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Transcriptomic analysis reveals the potential biological mechanism of AIS and lung adenocarcinoma.
INTRODUCTION: Acute ischemic stroke (AIS) and lung adenocarcinoma (LUAD) are associated with some of the highest morbidity and mortality rates worldwide. Despite reports on their strong correlation, the causal relationship is not fully understood. The study aimed to identify and annotate the biological functions of hub genes with clinical diagnostic efficacy in AIS and LUAD. METHODS: Transcriptome and single-cell datasets were obtained from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA). We identified the differentially expressed genes (DEGs) upregulated in AIS and LUAD and found 372 genes intersecting both datasets. Hub genes were identified using protein-protein interaction (PPI) networks, and the diagnostic and prognostic utility of these hub genes was then investigated using receiver operating characteristic (ROC) curves, survival analysis, and univariable Cox proportional hazard regression. Single-cell analysis was used to detect whether the hub genes were expressed in tumor epithelial cells. The immune microenvironment of AIS and LUAD was assessed using the CIBERSORT algorithm. The protein expression of these hub genes was tracked using the Human Protein Atlas (HPA). We calculated the number of positive cells using the digital pathology software QuPath. Finally, we performed molecular docking after using the Enrichr database to predict possible medicines. RESULTS: We identified the molecular mechanisms underlying hub genes in AIS and LUAD and found that CCNA2, CCNB1, CDKN2A, and CDK1 were highly expressed in AIS and LUAD tissue samples compared to controls. The hub genes were mainly involved in the following pathways: the cell cycle, cellular senescence, and the HIF-1 signaling pathway. Using immunohistochemical slices from the HPA database, we confirmed that these hub genes have a high diagnostic capability for AIS and LUAD. Further, their high expression is associated with poor prognosis. Finally, curcumin was tested as a potential medication using molecular docking modeling. DISCUSSION: Our findings suggest that the hub genes we found in this study contribute to the development and progression of AIS and LUAD by altering the cellular senescence pathway. Thus, they may be promising markers for diagnosis and prognosis.
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Single-Cell RNA-Sequencing Shift in the Interaction Pattern Between Glioma Stem Cells and Immune Cells During Tumorigenesis.
Glioblastoma is one of the most common neoplasms in the central nervous system characterized by limited immune response and unlimited expansion capability. Cancer stem cells (GSCs), a small fraction of the tumor cells, possess a pivotal regulation capability in the tumor microenvironment with a superior proliferation ability. We aimed to reveal the interaction between glioma stem cells (GSCs) and immune cells during tumorigenesis. Single-cell sequencing data from seven surgical specimens of glioblastoma patients and patient-derived GSCs cocultured with peripheral leukocytes were used for the analysis. Cell grouping and trajectory analysis were performed using Seurat and Monocle 3 packages in R software. The gene set of Cancer Genome Anatomy Project was used to define different cell types. Cells with the ability of proliferation and differentiation in glioblastoma tissue were defined as GSCs, which had a similar expression pattern to that in the GSCs in vitro. Astrocytes in glioblastoma were mainly derived from differentiated GSCs, while oligodendrocytes were most likely to be derived from different precursor cells. No remarkable evolutionary trajectory was observed among the subgroups of T cells in glioblastoma. The immune checkpoint interaction between GSCs and immune cells was changed from stimulatory to inhibitory during tumorigenesis. The patient-derived GSCs system is an ideal model for GSC research. The above research revealed that the interaction pattern between GSC glioma stem cells and immune cells during tumorigenesis provides a theoretical basis for GSC glioma stem cell-targeted immunotherapy.
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Mitochondrial single-stranded DNA binding protein 1 (SSBP1) high expression as a potential biomarker and association with poor prognosis in hepatocellular carcinoma (HCC).
