A Comprehensive Analysis of HOXB13 Expression in Hepatocellular Carcinoma.

Background and objectives: Hepatocellular carcinoma (HCC) is one of the most common malignancies worldwide and is caused by multiple factors. To explore novel targets for HCC treatment, we comprehensively analyzed the expression of HomeoboxB13 (HOXB13) and its role in HCC. Materials and Methods: The clinical significance of HCC was investigated using open gene expression databases, such as TIMER, UALCAN, KM, OSlihc, and LinkedOmics, and immunohistochemistry analysis. We also analyzed cell invasion and migration in HCC cell lines transfected with HOXB13-siRNA and their association with MMP9, E2F1, and MEIS1. Results: HOXB13 expression was higher in fibrolamellar carcinoma than in other histological subtypes. Its expression was associated with lymph node metastasis, histological stage, and tumor grade. It was positively correlated with immune cell infiltration of B cells (R = 0.246), macrophages (R = 0.182), myeloid dendritic cells (R = 0.247), neutrophils (R = 0.117), and CD4+ T cells (R = 0.258) and negatively correlated with immune cell infiltration of CD8+ T cells (R = -0.107). A positive correlation was observed between HOXB13, MMP9 (R = 0.176), E2F1 (R = 0.241), and MEIS1 (R = 0.189) expression (p < 0.001). The expression level of HOXB13 was significantly downregulated in both HepG2 and PLC/PFR/5 cell lines transfected with HOXB13-siRNA compared to that in cells transfected with NC siRNA (p < 0.05). Additionally, HOXB13 significantly affected cell viability and wound healing. Conclusions: HOXB13 overexpression may lead to poor prognosis in patients with HCC. Additional in vivo studies are required to improve our understanding of the biological role and the exact mechanism of action of HOXB13 in HCC.

Current advances in comprehensive omics data mining for oncology and cancer research.

The availability of a large amount of multiomics data enables data-driven discovery studies on cancers. High-throughput data on mutations, gene/protein expression, immune scores (tumor-infiltrating cells), drug screening, and RNAi (shRNAs and CRISPRs) screening are major integrated components of patient samples and cell line datasets. Improvements in data access and user interfaces make it easy for general scientists to carry out their data mining practices on integrated multiomics data platforms without computational expertise. Here, we summarize the extent of data integration and functionality of several portals and software that provide integrated multiomics data mining platforms for all cancer studies. Recent progress includes programming interfaces (APIs) for customized data mining. Precalculated datasets assist noncomputational users in quickly browsing data associations. Furthermore, stand-alone software provides fast calculations and smart functions, guiding optimal sampling and filtering options for the easy discovery of significant data associations. These efforts improve the utility of cancer omics big data for noncomputational users at all levels of cancer research. In the present review, we aim to provide analytical information guiding general scientists to find and utilize data mining tools for their research.

Helical magnetic micromotors decorated with nickel ferrocyanide for the active and rapid adsorption of radiocesium in water.

Separating radioactive cesium from nuclear waste and contaminated environments is critical to mitigate radiological hazards. In response to this need, remote-controllable and Cs-selective micromotor adsorbents have been considered as a promising technology for rapid in-situ cleanup while minimizing secondary waste and radiation exposure to workers. In this study, we demonstrate the active and rapid removal of a radioactive contaminant from water by leveraging the magnetic manipulation capabilities of a helical and magnetic Ni micromotor coated with Cs-selective nickel ferrocyanide (NiFC). The use of polyvinyl alcohol fibers as a template enables the straightforward preparation of the helical wire structure, allowing for precise control over the diameter and pitch of the helix through simple twisting with Ni wires. By harnessing Ni2+ ions eluted from the Ni micromotor in an acid solution, we successfully fabricate NiFC-coated Ni (NiFC/Ni) micromotors that exhibit a selective removal efficiency greater than 98% for 137Cs, even in the presence of high concentrations of competing Na+ ions. Under the influence of an external magnetic field, the NiFC/Ni micromotor demonstrates rapid motion, achieving a pulling motion (100 body lengths per second) through a magnetic gradient and a tumbling motion (46 body lengths per second) induced by a rotating magnetic field. The tumbling motion of the NiFC/Ni micromotor substantially improves the Cs adsorption rate, resulting in a rate that surpasses that achieved under nonmoving conditions by a factor of 21. This improved adsorption rate highlights the considerable potential of magnetically manipulated micromotor self-propulsion for efficient water-pollution treatment.

