Serum lipidome screening in patients with stage I non-small cell lung cancer

Klupczynska, Agnieszka; Plewa, Szymon; Kasprzyk, Mariusz; Dyszkiewicz, Wojciech; Kokot, Zenon J.; Matysiak, Jan

doi:10.1007/s10238-019-00566-7

Cited by 36 publications

(36 citation statements)

References 46 publications

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“…8 For example, serum levels of lysoPC (C26:0 and C26:1) and PC (C42:4 and C34:4) were different between stage I NSCLC and healthy patients. 22 Some elements and pathways of serum PC and PE profiles increased in patients with lung benign disease and early-stage NSCLC, as compared with healthy, whereas few (e.g., PC 15:0/18:1) significantly elevated in early-stage NSCLC patients alone. 2 It seems that patterns of lipid elements may be associated with the specificity of lung cancer and stage, rather than the intact lipid pathways.…”

Section: Discussionmentioning

confidence: 87%

“…18 In order to face the major challenge that most clinical information was descriptive and unmatched with the digital quantity of omics data, clinical phenomes were scored by DESS and integrated with genomic and proteomic data of patients with acute respiratory distress syndrome and chronic obstructive pulmonary disease. [19][20][21][22] Clinical phenomes were furthermore integrated with lipidomic profiles in patients with pulmonary embolism, acute pneumonia, and acute exacerbation of chronic obstructive pulmonary diseases, based on clinical trans-omics principle. 15 Lipidomic profiles difference between health and lung cancer has been defined and it depends upon methodologies of measurement and analysis, sample preparations and sources, and patient populations and status.…”

Section: Discussionmentioning

confidence: 99%

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Trans‐omic profiling between clinical phenoms and lipidomes among patients with different subtypes of lung cancer

Zhu

Zhang

et al. 2020

Clinical & Translational Med

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Lung cancer has high mortality, often accompanied with systemic metabolic disorders. The present study aimed at defining values of trans‐nodules cross‐clinical phenomic and lipidomic network layers in patients with adenocarcinoma (ADC), squamous cell carcinomas, or small cell lung cancer (SCLC). We measured plasma lipidomic profiles of lung cancer patients and found that altered lipid panels and concentrations varied among lung cancer subtypes, genders, ages, stages, metastatic status, nutritional status, and clinical phenome severity. It was shown that phosphatidylethanolamine elements (36:2, 18:0/18:2, and 18:1/18:1) were SCLC specific, whereas lysophosphatidylcholine (20:1 and 22:0 sn‐position‐1) and phosphatidylcholine (19:0/19:0 and 19:0/21:2) were ADC specific. There were statistically more lipids declined in male, <60 ages, late stage, metastasis, or body mass index < 22 . Clinical trans‐omics analyses demonstrated that one phenome in lung cancer subtypes might be generated from multiple metabolic pathways and metabolites, whereas a metabolic pathway and metabolite could contribute to different phenomes among subtypes, although those needed to be furthermore confirmed by bigger studies including larger population of patients in multicenters. Thus, our data suggested that trans‐omic profiles between clinical phenomes and lipidomes might have the value to uncover the heterogeneity of lipid metabolism among lung cancer subtypes and to screen out phenome‐based lipid panels as subtype‐specific biomarkers.

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Section: Discussionmentioning

confidence: 87%

Section: Discussionmentioning

confidence: 99%

Trans‐omic profiling between clinical phenoms and lipidomes among patients with different subtypes of lung cancer

Zhu

Zhang

et al. 2020

Clinical & Translational Med

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“…The down-regulation of them may be mainly due to the increased demand for membrane constituents during malignant transformation, cancer invasion and metastasis. Dysregulation of choline phospholipid metabolism is associated with carcinogenesis and cancer progression, which has been verified in many biomarker studies [26][27][28], including studies of GC [29]. Thus, the abnormal levels of LysoPC and PC may be considered as important biomarkers for GC patients.…”

Section: Discussionmentioning

confidence: 89%

Metabolomic profiling on plasma reveals potential biomarkers for screening and early diagnosis of gastric cancer and precancerous stages

