Lipidomic and metabolomic analysis reveals changes in biochemical pathways for non-small cell lung cancer tissues

Integration of clinical phenoms and metabolomics facilitates precision medicine for lung cancer

Lung cancer is a common malignancy that is frequently associated with systemic metabolic disorders. Early detection is pivotal to survival improvement. Although blood biomarkers have been used in its early diagnosis, missed diagnosis and misdiagnosis still exist due to the heterogeneity of lung cancer. Integration of multiple biomarkers or trans-omics results can improve the accuracy and reliability for lung cancer diagnosis. As metabolic reprogramming is a hallmark of lung cancer, metabolites, specifically lipids might be useful for lung cancer detection, yet systematic characterizations of metabolites in lung cancer are still incipient. The present study profiled the polar metabolome and lipidome in the plasma of lung cancer patients to construct an inclusive metabolomic atlas of lung cancer. A comprehensive analysis of lung cancer was also conducted combining metabolomics with clinical phenotypes. Furthermore, the differences in plasma lipid metabolites were compared and analyzed among different lung cancer subtypes. Alcohols, amides, and peptide metabolites were significantly increased in lung cancer, while carboxylic acids, hydrocarbons, and fatty acids were remarkably decreased. Lipid profiling revealed a significant increase in plasma levels of CER, PE, SM, and TAG in individuals with lung cancer as compared to those in healthy controls. Correlation analysis confirmed the association between a panel of metabolites and TAGs. Clinical trans-omics studies elucidated the complex correlations between lipidomic data and clinical phenotypes. The present study emphasized the clinical importance of lipidomics in lung cancer, which involves the correlation between metabolites and the expressions of other omics, ultimately influencing clinical phenotypes. This novel trans-omics network approach would facilitate the development of precision therapy for lung cancer.

Clinical advances in analytical profiling of signature lipids: implications for severe non-communicable and neurodegenerative diseases

BACKGROUND

Lipids play key roles in numerous biological processes, including energy storage, cell membrane structure, signaling, immune responses, and homeostasis, making lipidomics a vital branch of metabolomics that analyzes and characterizes a wide range of lipid classes. Addressing the complex etiology, age-related risk, progression, inflammation, and research overlap in conditions like Alzheimer's Disease, Parkinson's Disease, Cardiovascular Diseases, and Cancer poses significant challenges in the quest for effective therapeutic targets, improved diagnostic markers, and advanced treatments. Mass spectrometry is an indispensable tool in clinical lipidomics, delivering quantitative and structural lipid data, and its integration with technologies like Liquid Chromatography (LC), Magnetic Resonance Imaging (MRI), and few emerging Matrix-Assisted Laser Desorption Ionization- Imaging Mass Spectrometry (MALDI-IMS) along with its incorporation into Tissue Microarray (TMA) represents current advances. These innovations enhance lipidomics assessment, bolster accuracy, and offer insights into lipid subcellular localization, dynamics, and functional roles in disease contexts.

AIM OF THE REVIEW

The review article summarizes recent advancements in lipidomic methodologies from 2019 to 2023 for diagnosing major neurodegenerative diseases, Alzheimer's and Parkinson's, serious non-communicable cardiovascular diseases and cancer, emphasizing the role of lipid level variations, and highlighting the potential of lipidomics data integration with genomics and proteomics to improve disease understanding and innovative prognostic, diagnostic and therapeutic strategies.

KEY SCIENTIFIC CONCEPTS OF REVIEW

Clinical lipidomic studies are a promising approach to track and analyze lipid profiles, revealing their crucial roles in various diseases. This lipid-focused research provides insights into disease mechanisms, biomarker identification, and potential therapeutic targets, advancing our understanding and management of conditions such as Alzheimer's Disease, Parkinson's Disease, Cardiovascular Diseases, and specific cancers.

Salivary Lipids of Patients with Non-Small Cell Lung Cancer Show Perturbation with Respect to Plasma

A comprehensive lipid profile was analyzed in patients with non-small cell lung cancer (NSCLC) using nanoflow ultrahigh-performance liquid chromatography-electrospray ionization-tandem mass spectrometry. This study investigated 297 and 202 lipids in saliva and plasma samples, respectively, comparing NSCLC patients to healthy controls. Lipids with significant changes (>2-fold, p < 0.05) were further analyzed in each sample type. Both saliva and plasma exhibited similar lipid alteration patterns in NSCLC, but saliva showed more pronounced changes. Total triglycerides (TGs) increased (>2-3-fold) in plasma and saliva samples. Three specific TGs (50:2, 52:5, and 54:6) were significantly increased in NSCLC for both sample types. A common ceramide species (d18:1/24:0) and phosphatidylinositol 38:4 decreased in both plasma and saliva by approximately two-fold. Phosphatidylserine 36:1 was selectively detected in saliva and showed a subsequent decrease, making it a potential biomarker for predicting lung cancer. We identified 27 salivary and 10 plasma lipids as candidate markers for NSCLC through statistical evaluations. Moreover, this study highlights the potential of saliva in understanding changes in lipid metabolism associated with NSCLC.

