Paul, Sushmita

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.

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.

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.

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. Identification of microRNA-messengerRNA (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 a miRNA-mRNA module by maximizing relation of mRNAs with miRNA and functional similarity between selected mRNAs. Later using the knowledge of 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 proposed approach was found to generate more robust, integrated, and functionally enriched miRNA-mRNA modules in cervical cancer.

One of the important problems in functional genomics is how to select the disease genes. In this regard, the paper presents a new gene selection algorithm, termed as RelSim, to identify disease genes. It integrates judiciously the information of gene expression profiles and protein-protein interaction networks. A new similarity measure is introduced to compute the functional similarity between two genes. It is based on the information of protein-protein interaction networks. The new similarity measure offers an efficient way to calculate the functional similarity between two genes. The proposed algorithm selects a set of genes as disease genes, considering both microarray and protein-protein interaction data, by maximizing the relevance and functional similarity of the selected genes. While gene expression profiles are used to identify differentially expressed genes, the protein-protein interaction networks help to compute the functional similarity among genes. The performance of the proposed algorithm, along with a comparison with other related methods, is demonstrated on several colon cancer data sets.

Clustering functionally similar genes helps in understanding the mechanism of a biological pathway. It also provides information of those genes whose biological importance is previously not known. Clustering of genes is highly dependent on the similarity or dissimilarity criterion. Usually, microarray gene expression data is used to cluster genes. However, a microarray data may contain noise that may lead to undesired results. Therefore, incorporating gene ontology information may improve the clustering solutions. In this regard, an integrated dissimilarity measure is introduced for grouping functionally similar genes. It is comprised of city block distance and gene ontology based semantic dissimilarity. While, the city block distance is used to compute distance between two gene expression vectors, gene ontology based semantic dissimilarity measure is used for incorporating biological knowledge. The importance of the integrated dissimilarity measure is shown by incorporating it in different c-means clustering algorithms including rough-fuzzy clustering algorithms. In this work it has been shown that incorporation of integrated dissimilarity measure increases the functional similarity of cluster of genes as compared to the methods that are based on either type of dissimilarity measure. It is also observed that the rough-fuzzy clustering algorithm performs better with the new dissimilarity measure compared to different c-means clustering algorithms.

Ovarian cancer is a fatal gynecologic cancer. Altered expression of biomarkers leads to this deadly cancer. 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 the pathways associated with ovarian cancer have dysregulated miRNA as well as mRNA expression. Identification of miRNA-mRNA regulatory modules may help in understanding the mechanism of altered ovarian 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 ovarian cancer. A set of miRNA-mRNA modules are identified first than their association with ovarian cancer are studied exhaustively. The effectiveness of the proposed approach is compared with existing methods. The proposed approach is found to generate more robust integrated networks of miRNA-mRNA in ovarian cancer.

Cluster analysis is a technique that divides a given data set into a set of clusters in such a way that two objects from the same cluster are as similar as possible and the objects from different clusters are as dissimilar as possible. In this regard, a hybrid unsupervised learning algorithm, termed as rough-fuzzy c-means, is presented in this chapter. It comprises a judicious integration of the principles of rough sets and fuzzy sets. While the concept of lower and upper approximations of rough sets deals with uncertainty, vagueness, and incompleteness in class definition, the membership function of fuzzy sets enables efficient handling of overlapping partitions. The concept of crisp lower approximation and fuzzy boundary of a class, introduced in rough-fuzzy c-means, enables efficient selection of cluster prototypes. The effectiveness of the rough-fuzzy clustering algorithm, along with a comparison with other clustering algorithms, is demonstrated on grouping functionally similar genes from microarray data, identification of co-expressed microRNAs, and segmentation of brain magnetic resonance images using standard validity indices.