An in vivo functional genomics screen of nuclear receptors and their co-regulators identifies FOXA1 as an essential gene in lung tumorigenesis

Hight, Suzie K.; Mootz, Allison; Kollipara, Rahul K.; McMillan, Elizabeth A.; Yenerall, Paul; Otaki, Yoichi; Li, Long Shan; Avila, Kimberley; Peyton, Michael; Rodriguez‐Canales, Jaime; Mino, Barbara; Villalobos, Pamela; Girard, Luc; Dospoy, Patrick; Larsen, Jill E.; White, Michael A.; Heymach, John V.; Wistuba, Ignacio I.; Kittler, Ralf; Minna, John D.

doi:10.1016/j.neo.2020.04.005

Cited by 28 publications

(27 citation statements)

References 70 publications

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“…The GSE68465 ( Shedden et al, 2008 ), GSE42127 ( Hight et al, 2020 ), and GSE30219 ( Rousseaux et al, 2013 ) data sets (containing data from 443, 133, and 148 LUAC patients, respectively) were downloaded from the Gene Expression Omnibus database ( Edgar et al, 2002 ). GSE68465 was defined as an internal group, and the other data sets were combined as an external group (test group).…”

Section: Methodsmentioning

confidence: 99%

Prognostic Signature for Lung Adenocarcinoma Patients Based on Cell-Cycle-Related Genes

Jiang

Liao

et al. 2021

Front. Cell Dev. Biol.

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ObjectiveTo screen lung adenocarcinoma (LUAC)-specific cell-cycle-related genes (CCRGs) and develop a prognostic signature for patients with LUAC.MethodsThe GSE68465, GSE42127, and GSE30219 data sets were downloaded from the GEO database. Single-sample gene set enrichment analysis was used to calculate the cell cycle enrichment of each sample in GSE68465 to identify CCRGs in LUAC. The differential CCRGs compared with LUAC data from The Cancer Genome Atlas were determined. The genetic data from GSE68465 were divided into an internal training group and a test group at a ratio of 1:1, and GSE42127 and GSE30219 were defined as external test groups. In addition, we combined LASSO (least absolute shrinkage and selection operator) and Cox regression analysis with the clinical information of the internal training group to construct a CCRG risk scoring model. Samples were divided into high- and low-risk groups according to the resulting risk values, and internal and external test sets were used to prove the validity of the signature. A nomogram evaluation model was used to predict prognosis. The CPTAC and HPA databases were chosen to verify the protein expression of CCRGs.ResultsWe identified 10 LUAC-specific CCRGs (PKMYT1, ETF1, ECT2, BUB1B, RECQL4, TFRC, COCH, TUBB2B, PITX1, and CDC6) and constructed a model using the internal training group. Based on this model, LUAC patients were divided into high- and low-risk groups for further validation. Time-dependent receiver operating characteristic and Cox regression analyses suggested that the signature could precisely predict the prognosis of LUAC patients. Results obtained with CPTAC, HPA, and IHC supported significant dysregulation of these CCRGs in LUAC tissues.ConclusionThis prognostic prediction signature based on CCRGs could help to evaluate the prognosis of LUAC patients. The 10 LUAC-specific CCRGs could be used as prognostic markers of LUAC.

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Section: Methodsmentioning

confidence: 99%

Prognostic Signature for Lung Adenocarcinoma Patients Based on Cell-Cycle-Related Genes

Jiang

Liao

et al. 2021

Front. Cell Dev. Biol.

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“…The size of eligible patients was 1144. Subtype breakdown meta-analysis of the survival analysis from different studies was performed using the web server of Lung Cancer Explorer ( ), which integrated the following eligible genomic data of GSE72094 (n=442) ( 56 ), TCGA (LUAD, n=576; LUSC, n=552) ( ), GSE30219 (n=307) ( 57 ), GSE41271 (n=275) ( 58 ), GSE31210 (n=224) ( 59 ), GSE31210 (224) ( 60 ), GSE37745 (n=196) ( 61 ), GSE50081 (n=181) ( 62 ), GSE42127 (n=176) ( 63 ), GSE11969 (n=163) ( 64 ), GSE19188 (n=156) ( 65 ), GSE13213 (n=117) ( 66 ), GSE3141(n=111) ( 67 ), GSE74777 (107) ( 68 ), GSE26939 (n=101) ( 69 ), GSE1037 (n=80) ( 70 ), GSE29016 (n=72) ( 71 ), GSE12472 (n=63) ( 72 ), GSE31548 (n=50) ( 73 ), GSE10245 (n=48) ( 74 ), GSE11117 (n=44) ( 75 ).…”

Section: Methodsmentioning

confidence: 99%

The G4 Resolvase DHX36 Possesses a Prognosis Significance and Exerts Tumour Suppressing Function Through Multiple Causal Regulations in Non-Small Cell Lung Cancer

Cui

Cao

et al. 2021

Front. Oncol.

