A systematic review on metabolomics-based diagnostic biomarker discovery and validation in pancreatic cancer

Introduction

Metabolomics is an emerging approach for early detection of cancer. Along with the development of metabolomics, high-throughput technologies and statistical learning, the integration of multiple biomarkers has significantly improved clinical diagnosis and management for patients.

Objectives

In this study, we conducted a systematic review to examine recent advancements in the oncometabolomics-based diagnostic biomarker discovery and validation in pancreatic cancer.

Methods

PubMed, Scopus, and Web of Science were searched for relevant studies published before September 2017. We examined the study designs, the metabolomics approaches, and the reporting methodological quality following PRISMA statement.

Results and Conclusion

The included 25 studies primarily focused on the identification rather than the validation of predictive capacity of potential biomarkers. The sample size ranged from 10 to 8760. External validation of the biomarker panels was observed in nine studies. The diagnostic area under the curve ranged from 0.68 to 1.00 (sensitivity: 0.43–1.00, specificity: 0.73–1.00). The effects of patients’ bio-parameters on metabolome alterations in a context-dependent manner have not been thoroughly elucidated. The most reported candidates were glutamic acid and histidine in seven studies, and glutamine and isoleucine in five studies, leading to the predominant enrichment of amino acid-related pathways. Notably, 46 metabolites were estimated in at least two studies. Specific challenges and potential pitfalls to provide better insights into future research directions were thoroughly discussed. Our investigation suggests that metabolomics is a robust approach that will improve the diagnostic assessment of pancreatic cancer. Further studies are warranted to validate their validity in multi-clinical settings.

     

A metabolomics-based approach for non-invasive diagnosis of chromosomal anomalies

Introduction

Chromosomal anomalies (CA) are the most frequent fetal anomalies.

Objective

To evaluate the diagnostic performance of a machine learning ensemble model based on the maternal serum metabolomic fingerprint of fetal aneuploidies during the second trimester .

Methods

This is a case-control pilot study. Metabolomic profiles have been obtained on serum of 328 mothers (220 controls and 108 cases), using gas chromatography coupled to mass spectrometry. Eight machines learning and classification models were built and optimized. An ensemble model was built using a voting scheme. All samples were randomly divided into two sets. One was used as training set, the other one for diagnostic performance assessment.

Results

Ensemble machine learning model correctly classified all cases and controls. The accuracy was the same for trisomy 21 and 18; also, the other CA were correctly detected. Elaidic, stearic, linolenic, myristic, benzoic, citric and glyceric acid, mannose, 2-hydroxy butyrate, phenylalanine, proline, alanine and 3-methyl histidine were selected as the most relevant metabolites in class separation.

Conclusion

The proposed model, based on the maternal serum metabolomic fingerprint of fetal aneuploidies during the second trimester, correctly identifies all the cases of chromosomal abnormalities. Overall, this preliminary analysis appeared suggestive of a metabolic environment conductive to increased oxidative stress and a disturbance in the fetal central nervous system development. Maternal serum metabolomics can be a promising tool in the screening of chromosomal defects. Moreover, metabolomics allows to extend our knowledge about biochemical alterations caused by aneuploidies and responsible for the observed phenotypes.

     

A metabolomics-based approach for predicting stages of chronic kidney disease

Chronic kidney disease (CKD) is a major epidemiologic problem and a risk factor for cardiovascular events and cerebrovascular accidents. Because CKD shows irreversible progression, early diagnosis is desirable. Renal function can be evaluated by measuring creatinine-based estimated glomerular filtration rate (eGFR). This method, however, has low sensitivity during early phases of CKD. Cystatin C (CysC) may be a more sensitive predictor. Using a metabolomic method, we previously identified metabolites in CKD and hemodialysis patients. To develop a new index of renal hypofunction, plasma samples were collected from volunteers with and without CKD and metabolite concentrations were assayed by quantitative liquid chromatography/mass spectrometry. These results were used to construct a multivariate regression equation for an inverse of CysC-based eGFR, with eGFR and CKD stage calculated from concentrations of blood metabolites. This equation was able to predict CKD stages with 81.3% accuracy (range, 73.9–87.0% during 20 repeats). This procedure may become a novel method of identifying patients with early-stage CKD.     

