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Background: Disease-specific humoral immune response-related protein complexes in blood are associated with disease progression., Methods: Thirty-one patients with stage IIIB and IV non-small-cell lung cancer (NSCLC) were administered with oral dose of icotinib hydrochloride (150 mg twice daily or 125 mg 3 times daily) for a 28-continuous-day cycle until diseases progressed or unacceptable toxicity occurred. The levels of immunoinflammation-related protein complexes (IIRPCs) in a series of plasma samples from 31 NSCLC patients treated with icotinib hydrochloride were determined by an optimized native polyacrylamide gel electrophoresis., Results: Three characteristic patterns of the IIRPCs, named as patterns a, b, and c, respectively, were detected in plasma samples from 31 patients. Prior to the treatment, there were 18 patients in pattern a consisting of 5 IIRPCs, 9 in pattern b consisting of six IIRPCs, and 4 in pattern c without the IIRPCs. The levels of the IIRPCs in 27 patients were quantified. Our results indicate that the time length of humoral immune and inflammation response (TLHIIR) was closely associated with disease progression, and the median TLHIIR was 22.0 weeks, 95% confidence interval: 16.2 to 33.0 weeks, with a lead time of median 11 weeks relative to clinical imaging evidence confirmed by computed tomography or magnetic resonance imaging (the median progression-free survival, 34.0 weeks, 95% confidence interval: 27.9 to 49.0 weeks)., Conclusions: The complex relationships between humoral immune response, acquired resistance, and disease progression existed. Personalized IIRPCs could be indicators to monitor the disease progression., (Copyright © 2014 Elsevier B.V. All rights reserved.)

Changes in serum lipidome and in tissue lipidome are associated with cancer. In this study, tissue mass spectrometry imaging (MSI) and serum lipid profiling by matrix-assisted laser desorption/ionization-Fourier transform ion cyclotron resonance mass spectrometry (MALDI-FTICR MS) were performed to investigate significantly changed lipids in both tumor (malignant thyroid cancer (MTC) and benign thyroid tumor (BTT)) tissues and sera. Y-scatterplots of variable importance in the projection (VIP) values vs. fold change values indicate that change trends in the levels of ten lipids (i.e., phosphatidylcholine (PC)(34:1), PC(36:1), PC(38:6), phosphatidic acid (PA) (36:2), PA(36:3), PA(38:3), PA(38:4), PA(38:5), PA(40:5), and sphingomyelin (SM)(34:1)) in both tissues and sera from MTC patients, BTT patients, and normal individuals are significantly associated with these three types of pathophysiological status. In order to examine their diagnostic ability, 289 serum samples from 124 MTC patients, 43 BTT patients, and 122 normal controls were randomly divided into the training set and validation set. A biomarker of PC(34:1) exhibited excellent diagnostic ability to differentiate both MTC and BTT patients from normal individuals, with an area under the receiver operating characteristic (ROC) curve value of 0.984, a sensitivity of 96.4 %, and a specificity of 92.7 %. A panel which included PA(36:3) and SM(34:1) could distinguish between MTC and BTT, with an area under receiver operating characteristic curve (AUC) of 0.961, a sensitivity of 87.8 %, and a specificity of 92.9 %. It is worth noting that a panel consisting of PC(34:1), PA(36:3), and SM(34:1) could differentiate MTC patients from both BTT patients and normal individuals, with an AUC of 0.841, a sensitivity of 86.6 %, and a specificity of 75.5 %.

MicroRNAs (miRNAs) are small regulatory RNAs that play a significant role in eukaryotes by targeting mRNAs for cleavage or translational repression. Recent studies have also shown them to be associated with cellular changes following viral infection. Mink enteritis virus (MEV) is one of the most important viral pathogens in the mink industry. To study the involvement of miRNAs in the MEV infection process, we used Illumina's ultrahigh throughput approach to sequencing miRNA libraries from the feline kidney (F81) cell line before and after infection with MEV. Using this bioinformatics approach we identified 196 known mammalian miRNA orthologs belonging to 152 miRNA families in F81 cells. Additionally, 97 miRNA*s of these miRNAs were detected. As well as known miRNAs, 384 and 398 novel miRNA precursor candidates were identified in uninfected and MEV-infected F81 cells respectively that have not been reported in other mammals. In MEV-infected cells 3 miRNAs were significantly down-regulated and 4 up-regulated including 3 significantly. The majority (12 of 16) of randomly selected miRNA expression profiles by qRT-PCR were consistent with those identified by deep sequencing. A total of 88 miRNAs were predicted to target interferon-associated genes; 6 appear to target the 3'UTR of MEV-specific receptor transferring receptor mRNAs; and 8 to target the MEV mRNA coding region. No miRNAs coded by MEV itself were detected., (Copyright © 2014 Elsevier B.V. All rights reserved.)

