A multicentre clinical validation of AminoIndex Cancer Screening (AICS)

Tissue and plasma free amino acid detection by LC-MS/MS method in high grade glioma patients

PURPOSE

The changes in serum amino acid profiles are evaluated in different types of cancers and screening tests were developed for estimating the risk of cancer by rapid analysis of plasma free amino acid (PFAA) levels. There is scarce evidence about the metabolomics analysis of PFAA in malignant gliomas. The aim of the present study was to identify the most promising diagnostic amino acid biomarkers that could be objectively measured for high-grade glioma and to compare their level with the tissue counterpart.

METHODS

In this prospective study, we collected serum samples from 22 patients with the pathological diagnosis of high-grade diffuse glioma according to WHO 2016 classification and 22 healthy subjects, and brain tissue from 22 controls. Plasma and tissue amino acid concentrations were analyzed applying liquid chromatography-tandem mass spectrometry (LC-MS/MS) method.

RESULTS

Serum alanine, alpha-aminobutyric acid (AABA), lysine (Lys) and cysteine concentrations were significantly higher in high-grade glioma patients despite low levels of alanine and Lys in the tumor tissue. Aspartic acid, histidine and taurine were significantly decreased in both serum and tumors of glioma patients. A positive correlation was detected between tumor volumes and serum levels of latter three amino acids.

CONCLUSION

This study demonstrated potential amino acids which may have diagnostic value for high-grade glioma patients by utilizing LC-MS/MS method. Our results are preliminary to compare serum and tissue levels of amino acids in patients with malignant gliomas. The data presented here may provide feature ideas about the metabolic pathways in the pathogenesis of gliomas.

Development and internal validation of nomograms based on plasma metabolites to predict non-small cell lung cancer risk in smoking and nonsmoking populations

BACKGROUND

Lung cancer has significantly higher incidence and mortality rates worldwide. In this study, we analyzed the metabolic profiles of non-small cell lung cancer (NSCLC) patients and constructed prediction models for smokers and nonsmokers with internal validation.

METHODS

Plasma was collected from all patients enrolled for metabolic profiling by liquid chromatography-tandem mass spectrometry (LC-MS/MS). The total population was divided into two groups according to smoking or not. Statistical analysis of metabolites was performed separately for each group and prediction models were constructed.

RESULTS

A total of 1723 patients (1109 NSCLC patients and 614 healthy controls) were enrolled from the affiliated hospital during 2018 to 2021. After grouping by smoking history, each group was statistically analyzed and prediction models were constructed, which resulted in eight indicators (propionylcarnitine, arginine, citrulline, etc.) significantly associated with lung cancer risk for smokers and eight indicators (dodecanoylcarnitine, hydroxybutyrylcarnitine, asparagine, etc.) for nonsmokers (p < 0.05). The smoker model indicated an AUC of 0.860 in the training set and 0.850 in the validation set. The nonsmoker model showed an AUC of 0.783 in the training set and 0.762 in the validation set. Further calibration tests for both models indicated excellent goodness-of-fit results.

CONCLUSIONS

In this study, we found a series of metabolites significantly associated with lung cancer incidence and constructed respectively prediction models for NSCLC risk in smokers and nonsmokers, with internal validation to confirm the efficiency to discriminate lung cancer risk in both smoking and nonsmoking states.

Lipid biomarkers that reflect postoperative recurrence risk in lung cancer patients who smoke: a case-control study

BACKGROUND

The risk of postoperative recurrence is higher in lung cancer patients who smoke than non-smokers. However, objective evaluation of the postoperative recurrence risk is difficult using conventional pathological prognostic factors because of their lack of reproducibility. Consequently, novel objective biomarkers that reflect postoperative risk in lung cancer patients who smoke must be identified. Because cigarette smoking and oncogenesis alter lipid metabolism in lung tissue, we hypothesized that the lipid profiles in lung cancer tissues are influenced by cigarette smoking and can reflect the postoperative recurrence risk in smoking lung cancer patients. This study aimed to identify lipid biomarkers that reflect the smoking status and the postoperative recurrence risk.

