Genomic biomarkers in relation to PD-1 checkpoint blockade response.

25 Background: Somatic tumor mutational burden (TMB) and a T-cell inflamed gene expression profile (GEP) predict response to anti-PD-1/PD-L1 immunotherapies in multiple tumor types. We assessed the potential for GEP and TMB to jointly predict clinical response to pembrolizumab and to identify distinct, targetable patterns of biology that may modulate response/resistance. Methods: To assess the individual and joint clinical utility of TMB and GEP in a pan-tumor context, pembrolizumab-treated patients with advanced solid tumors and melanoma were stratified as 4 biomarker-defined clinical response groups (GEP low/TMB low, GEP low/TMB high, GEP high/TMB low, GEP high/TMB high; N > 300) based on cutoffs for TMB (ROC Youden Index associated) and GEP (selected via analysis of pan cancer data). TMB and GEP were used to guide transcriptome and exome analysis of tumors in 2 large databases (Moffitt, n = 2944; TCGA, n = 6978). Results: TMB and GEP had a low, but significant, correlation in these clinical datasets. ORR was highest in GEP high/TMB high (37-57%), modest in GEP high/TMB low (12-35%) and GEP low/TMB high (11-42%), and lowest in GEP low/TMB low (0-9%) groups. Within the Moffitt and TCGA databases, GEP and TMB again had a low correlation, demonstrating their potential joint utility for stratifying additional transcriptomic and genomic features of these datasets. Specific gene modules showed strong positive or negative and highly statistically significant associations with TMB, GEP or both in each dataset, and patterns were consistent between datasets. In particular, gene set enrichment analysis identified proliferative, stromal and vascular biology corresponding to specific TMB-defined subgroups within GEP high tumors. In TMB-high tumors, indication-dependent somatic DNA alterations in key cancer driver genes showed a strong negative association ( P< 1e-5) with GEP. Conclusions: This analysis shows that TMB and T-cell inflamed GEP score can stratify human cancers into groups with different response rates to pembrolizumab monotherapy, and identify patterns of underlying, targetable biology related to these groups. This approach may provide a precision medicine framework for evaluating anti-PD-1/L1-based combination therapy regimens. Clinical trial information: NCT01848834; NCT02054806; NCT01295827; NCT01866319.

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Enolase 1 Correlated With Cancer Progression and Immune-Infiltrating in Multiple Cancer Types: A Pan-Cancer Analysis

Frontiers in Oncology ◽

10.3389/fonc.2020.593706 ◽

2021 ◽

Vol 10 ◽

Author(s):

Wenhua Xu ◽

Wenna Yang ◽

Chunfeng Wu ◽

Xiaocong Ma ◽

Haoyu Li ◽

...

Keyword(s):

Cancer Progression ◽

Stress Protein ◽

Enrichment Analysis ◽

Tumor Stage ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Multiple Cancer ◽

Clinical Prognosis ◽

Multiple Tumor ◽

Normal Tissues

Enolase 1 (ENO1) is an oxidative stress protein expressed in endothelial cells. This study aimed to investigate the correlation of ENO1 with prognosis, tumor stage, and levels of tumor-infiltrating immune cells in multiple cancers. ENO1 expression and its influence on tumor stage and clinical prognosis were analyzed by UCSC Xena browser, Gene Expression Profiling Interactive Analysis (GEPIA), The Cancer Genome Atlas (TCGA), and GTEx Portal. The ENO1 mutation analysis was performed by cBio Portal, and demonstrated ENO1 mutation (1.8%) did not impact on tumor prognosis. The relationship between ENO1 expression and tumor immunity was analyzed by Tumor Immune Estimation Resource (TIMER) and GEPIA. The potential functions of ENO1 in pathways were investigated by Gene Set Enrichment Analysis. ENO1 expression was significantly different in tumor and corresponding normal tissues. ENO1 expression in multiple tumor tissues correlated with prognosis and stage. ENO1 showed correlation with immune infiltrates including B cells, CD8+ and CD4+ T cells, macrophages, neutrophils, and dendritic cells, and tumor purity. ENO1 was proved to be involved in DNA replication, cell cycle, apoptosis, glycolysis process, and other processes. These findings indicate that ENO1 is a potential prognostic biomarker that correlates with cancer progression immune infiltration.

