Metabolic pathways underlying GATA6 regulating Trastuzumab resistance in Gastric Cancer cells based on untargeted metabolomics

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Abstract

Trastuzumab has proven its effectiveness in gastric cancer with HER-2 gene-amplification, which has now developed resistance while the mechanism of which is not fully elucidated. Our previous studies demonstrated that the activity of GATA6 binding protein 6 (GATA6) enhanced prominently in trastuzumab resistant gastric cancer cell lines (NCI N87R and MKN45R). In the present study, we further confirmed the re-sensitization to trastuzumab and inhibition of mitochondrial functions of GATA6 knockout sublines (NCI N87R/ΔGATA6 and MKN45R/ΔGATA6). Moreover, we applied untargeted metabolomic profiling to investigate the potential roles of GATA6 in metabolism of NCI N87R and MKN45R. The UPLC system coupled with Q-Exactive Focus Orbitrap mass spectrometry, multivariate in combination with univariate analysis were performed for the screening of differential metabolites between resistant cells and GATA6 knockout sublines. A total of 68 and 59 endogenous metabolites were found to be altered significantly in NCI N87R/ΔGATA6 and MKN45R/ΔGATA6 cells compared with NCI N87R and MKN45R, respectively. Pathway analyses indicated disturbance of metabolic pathways after GATA6 knockout including tricarboxylic acid (TCA) cycle, glycolysis and energy-related amino acid pathways. An integrated proteomics-metabolomics revealed that sub-networks were closely related to TCA cycle, glycolysis, multiple amino acid and nucleotide metabolism. Western blot showed that TCA cycle and glycolysis-related molecules, including PKM, GLS, GLUL and LDHA, were downregulated in GATA6 knockout sublines. Taken together, these findings demonstrate that GATA6 is involved in metabolism reprogramming which might contribute to trastuzumab resistance in gastric cancer.

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Cancer statistics, 2020

Rebecca Siegel, Kimberly D Miller, Ahmedin Jemal (2020)

Each year, the American Cancer Society estimates the numbers of new cancer cases and deaths that will occur in the United States and compiles the most recent data on population-based cancer occurrence. Incidence data (through 2016) were collected by the Surveillance, Epidemiology, and End Results Program; the National Program of Cancer Registries; and the North American Association of Central Cancer Registries. Mortality data (through 2017) were collected by the National Center for Health Statistics. In 2020, 1,806,590 new cancer cases and 606,520 cancer deaths are projected to occur in the United States. The cancer death rate rose until 1991, then fell continuously through 2017, resulting in an overall decline of 29% that translates into an estimated 2.9 million fewer cancer deaths than would have occurred if peak rates had persisted. This progress is driven by long-term declines in death rates for the 4 leading cancers (lung, colorectal, breast, prostate); however, over the past decade (2008-2017), reductions slowed for female breast and colorectal cancers, and halted for prostate cancer. In contrast, declines accelerated for lung cancer, from 3% annually during 2008 through 2013 to 5% during 2013 through 2017 in men and from 2% to almost 4% in women, spurring the largest ever single-year drop in overall cancer mortality of 2.2% from 2016 to 2017. Yet lung cancer still caused more deaths in 2017 than breast, prostate, colorectal, and brain cancers combined. Recent mortality declines were also dramatic for melanoma of the skin in the wake of US Food and Drug Administration approval of new therapies for metastatic disease, escalating to 7% annually during 2013 through 2017 from 1% during 2006 through 2010 in men and women aged 50 to 64 years and from 2% to 3% in those aged 20 to 49 years; annual declines of 5% to 6% in individuals aged 65 years and older are particularly striking because rates in this age group were increasing prior to 2013. It is also notable that long-term rapid increases in liver cancer mortality have attenuated in women and stabilized in men. In summary, slowing momentum for some cancers amenable to early detection is juxtaposed with notable gains for other common cancers.

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The Emerging Hallmarks of Cancer Metabolism.

