Association of Untargeted Urinary Metabolomics and Lung Cancer Risk Among Never-Smoking Women in China

Socioeconomic characteristics, cancer mortality, and universal health coverage: A global analysis

BACKGROUND

Achieving universal health coverage (UHC) involves all individuals attaining accessible health interventions at an affordable cost. We examined current patterns and temporal trends of cancer mortality and UHC across sociodemographic index (SDI) settings, and quantified these association.

METHODS

We used data from the Global Burden of Disease Study 2019 and Our World in Data. The UHC effective coverage index was obtained to assess the potential population health gains delivered by health systems. The estimated annual percentage change (EAPC) with a 95% confidence interval (CI) was calculated to quantify the trend of cancer age-standardized mortality rate (ASMR). A generalized linear model was applied to estimate the association between ASMR and UHC.

FINDINGS

The high (EAPC = -0.9% [95% CI, -1.0%, -0.9%]) and high-middle (-0.9% [-1.0%, -0.8%]) SDI regions had the fastest decline in ASMR (per 100,000) for total cancers from 1990 to 2019. The overall UHC effective coverage index increased by 27.9% in the high-SDI quintile to 62.2% in the low-SDI quintile. A negative association was observed between ASMR for all-cancer (adjusted odds ratio [OR] = 0.87 [0.76, 0.99]), stomach (0.73 [0.56, 0.95]), breast (0.64 [0.52, 0.79]), cervical (0.42 [0.30, 0.60]), lip and oral cavity (0.55 [0.40, 0.75]), and nasopharynx (0.42 [0.26, 0.68]) cancers and high UHC level (the lowest as the reference).

CONCLUSIONS

Our findings strengthen the evidence base for achieving UHC to improve cancer outcomes.

FUNDING

This work is funded by the China National Natural Science Foundation and Chinese Academy of Medical Sciences Innovation Fund for Medical Science.

A nested case-control study of untargeted plasma metabolomics and lung cancer among never-smoking women within the prospective Shanghai Women's Health Study

The etiology of lung cancer in never-smokers remains elusive, despite 15% of lung cancer cases in men and 53% in women worldwide being unrelated to smoking. Here, we aimed to enhance our understanding of lung cancer pathogenesis among never-smokers using untargeted metabolomics. This nested case-control study included 395 never-smoking women who developed lung cancer and 395 matched never-smoking cancer-free women from the prospective Shanghai Women's Health Study with 15,353 metabolic features quantified in pre-diagnostic plasma using liquid chromatography high-resolution mass spectrometry. Recognizing that metabolites often correlate and seldom act independently in biological processes, we utilized a weighted correlation network analysis to agnostically construct 28 network modules of correlated metabolites. Using conditional logistic regression models, we assessed the associations for both metabolic network modules and individual metabolic features with lung cancer, accounting for multiple testing using a false discovery rate (FDR) < 0.20. We identified a network module of 121 features inversely associated with all lung cancer (p = .001, FDR = 0.028) and lung adenocarcinoma (p = .002, FDR = 0.056), where lyso-glycerophospholipids played a key role driving these associations. Another module of 440 features was inversely associated with lung adenocarcinoma (p = .014, FDR = 0.196). Individual metabolites within these network modules were enriched in biological pathways linked to oxidative stress, and energy metabolism. These pathways have been implicated in previous metabolomics studies involving populations exposed to known lung cancer risk factors such as traffic-related air pollution and polycyclic aromatic hydrocarbons. Our results suggest that untargeted plasma metabolomics could provide novel insights into the etiology and risk factors of lung cancer among never-smokers.

Lung cancer metabolomics: a pooled analysis in the Cancer Prevention Studies

BACKGROUND

A better understanding of lung cancer etiology and the development of screening biomarkers have important implications for lung cancer prevention.

METHODS

We included 623 matched case-control pairs from the Cancer Prevention Study (CPS) cohorts. Pre-diagnosis blood samples were collected between 1998 and 2001 in the CPS-II Nutrition cohort and 2006 and 2013 in the CPS-3 cohort and were sent for metabolomics profiling simultaneously. Cancer-free controls at the time of case diagnosis were 1:1 matched to cases on date of birth, blood draw date, sex, and race/ethnicity. Odds ratios (ORs) and 95% confidence intervals (CIs) were estimated using conditional logistic regression, controlling for confounders. The Benjamini-Hochberg method was used to correct for multiple comparisons.

RESULTS

Sphingomyelin (d18:0/22:0) (OR: 1.32; 95% CI: 1.15, 1.53, FDR = 0.15) and taurodeoxycholic acid 3-sulfate (OR: 1.33; 95% CI: 1.14, 1.55, FDR = 0.15) were positively associated with lung cancer risk. Participants diagnosed within 3 years of blood draw had a 55% and 48% higher risk of lung cancer per standard deviation increase in natural log-transformed sphingomyelin (d18:0/22:0) and taurodeoxycholic acid 3-sulfate level, while 26% and 28% higher risk for those diagnosed beyond 3 years, compared to matched controls. Lipid and amino acid metabolism accounted for 47% to 80% of lung cancer-associated metabolites at P < 0.05 across all participants and subgroups. Notably, ever-smokers exhibited a higher proportion of lung cancer-associated metabolites (P < 0.05) in xenobiotic- and lipid-associated pathways, whereas never-smokers showed a more pronounced involvement of amino acid- and lipid-associated metabolic pathways.

