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The expression of thrombospondin-4 correlates with disease severity in osteoarthritic knee cartilage
(2019)
Osteoarthritis (OA) is a progressive joint disease characterized by a continuous degradation of the cartilage extracellular matrix (ECM). The expression of the extracellular glycoprotein thrombospondin-4 (TSP-4) is known to be increased in injured tissues and involved in matrix remodeling, but its role in articular cartilage and, in particular, in OA remains elusive. In the present study, we analyzed the expression and localization of TSP-4 in healthy and OA knee cartilage by reverse transcription polymerase chain reaction (RT-PCR), immunohistochemistry, and immunoblot. We found that TSP-4 protein expression is increased in OA and that expression levels correlate with OA severity. TSP-4 was not regulated at the transcriptional level but we detected changes in the anchorage of TSP-4 in the altered ECM using sequential protein extraction. We were also able to detect pentameric and fragmented TSP-4 in the serum of both healthy controls and OA patients. Here, the total protein amount was not significantly different but we identified specific degradation products that were more abundant in sera of OA patients. Future studies will reveal if these fragments have the potential to serve as OA-specific biomarkers.
The KMT2A (MLL) gene rearrangements (KMT2A-r) are associated with a diverse spectrum of acute leukemias. Although most KMT2A-r are restricted to nine partner genes, we have recently revealed that KMT2A-USP2 fusions are often missed during FISH screening of these genetic alterations. Therefore, complementary methods are important for appropriate detection of any KMT2A-r. Here we use a machine learning model to unravel the most appropriate markers for prediction of KMT2A-r in various types of acute leukemia. A Random Forest and LightGBM classifier was trained to predict KMT2A-r in patients with acute leukemia. Our results revealed a set of 20 genes capable of accurately estimating KMT2A-r. The SKIDA1 (AUC: 0.839; CI: 0.799–0.879) and LAMP5 (AUC: 0.746; CI: 0.685–0.806) overexpression were the better markers associated with KMT2A-r compared to CSPG4 (also named NG2; AUC: 0.722; CI: 0.659–0.784), regardless of the type of acute leukemia. Of importance, high expression levels of LAMP5 estimated the occurrence of all KMT2A-USP2 fusions. Also, we performed drug sensitivity analysis using IC50 data from 345 drugs available in the GDSC database to identify which ones could be used to treat KMT2A-r leukemia. We observed that KMT2A-r cell lines were more sensitive to 5-Fluorouracil (5FU), Gemcitabine (both antimetabolite chemotherapy drugs), WHI-P97 (JAK-3 inhibitor), Foretinib (MET/VEGFR inhibitor), SNX-2112 (Hsp90 inhibitor), AZD6482 (PI3Kβ inhibitor), KU-60019 (ATM kinase inhibitor), and Pevonedistat (NEDD8-activating enzyme (NAE) inhibitor). Moreover, IC50 data from analyses of ex-vivo drug sensitivity to small-molecule inhibitors reveals that Foretinib is a promising drug option for AML patients carrying FLT3 activating mutations. Thus, we provide novel and accurate options for the diagnostic screening and therapy of KMT2A-r leukemia, regardless of leukemia subtype.
Background: Patients with chronic kidney disease (CKD) are at high risk of myocardial infarction. Cardiac troponins are the biomarkers of choice for the diagnosis of acute myocardial infarction (AMI) without ST‐segment elevation (NSTE). In patients with CKD, troponin levels are often chronically elevated, which reduces their diagnostic utility when NSTE‐AMI is suspected. The aim of this study was to derive a diagnostic algorithm for serial troponin measurements in patients with CKD and suspected NSTE‐AMI.
Methods and Results: Two cohorts, 1494 patients from a prospective cohort study with high‐sensitivity troponin I (hs‐cTnI) measurements and 7059 cases from a clinical registry with high‐sensitivity troponin T (hs‐cTnT ) measurements, were analyzed. The prospective cohort comprised 280 CKD patients (estimated glomerular filtration rate <60 mL/min/1.73 m2). The registry data set contained 1581 CKD patients. In both cohorts, CKD patients were more likely to have adjudicated NSTE‐AMI than non‐CKD patients. The specificities of hs‐cTnI and hs‐cTnT to detect NSTE‐AMI were reduced with CKD (0.82 versus 0.91 for hs‐cTnI and 0.26 versus 0.73 for hs‐cTnT) but could be restored by applying optimized cutoffs to either the first or a second measurement after 3 hours. The best diagnostic performance was achieved with an algorithm that incorporates serial measurements and rules in or out AMI in 69% (hs‐cTnI) and 55% (hs‐cTnT) of CKD patients.
Conclusions: The diagnostic performance of high‐sensitivity cardiac troponins in patients with CKD with suspected NSTE‐AMI is improved by use of an algorithm based on admission troponin and dynamic changes in troponin concentration.
Introduction: Affective disorders are a major global burden, with approximately 15% of people worldwide suffering from some form of affective disorder. In patients experiencing their first depressive episode, in most cases it cannot be distinguished whether this is due to bipolar disorder (BD) or major depressive disorder (MDD). Valid fluid biomarkers able to discriminate between the two disorders in a clinical setting are not yet available.
