation to cytokines and defense. Conclusions: This analysis may lead to better understanding of the mechanisms of cancer progression and facilitate establishing new biomarkers for prognosis prediction.

LncRNA FAM138B inhibits the progression of non-small cell lung cancer through miR-105-5p

As a type of lung cancer, non-small cell lung cancer (NSCLC) has the characteristics of high mortality and high recurrence rate, which poses a great threat to human life and health. Due to the high risk of surgical treatment and the slow recovery of wounds, non-coding RNAs, especially lncRNAs are used as new potential clinical prognostic markers to prevent and treat cancer in advance. This study aims to explore the role of FAM138B in NSCLC and its possibility as a prognostic biomarker. Real-timequantitative polymerase chain reaction (RT-qPCR) was used to detect the expression and overexpression level of lncRNA FAM138B (FAM138B) in cells and tissues. The CCK-8, Transwell migration and invasion methods were performed to observe the cell transfection.The interaction between FAM138B and miR-105-5p was predicted by the bioinformatics tool starBase v2.0, and verified by the luciferase reporter gene experiment. Kaplan-Meier and Cox regression analyses were used to determine the prognostic significance of FAM138B in NSCLC. The expression of FAM138B is down-regulated in NSCLC cells and tissues. Overexpression of FAM138B can inhibit the expression level of miR-105-5p in NSCLC cells, and the ability of NSCLC cells to proliferate, migrate and invade is downregulated. FAM138B targets miR-105-5p, and there is a negative correlation between FAM138B and miR-105-5p. It is confirmed that FAM138B inhibits the progression of NSCLC by targeting miR-105-5p and can be a potential prognostic biomarker for NSCLC.

IncRNA EPB41L4A-AS1 Mitigates the Proliferation of Non-Small-Cell Lung Cancer Cells through the miR-105-5p/GIMAP6 Axis

Non-small-cell lung cancer (NSCLC) is the major subtype of lung cancer, with a series of long non-coding RNAs (lncRNAs), microRNAs (miRNAs), and proteins involved in its pathogenesis. This study sought to investigate the functionality of lncRNA EPB41L4A antisense RNA 1 (lncRNA EPB41L4A-AS1) in the proliferation of NSCLC cells and provide a novel theoretical reference for NSCLC treatment. Levels of lncRNA EPB41L4A-AS1, miR-105-5p, and GTPase, IMAP family member 6 (GIMAP6) in tissues and cells were measured by RT-qPCR and the correlation between lncRNA EPB41L4A-AS1 and clinicopathological characteristics was analyzed. Cell proliferation was evaluated by cell counting kit-8 and colony formation assays. The subcellular localization of lncRNA EPB41L4A-AS1 was analyzed by the subcellular fractionation assay and the binding of miR-105-5p to lncRNA EPB41L4A-AS1 or GIMAP6 was analyzed by dual-luciferase and RNA pull-down assays. Functional rescue experiments were performed to analyze the role of miR-105-5p/GIMAP6 in NSCLC cell proliferation. lncRNA EPB41L4A-AS1 and GIMAP6 were downregulated while miR-105-5p was upregulated in NSCLC tissues and cells. lncRNA EPB41L4A-AS1 was correlated with tumor size and clinical staging and its overexpression reduced NSCLC cell proliferation. lncRNA EPB41L4A-AS1 was negatively correlated with miR-105-5p and positively correlated with GIMAP6 in NSCLC tissues, and lncRNA EPB41L4A-AS1 sponged miR-105-5p to promote GIMAP6 transcription in NSCLC cells. Overexpression of miR-105-5p or knockdown of GIMAP6 reversed the inhibition of lncRNA EPB41L4A-AS1 overexpression on NSCLC cell proliferation. lncRNA EPB41L4A-AS1 was downregulated in NSCLC and mitigated NSCLC cell proliferation through the miR-105-5p/GI-MAP6 axis.

An integrated model of FTO and METTL3 expression that predicts prognosis in lung squamous cell carcinoma patients

Background

Lung squamous cell carcinoma (LUSC) approximately accounts for a third of lung cancers. However, the role of N6-methyladenosine (m6A) in LUSC remains largely unknown according to previous studies.

Methods

In this study, we investigated the mutations, copy number variants (CNVs), expression of 20 m6A RNA methylation regulators, and clinical data from The Cancer Genome Atlas-LUSC (TCGA-LUSC). These data were used for the training cohort of screening potential biomarkers. The prognostic model of m6A RNA methylation regulators was constructed. A receiver operating characteristic (ROC) analysis was undertaken to determine the area under the curves (AUCs) (for 3- and 5-year survival) for the model. Additionally, the accuracy of the two-gene model was confirmed with external data verifications. Combined two-gene model and clinincal information were performed to construct a nomogram to predict patient’s prognostic risk assessment.

Results

Fat mass- and obesity-associated protein (FTO) and methyltransferase-like 3 (METTL3) were identified as potential prognostic biomarkers to evaluate benign and malignant tumors and prognosticate. The following prognostic model of m6A RNA methylation regulators was constructed: risk score = 0.162 × FTO − 0.069 × METTL3. Patients in low-risk group [median overall survival (mOS), 43.4 months] had longer survival than those with high-risk (mOS, 67.3 months) with P=0.0023. The smoking grade and risk score could be independent prognostic factors (P=0.00098 and P=0.0014, respectively). Ultimately, a nomogram was developed to assist clinicians to predict clinical outcomes.

Conclusions

FTO and METTL3 are potential prognostic biomarkers of LUSC. The two-gene model’s use of prognostic risk scores may provide guidance in the selection of therapeutic strategies.

Precise pathological classification of non-small cell lung adenocarcinoma and squamous carcinoma based on an integrated platform of targeted metabolome and lipidome

BACKGROUND

Non-small cell lung cancer (NSCLC) is the leading cause of cancer-related death worldwide. Lung adenocarcinoma (LUAD) and squamous cell carcinoma (LUSC) are the most common subtypes of NSCLC. Despite genetic differences between LUAD and LUSC have been clarified in depth, the metabolic differences of these two subtypes are still unclear.

METHODS

Totally, 128 plasma samples of NSCLC patients were collected before initial treatments, followed by determination of LC-ESI-Q TRAP-MS/MS. Differentially expressed metabolites were screened based on a strict standard.

RESULTS

Based on the integrated platform of targeted metabolome and lipidome, a total of 1141 endogenous metabolites (including 809 lipids) were finally detected in the plasma of NSCLC patients, including 16 increased and 3 decreased endogenous compounds in LUAD group when compared with LUSC group. Thereafter, a logistic regression model integrating four differential metabolites [2-(Methylthio) ethanol, Cortisol, D-Glyceric Acid, and N-Acetylhistamine] was established and could accurately differentiate LUAD and LUSC with an area under the ROC curve of 0.946 (95% CI 0.886-1.000). The cut-off value showed a satisfactory efficacy with 92.0% sensitivity and 92.9% specificity. KEGG functional enrichment analysis showed these differentially expressed metabolites could be further enriched in riboflavin metabolism, steroid hormone biosynthesis, prostate cancer, etc. The endogenous metabolites identified in this study have the potential to be used as novel biomarkers to distinguish LUAD from LUSC.

CONCLUSIONS

Our research might provide more evidence for exploring the pathogenesis and differentiation of NSCLC. This research could promote a deeper understanding and precise treatment of lung cancer.

MicroRNA-22 contributes to dexamethasone-induced osteoblast differentiation inhibition and dysfunction through targeting caveolin-3 expression in osteoblastic cells

Osteoporosis is a common complication of long-term use of glucocorticoids (GCs) characterized by the loss of bone mass and damage of the microarchitecture as well as osteoblast dysfunction. Previous studies have demonstrated that microRNA-22 (miR-22) is the negative modulator of osteogenesis that may target caveolin-3 (CAV3), which has been reported to enhance bone formation and inhibit the progression of osteoporosis as well as apoptosis. The present study aimed to investigate whether miR-22 may be involved in dexamethasone (DEX)-induced inhibition of osteoblast differentiation and dysfunction by regulating CAV3 expression. Reverse transcription-quantitative PCR (RT-qPCR) was performed to measure the expression of miR-22 and western blotting was performed to determine protein levels. The results demonstrated that miR-22 expression was upregulated in DEX-treated osteoblastic cells compared with the control group. In addition, miR-22 mimic aggravated, whereas miR-22 inhibitor mitigated DEX-induced damage in osteoblastic cells compared with the control groups. Additionally, CAV3 was identified as the target of miR-22 in osteoblasts using RT-qPCR, western blotting and dual-luciferase reporter gene assay analysis. The results also demonstrated that silencing of CAV3 blocked the beneficial effects of miR-22 inhibitor against DEX-induced cell damage and apoptosis in osteoblasts, as evidenced by the increased expression levels of cleaved caspase-3, Bax and alkaline phosphatase activity as well as decreased cell viability and Bcl-2 levels. Collectively, these results indicate a novel molecular mechanism by which miR-22 contributes to DEX-induced osteoblast dysfunction and apoptosis via the miR-22/CAV3 pathway.

