Lipidomics Identified Lyso-Phosphatidylcholine and Phosphatidylethanolamine as Potential Biomarkers for Diagnosis of Laryngeal Cancer

Nomogram for predicting survival in T1-T2 stage patients with supraglottic squamous cell carcinoma

BACKGROUND

Supraglottic squamous cell carcinoma (SGSCC) is characterized by low differentiation, rapid growth, and inconspicuous initial manifestations. Early detection and prompt treatment can significantly improve survival rates. The main focus of treatment is to maintain optimal laryngeal function.

METHODS

Using the Surveillance, Epidemiology, and End Results (SEER) database, we conducted univariate and multivariate Cox regression analyses to identify independent prognostic factors for T1-T2 SGSCC. We also enrolled 109 patients with T1-T2 SGSCC from the First Affiliated Hospital of Xinjiang Medical University as an external validation set. In addition, we developed a nomogram to predict the prognosis of T1-T2 SGSCC, assessed the predictive accuracy and discriminatory ability of the nomogram using the area under the curve (AUC), C-index, receiver operating characteristic (ROC) curve and calibration curve, and confirmed the clinical validity of the nomogram using decision curve analysis (DCA).

RESULTS

Our investigation identified nine prognostic indicators for T1-T2 SGSCC: age (≥ 65 years), marital status, American Joint Committee on Cancer (AJCC) stage (II-IV), grade (III-IV), M stage (M1), radiotherapy, chemotherapy, sex (female), and surgery. These variables were used to create accurate nomograms that predict overall and specific survival rates at 1, 3, and 5 years. The nomograms demonstrated superior prognostic value and accuracy compared to AJCC staging. Laryngectomy with partial laryngectomy is the preferred treatment option for T1-T2 SGSCC cases, providing superior overall survival (OS) and cancer-specific survival (CSS). Radiotherapy also improves OS and CSS. Our results were based on a comprehensive analysis of various indicators, including the C-index, ROC curve, calibration curve, and DCA curve.

CONCLUSION

Nomograms provide significant advantages in treatment decision making and diagnosis. Laryngectomy with partial laryngectomy is the most appropriate method for T1-T2 SGSCC cases. However, radiotherapy can also be used. Thus, patients with T1-T2 SGSCC should be evaluated to determine if combination therapy is the optimal treatment approach. Nevertheless, further research is needed to understand the role of chemotherapy. Overall, this study identified nine key predictors of future outcomes, aiding healthcare professionals in assessing risks and making treatment decisions for T1-T2 SGSCC patients.

Unraveling lipid and inflammation interplay in cancer, aging and infection for novel theranostic approaches

The synergistic relationships between Cancer, Aging, and Infection, here referred to as the CAIn Triangle, are significant determinants in numerous health maladies and mortality rates. The CAIn-related pathologies exhibit close correlations with each other and share two common underlying factors: persistent inflammation and anomalous lipid concentration profiles in the membranes of affected cells. This study provides a comprehensive evaluation of the most pertinent interconnections within the CAIn Triangle, in addition to examining the relationship between chronic inflammation and specific lipidic compositions in cellular membranes. To tackle the CAIn-associated diseases, a suite of complementary strategies aimed at diagnosis, prevention, and treatment is proffered. Our holistic approach is expected to augment the understanding of the fundamental mechanisms underlying these diseases and highlight the potential of shared features to facilitate the development of novel theranostic strategies.

TiO2@COF-based solid-phase microextraction combined with UHPLC-MS/MS for the rapid determination of potential biomarkers of phosphatidylcholines and lysophosphatidylcholines in head and neck cancers

Phosphatidylcholine (PC) and lysophosphatidylcholine (LPC), two types of phospholipids (PLs), have been reported to be closely correlated with head and neck cancers of laryngeal cancer (LC) and thyroid cancer (TC), which make their analysis crucial. TiO2@COF-based solid-phase microextraction (SPME) coupled to UHPLC-MS/MS was developed for the rapid and accurate detection of seven potential PL biomarkers from small amounts of serum in this work. The combination of TiO2 and COF proves to be effective for the extraction of the target analytes. Under optimal conditions, the developed TiO2@COF-based SPME-UHPLC-MS/MS method revealed good linearity (R2 ≥ 0.997) with LODs ranging from 0.05 to 0.38 ng/mL for PLs, the extraction recoveries and matrix effects ranging from 83.09-112.03% and 85.38-113.67%, respectively. As a high-throughput pretreatment method, satisfactory probe-to-probe reproducibility rates of 2.7-10.1% were obtained. Finally, the TiO2@COF-based SPME-UHPLC-MS/MS method was applied to analyze LPC 14:0, LPC 16:0, LPC 18:0, LPC 18:1, LPC 19:0, PC 16:0/18:1, and PC 18:0 in serum samples from early LC patients (n = 15), early TC patients (n = 15), and healthy volunteers (n = 15). The results indicated that cancer patients could be effectively differentiated from healthy controls using orthogonal partial least squares discriminant analysis (OPLS-DA). In conclusion, the established TiO2@COF-based SPME-UHPLC-MS/MS method is reliable for the rapid determination of the seven PLs in serum samples, which is promising for early diagnosis of head and neck cancers.

