A Boolean-based systems biology approach to predict novel genes associated with cancer: Application to colorectal cancer. BMC Syst Biol. 2011;5:35. [PMID: 21352556 PMCID: PMC3051904 DOI: 10.1186/1752-0509-5-35]

Glycosylated queuosines in tRNAs optimize translational rate and post-embryonic growth

Transfer RNA (tRNA) modifications are critical for protein synthesis. Queuosine (Q), a 7-deaza-guanosine derivative, is present in tRNA anticodons. In vertebrate tRNAs for Tyr and Asp, Q is further glycosylated with galactose and mannose to generate galQ and manQ, respectively. However, biogenesis and physiological relevance of Q-glycosylation remain poorly understood. Here, we biochemically identified two RNA glycosylases, QTGAL and QTMAN, and successfully reconstituted Q-glycosylation of tRNAs using nucleotide diphosphate sugars. Ribosome profiling of knockout cells revealed that Q-glycosylation slowed down elongation at cognate codons, UAC and GAC (GAU), respectively. We also found that galactosylation of Q suppresses stop codon readthrough. Moreover, protein aggregates increased in cells lacking Q-glycosylation, indicating that Q-glycosylation contributes to proteostasis. Cryo-EM of human ribosome-tRNA complex revealed the molecular basis of codon recognition regulated by Q-glycosylations. Furthermore, zebrafish qtgal and qtman knockout lines displayed shortened body length, implying that Q-glycosylation is required for post-embryonic growth in vertebrates.

CRNDE: A Pivotal Oncogenic Long Non-Coding RNA in Cancers

Colorectal Neoplasia Differentially Expressed (CRNDE), a long non-coding RNA that was initially identified as aberrantly expressed in colorectal cancer (CRC) has also been observed to exhibit elevated expression in various other human malignancies. Recent research has accumulated substantial evidence implicating CRNDE as an oncogenic player, exerting influence over critical cellular processes linked to cancer progression. Particularly, its regulatory interactions with microRNAs and proteins have been shown to modulate pathways that contribute to carcinogenesis and tumorigenesis. This review will comprehensively outline the roles of CRNDE in colorectal, liver, glioma, lung, cervical, gastric and prostate cancer, elucidating the mechanisms involved in modulating proliferation, apoptosis, migration, invasion, angiogenesis, and radio/chemoresistance. Furthermore, the review highlights CRNDE's potential as a multifaceted biomarker, owing to its presence in diverse biological samples and stable properties, thereby underscoring its diagnostic, therapeutic, and prognostic applications. This review aims to provide comprehensive insights of CRNDE-mediated oncogenesis and identify CRNDE as a promising target for future clinical interventions.

Multi-View Kernel Learning for Identification of Disease Genes

Gene expression data sets and protein-protein interaction (PPI) networks are two heterogeneous data sources that have been extensively studied, due to their ability to capture the co-expression patterns among genes and their topological connections. Although they depict different traits of the data, both of them tend to group co-functional genes together. This phenomenon agrees with the basic assumption of multi-view kernel learning, according to which different views of the data contain a similar inherent cluster structure. Based on this inference, a new multi-view kernel learning based disease gene identification algorithm, termed as DiGId, is put forward. A novel multi-view kernel learning approach is proposed that aims to learn a consensus kernel, which efficiently captures the heterogeneous information of individual views as well as depicts the underlying inherent cluster structure. Some low-rank constraints are imposed on the learned multi-view kernel, so that it can effectively be partitioned into k or fewer clusters. The learned joint cluster structure is used to curate a set of potential disease genes. Moreover, a novel approach is put forward to quantify the importance of each view. In order to demonstrate the effectiveness of the proposed approach in capturing the relevant information depicted by individual views, an extensive analysis is performed on four different cancer-related gene expression data sets and PPI network, considering different similarity measures.

A novel fusion between CDC42BPB and ALK in a patient with quadruple wild-type gastrointestinal stromal tumor

Background

Gastrointestinal stromal tumors (GISTs) are the most common type of mesenchymal tumor in gastrointestinal tract, with striking features of morphology and immunohistochemistry. But GISTs in pregnancy could seldom be found. Pathogenic activating mutations of the proto‐oncogene KIT and PDGFRA are detected in majority GISTs, and adjuvant imatinib therapy targeting KIT and PDGFRA mutations is recommended for patients with high‐risk GIST. However, some rare subgroups with distinct molecular features remain uncovered and more therapeutic targets need to be revealed.

Methods

The DNA/RNA samples were detected using the NGS‐based YuanSu450 gene panel. After identifying the CDC42BPB‐ALK fusion by NGS, this novel fusion was further confirmed by Sanger sequencing. Subsequently, FISH analysis was performed using the Vysis ALK Break Apart FISH Probe kit to testify the ALK status. ALK protein expression was confirmed by IHC (D5F3 and 5A4).

Results

Herein, we reported the first case of quadruple wild‐type (WT) GIST with ALK‐CDC42BPB fusion and ALK (D5F3) overexpression. In this study, we described a 33‐year‐old pregnant patient in lactation who had a massive space occupying lesion (with the maximum diameter of 22 cm) in the stomach and was eventually diagnosed as quadruple WT GIST (KIT^WT/PDGFRA^WT/SDH^WT/RAS‐P^WT).

Conclusion

We unexpectedly found that this GIST patient showed ALK (D5F3) overexpression and harbored a novel fusion CDC42BPB exon 24‐ALK in exon 20.

A zebrafish pparγ gene deletion reveals a protein kinase network associated with defective lipid metabolism

Peroxisome proliferator-activated receptor γ (Pparγ) is a master regulator of adipogenesis. Chronic pathologies such as obesity, cardiovascular diseases, and diabetes involve the dysfunction of this transcription factor. Here, we generated a zebrafish mutant in pparγ (KO) with CRISPR/Cas9 technology and revealed its regulatory network. We uncovered the hepatic phenotypes of these male and female KO, and then the male wild-type zebrafish (WT) and KO were fed with a high-fat (HF) or standard diet (SD). We next conducted an integrated analyze of the proteomics and phosphoproteomics profiles. Compared with WT, the KO showed remarkable hyalinization and congestion lesions in the liver of males. Strikingly, pparγ deletion protected against the influence of high-fat diet feeding on lipid deposition in zebrafish. Some protein kinases critical for lipid metabolism, including serine/threonine-protein kinase TOR (mTOR), ribosomal protein S6 kinase (Rps6kb1b), and mitogen-activated protein kinase 14A (Mapk14a), were identified to be highly phosphorylated in KO based on differential proteome and phosphoproteome analysis. Our study supplies a pparγ deletion animal model and provides a comprehensive description of pparγ-induced expression level alterations of proteins and their phosphorylation, which are vital to understand the defective lipid metabolism risks posed to human health.

Scalable Non-Linear Graph Fusion for Prioritizing Cancer-Causing Genes

In the past few decades, both gene expression data and protein-protein interaction (PPI)networks have been extensively studied, due to their ability to depict important characteristics of disease-associated genes. In this regard, the paper presents a new gene prioritization algorithm to identify and prioritize cancer-causing genes, integrating judiciously the complementary information obtained from two data sources. The proposed algorithm selects disease-causing genes by maximizing the importance of selected genes and functional similarity among them. A new quantitative index is introduced to evaluate the importance of a gene. It considers whether a gene exhibits a differential expression pattern across sick and healthy individuals, and has a strong connectivity in the PPI network, which are the important characteristics of a potential biomarker. As disease-associated genes are expected to have similar expression profiles and topological structures, a scalable non-linear graph fusion technique, termed as ScaNGraF, is proposed to learn a disease-dependent functional similarity network from the co-expression and common neighbor based similarity networks. The proposed ScaNGraF, which is based on message passing algorithm, efficiently combines the shared and complementary information provided by different data sources with significantly lower computational cost. A new measure, termed as DiCoIN, is introduced to evaluate the quality of a learned affinity network. The performance of the proposed graph fusion technique and gene selection algorithm is extensively compared with that of some existing methods, using several cancer data sets.

Navigating Multi-Scale Cancer Systems Biology Towards Model-Driven Clinical Oncology and Its Applications in Personalized Therapeutics

Rapid advancements in high-throughput omics technologies and experimental protocols have led to the generation of vast amounts of scale-specific biomolecular data on cancer that now populates several online databases and resources. Cancer systems biology models built using this data have the potential to provide specific insights into complex multifactorial aberrations underpinning tumor initiation, development, and metastasis. Furthermore, the annotation of these single- and multi-scale models with patient data can additionally assist in designing personalized therapeutic interventions as well as aid in clinical decision-making. Here, we have systematically reviewed the emergence and evolution of (i) repositories with scale-specific and multi-scale biomolecular cancer data, (ii) systems biology models developed using this data, (iii) associated simulation software for the development of personalized cancer therapeutics, and (iv) translational attempts to pipeline multi-scale panomics data for data-driven in silico clinical oncology. The review concludes that the absence of a generic, zero-code, panomics-based multi-scale modeling pipeline and associated software framework, impedes the development and seamless deployment of personalized in silico multi-scale models in clinical settings.

The Murine Reg3a Stimulated by Lactobacillus casei Promotes Intestinal Cell Proliferation and Inhibits the Multiplication of Porcine Diarrhea Causative Agent in vitro

Lactobacillus casei (L. casei), a normal resident of the gastrointestinal tract of mammals, has been extensively studied over the past few decades for its probiotic properties in clinical and animal models. Some studies have shown that some bacterium of Lactobacillus stimulate the production of antimicrobial peptides in intestinal cells to clear enteric pathogens, however, which antimicrobial peptides are produced by L. casei stimulation and its function are still not completely understood. In this study, we investigated the changes of antimicrobial peptides' expression after intragastric administration of L. casei to mice. The bioinformatics analysis revealed there were nine genes strongly associated with up-regulated DEGs. But, of these, only the antimicrobial peptide mReg3a gene was continuously up-regulated, which was also confirmed by qRT-PCR. We found out the mReg3a expressed in engineering E.coli promoted cell proliferation and wound healing proved by CCK-8 assay and wound healing assay. Moreover, the tight junction proteins ZO-1 and E-cadherin in mReg3a treatment group were significantly higher than that in the control group under the final concentration of 0.2 mg/ml both in Porcine intestinal epithelial cells (IPEC-J2) and Mouse intestinal epithelial cells (IEC-6) (p < 0.05). Surprisingly, the recombinant mReg3a not only inhibited Enterotoxigenic Escherichia coli (ETEC), but also reduced the copy number of the piglet diarrheal viruses, porcine epidemic diarrhea virus (PEDV), porcine transmissible gastroenteritis virus (TGEV), and porcine rotavirus (PoRV), indicating the antimicrobial peptides mReg3a may be feed additives to resist the potential of the intestinal bacterial and viral diarrhea disease.

Integration of the Microbiome, Metabolome and Transcriptomics Data Identified Novel Metabolic Pathway Regulation in Colorectal Cancer

Integrative multiomics data analysis provides a unique opportunity for the mechanistic understanding of colorectal cancer (CRC) in addition to the identification of potential novel therapeutic targets. In this study, we used public omics data sets to investigate potential associations between microbiome, metabolome, bulk transcriptomics and single cell RNA sequencing datasets. We identified multiple potential interactions, for example 5-aminovalerate interacting with Adlercreutzia; cholesteryl ester interacting with bacterial genera Staphylococcus, Blautia and Roseburia. Using public single cell and bulk RNA sequencing, we identified 17 overlapping genes involved in epithelial cell pathways, with particular significance of the oxidative phosphorylation pathway and the ACAT1 gene that indirectly regulates the esterification of cholesterol. These findings demonstrate that the integration of multiomics data sets from diverse populations can help us in untangling the colorectal cancer pathogenesis as well as postulate the disease pathology mechanisms and therapeutic targets.

Angel or Devil ? - CDK8 as the new drug target

Cyclin-dependent kinase 8 (CDK8) plays an momentous role in transcription regulation by forming kinase module or transcription factor phosphorylation. A large number of evidences have identified CDK8 as an important factor in cancer occurrence and development. In addition, CDK8 also participates in the regulation of cancer cell stress response to radiotherapy and chemotherapy, assists tumor cell invasion, metastasis, and drug resistance. Therefore, CDK8 is regarded as a promising target for cancer therapy. Most studies in recent years supported the role of CDK8 as a carcinogen, however, under certain conditions, CDK8 exists as a tumor suppressor. The functional diversity of CDK8 and its exceptional role in different types of cancer have aroused great interest from scientists but even more controversy during the discovery of CDK8 inhibitors. In addition, CDK8 appears to be an effective target for inflammation diseases and immune system disorders. Therefore, we summarized the research results of CDK8, involving physiological/pathogenic mechanisms and the development status of compounds targeting CDK8, provide a reference for the feasibility evaluation of CDK8 as a therapeutic target, and guidance for researchers who are involved in this field for the first time.

Colorectal Cancer Prediction Based on Weighted Gene Co-Expression Network Analysis and Variational Auto-Encoder

An effective feature extraction method is key to improving the accuracy of a prediction model. From the Gene Expression Omnibus (GEO) database, which includes 13,487 genes, we obtained microarray gene expression data for 238 samples from colorectal cancer (CRC) samples and normal samples. Twelve gene modules were obtained by weighted gene co-expression network analysis (WGCNA) on 173 samples. By calculating the Pearson correlation coefficient (PCC) between the characteristic genes of each module and colorectal cancer, we obtained a key module that was highly correlated with CRC. We screened hub genes from the key module by considering module membership, gene significance, and intramodular connectivity. We selected 10 hub genes as a type of feature for the classifier. We used the variational autoencoder (VAE) for 1159 genes with significantly different expressions and mapped the data into a 10-dimensional representation, as another type of feature for the cancer classifier. The two types of features were applied to the support vector machines (SVM) classifier for CRC. The accuracy was 0.9692 with an AUC of 0.9981. The result shows a high accuracy of the two-step feature extraction method, which includes obtaining hub genes by WGCNA and a 10-dimensional representation by variational autoencoder (VAE).

