ATF2 loss promotes tumor invasion in colorectal cancer cells via upregulation of cancer driver TROP2.

In cancer, the activating transcription factor 2 (ATF2) has pleiotropic functions in cellular responses to growth stimuli, damage, or inflammation. Due to only limited studies, the significance of ATF2 in colorectal cancer (CRC) is not well understood. We report that low ATF2 levels correlated with worse prognosis and tumor aggressiveness in CRC patients. NanoString gene expression and ChIP analysis confirmed trophoblast cell surface antigen 2 (TROP2) as a novel inhibitory ATF2 target gene. This inverse correlation was further observed in primary human tumor tissues. Immunostainings revealed that high intratumoral heterogeneity for ATF2 and TROP2 expression was sustained also in liver metastasis. Mechanistically, our in vitro data of CRISPR/Cas9-generated ATF2 knockout (KO) clones revealed that high TROP2 levels were critical for cell de-adhesion and increased cell migration without triggering EMT. TROP2 was enriched in filopodia and displaced Paxillin from adherens junctions. In vivo imaging, micro-computer tomography, and immunostainings verified that an ATF2KO/TROP2high status triggered tumor invasiveness in in vivo mouse and chicken xenograft models. In silico analysis provided direct support that ATF2low/TROP2high expression status defined high-risk CRC patients. Finally, our data demonstrate that ATF2 acts as a tumor suppressor by inhibiting the cancer driver TROP2. Therapeutic TROP2 targeting might prevent particularly the first steps in metastasis, i.e., the de-adhesion and invasion of colon cancer cells.

Mycobacterial Cord Factor Reprograms the Macrophage Response to IFN-γ towards Enhanced Inflammation yet Impaired Antigen Presentation and Expression of GBP1.

Mycobacteria survive in macrophages despite triggering pattern recognition receptors and T cell-derived IFN-γ production. Mycobacterial cord factor trehalose-6,6-dimycolate (TDM) binds the C-type lectin receptor MINCLE and induces inflammatory gene expression. However, the impact of TDM on IFN-γ-induced macrophage activation is not known. In this study, we have investigated the cross-regulation of the mouse macrophage transcriptome by IFN-γ and by TDM or its synthetic analogue trehalose-6,6-dibehenate (TDB). As expected, IFN-γ induced genes involved in Ag presentation and antimicrobial defense. Transcriptional programs induced by TDM and TDB were highly similar but clearly distinct from the response to IFN-γ. The glycolipids enhanced expression of a subset of IFN-γ-induced genes associated with inflammation. In contrast, TDM/TDB exerted delayed inhibition of IFN-γ-induced genes, including pattern recognition receptors, MHC class II genes, and IFN-γ-induced GTPases, with antimicrobial function. TDM downregulated MHC class II cell surface expression and impaired T cell activation by peptide-pulsed macrophages. Inhibition of the IFN-γ-induced GTPase GBP1 occurred at the level of transcription by a partially MINCLE-dependent mechanism that may target IRF1 activity. Although activation of STAT1 was unaltered, deletion of Socs1 relieved inhibition of GBP1 expression by TDM. Nonnuclear Socs1 was sufficient for inhibition, suggesting a noncanonical, cytoplasmic mechanism. Taken together, unbiased analysis of transcriptional reprogramming revealed a significant degree of negative regulation of IFN-γ-induced Ag presentation and antimicrobial gene expression by the mycobacterial cord factor that may contribute to mycobacterial persistence.

The AP-1 Transcription Factor c-Jun Promotes Arthritis by Regulating Cyclooxygenase-2 and Arginase-1 Expression in Macrophages.

