JOURNAL OF CELLULAR AND MOLECULAR MEDICINE最新文献

Human papillomavirus (HPV) infection can cause condyloma acuminatum (CA), which is characterized by a high incidence and a propensity for recurrence after treatment. Angiogenesis plays an important role in the occurrence and development of CA. Seryl-tRNA synthetase (SerRS) is a newly identified, potent anti-angiogenic factor that directly binds to the vascular endothelial growth factor (VEGFA) promoter, thereby suppressing its transcription. Emodin is a natural anthraquinone derivative that can promote SerRS expression. This study aimed to investigate the effects of emodin on CA and explore combined treatment strategies. The HPV-infected cell line SiHa was treated with either DMSO, emodin, ALA-PDT or a combination of emodin and ALA-PDT. We observed the effects on cell proliferation, apoptosis and the SerRS-VEGFA pathway. Our findings demonstrated that emodin targets angiogenesis through the SerRS-VEGFA pathway, resulting in the inhibition of SiHa cell proliferation and promotion of apoptosis (p < 0.001). To verify the therapeutic effect of emodin combined with ALA-PDT on HPV-associated tumours in vivo, we established an animal xenograft model by subcutaneously inoculating mice with SiHa cells (n = 4). The results showed that the combination of emodin and ALA-PDT significantly inhibited the expression of VEGFA to inhibit angiogenesis (p < 0.001), thus showing an inhibitory effect on tumour (p < 0.001). Furthermore, we determined that the mechanism underlying the decrease in VEGFA expression after emodin combined with ALA-PDT in CA may be attributed to the promotion of SerRS expression (p < 0.001). The combination of emodin and ALA-PDT holds promise as a novel therapeutic target for CA by targeting neovascularization in condyloma tissues.

New biomarkers for early diagnosis of gastric cancer (GC), the second leading cause of cancer-related death, are urgently needed. IGFBP7, known to play various roles in multiple tumours, is complexly regulated across diverse cancer types, as evidenced by our pancancer analysis. Bioinformatics analysis revealed that IGFBP7 expression was related to patient prognosis, tumour clinicopathological characteristics, tumour stemness, microsatellite instability and immune cell infiltration, as well as the expression of oncogenes and immune checkpoints. GSEA links IGFBP7 to several cancer-related pathways. IGFBP7 deficiency inhibited GC cell proliferation and migration in vitro. Furthermore, an in vivo nude mouse model revealed that IGFBP7 downregulation suppressed the tumorigenesis of GC cells. Western blotting analysis showed that the JAK1/2-specific inhibitor ruxolitinib could rescue alterations induced by IGFBP7 overexpression in GC cells. Additionally, our bioinformatics analysis and in vitro assays suggested that IGFBP7 is regulated by DNA methylation at the genetic level and that the RNA m⁶A demethylase FTO modulates it at the posttranscriptional level. This study emphasizes the clinical relevance of IGFBP7 in GC and its influence on cell proliferation and migration via the JAK/STAT signalling pathway. This study also highlights the regulation of IGFBP7 in GC by DNA and m⁶A RNA methylation.

Aging is a risk factor for various human disorders, including cancer. Current literature advocates that the primary principles of aging depend on the endogenous stress-induced DNA damage caused by reactive oxygen species 50 Hz low-frequency magnetic field was suggested to induce DNA damage and chromosomal instability. NF-kB, activated by DNA damage, is upregulated in age-related cancers and inhibition of NF-kB results in aging-related delayed pathologies. Metformin (Met), an NF-kB inhibitor, significantly reduces both NF-kB activation and expression in aging and cancer. This in vitro study, therefore, was set out to assess the effects of 5mT MF in 50 Hz frequency and Met treatment on the viability and proliferation of aged mouse NIH/3T3 fibroblasts and expression of RELA/p65, matrix metalloproteinases MMP2 and MMP9, and E-cadherin (CDH1) genes. The trypan blue exclusion assay was used to determine cell viability and the BrdU incorporation assay to determine cell proliferation. The MMP-2/9 protein analysis was carried out by immunocytochemistry, NF-kB activity by ELISA and the expressions of targeted genes by qRT–PCR methods. Four doses of Met (500 uM, 1 mM, 2 mM and 10 mM) suppressed both the proliferation and viability of fibroblasts exposed to the MF in a dose-dependent pattern, and the peak inhibition was recorded at the 10 mM dose. Met reduced the expression of NF-kB, and MMP2/9, elevated CDH1 expression and suppressed NF-kB activity. These findings suggest that Met treatment suppresses the carcinogenic potential of 50 Hz MFs in aged mouse fibroblasts, possibly through modulation of NF-kB activation and epithelial-mesenchymal transition modulation.