BACKGROUND: Single-stranded DNA binding protein 1 (SSBP1) is a DNA binding protein found in mitochondria, encoded by nuclear genes. SSBP1 plays a crucial role in responding to mitochondrial DNA (mtDNA) damage and maintaining genome stability, and it is linked to cancer occurrence and progression, but its role in hepatocellular carcinoma (HCC) is still unclear. Therefore, the aim of this research was to investigate the expression of SSBP1 and its potential clinical significance in HCC. METHODS: RNA-seq data and clinical information of HCC samples and normal liver samples were downloaded from The Cancer Genome Atlas (TCGA). The expression of SSBP1 in HCC and its correlation with clinical pathological indicators, prognosis, immune cells, and infiltration were analyzed using R software, while the diagnostic value of SSBP1 in HCC was evaluated. Using the R software, we conducted Gene Ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, and gene set enrichment analysis (GSEA) on the SSBP1 expression in HCC. Immunohistochemistry (IHC) detected SSBP1 expression in 31 HCC tissue pairs. Western blotting and quantitative reverse transcription polymerase chain reaction (qRT-PCR) quantified protein and mRNA levels in 6 fresh HCC tissue. Protein expression and distribution of SSBP1 in HCC cell lines were analyzed using qRT-PCR, Western blotting, and Immunofluorescence techniques. RESULTS: SSBP1 mRNA expression was significantly higher in HCC tissues compared to normal tissues (P<0.001) in both matched and unmatched samples. SSBP1 expression was correlated with gender and M stage (P<0.05), but not with other factors. High SSBP1 expression was identified as an independent risk factor for overall survival (OS) in HCC patients [hazard ratio =1.713, P=0.01]. Immunocell infiltration analysis showed a negative correlation between SSBP1 expression and level of naive B cells, but a positive correlation with memory B cells and macrophages (|Spearman's r| >0.2, P<0.05). The diagnostic value of SSBP1 mRNA expression for early diagnosis prognosis of HCC (area under the curve >0.50). The GO enrichment analysis of SSBP1 revealed that it was enriched for mitochondrial biological functions. KEGG analysis showed that SSBP1 was associated with multiple DNA replication, mismatch repair and homologous recombination pathways. GSEA analysis showed that the first three pathways strongly related to the high expression of SSBP1 were DNA repair, myc-targets-v1 and reactive oxygen species signaling pathways. Validation through IHC and Western blotting high SSBP1 protein expression in HCC tissue, as well as qRT-PCR and Western blotting results showed high expression in HCC cell lines. Immunofluorescence experiments indicated the localization of SSBP1 in the mitochondria of HCC cells. CONCLUSIONS: High expression of SSBP1 is an independent risk factor for poor prognosis in HCC patients and has good diagnostic value.
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Tumor type classification and candidate cancer-specific biomarkers discovery via semi-supervised learning.
Identifying cancer-related differentially expressed genes provides significant information for diagnosing tumors, predicting prognoses, and effective treatments. Recently, deep learning methods have been used to perform gene differential expression analysis using microarray-based high-throughput gene profiling and have achieved good results. In this study, we proposed a new robust multiple-datasets-based semi-supervised learning model, MSSL, to perform tumor type classification and candidate cancer-specific biomarkers discovery across multiple tumor types and multiple datasets, which addressed the following long-lasting obstacles: (1) the data volume of the existing single dataset is not enough to fully exert the advantages of deep learning; (2) a large number of datasets from different research institutions cannot be effectively used due to inconsistent internal variances and low quality; (3) relatively uncommon cancers have limited effects on deep learning methods. In our article, we applied MSSL to The Cancer Genome Atlas (TCGA) and the Gene Expression Comprehensive Database (GEO) pan-cancer normalized-level3 RNA-seq data and got 97.6% final classification accuracy, which had a significant performance leap compared with previous approaches. Finally, we got the ranking of the importance of the corresponding genes for each cancer type based on classification results and validated that the top genes selected in this way were biologically meaningful for corresponding tumors and some of them had been used as biomarkers, which showed the efficacy of our method.
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Aging weakens Th17 cell pathogenicity and ameliorates experimental autoimmune uveitis in mice.
Aging-induced changes in the immune system are associated with a higher incidence of infection and vaccination failure. Lymph nodes, which filter the lymph to identify and fight infections, play a central role in this process. However, careful characterization of the impact of aging on lymph nodes and associated autoimmune diseases is lacking. We combined single-cell RNA sequencing (scRNA-seq) with flow cytometry to delineate the immune cell atlas of cervical draining lymph nodes (CDLNs) of both young and old mice with or without experimental autoimmune uveitis (EAU). We found extensive and complicated changes in the cellular constituents of CDLNs during aging. When confronted with autoimmune challenges, old mice developed milder EAU compared to young mice. Within this EAU process, we highlighted that the pathogenicity of T helper 17 cells (Th17) was dampened, as shown by reduced GM-CSF secretion in old mice. The mitigated secretion of GM-CSF contributed to alleviation of IL-23 secretion by antigen-presenting cells (APCs) and may, in turn, weaken APCs' effects on facilitating the pathogenicity of Th17 cells. Meanwhile, our study further unveiled that aging downregulated GM-CSF secretion through reducing both the transcript and protein levels of IL-23R in Th17 cells from CDLNs. Overall, aging altered immune cell responses, especially through toning down Th17 cells, counteracting EAU challenge in old mice.
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Dissecting PTPN7-driven aggressiveness in IDH-wildtype astrocytomas: multi-omics, clinical validation, and spatial transcriptomics for prognostic insights.
