Abstract

Importance

   Chinese women have the highest rate of lung cancer among female
   never-smokers in the world, and the etiology is poorly understood.

Objective

   To assess the association between metabolomics and lung cancer risk
   among never-smoking women.

Design, Setting, and Participants

   This nested case-control study included 275 never-smoking female
   patients with lung cancer and 289 never-smoking cancer-free control
   participants from the prospective Shanghai Women’s Health Study
   recruited from December 28, 1996, to May 23, 2000. Validated food
   frequency questionnaires were used for the collection of dietary
   information. Metabolomic analysis was conducted from November 13, 2015,
   to January 6, 2016. Data analysis was conducted from January 6, 2016,
   to November 29, 2018.

Exposures

   Untargeted ultra-high-performance liquid chromatography–tandem mass
   spectrometry and nuclear magnetic resonance metabolomic profiles were
   characterized using prediagnosis urine samples. A total of 39 416
   metabolites were measured.

Main Outcomes and Measures

   Incident lung cancer.

Results

   Among the 564 women, those who developed lung cancer (275 participants;
   median [interquartile range] age, 61.0 [52-65] years) and those who did
   not develop lung cancer (289 participants; median [interquartile range]
   age, 62.0 [53-66] years) at follow-up (median [interquartile range]
   follow-up, 10.9 [9.0-11.7] years) were similar in terms of their
   secondhand smoke exposure, history of respiratory diseases, and body
   mass index. A peak metabolite, identified as 5-methyl-2-furoic acid,
   was significantly associated with lower lung cancer risk (odds ratio,
   0.57 [95% CI, 0.46-0.72]; P < .001; false discovery rate = 0.039).
   Furthermore, this peak was weakly correlated with self-reported dietary
   soy intake (ρ = 0.21; P < .001). Increasing tertiles of this metabolite
   were associated with lower lung cancer risk (in comparison with first
   tertile, odds ratio for second tertile, 0.52 [95% CI, 0.34-0.80]; and
   odds ratio for third tertile, 0.46 [95% CI, 0.30-0.70]), and the
   association was consistent across different histological subtypes and
   follow-up times. Additionally, metabolic pathway analysis found several
   systemic biological alterations that were associated with lung cancer
   risk, including 1-carbon metabolism, nucleotide metabolism, oxidative
   stress, and inflammation.

Conclusions and Relevance

   This prospective study of the untargeted urinary metabolome and lung
   cancer among never-smoking women in China provides support for the
   hypothesis that soy-based metabolites are associated with lower lung
   cancer risk in never-smoking women and suggests that biological
   processes linked to air pollution may be associated with higher lung
   cancer risk in this population.

Introduction

   Lung cancer is the leading cause of cancer mortality among men and
   women, accounting for 27% of all cancer deaths worldwide and nearly
   158 040 deaths in the United States in 2015.^[65]1 Although most cases
   of lung cancer are caused by active tobacco smoking, approximately 25%
   occur in never-smokers.^[66]2 Asian individuals (particularly Chinese
   women) have a higher rate of lung cancer among never-smokers compared
   with individuals of European descent^[67]3; however, the etiology and
   risk factors of this malignant neoplasm independent of smoking are
   poorly understood.^[68]4 Therefore, it is imperative to understand the
   etiology of lung cancer and identify new biomarker targets or
   metabolomic profiles as potential diagnostic and screening tools for
   lung cancer.

   Altered tumor metabolism, a distinctive hallmark of many cancers, is
   required to acquire sufficient nutrients to maintain deregulated and
   unlimited proliferation of the cancer cells.^[69]5 An agnostic approach
   using untargeted metabolic profiling allows more comprehensive coverage
   of downstream biological responses to environmental exposures, and can
   provide insight into underlying molecular disease mechanisms.^[70]6
   Metabolomic techniques have been used to identify metabolites in
   various cancers.^[71]7,[72]8,[73]9,[74]10,[75]11 However, most of these
   metabolomic studies of cancer were conducted using case-control study
   designs that compared the metabolite levels between individuals
   diagnosed with cancer and healthy controls, which could be prone to
   reverse causation bias. Targeted prospective studies have found some
   urinary metabolites to be associated with higher lung cancer risk,
   including creatine riboside,^[76]12 N-acetylneuraminic acid,^[77]12 and
   7-methylguanine.^[78]13 Both studies were conducted in populations of
   European descent and had small numbers of never-smokers.