METHODS

Primary tumor tissues of lung adenocarcinoma (ADC) (n = 26) and squamous cell carcinoma (SQCC) (n = 18) obtained from surgery were assigned to subgroups according to the patient's smoking status. The ADC cohort was divided into never smoker and smoker groups, while the SQCC cohort was divided into moderate smoker and heavy smoker groups. Extracted lipids from the tumor tissues were subjected to liquid chromatography-tandem mass spectrometry analysis. Lipids that were influenced by smoking status and reflected postoperative recurrence and pathological prognostic factors were screened.

RESULTS

Two and 12 lipid peaks in the ADC and SQCC cohorts showed a significant positive correlation with the Brinkman index, respectively. Among them, in the ADC cohort, a higher lipid level consisted of three phosphatidylcholine (PC) isomers, PC (14:0_18:2), PC (16:1_16:1), and PC (16:0_16:2), was associated with a shorter recurrence free period (RFP) and a greater likelihoods of progressed T-factor (≥ pT2) and pleural invasion. In the SQCC cohort, a lower m/z 736.5276 level was associated with shorter RFP and greater likelihood of recurrence.

CONCLUSIONS

From our data, we propose three PC isomers, PC (14:0_18:2), PC (16:1_16:1), and PC (16:0_16:2), and a lipid peak of m/z 736.5276 as novel candidate biomarkers for postoperative recurrence risk in lung ADC and SQCC patients who are smokers.

Alterations in the gastric microbiota and metabolites in gastric cancer: An update review

Gastric cancer (GC) is one of the leading causes of cancer mortality worldwide. Numerous studies have shown that the gastric microbiota can contribute to the occurrence and development of GC by generating harmful microbial metabolites, suggesting the possibility of discovering biomarkers. Metabolomics has emerged as an advanced promising analytical method for the analysis of microbiota-derived metabolites, which have greatly accelerated our understanding of host-microbiota metabolic interactions in GC. In this review, we briefly compiled recent research progress on the changes of gastric microbiota and its metabolites associated with GC. And we further explored the application of metabolomics and gastric microbiome association analysis in the diagnosis, prevention and treatment of GC.

Changes in serum amino acid levels in non-small cell lung cancer: a case-control study in Chinese population

Background

Previous studies have shown the alteration of amino acid (AA) profile in patients with non-small cell lung cancer (NSCLC). However, there is little data regarding AA profile in NSCLC in Chinese population. The aim of this study was to evaluate AA profile in Chinese NSCLC patients, explore its utility in sample classification and further discuss its related metabolic pathways.

Methods

The concentrations of 22 AAs in serum samples from 200 patients with NSCLC and 202 healthy controls were determined by liquid chromatography-tandem mass spectrometer (LC-MS/MS). AA levels in different tumor stages and histological types were also discussed. The performance of AA panel in classifying the cases and controls was evaluated in the training data set and validation data set based on the receiver operating characteristic (ROC) curve, and the important metabolic pathways were identified.

Results

The concentrations of tryptophan (Trp), phenylalanine (Phe), isoleucine (Ile), glycine (Gly), serine (Ser), aspartic acid (Asp), asparagine (Asn), cystein (Cys), glutamic acid (Glu), ornithine (Orn) and citrulline (Cit) were significantly altered in NSCLC patients compared with controls (all P-FDR < 0.05). Among these, four AAs including Asp, Cys, Glu and Orn were substantially up-regulated in NSCLC patients (FC ≥ 1.2). AA levels were significantly altered in patients with late-stage NSCLC, but not in those with early-stage when comparing with healthy controls. In terms of histological type, these AAs were altered in both adenocarcinoma and squamous cell carcinoma. For discrimination of NSCLC from controls, the area under the ROC curve (AUC) was 0.80 (95% CI [0.74-0.85]) in the training data set and 0.79 (95%CI [0.71-0.87]) in the validation data set. The AUCs for early-stage and late-stage NSCLC were 0.75 (95% CI [0.68-0.81]) and 0.86 (95% CI [0.82-0.91]), respectively. Moreover, the model showed a better performance in the classification of squamous cell carcinoma (AUC = 0.90, 95% CI [0.85-0.95]) than adenocarcinoma (AUC = 0.77, 95% CI [0.71-0.82]) from controls. Three important metabolic pathways were involved in the alteration of AA profile, including Gly, Ser and Thr metabolism; Ala, Asp and Glu metabolism; and Arg biosynthesis.

