Cancer Letters

Cancer Letters

Volume 470, 1 February 2020, Pages 75-83
Cancer Letters

Clinical lipidomics in understanding of lung cancer: Opportunity and challenge

https://doi.org/10.1016/j.canlet.2019.08.014Get rights and content

Highlights

  • Clinical lipidomics is important in identifying and validating biomarkers for early diagnosis and therapy for cancer.

  • Heterogeneity of systemic lipidomic profiles exists among patients with different lung cancer subtypes.

  • The integration of lipidomics with clinical phenomes and multi-omics will provide a new insight into understanding molecular mechanisms of lung cancer.

  • There is an urgent need to standardize lipidomic measurements and establish baseline and databases of lipidomic profiles in various populations.

Abstract

Disordered lipid metabolisms have been evidenced in lung cancer as well as its subtypes. Lipidomics with in-depth mining is considered as a critical member of the multiple omics family and a lipid-specific tool to understand disease-associated lipid metabolism and disease-specific dysfunctions of lipid species, discover biomarkers and targets for monitoring therapeutic strategies, and provide insights into lipid profiling and pathophysiological mechanisms in lung cancer. The present review describes the characters and patterns of lipidomic profiles in patients with different lung cancer subtypes, important values of comprehensive lipidomic profiles in understanding of lung cancer heterogeneity, urgent needs of standardized methodologies, potential mechanisms by lipid-associated enzymes and proteins, and the importance of integration between clinical phenomes and lipidomic profiles. The characteristics of lipidomic profiles in different lung cancer subtypes are extremely varied among study designs, objects, methods, and analyses. Preliminary data from recent studies demonstrate the specificity of lipidomic profiles specific for lung cancer stage, severity, subtype, and response to drugs. The heterogeneity of lipidomic profiles and lipid metabolism may be part of systems heterogeneity in lung cancer and be responsible for the development of drug resistance, although there are needs for direct evidence to show the existence of intra- or inter-lung cancer heterogeneity of lipidomic profiles. With an increasing understanding of expression profiles of genes and proteins, lipidomic profiles should be associated with activities of enzymes and proteins involved in the processes of lipid metabolism, which can be profiled with genomics and proteomics, and to provide the opportunity for the integration of lipidomic profiles with gene and protein expression profiles. The concept of clinical trans-omics should be emphasized to integrate data of lipidomics with clinical phenomics to identify disease-specific and phenome-specific biomarkers and targets, although there are still a large number of challenges to be overcome in the integration between clinical phenomes and lipidomic profiles.

Introduction

Lipidomics as a part of systems biology and systems-based analysis of lipids and their interacting partners was initially emphasized as an emerging and promising area of target and biomarker identification and validation [1]. It was proposed that it could be more important if the alterations of lipid metabolites and associated enzymes are integrated with phenomes of genomics and proteomics in human diseases. Roles of target genes in regulation of lipidomic profiles can be explored by charactering lipids and their interacting moieties under a clear genetic condition defined with RNA silencing [2], to elucidate specific roles of interactions between genes and lipid intermediates in cell signaling and functioning. With the rapid development of omics understanding and biotechnology, clinical lipidomics is recently defined "as a new integrative biomedicine to discover the correlation and regulation between a large scale of lipid elements measured and analyzed in liquid biopsies from patients with their phenomes and clinical phenotypes" [3]. As a new extension of lipidomics, clinical lipidomics more focuses on the comprehensive understanding of multi-dimensional profiles, pathways, and networks between lipids and their interacting moieties/elements with the of clinical proteomic, genomic, and phenomic data profiles. There are still a large number of challenges to be overcome, including instability, specificity, and sensitivity of methodology to characterize and quantify the complete lipid molecules in patient samples [4]. Clinical lipidomics has clear objectives to understand the systems biology-based mechanisms of metabolism, identify diagnostic markers specific for diseases, and serves as an effective and standardized measurement that is stable during handling, sensitive in response to changes, and efficient in data analysis. The important value of this emerging discipline is the ability to integrate patient phenotypes and clinical features to identify disease specificities and subtypes [5].

