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Role of BTLA in ovarian cancer and its clinical prognostic significance based on multi-omics analysis.
BACKGROUND: Ovarian cancer (OV) is a highly lethal gynecological malignancy with limited effective biomarkers for prognosis and precision immunotherapy. B and T lymphocyte attenuator (BTLA) is an immune checkpoint molecule involved in tumor immune regulation, but its role in OV remains unclear. METHODS: Transcriptomic data from The Cancer Genome Atlas (TCGA) were analyzed to evaluate BTLA expression and its potential clinical relevance in OV. Exploratory multivariable Cox regression analysis was performed to examine the association between BTLA expression and patient prognosis. Functional characteristics were investigated through gene set enrichment and variation analyses, protein-protein interaction analysis, and regulatory network construction. Single-cell RNA sequencing (ScRNA-seq) was applied to explore cellular heterogeneity and BTLA-related cell-cell communication. Drug sensitivity analyses and molecular docking were conducted to assess therapeutic relevance. Experimental assays were performed to validate BTLA expression. RESULTS: BTLA was significantly upregulated in OV tissues compared with normal controls. Higher BTLA expression was associated with patient prognosis in exploratory Cox regression and Kaplan-Meier (KM) analyses. Functional enrichment revealed associations with immune- and tumor-related pathways including epithelial mesenchymal transition (EMT), interleukin-10 signaling, and interferon responses. Single-cell analysis demonstrated BTLA-related transcriptional heterogeneity across cell populations, with epithelial cells showing the highest activity. Drug sensitivity profiling identified differential responses between BTLA expression groups, and molecular docking suggested moderate binding affinity between BTLA and genistein (Vina score = - 6.3 kcal/mol). Experimental validation confirmed increased BTLA expression at both mRNA and protein levels in OV tissues. CONCLUSION: These findings suggest that BTLA may be involved in the tumor immune microenvironment of OV and may provide preliminary clues as a potential biomarker and therapeutic target.
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Ligand-receptor interaction atlas within and between tumor cells and T cells in lung adenocarcinoma.
Purpose: Lung adenocarcinoma (LUAD) is the leading cause of cancer-related deaths worldwide. Although tumor cell-T cell interactions are known to play a fundamental role in promoting tumor progression, these interactions have not been explored in LUAD. Methods: The 10x genomics single-cell RNA sequencing (scRNA-seq) and gene expression data of LUAD patients were obtained from ArrayExpress, TCGA, and GEO databases. scRNA-seq data were analyzed and infiltrating tumor cells, epithelial cells, and T cells were identified in the tumor microenvironment. Differentially expressed ligand-receptor pairs were identified in tumor cells/normal epithelial cells and tumor T cells/non-tumor T cells based on corresponding scRNA-seq and gene expression data, respectively. These important interactions inside/across cancer cells and T cells in LUAD were systematically analyzed. Furthermore, a valid prognostic machine-learning model based on ligand-receptor interactions was built to predict the prognosis of LUAD patients. Flow cytometry and qRT-PCR were performed to validate the significantly differently expressed ligand-receptor pairs. Results: Overall, 39,692 cells in scRNA-seq data were included in our study after quality filtering. A total of 65 ligand-receptor pairs (17 upregulated and 48 downregulated), including LAMB1-ITGB1, CD70-CD27, and HLA-B-LILRB2, and 96 ligand-receptor pairs (41 upregulated and 55 downregulated), including CCL5-CCR5, SELPLG-ITGB2, and CXCL13-CXCR5, were identified in LUAD cancer cells and T cells, respectively. To explore the crosstalk between cancer cells and T cells, 114 ligand-receptor pairs, including 11 ligand-receptor pair genes that could significantly affect survival outcomes, were identified in our research. A machine-learning model was established to accurately predict the prognosis of LUAD patients and ITGB4, CXCR5, and MET were found to play an important role in prognosis in our model. Flow cytometry and qRT-PCR analyses indicated the reliability of our study. Conclusion: Our study revealed functionally significant interactions within and between cancer cells and T cells. We believe these observations will improve our understanding of potential mechanisms of tumor microenvironment contributions to cancer progression and help identify potential targets for immunotherapy in the future.