Natalya Pavlova, Craig B Thompson (2016)

Tumorigenesis is dependent on the reprogramming of cellular metabolism as both direct and indirect consequence of oncogenic mutations. A common feature of cancer cell metabolism is the ability to acquire necessary nutrients from a frequently nutrient-poor environment and utilize these nutrients to both maintain viability and build new biomass. The alterations in intracellular and extracellular metabolites that can accompany cancer-associated metabolic reprogramming have profound effects on gene expression, cellular differentiation, and the tumor microenvironment. In this Perspective, we have organized known cancer-associated metabolic changes into six hallmarks: (1) deregulated uptake of glucose and amino acids, (2) use of opportunistic modes of nutrient acquisition, (3) use of glycolysis/TCA cycle intermediates for biosynthesis and NADPH production, (4) increased demand for nitrogen, (5) alterations in metabolite-driven gene regulation, and (6) metabolic interactions with the microenvironment. While few tumors display all six hallmarks, most display several. The specific hallmarks exhibited by an individual tumor may ultimately contribute to better tumor classification and aid in directing treatment.

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Is Open Access

MetaboAnalyst 4.0: towards more transparent and integrative metabolomics analysis

Jasmine Chong, Othman Soufan, Carin Li … (2018)

Abstract We present a new update to MetaboAnalyst (version 4.0) for comprehensive metabolomic data analysis, interpretation, and integration with other omics data. Since the last major update in 2015, MetaboAnalyst has continued to evolve based on user feedback and technological advancements in the field. For this year's update, four new key features have been added to MetaboAnalyst 4.0, including: (1) real-time R command tracking and display coupled with the release of a companion MetaboAnalystR package; (2) a MS Peaks to Pathways module for prediction of pathway activity from untargeted mass spectral data using the mummichog algorithm; (3) a Biomarker Meta-analysis module for robust biomarker identification through the combination of multiple metabolomic datasets and (4) a Network Explorer module for integrative analysis of metabolomics, metagenomics, and/or transcriptomics data. The user interface of MetaboAnalyst 4.0 has been reengineered to provide a more modern look and feel, as well as to give more space and flexibility to introduce new functions. The underlying knowledgebases (compound libraries, metabolite sets, and metabolic pathways) have also been updated based on the latest data from the Human Metabolome Database (HMDB). A Docker image of MetaboAnalyst is also available to facilitate download and local installation of MetaboAnalyst. MetaboAnalyst 4.0 is freely available at http://metaboanalyst.ca.

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Author and article information

Journal

Journal ID (nlm-ta): Int J Med Sci

Journal ID (iso-abbrev): Int J Med Sci

Journal ID (publisher-id): ijms

Title: International Journal of Medical Sciences

Publisher: Ivyspring International Publisher (Sydney )

ISSN (Electronic): 1449-1907

Publication date Collection: 2020

Publication date (Electronic): 23 October 2020

Volume: 17

Issue: 18

Pages: 3146-3164

Affiliations

[1 ]School of Basic Medical Sciences, North Sichuan Medical College, Nanchong, Sichuan 637100, China.

[2 ]School of Pharmacy, North Sichuan Medical College, Nanchong, Sichuan 637100, China.

[3 ]Department of Laboratory Medicine, Affiliated Hospital of North Sichuan Medical College; Faculty of Laboratory Medicine, Center for Translational Medicine, North Sichuan Medical College, Nanchong, Sichuan 637000, China.

[4 ]Sichuan Key Laboratory of Medical Imaging and Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong, Sichuan 637000, China.

Author notes

✉ Corresponding author: Wenhu Liu, Ph.D., School of Pharmacy, North Sichuan Medical College, 55 Dongshun Rd. Nanchong, Sichuan 637100, China, E-mail: wenhuliu@ 123456cqu.edu.cn .

*These authors contributed equally to this work.

Competing Interests: The authors have declared that no competing interest exists.

Article

Publisher ID: ijmsv17p3146

DOI: 10.7150/ijms.50563

PMC ID: 7646115

PubMed ID: 33173435

SO-VID: 9beb6384-7504-453d-9853-3d86114388e0

License:

This is an open access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/). See http://ivyspring.com/terms for full terms and conditions.

Metabolic pathways underlying GATA6 regulating Trastuzumab resistance in Gastric Cancer cells based on untargeted metabolomics

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Karger: Oncology

Most cited references 40

Cancer statistics, 2020

The Emerging Hallmarks of Cancer Metabolism.

MetaboAnalyst 4.0: towards more transparent and integrative metabolomics analysis

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