CONCLUSIONS

This is the largest prospective study examining untargeted metabolic profiles regarding lung cancer risk. Sphingomyelin (d18:0/22:0), a sphingolipid, and taurodeoxycholic acid 3-sulfate, a bile salt, may be risk factors and potential screening biomarkers for lung cancer. Lipid and amino acid metabolism may contribute significantly to lung cancer etiology which varied by smoking status.

Integrative Omics Analysis Reveals Metabolic Features of Ground-Glass Opacity-Associated Lung Cancer

Background: Ground-glass opacity (GGO)-associated cancers are increasingly prevalent, exhibiting unique clinical and molecular features that suggest the need for a distinct treatment strategy. However, the metabolic characteristics and vulnerabilities of GGO-associated lung cancers remain unexplored. Methods: We conducted metabolomic and transcriptomic analyses on 40 pairs of GGO-associated lung cancer tissues and adjacent normal tissues. By integrating data from TCGA database and single-cell RNA sequencing, we aimed to identify aberrant metabolic pathways, establish a metabolite-associated gene signature, and pinpoint key metabolic genes. The physiological effect of key genes was detected in vitro and vivo assays. Results: We identified a 30-gene metabolite-associated signature and discovered aberrant metabolic pathways for GGO-associated lung cancer at both metabolic and transcriptional levels. Patients with this signature displayed specific prognostic and molecular features. Cox regression analysis, based on the Cancer Genome Atlas Program (TCGA) data, further narrowed down the metabolite-related gene signature, resulting in a 5-gene signature. Confirmed by single-cell RNA sequencing (GSE203360), the 5-gene signature was mainly expressed in cancer cells of GGO tissue. Real-time quantitative PCR (RT-qPCR) further validated the differential expression of these genes between GGO and adjacent normal tissue obtained from pulmonary surgery. Finally, our integrative analysis unveiled aberrant histidine metabolism at both the multi-omics and single-cell levels. Moreover, we identified MAOB as a key metabolic gene, demonstrating its ability to suppress cell proliferation, migration, and invasion in LUAD cell lines, both in vitro and in vivo. Conclusions: We identified a specific metabolite-associated gene signature and identified aberrant histidine metabolism in GGO-associated lung cancer from multiple perspectives. Notably, MAOB, a crucial component in histidine metabolism, demonstrated a significant inhibitory effect on the proliferation and metastasis of LUAD, indicating its potential significance in pathogenesis and therapeutic interventions.

Metabolomics Analysis and Diagnosis of Lung Cancer: Insights from Diverse Sample Types

Lung cancer is a highly fatal disease that poses a significant global health burden. The absence of characteristic clinical symptoms frequently results in the diagnosis of most patients at advanced stages of lung cancer. Although low-dose computed tomography (LDCT) screening has become increasingly prevalent in clinical practice, its high rate of false positives continues to present a significant challenge. In addition to LDCT screening, tumor biomarker detection represents a critical approach for early diagnosis of lung cancer; unfortunately, no tumor marker with optimal sensitivity and specificity is currently available. Metabolomics has recently emerged as a promising field for developing novel tumor biomarkers. In this paper, we introduce metabolic pathways, instrument platforms, and a wide variety of sample types for lung cancer metabolomics. Specifically, we explore the strengths, limitations, and distinguishing features of various sample types employed in lung cancer metabolomics research. Additionally, we present the latest advances in lung cancer metabolomics research that utilize diverse sample types. We summarize and enumerate research studies that have investigated lung cancer metabolomics using different metabolomic sample types. Finally, we provide a perspective on the future of metabolomics research in lung cancer. Our discussion of the potential of metabolomics in developing new tumor biomarkers may inspire further study and innovation in this dynamic field.

Integrated plasma metabolomics and lipidomics profiling highlights distinctive signature of hepatocellular carcinoma in HCV patients

BACKGROUND

Early diagnosis of hepatocellular carcinoma (HCC) is essential towards the improvement of prognosis and patient survival. Circulating markers such as α-fetoprotein (AFP) and micro-RNAs represent useful tools but still have limitations. Identifying new markers can be fundamental to improve both diagnosis and prognosis. In this approach, we harness the potential of metabolomics and lipidomics to uncover potential signatures of HCC.

METHODS

A combined untargeted metabolomics and lipidomics plasma profiling of 102 HCV-positive patients was performed by HILIC and RP-UHPLC coupled to Mass Spectrometry. Biochemical parameters of liver function (AST, ALT, GGT) and liver cancer biomarkers (AFP, CA19.9 e CEA) were evaluated by standard assays.