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Identification of ovarian cancer driver genes by using module network integration of multi-omics data

Interface Focus ◽

10.1098/rsfs.2013.0013 ◽

2013 ◽

Vol 3 (4) ◽

pp. 20130013 ◽

Cited By ~ 37

Author(s):

Olivier Gevaert ◽

Victor Villalobos ◽

Branimir I. Sikic ◽

Sylvia K. Plevritis

Keyword(s):

Gene Expression ◽

Ovarian Cancer ◽

Specific Gene ◽

Omics Data ◽

Driver Genes ◽

Module Network ◽

Cancer Driver ◽

Specific Gene Expression ◽

Cancer Drivers ◽

Disease Specific

The increasing availability of multi-omics cancer datasets has created a new opportunity for data integration that promises a more comprehensive understanding of cancer. The challenge is to develop mathematical methods that allow the integration and extraction of knowledge from large datasets such as The Cancer Genome Atlas (TCGA). This has led to the development of a variety of omics profiles that are highly correlated with each other; however, it remains unknown which profile is the most meaningful and how to efficiently integrate different omics profiles. We developed AMARETTO, an algorithm to identify cancer drivers by integrating a variety of omics data from cancer and normal tissue. AMARETTO first models the effects of genomic/epigenomic data on disease-specific gene expression. AMARETTO's second step involves constructing a module network to connect the cancer drivers with their downstream targets. We observed that more gene expression variation can be explained when using disease-specific gene expression data. We applied AMARETTO to the ovarian cancer TCGA data and identified several cancer driver genes of interest, including novel genes in addition to known drivers of cancer. Finally, we showed that certain modules are predictive of good versus poor outcome, and the associated drivers were related to DNA repair pathways.

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CLDN6 and CLDN10 are Associated with Immune Infiltration of Ovarian Cancer: A Study of Claudin Family

10.21203/rs.3.rs-40048/v1 ◽

2020 ◽

Author(s):

Peipei Gao ◽

Ting Peng ◽

Canhui Cao ◽

Shitong Lin ◽

Ping Wu ◽

...

Keyword(s):

Ovarian Cancer ◽

T Cell ◽

Tumor Microenvironment ◽

Immune Cells ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Gene Markers ◽

Immune Infiltration ◽

Kaplan Meier ◽

The Relationship

Abstract Background: Claudin family is a group of membrane proteins related to tight junction. There are many studies about them in cancer, but few studies pay attention to the relationship between them and the tumor microenvironment. In our research, we mainly focused on the genes related to the prognosis of ovarian cancer, and explored the relationship between them and the tumor microenvironment of ovarian cancer.Methods: The cBioProtal provided the genetic variation pattern of claudin gene family in ovarian cancer. The ONCOMINE database and Gene Expression Profiling Interactive Analysis (GEPIA) were used to exploring the mRNA expression of claudins in cancers. The prognostic potential of these genes was examined via Kaplan-Meier plotter. Immunologic signatures were enriched by gene set enrichment analysis (GSEA). The correlations between claudins and the tumor microenvironment of ovarian cancer were investigated via Tumor Immune Estimation Resource (TIMER).Results: In our research, claudin genes were altered in 363 (62%) of queried patients/samples. Abnormal expression levels of claudins were observed in various cancers. Among them, we found that CLDN3, CLDN4, CLDN6, CLDN10, CLDN15 and CLDN16 were significantly correlated with overall survival of patients with ovarian cancer. GSEA revealed that CLDN6 and CLDN10 were significantly enriched in immunologic signatures about B cell, CD4 T cell and CD8 T cell. What makes more sense is that CLDN6 and CLDN10 were found related to the tumor microenvironment. CLDN6 expression was negatively correlated with immune infiltration level in ovarian cancer, and CLDN10 expression was positively correlated with immune infiltration level in ovarian cancer. Further study revealed the CLDN6 expression level was negatively correlated with gene markers of various immune cells in ovarian cancer. And, the expression of CLDN10 was positive correlated with gene markers of immune cells in ovarian cancer.Conclusions: CLDN6 and CLDN10 were prognostic biomarkers, and correlated with immune infiltration in ovarian cancer. Our results revealed new roles for CLDN6 and CLDN10, and they were potential therapeutic targets in the treatment of ovarian cancer.

