Blood-based dynamic genomic signature for obsessive-compulsive disorder.

No biologically based diagnostic criteria are in clinical use today for obsessive-compulsive disorder (OCD), schizophrenia, and major depressive disorder (MDD), which are defined with reference to Diagnostic and Statistical Manual clinical symptoms alone. However, these disorders cannot always be well distinguished on clinical grounds and may also be comorbid. A biological blood-based dynamic genomic signature that can differentiate among OCD, MDD, and schizophrenia would therefore be of great utility. This study enrolled 77 patients with OCD, 67 controls with no psychiatric illness, 39 patients with MDD, and 40 with schizophrenia. An OCD-specific gene signature was identified using blood gene expression analysis to construct a predictive model of OCD that can differentiate this disorder from healthy controls, MDD, and schizophrenia using a logistic regression algorithm. To verify that the genes selected were not derived as a result of chance, the algorithm was tested twice. First, the algorithm was used to predict the cohort with true disease/control status and second, the algorithm predicted the cohort with disease/control status randomly reassigned (null set). A six-gene panel (COPS7A, FKBP1A, FIBP, TP73-AS1, SDF4, and GOLGA8A) discriminated patients with OCD from healthy controls, MDD, and schizophrenia in the training set (with an area under the receiver-operating-characteristic curve of 0.938; accuracy, 86%; sensitivity, 88%; and specificity, 85%). Our findings indicate that a blood transcriptomic signature can distinguish OCD from healthy controls, MDD, and schizophrenia. This finding further confirms the feasibility of using dynamic blood-based genomic signatures in psychiatric disorders and may provide a useful tool for clinical staff engaged in OCD diagnosis and decision making.

Blood gene signature for early hepatocellular carcinoma detection in patients with chronic hepatitis B.

PURPOSE: Up to 25% of chronic hepatitis B (CHB) patients eventually develop hepatocellular carcinoma (HCC), a disease with poor prognosis unless detected early. This study identifies a blood-based RNA biomarker panel for early HCC detection in CHB. MATERIALS AND METHODS: A genome-wide RNA expression study was performed using RNA extracted from blood samples from Malaysian patients (matched HCC, CHB, controls). Genes differentiating HCC from controls were selected for further testing using quantitative real-time polymerase chain reaction. Finally, a 6-gene biomarker panel was identified and characterized using a training set (cohort I = 126), and tested against 2 test sets (cohort II = 222; cohort III = 174). The total number of samples used for each group is: HCC + CHB = 143, CHB = 211, control = 168. RESULTS: Our gene panel displays a consistent trend distinguishing HCC from controls in our test sets, with an area under receiver-operating characteristic curve of 0.9 in cohort III. Our independent test set (cohort III) showed that the gene panel had a sensitivity of 70% with a specificity of 92%. The biomarker profile for HCC was consistently detected in a small subgroup of CHB patients, thus potentially predicting early, preclinical cases of cancer that should be screened more intensively. CONCLUSION: The biomarkers identified in this study can be used as the basis of a blood-based test for the detection of early HCC in CHB.

Blood gene expression signatures associate with heart failure outcomes.

Gene expression signatures in blood correlate with specific diseases. Such signatures may serve as valuable diagnostic and prognostic tools in disease management. Blood gene expression signatures associated with heart failure may be applied to predict prognosis, monitor disease progression, and optimize treatment. Blood gene expression profiles were generated for 71 subjects with heart failure and 15 controls without heart failure, using the Affymetrix GeneChip U133Plus2.0. Survival analysis identified 197 "mortality genes" that were significantly associated with patient outcome. Functional categorization showed that genes associated with T cell receptor signaling were most significantly overpresented. Cluster analysis of these T cell receptor signaling genes significantly categorized heart failure patients into three risk groups (P = 0.031) that were distinct from the three risk groups categorized by New York Heart Association (NYHA) Classification (P = 0.0002). By combining the analysis of clinical assessment (NYHA class) with T cell receptor signaling gene expression, we proposed a model that demonstrated an even greater differentiation of patients at risk (P = 0.0001). In this discovery study, we identified blood expression signatures associated with heart failure patient outcomes. Characterization of these mortality genes helped identify a set of T cell receptor signaling genes that may be of utility in predicting survival of heart failure patients. These data raise the possibility of prospectively risk stratifying patients with heart failure by integrating blood gene expression signatures with current clinical assessment.