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snRNA sequencing-based skeletal muscle analysis of Jiangquan black pigs with different average daily growth rates.
The Jiangquan black pigs, a new breed of swine obtained by introducing traits from Duroc pigs into Yimeng black pigs, exhibits fast growth rates and high meat quality. To understand how daily weight gain influences muscle development in this breed, we analyzed longissimus dorsi muscle cell subpopulations from Jiangquan black pigs using snRNA and bulk RNA sequencing. Thirteen distinct cell types (e.g., muscle stem cells, satellite cells, fibroblasts) were identified, and marker genes (PAX7, MYOD, MYOG) were found to exhibit stage-specific expression during differentiation. Pseudotime analysis revealed the differentiation trajectories of these cell populations, while cell cycle analysis uncovered the higher mitotic activity in satellite cells of the fast-growth versus slow-growth groups. Furthermore, cell communication analysis highlighted the interactions between muscle cells and other cell types. Finally, intergroup analysis revealed that 2,466 and 2,597 genes were differentially expressed in muscle stem cells and muscle satellite cells, respectively. These genes were enriched in disease-related pathways. This study provides a single-cell resolution atlas of porcine muscle development, offering insights into the genetic regulation of growth and potential targets for breeding optimization.
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Comprehensive Analysis of a Platelet- and Coagulation-Related Prognostic Gene Signature Identifies CYP19A1 as a Key Tumorigenic Driver of Colorectal Cancer.
BACKGROUND: The intricate interplay between the platelet-coagulation system and the progression of malignant tumors has profound therapeutic implications. However, a thorough examination of platelet and coagulation markers specific to colorectal cancer (CRC) is conspicuously absent in the current literature. Consequently, there is an urgent need for further exploration into the mechanistic underpinnings of these markers and their potential clinical applications. METHODS: By integrating RNA-seq data and clinicopathological information from patients with CRC in the cancer genome atlas, we identified genes related to the platelet-coagulation system using weighted gene co-expression networks and univariate Cox analysis. We established a prognostic risk model based on platelet- and coagulation-related genes using Lasso Cox regression analysis and validated the model in two independent CRC cohorts. We explored potential biological functional disparities between high-risk and low-risk groups through comprehensive bioinformatics analysis. RESULTS: Our findings indicate that colorectal cancer patients classified as high-risk generally exhibit poorer prognoses. Moreover, the model's risk scores were associated with the differential composition of the immune tumor microenvironment, suggesting its applicability to infer immunotherapy responsiveness. Cellular functional experiments and animal experiments indicated that CYP19A1 expression in CRC influences malignant phenotype and platelet activation. CONCLUSIONS: In summary, we present a novel platelet- and coagulation-related risk model for prognostic assessment of patients with CRC and confirm the important role of CYP19A1 in promoting malignant progression of CRC.
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In Vitro and In Vivo Development of the Human Airway at Single-Cell Resolution.
Bud tip progenitor cells give rise to all murine lung epithelial lineages and have been described in the developing human lung; however, the mechanisms controlling human bud tip differentiation into specific lineages are unclear. Here, we used homogeneous human bud tip organoid cultures and identified SMAD signaling as a key regulator of the bud tip-to-airway transition. SMAD induction led to the differentiation of airway-like organoids possessing functional basal cells capable of clonal expansion and multilineage differentiation. To benchmark in vitro-derived organoids, we developed a single-cell mRNA sequencing atlas of the human lung from 11.5 to 21 weeks of development, which revealed high degrees of similarity between the in vitro-derived and in vivo airway. Together, this work sheds light on human airway differentiation in vitro and provides a single-cell atlas of the developing human lung.
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Development and validation of a succinylation-related prognostic model for esophageal squamous cell carcinoma based on multi-omics bioinformatics analysis.