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Understanding Tissue-specific Gene Regulation

10.1101/110601 ◽

2017 ◽

Cited By ~ 1

Author(s):

Abhijeet R. Sonawane ◽

John Platig ◽

Maud Fagny ◽

Cho-Yi Chen ◽

Joseph N. Paulson ◽

...

Keyword(s):

Transcription Factor ◽

Transcriptional Control ◽

Tissue Specificity ◽

Enrichment Analysis ◽

Tissue Expression ◽

Gene Set Enrichment Analysis ◽

Specific Gene ◽

Tissue Specific ◽

Network Nodes ◽

Transcription Factor Expression

Although all human tissues carry out common processes, tissues are distinguished by gene expres-sion patterns, implying that distinct regulatory programs control tissue-specificity. In this study, we investigate gene expression and regulation across 38 tissues profiled in the Genotype-Tissue Expression project. We find that network edges (transcription factor to target gene connections) have higher tissue-specificity than network nodes (genes) and that regulating nodes (transcription factors) are less likely to be expressed in a tissue-specific manner as compared to their targets (genes). Gene set enrichment analysis of network targeting also indicates that regulation of tissue-specific function is largely independent of transcription factor expression. In addition, tissue-specific genes are not highly targeted in their corresponding tissue-network. However, they do assume bottleneck positions due to variability in transcription factor targeting and the influence of non-canonical regulatory interactions. These results suggest that tissue-specificity is driven by context-dependent regulatory paths, providing transcriptional control of tissue-specific processes.

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DNAJC10 Correlates with Tumor Immune Characteristics and Predicts the Prognosis of Glioma Patients

10.21203/rs.3.rs-761056/v1 ◽

2021 ◽

Author(s):

Feng Liu ◽

Zewei Tu ◽

Junzhe Liu ◽

Xiaoyan Long ◽

Bing Xiao ◽

...

Keyword(s):

T Cell ◽

Immune Cell ◽

Cox Regression ◽

Predictive Accuracy ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Functional Enrichment ◽

Time Dependent ◽

Prognostic Role

Abstract Background: A role of DNAJC10 has been reported in several cancers, but its function in glioma is not clear. The purpose of this study was to investigate the prognostic role and the underlying functions of DNAJC10 in glioma.Methods: Reverse transcription and quantitative polymerase chain reaction and western blotting were performed to quantify the relative DNAJC10 mRNA and protein expressions of clinical samples. Wilcoxon rank sum tests were used to compare DNAJC10 expression between or among glioma subgroups with different clinicopathological features. The overall survival (OS) rates of glioma patients with different DNAJC10 expression were compared with the Kaplan-Meier method (two-sided log-rank test). The prognosis-predictive accuracy of the DNAJC10 was evaluated by time-dependent receiver operating characteristic (ROC) curves. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes annotations were conducted using the “clusterProfiler” package. Single-sample gene set enrichment analysis was used to estimate immune cell infiltrations and immune-related function levels. The independent prognostic role of DNAJC10 was determined by univariate and multivariate Cox regression analyses. A DNAJC10-based nomogram model was established using multivariate Cox regression in the R package “rms.” Results: Higher DNAJC10 expression was observed in gliomas. It was upregulated in tumors with higher World Health Organization grade, isocitrate dehydrogenase wild-type status, 1p/19q non-co-deletion, and methylguanine-DNA methyltransferase unmethylated gliomas. Patients with gliomas with higher DNAJC10 expression had poorer prognoses than those with low-DNAJC10 gliomas. The predictive accuracy of 1/3/5-year OS of DNAJC10 was stable and robust using a time-dependent ROC model. Functional enrichment analysis recognized that T cell activation and T cell receptor signaling were enriched in higher DNAJC10 gliomas. Immune cell and stromal cell infiltrations, tumor mutation burden, copy number alteration burden, and immune checkpoint genes were also positively correlated with glioma DNAJC10 expression. A DNAJ10-based nomogram model was established and showed strong prognosis-predictive ability.Conclusion: Higher DNAJC10 expression correlates with poor prognosis of patients with glioma and is a potential and useful prognostic biomarker.