BACKGROUND: Gliomas, particularly IDH-wildtype astrocytomas, remain highly aggressive and resistant to current therapies. Despite advances in molecular classification, effective therapeutic targets are still limited. Consequently, identifying new targets is essential to improve patient survival. PTPN7, a tyrosine phosphatase implicated in MAPK signaling, is known to play roles in various malignancies but remains underexplored in gliomas. This study examines the prognostic significance, spatial distribution, and immune-related functions of PTPN7, aiming to elucidate its potential as a prognostic role and therapeutic target in glioma treatment. MATERIALS AND METHODS: We analyzed PTPN7 mRNA expression in gliomas via TCGA, CGGA, and single-cell RNA sequencing (GSE131928 and GSE89567). Kaplan determined prognostic significance-Meier and uni-/multi-variate Cox survival analyses. Gene set enrichment analysis (GSEA) was used to identify dysregulated pathways, immune signatures, and cell-type enrichments. We also applied CIBERSORT to evaluate the relationships between PTPN7 expression and 12-principal cell states and 22 immune populations. Spatial transcriptomics (Ivy Glioblastoma Atlas, 10 × Genomics Visium) mapped PTPN7 distribution; these findings were corroborated by immunohistochemistry-validated protein expression in 70 cases. RESULTS: Pan-cancer analysis revealed PTPN7 overexpression in multiple malignancies, including glioma. Notably, PTPN7 was significantly elevated in IDH-wildtype astrocytomas, correlating with higher tumor grades and poorer overall survival. GSEA indicated that high PTPN7 is linked to T-cell differentiation, macrophage/monocyte activation, and dendritic cell-associated pathways. Both immune deconvolution and single-cell analyses showed that PTPN7 positively correlates with myeloid series and T-cell populations, supported by additional GSEA findings. In the Ivy dataset and spatial transcriptomics, PTPN7 was concentrated in peri-necrotic, cellular tumor, and slightly lower in the infiltrating border regions, consistent with immune interaction sites. Immunohistochemical data further demonstrated high PTPN7 expression tracks with increased tumor grade, reaching statistical significance in IDH-wildtype astrocytomas and confirming its clinical relevance. CONCLUSION: This study positions PTPN7 as a prognostic biomarker and immune modulator in gliomas, particularly IDH-wildtype astrocytomas. Its expression correlates with tumor aggressiveness and immune infiltration, potentially driving glioma progression. Targeting PTPN7 may disrupt immune evasion and support tumor eradication, indicating a promising therapeutic avenue in immunotherapy-based strategies.
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Network toxicology integrated with machine learning and SHAP analysis identifies overlapping immune signatures between Di(2-ethylhexyl) phthalate (DEHP) and Sjögren's syndrome.
BACKGROUND: As a prevalent plasticizer in industrial manufacturing and daily-use products, di(2-ethylhexyl) phthalate (DEHP) has raised growing concerns regarding its safety and potential role in disease pathogenesis. Although direct epidemiological evidence linking DEHP exposure to Sjögren’s syndrome (SS) remains limited, DEHP has been reported to exert immunomodulatory and endocrine-disrupting effects that are highly relevant to the core pathological mechanisms of SS. Therefore, this study aimed to systematically explore the potential links between DEHP and SS using an integrated strategy combining toxicity prediction, network toxicology, machine learning, molecular docking, single-cell atlas interrogation, and in vitro validation. METHODS: The toxicity profile of DEHP was predicted using two complementary online platforms (ProTox 3.0 and ADMETlab 2.0). SS-related transcriptomic datasets were obtained from GEO, and potential DEHP targets were collected from CTD, SwissTargetPrediction, and SEA. Network toxicology analyses (intersection screening, PPI construction, and GO/KEGG enrichment) were performed to identify key genes and pathways. Machine learning models were trained to classify SS versus controls using the DEHP–SS intersect genes, and SHAP was applied to interpret key predictors. Molecular docking (AutoDock Vina) was conducted to estimate the binding propensity between DEHP and prioritized proteins. A single-cell atlas of mouse submandibular gland tissue (PanglaoDB; Mus musculus; SRA693675:SRS3206192) was queried to localize gene expression. Finally, in vitro experiments were performed in human submandibular gland epithelial cells (HSG) to validate STAT1 expression changes following DEHP exposure. RESULTS: In silico toxicity predictions suggested that DEHP may exhibit immunotoxic potential among multiple predicted toxicity endpoints. Network toxicology analysis identified 27 DEHP–SS intersect targets, and the PPI network highlighted STAT1, CCL2, CXCL10, MYD88, and IRF7 as highly connected hub genes. Across 113 machine learning models, Ridge/Elastic Net–based models demonstrated stable classification performance, and SHAP analysis prioritized five core predictors (STAT1, PHGDH, ISG15, CXCL10, and CCL2). Molecular docking suggested favorable binding energies ( ≤ − 5.0 kcal/mol) between DEHP and these five proteins. Single-cell atlas interrogation indicated that STAT1 is expressed in salivary mucous cells. In vitro experiments further demonstrated that DEHP exposure increases STAT1 mRNA and protein expression in HSG cells in a dose-dependent manner. Collectively, these results provide mechanistically plausible—yet non-causal—evidence linking DEHP to SS-relevant immune signatures. CONCLUSION: This study identifies an overlapping immune-response signature between predicted DEHP-associated targets and SS-related transcriptomic features, highlighting STAT1 as a prioritized node within this intersection. In vitro experiments demonstrate that DEHP exposure upregulates STAT1 expression in HSG cells. These findings provide hypothesis-generating evidence that DEHP may modulate interferon-related immune pathways relevant to SS, while not establishing causality.