BACKGROUND: Esophageal squamous cell carcinoma (ESCC), an aggressive malignancy with poor prognosis, requires reliable prognostic biomarkers. Protein succinylation, a critical post-translational modification implicated in cancer biology, has not yet been systematically investigated for its prognostic significance in ESCC. METHODS: Transcriptomic data from GSE53624 (n = 119) and The Cancer Genome Atlas (TCGA)-ESCC (n = 95) cohorts were analyzed in this study. Succinylation-related genes (SRGs) were identified via weighted gene co-expression network analysis (WGCNA), a curated SRG set, and differential expression analysis. A prognostic signature was constructed using LASSO Cox regression and validated internally and externally. Immune infiltration was assessed by CIBERSORT/ssGSEA. miRNA-mRNA and protein-protein interactions (PPIs) and drug sensitivity were evaluated. Gene localization was confirmed with single-cell RNA sequencing (scRNA-seq). RESULTS: 41 high-confidence SRGs associated with ESCC were identified. A robust seven-gene prognostic signature (IGFBP3, CMA1, FN1, CTSG, TIMP1, MBL2, and SP5) was established. Patients stratified into high- and low-risk groups exhibited significantly different overall survival (OS) in both the training cohort (GSE53624, P < 0.001) and the validation cohort (TCGA, P = 0.026). The risk score remained an independent prognostic factor and was incorporated into a predictive nomogram. High-risk tumors were characterized by an immunosuppressive tumor microenvironment (TME), with reduced eosinophil and natural killer (NK) cell infiltration and increased monocyte abundance. In addition, signature genes were associated with resistance to multiple anticancer agents. scRNA-seq analysis revealed predominant expression of these genes in malignant cells, fibroblasts, and myeloid cells. CONCLUSION: A novel seven-gene succinylation-related signature was established and validated as an independent prognostic biomarker for ESCC. This signature captures immunosuppressive features of high-risk tumors and may provide a foundation for individualized therapeutic strategies.
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Differences in maternal diet fiber content influence patterns of gene expression and chromatin accessibility in fetuses and piglets.
This study investigates the impact of maternal gestation diets with varying fiber contents on gene expression and chromatin accessibility in fetuses and piglets fed a low fiber diet post weaning. High-fiber maternal diets, enriched with sugar beet pulp or pea internal fiber, were compared to a low-fiber maternal diet to evaluate their effects on liver and muscle tissues. The findings demonstrate that maternal high-fiber diets significantly alter chromatin accessibility, predicted transcription factor activity and transcriptional landscape in both fetuses and piglets. A gene set enrichment analysis revealed over-expression of gene ontology terms related to metabolic processes and under-expression of those linked to immune responses in piglets from sows given the high-fiber diets during gestation. This suggests better metabolic health and immune tolerance of the fetus and offspring, in line with the documented epigenetic effects of short chain fatty acids on immune and metabolic pathways. A deconvolution analysis of the bulk RNA-seq data was performed using cell-type specific markers from a single cell transcriptome atlas of adult pigs. These results confirmed that the transcriptomic and chromatin accessibility data do not reflect different cell type compositions between maternal diet groups but rather phenotypic changes triggered by maternal nutrition in shaping the epigenetic and transcriptional environment of fetus and offspring. Our findings have implications for improving animal health and productivity as well as broader implications for human health, suggesting that optimizing maternal diet with high-fiber content could enhance metabolic health and immune function in the formative years after birth and potentially to adulthood.
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Establish TIIC signature score based the machine learning fusion in bladder cancer.
