The cancer secretome: a reservoir of biomarkers

Biomarkers are pivotal for cancer detection, diagnosis, prognosis and therapeutic monitoring. However, currently available cancer biomarkers have the disadvantage of lacking specificity and/or sensitivity. Developing effective cancer biomarkers becomes a pressing and permanent need. The cancer secretome, the totality of proteins released by cancer cells or tissues, provides useful tools for the discovery of novel biomarkers. The focus of this article is to review the recent advances in cancer secretome analysis. We aim to elaborate the approaches currently employed for cancer secretome studies, as well as its applications in the identification of biomarkers and the clarification of carcinogenesis mechanisms. Challenges encountered in this newly emerging field, including sample preparation, in vivo secretome analysis and biomarker validation, are also discussed. Further improvements on strategies and technologies will continue to drive forward cancer secretome research and enable development of a wealth of clinically valuable cancer biomarkers

A novel approach to detect differentially expressed genes from count-based digital databases by normalizing with housekeeping genes

AbstractSequence tag count-based gene expression analysis is potent for the identification of candidate genes relevant to the cancerous phenotype. With the public availability of count-based data, the computational approaches for differentially expressed genes, which are mainly based on Binomial or beta-Binomial distribution, become practical and important in cancer biology. It remains a permanent need to select a proper statistical model for these methods. In this study, we developed a novel Bayesian algorithm-based method, Electronic Differential Gene Expression Screener (EDGES), in which a statistical model was determined by geometric averaging of 12 common housekeeping genes. EDGES identified a set of differentially expressed genes in lung, breast and colorectal cancers by using publically available Serial Analysis of Gene Expression (SAGE) and Expressed Sequence Tag (EST data). Gene expression microarray analysis and quantitative reverse transcription real-time PCR demonstrated the effectiveness of this procedure. We conclude that current normalization of calibrators provides a new insight into count-based digital subtraction in cancer research

HSP60, a protein downregulated by IGFBP7 in colorectal carcinoma

<p>Abstract</p> <p>Background</p> <p>In our previous study, it was well defined that <it>IGFBP7 </it>was an important tumor suppressor gene in colorectal cancer (CRC). We aimed to uncover the downstream molecules responsible for <it>IGFBP7</it>'s behaviour in this study.</p> <p>Methods</p> <p>Differentially expressed protein profiles between PcDNA3.1(<it>IGFBP7</it>)-transfected RKO cells and the empty vector transfected controls were generated by two-dimensional gel electrophoresis (2-DE) and mass spectrometry (MS) identification. The selected differentially expressed protein induced by IGFBP7 was confirmed by western blot and ELISA. The biological behaviour of the protein was explored by cell growth assay and colony formation assay.</p> <p>Results</p> <p>Six unique proteins were found differentially expressed in PcDNA3.1(<it>IGFBP7</it>)-transfected RKO cells, including albumin (ALB), 60 kDa heat shock protein(HSP60), Actin cytoplasmic 1 or 2, pyruvate kinase muscle 2(PKM2), beta subunit of phenylalanyl-tRNA synthetase(FARSB) and hypothetical protein. The downregulation of HSP60 by IGFBP7 was confirmed by western blot and ELISA. Recombinant human HSP60 protein could increase the proliferation rate and the colony formation ability of PcDNA3.1(<it>IGFBP7</it>)-RKO cells.</p> <p>Conclusion</p> <p>HSP60 was an important downstream molecule of IGFBP7. The downregulation of HSP60 induced by IGFBP7 may be, at least in part, responsible for IGFBP7's tumor suppressive biological behaviour in CRC.</p

Integrated saccade latency as a measure of fatigue

INTRODUCTION: High workload, long working hours and inadequate sleep patterns can have deleterious effects on an individual’s performance. Fatigue is often linked with compromised cognitive and motor function. Our information processing system becomes overloaded and unable to monitor and suppress irrelevant information. Subsequent changes in oculomotor parameters and cortical processing times may therefore provide useful biomarkers to assess one’s state of fatigue. We propose a new non-invasive method to quantify fatigue by measuring Eye Movement And Intrinsic Latencies (EMAIL) without the use of any eye-tracking equipment. METHODS: The test is easy to perform and employs a Landolt C flanked by ring distractors. The test is presented at an eccentricity of 8°, randomly on either side of fixation point within ±5° elevation. The measurement variable is the time of presentation, δT. The subject’s task is to saccade to the peripheral target, register the orientation of the gap and respond by pressing one of four buttons. The EMAIL test measures the presentation time, δT, the subject needs to detect the peripheral target, generate an appropriate eye-movement and register the orientation of the gap. RESULTS: The EMAIL test was used to measure the stimulus presentation times needed to achieve 73% correct responses (using a one up, two down staircase). These times were subject specific and ranged from 165 to 200ms in the absence of fatigue. We investigated how, δT, is affected by exposure to other visually demanding tasks and levels of controlled fatigue. Measured integrated oculomotor responses such as latencies and visual processing times were found to increase significantly following demanding visual tasks by as much as 20ms, but only when fatigued. Preliminary findings using the EMAIL test also show that this technique can be used to investigate the effect of stimulants such as caffeine and depressants, such as alcohol. CONCLUSIONS: The EMAIL test provides a simple method to measure oculomotor parameters and to investigate how these are affected by fatigue. This method can be incorporated in the overall safety management system that is often needed in a number of work areas that involve visually-demanding and safety-critical tasks. The measured parameters provide information about an individual’s level of alertness and may also be of relevance in other industries in order to evaluate drugs developed to control fatigue