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Transcriptional profiling identifies an androgen receptor activity-low, stemness program associated with enzalutamide resistance

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1922207117 ◽

2020 ◽

Vol 117 (22) ◽

pp. 12315-12323 ◽

Cited By ~ 2

Author(s):

Joshi J. Alumkal ◽

Duanchen Sun ◽

Eric Lu ◽

Tomasz M. Beer ◽

George V. Thomas ◽

...

Keyword(s):

Androgen Receptor ◽

De Novo ◽

Statistical Significance ◽

Specific Antigen ◽

Enrichment Analysis ◽

Progression Free Survival ◽

Gene Set Enrichment Analysis ◽

Specific Gene ◽

Castration Resistant Prostate Cancer ◽

Gene Alterations

The androgen receptor (AR) antagonist enzalutamide is one of the principal treatments for men with castration-resistant prostate cancer (CRPC). However, not all patients respond, and resistance mechanisms are largely unknown. We hypothesized that genomic and transcriptional features from metastatic CRPC biopsies prior to treatment would be predictive of de novo treatment resistance. To this end, we conducted a phase II trial of enzalutamide treatment (160 mg/d) in 36 men with metastatic CRPC. Thirty-four patients were evaluable for the primary end point of a prostate-specific antigen (PSA)50 response (PSA decline ≥50% at 12 wk vs. baseline). Nine patients were classified as nonresponders (PSA decline <50%), and 25 patients were classified as responders (PSA decline ≥50%). Failure to achieve a PSA50 was associated with shorter progression-free survival, time on treatment, and overall survival, demonstrating PSA50’s utility. Targeted DNA-sequencing was performed on 26 of 36 biopsies, and RNA-sequencing was performed on 25 of 36 biopsies that contained sufficient material. Using computational methods, we measured AR transcriptional function and performed gene set enrichment analysis (GSEA) to identify pathways whose activity state correlated with de novo resistance.TP53gene alterations were more common in nonresponders, although this did not reach statistical significance (P= 0.055).ARgene alterations and AR expression were similar between groups. Importantly, however, transcriptional measurements demonstrated that specific gene sets—including those linked to low AR transcriptional activity and a stemness program—were activated in nonresponders. Our results suggest that patients whose tumors harbor this program should be considered for clinical trials testing rational agents to overcome de novo enzalutamide resistance.

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Gene Set Enrichment Analysis of Ki-67high CLL Clones Suggests Complex Interactions of B-Cell Receptor Signaling and Normal Cell Interactions in the Disease

Blood ◽

10.1182/blood.v118.21.2833.2833 ◽

2011 ◽

Vol 118 (21) ◽

pp. 2833-2833

Author(s):

Xiao J. Yan ◽

Daniel Kalenscher ◽

Erin Boyle ◽

Sophia Yancopoulos ◽

Rajendra N Damle ◽

...

