Dataset Information

Identification of LBX2 as a novel causal gene of lung adenocarcinoma.

ABSTRACT: BACKGROUND:Lung adenocarcinoma (LUAD) is the most predominant histological type of lung cancer with a poor prognosis. In this study, we demonstrate that LBX2 regulates cell proliferation, migration and invasion and the potential molecular mechanism in LUAD. METHODS:The Cancer Genome Atlas dataset was accessed to screen for novel genes and immunohistochemistry (IHC) assays were performed to determine the association between LBX2 expression and clinicopathological features of LUAD. 5-ethynyl-2'-deoxyuridine, colony formation and Real Time xCELLigence analysis system were used to evaluate the cell proliferation abilities of LUAD. Wound healing, transwell and Matrigel assays were used to detect cell migration and invasion capacities. Xenograft tumor models were used to assess the oncogenic role of LBX2 in vivo. RESULTS:We found that LBX2 was hyperexpressed in LUAD and correlated with clinicopathological features and poor prognosis in LUAD patients. Knockdown of LBX2 inhibited cell proliferation, migration and invasion of LUAD, whereas ectopic expression of LBX2 enhanced tumor growth, migration, and invasion. We further found that LBX2 might participate in epithelial-to-mesenchymal transition (EMT) progression and influence EMT-related gene expression. CONCLUSIONS:The current study suggests that LBX2 plays an oncogenic role in LUAD and may participate in tumor proliferation, migration, and invasion through EMT progression. KEY POINTS:Significant findings of the study LBX2 might participate in LUAD cell proliferation, migration and invasion via EMT progression. What this study adds LBX2 may represent a potential biomarker and a promising therapeutic target for LUAD.

SUBMITTER: Hu J

PROVIDER: S-EPMC7396393 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Identification of LBX2 as a novel causal gene of lung adenocarcinoma.

Hu Jingwen J Bai Yongkang Y Zhang Quanli Q Li Ming M Yin Rong R Xu Lin L

Thoracic cancer 20200622 8

<h4>Background</h4>Lung adenocarcinoma (LUAD) is the most predominant histological type of lung cancer with a poor prognosis. In this study, we demonstrate that LBX2 regulates cell proliferation, migration and invasion and the potential molecular mechanism in LUAD.<h4>Methods</h4>The Cancer Genome Atlas dataset was accessed to screen for novel genes and immunohistochemistry (IHC) assays were performed to determine the association between LBX2 expression and clinicopathological features of LUAD. ...[more]

PMID: 32567804

Similar Datasets

Project description:BackgroundLung cancer has become the most common cancer type and caused the most cancer deaths. Lung adenocarcinoma (LUAD) is one of the major types of lung cancer. Accumulating evidence suggests the tumor microenvironment is correlated with the tumor progress and the patient's outcome. This study aimed to establish a gene signature based on tumor microenvironment that can predict patients' outcomes for LUAD.MethodsDataset TCGA-LUAD, downloaded from the TCGA portal, were taken as training cohort, and dataset GSE72094, obtained from the GEO database, was set as validation cohort. In the training cohort, ESTIMATE algorithm was applied to find intersection differentially expressed genes (DEGs) among tumor microenvironment. Kaplan-Meier analysis and univariate Cox regression model were performed on intersection DEGs to preliminarily screen prognostic genes. Besides, the LASSO Cox regression model was implemented to build a multi-gene signature, which was then validated in the validation cohorts through Kaplan-Meier, Cox, and receiver operating characteristic curve (ROC) analyses. In addition, the correlation between tumor mutational burden (TMB) and risk score was evaluated by Spearman test. GSEA and immune infiltrating analyses were conducted for understanding function annotation and the role of the signature in the tumor microenvironment.ResultsAn eight-gene signature was built, and it was examined by Kaplan-Meier analysis, revealing that a significant overall survival difference was seen. The eight-gene signature was further proven to be independent of other clinico-pathologic parameters via the Cox regression analyses. Moreover, the ROC analysis demonstrated that this signature owned a better predictive power of LUAD prognosis. The eight-gene signature was correlated with TMB. Furthermore, GSEA and immune infiltrating analyses showed that the exact pathways related to the characteristics of eight-genes signature, and identified the vital roles of Mast cells resting and B cells naive in the prognosis of the eight-gene signature.ConclusionIdentifying the eight-gene signature (INSL4, SCN7A, STAP1, P2RX1, IKZF3, MS4A1, KLRB1, and ACSM5) could accurately identify patients' prognosis and had close interactions with Mast cells resting and B cells naive, which may provide insight into personalized prognosis prediction and new therapies for LUAD patients.

