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This study reports the discovery and evaluation of nanoparticle aided sensitive assays for glycovariants of MUC16 and MUC1 in a unique collection of paired ovarian cyst fluids and serum samples obtained at or prior to surgery for ovarian carcinoma suspicion. Selected glycovariants and the immunoassays for CA125, CA15-3 and HE4 were compared and validated in 347 cyst fluid and serum samples. Whereas CA125 and CA15-3 performed poorly in cyst fluid to separate carcinoma and controls, four glycovariants including MUC16MGL , MUC16STn , MUC1STn and MUC1Tn provided highly improved separations. In serum, the two STn glycovariants outperformed conventional CA125, CA15-3 and HE4 assays in all sub-categories analysed with main benefits obtained at high specificities and at postmenopausal and early-stage disease. Serum MUC16STn performed best at high specificity (90-99%), but sensitivity was also improved by the other glycovariants and CA15-3. The highly improved specificity, excellent analytical sensitivity, and robustness of the nanoparticle assisted glycovariant assays carry great promise for improved identification and early detection of ovarian carcinoma in routine differential diagnostics. This article is protected by copyright. All rights reserved. CC BY-NC 4.0Attribution-NonCommercial 4.0 InternationalFirst published: 09 May 2022Corresponding author: Kamlesh Gidwani (kamlesh.gidwani@utu.fi), Karin Sundfeldt (karin.sundfeldt@obgyn.gu.se)Funding:Swedish Cancer Foundation CAN-2018/834ALF-VGR Region, Sweden ALFGBG-721051, ALFGBG-932583Jane and Aatos Erkko Foundation, Finland 2018-2021Nordic Cancer Union, Denmark 194914

SummaryRevolutionary advances in AI and deep learning in recent years have resulted in an upsurge of papers exploring applications within the biomedical field. Within stem cell research, promising results have been reported from analyses of microscopy images to e.g., distinguish between pluripotent stem cells and differentiated cell types derived from stem cells. In this work, we investigated the possibility of using a deep learning model to predict the differentiation stage of pluripotent stem cells undergoing differentiation towards hepatocytes, based on morphological features of cell cultures. We were able to achieve close to perfect classification of images from early and late time points during differentiation, and this aligned very well with the experimental validation of cell identity and function. Our results suggest that deep learning models can distinguish between different cell morphologies, and provide alternative means of semi-automated functional characterization of stem cell cultures.

Cardiac hypertrophy is an important and independent risk factor for the development of cardiac myopathy that may lead to heart failure. The mechanisms underlying the development of cardiac hypertrophy are yet not well understood. To increase the knowledge about mechanisms and regulatory pathways involved in the progression of cardiac hypertrophy, we have developed a human induced pluripotent stem cell (hiPSC)-based in vitro model of cardiac hypertrophy and performed extensive characterization using a multi-omics approach. In a series of experiments, hiPSC-derived cardiomyocytes were stimulated with Endothelin-1 for 8, 24, 48, and 72 h, and their transcriptome and secreted proteome were analyzed. The transcriptomic data show many enriched canonical pathways related to cardiac hypertrophy already at the earliest time point, e.g., cardiac hypertrophy signaling. An integrated transcriptome–secretome analysis enabled the identification of multimodal biomarkers that may prove highly relevant for monitoring early cardiac hypertrophy progression. Taken together, the results from this study demonstrate that our in vitro model displays a hypertrophic response on both transcriptomic- and secreted-proteomic levels. The results also shed novel insights into the underlying mechanisms of cardiac hypertrophy, and novel putative early cardiac hypertrophy biomarkers have been identified that warrant further investigation to assess their potential clinical relevance.

Biomedical data are becoming increasingly multimodal and thereby capture the underlying complex relationships among biological processes. Deep learning (DL)-based data fusion strategies are a popular approach for modeling these nonlinear relationships. Therefore, we review the current state-of-the-art of such methods and propose a detailed taxonomy that facilitates more informed choices of fusion strategies for biomedical applications, as well as research on novel methods. By doing so, we find that deep fusion strategies often outperform unimodal and shallow approaches. Additionally, the proposed subcategories of fusion strategies show different advantages and drawbacks. The review of current methods has shown that, especially for intermediate fusion strategies, joint representation learning is the preferred approach as it effectively models the complex interactions of different levels of biological organization. Finally, we note that gradual fusion, based on prior biological knowledge or on search strategies, is a promising future research path. Similarly, utilizing transfer learning might overcome sample size limitations of multimodal data sets. As these data sets become increasingly available, multimodal DL approaches present the opportunity to train holistic models that can learn the complex regulatory dynamics behind health and disease. CC BY-NC 4.0Corresponding author: Sören Richard Stahlschmidt. Systems Biology Research Center, University of Skövde, Skövde, Sweden. E-mail: soren.richard.stahlschmidt@his.sePublished: 28 January 2022This work was supported by the University of Skövde, Sweden under grants from the Knowledge Foundation (20170302, 20200014).

