Robust Selection Algorithm (RSA) for Multi-Omic Biomarker Discovery; Integration with Functional Network Analysis to Identify miRNA Regulated Pathways in Multiple Cancers

MicroRNAs (miRNAs) play a crucial role in the maintenance of cellular homeostasis by regulating the expression of their target genes. As such, the dysregulation of miRNA expression has been frequently linked to cancer. With rapidly accumulating molecular data linked to patient outcome, the need for identification of robust multi-omic molecular markers is critical in order to provide clinical impact. While previous bioinformatic tools have been developed to identify potential biomarkers in cancer, these methods do not allow for rapid classification of oncogenes versus tumor suppressors taking into account robust differential expression, cutoffs, p-values and non-normality of the data. Here, we propose a methodology, Robust Selection Algorithm (RSA) that addresses these important problems in big data omics analysis. The robustness of the survival analysis is ensured by identification of optimal cutoff values of omics expression, strengthened by p-value computed through intensive random resampling taking into account any non-normality in the data and integration into multi-omic functional networks. Here we have analyzed pan-cancer miRNA patient data to identify functional pathways involved in cancer progression that are associated with selected miRNA identified by RSA. Our approach demonstrates the way in which existing survival analysis techniques can be integrated with a functional network analysis framework to efficiently identify promising biomarkers and novel therapeutic candidates across diseases.     

MicroRNA-203在结直肠癌研究中的进展

结直肠癌是我国常见的消化道恶性肿瘤。目前认为结直肠癌的形成是一个多因素、多步骤的过程,其具体发病机制尚不清楚。microRNA是一类非编码小分子RNA,能在转录后水平调控靶基因的表达,参与肿瘤的增殖、侵袭和转移,甚至调节肿瘤化疗敏感性。学者普遍认为mircroRNA-203(mir-203)是抗癌小分子RNA,但与以往不同的是mir-203在结直肠癌肿的表达水平仍存在争议。本文将概述mir-203在结直肠癌的发生、发展、诊断、预后以及药物抗性中发挥的各类生物学作用。阐述mir-203在结直肠癌不同信号通路中的作用,探索其在结直肠癌研究中的全部潜能。     

Hypoxic colorectal cancer-secreted exosomes deliver miR-210-3p to normoxic tumor cells to elicit a protumoral effect

Hypoxia, the most common feature in the tumor microenvironment, is closely related to tumor malignant progression and poor patient’s prognosis. Exosomes, initially recognized as cellular “garbage dumpsters”, are now known to be important mediums for mediating cellular communication in tumor microenvironment. However, the mechanisms of hypoxic tumor cell-derived exosomes facilitate colorectal cancer progression still need further exploration. In the present study, we found that exosomes from hypoxic colorectal cancer cells (H-Exos) promoted G1-S cycle transition and proliferation while preventing the apoptosis of colorectal cancer cells by transmitting miR-210-3p to normoxic tumor cells. Mechanistic investigation indicated that miR-210-3p from H-Exos elicited its protumoral effect via suppressing CELF2 expression. A preclinical study further confirmed that H-Exos could promote tumorigenesis in vivo. Clinically, the expression of miR-210-3p in circulating plasma exosomes was markedly upregulated in colorectal cancer patients, which were closely associated with multiple unfavorable clinicopathological features. Taken together, these results suggest that hypoxia may stimulate colorectal cancer cells to secrete miR-210-3p-enriched exosomes in tumor microenvironment, which elicit protumoral effects by inhibiting CELF2 expression. These findings provide new insights on the mechanism of colorectal cancer progression and potential therapeutic targets for colorectal cancer.     

N-glycosylation of Colorectal Cancer Tissues: A LIQUID CHROMATOGRAPHY AND MASS SPECTROMETRY-BASED INVESTIGATION

