Differentially expressed genes between the bone metastatic subpopulations and lung metastatic subpopulations of lung adenocarcinoma
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ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE249364 | GEO | 2024/06/30
REPOSITORIES: GEO
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Project description:Background: Lung adenocarcinoma (LUAD) is the leading cause of deaths worldwide, and metastasis accounts for the vast majority of cancer-related deaths. Driver mutations play important roles in treatment decision making for LUAD patients, while the complicated metastatic progress cannot be explained by genetic aberrations alone. Epigenomic reprogramming is particularly notable as an important signature of the metastasis transition. However, long noncoding RNAs (lncRNAs) hijacked by super-enhancer (SE), vital regulatory elements in epigenome, remain elusive in the progression of metastasis. Methods: SE associated lncRNAs microarray was utilized to identified the dysregulated lncRNAs related to metastasis. ChIP-seq, Hi-C data analysis and luciferase reporter assay were utilized to confirm LINC01977 was hijacked by SE. In vitro and in vivo assays were applied to elucidate effects of LINC01977 on the malignancy of LUAD. Results: In this study, we identified that LINC01977, a cancer-testis lncRNA, was up-regulated in tumor, which was driven by a …kb-long SE upstream of LINC01977 and sensitive to BRD4 inhibitor. LINC01977-antisense oligonucleotides (ASO) dramatically suppresses proliferation and invasion both in vitro and in vivo. Mechanically, LINC01977 promotes phosphorylation of SMAD3 to facilitate its nuclear retention, and it also act as a scaffold to cooperate the interaction between SMAD3 and CBP/P300, the transcriptional co-activators, to regulate the downstream target gene ZEB1. Interestingly, SMAD3 also positively regulated LINC09177 transcription by binding the promoter and active elements of its SE, which was medicated by canonical TGF-β signaling secreted by M2-like tumor-associated macrophages (TAM2). We also revealed that the expression of LINC01977 was positively correlated with TAM2 infiltration especially in early stage. Additionally, stage I LUAD patients with high LIN01977 expression predicated a shorter progression-free survival (PFS). Conclusions: LINC01977 promotes the malignant progression of LUAD through facilitating the interaction between SMAD3 and CBP/P300. The upregulation of LINC01977 was medicated by super-enhancer and TAM2 infiltration, dependent on canonical TGF-β signaling. LINC01977 could be a valuable therapeutic target especially for early stage patients.
2022-08-24 | GSE196584 | GEO
Project description:Differentially expressed genes between the bone metastatic subpopulations and lung metastatic subpopulations of lung adenocarcinoma
| PRJNA1048876 | ENA
Project description:We systematically profiled the genome-wide alternative splicing events in Lung Adenocarcinoma (LUAD) and Lung Squomous Cell Carcinoma (LUSC) against Lung Control through Human Transcriptome Array2.0. Non-invasive Stage IIIA non-small cell lung cancer (NSCLC) is heterogeneous in nature which makes it difficult to predict, diagnose and prognose owing to lower 5-year survival rate and 75-85% brain or bone metastasis. Hence, we hypothesized to develop transcript-based signature to categorize Stage IIIA-NSCLC-LUAD and LUSC, as well as identify markers which could indicate towards prognosis of disease. We were able to molecularly-categorize LUAD- and LUSC-tissue more precisely through HTA array2.0.
2020-09-21 | GSE138682 | GEO
Project description:The metastatic serum samples were collected from LUAD cohort, and were profiled to discover the metastatic serum secretome.
2024-09-18 | PXD056137 |
Project description:Long noncoding RNAs (lncRNAs) are known to regulate the development and progression of various cancers, however, few lncRNAs have been well characterized in lung adenocarcinoma (LUAD). Understanding the expression profile of lncRNAs and protein-coding genes is critical to develop new diagnosis and treatment strategies for LUAD and improving the prognosis of diagnosed patients. Five female LUAD patients with no smoking history were selected to profile lncRNA and protein-coding gene expression with microarrays. Paired tumor tissues and adjacent nontumor tissues were collected and confirmed by pathologists.
2015-03-10 | E-GEOD-66654 | biostudies-arrayexpress
Project description:Lung cancer is the most frequent cancer-related cause of death, and adenocarcinoma (LUAD) is the most frequent type. Despite the recent success of immunotherapies, survival of lung cancer patients has not significantly improved in the last decades. New therapies are necessary. We have previously identified sodium-glucose transporter 2 (SGLT2) as the major responsible for glucose uptake in LUAD, and we have showed that treatment with SGLT2 inhibitors significantly delays LUAD development and prolongs survival in murine models. However, our data shows that SGLT2 inhibitors also induce de-differentiation of LUAD cells, leading to a more aggressive phenotype and increased resistance to cisplatin. Glucose deprivation causes reduced αKG levels, leading to reduced activity of αKG-dependent histone demethylases and consequent histone hypermethylation. Supplementation of αKG or inhibition of the histone methyltransferase EZH2 reverse this phenotype, suggesting that this de-differentiated phenotype depends on insufficiency of αKG-dependent histone demethylases and unbalanced EZH2 activity. Consistently, double treatment with an SGLT2 inhibitor and an EZH2 inhibitor significantly reduces the tumor burden in a genetically engineered murine model of LUAD. We further characterized the effect of low glucose-induced tumor de-differentiation, identifying stabilization of hypoxia inducible factor 1α (HIF1α) as a major pathway responsible for the acquisition of a more aggressive phenotype following glucose deprivation. Finally, we identified an HIF1α-dependent transcriptional signature with prognostic significance in human LUAD. Our studies further our knowledge of the relationship between glucose metabolism and cell differentiation in cancer, characterizing the epigenetic adaptation of cancer cells to nutrient deprivation and identifying novel targets to prevent the development of resistance to metabolic therapies.