Sodium hydrosulfide alleviates dexamethasone-induced cell senescence and dysfunction through targeting the miR-22/sirt1 pathway in osteoblastic MC3T3-E1 cells

Glucocorticoid-induced osteoporosis is characterized by osteoblastic cell and microarchitecture dysfunction, as well as a loss of bone mass. Cell senescence contributes to the pathological process of osteoporosis and sodium hydrosulfide (NaHS) regulates the potent protective effects through delaying cell senescence. The aim of the present study was to investigate whether senescence could contribute to dexamethasone (Dex)-induced osteoblast impairment and to examine the effect of NaHS on Dex-induced cell senescence and damage. It was found that the levels of the senescence-associated markers, p53 and p21, were markedly increased in osteoblasts exposed to Dex. A p53 inhibitor reversed Dex-induced osteoblast injury, a process that was mitigated by NaHS administration through alleviating osteoblastic cell senescence. MicroRNA (miR)-22 blocked the impact of NaHS on Dex-induced osteoblast damage and senescence through targeting the regulation of Sirtuin 1 (sirt1) expression, as shown by the decreased cell viability and alkaline phosphatase activity, as well as an increased expression of p53 and p21. It was revealed that the sirt1 gene was the target of miR-22 in osteoblastic MC3T3-E1 cells through combining the results of dual luciferase reporter assays and reverse transcription-quantitative PCR, as well as western blot analyses. Silencing of sirt1 abolished the protective effect of NaHS against Dex-associated osteoblast senescence and injury. Taken together, the present study showed that NaHS prevents Dex-induced cell senescence and damage through targeting the miR-22/sirt1 pathway in osteoblastic MC3T3-E1 cells.

Extracting a biologically latent space of lung cancer epigenetics with variational autoencoders

Background

Lung cancer is one of the most malignant tumors, causing over 1,000,000 deaths each year worldwide. Deep learning has brought success in many domains in recent years. DNA methylation, an epigenetic factor, is used for model training in many studies. There is an opportunity for deep learning methods to analyze the lung cancer epigenetic data to determine their subtypes for appropriate treatment.

Results

Here, we employ variational autoencoders (VAEs), an unsupervised deep learning framework, on 450K DNA methylation data of TCGA-LUAD and TCGA-LUSC to learn latent representations of the DNA methylation landscape. We extract a biologically relevant latent space of LUAD and LUSC samples. It is showed that the bivariate classifiers on the further compressed latent features could classify the subtypes accurately. Through clustering of methylation-based latent space features, we demonstrate that the VAEs can capture differential methylation patterns about subtypes of lung cancer.

Conclusions

VAEs can distinguish the original subtypes from manually mixed methylation data frame with the encoded features of latent space. Further applications about VAEs should focus on fine-grained subtypes identification for precision medicine.

Simulated microgravity with floating environment promotes migration of non-small cell lung cancers

A migration of cancer is one of the most important factors affecting cancer therapy. Particularly, a cancer migration study in a microgravity environment has gained attention as a tool for developing cancer therapy. In this study, we evaluated the proliferation and migration of two types (adenocarcinoma A549, squamous cell carcinoma H1703) of non-small cell lung cancers (NSCLC) in a floating environment with microgravity. When we measured proliferation of two NSCLCs in the microgravity (MG) and ground-gravity (CONT), although initial cell adhesion in MG was low, a normalized proliferation rate of A549 in MG was higher than that in CONT. Wound healing results of A549 and H1703 showed rapid recovery in MG; particularly, the migration rate of A549 was faster than that of H1703 both the normal and low proliferating conditions. Gene expression results showed that the microgravity accelerated the migration of NSCLC. Both A549 and H1703 in MG highly expressed the migration-related genes MMP-2, MMP-9, TIMP-1, and TIMP-2 compared to CONT at 24 h. Furthermore, analysis of MMP-2 protein synthesis revealed weaker metastatic performance of H1703 than that of A549. Therefore, the simulated microgravity based cancer culture environment will be a potential for migration and metastasis studies of lung cancers.

