Acyl-Coenzyme A–Binding Protein Regulates Beta-Oxidation Required for Growth and Survival of Non–Small Cell Lung Cancer

Harris, Fredrick T.; Rahman, S.M. Jamshedur; Hassanein, Mohamed; Qian, Jun; Hoeksema, Megan D.; Chen, Heidi; Eisenberg, Rosana; Chaurand, Pierre; Caprioli, Richard M.; Shiota, Masakazu; Massion, Pierre P.

doi:10.1158/1940-6207.capr-14-0057

Cited by 41 publications

(29 citation statements)

References 47 publications

(44 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The unchanged palmitate and stearate oxidation contrasts with a recent report showing decreased palmitate oxidation in response to ACBP down‐regulation in cancer cells (Harris et al . ). The reason for this discrepancy is unclear but may be explained by differences in the cell type.…”

Section: Discussionmentioning

confidence: 97%

See 1 more Smart Citation

A novel role for central ACBP/DBI as a regulator of long‐chain fatty acid metabolism in astrocytes

Bouyakdan

Taïb

Budry

et al. 2015

Journal of Neurochemistry

View full text Add to dashboard Cite

Acyl-CoA-binding protein (ACBP) is a ubiquitously expressed protein that binds intracellular acyl-CoA esters. Several studies have suggested that ACBP acts as an acyl-CoA pool former and regulates long-chain fatty acids (LCFA) metabolism in peripheral tissues. In the brain, ACBP is known as Diazepam-Binding Inhibitor, a secreted peptide acting as an allosteric modulator of the GABA A receptor. However, its role in central LCFA metabolism remains unknown. In the present study, we investigated ACBP cellular expression, ACBP regulation of LCFA intracellular metabolism, FA profile, and FA metabolism-related gene expression using ACBP-deficient and control mice. ACBP was mainly found in astrocytes with high expression levels in the mediobasal hypothalamus. We demonstrate that ACBP deficiency alters the central LCFACoA profile and impairs unsaturated (oleate, linolenate) but not saturated (palmitate, stearate) LCFA metabolic fluxes in hypothalamic slices and astrocyte cultures. In addition, lack of ACBP differently affects the expression of genes involved in FA metabolism in cortical versus hypothalamic astrocytes. Finally, ACBP deficiency increases FA content and impairs their release in response to palmitate in hypothalamic astrocytes. Collectively, these findings reveal for the first time that central ACBP acts as a regulator of LCFA intracellular metabolism in astrocytes.

show abstract

Section: Discussionmentioning

confidence: 97%

“…) and oxidation (Harris et al . ). Furthermore, functional loss of ACBP decreases very long‐chain FA (VLCFA) content, suggesting that ACBP regulates LCFA elongation and hence VLCFA levels (Gaigg et al .…”

mentioning

confidence: 97%

A novel role for central ACBP/DBI as a regulator of long‐chain fatty acid metabolism in astrocytes

Bouyakdan

Taïb

Budry

et al. 2015

Journal of Neurochemistry

View full text Add to dashboard Cite

show abstract

“…shRNA generation and lentiviral infection was performed as in Refs. using the GIPz system targeted to SSTR2. Knockdown of SSTR2 was confirmed by immunoblotting.…”

Section: Methodsmentioning

confidence: 99%

Somatostatin receptor 2 signaling promotes growth and tumor survival in small‐cell lung cancer

Lehman

Hoeksema

Staub³

et al. 2018

Intl Journal of Cancer

Self Cite

View full text Add to dashboard Cite

Somatostatin receptor 2 (SSTR2) is overexpressed in a majority of neuroendocrine neoplasms, including small-cell lung carcinomas (SCLCs). SSTR2 was previously considered an inhibitory receptor on cell growth, but its agonists had poor clinical responses in multiple clinical trials. The role of this receptor as a potential therapeutic target in lung cancer merits further investigation. We evaluated the expression of SSTR2 in a cohort of 96 primary tumors from patients with SCLC and found 48% expressed SSTR2. Correlation analysis in both CCLE and an SCLC RNAseq cohort confirmed high-level expression and identified an association between NEUROD1 and SSTR2. There was a significant association with SSTR2 expression profile and poor clinical outcome. We tested whether SSTR2 expression might contribute to tumor progression through activation of downstream signaling pathways, using in vitro and in vivo systems and downregulated SSTR2 expression in lung cancer cells by shRNA. SSTR2 downregulation led to increased apoptosis and dramatically decreased tumor growth in vitro and in vivo in multiple cell lines with decreased AMPKα phosphorylation and increased oxidative metabolism. These results demonstrate a role for SSTR2 signaling in SCLC and suggest that SSTR2 is a poor prognostic biomarker in SCLC and potential future therapeutic signaling target.

show abstract

“…RABIF transcripts are overexpressed in pancreatic cancer (PA) tissues compared to normal pancreas [34]. DBI controls lung cancer (LU) progression by regulating β-oxidation [35]. MRPL48 is a candidate diagnostic marker and tumor suppressor for adrenocortical carcinoma [36].…”

Section: Resultsmentioning

confidence: 99%

A Self-Training Subspace Clustering Algorithm under Low-Rank Representation for Cancer Classification on Gene Expression Data

Xia

Han

et al. 2018

IEEE/ACM Trans. Comput. Biol. and Bioinf.

View full text Add to dashboard Cite

Accurate identification of the cancer types is essential to cancer diagnoses and treatments. Since cancer tissue and normal tissue have different gene expression, gene expression data can be used as an efficient feature source for cancer classification. However, accurate cancer classification directly using original gene expression profiles remains challenging due to the intrinsic high-dimension feature and the small size of the data samples. We proposed a new self-training subspace clustering algorithm under low-rank representation, called SSC-LRR, for cancer classification on gene expression data. Low-rank representation (LRR) is first applied to extract discriminative features from the high-dimensional gene expression data; self-training subspace clustering (SSC) method is then used to generate the cancer classification predictions. The SSC-LRR was tested on two separate benchmark datasets in control with four state of the art classification methods. It generated cancer classification predictions with an overall accuracy 89.7% and a general correlation 0.920, which are 18.9% and 24.4% higher than that of the best control method respectively. In addition, several genes (RNF114, HLA-DRB5, USP9Y and PTPN20) were identified by SSC-LRR as new cancer identifiers that deserve further clinical investigation. Overall, the study demonstrated a new sensitive avenue to recognize cancer classifications from large-scale gene expression data.

show abstract

Acyl-Coenzyme A–Binding Protein Regulates Beta-Oxidation Required for Growth and Survival of Non–Small Cell Lung Cancer

Cited by 41 publications

References 47 publications

A novel role for central ACBP/DBI as a regulator of long‐chain fatty acid metabolism in astrocytes

A novel role for central ACBP/DBI as a regulator of long‐chain fatty acid metabolism in astrocytes

Somatostatin receptor 2 signaling promotes growth and tumor survival in small‐cell lung cancer

A Self-Training Subspace Clustering Algorithm under Low-Rank Representation for Cancer Classification on Gene Expression Data

Contact Info

Product

Resources

About