Monocarboxylate transporters (MCTs) play a major role in up-regulation of glycolysis and adaptation to acidosis. However, the role of MCTs in gastric cancer (GC) is not fully understood. We investigated the potential utilization of a new cancer therapy for GC. We characterized the expression patterns of the MCT isoforms 1, 2, and 4 and investigated the role of MCT in GC through in vitro and in vivo tests using siRNA targeting MCTs. In GC cell lines, MCT1, 2, and 4 were up-regulated with different expression levels; MCT1 and MCT4 were more widely expressed in GC cell lines compared with MCT2. Inhibition of MCTs by siRNA or AR-C155858 reduced cell viability and lactate uptake in GC cell lines. The effect of inhibition of MCTs on tumor growth was also confirmed in xenograft models. Furthermore, MCT inhibition in GC cells increased the sensitivity of cells to radiotherapy or chemotherapy. Compared with normal gastric tissue, no significant alterations of expression levels in tumors were identified for MCT1 and MCT2, whereas a significant increase in MCT4 expression was observed. Most importantly, MCT4 was highly overexpressed in malignant cells of acsites and its silencing resulted in reduced tumor cell proliferation and lactate uptake in malignant ascites. Our study suggests that MCT4 is a clinically relevant target in GC with peritoneal carcinomatosis.
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http://dx.doi.org/10.18632/oncotarget.9523 | DOI Listing |
NPJ Precis Oncol
January 2025
Zentalis Pharmaceuticals, Inc., San Diego, CA, USA.
Upregulation of Cyclin E1 and subsequent activation of CDK2 accelerates cell cycle progression from G1 to S phase and is a common oncogenic driver in gynecological malignancies. WEE1 kinase counteracts the effects of Cyclin E1/CDK2 activation by regulating multiple cell cycle checkpoints. Here we characterized the relationship between Cyclin E1/CDK2 activation and sensitivity to the selective WEE1 inhibitor azenosertib.
View Article and Find Full Text PDFSci Rep
January 2025
School of Medicine, Nankai University, Tianjin, 300071, China.
Cholangiocarcinoma (CCA), a highly aggressive form of cancer, is known for its high mortality rate. A Disintegrin and Metalloprotease Domain-like Protein Decysin-1 (ADAMDEC1) can promote the development and metastasis in various tumors by degrading the extracellular matrix. However, its regulatory mechanism in CCA remains unclear.
View Article and Find Full Text PDFAnn Hematol
January 2025
Department of Hematology, Navy Medical Center of PLA, Naval Medical University, No. 338 West Huaihai Road, Changning District, Shanghai, 200052, China.
Multiple myeloma(MM) remains incurable with high relapse and chemoresistance rates. Differentially expressed genes(DEGs) between newly diagnosed myeloma and secondary plasma cell leukemia(sPCL) were subjected to a weighted gene co-expression network analysis(WGCNA). Drug resistant myeloma cell lines were established.
View Article and Find Full Text PDFSci Rep
January 2025
School of Physics, Engineering and Technology, University of York, Heslington, York, YO10 5DD, UK.
Prostate cancer is a disease which poses an interesting clinical question: Should it be treated? Only a small subset of prostate cancers are aggressive and require removal and treatment to prevent metastatic spread. However, conventional diagnostics remain challenged to risk-stratify such patients; hence, new methods of approach to biomolecularly sub-classify the disease are needed. Here we use an unsupervised self-organising map approach to analyse live-cell Raman spectroscopy data obtained from prostate cell-lines; our aim is to exemplify this method to sub-stratify, at the single-cell-level, the cancer disease state using high-dimensional datasets with minimal preprocessing.
View Article and Find Full Text PDFNat Commun
January 2025
Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, ON, Canada.
Spatial protein expression technologies can map cellular content and organization by simultaneously quantifying the expression of >40 proteins at subcellular resolution within intact tissue sections and cell lines. However, necessary image segmentation to single cells is challenging and error prone, easily confounding the interpretation of cellular phenotypes and cell clusters. To address these limitations, we present STARLING, a probabilistic machine learning model designed to quantify cell populations from spatial protein expression data while accounting for segmentation errors.
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