Purpose: To quantitatively analyze lipid molecules in tumors and adjacent tissues of intrahepatic cholangiocarcinoma (ICC), to establish diagnostic model and to examine lipid changes with clinical classification. Patients and Methods: We measured the quantity of 202 lipid molecules in 100 tumor observation points and 100 adjacent observation points of patients who were diagnosed with ICC. Principal component analysis (PCA) and orthogonal partial least squares-discriminant analysis (OPLS-DA) were handles, along with Student's t-test to identify specific metabolites. Prediction accuracy was validated in the validation set. Another logistic regression model was also established on the training set and validated on the validation set. Results: Distinct separation was obtained from PCA and OPLS-DA model. Ten differentiating metabolites were identified using PCA, OPLA-DA and Lasso regression: [m/z 722.5130], [m/z 863.5655], [m/z 436.2834], [m/z 474.2626], [m/z 661.4813], [m/z 750.5443], [m/z 571.2889], [m/z 836.5420], [m/z 772.5862] and [m/z 478.2939]. Using logical regression, a diagnostic equation: y = 3.4*[m/z 436.2834] - 3.773*[m/z 474.2626] + 3.82*[m/z 661.4813] - 4.394*[m/z 863.5655] + 10.165 based on four metabolites successfully differentiated cancerous areas from adjacent normal areas. The AUROC of the model reached 0.993 (95% CI: 0.985– 0.999) in the validation group. Compared with the adjacent non-tumor area, three characteristic metabolites FA (22:4), PA (P-18:0/0:0) and Glucosylceramide (d18:1/12:0) showed an increasing trend from stage I to stage II, while seven other metabolites LPA(16:0), PE(34:2), PE(36:4), PE(38:3), PE(40:6), PE(40:5) and LPE(16:0) showed a decreasing trend from stage I to stage II. Conclusion: We successfully identified lipid molecules in differentiating tumor tissue and adjacent tissue of ICC, established a discrimination logistic model which could be used as a classifier to classify tumor and non-tumor regions based on analysis in tumor margins and provided information for biomarker changes in ICC, and proposed to related lipid changes with clinical classification. [ABSTRACT FROM AUTHOR]

Subclinical mastitis (SCM) is a very common disease of dairy cows. Currently, somatic cell count (SCC) is used for SCM diagnoses. There are no prognostic tests to detect which cows may develop SCM during the dry-off period. Therefore, the objectives of this study were to identify metabolic alterations in the serum of pre-SCM cows during the dry-off period, at −8 and −4 weeks before calving, through a targeted mass spectrometry (MS) assay. Fifteen cows, free of any disease, and 10 cows affected only by SCM postpartum served as controls (CON) and the SCM group, respectively. Results showed 59 and 47 metabolites that differentiated (p ≤ 0.05) CON and pre-SCM cows at –8 and −4 weeks prior to the expected date of parturition, respectively. Regression analysis indicated that a panel of four serum metabolites (AUC = 0.92, p < 0.001) at −8 weeks and another four metabolites (AUC = 0.92, p < 0.01) at −4 weeks prior to parturition might serve as predictive biomarkers for SCM. Early identification of susceptible cows can enable development of better preventive measurements ahead of disease occurrence. [ABSTRACT FROM AUTHOR]

Changes in the levels of free fatty acids (FFAs) are closely associated with physiological status. Serum levels of C16:1, C18:3, C18:2, C18:1, C20:4, and C22:6 in 164 gastric cancer (GC) patients and 111 benign gastric disease (BGD) patients were significantly decreased compared with 252 healthy controls. Receiver operating characteristic analysis showed that the biomarker panel including C16:1, C18:3, C18:2, C20:4, and C22:6 presents a high diagnostic ability to differentiate early-stage GC patients from healthy controls plus BGD patients, with a sensitivity of 80.6% and a specificity of 72.7%. [ABSTRACT FROM AUTHOR]