Screening of the siGPCR library in combination with cisplatin against lung cancers.

The screening of siRNAs targeting 390 human G protein-coupled receptors (GPCRs) was multiplexed in combination with cisplatin against lung cancer cells. While the cell viability measure hardly captured the anticancer effect of siGPCRs, the direct cell count revealed the anticancer potential of diverse GPCRs (46 hits with > twofold growth inhibition, p-value < 0.01). In combined treatment with cisplatin, siRNAs against five genes (ADRA2A, F2RL3, NPSR1, NPY and TACR3) enhanced the anti-proliferation efficacy on cancer cells and reduced the self-recovery ability of surviving cells after the removal of the combined treatment. Further on-target validation confirmed that the knockdown of TACR3 expression exhibited anticancer efficacy under both single and combined treatment with cisplatin. Q-omics ( http://qomics.io ) analysis showed that high expression of TACR3 was unfavorable for patient survival, particularly with mutations in GPCR signaling pathways. The present screening data provide a useful resource for GPCR targets and biomarkers for improving the efficacy of cisplatin treatment.

Q-omics: Smart Software for Assisting Oncology and Cancer Research.

The rapid increase in collateral omics and phenotypic data has enabled data-driven studies for the fast discovery of cancer targets and biomarkers. Thus, it is necessary to develop convenient tools for general oncologists and cancer scientists to carry out customized data mining without computational expertise. For this purpose, we developed innovative software that enables user-driven analyses assisted by knowledge-based smart systems. Publicly available data on mutations, gene expression, patient survival, immune score, drug screening and RNAi screening were integrated from the TCGA, GDSC, CCLE, NCI, and DepMap databases. The optimal selection of samples and other filtering options were guided by the smart function of the software for data mining and visualization on Kaplan-Meier plots, box plots and scatter plots of publication quality. We implemented unique algorithms for both data mining and visualization, thus simplifying and accelerating user-driven discovery activities on large multiomics datasets. The present Q-omics software program (v0.95) is available at http://qomics.sookmyung.ac.kr.

Analysis of Cross-Association between mRNA Expression and RNAi Efficacy for Predictive Target Discovery in Colon Cancers.

The availability of large-scale, collateral mRNA expression and RNAi data from diverse cancer cell types provides useful resources for the discovery of anticancer targets for which inhibitory efficacy can be predicted from gene expression. Here, we calculated bidirectional cross-association scores (predictivity and descriptivity) for each of approximately 18,000 genes identified from mRNA and RNAi (i.e., shRNA and sgRNA) data from colon cancer cell lines. The predictivity score measures the difference in RNAi efficacy between cell lines with high vs. low expression of the target gene, while the descriptivity score measures the differential mRNA expression between groups of cell lines exhibiting high vs. low RNAi efficacy. The mRNA expression of 90 and 74 genes showed significant (p < 0.01) cross-association scores with the shRNA and sgRNA data, respectively. The genes were found to be from diverse molecular classes and have different functions. Cross-association scores for the mRNA expression of six genes (CHAF1B, HNF1B, HTATSF1, IRS2, POLR2B and SATB2) with both shRNA and sgRNA efficacy were significant. These genes were interconnected in cancer-related transcriptional networks. Additional experimental validation confirmed that siHNF1B efficacy is correlated with HNF1B mRNA expression levels in diverse colon cancer cell lines. Furthermore, KIF26A and ZIC2 gene expression, with which shRNA efficacy displayed significant scores, were found to correlate with the survival rate from colon cancer patient data. This study demonstrates that bidirectional predictivity and descriptivity calculations between mRNA and RNAi data serve as useful resources for the discovery of predictive anticancer targets.

Overexpression and Selective Anticancer Efficacy of ENO3 in STK11 Mutant Lung Cancers.

Oncogenic gain-of-function mutations are clinical biomarkers for most targeted therapies, as well as represent direct targets for drug treatment. Although loss-of-function mutations involving the tumor suppressor gene, STK11 (LKB1) are important in lung cancer progression, STK11 is not the direct target for anticancer agents. We attempted to identify cancer transcriptome signatures associated with STK11 loss-offunction mutations. Several new sensitive and specific gene expression markers (ENO3, TTC39C, LGALS3, and MAML2) were identified using two orthogonal measures, i.e., fold change and odds ratio analyses of transcriptome data from cell lines and tissue samples. Among the markers identified, the ENO3 gene over-expression was found to be the direct consequence of STK11 loss-of-function. Furthermore, the knockdown of ENO3 expression exhibited selective anticancer effect in STK11 mutant cells compared with STK11 wild type (or recovered) cells. These findings suggest that ENO3 -based targeted therapy might be promising for patients with lung cancer harboring STK11 mutations.

