Multifaceted enrichment analysis of RNA-RNA crosstalk reveals cooperating micro-societies in human colorectal cancer

Alterations in the balance of mRNA and microRNA (miRNA) expression profiles contribute to the onset and development of colorectal cancer. The regulatory functions of individual miRNA-gene pairs are widely acknowledged, but group effects are largely unexplored. We performed an integrative analysis of mRNA–miRNA and miRNA–miRNA interactions using high-throughput mRNA and miRNA expression profiles obtained from matched specimens of human colorectal cancer tissue and adjacent non- tumorous mucosa. This investigation resulted in a hypernetwork-based model, whose functional back- bone was fulfilled by tight micro-societies of miR- NAs. These proved to modulate several genes that are known to control a set of significantly enriched cancer-enhancer and cancer-protection biological processes, and that an array of upstream regulatory analyses demonstrated to be dependent on miR-145, a cell cycle and MAPK signalling cascade master regulator. In conclusion, we reveal miRNA-gene clusters and gene families with close functional relationships and highlight the role of miR-145 as potent upstream regulator of a complex RNA–RNA crosstalk, which mechanistically modulates several signalling path- ways and regulatory circuits that when deranged are relevant to the changes occurring in colorectal carcinogenesis

Dekodiranje molekulskog aspekta oštećenja oka prouzročenog ionizirajućim zračenjem pomoću rudarenja genomskih podataka

Even at low levels, exposure to ionising radiation can lead to eye damage. However, the underlying molecular mechanisms are not yet fully understood. We aimed to address this gap with a comprehensive in silico approach to the issue. For this purpose we relied on the Comparative Toxicogenomics Database (CTD), ToppGene Suite, Cytoscape, GeneMANIA, and Metascape to identify six key regulator genes associated with radiation-induced eye damage (ATM, CRYAB, SIRT1, TGFB1, TREX1, and YAP1), all of which have physical interactions. Some of the identified molecular functions revolve around DNA repair mechanisms, while others are involved in protein binding, enzymatic activities, metabolic processes, and post-translational protein modifications. The biological processes are mostly centred on response to DNA damage, the p53 signalling pathway in particular. We identified a significant role of several miRNAs, such as hsa-miR-183 and hsamiR-589, in the mechanisms behind ionising radiation-induced eye injuries. Our study offers a valuable method for gaining deeper insights into the adverse effects of radiation exposure.Izloženost ionizirajućem zračenju čak i pri niskim razinama može pridonijeti nastanku oštećenja oka. Međutim, osnovni molekulski mehanizmi i dalje nisu potpuno razjašnjeni. Cilj našega istraživanja bio je ispuniti tu nedostajuću kariku primjenom sveobuhvatnog in silico pristupa problemu. U tu svrhu, pomoću genomskih baza podataka, portala i poslužitelja (Comparative Toxicogenomics Database, ToppGene Suite portal, Cytoscape, GeneMANIA i Metascape), identificirano je šest ključnih regulacijskih gena koji su povezani s oštećenjem oka prouzročenog ionizirajućim zračenjem (ATM, CRYAB, SIRT1, TGFB1, TREX1 i YAP1) i koji su svi bili u fizičkoj interakciji. Neke od identificiranih molekulskih funkcija odnosile su se na mehanizme popravka oštećenja DNA, a druge su bile uključene u vezanje proteina, enzimsku aktivnost, metaboličke procese i posttranslacijske modifikacije proteina. Biološki procesi uglavnom su bili povezani s odgovorom na oštećenje DNA, pogotovo sa signalnim putem p53. Uočena je i značajna uloga nekoliko miRNA, poput hsa-miR-183 i hsa-miR-589, u mehanizmima povezanima s oštećenjem oka prouzročenog ionizirajućim zračenjem. Osim toga, u ovom je istraživanju opisana korisna metoda za ispitivanje štetnih učinaka izloženosti zračenju

Identification of gene expression logical invariants in Arabidopsis.

Numerous gene expression datasets from diverse tissue samples from the plant variety Arabidopsis thaliana have been already deposited in the public domain. There have been several attempts to do large scale meta-analyses of all of these datasets. Most of these analyses summarize pairwise gene expression relationships using correlation, or identify differentially expressed genes in two conditions. We propose here a new large scale meta-analysis of the publicly available Arabidopsis datasets to identify Boolean logical relationships between genes. Boolean logic is a branch of mathematics that deals with two possible values. In the context of gene expression datasets we use qualitative high and low expression values. A strong logical relationship between genes emerges if at least one of the quadrants is sparsely populated. We pointed out serious issues in the data normalization steps widely accepted and published recently in this context. We put together a web resource where gene expression relationships can be explored online which helps visualize the logical relationships between genes. We believe that this website will be useful in identifying important genes in different biological context. The web link is http://hegemon.ucsd.edu/plant/