Project description:BackgroundLung adenocarcinoma (LUAD) as a frequent type of lung cancer has a 5-year overall survival rate of lower than 20% among patients with advanced lung cancer. This study aims to construct a risk model to guide immunotherapy in LUAD patients effectively.Materials and methodsLUAD Bulk RNA-seq data for the construction of a model, single-cell RNA sequencing (scRNA-seq) data (GSE203360) for cell cluster analysis, and microarray data (GSE31210) for validation were collected from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database. We used the Seurat R package to filter and process scRNA-seq data. Sample clustering was performed in the ConsensusClusterPlus R package. Differentially expressed genes (DEGs) between two groups were mined by the Limma R package. MCP-counter, CIBERSORT, ssGSEA, and ESTIMATE were employed to evaluate immune characteristics. Stepwise multivariate analysis, Univariate Cox analysis, and Lasso regression analysis were conducted to identify key prognostic genes and were used to construct the risk model. Key prognostic gene expressions were explored by RT-qPCR and Western blot assay.ResultsA total of 27 immune cell marker genes associated with prognosis were identified for subtyping LUAD samples into clusters C3, C2, and C1. C1 had the longest overall survival and highest immune infiltration among them, followed by C2 and C3. Oncogenic pathways such as VEGF, EFGR, and MAPK were more activated in C3 compared to the other two clusters. Based on the DEGs among clusters, we confirmed seven key prognostic genes including CPA3, S100P, PTTG1, LOXL2, MELTF, PKP2, and TMPRSS11E. Two risk groups defined by the seven-gene risk model presented distinct responses to immunotherapy and chemotherapy, immune infiltration, and prognosis. The mRNA and protein level of CPA3 was decreased, while the remaining six gene levels were increased in clinical tumor tissues.ConclusionImmune cell markers are effective in clustering LUAD samples into different subtypes, and they play important roles in regulating the immune microenvironment and cancer development. In addition, the seven-gene risk model may serve as a guide for assisting in personalized treatment in LUAD patients.

Project description:Previous literatures have suggested the importance of inflammatory response during lung adenocarcinoma (LUAD) development. This study aimed at exploring the inflammation-related genes and developing a prognostic signature for predicting the prognosis of LUAD. Survival‑associated inflammation-related genes were identified by univariate Cox regression analysis in the dataset of The Cancer Genome Atlas (TCGA). The least absolute shrinkage and selection operator (LASSO) penalized Cox regression model was used to derive a risk signature which is significantly negatively correlated with OS and divide samples into high-, medium- and low-risk group. Univariate and multivariate Cox analyses suggested that the level of risk group was an independent prognostic factor of the overall survival (OS). Time-dependent receiver operating characteristic (ROC) curve indicated the AUC of 1-, 3- and 5-years of the risk signature was 0.715, 0.719, 0.699 respectively. A prognostic nomogram was constructed by integrating risk group and clinical features. The independent dataset GSE30219 of Gene Expression Omnibus (GEO) was used for verification. We further explored the differences among risk groups in Gene set enrichment analysis (GSEA), tumor mutation and tumor microenvironment. Furthermore, Single Sample Gene Set Enrichment Analysis (ssGSEA) and the results of Cell-type Identification By Estimating Relative Subsets Of RNA Transcripts (CIBERSORT) suggested the status of immune cell infiltration was highly associated with risk groups. We demonstrated the prediction effect of CTLA-4 and PD-1/PD-L1 inhibitors in the low-risk group was better than that in the high-risk group using two methods of immune score include immunophenoscore from The Cancer Immunome Atlas (TCIA) and TIDE score from Tumor Immune Dysfunction and Exclusion (TIDE). In addition, partial targeted drugs and chemotherapy drugs for lung cancer had higher drug sensitivity in the high-risk group. Our findings provide a foundation for future research targeting inflammation-related genes to predictive prognosis and some reference significance for the selection of immunotherapy and drug regimen for lung adenocarcinoma.