RESULTS

HCC was characterized by an elevation of short and long-chain acylcarnitines, asymmetric dimethylarginine, methylguanine, isoleucylproline and a global reduction of lysophosphatidylcholines. A supervised PLS-DA model showed that the predictive accuracy for HCC class of metabolomics and lipidomics was superior to AFP for the test set (100.00% and 94.40% vs 55.00%). Additionally, the model was applied to HCC patients with AFP values < 20 ng/mL, and, by using only the top 20 variables selected by VIP scores achieved an Area Under Curve (AUC) performance of 0.94.

CONCLUSION

These exploratory findings highlight how metabo-lipidomics enables the distinction of HCC from chronic HCV conditions. The identified biomarkers have high diagnostic potential and could represent a viable tool to support and assist in HCC diagnosis, including AFP-negative patients.

Characteristics of Cancer Epidemiology Studies That Employ Metabolomics: A Scoping Review

An increasing number of cancer epidemiology studies use metabolomics assays. This scoping review characterizes trends in the literature in terms of study design, population characteristics, and metabolomics approaches and identifies opportunities for future growth and improvement. We searched PubMed/MEDLINE, Embase, Scopus, and Web of Science: Core Collection databases and included research articles that used metabolomics to primarily study cancer, contained a minimum of 100 cases in each main analysis stratum, used an epidemiologic study design, and were published in English from 1998 to June 2021. A total of 2,048 articles were screened, of which 314 full texts were further assessed resulting in 77 included articles. The most well-studied cancers were colorectal (19.5%), prostate (19.5%), and breast (19.5%). Most studies used a nested case-control design to estimate associations between individual metabolites and cancer risk and a liquid chromatography-tandem mass spectrometry untargeted or semi-targeted approach to measure metabolites in blood. Studies were geographically diverse, including countries in Asia, Europe, and North America; 27.3% of studies reported on participant race, the majority reporting White participants. Most studies (70.2%) included fewer than 300 cancer cases in their main analysis. This scoping review identified key areas for improvement, including needs for standardized race and ethnicity reporting, more diverse study populations, and larger studies.

Isoflavone and soy food intake and risk of lung cancer in never smokers: report from prospective studies in Japan and China

PURPOSE

Evidence from several cohorts has suggested that a higher intake of isoflavone is associated with lower risk of lung cancer in never smokers, but the association has not been investigated by histologic type of lung cancer. Adenocarcinoma is a common histologic type found in never smokers. We hypothesized that a higher intake of isoflavone is associated with a lower risk of lung adenocarcinoma among never smokers. Here, we examined the associations of isoflavone and soy food intake with lung cancer and its histologic types in never smokers.

METHODS

We performed a pooled analysis using data from the Japan Public Health Center-based Prospective Study, Shanghai Women's Health Study and Shanghai Men's Study with 147,296 never smokers aged 40-74 years with no history of cancer. During 1,990,040 person-years of follow-up, 1247 lung cancer cases were documented. Dietary isoflavone and soy food intake were assessed using a food-frequency questionnaire. Multivariable Cox proportional hazards models assessed the associations between isoflavone and soy intake with incidence of lung cancer by histologic type.

RESULTS

A higher intake of dietary isoflavone and soy food were associated with reduced risk of lung adenocarcinoma. The multivariable hazard ratios (HRs) (95% CI) of risk of lung adenocarcinoma for the highest versus lowest intakes of isoflavone and soy food were 0.74 (0.60-0.92) and 0.78 (0.63-0.96), respectively. The multivariable HRs of risk of lung adenocarcinoma associated with each 10 mg/day increase in isoflavone and each 50 g/day increase in soy food intake were 0.81 (0.70-0.94) and 0.84 (0.73-0.96), respectively.

CONCLUSION

Higher intake of isoflavone and soy food was associated with lower risk of lung adenocarcinoma in never smokers.

Liquid Biopsy for Lung Cancer: Up-to-Date and Perspectives for Screening Programs

Lung cancer is the deadliest cancer worldwide. Tissue biopsy is currently employed for the diagnosis and molecular stratification of lung cancer. Liquid biopsy is a minimally invasive approach to determine biomarkers from body fluids, such as blood, urine, sputum, and saliva. Tumor cells release cfDNA, ctDNA, exosomes, miRNAs, circRNAs, CTCs, and DNA methylated fragments, among others, which can be successfully used as biomarkers for diagnosis, prognosis, and prediction of treatment response. Predictive biomarkers are well-established for managing lung cancer, and liquid biopsy options have emerged in the last few years. Currently, detecting EGFR p.(Tyr790Met) mutation in plasma samples from lung cancer patients has been used for predicting response and monitoring tyrosine kinase inhibitors (TKi)-treated patients with lung cancer. In addition, many efforts continue to bring more sensitive technologies to improve the detection of clinically relevant biomarkers for lung cancer. Moreover, liquid biopsy can dramatically decrease the turnaround time for laboratory reports, accelerating the beginning of treatment and improving the overall survival of lung cancer patients. Herein, we summarized all available and emerging approaches of liquid biopsy-techniques, molecules, and sample type-for lung cancer.

