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.

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.

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.

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.

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.

Use of blood based biomarkers in the evaluation of Crohn's disease and ulcerative colitis.

Despite significant improvements in our understanding of Crohn's disease (CD) and ulcerative colitis (UC) in recent years, questions remain regarding the best approaches to assessment and management of these chronic diseases during periods of both relapse and remission. Various serologic biomarkers have been used in the evaluation of patients with both suspected and documented inflammatory bowel disease (IBD), and while each has potential utility in the assessment of patients with IBD, potential limitation remain with each method of assessment. Given these potential shortcomings, there has been increased interest in other means of evaluation of patients with IBD, including an expanding interest in the role of gene expression profiling. Among patients with IBD, gene expression profiles obtained from whole blood have been used to differentiate active from inactive CD, as well as to differentiate between CD, UC, and non-inflammatory diarrheal conditions. There are many opportunities for a non-invasive, blood based test to aid in the assessment of patients with IBD, particularly when considering more invasive means of evaluation including endoscopy with biopsy. Furthermore, as the emphasis on personalized medicine continues to increase, the potential ability of gene expression analysis to predict patient response to individual therapies offers great promise. While whole blood gene expression analysis may not completely replace more traditional means of evaluating patients with suspected or known IBD, it does offer significant potential to expand our knowledge of the underlying genes involved in the development of these diseases.

Blood-based biomarkers used to predict disease activity in Crohn's disease and ulcerative colitis.

BACKGROUND: Identifying specific genes that are differentially expressed during inflammatory bowel disease flares may help stratify disease activity. The aim of this study was to identify panels of genes to be able to distinguish disease activity in Crohn's disease (CD) and ulcerative colitis (UC). METHODS: Patients were grouped into categories based on disease and severity determined by histological grading. Whole blood was collected by PAXgene Blood RNA collection tubes, (PreAnalytiX) and gene expression analysis using messenger RNA was conducted. Logistic regression was performed on multiple combinations of common probe sets, and data were evaluated in terms of discrimination by computing the area under the receiving operator characteristic curve (ROC-AUC). RESULTS: Nine inactive CD, 8 mild CD, 10 moderate-to-severe CD, 9 inactive UC, 8 mild UC, 10 moderate-to-severe UC, and 120 controls were hybridized to Affymetrix U133 Plus 2 microarrays. Panels of 6 individual genes discriminated the stages of disease activity: CD with mild severity {ROC-AUC, 0.89 (95% confidence interval [CI], 0.84%-0.95%)}, CD with moderate-to-severe severity (ROC-AUC 0.98 [95% CI, 0.97-1.0]), UC with mild severity (ROC-AUC 0.92 [95% CI, 0.87-0.96]), and UC with moderate-to-severe severity (ROC-AUC 0.99 [95% CI, 0.97-1.0]). Validation by real-time reverse transcription-PCR confirmed the Affymetrix microarray data. CONCLUSIONS: The specific whole blood gene panels reliably distinguished CD and UC and determined the activity of disease, with high sensitivity and specificity in our cohorts of patients. This simple serological test has the potential to become a biomarker to determine the activity of disease.

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.

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.

Blood RNA biomarker panel detects both left- and right-sided colorectal neoplasms: a case-control study.

BACKGROUND: Colonoscopy is widely regarded to be the gold standard for colorectal cancer (CRC) detection. Recent studies, however, suggest that the effectiveness of colonoscopy is mostly confined to tumors on the left side of the colon (descending, sigmoid, rectum), and that the technology has poor tumor detection for right-sided (cecum, ascending, transverse) lesions. A minimally invasive test that can detect both left-sided and right-sided lesions could increase the effectiveness of screening colonoscopy by revealing the potential presence of neoplasms in the right-sided "blind spot". METHODS: We previously reported on a seven-gene, blood-based biomarker panel that effectively stratifies a patient's risk of having CRC. For the current study, we assessed the effectiveness of the seven-gene panel for the detection of left- and right-sided CRC lesions. Results were evaluated for 314 patients with CRC (left-sided: TNM I, 65; TNM II, 57; TNM III, 60; TNM IV, 17; unknown, 9. right-sided: TNM I, 28; TNM II, 29; TNM III, 38; TNM IV, 12; unknown, 1 and including two samples with both left and right lesions) and 328 control samples. Blood samples were obtained prior to clinical staging and therapy. Most CRC subjects had localized disease (stages I and II, 58%); regional (stage III) and systemic (stage IV) disease represented 32% and 9%, respectively, of the study population. RESULTS: The panel detected left-sided (74%, 154/208) and right-sided (85%, 92/108) lesions with an overall sensitivity of 78% (215/316) at a specificity of 66% (215/328). Treatable cancer (stages I to III) was detected with left-sided lesion sensitivity of 76% (138/182) and right-sided sensitivity of 84% (80/95). CONCLUSION: This seven-gene biomarker panel detected right-sided CRC lesions across all cancer stages with a sensitivity that is at least equal to that for left-sided lesions. This study supports the use of this panel as the basis for a patient-friendly, blood-based test that can be easily incorporated into a routine physical examination in advance of colonoscopy to provide a convenient companion diagnostic and a pre-screening alert, ultimately leading to enhanced CRC screening effectiveness.