Similarities and differences in peripheral blood gene-expression signatures of individuals with schizophrenia and their first-degree biological relatives.

Several studies have evaluated the potential utility of blood-based whole-transcriptome signatures as a source of biomarkers for schizophrenia. This endeavor has been complicated by the fact that individuals with schizophrenia typically differ from appropriate comparison subjects on more than just the presence of the disorder; for example, individuals with schizophrenia typically receive antipsychotic medications, and have been dealing with the sequelae of this chronic illness for years. The inability to control such factors introduces a considerable degree of uncertainty in the results to date. To overcome this, we performed a blood-based gene-expression profiling study of schizophrenia patients (n = 9) as well as their unmedicated, nonpsychotic, biological siblings (n = 9) and unaffected comparison subjects (n = 12). The unaffected biological siblings, who may harbor some of the genetic predisposition to schizophrenia, exhibited a host of gene-expression differences from unaffected comparison subjects, many of which were shared by their schizophrenic siblings, perhaps indicative of underlying risk factors for the disorder. Several genes that were dysregulated in both individuals with schizophrenia and their siblings related to nucleosome and histone structure and function, suggesting a potential epigenetic mechanism underlying the risk state for the disorder. Nonpsychotic siblings also displayed some differences from comparison subjects that were not found in their affected siblings, suggesting that the dysregulation of some genes in peripheral blood may be indicative of underlying protective factors. This study, while exploratory, illustrated the potential utility and increased informativeness of including unaffected first-degree relatives in research in pursuit of peripheral biomarkers for schizophrenia.

Immune-related gene expression signatures in colorectal cancer.

The immune system is crucial in regulating colorectal cancer (CRC) tumorigenesis. Identification of immune-related transcriptomic signatures derived from the peripheral blood of patients with CRC would provide insights into CRC pathogenesis, and suggest novel clues to potential immunotherapy strategies for the disease. The present study collected blood samples from 59 patients with CRC and 62 healthy control patients and performed whole blood gene expression profiling using microarray hybridization. Immune-related gene expression signatures for CRC were identified from immune gene datasets, and an algorithmic predictive model was constructed for distinguishing CRC from controls. Model performance was characterized using an area under the receiver operating characteristic curve (ROC AUC). Functional categories for CRC-specific gene expression signatures were determined using gene set enrichment analyses. A Kaplan-Meier plotter survival analysis was also performed for CRC-specific immune genes in order to characterize the association between gene expression and CRC prognosis. The present study identified five CRC-specific immune genes [protein phosphatase 3 regulatory subunit Bα (PPP3R1), amyloid ß precursor protein, cathepsin H, proteasome activator subunit 4 and DEAD-Box Helicase 3 X-Linked]. A predictive model based on this five-gene panel showed good discriminatory power (independent test set sensitivity, 83.3%; specificity, 94.7%, accuracy, 89.2%; ROC AUC, 0.96). The candidate genes were involved in pathways associated with 'adaptive immune responses', 'innate immune responses' and 'cytokine signaling'. The survival analysis found that a high level of PPP3R1 expression was associated with a poor CRC prognosis. The present study identified five CRC-specific immune genes that were potential diagnostic biomarkers for CRC. The biological function analysis indicated a close association between CRC pathogenesis and the immune system, and may reveal more information about the immunogenic and pathogenic mechanisms driving CRC in the future. Overall, the association between PPP3R1 expression and survival of patients with CRC revealed potential new targets for CRC immunotherapy.

A blood-based biomarker panel for stratifying current risk for colorectal cancer.

