L-Type Amino Acid Transporter 1 Regulates Cancer Stemness and the Expression of Programmed Cell Death 1 Ligand 1 in Lung Cancer Cells

Liu, Yiheng; Li, Yuling; Shen, Huan-Ting; Chien, Peng-Ju; Sheu, Gwo‐Tarng; Wang, Bing‐Yen; Chang, Wen‐Wei

doi:10.3390/ijms222010955

Cited by 18 publications

(13 citation statements)

References 36 publications

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“…Many lung cancers show increased expression of ATP1A1 and targeting ATP1A1 with siRNA or drugs (cardenolides) has an antitumor effect [58]. SLC3A2 heterodimerizes with SLC7A5/LAT1, which has a well-established role in lung cancer [59][60][61]. Conversely, our results suggest that erlotinib treatment may result in localization of glycolysis enzymes away from the plasma membrane (Figure 4A).…”

Section: Discussionmentioning

confidence: 75%

Network models of protein phosphorylation, acetylation, and ubiquitination connect metabolic and cell signaling pathways in lung cancer

Ross

Zhang

Akcora

et al. 2022

Preprint

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We analyzed large-scale post-translational modification (PTM) data to outline cell signaling pathways affected by tyrosine kinase inhibitors (TKIs) in ten lung cancer cell lines. Tyrosine phosphorylated, lysine ubiquitinated, and lysine acetylated proteins were concomitantly identified using sequential enrichment of post translational modification (SEPTM) proteomics. Machine learning was used to identify PTM clusters that represent functional modules that respond to TKIs. To model lung cancer signaling at the protein level, PTM clusters were used to create a co-cluster correlation network (CCCN) and select protein-protein interactions (PPIs) from a large network of curated PPIs to create a cluster-filtered network (CFN). Next, we constructed a Pathway Crosstalk Network (PCN) by connecting pathways from NCATS BioPlanet whose member proteins have PTMs that co-cluster. Interrogating the CCCN, CFN, and PCN individually and in combination yields insights into the response of lung cancer cells to TKIs. We highlight examples where cell signaling pathways involving EGFR and ALK exhibit crosstalk with BioPlanet pathways: Transmembrane transport of small molecules; and Glycolysis and gluconeogenesis. These data identify known and previously unappreciated connections between receptor tyrosine kinase (RTK) signal transduction and oncogenic metabolic reprogramming in lung cancer. Comparison to a CFN generated from a previous multi-PTM analysis of lung cancer cell lines reveals a common core of PPIs involving heat shock/chaperone proteins, metabolic enzymes, cytoskeletal components, and RNA-binding proteins. Elucidation of points of crosstalk among signaling pathways employing different PTMs reveals new potential drug targets and candidates for synergistic attack through combination drug therapy.

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Section: Discussionmentioning

confidence: 75%

Network models of protein phosphorylation, acetylation, and ubiquitination connect metabolic and cell signaling pathways in lung cancer

Ross

Zhang

Akcora

et al. 2022

Preprint

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“…The levels of S100P mRNA and protein expression are higher in lung cancer, and upregulated S100P increases cancer cell migration and metastasis ( Hsu et al, 2015 ). The levels of SLC7A5 mRNA and protein expression are increased in NSCLC and are correlated with grade, pathologic stage, and OS ( Takeuchi et al, 2010 ; Liu et al, 2021 ). The levels of COL1A1 mRNA expression in lung cancer are increased and are significantly correlated with tumor diameter, metastasis, and OS ( Geng et al, 2021 ; Hou et al, 2021 ).…”

Section: Discussionmentioning

confidence: 99%

Construction of a redox-related gene signature for overall survival prediction and immune infiltration in non-small-cell lung cancer

Miao

Yang

Gao

et al. 2022

Front. Mol. Biosci.