Metabolomics of head and neck cancer in biofluids: an integrative systematic review

INTRODUCTION

Head and neck cancer (HNC) is the fifth most common cancer globally. Diagnosis at early stages are critical to reduce mortality and improve functional and esthetic outcomes associated with HNC. Metabolomics is a promising approach for discovery of biomarkers and metabolic pathways for risk assessment and early detection of HNC.

OBJECTIVES

To summarize and consolidate the available evidence on metabolomics and HNC in plasma/serum, saliva, and urine.

METHODS

A systematic search of experimental research was executed using PubMed and Web of Science. Available data on areas under the curve was extracted. Metabolic pathway enrichment analysis were performed to identify metabolic pathways altered in HNC. Fifty-four studies were eligible for data extraction (33 performed in plasma/serum, 15 in saliva and 6 in urine).

RESULTS

Metabolites with high discriminatory performance for detection of HNC included single metabolites and combination panels of several lysoPCs, pyroglutamate, glutamic acid, glucose, tartronic acid, arachidonic acid, norvaline, linoleic acid, propionate, acetone, acetate, choline, glutamate and others. The glucose-alanine cycle and the urea cycle were the most altered pathways in HNC, among other pathways (i.e. gluconeogenesis, glycine and serine metabolism, alanine metabolism, etc.). Specific metabolites that can potentially serve as complementary less- or non-invasive biomarkers, as well as metabolic pathways integrating the data from the available studies, are presented.

CONCLUSION

The present work highlights utility of metabolite-based biomarkers for risk assessment, early detection, and prognostication of HNC, as well as facilitates incorporation of available metabolomics studies into multi-omics data integration and big data analytics for personalized health.

Clinical diagnostic value of amino acids in laryngeal squamous cell carcinomas

Background

Early diagnosis and treatment can improve the survival rates of patients with laryngeal squamous cell carcinoma (LSCC). Therefore, it is necessary to discover new biomarkers for laryngeal cancer screening and early diagnosis.

Methods

We collected fasting plasma from LSCC patients and healthy volunteers, as well as cancer and para-carcinoma tissues from LSCC patients, and performed quantitative detection of amino acid levels using liquid chromatography-mass spectrometry. We used overall analysis and multivariate statistical analysis to screen out the statistically significant differential amino acids in the plasma and tissue samples, conducted receiver operating characteristic (ROC) analysis of the differential amino acids to evaluate the sensitivity and specificity of the differential amino acids, and finally determined the diagnostic value of amino acids for laryngeal cancer. Additionally, we identified amino acids in the plasma and tissue samples that are valuable for the early diagnosis of laryngeal cancer classified according to the tumor-node-metastasis (TNM) classification.

Results

Asparagine (Asp) and homocysteine (Hcy) were two amino acids of common significance in plasma and tissue samples, and their specificity and sensitivity analysis showed that they may be new biomarkers for the diagnosis and treatment of LSCC. According to the TNM staging system, phenylalanine (Phe) and isoleucine (Ile) were screened out in the plasma of LSCC patients at early (I and II) and advanced (III and IV) stages; ornithine hydrochloride (Orn), glutamic acid (Glu), and Glycine (Gly) were selected in the tissue. These dysregulated amino acids found in LSCC patients may be useful as clinical biomarkers for the early diagnosis and screening of LSCC.

Identification of metabolic biomarkers for diagnosis of epithelial ovarian cancer using internal extraction electrospray ionization mass spectrometry (iEESI-MS)

BACKGROUND

Epithelial ovarian cancer (EOC) is the leading cause of death from gynecologic malignancies. The poor prognosis of EOC is mainly due to its asymptomatic early stage, lack of effective screening methods, and a late diagnosis in the advanced stages of the disease.

OBJECTIVE

This study investigated metabolomic abnormalities in epithelial ovarian cancers.

METHODS

Our study developed a novel strategy to rapidly identify the metabolic biomarkers in the plasma of the EOC patients using Internal Extraction Electrospray Ionization Mass Spectrometry (IEESI-MS) and Liquid Chromatography-mass Spectrometry (HPLC-MS), which could distinguish the differential metabolites in between plasma samples collected from 98 patients with epithelial ovarian cancer, including 78 cases with original (P), and 20 cases with self-configuration (ZP), as well as 60 healthy subjects, including 30 cases in the original sample (H), 30 cases in self-configuration (ZH), and 6 cases in a blind sample (B).