A novel nomogram model combining CT texture features and urine energy metabolism to differentiate single benign from malignant pulmonary nodule

Objective

To investigate a novel diagnostic model for benign and malignant pulmonary nodule diagnosis based on radiomic and clinical features, including urine energy metabolism index.

Methods

A total of 107 pulmonary nodules were prospectively recruited and pathologically confirmed as malignant in 86 cases and benign in 21 cases. A chest CT scan and urine energy metabolism test were performed in all cases. A nomogram model was established in combination with radiomic and clinical features, including urine energy metabolism levels. The nomogram model was compared with the radiomic model and the clinical feature model alone to test its diagnostic validity, and receiver operating characteristic (ROC) curves were plotted to assess diagnostic validity.

Results

The nomogram was established using a logistic regression algorithm to combine radiomic features and clinical characteristics including urine energy metabolism results. The predictive performance of the nomogram was evaluated using the area under the ROC and calibration curve, which showed the best performance, area under the curve (AUC) = 0.982, 95% CI = 0.940-1.000, compared to clinical and radiomic models in the testing cohort. The clinical benefit of the model was assessed using the decision curve analysis (DCA) and using the nomogram for benign and malignant pulmonary nodules, and preoperative prediction of benign and malignant pulmonary nodules using nomograms showed better clinical benefit.

Conclusion

This study shows that a coupled model combining CT imaging features and clinical features (including urine energy metabolism) in combination with the nomogram model has higher diagnostic performance than the radiomic and clinical models alone, suggesting that the combination of both methods is more advantageous in identifying benign and malignant pulmonary nodules.

Systematic Review of NMR-Based Metabolomics Practices in Human Disease Research

Nuclear magnetic resonance (NMR) spectroscopy is one of the principal analytical techniques for metabolomics. It has the advantages of minimal sample preparation and high reproducibility, making it an ideal technique for generating large amounts of metabolomics data for biobanks and large-scale studies. Metabolomics is a popular “omics” technology and has established itself as a comprehensive exploratory biomarker tool; however, it has yet to reach its collaborative potential in data collation due to the lack of standardisation of the metabolomics workflow seen across small-scale studies. This systematic review compiles the different NMR metabolomics methods used for serum, plasma, and urine studies, from sample collection to data analysis, that were most popularly employed over a two-year period in 2019 and 2020. It also outlines how these methods influence the raw data and the downstream interpretations, and the importance of reporting for reproducibility and result validation. This review can act as a valuable summary of NMR metabolomic workflows that are actively used in human biofluid research and will help guide the workflow choice for future research.

Serum Metabolomics Profiling Reveals Metabolic Alterations Prior to a Diagnosis with Non-Small Cell Lung Cancer among Chinese Community Residents: A Prospective Nested Case-Control Study

The present high mortality of lung cancer in China stems mainly from the lack of feasible, non-invasive and early disease detection biomarkers. Serum metabolomics profiling to reveal metabolic alterations could expedite the disease detection process and suggest those patients who are harboring disease. Using a nested case-control design, we applied ultra-high-performance liquid chromatography/mass spectrometry (LC-MS)-based serum metabolomics to reveal the metabolomic alterations and to indicate the presence of non-small cell lung cancer (NSCLC) using serum samples collected prior to disease diagnoses. The studied serum samples were collected from 41 patients before a NSCLC diagnosis (within 3.0 y) and 38 matched the cancer-free controls from the prospective Shanghai Suburban Adult Cohort. The NSCLC patients markedly presented cellular metabolism alterations in serum samples collected prior to their disease diagnoses compared with the cancer-free controls. In total, we identified 18 significantly expressed metabolites whose relative abundance showed either an upward or a downward trend, with most of them being lipid and lipid-like molecules, organic acids, and nitrogen compounds. Choline metabolism in cancer, sphingolipid, and glycerophospholipid metabolism emerged as the significant metabolic disturbance of NSCLC. The metabolites involved in these biological processes may be the distinctive features associated with NSCLC prior to a diagnosis.

A tipping point in cancer epidemiology: embracing a life course exposomic framework

The pathogenesis of multifactorial malignant diseases, with variable onset, severity, and natural history, reflects development-specific exposures and individual responses to these exposures influenced by underlying genetic predisposition. Embedded in life course theory, exposomics provides a framework to more fully elucidate how environmental factors alter cancer risk, disease course, and response to treatment across the lifespan.

