SEPHS1: Its evolution, function and roles in development and diseases

Bang, Jeyoung; Kang, Dong‐Hyun; Jung, Jisu; Yoo, Tack-Jin; Shim, Myung Sup; Gladyshev, Vadim N.; Tsuji, Petra A.; Hatfield, Dolph L.; Kim, Jin Hong; Lee, Byeong Jae

doi:10.1016/j.abb.2022.109426

Cited by 6 publications

(5 citation statements)

References 73 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Selenophosphate synthetase (SEPHS) was originally discovered in prokaryotes as an enzyme that catalyzes selenophosphate synthesis using inorganic selenium and ATP as substrates. Dysregulated SEPHS1 is associated with the pathogenesis of various diseases including cancer, Crohn's disease, and osteoarthritis [17]. Especially, increased expression of SEPHS1 may indicate poor prognosis of hepatocellular carcinoma patients [18].…”

Section: Discussionmentioning

confidence: 99%

Prognostic Exploration of Metabolism-Related Genes in Hepatocellular Carcinoma and Pan-Cancer

Huang,

Liu,

et al. 2023

Preprint

View full text Add to dashboard Cite

Background The aim of this study is to delve into the potential value of metabolism-related genes in the prognosis assessment of cancer. By analyzing transcriptomics data and clinical information of various types of cancer from public databases, we screen out metabolism-related genes associated with prognosis and construct a prognostic model, offering new solutions for the prognosis assessment and personalized treatment of cancer patients. Methods Initially, we obtain metabolomics data and clinical information of various types of cancer from public databases (such as TCGA, GTEx, UCSC), including gene expression data, patient survival information, etc. Subsequently, we acquire a list of metabolism-related genes from the KEGG database and match it with the gene expression data in cancer samples to screen out differentially expressed metabolism-related genes. We then use univariate Cox regression analysis to analyze prognosis-related genes and employ LASSO and random survival forest algorithms for feature selection, choosing the most important metabolic features. Based on the selected metabolic features, we construct a prognostic model using various machine learning algorithms, including The NonLinear CoxPH, Extra Survival Trees, etc., and optimize the parameters. Finally, we apply the constructed pan-cancer prognostic model to datasets of other types of cancer for validation and performance evaluation. Results In HCC, we identified 407 differentially expressed genes related to metabolism. After Cox testing and prognosis-related analysis, we screened out 561 differentially expressed genes related to prognosis, and used random forest and LASSO regression algorithms to select the most important features, ultimately obtaining 7 metabolic features with significant predictive power. Subsequently, we reconstructed the random survival forest model based on these 7 metabolic features and verified the predictive performance of the model by drawing ROC curves (1–5 year AUC value > 0.89). The application results of the prognostic model in pan-cancer showed that it exhibited good predictive effects in 10 of the 33 types of cancer in the TCGA database (C-index > 0.75, IBS < 0.25), proving the potential value of metabolic features as prognostic markers in cancer. Conclusion This study constructs an effective pan-cancer prognostic model through comprehensive analysis of metabolomics data and clinical information in public databases, which can predict the prognosis of cancer patients. At the same time, we observed variations in several metabolic features among different types of cancer, offering new insights into predicting molecular subtypes and responses to diverse treatment plans. The findings from this study serve as a reference for individualized treatment decisions and precision medicine for cancer patients, while also contributing novel ideas and methods to advance the field of metabolomics.

show abstract

Section: Discussionmentioning

confidence: 99%

Prognostic Exploration of Metabolism-Related Genes in Hepatocellular Carcinoma and Pan-Cancer

Huang,

Liu,

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…This site is formed by several amino acid residues that are critical for catalysis, including a Cys residue, which is involved in the SeCys formation. Other residues are vital for the catalytic capacity of SEPHS2, such as lysine and aspartate, which are involved in ATP binding and stabilization of the intermediate state [ 109 , 110 , 111 ].…”

Section: Selenoproteinsmentioning

confidence: 99%

Selenoproteins in Health

Qi,

Duan,

2023

Molecules

View full text Add to dashboard Cite

Selenium (Se) is a naturally occurring essential micronutrient that is required for human health. The existing form of Se includes inorganic and organic. In contrast to the inorganic Se, which has low bioavailability and high cytotoxicity, organic Se exhibits higher bioavailability, lower toxicity, and has a more diverse composition and structure. This review presents the nutritional benefits of Se by listing and linking selenoprotein (SeP) functions to evidence of health benefits. The research status of SeP from foods in recent years is introduced systematically, particularly the sources, biochemical transformation and speciation, and the bioactivities. These aspects are elaborated with references for further research and utilization of organic Se compounds in the field of health.

show abstract

“…Disruption of metabolic pathways, such as the selenium pathway, by depleting selenophosphate synthetase 1 (SEPHS1) increases ROS levels and leads to DNA damage [78]. The Sephs1-deficient cells have reduced expression of those stress-related selenoproteins that act as oxidoreductases (including glutathione peroxidase 1 (GPX1) and methionine sulfoxide reductase B1 (MSRB1)) [79].…”

Section: Dna Damagementioning

confidence: 99%

An Integrated View of Stressors as Causative Agents in OA Pathogenesis

et al. 2023

View full text Add to dashboard Cite

Cells in the body are exposed to dynamic external and internal environments, many of which cause cell damage. The cell’s response to this damage, broadly called the stress response, is meant to promote survival and repair or remove damage. However, not all damage can be repaired, and sometimes, even worse, the stress response can overtax the system itself, further aggravating homeostasis and leading to its loss. Aging phenotypes are considered a manifestation of accumulated cellular damage and defective repair. This is particularly apparent in the primary cell type of the articular joint, the articular chondrocytes. Articular chondrocytes are constantly facing the challenge of stressors, including mechanical overloading, oxidation, DNA damage, proteostatic stress, and metabolic imbalance. The consequence of the accumulation of stress on articular chondrocytes is aberrant mitogenesis and differentiation, defective extracellular matrix production and turnover, cellular senescence, and cell death. The most severe form of stress-induced chondrocyte dysfunction in the joints is osteoarthritis (OA). Here, we summarize studies on the cellular effects of stressors on articular chondrocytes and demonstrate that the molecular effectors of the stress pathways connect to amplify articular joint dysfunction and OA development.

show abstract

SEPHS1: Its evolution, function and roles in development and diseases

Cited by 6 publications

References 73 publications

Prognostic Exploration of Metabolism-Related Genes in Hepatocellular Carcinoma and Pan-Cancer

Prognostic Exploration of Metabolism-Related Genes in Hepatocellular Carcinoma and Pan-Cancer

Selenoproteins in Health

An Integrated View of Stressors as Causative Agents in OA Pathogenesis

Contact Info

Product

Resources

About