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Chang SM, Yang M, Lu W, Huang YJ, Huang Y, Hung H, Miecznikowski JC, Lu TP, Tzeng JY. Gene-Set Integrative Analysis of Multi-Omics Data Using Tensor-based Association Test. Bioinformatics 2021; 37:2259-2265. [PMID: 33674827 PMCID: PMC8388036 DOI: 10.1093/bioinformatics/btab125] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2020] [Revised: 12/30/2020] [Accepted: 02/24/2021] [Indexed: 11/12/2022] Open
Abstract
MOTIVATION Facilitated by technological advances and the decrease in costs, it is feasible to gather subject data from several omics platforms. Each platform assesses different molecular events, and the challenge lies in efficiently analyzing these data to discover novel disease genes or mechanisms. A common strategy is to regress the outcomes on all omics variables in a gene set. However, this approach suffers from problems associated with high-dimensional inference. RESULTS We introduce a tensor-based framework for variable-wise inference in multi-omics analysis. By accounting for the matrix structure of an individual's multi-omics data, the proposed tensor methods incorporate the relationship among omics effects, reduce the number of parameters, and boost the modeling efficiency. We derive the variable-specific tensor test and enhance computational efficiency of tensor modeling. Using simulations and data applications on the Cancer Cell Line Encyclopedia (CCLE), we demonstrate our method performs favorably over baseline methods and will be useful for gaining biological insights in multi-omics analysis. AVAILABILITY AND IMPLEMENTATION R function and instruction are available from the authors' website: https://www4.stat.ncsu.edu/∼jytzeng/Software/TR.omics/TRinstruction.pdf. SUPPLEMENTARY INFORMATION Supplementary materials are available at Bioinformatics online.
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Affiliation(s)
- Sheng-Mao Chang
- Department of Statistics, National Cheng Kung University, Tainan, Taiwan
| | - Meng Yang
- Department of Statistics, North Carolina State University, Raleigh NC, 27695, USA
| | - Wenbin Lu
- Department of Statistics, North Carolina State University, Raleigh NC, 27695, USA
| | - Yu-Jyun Huang
- Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taipei, Taiwan
| | - Yueyang Huang
- Bioinformatics Research Center, North Carolina State University, Raleigh NC, 27695, USA
| | - Hung Hung
- Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taipei, Taiwan
| | | | - Tzu-Pin Lu
- Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taipei, Taiwan
| | - Jung-Ying Tzeng
- Department of Statistics, National Cheng Kung University, Tainan, Taiwan.,Department of Statistics, North Carolina State University, Raleigh NC, 27695, USA.,Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taipei, Taiwan.,Bioinformatics Research Center, North Carolina State University, Raleigh NC, 27695, USA
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2
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Jiang Y, Huang Y, Du Y, Zhao Y, Ren J, Ma S, Wu C. Identification of Prognostic Genes and Pathways in Lung Adenocarcinoma Using a Bayesian Approach. Cancer Inform 2020; 16:1176935116684825. [PMID: 33354107 PMCID: PMC7736146 DOI: 10.1177/1176935116684825] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2016] [Accepted: 11/24/2016] [Indexed: 01/02/2023] Open
Abstract
Lung cancer is the leading cause of cancer-associated mortality in the United States and the world. Adenocarcinoma, the most common subtype of lung cancer, is generally diagnosed at the late stage with poor prognosis. In the past, extensive effort has been devoted to elucidating lung cancer pathogenesis and pinpointing genes associated with survival outcomes. As the progression of lung cancer is a complex process that involves coordinated actions of functionally associated genes from cancer-related pathways, there is a growing interest in simultaneous identification of both prognostic pathways and important genes within those pathways. In this study, we analyse The Cancer Genome Atlas lung adenocarcinoma data using a Bayesian approach incorporating the pathway information as well as the interconnections among genes. The top 11 pathways have been found to play significant roles in lung adenocarcinoma prognosis, including pathways in mitogen-activated protein kinase signalling, cytokine-cytokine receptor interaction, and ubiquitin-mediated proteolysis. We have also located key gene signatures such as RELB, MAP4K1, and UBE2C. These results indicate that the Bayesian approach may facilitate discovery of important genes and pathways that are tightly associated with the survival of patients with lung adenocarcinoma.
