Lung cancer is a leading cause of cancer‐related deaths with an increasing incidence and poor prognoses. To further understand the regulatory mechanisms of lipidomic profiles in lung cancer subtypes, we measure the profiles of plasma lipidome between health and patients with lung cancer or among patients with squamous cell carcinomas, adenocarcinoma or small cell lung cancer and to correct lipidomic and genomic profiles of lipid‐associated enzymes and proteins by integrating the data of large‐scale genome screening. Our studies demonstrated that circulating levels of PS and lysoPS significantly increased, while lysoPE and PE decreased in patients with lung cancer. Our data indicate that lung cancer‐specific and subtype‐specific lipidomics in the circulation are important to understand mechanisms of systemic metabolisms and identify diagnostic biomarkers and therapeutic targets. The carbon atoms, dual bonds or isomerism in the lipid molecule may play important roles in lung cancer cell differentiations and development. This is the first try to integrate lipidomic data with lipid protein‐associated genomic expression among lung cancer subtypes as the part of clinical trans‐omics. We found that a large number of lipid protein‐associated genes significantly change among cancer subtypes, with correlations with altered species and spatial structures of lipid metabolites.

Background The morbidity and mortality of patients with critical illnesses remain high in pulmonary critical care units and a poorly understood correlation between alterations of lipid elements and clinical phenomes remain unelucidated. Methods In the present study, we investigated plasma lipidomic profiles of 30 patients with severe acute pneumonia (SAP), acute pulmonary embolism (APE), and acute exacerbation of chronic pulmonary diseases (AECOPD) or 15 healthy with the aim to compare disease specificity of lipidomic patterns. We defined the specificity of lipidomic profiles in SAP by comparing it to both APE and AECOPD. Analysis of the correlation between altered lipid elements and clinical phenotypes using the lipid-QTL model was then carried out. Results We integrated lipidomic profiles with clinical phenomes measured by score values from the digital evaluation score system and found phenome-associated lipid elements to identify disease-specific lipidomic profiling. The present study demonstrates that lipidomic profiles of patients with acute lung diseases are different from healthy lungs, and there are also disease-specific portions of lipidomics among SAP, APE, or AECOPD. The comprehensive profiles of clinical phenomes or lipidomics are valuable in describing the disease specificity of patient phenomes and lipid elements. The combination of clinical phenomes with lipidomic profiles provides more detailed disease-specific information on panels of lipid elements When compared to the use of each separately. Conclusions Integrating biological functions with disease specificity, we believe that clinical lipidomics may create a new alternative way to understand lipid-associated mechanisms of critical illnesses and develop a new category of disease-specific biomarkers and therapeutic targets. Electronic supplementary material The online version of this article (10.1186/s12967-019-1898-z) contains supplementary material, which is available to authorized users.

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