Project description:Background: Lung adenocarcinoma (LUAD) is one of the main types of lung cancer. Because of its low early diagnosis rate, poor late prognosis, and high mortality, it is of great significance to find biomarkers for diagnosis and prognosis. Methods: Five hundred and twelve LUADs from The Cancer Genome Atlas were used for differential expression analysis and short time-series expression miner (STEM) analysis to identify the LUAD-development characteristic genes. Survival analysis was used to identify the LUAD-unfavorable genes and LUAD-favorable genes. Gene set variation analysis (GSVA) was used to score individual samples against the two gene sets. Receiver operating characteristic (ROC) curve analysis and univariate and multivariate Cox regression analysis were used to explore the diagnostic and prognostic ability of the two GSVA score systems. Two independent data sets from Gene Expression Omnibus (GEO) were used for verifying the results. Functional enrichment analysis was used to explore the potential biological functions of LUAD-unfavorable genes. Results: With the development of LUAD, 185 differentially expressed genes (DEGs) were gradually upregulated, of which 84 genes were associated with LUAD survival and named as LUAD-unfavorable gene set. While 237 DEGs were gradually downregulated, of which 39 genes were associated with LUAD survival and named as LUAD-favorable gene set. ROC curve analysis and univariate/multivariate Cox proportional hazards analyses indicated both of LUAD-unfavorable GSVA score and LUAD-favorable GSVA score were a biomarker of LUAD. Moreover, both of these two GSVA score systems were an independent factor for LUAD prognosis. The LUAD-unfavorable genes were significantly involved in p53 signaling pathway, Oocyte meiosis, and Cell cycle. Conclusion: We identified and validated two LUAD-development characteristic gene sets that not only have diagnostic value but also prognostic value. It may provide new insight for further research on LUAD.

Project description:Background:Lung cancer has the highest morbidity and mortality worldwide, and lung adenocarcinoma (LADC) is the most common pathological subtype. Accumulating evidence suggests the tumor microenvironment (TME) is correlated with the tumor progress and the patient's outcome. As the major components of TME, the tumor-infiltrated immune cells and stromal cells have attracted more and more attention. In this study, differentially expressed immune and stromal signature genes were used to construct a TME-related prognostic model for predicting the outcomes of LADC patients. Methods:The expression profiles of LADC samples with clinical information were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). The differentially expressed genes (DEGs) related to the TME of LADC were identified using TCGA dataset by Wilcoxon rank sum test. The prognostic effects of TME-related DEGs were analyzed using univariate Cox regression. Then, the least absolute shrinkage and selection operator (LASSO) regression was performed to reduce the overfit and the number of genes for further analysis. Next, the prognostic model was constructed by step multivariate Cox regression and risk score of each sample was calculated. Then, survival and Receiver Operating Characteristic (ROC) analyses were conducted to validate the model using TCGA and GEO datasets, respectively. The Kyoto Encyclopedia of Genes and Genomes analysis of gene signature was performed using Gene Set Enrichment Analysis (GSEA). Finally, the overall immune status, tumor purity and the expression profiles of HLA genes of high- and low-risk samples was further analyzed to reveal the potential mechanisms of prognostic effects of the model. Results:A total of 93 TME-related DEGs were identified, of which 23 DEGs were up-regulated and 70 DEGs were down-regulated. The univariate cox analysis indicated that 23 DEGs has the prognostic effects, the hazard ratio ranged from 0.65 to 1.25 (p < 0.05). Then, seven genes were screened out from the 23 DEGs by LASSO regression method and were further analyzed by step multivariate Cox regression. Finally, a three-gene (ADAM12, Bruton Tyrosine Kinase (BTK), ERG) signature was constructed, and ADAM12, BTK can be used as independent prognostic factors. The three-gene signature well stratified the LADC patients in both training (TCGA) and testing (GEO) datasets as high-risk and low-risk groups, the 3-year area under curve (AUC) of ROC curves of three GEO sets were 0.718 (GSE3141), 0.646 (GSE30219) and 0.643 (GSE50081). The GSEA analysis indicated that highly expressed ADAM12, BTK, ERG mainly correlated with the activation of pathways involving in focal adhesion, immune regulation. The immune analysis indicated that the low-risk group has more immune activities and higher expression of HLA genes than that of the high-risk group. In sum, we identified and constructed a three TME-related DEGs signature, which could be used to predict the prognosis of LADC patients.

Dataset Information

Identification of LBX2 as a novel causal gene of lung adenocarcinoma.

Publications

Identification of LBX2 as a novel causal gene of lung adenocarcinoma.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets