PKM2 as a biomarker for chemosensitivity to front-line platinum-based chemotherapy in patients with metastatic non-small-cell lung cancer

Papadaki, Chara; Sfakianaki, Maria; Lagoudaki, Eleni; Giagkas, G.; Ioannidis, Georgios; Trypaki, Maria; Tsakalaki, E.; Voutsina, Alexandra; Koutsopoulos, Anastasios; Mavroudis, Dimitriοs; Georgoulias, Vassilis; Souglakos, John

doi:10.1038/bjc.2014.492

Cited by 55 publications

(43 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…PKM2, as the critical isoenzyme of PK, serves a role in maintaining the metabolic program of cancer cells (34). inhibition of PKM2 expression was previously reported to decrease glucose consumption and lactate production (35), and elevated expression of PKM2 has been reported in colon and lung cancer (36,37). Previous studies have suggested that PKM2 contributes to cancer metabolism by promoting hypoxia-inducible factor-1 expression or coordinating with c-Myc (38,39).…”

Section: Discussionmentioning

confidence: 99%

Long non‑coding RNA BCYRN1 promotes glycolysis and tumor progression by regulating the miR‑149/PKM2 axis in non‑small‑cell lung cancer

et al. 2020

View full text Add to dashboard Cite

cancer cells use aerobic glycolysis to sustain their proliferation. long non-coding rna brain cytoplasmic rna 1 (BcYrn1) has been reported to act as an oncogene in non-small-cell lung cancer (nSclc). The present study investigated the role of BcYrn1 in nSclc glycolysis. BcYrn1 expression was detected in nSclc cells and tissues using reverse transcription-quantitative Pcr. The effect of BcYrn1 on aerobic glycolysis was examined by measuring nSclc cell glucose catabolism and lactate synthesis. The relationships between BcYrn1 and microrna (mir)-149, and between mir-149 and pyruvate kinase M1/2 (PKM2) were measured using a dual-luciferase reporter assay. cell proliferation and invasion were analyzed by the cell counting kit-8 assay and the Matrigel invasion assay, respectively. High BcYrn1 expression was observed in nSclc tissues and cells compared with the corresponding controls. BcYrn1 induced glycolysis and upregulated the expression levels of PKM2 in nSclc cells. in addition, BcYrn1 regulated mir-149 expression levels, and mir-149 inhibitor rescued the effects of si-BcYrn1 on glucose consumption and lactate production. miR-149 knockdown significantly enhanced the expression of PKM2. Furthermore, PKM2 inhibition significantly reversed the effects of mir-149 inhibitor on glucose catabolism and lactate synthesis. Furthermore, PKM2 was involved in nSclc cell proliferation and invasion, and BcYrn1 knockdown and mir-149 overexpression inhibited both processes. The present study suggested that BcYrn1 was involved in cell glycolysis, proliferation and invasion during nSclc via regulating mir-149 and PKM2.

show abstract

Section: Discussionmentioning

confidence: 99%

Long non‑coding RNA BCYRN1 promotes glycolysis and tumor progression by regulating the miR‑149/PKM2 axis in non‑small‑cell lung cancer

et al. 2020

View full text Add to dashboard Cite

show abstract

“…These findings also highlight a role of PKM2 in facilitating cancer cells to survive chemotherapy, the application of DNA damage in treating diseases, making it a new target in preventing cancer development and chemotherapy resistance (38).…”

Section: Discussionmentioning

confidence: 82%

A Novel Role for Pyruvate Kinase M2 as a Corepressor for P53 during the DNA Damage Response in Human Tumor Cells

Wang

et al. 2016

Journal of Biological Chemistry

View full text Add to dashboard Cite

Edited by Patrick SungThe pyruvate kinase (PK) is a rate-limiting glycolytic enzyme catalyzing the dephosphorylation of phosphoenolpyruvate to pyruvate, yielding one molecule of ATP. The M2 isoform of PK (PKM2) is predominantly expressed in normal proliferating cells and tumors, and both metabolic and non-metabolic activities for the enzyme in promoting tumor cell proliferation have been identified. However, the exact roles of PKM2 in tumor initiation, growth and maintenance are not yet fully understood. Using immunoprecipitation-coupled LC-MS/MS in MCF7 cells exposed to DNA-damaging agent, we report that the nuclear PKM2 interacts directly with P53 protein, a critical safeguard for genome stability. Specifically, PKM2 inhibits P53-dependent transactivation of the P21 gene by preventing P53 binding to the P21 promoter, leading to a nonstop G 1 phase. As a result, PKM2 expression provides a growth advantage for tumor cells in the presence of a DNA damage stimulus. In addition, PKM2 interferes with phosphorylation of P53 at serine 15, known to stimulate P53 activity by the ATM serine/threonine kinase. These findings reveal a new role for PKM2 in modulating the DNA damage response and illustrate a novel mechanism of PKM2 participating in tumorigenesis.Pyruvate kinase (PK), 4 a rate-limiting enzyme of glycolysis, catalyzes the transfer of phosphate from phosphoenolpyruvate (PEP) to ADP, yielding one molecule of ATP and pyruvate. The PK consists of four isoforms, the PKLR gene-encoded PKL and PKR isoforms and the PKM gene-encoded M1 (PKM1) and M2 (PKM2) isoforms, which express in different types of mammalian cells and tissues. PKM1 constitutively forms stable tetramers (the active form of PK) and is expressed in normal adult tissues that require high levels of energy, such as the heart, brain, and skeletal muscle. In contrast, PKM2 exists in either tetramers or dimers with less activity and metabolic intermediate fructose-1,6-bisphosphate (FBP) can allosterically activate PKM2 by promoting the formation of tetramer from dimer (1, 2). PKM2 is selectively expressed in normal proliferating cells as well as tumor cells, indicating an indispensable role in cancer development.There are two known mechanisms by which PKM2 favors tumor cell growth, one dependent on and one independent of glycolysis. As a glycolytic enzyme, PKM2 in tumor cells shifts glucose metabolism from oxidative phosphorylation to glycolysis under normoxic conditions, a phenomenon termed the Warburg effect or aerobic glycolysis (3). On the other hand, non-metabolic function of PKM2, which is mainly associated with the nuclear PKM2, has been identified as a major contributor during tumorigenesis. For example, nuclear PKM2 displays kinase activity in phosphorylating a number of protein substrates, including histone H3 (4, 5), signal transducer and activator of transcription 3 (stat3) (6, 7), Bub3 (8), and myosin light chain 2 (MLC2) (9), for which PKM2 uses the high-energy phosphate from PEP but not ATP as a phosphate donor. The PKM2-induced phosphorylation...