Conclusions

The levels of several AAs in serum were altered in Chinese NSCLC patients. These altered AAs may be utilized to classify the cases from the controls. Gly, Ser and Thr metabolism; Ala, Asp and Glu metabolism and Arg biosynthesis pathways may play roles in metabolism of the NSCLC patient.

Decreased sphingomyelin (t34:1) is a candidate predictor for lung squamous cell carcinoma recurrence after radical surgery: a case-control study

Background

To reduce disease recurrence after radical surgery for lung squamous cell carcinomas (SQCCs), accurate prediction of recurrent high-risk patients is required for efficient patient selection for adjuvant chemotherapy. Because treatment modalities for recurrent lung SQCCs are scarce compared to lung adenocarcinomas (ADCs), accurately selecting lung SQCC patients for adjuvant chemotherapy after radical surgery is highly important. Predicting lung cancer recurrence with high objectivity is difficult with conventional histopathological prognostic factors; therefore, identification of a novel predictor is expected to be highly beneficial. Lipid metabolism alterations in cancers are known to contribute to cancer progression. Previously, we found that increased sphingomyelin (SM)(d35:1) in lung ADCs is a candidate for an objective recurrence predictor. However, no lipid predictors for lung SQCC recurrence have been identified to date. This study aims to identify candidate lipid predictors for lung SQCC recurrence after radical surgery.

Methods

Recurrent (n = 5) and non-recurrent (n = 6) cases of lung SQCC patients who underwent radical surgery were assigned to recurrent and non-recurrent groups, respectively. Extracted lipids from frozen tissue samples of primary lung SQCC were analyzed by liquid chromatography-tandem mass spectrometry. Candidate lipid predictors were screened by comparing the relative expression levels between the recurrent and non-recurrent groups. To compare lipidomic characteristics associated with recurrent SQCCs and ADCs, a meta-analysis combining SQCC (n = 11) and ADC (n = 20) cohorts was conducted.

Results

Among 1745 screened lipid species, five species were decreased (≤ 0.5 fold change; P < 0.05) and one was increased (≥ 2 fold change; P < 0.05) in the recurrent group. Among the six candidates, the top three final candidates (selected by AUC assessment) were all decreased SM(t34:1) species, showing strong performance in recurrence prediction that is equivalent to that of histopathological prognostic factors. Meta-analysis indicated that decreases in a limited number of SM species were observed in the SQCC cohort as a lipidomic characteristic associated with recurrence, in contrast, significant increases in a broad range of lipids (including SM species) were observed in the ADC cohort.

Conclusion

We identified decreased SM(t34:1) as a novel candidate predictor for lung SQCC recurrence. Lung SQCCs and ADCs have opposite lipidomic characteristics concerning for recurrence risk.

Trial registration

This retrospective study was registered at the UMIN Clinical Trial Registry (UMIN000039202) on January 21, 2020.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-08948-5.

The gut microbiota associated with high-Gleason prostate cancer

We have found that intestinal bacteria and their metabolites, short-chain fatty acids (SCFAs), promote cancer growth in prostate cancer (PCa) mouse models. To clarify the association between gut microbiota and PCa in humans, we analyzed the gut microbiota profiles of men with suspected PCa. One hundred and fifty-two Japanese men undergoing prostate biopsies (96 with cancer and 56 without cancer) were included in the study and randomly divided into two cohorts: a discovery cohort (114 samples) and a test cohort (38 samples). The gut microbiota was compared between two groups, a high-risk group (men with Grade group 2 or higher PCa) and a negative + low-risk group (men with negative biopsy or Grade group 1 PCa), using 16S rRNA gene sequencing. The relative abundances of Rikenellaceae, Alistipes, and Lachnospira, all SCFA-producing bacteria, were significantly increased in high-risk group. In receiver operating characteristic curve analysis, the index calculated from the abundance of 18 bacterial genera which were selected by least absolute shrinkage and selection operator regression detected high-risk PCa in the discovery cohort with higher accuracy than the prostate specific antigen test (area under the curve [AUC] = 0.85 vs 0.74). Validation of the index in the test cohort showed similar results (AUC = 0.81 vs 0.67). The specific bacterial taxa were associated with high-risk PCa. The gut microbiota profile could be a novel useful marker for the detection of high-risk PCa and could contribute to the carcinogenesis of PCa.