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Systematic identification of post-transcriptional regulatory modules.
In our cells, a limited number of RNA binding proteins (RBPs) are responsible for all aspects of RNA metabolism across the entire transcriptome. To accomplish this, RBPs form regulatory units that act on specific target regulons. However, the landscape of RBP combinatorial interactions remains poorly explored. Here, we perform a systematic annotation of RBP combinatorial interactions via multimodal data integration. We build a large-scale map of RBP protein neighborhoods by generating in vivo proximity-dependent biotinylation datasets of 50 human RBPs. In parallel, we use CRISPR interference with single-cell readout to capture transcriptomic changes upon RBP knockdowns. By combining these physical and functional interaction readouts, along with the atlas of RBP mRNA targets from eCLIP assays, we generate an integrated map of functional RBP interactions. We then use this map to match RBPs to their context-specific functions and validate the predicted functions biochemically for four RBPs. This study provides a detailed map of RBP interactions and deconvolves them into distinct regulatory modules with annotated functions and target regulons. This multimodal and integrative framework provides a principled approach for studying post-transcriptional regulatory processes and enriches our understanding of their underlying mechanisms.
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Dissecting the impact of transcription factor dose on cell reprogramming heterogeneity using scTF-seq.
Reprogramming often yields heterogeneous cell fates, yet the underlying mechanisms remain poorly understood. To address this, we developed single-cell transcription factor sequencing (scTF-seq), a single-cell technique that induces barcoded, doxycycline-inducible TF overexpression and quantifies TF dose-dependent transcriptomic changes. Applied to mouse embryonic multipotent stromal cells, scTF-seq generated a gain-of-function atlas for 384 mouse TFs, identifying key regulators of lineage specification, cell cycle control and their interplay. Leveraging single-cell resolution, we uncovered how TF dose shapes reprogramming heterogeneity, revealing both dose-dependent and stochastic cell state transitions. We classified TFs into low-capacity and high-capacity groups, with the latter further subdivided by dose sensitivity. Combinatorial scTF-seq demonstrated that TF interactions can shift from synergistic to antagonistic depending on the relative dose. Altogether, scTF-seq enables the dissection of TF function, dose and cell fate control, providing a high-resolution framework to understand and predict reprogramming outcomes, advancing gene regulation research and the design of cell engineering strategies.
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Integrated Multiomics Analysis and Machine Learning Approaches in Bladder Cancer: Unveiling the Impact of Immunogenic Cell Death and Its Key Gene SLC2A3 on Prognosis and Personalized Treatment Strategies.
Recently, immunogenic cell death (ICD) has been identified as a regulatory cell death mechanism that induces an adaptive immune response, thereby improving enhancing the efficacy of immunotherapy and contributing to improved prognosis in bladder cancer (BLCA). This study established a risk signature based on ICD and identified ICD-related genes as diagnostic markers and therapeutic targets for BLCA. Thirty-two key ICD-risk genes (IRGs) were screened from correlation and univariate Cox regression analyses. Data obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases regarding BLCA and normal samples were categorized based on IRGs. ICD-based prognostic signature was built based on IRGs, stratifying BLCA patients into high- and low-risk groups. SLC2A3 was identified as a hub gene by 101 combinatorial machine learning algorithms and 10 individual machine learning algorithms. Based on single-cell sequencing data, we identified the cancer-associated fibroblasts (CAFs), the key cell population exhibiting high SLC2A3 expression. Functional analyses were performed to explore the potential value of SLC2A3 as a target for precision therapy. A prognostic signature was constructed using IRGs, indicating significant differences in the tumor microenvironment (TME) and treatment response between different risk groups. We identified SLC2A3 as the most critical IRG exhibiting high expression in the fibroblast population of patients with BLCA, especially in CAFs, which play an important role in BLCA progression. We found that inhibiting SLC2A3 expression may enhance the effectiveness of immunotherapy and the identified potential drugs targeting SLC2A3. We demonstrated that the identified IRGs serve as risk factors for clinical prognosis in BLCA and successfully constructed an ICD-based prognostic signature. Additionally, SLC2A3 holds potential as a therapeutic target to advance precision and personalized treatment strategies for BLCA, in combination with immunotherapy.