RESULTS

Our study detected 880 metabolites based on criteria variable importance in projection (VIP) > 1, among which 26 metabolites were selected for further identification. They are mainly metabolism-related lipids, amino acids, nucleic acids, and others. The metabolic pathways associated with the differential metabolites were explored by the KEGG analysis, a comprehensive database that integrates genome, chemistry, and system function information. The abnormal metabolites of EOC patients identified by IEESI-MS and HPLC-MS included Lysophosphatidylcholine (16:0) [Lyso PC (16:0)], L-Phenylalanine, L-Leucine, Phenylpyruvic acid, L-Tryptophan, and L-Histidine.

CONCLUSIONS

Identifying the abnormal metabolites of EOC patients through metabolomics analyses could provide a new strategy to identify valuable potential biomarkers for the screening and early diagnosis of EOC.

Lipidome Analysis of Oropharyngeal Tumor Tissues Using Nanosecond Infrared Laser (NIRL) Tissue Sampling and Subsequent Mass Spectrometry

Ultrashort pulse infrared lasers can simultaneously sample and homogenize biological tissue using desorption by impulsive vibrational excitation (DIVE). With growing attention on alterations in lipid metabolism in malignant disease, mass spectrometry (MS)-based lipidomic analysis has become an emerging topic in cancer research. In this pilot study, we investigated the feasibility of tissue sampling with a nanosecond infrared laser (NIRL) for the subsequent lipidomic analysis of oropharyngeal tissues, and its potential to discriminate oropharyngeal squamous cell carcinoma (OPSCC) from non-tumorous oropharyngeal tissue. Eleven fresh frozen oropharyngeal tissue samples were ablated. The produced aerosols were collected by a glass fiber filter, and the lipidomes were analyzed with mass spectrometry. Data was evaluated by principal component analysis and Welch's t-tests. Lipid profiles comprised 13 lipid classes and up to 755 lipid species. We found significant inter- and intrapatient alterations in lipid profiles for tumor and non-tumor samples (p-value < 0.05, two-fold difference). Thus, NIRL tissue sampling with consecutive MS lipidomic analysis is a feasible and promising approach for the differentiation of OPSCC and non-tumorous oropharyngeal tissue and may provide new insights into lipid composition alterations in OPSCC.

Identification and characterization of lysophosphatidylcholine 14:0 as a biomarker for drug-induced lung disease

Drug-induced interstitial lung disease (DILD) occurs when drug exposure causes inflammation of the lung interstitium. DILD can be caused by different types of drugs, and some DILD patterns results in a high mortality rate; hence, DILD poses a serious problem in clinical practice as well as drug development, and strategies to diagnose and distinguish DILD from other lung diseases are necessary. We aimed to identify novel biomarkers for DILD by performing lipidomics analysis on plasma samples from patients with acute and recovery phase DILD. Having identified lysophosphatidylcholines (LPCs) as candidate biomarkers for DILD, we determined their concentrations using validated liquid chromatography/mass spectrometry biomarker assays. In addition, we evaluated the ability of LPCs to discriminate patients with acute phase DILD from those with recovery phase DILD, DILD-tolerant, or other lung diseases, and characterized their association with clinical characteristics. Lipidomics analysis revealed a clear decrease in LPC concentrations in the plasma of patients with acute phase DILD. In particular, LPC(14:0) had the highest discriminative index against recovery phase and DILD-tolerant patients. LPC(14:0) displayed no clear association with causal drugs, or subjects' backgrounds, but was associated with disease severity. Furthermore, LPC(14:0) was able to discriminate between patients with DILD and other lung diseases, including idiopathic interstitial pneumonia and lung disease associated with connective tissue disease. LPC(14:0) is a promising biomarker for DILD that could improve the diagnosis of DILD and help to differentiate DILD from other lung diseases, such as idiopathic interstitial pneumonia and connective tissue disease.

MALDI Mass Spectrometry Imaging Highlights Specific Metabolome and Lipidome Profiles in Salivary Gland Tumor Tissues