Colorectal cancer is the third most common cancer worldwide with an annual incidence of ～1 million cases and an annual mortality rate of ～655,000 individuals. There is an urgent need for identifying novel targets to develop more sensitive, reliable, and specific tests for early stage detection of colon cancer. Post-translational modifications are known to play an important role in cancer progression and immune surveillance of tumors. In the present study, we compared the N-glycan profiles from 13 colorectal cancer tumor tissues and corresponding control colon tissues. The N-glycans were enzymatically released, purified, and labeled with 2-aminobenzoic acid. Aliquots were profiled by hydrophilic interaction liquid chromatography (HILIC-HPLC) with fluorescence detection and by negative mode MALDI-TOF-MS. Using partial least squares discriminant analysis to investigate the N-glycosylation changes in colorectal cancer, an excellent separation and prediction ability were observed for both HILIC-HPLC and MALDI-TOF-MS data. For structure elucidation, information from positive mode ESI-ion trap-MS/MS and negative mode MALDI-TOF/TOF-MS was combined. Among the features with a high separation power, structures containing a bisecting GlcNAc were found to be decreased in the tumor, whereas sulfated glycans, paucimannosidic glycans, and glycans containing a sialylated Lewis type epitope were shown to be increased in tumor tissues. In addition, core-fucosylated high mannose N-glycans were detected in tumor samples. In conclusion, the combination of HILIC and MALDI-TOF-MS profiling of N-glycans with multivariate statistical analysis demonstrated its potential for identifying N-glycosylation changes in colorectal cancer tissues and provided new leads that might be used as candidate biomarkers.     

Multi‐modal meta‐analysis of cancer cell line omics profiles identifies ECHDC1 as a novel breast tumor suppressor

Molecular and functional profiling of cancer cell lines is subject to laboratory‐specific experimental practices and data analysis protocols. The current challenge therefore is how to make an integrated use of the omics profiles of cancer cell lines for reliable biological discoveries. Here, we carried out a systematic analysis of nine types of data modalities using meta‐analysis of 53 omics studies across 12 research laboratories for 2,018 cell lines. To account for a relatively low consistency observed for certain data modalities, we developed a robust data integration approach that identifies reproducible signals shared among multiple data modalities and studies. We demonstrated the power of the integrative analyses by identifying a novel driver gene, ECHDC1, with tumor suppressive role validated both in breast cancer cells and patient tumors. The multi‐modal meta‐analysis approach also identified synthetic lethal partners of cancer drivers, including a co‐dependency of PTEN deficient endometrial cancer cells on RNA helicases.     

Identification of key biomarkers for thyroid cancer by integrative gene expression profiles

Thyroid cancer is a frequently diagnosed malignancy and the incidence has been increased rapidly in recent years. Despite the favorable prognosis of most thyroid cancer patients, advanced patients with metastasis and recurrence still have poor prognosis. Therefore, the molecular mechanisms of progression and targeted biomarkers were investigated for developing effective targets for treating thyroid cancer. Eight chip datasets from the gene expression omnibus database were selected and the inSilicoDb and inSilicoMerging R/Bioconductor packages were used to integrate and normalize them across platforms. After merging the eight gene expression omnibus datasets, we obtained one dataset that contained the expression profiles of 319 samples (188 tumor samples plus 131 normal thyroid tissue samples). After screening, we identified 594 significantly differentially expressed genes (277 up-regulated genes plus 317 down-regulated genes) between the tumor and normal tissue samples. The differentially expressed genes exhibited enrichment in multiple signaling pathways, such as p53 signaling. By building a protein–protein interaction network and module analysis, we confirmed seven hub genes, and they were all differentially expressed at all the clinical stages of thyroid cancer. A diagnostic seven-gene signature was established using a logistic regression model with the area under the receiver operating characteristic curve (AUC) of 0.967. Seven robust candidate biomarkers predictive of thyroid cancer were identified, and the obtained seven-gene signature may serve as a useful marker for thyroid cancer diagnosis and prognosis.     

The discovery of putative urine markers for the specific detection of prostate tumor by integrative mining of public genomic profiles

Urine has emerged as an attractive biofluid for the noninvasive detection of prostate cancer (PCa). There is a strong imperative to discover candidate urinary markers for the clinical diagnosis and prognosis of PCa. The rising flood of various omics profiles presents immense opportunities for the identification of prospective biomarkers. Here we present a simple and efficient strategy to derive candidate urine markers for prostate tumor by mining cancer genomic profiles from public databases. Prostate, bladder and kidney are three major tissues from which cellular matters could be released into urine. To identify urinary markers specific for PCa, upregulated entities that might be shed in exosomes of bladder cancer and kidney cancer are first excluded. Through the ontology-based filtering and further assessment, a reduced list of 19 entities encoding urinary proteins was derived as putative PCa markers. Among them, we have found 10 entities closely associated with the process of tumor cell growth and development by pathway enrichment analysis. Further, using the 10 entities as seeds, we have constructed a protein-protein interaction (PPI) subnetwork and suggested a few urine markers as preferred prognostic markers to monitor the invasion and progression of PCa. Our approach is amenable to discover and prioritize potential markers present in a variety of body fluids for a spectrum of human diseases.     