   The Shanghai Women’s Health Study (SWHS) is a population-based
   prospective cohort of 73 363 female participants who were predominantly
   never-smokers. This study provides a unique opportunity to
   prospectively evaluate the role of the metabolome in lung cancer
   development. To our knowledge, this is the first prospective study to
   investigate prediagnostic metabolic biomarkers and pathway alterations
   in urine samples using an untargeted approach among never-smoking
   women, which may provide novel insights into the etiology and
   mechanisms associated with lung cancer development in never-smokers.

Methods

Study Population

   From December 28, 1996, to May 23, 2000, the SWHS recruited 73 363
   Chinese women aged 40 to 70 years who were primarily never-smokers. In
   this nested case-control study, we performed metabolomic profiling of
   all 275 female patients with incident lung cancer and 289 female
   healthy controls who were individually matched on date of birth (±2
   years) and date of spot urine sample collection (±3 months). We
   followed the Strengthening the Reporting of Observational Studies in
   Epidemiology ([79]STROBE) reporting guideline. Information on
   demographic characteristics, lifestyle, diet, and medical history was
   obtained using a detailed survey administered in person at
   baseline.^[80]14 A comprehensive and validated food frequency
   questionnaire was used to assess dietary intake during the year prior
   to the interview. Urine samples were collected into a sterilized cup
   containing 125 mg of ascorbic acid to prevent oxidation of labile
   metabolites. After collection, the samples were kept at approximately
   0°C to –4°C and were processed within 6 hours for long-term storage at
   −70°C.^[81]14 All study participants provided written informed consent
   prior to participation, and the study protocols were approved by
   institutional review boards of all participating institutes and the
   National Cancer Institute.

Follow-up and Lung Cancer Identification

   The participants were followed up with a combination of in-person
   surveys and periodic linkage to cancer and vital statistics
   registries.^[82]14 Briefly, cohort members were followed up for
   diagnosis of cancer by in-person surveys every 2 to 3 years and annual
   record linkage with databases from the population-based Shanghai Cancer
   Registry, Shanghai Vital Statistics Registry, and Shanghai Resident
   Registry. All possible cancer diagnoses were verified through home
   visits and a review of medical records by a panel of oncologists. Lung
   cancer was defined as code 162 under the International Classification
   of Diseases, Ninth Revision. All female patients with lung cancer were
   included as soon as they were diagnosed after the baseline recruitment
   through December 31, 2009. Lung cancer diagnosed before the recruitment
   was excluded. Information on histological subtypes of lung cancer was
   extracted from medical records. Histological groups of lung cancer were
   classified according to the morphology code of International
   Classification of Diseases for Oncology, Second Edition
   (8140/3).^[83]15

Urinary Metabolomics

   All urine samples (patient and pooled quality control samples) were
   analyzed in an untargeted fashion by ultra-high-performance liquid
   chromatography–tandem mass spectrometry (UPLC-MS) and 600-MHz hydrogen
   1 nuclear magnetic resonance (NMR) spectroscopy at Imperial College
   London, United Kingdom, acquired between November 13, 2015, and January
   6, 2016. The spectral data for the pooled quality control sample was
   collected after every 5 patient samples. The measurements were carried
   out according to established protocols used for metabolic phenotyping
   of biofluids.^[84]16,[85]17 For the UPLC-MS measurements, metabolic
   profiling was performed on an ACQUITY UPLC System (Waters Corp) coupled
   to a Xevo Triple Quadrupole Mass Spectrometer (Waters Corp) following a
   standard set of protocols.^[86]18 Mass spectrometry was performed using
   electrospray in both positive and negative ionization (electrospray
   ionization [ESI]+ and ESI−) modes under conditions described
   previously.^[87]19 This UPLC-MS method allows for a high-resolution,
   accurate, and simultaneous detection of thousands of peaks that
   correspond to individual ions with a unique mass to charge (m/z) ratio
   and retention time.^[88]20