Keyword(s):

T Cell ◽

B Cell ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Gene Set Enrichment ◽

Ki 67 ◽

Mutation Status ◽

Gene Set ◽

Gene Sets ◽

Bcr Signaling

Abstract Abstract 2833 Introduction: In chronic lymphocytic leukemia (CLL), clonally expanded CD5+ B lymphocytes eventually overwhelm healthy immune cells, hindering normal immune function. To determine mechanisms fueling this expansion, gene expression data were gathered by microarray analysis of cells from CLL patients. Samples were grouped based on Ki-67 expression, an indicator of proliferation. To determine mechanisms correlating with B-cell proliferation and impacting on CLL B-cell biology, microarray profiles were compared using Gene Set Enrichment Analysis (GSEA) [Subramanian A, et al. PNAS 2005]. Methods: Samples were analyzed for intracellular expression of Ki-67 by flow cytometry and divided into 2 groups based on Ki-67 expression (cutoff at 5%). RNA was then purified from CD5+CD19+ CLL cells and gene expression microarray assays were performed using Illumina HumanHT12 beadchips. GSEA was carried out using a library of signatures by Dr. Louis Staudt [Shaffer AL, et al. Immunol Rev 2006] containing 305 gene sets encompassing 13, 564 genes biased towards hematopoietic signatures. Results: Of 61 cases, 14 were Ki-67high and 47 were Ki-67low. When time-to-first-treatment (TTFT) was compared between the groups, Ki67high patients had significantly shorter TTFT (2.76 yrs) compared to Ki-67low patients (23.46 yrs; P<0.0001). By GSEA, we determined 255/285 gene sets were upregulated in the Ki-67high group with 50 gene sets significantly enriched at a false discovery rate (FDR) <25%. For the Ki-67low group, 30/285 gene sets were upregulated with only one significant at FDR <25%. IGHV unmutated CLL (U-CLL) was enriched in only one gene set, termed CLLUNMUT-1, while mutated CLL (M-CLL) was only enriched in CLLMUT-1. CD38high and CD38low subsets were similarly enriched in these two gene sets, with 4 additional gene sets in the CD38high group, including MYD88UP-4 and IFN-2. Of the 50 significantly enriched gene sets in the Ki-67high group, 17 relate to signaling pathways, 16 to cellular differentiation, 6 to cellular processes, 4 to transcription factor targets, and the remaining 7 relate to cancer. Of these, the percentage of the signaling component is up 13% from its representation in the original Staudt library. The top 5 gene sets enriched in the Ki-67high group are: upregulated U-CLL compared to M-CLL (CLLUNMUT-1), myeloid tissue compared to other tissues (MYELOID-1), T cell cytokine induced proliferation (TCYTUP-8), BCR crosslinking CLL B cells (CLLBCRUP-1) and BDCA4+ dendritic cells compared to other hematopoietic cells (DC-1). The total number of genes enriched in these 50 sets is 769, with 217 genes shared in two or more gene sets. Twenty genes were enriched in the CLL BCR signature, CLLBCRUP-1 [Herishanu Y, et al. Blood 2011]. Of these, WARS, IRF4, MX1, OAS1, and NAMPT are also enriched in the T cell cytokine induced and T cell activation signatures. Only one gene set was enriched in the Ki-67low group, CLLMUT-1, upregulated in M-CLL compared to U-CLL. CD274 (PD-L1) was consistently elevated in the Ki-67low group in all the patients, irrespective of IGHV mutation status. Discussion: The observed GSEA profiles in Ki-67high patients correlated with gene signatures biased towards BCR signaling, signal transduction, and hematopoietic cancer, consistent with the Ki-67high group containing more (recently) proliferating cells influenced at least in part by BCR signaling. The profiles also suggest that additional cells (T lymphocytes and dendritic cells) may be involved. It is notable these gene sets were not observed for CLL patients subgrouped by IGHV mutation status or by CD38, and that these other subsets did not show as pronounced a distinction by GSEA profiling. Disclosures: No relevant conflicts of interest to declare.

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T315I-Mutated BCR-ABL Induces a Distinct and Specific Molecular Signature With High Expression Of Zinc Finger (ZNF) Transcription Factors

Blood ◽

10.1182/blood.v122.21.4899.4899 ◽

2013 ◽

Vol 122 (21) ◽

pp. 4899-4899

Author(s):

Christophe Desterke ◽

Djamel Aggoune ◽

Marie Laure Bonnet ◽

Nais Prade ◽

Jean-Claude Chomel ◽

...

