Objective: The study aimed to explore the miRNA and mRNA biomarkers in post-stroke depression (PSD) and to develop a miRNA–mRNA regulatory network to reveal its potential pathogenesis.Methods: The transcriptomic expression profile was obtained from the GEO database using the accession numbers GSE117064 (miRNAs, stroke vs. control) and GSE76826 [mRNAs, late-onset major depressive disorder (MDD) vs. control]. Differentially expressed miRNAs (DE-miRNAs) were identified in blood samples collected from stroke patients vs. control using the Linear Models for Microarray Data (LIMMA) package, while the weighted correlation network analysis (WGCNA) revealed co-expressed gene modules correlated with the subject group. The intersection between DE-miRNAs and miRNAs identified by WGCNA was defined as stroke-related miRNAs, whose target mRNAs were stroke-related genes with the prediction based on three databases (miRDB, miRTarBase, and TargetScan). Using the GSE76826 dataset, the differentially expressed genes (DEGs) were identified. Overlapped DEGs between stroke-related genes and DEGs in late-onset MDD were retrieved, and these were potential mRNA biomarkers in PSD. With the overlapped DEGs, three machine-learning methods were employed to identify gene signatures for PSD, which were established with the intersection of gene sets identified by each algorithm. Based on the gene signatures, the upstream miRNAs were predicted, and a miRNA–mRNA network was constructed.Results: Using the GSE117064 dataset, we retrieved a total of 667 DE-miRNAs, which included 420 upregulated and 247 downregulated ones. Meanwhile, WGCNA identified two modules (blue and brown) that were significantly correlated with the subject group. A total of 117 stroke-related miRNAs were identified with the intersection of DE-miRNAs and WGCNA-related ones. Based on the miRNA-mRNA databases, we identified a list of 2,387 stroke-related genes, among which 99 DEGs in MDD were also embedded. Based on the 99 overlapped DEGs, we identified three gene signatures (SPATA2, ZNF208, and YTHDC1) using three machine-learning classifiers. Predictions of the three mRNAs highlight four miRNAs as follows: miR-6883-5p, miR-6873-3p, miR-4776-3p, and miR-6738-3p. Subsequently, a miRNA–mRNA network was developed.Conclusion: The study highlighted gene signatures for PSD with three genes (SPATA2, ZNF208, and YTHDC1) and four upstream miRNAs (miR-6883-5p, miR-6873-3p, miR-4776-3p, and miR-6738-3p). These biomarkers could further our understanding of the pathogenesis of PSD.

We report a Pd-based hexagonal prismatic coordination cage of Pd-TMPP(Au) featuring a highly entropic ligand combination, viz. H₂TMPP, TMPP(Pd), and TMPP(Au) metalloligands to yield double-arrow-shaped polycrystals. Pd-TMPP(Au) exhibits concentration-dependent cytotoxicity against hepatocellular carcinoma cell lines HuH-7, Hep-G2, and PLC/PRF/5, which is augmented by light irradiation.

Objectives: Metastasis of tumor cells is the leading reason for mortality among patients diagnosed with gastric cancer (GC). Emerging evidence indicated a strong correlation between programmed cell death (PCD) and the invasion and metastasis of tumor cells. Therefore, we aimed to develop a programmed cell death signature to assess the prognosis and therapeutic efficacy in GC patients. Methods: Here, we collected 1911 PCD-related genes from 19 different PCD patterns, and developed an immune-derived multiple programmed cell death index (MPCDI) using the integrating machine learning and multi-omics analysis, and systematically dissected heterogeneity in GC patients. Subsequently, we divided GC patients into two categories, namely high-MPCDI group and low-MPCDI group, using the median MPCDI as the threshold. We performed a comprehensive analysis of the clinical characteristics, somatic mutations, immune infiltration, drug sensitivity, and immunotherapeutic efficacy of the two groups. Results: Survival and immunotherapy response analyses indicated that the high-MPCDI patients experienced a poorer overall survival (p=0.018) and were more resistant to commonly used chemotherapeutic drugs but benefited from immunotherapy compared to the low-MPCDI patients. In addition, MPCDI was confirmed as a standalone risk factor for overall survival, and nomograms can provide a precise tool for the clinical diagnosis of GC patients. Conclusions: Taken together, the MPCDI can serve as a robust clinical diagnostic classifier to guide medication administration and improve outcomes in GC patients.