Activation of proinflammatory macrophages is associated with the inflammatory state of rheumatoid arthritis. Their polarization and activation are controlled by transcription factors such as NF-κB and the AP-1 transcription factor member c-Fos. Surprisingly, little is known about the role of the AP-1 transcription factor c-Jun in macrophage activation. In this study, we show that mRNA and protein levels of c-Jun are increased in macrophages following pro- or anti-inflammatory stimulations. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment cluster analyses of microarray data using wild-type and c-Jun-deleted macrophages highlight the central function of c-Jun in macrophages, in particular for immune responses, IL production, and hypoxia pathways. Mice deficient for c-Jun in macrophages show an amelioration of inflammation and bone destruction in the serum-induced arthritis model. In vivo and in vitro gene profiling, together with chromatin immunoprecipitation analysis of macrophages, revealed direct activation of the proinflammatory factor cyclooxygenase-2 and indirect inhibition of the anti-inflammatory factor arginase-1 by c-Jun. Thus, c-Jun regulates the activation state of macrophages and promotes arthritis via differentially regulating cyclooxygenase-2 and arginase-1 levels.

Genome-wide analysis of multi-view data of miRNA-seq to identify miRNA biomarkers for stomach cancer.

Stomach cancer is one of the leading causes of cancer-related deaths worldwide. More than 80% diagnosis of this cancer occur at later stages leading to low 5-year survival rate. This emphasizes the need to have better prognostic techniques for stomach cancer. In this regard, the Next-Generation Sequencing of whole genome and multi-view approach to omics may reveal the underlying molecular complexity of stomach cancer using high throughput expression data of miRNA. Generally, miRNAs are small, non-coding RNAs, which cause downregulation of target mRNAs. They also show differential expression for a specific biological condition like stage or histological type of stomach cancer, highlighting their importance as potential biomarkers. Analyzing miRNA expression data is a challenging task due to the existence of large number of miRNAs and less sample size. A small set of miRNAs will be helpful in designing efficient diagnostic and prognostic tool. In this regard, here a computational framework is proposed that selects different sets of miRNAs for five different categories of clinical outcomes viz. condition, clinical stage, age, histological type, and survival status. First, the miRNAs are ranked using four feature ranking methods. These ranks are used to find an ensemble rank based on adaptive weight. Second, the top 100 miRNAs from each category are used to find the miRNAs that are common to all categories as well as miRNAs that belong to only one category. Finally, the results have been validated quantitatively and through biological significance analysis.

µHEM for identification of differentially expressed miRNAs using hypercuboid equivalence partition matrix.

BACKGROUND: The miRNAs, a class of short approximately 22-nucleotide non-coding RNAs, often act post-transcriptionally to inhibit mRNA expression. In effect, they control gene expression by targeting mRNA. They also help in carrying out normal functioning of a cell as they play an important role in various cellular processes. However, dysregulation of miRNAs is found to be a major cause of a disease. It has been demonstrated that miRNA expression is altered in many human cancers, suggesting that they may play an important role as disease biomarkers. Multiple reports have also noted the utility of miRNAs for the diagnosis of cancer. Among the large number of miRNAs present in a microarray data, a modest number might be sufficient to classify human cancers. Hence, the identification of differentially expressed miRNAs is an important problem particularly for the data sets with large number of miRNAs and small number of samples. RESULTS: In this regard, a new miRNA selection algorithm, called µHEM, is presented based on rough hypercuboid approach. It selects a set of miRNAs from a microarray data by maximizing both relevance and significance of the selected miRNAs. The degree of dependency of sample categories on miRNAs is defined, based on the concept of hypercuboid equivalence partition matrix, to measure both relevance and significance of miRNAs. The effectiveness of the new approach is demonstrated on six publicly available miRNA expression data sets using support vector machine. The.632+ bootstrap error estimate is used to minimize the variability and biasedness of the derived results. CONCLUSIONS: An important finding is that the µHEM algorithm achieves lowest B.632+ error rate of support vector machine with a reduced set of differentially expressed miRNAs on four expression data sets compare to some existing machine learning and statistical methods, while for other two data sets, the error rate of the µHEM algorithm is comparable with the existing techniques. The results on several microarray data sets demonstrate that the proposed method can bring a remarkable improvement on miRNA selection problem. The method is a potentially useful tool for exploration of miRNA expression data and identification of differentially expressed miRNAs worth further investigation.

Capturing the latent space of an Autoencoder for multi-omics integration and cancer subtyping.