In this study, Fourier transform ion cyclotron resonance mass spectrometry (FTICR MS) coupled with chip-based direct-infusion nanoelectrospray ionization source (CBDInanoESI) in a negative ion mode is first employed to evaluate the effect of serum and its corresponding supernatant matrixes on the recoveries of serum free fatty acids (FFAs) based on spike-and-recovery experimental strategy by adding analytes along with analog internal standard (IS). The recoveries between serum (69.8–115.6%) and the supernatant (73.6–99.0%) matrixes are almost identical. Multiple point internal standard calibration curves between the concentration ratios of individual fatty acids to ISs, (C17:1 as IS of C16:1, C18:3, C18:2, or C18:1 or C21:0 as IS of C20:4 or C22:6) versus their corresponding intensity ratios were constructed for C16:1, C18:3, C18:2, C18:1, C20:4 and C22:6, respectively, with correlation coefficients of greater than 0.99, lower limits of detection between 0.3 and 1.8 nM, and intra- and inter-day precision (relative standard deviations <18%), along with the linear dynamic range of three orders of magnitude. Sequentially, this advanced analytical platform was applied to perform simultaneous quantitative and qualitative analysis of multiple targets, e.g., serum supernatant unsaturated FFAs from 361 participants including 95 patients with pancreatic cancer (PC), 61 patients with pancreatitis and 205 healthy controls. Experimental results indicate that the levels of C18:1, C18:2, C18:3, C20:4 and C22:6, as well as the level ratios of C18:2/C18:1 and C18:3/C18:1 of the PC patients were significantly decreased compared with those of healthy controls and the patients with pancreatitis (p < 0.01). It is worth noting that the ratio of C18:2/C18:1, polyunsaturated fatty acids (PUFAs) (C18:2, C18:3, C20:4, and C22:6), panel a (C16:1, C18:3, C18:2, C20:4 and C22:6) and panel b (C18:2/C18:1 and C18:3/C18:1) performed excellent diagnostic ability, with an area under the receiver operating characteristic curve of ≥0.869, sensitivity of ≥85.7%, and specificity of ≥86.7% for differentiating the early stage PC from non-cancer subjects, which are greatly higher than those of clinically used serum biomarker CA 19-9. More importantly, this platform can also provide a fast and easy way to quantify the levels of FFAs in less than 30 s per sample. [ABSTRACT FROM AUTHOR]

[Display omitted] • Lipids distribution of early gastric cancer (EGC) was detected by mass spectrometry imaging. • Lipids distribution in early gastric cancerous region was different from that in paracancerous region. • Serum phosphatidylcholine (32:0) and phosphatidylcholine (34:3) may serve as biomarkers for diagnosis of EGC. • Fatty acid metabolic enzymes expressed differently in early gastric cancerous regions. Early detection of gastric cancer is an effective way to decrease the associated mortality. Efficient methods are needed to provide more sensitive biomarkers for detection of early gastric cancer (EGC). We performed liquid extraction-electrosonic spray ionization mass spectrometry imaging and immunofluorescent staining of enzymes related to lipid synthesis on cancerous and paracancerous tissues obtained from six EGC patients and investigated the serum lipid profiles. Areas under the receiver operating characteristic curves were used to determine the diagnostic accuracy of putative biomarkers. Five lipids detected in the positive ion mode and seven in negative ion mode displayed higher relative intensities in the cancerous than the paracancerous tissues. Seven lipids detected in the positive ion mode and seven in the negative ion mode displayed lower relative intensities in the cancerous than the paracancerous regions. Phosphatidylcholine (34:3) and phosphatidylcholine (36:1) showed higher relative intensities in the cancerous than in the paracancerous tissues and showed higher relative intensities in the serum of EGC patients than healthy controls. Phosphatidylcholine (32:0) showed lower relative intensities in the cancerous than in the paracancerous tissues and lower relative intensities in the serum of EGC patients than healthy controls. The areas under the receiver operating characteristic curves of serum phosphatidylcholine (32:0) and phosphatidylcholine (34:3) for identifying EGC patients were 1.000 and 0.978, respectively. Regions associated with EGC showed different lipid distributions and enzyme expression from the paracancerous regions. Serum Phosphatidylcholine (32:0) and phosphatidylcholine (34:3) are potential biomarkers for discriminating between EGC patients and healthy controls. [ABSTRACT FROM AUTHOR]

