Lung Cancer Gene Expression Datasets Research Articles

A Novel Meta-Analysis-Based Regularized Orthogonal Matching Pursuit Algorithm to Predict Lung Cancer with Selected Biomarkers

Biomarker selection for predictive analytics encounters the problem of identifying a minimal-size subset of genes that is maximally predictive of an outcome of interest. For lung cancer gene expression datasets, it is a great challenge to handle the characteristics of small sample size, high dimensionality, high noise as well as the low reproducibility of important biomarkers in different studies. In this paper, our proposed meta-analysis-based regularized orthogonal matching pursuit (MA-ROMP) algorithm not only gains strength by using multiple datasets to identify important genomic biomarkers efficiently, but also keeps the selection flexible among datasets to take into account data heterogeneity through a hierarchical decomposition on regression coefficients. For a case study of lung cancer, we downloaded GSE10072, GSE19188 and GSE19804 from the GEO database with inconsistent experimental conditions, sample preparation methods, different study groups, etc. Compared with state-of-the-art methods, our method shows the highest accuracy, of up to 95.63%, with the best discriminative ability (AUC 0.9756) as well as a more than 15-fold decrease in its training time. The experimental results on both simulated data and several lung cancer gene expression datasets demonstrate that MA-ROMP is a more effective tool for biomarker selection and learning cancer prediction.

USNAP: fast unique dense region detection and its application to lung cancer.

Many real-world problems can be modeled as annotated graphs. Scalable graph algorithms that extract actionable information from such data are in demand since these graphs are large, varying in topology, and have diverse node/edge annotations. When these graphs change over time they create dynamic graphs, and open the possibility to find patterns across different time points. In this article, we introduce a scalable algorithm that finds unique dense regions across time points in dynamic graphs. Such algorithms have applications in many different areas, including the biological, financial, and social domains. There are three important contributions to this manuscript. First, we designed a scalable algorithm, USNAP, to effectively identify dense subgraphs that are unique to a time stamp given a dynamic graph. Importantly, USNAP provides a lower bound of the density measure in each step of the greedy algorithm. Second, insights and understanding obtained from validating USNAP on real data show its effectiveness. While USNAP is domain independent, we applied it to four non-small cell lung cancer gene expression datasets. Stages in non-small cell lung cancer were modeled as dynamic graphs, and input to USNAP. Pathway enrichment analyses and comprehensive interpretations from literature show that USNAP identified biologically relevant mechanisms for different stages of cancer progression. Third, USNAP is scalable, and has a time complexity of O(m+mc log nc+nc log nc), where m is the number of edges, and n is the number of vertices in the dynamic graph; mc is the number of edges, and nc is the number of vertices in the collapsed graph. The code of USNAP is available at https://www.cs.utoronto.ca/~juris/data/USNAP22.

Expression of ATP/GTP Binding Protein 1 Has Prognostic Value for the Clinical Outcomes in Non-Small Cell Lung Carcinoma.

ATP/GTP binding protein 1 (AGTPBP1) encodes a crucial protein, cytosolic carboxypeptidase 1 (CCP1), which plays a role in modulating the polyglutamylation of tubulin and has been studied in degenerative diseases. However, the role of AGTPBP1 in malignancy has not been completely studied yet. In this study, we examined the role of AGTPBP1 in cancer progression, its association with patient survival, and related mechanisms in lung cancer, using the A549 cell line and lung cancer gene expression datasets. AGTPBP1 knockdown increased the proliferation, migration, sphere formation, and drug resistance of A549 cells. Lung cancer datasets revealed significantly lower mRNA and protein expression levels of AGTPBP1 in lung cancer tissues, as compared to those in normal tissues. Importantly, AGTPBP1 expression positively correlated with patient survival. Analysis of co-expressed genes revealed that AGTPBP1 expression positively correlated with immune infiltration in lung cancer. Our results conclusively suggested that AGTPBP1 expression was correlated with cancer progression and immune infiltration in lung cancer.

