Multi-omic diagnostics of prostate cancer in the presence of benign prostatic hyperplasia.

There is an unmet need for improved diagnostic testing and risk prediction for cases of prostate cancer (PCa) to improve care and reduce overtreatment of indolent disease. Here we have analysed the serum proteome and lipidome of 262 study participants by liquid chromatography-mass spectrometry, including participants diagnosed with PCa, benign prostatic hyperplasia (BPH), or otherwise healthy volunteers, with the aim of improving biomarker specificity. Although a two-class machine learning model separated PCa from controls with sensitivity of 0.82 and specificity of 0.95, adding BPH resulted in a statistically significant decline in specificity for prostate cancer to 0.76, with half of BPH cases being misclassified by the model as PCa. A small number of biomarkers differentiating between BPH and prostate cancer were identified, including proteins in MAP Kinase pathways, as well as in lipids containing oleic acid; these may offer a route to greater specificity. These results highlight, however, that whilst there are opportunities for machine learning, these will only be achieved by use of appropriate training sets that include confounding comorbidities, especially when calculating the specificity of a test.

A Novel Blood Proteomic Signature for Prostate Cancer.

Prostate cancer is the most common malignant tumour in men. Improved testing for diagnosis, risk prediction, and response to treatment would improve care. Here, we identified a proteomic signature of prostate cancer in peripheral blood using data-independent acquisition mass spectrometry combined with machine learning. A highly predictive signature was derived, which was associated with relevant pathways, including the coagulation, complement, and clotting cascades, as well as plasma lipoprotein particle remodeling. We further validated the identified biomarkers against a second cohort, identifying a panel of five key markers (GP5, SERPINA5, ECM1, IGHG1, and THBS1) which retained most of the diagnostic power of the overall dataset, achieving an AUC of 0.91. Taken together, this study provides a proteomic signature complementary to PSA for the diagnosis of patients with localised prostate cancer, with the further potential for assessing risk of future development of prostate cancer. Data are available via ProteomeXchange with identifier PXD025484.

High Throughput LC-MS Platform for Large Scale Screening of Bioactive Polar Lipids in Human Plasma and Serum.

Lipids play a key role in many biological processes, and their accurate measurement is critical to unraveling the biology of diseases and human health. A high throughput HILIC-based (LC-MS) method for the semiquantitative screening of over 2000 lipids, based on over 4000 MRM transitions, was devised to produce an accessible and robust lipidomic screen for phospholipids in human plasma/serum. This methodology integrates many of the advantages of global lipid analysis with those of targeted approaches. Having used the method as an initial "wide class" screen, it can then be easily adapted for a more targeted analysis and quantification of key, dysregulated lipids. Robustness was assessed using 1550 continuous injections of plasma extracts onto a single column and via the evaluation of columns from 5 different batches of stationary phase. Initial screens in positive (239 lipids, 431 MRM transitions) and negative electrospray ionization (ESI) mode (232 lipids, 446 MRM transitions) were assessed for reproducibility, sensitivity, and dynamic range using analysis times of 8 min. The total number of lipids monitored using these screening methods was 433 with an overlap of 38 lipids in both modes. A polarity switching method for accurate quantification, using the same LC conditions, was assessed for intra- and interday reproducibility, accuracy, dynamic range, stability, carryover, dilution integrity, and matrix interferences and found to be acceptable. This polarity switching method was then applied to lipids important in the stratification of human prostate cancer samples.

A Prostate Cancer Proteomics Database for SWATH-MS Based Protein Quantification.

Prostate cancer is the most frequent form of cancer in men, accounting for more than one-third of all cases. Current screening techniques, such as PSA testing used in conjunction with routine procedures, lead to unnecessary biopsies and the discovery of low-risk tumours, resulting in overdiagnosis. SWATH-MS is a well-established data-independent (DI) method requiring prior knowledge of targeted peptides to obtain valuable information from SWATH maps. In response to the growing need to identify and characterise protein biomarkers for prostate cancer, this study explored a spectrum source for targeted proteome analysis of blood samples. We created a comprehensive prostate cancer serum spectral library by combining data-dependent acquisition (DDA) MS raw files from 504 patients with low, intermediate, or high-grade prostate cancer and healthy controls, as well as 304 prostate cancer-related protein in silico assays. The spectral library contains 114,684 transitions, which equates to 18,479 peptides translated into 1227 proteins. The robustness and accuracy of the spectral library were assessed to boost confidence in the identification and quantification of prostate cancer-related proteins across an independent cohort, resulting in the identification of 404 proteins. This unique database can facilitate researchers to investigate prostate cancer protein biomarkers in blood samples. In the real-world use of the spectrum library for biomarker detection, using a signature of 17 proteins, a clear distinction between the validation cohort's pre- and post-treatment groups was observed. Data are available via ProteomeXchange with identifier PXD028651.

High-Throughput Microbore Ultrahigh-Performance Liquid Chromatography-Ion Mobility-Enabled-Mass Spectrometry-Based Proteomics Methodology for the Exploratory Analysis of Serum Samples from Large Cohort Studies.

The deployment of proteomic analysis in clinical studies represents a significant opportunity to detect and validate biomarkers in translational medicine, improve disease understanding, and provide baseline information on population health. However, comprehensive proteome studies usually employ nanoscale chromatography and often require several hours of analysis/sample. Here, we describe a high-throughput liquid chromatography tandem mass spectrometry (LC/MS/MS) methodology using 1 mm scale chromatography requiring only 15 min/sample, coupled to ion mobility-enabled mass spectrometry. The short run time effected a 6-fold increase in productivity compared with nanoscale LC/MS. The method demonstrated excellent reproducibility with retention time coefficient of variations of less than 0.05% and peak area reproducibility ranging from 5 to 15%. The 1 mm system produced similar chromatographic peak capacity values to the nanoscale miniaturized system, detecting 90% of the Escherichia coli proteins identified by the 75 µm LC/MS system (albeit based on only 75% of the peptides found by the latter). Application to the analysis of serum samples from a human prostate cancer study group resulted in the identification of a total of 533 proteins revealing the differential expression of proteins linked to patients receiving hormone-radiotherapy or undergoing surgery.

Curcuma longa (Turmeric): An auspicious spice for antibacterial, phytochemical and antioxidant activities.

Turmeric, a recognized spice, is known for miscellaneous health benefits in addition to culinary uses. In this study, in vitro evaluation of turmeric ethanol, methanol and aqueous extracts were mediated by disc diffusion, agar well method and phytochemical analysis. Purification of curcumin from turmeric was assisted by silica gel, TLC and HPLC for evaluation of its antioxidant and DNA protection activity. The sensitivity of alcoholic extracts against bacterial species differed, yet Staphylococcus aureus sub sp. Aureus and Bacillus subtilis both exhibited pronounced inhibition in disc diffusion and agar well method respectively. Overall, the crude ethanol extract of turmeric has an enhanced inhibitory effect on the growth of different bacterial species with a mean of 9.4±1.00 mm compared to 8.8±0.58 mm in case of crude methanol extract. Phytochemical analysis confirmed the presence of carbohydrates, flavonoids, coumarins, steroids, saponnins, tannins and phenols. Purification of curcumin through HPLC gave the main peak with 55% of acetonitrile at a retention time of 61- 65 minutes. Lower concentration of purified curcumin has protective effects on human DNA but increased concentrations instigate damaging effects. Its percentage scavenging ability was highest (91.84%) at 45 µg and per unit increase in the concentration prompted 6 units increase in percentage inhibition with a linear regression, R2= 0.914. All these traits boost its significance in herbal medicine with varied antimicrobial and pharmacological activities.