Enhanced identification of significant regulators of gene expression

Background

Diseases like cancer will lead to changes in gene expression, and it is relevant to identify key regulatory genes that can be linked directly to these changes. This can be done by computing a Regulatory Impact Factor (RIF) score for relevant regulators. However, this computation is based on estimating correlated patterns of gene expression, often Pearson correlation, and an assumption about a set of specific regulators, normally transcription factors. This study explores alternative measures of correlation, using the Fisher and Sobolev metrics, and an extended set of regulators, including epigenetic regulators and long non-coding RNAs (lncRNAs). Data on prostate cancer have been used to explore the effect of these modifications.

Results

A tool for computation of RIF scores with alternative correlation measures and extended sets of regulators was developed and tested on gene expression data for prostate cancer. The study showed that the Fisher and Sobolev metrics lead to improved identification of well-documented regulators of gene expression in prostate cancer, and the sets of identified key regulators showed improved overlap with previously defined gene sets of relevance to cancer. The extended set of regulators lead to identification of several interesting candidates for further studies, including lncRNAs. Several key processes were identified as important, including spindle assembly and the epithelial-mesenchymal transition (EMT).

Conclusions

The study has shown that using alternative metrics of correlation can improve the performance of tools based on correlation of gene expression in genomic data. The Fisher and Sobolev metrics should be considered also in other correlation-based applications.

Serum miR-101-3p combined with pepsinogen contributes to the early diagnosis of gastric cancer

BACKGROUND

This study aimed to explore the diagnostic value of serum miR-101-3p combined with pepsinogen (PG) on early diagnosis of gastric cancer (GC).

METHODS

A total of 61 atrophic gastritis (AG) and 86 GC patients, and 50 healthy volunteers were enrolled. The serum expression of miR-101-3p was measured by qRT-PCR. The serum content of carcinoembryonic antigen (CEA) was measured by Electrochemiluminescence immunoassay. The serum contents of PGI and PGII were measured by Enzyme linked immunosorbent assay. The diagnostic value of serum markers on AG and GC was analyzed by receiver operating characteristic (ROC) analysis.

RESULTS

The expression of miR-101-3p, the content of PGI and the ratio of PGI/II were significantly decreased, and the content of PGII was significantly increased in AG patients compared with those in normal controls. The changes of the above serum indicators were more obvious in GC patients than those in AG patients. The content of CEA was significantly higher in GC patients than that in AG patients. In addition, the expression of miR-101-3p was negatively associated with the submucosal infiltration in GC patients. MiR-101-3p exhibited high diagnostic value on AG (AUC 0.8493, sensitivity 80.33%, specificity 80%) and GC (AUC 0.8749, sensitivity 72.09%, specificity 86.49%). MiR-101-3p + PGI + PGI/II (AUC 0.856, sensitivity 80.23%, specificity 77.05%) exhibited a high diagnostic value in distinguishing between AG and GC.

CONCLUSIONS

MiR-101-3p was a potential diagnostic marker for AG and GC. MiR-101-3p + PGI + PGI/II was effective in distinguishing between AG and GC.

Role of Regenerating Islet-Derived Protein 3A in Gastrointestinal Cancer

Regenerating islet-derived protein 3A (Reg3A), a protein mainly expressed in the digestive system, has been found over-expressed in many kinds of gastrointestinal cancer, including hepatocellular carcinoma, pancreatic cancer, gastric cancer, and colorectal cancer, therefore has been considered as a promising tumor marker. In recent years, considerable attention has been focused on the tumorigenesis effects of Reg3A, which were mainly manifested as cell proliferation promotion, cell apoptosis inhibition, the regulation of cancer cell migration and invasion. In particular, based on the significant up-regulation of Reg3A during pancreatic inflammation as well as its tumorigenic potential, Reg3A has been considered to play a key role in inflammation-linked pancreatic carcinogenesis. In addition, we here systematically generalized the reported Reg3A-related signaling molecules, which included JAK2-STAT3- NF-κB, SOCS3, EXTL3-PI3K-Akt, GSK3β, Wnt/β-catenin as well as some invasion and migration-related genes (Snail, MMP-2, MMP-9, E-cadherin, RhoC, and MTA1). And gp130, EGFR, EXTL3, and Fibronectin 1 might act as potential receptors for Reg3A.

OMICfpp: a fuzzy approach for paired RNA-Seq counts

Background

RNA sequencing is a widely used technology for differential expression analysis. However, the RNA-Seq do not provide accurate absolute measurements and the results can be different for each pipeline used. The major problem in statistical analysis of RNA-Seq and in the omics data in general, is the small sample size with respect to the large number of variables. In addition, experimental design must be taken into account and few tools consider it.

Results

We propose OMICfpp, a method for the statistical analysis of RNA-Seq paired design data. First, we obtain a p-value for each case-control pair using a binomial test. These p-values are aggregated using an ordered weighted average (OWA) with a given orness previously chosen. The aggregated p-value from the original data is compared with the aggregated p-value obtained using the same method applied to random pairs. These new pairs are generated using between-pairs and complete randomization distributions. This randomization p-value is used as a raw p-value to test the differential expression of each gene. The OMICfpp method is evaluated using public data sets of 68 sample pairs from patients with colorectal cancer. We validate our results through bibliographic search of the reported genes and using simulated data set. Furthermore, we compared our results with those obtained by the methods edgeR and DESeq2 for paired samples. Finally, we propose new target genes to validate these as gene expression signatures in colorectal cancer. OMICfpp is available at http://www.uv.es/ayala/software/OMICfpp_0.2.tar.gz.

Conclusions

Our study shows that OMICfpp is an accurate method for differential expression analysis in RNA-Seq data with paired design. In addition, we propose the use of randomized p-values pattern graphic as a powerful and robust method to select the target genes for experimental validation.

Electronic supplementary material

The online version of this article (10.1186/s12864-019-5496-5) contains supplementary material, which is available to authorized users.