Dissecting phenotypic responses of the druggable targetome in cancers.

Although a large amount of screening data comprising target genes and/or drugs tested against cancer cell line panels are available, different assay conditions and readouts limit the integrated analysis and batch-to-batch comparison of these data. Here, we systematically produced and analyzed the anticancer effect of the druggable targetome to understand the varied phenotypic outcomes of diverse functional classes of target genes. A library of siRNAs targeting ~4,800 druggable genes was screened against cancer cell lines under 2D and/or 3D assay conditions. The anticancer effect was simultaneously measured by quantifying cell proliferation and/or viability. Hit rates varied significantly depending on assay conditions and/or phenotypic readouts. Functional classes of hit genes were correlated with the microenvironment difference between the 2D monolayer cell proliferation and 3D sphere formation assays. Furthermore, multiplexing of cell proliferation and viability measures enabled us to compare the sensitivity and resistance responses to the gene knockdown. Many target genes that inhibited cell proliferation increased the single-cell-level viability of surviving cells, leading to an increase in self-renewal potential. In this study, combinations of parallel 2D/3D assays and multiplexing of cell proliferation and viability measures provided functional insights into the varied phenotypic outcomes of the cancer targetome.

ß-catenin/TCF activity regulates IGF-1R tyrosine kinase inhibitor sensitivity in colon cancer.

The availability of large-scale drug screening data on cell line panels provides a unique opportunity to identify predictive biomarkers for targeted drug efficacy. Analysis of diverse drug data on ~990 cancer cell lines revealed enhanced sensitivity of insulin-like growth factor 1 receptor/ Insulin Receptor (IGF-1R/IR) tyrosine kinase inhibitors (TKIs) in colon cancer cells. Interestingly, ß-catenin/TCF(T cell factor)-responsive promoter activity exhibited a significant positive association with IGF-1R/IR TKI response, while the mutational status of direct upstream genes, such as CTNNB1 and APC, was not significantly associated with the response. The ß-catenin/TCF activity high cell lines express components of IGF-1R/IR signaling more than the low cell lines explaining their enhanced sensitivity against IGF-1R/IR TKI. Reinforcing ß-catenin/TCF responsive promoter activity by introducing CTNNB1 gain-of-function mutations into IGF-1R/IR TKI-resistant cells increased the expression and activity of IGF-1R/IR signaling components and also sensitized the cells to IGF-1R/IR TKIs in vitro and in vivo. Analysis of TCGA data revealed that the stronger ß-catenin/TCF responsive promoter activity was associated with higher IGF-1R and IGF2 transcription in human colon cancer specimens as well. Collectively, compared to the mutational status of upstream genes, ß-catenin/TCF responsive promoter activity has potential to be a stronger predictive positive biomarker for IGF-1R/IR TKI responses in colon cancer cells. The present study highlights the potential of transcriptional activity as therapeutic biomarkers for targeted therapies, overcoming the limited ability of upstream genetic mutations to predict responses.

QSurface: fast identification of surface expression markers in cancers.

BACKGROUND: Cell surface proteins have provided useful targets and biomarkers for advanced cancer therapies. The recent clinical success of antibody-drug conjugates (ADCs) highlights the importance of finding selective surface antigens for given cancer subtypes. We thus attempted to develop stand-alone software for the analysis of the cell surface transcriptome of patient cancer samples and to prioritize lineage- and/or mutation-specific over-expression markers in cancer cells. RESULTS: A total of 519 genes were selected as surface proteins, and their expression was profiled in 14 cancer subtypes using patient sample transcriptome data. Lineage/mutation-oriented analysis was used to identify subtype-specific surface markers with statistical confidence. Experimental validation confirmed the unique over-expression of predicted surface markers (MUC4, MSLN, and SLC7A11) in lung cancer cells at the protein level. The differential cell surface gene expression of cell lines may differ from that of tissue samples due to the absence of the tumor microenvironment. CONCLUSIONS: In the present study, advanced 3D models of lung cell lines successfully reproduced the predicted patterns, demonstrating the physiological relevance of cell line-based 3D models in validating surface markers from patient tumor data. Also QSurface software is freely available at http://compbio.sookmyung.ac.kr/~qsurface .

Transcriptome modeling and phenotypic assays for cancer precision medicine.

