Background: Memory B cells and microRNAs (miRNAs) play important roles in the progression of gastric adenocarcinoma (GAC), also known as stomach adenocarcinoma (STAD). However, few studies have investigated the use of memory B-cell-associated miRNAs in predicting the prognosis of STAD. Methods: We identified the marker genes of memory B cells by single-cell RNA sequencing (scRNA-seq) and identified the miRNAs associated with memory B cells by constructing an mRNA‒miRNA coexpression network. Then, univariate Cox, random survival forest (RSF), and stepwise multiple Cox regression (StepCox) algorithms were used to identify memory B-cell-associated miRNAs that were significantly related to overall survival (OS). A prognostic risk model was constructed and validated using these miRNAs, and patients were divided into a low-risk group and a high-risk group. In addition, the differences in clinicopathological features, tumour microenvironment, immune blocking therapy, and sensitivity to anticancer drugs in the two groups were analysed. Results: Four memory B-cell-associated miRNAs (hsa-mir-145, hsa-mir-125b-2, hsa-mir-100, hsa-mir-221) with significant correlations to OS were identified and used to construct a prognostic model. Time-dependent receiver operating characteristic (ROC) curve analysis confirmed the feasibility of the model. Kaplan‒Meier (K‒M) survival curve analysis showed that the prognosis was poor in the high-risk group. Comprehensive analysis showed that patients in the high-risk group had higher immune scores, matrix scores, and immune cell infiltration and a poor immune response. In terms of drug screening, we predicted eight drugs with higher sensitivity in the high-risk group, of which CGP-60474 was associated with the greatest sensitivity. Conclusions: In summary, we identified memory B-cell-associated miRNA prognostic features and constructed a novel risk model for STAD based on scRNA-seq data and bulk RNA-seq data. Among patients in the high-risk group, STAD showed the highest sensitivity to CGP-60474. This study provides prognostic insights into individualized and precise treatment for STAD patients.

Immune activation has been shown to play a critical role in the development of schizophrenia; however its underlying mechanism remains unknown. Our report demonstrates a high-quality protein interaction network for schizophrenia (SCZ Network), constructed using our “neighborhood walk” approach in combination with “random walk with restart”. The spatiotemporal expression pattern of the genes in this disease network revealed two developmental stages sensitive to perturbation by immune activation: mid-to late gestation, and adolescence. Furthermore, we induced immune activation at these stages in mice, carried out transcriptome sequencing on the mouse brains, and illustrated clear potential molecular pathways and key regulators correlating maternal immune activation during gestation and an increased risk for schizophrenia after a second immune activation at puberty. This work provides not only valuable resources for the study on molecular mechanisms underlying schizophrenia, but also a systematic strategy for the discovery of molecular pathways of complex mental disorders.

Aberrant alternative splicing and abnormal alternative splicing events (ASEs) in glioblastoma multiforme (GBM) remain largely elusive. The prognostic-associated ASEs in GBM were identified and summarized into 123 genes using GBM and LGG datasets from ASCancer Atlas and TCGA. The eleven genes (C2, COL3A1, CTSL, EIF3L, FKBP9, FN1, HPCAL1, HSPB1, IGFBP4, MANBA, PRKAR1B) were screened to develop an alternative splicing prognostic risk score (ASRS) model through machine learning algorithms. The model was trained on the TCGA-GBM cohort and validated with four external datasets from CGGA and GEO, achieving AUC values of 0.808, 0.814, 0.763, 0.859, and 0.836 for 3-year survival rates, respectively. ASRS could be an independent prognostic factor for GBM patients (HR > 1.8 across three datasets) through multivariate Cox regression analysis. The high-risk group demonstrated poorer prognosis, elevated immune scores, increased levels of immune cell infiltration, and greater differences in drug sensitivity. We found that FN1, used for model construction, contained 4 abnormal ASEs resulting in high expression of non-canonical transcripts and the presence of premature termination codon. These abnormal ASEs may be regulated by tumour-related splicing factors according to the PPI network. Furthermore, both mRNA and protein levels of FN1 were highly expressed in GBM compared to LGG, correlating with poor prognosis in GBM. In conclusion, our findings highlight the role of ASEs in affecting the progression of GBM, and the model showed a potential application for prognostic risk of patients. FN1 may serve as a promising splicing biomarker for GBM, and mechanisms of processes of aberrant splicing need to be revealed in the future.