DNA methylation and associated gene expression in blood prior to lung cancer diagnosis in the Norwegian Women and Cancer cohort

The majority of lung cancer is caused by tobacco smoking, and lung cancer-relevant epigenetic markers have been identified in relation to smoking exposure. Still, smoking-related markers appear to mediate little of the effect of smoking on lung cancer. Thus in order to identify disease-relevant markers and enhance our understanding of pathways, a wide search is warranted. Through an epigenome-wide search within a case-control study (131 cases, 129 controls) nested in a Norwegian prospective cohort of women, we found 25 CpG sites associated with lung cancer. Twenty-three were classified as associated with smoking (LC-AwS), and two were classified as unassociated with smoking (LC-non-AwS), as they remained associated with lung cancer after stringent adjustment for smoking exposure using the comprehensive smoking index (CSI): cg10151248 (PC, CSI-adjusted odds ratio (OR) = 0.34 [0.23-0.52] per standard deviation change in methylation) and cg13482620 (B3GNTL1, CSI-adjusted OR = 0.33 [0.22-0.50]). Analysis among never smokers and a cohort of smoking-discordant twins confirmed the classification of the two LC-non-AwS CpG sites. Gene expression profiles demonstrated that the LC-AwS CpG sites had different enriched pathways than LC-non-AwS sites. In conclusion, using blood-derived DNA methylation and gene expression profiles from a prospective lung cancer case-control study in women, we identified 25 CpG lung cancer markers prior to diagnosis, two of which were LC-non-AwS markers and related to distinct pathways.

Colorectal neoplasia differentially expressed: a long noncoding RNA with an imperative role in cancer

Colorectal neoplasia differentially expressed (CRNDE), as a long noncoding RNA (lncRNA), has attracted increasing attention in recent years and has been documented to be at abnormally high expression in various types of cancer, such as colorectal cancer, glioma, hepatocellular carcinoma, lung cancer, and breast cancer. It could not only be used as a clinical biomarker for the early diagnosis and prognosis evaluation in a variety of cancers but also promote the development and progress of various tumor cells. Moreover, it is involved in the targeting regulation of multiple microRNAs and the activation/inhibition of multiple signaling pathways. In this review, we presented a systematic summary of the potential carcinogenicity and clinical value of CRNDE in the current evidence, so as to provide reference for early diagnosis, prognosis evaluation, and targeted therapy of various clinical cancers.

A novel joint analysis framework improves identification of differentially expressed genes in cross disease transcriptomic analysis

Motivation

Detecting differentially expressed (DE) genes between disease and normal control group is one of the most common analyses in genome-wide transcriptomic data. Since most studies don’t have a lot of samples, researchers have used meta-analysis to group different datasets for the same disease. Even then, in many cases the statistical power is still not enough. Taking into account the fact that many diseases share the same disease genes, it is desirable to design a statistical framework that can identify diseases’ common and specific DE genes simultaneously to improve the identification power.

Results

We developed a novel empirical Bayes based mixture model to identify DE genes in specific study by leveraging the shared information across multiple different disease expression data sets. The effectiveness of joint analysis was demonstrated through comprehensive simulation studies and two real data applications. The simulation results showed that our method consistently outperformed single data set analysis and two other meta-analysis methods in identification power. In real data analysis, overall our method demonstrated better identification power in detecting DE genes and prioritized more disease related genes and disease related pathways than single data set analysis. Over 150% more disease related genes are identified by our method in application to Huntington’s disease. We expect that our method would provide researchers a new way of utilizing available data sets from different diseases when sample size of the focused disease is limited.

Electronic supplementary material

The online version of this article (10.1186/s13040-018-0163-y) contains supplementary material, which is available to authorized users.

Significance and Functional Similarity for Identification of Disease Genes

One of the most significant research issues in functional genomics is insilico identification of disease related genes. In this regard, the paper presents a new gene selection algorithm, termed as SiFS, for identification of disease genes. It integrates the information obtained from interaction network of proteins and gene expression profiles. The proposed SiFS algorithm culls out a subset of genes from microarray data as disease genes by maximizing both significance and functional similarity of the selected gene subset. Based on the gene expression profiles, the significance of a gene with respect to another gene is computed using mutual information. On the other hand, a new measure of similarity is introduced to compute the functional similarity between two genes. Information derived from the protein-protein interaction network forms the basis of the proposed SiFS algorithm. The performance of the proposed gene selection algorithm and new similarity measure, is compared with that of other related methods and similarity measures, using several cancer microarray data sets.

Biological Networks for Cancer Candidate Biomarkers Discovery

Due to its extraordinary heterogeneity and complexity, cancer is often proposed as a model case of a systems biology disease or network disease. There is a critical need of effective biomarkers for cancer diagnosis and/or outcome prediction from system level analyses. Methods based on integrating omics data into networks have the potential to revolutionize the identification of cancer biomarkers. Deciphering the biological networks underlying cancer is undoubtedly important for understanding the molecular mechanisms of the disease and identifying effective biomarkers. In this review, the networks constructed for cancer biomarker discovery based on different omics level data are described and illustrated from recent advances in the field.

Recombinant Reg3α protein protects against experimental acute pancreatitis in mice

Regenerating gene 3α (Reg3α) protein is a trophic factor that stimulates cell and tissue proliferation, neogenesis and also acts against apoptosis and necrosis. In order to explore the potential roles of recombinant Reg3α (rReg3α), we produced a mature rReg3α polypeptide for direct administration in l-arginine (L-Arg) induced acute pancreatitis (AP) in mice. Our results showed that rReg3α stimulated cell proliferation through Erk1/2 and p38 phosphorylation and also cyclin D1 upregulation mediated by Akt/ATF-2 signaling. Moreover, rReg3α administration significantly reduced the pancreatic damage caused by L-Arg injection, as shown in histological examination and serum amylase, lipase and C-reactive protein (CRP) assays. Not only acinar cell necrosis but also apoptosis found in the pancreas of AP mice were alleviated by rReg3α. Finally, upregulated Bcl-2 and Bcl-xL and suppressed poly (ADP-ribose) synthetase/polymerase (PARP) levels were detected as being relevant to the mechanism of rReg3α protection. We therefore conclude that rReg3α acts as a protective polypeptide against AP in mice by enhancing Bcl-2 and Bcl-xL expressions and suppressing PARP level.

The putative oncogene, CRNDE, is a negative prognostic factor in ovarian cancer patients

The CRNDE gene seems to play an oncogenic role in cancers, though its exact function remains unknown. Here, we tried to assess its usefulness as a molecular prognostic marker in ovarian cancer. Based on results of our microarray studies, CRNDE transcripts were further analyzed by Real-Time qPCR-based profiling of their expression. The qPCR study was conducted with the use of personally designed TaqMan assays on 135 frozen tissue sections of ovarian carcinomas from patients treated with platinum compounds and either cyclophosphamide (PC, N = 32) or taxanes (TP, N = 103). Elevated levels of two different CRNDE transcripts were a negative prognostic factor; they increased the risk of death and recurrence in the group of patients treated with TP, but not PC (DNA-damaging agents only). Higher associations were found for overexpression of the short CRNDE splice variant (FJ466686): HR 6.072, 95% CI 1.814-20.32, p = 0.003 (the risk of death); HR 15.53, 95% CI 3.812-63.28, p < 0.001 (the risk of recurrence). Additionally, accumulation of the TP53 protein correlated with decreased expression of both CRNDE transcripts in tumor cells. Our results depict CRNDE as a potential marker of poor prognosis in women with ovarian carcinomas, and suggest that its significance depends on the therapeutic regimen used.