Serum metabolomics as a novel diagnostic approach for pancreatic cancer

Pancreatic cancer is one of the leading causes of cancer-related death, and there is currently little hope of a cure because there are no effective biomarkers for its early detection. Therefore, the search for novel biomarkers that would allow the early detection of pancreatic cancer is ongoing. In this study, the differences between the metabolomes of pancreatic cancer patients with Stage III, Stage IVa, or Stage IVb disease (n = 20) and healthy volunteers (n = 9) were evaluated by metabolomics, which is the endpoint of the Omics cascade and therefore the last step in the cascade before the phenotype. In our experimental conditions using gas chromatography mass spectrometry (GC/MS), a total of 60 metabolites were detected in serum, and the levels of 18 of the 60 metabolites were significantly changed in pancreatic cancer patients compared with those in healthy volunteers. Then, Principal Component Analysis (PCA), which is a basic form of Multiple Classification Analysis, was performed, and the PCA scores plots based on the 60 metabolites highlighted the metabolomic differences between the pancreatic cancer patients and healthy volunteers. The differences between different stages of pancreatic cancer were also assessed by Partial Least Squares Discriminant Analysis (PLS-DA), which is one of Multiple Classification Analysis, and we found that it was possible to discriminate among the Stage III, Stage IVa, and Stage IVb groups. In addition, values of the 9 metabolites in 1 Stage I pancreatic cancer patient were similar to those obtained from the Stage III, Stage IVa, and Stage IVb pancreatic cancer patients. Our findings will aid the discovery of novel biomarkers that allow the early detection of pancreatic cancer by metabolomic approaches.     

MicroRNA expression profile in serum reveals novel diagnostic biomarkers for endometrial cancer

Purpose: Circulating microRNAs (miRNAs) prove to be promising diagnostic biomarkers for various cancers, including endometrial cancer (EC). The present study aims to identify serum microRNAs that can serve as potential biomarkers for EC diagnosis.Patients and methods: A total of 92 EC and 102 normal control (NC) serum samples were analyzed using quantitative real-time polymerase chain reaction (qRT-PCR) in this four-phase experiment. The logistic regression method was used to construct a diagnostic model based on the differentially expressed miRNAs in serum. The receiver operating characteristic (ROC) curve analysis was performed to evaluate the diagnostic value. To further validate the diagnostic capacity of the identified signature, the 6-miRNA marker was compared with previously reported biomarkers and verified in three public datasets. In addition, the expression characteristics of the identified miRNAs were further explored in tissue and serum exosomes samples.Results: Six miRNAs (miR-143-3p, miR-195-5p, miR-20b-5p, miR-204-5p, miR-423-3p, and miR-484) were significantly overexpressed in the serum of EC compared with NCs. Areas under the ROC of the 6-miRNA signatures were 0.748, 0.833, and 0.967 for the training, testing, and the external validation phases, respectively. The identified signature has a very stable diagnostic performance in the large cohorts of three public datasets. Compared with previously identified miRNA biomarkers, the 6-miRNA signature in the present study has superior performance in diagnosing EC. Moreover, the expression of miR-143-3p and miR-195-5p in tissues and the expression of miR-20b-5p in serum exosomes were consistent with those in serum.Conclusions: We established a 6-miRNA signature in serum and they could function as potential non-invasive biomarker for EC diagnosis.     

A metabolomics-based approach for non-invasive screening of fetal central nervous system anomalies

Background

Central nervous system anomalies represent a wide range of congenital birth defects, with an incidence of approximately 1% of all births. They are currently diagnosed using ultrasound evaluation. However, there is strong need for a more accurate and less operator-dependent screening method.

Objectives

To perform a characterization of maternal serum in order to build a metabolomic fingerprint resulting from congenital anomalies of the central nervous system.

Methods

This is a case–control pilot study. Metabolomic profiles were obtained from serum of 168 mothers (98 controls and 70 cases), using gas chromatography coupled to mass spectrometry. Nine machine learning and classification models were built and optimized. An ensemble model was built based on results from the individual models. All samples were randomly divided into two groups. One was used as training set, the other one for diagnostic performance assessment.

Results

Ensemble machine learning model correctly classified all cases and controls. Propanoic, lactic, gluconic, benzoic, oxalic, 2-hydroxy-3-methylbutyric, acetic, lauric, myristic and stearic acid and myo-inositol and mannose were selected as the most relevant metabolites in class separation.

Conclusion

The metabolomic signature of second trimester maternal serum from pregnancies affected by a fetal central nervous system anomaly is quantifiably different from that of a normal pregnancy. Maternal serum metabolomics is therefore a promising tool for the accurate and sensitive screening of such congenital defects. Moreover, the details of the most relevant metabolites and their respective biochemical pathways allow better understanding of the overall pathophysiology of affected pregnancies.