   Each UPLC-MS data set was preprocessed using Progenisis (Non Linear
   Dynamics) to convert the 3-dimensional UPLC-MS raw data into
   time-aligned detected features of retention time, m/z ratio, and
   intensity (peak area). The ion intensities for each peak detected were
   then normalized, within each sample, to the median peak intensity in
   that sample. The metabolic profiling data were preprocessed using the
   freely available XCMS software (Scripps Research Institute) to detect
   and identify peaks, align retention time, and perform normalization of
   the raw mass spectrometry data before subsequent statistical
   analysis.^[89]19,[90]21 The resulting features were filtered to remove
   features with a coefficient of variation greater than 30% across the
   quality control samples. The NMR data were imported into MATLAB R2012b
   (Natick) using the Metaspectra program script, using a resolution of
   0.00055. The data were then aligned using recursive segmentwise peak
   alignment^[91]22 and normalized by probabilistic quotient to account
   for and exclude systematic sources of bias within samples not due to
   biological processes or the environment. Statistical recoupling of
   variables^[92]23 was then used for peak detection. The resulting tables
   were imported to SIMCA-P+ software version 13.0.2 (Umetrics) to perform
   multivariate statistical analysis. In total, there were 20 555 UPLC-MS
   peaks in positive mode, 18 475 in negative mode, and 386 NMR peaks.

Statistical Analysis

   A small constant (1 × 10^−9) was added to all metabolite levels, which
   were then natural log–transformed to approximate normal distributions
   and subsequently analyzed as both continuous and categorical variables
   (based on tertiles among controls). Principal component analysis,
   partial least-squares–discriminant analysis, and orthogonal projection
   on latent structures–discriminant analysis (OPLS-DA) were performed on
   all data to check for batch effects and clustering of samples.

   The characteristics of patients with lung cancer and control
   individuals were compared using the Wilcoxon rank sum test for
   continuous variables and Fisher exact test for categorical variables.
   Analyses were restricted to lifetime never-smokers (275 female patients
   with lung cancer and 289 controls). Separate unconditional logistic
   regression models were used to estimate the odds ratios (ORs) and 95%
   confidence intervals for the associations between each metabolite and
   lung cancer risk. Tests of trend were calculated by treating the level
   of each metabolite as a continuous variable. We also stratified the
   analysis by follow-up time (<9 years [the median duration of follow-up]
   or ≥9 years) and major lung cancer subtypes (adenocarcinoma,
   nonadenocarcinoma). Because dietary intake has previously been reported
   to be associated with altered metabolites in urine samples, using a
   multivariable linear regression model, we evaluated the associations
   between the most significant metabolite and prediagnosis dietary
   factors among female patients with lung cancer and controls. In
   addition, because soy food intake has been previously found to be
   associated with risk of lung cancer in the SWHS,^[93]24 we further
   looked at grouped soy intake, which included 11 soy food items that are
   commonly consumed in Shanghai, including soy milk, tofu, fried tofu,
   dried or pressed tofu, fresh green soy beans, dry soy beans, soy
   sprouts, and other soy products. In addition, mediation analysis was
   used to estimate the proportion of the mediated effect via the
   significant metabolites on the association between soy consumption and
   lung cancer risk.^[94]25 Missing data were excluded from all analyses.
   All models were adjusted for age (continuous), body mass index, history
   of respiratory diseases (categorical; ever or never) and environmental
   tobacco smoke (categorical; ever or never). Correlations among
   significant metabolites, as well as between significant metabolites and
   17 dietary factors, were evaluated using Spearman correlation
   coefficients.

   All statistical analyses were conducted using R statistical software
   version 3.2.2 (R Project for Statistical Computing) and SAS statistical
   software version 4.3 (SAS Institute). All tests with a false discovery
   rate (FDR) of less than 0.10 were considered statistically significant.
   All P values were reported based on 2-sided tests and P < .05 was
   considered statistically significant. We calculated FDRs using the
   Benjamini-Hochberg method to account for multiple comparisons.^[95]26

Metabolite Identification

   The metabolic phenotypes of female patients with lung cancer and
   controls were further evaluated using pathway enrichment to identify
   biological processes associated with lung cancer diagnosis. In this
   analysis, we tested all metabolic features for their association with
   lung cancer diagnosis. Pathway enrichment analysis was completed using
   all metabolic features with P < .05. This threshold minimizes type II
   error (false-negatives) at the increased risk of type I error
   (false-positives). The pathway enrichment approach uses a random
   sampling framework to identify a null distribution of metabolites
   across a metabolic network; comparison of the null distribution with
   annotated metabolites from signals with P < .05 removes
   false-positives, which would be randomly distributed across the
   metabolic map, and allows isolation of biological effects. The pathway
   enrichment was completed separately for positive- and negative-mode
   data using the mummichog analysis approach^[96]27; pathways with
   P < .05 were selected for further characterization. Metabolites were
   identified and annotated based on accurate mass (for UPLC-MS) and by
   using in-house and available online databases such as the Human
   Metabolome Database and METLIN.

