Alternative polyadenylation (APA) is an important post-transcriptional regulatory mechanism that recognizes different polyadenylation signals on transcripts, resulting in transcripts with different lengths of 3′ untranslated regions and thereby influencing a series of biological processes. Recent studies have highlighted the important roles of APA in human. However, APA profiles in other animals have not been fully recognized, and there is no database that provides comprehensive APA information for other animals except human. Here, by using the RNA sequencing data collected from public databases, we systematically characterized the APA profiles in 9244 samples of 18 species. In total, we identified 342 952 APA events with a median of 17 020 per species using the DaPars2 algorithm, and 315 691 APA events with a median of 17 953 per species using the QAPA algorithm in these 18 species, respectively. In addition, we predicted the polyadenylation sites (PAS) and motifs near PAS of these species. We further developed Animal-APAdb, a user-friendly database (http://gong_lab.hzau.edu.cn/Animal-APAdb/) for data searching, browsing and downloading. With comprehensive information of APA events in different tissues of different species, Animal-APAdb may greatly facilitate the exploration of animal APA patterns and novel mechanisms, gene expression regulation and APA evolution across tissues and species.

Glioblastoma is the most common type of glioma with a high incidence and poor prognosis, and effective medical treatment remains challenging. Pseudouridine (Ψ) is the first post-transcriptional modification discovered and one of the most abundant modifications to RNA. However, the prognostic value of Ψ-related lncRNAs (ΨrLs) for glioma patients has never been systematically evaluated. This study aims to construct a risk model based on ΨrLs signature and to validate the predictive efficiency of the model. Transcriptomic data, genomic data, and relevant clinical data of glioma patients were extracted from the Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA). ΨrLs with significant correlation with Ψ-related genes were identified, and univariate Cox regression, least absolute shrinkage and selection operator (LASSO) regression, and multivariate Cox regression were used to further select biomarkers and construct a ΨrLs signature risk model. Then, the expression of lncRNAs of ΨrLs signature in multiple glioma cell lines was detected by qPCR. Further, ROC analysis, stratification analysis, correlation analysis, survival analysis, nomogram, enrichment analysis, immune infiltration analysis, chemoradiotherapy sensitivity analysis, somatic mutation, and recurrent copy number variation (CNV) analysis were used to validate the predictive efficiency of ΨrLs signature in TCGA and CGGA datasets. A four-lncRNA ΨrLs signature (DNAJC27-AS1, GDNF-AS1, ZBTB20-AS4, and DNMBP-AS1) risk model was constructed. By ROC analysis, stratified analysis, correlation analysis, survival analysis, and nomogram, the signature showed satisfactory predictive efficiency. Functional enrichment analysis revealed the differences in immune-related biological processes between high- and low-risk groups. Immune infiltration analysis showed that the high-risk group had lower tumor purity and higher stromal, immune and ESTIMATE scores. Mitoxantrone was identified as effective drug for low-risk group of glioma patients. Key genes in glioma development, including IDH1, EGFR, PTEN, etc., were differentially mutated between risk groups. The main recurrent CNVs in low-risk groups were 19q13.42 deletion and 7q34 amplification; 10q23.31 deletion and 12q14.1 in the high-risk group. Our study identified a four-lncRNA ΨrLs signature that effectively predicts the prognosis of glioma patients and may serve as a diagnostic tool. Risk scores of glioma patients generated by the signature is associated with immune-related biological processes and chemoradiotherapy sensitivity. These findings may inform the development of more targeted and effective therapies for glioma patients.

Background. Glioma is the most common primary brain tumor, representing approximately 80.8 of malignant tumors. Necroptosis triggers and enhances antitumor immunity and is expected to be a new target for tumor immunotherapy. The effectiveness of necroptosis-related lncRNAs as potential therapeutic targets for glioma has not been elucidated. Methods. We acquired RNA-seq data sets from LGG and GBM samples, and the corresponding clinical characteristic information is from TCGA. Normal brain tissue data is from GTEX. Based on TCGA and GTEx, we used univariate Cox regression to sort out survival-related lncRNAs. Lasso regression models were then built. Then, we performed a separate Kaplan-Meier analysis of the lncRNAs used for modeling. We validated different risk groups via OS, DFS, enrichment analysis, comprehensive immune analysis, and drug sensitivity. Results. We constructed a 12 prognostic lncRNAs model after bioinformatic analysis. Subsequently, the risk score of every glioma patient was calculated based on correlation coefficients and expression levels, and the patients were split into low- and high-risk groups according to the median value of the risk score. A nomogram was established for every glioma patient to predict prognosis. Besides, we found significant differences in OS, DFS, immune infiltration and checkpoints, and immune therapy between different risk subgroups. Conclusion. Predictive models of 12 necroptosis-related lncRNAs can facilitate the assessment of the prognosis and molecular characteristics of glioma patients and improve treatment modalities.

Diffuse glioma, the most prevalent and malignant intracranial tumor, presents a formidable challenge due to its immunosuppressive microenvironment, which complicates conventional therapeutic approaches. This study conducted a comprehensive prognostic meta‐analysis involving 2,968 patients with diffuse glioma and established a comprehensive machine learning framework with nested resampling of 18 machine learning algorithms, and developed the Immune Glioma Survival Signature (IGLoS). This signature, comprising CCL19, ICOSLG, IL11, PTGES, TNFAIP3, and TRAF3IP3, has been demonstrated to predict survival outcomes across a range of cancers and to correlate with tumor progression at the level of multi‐omics. It is noteworthy that the IGLoS score enables precise patient stratification for personalized cancer treatments and elucidates pivotal resistance mechanisms to immunotherapy. Furthermore, siRNA screening has underscored the critical role of TRAF3IP3 in modulating PDL1 expression and immune pathways, with implications on the ERK pathway and NFATC2 involvement. Through single‐cell analysis of published and in‐house datasets, TRAF3IP3 exhibited selective enrichment in NPC‐like and MES‐like tumor cells, and showed a dual functionality in mediating T‐Cell Exhaustion. Targeting TRAF3IP3 emerges as a promising avenue to combat immunotherapy resistance, particularly in glioma, thus paving the way for precision medicine.

Glioblastoma (GBM) is the most aggressive and malignant type of primary brain tumor, with a median survival time of less than two years and a uniformly poor prognosis, despite multimodal therapeutic approaches, which highlights an urgent need for novel therapeutic targets. In this study, by integrative multi-omics analysis from CPTAC database, DepMap database and seven independent GBM cohorts, four hub genes (CD44, SURF4, IGSF3 and RALGAPA1) were identified as essential genes regulated by cancer driver genes with robust prognostic value. GBM multi-omics data from public and in-house cohorts validated that CD44 and SURF4 might be synthetic lethal partners of loss-of-function tumor suppressor genes. Analysis for immune-related pathway activity revealed complex regulation relationships of the four hub genes in tumor microenvironment (TME). Further investigation on SURF4 in pathway activity, immune therapy response and drug sensitivity proposed that SURF4 emerged as a promising therapeutic target for GBM, even for pan-cancer. Pan-cancer multi-omics exploration suggested that RALGAPA1 may be a tumor suppressor gene. By screening the first-generation and second-generation DepMap database, four genes (CCDC106, GAL3ST1, GDI2 and HSF1) might be considered as synthetic targets after mutation of RALGAPA1 as a tumor suppressor gene.