Analysis of SIGLEC12 expression, IMMUNOMODULATION and prognostic value in RENAL cancer using multiomic databases

© 2024 The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY), https://creativecommons.org/licenses/by/4.0/Siglecs belong to a family of immune regulatory receptors predominantly found on hematopoietic cells. They interact with Sia, resulting in the activation or inhibition of the immune response. Previous reports have suggested that the SIGLEC12 gene, which encodes the Siglec-XII protein, is expressed in the epithelial tissues and upregulated in carcinomas. However, studies deciphering the role of Siglec-XII in renal cancer (RC) are still unavailable, and here we provide insights on this question. We conducted expression analysis using the Human Protein Atlas and UALCAN databases. The impact of SIGLEC12 on RC prognosis was determined using the KM plotter, and an assessment of immune infiltration with SIGLEC12 was performed using the TIMER database. GSEA was conducted to identify the pathways affected by SIGLEC12. Finally, using GeneMania, we identified Siglec-XII interacting proteins. Our findings indicated that macrophages express SIGLEC12 in the kidney. Furthermore, we hypothesize that Siglec-XII expression might be involved in the increase of primary RC, but this effect may not be dependent on the age of the patient. In the tumour microenvironment, oncogenic pathways appeared to be upregulated by SIGLEC12. Similarly, our analysis suggested that SIGLEC12-related kidney renal papillary cell carcinomas may be more suitable for targeted immunotherapy, such as CTLA-4 and PD-1/PD-L1 inhibitors. These preliminary results suggested that high expression of SIGLEC12 is associated with poor prognosis for RC. Future studies to assess its clinical utility are necessitated.Peer reviewe

Arabidopsis Coexpression Tool:a tool for gene coexpression analysis in Arabidopsis thaliana

Gene coexpression analysis refers to the discovery of sets of genes which exhibit similar expression patterns across multiple transcriptomic data sets, such as microarray experiment data of public repositories. Arabidopsis Coexpression Tool (ACT), a gene coexpression analysis web tool for Arabidopsis thaliana, identifies genes which are correlated to a driver gene. Primary microarray data from ATH1 Affymetrix platform were processed with Single-Channel Array Normalization algorithm and combined to produce a coexpression tree which contains ∼21,000 A. thaliana genes. ACT was developed to present subclades of coexpressed genes, as well as to perform gene set enrichment analysis, being unique in revealing enriched transcription factors targeting coexpressed genes. ACT offers a simple and user-friendly interface producing working hypotheses which can be experimentally verified for the discovery of gene partnership, pathway membership, and transcriptional regulation. ACT analyses have been successful in identifying not only genes with coordinated ubiquitous expressions but also genes with tissue-specific expressions

Investigating the Mechanism of Arsenic-induced Ferroptosis in the Skin

Background: Ferroptosis, an oxidative and iron-dependent cell death, is a new type of regulated cell death. There are few studies on the mechanisms of ferroptosis in the skin and related diseases. Arsenic is shown to induce ferroptosis cell death. This study aimed to decipher the relationship between arsenic exposure and ferroptosis cell death in the skin. Methods: Arsenic-gene interactions were obtained. Then, skin-specific arsenic-gene interactions were screened. Ferroptosis-related genes were identified. Analysis of functional and biological interactions was performed to identify possible mechanisms. Results: The arsenic-gene interactions and the ferroptosis-related genes showed an overlap of 59 genes. Functional enrichment, protein-protein interaction, and transcription factor (TF)/miRNA target gene interaction analyses were used to look into the mechanism of arsenic-induced ferroptosis in the skin. ACTB, CTNNB1, HSPA8, SRC, RACK1, CD44, and SQSTM1 were identified as key proteins. Gene ontology analysis of these proteins indicated the mitochondrial morphology and functionality changes following arsenic-induced ferroptosis in the skin. HIF1A and SP1 TFs regulate a large number of genes compared to other TFs. Ten miRNAs with high interaction with ferroptosis-associated genes were identified. Conclusion: This work investigated the mechanism of arsenic-induced ferroptosis in the skin and identified key genes and regulators, and functional analysis highlighted the role of mitochondria in this skin exposure

Integrative OMICS Data-Driven Procedure Using a Derivatized Meta-Analysis Approach