BACKGROUND AND OBJECTIVE: The motivation behind cancer subtyping is to identify subgroups of cancer patients with distinguishable phenotypes of clinical importance. It can assist in advancement of subtype-targeted based treatments. Subtype identification is a complicated task, therefore requires multi-omics data integration to identify the precise patients' subgroup. Over the years, several computational attempts have been made to identify the cancer subtypes accurately using integrative multi-omics analysis. Some studies have used Autoencoders (AE) to capture multi-omics feature integration in lower dimensions for identifying subtypes in specific types of cancer. However, capturing the highly informative latent space by learning the deep architectures of AE to attain a satisfactory generalized performance is required. Therefore, in this study, a novel AE-assisted cancer subtyping framework is presented that utilizes the compressed latent space of a Sparse AE neural network for multi-omics clustering. METHODS: The proposed framework first performs a supervised feature selection based on the survival status of the patients. The selected features from each of the omic data are passed to the AE. The information embedded in the latent space of the trained AE neural networks are then used for cancer subtyping using Spectral clustering. The AE architecture designed in this study exhaustively searches the best compression for multi-omics data by varying the number of neurons in the hidden layers and penalizing activations within the layers. RESULTS AND CONCLUSION: The proposed framework is applied to five different multi-omics cancer datasets taken from The Cancer Genome Atlas. It is observed that for getting a robust information bottleneck, a compression of 10-20% of the input features along with an L1 regularization penalty of 0.01 or 0.001 performs well for most of the cancer datasets. Clustering performed on this latent representation generates clusters with better silhouette scores and significantly varying survival patterns. For further biological assessment, differential expression analysis is performed between the identified subtypes of Glioblastoma multiforme (GBM), followed by enrichment analysis of the differentially expressed biomarkers. Several pathways and disease ontology terms coherent to GBM are found to be significantly associated. Varying responses of the identified GBM subtypes towards the drug Temozolomide is also tested to demonstrate its clinical importance. Hence, the study shows that AE-assisted multi-omics integration can be used for the prediction of clinically significant cancer subtypes.

A Feature Weighting-Assisted Approach for Cancer Subtypes Identification From Paired Expression Profiles.

Identification of cancer subtypes is critically important for understanding the heterogeneity present in tumors. Projects like The Cancer Genome Atlas (TCGA), have made available the data-sets containing expression profiles of multiple types of biomarkers across the same set of samples. Availability of these types of data-sets help in capturing heterogeneity underlying, complex biological processes and phenotypes. Further, by integrating information from multiple sources, homogeneous groups for cancer can be identified. However, there is a lack of computational approaches to identify histological subtypes among the patients suffering from different types of cancers. Assigning weight to the biomarkers prior to the integration of multiple information sources for the same set of samples can play an important role in cancer subtypes identification, which has not been explored previously. Sub-typing of cancers can help in analyzing shared molecular profiles between different histological subtypes of solid tumors. This can further help in designing appropriate therapies and treatments. A novel method for feature weighting based on robust regression fit is developed in this study. This method assigns a weight to every biomarker on the basis of variability present across the samples. Later, this weight is utilized to find similarity between patients individually from each of the information sources. In this study, the two information sources that have been utilized are miRNA and mRNA expression profiles across the same set of samples. Patient-similarity networks, that are generated from each of the expression profiles are then integrated using the approach of Similarity Network Fusion. Finally, Spectral clustering is applied on the fused network to identify similar groups of patients that represent a cancer subtype. To establish the efficiency of the proposed approach, it has been applied to three types of cancer data-sets and is also compared with the other existing methods.

Recursive integration of synergised graph representations of multi-omics data for cancer subtypes identification.