Colorectal cancer (CRC) is often curable and preventable using current screening modalities. Unfortunately, screening compliance remains low, partly due to patient dissatisfaction with faecal/endoscopic testing. Recent guidelines advise CRC screening should begin with risk stratification. A blood-based test providing clinically actionable CRC risk information would likely improve screening compliance and enhance clinical decision making. We analyzed 196 gene expression profiles to select candidate CRC biomarkers. qRT-PCR was performed on 642 samples to develop a 7-gene biomarker panel using 112 CRC/120 controls (training set) and 202 CRC/208 controls (independent, blind test set). Panel performance characteristics and disease prevalence (0.7%) were then used to develop a scale assessing an individual's current risk of having CRC based on his/her gene signature. A 7-gene panel (ANXA3, CLEC4D, LMNB1, PRRG4, TNFAIP6, VNN1 and IL2RB) discriminated CRC in the training set (area under the receiver-operating-characteristic curve (ROC AUC), 0.80; accuracy, 73%; sensitivity, 82%; specificity 64%). The independent blind test set confirmed performance (ROC AUC, 0.80; accuracy, 71%; sensitivity, 72%; specificity, 70%). Individual gene profiles were compared against the population results and used to calculate the current relative risk for CRC. We have developed a 7-gene, blood-based biomarker panel that can stratify subjects according to their current relative risk across a broad range in an average-risk population. Across the continuous spectrum of risk as defined by the current relative risk scale, it is possible to identify clinically meaningful reference points that can assist patients and physicians in CRC screening decision making.

Differential regulation of peripheral leukocyte genes in patients with active Crohn's disease and Crohn's disease in remission.

GOALS: Assessment of disease severity is a frequent challenge in the management of Crohn's disease. Noninvasive, accurate markers for monitoring disease activity are urgently required. Specific gene expression patterns and molecular biomarkers associated with active Crohn's disease could serve as such markers, thereby providing a novel approach to disease activity monitoring. BACKGROUND: Gene expression profiling in circulating leukocytes has shown promise in several medical conditions and blood may provide an easily accessible surrogate tissue for using gene expression profiling to assess activity of Crohn's disease. STUDY: In this study, we compared genome-wide transcription profiles of circulating leukocytes in patients with active and quiescent Crohn's disease. RESULTS: We observed complex changes in blood gene expression patterns in active Crohn's disease: genes of various functional categories were differentially regulated between active and inactive Crohn's disease. We specifically identified a number of inflammatory molecules overexpressed or underexpressed in active Crohn's disease and validated a subset of these genes by real-time reverse transcription-polymerase chain reaction. CONCLUSIONS: The genes differentially regulated in peripheral leukocytes represent potential new biomarkers for assessing the activity of Crohn's disease.

Peripheral blood transcriptome identifies high-risk benign and malignant breast lesions.

BACKGROUND: Peripheral blood transcriptome profiling is a potentially important tool for disease detection. We utilize this technique in a case-control study to identify candidate transcriptomic biomarkers able to differentiate women with breast lesions from normal controls. METHODS: Whole blood samples were collected from 50 women with high-risk breast lesions, 57 with breast cancers and 44 controls (151 samples). Blood gene expression profiling was carried out using microarray hybridization. We identified blood gene expression signatures using AdaBoost, and constructed a predictive model differentiating breast lesions from controls. Model performance was then characterized by AUC sensitivity, specificity and accuracy. Biomarker biological processes and functions were analyzed for clues to the pathogenesis of breast lesions. RESULTS: Ten gene biomarkers were identified (YWHAQ, BCLAF1, WSB1, PBX2, DDIT4, LUC7L3, FKBP1A, APP, HERC2P2, FAM126B). A ten-gene panel predictive model showed discriminatory power in the test set (sensitivity: 100%, specificity: 84.2%, accuracy: 93.5%, AUC: 0.99). These biomarkers were involved in apoptosis, TGF-beta signaling, adaptive immune system regulation, gene transcription and post-transcriptional protein modification. CONCLUSION: A promising method for the detection of breast lesions is reported. This study also sheds light on breast cancer/immune system interactions, providing clues to new targets for breast cancer immune therapy.

