BackgroundHepatocellular Carcinoma is the third most common cause of cancer related death worldwide, often diagnosed by measuring serum AFP; a poor performance stand-alone biomarker. With the aim of improving on this, our study focuses on plasma proteins identified by Mass Spectrometry in order to investigate and validate differences seen in the respective proteomes of controls and subjects with LC and HCC.MethodsMass Spectrometry analysis using liquid chromatography electro spray ionization quadrupole time-of-flight was conducted on 339 subjects using a pooled expression profiling approach. ELISA assays were performed on four significantly differentially expressed proteins to validate their expression profiles in subjects from the Gambia and a pilot group from Nigeria. Results from this were collated for statistical multiplexing using logistic regression analysis.ResultsTwenty-six proteins were identified as differentially expressed between the three subject groups. Direct measurements of four; hemopexin, alpha-1-antitrypsin, apolipoprotein A1 and complement component 3 confirmed their change in abundance in LC and HCC versus control patients. These trends were independently replicated in the pilot validation subjects from Nigeria. The statistical multiplexing of these proteins demonstrated performance comparable to or greater than ALT in identifying liver cirrhosis or carcinogenesis. This exercise also proposed preliminary cut offs with achievable sensitivity, specificity and AUC statistics greater than reported AFP averages.ConclusionsThe validated changes of expression in these proteins have the potential for development into high-performance tests usable in the diagnosis and or monitoring of HCC and LC patients. The identification of sustained expression trends strengthens the suggestion of these four proteins as worthy candidates for further investigation in the context of liver disease. The statistical combinations also provide a novel inroad of analyses able to propose definitive cut-offs and combinations for evaluation of performance.
Background: Hepatocellular Carcinoma is the third most common cause of cancer related death worldwide, often diagnosed by measuring serum AFP; a poor performance stand-alone biomarker. With the aim of improving on this, our study focuses on plasma proteins identified by Mass Spectrometry in order to investigate and validate differences seen in the respective proteomes of controls and subjects with LC and HCC.Methods: Mass Spectrometry analysis using liquid chromatography electro spray ionization quadrupole time-of-flight was conducted on 339 subjects using a pooled expression profiling approach. ELISA assays were performed on four significantly differentially expressed proteins to validate their expression profiles in subjects from the Gambia and a pilot group from Nigeria. Results from this were collated for statistical multiplexing using logistic regression analysis.Results: Twenty-six proteins were identified as differentially expressed between the three subject groups. Direct measurements of four; hemopexin, alpha-1-antitrypsin, apolipoprotein A1 and complement component 3 confirmed their change in abundance in LC and HCC versus control patients. These trends were independently replicated in the pilot validation subjects from Nigeria. The statistical multiplexing of these proteins demonstrated performance comparable to or greater than ALT in identifying liver cirrhosis or carcinogenesis. This exercise also proposed preliminary cut offs with achievable sensitivity, specificity and AUC statistics greater than reported AFP averages. Conclusions:The validated changes of expression in these proteins have the potential for development into highperformance tests usable in the diagnosis and or monitoring of HCC and LC patients. The identification of sustained expression trends strengthens the suggestion of these four proteins as worthy candidates for further investigation in the context of liver disease. The statistical combinations also provide a novel inroad of analyses able to propose definitive cutoffs and combinations for evaluation of performance.
Objective: The risk of cardiovascular (CVD) events increases for four weeks following a respiratory infection. This short-term increase in risk may be an opportunity for clinical intervention, providing one could predict the risk of infection-related CVD. We therefore sought to develop and validate a risk prediction model for use in patients with respiratory infections in primary care. Design and method: We used routinely collected data from two UK general practice databases, provided by Clinical Practice Research Datalink (CPRD). We used cohorts of patients without pre-existing CVD, starting from their first respiratory infection after the age of 40. We followed patients for 28 days. Our outcome was a composite of cerebral and cardiac events. We used logistic regression to develop risk prediction models in CPRD Aurum, and validated it in CPRD Gold. We selected predictors by asking clinicians to prioritize a list of potential predictors. This model uses ten variables. Results: The development cohort comprised 3.8 million patients with respiratory infections, and 7,081 CVD events in the following 28 days (0.18%). The validation cohort comprised 2.6 million patients with infections, and 6,868 events (0.26%). In external validation, we estimated a C statistic of 0.86. Using a 1% threshold of risk as a cut off, the positive predictive value was 3% (3.2 to 3.4%), with a negative predictive value close to one. Conclusions: It is possible to predict risk of primary infection-related CVD. Identifying patients at greatest risk of infection-related CVD opens up possibilities for further research applications.
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