     

A novel circular RNA hsa_circ_0060927 may serve as a potential diagnostic biomarker for human colorectal cancer

Molecular Biology Reports - CYP24A1 plays a role in strictly regulated vitamin D metabolism pathway and has been nominated as a prognostic biomarker for colorectal cancer (CRC). Increasing evidence...     

Serum miR‐92a‐1 is a novel diagnostic biomarker for colorectal cancer

Colorectal cancer (CRC) is one of the most common cancers worldwide, with high mortality. Abnormally expressed microRNAs (miRNAs) are considered novel biomarkers in cancer diagnosis. The aim of this study was to investigate the diagnostic value of miR‐92a‐1 in patients with CRC. Serum samples were collected from 148 patients pathologically diagnosed with CRC and 68 gender‐ and age‐matched healthy volunteers. Quantitative real‐time polymerase chain reaction (qRT‐PCR) was used to measure serum miR‐92a‐1 level. Relationship between miR‐92a‐1 and clinicopathological features of CRC cases was analysed via chi‐square test. Receiver operating characteristic (ROC) curve was plotted to estimate the diagnostic value of miR‐92a‐1 in CRC. Serum miR‐92a‐1 was significantly up‐regulated in CRC patients compared with healthy individuals (P < .001). Moreover, miR‐92a‐1 expression was correlated with TNM stage (P = .02), histological stage (P = .003), lymph node metastasis (P = .003) and distant metastasis (P < .001). ROC analysis showed that the area under the ROC curve (AUC) was 0.914, suggesting high diagnostic accuracy of miR‐92a‐1 in ROC. The optimal cut‐off value was 1.485, with a sensitivity of 81.8% and a specificity of 95.6%. MiR‐92a‐1 is increased in CRC patients and correlated with aggressive clinical characteristics. Serum miR‐92a‐1 may be a potential diagnostic biomarker for CRC.     

A novel approach and protocol for discovering extremely low-abundance proteins in serum

The proteomic analysis of serum (plasma) has been a major approach to determining biomarkers essential for early disease diagnoses and drug discoveries. The determination of these biomarkers, however, is analytically challenging since the dynamic concentration range of serum proteins/peptides is extremely wide (more than 10 orders of magnitude). Thus, the reduction in sample complexity prior to proteomic analyses is essential, particularly in analyzing low-abundance protein biomarkers. Here, we demonstrate a novel approach to the proteomic analyses of human serum that uses an originally developed serum protein separation device and a sequentially linked 3-D-LC-MS/MS system. Our hollow-fiber-membrane-based serum pretreatment device can efficiently deplete high-molecular weight proteins and concentrate low-molecular weight proteins/peptides automatically within 1 h. Four independent analyses of healthy human sera pretreated using this unique device, followed by the 3-D-LC-MS/MS successfully produced 12 000-13 000 MS/MS spectra and hit around 1800 proteins (>95% reliability) and 2300 proteins (>80% reliability). We believe that the unique serum pretreatment device and proteomic analysis protocol reported here could be a powerful tool for searching physiological biomarkers by its high throughput (3.7 days per one sample analysis) and high performance of finding low abundant proteins from serum or plasma samples.     

LncRNAs as potential diagnostic and prognostic biomarkers in gastric cancer: A novel approach to personalized medicine

Gastric cancer is the third leading cause of cancer death with 5-year survival rate of about 30–35%. Since early detection is associated with decreased mortality, identification of novel biomarkers for early diagnosis and proper management of patients with the best response to therapy is urgently needed. Long noncoding RNAs (lncRNAs) due to their high specificity, easy accessibility in a noninvasive manner, as well as their aberrant expression under different pathological and physiological conditions, have received a great attention as potential diagnostic, prognostic, or predictive biomarkers. They may also serve as targets for treating gastric cancer. In this review, we highlighted the role of lncRNAs as tumor suppressors or oncogenes that make them potential biomarkers for the diagnosis and prognosis of gastric cancer. Relatively, lncRNAs such as H19, HOTAIR, UCA1, PVT1, tissue differentiation-inducing nonprotein coding, and LINC00152 could be potential diagnostic and prognostic markers in patients with gastric cancer. Also, the impact of lncRNAs such as ecCEBPA, MLK7-AS1, TUG1, HOXA11-AS, GAPLINC, LEIGC, multidrug resistance-related and upregulated lncRNA, PVT1 on gastric cancer epigenetic and drug resistance as well as their potential as therapeutic targets for personalized medicine was discussed.     