Use of peripheral blood transcriptomic biomarkers to distinguish high-grade cervical squamous intraepithelial lesions from low-grade lesions.

It is crucial to classify cervical lesions into high-grade squamous intraepithelial lesions (HSILs) and low-grade SILs (LSILs), as LSILs are conservatively treated by observation, based on an expectation of natural regression, whereas HSILs usually require electrosurgical excision. In the present study, peripheral blood gene expression profiles were analyzed to identify transcriptomic biomarkers distinguishing HSILs from LSILs. A total of 102 blood samples were collected from women with cervical SILs (66 HSIL and 36 LSIL) for microarray hybridization. Candidate gene signatures were identified using AdaBoost algorithms, and a predictive model was constructed using logistic regression to differentiate HSILs from LSILs. To correct for possible bias as a result of the limited sample size and to verify the stability of the predictive model, a two-fold cross validation and null set analysis was conducted over 1,000 iterations. The functions of the transcriptomic biomarkers were then analyzed to elucidate the pathogenesis of cervical SIL. A total of 10 transcriptomic genes (STMN3, TRPC4AP, DYRK2, AGK, KIAA0319L, GRPEL1, ZFC3H1, LYL1, ITGB1 and ARHGAP18) were identified. The predictive model based on the 10-gene panel exhibited well-discriminated power. A cross validation process using known disease status exhibited almost the same performance as that of the predictive model, whereas null-set analysis with randomly reassigned disease status exhibited much lower predictive performance for distinguishing HSILs from LSILs. These biomarkers were involved in the 'Rho GTPase cycle', 'mitochondrial protein import', 'oncogenic MAPK signaling', 'integrin cell surface interaction' and 'signaling by BRAF and RAF fusions'. In conclusion, peripheral blood gene expression analysis is a promising method for distinguishing HSILs from LSILs. The present study proposes 10 candidate genes that could be used in the future as diagnostic biomarkers and potential therapeutic targets for cervical SILs. A simple, non-invasive blood test would be clinically useful in the diagnosis and classification of patients with cervical SILs.

Novel blood-based, five-gene biomarker set for the detection of colorectal cancer.

PURPOSE: We applied a unique method to identify genes expressed in whole blood that can serve as biomarkers to detect colorectal cancer (CRC). EXPERIMENTAL DESIGN: Total RNA was isolated from 211 blood samples (110 non-CRC, 101 CRC). Microarray and quantitative real-time PCR were used for biomarker screening and validation, respectively. RESULTS: From a set of 31 RNA samples (16 CRC, 15 controls), we selected 37 genes from analyzed microarray data that differed significantly between CRC samples and controls (P < 0.05). We tested these genes with a second set of 115 samples (58 CRC, 57 controls) using quantitative real-time PCR, validating 17 genes as differentially expressed. Five of these genes were selected for logistic regression analysis, of which two were the most up-regulated (CDA and MGC20553) and three were the most down-regulated (BANK1, BCNP1, and MS4A1) in CRC patients. Logit (P) of the five-gene panel had an area under the curve of 0.88 (95% confidence interval, 0.81-0.94). At a cutoff of logit (P) >+0.5 as disease (high risk), <-0.5 as control (low risk), and in between as an intermediate zone, the five-gene biomarker combination yielded a sensitivity of 94% (47 of 50) and a specificity of 77% (33 of 43). The intermediate zone contained 22 samples. We validated the predictive power of these five genes with a novel third set of 92 samples, correctly identifying 88% (30 of 34) of CRC samples and 64% (27 of 42) of non-CRC samples. The intermediate zone contained 16 samples. CONCLUSION: Our results indicate that the five-gene biomarker panel can be used as a novel blood-based test for CRC.

The peripheral-blood transcriptome: new insights into disease and risk assessment.