Cancer precision medicine requires clinically actionable biomarkers for patient stratification and a better prediction of clinical outcome. Although thousands of cancer-enriched mutated genes have been reported by global sequencing projects, to date, only a few oncogenic mutations have been confirmed as effective biomarkers in cancer therapies. The low frequency and varied profile (i.e., allele frequency, mutation position) of mutant genes among cancer types limit the utility of predictive biomarkers. The recent explosion of cancer transcriptome and phenotypic screening data provides another opportunity for finding transcript-level biomarkers and targets, thus overcoming the limitation of cancer mutation analyses. Technological developments enable the rapid and extensive discovery of potential target-biomarker combinations from large-scale transcriptome-level screening combined with physiologically relevant phenotypic assays. Here, we summarized recent progress as well as discussed the outlook of transcriptome-oriented data mining strategies and phenotypic assays for the identification of non-genetic biomarkers and targets in cancer drug discovery.

Patient sample-oriented analysis of gene expression highlights extracellular signatures in breast cancer progression.

Although a large collection of cancer cell lines are useful surrogates for patient samples, the physiological relevance of observed molecular phenotypes in cell lines remains controversial. Because transcriptome data are a representative set of molecular phenotypes in cancers, we systematically analyzed the discrepancy of global gene expression profiles between patient samples and cell lines in breast cancers. While the majority of genes exhibited general consistency between patient samples and cell lines, the expression of genes in the categories of extracellular matrix, collagen trimers, receptor activity, catalytic activity and transporter activity were significantly up-regulated only in tissue samples. Genes in the extracellular matrix, particularly collagen trimers, showed a wide variation of expression in tissue, but minimal expression and variation in cell lines. Further analysis of tissue samples exclusively revealed that collagen genes exhibited a cancer stage-dependent expressional variation based on their supramolecular structure. Prognostic collagen biomarkers associated with survival rate were also readily predicted from tissue-oriented transcriptome analysis. This study presents the limitations of cell lines and the exclusive features of tissue samples in terms of functional categories of the cancer transcriptome.

Loss-of-function screens of druggable targetome against cancer stem-like cells.

Cancer stem-like cells (CSLCs) contribute to the initiation and recurrence of tumors and to their resistance to conventional therapies. In this study, small interfering RNA (siRNA)-based screening of ∼4800 druggable genes in 3-dimensional CSLC cultures in comparison to 2-dimensional bulk cultures of U87 glioma cells revealed 3 groups of genes essential for the following: survival of the CSLC population only, bulk-cultured population only, or both populations. While diverse biologic processes were associated with siRNAs reducing the bulk-cultured population, CSLC-eliminating siRNAs were enriched in a few functional categories, such as lipid metabolism, protein metabolism, and gene expression. Interestingly, siRNAs that selectively reduced CSLC only were found to target genes for cholesterol and unsaturated fatty acid synthesis. The lipidomic profile of CSLCs revealed increased levels of monounsaturated lipids. Pharmacologic blockage of these target pathways reduced CSLCs, and this effect was eliminated by addition of downstream metabolite products. The present CSLC-sensitive target categories provide a useful resource that can be exploited for the selective elimination of CSLCs.-Song, M., Lee, H., Nam, M.-H., Jeong, E., Kim, S., Hong, Y., Kim, N., Yim, H. Y., Yoo, Y.-J., Kim, J. S., Kim, J.-S., Cho, Y.-Y., Mills, G. B., Kim, W.-Y., Yoon, S. Loss-of-function screens of druggable targetome against cancer stem-like cells.

Cardiac glycosides display selective efficacy for STK11 mutant lung cancer.

Although STK11 (LKB1) mutation is a major mediator of lung cancer progression, targeted therapy has not been implemented due to STK11 mutations being loss-of-function. Here, we report that targeting the Na(+)/K(+)-ATPase (ATP1A1) is synthetic lethal with STK11 mutations in lung cancer. The cardiac glycosides (CGs) digoxin, digitoxin and ouabain, which directly inhibit ATP1A1 function, exhibited selective anticancer effects on STK11 mutant lung cancer cell lines. Restoring STK11 function reduced the efficacy of CGs. Clinically relevant doses of digoxin decreased the growth of STK11 mutant xenografts compared to wild type STK11 xenografts. Increased cellular stress was associated with the STK11-specific efficacy of CGs. Inhibiting ROS production attenuated the efficacy of CGs, and STK11-AMPK signaling was important in overcoming the stress induced by CGs. Taken together, these results show that STK11 mutation is a novel biomarker for responsiveness to CGs. Inhibition of ATP1A1 using CGs warrants exploration as a targeted therapy for STK11 mutant lung cancer.