Multiplexed nanomaterial-assisted laser desorption/ionization for pan-cancer diagnosis and classification

As cancer is increasingly considered a metabolic disorder, it is postulated that serum metabolite profiling can be a viable approach for detecting the presence of cancer. By multiplexing mass spectrometry fingerprints from two independent nanostructured matrixes through machine learning for highly sensitive detection and high throughput analysis, we report a laser desorption/ionization (LDI) mass spectrometry-based liquid biopsy for pan-cancer screening and classification. The Multiplexed Nanomaterial-Assisted LDI for Cancer Identification (MNALCI) is applied in 1,183 individuals that include 233 healthy controls and 950 patients with liver, lung, pancreatic, colorectal, gastric, thyroid cancers from two independent cohorts. MNALCI demonstrates 93% sensitivity at 91% specificity for distinguishing cancers from healthy controls in the internal validation cohort, and 84% sensitivity at 84% specificity in the external validation cohort, with up to eight metabolite biomarkers identified. In addition, across those six different cancers, the overall accuracy for identifying the tumor tissue of origin is 92% in the internal validation cohort and 85% in the external validation cohort. The excellent accuracy and minimum sample consumption make the high throughput assay a promising solution for non-invasive cancer diagnosis.

As cancer is increasingly considered a metabolic disorder, it is postulated that serum metabolite profiling can be a viable approach for detecting the presence of cancer. Here, the authors report a machine learning model using mass spectrometry-based liquid biopsy data for pan-cancer screening and classification.

Integrated molecular response of exposure to traffic-related pollutants in the US trucking industry

Exposure to traffic-related pollutants, including diesel exhaust, is associated with increased risk of cardiopulmonary disease and mortality; however, the precise biochemical pathways underlying these effects are not known. To investigate biological response mechanisms underlying exposure to traffic related pollutants, we used an integrated molecular response approach that included high-resolution metabolomic profiling and peripheral blood gene expression to identify biological responses to diesel exhaust exposure. Plasma samples were collected from 73 non-smoking males employed in the US trucking industry between February 2009 and October 2010, and analyzed using untargeted high-resolution metabolomics to characterize metabolite associations with shift- and week-averaged levels of elemental carbon (EC), organic carbon (OC) and particulate matter with diameter ≤ 2.5 μm (PM2.5). Metabolic associations with EC, OC and PM2.5 were evaluated for biochemical processes known to be associated with disease risk. Annotated metabolites associated with exposure were then tested for relationships with the peripheral blood transcriptome using multivariate selection and network correlation. Week-averaged EC and OC levels, which were averaged across multiple shifts during the workweek, resulted in the greatest exposure-associated metabolic alterations compared to shift-averaged exposure levels. Metabolic changes associated with EC exposure suggest increased lipid peroxidation products, biomarkers of oxidative stress, thrombotic signaling lipids, and metabolites associated with endothelial dysfunction from altered nitric oxide metabolism, while OC exposures were associated with antioxidants, oxidative stress biomarkers and critical intermediates in nitric oxide production. Correlation with whole blood RNA gene expression provided additional evidence of changes in processes related to endothelial function, immune response, inflammation, and oxidative stress. We did not detect metabolic associations with PM2.5. This study provides an integrated molecular assessment of human exposure to traffic-related air pollutants that includes diesel exhaust. Metabolite and transcriptomic changes associated with exposure to EC and OC are consistent with increased risk of cardiovascular diseases and the adverse health effects of traffic-related air pollution.

A role for club cells in smoking-associated lung adenocarcinoma

The cellular origin of lung adenocarcinoma remains a focus of intense research efforts. The marked cellular heterogeneity and plasticity of the lungs, as well as the vast variety of molecular subtypes of lung adenocarcinomas perplex the field and account for the extensive variability of experimental results. While most experts would agree on the cellular origins of other types of thoracic tumours, great controversy exists on the tumour-initiating cells of lung adenocarcinoma, since this histologic subtype of lung cancer arises in the distal pulmonary regions where airways and alveoli converge, occurs in smokers as well as nonsmokers, is likely caused by various environmental agents, and is marked by vast molecular and pathologic heterogeneity. Alveolar type II, club, and their variant cells have all been implicated in lung adenocarcinoma progeny and the lineage hierarchies in the distal lung remain disputed. Here we review the relevant literature in this rapidly expanding field, including results from mouse models and human studies. In addition, we present a case for club cells as cells of origin of lung adenocarcinomas that arise in smokers.

Multiple lung epithelial cells are targets of carcinogenic hits. Club cells are such cells that can metabolically activate tobacco pre-carcinogens, being thus positioned as cells of origin of lung adenocarcinomas in smokers.https://bit.ly/3iOshcy

Metabolic network-based identification of plasma markers for non-small cell lung cancer