Genome-wide integrative analysis revealed a correlation between lengths of copy number segments and corresponding gene expression profile

Microarray analysis has been applied to comprehensively reveal the abnormalities of DNA copy number (CN) and gene expression in human cancer research during the last decade. These analyses have individually contributed to identify the genes associated with carcinogenesis, progression, metastasis of tumor cells and poor prognosis of cancer patients. However, it is known that the correlation between profiles of CN and gene expression does not highly correlate. Factors which determine the degree of correlation remain largely unexplained. To investigate one such factor, we performed trend analyses between the lengths of CN segments and corresponding gene expression profiles from microarray data in hepatocellular carcinoma (HCC) and colorectal carcinoma (CRC). Significant correlations were observed in CN gain of HCC and CRC (p<0.05). The trend of the CN loss showed a significant correlation in HCC although there was no correlation between the length of CN loss segments and gene expression in CRC. Our findings suggest that the influence of CN on gene expression highly depends on the length of CN region, especially in the case of CN gain. To the best of our knowledge, this is the first study describing the correlation between lengths of CNA segments and expression profiles of corresponding genes.     

Cancer markers: Integratively annotated classification

Translational cancer genomics research aims to ensure that experimental knowledge is subject to computational analysis, and integrated with a variety of records from omics and clinical sources. The data retrieval from such sources is not trivial, due to their redundancy and heterogeneity, and the presence of false evidence. In silico marker identification, therefore, remains a complex task that is mainly motivated by the impact that target identification from the elucidation of gene co-expression dynamics and regulation mechanisms, combined with the discovery of genotype–phenotype associations, may have for clinical validation. Based on the reuse of publicly available gene expression data, our aim is to propose cancer marker classification by integrating the prediction power of multiple annotation sources. In particular, with reference to the functional annotation for colorectal markers, we indicate a classification of markers into diagnostic and prognostic classes combined with susceptibility and risk factors.     

Layered Signaling Regulatory Networks Analysis of Gene Expression Involved in Malignant Tumorigenesis of Non-Resolving Ulcerative Colitis via Integration of Cross-Study Microarray Profiles

Background

Ulcerative colitis (UC) was the most frequently diagnosed inflammatory bowel disease (IBD) and closely linked to colorectal carcinogenesis. By far, the underlying mechanisms associated with the disease are still unclear. With the increasing accumulation of microarray gene expression profiles, it is profitable to gain a systematic perspective based on gene regulatory networks to better elucidate the roles of genes associated with disorders. However, a major challenge for microarray data analysis is the integration of multiple-studies generated by different groups.

Methodology/Principal Findings

In this study, firstly, we modeled a signaling regulatory network associated with colorectal cancer (CRC) initiation via integration of cross-study microarray expression data sets using Empirical Bayes (EB) algorithm. Secondly, a manually curated human cancer signaling map was established via comprehensive retrieval of the publicly available repositories. Finally, the co-differently-expressed genes were manually curated to portray the layered signaling regulatory networks.

Results

Overall, the remodeled signaling regulatory networks were separated into four major layers including extracellular, membrane, cytoplasm and nucleus, which led to the identification of five core biological processes and four signaling pathways associated with colorectal carcinogenesis. As a result, our biological interpretation highlighted the importance of EGF/EGFR signaling pathway, EPO signaling pathway, T cell signal transduction and members of the BCR signaling pathway, which were responsible for the malignant transition of CRC from the benign UC to the aggressive one.

Conclusions

The present study illustrated a standardized normalization approach for cross-study microarray expression data sets. Our model for signaling networks construction was based on the experimentally-supported interaction and microarray co-expression modeling. Pathway-based signaling regulatory networks analysis sketched a directive insight into colorectal carcinogenesis, which was of significant importance to monitor disease progression and improve therapeutic interventions.     

Detection and identification of heat shock protein 10 as a biomarker in colorectal cancer by protein profiling

Although colorectal cancer is one of the best-characterized tumors with regard to the multistep progression, it remains one of the most frequent and deadly neoplasms. For a better understanding of the molecular mechanisms behind the process of tumorigenesis and tumor progression, changes in protein expression between microdissected normal and tumorous colonic epithelium were analyzed. Cryostat sections from colorectal tumors, adenoma tissue, and adjacent normal mucosa were laser-microdissected and analyzed using ProteinChip Arrays. The derived MS profiles exhibited numerous statistical differences. One peak showing significantly high expression in the tumor was purified by reverse-phase chromatography and SDS-PAGE. The protein band of interest was passively eluted from the gel and identified as heat shock protein 10 (HSP 10) by tryptic digestion, peptide mapping, and MS/MS analysis. This tumor marker was further characterized by immunohistochemistry. Analysis of HSP 10-positive tissue by ProteinChip technology confirmed the identity of this protein. This work demonstrates that biomarker in colorectal cancer can be detected, identified, and assessed by a proteomic approach comprising tissue microdissection, protein profiling, and immunological techniques. In our experience, histological defined microdissected tissue areas should be used to identify proteins that might be responsible for tumorigenesis.     