Background Memory B cells and microRNA (miRNA) play important roles in the progression of gastric adenocarcinoma (STAD). However, there are few studies on utility of memory B cell-associated miRNAs for prognosis of STAD. Methods We identified the marker genes of memory B cells by single-cell RNA sequencing (scRNA-seq) and identified the miRNAs associated with memory B cells by constructing an mRNA-miRNA co-expression network. Then, univariate Cox, random survival forest (RSF), and stepwise multiple Cox regression (StepCox) algorithms were used to identify memory B cell-associated miRNAs that were significantly related to overall survival (OS). A prognostic risk model was constructed and validated using these miRNAs, and patients were divided into a low-risk group and a high-risk group. In addition, the differences in clinicopathological features, tumour microenvironment, immune blocking therapy, and sensitivity of anticancer drugs in the two high versus low risk groups were analyzed. Results Four memory B cell-associated miRNAs (hsa-mir-145, hsa-mir-125b-2, hsa-mir-100, hsa-mir-221) with significantly correlation to OS were identified and used to construct a prognostic model. Time-dependent receiver operating characteristic (ROC) curve analysis confirmed the feasibility of the model. Kaplan-Meier (K-M) survival curve analysis showed that the prognosis was poor in the high-risk group. The comprehensive analysis showed that the patients in the high-risk group have higher immune scores, matrix scores, immune cell infiltration, and poor effect of immune response. In addition, in terms of drug screening, we predicted eight drugs with higher sensitivity in the high-risk group, of which CGP-60474 was the most sensitive. Conclusions In summary, we identified memory B cell-associated miRNAs prognostic features and constructed a novel risk model for STAD based on scRNA-seq data and bulk RNA-seq data. Patients in the high-risk group showed the highest sensitivity to GCP-60474. This study provides prognostic insights for individualized and accurate treatment of STAD patients.

Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal malignancy with a five-year survival rate below 10%. Cancer-associated fibroblasts (CAFs) promote epithelial–mesenchymal transition (EMT) and metastasis, yet the specific CAF subtypes and molecular axes driving PDAC progression remain incompletely understood. Here, using multi-omics data from PDAC samples, we identified a malignant ductal subpopulation, termed Ductal-T0, characterized by the highest EMT activity and prominent acquisition of myofibroblastic CAF (myCAF)-like transcriptional programs. Computationally, we predicted that myCAF-secreted THBS2 and FN1 engage the ITGA3/ITGB1/SDC1/SDC4 receptor axes in Ductal-T0 cells, which could activate TNF, NF-κB, TGF-β, and PI3K-AKT-signaling pathways to promote EMT. Pseudotime trajectory and velocity analyses suggested that Ductal-T0 cells exhibited the highest propensity to acquire myCAF-like features among all ductal subpopulations. Survival analysis revealed that an increased proportion of Ductal-T0 cells and elevated abundance of THBS2-ITGA3/ITGB1 and THBS2-SDC1 ligand–receptor pairs were significantly associated with poor prognosis. Spatial transcriptomics further revealed that myCAFs and Ductal-T0 cells co-localized at the tumor margin, which may contribute to reduced immune cell presence via dense extracellular matrix (ECM) barrier formation—a computationally inferred model of EMT-associated immune exclusion and metastatic progression—and identify THBS2 as a promising candidate for future therapeutic investigation to disrupt CAF–tumor crosstalk in PDAC.