Metabolic markers, offering sensitive information on biological dysfunction, play important roles in diagnosing and treating cancers. However, the discovery of effective markers is limited by the lack of well-established metabolite selection approaches. Here, we propose a network-based strategy to uncover the metabolic markers with potential clinical availability for non-small cell lung cancer (NSCLC). First, an integrated mass spectrometry-based untargeted metabolomics was used to profile the plasma samples from 43 NSCLC patients and 43 healthy controls. We found that a series of 39 metabolites were altered significantly. Relying on the human metabolic network assembled from Kyoto Encyclopedia of Genes and Genomes (KEGG) database, we mapped these differential metabolites to the network and constructed an NSCLC-related disease module containing 23 putative metabolic markers. By measuring the PageRank centrality of molecules in this module, we computationally evaluated the network-based importance of the 23 metabolites and demonstrated that the metabolism pathways of aromatic amino acids and long-chain fatty acids provided potential molecular targets of NSCLC (i.e., IL4l1 and ACOT2). Combining network-based ranking and support-vector machine modeling, we further found a panel of eight metabolites (i.e., pyruvate, tryptophan, and palmitic acid) that showed a high capability to differentiate patients from controls (accuracy > 97.7%). In summary, we present a meaningful network method for metabolic marker discovery and have identified eight strong candidate metabolites for NSCLC diagnosis.

Crosstalk between H1975 tumor cells and platelets to induce the proliferation, migration and tube formation of vascular endothelial cells

Activated platelets (PLTs) participate in the regulation of tumor angiogenesis, and tumors can activate PLTs. Whether co-culture of lung carcinoma with PLTs improves the function of human umbilical vein endothelial cells (HUVECs) requires further investigation. The present study aimed to investigate the impact of H1975 cell crosstalk with PLTs on the proliferation, migration and tube formation of HUVECs. Following generation of cell-derived supernatants and construction of the co-culture system, Cell Counting Kit-8, flow cytometry, transmission electron microscopy and a meter for epithelial measurement were performed to detect the proliferative ability of HUVECs. Furthermore, the wound healing and Transwell migration assays were performed to detect the migratory ability of HUVECs. A tube formation assay was performed to assess angiogenesis, ELISA was applied to detect the content of vascular endothelial growth factor (VEGF) and western blotting was carried out to measure the expression levels of VEGF receptor 2 (VEGFR2) in HUVECs. Compared with single-cultured HUVECs (control), co-culture with H1975 cells and PLTs (Exp_HP) improved cell proliferation, increased the proportion of cells in the S-phase, destroyed the cell ultrastructure and decreased transepithelial electrical resistance in HUVECs. In addition, a higher relative migration rate, greater number of migrated cells, stronger tube-forming ability and increased expression of VEGF and VEGFR2 were detected in the Exp_HP group compared with the control group. The properties of HUVECs in Exp_H (co-cultured with H1975 cells) were similar to those in Exp_HP, but significantly weaker. Taken together, the results of the present study suggest that tumor cells interacting with PLTs may play an important role in tumor angiogenesis by affecting or mediating changes in the properties of vascular endothelial cells (VECs).

Neonatal heel prick mass spectrometry identifies metabolic predictors of AML latency

Ongoing research efforts that consider cancer as a disease of dramatically altered cellular metabolism have accelerated interest in snapshot metabolomics in various human tissues. In this issue of Leukemia Research, Petrick et al performed metabolomic analysis on newborn blood spots and found a number of unexpected ceramide and sphingolipid compounds that may play a role in the development and latency of pediatric acute myeloid leukemia (AML). The chemical complexity and range of cellular metabolites massively exceeds the relatively limited building blocks of the transcriptome or the proteome and has high potential to find novel leukemia-specific macromolecular synthesis pathways, metabolic vulnerabilities and biomarkers.

Untargeted metabolomics of newborn dried blood spots reveals sex-specific associations with pediatric acute myeloid leukemia

The etiology of pediatric acute myeloid leukemia (AML) is largely unknown, but evidence for mutations in utero and long latency periods suggests that environmental factors play a role. Therefore, we used untargeted metabolomics of archived newborn dried blood spots (DBS) to investigate neonatal exposures as potential causal risk factors for AML. Untargeted metabolomics profiling was performed on DBS punches from 48 pediatric patients with AML and 46 healthy controls as part of the California Childhood Leukemia Study (CCLS). Because sex disparities are suggested by differences in AML incidence rates, metabolite features associated with AML were identified in analyses stratified by sex. There was no overlap between the 16 predictors of AML in females and 15 predictors in males, suggesting that neonatal metabolomic profiles of pediatric AML risk are sex-specific. In females, four predictors of AML were putatively annotated as ceramides, a class of metabolites that has been linked with cancer cell proliferation. In females, two metabolite predictors of AML were strongly correlated with breastfeeding duration, indicating a possible biological link between this putative protective risk factor and childhood leukemia. In males, a heterogeneous metabolite profile of AML predictors was observed. Replication with larger participant numbers is required to validate findings.

Urinary Biomarkers for Early Diagnosis of Lung Cancer

Lung cancer is the leading cause of cancer deaths worldwide. Its early detection has the potential to significantly impact the burden of the disease. The screening and diagnostic techniques in current use suffer from limited specificity. The need therefore arises for a reliable biomarker to identify the disease earlier, which can be integrated into a test. This test would also allow for the recurrence risk after surgery to be stratified. In this context, urine could represent a non-invasive alternative matrix, with the urinary metabolomic profile offering a potential source for the discovery of diagnostic biomarkers. This paper aims to examine the current state of research and the potential for translation into clinical practice.