Objectives To evaluate the impact of different biologic, histopathologic and lifestyle factors on serum levels of human epididymis protein 4 (HE4) and Cancer antigen 125 (CA125) in the diagnostic work up of women with an ovarian cyst or pelvic tumor. Methods The statistical evaluation was performed on a population of 445 women diagnosed with a benign ovarian disease, included in a large Swedish multicenter trial (ClinicalTrials.gov NCT03193671). Multivariable logistic regression analyses were performed to distinguish between the true negatives and false positives through adjusting for biologic, histopathologic and lifestyle factors on serum samples of CA125 and HE4 separately. The likelihood ratio test was used to determine statistical significance and Benjamini-Hochberg correction to adjust for multiple testing. Results A total of 31% of the women had false positive CA125 but only 9% had false positive results of HE4. Smoking (OR 6.62 95% CI 2.93–15.12) and impaired renal function, measured by eGFR (OR 0.18 95% CI 0.08–0.39), were independently predictive of falsely elevated serum levels of HE4. Endometriosis was the only variable predictive of falsely elevated serum levels of CA125 (OR 7.96 95% CI 4.53–14.39). Age correlated with increased serum levels of HE4. Conclusions Smoking, renal failure, age and endometriosis are factors that independently should be considered when assessing serum levels of HE4 and CA125 in women with an ovarian cyst or pelvic mass to avoid false indications of malignant disease.

A common approach for analyzing large-scale molecular data is to cluster objects sharing similar characteristics. This assumes that genes with highly similar expression profiles are likely participating in a common molecular process. Biological systems are extremely complex and challenging to understand, with proteins having multiple functions that sometimes need to be activated or expressed in a time-dependent manner. Thus, the strategies applied for clustering of these molecules into groups are of key importance for translation of data to biologically interpretable findings. Here we implemented a multi-assignment clustering (MAsC) approach that allows molecules to be assigned to multiple clusters, rather than single ones as in commonly used clustering techniques. When applied to high-throughput transcriptomics data, MAsC increased power of the downstream pathway analysis and allowed identification of pathways with high biological relevance to the experimental setting and the biological systems studied. Multi-assignment clustering also reduced noise in the clustering partition by excluding genes with a low correlation to all of the resulting clusters. Together, these findings suggest that our methodology facilitates translation of large-scale molecular data into biological knowledge. The method is made available as an R package on GitLab (https://gitlab.com/wolftower/masc). CC BY 4.0

Ovarian cancer is the most lethal gynecologic cancer. Pre-diagnostic testing lacks sensitivity and specificity, and surgery is often the only way to secure the diagnosis. Exploring new biomarkers is of great importance, but the rationale of combining validated well-established biomarkers and algorithms could be a more effective way forward. We hypothesized that we can improve differential diagnostics and reduce false positives by combining (a) risk of malignancy index (RMI) with serum HE4, (b) risk of ovarian malignancy algorithm (ROMA) with a transvaginal ultrasound score or (c) adding HE4 to CA125 in a simple algorithm. With logistic regression modeling, new algorithms were explored and validated using leave-one-out cross validation. The analyses were performed in an existing cohort prospectively collected prior to surgery, 2013&ndash, 2016. A total of 445 benign tumors and 135 ovarian cancers were included. All presented models improved specificity at cut-off compared to the original algorithm, and goodness of fit was significant (p &lt, 0.001). Our findings confirm that HE4 is a marker that improves specificity without hampering sensitivity or diagnostic accuracy in adnexal tumors. We provide in this study &ldquo, easy-to-use&rdquo, algorithms that could aid in the triage of women to the most appropriate level of care when presenting with an unknown ovarian cyst or suspicious ovarian cancer.