Magnesium homeostasis in colon carcinoma LoVo cells sensitive or resistant to doxorubicin

Neoplastic cells accumulate magnesium, an event which provides selective advantages and is frequently associated with TRPM7overexpression. Little is known about magnesium homeostasis in drug-resistant cancer cells. Therefore, we used the colon cancer LoVo cell model and compared doxorubicin-resistant to sensitive cells. In resistant cells the concentration of total magnesium is higher while its influx capacity is lower than in sensitive cells. Accordingly, resistant cells express lower amounts of the TRPM6 and 7, both involved in magnesium transport. While decreased TRPM6 levels are due to transcriptional regulation, post-transcriptional events are involved in reducing the amounts of TRPM7. Indeed, the calpain inhibitor calpeptin markedly increases the levels of TRPM7 in resistant cells. In doxorubicin-sensitive cells, silencing TRPM7 shifts the phenotype to one more similar to resistant cells, since in these cells silencing TRPM7 significantly decreases the influx of magnesium, increases its intracellular concentration and increases resistance to doxorubicin. On the other hand, calpain inhibition upregulates TRPM7, decreases intracellular magnesium and enhances the sensitivity to doxorubicin of resistant LoVo cells. We conclude that in LoVo cells drug resistance is associated with alteration of magnesium homeostasis through modulation of TRPM7. Our data suggest that TRPM7 expression may be an additional undisclosed player in chemoresistance.

Non-coding landscapes of colorectal cancer

For two decades Vogelstein’s model has been the paradigm for describing the sequence of molecular changes within protein-coding genes that would lead to overt colorectal cancer (CRC). This model is now too simplistic in the light of recent studies, which have shown that our genome is pervasively transcribed in RNAs other than mRNAs, denominated non-coding RNAs (ncRNAs). The discovery that mutations in genes encoding these RNAs [i.e., microRNAs (miRNAs), long non-coding RNAs, and circular RNAs] are causally involved in cancer phenotypes has profoundly modified our vision of tumour molecular genetics and pathobiology. By exploiting a wide range of different mechanisms, ncRNAs control fundamental cellular processes, such as proliferation, differentiation, migration, angiogenesis and apoptosis: these data have also confirmed their role as oncogenes or tumor suppressors in cancer development and progression. The existence of a sophisticated RNA-based regulatory system, which dictates the correct functioning of protein-coding networks, has relevant biological and biomedical consequences. Different miRNAs involved in neoplastic and degenerative diseases exhibit potential predictive and prognostic properties. Furthermore, the key roles of ncRNAs make them very attractive targets for innovative therapeutic approaches. Several recent reports have shown that ncRNAs can be secreted by cells into the extracellular environment (i.e., blood and other body fluids): this suggests the existence of extracellular signalling mechanisms, which may be exploited by cells in physiology and pathology. In this review, we will summarize the most relevant issues on the involvement of cellular and extracellular ncRNAs in disease. We will then specifically describe their involvement in CRC pathobiology and their translational applications to CRC diagnosis, prognosis and therapy.

Identification of a DNA methylation signature to predict disease-free survival in locally advanced rectal cancer

In locally advanced rectal cancer a preoperative predictive biomarker is necessary to adjust treatment specifically for those patients expected to suffer relapse. We applied whole genome methylation CpG island array analyses to an initial set of patients (n=11) to identify differentially methylated regions (DMRs) that separate a good from a bad prognosis group. Using a quantitative high-resolution approach, candidate DMRs were first validated in a set of 61 patients (test set) and then confirmed DMRs were further validated in additional independent patient cohorts (n=71, n=42). We identified twenty highly discriminative DMRs and validated them in the test set using the MassARRAY technique. Ten DMRs could be confirmed which allowed separation into prognosis groups (p=0.0207, HR=4.09). The classifier was validated in two additional cohorts (n=71, p=0.0345, HR=3.57 and n=42, p=0.0113, HR=3.78). Interestingly, six of the ten DMRs represented regions close to the transcriptional start sites of genes which are also marked by the Polycomb Repressor Complex component EZH2. In conclusion we present a classifier comprising 10 DMRs which predicts patient prognosis with a high degree of accuracy. These data may now help to discriminate between patients that may respond better to standard treatments from those that may require alternative modalities.

The Novel Gene CRNDE Encodes a Nuclear Peptide (CRNDEP) Which Is Overexpressed in Highly Proliferating Tissues

CRNDE, recently described as the lncRNA-coding gene, is overexpressed at RNA level in human malignancies. Its role in gametogenesis, cellular differentiation and pluripotency has been suggested as well. Herein, we aimed to verify our hypothesis that the CRNDE gene may encode a protein product, CRNDEP. By using bioinformatics methods, we identified the 84-amino acid ORF encoded by one of two CRNDE transcripts, previously described by our research team. This ORF was cloned into two expression vectors, subsequently utilized in localization studies in HeLa cells. We also developed a polyclonal antibody against CRNDEP. Its specificity was confirmed in immunohistochemical, cellular localization, Western blot and immunoprecipitation experiments, as well as by showing a statistically significant decrease of endogenous CRNDEP expression in the cells with transient shRNA-mediated knockdown of CRNDE. Endogenous CRNDEP localizes predominantly to the nucleus and its expression seems to be elevated in highly proliferating tissues, like the parabasal layer of the squamous epithelium, intestinal crypts or spermatocytes. After its artificial overexpression in HeLa cells, in a fusion with either the EGFP or DsRed Monomer fluorescent tag, CRNDEP seems to stimulate the formation of stress granules and localize to them. Although the exact role of CRNDEP is unknown, our preliminary results suggest that it may be involved in the regulation of the cell proliferation. Possibly, CRNDEP also participates in oxygen metabolism, considering our in silico results, and the correlation between its enforced overexpression and the formation of stress granules. This is the first report showing the existence of a peptide encoded by the CRNDE gene.

Prediction of cancer proteins by integrating protein interaction, domain frequency, and domain interaction data using machine learning algorithms

Many proteins are known to be associated with cancer diseases. It is quite often that their precise functional role in disease pathogenesis remains unclear. A strategy to gain a better understanding of the function of these proteins is to make use of a combination of different aspects of proteomics data types. In this study, we extended Aragues's method by employing the protein-protein interaction (PPI) data, domain-domain interaction (DDI) data, weighted domain frequency score (DFS), and cancer linker degree (CLD) data to predict cancer proteins. Performances were benchmarked based on three kinds of experiments as follows: (I) using individual algorithm, (II) combining algorithms, and (III) combining the same classification types of algorithms. When compared with Aragues's method, our proposed methods, that is, machine learning algorithm and voting with the majority, are significantly superior in all seven performance measures. We demonstrated the accuracy of the proposed method on two independent datasets. The best algorithm can achieve a hit ratio of 89.4% and 72.8% for lung cancer dataset and lung cancer microarray study, respectively. It is anticipated that the current research could help understand disease mechanisms and diagnosis.