Meta-Analysis Based on Nonconvex Regularization

The widespread applications of high-throughput sequencing technology have produced a large number of publicly available gene expression datasets. However, due to the gene expression datasets have the characteristics of small sample size, high dimensionality and high noise, the application of biostatistics and machine learning methods to analyze gene expression data is a challenging task, such as the low reproducibility of important biomarkers in different studies. Meta-analysis is an effective approach to deal with these problems, but the current methods have some limitations. In this paper, we propose the meta-analysis based on three nonconvex regularization methods, which are L1/2 regularization (meta-Half), Minimax Concave Penalty regularization (meta-MCP) and Smoothly Clipped Absolute Deviation regularization (meta-SCAD). The three nonconvex regularization methods are effective approaches for variable selection developed in recent years. Through the hierarchical decomposition of coefficients, our methods not only maintain the flexibility of variable selection and improve the efficiency of selecting important biomarkers, but also summarize and synthesize scientific evidence from multiple studies to consider the relationship between different datasets. We give the efficient algorithms and the theoretical property for our methods. Furthermore, we apply our methods to the simulation data and three publicly available lung cancer gene expression datasets, and compare the performance with state-of-the-art methods. Our methods have good performance in simulation studies, and the analysis results on the three publicly available lung cancer gene expression datasets are clinically meaningful. Our methods can also be extended to other areas where datasets are heterogeneous.

Bicluster Method to Predict Gene Patterns to Classify Differential Gene Expressions in Non-Small Cell Lung Cancer

In recent years, there are numerous efforts to overcome the constraints of data mining approaches to classify "BIG DATA". There are several types of data which has identical and similar expressions but there are dependent classification algorithms to predict these classes of expressions. Totally Different algorithms have been developed and enforced to research and differentiate the categories of data groups based on their functions. Zero-suppressed Binary Decision Diagram (ZBDD) algorithms help to classify the data with several categories. In the present study, lung cancer gene expression datasets 25 samples contain 10 mouth buccal cavity epithelial tissue samples and 15 nasal epithelial tissue samples from never smokers and current smokers were used to classify the genes and their expressions with various conditions. Using R and BioConductor software to normalize and predict differential expressed genes by Affy, Affycore tools and Limma packages to predict the gene expression with various functional properties. ZBDD algorithm and parallel coordination helps to predict the functional genes and the results shows 345 nasal epithelial genes were predict of which 54 genes were present in bicluster and 35 genes from mouth epithelial tissues show 14 were present in ZBDD bicluster. The results conclude that ZBDD algorithm has great advantage to classify big data and this algorithm can introduce in any large datasets for the accurate predict of large datasets.

Differential expression and functional analysis of lung cancer gene expression datasets: A systems biology perspective.

There is an inherent need to identify differentially expressed genes (DEGs), characterize these genes and provide functional enrichment analysis to the publicly available lung cancer datasets, primarily coming from next-generation sequencing data or microarray gene expression studies. The risk of lung cancer in patients with smokers is manifold, and with chronic obstructive pulmonary disease (COPD) it is 2- to 5-fold greater, compared with smokers without COPD. In the present study, differential expression analysis and gene functional enrichment analysis of lung cancer gene expression datasets obtained from NCBI-GEO were performed. The result identifies a significant number of DEGs which have at least a 2-fold change in their expression. Among them, six genes were found to have a 4-fold change in the expression level, and 47 genes exhibited a 3-fold change in the expression. It was also observed that most of the genes were upregulated and few genes were downregulated.

Smoker and non-smoker lung adenocarcinoma is characterized by distinct tumor immune microenvironments

ABSTRACTTobacco smoking causes DNA damages in epithelial cells and immune dysfunction in the lung, which collectively contribute to lung carcinogenesis and progression. However, potential mechanisms by which tumor-infiltrating immune cells contribute to lung cancer survival and their differential contributions in ever-smokers and never-smokers are not well studied. Here, we performed integrative analysis of 11 lung cancer gene-expression datasets, including 1,111 lung adenocarcinomas and 200 adjacent normal lung samples. Distinct pathways were altered in lung carcinogenesis in ever-smokers and never-smokers. Never-smoker patients had a better outcome than ever-smoker patients. We characterized compositional patterns of 21 types of immune cells in lung adenocarcinomas and revealed the complex association between immune cell composition and clinical outcomes. Interestingly, we found two subsets of immune cells, mast cells and CD4+ memory T cells, which had completely opposite associations with outcomes in resting and activated status. We further discovered that several chemokines and their associated receptors (e.g., CXCL11-CX3CR1 axis) were selectively altered in lung tumors in response to cigarette smoking and their abundances showed stronger correlation with fractions of these immune subsets in ever-smokers than never-smokers. The status switched from the resting to activated forms in mast cells and CD4+ memory T cells might manifest some important processes induced by cigarette smoking during tumor development and progression. Our findings suggested that aberrant activation of mast cells and CD4+ memory T cells plays crucial roles in cigarette smoking-induced immune dysfunction in the lung, which contributes to tumor development and progression.

