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Biomarker Candidates for Tumors Identified from Deep-Profiled Plasma Stem Predominantly from the Low Abundant Area.

ABSTRACT: The plasma proteome has the potential to enable a holistic analysis of the health state of an individual. However, plasma biomarker discovery is difficult due to its high dynamic range and variability. Here, we present a novel automated analytical approach for deep plasma profiling and applied it to a 180-sample cohort of human plasma from lung, breast, colorectal, pancreatic, and prostate cancers. Using a controlled quantitative experiment, we demonstrate a 257% increase in protein identification and a 263% increase in significantly differentially abundant proteins over neat plasma. In the cohort, we identified 2732 proteins. Using machine learning, we discovered biomarker candidates such as STAT3 in colorectal cancer and developed models that classify the diseased state. For pancreatic cancer, a separation by stage was achieved. Importantly, biomarker candidates came predominantly from the low abundance region, demonstrating the necessity to deeply profile because they would have been missed by shallow profiling.

SUBMITTER: Tognetti M

PROVIDER: S-EPMC9251764 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature

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Biomarker Candidates for Tumors Identified from Deep-Profiled Plasma Stem Predominantly from the Low Abundant Area.

Tognetti Marco M Sklodowski Kamil K Müller Sebastian S Kamber Dominique D Muntel Jan J Bruderer Roland R Reiter Lukas L

Journal of proteome research 20220523 7

The plasma proteome has the potential to enable a holistic analysis of the health state of an individual. However, plasma biomarker discovery is difficult due to its high dynamic range and variability. Here, we present a novel automated analytical approach for deep plasma profiling and applied it to a 180-sample cohort of human plasma from lung, breast, colorectal, pancreatic, and prostate cancers. Using a controlled quantitative experiment, we demonstrate a 257% increase in protein identificati ...[more]

PMID: 35605973

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Project description:Placenta accreta spectrum (PAS) presents a significant obstetric challenge, associated with considerable maternal and fetal-neonatal morbidity and mortality. Nevertheless, it is imperative to acknowledge that a noteworthy subset of PAS cases remains undetected until the time of delivery, thereby contributing to an augmented incidence of morbidity among the affected individuals. The delayed identification of PAS not only hinders timely intervention but also exacerbates the associated health risks for both the maternal and fetal outcomes. This underscores the urgency to innovate strategies for early PAS diagnosis. In this study, we aimed to explore plasma proteins as potential diagnostic biomarkers for PAS. Integrated transcriptome and proteomic analyses were conducted to establish a novel diagnostic approach. A cohort of 15 pregnant women diagnosed with PAS and delivering at Inonu University Faculty of Medicine between 01/04/2021 and 01/01/2023, along with a matched control group of 15 pregnant women without PAS complications, were enrolled. Plasma protein identification utilized enzymatic digestion and liquid chromatography-tandem mass spectrometry techniques. Proteomic analysis identified 228 plasma proteins, of which 85 showed significant differences (P < 0.001) between PAS and control cases. We refined this to a set of 20 proteins for model construction, resulting in a highly accurate classification model (96.9% accuracy). Notable associations were observed for proteins encoded by P01859 (Immunoglobulin heavy constant gamma 2), P02538 (Keratin type II cytoskeletal 6A), P29622 [Kallistatin (also known as Serpin A4)], P17900 (Ganglioside GM2 activator Calmodulin-like protein 5), and P01619 (Immunoglobulin kappa variable 3-20), with fold changes indicating their relevance in distinguishing PAS from control groups. In conclusion, our study has identified novel plasma proteins that could serve as potential biomarkers for early diagnosis of PAS in pregnant women. Further research and validation in larger PAS cohorts are necessary to determine the clinical utility and reliability of these proteomic biomarkers for diagnosing PAS.

Project description:Introduction: Zoonotic transition of Influenza A viruses is the cause of epidemics with high rates of morbidity and mortality. Predicting which viral strains are likely to transition from their genetic sequence could help in the prevention and response against these zoonotic strains. We hypothesized that features predictive of viral hosts could be leveraged to identify biomarkers of zoonotic viral transition. Methods: We trained deep learning models to predict viral hosts based on the virus mRNA or protein sequences. Our multi-host dataset contained 848,630 unique nucleotide sequences obtained from the NCBI Influenza Virus and Influenza Research Databases. Each sequence, representing one gene from one viral strain, was classified into one of the three host categories: Avian, Human, and Swine. Trained models were analyzed using various neural network interpretation methods to identify interesting candidates for zoonotic transition biomarkers. Results: Using mRNA sequences as input led to higher prediction accuracies than amino acids, suggesting that the codon sequence contains information relevant to viral hosts that is lost during protein translation. UMAP visualization of the latent space of our classifiers showed that viral sequences clustered according to their host of origin. Interestingly, sequences from pandemic zoonotic viral strains localized at the margins between hosts, while zoonotic sequences incapable of Human-to-Human transmission localized with non-zoonotic viruses from the same host. In addition, host prediction for pandemic zoonotic sequences had low prediction accuracy, which was not the case for the other zoonotic strains. This supports our hypothesis that ambiguously predicted viral sequences bear features associated with cross-species infectivity. Finally, we compared misclassified sequences to well-classified ones to extract interesting candidates for zoonotic transition biomarkers. While features varied significantly between pairs of species and viral genes, several codons were conserved in Swine-to-Human and Avian-to-Human misclassified sequences, and in particular in the NA, HA, and NP genes, suggesting their importance for zoonosis in Humans. Discussion: Analysis of viral sequences using neural network interpretation approaches revealed important genetic differences between zoonotic viruses with pandemic potential, compared to non-zoonotic viral strains or zoonotic viruses incapable of Human-to-Human transmission.

Dataset Information

Biomarker Candidates for Tumors Identified from Deep-Profiled Plasma Stem Predominantly from the Low Abundant Area.

Publications

Biomarker Candidates for Tumors Identified from Deep-Profiled Plasma Stem Predominantly from the Low Abundant Area.

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