Material and Methods: Seventy depressed patients suffering from BD (bipolar I and II subtypes) and 42 patients with major MDD were recruited and blood samples were taken for proteomic analyses after 8 h fasting. Proteomic profiles were analyzed using the Multiplex Immunoassay platform from Myriad Rules Based Medicine (Myriad RBM; Austin, Texas, USA). Human DiscoveryMAPTM was used to measure the concentration of various proteins, peptides, and small molecules. A multivariate predictive model was consequently constructed to differentiate between BD and MDD.
Results: Based on the various proteomic profiles, the algorithm could discriminate depressed BD patients from MDD patients with an accuracy of 67%.
Discussion: The results of this preliminary study suggest that future discrimination between bipolar and unipolar depression in a single case could be possible, using predictive biomarker models based on blood proteomic profiling.
Serum GFAP for stroke diagnosis in regions with limited access to brain imaging (BE FAST India)
(2021)
Introduction: Despite a high burden of stroke, access to rapid brain imaging is limited in many middle- and low-income countries. Previous studies have described the astroglial protein GFAP (glial fibrillary acidic protein) as a biomarker of intracerebral hemorrhage. The aim of this study was to test the diagnostic accuracy of GFAP for ruling out intracranial hemorrhage in a prospective cohort of Indian stroke patients. Patients and methods: This study was conducted in an Indian tertiary hospital (Christian Medical College, Ludhiana). Patients with symptoms suggestive of acute stroke admitted within 12 h of symptom onset were enrolled. Blood samples were collected at hospital admission. Single Molecule Array technology was used for determining serum GFAP concentrations. Results: A total number of 155 patients were included (70 intracranial hemorrhage, 75 ischemic stroke, 10 stroke mimics). GFAP serum concentrations were elevated in intracranial hemorrhage patients compared to ischemic stroke patients [median (interquartile range) 2.36 µg/L (0.61–7.16) vs. 0.18 µg/L (0.11–0.38), p < 0.001]. Stroke mimics patients had a median GFAP serum level of 0.14 µg/L (0.09–0.26). GFAP values below the cut-off of 0.33 µg/L (area under the curve 0.871) ruled out intracranial hemorrhage with a negative predictive value of 89.7%, (at a sensitivity for detecting intracranial hemorrhage of 90.0%). Discussion: The high negative predictive value of a GFAP test system allows ruling out patients with intracranial hemorrhage. Conclusion: In settings where immediate brain imaging is not available, this would enable to implement secondary prevention (e.g., aspirin) in suspected ischemic stroke patients as soon as possible.
We recently reported that expression levels of tumor necrosis factor (TNF) receptors, TNFR1 and TNFR2, are significantly changed in the brains and cerebral spinal cerebral fluid (CSF) with Alzheimer's disease (AD). Moreover, we also found that, in an Alzheimer's mouse model, genetic deletion of TNF receptor (TNFR1) reduces amyloid plaques and amyloid beta peptides (Abeta) production through beta-secretase (BACE1) regulation. TNF-alpha converting enzyme (TACE/ADAM-17) does not only cleave pro- TNF-alpha but also TNF receptors, however, whether the TACE activity was changed in the CSF was not clear. In this study, we examined TACE in the CSF in 32 AD patients and 27 age-matched healthy controls (HCs). Interestingly, we found that TACE activity was significantly elevated in the CSF from AD patients compared with HCs. Furthermore, we also assayed the CSF levels of TACE cleaved soluble forms of TNFR1 and TNFR2 in the same patients. We found that AD patients had higher levels of both TACE cleaved soluble TNFR1 (sTNFR1) and TNFR2 (sTNFR2) in the CSF compared with aged- and gender-matched healthy controls. Levels of sTNFR1 correlated strongly with the levels of sTNFR2 (rs = 0.567-0.663, p < 0.01). The levels of both sTNFR1 and sTNFR2 significantly correlated with the TACE activity (rs = 0.491-0.557, p < 0.05). To examine if changes in TACE activity and in levels of cleaved soluble TNFRs are an early event in the course of Alzheimer's disease, we measured these molecules in the CSF from 47 patients with mild cognitive impairment (MCI) which is considered as a preclinical stage of AD. Unexpectedly, we found significantly higher levels of TACE activity and soluble TNFR in the MCI group. These results suggest that TACE activity and soluble TNF receptors may be potential diagnostic candidate biomarkers in AD and MCI.
