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Free amino acid profiling of gastric juice as a method for discovering potential biomarkers of early gastric cancer.

ABSTRACT: Background: Prior studies showed that aromatic amino acids (AAAs) could be used as potential gastric juice biomarkers in screening gastric cancer (GC). To identify new biomarkers for early diagnosis of GC, the characteristics of gastric juice free amino acid (GJFAA) profiling was determined. Method: First, gastric juice was collected from 130 consecutive patients who underwent gastroscopy. They were divided into GC group (n = 47) and non-neoplastic gastric disease (NGD) group (n = 83) according to the pathological diagnosis. The concentrations of 34 GJFAAs were examined by amino acid analyzer. Multivariate and univariate analyses were used for comparing the alterations of GJFAA profiles between the two groups. Then candidate differential GJFAAs were verified by LC-MS/MS in another set of patients, which included 32 GC patients and 38 NGD patients. The diagnostic performance of GJFAAs was evaluated by ROC curve. Results: Significant alterations in GJFAA profiles were observed in GC patients compared to NGD patients in the training set. A total of 14 amino acids were screened as differential GJFAAs. Leucine, threonine and serine were the most frequently altered. Combined AUC of the three non-AAAs [0.869 (95% CI, 0.805-0.934)] was superior to the combined three AAAs [0.841 (95% CI, 0.773-0.908)]. In addition, a combined AUC comprisingthe six ones was further improved to 0.871 (95% CI, 0.809-0.933) in the diagnosis of GC. A similar variation trend and diagnostic value were observed in the validation set. Conclusion: This study indicates the potential of GJFAA profiling as a promising approach for the early detection and screening of GC.

SUBMITTER: Liu J

PROVIDER: S-EPMC6958230 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Free amino acid profiling of gastric juice as a method for discovering potential biomarkers of early gastric cancer.

Liu Jian J Lin Sanren S Li Zhengpeng Z Zhou Liya L Yan Xiue X Xue Yan Y Meng Lingmei L Lu Jingjing J Suo Baojun B Jiang Wenjun W

International journal of clinical and experimental pathology 20180501 5

<i>Background:</i> Prior studies showed that aromatic amino acids (AAAs) could be used as potential gastric juice biomarkers in screening gastric cancer (GC). To identify new biomarkers for early diagnosis of GC, the characteristics of gastric juice free amino acid (GJFAA) profiling was determined. <i>Method:</i> First, gastric juice was collected from 130 consecutive patients who underwent gastroscopy. They were divided into GC group (n = 47) and non-neoplastic gastric disease (NGD) group (n = ...[more]

PMID: 31938344

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Project description:BACKGROUND: Recently, rapid advances have been made in metabolomics-based, easy-to-use early cancer detection methods using blood samples. Among metabolites, profiling of plasma free amino acids (PFAAs) is a promising approach because PFAAs link all organ systems and have important roles in metabolism. Furthermore, PFAA profiles are known to be influenced by specific diseases, including cancers. Therefore, the purpose of the present study was to determine the characteristics of the PFAA profiles in cancer patients and the possibility of using this information for early detection. METHODS AND FINDINGS: Plasma samples were collected from approximately 200 patients from multiple institutes, each diagnosed with one of the following five types of cancer: lung, gastric, colorectal, breast, or prostate cancer. Patients were compared to gender- and age- matched controls also used in this study. The PFAA levels were measured using high-performance liquid chromatography (HPLC)-electrospray ionization (ESI)-mass spectrometry (MS). Univariate analysis revealed significant differences in the PFAA profiles between the controls and the patients with any of the five types of cancer listed above, even those with asymptomatic early-stage disease. Furthermore, multivariate analysis clearly discriminated the cancer patients from the controls in terms of the area under the receiver-operator characteristics curve (AUC of ROC >0.75 for each cancer), regardless of cancer stage. Because this study was designed as case-control study, further investigations, including model construction and validation using cohorts with larger sample sizes, are necessary to determine the usefulness of PFAA profiling. CONCLUSIONS: These findings suggest that PFAA profiling has great potential for improving cancer screening and diagnosis and understanding disease pathogenesis. PFAA profiles can also be used to determine various disease diagnoses from a single blood sample, which involves a relatively simple plasma assay and imposes a lower physical burden on subjects when compared to existing screening methods.

Project description:Discovering amino acid (AA) patterns on protein binding sites has recently become popular. We propose a method to discover the association relationship among AAs on binding sites. Such knowledge of binding sites is very helpful in predicting protein-protein interactions. In this paper, we focus on protein complexes which have protein-protein recognition. The association rule mining technique is used to discover geographically adjacent amino acids on a binding site of a protein complex. When mining, instead of treating all AAs of binding sites as a transaction, we geographically partition AAs of binding sites in a protein complex. AAs in a partition are treated as a transaction. For the partition process, AAs on a binding site are projected from three-dimensional to two-dimensional. And then, assisted with a circular grid, AAs on the binding site are placed into grid cells. A circular grid has ten rings: a central ring, the second ring with 6 sectors, the third ring with 12 sectors, and later rings are added to four sectors in order. As for the radius of each ring, we examined the complexes and found that 10Å is a suitable range, which can be set by the user. After placing these recognition complexes on the circular grid, we obtain mining records (i.e. transactions) from each sector. A sector is regarded as a record. Finally, we use the association rule to mine these records for frequent AA patterns. If the support of an AA pattern is larger than the predetermined minimum support (i.e. threshold), it is called a frequent pattern. With these discovered patterns, we offer the biologists a novel point of view, which will improve the prediction accuracy of protein-protein recognition. In our experiments, we produced the AA patterns by data mining. As a result, we found that arginine (arg) most frequently appears on the binding sites of two proteins in the recognition protein complexes, while cysteine (cys) appears the fewest. In addition, if we discriminate the shape of binding sites between concave and convex further, we discover that patterns {arg, glu, asp} and {arg, ser, asp} on the concave shape of binding sites in a protein more frequently (i.e. higher probability) make contact with {lys} or {arg} on the convex shape of binding sites in another protein. Thus, we can confidently achieve a rate of at least 78%. On the other hand {val, gly, lys} on the convex surface of binding sites in proteins is more frequently in contact with {asp} on the concave site of another protein, and the confidence achieved is over 81%. Applying data mining in biology can reveal more facts that may otherwise be ignored or not easily discovered by the naked eye. Furthermore, we can discover more relationships among AAs on binding sites by appropriately rotating these residues on binding sites from a three-dimension to two-dimension perspective. We designed a circular grid to deposit the data, which total to 463 records consisting of AAs. Then we used the association rules to mine these records for discovering relationships. The proposed method in this paper provides an insight into the characteristics of binding sites for recognition complexes.

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Free amino acid profiling of gastric juice as a method for discovering potential biomarkers of early gastric cancer.

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Free amino acid profiling of gastric juice as a method for discovering potential biomarkers of early gastric cancer.

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