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Abstract Glioblastoma multiforme (GBM) is the most aggressive type of primary brain tumor, and the presence of glioma stem cells (GSCs) has been linked to its resistance to treatments and recurrence. Additionally, aberrant glycosylation has been implicated in the aggressiveness of cancers. However, the influence and underlying mechanism of N-glycosylation on the GSC phenotype and GBM malignancy remain elusive. Here, we performed an in-silico analysis approach on publicly available datasets to examine the function of N-glycosylation-related genes in GSCs and gliomas, accompanied by a qRT-PCR validation experiment. We found that high α-1,2-mannosidase MAN1C1 is associated with immunological functions and worse survival of glioma patients. Differential gene expression analysis and qRT-PCR validation revealed that MAN1C1 is highly expressed in GSCs. Furthermore, higher MAN1C1 expression predicts worse outcomes in glioma patients. Also, MAN1C1 expression is increased in the perinecrotic region of GBM and is associated with immunological and inflammatory functions, a hallmark of the GBM mesenchymal subtype. Further analysis confirmed that MAN1C1 expression is closely associated with infiltrating immune cells and disrupted immune response in the GBM microenvironment. These suggest that MAN1C1 is a potential biomarker for gliomas and may be important as an immunotherapeutic target for GBM.

Anticancer peptides are emerging anticancer drug that offers fewer side effects and is more effective than chemotherapy and targeted therapy. Predicting anticancer peptides from sequence information is one of the most challenging tasks in immunoinformatics. In the past ten years, machine learning-based approaches have been proposed for identifying ACP activity from peptide sequences. These methods include our previous method MLACP (developed in 2017) which made a significant impact on anticancer research. MLACP tool has been widely used by the research community, however, its robustness must be improved significantly for its continued practical application. In this study, the first large non-redundant training and independent datasets were constructed for ACP research. Using the training dataset, the study explored a wide range of feature encodings and developed their respective models using seven different conventional classifiers. Subsequently, a subset of encoding-based models was selected for each classifier based on their performance, whose predicted scores were concatenated and trained through a convolutional neural network (CNN), whose corresponding predictor is named MLACP 2.0. The evaluation of MLACP 2.0 with a very diverse independent dataset showed excellent performance and significantly outperformed the recent ACP prediction tools. Additionally, MLACP 2.0 exhibits superior performance during cross-validation and independent assessment when compared to CNN-based embedding models and conventional single models. Consequently, we anticipate that our proposed MLACP 2.0 will facilitate the design of hypothesis-driven experiments by making it easier to discover novel ACPs. The MLACP 2.0 is freely available at https://balalab-skku.org/mlacp2.

Tomato yellow leaf curl virus (TYLCV) dispersed across different countries, specifically to subtropical regions, associated with more severe symptoms. Since TYLCV was first isolated in 1931, it has been a menace to tomato industrial production worldwide over the past century. Three groups were newly isolated from TYLCV-resistant tomatoes in 2022; however, their functions are unknown. The development of machine learning (ML)-based models using characterized sequences and evaluating blind predictions is one of the major challenges in interdisciplinary research. The purpose of this study was to develop an integrated computational framework for the accurate identification of symptoms (mild or severe) based on TYLCV sequences (isolated in Korea). For the development of the framework, we first extracted 11 different feature encodings and hybrid features from the training data and then explored 8 different classifiers and developed their respective prediction models by using randomized 10-fold cross-validation. Subsequently, we carried out a systematic evaluation of these 96 developed models and selected the top 90 models, whose predicted class labels were combined and considered as reduced features. On the basis of these features, a multilayer perceptron was applied and developed the final prediction model (IML-TYLCVs). We conducted blind prediction on 3 groups using IML-TYLCVs, and the results indicated that 2 groups were severe and 1 group was mild. Furthermore, we confirmed the prediction with virus-challenging experiments of tomato plant phenotypes using infectious clones from 3 groups. Plant virologists and plant breeding professionals can access the user-friendly online IML-TYLCVs web server at https://balalab-skku.org/IML-TYLCVs, which can guide them in developing new protection strategies for newly emerging viruses.