Biology Direct最新文献

Background: Pancreatic cancer is characterized by a complex tumor microenvironment that hinders effective immunotherapy. Identifying key factors that regulate the immunosuppressive landscape is crucial for improving treatment strategies.

Methods: We constructed a prognostic and risk assessment model for pancreatic cancer using 101 machine learning algorithms, identifying OSBPL3 as a key gene associated with disease progression and prognosis. We integrated multi-dataset analyses, single-cell transcriptomic data, and functional experiments to explore the role of OSBPL3 in pancreatic cancer.

Results: Our risk prediction model, developed using machine learning algorithms, demonstrated high predictive accuracy across multiple datasets. Notably, the "rf" algorithm model showed an AUC of 1 in the training set and AUCs of 0.887 and 0.977 in two validation datasets. Ridge regression analysis identified OSBPL3 as a core prognostic feature gene. High OSBPL3 expression in pancreatic cancer samples was associated with immunosuppressive characteristics, including reduced CD8 + T cell infiltration and increased immunosuppressive cell populations such as Treg cells and M2 macrophages. Transcriptomic sequencing following OSBPL3 knockdown revealed enrichment of immune-related pathways, suggesting OSBPL3's influence on the immune microenvironment. Single-cell analyses further confirmed OSBPL3's role in shaping the immunosuppressive landscape by modulating Treg cells and M2 macrophages. Additionally, OSBPL3 expression was linked to resistance to immunotherapy, with high OSBPL3 expression associated with lower Immunophenoscore (IPS) scores, indicating poor responsiveness to immunotherapy.

Conclusions: Our study reveals OSBPL3 as a critical regulator of the immunosuppressive microenvironment in pancreatic cancer and a potential therapeutic target. Targeting OSBPL3 may enhance the efficacy of immunotherapy and improve patient outcomes. Further research is warranted to explore OSBPL3 as a biomarker for predicting immunotherapy response and as a potential therapeutic target in combination with anti-PD1 therapy.

Background: Endothelial cells are integral components of the tumor microenvironment and play a multifaceted role in tumor immunotherapy. Targeting endothelial cells and related signaling pathways can improve the effectiveness of immunotherapy by normalizing tumor blood vessels and promoting immune cell infiltration. However, to date, there have been no comprehensive studies analyzing the role of endothelial cells in the diagnosis and treatment of prostate adenocarcinoma (PRAD).

Method: By integrating clinical and transcriptomic data from TCGA-PRAD, we initially identified key endothelial cell-related genes in PRAD samples through single-cell analysis. Subsequently, cluster analysis was employed to classify PRAD samples based on the expression of these endothelial cell-related genes, allowing us to explore their correlation with patient prognosis and immunotherapy outcomes. A diagnostic model was then constructed and validated using a combination of 108 machine learning algorithms. The XGBoost and Random Forest algorithms highlighted the significant role of COL1A1, and we further analyzed the expression and correlation of COL1A1, AR, and EGFR through multiplex immunofluorescence staining. In vitro experimental analysis of the impact of COL1A1 on the progression of PRAD.

Results: Single-cell analysis identified 12 differential prognostic genes associated with endothelial cells. Cluster analysis confirmed a strong correlation between endothelial cell-related genes and both prostate cancer prognosis and immunotherapy responses. Diagnostic models developed using various machine learning techniques demonstrated the significant predictive capability of these 12 genes in the diagnosis of prostate cancer. Furthermore, based on patients' prognostic information, multiple machine learning analyses highlighted the critical role of COL1A1. Immunofluorescence analysis results confirmed that COL1A1 is highly expressed in prostate cancer and is positively correlated with both AR and EGFR. In vitro experiments confirm that reducing COL1A1 expression levels can inhibit PRAD progression.

Conclusion: This study provides a comprehensive analysis of the role of endothelial cell-related genes in the diagnosis, prognosis, and immunotherapy of prostate cancer. The findings, supported by various machine learning algorithms and experimental results, highlight COL1A1 as a significant target for the diagnosis and immunotherapy of PRAD.

Background: Multiple myeloma (MM) is a hematological malignancy characterized by uncontrolled proliferation of plasma cells and is currently incurable. Despite advancements in therapeutic strategies, resistance to proteasome inhibitors, particularly bortezomib (BTZ), poses a substantial challenge to disease management. This study aimed to explore the efficacy of boanmycin, a novel antitumor antibiotic, in overcoming resistance to BTZ in MM.