Future personalized medicine strategies for assessing an individual's health require, ideally, a noninvasive system that is capable of integrating numerous interactive factors, including gender, age, genetics, behavior, environment and comorbidities. Several microarray-based methods developed to meet this goal are currently under investigation. However, most rely on tissue biopsies, which are not readily available or accessible. As an alternative, several recent studies have investigated the use of human peripheral blood cells as surrogate biopsy material. Such studies are based on the assumption that molecular profiling of circulating blood might reflect physiological and pathological events occurring in different tissues of the body. This has led to the development of novel methods for identifying and monitoring blood biomarkers to probe an individual's health status. Here, we discuss the rationale and clinical potential of profiling the peripheral-blood transcriptome.

Essential role of ICAM-1/CD18 in mediating EPC recruitment, angiogenesis, and repair to the infarcted myocardium.

Bone marrow-derived endothelial progenitor cells (EPCs) have the ability to migrate to ischemic organs. However, the signals that mediate trafficking and recruitment of these cells are not well understood. Using a functional genomics strategy, we determined the genes that were upregulated in the ischemic myocardium and might be involved in EPC recruitment. Among them, CD18 and its ligand ICAM-1 are particularly intriguing because CD18 and its heterodimer binding chains CD11a and CD11b were correspondingly expressed in ex vivo-expanded EPCs isolated from rat and murine bone marrows. To further verify the functional role of CD18 in mediating EPC recruitment and repair to the infarcted myocardium, we used neutralizing antibody to block CD18. Blockade of CD18 in EPCs significantly inhibited their attachment capacity in vitro and reduced their recruitment to the ischemic myocardium in vivo by 95%. Moreover, mice receiving EPCs that were treated with control isotype IgG exhibited significantly increased capillary density in the infarct border zone, reduced cardiac dilatation, ventricular wall thinning, and fibrosis when compared with myocardial infarction mice receiving PBS and CD18 blockade reversed the EPC-mediated improvements to the infarcted heart. Thus, our results suggest an essential role of CD18 in mediating EPC recruitment and the subsequent functional effects on the infarcted heart.

Gene expression signature for detection of gastric cancer in peripheral blood.

Gastric cancer (stomach cancer) is the fifth most common malignancy and the third leading cause of cancer-associated mortality worldwide. Identifying gastric cancer patients at an early and curable stage of the disease is essential if mortality rates for this disease are to decrease. A non-invasive blood-based test that is an indicator of gastric cancer risk would likely be of benefit in identifying gastric cancer patients at an early stage, and such a test may enhance clinical decision making. This study identified a four-gene expression signature in peripheral blood samples associated with gastric cancer. A total of 216 blood samples were collected, including those from 36 gastric cancer patients, 55 healthy controls and 125 patients with other carcinomas, and gene expression profiles were examined using an Affymetrix Gene Profiling microarray. Blood gene expression profiles were compared between patients with stomach cancer, healthy controls and patients affected with other malignancies. A four-gene panel was identified comprising purine-rich element binding protein B, structural maintenance of chromosomes 1A, DENN/MADD domain containing 1B and programmed cell death 4. The four-gene panel discriminated gastric cancer with an area under the receiver-operating-characteristic curve of 0.99, an accuracy of 95%, sensitivity of 92% and specificity of 96%. The non-invasive nature of the blood test, together with the relatively high accuracy of the four-gene panel may assist physicians in gastric cancer screening decision making.

Identifying leukocyte gene expression patterns associated with plasma lipid levels in human subjects.