2022-12-31 | E-MTAB-11253 | biostudies-arrayexpress
Project description:To illuminate the evolutionary trajectory of LUAD from AIS to IAC, high-throughput scRNA-seq and ST data were generated and integrated to create a large-scale, single-cell spatiotemporal atlas of LUAD. We compiled a multi-omics atlas of the early-stage LUAD invasion process that reflects the heterogeneity of cancer cells, the competitive polyclonal origin of LUAD, signalling interactions between cancer cells and the TME, and the pseudo-chronological nature of LUAD invasion. The spatial distribution characteristics of LUAD cells revealed the spatial heterogeneity of LUAD and the mechanism of spatial immune escape in LUAD, which provides strong evidence supporting clinical diagnosis and surgical intervention at the single-cell level. The seurat object of ST data are available on https://drive.google.com/drive/folders/1dkGF4kKMbHSm04DKHg6P_y_iTD1pXCH8?usp=sharing.
2021-11-30 | GSE189487 | GEO
Project description:BackgroundCircular RNAs (circRNAs) may function as the decoys for microRNAs (miRNAs) or proteins, the templates for translation, and the sources of pseudogene generation. The purpose of this study is to determine the diagnostic circRNAs, which are related to lung adenocarcinoma (LUAD), that adsorb miRNAs on the basis of the competing endogenous RNA (ceRNA) hypothesis.MethodsThe differentially expressed circRNAs (DEcircRNAs) in LUAD were revealed by the microarray data (GSE101586 and GSE101684) that were obtained from the Gene Expression Omnibus (GEO) database. The miRNAs that were targeted by the DEcircRNAs were predicted with the CircInteractome, and the target mRNAs of the miRNAs were found by the miRDB and the TargetScan. The ceRNA network was built by the Cytoscape. The potential biological roles and the regulatory mechanisms of the circRNAs were investigated by the Gene Ontology (GO) enrichment analysis and the Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. The expression of the host genes of circRNAs was examined by the Ualcan. The survival analysis was performed by the Kaplan-Meier plotter.ResultsIn comparison with normal lung tissues, LUAD tissues contained 7 overlapping cancer-specific DEcircRNAs with 294 miRNA response elements (MREs). Among the 7 DEcircRNAs, 3 circRNAs (hsa_circ_0072088, hsa_circ_0003528, and hsa_circ_0008274) were upregulated and 4 circRNAs (hsa_circ_0003162, hsa_circ_0029426, hsa_circ_0049271, and hsa_circ_0043256) were downregulated. A circRNA-miRNA-mRNA regulatory network, which included 33 differentially expressed miRNAs (DEmiRNAs) and 2007 differentially expressed mRNAs (DEmRNAs), was constructed. These mRNAs were enriched in the biological function of cell-cell adhesion, response to hypoxia, and stem cell differentiation and were involved in the PI3K-Akt signaling, HIF-1 signaling, and cAMP signaling pathways.ConclusionOur results indicated that 7 DEcircRNAs could have diagnostic value for LUAD. Additionally, the circRNAs-mediated ceRNA network might provide a novel perspective into unraveling the pathogenesis and progression of LUAD.
| S-EPMC8448594 | biostudies-literature
Project description:To illuminate the evolutionary trajectory of LUAD from AIS to IAC, high-throughput scRNA-seq and ST data were generated and integrated to create a large-scale, single-cell spatiotemporal atlas of LUAD. We compiled a multi-omics atlas of the early-stage LUAD invasion process that reflects the heterogeneity of cancer cells, the competitive polyclonal origin of LUAD, signalling interactions between cancer cells and the TME, and the pseudo-chronological nature of LUAD invasion. The spatial distribution characteristics of LUAD cells revealed the spatial heterogeneity of LUAD and the mechanism of spatial immune escape in LUAD, which provides strong evidence supporting clinical diagnosis and surgical intervention at the single-cell level.
2021-11-30 | GSE189357 | GEO
Project description:In this study, we introduce and use Efficiency Analysis to compare differences in the apparent internal and external consistency of competing normalization methods and tests for identifying differentially expressed genes. Using publicly available data, two lung adenocarcinoma datasets were analyzed using caGEDA (http://bioinformatics2.pitt.edu/GE2/GEDA.html) to measure the degree of differential expression of genes existing between two populations. The datasets were randomly split into at least two subsets, each analyzed for differentially expressed genes between the two sample groups, and the gene lists compared for overlapping genes. Efficiency Analysis is an intuitive method that compares the differences in the percentage of overlap of genes from two or more data subsets, found by the same test over a range of testing methods. Tests that yield consistent gene lists across independently analyzed splits are preferred to those that yield less consistent inferences. For example, a method that exhibits 50% overlap in the 100 top genes from two studies should be preferred to a method that exhibits 5% overlap in the top 100 genes. The same procedure was performed using all available normalization and transformation methods that are available through caGEDA. The 'best' test was then further evaluated using internal cross-validation to estimate generalizable sample classification errors using a Naïve Bayes classification algorithm. A novel test, termed D1 (a derivative of the J5 test) was found to be the most consistent, and to exhibit the lowest overall classification error, and highest sensitivity and specificity. The D1 test relaxes the assumption that few genes are differentially expressed. Efficiency Analysis can be misleading if the tests exhibit a bias in any particular dimension (e.g. expression intensity); we therefore explored intensity-scaled and segmented J5 tests using data in which all genes are scaled to share the same intensity distribution range. Efficiency Analysis correctly predicted the 'best' test and normalization method using the Beer dataset and also performed well with the Bhattacharjee dataset based on both efficiency and classification accuracy criteria.
| S-EPMC2623303 | biostudies-literature