Methods: BTZ-resistant cells were generated over a period of at least 6 months by gradually increasing the concentration of BTZ. The viability of MM cell lines and patient bone marrow mononuclear cells (BMMCs) was measured via the CCK8 reagent. The protein levels of cleaved caspase 3, cleaved caspase 7, cleaved PARP, PARP, p-JNK, JNK, and γ-H2AX were analyzed through Western blot. Cellular morphology was observed via transmission electron microscopy. Colony formation ability was evaluated, and cell apoptosis and the cell cycle were detected through flow cytometry. Xenograft experiments were conducted to evaluate the growth of MM cells in vivo.

Results: Our results demonstrated that boanmycin effectively inhibited cell proliferation and colony formation, and triggered apoptosis in both BTZ-sensitive and BTZ-resistant MM cells. The combination of boanmycin with BTZ had greater inhibitory effects than either drug alone. Furthermore, boanmycin significantly suppressed MM cell growth in immunodeficient mouse xenograft models without inducing distinct toxic side effects. Notably, boanmycin markedly killed patient-derived MM cells ex vivo. Mechanistically, boanmycin not only disrupts the cell cycle and causes DNA damage but also exerts its antitumor effects by inducing endoplasmic reticulum (ER) functional impairment.

Conclusions: Our findings highlight the potential of boanmycin as a promising novel therapeutic option for treating MM, particularly in patients with BTZ resistance.

Background: Liquid-liquid phase separation (LLPS) is essential for the formation of membraneless organelles and significantly influences cellular compartmentalization, chromatin remodeling, and gene regulation. Previous research has highlighted the critical function of liquid-liquid biopolymers in the development of hepatocellular carcinoma (HCC).

Methods: This study conducted a comprehensive review of 3,685 liquid-liquid biopolymer regulators, leading to the development of a LLPS related Prognostic Risk Score (LPRS) for HCC through bootstrap-based univariate Cox, Random Survival Forest (RSF), and LASSO analyses. A prognostic nomogram for HCC patients was developed using LPRS and other clinicopathological factors. We utilized SurvSHAP to identify key genes within the LPRS influencing HCC prognosis. To validate our findings, we collected 49 HCC cases along with adjacent tissue samples and confirmed the correlation between DCAF13 expression and HCC progression through qRT-PCR analysis and in vitro experiments.

Results: LPRS was established with 8 LLPS-related genes (TXN, CBX2, DCAF13, SLC2A1, KPNA2, FTCD, MAPT, and SAC3D1). Further research indicated that a high LPRS is closely associated with vascular invasion, histological grade (G3-G4), and TNM stage (III-IV) in HCC, concurrently establishing LPRS as an independent risk factor for prognosis. A nomogram that integrates LPRS with TNM staging and patient age markedly improves the predictive accuracy of survival outcomes for HCC patients. Our findings suggest that increased DCAF13 expression in HCC plays a crucial role in cancer progression and angiogenesis. Navitoclax has emerged as a promising treatment for HCC patients with high LPRS levels, offering a novel therapeutic direction by targeting LLPS.

Conclusion: We have formulated a novel LPRS model that is capable of accurately predicting the clinical prognosis and drug sensitivity of HCC. DCAF13 might play a pivotal role in malignant progression mediated by LLPS.

Background: Integrating multi-layered information can enhance the accuracy of genomic prediction for complex traits. However, the improvement and application of effective strategies for genomic prediction (GP) using multi-omics data remains challenging.

Methods: We generated 11 feature sets for sequencing variants from genomics, transcriptomics, metabolomics, and epigenetics data in beef cattle, then we assessed the contribution of functional variants using genomic restricted maximum likelihood (GREML). We next estimated and ranked variant scores for 43 economically important traits, and compared the prediction accuracy of the top and bottom sets using genomic best linear unbiased prediction (GBLUP) and BayesB model. In addition, we annotated the variants from GWAS with functional feature sets and performed enrichment analysis.