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Background: An imbalance in the redox homeostasis has been reported in multiple cancers and is associated with a poor prognosis of disease. However, the prognostic value of redox-related genes in non-small-cell lung cancer (NSCLC) remains unclear.Methods: RNA sequencing data, DNA methylation data, mutation, and clinical data of NSCLC patients were downloaded from The Cancer Genome Atlas and Gene Expression Omnibus databases. Redox-related differentially expressed genes (DEGs) were used to construct the prognostic signature using least absolute shrinkage and selection operator (LASSO) regression analysis. Kaplan–Meier survival curve and receiver operator characteristic (ROC) curve analyses were applied to validate the accuracy of the gene signature. Nomogram and calibration plots of the nomogram were constructed to predict prognosis. Pathway analysis was performed using gene set enrichment analysis. The correlations of risk score with tumor stage, immune infiltration, DNA methylation, tumor mutation burden (TMB), and chemotherapy sensitivity were evaluated. The prognostic signature was validated using GSE31210, GSE26939, and GSE68465 datasets. Real-time polymerase chain reaction (PCR) was used to validate dysregulated genes in NSCLC.Results: A prognostic signature was constructed using the LASSO regression analysis and was represented as a risk score. The high-risk group was significantly correlated with worse overall survival (OS) (p < 0.001). The area under the ROC curve (AUC) at the 5-year stage was 0.657. The risk score was precisely correlated with the tumor stage and was an independent prognostic factor for NSCLC. The constructed nomogram accurately predicted the OS of patients after 1-, 3-, and 5-year periods. DNA replication, cell cycle, and ECM receptor interaction were the main pathways enriched in the high-risk group. In addition, the high-risk score was correlated with higher TMB, lower methylation levels, increased infiltrating macrophages, activated memory CD4+ T cells, and a higher sensitivity to chemotherapy. The signature was validated in GSE31210, GSE26939, and GSE68465 datasets. Real-time PCR validated dysregulated mRNA expression levels in NSCLC.Conclusions: A prognostic redox-related gene signature was successfully established in NSCLC, with potential applications in the clinical setting.

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“…GRM1 (glutamine receptor 1), an important receptor of glutamine metabolism pathway, was also overexpressed in lung cancer and promoted tumor development and progression through activating the MAPK and AKT/mTOR pathways [38]. Liu et al demonstrated that the upregulation of LAT1 in pemetrexed-resistant NSCLC cells was correlated with increased cancer stem cell activity, and the inhibition of LAT1 by RNA interference or JPH 203 (a small molecule inhibitor of LAT1) reduced the selfrenewal capability that mediated via the Akt/mTOR pathway [39]. These evidences suggest a relationship between GM and mTOR signaling pathway, which requires further validation in the future.…”

Section: Discussionmentioning

confidence: 99%

“…LAT1 silencing caused the downregulation of programmed cell death 1 ligand 1 (PD-L1) on lung cancer cells. The PD-L1+/LAT1 + subpopulation of NSCLC cells exhibited great Cancer Stem Cell (CSC) activity [39]. In addition, glutamine depletion combination with anti-PD-L1 strongly enhanced antitumor e cacy of T cells by upregulation of Fas/CD95 levels in mice [47].…”

Section: Discussionmentioning

confidence: 99%

A novel glutamine metabolism-related gene signature for prognostic prediction in patients with lung adenocarcinoma

Jiang¹,

Wen

et al. 2022

Preprint

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Background Lung adenocarcinoma (LUAD) is the major non-small-cell lung cancer pathological subtype with poor prognosis worldwide. Glutamine metabolism (GM) plays an important role in tumor development and progression. Herein, we aimed to construct a GM-associated prognostic gene signature to predict survival in patients with LUAD. Methods The gene expression profiles of patients with LUAD were downloaded from the Cancer Genome Atlas (TCGA), and two independent datasets for validation were downloaded from the Gene Expression Omnibus (GEO). 41 GM-related genes were downloaded from the previous literatures and used to establish GM-related risk model (names as Score). The prognostic GM-associated gene signatures were identified using univariate Cox and LASSO analyses. Kaplan-Meier and receiver operating characteristic (ROC) curves were performed to validate the predictive efficacy of the prognostic signatures. The correlation between the GMScore and tumor-infiltrated immune cells was evaluated using CIBERSORTx analysis. A nomogram was used to predict the survival probability of LUAD based on GMScore. Results We constructed a prognostic signature comprising 7 GM-related genes (AMDHD1, GAPDH, GCLC, GLS2, HAL, SLC38A2, SLC7A5). The Kaplan-Meier curves validated the reliable predictive ability of the prognostic signature in TCGA and two GEO datasets (p < 0.001, p = 0.001, and p = 0.003, respectively). The area under the curve (AUC) of the ROC curves validated the predictive accuracy of the risk model. We constructed a nomogram to predict the survival probability of LUAD, and the calibration curves showed well predictive accuracy. Finally, functional analysis also revealed distinct immune status between high and low GMScore groups in LUAD. Conclusion Our study constructed a GM-related gene signature, and indicated that GMScore could serve as a novel biomarker for predicting patients' survival in LUAD.

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L-Type Amino Acid Transporter 1 Regulates Cancer Stemness and the Expression of Programmed Cell Death 1 Ligand 1 in Lung Cancer Cells

Cited by 18 publications

References 36 publications

Network models of protein phosphorylation, acetylation, and ubiquitination connect metabolic and cell signaling pathways in lung cancer

Network models of protein phosphorylation, acetylation, and ubiquitination connect metabolic and cell signaling pathways in lung cancer

Construction of a redox-related gene signature for overall survival prediction and immune infiltration in non-small-cell lung cancer

A novel glutamine metabolism-related gene signature for prognostic prediction in patients with lung adenocarcinoma

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