Attribute selection using fuzzy roughset based customized similarity measure for lung cancer microarray gene expression data

Microarray gene expression data plays a prominent role in feature selection that helps in diagnosis and treatment of a wide variety of diseases. Microarray gene expression data contains redundant feature genes of high dimensionality and smaller training and testing samples. This paper proposes a customized similarity measure using fuzzy rough quick reduct algorithm for attribute selection. Information Gain based entropy is used to reduce the dimensionality in the first stage and the proposed fuzzy rough quick reduct method that defines a customized similarity measure for selecting the minimum number of informative genes and removing the redundant genes is employed at the second stage. The proposed method is evaluated using leukemia, lung and ovarian cancer gene expression datasets on a random forest classifier. The proposed method produces 97.22%, 99.45% and 99.6% classifier accuracy on leukemia, lung and ovarian cancer gene expression datasets respectively. The research study is carried out using the R open source software package. The proposed method shows substantial improvement in the performance with respect to various statistical parameters like classification accuracy, precision, recall, f-measure and region of characteristic compared to available methods in literature.

Abstract A037: Differences in BRCAness/PARP inhibitor response signatures and homologous recombination gene expression across lung adenocarcinoma and squamous cell carcinoma gene expression subtypes

Abstract Background: Gene expression-based subtyping in lung adenocarcinoma (AD) and lung squamous cell carcinoma (SQ) classifies AD and SQ tumors into distinct subtypes with variable expression of underlying biology including DNA damage response genes. These subtypes are linked to differences in chemotherapy sensitivity, and may impact response to therapeutics like PARP inhibitors. Methods: Using The Cancer Genome Atlas (TCGA) lung cancer gene expression datasets (AD n=515 and SQ n=501), AD subtypes (Terminal Respiratory Unit (TRU), Proximal Proliferative (PP), and Proximal Inflammatory (PI)) and SQ subtypes (Primitive, Classical, Secretory, and Basal) were defined using gene expression based centroid predictors. Association between AD and SQ expression subtypes and 3 published BRCAness/PARP inhibitor response signatures developed in ovarian and/or breast cancer (Konstantinopoulos et al., PMID 20547991; Daemen et al., PMID 22875744; McGrail et al., PMID 28649435) was examined using linear regression. Association between subtypes and expression of 15 recognized homologous recombination (HR) related genes (ATM, ATR, BRCA1, BRCA2, BRIP1, CDK12, CHEK1, CHEK2, FANCA, FANCI, FANCD2, MRE11A, RAD51C, RAD51L1, PTEN) was also examined using linear regression, and association tests included adjustment for the 3 BRCAness/PARP inhibitor response signatures and proliferation score. Results: AD and SQ subtypes showed strong association with the 3 BRCAness/PARP inhibitor response signatures (F-test p-values 7.7e-05, 5.9e-13, 9.4e-33 in AD and 1.9e-05, 9.0e-13, 2.7e-19 in SQ). AD and SQ subtypes showed strong association with 15 HR genes (max and median F-test p-values were 8.5e-04 and 7.5e-25 in AD, and 7.3e-04 and 1.4e-12 in SQ). The TRU subtype in AD showed low expression relative to the other AD subtypes for a majority of the HR genes, including BRCA1. In SQ, the same was true for the basal and secretory subtypes. Simultaneous adjustment for the 3 BRCAness/PARP inhibitor response signatures, as well as for proliferation, reduced association strength between subtype and HR gene expression in AD and less so in SQ. In AD, association between subtype and gene expression remained significant for 4 HR genes (using Bonferroni correction for 15 tests), including CHECK2, FANCI, BRIP1, and RAD51L1. In SQ, association between subtype and gene expression remained significant for all HR genes except CHEK1 and FANCA, (median and min Bonferroni-adjusted p-value 2.9e-04 and 2.6e-21). Conclusions: Intrinsic biologic subtypes of lung AD and SQ are associated with published BRCAness/PARP inhibitor response signatures and reveal differential expression of several HR-related genes. Evaluation of these subtypes, particularly in SQ, as potential biomarkers of PARP inhibitor sensitivity should be investigated. Citation Format: Gregory Mayhew, Chuck Perou, D Neil Hayes, Myla Lai-Goldman, Hawazin Faruki. Differences in BRCAness/PARP inhibitor response signatures and homologous recombination gene expression across lung adenocarcinoma and squamous cell carcinoma gene expression subtypes [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2017 Oct 26-30; Philadelphia, PA. Philadelphia (PA): AACR; Mol Cancer Ther 2018;17(1 Suppl):Abstract nr A037.

Inhibiting proliferation and migration of lung cancer using small interfering RNA targeting on Aldo-keto reductase family 1 member B10.

