Fuse feeds as one: cross-modal framework for general identification of AMPs.

Antimicrobial peptides (AMPs) are promising candidates for the development of new antibiotics due to their broad-spectrum activity against a range of pathogens. However, identifying AMPs through a huge bunch of candidates is challenging due to their complex structures and diverse sequences. In this study, we propose SenseXAMP, a cross-modal framework that leverages semantic embeddings of and protein descriptors (PDs) of input sequences to improve the identification performance of AMPs. SenseXAMP includes a multi-input alignment module and cross-representation fusion module to explore the hidden information between the two input features and better leverage the fusion feature. To better address the AMPs identification task, we accumulate the latest annotated AMPs data to form more generous benchmark datasets. Additionally, we expand the existing AMPs identification task settings by adding an AMPs regression task to meet more specific requirements like antimicrobial activity prediction. The experimental results indicated that SenseXAMP outperformed existing state-of-the-art models on multiple AMP-related datasets including commonly used AMPs classification datasets and our proposed benchmark datasets. Furthermore, we conducted a series of experiments to demonstrate the complementary nature of traditional PDs and protein pre-training models in AMPs tasks. Our experiments reveal that SenseXAMP can effectively combine the advantages of PDs to improve the performance of protein pre-training models in AMPs tasks.

Identification of Prognostic Biomarkers for Glioblastoma Based on Transcriptome and Proteome Association Analysis.

OBJECTIVE: Glioblastoma multiforme (GBM) is the most malignant primary brain tumor in adults. This study aimed to identify significant prognostic biomarkers related to GBM. METHODS: We collected 3 GBM and 3 healthy human brain samples for transcriptome and proteomic sequencing analysis. Differentially expressed genes (DEGs) between GBM and control samples were identified using the edge R package in R. Functional enrichment analyses, prediction of long noncoding RNA target genes, and protein-protein interaction network analyses were performed. Subsequently, transcriptomic and proteomic association analyses, validation using The Cancer Genome Atlas (TCGA) database, and survival and prognostic analyses were conducted. Then the hub genes directly related to GBM were screened. Finally, the expression of key genes was verified by quantitative polymerase chain reaction (qPCR). RESULTS: Totally, 1140 transcripts and 503 proteins were significantly up- or down-regulated. A total of 25 genes were upregulated and 62 were downregulated at both the transcriptome and proteome levels. Results from TCGA database showed that 84 of these 87 genes matched with transcriptome sequencing results. A Cox regression analysis suggested that Fibronectin 1(FN1) was a prognostic risk factor. The qPCR results showed that FN1 was significantly upregulated in GBM samples. CONCLUSIONS: FN1 may play a role in GBM progression through ECM-receptor interaction and PI3K-Akt signaling pathways. FN1 may be considered as a prognostic biomarkers related to GBM.

Pan-cancer analysis of ASB3 and the potential clinical implications for immune microenvironment of glioblastoma multiforme.

Background: Ankyrin repeat and SOCS Box containing 3 (ASB3) is an E3 ubiquitin ligase. It has been reported to regulate the progression of some cancers, but no systematic pan-cancer analysis has been conducted to explore its function in prognosis and immune microenvironment. Method: In this study, mRNA expression data were downloaded from TCGA and GTEx database. Next generation sequencing data from 14 glioblastoma multiforme (GBM) samples by neurosurgical resection were used as validation dataset. Multiple bioinformatics methods (ssGSEA, Kaplan-Meier, Cox regression analysis, GSEA and online tools) were applied to explore ASB3 expression, gene activity, prognosis of patients in various cancers, and its correlation with clinical information, immune microenvironment and pertinent signal pathways in GBM. The biological function of ASB3 in tumor-infiltrating lymphocytes (TILs) was verified using an animal model. Results: We found that ASB3 was aberrant expressed in a variety of tumors, especially in GBM, and significantly correlated with the prognosis of cancer patients. The level of ASB3 was related to the TMB, MSI and immune cell infiltration in some cancer types. ASB3 had a negative association with immune infiltration and TME, including regulatory T cells (Tregs), cancer-associated fibroblasts, immunosuppressors and related signaling pathways in GBM. ASB3 overexpression reduced the proportion of Tregs in TILs. GSEA and PPI analysis also showed negative correlation between ASB3 expression and oncogenetic signaling pathways in GBM. Conclusion: A comprehensive pan-cancer analysis of ASB3 showed its potential function as a biomarker of cancer prognosis and effective prediction of immunotherapy response. This study not only enriches the understanding of the biological function of ASB3 in pan-cancer, especially in GBM immunity, but also provides a new reference for the personalized immunotherapy of GBM.