show abstract

“…identification of eight prognostic biomarkers for feature selection. We referred to seven well-known biomarkers that are highly correlated with NSCLC according to previous studies (EPCAM, HIF1A, PKM, PTK7, ALCAM, CADM1, and SLC2A1) [5][6][7][8][9][10][11]24,25 . Based on each of these seven biomarkers, we separated the 614 ADC patients into biomarker-and biomarker+ subgroups according to expressions by StepMiner 26 (Fig.…”

Section: Figurementioning

confidence: 99%

Overall survival prediction of non-small cell lung cancer by integrating microarray and clinical data with deep learning

Lai

Chen

Hsu

et al. 2020

Sci Rep

128

View full text Add to dashboard Cite

non-small cell lung cancer (nScLc) is one of the most common lung cancers worldwide. Accurate prognostic stratification of NSCLC can become an important clinical reference when designing therapeutic strategies for cancer patients. With this clinical application in mind, we developed a deep neural network (Dnn) combining heterogeneous data sources of gene expression and clinical data to accurately predict the overall survival of nScLc patients. Based on microarray data from a cohort set (614 patients), seven well-known NSCLC biomarkers were used to group patients into biomarker-and biomarker+ subgroups. then, by using a systems biology approach, prognosis relevance values (pRV) were then calculated to select eight additional novel prognostic gene biomarkers. finally, the combined 15 biomarkers along with clinical data were then used to develop an integrative DNN via bimodal learning to predict the 5-year survival status of NSCLC patients with tremendously high accuracy (AUC: 0.8163, accuracy: 75.44%). Using the capability of deep learning, we believe that our prediction can be a promising index that helps oncologists and physicians develop personalized therapy and build the foundation of precision medicine in the future.Lung cancer is the worldwide leading cause of cancer-related mortality, with non-small cell lung cancer (NSCLC) accounting for approximately 85% of all lung cancer patients 1 . The most common NSCLC subtypes are adenocarcinoma (ADC), squamous cell carcinoma (SQC), and large cell carcinoma. Although the overall 5-year survival rate of patients diagnosed with stage I ADC was 63%, nearly 35% of patients relapsed after surgery with a poor prognosis 2 . Adjuvant treatments have been considered ideal for ADC patients with the highest risk of recurrence or death to increase survival rates 3 . Therefore, prognostic stratification is crucial for categorizing patients to help doctors make decisions on therapeutic strategies.Recently, researchers have developed predictive methods based on gene expression profiles to classify lung cancer patients with distinct clinical outcomes, including relapse and overall survival 4 . Previous studies have shown the importance of biomarkers for NSCLC, such as EPCAM, HIF1A, PKM, PTK7, ALCAM, CADM1, and SLC2A1, which were used as a single biomarker for predicting prognostic condition or metastasis 5-11 . However, cancer is a systemic disease with complicated and illusive mechanisms that often involves multiple genes and cross-talk between pathways. Therefore, extending our understanding of NSCLC via the single gene biomarkers by studying the interactions between genes is essential for more accurate prognostic prediction.Machine learning algorithms are powerful tools that apply input features (biomarkers) to capture the complicated interdependencies between these features to accurately predict clinical outcomes 12 . In addition, predicting cancer prognosis can be improved by appropriately modeling the interactions between biomarkers compared with the single biomarker approach ...

show abstract

PKM2 as a biomarker for chemosensitivity to front-line platinum-based chemotherapy in patients with metastatic non-small-cell lung cancer

Cited by 55 publications

References 44 publications

Long non‑coding RNA BCYRN1 promotes glycolysis and tumor progression by regulating the miR‑149/PKM2 axis in non‑small‑cell lung cancer

Long non‑coding RNA BCYRN1 promotes glycolysis and tumor progression by regulating the miR‑149/PKM2 axis in non‑small‑cell lung cancer

A Novel Role for Pyruvate Kinase M2 as a Corepressor for P53 during the DNA Damage Response in Human Tumor Cells

Overall survival prediction of non-small cell lung cancer by integrating microarray and clinical data with deep learning

Contact Info

Product

Resources

About