RESUMEN
Lack of the established noninvasive diagnostic biomarkers causes delay in diagnosis of lung cancer (LC). The aim of this study was to explore the association between inflammatory and cancer-associated plasma proteins and LC and thereby discover potential biomarkers. Patients referred for suspected LC and later diagnosed with primary LC, other cancers, or no cancer (NC) were included in this study. Demographic information and plasma samples were collected, and diagnostic information was later retrieved from medical records. Relative quantification of 92 plasma proteins was carried out using the Olink Immuno-Onc-I panel. Association between expression levels of panel of proteins with different diagnoses was assessed using generalized linear model (GLM) with the binomial family and a logit-link function, considering confounder effects of age, gender, smoking, and pulmonary diseases. The analysis showed that the combination of five plasma proteins (CD83, GZMA, GZMB, CD8A, and MMP12) has higher diagnostic performance for primary LC in both early and advanced stages compared with NC. This panel demonstrated lower diagnostic performance for other cancer types. Moreover, inclusion of four proteins (GAL9, PDCD1, CD4, and HO1) to the aforementioned panel significantly increased the diagnostic performance for primary LC in advanced stage as well as for other cancers. Consequently, the collective expression profiles of select plasma proteins, especially when analyzed in conjunction, might have the potential to distinguish individuals with LC from NC. This suggests their utility as predictive biomarkers for identification of LC patients. The synergistic application of these proteins as biomarkers could pave the way for the development of diagnostic tools for early-stage LC detection.
RESUMEN
The lethal malaria parasite Plasmodium falciparum needs to constantly respond and adapt to changes within the human host in order to survive and transmit. One such change is composed of nutritional limitation, which is augmented with increased parasite loads and intimately linked to severe disease development. Extracellular vesicles released from infected red blood cells have been proposed as important mediators of disease pathogenesis and intercellular communication but whether important for the parasite response to nutritional availability is unknown. Therefore, we investigated the abundance and small RNA cargo of extracellular vesicles released upon short-term nutritional starvation of P. falciparum in vitro cultures. We show that primarily ring-stage parasite cultures respond to glucose and amino acid deprivation with an increased release of extracellular vesicles. Small RNA sequencing of these extracellular vesicles further revealed human miRNAs and parasitic tRNA fragments as the main constituent biotypes. Short-term starvations led to alterations in the transcriptomic profile, most notably in terms of the over-represented biotypes. These data suggest a potential role for extracellular vesicles released from P. falciparum infected red blood cells in the response to nutritional perturbations, their potential as prognostic biomarkers and point towards an evolutionary conserved role among protozoan parasites.
Asunto(s)
Vesículas Extracelulares , Malaria Falciparum , Parásitos , Animales , Humanos , Plasmodium falciparum/genética , ARN/metabolismo , Comunicación Celular/genética , Eritrocitos/metabolismo , Malaria Falciparum/parasitología , Parásitos/genética , Vesículas Extracelulares/metabolismo , Proteínas Protozoarias/genéticaRESUMEN
BACKGROUND: DNA methylation profiling of pediatric brain tumors offers a new way of diagnosing and subgrouping these tumors which improves current clinical diagnostics based on histopathology. We have therefore developed the MethPed classifier, which is a multiclass random forest algorithm, based on DNA methylation profiles from many subgroups of pediatric brain tumors. RESULTS: We developed an R package that implements the MethPed classifier, making it easily available and accessible. The package can be used for estimating the probability that an unknown sample belongs to each of nine pediatric brain tumor diagnoses/subgroups. CONCLUSIONS: The MethPed R package efficiently classifies pediatric brain tumors using the developed MethPed classifier. MethPed is available via Bioconductor: http://bioconductor.org/packages/MethPed/.