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Simple Summary: Researchers are often interested in detecting whether there are any differences in gene expression levels between two types of cells. To do this, gene expression levels are measured, and specific computer programs are used to detect these differences. Historically, microarrays were used to measure gene expression, but they are now being supplanted by newer, more efficient technologies such as RNA sequencing. BioTEA allows users to perform the differential expression analysis of microarray-derived data easily, quickly and in a reproducible way. It combines all the steps needed to directly start from the gene expression levels and obtain a list of genes that are differentially expressed between the two cell types of interest. In this way, the large amount of publicly available microarray data can still be analyzed in the modern era. Differential expression analyses can be rather complex to run, but BioTEA makes them straightforward, so that even non-bioinformaticians can perform them with ease. BioTEA is free and open-source. Tens of thousands of gene expression data sets describing a variety of model organisms in many different pathophysiological conditions are currently stored in publicly available databases such as the Gene Expression Omnibus (GEO) and ArrayExpress (AE). As microarray technology is giving way to RNA-seq, it becomes strategic to develop high-level tools of analysis to preserve access to this huge amount of information through the most sophisticated methods of data preparation and processing developed over the years, while ensuring, at the same time, the reproducibility of the results. To meet this need, here we present bioTEA (biological Transcript Expression Analyzer), a novel software tool that combines ease of use with the versatility and power of an R/Bioconductor-based differential expression analysis, starting from raw data retrieval and preparation to gene annotation. BioTEA is an R-coded pipeline, wrapped in a Python-based command line interface and containerized with Docker technology. The user can choose among multiple options—including gene filtering, batch effect handling, sample pairing, statistical test type—to adapt the algorithm flow to the structure of the particular data set. All these options are saved in a single text file, which can be easily shared between different laboratories to deterministically reproduce the results. In addition, a detailed log file provides accurate information about each step of the analysis. Overall, these features make bioTEA an invaluable tool for both bioinformaticians and wet-lab biologists interested in transcriptomics. BioTEA is free and open-source. [ABSTRACT FROM AUTHOR]

Melanoma is a relatively rare disease worldwide; nevertheless, it has a great relevance in some countries, such as in Europe. In order to shed some light upon the transcriptional profile of skin melanoma, we compared the gene expression of six independent tumours (all progressed towards metastatic disease and with wild type BRAF) to the expression profile of non-dysplastic melanocytes (considered as a healthy control) in a pilot study. Paraffin-embedded samples were manually micro-dissected to obtain enriched samples, and then, RNA was extracted and analysed through a microarray-based approach. An exhaustive bioinformatics analysis was performed to identify differentially expressed transcripts between the two groups, as well as enriched functional terms. Overall, 50 up- and 19 downregulated transcripts were found to be significantly changed in the tumour compared to the control tissue. Among the upregulated transcripts, the majority belonged to the immune response group and to the proteasome, while most of the downregulated genes were related to cytosolic ribosomes. A Gene Set Enrichment Analysis (GSEA), along with the RNA-Seq data retrieved from the TCGA/GTEx databases, confirmed the general trend of downregulation affecting cytoribosome proteins. In contrast, transcripts coding for mitoribosome proteins showed the opposite trend. [ABSTRACT FROM AUTHOR]

Simple Summary: In this study, we analyzed a cohort of six colorectal cancer patients harboring KRAS mutations and with wild-type BRAF from a transcriptional perspective, with the aim of elucidating the role of the stromal cells in tumor progression. Specifically, paraffin-embedded specimens were subjected to microdissection and hybridized on Agilent-026652 microarrays to compare the gene expression of tumor samples composed of neoplastic epithelial samples against the neighboring stromal tissue. A paired rank-product test led to the detection of 193 differentially expressed genes. Subsequent functional enrichment analysis pointed to extracellular matrix constituents, angiogenesis, and cell migration as the main biological processes enhanced in stromata, while the tumor compartment was characterized by an overexpression of many ribosomal protein genes. A further gene set enrichment analysis against a comprehensive ribosomal protein gene set finally revealed that only cytosolic ribosomes (80S) were affected by such upregulation, while mitochondrial ribosomes were virtually unaltered. Because of its high incidence and poor prognosis, colorectal cancer (CRC) represents an important health issue in several countries. As with other carcinomas, the so-called tumour microenvironment (TME) has been shown to play key roles in CRC progression and related therapeutical outcomes, even though a deeper understanding of the underlying molecular mechanisms is needed to devise new treatment strategies. For some years now, omics technologies and consolidated bioinformatics pipelines have allowed scientists to access large amounts of biologically relevant information, even when starting from small tissue samples; thus, in order to shed new light upon the role of the TME in CRC, we compared the gene expression profiles of 6 independent tumour tissues (all progressed towards metastatic disease) to the expression profile of the surrounding stromata. To do this, paraffin-embedded whole tissues were first microdissected to obtain samples enriched with tumour and stromal cells, respectively. Afterwards, RNA was extracted and analysed using a microarray-based approach. A thorough bioinformatics analysis was then carried out to identify transcripts differentially expressed between the two groups and possibly enriched functional terms. Overall, 193 genes were found to be significantly downregulated in tumours compared to the paired stromata. The functional analysis of the downregulated gene list revealed three principal macro areas of interest: the extracellular matrix, cell migration, and angiogenesis. Conversely, among the upregulated genes, the main alterations detected by the functional annotation were related to the ribosomal proteins (rProteins) of both the large (60S) and small (40S) subunits of the cytosolic ribosomes. Subsequent gene set enrichment analysis (GSEA) confirmed the massive overexpression of most cytosolic—but not mitochondrial—ribosome rProteins. [ABSTRACT FROM AUTHOR]