Differential expression of genes that encode glycolysis enzymes in kidney and lung cancer in humans

Oparina, N. Yu.; Snezhkina, Anastasiya V.; Садритдинова, А. Ф.; Veselovsky, Vladimir A.; Dmitriev, Alexey A.; Senchenko, V. N.; Melnikova, N. V.; Speranskaya, A.S.; Дарий, М. В.; Степанов, О. А.; Barkhatov, I. M.; Kudryavtseva, Anna V.

doi:10.1134/s1022795413050104

Cited by 23 publications

(16 citation statements)

References 30 publications

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“…ALDOA catalyzes the reversible conversion of fructose-1,6-bisphosphate to glyceraldehyde-3-phosphate (GAP) and dihydroxyacetone phosphate (DHAP) 40 . It is widely accepted that ALDOA is highly expressed and is correlated with poor survival outcomes in many kinds of harmful cancers [10][11][12][13] . We also found that ALDOA is c Top panel shows a schematic image of a construction containing KCNQ1OT1 wild type combined with MS2 binding sequence.…”

Section: Discussionmentioning

confidence: 99%

“…Fructose-bisphosphate aldolase A (ALDOA), a glycolytic enzyme, plays an important role in glycolysis and gluconeogenesis 9 . Notably, ALDOA is highly expressed and acts as an oncogene in many types of cancers including lung squamous cell carcinomas, colorectal cancer, hepatocellular carcinomas, and oral squamous cell carcinomas [10][11][12][13] . Previous studies have reported that ALDOA is upregulated in OS and is a negative survival marker of these patients 14,15 .…”

Section: Introductionmentioning

confidence: 99%

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LncRNA KCNQ1OT1 sponges miR-34c-5p to promote osteosarcoma growth via ALDOA enhanced aerobic glycolysis

et al. 2020

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Metabolic switch from oxidative phosphorylation to aerobic glycolysis, which is also called the Warburg effect, is a hallmark of osteosarcoma (OS) and leads to the enhancement of cell chemoresistance, growth, metastasis, and invasion. Emerging evidence indicates that long non-coding RNA (lncRNA) plays a crucial role in the Warburg effect of cancer cells. Here, we report that lncRNA KCNQ1OT1 was upregulated in OS. Meanwhile, functional experiments demonstrated that the KCNQ1OT1 facilitated proliferation and suppressed apoptosis of OS cells. In addition, KCNQ1OT1 contributed to the Warburg effect by stimulating aldolase A (ALDOA) expression. Furthermore, using bioinformatics analysis, luciferase reporter, RNA immunoprecipitation, and RNA pull-down assay, we identified that KCNQ1OT1 functions as a competing endogenous RNA (ceRNA) by sponging miR-34c-5p, which inhibited ALDOA expression by directly targeting its 3ʹUTR. Taken together, these data identified a key role of KCNQ1OT1 in glucose metabolism reprogramming of OS. Targeting the KCNQ1OT1/miR-34c-5p/ALDOA axis may be a potential therapeutic target in OS treatment.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

LncRNA KCNQ1OT1 sponges miR-34c-5p to promote osteosarcoma growth via ALDOA enhanced aerobic glycolysis

et al. 2020

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“…Roles of DGKH and PFKP in lung cancer have not been fully discussed. However, Oparina et al proved that expression stability of the glycolytic enzyme encoding genes affecting the malignant and mRNA expression level of PFKP in kidney cancer was higher than the normal samples [28]. Molatore and his colleagues proved that DGKH was up-regulated in rats with pheochromocytoma and may be a biomarker for clinical use [29].…”

Section: Discussionmentioning

confidence: 99%

Identification of lung cancer oncogenes based on the mRNA expression and single nucleotide polymorphism profile data

