LCMD: Lung Cancer Metabolome Database

Abstract

Lung cancer, one of the most common causes of cancer-related death worldwide, has been associated with high treatment cost and imposed great burdens. The 5-year postoperative survival rate of lung cancer (13%) is lower than many other leading cancers indicating the urgent needs to dissect its pathogenic mechanisms and discover specific biomarkers. Although several proteins have been proposed to be potential candidates for the diagnosis of lung cancer, they present low accuracy in clinical settings. Metabolomics has thus emerged as a very promising tool for biomarker discovery. To date, many lung cancer-related metabolites have been highlighted in the literature but no database is available for scientists to retrieve this information. Herein, we construct and introduce the first Lung Cancer Metabolome Database (LCMD), a freely available online database depositing 2013 lung cancer-related metabolites identified from 65 mass spectrometry-based lung cancer metabolomics studies. Researchers are able to explore LCMD via two ways. Firstly, by applying various filters in the “Browse Metabolites” mode, users can access a list of lung cancer-related metabolites that satisfy the filter specifications. For each metabolite, users can acquire the value of the fold change (cancer/normal), statistical significance (p-value) of the fold change, and the comparative research designs of all the mass spectrometry-based lung cancer metabolomics studies that identify this metabolite. Secondly, by applying various filters in the “Browse Studies” mode, users can obtain a list of mass spectrometry-based lung cancer metabolomics studies that satisfy the filter specifications. For each study, users can view the type of studied specimen, mass spectrometry (MS) method, MS data processing software, and differential analysis method, as well as all the identified lung cancer-related metabolites. Furthermore, the overview of each study is clearly illustrated by a graphical summary. The LCMD ( http://cosbi7.ee.ncku.edu.tw/LCMD/ ) is the first database that brings together the meaningful information of lung cancer-related metabolites. The development of the LCMD is envisioned to promote the biomarker discovery of lung cancer.

Advances and Trends in Omics Technology Development

“…Genome Sequence Archive (GSA) ( 278 ) developed by the Chinese National Genomic Data Center has been launched to compliment this giant-size database portfolio, with GSA-human recently announced as part of GSA to provide a repository for human genetic related omics data ( 279 ). Various databases smaller in size and/or with more specialized focuses have been established by different groups such as “LCMD” for lung cancer metabolome ( 280 ), “DBSAV” for deleterious single amino acid variation prediction in human proteome ( 281 ), “SARS-CoV-2 3D” for coronavirus proteome ( 282 ), and “CMVdb” for cytomegalovirus multi-omes ( 283 ). A plethora of machine-learning algorithms and computational tools have been developed to interrogate these omics data toward gained knowledge or new discoveries such as WeiBI for PPI taking into account of functional enrichment ( 284 ), DTI-MLCD for drug-target interactions utilizing multi-label learning ( 285 ), and MDF-SA-DDI for predicting interaction events between two drugs based on a transformer self-attention mechanism ( 286 ).…”

Identifying Hair Biomarker Candidates for Alzheimer’s Disease Using Three High Resolution Mass Spectrometry-Based Untargeted Metabolomics Strategies

“…Various HRMS-based untargeted metabolomics strategies for biomarker discovery have been developed and proposed, such as a combination of full-scan and targeted MS/MS, data-dependent acquisition (DDA), and data-independent acquisition (DIA). − For example, 26 papers reported the use of the HRMS-based untargeted metabolomics strategy to discover lung cancer biomarkers, as shown in Table S1 . Among these, it was reported that a full scan with targeted MS/MS was performed in 17 papers, DDA mode was utilized in eight papers, and DIA mode was used in one paper.…”

“…24−27 For example, 26 papers reported the use of the HRMS-based untargeted metabolomics strategy to discover lung cancer biomarkers, as shown in Table S1. 28 Among these, it was reported that a full scan with targeted MS/MS was performed in 17 papers, DDA mode was utilized in eight papers, and DIA mode was used in one paper. The combination procedure was performed using the full scan method to obtain metabolic signals in the individual biological samples, including abundance, accurate m/z, and retention time.…”

How do prolonged anchorage-free lifetimes strengthen non-small-cell lung cancer cells to evade anoikis? – A link with altered cellular metabolomics

“…Some clinical research attempts to optimize lung cancer diagnosis by searching for the metabolites associated with the disease incidence, using state-of-the-art facilities in molecular biology [ 16 – 21 ]. Over 2000 metabolites have been profiled in various specimens (e.g., sera, plasma, and tissues) derived from lung cancer patients [ 21 ], while very few of them are identified statistically as lung cancer biomarkers [ 16 – 21 ]. To date, over 600 metabolites remain unknown for their classification and roles in lung cancer metabolism [ 21 ].…”

“…Over 2000 metabolites have been profiled in various specimens (e.g., sera, plasma, and tissues) derived from lung cancer patients [ 21 ], while very few of them are identified statistically as lung cancer biomarkers [ 16 – 21 ]. To date, over 600 metabolites remain unknown for their classification and roles in lung cancer metabolism [ 21 ]. Only a few studies unveil the metabolic alterations associated with anoikis resistance, such as elevations in glycolysis, asparagine bioavailability, and fatty acid uptake [ 22 ].…”