• Three metabolites panel serves as new biomarkers for early ovarian cancer detection. • The panel surpasses CA125 in benign conditions vs. malignant ovarian cancer diagnosis. • Glycerophospholipid pathways play vital roles in ovarian cancer progression. Ovarian cancer (OC) is a major global cause of death among gynecological cancers, with a high mortality rate. Early diagnosis, distinguishing between benign conditions and early malignant OC forms, is vital for successful treatment. This research investigates serum metabolites to find diagnostic biomarkers for early OC identification. Metabolomic profiles derived from the serum of 60 patients with benign conditions and 60 patients with malignant OC were examined using ultra-performance liquid chromatography coupled with tandem mass spectrometry (UPLC-MS/MS). Comparative analysis revealed differential metabolites linked to OC, aiding biomarker identification for early-diagnosis of OC via machine learning features. The predictive ability of these biomarkers was evaluated against the traditional biomarker, cancer antigen 125 (CA125). 84 differential metabolites were identified, including 2-Thiothiazolidine-4-carboxylic acid (TTCA), Methionyl-Cysteine, and Citrulline that could serve as potential biomarkers to identify benign conditions and malignant OC. In the diagnosis of early-stage OC, the area under the curve (AUC) for Citrulline was 0.847 (95 % Confidence Interval (CI): 0.719–0.974), compared to 0.770 (95 % CI: 0.596–0.944) for TTCA, and 0.754 for Methionine-Cysteine (95 % CI: 0.589–0.919). These metabolites demonstrate a superior diagnostic capability relative to CA125, which has an AUC of 0.689 (95 % CI: 0.448–0.931). Among these biomarkers, Citrulline stands out as the most promising. Additionally, in the diagnosis of benign conditions and malignant OC, using logistic regression to combine potential biomarkers with CA125 has an AUC of 0.987 (95 % CI: 0.9708–1) has been proven to be more effective than relying solely on the traditional biomarker CA125 with an AUC of 0.933 (95 % CI: 0.870–0.996). Furthermore, among all the differential metabolites, lipid metabolites dominate, significantly impacting glycerophospholipid metabolism pathway. The discovered serum metabolite biomarkers demonstrate excellent diagnostic performance for distinguishing between benign conditions and malignant OC and for early diagnosis of malignant OC. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • MALDI-MSI could in situ detect nucleosides and nucleotides. • Significantly increased nucleotides in cancerous areas were observed. • Significantly decreased nucleosides in cancerous areas were observed. • Ratio of nucleoside to nucleoside monophosphate could distinguish GC pathological states. Abstract In situ detection of nucleoside sand nucleotides still remains a challenge. In this study, we used matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) to investigate the distribution of small molecule metabolites(SMMs, typically<500 Da) in gastric cancer (GC) tissues (n = 19). Significantly increased nucleotides(i.e. uridine monophosphate, adenosine monophosphate, and guanosine monophosphate) and significantly decreased nucleosides (i.e. inosine, guanosine, and uridine) in cancerous areas were observed relative to adjacent noncancerous areas. Statistical analysis revealed a clear separation between cancerous areas and adjacent noncancerous areas for 19 GC tissue samples based on the differential expressions of SMMs such as nucleosides and nucleotides. The level ratio of nucleoside to its corresponding nucleoside monophosphate may be potential biomarkers for distinguish the pathological states of GC tissue. [ABSTRACT FROM AUTHOR]

Background Changes in the levels of lipids are associated with breast cancer (BC). Methods Disease-specific serum free fatty acids (FFAs) were quantified using chip-based direct-infusion nanoelectrospray ionization-Fourier transform ion cyclotron resonance mass spectrometry (CBDInanoESI-FTICR MS) in the negative ion mode. Multiple point internal standard calibration curves between the concentration ratios of fatty acids (i.e., C 16:1 , C 18:3 , C 18:2 , C 18:1 , C 20:4 , and C 22:6 ) to internal standards (C 17:1 for C 16:1 , C 18:3 , C 18:2 , and C 18:1 , C 21:0 for C 20:4 and C 22:6 ) and their corresponding intensity ratios were established with a correlation coefficient of greater than 0.986. Results Data from 342 serum samples including 202 healthy controls and 140 BC patients indicate that serum concentrations of FFAs in patients with BC were significantly decreased compared with those in healthy controls. A panel of C 16:1 , C 18:3 , C 18:2 , C 20:4 , and C 22:6 showed an excellent diagnostic ability to differentiate the patients with early stage BC from healthy controls, with the area under the receiver operating characteristics (ROC) curve of 0.953, a sensitivity of 83.3%, and a specificity of 87.1%. Conclusion Our findings suggest that these FFAs may be a valuable biomarker panel for the early-stage detection of BC. [ABSTRACT FROM AUTHOR]

Abstract: Background: There are no effective clinical biomarkers for early and specific detection of lung cancer (LC). The changes in the levels of some serum metabolites of LC patients are associated with patient gender and LC stages. Methods: Serum metabolites of the LC patients (n=58) and healthy controls (n=495) were performed using direct-infusion positive ion electrospray ionization-Fourier transform ion cyclotron resonance mass spectrometry in combination with univariate and partial least squares discriminant analyses (PLS-DA). Results: Univariate analysis indicated that 141 of the 212 serum metabolites were significantly changed in the LC patients compared with healthy controls. PLS-DA model, based on the 141 metabolites, demonstrated the differential metabolite distribution in single-sex LC patients compared with the corresponding healthy controls, and also in LC females compared with LC males. Several lipids comprising fatty acid derivations, sphingomyelin (SM) and lysophosphatidylcholine (LPC) were associated with the LC progression. Classification using oleamide, long chain acyl carnitines, LPC(18:1), LPC(20:4), LPC(20:3), LPC(22:6), and SM(16:0/1) as a biomarker panel resulted in remarkable separation between the LC patients and healthy controls with sensitivity and specificity of 100% and 91%, respectively. Conclusion: The serum metabolites found in this study may play essential roles in gender-specific LC detection and early diagnosis of cancer. [Copyright &y& Elsevier]