Functional genomics analysis reveals a MYC signature associated with a poor clinical prognosis in liposarcomas

Liposarcomas, which are malignant fatty tumors, are the second most common soft-tissue sarcomas. Several histologically defined liposarcoma subtypes exist, yet little is known about the molecular pathology that drives the diversity in these tumors. We used functional genomics to classify a panel of diverse liposarcoma cell lines based on hierarchical clustering of their gene expression profiles, indicating that liposarcoma gene expression profiles and histologic classification are not directly correlated. Boolean probability approaches based on cancer-associated properties identified differential expression in multiple genes, including MYC, as potentially affecting liposarcoma signaling networks and cancer outcome. We confirmed our method with a large panel of lipomatous tumors, revealing that MYC protein expression is correlated with patient survival. These data encourage increased reliance on genomic features in conjunction with histologic features for liposarcoma clinical characterization and lay the groundwork for using Boolean-based probabilities to identify prognostic biomarkers for clinical outcome in tumor patients.

Large-scale integration of small molecule-induced genome-wide transcriptional responses, Kinome-wide binding affinities and cell-growth inhibition profiles reveal global trends characterizing systems-level drug action

The Library of Integrated Network-based Cellular Signatures (LINCS) project is a large-scale coordinated effort to build a comprehensive systems biology reference resource. The goals of the program include the generation of a very large multidimensional data matrix and informatics and computational tools to integrate, analyze, and make the data readily accessible. LINCS data include genome-wide transcriptional signatures, biochemical protein binding profiles, cellular phenotypic response profiles and various other datasets for a wide range of cell model systems and molecular and genetic perturbations. Here we present a partial survey of this data facilitated by data standards and in particular a robust compound standardization workflow; we integrated several types of LINCS signatures and analyzed the results with a focus on mechanism of action (MoA) and chemical compounds. We illustrate how kinase targets can be related to disease models and relevant drugs. We identified some fundamental trends that appear to link Kinome binding profiles and transcriptional signatures to chemical information and biochemical binding profiles to transcriptional responses independent of chemical similarity. To fill gaps in the datasets we developed and applied predictive models. The results can be interpreted at the systems level as demonstrated based on a large number of signaling pathways. We can identify clear global relationships, suggesting robustness of cellular responses to chemical perturbation. Overall, the results suggest that chemical similarity is a useful measure at the systems level, which would support phenotypic drug optimization efforts. With this study we demonstrate the potential of such integrated analysis approaches and suggest prioritizing further experiments to fill the gaps in the current data.

Low expression of novel lncRNA RP11-462C24.1 suggests a biomarker of poor prognosis in colorectal cancer

Long noncoding RNAs (lncRNAs) have recently emerged as a major class of regulatory molecules, which were involved in a broad range of biological processes and complex diseases. Research on lncRNAs may shed light on tumorigenesis and progression of colorectal cancer (CRC). The purpose of the present study was to identify lncRNAs correlated with CRC and then investigate their potential functions. We selected 92 patients for this prospective study and then collected the tumor samples and clinical records. First, the global lncRNA expression profiles in tumor and adjacent normal tissues of patients with non-metastatic CRC and patients with metastatic CRC were measured by microarray assay. Then, a noteworthy lncRNAs RP11-462C24.1 whose function was previously unknown was explored in detail on the aspect of the association of its expression level and clinicopathological features of CRC and patients' survival. We found that RP11-462C24.1 expression level was lower in cancer tissues compared with adjacent normal samples (P < 0.001). Furthermore, its expression level was lower in CRC patients with metastasis than those without metastasis (P = 0.049). That is, RP11-462C24.1 expression level decreased as the malignant degree of CRC increased. In addition, low expression of RP11-462C24.1 significantly correlated with more distant metastasis (P = 0.011). The areas under ROC curves were 0.78 and 0.65 for RP11-462C24.1, distinguishing CRC from normal tissue and distinguishing CRC without metastasis from CRC with metastasis, respectively. Multivariate analysis identified that RP11-462C24.1 was an independent predictor for patients prognosis (P = 0.005). Furthermore, Kaplan-Meier analysis showed that patients with low expression of RP11-462C24.1 had a poor disease-free survival (P < 0.001). This is the first study that correlates RP11-462C24.1 expression profile with malignancy grade in human CRC. Our results showed that RP11-462C24.1 could be a potential novel prognostic marker for CRC, and thus, provided a new strategy for CRC diagnosis. Meanwhile, our findings indicated the potential roles of RP11-462C24.1 in tumorigenesis and progression of CRC, which gave a clue for future studies.

RNF14 is a regulator of mitochondrial and immune function in muscle

BACKGROUND

Muscle development and remodelling, mitochondrial physiology and inflammation are thought to be inter-related and to have implications for metabolism in both health and disease. However, our understanding of their molecular control is incomplete.

RESULTS

In this study we have confirmed that the ring finger 14 protein (RNF14), a poorly understood transcriptional regulator, influences the expression of both mitochondrial and immune-related genes. The prediction was based on a combination of network connectivity and differential connectivity in cattle (a non-model organism) and mice data sets, with a focus on skeletal muscle. They assigned similar probability to mammalian RNF14 playing a regulatory role in mitochondrial and immune gene expression. To try and resolve this apparent ambiguity we performed a genome-wide microarray expression analysis on mouse C2C12 myoblasts transiently transfected with two Rnf14 transcript variants that encode 2 naturally occurring but different RNF14 protein isoforms. The effect of both constructs was significantly different to the control samples (untransfected cells and cells transfected with an empty vector). Cluster analyses revealed that transfection with the two Rnf14 constructs yielded discrete expression signatures from each other, but in both cases a substantial set of genes annotated as encoding proteins related to immune function were perturbed. These included cytokines and interferon regulatory factors. Additionally, transfection of the longer transcript variant 1 coordinately increased the expression of 12 (of the total 13) mitochondrial proteins encoded by the mitochondrial genome, 3 of which were significant in isolated pair-wise comparisons (Mt-coxII, Mt-nd2 and mt-nd4l). This apparent additional mitochondrial function may be attributable to the RWD protein domain that is present only in the longer RNF14 isoform.

CONCLUSIONS

RNF14 influences the expression of both mitochondrial and immune related genes in a skeletal muscle context, and has likely implications for the inter-relationship between bioenergetic status and inflammation.

Decision tree-based method for integrating gene expression, demographic, and clinical data to determine disease endotypes

Background

Complex diseases are often difficult to diagnose, treat and study due to the multi-factorial nature of the underlying etiology. Large data sets are now widely available that can be used to define novel, mechanistically distinct disease subtypes (endotypes) in a completely data-driven manner. However, significant challenges exist with regard to how to segregate individuals into suitable subtypes of the disease and understand the distinct biological mechanisms of each when the goal is to maximize the discovery potential of these data sets.