Results: We observed significant enrichments for 32 functional categories in 11 feature sets. The evolutionary related sets (conservation regions and selection signatures) contributed significantly to heritability (31.78-fold and 14.48-fold enrichment), while metabolomics and transcriptomics showed low heritability enrichments. We observed a significant increase in prediction accuracy using the top feature set variants compared to whole-genome sequencing (WGS) data. The prediction accuracy based on the top 10% variant set showed an average increase of 11.6% and 7.54% using BayesB and GBLUP across traits, respectively. Notably, the greatest increase of 31.52% was obtained for spleen weight (SW) using BayesB. Also, we found that the top 10% of variants show strong enrichment with weight related QTLs based on the Cattle QTL database.

Conclusions: Our findings suggest that integrating biological prior information from multiple layers can enhance our understanding of the genetic architecture underlying complex traits and further improve genomic prediction in beef cattle.

Background: Prostate cancer is the most common diagnosed tumor and the fifth cancer related death among men in Europe. Although several genetic alterations such as ERG-TMPRSS2 fusion, MYC amplification, PTEN deletion and mutations in p53 and BRCA2 genes play a key role in the pathogenesis of prostate cancer, specific gene alteration signature that could distinguish indolent from aggressive prostate cancer or may aid in patient stratification for prognosis and/or clinical management of patients with prostate cancer is still missing. Therefore, here, by a multi-omics approach we describe a prostate cancer carrying the fusion of TMPRSS2 with ERG gene and deletion of 16q chromosome arm.

Results: We have observed deletion of KDM6A gene, which may represent an additional genomic alteration to be considered for patient stratification. The cancer hallmarks gene signatures highlight intriguing molecular aspects that characterize the biology of this tumor by both a high hypoxia and immune infiltration scores. Moreover, our analysis showed a slight increase in the Tumoral Mutational Burden, as well as an over-expression of the immune checkpoints. The omics profiling integrating hypoxia, ROS and the anti-cancer immune response, optimizes therapeutic strategies and advances personalized care for prostate cancer patients.

Conclusion: The here data reported can lay the foundation for predicting a poor prognosis for the studied prostate cancer, as well as the possibility of targeted therapies based on the modulation of hypoxia, ROS, and the anti-cancer immune response.

Background: Intrauterine adhesion (IUA) is a common cause of clinically refractory infertility, and there exists significant heterogeneity in the treatment outcomes among IUA patients with the similar severity after transcervical resection of adhesion(TCRA). The underlying mechanism of different treatment outcomes occur remains elusive, and the precise contribution of various cell subtypes in this process remains uncertain.

Results: Here, we performed single-cell transcriptome sequencing on 10 human endometrial samples to establish a single-cell atlas differences between patients who responded to estrogen therapy and those who did not. The results showed increased infiltration of immune cells such as monocyte macrophages, T cells, and natural killer (NK) cells in patients who did not respond to estrogen therapy. Our findings indicate that distinct fibroblast subsets are implicated in the modulation of the Wnt, Hippo, and Hedgehog signaling pathways, as evidenced by functional enrichment analyses. This may have implications for the therapeutic efficacy in patients with IUA. Furthermore, we delineated the markers and transcriptional status of different macrophage subsets and identified two cell clusters, CXCL10high and CCL4L2high macrophage subsets, which are intimately associated with inflammation and fibrosis. The state of fibrosis and inflammatory response in human endometrial tissues with disparate treatment outcomes is revealed, and providing evidence to clarify the underlying determinants of sensitivity to estrogen therapy.

Conclusions: We described the transcriptional status of different cell subtypes in the two groups of patients, providing new ideas for exploring the molecular mechanism of the difference in the effectiveness of estrogen therapy in patients, and providing theoretical basis for providing precise and individualized treatment plans for IUA patients.

Spermatogonial stem cells (SSCs) form haploid gametes through the precisely regulated process of spermatogenesis. Within the testis, SSCs undergo self-renewal through mitosis, differentiation, and then enter meiosis to generate mature spermatids. This study utilized single-cell RNA sequencing on 26,888 testicular cells obtained from five Holstein bull testes, revealing the presence of five distinct germ cell types and eight somatic cell types in cattle testes. Gene expression profiling and enrichment analysis were utilized to uncover the varied functional roles of different cell types involved in cattle spermatogenesis. Additionally, unique gene markers specific to each testicular cell type were identified. Moreover, differentially expressed genes in spermatogonia exhibited notable enrichment in GO terms and KEGG pathway linked to alternative splicing. Notably, our study has shown that the activity of the YY1 regulation displays distinct expression patterns in spermatogonia, specifically targeting spliceosome proteins including RBM39, HNRNPA2B1, HNRNPH3, CPSF1, PCBP1, SRRM1, and SRRM2, which play essential roles in mRNA splicing. These results emphasize the importance of mRNA processing in spermatogonia within cattle testes, providing a basis for further investigation into their involvement in spermatogonial development.