Assessment of Blood Plasma Free-amino Acid Levels in Infertile Men

BACKGROUND/AIM

The objective of this study was to investigate the significance of the blood levels of free amino acids (AAs) in infertile men.

PATIENTS AND METHODS

Ninety-three men who underwent examinations for infertility were included. The concentrations of 20 AAs were measured and compared in four groups (normospermia, obstructive azoospermia, oligozoospermia, non-obstructive azoospermia) based on semen analysis and clinical parameters.

RESULTS

When the 93 men were divided into normospermia, obstructive azoospermia, oligozoospermia, and non-obstructive azoospermia groups, no significant differences were found in the concentrations of the 20 AAs between them. We then compared 49 men diagnosed with normozoospermia or oligozoospermia according to the median sperm motility and morphology abnormalities rates (30% and 20%, respectively). Men with low motility rates had significantly lower levels of tryptophan and alanine, and men with high abnormal morphology rates had significantly lower levels of aspartate and glutamate.

CONCLUSION

AAs are probably involved in the pathogenesis of male infertility, particularly oligozoospermia.

Plasma amino acids indicate glioblastoma with ATRX loss

Glioblastoma (GB) is the most common primary brain tumour in adults. The lack of molecular biomarker, non-specific symptoms and fast growth rate often result in a significant delay in diagnosis. Despite multimodal treatment, the prognosis remains poor. Here, we verified the hypothesis that amino acids (AA) regulating the critical metabolic pathways necessary for maintenance, growth, reproduction, and immunity of an organism, may constitute a favourable target in GB biomarker research. We measured the plasma amino acids levels in 18 GB patients and 15 controls and performed the quantitative and qualitative metabolomic analysis of free AA applying high-performance liquid chromatography quadrupole time-of-flight mass spectrometry (LC-QTOF-MS). We present both the raw data and the results of our statistical analysis. The majority of AA were lowered in the study group in comparison to the control group. Five of these (arginine, glutamic acid, glutamine, glycine, and histidine) differed significantly (all p < 10^-5 and AUC > 0.9). Plasma levels of leucine and phenylalanine decreased in the case of GB with lost alpha-thalassemia/mental retardation X-linked (ATRX) expression on immunohistochemistry (p = 0.003 and 0.045, respectively). We demonstrated for the first time that certain plasma-free AA levels of GB patients were significantly different from those in healthy volunteers. Target profiling of plasma-free AA, identified utilizing LC-QTOF-MS, may present prognostic value by indicating GB patients with lost ATRX expression. The on-going quest for glioma biomarkers still aims to determine the detailed metabolic profile and evaluate its impact on therapy and prognosis.

Use of plasma-free amino acids as biomarkers for detecting and predicting disease risk

This paper reviews developments regarding the use of plasma-free amino acid (PFAA) profiles as biomarkers for detecting and predicting disease risk. This work was initiated and first published in 2006 and was subsequently developed by Ajinomoto Co., Inc. After commercialization in 2011, PFAA-based tests were adopted in over 1500 clinics and hospitals in Japan, and numerous clinician-led studies have been performed to validate these tests. Evidence is accumulating that PFAA profiles can be used for diabetes prediction and evaluation of frailty; in particular, decreased plasma essential amino acids could contribute to the pathophysiology of severe frailty. Integration of PFAA evaluation as a biomarker and effective essential amino acid supplementation, which improves physical and mental functions in the elderly, could facilitate the development of precision nutrition, including personalized solutions. This present review provides the background for the technology as well as more recent clinical findings, and offers future possibilities regarding the implementation of precision nutrition.