Based on accumulating evidence of a role of lipid signaling in many physiological and pathophysiological processes including psychiatric diseases, the present data driven analysis was designed to gather information needed to develop a prospective biomarker, using a targeted lipidomics approach covering different lipid mediators. Using unsupervised methods of data structure detection, implemented as hierarchal clustering, emergent self-organizing maps of neuronal networks, and principal component analysis, a cluster structure was found in the input data space comprising plasma concentrations of d = 35 different lipid-markers of various classes acquired in n = 94 subjects with the clinical diagnoses depression, bipolar disorder, ADHD, dementia, or in healthy controls. The structure separated patients with dementia from the other clinical groups, indicating that dementia is associated with a distinct lipid mediator plasma concentrations pattern possibly providing a basis for a future biomarker. This hypothesis was subsequently assessed using supervised machine-learning methods, implemented as random forests or principal component analysis followed by computed ABC analysis used for feature selection, and as random forests, k-nearest neighbors, support vector machines, multilayer perceptron, and naïve Bayesian classifiers to estimate whether the selected lipid mediators provide sufficient information that the diagnosis of dementia can be established at a higher accuracy than by guessing. This succeeded using a set of d = 7 markers comprising GluCerC16:0, Cer24:0, Cer20:0, Cer16:0, Cer24:1, C16 sphinganine, and LacCerC16:0, at an accuracy of 77%. By contrast, using random lipid markers reduced the diagnostic accuracy to values of 65% or less, whereas training the algorithms with randomly permuted data was followed by complete failure to diagnose dementia, emphasizing that the selected lipid mediators were display a particular pattern in this disease possibly qualifying as biomarkers.
Hepatitis C virus (HCV) substantially affects lipid metabolism, and remodeling of sphingolipids appears to be essential for HCV persistence in vitro. The aim of the current study is the evaluation of serum sphingolipid variations during acute HCV infection. We enrolled prospectively 60 consecutive patients with acute HCV infection, most of them already infected with human immunodeficiency virus (HIV), and serum was collected at the time of diagnosis and longitudinally over a six-month period until initiation of antiviral therapy or confirmed spontaneous clearance. Quantification of serum sphingolipids was performed by liquid chromatography-tandem mass spectrometry (LC-MS/MS). Spontaneous clearance was observed in 11 out of 60 patients (18.3%), a sustained viral response (SVR) in 43 out of 45 patients (95.5%) receiving an antiviral treatment after follow-up, whereas persistence of HCV occurred in six out of 60 patients (10%). C24-ceramide (C24-Cer)-levels increased at follow-up in patients with spontaneous HCV eradication (p < 0.01), as compared to baseline. Sphingosine and sphinganine values were significantly upregulated in patients unable to clear HCV over time compared to patients with spontaneous clearance of HCV infection on follow-up (p = 0.013 and 0.006, respectively). In summary, the persistence of HCV after acute infection induces a downregulation of C24Cer and a simultaneous elevation of serum sphingosine and sphinganine concentrations.
Despite the implementation of consolidative immune checkpoint inhibition after definitive chemoradiotherapy (CRT), the prognosis for locally advanced non-small-cell lung cancer (NSCLC) remains poor. We assessed the impact of the C-reactive protein (CRP) to albumin ratio (CAR) as an inflammation-based prognostic score in patients with locally advanced NSCLC treated with CRT. We retrospectively identified and analyzed 52 patients with primary unresectable NSCLC (UICC Stage III) treated with definitive/neoadjuvant CRT between 2014 and 2019. CAR was calculated by dividing baseline CRP by baseline albumin levels and correlated with clinicopathologic parameters to evaluate prognostic impact. After dichotomizing patients by the median, univariate and multivariate Cox regression analyses were performed. An increased CAR was associated with advanced T-stage (p = 0.018) and poor performance status (p = 0.004). Patients with pre-therapeutic elevated CAR had significantly lower hemoglobin and higher leukocyte levels (hemoglobin p = 0.001, leukocytes p = 0.018). High baseline CAR was shown to be associated with worse local control (LPFS, p = 0.006), shorter progression-free survival (PFS, p = 0.038) and overall survival (OS, p = 0.022), but not distant metastasis-free survival (DMFS). Multivariate analysis confirmed an impaired outcome in patients with high CAR (LPFS: HR 3.562, 95% CI 1.294–9.802, p = 0.011). CAR is an easily available and independent prognostic marker after CRT in locally advanced NSCLC. CAR may be a useful biomarker for patient stratification to individualize treatment concepts.
Uterine cervical cancer is one of the leading causes of cancer-related mortality in women worldwide. Each year, over half a million new cases are estimated, resulting in more than 300,000 deaths. While less-invasive, fertility-preserving surgical procedures can be offered to women in early stages, treatment for locally advanced disease may include radical hysterectomy, primary chemoradiotherapy (CRT) or a combination of these modalities. Concurrent platinum-based chemoradiotherapy regimens remain the first-line treatments for locally advanced cervical cancer. Despite achievements such as the introduction of angiogenesis inhibitors, and more recently immunotherapies, the overall survival of women with persistent, recurrent or metastatic disease has not been extended significantly in the last decades. Furthermore, a broad spectrum of molecular markers to predict therapy response and survival and to identify patients with high- and low-risk constellations is missing. Implementation of these markers, however, may help to further improve treatment and to develop new targeted therapies. This review aims to provide comprehensive insights into the complex mechanisms of cervical cancer pathogenesis within the context of molecular markers for predicting treatment response and prognosis.