Blood-based biomarkers of aggressive prostate cancer.

PURPOSE: Prostate cancer is a bimodal disease with aggressive and indolent forms. Current prostate-specific-antigen testing and digital rectal examination screening provide ambiguous results leading to both under-and over-treatment. Accurate, consistent diagnosis is crucial to risk-stratify patients and facilitate clinical decision making as to treatment versus active surveillance. Diagnosis is currently achieved by needle biopsy, a painful procedure. Thus, there is a clinical need for a minimally-invasive test to determine prostate cancer aggressiveness. A blood sample to predict Gleason score, which is known to reflect aggressiveness of the cancer, could serve as such a test. MATERIALS AND METHODS: Blood mRNA was isolated from North American and Malaysian prostate cancer patients/controls. Microarray analysis was conducted utilizing the Affymetrix U133 plus 2·0 platform. Expression profiles from 255 patients/controls generated 85 candidate biomarkers. Following quantitative real-time PCR (qRT-PCR) analysis, ten disease-associated biomarkers remained for paired statistical analysis and normalization. RESULTS: Microarray analysis was conducted to identify 85 genes differentially expressed between aggressive prostate cancer (Gleason score ≥8) and controls. Expression of these genes was qRT-PCR verified. Statistical analysis yielded a final seven-gene panel evaluated as six gene-ratio duplexes. This molecular signature predicted as aggressive (ie, Gleason score ≥8) 55% of G6 samples, 49% of G7(3+4), 79% of G7(4+3) and 83% of G8-10, while rejecting 98% of controls. CONCLUSION: In this study, we have developed a novel, blood-based biomarker panel which can be used as the basis of a simple blood test to identify men with aggressive prostate cancer and thereby reduce the overdiagnosis and overtreatment that currently results from diagnosis using PSA alone. We discuss possible clinical uses of the panel to identify men more likely to benefit from biopsy and immediate therapy versus those more suited to an "active surveillance" strategy.

Whole blood transcriptome correlates with treatment response in nasopharyngeal carcinoma.

BACKGROUND: Treatment protocols for nasopharyngeal carcinoma (NPC) developed in the past decade have significantly improved patient survival. In most NPC patients, however, the disease is diagnosed at late stages, and for some patients treatment response is less than optimal. This investigation has two aims: to identify a blood-based gene-expression signature that differentiates NPC from other medical conditions and from controls and to identify a biomarker signature that correlates with NPC treatment response. METHODS: RNA was isolated from peripheral whole blood samples (2 x 10 ml) collected from NPC patients/controls (EDTA vacutainer). Gene expression patterns from 99 samples (66 NPC; 33 controls) were assessed using the Affymetrix array. We also collected expression data from 447 patients with other cancers (201 patients) and non-cancer conditions (246 patients). Multivariate logistic regression analysis was used to obtain biomarker signatures differentiating NPC samples from controls and other diseases. Differences were also analysed within a subset (n=28) of a pre-intervention case cohort of patients whom we followed post-treatment. RESULTS: A blood-based gene expression signature composed of three genes - LDLRAP1, PHF20, and LUC7L3 - is able to differentiate NPC from various other diseases and from unaffected controls with significant accuracy (area under the receiver operating characteristic curve of over 0.90). By subdividing our NPC cohort according to the degree of patient response to treatment we have been able to identify a blood gene signature that may be able to guide the selection of treatment. CONCLUSION: We have identified a blood-based gene signature that accurately distinguished NPC patients from controls and from patients with other diseases. The genes in the signature, LDLRAP1, PHF20, and LUC7L3, are known to be involved in carcinoma of the head and neck, tumour-associated antigens, and/or cellular signalling. We have also identified blood-based biomarkers that are (potentially) able to predict those patients who are more likely to respond to treatment for NPC. These findings have significant clinical implications for optimizing NPC therapy.

GeneNews Limited: bringing the blood transcriptome to personalized medicine.

GeneNews Limited (TSX:GEN) is a molecular diagnostics company, uniquely positioned to deliver on the promise of personalized medicine. GeneNews has developed and patented the Sentinel Principle(®), an innovative approach to identify clinically actionable disease biomarkers from the blood transcriptome. Novel biomarker panels have been identified to address unmet clinical needs in a broad spectrum of malignancy, including those for oncology, cardiovascular, metabolic and neurological related disorders. Currently available products and services from GeneNews are: ColonSentry™, the world's first blood-based molecular test for colorectal cancer, and; the SentinelGX™ Pharmacogenomic and Companion Diagnostic BloodRNA™ services, established to effectively support pharmaceutical partners in their companion diagnostic, development efforts.