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Lung cancer is one of the most prevalent cancers in both men and women worldwide. The nucleic acid G4 structures have been implicated in the transcriptional programmes of cancer-related genes in some cancers such as lung cancer. However, the role of the dominant G4 resolvase DHX36 in the progression of lung cancer remains unknown. In this study, by bioinformatic analysis of public datasets (TCGA and GEO), we find DHX36 is an independent prognosis indicator in non-small-cell lung carcinoma (NSCLC) with subtype dependence. The stable lentiviral knockdown of the DHX36 results in accelerated migration and aggregation of the S-phase subpopulation in lung cancer cells. The reduction of DHX36 level de-sensitises the proliferation response of lung cancer cells to chemotherapeutic drugs such as paclitaxel with cell dependence. The knockdown of this helicase leads to promoted tumour growth, demonstrated by a 3D fluorescence spheroid lung cancer model, and the stimulation of cell colony formation as shown by single-cell cultivation. High throughput proteomic array indicates that DHX36 functions in lung cancer cells through regulating multiple signalling pathways including activation of protein activity, protein autophosphorylation, Fc-receptor signalling pathway, response to peptide hormone and stress-activated protein kinase signalling cascade. A causal transcriptomic analysis suggests that DHX36 is significantly associated with mRNA surveillance, RNA degradation, DNA replication and Myc targets. Therefore, we unveil that DHX36 presents clinical significance and plays a role in tumour suppression in lung cancer, and propose a potentially new concept for an anti-cancer therapy based on helicase-specific targeting.

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Section: Methodsmentioning

confidence: 99%

“…GSE99254 [10] is a single-cell sequencing cohort with 14 samples and 12346 non-small-cell lung cancer cells and was obtained from the GEO (http://www.ncbi.nlm.nih.gov/geo/) database with the GPL16791 platform and GPL20301 platform. Meanwhile, GSE42127 [11] was also downloaded to verify the conclusion. [7] were used to evaluate stromal and immune cells in malignant tumor tissues in this study and estimate the proportion of stromal and immune cells in the tumor microenvironment based on the gene matrix.…”

Section: Data Sourcesmentioning

confidence: 99%

Tumor Purity Coexpressed Genes Related to Immune Microenvironment and Clinical Outcomes of Lung Adenocarcinoma

Bai

Pan

Sun

2021

Journal of Oncology

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Purpose. Lung cancer tissue includes tumor tissue, stromal cells, immune cells, and epithelial cells. These nontumor cells dilute the tumor purity in lung cancer tissues. Tumor purity plays an essential role in the immune response to lung cancer. At present, the biological processes related to the purity of lung cancer tumors remains unclear. Methods. We measured tumor purity in 486 lung carcinoma tissues from TCGA-LUAD FPKM by using the “estimate” R package. Lung carcinoma tumor mutation burden was calculated by analyzing TCGA single nucleotide polymorphism data. The immune cell proportion was also evacuated via the CIBERSORT method. Lung carcinoma samples with P < 0.05 were considered significant. Based on the tumor purity and lung carcinoma gene matrix, we performed weighted gene coexpression network analysis (WGCNA), and the tumor purity-related module was identified. Then, we analyzed the functions of the factors involved in the module. We screened the coexpressed factors related to clinical outcome and immunophenotype. Finally, expression levels of these factors were measured at tissue and single-cell levels. Results. A lung cancer tumor purity correlated coexpression network was determined. Five coexpressed genes (CD4, CD53, EVI2B, PLEK, and SASH3) were identified as tumor purity coexpressed genes that negatively correlated with tumor purity. Because the factors in the coexpression network often participate in similar biological processes, we found that CD4, CD53, EVI2B, PLEK, and SASH3 were most related to positive regulation of cytokine production and interleukin−2 production through functional enrichment. In a clinical phenotype analysis, we found that these five factors can be used as independent prognostic risk factors. We found that these factors were significantly negatively correlated with tumor purity and positively correlated with the immune score in the immunophenotyping analysis. Using GSEA analysis, we found that the antigen processing and presentation pathway were related to the five tumor coexpressed genes mentioned above. SASH3 and CD53 were used to conduct a prognostic model based on the interaction analysis of the Support Vector Machine and the Least Absolute Shrinkage and Selection Operator. SASH3 was verified to be related to CD8A using a single-cell analysis. Conclusion. Tumor purity-related coexpression factors in the tumor microenvironment have essential clinical, genomic, and biological significance in lung cancer. These coexpression factors (SASH3 and CD53) can be used to classify tumor purity phenotypes and to predict clinical outcomes.

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An in vivo functional genomics screen of nuclear receptors and their co-regulators identifies FOXA1 as an essential gene in lung tumorigenesis

Cited by 28 publications

References 70 publications

Prognostic Signature for Lung Adenocarcinoma Patients Based on Cell-Cycle-Related Genes

Prognostic Signature for Lung Adenocarcinoma Patients Based on Cell-Cycle-Related Genes

The G4 Resolvase DHX36 Possesses a Prognosis Significance and Exerts Tumour Suppressing Function Through Multiple Causal Regulations in Non-Small Cell Lung Cancer

Tumor Purity Coexpressed Genes Related to Immune Microenvironment and Clinical Outcomes of Lung Adenocarcinoma

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