What can humans learn from flies about adenomatous polyposis coli?

Somatic or inherited mutations in the adenomatous polyposis coli (APC) gene are a frequent cause of colorectal cancer in humans. APC protein has an important tumor suppression function to reduce cellular levels of the signaling protein beta-catenin and, thereby, inhibit beta-catenin and T-cell-factor-mediated gene expression. In addition, APC protein binds to microtubules in vertebrate cells and localizes to actin-rich adherens junctions in epithelial cells of the fruit fly Drosophila (Fig. 1). Very little is known, however, about the function of these cytoskeletal associations. Recently, Hamada and Bienz have described a potential role for Drosophila E-APC in cellular adhesion, which offers new clues to APC function in embryonic development, and potentially colorectal adenoma formation and tumor progression in humans.     

Estimating gene expression from DNA methylation and copy number variation: A deep learning regression model for multi-omics integration

Gene expression analysis plays a significant role for providing molecular insights in cancer. Various genetic and epigenetic factors (being dealt under multi-omics) affect gene expression giving rise to cancer phenotypes. A recent growth in understanding of multi-omics seems to provide a resource for integration in interdisciplinary biology since they altogether can draw the comprehensive picture of an organism's developmental and disease biology in cancers. Such large scale multi-omics data can be obtained from public consortium like The Cancer Genome Atlas (TCGA) and several other platforms. Integrating these multi-omics data from varied platforms is still challenging due to high noise and sensitivity of the platforms used. Currently, a robust integrative predictive model to estimate gene expression from these genetic and epigenetic data is lacking. In this study, we have developed a deep learning-based predictive model using Deep Denoising Auto-encoder (DDAE) and Multi-layer Perceptron (MLP) that can quantitatively capture how genetic and epigenetic alterations correlate with directionality of gene expression for liver hepatocellular carcinoma (LIHC). The DDAE used in the study has been trained to extract significant features from the input omics data to estimate the gene expression. These features have then been used for back-propagation learning by the multilayer perceptron for the task of regression and classification. We have benchmarked the proposed model against state-of-the-art regression models. Finally, the deep learning-based integration model has been evaluated for its disease classification capability, where an accuracy of 95.1% has been obtained.     

Prostate cancer expression profiling by cDNA sequencing analysis.

Prostate cancer is a frequently diagnosed solid tumor that is originated mostly from prostate epithelium. One of the key issues in prostate cancer research is to develop molecular markers that can effectively detect and distinguish the progression and malignancy of prostate tumors. Automated, single-pass cDNA sequencing was utilized to rapidly identify expressed genes in a number of cDNA libraries constructed from various normal and tumor prostatic tissues. These included cell lines as well as short-term epithelial culture. A total of 6604 expressed sequence tags (ESTs) were generated and searched against on-line nucleotide and protein databases. A relational database centric software system was constructed to process, store, and analyze EST data rapidly. cDNA contigs were also obtained by assembly of multiple EST sequences. Protein structural signatures were annotated using motif analysis tools including BLOCKS and an in-house-designed neural network. Cross-library comparisons revealed their unique gene expression profiles. Several differentially expressed cDNA clones were identified, and their expression patterns were confirmed by RNA dot blot and RT-PCR analyses.     

SETDB1 confers colorectal cancer metastasis by regulation of WNT/β-catenin signaling

BackgroundMetastasis is a critical step in tumor development; however, its specific molecular mechanism is still not fully understood. SETDB1 overexpression is associated with tumor progression and poor prognosis. Here, we explored a novel mechanism by which SETDB1 promotes tumor metastasis in colorectal cancer.MethodsWe conducted database and clinical specimen analysis to determine the expression level of SETDB1 in colorectal cancer, as well as the prognosis of colorectal cancer with overexpressed SETDB1. We used wound healing assays, Transwell assays, and animal studies to study the effect of SETDB1 on colorectal cancer. We performed western blotting, qRT–PCR, immunofluorescence, and co-immunoprecipitation to explore the underlying associations between SETDB1 and β-catenin. We further used wound healing assays, Transwell assays, and animal studies to verify the relationship between SETDB1 and Wnt/β-catenin.ResultsSETDB1 expression was upregulated in colorectal cancer and correlated with poor prognosis. Low expression of SETDB1 decreased invasion and metastasis in colorectal cancer. Low-expression of SETDB1 in colorectal tumor cells decreased β-catenin expression and its nuclear import. We also found that SETDB1 can bind and directly methylate β-catenin, Lastly, we discovered that this metastatic ability could be decreased by activating the Wnt/β-catenin pathway with SETDB1 knock-down.ConclusionSETDB1 is highly expressed in colorectal cancer and plays an important role in the invasion and metastasis through the Wnt/β-catenin pathway. It does so by direct methylation of β-catenin. This novel SETDB1/Wnt/β-catenin pathway provides a new strategy for the treatment of colorectal cancer.     