Results

   Among the 564 women, those who developed lung cancer (275 participants;
   median [interquartile range {IQR}] age, 61.0 [52-65] years) and those
   who did not develop lung cancer (289 participants; median [IQR] age,
   62.0 [53-66] years) at follow-up were similar in terms of secondhand
   smoke exposure, history of respiratory diseases, and body mass index
   ([97]Table 1). The median (IQR) time from the baseline questionnaire to
   lung cancer diagnosis was 9.26 (6.69-11.05) years.

Table 1. Characteristics of the Nested Case-Control Study of Never-Smoking
Women in the Shanghai Women’s Health Study.

   Characteristic Lung Cancer (n = 275) No Lung Cancer (n = 289) P
   Value[98]^a
   Environmental tobacco smoke, No. (%)
   Never 70 (25.5) 71 (24.6) .86
   Ever 187 (68) 200 (69.2)
   Missing 18 (6.5) 18 (6.2)
   History of respiratory diseases, No. (%)
   Yes 28 (10.2) 22 (7.6) .36
   No 247 (89.8) 267 (92.4)
   Age at baseline, median (IQR), y 61.0 (52-65) 62.0 (53-66) .42
   BMI, median (IQR) 24.3 (22.4-26.6) 24.3 (22.0-26.9) .84
   Time to diagnosis, median (IQR), y 9.26 (6.69-11.1) NA NA
   Histological subtypes, No. (%)
   Adenocarcinoma 135 (49.1) NA NA
   Squamous cell carcinoma 9 (3.3) NA
   Other histology subtypes 19 (6.9) NA
   Unclassified 104 (37.8) NA
   No ICD-O-2 code 8 (2.9) NA
   [99]Open in a new tab

   Abbreviations: BMI, body mass index (calculated as weight in kilograms
   divided by height in meters squared); ICD-O-2, International
   Classification of Diseases for Oncology, Second Revision; IQR,
   interquartile range; NA, not applicable.
   ^^a

   P values were obtained from Wilcoxon signed-rank test for continuous
   variables and Fisher exact test for categorical variables.

   Using an FDR cutoff of 10% to adjust for multiple comparisons, 3
   urinary metabolites by UPLC-MS were significantly associated with lower
   lung cancer risk ([100]Table 2). The statistically significant
   metabolites were pos_2.61_127.0382m/z (OR, 0.57 [95% CI, 0.46-0.72];
   P < .001; FDR = 0.039), neg_2.60_369.0408m/z (OR, 0.97 [95% CI,
   0.96-0.98]; P < .001; FDR = 0.039), and pos_2.61_184.0325n (OR, 0.55
   [95% CI, 0.43-0.71]; P < .001; FDR = 0.065). The 3 significant
   metabolites were highly correlated with each other (Spearman
   correlations ranged from 0.67-0.89) (eTable 1 in the [101]Supplement).
   In addition, results were comparable when stratified by follow-up time
   (<9 years of follow-up and ≥9 years of follow-up) after urine sample
   collection ([102]Table 2). Similar results were also obtained after
   excluding 4 women who were diagnosed within 2 years after the baseline
   interview (data not shown). Furthermore, similar associations were
   observed among patients with lung adenocarcinoma (135 patients [49.1%])
   and those with unclassified lung cancer (104 patients [37.8%]) (eTable
   2 and eTable 3 in the [103]Supplement). None of the NMR features were
   significantly associated with lung cancer risk (data not shown).

Table 2. Associations Between Urinary Metabolites and Lung Cancer Risk,
Stratified by Follow-up Time.