The wealth of high-throughput data has opened up new opportunities to analyze and describe biological processes at higher resolution, ultimately leading to a significant acceleration of scientific output using high-throughput data from the different omics layers and the generation of databases to store and report raw datasets. The great variability among the techniques and the heterogeneous methodologies used to produce this data have placed meta-analysis methods as one of the approaches of choice to correlate the resultant large-scale datasets from different research groups. Through multi-study meta-analyses, it is possible to generate results with greater statistical power compared to individual analyses. Gene signatures, biomarkers and pathways that provide new insights of a phenotype of interest have been identified by the analysis of large-scale datasets in several fields of science. However, despite all the efforts, a standardized regulation to report large-scale data and to identify the molecular targets and signaling networks is still lacking. Integrative analyses have also been introduced as complementation and augmentation for meta-analysis methodologies to generate novel hypotheses. Currently, there is no universal method established and the different methods available follow different purposes. Herein we describe a new unifying, scalable and straightforward methodology to meta-analyze different omics outputs, but also to integrate the significant outcomes into novel pathways describing biological processes of interest. The significance of using proper molecular identifiers is highlighted as well as the potential to further correlate molecules from different regulatory levels. To show the methodology's potential, a set of transcriptomic datasets are meta-analyzed as an example

DGLinker: flexible knowledge-graph prediction of disease-gene associations

As a result of the advent of high-throughput technologies, there has been rapid progress in our understanding of the genetics underlying biological processes. However, despite such advances, the genetic landscape of human diseases has only marginally been disclosed. Exploiting the present availability of large amounts of biological and phenotypic data, we can use our current understanding of disease genetics to train machine learning models to predict novel genetic factors associated with the disease. To this end, we developed DGLinker, a webserver for the prediction of novel candidate genes for human diseases given a set of known disease genes. DGLinker has a user-friendly interface that allows non-expert users to exploit biomedical information from a wide range of biological and phenotypic databases, and/or to upload their own data, to generate a knowledge-graph and use machine learning to predict new disease-associated genes. The webserver includes tools to explore and interpret the results and generates publication-ready figures. DGLinker is available at https://dglinker.rosalind.kcl.ac.uk. The webserver is free and open to all users without the need for registration

Potential genomic biomarkers of obesity and its comorbidities for phthalates and bisphenol A mixture: In silico toxicogenomic approach

This in silico toxicogenomic study aims to explore the relationship between phthalates and bisphenol A (BPA) co-exposure and obesity, as well as its comorbid conditions, in order to construct a possible set of genomic biomarkers. The Comparative Toxicogenomics Database (CTD; http://ctd.mdibl.org) was used as the main data mining tool, along with GeneMania (https://genemania.org), ToppGene Suite (https://toppgene.cchmc.org) and DisGeNET (http://www. disgenet.org). Among the phthalates, bis(2-ethylhexyl) phthalate (DEHP) and dibutyl phthalate (DBP) were chosen as the most frequently curated phthalates in CTD, which also share similar mechanisms of toxicity. DEHP, DBP and BPA interacted with 84, 90 and 194 obesity-related genes/proteins, involved in 67, 65 and 116 pathways, respectively. Among these, 53 genes/proteins and 42 pathways were common to all three substances. 31 genes/proteins had matching interactions for all three investigated substances, while more than half of these genes/proteins (56.49%) were in co-expression. 7 of the common genes/proteins (6 relevant to humans: CCL2, IL6, LPL, PPARG, SERPINE1, and TNF) were identified in all the investigated obesity comorbidities, while PPARG and LPL were most closely linked to obesity. These genes/proteins could serve as a target for further in vitro and in vivo studies of molecular mechanisms of DEHP, DBP and BPA mixture obesogenic properties. Analysis reported here should be applicable to any mixture of environmental chemicals and any disease present in CTD

FAM111A is a novel molecular marker for oocyte aging

Aging is the main cause of decline in oocyte quality, which can further trigger the failure of assisted reproductive technology (ART). Exploring age-related genes in oocytes is an important way to investigate the molecular mechanisms involved in oocyte aging. To provide novel insight into this field, we performed a pooled analysis of publicly available datasets, using the overlapping results of two statistical methods on two Gene Expression Omnibus (GEO) datasets. The methods utilized in the current study mainly include Spearman rank correlation, the Wilcoxon signed-rank test, t-tests, Venn diagrams, Gene Ontology (GO), Protein–Protein Interaction (PPI), Gene Set Enrichment Analysis (GSEA), Gene Set Variation Analysis (GSVA), and receiver operating characteristic (ROC) curve analysis. We identified hundreds of age-related genes across different gene expression datasets of in vitro maturation-metaphase II (IVM-MII) oocytes. Age-related genes in IVM-MII oocytes were involved in the biological processes of cellular metabolism, DNA replication, and histone modifications. Among these age-related genes, FAM111A expression presented a robust correlation with age, seen in the results of different statistical methods and different datasets. FAM111A is associated with the processes of chromosome segregation and cell cycle regulation. Thus, this enzyme is potentially an interesting novel marker for the aging of oocytes, and warrants further mechanistic study