Pathological cardiac hypertrophy, a common feature in various cardiovascular diseases, can be more effectively managed through combination therapies using natural compounds. Harmine, a β-carboline alkaloid found in plants, possesses numerous pharmacological functions, including alleviating cardiac hypertrophy. Similarly, Selenomethionine (SE), a primary organic selenium source, has been shown to mitigate cardiac autophagy and alleviate injury. To explores the therapeutic potential of combining Harmine with SE to treat cardiac hypertrophy. The synergistic effects of SE and harmine against cardiac hypertrophy were assessed in vitro with angiotensin II (AngII)-induced hypertrophy and in vivo using a Myh6^R404Q mouse model. Co-administration of SE and harmine significantly reduced hypertrophy-related markers, outperforming monotherapies. Transcriptomic and metabolic profiling revealed substantial alterations in key metabolic and signalling pathways, particularly those involved in energy metabolism. Notably, the combination therapy led to a marked reduction in the activity of key glycolytic enzymes. Importantly, the addition of the glycolysis inhibitor 2-deoxy-D-glucose (2-DG) did not further potentiate these effects, suggesting that the antihypertrophic action is predominantly mediated through glycolytic inhibition. These findings highlight the potential of SE and harmine as a promising combination therapy for the treatment of cardiac hypertrophy.

Long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) are two typical types of non-coding RNAs that interact and play important regulatory roles in many animal organisms. Exploring the unknown interactions between lncRNAs and miRNAs contributes to a better understanding of their functional involvement. Currently, studying the interactions between lncRNAs and miRNAs heavily relies on laborious biological experiments. Therefore, it is necessary to design a computational method for predicting lncRNA–miRNA interactions. In this work, we propose a method called MPGK-LMI, which utilizes a graph attention network (GAT) to predict lncRNA–miRNA interactions in animals. First, we construct a meta-path similarity matrix based on known lncRNA–miRNA interaction information. Then, we use GAT to aggregate the constructed meta-path similarity matrix and the computed Gaussian kernel similarity matrix to update the feature matrix with neighbourhood information. Finally, a scoring module is used for prediction. By comparing with three state-of-the-art algorithms, MPGK-LMI achieves the best results in terms of performance, with AUC value of 0.9077, AUPR of 0.9327, ACC of 0.9080, F1-score of 0.9143 and precision of 0.8739. These results validate the effectiveness and reliability of MPGK-LMI. Additionally, we conduct detailed case studies to demonstrate the effectiveness and feasibility of our approach in practical applications. Through these empirical results, we gain deeper insights into the functional roles and mechanisms of lncRNA–miRNA interactions, providing significant breakthroughs and advancements in this field of research. In summary, our method not only outperforms others in terms of performance but also establishes its practicality and reliability in biological research through real-case analysis, offering strong support and guidance for future studies and applications.

The unique non-coding RNA molecule known as circular RNA (circRNA) is distinguished from conventional linear RNA by having a longer half-life, greater degree of conservation and inherent solidity. Extensive research has demonstrated the profound impact of circRNA expression on cellular drug sensitivity and therapeutic efficacy. There is an immediate need for the creation of efficient computational techniques to anticipate the potential correlations between circRNA and drug sensitivity, as classical biological research approaches are time-consuming and costly. In this work, we introduce a novel deep learning model called SNMGCDA, which aims to forecast the relationships between circRNA and drug sensitivity. SNMGCDA incorporates a diverse range of similarity networks, enabling the derivation of feature vectors for circRNAs and drugs using three distinct calculation methods. First, we utilize a sparse autoencoder for the extraction of drug characteristics. Subsequently, the application of non-negative matrix factorization (NMF) enables the identification of relationships between circRNAs and drugs based on their shared features. Additionally, the multi-head graph attention network is employed to capture the characteristics of circRNAs. After acquiring the characteristics from these three separate components, we combine them to form a unified and inclusive feature vector for each cluster of circRNA and drug. Finally, the relevant feature vectors and labels are inputted into a multilayer perceptron (MLP) to make predictions. The outcomes of the experiment, obtained through 5-fold cross-validation (5-fold CV) and 10-fold cross-validation (10-fold CV), demonstrate SNMGCDA outperforms five other state-of-art methods in terms of performance. Additionally, the majority of case studies have predominantly confirmed newly discovered correlations by SNMGCDA, thereby emphasizing its reliability in predicting potential relationships between circRNAs and drugs.