Keyword(s):

Transcription Factors ◽

Hierarchical Clustering ◽

Clonal Selection ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Classification Error ◽

Specific Gene ◽

Gene Set ◽

Abl Kinase ◽

T315i Mutation

Abstract Chronic myeloid leukemia (CML) is the paradigm of malignancy treated by targeted therapies by the use of tyrosine kinase inhibitors (TKI), essentially Imatinib, Dasatinib and Nilotinib. Despite their major efficiency, especially as first line therapies, resistance to these drugs develop partly due to genetic instability inherent to CML. BCR-ABL-kinase mutations remain the first cause of resistance, which appears to be due to clonal selection of cells bearing a given mutation under TKI therapies. Amongst these mutations, the “gatekeeper” T315I mutant is a major concern as it confers resistance to all three TKI clinically used and patients with this mutation have a poor prognosis. The inaccessibility of the TKI to the ABL kinase pocket might not be the only “mechanistic” cause of resistance and it has been suggested that T315I-mutated BCR-ABL (Skaggs BJ et al, 2006) could induce a specific phosphoproteome signature. To evaluate this possibility, we decided to determine if a specific gene expression profiling can be associated with T315I-mutated BCR-ABL, as compared to native BCR-ABL. The human hematopoietic cell line UT7 was transfected with retroviral vectors encoding for native BCR-ABL (UT7.11) or BCR-ABL with the T315I mutation (UT7.T315I). The cell lines were characterized by their cell growth, Western blotting and sequencing. UT7.11 cells were sensitive to Imatinib, Dasatinib and Nilotinib as well as to Ponatinib whereas UT7-T315I cells were resistant to all three TKI except for Ponatinib. Affymetrix microarrays were performed in triplicate on each of three groups (UT7, UT7.11, UT7.T315I). The datas were normalized using the dchip software. Bioinformatics analyzes were performed with R software (packages FactoMineR, limma, PAMR) Mev in TM4 software, enrichment analysis with the GSEA software (Broad institute). The principal component analysis (PCA) showed that the overall RNA expression of UT7.T315I was different from that of UT7.11 (native BCR-ABL) and parental UT7. On factorial map, UT7.11 was found more distant from parental UT7 than UT7.T315I. The contrast analysis of the linear model by the algorithm limma between the 3 groups, showed a strong differential signature of UT7.11 as compared to parental UT7 and UT7.T315I (respectively 4792 and 4813 genes). Only 800 genes were found to be differentially expressed between UT7.T315I and parental UT7. In hierarchical clustering, the total signature obtained in limma confirmed a closed profile between parental UT7 and UT7.T315I. Among the results of the limma model, we identified a 286 specific genes signature for UT7.T315I (both different from parental UT7 and UT7.11 and also not regulated between UT7.11 and UT7). This specific list of UT7.T315I was validated with the T315I group sample segregation by different multivariate methods: PCA, hierarchical clustering and non-negative matrix factorization. Among this T315I-specific gene list limma, 34 ZNF family genes were found (11.88%). Predicting class algorithm based on shunkren centroid (PAMR) separated the three group samples with low classification error and a global list of 368 genes: only 75 genes predicted UT7.T315I group and from this list 13 were in the ZNF gene family (13.33%). By the method of gene set enrichment analysis (GSEA), we explored the top 100 ranked genes as upregulated in UT7.T315I by comparing the two other sample groups. This gene set showed a high representation of ZNF family genes (25%). The design of a gene set with ZNF family genes selected showed a positive enrichment of ZNF (NES = +1.35, p-value <0.001) in the UT7.T315I by comparing the two other groups. The majority of these genes is localized in 19q13.41 (ZNF cluster 282). They exhibit C2H2 and Kruppel-associated box (KRAB) domains in their sequence. Interestingly the overexpression of KRAB-ZNF transcription factors has been recently reported in patients with gastrointestinal stromal tumors (GIST) as conferring resistance to Imatinib Mesylate (Rink L., PLOS One 2013). In conclusion, our work revealed for the first time a specific signature of the T315I mutation which includes a strong representation of the ZNF family. The identification of this signature could be interest for future drug screening strategies in advanced phase CML patients progressing under Ponatinib. Current experiments are underway to validate these results by analyzing the expression of ZNF family of genes in primary CML cells with T315I mutation. Disclosures: Turhan: Bristol Myers Squibb, Novartis: Consultancy, Honoraria.