Glucose starvation induces mutation and lineage-dependent adaptive responses in a large collection of cancer cell lines.

Tolerance of glucose deprivation is an important factor for cancer proliferation, survival, migration and progression. To systematically understand adaptive responses under glucose starvation in cancers, we analyzed reverse phase protein array (RPPA) data of 115 protein antibodies across a panel of approximately 170 heterogeneous cancer cell lines, cultured under normal and low glucose conditions. In general, glucose starvation broadly altered levels of many of the proteins and phosphoproteins assessed across the cell lines. Many mTOR pathway components were selectively sensitive to glucose stress, although the change in their levels still varied greatly across the cell line set. Furthermore, lineage- and genotype-based classification of cancer cell lines revealed mutation-specific variation of protein expression and phosphorylation in response to glucose starvation. Decreased AKT phosphorylation (S473) was significantly associated with PTEN mutation under glucose starvation conditions in lung cancer cell lines. The present study (see TCPAportal.org for data resource) provides insight into adaptive responses to glucose deprivation under diverse cellular contexts.

Endovascular Treatment for Acute Ischemic Stroke Patients over 80 Years of Age.

OBJECTIVE: We evaluated the effect of endovascular treatment (EVT) for acute ischemic stroke in patients over 80 years of age. MATERIALS AND METHODS: The records of 156 acute stroke patients aged over 80 years who were considered as candidates for EVT were analyzed. Fifty-six patients (35.9%, EVT group) underwent EVT and 100 patients (64.1%, non-EVT group) did not. Outcomes, in terms of functional outcomes and rates of symptomatic hemorrhage, in-hospital morbidity and mortality, were compared between groups. Each comparison was adjusted for age, time from onset, initial National Institute of Health Stroke Scale, and pre-stroke modified Rankin Scale (mRS). RESULTS: More patients in the EVT group achieved good outcomes (mRS score of 0-2) at 3 months (35.7% vs. 11.0%, adjusted odds ratio [OR] 4.779 [95% confidence interval 1.972-11.579], p = 0.001) and 12 months (35.7% vs. 14.0%, adjusted OR 3.705 [1.574-8.722], p = 0.003) after stroke. During admission, rates of hospital-acquired infection including pneumonia (12.5% vs. 29.0%, adjusted OR 0.262 [0.098-0.703], p = 0.008) and urinary tract infection (16.0% vs. 34.0%, adjusted OR 0.256 [0.099-0.657], p = 0.005) were significantly lower in the EVT group. More symptomatic hemorrhages (10.7% vs. 2.0%, adjusted OR 6.859 [1.139-41.317], p = 0.036) occurred in the EVT group, but no significant difference was observed in in-hospital mortality rate (12.5% vs. 8.0%, adjusted OR 1.380 [0.408-4.664], p = 0.604). CONCLUSION: EVT improved functional outcome and reduced the risk of hospital-acquired infections in acute stroke patients over 80 years of age without increasing the risk of in-hospital mortality, although symptomatic hemorrhage occurred more frequently after EVT.

Analysis of image-based phenotypic parameters for high throughput gene perturbation assays.

Although image-based phenotypic assays are considered a powerful tool for siRNA library screening, the reproducibility and biological implications of various image-based assays are not well-characterized in a systematic manner. Here, we compared the resolution of high throughput assays of image-based cell count and typical cell viability measures for cancer samples. It was found that the optimal plating density of cells was important to obtain maximal resolution in both types of assays. In general, cell counting provided better resolution than the cell viability measure in diverse batches of siRNAs. In addition to cell count, diverse image-based measures were simultaneously collected from a single screening and showed good reproducibility in repetitions. They were classified into a few functional categories according to biological process, based on the differential patterns of hit (i.e., siRNAs) prioritization from the same screening data. The presented systematic analyses of image-based parameters provide new insight to a multitude of applications and better biological interpretation of high content cell-based assays.

Standard vs Modified Antiplatelet Preparation for Preventing Thromboembolic Events in Patients With High On-Treatment Platelet Reactivity Undergoing Coil Embolization for an Unruptured Intracranial Aneurysm: A Randomized Clinical Trial.