Lung cancer is the leading cause of global cancer-associated mortality. Genomic alterations in lung cancers have not been widely characterized, however, the molecular mechanism of tumor initiation and progression remain unknown, and no molecularly targeted have been specifically developed for its treatment and diagnosis. The present study observed the upregulation of Aldo-keto reductase family 1 member Bio10 (AKR1B10) lung cancer tissues by analyzing two public lung cancer gene expression datasets. Further experiments in silencing AKR1B10 demonstrated that the expression of AKR1B10 was associated with cell proliferation, cell cycle, adhesion and invasion, as well as extracellular-signal-regulated kinase/mitogen activated protein kinase signal pathway. The overexpression of AKR1B10 in lung cancer indicates the important role of AKR1B10 in tumorigenesis. These findings suggest that AKR1B10 could be a potential diagnosis and treatment mark of lung cancer.

Abstract A10: SLC7A11 contributes to the pathogenesis of lung cancer

Abstract Introduction: Many tumors increase uptake and dependence on nutrients such as glucose, cysteine or glutamine. These basic observations on cancer cell metabolism have opened multiple new diagnostic and therapeutic avenues in cancer. Here, we investigated the function of SLC7A11 (xCT), a cystine/glutamate anti-porter, in the pathogenesis of lung cancer. Recently, we found xCT to be strongly overexpressed at the cell surface of lung cancers and began to investigate the mechanisms by which xCT may contribute to lung cancer progression. Methods: The expression of xCT and its related genes in 411 patients with non small cell lung cancer (NSCLC) were evaluated in publically available lung cancer gene expression datasets. The association between xCT expression and overall survival, stage of diseases were tested in tissue microarray datasets. Gain of function studies for xCT were undertaken in the normal airway epithelium cell lines (BEAS2B and 16HBE) to investigate the role of xCT in regulating cell metabolism, growth and survival in normal and malignant lung epithelial cells. Results: Our results shows that xCT proein is overexpressed in 38% of NSCLC patients. We also found that expression of xCT mRNA is strongly associated with disease stage (p<0.01), smoking history (p =0.0202) and overrall survival (p<0.01). Overexpression of xCT in normal airway epithelium cells (16HBE and BEAS2B) induces the colony formaion in soft agar and tumor formation in nude mice. Targeting these cells with xCT overexpression by sulfasalazine significantly reduced tumor burden in mice by 50%. Furthermore, the overexpresion of xCT promotes increased glucose consumption, glutamine consumption and lactate production. Conclusion: Our results demonstrate that xCT is highly expressed in a subset of NSCLCs. The induction of xCT in transformed aiway epithelial cells causes tumor formation in nude mice. The mechanism of malignant transformation involves the Warburg effect. Further investigation into the mechanisms of transformation may help in answering fundamental questions pertaining to how glutamate/cystine exchange contributes to lung cancer development and progression and also in establishing xCT as a potential novel therapeutic target in lung cancer. Citation Format: Xiangming Ji Ji, Jun Qian, Jamshedur Rahman, Brad Harris, Megan Hoeksema, Heidi Chen, Rosana Eisenberg, Jamey Young. SLC7A11 contributes to the pathogenesis of lung cancer. [abstract]. In: Proceedings of the AACR Special Conference: Metabolism and Cancer; Jun 7-10, 2015; Bellevue, WA. Philadelphia (PA): AACR; Mol Cancer Res 2016;14(1_Suppl):Abstract nr A10.

Validation of the Lung Subtyping Panel in Multiple Fresh-Frozen and Formalin-Fixed, Paraffin-Embedded Lung Tumor Gene Expression Data Sets.