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.

Blood-based biomarkers can differentiate ulcerative colitis from Crohn's disease and noninflammatory diarrhea.

BACKGROUND: Blood gene expression profiling has been used in several studies to identify patients with a number of conditions and diseases. A blood test with the ability to differentiate Crohn's disease (CD) from ulcerative colitis (UC) and noninflammatory diarrhea would be useful in the clinical management of these diseases. METHODS: Affymetrix U133Plus 2.0 GeneChip oligonucleotide arrays were used to generate whole blood gene expression profiles for 21 patients with UC, 24 patients with CD, and 10 control patients with diarrhea, but without colonic pathology. RESULTS: A supervised learning method (logistic regression) was used to identify specific panels of probe sets which were able to discriminate between UC and CD and from controls. The UC panel consisted of the four genes, CD300A, KPNA4, IL1R2, and ELAVL1; the CD panel comprised the four genes CAP1, BID, NIT2, and NPL. These panels clearly differentiated between CD and UC. CONCLUSIONS: Gene expression profiles from blood can differentiate patients with CD from those with UC and from noninflammatory diarrheal disorders.

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.

A case-controlled validation study of a blood-based seven-gene biomarker panel for colorectal cancer in Malaysia.

BACKGROUND: Colorectal cancer (CRC) screening is key to CRC prevention and mortality reduction, but patient compliance with CRC screening is low. We previously reported a blood-based test for CRC that utilizes a seven-gene panel of biomarkers. The test is currently utilized clinically in North America for CRC risk stratification in the average-risk North American population in order to improve screening compliance and to enhance clinical decision making. METHODS: In this study, conducted in Malaysia, we evaluated the seven-gene biomarker panel validated in a North American population using blood samples collected from local patients. The panel employs quantitative RT-PCR (qRT-PCR) to analyze gene expression of the seven biomarkers (ANXA3, CLEC4D, TNFAIP6, LMNB1, PRRG4, VNN1 and IL2RB) that are differentially expressed in CRC patients as compared with controls. Blood samples from 210 patients (99 CRC and 111 controls) were collected, and total blood RNA was isolated and subjected to quantitative RT-PCR and data analysis. RESULTS: The logistic regression analysis of seven-gene panel has an area under the curve (AUC) of 0.76 (95% confidence interval: 0.70 to 0.82), 77% specificity, 61% sensitivity and 70% accuracy, comparable to the data obtained from the North American investigation of the same biomarker panel. CONCLUSIONS: Our results independently confirm the results of the study conducted in North America and demonstrate the ability of the seven biomarker panel to discriminate CRC from controls in blood samples drawn from a Malaysian population.

Teratocarcinoma-derived growth factor-1 is upregulated in aldosterone-producing adenomas and increases aldosterone secretion and inhibits apoptosis in vitro.

Aldosterone-producing adenomas (APA) are a frequent cause of secondary hypertension characterized by autonomous hypersecretion of aldosterone. However, the molecular mechanisms involved in adrenal tumorigenesis and deregulated aldosterone secretion are currently unknown. To identify putative functional genes, a transcriptional screening was performed on 8 APA and 3 normal adrenals (NA) using oligonucleotide microarrays. Data were next validated on an expanded set of samples by real-time PCR (APA, n=19; NA, n=10). The epidermal growth factor-like teratocarcinoma-derived growth factor-1 (TDGF-1) was upregulated in APA compared with NA (14.7-fold and 21.4-fold by microarray and real-time PCR, respectively). In vitro studies and Western blot analysis using the NCI H295R adrenocortical cell line showed that TDGF-1 increased Akt phosphorylation on Thr308 and Ser473, consistent with activation of phosphatidylinositol 3-kinase/Akt signaling, and also demonstrated a concomitant inactivation of the Akt substrate glycogen synthesis kinase-3beta via Ser9 phosphorylation. Furthermore, TDGF-1 mediated a 3.8+/-0.4-fold increase in aldosterone secretion (n=4) that was specifically blocked by the phosphatidylinositol 3-kinase inhibitors wortmannin (50 nmol/L) and LY294002 (20 micromol/L). Finally, TDGF-1 protected H295R cells from apoptosis induced by staurosporine, causing a decrease in caspase-3 activity, a reduction in the inactivation of poly(ADP-ribose) polymerase, and an inhibition of DNA fragmentation, detected by the TUNEL reaction and fluorescence microscopy that was blocked by LY294002. Taken together, our data suggest that TDGF-1, which is significantly upregulated in APA and mediates aldosterone hypersecretion and deregulated growth in adrenocortical cells in vitro, may represent a key player in the development and pathophysiology of primary aldosteronism.

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.

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.