   Metabolite[104]^a OR (95% CI)[105]^b P Value FDR Follow-up
   <9 y[106]^c ≥9 y[107]^d
   OR (95% CI)[108]^b P Value OR (95% CI)[109]^b P Value
   pos_2.61_127.0382m/z 0.57 (0.46-0.72) <.001 0.039 0.51 (0.38-0.70)
   <.001 0.62 (0.48-0.81) <.001
   neg_2.60_369.0408m/z 0.97 (0.96-0.98) <.001 0.039 0.97 (0.95-0.98)
   <.001 0.97 (0.96-0.99) <.001
   pos_2.61_184.0325n 0.55 (0.43-0.71) <.001 0.065 0.49 (0.35-0.69) <.001
   0.60 (0.44-0.82) .001
   [110]Open in a new tab

   Abbreviations: FDR, false discovery rate; OR, odds ratio.
   ^^a

   Metabolites were natural log–transformed.
   ^^b

   All models were adjusted for age, body mass index, history of
   respiratory diseases, and secondhand smoke exposure.
   ^^c

   Sample sizes were 131 with lung cancer and 289 without lung cancer.
   ^^d

   Sample sizes were 144 with lung cancer and 289 without lung cancer.

   One of the significant metabolites (pos_2.61_127.0382m/z) was
   identified as 5-methyl-2-furoic acid based on its fragmentation pattern
   by tandem mass spectrometry (eTable 4 in the [111]Supplement). There
   was a significant monotonic dose-response association between
   5-methyl-2-furoic acid and lung cancer risk when analyzed as tertiles
   ([112]Table 3). Increasing tertiles of the metabolite level were
   significantly associated with lower lung cancer risk (in comparison
   with first tertile, OR for second tertile, 0.52 [95% CI, 0.34-0.80];
   and OR for third tertile, 0.46 [95% CI, 0.30-0.70]; P for
   trend < .001). Similar trends were observed for the 2 other significant
   metabolites (eTable 5 in the [113]Supplement).

Table 3. Associations Between 5-Methyl-2-Furoic Acid and Lung Cancer, by
Tertiles and Stratified by Follow-up Time.

   Log Metabolite Tertiles Patients With Lung Cancer, No. Patients Without
   Lung Cancer, No. OR (95% CI)[114]^a P Value P for Trend
   First tertile, median = 10.5 139 96 1 [Reference] NA <.001
   Second tertile, median = 11.3 71 95 0.52 (0.34-0.80) .003
   Third tertile, median = 12.1 64 96 0.46 (0.30-0.70) <.001
   <9-y follow-up
   First tertile, median = 10.5 69 96 1 [Reference] NA <.001
   Second tertile, median = 11.3 34 96 0.51 (0.30-0.86) .01
   Third tertile, median = 12.1 27 96 0.36 (0.20-0.63) <.001
   ≥9-y follow-up
   First tertile, median = 10.4 70 96 1 [Reference] NA .02
   Second tertile, median = 11.2 37 96 0.53 (0.32-0.89) .02
   Third tertile, median = 12.1 37 96 0.56 (0.34-0.93) .03
   [115]Open in a new tab

   Abbreviations: OR, odds ratio; NA, not applicable.
   ^^a

   All models were adjusted for age, body mass index, history of
   respiratory diseases, and secondhand smoke exposure.

   Because dietary intake has been previously reported to be associated
   with altered metabolites in urine samples, we also assessed the
   correlation of each available dietary factor with the level of
   5-methyl-2-furoic acid. Consumption of soy food, including soy milk,
   tofu, fried tofu, dried or pressed tofu, fresh green soy beans, dry soy
   beans, soy sprouts, and other soy products, in the past year had a weak
   positive correlation with levels of 5-methyl-2-furoic acid among
   controls (ρ = 0.21; P < .001) (eTable 6 in the [116]Supplement). Using
   mediation analysis, we found that 25% of the association between soy
   consumption and higher lung cancer risk was significantly mediated via
   5-methyl-2-furoic acid.

