Sedoheptulose Kinase SHPK Expression in Glioblastoma: Emerging Role of the Nonoxidative Pentose Phosphate Pathway in Tumor Proliferation

Abstract

Glioblastoma (GBM) is the most common form of malignant brain cancer and is considered the deadliest human cancer. Because of poor outcomes in this disease, there is an urgent need for progress in understanding the molecular mechanisms of GBM therapeutic resistance, as well as novel and innovative therapies for cancer prevention and treatment. The pentose phosphate pathway (PPP) is a metabolic pathway complementary to glycolysis, and several PPP enzymes have already been demonstrated as potential targets in cancer therapy. In this work, we aimed to evaluate the role of sedoheptulose kinase (SHPK), a key regulator of carbon flux that catalyzes the phosphorylation of sedoheptulose in the nonoxidative arm of the PPP. SHPK expression was investigated in patients with GBM using microarray data. SHPK was also overexpressed in GBM cells, and functional studies were conducted. SHPK expression in GBM shows a significant correlation with histology, prognosis, and survival. In particular, its increased expression is associated with a worse prognosis. Furthermore, its overexpression in GBM cells confirms an increase in cell proliferation. This work highlights for the first time the importance of SHPK in GBM for tumor progression and proposes this enzyme and the nonoxidative PPP as possible therapeutic targets.

Identification of crucial inflammaging related risk factors in multiple sclerosis

“…The Markov Chain Monte Carlo (MCMC) method was used to assess the sensitivity indices between inflammaging and MS. As a result, the 35 sensitive triples (by calculating the absolute difference frequency) were shown in Table 3 (Sarasin-Filipowicz et al, 2009 ; Chen and D'Mello, 2010 ; Bergbold and Lemberg, 2013 ; Liu et al, 2013 ; Malhotra et al, 2013 ; Wan, 2014 ; Charbit et al, 2015 ; Fusco et al, 2015 ; An et al, 2017 ; Arentsen et al, 2017 ; Maridas et al, 2017 ; Mathur et al, 2017 ; Xiao et al, 2019 ; Immler et al, 2020 ; Sato et al, 2020 ; Tong et al, 2020 ; Buhelt et al, 2021 ; Correale, 2021 ; Fadul et al, 2021 ; Ma et al, 2021 ; Peng et al, 2021 ; Bogacka et al, 2022 ; Franceschi et al, 2022 ; Hjæresen et al, 2022 ; Khurana and Goswami, 2022 ; Liu S. et al, 2022 ; Schebb et al, 2022 ; Watanabe et al, 2022 ; Saeidi et al, 2023 ). For example, the sensitive triple with maximum difference was “TMPRSS13-USP18-DCHS2” (the absolute difference value was 0.270469).…”

“…The energy metabolism was also involved in the development of MS. For example, in the integrated inflammaging model ( Table 2 ), the aging marker BMP8A, enabled anti-adiposity by promoting fatty acid oxidation and inhibiting adipocyte differentiation (Zhong et al, 2023 ); the inflammatory marker IGFBP4 was an important regulator of adipose tissue development (Maridas et al, 2017 ). In addition, in MCMC ( Table 4 ), the inflammation marker SHPK catalyzed the phosphorylation of sedoheptulose in the non-oxidative arm of the pentose phosphate pathway (Franceschi et al, 2022 ). Furthermore, in the enrichment analysis ( Supplementary Table S6 ), the KEGG pathway with minimum FDR was “NICOTINATE AND NICOTINAMIDE METABOLISM” (FDR = 0.001626), which produced the biologically active coenzymes NAD (its phosphate analog was the NADP) (Gasperi et al, 2019 ).…”

Uncovering Hidden Cancer Self-Dependencies through Analysis of shRNA-Level Dependency Scores

“…On the other hand, as depicted in Figure 4a, nine identified amplification driver genes by NBDep are in the IntOGen gene list, namely ERBB2, CDK4, FLT3, MAP2K1, KRAS, NRAS, GNAS, RUNX1 as oncogenes and LATS2 as a tumor suppressor. Additionally, other amplification driver genes identified by NBDep, such as ITGAE, ALKBH3, SHPK, and GJB3 have been previously reported to play a significant role in cancer [36][37][38][39]. NBDep also identified 23 non-missense driver genes that were previously reported in IntOGen, namely SIN3A, NTRK1, FN1, MGA, MAP3K1, PTEN, DROSHA, WRN, RELA, CDX2, KDR, CDKN1B, POLD1, PIK3R1, NUP214, PML, GATA3, TOP2A, APC, TP53, RAD21, GNAI2, and NXF1.…”

Dexamethasone effects on the expression and content of glycosylated components of mouse brain tissue

“…The end of определена в каждом образце по визyaльнo-аналоговой шкале, согласно S. Franceschi и соавт. [20]: в образце рассчитывали процент ядер клеток с различной интенсивностью окраски (3 балла -сильная, 2 баллаумеренная и 1 балл -слабая). Поскольку во всех группах ядерная экспрессия GR была практически тотальной и различалась только по интенсивности окрашивания клеточных ядер, после полуколичественной оценки экспрессии по 3-балльной шкале каждое животное во всех группах было отнесено к одному из 3 паттернов фенотипа, которые расценивались как высокий, умеренный и низкий.…”