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Affiliation(s)
- Yu Jiang
- Division of Epidemiology, Biostatistics
and Environmental Health, School of Public Health, University of Memphis, Memphis,
TN, USA
- Cooperative Studies Program, VA
Connecticut Healthcare System, West Haven, CT, USA
| | - Yuan Huang
- Cooperative Studies Program, VA
Connecticut Healthcare System, West Haven, CT, USA
- Department of Biostatistics, Yale
University, New Haven, CT, USA
| | - Yinhao Du
- Department of Statistics, Kansas State
University, Manhattan, KS, USA
| | - Yinjun Zhao
- Department of Biostatistics, Yale
University, New Haven, CT, USA
| | - Jie Ren
- Department of Statistics, Kansas State
University, Manhattan, KS, USA
| | - Shuangge Ma
- Cooperative Studies Program, VA
Connecticut Healthcare System, West Haven, CT, USA
- Department of Biostatistics, Yale
University, New Haven, CT, USA
| | - Cen Wu
- Department of Statistics, Kansas State
University, Manhattan, KS, USA
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3
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Li Z, Wang W, Meng L, Zhang Y, Zhang J, Li C, Wu Y, Feng F, Zhang Q. Identification and analysis of key lncRNAs in malignant-transformed BEAS-2B cells induced with coal tar pitch by microarray analysis. ENVIRONMENTAL TOXICOLOGY AND PHARMACOLOGY 2020; 79:103376. [PMID: 32470693 DOI: 10.1016/j.etap.2020.103376] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/26/2019] [Revised: 03/23/2020] [Accepted: 03/23/2020] [Indexed: 06/11/2023]
Abstract
This study aims to explore the key and differentially expressed long non-coding RNAs (lncRNAs) and elucidates their possible mechanisms in malignant-transformed Human bronchial epithelial (BEAS-2B) cells induced by coal tar pitch extracts (CTPE). BEAS-2B cells were stimulated with 2.4 μg/ml CTPE, then passaged for three times which were named CTPE1 and then passaged until passage 30 (CTPE30). The results showed that cells of CTPE30 appeared abnormal morphology. Furthermore, migration, clonality and proliferation of cells in CTPE group were significantly increased compared with those in control groups. However, the apoptosis of cells in CTPE group was inhibited. A total of 569 differentially expressed mRNAs and 707 differentially expressed lncRNAs were screened out, among which four lncRNAs were validated and were consistent with the microarray results. 32 target genes were screened out by Co-expression network. The study suggests that differentially expressed lncRNAs may play a potential role in lung carcinogenesis.
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Affiliation(s)
- Zhongqiu Li
- Department of Toxicology, College of Public Health, Zhengzhou University, Zhengzhou, Henan province, 450001, China
| | - Weiguang Wang
- Rizhao Center for Disease Control and Prevention, Rizhao, Shandong province, 276800, China
| | - Liya Meng
- Department of Toxicology, College of Public Health, Zhengzhou University, Zhengzhou, Henan province, 450001, China
| | - Yaping Zhang
- Department of Toxicology, College of Public Health, Zhengzhou University, Zhengzhou, Henan province, 450001, China
| | - Jiatong Zhang
- Hospital of Zhengzhou University, Zhengzhou, Henan province, China
| | - Chunyang Li
- Department of Toxicology, College of Public Health, Zhengzhou University, Zhengzhou, Henan province, 450001, China
| | - Yongjun Wu
- Department of Toxicology, College of Public Health, Zhengzhou University, Zhengzhou, Henan province, 450001, China
| | - Feifei Feng
- Department of Toxicology, College of Public Health, Zhengzhou University, Zhengzhou, Henan province, 450001, China.
| | - Qiao Zhang
- Department of Toxicology, College of Public Health, Zhengzhou University, Zhengzhou, Henan province, 450001, China.