Cardiac hypertrophy is an important and independent risk factor for the development of heart failure. To better understand the mechanisms and regulatory pathways involved in cardiac hypertrophy, there is a need for improved in vitro models. In this study, we investigated how hypertrophic stimulation affected human induced pluripotent stem cell (iPSC)-derived cardiomyocytes (CMs). The cells were stimulated with endothelin-1 (ET-1) for 8, 24, 48, 72, or 96 h. Parameters including cell size, ANP-, proBNP-, and lactate concentration were analyzed. Moreover, transcriptional profiling using RNA-sequencing was performed to identify differentially expressed genes following ET-1 stimulation. The results show that the CMs increase in size by approximately 13% when exposed to ET-1 in parallel to increases in ANP and proBNP protein and mRNA levels. Furthermore, the lactate concentration in the media was increased indicating that the CMs consume more glucose, a hallmark of cardiac hypertrophy. Using RNA-seq, a hypertrophic gene expression pattern was also observed in the stimulated CMs. Taken together, these results show that hiPSC-derived CMs stimulated with ET-1 display a hypertrophic response. The results from this study also provide new molecular insights about the underlying mechanisms of cardiac hypertrophy and may help accelerate the development of new drugs against this condition., Summary: This research describes a functional in vitro model of cardiac hypertrophy, and how the time of stimulation with endothelin-1 affects the cells.

There is a strong anticipated future for human pluripotent stem cell-derived hepatocytes (hiPS-HEP), but so far their use has been limited due to insufficient functionality. We investigated the potential of hiPS-HEP as an in vitro model for metabolic diseases by combining transcriptomics with multiple functional assays. The transcriptomics analysis revealed that 86% of the genes were expressed at similar levels in hiPS-HEP as in human primary hepatocytes (hphep). Adult characteristics of the hiPS-HEP were confirmed by the presence of important hepatocyte features, e.g. Albumin secretion and expression of major drug metabolizing genes. Normal energy metabolism is crucial for modeling metabolic diseases, and both transcriptomics data and functional assays showed that hiPS-HEP were similar to hphep regarding uptake of glucose, LDL and fatty acids. Importantly, the inflammatory state of the hiPS-HEP was low under standard conditions, but in response to lipid accumulation and ER stress the inflammation marker TNFα was upregulated. Furthermore, hiPS-HEP could be co-cultured with primary hepatic stellate cells both in 2D and in 3D spheroids, paving the way for using these co-cultures for modeling NASH. Taken together, hiPS-HEP have the potential to serve as an in vitro model for metabolic diseases. Furthermore, differently expressed genes identified in this study can serve as targets for future improvements of the hiPS-HEP.

Biomarkers for early detection of ovarian tumors are urgently needed. Tumors of the ovary grow within cysts and most are benign. Surgical sampling is the only way to ensure accurate diagnosis, but often leads to morbidity and loss of female hormones. The present study explored the deep proteome in well-defined sets of ovarian tumors, FIGO stage I, Type 1 (low-grade serous, mucinous, endometrioid; n = 9), Type 2 (high-grade serous; n = 9), and benign serous (n = 9) using TMT–LC–MS/MS. Data are available via ProteomeXchange with identifier PXD010939. We evaluated new bioinformatics tools in the discovery phase. This innovative selection process involved different normalizations, a combination of univariate statistics, and logistic model tree and naive Bayes tree classifiers. We identified 142 proteins by this combined approach. One biomarker panel and nine individual proteins were verified in cyst fluid and serum: transaldolase-1, fructose-bisphosphate aldolase A (ALDOA), transketolase, ceruloplasmin, mesothelin, clusterin, tenascin-XB, laminin subunit gamma-1, and mucin-16. Six of the proteins were found significant (p

Hepatocytes derived from human pluripotent stem cells (hPSC-HEP) have the potential to replace presently used hepatocyte sources applied in liver disease treatment and models of drug discovery and development. Established hepatocyte differentiation protocols are effective and generate hepatocytes, which recapitulate some key features of their in vivo counterparts. However, generating mature hPSC-HEP remains a challenge. In this study, we applied transcriptomics to investigate the progress of in vitro hepatic differentiation of hPSCs at the developmental stages, definitive endoderm, hepatoblasts, early hPSC-HEP, and mature hPSC-HEP, to identify functional targets that enhance efficient hepatocyte differentiation. Using functional annotation, pathway and protein interaction network analyses, we observed the grouping of differentially expressed genes in specific clusters representing typical developmental stages of hepatic differentiation. In addition, we identified hub proteins and modules that were involved in the cell cycle process at early differentiation stages. We also identified hub proteins that differed in expression levels between hPSC-HEP and the liver tissue controls. Moreover, we identified a module of genes that were expressed at higher levels in the liver tissue samples than in the hPSC-HEP. Considering that hub proteins and modules generally are essential and have important roles in the protein-protein interactions, further investigation of these genes and their regulators may contribute to a better understanding of the differentiation process. This may suggest novel target pathways and molecules for improvement of hPSC-HEP functionality, having the potential to finally bring this technology to a wider use.