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TS-GOEA: a web tool for tissue-specific gene set enrichment analysis based on gene ontology

BMC Bioinformatics ◽

10.1186/s12859-019-3125-6 ◽

2019 ◽

Vol 20 (S18) ◽

Author(s):

Jiajie Peng ◽

Guilin Lu ◽

Hansheng Xue ◽

Tao Wang ◽

Xuequn Shang

Keyword(s):

Gene Ontology ◽

Enrichment Analysis ◽

Experimental Results ◽

Gene Set Enrichment Analysis ◽

Specific Gene ◽

Web Tool ◽

Tissue Specific ◽

Analysis Tools ◽

Go Enrichment ◽

Go Enrichment Analysis

Abstract Background The Gene Ontology (GO) knowledgebase is the world’s largest source of information on the functions of genes. Since the beginning of GO project, various tools have been developed to perform GO enrichment analysis experiments. GO enrichment analysis has become a commonly used method of gene function analysis. Existing GO enrichment analysis tools do not consider tissue-specific information, although this information is very important to current research. Results In this paper, we built an easy-to-use web tool called TS−GOEA that allows users to easily perform experiments based on tissue-specific GO enrichment analysis. TS−GOEA uses strict threshold statistical method for GO enrichment analysis, and provides statistical tests to improve the reliability of the analysis results. Meanwhile, TS−GOEA provides tools to compare different experimental results, which is convenient for users to compare the experimental results. To evaluate its performance, we tested the genes associated with platelet disease with TS−GOEA. Conclusions TS−GOEA is an effective GO analysis tool with unique features. The experimental results show that our method has better performance and provides a useful supplement for the existing GO enrichment analysis tools. TS−GOEA is available at http://120.77.47.2:5678.

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Dual KS: Defining Gene Sets with Tissue Set Enrichment Analysis

Cancer Informatics ◽

10.4137/cin.s2892 ◽

2010 ◽

Vol 9 ◽

pp. CIN.S2892 ◽

Cited By ~ 1

Author(s):

Yarong Yang ◽

Eric J. Kort ◽

Nader Ebrahimi ◽

Zhongfa Zhang ◽

Bin T. Teh

Keyword(s):

Error Rate ◽

Expression Patterns ◽

Enrichment Analysis ◽

Gene Signature ◽

Gene Set Enrichment Analysis ◽

Specific Gene ◽

Gene Signatures ◽

Bioconductor Project ◽

Microarray Studies ◽

Estimated Error

Background Gene set enrichment analysis (GSEA) is an analytic approach which simultaneously reduces the dimensionality of microarray data and enables ready inference of the biological meaning of observed gene expression patterns. Here we invert the GSEA process to identify class-specific gene signatures. Because our approach uses the Kolmogorov-Smirnov approach both to define class specific signatures and to classify samples using those signatures, we have termed this methodology “Dual-KS” (DKS). Results The optimum gene signature identified by the DKS algorithm was smaller than other methods to which it was compared in 5 out of 10 datasets. The estimated error rate of DKS using the optimum gene signature was smaller than the estimated error rate of the random forest method in 4 out of the 10 datasets, and was equivalent in two additional datasets. DKS performance relative to other benchmarked algorithms was similar to its performance relative to random forests. Conclusions DKS is an efficient analytic methodology that can identify highly parsimonious gene signatures useful for classification in the context of microarray studies. The algorithm is available as the dualKS package for R as part of the bioconductor project.

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