Untargeted Urinary Metabolomics and Children's Exposure to Secondhand Smoke: The Influence of Individual Differences

Children’s exposure to secondhand smoke (SHS) is a severe public health problem. There is still a lack of evidence regarding panoramic changes in children’s urinary metabolites induced by their involuntary exposure to SHS, and few studies have considered individual differences. This study aims to clarify the SHS-induced changes in urinary metabolites in preschool children by using cross-sectional and longitudinal metabolomics analyses. Urinary metabolites were quantified by using untargeted ultra high-performance liquid chromatography-mass spectrometry (UPLC^(c)-MS/MS). Urine cotinine-measured SHS exposure was examined to determine the exposure level. A cross-sectional study including 17 children in a low-exposure group, 17 in a medium-exposure group, and 17 in a high-exposure group was first conducted. Then, a before–after study in the cohort of children was carried out before and two months after smoking-cessation intervention for family smokers. A total of 43 metabolites were discovered to be related to SHS exposure in children in the cross-sectional analysis (false discovery rate (FDR) corrected p < 0.05, variable importance in the projection (VIP) > 1.0). Only three metabolites were confirmed to be positively associated with children’s exposure to SHS (FDR corrected p < 0.05) in a follow-up longitudinal analysis, including kynurenine, tyrosyl-tryptophan, and 1-(3-pyridinyl)-1,4-butanediol, the latter of which belongs to carbonyl compounds, peptides, and pyridines. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis indicated that 1-(3-pyridinyl)-1,4-butanediol and kynurenine were significantly enriched in xenobiotic metabolism by cytochrome P450 (p = 0.040) and tryptophan metabolism (p = 0.030), respectively. These findings provide new insights into the pathophysiological mechanism of SHS and indicate the influence of individual differences in SHS-induced changes in urinary metabolites in children.

Plasma and Urine Metabolite Profiles Impacted by Increased Dietary Navy Bean Intake in Colorectal Cancer Survivors: A Randomized-Controlled Trial

Navy beans contain bioactive phytochemicals with colon cancer prevention properties as demonstrated in carcinogen-induced animal models. Human studies support that dietary navy bean intake modulates metabolism by the gut microbiome. This study investigated the effect of navy bean ingestion on plasma and urine metabolite profiles of overweight and obese colorectal cancer survivors. Twenty participants completed a single-blinded, randomized-controlled dietary intervention with precooked navy beans (35 g bean powder/day) or control (0 g/day) for 4 weeks. Plasma and urine were collected at baseline, 2 weeks, and 4 weeks following consumption. Nontargeted metabolomics was applied to study meals and snacks, navy beans, plasma, and urine. Increased navy bean consumption was hypothesized to (i) delineate dietary biomarkers and (ii) promote metabolic shifts relevant for cancer protection in the plasma and urine metabolome. At 4 weeks, 16 plasma and 16 urine metabolites were significantly different in the navy bean intervention group compared with placebo control (P < 0.05). Increased plasma 2,3-dihydroxy-2-methylbutyrate (1.34-fold), S-methylcysteine (1.92-fold), and pipecolate (3.89-fold), and urine S-adenosylhomocysteine (2.09-fold) and cysteine (1.60-fold) represent metabolites with cancer-protective actions following navy bean consumption. Diet-derived metabolites were detected in plasma or urine and confirmed for presence in the navy bean intervention meals and snacks. These included 3-(4-hydroxyphenyl)propionate, betaine, pipecolate, S-methylcysteine, choline, eicosapentaenoate (20:5n3), benzoate, S-adenosylhomocysteine, N-delta-acetylornithine, cysteine, 3-(4-hydroxyphenyl)lactate, gentisate, hippurate, 4-hydroxyhippurate, and salicylate. The navy bean dietary intervention for 4 weeks showed changes to pathways of metabolic importance to colorectal cancer prevention and merit continued attention for dietary modulation in future high-risk cohort investigations. PREVENTION RELEVANCE: This clinical study suggests that increased consumption of navy beans would deliver bioactive metabolites to individuals at high risk for colorectal cancer recurrence and produce metabolic shifts in plasma and urine profiles.

Urine Metabolomics Analysis in Patients With Normoalbuminuric Diabetic Kidney Disease

Objective

Diabetic kidney disease (DKD) leads to low high albuminuria and gradually progresses to very high albuminuria with kidney insufficiency. However, about 20–40% of DKD is normoalbuminuric DKD (NADKD), which has impaired kidney function but normal urine albumin. This study is to investigate the urine metabolomic profiles of patients with NADKD and albuminuria DKD (ADKD).

Methods

In total, 95 patients were divided into a simple diabetes mellitus group (SDM group), an ADKD group, and a NADKD group. All subjects were analyzed for urine metabolites using non-targeted metabolomics based on ultra-performance liquid chromatography – tandem mass spectrometry.

