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Investigating the gene expression networks that regulate cancer initiation and development is crucial but remains mostly incomplete. With the innovations in RNA-sequencing technologies and computational biology, long noncoding RNAs (lncRNAs) are being known and characterized at a speedy pace. Recent findings reveal that lncRNAs are involved in serial steps of cancer development. These lncRNAs act with DNA, RNA, protein molecules and/or their combos, acting as a vital regulator in chromatin organization, as well as transcriptional and posttranscriptional regulation. Their aberrant expression confers the neoplastic cell capacities for neoplasm initiation, growth, and metastasis. Here we emphasize their aberrant expression and performance in cancers. We found that Chromosome 11 consists of the highest number of lncRNA specific to different 14 cancers related to obesity. Among these cancers, breast cancer has the highest number of lncRNA associated with it. The interacting partners of the lncRNAs were analysed and domain specific interactions were studied. The results showed that the lncRNAs H19 and MALAT1 can act as potential biomarkers for different cancers. [ABSTRACT FROM AUTHOR]

MicroRNAs (miRNAs) negatively regulate mRNAs at post-transcriptional level and thus can regulate different biological processes. Here we analyze the sequence characteristics and length distribution of miRNA sequences of six monocot and six dicot plants. We observe a species specific nucleotide preference of miRNAs. Although the first position of 5' end of mature miRNAs is U rich, there exists a wide variation. While the GC% of monocot mature miRNAs in general is nearly equal to the AU%, the GC% of stress induced miRNAs is significantly higher than AU%. Thus, higher content of GC% can be used as a signature of stress induced miRNAs in monocot. The length distribution of mature miRNAs shows the highest peak at 21 nucleotides with some other minor peaks at 20, 22 and 24 nucleotides. The synthesis of some miRNAs has positional preference either to 5' or to 3' arm of their precursors, but they are different in monocot and dicot. [ABSTRACT FROM AUTHOR]

The identification of disease-causing genes is the first and most important step toward understanding the biological mechanisms underlying a disease. Microarray analysis is one such powerful method that is widely used to identify genes that are expressed differently in two or more conditions (disease vs. normal). Because of its large library of statistical R packages and user-friendly interface, the R programming language provides a platform for microarray analysis. In this chapter, we will go over how to identify disease-causing culprit genes from the raw microarray data, using various packages of R programming. The pipeline overviews the steps in microarray analysis, such as data pre-processing, normalization, and statistical analysis using visualization techniques such as heatmaps, box plots, and so on. To better understand the function of the altered genes, gene ontology and pathway analysis are performed., (© 2024. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)