Showing papers by "Sushmita Paul published in 2019"

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

Abstract: 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.

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

Abstract: 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

Abstract: 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 $\mu$ Sim. 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.

Importance of Feature Weighing in Cervical Cancer Subtypes Identification

Abstract: Cancer subtypes identification is very important for the advancement of precision cancer disease diagnosis and therapy. It is one of the important components of the personalized medicine framework. Cervical cancer (CC) is one of the leading gynecological cancers that causes deaths in women worldwide. However, there is a lack of studies to identify histological subtypes among the patients suffering from tumor of the uterine cervix. Hence, sub-typing of cancer can help in analyzing shared molecular profiles between different histological subtypes of solid tumors of uterine cervix. With the advancement in technology, large scale multi-omics data are generated. The integration of genomics data generated from different platforms helps in capturing complementary information about the patients. Several computational approaches have been developed that integrate muti-omics data for cancer sub-typing. In this study, mRNA (messenger RNA) and miRNA (microRNA) expression data are integrated to identify the histological subtypes of CC. In this regard, a method is proposed that ranks the biomarkers (mRNA and miRNA) on the basis of their varying expression across the samples. The ranking method generates a weight for every biomarker which is further used to identify the similarity between the samples. A well-known approach named Similarity Network Fusion (SNF) is then applied, followed by Spectral clustering, to identify groups of related samples. This study focuses on the role of weighing the biomarkers prior to their integration and application of the clustering algorithm. The weighing method proposed in this study is compared with some other methods and proved to be more efficient. The proposed method helps in identifying histological subtypes of CC and can also be applied to other types of cancer data where histological subtypes play a key role in designing treatments and therapies.

Density-Based Clustering of Functionally Similar Genes Using Biological Knowledge

Abstract: Clustering is used to identify natural groups present in the data. It has been applied widely for analyzing gene expression data to discover gene clusters that might be involved in same biological processes. This information is very important for analyzing data of fatal diseases like cancers and identifying potential diagnostic and prognostic markers. Existing clustering methods used in this regard are computationally efficient, but do not always produce biologically meaningful results. Additionally, they have one or the other shortcomings; either they are not able to deal with arbitrary-shaped clusters, require number of clusters to be specified previously or are not efficient in dealing with noise present in biological data. In this study, a new density-based clustering method specific for gene expression data is introduced that overcomes the above shortcomings and produces biologically enriched clusters of functionally similar genes by incorporating biological information from Gene Ontology (GO). The proposed method integrates the GO semantic similarity information and the correlation information between the genes for obtaining clusters. The clusters are further validated for their biological relevance using Disease Ontology, KEGG Pathway enrichment and protein-protein interaction network analysis.

Identification of Four miRNAs by Analysing Multi-View miRNA-seq Data of Stomach Cancer

Abstract: Stomach cancer continues to be one of the most common cancer types in the world. Poor prognosis and late detection are major challenges in the diagnosis and treatment of this cancer. Currently, Next-Generation Sequencing (NGS) of genome has revolutionized stomach cancer research by providing multi-view data which can be used for better understanding of the underlying molecular mechanism of this disease. Micro-RNA (miRNA) sequencing data is one such high resolution expression data. MiRNAs are evolutionary conserved, small, non-coding RNAs that play a role in post-transcriptional regulation of gene expression. They are proven to show abnormal expression for a specific biological condition like tumor or age or survival of stomach cancer which makes them potential biomarkers for this cancer type. MiRNA data analysis comes with a challenge of less number of samples as compared to the number of miRNAs. Finding a small set of miRNAs is needed to identify potential biomarkers. In this regard, here, a method for selecting a small subset of miRNAs from the entire miRNA expression data has been proposed that selects miRNAs, common to three different categories of clinical outcomes, viz. condition, age, and survival status. First, three feature selection methods have been used to rank miRNAs individually for different categories. These ranks are then used to compute an ensemble of ranks of each miRNA using adaptive weight method for each category. Second, the top 100 miRNAs from each category have been used to find the miRNAs that are common to all categories. As a result, four miRNAs are found which are validated using classification of subclass under each category, miRNA-Gene-TF network, PPI network, expression analysis using box plots, KEGG pathway and GO enrichment analysis.