Long non-coding RNA-HAGLR suppressed tumor growth of lung adenocarcinoma through epigenetically silencing E2F1

Emerging evidence indicates that long noncoding RNAs (LncRNAs) are new players in gene regulation but their mechanisms of action are mainly undocumented. In this study, we investigated LncRNA alterations that contribute to lung cancer by analyzing published microarray data in Gene Expression Obminus (GEO) and The Cancer Genome Atlas RNA (TCGA) sequencing data. Here, we reported that HAGLR (also called HOXD-AS1) was frequently down-regulated in lung adenocarcinoma (LUAD) tissues, and decreased HAGLR expression was clinically associated with shorter survival of LUAD patients. Preclinical studies using multiple LUAD cells and in vivo mouse model indicated that HAGLR could attenuate LUAD cell growth in vitro and in vivo. Mechanistically, HAGLR could physically interact with DNMT1, and recruit DNMT1 on E2F1 promoter to increase local DNA methylation. Overall, our study demonstrated that HAGLR promoted LUAD progression by recruiting DNMT1 to modulate the promoter methylation and expression of E2F1, which expanded potential therapeutic strategies for LUAD treatment.

Molecular and Morphological Profiling of Lung Cancer: A Foundation for "Next-Generation" Pathologists and Oncologists

The pathological diagnosis of lung cancer has largely been based on the morphological features observed microscopically. Recent innovations in molecular and genetic technology enable us to compare conventional histological classifications, protein expression status, and gene abnormalities. The introduction of The Cancer Genome Atlas (TCGA) project along with the widespread use of the next-generation sequencer (NGS) have facilitated access to enormous data regarding the molecular profiles of lung cancer. The World Health Organization classification of lung cancer, which was revised in 2015, is based on this progress in molecular pathology; moreover, immunohistochemistry has come to play a larger role in diagnosis. In this article, we focused on genetic and epigenetic abnormalities in non-small cell carcinoma (adenocarcinoma and squamous cell carcinoma), neuroendocrine tumor (including carcinoids, small cell carcinoma, and large cell neuroendocrine carcinoma), and carcinoma with rare histological subtypes. In addition, we summarize the therapeutic targeted reagents that are currently available and undergoing clinical trials. A good understanding of the morphological and molecular profiles will be necessary in routine practice when the NGS platform is widely used.

Penalized integrative semiparametric interaction analysis for multiple genetic datasets

In this article, we consider a semiparametric additive partially linear interaction model for the integrative analysis of multiple genetic datasets. The goals are to identify important genetic predictors and gene-gene interactions and to estimate the nonparametric functions that describe the environmental effects at the same time. To find the similarities and differences of the genetic effects across different datasets, we impose a group structure on the regression coefficients matrix under the homogeneity assumption, ie, models for different datasets share the same sparsity structure, but the coefficients may differ across datasets. We develop an iterative approach to estimate the parameters of main effects, interactions and nonparametric functions, where a reparametrization of interaction parameters is implemented to meet the strong hierarchy assumption. We demonstrate the advantages of the proposed method in identification, estimation, and prediction in a series of numerical studies. We also apply the proposed method to the Skin Cutaneous Melanoma data and the lung cancer data from the Cancer Genome Atlas.

Differentially mutated subnetworks discovery

PROBLEM

We study the problem of identifying differentially mutated subnetworks of a large gene-gene interaction network, that is, subnetworks that display a significant difference in mutation frequency in two sets of cancer samples. We formally define the associated computational problem and show that the problem is NP-hard.

ALGORITHM

We propose a novel and efficient algorithm, called DAMOKLE, to identify differentially mutated subnetworks given genome-wide mutation data for two sets of cancer samples. We prove that DAMOKLE identifies subnetworks with statistically significant difference in mutation frequency when the data comes from a reasonable generative model, provided enough samples are available.

EXPERIMENTAL RESULTS

We test DAMOKLE on simulated and real data, showing that DAMOKLE does indeed find subnetworks with significant differences in mutation frequency and that it provides novel insights into the molecular mechanisms of the disease not revealed by standard methods.

An immunogenic NSCLC microenvironment is associated with favorable survival in lung adenocarcinoma

The tumor microenvironment consists of an intricately organized system through which immune cells and cancer cells may communicate to regulate anti-tumor immunogenicity. To this end, non-small cell lung cancer (NSCLC) has been shown to activate a variety of immunological mechanisms, thereby broadening our understanding of lung cancer immunobiology. However, while recent work has highlighted the importance of NSCLC immunology and prognosis, studies have not yet examined the tumor microenvironment (TME) globally in regards to the survival outcomes between two major NSCLC subtypes: lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC). In the present study, we identify an immunogenic tumor microenvironment state in NSCLC that is enriched for the lung adenocarcinoma subtype. By utilizing TME cell enrichment scores and RNA-seq expression data, we show that the inflamed TME is associated with favorable patient survival in lung adenocarcinoma, but this does not hold true for lung squamous cell carcinoma. Moreover, differentially regulated pathways between immune-inflamed and immune-excluded tumors within LUAD and LUSC were not subtype specific. Instead, immune-inflamed LUSC samples possessed elevated immune checkpoint marker expression when compared to those of the LUAD samples, thereby offering a putative explanation for our prognostic observations. These results shed light on the immunological prognostic effects within lung cancer and may encourage further TME exploration between these two subtypes as the landscape of NSCLC therapy progresses.

