Multi-Factored Gene-Gene Proximity Measures Exploiting Biological Knowledge Extracted from Gene Ontology: Application in Gene Clustering

To describe the cellular functions of proteins and genes, a potential dynamic vocabulary is Gene Ontology (GO), which comprises of three sub-ontologies namely, Biological-process, Cellular-component, and Molecular-function. It has several applications in the field of bioinformatics like annotating/measuring gene-gene or protein-protein semantic similarity, identifying genes/proteins by their GO annotations for disease gene and target discovery, etc. To determine semantic similarity between genes, several semantic measures have been proposed in literature, which involve information content of GO-terms, GO tree structure, or the combination of both. But, most of the existing semantic similarity measures do not consider different topological and information theoretic aspects of GO-terms collectively. Inspired by this fact, in this article, we have first proposed three novel semantic similarity/distance measures for genes covering different aspects of GO-tree. These are further implanted in the frameworks of well-known multi-objective and single-objective based clustering algorithms to determine functionally similar genes. For comparative analysis, 10 popular existing GO based semantic similarity/distance measures and tools are also considered. Experimental results on Mouse genome, Yeast, and Human genome datasets evidently demonstrate the supremacy of multi-objective clustering algorithms in association with proposed multi-factored similarity/distance measures. Clustering outcomes are further validated by conducting some biological/statistical significance tests. Supplementary information is available at https://www.iitp.ac.in/sriparna/journals.html.

Novel symmetry-based gene-gene dissimilarity measures utilizing Gene Ontology: Application in gene clustering

In recent years DNA microarray technology, leading to the generation of high-volume biological data, has gained significant attention. To analyze this high volume gene-expression data, one such powerful tool is Clustering. For any clustering algorithm, its efficiency majorly depends upon the underlying similarity/dissimilarity measure. During the analysis of such data often there is a need to further explore the similarity of genes not only with respect to their expression values but also with respect to their functional annotations, which can be obtained from Gene Ontology (GO) databases. In the existing literature, several novel clustering and bi-clustering approaches were proposed to identify co-regulated genes from gene-expression datasets. Identifying co-regulated genes from gene expression data misses some important biological information about functionalities of genes, which is necessary to identify semantically related genes. In this paper, we have proposed sixteen different semantic gene-gene dissimilarity measures utilizing biological information of genes retrieved from a global biological database namely Gene Ontology (GO). Four proximity measures, viz. Euclidean, Cosine, point symmetry and line symmetry are utilized along with four different representations of gene-GO-term annotation vectors to develop total sixteen gene-gene dissimilarity measures. In order to illustrate the profitability of developed dissimilarity measures, some multi-objective as well as single-objective clustering algorithms are applied utilizing proposed measures to identify functionally similar genes from Mouse genome and Yeast datasets. Furthermore, we have compared the performance of our proposed sixteen dissimilarity measures with three existing state-of-the-art semantic similarity and distance measures.

Circular RNA Complement Factor H (CFH) Promotes Glioma Progression by Sponging miR-149 and Regulating AKT1

Background

Circular RNAs (circRNAs) are widely expressed in mammals and can regulate the development and progression of human tumors. has_circ_0015758 (circ-CFH) is an exon circRNA transcript from the GRCh37/hg19 fragment of chromosome 1 and is homologous to the protein-coding gene complement factor H (CFH). Currently, the function of circ-CFH in glioma remains unclear.

Material/Methods

In our study, circ-CFH, miR-149, and Akt1 mRNA expression levels were analyzed by qRT-PCR assays. To investigate the function of circ-CFH in cell proliferation, circ-CFH knockdown models were established by using circ-CFH siRNAs. Cell proliferation abilities were measured by CCK-8 and colony formation assays and in vivo experiments. In addition, the interaction between circ-CFH and miR-149 was assessed by luciferase reporter assays.

Results

Circ-CFH expression was significantly upregulated in glioma tissue and was correlated with tumor grade. Circ-CFH expression levels were also markedly higher in U251 and U373 glioma cell lines. Circ-CFH knockdown inhibited cell proliferation and colony formation abilities. Luciferase assays indicated that circ-CFH functions as a miR-149 sponge and inhibits its function in U251 and U373 cells. Subsequently, AKT1 was identified as a direct target of the circ-CFH/miR-149 axis.

Conclusions

Circ-CFH promotes glioma progression by sponging miR-149 and regulating the AKT1 signaling pathway. The circ-CFH/miR-149/AKT1 regulation axis may be a potential target for glioma therapy.

Deciphering unclassified tumors of non-small-cell lung cancer through radiomics

BACKGROUND

Tumors are highly heterogeneous at the phenotypic, physiologic, and genomic levels. They are categorized in terms of a differentiated appearance under a microscope. Non-small-cell lung cancer tumors are categorized into three main subgroups: adenocarcinoma, squamous cell carcinoma, and large cell carcinoma. In approximately 20% of pathology reports, they are returned unclassified or classified as not-otherwise-specified (NOS) owing to scant materials or poor tumor differentiation.

METHOD

We present a radiomic interrogation of molecular spatial variations to decode unclassified NOS tumor architecture quantitatively. Twelve spatial descriptors with various displacements and directions were extracted and profiled with respect to the subgroups. The profiled descriptors were used to decipher the NOS tumor morphologic clues from the imaging phenotype perspective. This profiler was built as an extended version of a conventional support-vector-machine classifier, wherein a genetic algorithm and correlation analysis were embedded to define the molecular signatures of poorly differentiated tumors using well-differentiated-tumor information.

RESULTS

Sixteen multi-class classifier models with an 81% average accuracy and descriptor subset size ranging from 12 to 144 were reported. The average area under the curve was 86.3% at a 95% confidence interval and a 0.03-0.08 standard error. Correlation analysis returned an unclassified NOS membership matrix with respect to the cell-architecture similarity score for the subgroups. The best model demonstrated 53% NOS reduction.

CONCLUSION

The membership matrix is expected to assist pathologists and oncologists in cases of unresectable tumors or scant biopsy materials for histological subtyping and cancer therapy.

Simultaneous Clustering and Feature Weighting Using Multiobjective Optimization for Identifying Functionally Similar miRNAs

MicroRNAs (miRNAs) are a type of RNAs, which are responsible for monitoring the gene expression values. Recent research asserts that miRNAs form some clustering on chromosomes. The miRNAs belonging to a particular cluster are highly similar in terms of their activity and they are termed as "coregulated" miRNAs. The current paper presents an approach that simultaneously performs two tasks: i) clustering of miRNAs into different categories based on some similarity measures ii) identification of proper weight values for different time points with respect to which expression values are available. In general, a large number of expression values are available for a given miRNA data set. All these values may not be suitable to be used equally to measure the similarity between two miRNAs. In the current study, the problem of proper selection of weight values for different time points and then determining the proper partitioning from the given miRNA data set utilizing the similarity computed using the new set of weight values is formulated as an optimization problem where several cluster validity indices are optimized as the goodness measures. To that end, a multiobjective differential evolution based optimization technique is utilized. The supremacy of the proposed technique is tested on three miRNA data sets in comparison to some recent approaches in terms of some popular performance measures like Silhouette index and DB-index. The observations are further supported by statistical and biological significance tests. Supplementary information is available at https://www.iitp.ac.in/~sriparna/journals.html.