Salivary gland tumors are relatively uncommon neoplasms that represent less than 5% of head and neck tumors, and about 90% are in the parotid gland. The wide variety of histologies and tumor characteristics makes diagnosis and treatment challenging. In the present study, Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) was used to discriminate the pathological regions of patient-derived biopsies of parotid neoplasms by metabolomic and lipidomic profiles. Fresh frozen parotid tissues were analyzed by MALDI time-of-flight (TOF) MSI, both in positive and negative ionization modes, and additional MALDI-Fourier-transform ion cyclotron resonance (FT-ICR) MSI was carried out for metabolite annotation. MALDI-TOF-MSI spatial segmentation maps with different molecular signatures were compared with the histologic annotation. To maximize the information related to specific alterations between the pathological and healthy tissues, unsupervised (principal component analysis, PCA) and supervised (partial least squares-discriminant analysis, PLS-DA) multivariate analyses were performed presenting a 95.00% accuracy in cross-validation. Glycerophospholipids significantly increased in tumor tissues, while sphingomyelins and triacylglycerols, key players in the signaling pathway and energy production, were sensibly reduced. In addition, a significant increase of amino acids and nucleotide intermediates, consistent with the bioenergetics request of tumor cells, was observed. These results underline the potential of MALDI-MSI as a complementary diagnostic tool to improve the specificity of diagnosis and monitoring of pharmacological therapies.

Multiplatform Metabolomics Studies of Human Cancers With NMR and Mass Spectrometry Imaging

The status of metabolomics as a scientific branch has evolved from proof-of-concept to applications in science, particularly in medical research. To comprehensively evaluate disease metabolomics, multiplatform approaches of NMR combining with mass spectrometry (MS) have been investigated and reported. This mixed-methods approach allows for the exploitation of each individual technique's unique advantages to maximize results. In this article, we present our findings from combined NMR and MS imaging (MSI) analysis of human lung and prostate cancers. We further provide critical discussions of the current status of NMR and MS combined human prostate and lung cancer metabolomics studies to emphasize the enhanced metabolomics ability of the multiplatform approach.

Untargeted metabolomics reveals alterations in the metabolic reprogramming of prostate cancer cells by double-stranded DNA-modified gold nanoparticles

Metabolic reprogramming plays an important role in the development of prostate cancer (PCa). However, there are few reports on the effects of nanomaterials as vectors on cancer metabolic reprogramming. Herein, a type of nanoparticle with good biocompatibility was synthesized by modifying the double-stranded of DNA containing a sulfhydryl group on the surface of gold nanoparticles (AuNPs-dsDNA) through salt-aging conjugation methods. The resultant AuNPs-dsDNA complexes possessed low toxicity to PC3 and DU145 cells in vitro. There was also no obvious hepatorenal toxicity after intravenous injection of AuNPs-dsDNA complexes in vivo, which indicated that these nanoparticles had good biological compatibilities. We investigated their biological functions using prostate cancer cells. Seahorse assay showed that AuNPs-dsDNA complexes could increase glycolysis and glycolysis capacity both in PC3 and DU145 cells. We further detected the expression of glycolysis-related genes by qPCR assay, and found that PKM2, PDHA, and LDHA were significantly upregulated. Furthermore, untargeted metabolomics revealed that PC (18:2(9Z,12Z)/18:2(9Z,12Z)) and PC (18:0/18:2 (9Z,12Z)) levels were decreased and inosinic acid level was increased in PC3 cells. Whereas (3S,6E,10E)-1,6,10,14-Phytatetraen-3-ol, Plasmenyl-PE 36:5 and Cer (d18:2/18:2) were decreased, PE 21:3 and 1-pyrrolidinecarboxaldehyde were increased in DU145 cells after co-culturing with AuNPs-dsDNA. In summary, we found that AuNPs and AuNPs-dsDNA complexes possibly regulate the metabolic reprogramming of cancer cells mainly through the lipid metabolic pathways, which could compensate for the previously mentioned phenomenon of enhanced glycolysis and glycolysis capacity. This will provide an important theoretical basis for our future research on the characteristic targeted design of nanomaterials for cancer metabolism.

Integrated Transcriptomics and Metabolomics Analyses of Stress-Induced Murine Hair Follicle Growth Inhibition

Psychological stress plays an important role in hair loss, but the underlying mechanisms are not well-understood, and the effective therapies available to regrow hair are rare. In this study, we established a chronic restraint stress (CRS)-induced hair growth inhibition mouse model and performed a comprehensive analysis of metabolomics and transcriptomics. Metabolomics data analysis showed that the primary and secondary metabolic pathways, such as carbohydrate metabolism, amino acid metabolism, and lipid metabolism were significantly altered in skin tissue of CRS group. Transcriptomics analysis also showed significant changes of genes expression profiles involved in regulation of metabolic processes including arachidonic acid metabolism, glutathione metabolism, glycolysis gluconeogenesis, nicotinate and nicotinamide metabolism, purine metabolism, retinol metabolism and cholesterol metabolism. Furthermore, RNA-Seq analyses also found that numerous genes associated with metabolism were significantly changed, such as Hk-1, in CRS-induced hair growth inhibition. Overall, our study supplied new insights into the hair growth inhibition induced by CRS from the perspective of integrated metabolomics and transcriptomics analyses.