Changed Profile of Expression of Splicing Regulator Genes in Response to Exercise

Tonevitsky, E. A.; Trushkin, E. V.; Shkurnikov, M. Yu.; Акимов, Е. Б.; Sakharov, D. A.

doi:10.1007/s10517-009-0593-0

Cited by 7 publications

(4 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…On the list, there are also several stress-responding genes such as HSP70 (#13 & #41) [42] as well as cancer-related genes such as TCTP (#57) [43]. It is worth noting that six genes, PTB (#1) [44], DDX46 (#3) [45], EF2 (#15 & #34) [46], hnRNP K (#16) [47], and eRF1 (# 26) [48] are all involved in mRNA splicing or processing and RNA translation initiation or termination, indicating alteration of mRNA secondary structure and protein synthesis may be targeted by both TNT and RDX. More work should be devoted to exploring biological functions and interactions of the 58 genes that may lead or be linked to toxicological effects or biochemical endpoints.…”

Section: Discussionmentioning

confidence: 99%

Identification and Optimization of Classifier Genes from Multi-Class Earthworm Microarray Dataset

et al. 2010

View full text Add to dashboard Cite

Monitoring, assessment and prediction of environmental risks that chemicals pose demand rapid and accurate diagnostic assays. A variety of toxicological effects have been associated with explosive compounds TNT and RDX. One important goal of microarray experiments is to discover novel biomarkers for toxicity evaluation. We have developed an earthworm microarray containing 15,208 unique oligo probes and have used it to profile gene expression in 248 earthworms exposed to TNT, RDX or neither. We assembled a new machine learning pipeline consisting of several well-established feature filtering/selection and classification techniques to analyze the 248-array dataset in order to construct classifier models that can separate earthworm samples into three groups: control, TNT-treated, and RDX-treated. First, a total of 869 genes differentially expressed in response to TNT or RDX exposure were identified using a univariate statistical algorithm of class comparison. Then, decision tree-based algorithms were applied to select a subset of 354 classifier genes, which were ranked by their overall weight of significance. A multiclass support vector machine (MC-SVM) method and an unsupervised K-mean clustering method were applied to independently refine the classifier, producing a smaller subset of 39 and 30 classifier genes, separately, with 11 common genes being potential biomarkers. The combined 58 genes were considered the refined subset and used to build MC-SVM and clustering models with classification accuracy of 83.5% and 56.9%, respectively. This study demonstrates that the machine learning approach can be used to identify and optimize a small subset of classifier/biomarker genes from high dimensional datasets and generate classification models of acceptable precision for multiple classes.

show abstract

Section: Discussionmentioning

confidence: 99%

Identification and Optimization of Classifier Genes from Multi-Class Earthworm Microarray Dataset

et al. 2010

View full text Add to dashboard Cite

show abstract

“…Reduced HNRNPR expression leads to a reduction in MHC‐I mRNA, and subsequent escape from CTL attack (Reches et al, 2016). HNRNPR is also involved in the formation of precatalytic spliceosomes, which leads to the modification of spliceosome activity and the choice of alternative exons (Tonevitsky et al, 2009). In addition, HNRNPR can interact with other hnRNPs to form complexes and regulate pre‐mRNA and mature mRNA (Hassfeld et al, 1998).…”

Section: Introductionmentioning

confidence: 99%

Pan‐cancer analysis of the oncogenic role of HNRNPR in human tumors

Yang

Sun

Xiang

2023

Cell Biology International

View full text Add to dashboard Cite

Heterogeneous nuclear ribonucleoproteins (hnRNPs) are potential cancer biomarkers. Little is known about the role of HNRNPR, an essential member of the hnRNP family, in human tumours. This study aims to explore the potential value of HNRNPR across cancers, based on The Cancer Genome Atlas (TCGA). The expression level, mutation, DNA methylation, phosphorylation status, survival status, pathological stage, tumor mutation burden (TMB), microsatellite instability (MSI), immune cell infiltration, and immune signature related to HNRNPR were analyzed. HNRNPR expression level was increased in several types of cancer and was associated with poor prognosis, especially in liver hepatocellular carcinoma (LIHC). HNRNPR was also correlated with antitumour immunity, and associated with TMB, MSI, and immune cell activation status across cancers. Furthermore, nomograms were established to predict the prognosis of LIHC, based on HNRNPR and other clinical characteristics. Functional enrichment analysis showed the mechanisms of HNRNPR‐mediated LIHC progression. Loss‐of‐function experiments demonstrated that inhibition of HNRNPR could remarkably suppress hepatocellular carcinoma (HCC) cell proliferation, migration, invasion, and Epithelial‐Mesenchymal Transition abilities. Our study offers a comprehensive understanding of the oncogenic roles of HNRNPR across different tumours, and demonstrates that HNRNPR might foster the proliferation, migration, and invasion abilities of HCC cells.

show abstract

“…The same was shown for antisense genes, which are expected to regulate their sense partners [8], and housekeeping genes, which expression level is continuously maintained on the almost constant level [9]. Recently genes which expression is increased in response to physical stress were described [10,11]. One could expect that they will show similar regularities in total length and intron number, as their transcription takes place under harsh conditions.…”

Section: Introductionmentioning

confidence: 78%

“…Our research was based on the results of the microarray analysis obtained in [10,11]. In that work 11 well-trained skiers performed ramp-test for about 15 minutes, after which changes in gene expression in blood mononuclear cells were analyzed with Affymetrix GeneChip 1.0 ST Arrays.…”

Section: Methodsmentioning

confidence: 99%

Structural Pecularities of Human Genes Which Expression Increases in Response to Stress

Riabenko¹,

Tonevitsky²,

Tonevitsky³

et al. 2011

Am. J. Biomed. Sci.

View full text Add to dashboard Cite

We studied structural peculiarities of 123 human genes which were previously identified as genes of early response to exercise stress. Compared to protein-coding genes in general, their structure revealed some peculiarities: they are shorter in both coding and non-coding regions and contain fewer introns. This result is in good agreement with the similar results reported for stress-responding genes in other species (yeasts, thale cress, and mice).

show abstract

Changed Profile of Expression of Splicing Regulator Genes in Response to Exercise

Cited by 7 publications

References 11 publications

Identification and Optimization of Classifier Genes from Multi-Class Earthworm Microarray Dataset

Identification and Optimization of Classifier Genes from Multi-Class Earthworm Microarray Dataset

Pan‐cancer analysis of the oncogenic role of HNRNPR in human tumors

Structural Pecularities of Human Genes Which Expression Increases in Response to Stress

Contact Info

Product

Resources

About