Serum untargeted metabolomics reveal metabolic alteration of non-small cell lung cancer and refine disease detection

This study was performed to characterize the metabolic alteration of non-small-cell lung cancer (NSCLC) and discover blood-based metabolic biomarkers relevant to lung cancer detection. An untargeted metabolomics-based approach was applied in a case-control study with 193 NSCLC patients and 243 healthy controls. Serum metabolomics were determined by using an ultra high performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) method. We screened differential metabolites based on univariate and multivariate analysis, followed by identification of the metabolites and related pathways. For NSCLC detection, machine learning was employed to develop and validate the model based on the altered serum metabolite features. The serum metabolic pattern of NSCLC was definitely different from the healthy condition. In total, 278 altered features were found in the serum of NSCLC patients comparing with healthy people. About one-fifth of the abundant differential features were identified successfully. The altered metabolites were enriched in metabolic pathways such as phenylalanine metabolism, linoleic acid metabolism, and biosynthesis of bile acids. We demonstrated a panel of 10 metabolic biomarkers which representing excellent discriminating capability for NSCLC discrimination, with a combined area under the curve (AUC) in the validation set of 0.95 (95% CI: 0.91-0.98). Moreover, this model showed a desirable performance for the detection of NSCLC at an early stage (AUC = 0.95, 95% CI: 0.92-0.97). Our study offers a perspective on NSCLC metabolic alteration. The finding of the biomarkers might shed light on the clinical detection of lung cancer, especially for those cancers in an early stage in Chinese population.

Plasma metabolomics analyses highlight the multifaceted effects of noise exposure and the diagnostic power of dysregulated metabolites for noise-induced hearing loss in steel workers

Noise exposure can lead to various kinds of disorders. Noise-induced hearing loss (NIHL) is one of the leading disorders confusing the noise-exposed workers. It is essential to identify NIHL markers for its early diagnosis and new therapeutic targets for its treatment. In this study, a total of 90 plasma samples from 60 noise-exposed steel factory male workers (the noise group) with (NIHL group, n = 30) and without NIHL (non-NIHL group, n = 30) and 30 male controls without noise exposure (control group) were collected. Untargeted human plasma metabolomic profiles were determined with HPLC-MS/MS. The levels of the metabolites in the samples were normalized to total peak intensity, and the processed data were subjected to multivariate data analysis. The Wilcoxon test and orthogonal partial least square-discriminant analysis (OPLS-DA) were performed. With the threshold of p &lt; 0.05 and the variable importance of projection (VIP) value &gt;1, 469 differential plasma metabolites associated with noise exposure (DMs-NE) were identified, and their associated 58 KEGG pathways were indicated. In total, 33 differential metabolites associated with NIHL (DMs-NIHL) and their associated 12 KEGG pathways were identified. There were six common pathways associated with both noise exposure and NIHL. Through multiple comparisons, seven metabolites were shown to be dysregulated in the NIHL group compared with the other two groups. Through LASSO regression analysis, two risk models were constructed for NIHL status predication which could discriminate NIHL from non-NIHL workers with the area under the curve (AUC) values of 0.840 and 0.872, respectively, indicating their efficiency in NIHL diagnosis. To validate the results of the metabolomics, cochlear gene expression comparisons between susceptible and resistant mice in the GSE8342 dataset from Gene Expression Omnibus (GEO) were performed. The immune response and cell death-related processes were highlighted for their close relations with noise exposure, indicating their critical roles in noise-induced disorders. We concluded that there was a significant difference between the metabolite’s profiles between NIHL cases and non-NIHL individuals. Noise exposure could lead to dysregulations of a variety of biological pathways, especially immune response and cell death-related processes. Our results might provide new clues for noise exposure studies and NIHL diagnosis.

Non-Small Cell Lung Cancer Detection and Subtyping by UPLC-HRMS-Based Tissue Metabolomics

Non-small cell lung cancer (NSCLC) is the prevalent histological subtype of lung cancer. In this study, we performed ultraperformance liquid chromatography-high-resolution mass spectrometry (UPLC-HRMS)-based metabolic profiling of 227 tissue samples from 79 lung cancer patients with adenocarcinoma (AC) or squamous cell carcinoma (SCC). Orthogonal partial least squares-discriminant analysis (oPLS-DA) analyses showed that AC, SCC, and NSCLC tumors were discriminated from adjacent noncancerous tissue (ANT) and distant noncancerous tissue (DNT) samples with good accuracies (91.3, 100, and 88.3%), sensitivities (85.7, 100, and 83.9%), and specificities (94.3, 100, and 90.7%), using 12, 4, and 7 discriminant metabolites, respectively. The discriminant panel for AC detection included valine, sphingosine, glutamic acid γ-methyl ester, and lysophosphatidylcholine (LPC) (16:0), levels of which in tumor tissues were significantly altered. Valine, sphingosine, LPC (18:1), and leucine derivatives were used for SCC detection. The discrimination between AC and SCC had 96.8% accuracy, 98.2% sensitivity, and 85.7% specificity using a five-metabolite panel, of which valine and creatine had significant differences. The classification models were further verified with external validation sets, showing a promising prospect for NSCLC tissue detection and subtyping.