Li¹,

Xiao²,

Li³

et al. 2020

Preprint

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Background: Gastric cancer (GC) remains one of the most common cancers all over the world. The greatest challenge for GC is that it is often detected at advanced stages, leading to the loss of optimum time for treatment and giving rise to poor prognosis. Thus, there is a critical need to develop effective and noninvasive strategies for early diagnosis of the disease process. Methods: In total, 82 participants were enrolled in the study, including 50 chronic superficial gastritis (CSG) patients, 7 early gastric cancer (EGC) and 25 advanced gastric cancer (AGC) ones. Metabolites profiling on patient plasma was performed using ultra-high performance liquid chromatography coupled with quadrupole-time-of-flight mass spectrometry ( UPLC-Q-TOF/MS ). Principal components analysis as well as orthogonal partial least squares-discriminant analysis was utilized to evaluate the variation on endogenous metabolites for GC patients and to screen potential biomarkers. Furthermore, the biomarker panels detected above were used to create logistic regression models, which discrimination efficiency and accuracy was ascertained by receiver operating characteristic curve (ROC) analysis. Metabolic pathways were carried out on MetaboAnalyst. Results: Totally 50 metabolites were detected differentially expressed among CSG, EGC and AGC patients. L-carnitine, L-proline, pyruvaldehyde, phosphatidylcholines (PC) (14:0/18:0), lysophosphatidylcholine (14:0) (LysoPC 14:0), lysinoalanine were defined as the potential biomarker panel for the diagnosis among CSG and EGC patients. Compared with EGC patients, 6 significantly changed metabolites, PC(O-18:0/0:0) and LysoPC(20:4(5Z,8Z,11Z,14Z)) were found to be up-regulated, whereas L-proline, L-valine, adrenic acid and pyruvaldehyde to be down-regulated in AGC patients. ROC analysis demonstrated a high diagnostic performance for metabolite panels with area under the curve (AUC) of 0.931 to 1. Moreover, the metabolomic pathway analysis revealed several metabolism pathway disruptions, including amino acid and lipid metabolisms, in GC patients. Conclusions: In this study, a total of six differential metabolites that contributed to GC and precancerous stages were identified, respectively. The biomarker panels further improve diagnostic performance for detecting GC, with AUC values of more than 93.1%. It indicated that the biomarker panels may be sensitive to the early diagnosis of GC disease, which can be used as a promising diagnostic and prognostic tool for disease stratification studies.

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“…levels. Yet another study found that PC levels were elevated in LC patient serum [44]. Such elevated PC levels may be due to an increased need for these membrane phospholipids in cancer cells, given that they are primary components of the cell membrane [45,46].…”

Section: Discussionmentioning

confidence: 98%

Clinical significance of circulating tumor cells and metabolic signatures in lung cancer after surgical removal

Yang

et al. 2020

Preprint

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Background Lung cancer (LC) remains the deadliest form of cancer globally. While surgery remains the optimal treatment strategy for individuals with early-stage LC, what the metabolic consequences are of such surgical intervention remains uncertain. Methods Negative enrichment-fluorescence in situ hybridization (NE-FISH) was used in an effort to detect circulating tumor cells (CTCs) in pre- and post-surgery peripheral blood samples from 51 LC patients. In addition, targeted metabolomics analyses, multivariate statistical analyses, and pathway analyses were used to explore surgery-associated metabolic changes. Results LC patients had significantly higher CTC counts relative to healthy controls with 66.67% of LC patients having at least 1 detected CTC. CTC counts were associated with clinical outcomes following surgery. In a targeted metabolomics analysis, we detected 34 amino acids, 164 lipids, and 24 fatty acids. When comparing LC patients before and after surgery to control patients, metabolic shifts were detected via PLS-DA and pathway analysis. Further surgery-associated metabolic changes were identified when comparing LA and LB groups. We identified SM 42:4, Ser, Sar, Gln, and LPC 18:0 for inclusion in a biomarker panel for early-stage LC detection based upon an AUC of 0.965 (95% CI = 0.900–1.000). This analysis revealed that SM 42:2, SM 35:1, PC (16:0/14:0), PC (14:0/16:1), Cer (d18:1/24:1), and SM 38:3 may offer diagnostic and prognostic benefits in LC. Conclusions These findings suggest that CTC detection and plasma metabolite profiling may be an effective means of diagnosing early-stage LC and identifying patients at risk for disease recurrence.

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Serum lipidome screening in patients with stage I non-small cell lung cancer

Cited by 36 publications

References 46 publications

Trans‐omic profiling between clinical phenoms and lipidomes among patients with different subtypes of lung cancer

Trans‐omic profiling between clinical phenoms and lipidomes among patients with different subtypes of lung cancer

Metabolomic profiling on plasma reveals potential biomarkers for screening and early diagnosis of gastric cancer and precancerous stages

Clinical significance of circulating tumor cells and metabolic signatures in lung cancer after surgical removal

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