Results

The urine metabolomic profiles of the SDM group, NADKD group, and ADKD group were significantly different, and 65 different metabolites were identified among the three groups. Metabolic pathway analysis of these differential metabolites found that the top three significantly changed metabolic pathways were linoleic acid metabolism, citrate cycle, and, arginine and proline metabolism. There are 12 metabolites enriched in these three metabolic pathways. In detail, compared with those in the SDM group, the levels of γ-linolenic acid in the ADKD group were increased significantly, while the levels of succinic acid, cis-aconitic acid, citric acid, L-proline, L-erythro-4-hydroxyglutamate, N-methylhydantoin, N-carbamoylputrescine, spermidine, and 5-aminopentanoic acid were reduced significantly; compared with those in the NADKD group, the levels of linoleic acid, γ-linolenic acid, and L-malic acid in the ADKD group were increased significantly (P < 0.05), while the levels of L-proline, L-erythro-4-hydroxyglutamate, N-carbamoylputrescine, and spermidine were significantly reduced (P < 0.05). However, there were no significant difference between the SDM group and NADKD group (P > 0.05).

Conclusion

The urine metabolomic profiles between the NADKD group and the ADKD group are significantly different. Specifically, these two groups have distinct levels of linoleic acid, γ-linolenic acid, L-malic acid, L-proline, L-erythro-4-hydroxyglutamate, N-carbamoylputrescine, and spermidine.

Association between Metabolites and the Risk of Lung Cancer: A Systematic Literature Review and Meta-Analysis of Observational Studies

Globally, lung cancer is the most prevalent cancer type. However, screening and early detection is challenging. Previous studies have identified metabolites as promising lung cancer biomarkers. This systematic literature review and meta-analysis aimed to identify metabolites associated with lung cancer risk in observational studies. The literature search was performed in PubMed and EMBASE databases, up to 31 December 2019, for observational studies on the association between metabolites and lung cancer risk. Heterogeneity was assessed using the I2 statistic and Cochran's Q test. Meta-analyses were performed using either a fixed-effects or random-effects model, depending on study heterogeneity. Fifty-three studies with 297 metabolites were included. Most identified metabolites (252 metabolites) were reported in individual studies. Meta-analyses were conducted on 45 metabolites. Five metabolites (cotinine, creatinine riboside, N-acetylneuraminic acid, proline and r-1,t-2,3,c-4-tetrahydroxy-1,2,3,4-tetrahydrophenanthrene) and five metabolite groups (total 3-hydroxycotinine, total cotinine, total nicotine, total 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanol (sum of concentrations of the metabolite and its glucuronides), and total nicotine equivalent (sum of total 3-hydroxycotinine, total cotinine and total nicotine)) were associated with higher lung cancer risk, while three others (folate, methionine and tryptophan) were associated with lower lung cancer risk. Significant heterogeneity was detected across most studies. These significant metabolites should be further evaluated as potential biomarkers for lung cancer.

Machine learning of serum metabolic patterns encodes early-stage lung adenocarcinoma

Early cancer detection greatly increases the chances for successful treatment, but available diagnostics for some tumours, including lung adenocarcinoma (LA), are limited. An ideal early-stage diagnosis of LA for large-scale clinical use must address quick detection, low invasiveness, and high performance. Here, we conduct machine learning of serum metabolic patterns to detect early-stage LA. We extract direct metabolic patterns by the optimized ferric particle-assisted laser desorption/ionization mass spectrometry within 1 s using only 50 nL of serum. We define a metabolic range of 100-400 Da with 143 m/z features. We diagnose early-stage LA with sensitivity~70-90% and specificity~90-93% through the sparse regression machine learning of patterns. We identify a biomarker panel of seven metabolites and relevant pathways to distinguish early-stage LA from controls (p < 0.05). Our approach advances the design of metabolic analysis for early cancer detection and holds promise as an efficient test for low-cost rollout to clinics.

Analysis of differential metabolites in lung cancer patients based on metabolomics and bioinformatics

Aim: Based on metabonomics, the metabolic markers of lung cancer patients were analyzed, combined with bioinformatics to explore the underlying disease mechanism. Materials & methods: Based on case-control design, using UPLC-Q-TOF/MS, urine metabolites were detected in discovery and validation set. Multivariate statistical analysis were performed to identify potential markers for lung cancer. A network analysis was constructed to integrate lung cancer disease targets with the above metabolic markers, and its possible mechanism and biological significance were explained. Results: A total of 35 potential markers were identified, 11 of which overlapped. Five key markers have a good linear correlation with serum biochemical indicators. Conclusion: The occurrence and development of lung cancer are closely related to disturbance of D-Glutamine and D-glutamate metabolism, amino acid imbalance. This test was registered on China clinical trial registration center (www.chictr.org.cn/index.aspx), registration number was ChiCTR1900025543.