Identification of cancer omics commonality and difference via community fusion

The analysis of cancer omics data is a "classic" problem; however, it still remains challenging. Advancing from early studies that are mostly focused on a single type of cancer, some recent studies have analyzed data on multiple "related" cancer types/subtypes, examined their commonality and difference, and led to insightful findings. In this article, we consider the analysis of multiple omics datasets, with each dataset on one type/subtype of "related" cancers. A Community Fusion (CoFu) approach is developed, which conducts marker selection and model building using a novel penalization technique, informatively accommodates the network community structure of omics measurements, and automatically identifies the commonality and difference of cancer omics markers. Simulation demonstrates its superiority over direct competitors. The analysis of TCGA lung cancer and melanoma data leads to interesting findings.

Transcription Factors Contribute to Differential Expression in Cellular Pathways in Lung Adenocarcinoma and Lung Squamous Cell Carcinoma

Lung cancers are broadly classified into small cell lung cancers and non-small cell lung cancers (NSCLC). Lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) are two common subtypes of NSCLC, and despite the fact that both occur in lung tissues, these two subtypes show a number of different pathological characteristics. To investigate the differences and seek potential therapy targets, we used bioinformatics methods to analyze RNA-Seq data from different aspects. The previous studies and comparative pathway enrichment analysis on publicly available data showed that expressed or inhibited genes are different in two cancer subtypes through important pathways. Some of these genes could not only affect cell function through expression, but also could regulate other genes' expression by binding to a specific DNA sequence. This kind of genes is called transcription factor (TF) or sequence-specific DNA-binding factor. Transcription factors play important roles in controlling gene expression in carcinoma pathways. Our results revealed transcription factors that may cause differential expression of genes in cellular pathways of LUAD and LUSC, which provide new clues for study and treatment. Once such TF is NFE2l2 which may regulate genes in the Wnt signaling pathway, and the MAPK signaling pathway, thus leading to an increase the cell growth, cell division, and gene transcription. Another TF-XBP1 has high correlation with genes related to cell adhesion molecules and cytokine-cytokine receptor interaction pathways that may further affect the immune system. Moreover, the two TF and high correlated genes also show similar patterns in an independent GEO data set.

Diagnosis value of aberrantly expressed microRNA profiles in lung squamous cell carcinoma: a study based on the Cancer Genome Atlas

Background

Lung cancer is considered as one of the most frequent and deadly cancers with high mortality all around the world. It is critical to find new biomarkers for early diagnosis of lung cancer, especially lung squamous cell carcinoma (LUSC). The Cancer Genome Atlas (TCGA) is a database which provides both cancer and clinical information. This study is a comprehensive analysis of a novel diagnostic biomarker for LUSC, based on TCGA.

Methods and Results

The present study investigated LUSC-specific key microRNAs (miRNAs) from large-scale samples in TCGA. According to exclusion criteria and inclusion criteria, the expression profiles of miRNAs with related clinical information of 332 LUSC patients were obtained. Most aberrantly expressed miRNAs were identified between tumor and normal samples. Forty-two LUSC-specific intersection miRNAs (fold change >2, p < 0.05) were obtained by an integrative computational method, among them six miRNAs were found to be aberrantly expressed concerning characteristics of patients (gender, lymphatic metastasis, patient outcome assessment) through Student t-test. Five miRNAs correlated with overall survival (log-rank p < 0.05) were obtained through the univariate Cox proportional hazards regression model and Mantel–Haenszel test. Then, five miRNAs were randomly selected to validate the expression in 47 LUSC patient tissues using quantitative real-time polymerase chain reaction. The results showed that the test findings were consistent with the TCGA findings. Also, the diagnostic value of the specific key miRNAs was determined by areas under receiver operating characteristic curves. Finally, 577 interaction mRNAs as the targets of 42 LUSC-specific intersection miRNAs were selected for further bioinformatics analysis.

Conclusion

This study indicates that this novel microRNA expression signature may be a useful biomarker of the diagnosis for LUSC patients, based on bioinformatics analysis.