Background: Alveolar macrophages (AMs) is critical to exacerbate acute lung injury (ALI) induced by lipopolysaccharide (LPS) via inhibiting inflammation, which could by shifted by mesenchymal stem cell-derived exosomes (MSC-exos). But the underlying rationale is not fully clarified. Our study aimed to analyze the significance of itaconic acid (ITA) in mediating the protective effects of MSC-exos on LPS-induced ALI.

Methods: MSC-exos were used to treat pulmonary microvascular endothelial cells (PMVECs) co-cultured with AMs under LPS stimulation. si-IRG1 was transfected to AMs. PMVEC permeability, apoptosis rates, and inflammatory cytokine levels were assessed. In vivo, C57BL/6 wild-type (WT) and Irg1-/- mice were employed to explore the protection of MSC-exos against LPS-induced ALI. The lung injury was determined by histological and biochemical assays. ITA levels were measured using gas chromatography-mass spectrometry. Western blot and flow cytometry analyses were performed to assess M1/M2 polarization.

Results: Co-culture with AMs significantly increased PMVEC permeability, apoptosis rates, IL-6, TNF-α levels and Claudin-5 and ZO-1 expression induced by LPS treatment, which were attenuated by MSC-exos accompanied by enhanced ITA level. After si-IRG1 transfection, MSC-exos' protective efficacy was reversed, with suppressed M2 polarization. In vivo, MSC-exos alleviated alveolar structure disruption, pulmonary edema, inflammation and increased ITA concentration in WT mice but had reduced effects in Irg1-/- mice, with neglected M2 polarization.

Conclusions: ITA secretion facilitated the MSC-exos' protective benefits on LPS-induced PMVEC damage and ALI in mice by promoting AM M2 polarization, highlighting a potential therapeutic strategy for ALI and related inflammatory lung diseases.

Oral squamous cell carcinoma (OSCC) is the most frequent type of oral malignancy with high metastasis and poor prognosis. The deubiquitinating enzyme Ubiquitin Specific Peptidase 44 (USP44) regulates the mitotic checkpoint, and its deficiency leads to aneuploidy and increases tumor incidence. However, the role of USP44 in OSCC is not well understood. Herein, we analyzed mRNA sequencing data of OSCC samples downloaded from the TCGA and GEO databases and found that USP44 was decreased in human OSCC tissues and was positively correlated to the survival of OSCC patients. To investigate the biological impact of USP44, we used recombinant lentiviruses to overexpress or knockdown USP44 expression in OSCC cell lines, which were also injected subcutaneously or into the lateral tail vein of Male BALB/c nude mice to model tumorigenesis or lung metastasis in vivo, respectively. The results showed that overexpression of USP44 inhibited malignant cell phenotypes in vitro and suppressed tumor growth and lung metastasis in vivo, while its downregulation had the opposite effects. Comprehensive proteomic analyses through Co-IP mass spectrometry and label-free quantitative LC-MS/MS methods identified 112 differentially expressed proteins positively regulated by USP44, among which 13 were involved in cancer-related pathways including apoptotic signaling and cell cycle regulation. PPI analysis identified Hexamethylene Bis-Acetamide-Inducible Protein 1 (HEXIM1) as the hub protein. Upregulation of USP44 enhanced HEXIM1 protein stability, leading to its higher expression in OSCC cells. Silencing of HEXIM1 further enhanced the malignant phenotype of OSCC cells. At the same time, HEXIM1 knockdown reversed the antitumor effects of USP44. These findings demonstrated that USP44 acted as a critical tumor suppressor in OSCC by inhibiting cell proliferation and metastasis through the stabilization of HEXIM1 protein, suggesting that USP44-HEXIM1 axis is a promising target for OSCC therapy.