Cancer subtypes identification is one of the critical steps toward advancing personalized anti-cancerous therapies. Accumulation of a massive amount of multi-platform omics data measured across the same set of samples provides an opportunity to look into this deadly disease from several views simultaneously. Few integrative clustering approaches are developed to capture shared information from all the views to identify cancer subtypes. However, they have certain limitations. The challenge here is identifying the most relevant feature space from each omic view and systematically integrating them. Both the steps should lead toward a global clustering solution with biological significance. In this respect, a novel multi-omics clustering algorithm named RISynG (Recursive Integration of Synergised Graph-representations) is presented in this study. RISynG represents each omic view as two representation matrices that are Gramian and Laplacian. A parameterised combination function is defined to obtain a synergy matrix from these representation matrices. Then a recursive multi-kernel approach is applied to integrate the most relevant, shared, and complementary information captured via the respective synergy matrices. At last, clustering is applied to the integrated subspace. RISynG is benchmarked on five multi-omics cancer datasets taken from The Cancer Genome Atlas. The experimental results demonstrate RISynG's efficiency over the other approaches in this domain.

A review on methods for predicting miRNA-mRNA regulatory modules.

Identification of complex interactions between miRNAs and mRNAs in a regulatory network helps better understand the underlying biological processes. Previously, identification of these interactions was based on sequence-based predicted target binding information. With the advancement in high-throughput omics technologies, miRNA and mRNA expression for the same set of samples are available. This helps develop more efficient and flexible approaches that work by integrating miRNA and mRNA expression profiles with target binding information. Since these integrative approaches of miRNA-mRNA regulatory modules (MRMs) detection is sufficiently able to capture the minute biological details, 26 such algorithms/methods/tools for MRMs identification are comprehensively reviewed in this article. The study covers the significant features underlying every method. Therefore, the methods are classified into eight groups based on mathematical approaches to understand their working and suitability for one's study. An algorithm could be selected based on the available information with the users and the biological question under investigation.

CorGO: An Integrated Method for Clustering Functionally Similar Genes.

Identification of groups of co-expressed or co-regulated genes is critical for exploring the underlying mechanism behind a particular disease like cancer. Condition-specific (disease-specific) gene-expression profiles acquired from different platforms are widely utilized by researchers to get insight into the regulatory mechanism of the disease. Several clustering algorithms are developed using gene expression profiles to identify the group of similar genes. These algorithms are computationally efficient but are not able to capture the functional similarity present between the genes, which is very important from a biological perspective. In this study, an algorithm named CorGO is introduced, that specifically deals with the identification of functionally similar gene-clusters. Two types of relationships are calculated for this purpose. Firstly, the Correlation (Cor) between the genes are captured from the gene-expression data, which helps in deciphering the relationship between genes based on its expression across several diseased samples. Secondly, Gene Ontology (GO)-based semantic similarity information available for the genes is utilized, that helps in adding up biological relevance to the identified gene-clusters. A similarity measure is defined by integrating these two components that help in the identification of homogeneous and functionally similar groups of genes. CorGO is applied to four different types of gene expression profiles of different types of cancer. Gene-clusters identified by CorGO, are further validated by pathway enrichment, disease enrichment, and network analysis. These biological analyses demonstrated significant connectivity and functional relatedness within the genes of the same cluster. A comparative study with commonly used clustering algorithms is also performed to show the efficacy of the proposed method.

Selenium to selenoproteins - role in COVID-19.

The disruption of antioxidant defense has been demonstrated in severe acute respiratory syndrome due to SARS-CoV infection. Selenium plays a major role in decreasing the ROS produced in response to various viral infections. Selenoprotein enzymes are essential in combating oxidative stress caused due to excessive generation of ROS. Selenium also has a role in inhibiting the activation of NF-κB, thus alleviating inflammation. In viral infections, selenoproteins have also been found to inhibit type I interferon responses, modulate T cell proliferation and oxidative burst in macrophages, and inhibit viral transcriptional activators. Potential virally encoded selenoproteins have been identified by computational analysis in different viral genomes like HIV-1, Japanese encephalitis virus (JEV), and hepatitis C virus. This review discusses the role and the possible mechanisms of selenium, selenoproteins, and virally encoded selenoproteins in the pathogenicity of viral infections. Identification of potential selenoproteins in the COVID 19 genome by computational tools will give insights further into their role in the pathogenesis of viral infections.