A serum glycomics approach to breast cancer biomarkers

Because the glycosylation of proteins is known to change in tumor cells during the development of breast cancer, a glycomics approach is used here to find relevant biomarkers of breast cancer. These glycosylation changes are known to correlate with increasing tumor burden and poor prognosis. Current antibody-based immunochemical tests for cancer biomarkers of ovarian (CA125), breast (CA27.29 or CA15-3), pancreatic, gastric, colonic, and carcinoma (CA19-9) target highly glycosylated mucin proteins. However, these tests lack the specificity and sensitivity for use in early detection. This glycomics approach to find glycan biomarkers of breast cancer involves chemically cleaving oligosaccharides (glycans) from glycosylated proteins that are shed or secreted by breast cancer tumor cell lines. The resulting free glycan species are analyzed by MALDI-FT-ICR MS. Further structural analysis of the glycans can be performed in FTMS through the use of tandem mass spectrometry with infrared multiphoton dissociation. Glycan profiles were generated for each cell line and compared. These methods were then used to analyze sera obtained from a mouse model of breast cancer and a small number of serum samples obtained from human patients diagnosed with breast cancer or patients with no known history of breast cancer. In addition to the glycosylation changes detected in mice as mouse mammary tumors developed, glycosylation profiles were found to be sufficiently different to distinguish patients with cancer from those without. Although the small number of patient samples analyzed so far is inadequate to make any legitimate claims at this time, these promising but very preliminary results suggest that glycan profiles may contain distinct glycan biomarkers that may correspond to glycan "signatures of cancer."     

Periostin: novel diagnostic and therapeutic target for cancer

Periostin is a secreted protein that shares a structural homology to the axon guidance protein fasciclin I (FAS1) in insects and was originally named as osteoblast-specific factor-2 (Osf2). Periostin is particularly highly homologus to Betaig-h3, which promotes cell adhesion and spreading of fibroblasts. It has recently been reported that Periostin was frequently overexpressed in various types of human cancers. Although the detailed function of Periostin is still unclear, Periostin-integrin interaction through FAS1 domain is thought to be involved in tumor development. In addition, Periostin stimulates metastatic growth by promoting cancer cell survival, invasion and angiogenesis. Therefore, Periostin can be a useful marker to predict the behavior of cancer. This review summarizes the recent understanding of Periostin roles in tumor development and speculates on the usefulness of Periostin as a therapeutic and diagnostic target for cancer.     

Plasma metabonomics as a novel diagnostic approach for major depressive disorder

Major depressive disorder (MDD) is a socially detrimental psychiatric disorder, contributing to increased healthcare expenditures and suicide rates. However, no empirical laboratory-based tests are available to support the diagnosis of MDD. In this study, a NMR-based plasma metabonomic method for the diagnosis of MDD was tested. Proton nuclear magnetic resonance ((1)H NMR) spectra of plasma sampled from first-episode drug-na??ve depressed patients (n = 58) and healthy controls (n = 42) were recorded and analyzed by orthogonal partial least-squares discriminant analysis (OPLS-DA). The OPLS-DA score plots of the spectra demonstrated that the depressed patient group was significantly distinguishable from the healthy control group. Moreover, the method accurately diagnosed blinded samples (n = 26) in an independent replication cohort with a sensitivity and specificity of 92.8% and 83.3%, respectively. Taken together, NMR-based plasma metabonomics may offer an accurate empirical laboratory-based method applicable to the diagnosis of MDD.     

Identification of PSME3 as a novel serum tumor marker for colorectal cancer by combining two-dimensional polyacrylamide gel electrophoresis with a strictly mass spectrometry-based approach for data analysis

