Sequence logos are used to visually display conservations and variations in short sequences. They can indicate the fixed patterns or conserved motifs in a batch of DNA or protein sequences. However, most of the popular sequence logo generators are based on the assumption that all the input sequences are from the same homologous group, which will lead to an overlook of the heterogeneity among the sequences during the sequence logo making process. Heterogeneous groups of sequences may represent clades of different evolutionary origins, or genes families with different functions. Therefore, it is essential to divide the sequences into different phylogenetic or functional groups to reveal their specific sequence motifs and conservation patterns. To solve these problems, we developed MetaLogo, which can automatically cluster the input sequences after multiple sequence alignment and phylogenetic tree construction, and then output sequence logos for multiple groups and aligned them in one figure. User-defined grouping is also supported by MetaLogo to allow users to investigate functional motifs in a more delicate and dynamic perspective. MetaLogo can highlight both the homologous and nonhomologous sites among sequences. MetaLogo can also be used to annotate the evolutionary positions and gene functions of unknown sequences, together with their local sequence characteristics. We provide users a public MetaLogo web server (http://metalogo.omicsnet.org), a standalone Python package (https://github.com/labomics/MetaLogo), and also a built-in web server available for local deployment. Using MetaLogo, users can draw informative, customized and publishable sequence logos without any programming experience to present and investigate new knowledge on specific sequence sets.

Compared with clinically functioning pituitary adenoma (FPA), clinically non-functioning pituitary adenoma (NFPA) lacks of detectable hypersecreting serum hormones and related symptoms which make it difficult to predict the prognosis and monitoring for postoperative tumour regrowth. We aim to investigate whether the expression of selected tumour-related proteins and clinical features could be used as tumour markers to effectively predict the regrowth of NFPA. Tumour samples were collected from 295 patients with NFPA from Beijing Tiantan Hospital. The expression levels of 41 tumour-associated proteins were assessed using tissue microarray analyses. Clinical characteristics were analysed via univariate and multivariate logistic regression analyses. Logistic regression algorithm was applied to build a prediction model based on the expression levels of selected proteins and clinical signatures, which was then assessed in the testing set. Three proteins and two clinical signatures were confirmed to be significantly related to the regrowth of NFPA, including cyclin-dependent kinase inhibitor 2A (CDKN2A/p16), WNT inhibitory factor 1 (WIF1), tumour growth factor beta (TGF-β), age and tumour volume. A prediction model was generated on the training set, which achieved a fivefold predictive accuracy of 81.2%. The prediction ability was validated on the testing set with an accuracy of 83.9%. The area under the receiver operating characteristic curves (AUC) for the signatures were 0.895 and 0.881 in the training and testing sets, respectively. The prediction model could effectively predict the regrowth of NFPA, which may facilitate the prognostic evaluation and guide early interventions.

Background: Necroptosis has emerged as a critical regulator in tumor progression and therapeutic response, yet its prognostic significance and influence on the tumor microenvironment (TME) in gastric cancer (GC) remain poorly characterized. Therefore, it is needed to unveil the intricate relationship between GC and necroptosis. Methods: We conducted an integrative analysis of bulk and single-cell data to delineate the prognostic landscape of necroptosis in GC. A necroptosis-related (NR) model was established using transcriptomic data from the TCGA cohort and validated in an external GEO cohort. To translate this model into clinical practice, a prognostic nomogram incorporating NR scores and clinical parameters was constructed. The molecular mechanisms underlying NR variations were investigated through stemness analysis, pathway enrichment, immune cell infiltration profiling, and intercellular communication networks. Results: Consensus clustering analysis revealed pronounced heterogeneity in GC by stratifying patients into two survival-associated NR clusters (cluster 1 and cluster 2) with differences in TME characteristics. Employing machine learning approaches, a NR model was developed and validated, which demonstrated predictive ability for survival outcomes. Meanwhile, the nomogram demonstrated clinically relevant predictive accuracy, with time-dependent area under curve (AUC) values of 0.701 (1-year), 0.715 (3-year), and 0.753 (5-year). Notably, this NR model enabled robust stratification of GC patients into two distinct subgroups, both in bulk and single-cell datasets. Single-cell resolution analysis revealed patients with varying NR scores were characterized by aberrant enrichment of endothelial cells, epithelial cells, and fibroblasts, highlighting the profound impact of necroptosis on cellular composition within the TME. Conclusions: Our novel NR model deciphers the dynamic interplay between necroptosis and TME remodeling in GC, enabling robust patient stratification across both bulk and single-cell data and revealing a necroptosis-driven environment characterized by aberrant expansion of endothelial cells, epithelial cells, and fibroblasts. The clinically applicable nomogram offers a precision medicine framework for risk assessment.

Clustering analysis for single-cell RNA sequencing (scRNA-seq) data is essential for characterizing cellular heterogeneity. However, batch information caused by batch effects is often confused with the intrinsic biological information in scRNA-seq data, which makes accurate clustering quite challenging. A Deep Adaptive Clustering with Adversarial Learning method (DACAL) is proposed here. DACAL jointly optimizes the batch correcting and clustering processes to remove batch effects while retaining biological information. DACAL achieves batch correction and adaptive clustering without requiring manually specified cell types or resolution parameters. DACAL is compared with other widely used batch correction and clustering methods on human pancreas datasets from different sequencing platforms and mouse mammary datasets from different laboratories. The results demonstrate that DACAL can correct batch effects efficiently and adaptively find accurate cell types, outperforming competing methods. Moreover, it can obtain cell subtypes with biological meanings.