   Regression analysis included 152 positive-mode and 186 negative-mode
   features associated with lung cancer risk at P < .05; only 4 features
   were present with Bonferroni correction less than .05. Pathway
   enrichment identified 40 metabolic pathways associated with lung cancer
   risk at P < .05, including 8 pathways meeting a Bonferroni-corrected
   P < .05 ([117]Figure). Eight were detected in both modes and included
   amino acid metabolism, nucleotide metabolism, nitrogen catabolism, and
   B vitamin pathways. Metabolic alterations associated with lung cancer
   risk suggest systemic changes to key biological processes, including
   1-carbon metabolism (vitamin B[9] [folate] metabolism; glycine, serine,
   alanine, and threonine metabolism), oxidative stress pathways
   (ascorbate [vitamin C] and aldarate metabolism, methionine and cysteine
   metabolism, glutamate metabolism), nucleotide metabolism (purine
   metabolism), and inflammation (leukotriene metabolism).

Figure. Metabolic Pathways Associated With Lung Cancer Risk Using Features
With P < .05.

   [118]Figure.
   [119]Open in a new tab

   Pathway enrichment was identified using the mummichog analysis approach
   and identified 40 metabolic pathways associated with lung cancer risk,
   including 8 pathways meeting a Bonferroni-corrected P < .05. Circle
   size is proportional to the number of metabolites associated with lung
   cancer diagnosis from each pathway. The placement of circles along the
   x-axis indicate the statistical significance of each pathway. The
   dotted line indicates the significance level using Bonferroni P = .05.
   CoA indicates coenzyme A.

Discussion

   To our knowledge, this is the first large-scale prospective study of
   untargeted de novo metabolomics in association with lung cancer among
   never-smoking Chinese women. We found 3 metabolites associated with a
   reduction of lung cancer risk after accounting for multiple
   comparisons. These metabolites were highly correlated with each other
   and may represent the same chemical family and/or source. One of these
   metabolites (pos_2.61_127.0382m/z) was identified as 5-methyl-2-furoic
   acid, which belongs to the family of furoic acid derivatives. It was
   weakly correlated with dietary soy intake in our study population,
   suggesting that this metabolite may be partially derived from
   soy-related dietary products. Metabolic alterations identified in the
   pathway enrichment analysis suggest lung cancer risk is associated with
   an increased oxidative state and depletion of systemic antioxidant
   pathways.

   Furan fatty acids are related compounds that occur naturally in foods
   such as soybean oil^[120]28 and have also been shown to have
   anti-inflammatory and antioxidant properties in vivo.^[121]29
   Methylated and furan-containing fatty acids have also been demonstrated
   to protect against reactive oxygen species–mediated damage in vivo by
   acting as scavengers of reactive oxygen species.^[122]30 Interestingly,
   we found higher levels of the furoic acid metabolite in urine to be
   associated with a lower risk of lung cancer. Another furoic acid
   derivative, 5-tetradecyl-oxy-2-furoic acid (TOFA), has been reported to
   reduce fatty acid synthesis by inhibiting acetyl coenzyme A
   carboxylase-α (ACCA), thereby increasing apoptosis rates and
   eradicating prostate cancer cells via the mitochondrial
   pathway.^[123]31 One study^[124]32 identified TOFA as a potent
   inhibitor of stearoyl coenzyme A desaturase 1 (SCD1), which is an
   enzyme in the fatty-acid synthesis pathway that depletes
   monounsaturated fatty acids and therefore restricts cancer cell
   proliferation. Furoic acid is derived from furans, which are commonly
   used as food additives, and preservatives, which are usually present in
   high levels in heat-treated foods such as soy sauce, baked beans, and
   brewed coffee.^[125]33

   Dietary soy intake was previously shown to be associated with a 37%
   lower risk of lung cancer among a population of Chinese women in
   Shanghai, comparing the highest quintile with the lowest
   quintile.^[126]24 This is consistent with our findings in the same
   population of a significant protective association between 5-methyl
   furan-2-carboxylic acid and lung cancer risk, and a significant weak
   correlation between this metabolite and dietary soy intake. Soy foods
   contain high concentrations of isoflavones, a type of phytoestrogen,
   which possess antiestrogenic and antioxidative effects.^[127]34 It has
   been hypothesized that the bioactive compounds of dietary soy may
   contain antioxidant and anti-inflammatory properties that inhibit tumor
   growth and invasion and induce apoptosis.^[128]35,[129]36,[130]37