Results

A multi-step decision tree-based method is described for defining endotypes based on gene expression, clinical covariates, and disease indicators using childhood asthma as a case study. We attempted to use alternative approaches such as the Student’s t-test, single data domain clustering and the Modk-prototypes algorithm, which incorporates multiple data domains into a single analysis and none performed as well as the novel multi-step decision tree method. This new method gave the best segregation of asthmatics and non-asthmatics, and it provides easy access to all genes and clinical covariates that distinguish the groups.

Conclusions

The multi-step decision tree method described here will lead to better understanding of complex disease in general by allowing purely data-driven disease endotypes to facilitate the discovery of new mechanisms underlying these diseases. This application should be considered a complement to ongoing efforts to better define and diagnose known endotypes. When coupled with existing methods developed to determine the genetics of gene expression, these methods provide a mechanism for linking genetics and exposomics data and thereby accounting for both major determinants of disease.

RNA nanotechnology for computer design and in vivo computation

Molecular-scale computing has been explored since 1989 owing to the foreseeable limitation of Moore's law for silicon-based computation devices. With the potential of massive parallelism, low energy consumption and capability of working in vivo, molecular-scale computing promises a new computational paradigm. Inspired by the concepts from the electronic computer, DNA computing has realized basic Boolean functions and has progressed into multi-layered circuits. Recently, RNA nanotechnology has emerged as an alternative approach. Owing to the newly discovered thermodynamic stability of a special RNA motif (Shu et al. 2011 Nat. Nanotechnol. 6, 658-667 (doi:10.1038/nnano.2011.105)), RNA nanoparticles are emerging as another promising medium for nanodevice and nanomedicine as well as molecular-scale computing. Like DNA, RNA sequences can be designed to form desired secondary structures in a straightforward manner, but RNA is structurally more versatile and more thermodynamically stable owing to its non-canonical base-pairing, tertiary interactions and base-stacking property. A 90-nucleotide RNA can exhibit 4⁹⁰ nanostructures, and its loops and tertiary architecture can serve as a mounting dovetail that eliminates the need for external linking dowels. Its enzymatic and fluorogenic activity creates diversity in computational design. Varieties of small RNA can work cooperatively, synergistically or antagonistically to carry out computational logic circuits. The riboswitch and enzymatic ribozyme activities and its special in vivo attributes offer a great potential for in vivo computation. Unique features in transcription, termination, self-assembly, self-processing and acid resistance enable in vivo production of RNA nanoparticles that harbour various regulators for intracellular manipulation. With all these advantages, RNA computation is promising, but it is still in its infancy. Many challenges still exist. Collaborations between RNA nanotechnologists and computer scientists are necessary to advance this nascent technology.

Modeling of the hypothalamic-pituitary-adrenal axis-mediated interaction between the serotonin regulation pathway and the stress response using a Boolean approximation: a novel study of depression

Major depressive disorder (MDD) is a multifactorial disorder known to be influenced by both genetic and environmental factors. MDD presents a heritability of 37%, and a genetic contribution has also been observed in studies of family members of individuals with MDD that imply that the probability of suffering the disorder is approximately three times higher if a first-degree family member is affected. Childhood maltreatment and stressful life events (SLEs) have been established as critical environmental factors that profoundly influence the onset of MDD. The serotonin pathway has been a strong candidate for genetic studies, but it only explains a small proportion of the heritability of the disorder, which implies the involvement of other pathways. The serotonin (5-HT) pathway interacts with the stress response pathway in a manner mediated by the hypothalamic-pituitary-adrenal (HPA) axis. To analyze the interaction between the pathways, we propose the use of a synchronous Boolean network (SBN) approximation. The principal aim of this work was to model the interaction between these pathways, taking into consideration the presence of selective serotonin reuptake inhibitors (SSRIs), in order to observe how the pathways interact and to examine if the system is stable. Additionally, we wanted to study which genes or metabolites have the greatest impact on model stability when knocked out in silico. We observed that the biological model generated predicts steady states (attractors) for each of the different runs performed, thereby proving that the system is stable. These attractors changed in shape, especially when anti-depressive drugs were also included in the simulation. This work also predicted that the genes with the greatest impact on model stability were those involved in the neurotrophin pathway, such as CREB, BDNF (which has been associated with major depressive disorder in a variety of studies) and TRkB, followed by genes and metabolites related to 5-HT synthesis.

Cancer systems biology in the genome sequencing era: part 2, evolutionary dynamics of tumor clonal networks and drug resistance

A tumor often consists of multiple cell subpopulations (clones). Current chemo-treatments often target one clone of a tumor. Although the drug kills that clone, other clones overtake it and the tumor recurs. Genome sequencing and computational analysis allows to computational dissection of clones from tumors, while singe-cell genome sequencing including RNA-Seq allows profiling of these clones. This opens a new window for treating a tumor as a system in which clones are evolving. Future cancer systems biology studies should consider a tumor as an evolving system with multiple clones. Therefore, topics discussed in Part 2 of this review include evolutionary dynamics of clonal networks, early-warning signals (e.g., genome duplication events) for formation of fast-growing clones, dissecting tumor heterogeneity, and modeling of clone-clone-stroma interactions for drug resistance. The ultimate goal of the future systems biology analysis is to obtain a 'whole-system' understanding of a tumor and therefore provides a more efficient and personalized management strategies for cancer patients.

Identification of lung-cancer-related genes with the shortest path approach in a protein-protein interaction network

Lung cancer is one of the leading causes of cancer mortality worldwide. The main types of lung cancer are small cell lung cancer (SCLC) and nonsmall cell lung cancer (NSCLC). In this work, a computational method was proposed for identifying lung-cancer-related genes with a shortest path approach in a protein-protein interaction (PPI) network. Based on the PPI data from STRING, a weighted PPI network was constructed. 54 NSCLC- and 84 SCLC-related genes were retrieved from associated KEGG pathways. Then the shortest paths between each pair of these 54 NSCLC genes and 84 SCLC genes were obtained with Dijkstra's algorithm. Finally, all the genes on the shortest paths were extracted, and 25 and 38 shortest genes with a permutation P value less than 0.05 for NSCLC and SCLC were selected for further analysis. Some of the shortest path genes have been reported to be related to lung cancer. Intriguingly, the candidate genes we identified from the PPI network contained more cancer genes than those identified from the gene expression profiles. Furthermore, these genes possessed more functional similarity with the known cancer genes than those identified from the gene expression profiles. This study proved the efficiency of the proposed method and showed promising results.

Identification of ovarian cancer associated genes using an integrated approach in a Boolean framework

Background

Cancer is a complex disease where molecular mechanism remains elusive. A systems approach is needed to integrate diverse biological information for the prognosis and therapy risk assessment using mechanistic approach to understand gene interactions in pathways and networks and functional attributes to unravel the biological behaviour of tumors.