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4
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Relli V, Trerotola M, Guerra E, Alberti S. Distinct lung cancer subtypes associate to distinct drivers of tumor progression. Oncotarget 2018; 9:35528-35540. [PMID: 30473748 PMCID: PMC6238974 DOI: 10.18632/oncotarget.26217] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2018] [Accepted: 09/26/2018] [Indexed: 12/12/2022] Open
Abstract
The main non–small-cell lung cancer (NSCLC) histopathological subtypes are lung adenocarcinomas (LUAD) and lung squamous cell carcinomas (LUSC). To identify candidate progression determinants of NSCLC subtypes, we explored the transcriptomic signatures of LUAD versus LUSC. We then investigated the prognostic impact of the identified tumor-associated determinants. This was done utilizing DNA microarray data from 2,437 NSCLC patients. An independent analysis of a case series of 994 NSCLC was conducted by next-generation sequencing, together with gene expression profiling from GEO (https://www.ncbi.nlm.nih.gov/geo/). This work led us to identify 69 distinct tumor prognostic determinants, which impact on LUAD or LUSC clinical outcome. These included key drivers of tumor growth and cell cycle, transcription factors and metabolic determinants. Such disease determinants appeared vastly different in LUAD versus LUSC, and often had opposite impact on clinical outcome. These findings indicate that distinct tumor progression pathways are at work in the two NSCLC subtypes. Notably, most prognostic determinants would go inappropriately assessed or even undetected when globally investigating unselected NSCLC. Hence, differential consideration for NSCLC subtypes should be taken into account in current clinical evaluation procedures for lung cancer.
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Affiliation(s)
- Valeria Relli
- Unit of Cancer Pathology, CeSI-MeT, University "G. d'Annunzio", Chieti, Italy
| | - Marco Trerotola
- Unit of Cancer Pathology, CeSI-MeT, University "G. d'Annunzio", Chieti, Italy.,Department of Medical, Oral and Biotechnological Sciences, University "G. d'Annunzio", Chieti, Italy
| | - Emanuela Guerra
- Unit of Cancer Pathology, CeSI-MeT, University "G. d'Annunzio", Chieti, Italy.,Department of Medical, Oral and Biotechnological Sciences, University "G. d'Annunzio", Chieti, Italy
| | - Saverio Alberti
- Unit of Cancer Pathology, CeSI-MeT, University "G. d'Annunzio", Chieti, Italy.,Department of Biomedical Sciences, Dentistry, Morphological and Functional Imaging, University of Messina, Messina, Italy
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5
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Identification of a robust subpathway-based signature for acute myeloid leukemia prognosis using an miRNA integrated strategy. PLoS One 2018; 13:e0194245. [PMID: 29570744 PMCID: PMC5865743 DOI: 10.1371/journal.pone.0194245] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2017] [Accepted: 02/27/2018] [Indexed: 01/22/2023] Open
Abstract
Acute myeloid leukemia (AML) is a heterogeneous disease, and survival signatures are urgently needed to better monitor treatment. MiRNAs displayed vital regulatory roles on target genes, which was necessary involved in the complex disease. We therefore examined the expression levels of miRNAs and genes to identify robust signatures for survival benefit analyses. First, we reconstructed subpathway graphs by embedding miRNA components that were derived from low-throughput miRNA-gene interactions. Then, we randomly divided the data sets from The Cancer Genome Atlas (TCGA) into training and testing sets, and further formed 100 subsets based on the training set. Using each subset, we identified survival-related miRNAs and genes, and identified survival subpathways based on the reconstructed subpathway graphs. After statistical analyses of these survival subpathways, the most robust subpathways with the top three ranks were identified, and risk scores were calculated based on these robust subpathways for AML patient prognoses. Among these robust subpathways, three representative subpathways, path: 05200_10 from Pathways in cancer, path: 04110_20 from Cell cycle, and path: 04510_8 from Focal adhesion, were significantly associated with patient survival in the TCGA training and testing sets based on subpathway risk scores. In conclusion, we performed integrated analyses of miRNAs and genes to identify robust prognostic subpathways, and calculated subpathway risk scores to characterize AML patient survival.
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Qi L, Li T, Shi G, Wang J, Li X, Zhang S, Chen L, Qin Y, Gu Y, Zhao W, Guo Z. An individualized gene expression signature for prediction of lung adenocarcinoma metastases. Mol Oncol 2017; 11:1630-1645. [PMID: 28922552 PMCID: PMC5663997 DOI: 10.1002/1878-0261.12137] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2017] [Revised: 09/01/2017] [Accepted: 09/06/2017] [Indexed: 12/17/2022] Open
Abstract
Our laboratory previously reported an individual‐level signature consisting of nine gene pairs, named 9‐GPS. This signature was developed by training on microarray expression data and validated using three independent integrated microarray data sets, with samples of stage I non‐small‐cell lung cancer after complete surgical resection. In this study, we first validated the cross‐platform robustness of 9‐GPS by demonstrating that 9‐GPS could significantly stratify the overall survival of 213 stage I lung adenocarcinoma (LUAD) patients detected with RNA‐sequencing platform in The Cancer Genome Atlas (TCGA; log‐rank P = 0.0318, C‐index = 0.55). Applying 9‐GPS to all the 423 stage I‐IV LUAD samples in TCGA, the predicted high‐risk samples were significantly enriched with clinically diagnosed metastatic samples (Fisher's exact test, P = 0.0015). We further modified the voting rule of 9‐GPS and found that the modified 9‐GPS had a better performance in predicting metastasis states (Fisher's exact test, P < 0.0001). With the aid of the modified 9‐GPS for reclassifying the metastasis states of patients with LUAD, the reclassified metastatic samples presented clearer transcriptional and genomic characteristics compared to the reclassified nonmetastatic samples. Finally, regulator network analysis identified TP53 and IRF1 with frequent genomic aberrations in the reclassified metastatic samples, indicating their key roles in driving tumor metastasis. In conclusion, 9‐GPS is a robust signature for identifying early‐stage LUAD patients with potential occult metastasis. This occult metastasis prediction was associated with clear transcriptional and genomic characteristics as well as the clinical diagnoses.