Context .- A histologic classification of lung cancer subtypes is essential in guiding therapeutic management. Objective .- To complement morphology-based classification of lung tumors, a previously developed lung subtyping panel (LSP) of 57 genes was tested using multiple public fresh-frozen gene-expression data sets and a prospectively collected set of formalin-fixed, paraffin-embedded lung tumor samples. Design .- The LSP gene-expression signature was evaluated in multiple lung cancer gene-expression data sets totaling 2177 patients collected from 4 platforms: Illumina RNAseq (San Diego, California), Agilent (Santa Clara, California) and Affymetrix (Santa Clara) microarrays, and quantitative reverse transcription-polymerase chain reaction. Gene centroids were calculated for each of 3 genomic-defined subtypes: adenocarcinoma, squamous cell carcinoma, and neuroendocrine, the latter of which encompassed both small cell carcinoma and carcinoid. Classification by LSP into 3 subtypes was evaluated in both fresh-frozen and formalin-fixed, paraffin-embedded tumor samples, and agreement with the original morphology-based diagnosis was determined. Results .- The LSP-based classifications demonstrated overall agreement with the original clinical diagnosis ranging from 78% (251 of 322) to 91% (492 of 538 and 869 of 951) in the fresh-frozen public data sets and 84% (65 of 77) in the formalin-fixed, paraffin-embedded data set. The LSP performance was independent of tissue-preservation method and gene-expression platform. Secondary, blinded pathology review of formalin-fixed, paraffin-embedded samples demonstrated concordance of 82% (63 of 77) with the original morphology diagnosis. Conclusions .- The LSP gene-expression signature is a reproducible and objective method for classifying lung tumors and demonstrates good concordance with morphology-based classification across multiple data sets. The LSP panel can supplement morphologic assessment of lung cancers, particularly when classification by standard methods is challenging.

Abstract 1185: Regulation of neutral amino acid transporters gene expression profile in non-small cell lung cancer

Abstract INTRODUCTION: In a recent study, we showed that SLC1A5, a key glutamine (Gln) transporter, regulates tumor growth and survival in NSCLC. Here we sought to characterize the expression of other Gln transporters in lung cancer and to test whether the inhibition of SLC1A5 activity induced a change in expression of other amino acid transporters in NSCLC. METHODS: Neutral amino acid transporters reported in the cancer literature were evaluated in publically available lung cancer gene expression datasets. Among those, SLC38A1, SLC1A4, SLC1A5, SLC7A5, SLC7A11, SLC7A8 were selected for the in vitro validation. The expression of these genes was evaluated in two lung squamous carcinoma cell lines (HCC15, H226) and one adenocarcinoma cell line (A549) by RT-PCR and then quantified by qPCR, using SYBR® Green Supermix protocol. The expression of the same genes was assessed in response to transient SLC1A5 knockdown in HCC15 and A549 cell lines by qPCR. RESULTS: Amino acid transporters SLC38A1, SLC1A5, SLC7A5 and SLC7A11 are expressed in NSCLCs, a finding that was confirmed in the three cell lines studied. SLC1A4 was found to have the lowest expression and SLC7A8 no expression in any of cell lines. In response to SLC1A5 transient knockdown in A549, SLC38A1, SLC1A4 and SLC7A5 were significantly up-regulated (p<0.02); in contrast, in HCC15, SLC38A1, SLC1A4 and SLC7A5 were significantly down-regulated (p<0.02) and SLC7A11 was up-regulated (p<0.13). CONCLUSION: Our results show that SLC38A1, SLC1A5, SLC7A5 and SLC7A11 have the highest level of gene expression in NSCLC. We identified a possible compensatory mechanism to satisfy Gln transport and cellular function upon SLC1A5 silencing involving different transporters in a cell-specific manner. Compensatory mechanisms in response to SLC1A5 knockdown deserve further investigation as it may have implications in strategies aiming at inhibiting its activity. Further expression, localization and functional studies will follow to better understand changes occurring in lung cancer progression. Key words: Non-small cell lung cancer, SLC1A5, expression, amino acid, transporters Citation Format: Pierre P. Massion, Maria Senosain, Mohamed Hassanein, Xiangming Ji, Jun Qian, Megan hoeksema. Regulation of neutral amino acid transporters gene expression profile in non-small cell lung cancer. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 1185. doi:10.1158/1538-7445.AM2015-1185

Harmony Search PSO Clustering for Tumor and Cancer Gene Expression Dataset

Enormous quantity of gene expression data from diverse data sources are accumulated due to the modern advancement in microarray technology that leads to major computational challenges. The foremost step towards addressing this challenge is to cluster genes which reveal hidden gene expression patterns and natural structures to find the interesting patterns from the underlying data that in turn helps in disease diagnosis and drug development. Particle Swarm Optimization (PSO) technique is extensively used for many practical applications but fails in finding the initial seeds to generate clusters and thus reduces the clustering accuracy. One of the meta-heuristic optimization algorithms called Harmony Search is free from divergence and helps to find out the near-global optimal solutions by searching the entire solution space. This paper proposes a novel Harmony Search Particle Swarm Optimization (HSPSO) clustering algorithm and is applied for Brain Tumor, Colon Cancer, Leukemia Cancer and Lung Cancer gene expression datasets for clustering. Experimental results show that the proposed algorithm produces clusters with better compactness and accuracy, in comparison with K-means clustering, PSO clustering (swarm clustering) and Fuzzy PSO clustering.