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. 2024 Sep 17;16(18):3179.
doi: 10.3390/cancers16183179.

Metabolomic Profiling of Pulmonary Neuroendocrine Neoplasms

Clémence Boullier  1   2 , Fabien C Lamaze  1 , Jean-François Haince  3 , Rashid Ahmed Bux  3 , Michèle Orain  1 , Jiamin Zheng  4 , Lun Zhang  4 , David S Wishart  4 , Yohan Bossé  1   2 , Venkata S K Manem  2   5   6 , Philippe Joubert  1   2
Affiliations

Metabolomic Profiling of Pulmonary Neuroendocrine Neoplasms

Clémence Boullier et al. Cancers (Basel). .

Abstract

Background/objectives: Pulmonary neuroendocrine neoplasms (NENs) account for 20% of malignant lung tumors. Their management is challenging due to their diverse clinical features and aggressive nature. Currently, metabolomics offers a range of potential cancer biomarkers for diagnosis, monitoring tumor progression, and assessing therapeutic response. However, a specific metabolomic profile for early diagnosis of lung NENs has yet to be identified. This study aims to identify specific metabolomic profiles that can serve as biomarkers for early diagnosis of lung NENs.

Methods: We measured 153 metabolites using liquid chromatography combined with mass spectrometry (LC-MS) in the plasma of 120 NEN patients and compared them with those of 71 healthy individuals. Additionally, we compared these profiles with those of 466 patients with non-small-cell lung cancers (NSCLCs) to ensure clinical relevance.

Results: We identified 21 metabolites with consistently altered plasma concentrations in NENs. Compared to healthy controls, 18 metabolites were specific to carcinoid tumors, 5 to small-cell lung carcinomas (SCLCs), and 10 to large-cell neuroendocrine carcinomas (LCNECs). These findings revealed alterations in various metabolic pathways, such as fatty acid biosynthesis and beta-oxidation, the Warburg effect, and the citric acid cycle.

Conclusions: Our study identified biomarker metabolites in the plasma of patients with each subtype of lung NENs and demonstrated significant alterations in several metabolic pathways. These metabolomic profiles could potentially serve as biomarkers for early diagnosis and better management of lung NENs.

Keywords: blood metabolites; carcinoid tumors; large-cell neuroendocrine carcinoma (LCNEC); lung; metabolism; metabolomic profile; neuroendocrine neoplasm (NEN); small-cell lung carcinoma (SCLC).

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Conflict of interest statement

Rashid Ahmed Bux is President and CEO of BioMark Diagnostics Inc. and is a shareholder. Jean-Francois Haince is General Manager of BioMark Diagnostic Solutions Inc. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as potential conflicts of interest.

Figures

Figure 1
Figure 1
Volcano plots with plasma metabolite concentrations. Metabolite selection based on Mann–Whitney test and Bonferroni correction (p-value threshold = 0.0003); overexpressed metabolites = red, underexpressed = blue: (a) NENs grouped together, (b) carcinoids tumors, (c) SCLCs or (d) LCNECs compared with healthy controls. (e) SCLCs or (f) LCNECs compared to NSCLCs.
Figure 1
Figure 1
Volcano plots with plasma metabolite concentrations. Metabolite selection based on Mann–Whitney test and Bonferroni correction (p-value threshold = 0.0003); overexpressed metabolites = red, underexpressed = blue: (a) NENs grouped together, (b) carcinoids tumors, (c) SCLCs or (d) LCNECs compared with healthy controls. (e) SCLCs or (f) LCNECs compared to NSCLCs.
Figure 1
Figure 1
Volcano plots with plasma metabolite concentrations. Metabolite selection based on Mann–Whitney test and Bonferroni correction (p-value threshold = 0.0003); overexpressed metabolites = red, underexpressed = blue: (a) NENs grouped together, (b) carcinoids tumors, (c) SCLCs or (d) LCNECs compared with healthy controls. (e) SCLCs or (f) LCNECs compared to NSCLCs.
Figure 2
Figure 2
Representation of variations in concentrations of metabolites differentially expressed in plasma under different conditions and characterization of their discriminative capacity: (a) Venn diagram of the 23 metabolites that have significantly different concentrations between patients with carcinoid tumors, SCLCs, and LCNECs compared to healthy individuals. (b) Heatmap plot with plasma concentrations of these 23 metabolites. Concentrations were normalized by z-score. Clinical characteristics are integrated: gender, BMI, smoking, and pathological stage.
Figure 3
Figure 3
Metabolic pathways expected to be affected by the presence of NENs: (a) Enrichment analysis with 9 non-lipid metabolites (among the 21) differentially expressed in patients with NENs compared to healthy people. (b) Enrichment analysis with 8 non-lipid metabolites (among 18) differentially expressed in patients with carcinoid tumors compared to healthy people. (c) Enrichment analysis with 4 non-lipid metabolites (among 5) differentially expressed in patients with SCLCs compared to healthy people. (d) Enrichment analysis with 5 non-lipid metabolites (among 10) differentially expressed in patients with LCNECs compared to healthy people. Representation of the top 25 pathways among the 26 enriched. Set enrichment analysis (MSEA) performed with MetaboAnalyst 6.0. * indicates significantly enriched metabolic pathways (p < 0.05).
Figure 3
Figure 3
Metabolic pathways expected to be affected by the presence of NENs: (a) Enrichment analysis with 9 non-lipid metabolites (among the 21) differentially expressed in patients with NENs compared to healthy people. (b) Enrichment analysis with 8 non-lipid metabolites (among 18) differentially expressed in patients with carcinoid tumors compared to healthy people. (c) Enrichment analysis with 4 non-lipid metabolites (among 5) differentially expressed in patients with SCLCs compared to healthy people. (d) Enrichment analysis with 5 non-lipid metabolites (among 10) differentially expressed in patients with LCNECs compared to healthy people. Representation of the top 25 pathways among the 26 enriched. Set enrichment analysis (MSEA) performed with MetaboAnalyst 6.0. * indicates significantly enriched metabolic pathways (p < 0.05).
Figure 4
Figure 4
Machine learning with potential plasma metabolite biomarkers: (a) Study design. Plasma samples from a total of 657 participants were analyzed by mass spectrometry. Statistical tests using the concentrations of 153 metabolites for each sample were carried out to try and discriminate a particular profile of NENs. First, a selection step based on a Bonferroni-corrected Mann–Whitney test with a bootstrap method identified metabolites differentially expressed in NENs. Finally, among these metabolites, those with predictive potential were used to build logistic regression models. (b–e) Confusion matrices for all logistic regression models constructed. Test population = entire cohort. (b) Prediction of NEN patients and healthy controls using concentrations of the 11 metabolites used in the model. Cross-validation accuracy (95% CI) = 93.16% (87.69%–98.62%); Sensitivity = 95.77%; Specificity = 96.67%. (c) Prediction of carcinoid tumor patients and healthy controls using concentrations of the 7 metabolites used in the model. Cross-validation accuracy (95% CI) = 92.60% (84.20%–100%); Sensitivity = 95.78%; Specificity = 94%. (d) Prediction of patients with SCLCs and healthy controls using concentrations of the 4 metabolites used in the model. Cross-validation accuracy (95% CI) = 90.08% (82.70%–97.46%); Sensitivity = 97.19%; Specificity = 85%. (e) Prediction of LCNEC patients and healthy controls using concentrations of the 4 metabolites used in the model. Cross-validation accuracy = 92.09% (85.06%–99.13%); Sensitivity = 97.19%; Specificity = 86.67%.
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