This retrospective transcriptomic study leveraged bioinformatics and machine learning algorithms to identify novel gene biomarkers and explore immune cell infiltration profiles associated with chronic obstructive pulmonary disease (COPD). Utilizing an integrated analysis of metadata encompassing six gene expression omnibus (GEO) microarray datasets, 987 differentially expressed genes were identified. Further gene ontology and pathway enrichment analyses revealed the enrichment of these genes across various biological processes and pathways. Moreover, a systematic integration of two machine learning algorithms along with pathway-gene correlations identified six candidate biomarkers, which were validated in a separate cohort comprising six additional microarray datasets, ultimately identifying ADD3 and GNAS as diagnostic biomarkers for COPD. Subsequently, the diagnostic efficacy of ADD3 and GNAS was assessed, and the impact of their expression levels on overall survival was further evaluated and quantified in the validation cohort. Examination of immune cell subtype infiltration found increased proportions of cytotoxic CD8⁺ T cells, resting and activated NK cells, along with decreased M0 and M2 macrophages, in COPD versus control samples. Correlation analyses also uncovered significant associations between ADD3 and GNAS expression and infiltration of various immune cell types. In conclusion, this study elucidates crucial COPD diagnostic biomarkers and immune cell profiles which may illuminate the immunopathological drivers of COPD progression, representing personalized therapeutic targets warranting further investigation.

Colorectal cancer (CRC) is a relatively common malignancy clinically and the second leading cause of cancer-related deaths. Recent studies have identified T-cell exhaustion as playing a crucial role in the pathogenesis of CRC. A long-standing challenge in the clinical management of CRC is to understand how T cells function during its progression and metastasis, and whether potential therapeutic targets for CRC treatment can be predicted through T cells. Here, we propose DeepTEX, a multi-omics deep learning approach that integrates cross-model data to investigate the heterogeneity of T-cell exhaustion in CRC. DeepTEX uses a domain adaptation model to align the data distributions from two different modalities and applies a cross-modal knowledge distillation model to predict the heterogeneity of T-cell exhaustion across diverse patients, identifying key functional pathways and genes. DeepTEX offers valuable insights into the application of deep learning in multi-omics, providing crucial data for exploring the stages of T-cell exhaustion associated with CRC and relevant therapeutic targets.

Xie L, Li W, Zheng XQ, et al. Treponema pallidum membrane protein Tp47 induced autophagy and inhibited cell migration in HMC3 cells via the PI3K/AKT/FOXO1 pathway. J Cell Mol Med. 2023;27(20):3065-3074. doi: 10.1111/jcmm.17872

In Xie et al.,¹ the published article contains errors in Figure 1, Figure 2D, Figure 3E and Figure 5D. This issue occurred because of a mix-up in the organization of our original data, likely due to the large volume of images involved. The corrected Figure 1, Figure 2, Figure 3 and Figure 5. are below. The authors confirm that the conclusions of this article remain unchanged.

Global impact of viral diseases specially Monkeypox (mpox) and Marburg virus, emphasizing the urgent need for effective drug interventions. Oxymatrine is an alkaloid which has been selected and modified using various functional groups to enhance its efficacy. The modifications were evaluated using various computatioanal analysis such as pass prediction, molecular docking, ADMET, and molecular dynamic simulation. Mpox and Marburg virus were chosen as target diseases based on their maximum pass prediction spectrum against viral disease. After that, molecular docking, dynamic simulation, DFT, calculation and ADMET prediction were determined. The main objective of this study was to enhance the efficacy of oxymatrine derivatives through functional group modifications and computational analyses to develop effective drug candidates against mpox and Marburg viruses. The calculated binding affinities indicated strong interactions against both mpox virus and Marburg virus. After that, the molecular dynamic simulation was conducted at 100 ns, which confirmed the stability of the binding interactions between the modified oxymatrine derivatives and target proteins. Then, the modified oxymatrine derivatives conducted theoretical ADMET profiling, which demonstrated their potential for effective drug development. Moreover, HOMO-LUMO calculation was performed to understand the chemical reactivity and physicochemical properties of compounds. This computational analysis indicated that modified oxymatrine derivatives for the treatment of mpox and Marburg virus suggested effective drug candidates based on their binding affinity, drug-like properties, stability and chemical reactivity. However, further experimental validation is necessary to confirm their clinical value and efficacy as therapeutic candidates.