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Mei²,

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et al. 2015

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This study aimed to identify the oncogenes associated with lung cancer based on the mRNA and single nucleotide polymorphism (SNP) profile data. The mRNA expression profile data of GSE43458 (80 cancer and 30 normal samples) and SNP profile data of GSE33355 (61 pairs of lung cancer samples and control samples) were downloaded from Gene Expression Omnibus database. Common genes between the mRNA profile and SNP profile were identified as the lung cancer oncogenes. Risk subpathways of the selected oncogenes with the SNP locus were analyzed using the iSubpathwayMiner package in R. Moreover, protein-protein interaction (PPI) network of the oncogenes was constructed using the HPRD database and then visualized using the Cytoscape. Totally, 3004 DEGs (1105 up-regulated and 1899 down-regulated) and 125 significant SNPs closely related to 174 genes in the lung cancer samples were identified. Also, 39 common genes, like PFKP (phosphofructokinase, platelet) and DGKH-rs11616202 (diacylglycerol kinase, eta) that enriched in sub-pathways such as galactose metabolism, fructose and mannose metabolism, and pentose phosphate pathway, were identified as the lung cancer oncogenes. Besides, PIK3R1 (phosphoinositide-3-kinase, regulatory subunit 1), RORA (RAR-related orphan receptor A), MAGI3 (membrane associated guanylate kinase, WW and PDZ domain containing 3), PTPRM (protein tyrosine phosphatase, receptor type, M), and BMP6 (bone morphogenetic protein 6) were the hub genes in PPI network. Our study suggested that PFKP and DGKH that enriched in galactose metabolism, fructose and mannose metabolism pathway, as well as PIK3R1, RORA, and MAGI3, may be the lung cancer oncogenes. Key words: lung cancer, single nucleotide polymorphism (SNP), function analysis, differentially expressed gene, oncogenesAbbreviations: SNP -single nucleotide polymorphism; PPI -proteinprotein interaction; DEGs -differentially expressed genes Lung cancer is one of the most common malignancies with an increasing morbidity and mortality and is a worldwide leading cause of cancer-related death with a 5-year survival rate from 13% to 15% [1]. Mechanism of lung cancer is complicate, mainly due to the late diagnosis and lack of effective treatment [2]. Therefore, underlying the molecular profiles of lung cancer as well as elucidating the roles of oncogenes and tumor suppressors in the development of this malignancy is expected to identify the molecular targets for lung cancer prediction and treatment.Previous studies have demonstrated that environmental factors like smoking and air pollution, and gene polymorphisms were the main factors contributing to the lung cancer progression and metastasis [3]. Chemical constituents in tobacco smoke including compounds of the carcinogenic polycyclic type were the carcinogen for lung cancer [4]. A previous study reveals that mutations of the tumor suppressor genes are a main reason for lung cancer progression, such as the prevalence of p53 mutational patterns G to T transversions is 30% and p53 mutations in lung cancer can be attribu...

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“…Tumor development is closely associated with the accumulation of somatic mutations, which may be due to various processes such as endogenous and exogenous DNA damage, defective mechanisms of DNA replication, modification, and repair [2,3]. These cause the changes in expression profiles of many genes, including activation of oncogenes and inactivation of tumor suppressor genes that lead to alterations in signaling pathways, cellular metabolism, and proliferation [4][5][6][7][8][9][10][11][12][13][14]. Distinct combinations of mutation types ("mutational signatures") depend on different mutation processes; multiple mutation processes generate jumbled composite signatures.…”

Section: Introductionmentioning

confidence: 99%

Mutational load in carotid body tumor

et al. 2019

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Background: Carotid body tumor (CBT) is a rare neoplasm arising from paraganglion located near the bifurcation of the carotid artery. There is great intra-tumor heterogeneity, and CBT development could be associated with both germline and somatic allelic variants. Studies on the molecular genetics of CBT are limited, and the molecular mechanisms of its pathogenesis are not fully understood. This work is focused on the estimation of mutational load (ML) in CBT. Methods: Using the NextSeq 500 platform, we performed exome sequencing of tumors with matched lymph node tissues and peripheral blood obtained from six patients with CBT. To obtain reliable results in tumors with low ML, we developed and successfully applied a complex approach for the analysis of sequencing data. ML was evaluated as the number of somatic variants per megabase (Mb) of the target regions covered by the Illumina TruSeq Exome Library Prep Kit. Results: The ML in CBT varied in the range of 0.09-0.28/Mb. Additionally, we identified several pathogenic/likely pathogenic somatic and germline allelic variants across six patients studied (including TP53 variants). Conclusions: Using the developed approach, we estimated the ML in CBT, which is much lower than in common malignant tumors. Identified variants in known paraganglioma/pheochromocytoma-causative genes and novel genes could be associated with the pathogenesis of CBT. The obtained results expand our knowledge of the mutation process in CBT as well as the biology of tumor development.

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Differential expression of genes that encode glycolysis enzymes in kidney and lung cancer in humans

Cited by 23 publications

References 30 publications

LncRNA KCNQ1OT1 sponges miR-34c-5p to promote osteosarcoma growth via ALDOA enhanced aerobic glycolysis

LncRNA KCNQ1OT1 sponges miR-34c-5p to promote osteosarcoma growth via ALDOA enhanced aerobic glycolysis

Identification of lung cancer oncogenes based on the mRNA expression and single nucleotide polymorphism profile data

Mutational load in carotid body tumor

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