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Affiliation(s)
- Lishuang Qi
- College of Bioinformatics Science and TechnologyHarbin Medical UniversityChina
| | - Tianhao Li
- College of Bioinformatics Science and TechnologyHarbin Medical UniversityChina
| | - Gengen Shi
- College of Bioinformatics Science and TechnologyHarbin Medical UniversityChina
| | - Jiasheng Wang
- College of Bioinformatics Science and TechnologyHarbin Medical UniversityChina
| | - Xin Li
- College of Bioinformatics Science and TechnologyHarbin Medical UniversityChina
| | - Sainan Zhang
- College of Bioinformatics Science and TechnologyHarbin Medical UniversityChina
| | - Libin Chen
- College of Bioinformatics Science and TechnologyHarbin Medical UniversityChina
| | - Yuan Qin
- College of Bioinformatics Science and TechnologyHarbin Medical UniversityChina
| | - Yunyan Gu
- College of Bioinformatics Science and TechnologyHarbin Medical UniversityChina
| | - Wenyuan Zhao
- College of Bioinformatics Science and TechnologyHarbin Medical UniversityChina
| | - Zheng Guo
- College of Bioinformatics Science and TechnologyHarbin Medical UniversityChina
- Department of BioinformaticsKey Laboratory of Ministry of Education for Gastrointestinal CancerSchool of Basic Medical SciencesFujian Medical UniversityFuzhouChina
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7
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Toviwek B, Suphakun P, Choowongkomon K, Hannongbua S, Gleeson MP. Synthesis and evaluation of the NSCLC anti-cancer activity and physical properties of 4-aryl- N -phenylpyrimidin-2-amines. Bioorg Med Chem Lett 2017; 27:4749-4754. [DOI: 10.1016/j.bmcl.2017.08.063] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2017] [Revised: 08/19/2017] [Accepted: 08/25/2017] [Indexed: 10/18/2022]
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8
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Charkiewicz R, Niklinski J, Claesen J, Sulewska A, Kozlowski M, Michalska-Falkowska A, Reszec J, Moniuszko M, Naumnik W, Niklinska W. Gene Expression Signature Differentiates Histology But Not Progression Status of Early-Stage NSCLC. Transl Oncol 2017; 10:450-458. [PMID: 28456114 PMCID: PMC5408153 DOI: 10.1016/j.tranon.2017.01.015] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2016] [Revised: 01/25/2017] [Accepted: 01/31/2017] [Indexed: 01/10/2023] Open
Abstract
Advances in molecular analyses based on high-throughput technologies can contribute to a more accurate classification of non-small cell lung cancer (NSCLC), as well as a better prediction of both the disease course and the efficacy of targeted therapies. Here we set out to analyze whether global gene expression profiling performed in a group of early-stage NSCLC patients can contribute to classifying tumor subtypes and predicting the disease prognosis. Gene expression profiling was performed with the use of the microarray technology in a training set of 108 NSCLC samples. Subsequently, the recorded findings were validated further in an independent cohort of 44 samples. We demonstrated that the specific gene patterns differed significantly between lung adenocarcinoma (AC) and squamous cell lung carcinoma (SCC) samples. Furthermore, we developed and validated a novel 53-gene signature distinguishing SCC from AC with 93% accuracy. Evaluation of the classifier performance in the validation set showed that our predictor classified the AC patients with 100% sensitivity and 88% specificity. We revealed that gene expression patterns observed in the early stages of NSCLC may help elucidate the histological distinctions of tumors through identification of different gene-mediated biological processes involved in the pathogenesis of histologically distinct tumors. However, we showed here that the gene expression profiles did not provide additional value in predicting the progression status of the early-stage NSCLC. Nevertheless, the gene expression signature analysis enabled us to perform a reliable subclassification of NSCLC tumors, and it can therefore become a useful diagnostic tool for a more accurate selection of patients for targeted therapies.