RFCM3: Computational Method for Identification of miRNA-mRNA Regulatory Modules in Cervical Cancer.

Cervical cancer is a leading severe malignancy throughout the world. Molecular processes and biomarkers leading to tumor progression in cervical cancer are either unknown or only partially understood. An increasing number of studies have shown that microRNAs play an important role in tumorigenesis so understanding the regulatory mechanism of miRNAs in gene-regulatory network will help elucidate the complex biological processes that occur during malignancy. Functional genomics data provides opportunities to study the aberrant microRNA-messenger RNA (miRNA-mRNA) interaction. Identification of miRNA-mRNA regulatory modules will aid deciphering aberrant transcriptional regulatory network in cervical cancer but is computationally challenging. In this regard, an algorithm, termed as relevant and functionally consistent miRNA-mRNA modules (RFCM3), is proposed. It integrates miRNA and mRNA expression data of cervical cancer for identification of potential miRNA-mRNA modules. It selects set of miRNA-mRNA modules by maximizing relation of mRNAs with miRNA and functional similarity between selected mRNAs. Later, using the knowledge of the miRNA-miRNA synergistic network different modules are fused and finally a set of modules are generated containing several miRNAs as well as mRNAs. This type of module explains the underlying biological pathways containing multiple miRNAs and mRNAs. The effectiveness of the proposed approach over other existing methods has been demonstrated on a miRNA and mRNA expression data of cervical cancer with respect to enrichment analyses and other standard metrices. The prognostic value of the genes in a module with respect to cervical cancer is also demonstrated. The approach was found to generate more robust, integrated, and functionally enriched miRNA-mRNA modules in cervical cancer.

EMT transcription factor ZEB1 alters the epigenetic landscape of colorectal cancer cells.

Epigenetic deregulation remarkably triggers mechanisms associated with tumor aggressiveness like epithelial-mesenchymal transition (EMT). Since EMT is a highly complex, but also reversible event, epigenetic processes such as DNA methylation or chromatin alterations must be involved in its regulation. It was recently described that loss of the cell cycle regulator p21 was associated with a gain in EMT characteristics and an upregulation of the master EMT transcription factor ZEB1. In this study, in silico analysis was performed in combination with different in vitro and in vivo techniques to identify and verify novel epigenetic targets of ZEB1, and to proof the direct transcriptional regulation of SETD1B by ZEB1. The chorioallantoic-membrane assay served as an in vivo model to analyze the ZEB1/SETD1B interaction. Bioinformatical analysis of CRC patient data was used to examine the ZEB1/SETD1B network under clinical conditions and the ZEB1/SETD1B network was modeled under physiological and pathological conditions. Thus, we identified a self-reinforcing loop for ZEB1 expression and found that the SETD1B associated active chromatin mark H3K4me3 was enriched at the ZEB1 promoter in EMT cells. Moreover, clinical evaluation of CRC patient data showed that the simultaneous high expression of ZEB1 and SETD1B was correlated with the worst prognosis. Here we report that the expression of chromatin modifiers is remarkably dysregulated in EMT cells. SETD1B was identified as a new ZEB1 target in vitro and in vivo. Our study demonstrates a novel example of an activator role of ZEB1 for the epigenetic landscape in colorectal tumor cells.

Integration of miRNA and mRNA Expression Data for Understanding Etiology of Gynecologic Cancers.

Dysregulation of miRNA-mRNA regulatory networks is very common phenomenon in any diseases including cancer. Altered expression of biomarkers leads to these gynecologic cancers. Therefore, understanding the underlying biological mechanisms may help in developing a robust diagnostic as well as a prognostic tool. It has been demonstrated in various studies that the pathways associated with gynecologic cancer have dysregulated miRNA as well as mRNA expression. Identification of miRNA-mRNA regulatory modules may help in understanding the mechanism of altered gynecologic cancer pathways. In this regard, an existing robust mutual information-based Maximum-Relevance Maximum-Significance algorithm has been used for identification of miRNA-mRNA regulatory modules in gynecologic cancer. A set of miRNA-mRNA modules are identified first than their association with gynecologic cancer are studied exhaustively. The effectiveness of the proposed approach is compared with the existing methods. The proposed approach is found to generate more robust integrated networks of miRNA-mRNA in gynecologic cancer.