IMPORTANCE: Thromboembolism is the most common complication in coiling for an unruptured aneurysm and is frequent in patients with high on-treatment platelet reactivity (HTPR) who are prescribed a standard antiplatelet preparation for its prevention. OBJECTIVE: To evaluate the effect of a modified antiplatelet preparation compared with a standard preparation in patients with HTPR undergoing coiling. DESIGN, SETTING, AND PARTICIPANTS: A prospective randomized open-label active-control trial with blinded outcome assessment at the Seoul National University Bundang Hospital from May 27, 2013, to April 7, 2014. Patients with HTPR were randomly assigned (1 to 1) to the standard or modified preparation group. Patients without HTPR were assigned to the non-HTPR group. A total of 228 patients undergoing coiling for unruptured aneurysms were enrolled and allocated to the study, 126 in the HTPR group (63 to the standard preparation group and 63 to the modified preparation group) and 102 to the non-HTPR group. Intent-to-treat analysis was performed. INTERVENTIONS: The modified preparation (HTPR to aspirin, 300 mg of aspirin and 75 mg of clopidogrel bisulfate; and HTPR to clopidogrel, 200 mg of cilostazol added to the standard regimen) was performed before coiling in the modified preparation group. Standard preparation (100 mg of aspirin and 75 mg of clopidogrel) was maintained in the standard preparation and non-HTPR groups. MAIN OUTCOMES AND MEASURES: The primary outcome was a thromboembolic event defined as thromboembolism during coiling and a transient ischemic attack or ischemic stroke within 7 days after coiling. The principal secondary outcome was a bleeding complication according to Thrombolysis in Myocardial Infarction bleeding criteria within 30 days after coil embolization. RESULTS: The thromboembolic event rate was low in the modified preparation group (1 of 63 [1.6%]) compared with the standard preparation group (7 of 63 [11.1%]; adjusted risk difference, -11.7% [95% CI, -21.3% to -2.0%]; P = .02), which had a higher thromboembolic risk than the non-HTPR group (1 of 102 [1.0%]; adjusted risk difference, 8.6% [95% CI, 1.0% to 16.3%]; P = .03). All bleeding complications were of minimal grade according to Thrombolysis in Myocardial Infarction bleeding criteria. The bleeding rate was not different between the modified (6 of 63 [9.5%]) and standard (4 of 63 [6.3%]) preparation groups (adjusted risk difference, 5.6% [95% CI, -4.2% to 15.4%]; P = .26). CONCLUSIONS AND RELEVANCE: Modified antiplatelet preparation for patients with HTPR compared with standard antiplatelet preparation reduced the thromboembolic event rate in coiling for an unruptured aneurysm without increasing bleeding. TRIAL REGISTRATION: Clinical Research Information Service Identifier: KCT0000804.

MACE: mutation-oriented profiling of chemical response and gene expression in cancers.

SUMMARY: The mutational status of specific cancer lineages can affect the sensitivity to or resistance against cancer drugs. The MACE database provides web-based interactive tools for interpreting large chemical screening and gene expression datasets of cancer cell lines in terms of mutation and lineage categories. GI50 data of chemicals against individual NCI60 cell lines were normalized and organized to statistically identify mutation- or lineage-specific chemical responses. Similarly, DNA microarray data on NCI60 cell lines were processed to analyze mutation- or lineage-specific gene expression signatures. A combined analysis of GI50 and gene expression data to find potential associations between chemicals and genes is also a capability of this system. This database will provide extensive, systematic information to identify lineage- or mutation-specific anticancer agents and related gene targets. AVAILABILITY AND IMPLEMENTATION: The MACE web database is available at http://mace.sookmyung.ac.kr/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. CONTACT: yoonsj@sookmyung.ac.kr.

Dissecting the global variation of gene expression for the functional interpretation of transcriptome data.

To perform their biological functions, individual genes exhibit varying ranges of expression levels. Thus, considering the intrinsic variability of gene expression can improve geneset-based functional analyses which are typically used to interpret transcriptome data. Through the extensive quantitative analysis of the expressional variability of individual genes using large collections of transcriptome and proteome data, we found the existence of the intrinsic variability of gene expression at the transcriptional level. Interestingly, genes under post-translational regulation were not sensitively regulated at the transcriptional level. Because genes have intrinsically different levels of regulation at the transcription and translation stages, the functional geneset-based interpretation of transcriptome data should only include genes that are significantly varied at the transcriptional level. Thus, by removing genes with low transcriptional variation from the DNA microarray data, we showed that geneset enrichment analysis could provide improved resolution in prioritizing target functional pathways in several different experimental datasets.