Search Results (8)

Lung cancer is a global health challenge, hindered by delayed diagnosis and the disease’s complex molecular landscape. Accurate patient survival prediction is critical, motivating the exploration of various -omics datasets using machine learning methods. Leveraging multi-omics data, this study seeks to enhance the accuracy of survival prediction by proposing new feature extraction techniques combined with unbiased feature selection. Two lung adenocarcinoma multi-omics datasets, originating from the TCGA and CPTAC-3 projects, were employed for this purpose, emphasizing gene expression, methylation, and mutations as the most relevant data sources that provide features for the survival prediction models. Additionally, gene set aggregation was shown to be the most effective feature extraction method for mutation and copy number variation data. Using the TCGA dataset, we identified 32 molecular features that allowed the construction of a 2-year survival prediction model with an AUC of 0.839. The selected features were additionally tested on an independent CPTAC-3 dataset, achieving an AUC of 0.815 in nested cross-validation, which confirmed the robustness of the identified features. Full article

Due to the emergence of new variants of the SARS-CoV-2 coronavirus, the question of how the viral genomes evolved, leading to the formation of highly infectious strains, becomes particularly important. Three major emergent strains, Alpha, Beta and Delta, characterized by a significant number of missense mutations, provide a natural test field. We accumulated and aligned 4.7 million SARS-CoV-2 genomes from the GISAID database and carried out a comprehensive set of analyses. This collection covers the period until the end of October 2021, i.e., the beginnings of the Omicron variant. First, we explored combinatorial complexity of the genomic variants emerging and their timing, indicating very strong, albeit hidden, selection forces. Our analyses show that the mutations that define variants of concern did not arise gradually but rather co-evolved rapidly, leading to the emergence of the full variant strain. To explore in more detail the evolutionary forces at work, we developed time trajectories of mutations at all 29,903 sites of the SARS-CoV-2 genome, week by week, and stratified them into trends related to (i) point substitutions, (ii) deletions and (iii) non-sequenceable regions. We focused on classifying the genetic forces active at different ranges of the mutational spectrum. We observed the agreement of the lowest-frequency mutation spectrum with the Griffiths–Tavaré theory, under the Infinite Sites Model and neutrality. If we widen the frequency range, we observe the site frequency spectra much more consistently with the Tung–Durrett model assuming clone competition and selection. The coefficients of the fitting model indicate the possibility of selection acting to promote gradual growth slowdown, as observed in the history of the variants of concern. These results add up to a model of genomic evolution, which partly fits into the classical drift barrier ideas. Certain observations, such as mutation “bands” persistent over the epidemic history, suggest contribution of genetic forces different from mutation, drift and selection, including recombination or other genome transformations. In addition, we show that a “toy” mathematical model can qualitatively reproduce how new variants (clones) stem from rare advantageous driver mutations, and then acquire neutral or disadvantageous passenger mutations which gradually reduce their fitness so they can be then outcompeted by new variants due to other driver mutations. Full article

We aimed to identify miRNAs and pathways specifically deregulated in adolescent and young adult (AYA) T-ALL patients. Small RNA-seq showed no major differences between AYA and pediatric T-ALL, but it revealed downregulation of miR-143-3p in T-ALL patients. Prediction algorithms identified several known and putative oncogenes targeted by this miRNA, including KRAS, FGF1, and FGF9. Pathway analysis indicated signaling pathways related to cell growth and proliferation, including FGFR signaling and PI3K-AKT signaling, with the majority of genes overrepresented in these pathways being predicted targets of hsa-miR-143-3p. By luciferase reporter assays, we validated direct interactions of this miRNA with KRAS, FGF1 and FGF9. In cell proliferation assays, we showed reduction of cell growth upon miR-143-3p overexpression in two T-ALL cell lines. Our study is the first description of the miRNA transcriptome in AYA T-ALL patients and the first report on tumor suppressor potential of miR-143-3p in T-ALL. Downregulation of this miRNA in T-ALL patients might contribute to enhanced growth and viability of leukemic cells. We also discuss the potential role of miR-143-3p in FGFR signaling. Although this requires more extensive validation, it might be an interesting direction, since FGFR inhibition proved promising in preclinical studies in various cancers. Full article