High-grade serous ovarian cancer (HGSOC) is the most common and aggressive subtype of epithelial ovarian cancer, often diagnosed at advanced stages with a poor prognosis. Paclitaxel (PTX), a standard chemotherapeutic agent for HGSOC, exerts cytotoxic effects on cancer cells and modulates the tumor microenvironment. This study aimed to elucidate the molecular mechanisms of PTX in HGSOC using bioinformatics, machine learning, network pharmacology, and molecular docking, to identify potential diagnostic biomarkers and therapeutic targets. We identified differentially expressed genes (DEGs) between HGSOC and normal ovarian tissues using the GSE54388 dataset from the Gene Expression Omnibus database. The intersection of these DEGs with PTX targets, identified from the Swiss Target Prediction database, yielded 15 overlapping genes. These genes were analyzed via protein–protein interaction (PPI) network analysis to identify significant interaction relationships. Kaplan–Meier survival analysis was then performed to assess the prognostic significance of these genes. Their protein expression patterns in HGSOC tissues were validated using the Human Protein Atlas (HPA) database. Functional enrichment analysis was conducted using Gene Ontology and the Kyoto Encyclopedia of Genes and Genomes. A combined diagnostic model was developed using LASSO regression and validated in two independent external datasets (GSE26712 and GSE12470). Molecular docking experiments were conducted to confirm the binding affinity of PTX to key proteins. Immune infiltration analysis was performed to assess the tumor microenvironment, revealing significant differences in immune cell composition between normal and tumor tissues. A total of 2267 DEGs were identified, with 15 overlapping genes related to PTX targets. After PPI network analysis, Kaplan–Meier survival analysis, and HPA validation, five key genes (AURKA, CBX7, CCNA2, HSP90AA1, and TUBB3) were identified as associated with HGSOC progression. The combined diagnostic model demonstrated high accuracy in distinguishing HGSOC from normal tissues, with AUC values of 0.9892 and 0.9465 in the GSE26712 and GSE12470 validation datasets, respectively. Molecular docking confirmed stable binding of PTX to these key proteins, suggesting their role in PTX’s therapeutic effects. Immune infiltration analysis revealed significant differences in immune cell composition between normal and tumor tissues, highlighting the potential impact of these genes on the tumor microenvironment. In summary, our findings provide a theoretical basis for improving clinical diagnosis and elucidating the underlying mechanisms of HGSOC.

Reactive oxygen species (ROS)-mediated cancer therapy has attracted extensive attention due to its high spatiotemporal selectivity and minimal side effects. Herein, we report a nonanuclear Pd-based coordination cage of Pd₆-TMPP(Ni), constructed from Ni-chelated TMPP(Ni) as the metalloligand and Pd²⁺ ions (H₂TMPP = meso-tetrakis (6-methylpyridin-3-yl) porphyrin). Pd₆-TMPP(Ni) integrates dual ROS-generation for cancer therapy, viz., hydroxyl radical (•OH) production and photoinduced singlet oxygen (¹O₂) generation. In vitro cytotoxicity assays against cancer cell lines NCI-H82 (lung cancer), A549 (lung cancer), KYSE-510 (esophageal cancer), and Te-1 (esophageal cancer) reveal its potent dark toxicity (IC₅₀: 1.9–2.1 μmol L⁻¹) and phototoxicity (IC₅₀: 0.8–1.5 μmol L⁻¹), which is attributed to enhanced intracellular ROS accumulation. This work develops a versatile therapeutic platform that harnesses Ni-induced •OH for chemodynamic therapy (CDT) and porphyrin-generated ¹O₂ for photodynamic therapy (PDT), thereby mitigating the oxygen dependence of conventional PDT.