Results

We weighted the functional attributes based on various functional properties observed between cancerous and non-cancerous genes reported from literature. This weighing schema was then encoded in a Boolean logic framework to rank differentially expressed genes. We have identified 17 genes to be differentially expressed from a total of 11,173 genes, where ten genes are reported to be down-regulated via epigenetic inactivation and seven genes are up-regulated. Here, we report that the overexpressed genes IRAK1, CHEK1 and BUB1 may play an important role in ovarian cancer. We also show that these 17 genes can be used to form an ovarian cancer signature, to distinguish normal from ovarian cancer subjects and that the set of three genes, CHEK1, AR, and LYN, can be used to classify good and poor prognostic tumors.

Conclusion

We provided a workflow using a Boolean logic schema for the identification of differentially expressed genes by integrating diverse biological information. This integrated approach resulted in the identification of genes as potential biomarkers in ovarian cancer.

CRNDE: A Long Non-Coding RNA Involved in CanceR, Neurobiology, and DEvelopment

CRNDE is the gene symbol for Colorectal Neoplasia Differentially Expressed (non-protein-coding), a long non-coding RNA (lncRNA) gene that expresses multiple splice variants and displays a very tissue-specific pattern of expression. CRNDE was initially identified as a lncRNA whose expression is highly elevated in colorectal cancer, but it is also upregulated in many other solid tumors and in leukemias. Indeed, CRNDE is the most upregulated lncRNA in gliomas and here, as in other cancers, it is associated with a "stemness" signature. CRNDE is expressed in specific regions within the human and mouse brain; the mouse ortholog is high in induced pluripotent stem cells and increases further during neuronal differentiation. We suggest that CRNDE is a multifunctional lncRNA whose different splice forms provide specific functional scaffolds for regulatory complexes, such as the polycomb repressive complex 2 (PRC2) and CoREST chromatin-modifying complexes, which CRNDE helps pilot to target genes.

Colorectal Neoplasia Differentially Expressed (CRNDE), a Novel Gene with Elevated Expression in Colorectal Adenomas and Adenocarcinomas

An uncharacterized gene locus (Chr16:hCG_1815491), now named colorectal neoplasia differentially expressed (gene symbol CRNDE), is activated early in colorectal neoplasia. The locus is unrelated to any known protein-coding gene. Microarray analysis of 454 tissue specimens (discovery) and 68 previously untested specimens (validation) showed elevated expression of CRNDE in >90% of colorectal adenomas and adenocarcinomas. These findings were confirmed and extended by exon microarray studies and RT-PCR assays. CRNDE transcription start sites were identified in CaCo2 and HCT116 cells by 5'-RACE. The major transcript isoforms in colorectal cancer (CRC) cell lines and colorectal tissue are CRNDE-a, -b, -d, -e, -f, -h, and -j. Except for CRNDE-d, the known CRNDE splice variants are upregulated in neoplastic colorectal tissue; expression levels for CRNDE-h alone demonstrate a sensitivity of 95% and specificity of 96% for adenoma versus normal tissue. A quantitative RT-PCR assay measuring CRNDE-h RNA levels in plasma was (with a threshold of 2(-ΔCt) = 2.8) positive for 13 of 15 CRC patients (87%) but only 1 of 15 healthy individuals (7%). We conclude that individual CRNDE transcripts show promise as tissue and plasma biomarkers, potentially exhibiting high sensitivity and specificity for colorectal adenomas and cancers.

Beyond differential expression: the quest for causal mutations and effector molecules

High throughput gene expression technologies are a popular choice for researchers seeking molecular or systems-level explanations of biological phenomena. Nevertheless, there has been a groundswell of opinion that these approaches have not lived up to the hype because the interpretation of the data has lagged behind its generation. In our view a major problem has been an over-reliance on isolated lists of differentially expressed (DE) genes which – by simply comparing genes to themselves – have the pitfall of taking molecular information out of context. Numerous scientists have emphasised the need for better context. This can be achieved through holistic measurements of differential connectivity in addition to, or in replacement, of DE. However, many scientists continue to use isolated lists of DE genes as the major source of input data for common readily available analytical tools. Focussing this opinion article on our own research in skeletal muscle, we outline our resolutions to these problems – particularly a universally powerful way of quantifying differential connectivity. With a well designed experiment, it is now possible to use gene expression to identify causal mutations and the other major effector molecules with whom they cooperate, irrespective of whether they themselves are DE. We explain why, for various reasons, no other currently available experimental techniques or quantitative analyses are capable of reaching these conclusions.

Identification of colorectal cancer related genes with mRMR and shortest path in protein-protein interaction network

One of the most important and challenging problems in biomedicine and genomics is how to identify the disease genes. In this study, we developed a computational method to identify colorectal cancer-related genes based on (i) the gene expression profiles, and (ii) the shortest path analysis of functional protein association networks. The former has been used to select differentially expressed genes as disease genes for quite a long time, while the latter has been widely used to study the mechanism of diseases. With the existing protein-protein interaction data from STRING (Search Tool for the Retrieval of Interacting Genes), a weighted functional protein association network was constructed. By means of the mRMR (Maximum Relevance Minimum Redundancy) approach, six genes were identified that can distinguish the colorectal tumors and normal adjacent colonic tissues from their gene expression profiles. Meanwhile, according to the shortest path approach, we further found an additional 35 genes, of which some have been reported to be relevant to colorectal cancer and some are very likely to be relevant to it. Interestingly, the genes we identified from both the gene expression profiles and the functional protein association network have more cancer genes than the genes identified from the gene expression profiles alone. Besides, these genes also had greater functional similarity with the reported colorectal cancer genes than the genes identified from the gene expression profiles alone. All these indicate that our method as presented in this paper is quite promising. The method may become a useful tool, or at least plays a complementary role to the existing method, for identifying colorectal cancer genes. It has not escaped our notice that the method can be applied to identify the genes of other diseases as well.

Dysregulation of CDK8 and Cyclin C in tumorigenesis

Appropriately controlled gene expression is fundamental for normal growth and survival of all living organisms. In eukaryotes, the transcription of protein-coding mRNAs is dependent on RNA polymerase II (Pol II). The multi-subunit transcription cofactor Mediator complex is proposed to regulate most, if not all, of the Pol II-dependent transcription. Here we focus our discussion on two subunits of the Mediator complex, cyclin-dependent kinase 8 (CDK8) and its regulatory partner Cyclin C (CycC), because they are either mutated or amplified in a variety of human cancers. CDK8 functions as an oncoprotein in melanoma and colorectal cancers, thus there are considerable interests in developing drugs specifically targeting the CDK8 kinase activity. However, to evaluate the feasibility of targeting CDK8 for cancer therapy and to understand how their dysregulation contributes to tumorigenesis, it is essential to elucidate the in vivo function and regulation of CDK8-CycC, which are still poorly understood in multi-cellular organisms. We summarize the evidence linking their dysregulation to various cancers and present our bioinformatics and computational analyses on the structure and evolution of CDK8. We also discuss the implications of these observations in tumorigenesis. Because most of the Mediator subunits, including CDK8 and CycC, are highly conserved during eukaryotic evolution, we expect that investigations using model organisms such as Drosophila will provide important insights into the function and regulation of CDK8 and CycC in different cellular and developmental contexts.