BACKGROUND: Bladder cancer is a prevalent malignant tumor with high heterogeneity. Current treatments, such as transurethral resection of bladder tumor (TURBT) and intravesical Bacillus Calmette-Guérin (BCG) therapy, still have limitations, with approximately 30% of non-muscle-invasive bladder cancer (NMIBC) progressing to muscle-invasive bladder cancer (MIBC), and a substantial number of MIBC patients experiencing recurrence after surgery. Immunotherapy has shown potential benefits, but accurate prediction of its prognostic effects remains challenging. METHODS: We analyzed bladder cancer RNA-seq data and clinical information from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, and used various machine learning algorithms to screen for feature RNAs related to tumor-infiltrating immune cells (TIICs) from single-cell data. Based on these RNAs, we established a TIIC signature score and evaluated its relationship with overall survival (OS) and immunotherapy response in bladder cancer patients. RESULTS: The study identified 171 TIIC-RNAs and selected 11 TIIC-RNAs with prognostic value through survival analysis. The TIIC signature score established using a machine learning fusion method was significantly associated with OS and showed good predictive performance in different datasets. Additionally, the signature score was negatively correlated with immunotherapy response, with patients with low TIIC feature scores showing better survival outcomes after immunotherapy. Further biological functional analysis revealed a close association between the TIIC signature score and immune regulation processes, cellular metabolism, and genetic variations. CONCLUSION: This study successfully constructed and validated an RNA signature scoring system based on tumor-infiltrating immune cell (TIIC) features, which can effectively predict OS and the effectiveness of immunotherapy in bladder cancer patients.
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A SUMOylation/immune-related gene signature predicts the prognosis and immunotherapy efficacy of patients with triple-negative breast cancer.
BACKGROUND: SUMOylation, a type of posttranslational protein modification, is linked to numerous biological processes, including tumorigenesis, the immunological response, and DNA repair. Immune-related genes are crucial for immune monitoring and the formation of the tumor immunological microenvironment. The aim of this work was to create a survival prediction model that combines SUMOylation and immune-related gene expression to guide personalized treatment strategies for triple-negative breast cancer (TNBC) patients. METHODS: RNA sequencing (RNA-seq) data and clinical information from TNBC patients were obtained from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO). An unsupervised clustering method was applied to identify different SUMOylation-related subclusters in TNBC. Using immune cell infiltration levels and immunological scores, we performed weighted gene coexpression network analysis (WGCNA) to identify SUMOylated and immune-related genes (SIRGs). Three machine learning methods, namely, least absolute shrinkage and selection operator (LASSO), support vector machine recursive feature elimination (SVM-RFE), and random forest (RF), were utilized to construct a risk prognosis model from the SIRGs. The model genes were also analyzed through single-cell sequencing. Furthermore, we explored the correlations between risk score and patient prognosis, immune cell infiltration, and immunotherapy. The expression of characteristic genes was ultimately validated by quantitative real-time polymerase chain reaction (qRT-PCR) and immunohistochemistry (IHC). RESULTS: The expression levels of genes (MITD1, IL12B, and ZP1) identified by machine learning were used to develop a prognostic model and nomogram. The low-risk patients had significantly better survival rates than the high-risk patients in both the training and validation cohorts. Specifically, patients in the low-risk group had increased immune cell infiltration, immune checkpoint gene expression, and sensitivity to immunotherapy. CONCLUSION: The constructed SIRG-related prognostic model can accurately predict prognosis and treatment efficacy for patients with TNBC.
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A single-cell atlas of large B cell lymphomas reveals distinct malignant archetypes predictive of clinical outcomes.
Large B cell lymphomas (LBCLs) exhibit significant heterogeneity which leads to disparate treatment response, with up to 40% of patients developing refractory disease or relapsing within the first two years. To understand the cellular and molecular basis of this heterogeneity, we performed single-cell RNA-seq, BCR-seq, and TCR-seq on LBCL tumor biopsies, generating an atlas of 63 LBCL cases, mostly classified as diffuse LBCL, not otherwise specified (DLBCL NOS). We identified five conserved transcriptional archetypes of malignant B cells that co-occur within individual tumors, with varying proportions across patients. Notably, high abundance of Archetype 4, characterized by memory B cell features and quiescence markers, correlated with poor event-free survival following standard immunochemotherapy, a finding which was validated in independent cohorts through deconvolution of bulk RNA-seq data. Our study provides a novel framework for patient stratification based on the quantification of malignant cellular states, with implications for precision therapy of LBCLs.