An Integrated Approach for Identification of Functionally Similar MicroRNAs in Colorectal Cancer.

Colorectal cancer (CRC) is one of the most prevalent cancers around the globe. However, the molecular reasons for pathogenesis of CRC are still poorly understood. Recently, the role of microRNAs or miRNAs in the initiation and progression of CRC has been studied. MicroRNAs are small, endogenous noncoding RNAs found in plants, animals, and some viruses, which function in RNA silencing and posttranscriptional regulation of gene expression. Their role in CRC development is studied and they are found to be potential biomarkers in diagnosis and treatment of CRC. Therefore, identification of functionally similar CRC related miRNAs may help in the development of a prognostic tool. In this regard, this paper presents a new algorithm, called µSim. It is an integrative approach for identification of functionally similar miRNAs associated with CRC. It integrates judiciously the information of miRNA expression data and miRNA-miRNA functionally synergistic network data. The functional similarity is calculated based on both miRNA expression data and miRNA-miRNA functionally synergistic network data. The effectiveness of the proposed method in comparison to other related methods is shown on four CRC miRNA data sets. The proposed method selected more significant miRNAs related to CRC as compared to other related methods.

Differential Expression Profile of NLRs and AIM2 in Glioma and Implications for NLRP12 in Glioblastoma.

Gliomas are the most prevalent primary brain tumors with immense clinical heterogeneity, poor prognosis and survival. The nucleotide-binding domain, and leucine-rich repeat containing receptors (NLRs) and absent-in-melanoma 2 (AIM2) are innate immune receptors crucial for initiation and progression of several cancers. There is a dearth of reports linking NLRs and AIM2 to glioma pathology. NLRs are expressed by cells of innate immunity, including monocytes, macrophages, dendritic cells, endothelial cells, and neutrophils, as well as cells of the adaptive immune system. NLRs are critical regulators of major inflammation, cell death, immune and cancer-associated pathways. We used a data-driven approach to identify NLRs, AIM2 and NLR-associated gene expression and methylation patterns in low grade glioma and glioblastoma, using The Cancer Genome Atlas (TCGA) patient datasets. Since TCGA data is obtained from tumor tissue, comprising of multiple cell populations including glioma cells, endothelial cells and tumor-associated microglia/macrophages we have used multiple cell lines and human brain tissues to identify cell-specific effects. TCGA data mining showed significant differential NLR regulation and strong correlation with survival in different grades of glioma. We report differential expression and methylation of NLRs in glioma, followed by NLRP12 identification as a candidate prognostic marker for glioma progression. We found that Nlrp12 deficient microglia show increased colony formation while Nlrp12 deficient glioma cells show decreased cellular proliferation. Immunohistochemistry of human glioma tissue shows increased NLRP12 expression. Interestingly, microglia show reduced migration towards Nlrp12 deficient glioma cells.

Identification of miRNA-mRNA Modules in Colorectal Cancer Using Rough Hypercuboid Based Supervised Clustering.

Differences in the expression profiles of miRNAs and mRNAs have been reported in colorectal cancer. Nevertheless, information on important miRNA-mRNA regulatory modules in colorectal cancer is still lacking. In this regard, this study presents an application of the RH-SAC algorithm on miRNA and mRNA expression data for identification of potential miRNA-mRNA modules. First, a set of miRNA rules was generated using the RH-SAC algorithm. The mRNA targets of the selected miRNAs were identified using the miRTarBase database. Next, the expression values of target mRNAs were used to generate mRNA rules using the RH-SAC. Then all miRNA-mRNA rules have been integrated for generating networks. The RH-SAC algorithm unlike other existing methods selects a group of co-expressed miRNAs and mRNAs that are also differentially expressed. In total 17 miRNAs and 141 mRNAs were selected. The enrichment analysis of selected mRNAs revealed that our method selected mRNAs that are significantly associated with colorectal cancer. We identified novel miRNA/mRNA interactions in colorectal cancer. Through experiment, we could confirm that one of our discovered miRNAs, hsa-miR-93-5p, was significantly up-regulated in 75.8% CRC in comparison to their corresponding non-tumor samples. It could have the potential to examine colorectal cancer subtype specific unique miRNA/mRNA interactions.