Plasma lipid levels have been known to be risk factors for atherosclerosis for decades, and in recent years it has become accepted that inflammation is a crucial event in the pathogenesis of atherosclerosis. In this study, we investigated the relationship between plasma lipids and leukocytes by profiling and analyzing leukocyte gene expression in response to plasma lipid levels. We discovered several interesting patterns of leukocyte gene expression: (1) the expression of a number of immune response- and inflammation-related genes are correlated with plasma lipid levels; (2) genes involved in lipid metabolism and in the electron transport chain were positively correlated with triglycerides and low-density lipoprotein cholesterol (LDL) levels, and negatively correlated with high-density lipoprotein cholesterol (HDL) levels; (3) genes involved in platelet activation were negatively correlated with HDL levels; (4) transcription factors regulating lipogenesis-related genes were correlated with plasma lipid levels; (5) a number of genes correlated with plasma lipid levels were found to be located in the regions of known quantitative trait loci (QTLs) associated with hyperlipemia. Our findings suggest that leukocytes respond to changing plasma lipid levels by regulating a network of genes, including genes involved in immune response, and lipid and fatty acid metabolism.

Novel blood biomarkers of human urinary bladder cancer.

PURPOSE: Recent data indicate that cDNA microarray gene expression profile of blood cells can reflect disease states and thus have diagnostic value. We tested the hypothesis that blood cell gene expression can differentiate between bladder cancer and other genitourinary cancers as well as between bladder cancer and healthy controls. EXPERIMENTAL DESIGN: We used Affymetrix U133 Plus 2.0 GeneChip (Affymetrix, Santa Clara, CA) to profile circulating blood total RNA from 35 patients diagnosed with one of three types of genitourinary cancer [bladder cancer (n = 16), testicular cancer (n = 10), and renal cell carcinoma (n = 9)] and compared their cDNA profiles with those of 10 healthy subjects. We then verified the expression levels of selected genes from the Affymetrix results in a larger number of bladder cancer patients (n = 40) and healthy controls (n = 27). RESULTS: Blood gene expression profiles distinguished bladder cancer patients from healthy controls and from testicular and renal cancer patients. Differential expression of a combined set of seven gene transcripts (insulin-like growth factor-binding protein 7, sorting nexin 16, chondroitin sulfate proteoglycan 6, and cathepsin D, chromodomain helicase DNA-binding protein 2, nell-like 2, and tumor necrosis factor receptor superfamily member 7) was able to discriminate bladder cancer from control samples with a sensitivity of 83% (95% confidence interval, 67-93%) and a specificity of 93% (95% confidence interval, 76-99%). CONCLUSION: We have shown that the gene expression profile of circulating blood cells can distinguish bladder cancer from other types of genitourinary cancer and healthy controls and can be used to identify novel blood markers for bladder cancer.

"Chip"ping away at heart failure.

Studies in the field of microarray technology have exploded onto the scene to delve into the unknown underlying mechanisms and pathways in molecular disease. Diseases of the cardiovascular system, particularly those with unexplained molecular etiologies, such as heart failure, have more recently been investigated using array technology. Our laboratory has sought to examine gene expression profiles of human heart failure using a 10,000+ element cardiovascular-based complementary DNA microarray constructed in-house, termed the "CardioChip." Our studies have identified panels of genes, such as those encoding sarcomeric and cytoskeletal proteins, stress proteins, and Ca2+ regulators, that are differentially expressed in disease conditions as compared with samples from nonfailing hearts. Microarrays are effective tools for examining molecular portraits of the cardiovascular disease condition.

Discovery of Ca2+-relevant and differentiation-associated genes downregulated in esophageal squamous cell carcinoma using cDNA microarray.

To identify genes that are differentially expressed in human esophageal squamous cell carcinoma (ESCC), we have developed a cDNA microarray representing 34 176 clones to analyse gene expression profiles in ESCC. A total of 77 genes (including 31 novel genes) were downregulated, and 15 genes (including one novel gene) were upregulated in cancer tissues compared with their normal counterparts. Immunohistochemistry and Northern blot analysis were carried out to verify the cDNA microarray results. It was revealed that genes involved in squamous cell differentiation were coordinately downregulated, including annexin I, small proline-rich proteins (SPRRs), calcium-binding S100 proteins (S100A8, S100A9), transglutaminase (TGM3), cytokeratins (KRT4, KRT13), gut-enriched Krupple-like factor (GKLF) and cystatin A. Interestingly, most of the downregulated genes encoded Ca(2+)-binding or -modulating proteins that constitute the cell envelope (CE). Moreover, genes associated with invasion or proliferation were upregulated, including genes such as fibronectin, secreted protein acidic and rich in cystein (SPARC), cathepsin B and KRT17. Functional analysis of the alteration in the expression of GKLF suggested that GKLF might be able to regulate the expression of SPRR1A, SPRR2A and KRT4 in ESCC. This study provides new insights into the role of squamous cell differentiation-associated genes in ESCC initiation and progression.