Preoperative AminoIndex Cancer Screening (AICS) abnormalities predict postoperative recurrence in patients undergoing curative resection for non-small cell lung cancer

BACKGROUND

AminoIndex™ Cancer Screening (AICS (lung)) was developed as a screening test for lung cancer using a multivariate analysis of plasma-free amino acid (PFAA) profiles. According to the developed index composed of PFAA, the probability of lung cancer was categorized into AICS (lung) ranks A, B, and C in order of increasing risk. The aim of the present study was to investigate the relationship between the preoperative AICS (lung) rank and surgical outcomes in patients who underwent curative resection for non-small cell lung cancer (NSCLC).

METHODS

Preoperative blood samples were collected from 297 patients who underwent curative resection for NSCLC between 2006 and 2015. PFAA concentrations were measured. The relationship between the preoperative AICS (lung) rank and clinicopathological factors was examined. The effects of the preoperative AICS (lung) rank on postoperative outcomes were also analyzed.

RESULTS

The AICS (lung) rank was A in 93 patients (31.3%), B in 82 (27.6%), and C in 122 (41.1%). The AICS (lung) rank did not correlate with any clinicopathological factors, except for age. Based on follow-up data (median follow-up period of 6 years), postoperative recurrence was observed in 22 rank A patients (23.7%), 15 rank B (18.3%) and 49 rank C (40.2%). In the univariate analysis, preoperative AICS (lung) rank C was a worse factor of recurrence-free survival (p = 0.0002). The multivariate analysis identified preoperative AICS (lung) rank C (HR: 2.17, p = 0.0005) as a significant predictor of postoperative recurrence, particularly in patients with early-stage disease or adenocarcinoma.

CONCLUSION

Preoperative AICS (lung) rank C is a high-risk predictor of postoperative recurrence in patients undergoing curative resection for NSCLC.

Liquid biopsy as a perioperative biomarker of digestive tract cancers: review of the literature

Tissue biopsies are the gold-standard for investigating the molecular characterization of tumors. However, a "solid" biopsy is an invasive procedure that cannot capture real-time tumor dynamics and may yield inaccurate information because of intratumoral heterogeneity. In this review, we summarize the current state of knowledge about surgical treatment-associated "liquid" biopsy for patients with digestive organ tumors. A liquid biopsy is a technique involving the sampling and testing of non-solid biological materials, including blood, urine, saliva, and ascites. Previous studies have reported the potential value of blood-based biomarkers, circulating tumor cells, and cell-free nucleic acids as facilitators of cancer treatment. The applications of a liquid biopsy in a cancer treatment setting include screening and early diagnosis, prognostication, and outcome and recurrence monitoring of cancer. This technique has also been suggested as a useful tool in personalized medicine. The transition to precision medicine is still in its early stages. Soon, however, liquid biopsy is likely to form the basis of patient selection for molecular targeted therapies, predictions regarding chemotherapy sensitivity, and real-time evaluations of therapeutic effects.

Metabolomic Biomarkers for Detection, Prognosis and Identifying Recurrence in Endometrial Cancer

Metabolic reprogramming is increasingly recognised as one of the defining hallmarks of tumorigenesis. There is compelling evidence to suggest that endometrial cancer develops and progresses in the context of profound metabolic dysfunction. Whilst the incidence of endometrial cancer continues to rise in parallel with the global epidemic of obesity, there are, as yet, no validated biomarkers that can aid risk prediction, early detection, prognostic evaluation or surveillance. Advances in high-throughput technologies have, in recent times, shown promise for biomarker discovery based on genomic, transcriptomic, proteomic and metabolomic platforms. Metabolomics, the large-scale study of metabolites, deals with the downstream products of the other omics technologies and thus best reflects the human phenotype. This review aims to provide a summary and critical synthesis of the existing literature with the ultimate goal of identifying the most promising metabolite biomarkers that can augment current endometrial cancer diagnostic, prognostic and recurrence surveillance strategies. Identified metabolites and their biochemical pathways are discussed in the context of what we know about endometrial carcinogenesis and their potential clinical utility is evaluated. Finally, we underscore the challenges inherent in metabolomic biomarker discovery and validation and provide fresh perspectives and directions for future endometrial cancer biomarker research.