Model-based genotype-phenotype mapping used to investigate gene signatures of immune sensitivity and resistance in melanoma micrometastasis.

In this paper, we combine kinetic modelling and patient gene expression data analysis to elucidate biological mechanisms by which melanoma becomes resistant to the immune system and to immunotherapy. To this end, we systematically perturbed the parameters in a kinetic model and performed a mathematical analysis of their impact, thereby obtaining signatures associated with the emergence of phenotypes of melanoma immune sensitivity and resistance. Our phenotypic signatures were compared with published clinical data on pretreatment tumor gene expression in patients subjected to immunotherapy against metastatic melanoma. To this end, the differentially expressed genes were annotated with standard gene ontology terms and aggregated into metagenes. Our method sheds light on putative mechanisms by which melanoma may develop immunoresistance. Precisely, our results and the clinical data point to the existence of a signature of intermediate expression levels for genes related to antigen presentation that constitutes an intriguing resistance mechanism, whereby micrometastases are able to minimize the combined anti-tumor activity of complementary responses mediated by cytotoxic T cells and natural killer cells, respectively. Finally, we computationally explored the efficacy of cytokines used as low-dose co-adjuvants for the therapeutic anticancer vaccine to overcome tumor immunoresistance.

Rough hypercuboid based supervised clustering of miRNAs.

The microRNAs are small, endogenous non-coding RNAs found in plants, animals, and some viruses, which function in RNA silencing and post-transcriptional regulation of gene expression. It is suggested by various genome-wide studies that a substantial fraction of miRNA genes is likely to form clusters. The coherent expression of the miRNA clusters can then be used to classify samples according to the clinical outcome. In this regard, a new clustering algorithm, termed as rough hypercuboid based supervised attribute clustering (RH-SAC), is proposed to find such groups of miRNAs. The proposed algorithm is based on the theory of rough set, which directly incorporates the information of sample categories into the miRNA clustering process, generating a supervised clustering algorithm for miRNAs. The effectiveness of the new approach is demonstrated on several publicly available miRNA expression data sets using support vector machine. The so-called B.632+ bootstrap error estimate is used to minimize the variability and biasedness of the derived results. The association of the miRNA clusters to various biological pathways is also shown by doing pathway enrichment analysis.

City block distance and rough-fuzzy clustering for identification of co-expressed microRNAs.

The microRNAs or miRNAs are short, endogenous RNAs having ability to regulate mRNA expression at the post-transcriptional level. Various studies have revealed that miRNAs tend to cluster on chromosomes. The members of a cluster that are in close proximity on chromosomes are highly likely to be processed as co-transcribed units. Therefore, a large proportion of miRNAs are co-expressed. Expression profiling of miRNAs generates a huge volume of data. Complicated networks of miRNA-mRNA interaction increase the challenges of comprehending and interpreting the resulting mass of data. In this regard, this paper presents a clustering algorithm in order to extract meaningful information from miRNA expression data. It judiciously integrates the merits of rough sets, fuzzy sets, the c-means algorithm, and the normalized range-normalized city block distance to discover co-expressed miRNA clusters. While the membership functions of fuzzy sets enable efficient handling of overlapping partitions in a noisy environment, the concept of lower and upper approximations of rough sets deals with uncertainty, vagueness, and incompleteness in cluster definition. The city block distance is used to compute the membership functions of fuzzy sets and to find initial partition of a data set, and therefore helps to handle minute differences between two miRNA expression profiles. The effectiveness of the proposed approach, along with a comparison with other related methods, is demonstrated for several miRNA expression data sets using different cluster validity indices. Moreover, the gene ontology is used to analyze the functional consistency and biological significance of generated miRNA clusters.