3-D Visualization and Quantitation of Microvessels in Transparent Human Colorectal Carcinoma

Microscopic analysis of tumor vasculature plays an important role in understanding the progression and malignancy of colorectal carcinoma. However, due to the geometry of blood vessels and their connections, standard microtome-based histology is limited in providing the spatial information of the vascular network with a 3-dimensional (3-D) continuum. To facilitate 3-D tissue analysis, we prepared transparent human colorectal biopsies by optical clearing for in-depth confocal microscopy with CD34 immunohistochemistry. Full-depth colons were obtained from colectomies performed for colorectal carcinoma. Specimens were prepared away from (control) and at the tumor site. Taking advantage of the transparent specimens, we acquired anatomic information up to 200 μm in depth for qualitative and quantitative analyses of the vasculature. Examples are given to illustrate: (1) the association between the tumor microstructure and vasculature in space, including the perivascular cuffs of tumor outgrowth, and (2) the difference between the 2-D and 3-D quantitation of microvessels. We also demonstrate that the optically cleared mucosa can be retrieved after 3-D microscopy to perform the standard microtome-based histology (H&E staining and immunohistochemistry) for systematic integration of the two tissue imaging methods. Overall, we established a new tumor histological approach to integrate 3-D imaging, illustration, and quantitation of human colonic microvessels in normal and cancerous specimens. This approach has significant promise to work with the standard histology to better characterize the tumor microenvironment in colorectal carcinoma.     

Biological and functional analysis of statistically significant pathways deregulated in colon cancer by using gene expression profiles

Gene expression profiling offers a great opportunity for studying multi-factor diseases and for understanding the key role of genes in mechanisms which drive a normal cell to a cancer state. Single gene analysis is insufficient to describe the complex perturbations responsible for cancer onset, progression and invasion. A deeper understanding of the mechanisms of tumorigenesis can be reached focusing on deregulation of gene sets or pathways rather than on individual genes. We apply two known and statistically well founded methods for finding pathways and biological processes deregulated in pathological conditions by analyzing gene expression profiles. In particular, we measure the amount of deregulation and assess the statistical significance of predefined pathways belonging to a curated collection (Molecular Signature Database) in a colon cancer data set. We find that pathways strongly involved in different tumors are strictly connected with colon cancer. Moreover, our experimental results show that the study of complex diseases through pathway analysis is able to highlight genes weakly connected to the phenotype which may be difficult to detect by using classical univariate statistics. Our study shows the importance of using gene sets rather than single genes for understanding the main biological processes and pathways involved in colorectal cancer. Our analysis evidences that many of the genes involved in these pathways are strongly associated to colorectal tumorigenesis. In this new perspective, the focus shifts from finding differentially expressed genes to identifying biological processes, cellular functions and pathways perturbed in the phenotypic conditions by analyzing genes co-expressed in a given pathway as a whole, taking into account the possible interactions among them and, more importantly, the correlation of their expression with the phenotypical conditions.     

Role of ghrelin axis in colorectal cancer: a novel association

Recently discovered orexigenic peptide, ghrelin, which is primarily produced by gastrointestinal tract, has been implicated in the malignant cell proliferation and invasion, presumably through an autocrine/paracrine mechanism. This study was aimed to identify the role of endogenously produced ghrelin in colorectal cancer progression. Malignant intestinal epithelial cells differentially over-express ghrelin receptors and produce more ghrelin as compared to normal human colonocytes, leading to their enhanced proliferative and invasive behavior. Though, systemically available endocrine ghrelin levels in patients with colorectal cancer do not exhibit significant correlation with any tumor stage or grade, however, locally produced autocrine tissue ghrelin strongly correlates both with advancing colorectal malignancy in a stage-dependent manner and BMI of the colorectal patients. We conclude that ghrelin might play an important role in promoting colorectal malignancy.