Gland Instance Segmentation by Deep Multichannel Side Supervision

Abstract. In this paper, we propose a new image instance segmentation method that segments individual glands (instances) in colon histology images. This is a task called instance segmentation that has recently become increasingly important. The problem is challenging since not only do the glands need to be segmented from the complex background, they are also required to be individually identified. Here we leverage the idea of image-to-image prediction in recent deep learning by building a framework that automatically exploits and fuses complex multichannel information, regional and boundary patterns, with side supervision (deep supervision on side responses) in gland histology images. Our proposed system, deep multichannel side supervision (DMCS), alleviates heavy feature design due to the use of convolutional neural networks guided by side supervision. Compared to methods reported in the 2015 MICCAI Gland Segmentation Challenge, we observe state-of-the-art results based on a number of evaluation metrics

Prognosis Prediction of Colorectal Cancer Using Gene Expression Profiles

Background: Investigation on prognostic markers for colorectal cancer (CRC) deserves efforts, but data from China are scarce. This study aimed to build a prognostic algorithm using differentially expressed gene (DEG) profiles and to compare it with the TNM staging system in their predictive accuracy for CRC prognosis in Chinese patients.Methods: DEGs in six paired tumor and corresponding normal tissues were determined using RNA-Sequencing. Subsequently, matched tumor and normal tissues from 127 Chinese patients were assayed for further validation. Univariate and multivariate Cox regressions were used to identify informative DEGs. A predictive index (PI) was derived as a linear combination of the products of the DEGs and their Cox regression coefficients. The combined predictive accuracy of the DEGs-based PI and tumors' TNM stages was also examined by a logistic regression model including the two predictors. The predictive performance was evaluated with the area under the receiver operating characteristics (AUCs).Results: Out of 75 candidate DEGs, we identified 10 DEGs showing statistically significant associations with CRC survival. A PI based on these 10 DEGs (PI-10) predicted CRC survival probability more accurately than the TNM staging system [AUCs for 3-year survival probability 0.73 (95% confidence interval: 0.64, 0.81) vs. 0.68 (0.59, 0.76)] but comparable to a simplified PI (PI-5) using five DEGs (LOC646627, BEST4, KLF9, ATP6V1A, and DNMT3B). The predictive accuracy was improved further by combining PI-5 and the TNM staging system [AUC for 3-year survival probability: 0.72 (0.63, 0.80)].Conclusion: Prognosis prediction based on informative DEGs might yield a higher predictive accuracy in CRC prognosis than the TNM staging system does

A transcriptome anatomy of human colorectal cancers

BACKGROUND: Accumulating databases in human genome research have enabled integrated genome-wide study on complicated diseases such as cancers. A practical approach is to mine a global transcriptome profile of disease from public database. New concepts of these diseases might emerge by landscaping this profile. METHODS: In this study, we clustered human colorectal normal mucosa (N), inflammatory bowel disease (IBD), adenoma (A) and cancer (T) related expression sequence tags (EST) into UniGenes via an in-house GetUni software package and analyzed the transcriptome overview of these libraries by GOTree Machine (GOTM). Additionally, we downloaded UniGene based cDNA libraries of colon and analyzed them by Xprofiler to cross validate the efficiency of GetUni. Semi-quantitative RT-PCR was used to validate the expression of β-catenin and. 7 novel genes in colorectal cancers. RESULTS: The efficiency of GetUni was successfully validated by Xprofiler and RT-PCR. Genes in library N, IBD and A were all found in library T. A total of 14,879 genes were identified with 2,355 of them having at least 2 transcripts. Differences in gene enrichment among these libraries were statistically significant in 50 signal transduction pathways and Pfam protein domains by GOTM analysis P < 0.01 Hypergeometric Test). Genes in two metabolic pathways, ribosome and glycolysis, were more enriched in the expression profiles of A and IBD than in N and T. Seven transmembrane receptor superfamily genes were typically abundant in cancers. CONCLUSION: Colorectal cancers are genetically heterogeneous. Transcription variants are common in them. Aberrations of ribosome and glycolysis pathway might be early indicators of precursor lesions in colon cancers. The electronic gene expression profile could be used to highlight the integral molecular events in colorectal cancers