Neutral Desorption Extractive Electrospray Ionization Mass Spectrometry Analysis Sputum for Non-Invasive Lung Adenocarcinoma Detection

Zheng, Qi; Zhang, Jianyong; Wang, Xinchen; Zhang, Wen‐Xiong; Xiao, Yipo; Sheng, Hu; Xu, Jianjun

doi:10.2147/ott.s269300

Cited by 7 publications

(4 citation statements)

References 63 publications

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“… 18 , 19 Specifically, such reports have been made for renal cell cancer, 7 liver, 8 breast cancer, 10 and colorectal cancer. 12 Regarding lung cancer, several lipid metabolites have been reported as candidates in sputum analysis, 20 but definitive biomarkers that include other organ malignancies have not yet been clarified. In this study, our system could not specify the metabolites from which the m/z was derived for each identification of PLAC, CRMT, and normal lung.…”

Section: Discussionmentioning

confidence: 99%

Utility of mass spectrometry and artificial intelligence for differentiating primary lung adenocarcinoma and colorectal metastatic pulmonary tumor

et al. 2021

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Background: Rapid intraoperative diagnosis for unconfirmed pulmonary tumor is extremely important for determining the optimal surgical procedure (lobectomy or sublobar resection). Attempts to diagnose malignant tumors using mass spectrometry (MS) have recently been described. This study evaluated the usefulness of MS and artificial intelligence (AI) for differentiating primary lung adenocarcinoma (PLAC) and colorectal metastatic pulmonary tumor. Methods: Pulmonary samples from 40 patients who underwent pulmonary resection for PLAC (20 tumors, 20 normal lungs) or pulmonary metastases originating from colorectal metastatic pulmonary tumor (CRMPT) (20 tumors, 20 normal lungs) were collected and analyzed retrospectively by probe electrospray ionization-MS. AI using random forest (RF) algorithms was employed to evaluate the accuracy of each combination. Results: The accuracy of the machine learning algorithm applied using RF to distinguish malignant tumor (PLAC or CRMPT) from normal lung was 100%. The algorithms offered 97.2% accuracy in differentiating PLAC and CRMPT. Conclusions: MS combined with an AI system demonstrated high accuracy not only for differentiating cancer from normal tissue, but also for differentiating between PLAC and CRMPT with a short working time. This method shows potential for application as a support tool facilitating rapid intraoperative diagnosis to determine the surgical procedure for pulmonary resection.

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Section: Discussionmentioning

confidence: 99%

Utility of mass spectrometry and artificial intelligence for differentiating primary lung adenocarcinoma and colorectal metastatic pulmonary tumor

et al. 2021

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“…ND-EESI-MS and collision induced dissociation (CID) were reported to detect sputum metabolites from 143 spontaneous sputum samples. In this study, 19 altered metabolites were detected by ND-EESI-MS from 76 cases of LAC and 67 cases of control (Zheng et al, 2021). Moreover, ND-EESI technology can also be used for the detection of exogenous substances.…”

Section: Application Of Eesi-ms In Analysis Of Pharmaceutical Chemicalsmentioning

confidence: 91%

“…Due to its extremely high tolerance of dirty matrix, EESI-MS has been widely used in the safety evaluation of medicine (Chen et al, 2010;Zeng et al, 2016;Zheng et al, 2022), food analysis (Deng et al, 2017;Xu et al, 2017;Lu et al, 2021b), life science (Hu et al, 2011;Ning et al, 2013;Ke et al, 2019), environmental science (Liu et al, 2011;Liu et al, 2012;Liu et al, 2020), chemical industry (Zhu et al, 2008;Li X. et al, 2013;Zhang et al, 2018) and other fields. In addition, based on EESI-MS, Neutral-Desorption Extractive Electrospray Ionization Mass Spectrometry (ND-EESI-MS) (Wu et al, 2019;Zheng et al, 2021) and Internal Extractive Electrospray Ionization Mass Spectrometry (iEESI-MS) (Lu et al, 2021a;Zheng et al, 2022) were further developed. Herein, we provide an overview of the typical effects of operation conditions on the analytical performance of EESI-MS for various pharmaceutical compounds, pharmaceutical preparations, and herbal medicine materials.…”

Section: Introductionmentioning

confidence: 99%

Extractive electrospray ionization mass spectrometry for analytical evaluation and synthetic preparation of pharmaceutical chemicals

Qin

Qian

et al. 2023

Front. Pharmacol.

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Extraction electrospray ionization mass spectrometry (EESI-MS), due to the unique configuration of its ionization module, enables the effective ionization of trace molecules of interest in samples containing complex matrices with high sensitivity, high selectivity and high responding speed without requiring sample pretreatment, and allows high-energy molecular species to undergo specially designed reactions for advanced functionalization. The typical effects of operating conditions on the analytical performance of extraction electrospray ionization mass spectrometry for various pharmaceutical compounds, pharmaceutical preparations and herbal materials were systematically reviewed. The application prospect of extraction electrospray ionization in molecular functionalization for advanced drug discovery is also briefly introduced.

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“… 106 Meanwhile, due to the changes in fatty acid metabolism in cancers, certain specific fatty acids may serve as potential biomarkers for cancer diagnosis. 107 , 108 …”

Section: Metabolite Sensing and Epigenetic Regulation Of Fatty Acid M...mentioning

confidence: 99%

Signaling pathways in cancer metabolism: mechanisms and therapeutic targets

You

Xie

Zhang

et al. 2023

Sig Transduct Target Ther

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A wide spectrum of metabolites (mainly, the three major nutrients and their derivatives) can be sensed by specific sensors, then trigger a series of signal transduction pathways and affect the expression levels of genes in epigenetics, which is called metabolite sensing. Life body regulates metabolism, immunity, and inflammation by metabolite sensing, coordinating the pathophysiology of the host to achieve balance with the external environment. Metabolic reprogramming in cancers cause different phenotypic characteristics of cancer cell from normal cell, including cell proliferation, migration, invasion, angiogenesis, etc. Metabolic disorders in cancer cells further create a microenvironment including many kinds of oncometabolites that are conducive to the growth of cancer, thus forming a vicious circle. At the same time, exogenous metabolites can also affect the biological behavior of tumors. Here, we discuss the metabolite sensing mechanisms of the three major nutrients and their derivatives, as well as their abnormalities in the development of various cancers, and discuss the potential therapeutic targets based on metabolite-sensing signaling pathways to prevent the progression of cancer.

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Neutral Desorption Extractive Electrospray Ionization Mass Spectrometry Analysis Sputum for Non-Invasive Lung Adenocarcinoma Detection

Cited by 7 publications

References 63 publications

Utility of mass spectrometry and artificial intelligence for differentiating primary lung adenocarcinoma and colorectal metastatic pulmonary tumor

Utility of mass spectrometry and artificial intelligence for differentiating primary lung adenocarcinoma and colorectal metastatic pulmonary tumor

Extractive electrospray ionization mass spectrometry for analytical evaluation and synthetic preparation of pharmaceutical chemicals

Signaling pathways in cancer metabolism: mechanisms and therapeutic targets

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