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Affiliation(s)
- Radoslaw Charkiewicz
- Department of Clinical Molecular Biology, Medical University of Bialystok, Waszyngtona 13, Bialystok 15-269, Poland
| | - Jacek Niklinski
- Department of Clinical Molecular Biology, Medical University of Bialystok, Waszyngtona 13, Bialystok 15-269, Poland
| | - Jürgen Claesen
- Interuniversity Institute for Biostatistics and Statistical Bioinformatics, Hasselt University, Diepenbeek 3590, Belgium
| | - Anetta Sulewska
- Department of Clinical Molecular Biology, Medical University of Bialystok, Waszyngtona 13, Bialystok 15-269, Poland
| | - Miroslaw Kozlowski
- Department of Thoracic Surgery, Medical University of Bialystok, Marii Sklodowskiej-Curie 24a, Bialystok 15-276, Poland
| | - Anna Michalska-Falkowska
- Department of Clinical Molecular Biology, Medical University of Bialystok, Waszyngtona 13, Bialystok 15-269, Poland
| | - Joanna Reszec
- Department of Medical Pathomorphology, Medical University of Bialystok, Waszyngtona 13, Bialystok 15-269, Poland
| | - Marcin Moniuszko
- Department of Regenerative Medicine and Immune Regulation, Medical University of Bialystok, Waszyngtona 13, Bialystok, 15-269, Poland
| | - Wojciech Naumnik
- Department of Clinical Molecular Biology, Medical University of Bialystok, Waszyngtona 13, Bialystok 15-269, Poland; First Department of Lung Diseases, Medical University of Bialystok, Zurawia 14, Bialystok 15-540, Poland
| | - Wieslawa Niklinska
- Department of Histology and Embryology, Medical University of Bialystok, Waszyngtona 13, Bialystok 15-269, Poland.
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Zinovyeva MV, Kostina MB, Monastyrskaya GS, Sass AV, Filyukova OB, Vinogradova TV, Kopantzev EP, Sverdlov ED. Genetic markers for lung and esophagus common precursor cells in human development. DOKL BIOCHEM BIOPHYS 2015; 463:203-8. [DOI: 10.1134/s1607672915040031] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2015] [Indexed: 11/23/2022]
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10
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Pathway-based gene signatures predicting clinical outcome of lung adenocarcinoma. Sci Rep 2015; 5:10979. [PMID: 26042604 PMCID: PMC4455286 DOI: 10.1038/srep10979] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2014] [Accepted: 05/11/2015] [Indexed: 01/24/2023] Open
Abstract
Lung adenocarcinoma is often diagnosed at an advanced stage with poor prognosis. Patients with different clinical outcomes may have similar clinico-pathological characteristics. The results of previous studies for biomarkers for lung adenocarcinoma have generally been inconsistent and limited in clinical application. In this study, we used inverse-variance weighting to combine the hazard ratios for the four datasets and performed pathway analysis to identify prognosis-associated gene signatures. A total of 2,418 genes were found to be significantly associated with overall survival. Of these, a 21-gene signature in the HMGB1/RAGE signalling pathway and a 31-gene signature in the clathrin-coated vesicle cycle pathway were significantly associated with prognosis of lung adenocarcinoma across all four datasets (all p-values < 0.05, log-rank test). We combined the scores for the three pathways to derive a combined pathway-based risk (CPBR) score. Three pathway-based signatures and CPBR score also had more predictive power than single genes. Finally, the CPBR score was validated in two independent cohorts (GSE14814 and GSE13213 in the GEO database) and had significant adjusted hazard ratios 2.72 (p-value < 0.0001) and 1.71 (p-value < 0.0001), respectively. These results could provide a more complete picture of the lung cancer pathogenesis.