Differential gene expression of blood-derived cell lines in familial combined hyperlipidemia.

OBJECTIVES: The genetic background of familial combined hyperlipidemia (FCHL) is currently unclear. We propose transcriptional profiling as a complementary tool for its understanding. Two hypotheses were tested: the existence of a disease-specific modification of gene expression in FCHL and the detectability of such a transcriptional profile in blood derived cell lines. METHODS AND RESULTS: We established lymphoblastic cell lines from FCHL patients and controls. The cells were cultured in fixed conditions and their basal expression profile was compared using microarrays; 166 genes were differentially expressed in FCHL-derived cell lines compared with controls, with enrichment in metabolism-related genes. Of note was the upregulation of EGR-1, previously found to be upregulated in the adipose tissue of FCHL patients, the upregulation of DCHR-7, the downregulation of LYPLA2, and the differential expression of several genes previously unrelated to FCHL. A cluster of potential EGR-1-regulated transcripts was also differentially expressed in FCHL cells. CONCLUSIONS: Our data indicate that in FCHL, a disease-specific transcription profile is detectable in immortalized cell lines easily obtained from peripheral blood and provide complementary information to classical genetic approaches to FCHL and/or the metabolic syndrome.

Cloning and characterization of a novel cardiac-specific kinase that interacts specifically with cardiac troponin I.

Cardiac-restricted genes play important roles in cardiovascular system. In an effort to identify such novel genes we identified a novel cardiac-specific kinase gene TNNI3K localized on 1p31.1 based on bioinformatics analyses. Sequence analysis suggested that TNNI3K is a distant family member of integrin-linked kinase. Northern blot and 76-tissue array analyses showed that TNNI3K is highly expressed in heart, but is undetectable in other tissues. Immunohistochemical analysis predominantly localized TNNI3K in the nucleus of cardiac myocytes. In vitro kinase assay showed that TNNI3K is a functional kinase. The yeast two-hybrid system showed that TNNI3K could directly interact with cardiac troponin I, results that were further confirmed by coimmunoprecipitation in vivo. Our data suggest that TNNI3K is a cardiac-specific kinase and play important roles in cardiac system.

Mining the Dynamic Genome: A Method for Identifying Multiple Disease Signatures Using Quantitative RNA Expression Analysis of a Single Blood Sample.

BACKGROUND: Blood has advantages over tissue samples as a diagnostic tool, and blood mRNA transcriptomics is an exciting research field. To realize the full potential of blood transcriptomic investigations requires improved methods for gene expression measurement and data interpretation able to detect biological signatures within the "noisy" variability of whole blood. METHODS: We demonstrate collection tube bias compensation during the process of identifying a liver cancer-specific gene signature. The candidate probe set list of liver cancer was filtered, based on previous repeatability performance obtained from technical replicates. We built a prediction model using differential pairs to reduce the impact of confounding factors. We compared prediction performance on an independent test set against prediction on an alternative model derived by Weka. The method was applied to an independent set of 157 blood samples collected in PAXgene tubes. RESULTS: The model discriminated liver cancer equally well in both EDTA and PAXgene collected samples, whereas the Weka-derived model (using default settings) was not able to compensate for collection tube bias. Cross-validation results show our procedure predicted membership of each sample within the disease groups and healthy controls. CONCLUSION: Our versatile method for blood transcriptomic investigation overcomes several limitations hampering research in blood-based gene tests.