Superoxide radicals, together with nitric oxide (NO), determine the oxidative status of cells, which use different pathways to control their levels in response to stressing conditions. Using gene expression data available in the Cancer Cell Line Encyclopedia and microarray results, we compared the expression of genes engaged in pathways controlling reactive oxygen species and NO production, neutralization, and changes in response to the exposure of cells to ionizing radiation (IR) in human cancer cell lines originating from different tissues. The expression of NADPH oxidases and NO synthases that participate in superoxide radical and NO production was low in all cell types. Superoxide dismutase, glutathione peroxidase, thioredoxin, and peroxiredoxins participating in radical neutralization showed high expression in nearly all cell types. Some enzymes that may indirectly influence superoxide radical and NO levels showed tissue-specific expression and differences in response to IR. Using fluorescence microscopy and specific dyes, we followed the levels and the distribution of superoxide and NO radicals in living melanoma cells at different times after exposure to IR. Directly after irradiation, we observed an increase of superoxide radicals and NO coexistent in the same subcellular locations, suggesting a switch of NO synthase to the production of superoxide radicals. Full article

T-cell acute lymphoblastic leukemia (T-ALL) is an aggressive malignancy arising from T lymphocyte precursors. We have previously shown by miRNA-seq, that miRNAs from the mir-106a-363 cluster are overexpressed in pediatric T-ALL. In silico analysis indicated their potential involvement in the regulation of apoptosis. Here, we aimed to test the hypothesis on the pro-tumorigenic roles of these miRNAs in T-ALL cells in vitro. We demonstrate, for the first time, that hsa-miR-20b-5p and hsa-miR-363-3p from the mir-106a-363 cluster, when upregulated in T-ALL cells in vitro, protect leukemic cells from apoptosis, enhance proliferation, and contribute to growth advantage. We show, using dual luciferase reporter assays, Ago2-RNA immunoprecipitation, RT-qPCR, and Western blots, that the oncogenic effects of these upregulated miRNAs might, at least in part, be mediated by the downregulation of two important tumor suppressor genes, PTEN and BIM, targeted by both miRNAs. Additionally, we demonstrate the cooperative effects of these two miRNAs by simultaneous inhibition of both miRNAs as compared to the inhibition of single miRNAs. We postulate that hsa-miR-20b-5p and hsa-miR-363-3p from the mir-106a-363 cluster might serve as oncomiRs in T-ALL, by contributing to post-transcriptional repression of key tumor suppressors, PTEN and BIM. Full article

Optimal endogenous controls enable reliable normalization of microRNA (miRNA) expression in reverse-transcription quantitative PCR (RT-qPCR). This is particularly important when miRNAs are considered as candidate diagnostic or prognostic biomarkers. Universal endogenous controls are lacking, thus candidate normalizers must be evaluated individually for each experiment. Here we present a strategy that we applied to the identification of optimal control miRNAs for RT-qPCR profiling of miRNA expression in T-cell acute lymphoblastic leukemia (T-ALL) and in normal cells of T-lineage. First, using NormFinder for an iterative analysis of miRNA stability in our miRNA-seq data, we established the number of control miRNAs to be used in RT-qPCR. Then, we identified optimal control miRNAs by a comprehensive analysis of miRNA stability in miRNA-seq data and in RT-qPCR by analysis of RT-qPCR amplification efficiency and expression across a variety of T-lineage samples and T-ALL cell line culture conditions. We then showed the utility of the combination of three miRNAs as endogenous normalizers (hsa-miR-16-5p, hsa-miR-25-3p, and hsa-let-7a-5p). These miRNAs might serve as first-line candidate endogenous controls for RT-qPCR analysis of miRNAs in different types of T-lineage samples: T-ALL patient samples, T-ALL cell lines, normal immature thymocytes, and mature T-lymphocytes. The strategy we present is universal and can be transferred to other RT-qPCR experiments. Full article

High density oligonucleotide microarrays present a big challenge for statistical data processing methods which aim to separate changes induced by experimental factors from those caused by artifacts and measurement inaccuracies. Despite huge advances in the field of microarray probe design methods, the signal variation between probes that target a single transcript is substantially larger than their between-replicate array variability, suggesting a large influence of various probe-specific effects that introduce bias to the data. In this work we present the influence of probe-related design variations on the expression intensities of individual probes, focusing on five potential sources of high probe signal variance: the GC composition of the probe, the distance between individual probe target sites, G-quadruplex formation in the probe sequence, the occurrence of sequence motifs complementary to the oligo(dT) primer, and the specificity of unrecognized alternative splicing probeset assignment. By focusing on two high quality microarray datasets based on two distinct array designs we show the extent of variance between probes that target a specific transcript providing guidelines for the future design of microarrays and data processing methods. Full article