Lung cancer become number one in both occurrence and mortality among cancers, with a large population of potential candidates who are exposed to smoking, air pollution, chemicals, or chronic diseases consistently [6]. A number of new target drugs are being discovered and firstly applied for patients with lung cancer. The sensitivity to those drugs and reoccurrence after therapies vary extensively among patients due to the existence of intra- and inter-tumor heterogeneity. For example, a precise self-validation system was proposed recently as a specifically selected strategy of treatment for patients with lung cancer gene mutations and heterogeneity based on hereditary and somatic gene changes, mutation, and heterogeneity [7]. The specificity and characterization of intra- or inter-lung cancer heterogeneity as well as associated signaling pathways and regulators may play decisive roles, while lipid metabolism disorders in lung cancer are suggested to be more important to accurately diagnose disease severities and stages, estimate response to therapy and prognosis, and develop new therapeutic targets and disease-specific biomarkers [8]. In the present article, we overview the characters and patterns of lipidomic profiles in patients with lung cancer and subtypes, and emphasize the important values of comprehensive lipidomic profiles in the understanding of lung cancer heterogeneity, headline the urgent need for standardized methodologies for measurements and analyses. We also illustrate potential mechanisms by lipid-associated enzymes and proteins can alter lipidomic profiles. Finally, we describe the importance of integration between clinical phenomes and lipidomic profiles in identification of disease-specific and phenome-specific lipid elements.

Section snippets

Lipidomics profiles in lung cancer and subtypes

Lipidomics with in-depth mining is considered a critical member of multiple omics family and a lipid-specific tool to understand disease-associated lipid metabolism and specific dysfunctions of lipid species, discover biomarkers and targets for monitoring therapeutic strategies, and provide insights into lipid profiling and pathophysiological mechanisms in lung cancer [9]. There is increasing evidence to show the characters of lipidomic profiles in lung cancer and subtypes, though the findings

Lipidomics in lung cancer heterogeneity

Lung cancer heterogeneity is more comprehensive and sophistic than expected and exists between patients, cancer types, locations, cells, molecules, or dimensions of genome constructions [15,16]. The heterogeneity of lung cancer can lead to evolution during which new heterogeneity can be generated. Both hereditary and somatic gene changes during evolution and genomic instability can cause the progression of gene mutations and heterogeneity [7]. Therapy per se can also alter characteristics of

Urgent needs of standardized methodologies

Methodologies and strategies of lipidomics are rapidly developing with understanding of lipidomics, carefully categorized into more accurate moieties of lipidomic profiles, and comprehensively utilized for understanding characters and specificities of interaction and network matrices using new concepts of clinical trans-omics or cell-cell communications [29,30]. LPCs quantification based on liquid chromatography mass spectrometry (LC-MS)/MS parameter prediction with multiple statistical data

Understanding of lipid-associated enzymes and proteins-based mechanisms

A large number of enzymes and proteins are involved in the processes of lipid metabolism, which can be profiled with genomics and proteomics and provide the opportunity for the integration of lipidomic profiles with gene and protein expression profiles. For example, acetyl-CoA acetyltransferase 2 (ACAT2) was selected as a potential target gene from studies on lipidomic profiling. ACAT2 encodes the cytosolic acetoacetyl-CoA thiolase to regulate synthesis and degradation of ketone bodies or fatty

Importance of integration between clinical phenomes and lipidomics

With the wide application of multiple omics in clinical research, the concept of clinical trans-omics has been emphasized to integrate data of molecular multi-omics, e.g., genomics, proteomics, metabolomics, and lipidomics, with clinical phenomics to identify diagnostic biomarkers and therapeutic targets [28]. Lipidomics demonstrates the characters and profiles of systems-level lipid elements, while clinical trans-omics will provide the full picture of patient phenome-based lipid networks for

Conclusion

Disordered lipid metabolisms have been evidenced in lung cancer as well as its subtypes. Lipidomics with an in-depth mining is considered as a critical member of the multiple omics family and a lipid-specific tool to understand disease-associated lipid metabolism and specific dysfunctions of lipid species, discover biomarkers and targets for monitoring therapeutic strategies, and provide insights into lipid profiling and pathophysiological mechanisms in lung cancer. The characters of lipidomic

Contributions

ZLL and ZBJ contributed to manuscript preparation and writing, and ZLL draw all figures. ZYM, HZ, ZJQ and WXD contribute to the manuscript strategy and design, review, and editing.

Declaration of competing interest

The authors declare no conflict of interest.

Acknowledgements

The work was supported by Zhongshan Distinguished Professor Grant (XDW), The National Nature Science Foundation of China (91230204, 81270099, 81320108001, 81270131, 81300010, 81700008, 81873409), The Shanghai Committee of Science and Technology (12JC1402200, 12431900207, 11410708600, 14431905100), Operation funding of Shanghai Institute of Clinical Bioinformatics, Ministry of Education for Academic Special Science and Research Foundation for PhD Education (20130071110043), and National Key

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