   Evaluating pathway (rather than individual metabolite) changes
   associated with disease risk provides a systems biology approach that
   can be used to understand the metabolic effects of lung cancer
   pathobiology. In the present study, we identified alterations in
   pathways that suggest dysregulation of 1-carbon metabolism and related
   pathways, including folate, methionine and cysteine metabolism, purine
   metabolism, and glycine, serine, alanine, and threonine metabolism.
   One-carbon metabolism is critical in intermediary metabolism and
   supports multiple biological processes, including nucleotide
   metabolism, amino acid metabolism, and homeostasis (methionine and
   cysteine, serine, and glycine).^[131]38

   Our study suggests that metabolic alterations in oxidative stress and
   inflammatory pathways, which could arise from multiple exogenous and
   endogenous exposures and biological processes, are associated with lung
   cancer risk in never-smoking women in China. It is, however, intriguing
   that these same pathways have also been linked to exposure to
   environmental pollutants that are suspected to be associated with
   higher lung cancer disease risk,^[132]39,[133]40 including
   traffic-related air pollution.^[134]41,[135]42,[136]43,[137]44 Taken
   together, the overlap in oxidative stress and inflammation metabolites
   suggest environmental exposures and lung cancer risk in this population
   may share similarities in metabolic alterations and provide an indirect
   link between the two. Additional studies are needed to evaluate these
   possible associations.

   One major strength of this study is that the metabolites were measured
   in never-smoking women, thereby eliminating the possibility of
   confounding by active smoking, which has occurred in other studies.
   Other strengths of this study include the prospective design, in which
   the collection of urinary metabolomic biomarkers preceded lung cancer
   diagnosis, which provides exposure-outcome temporality, and the
   untargeted measurement of metabolites, which provides increased
   opportunities for making novel discoveries.

Limitations

   There were limitations to this study. One is that a notable proportion
   of the cohort with lung cancer (40.7%) had an unclassified histological
   subtype. However, analysis of unclassified lung cancer yielded results
   that were similar to analysis of all lung cancer and of lung
   adenocarcinoma only. The source of the dietary data was a food
   frequency questionnaire designed to assess dietary patterns in the past
   year when the baseline interview was conducted. The exact question
   asked in the questionnaire was, “Would you please tell me if you ate
   those foods and how much you ate in general over the past year?” Given
   that most metabolites have relatively short half-lives, the weak
   correlation between 5-methyl-2-furoic acid and dietary soy intake may
   have been stronger if we had questionnaire or diary data for the last
   few days before urine was collected. In addition, the urine samples
   were only collected once and we were unable to assess the temporal
   trends of metabolites over time.

Conclusions

   This nested case-control study found that a urinary metabolite,
   5-methyl-2-furoic acid, was associated with decreased risk of lung
   cancer in a prospective cohort of never-smoking women. The identified
   significant metabolite (5-methyl-2-furoic acid) was weakly correlated
   with self-reported usual dietary intake of soy. Furthermore, our
   finding is consistent with recent epidemiological evidence showing that
   higher dietary soy consumption may be associated with decreased lung
   cancer risk among never-smokers, strengthening the biological relevance
   of the identified metabolite. In addition, metabolic pathways
   associated with lung cancer risk suggest systemic biological
   alterations were present, and a number of pathways identified in the
   present study have been linked to results of previous metabolomic
   studies of populations exposed to air pollution, a suspected risk
   factor for lung cancer. Additional studies are warranted to replicate
   and further characterize the associations reported in our study, which
   provide additional clues about the exposures and biological processes
   that may contribute to the risk of lung cancer in never-smoking
   populations in Asia.
   Supplement.

   eTable 1. Spearman Correlations Between Significant Metabolites (<10%
   FDR) in Controls Only, Cases Only and Overall Study Population

   eTable 2. Associations Between Urinary Metabolites and Lung Cancer
   Risk, Stratified by Lung Cancer Subtypes

   eTable 3. Associations Between the Most Significant Metabolite
   (5-Methyl-2-Furoic Acid) and Lung Cancer in Tertiles, Stratified by
   Lung Cancer Subtypes

   eTable 4. Identification of the Top Three Metabolites

   eTable 5. Associations Between neg_2.60_369.0408mz, pos_2.61_184.0325n
   and Lung Cancer, by Tertiles

   eTable 6. Univariate Linear Regression and Correlation Between the Most
   Significant Metabolite (5-Methyl-2-Furoic Acid) and Diet Variables
   Among Controls
   [138]Click here for additional data file.^ (24.6KB, pdf)

References