The purpose of this study was to identify and validate novel serological protein biomarkers of human colorectal cancer (CRC). Proteins from matched CRC and adjacent normal tissue samples were resolved by two-dimensional gel electrophoresis. From each gel all spots were excised, and enveloped proteins were identified by MS. By comparison of the resulting protein profiles, dysregulated proteins can be identified. A list of all identified proteins and validation of five exemplarily selected proteins, elevated in CRC was reported previously (Roessler, M., Rollinger, W., Palme, S., Hagmann, M. L., Berndt, P., Engel, A. M., Schneidinger, B., Pfeffer, M., Andres, H., Karl, J., Bodenmuller, H., Ruschoff, J., Henkel, T., Rohr, G., Rossol, S., Rosch, W., Langen, H., Zolg, W., and Tacke, M. (2005) Identification of nicotinamide N-methyltransferase as a novel serum tumor marker for colorectal cancer. Clin. Cancer Res. 11, 6550-6557). Here we describe identification and initial validation of another potential marker protein for CRC. Comparison of tissue protein profiles revealed strong elevation of proteasome activator complex subunit 3 (PSME3) expression in CRC tissue. This dysregulation was not detectable based on the spot pattern. The PSME3-containing spot on tumor gels showed no visible difference to the corresponding spot on matched control gels. MS analysis revealed the presence of two proteins, PSME3 and annexin 4 (ANXA4) in one and the same spot on tumor gels, whereas the matched spot contained only one protein, ANXA4, on control gels. Therefore, dysregulation of PSME3 was masked by ANXA4 and could only be recognized by MS-based analysis but not by image analysis. To validate this finding, antibody to PSME3 was developed, and up-regulation in CRC was confirmed by Western blot analysis and immunohistochemistry. Finally by developing a highly sensitive immunoassay, PSME3 could be detected in human sera and was significantly elevated in CRC patients compared with healthy donors and patients with benign bowel disease. We propose that PSME3 be considered a novel serum tumor marker for CRC that may have significance in the detection and in the management of patients with this disease. Further studies are needed to fully assess the potential clinical value of this marker candidate.     

Metabolomics approach for predicting response to neoadjuvant chemotherapy for colorectal cancer

Introduction

Colorectal cancer (CRC) is a clinically heterogeneous disease, which necessitates a variety of treatments and leads to different outcomes. Only some CRC patients will benefit from neoadjuvant chemotherapy (NACT).

Objectives

An accurate prediction of response to NACT in CRC patients would greatly facilitate optimal personalized management, which could improve their long-term survival and clinical outcomes.

Methods

In this study, plasma metabolite profiling was performed to identify potential biomarker candidates that can predict response to NACT for CRC. Metabolic profiles of plasma from non-response (n?=?30) and response (n?=?27) patients to NACT were studied using UHPLC–quadruple time-of-flight)/mass spectrometry analyses and statistical analysis methods.

Results

The concentrations of nine metabolites were significantly different when comparing response to NACT. The area under the receiver operating characteristic curve value of the potential biomarkers was up to 0.83 discriminating the non-response and response group to NACT, superior to the clinical parameters (carcinoembryonic antigen and carbohydrate antigen 199).

Conclusion

These results show promise for larger studies that could result in more personalized treatment protocols for CRC patients.

     

A novel prognostic signature of immune‐related genes for patients with colorectal cancer

Colorectal cancer (CRC) is one of the most commonly diagnosed cancers with an estimated 1.8 million new cases worldwide and associated with high mortality rates of 881 000 CRC‐related deaths in 2018. Screening programs and new therapies have only marginally improved the survival of CRC patients. Immune‐related genes (IRGs) have attracted attention in recent years as therapeutic targets. The aim of this study was to identify an immune‐related prognostic signature for CRC. To this end, we combined gene expression and clinical data from the CRC data sets of The Cancer Genome Atlas (TCGA) into an integrated immune landscape profile. We identified a total of 476 IRGs that were differentially expressed in CRC vs normal tissues, of which 18 were survival related according to univariate Cox analysis. Stepwise multivariate Cox proportional hazards analysis established an immune‐related prognostic signature consisting of SLC10A2, FGF2, CCL28, NDRG1, ESM1, UCN, UTS2 and TRDC. The predictive ability of this signature for 3‐ and 5‐year overall survival was determined using receiver operating characteristics (ROC), and the respective areas under the curve (AUC) were 79.2% and 76.6%. The signature showed moderate predictive accuracy in the validation and GSE38832 data sets as well. Furthermore, the 8‐IRG signature correlated significantly with tumour stage, invasion, lymph node metastasis and distant metastasis by univariate Cox analysis, and was established an independent prognostic factor by multivariate Cox regression analysis for CRC. Gene set enrichment analysis (GSEA) revealed a relationship between the IRG prognostic signature and various biological pathways. Focal adhesions and ECM‐receptor interactions were positively correlated with the risk scores, while cytosolic DNA sensing and metabolism‐related pathways were negatively correlated. Finally, the bioinformatics results were validated by real‐time RT?qPCR. In conclusion, we identified and validated a novel, immune‐related prognostic signature for patients with CRC, and this signature reflects the dysregulated tumour immune microenvironment and has a potential for better CRC patient management.