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Lu TP, Hsiao CK, Lai LC, Tsai MH, Hsu CP, Lee JM, Chuang EY. Identification of regulatory SNPs associated with genetic modifications in lung adenocarcinoma. BMC Res Notes 2015; 8:92. [PMID: 25889623 PMCID: PMC4384239 DOI: 10.1186/s13104-015-1053-8] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2014] [Accepted: 03/11/2015] [Indexed: 11/28/2022] Open
Abstract
Background Although much research effort has been devoted to elucidating lung cancer, the molecular mechanism of tumorigenesis still remains unclear. A major challenge to improve the understanding of lung cancer is the difficulty of identifying reproducible differentially expressed genes across independent studies, due to their low consistency. To enhance the reproducibility of the findings, an integrated analysis was performed to identify regulatory SNPs. Thirty-two pairs of tumor and adjacent normal lung tissue specimens were analyzed using Affymetrix U133plus2.0, Affymetrix SNP 6.0, and Illumina Infinium Methylation microarrays. Copy number variations (CNVs) and methylation alterations were analyzed and paired t-tests were used to identify differentially expressed genes. Results A total of 505 differentially expressed genes were identified, and their dysregulated patterns moderately correlated with CNVs and methylation alterations based on the hierarchical clustering analysis. Subsequently, three statistical approaches were performed to explore regulatory SNPs, which revealed that the genotypes of 551 and 66 SNPs were associated with CNV and changes in methylation, respectively. Among them, downstream transcriptional dysregulation was observed in 9 SNPs for CNVs and 4 SNPs for methylation alterations. Conclusions In summary, these identified SNPs concurrently showed the same direction of gene expression changes with genetic modifications, suggesting their pivotal roles in the genome for non-smoking women with lung adenocarcinoma. Electronic supplementary material The online version of this article (doi:10.1186/s13104-015-1053-8) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Tzu-Pin Lu
- Department of Public Health, Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taipei, Taiwan.
| | - Chuhsing K Hsiao
- Department of Public Health, Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taipei, Taiwan. .,Bioinformatics and Biostatistics Core, Center of Genomic Medicine, National Taiwan University, Taipei, Taiwan.
| | - Liang-Chuan Lai
- Bioinformatics and Biostatistics Core, Center of Genomic Medicine, National Taiwan University, Taipei, Taiwan. .,Graduate Institute of Physiology, National Taiwan University, Taipei, Taiwan.
| | - Mong-Hsun Tsai
- Bioinformatics and Biostatistics Core, Center of Genomic Medicine, National Taiwan University, Taipei, Taiwan. .,Institute of Biotechnology, National Taiwan University, Taipei, Taiwan.
| | - Chung-Ping Hsu
- Division of Thoracic Surgery, Taichung Veterans General Hospital, Taichung, Taiwan.
| | - Jang-Ming Lee
- Department of Surgery, National Taiwan University Hospital, Taipei, Taiwan.
| | - Eric Y Chuang
- Bioinformatics and Biostatistics Core, Center of Genomic Medicine, National Taiwan University, Taipei, Taiwan. .,Graduate Institute of Biomedical Electronics and Bioinformatics and Department of Electrical Engineering, National Taiwan University, Taipei, Taiwan.
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12
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Identification of gene expression biomarkers for predicting radiation exposure. Sci Rep 2014; 4:6293. [PMID: 25189756 PMCID: PMC4155333 DOI: 10.1038/srep06293] [Citation(s) in RCA: 32] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2014] [Accepted: 08/19/2014] [Indexed: 12/19/2022] Open
Abstract
A need for more accurate and reliable radiation dosimetry has become increasingly important due to the possibility of a large-scale radiation emergency resulting from terrorism or nuclear accidents. Although traditional approaches provide accurate measurements, such methods usually require tedious effort and at least two days to complete. Therefore, we provide a new method for rapid prediction of radiation exposure. Eleven microarray datasets were classified into two groups based on their radiation doses and utilized as the training samples. For the two groups, Student's t-tests and resampling tests were used to identify biomarkers, and their gene expression ratios were used to develop a prediction model. The performance of the model was evaluated in four independent datasets, and Ingenuity pathway analysis was performed to characterize the associated biological functions. Our meta-analysis identified 29 biomarkers, showing approximately 90% and 80% accuracy in the training and validation samples. Furthermore, the 29 genes significantly participated in the regulation of cell cycle, and 19 of them are regulated by three well-known radiation-modulated transcription factors: TP53, FOXM1 and ERBB2. In conclusion, this study demonstrates a reliable method for identifying biomarkers across independent studies and high and reproducible prediction accuracy was demonstrated in both internal and external datasets.
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