We present a novel machine learning approach for the classification of cancer samples using expression data. We refer to the method as "decision trunks," since it is loosely based on decision trees, but contains several modifications designed to achieve an algorithm that: (1) produces smaller and more easily interpretable classifiers than decision trees; (2) is more robust in varying application scenarios; and (3) achieves higher classification accuracy. The decision trunk algorithm has been implemented and tested on 26 classification tasks, covering a wide range of cancer forms, experimental methods, and classification scenarios. This comprehensive evaluation indicates that the proposed algorithm performs at least as well as the current state of the art algorithms in terms of accuracy, while producing classifiers that include on average only 2-3 markers. We suggest that the resulting decision trunks have clear advantages over other classifiers due to their transparency, interpretability, and their correspondence with human decision-making and clinical testing practices. © the author(s), publisher and licensee Libertas Academica Ltd. CC BY-NC 3.0© the author(s), publisher and licensee Libertas Academica Ltd. This is an open access article. Unrestricted non-commercial use is permitted provided the original work is properly cited.

Background Endometrial cancer (EC) is the most frequently diagnosed gynecological malignancy and the fourth most common cancer diagnosis overall among women. As with many other forms of cancer, it has been shown that certain miRNAs are differentially expressed in EC and these miRNAs are believed to play important roles as regulators of processes involved in the development of the disease. With the rapidly growing number of studies of miRNA expression in EC, there is a need to organize the data, combine the findings from experimental studies of EC with information from various miRNA databases, and make the integrated information easily accessible for the EC research community. Findings The miREC database is an organized collection of data and information about miRNAs shown to be differentially expressed in EC. The database can be used to map connections between miRNAs and their target genes in order to identify specific miRNAs that are potentially important for the development of EC. The aim of the miREC database is to integrate all available information about miRNAs and target genes involved in the development of endometrial cancer, and to provide a comprehensive, up-to-date, and easily accessible source of knowledge regarding the role of miRNAs in the development of EC. Database URL: http://www.mirecdb.orgwebcite. Conclusions Several databases have been published that store information about all miRNA targets that have been predicted or experimentally verified to date. It would be a time-consuming task to navigate between these different data sources and literature to gather information about a specific disease, such as endometrial cancer. The miREC database is a specialized data repository that, in addition to miRNA target information, keeps track of the differential expression of genes and miRNAs potentially involved in endometrial cancer development. By providing flexible search functions it becomes easy to search for EC-associated genes and miRNAs from different starting points, such as differential expression and genomic loci (based on genomic aberrations). CC BY 4.0© 2015 Ulfenborg et al.; licensee BioMed Central. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

The significance of miRNAs has been clarified over the last decade as thousands of these small non-coding RNAs have been found in a wide variety of species. By binding to specific target mRNAs, miRNAs act as negative regulators of gene expression in many different biological processes. Computational approaches for discovery of miRNAs in genomes usually take the form of an algorithm that scans sequences for miRNA-characteristic hairpins, followed by classification of those hairpins as miRNAs or non-miRNAs. In this study, two new approaches to genome-scale miRNA discovery are presented and evaluated. These methods, one ensemble-based and one using logistic regression, have been designed to detect miRNA candidates without relying on conservation or transcriptome data, and to achieve high-confidence predictions in reasonable computational time. GenoScan achieves high accuracy with a good balance between sensitivity and specificity. In a benchmark evaluation including 15 previously published methods, the regression-based approach in GenoScan achieved the highest classification accuracy.

In silico prediction of novel miRNAs from genomic sequences remains a challenging problem. This study presents a genome-wide miRNA discovery software package called GenoScan and evaluates two hairpin classification methods. These methods, one ensemble-based and one using logistic regression were benchmarked along with 15 published methods. In addition, the sequence-folding step is addressed by investigating the impact of secondary structure prediction methods and the choice of input sequence length on prediction performance. Both the accuracy of secondary structure predictions and the miRNA prediction are evaluated. In the benchmark of hairpin classification methods, the regression model achieved highest classification accuracy. Of the structure prediction methods evaluated, ContextFold achieved the highest agreement between predicted and experimentally determined structures. However, both the choice of secondary structure prediction method and input sequence length had limited impact on hairpin classification performance.