BioFactors最新文献

Mitochondrial quality regulation plays an important role in affecting the treatment sensitivity of multiple myeloma (MM). We aimed to develop a mitochondrial quality regulation genes (MQRGs)-related prognostic model for MM patients. The Genomic Data Commons-MM of bulk RNA-seq, mutation, and single-cell RNA-seq (scRNA-seq) dataset were downloaded, and the MQRGs gene set was collected previous study. “maftools” and CIBERSORT were used for mutation and immune-infiltration analysis. Subsequently, the “ConsensusClusterPlus” was used to perform the unsupervised clustering analysis, “survminer” and “ssGSEA” R package was used for the Kaplan–Meier survival and enrichment analysis, “limma” R, univariate and Least Absolute Shrinkage and Selection Operator Cox were used for RiskScore model. The “timeROC” R package was used for Receiver Operating Characteristic Curve analysis. Finally, the “Seurat” R package was used for scRNA-seq analysis. These MQRGs are mainly located on chromosome-1,2,3,7, and 22 and had significant expression differences among age, gender, and stage groups, in which PPARGC1A and PPARG are the high mutation genes. Most MQRGs expression are closely associated with the plasma cells infiltration and can divide the patients into 2 different prognostic clusters (C1, C2). Then, 8 risk models were screened from 60 DEGs for RiskScore, which is an independent prognostic factor and effectively divided the patients into high and low risk groups with significant difference of immune checkpoint expression. Nomogram containing RiskScore can accurately predict patient prognosis, and a series of specific transcription factor PRDM1 and IRF1 were identified. We described the based molecular features and developed a high effective MQRGs-related prognostic model in MM.

G-protein-coupled receptors (GPRs) are critical regulators of various biological behaviors, and their role in gastric cancer (GC) progression is gaining increasing attention. Among them, the immune regulatory mechanisms mediated by chemokine receptor 4 (CXCR4) remain insufficiently understood. This study aims to explore the immune regulatory functions of CXCR4 and the heterogeneity of the tumor microenvironment (TME) by examining GPR-related gene expression in GC. Through multi-omics approaches, including spatial transcriptomics and single-cell RNA sequencing, we investigated the oncogenic mechanisms of CXCR4, particularly its role in T cell immune exhaustion. In vitro experiments, including ELISA, PCR, CCK8 assays, cell scratch assays, and colony formation assays, were used to validate the role of CXCR4 in the migration and invasion of AGS and SNU-1 cell lines. CXCR4 silencing using siRNA further demonstrated its regulatory effects on these cellular processes. Our results revealed a strong correlation between elevated CXCR4 expression and increased exhaustion of regulatory T cells (Tregs) in the TME. Furthermore, heightened CXCR4 expression was linked to increased TME heterogeneity, driven by oxidative stress and activation of the NF-κB pathway, promoting immune evasion and tumor progression. Silencing CXCR4 significantly inhibited the invasive and proliferative abilities of AGS and SNU-1 cells, while also reducing the expression of pro-inflammatory cytokines IL-1β and interleukin-6, thus alleviating chronic inflammation and improving TME conditions. In conclusion, our comprehensive investigation highlights CXCR4 as a key mediator of TME dynamics and immune modulation in GC. Targeting CXCR4 presents a promising therapeutic strategy to slow tumor progression by reducing Tregs-mediated immune exhaustion and TME heterogeneity, positioning it as a novel therapeutic target in GC treatment.

Non-alcoholic fatty liver disease (NAFLD) is a liver pathology concomitant with metabolic disarrangement. This study assessed the therapeutic impacts of delphinidin, an anthocyanin, or α-amyrin, a pentacyclic triterpenoid, on NAFLD in rats and the underlying mechanisms involved. NAFLD was established by feeding a high-fat diet (HFD) for 10 weeks, either alone or in combination with delphinidin (40 mg/kg, oral) or α-amyrin (20 mg/kg, oral). Delphinidin or α-amyrin ameliorated the metabolic and histopathological perturbations induced by HFD. These compounds markedly attenuated NAFLD-induced hepatic steatosis, as evidenced by a substantial decrease in body weight, insulin resistance, and liver and adipose tissue indices. Alongside normalization of the atherogenic index, both improved HFD-mediated abnormalities in serum lipids, liver enzymes, leptin, and ghrelin levels. Moreover, their intervention activated the NFE2 like bZIP transcription factor 2 and heme oxygenase 1 pathways and abrogated HFD-triggered activation of mitogen-activated protein kinase 1 signaling. These remedies inhibited hepatic apoptosis and modulated the gene expression of lipogenic enzymes. Furthermore, histological analysis corroborated the suppression of lipid accumulation and amelioration of hepatic architecture in the treated rats. Our findings highlight the hepatoprotective value of delphinidin or α-amyrin against NAFLD and related metabolic diseases through their insulin-sensitizing, anti-inflammatory, antioxidant, and antiapoptotic effects.

Lung cancer continues to be the leading cause of cancer-related mortality worldwide. Early detection and a comprehensive understanding of tumor-immune interactions are crucial for improving patient outcomes. This study aimed to develop a novel biomarker panel utilizing peripheral blood transcriptomics and machine learning algorithms for early lung cancer diagnosis, while simultaneously providing insights into tumor-immune crosstalk mechanisms. Leveraging a training cohort (GSE135304), we employed multiple machine learning algorithms to formulate a Lung Cancer Diagnostic Score (LCDS) based on peripheral blood transcriptomic features. The LCDS model's performance was evaluated using the area under the receiver operating characteristic (ROC) curve (AUC) in multiple validation cohorts (GSE42834, GSE157086, and an in-house dataset). Peripheral blood samples were obtained from 20 lung cancer patients and 10 healthy control subjects, representing an in-house cohort recruited at the Sixth People's Hospital of Chengdu. We employed advanced bioinformatics techniques to explore tumor-immune interactions through comprehensive immune infiltration and pathway enrichment analyses. Initial screening identified 844 differentially expressed genes, which were subsequently refined to 87 genes using the Boruta feature selection algorithm. The random forest (RF) algorithm demonstrated the highest accuracy in constructing the LCDS model, yielding a mean AUC of 0.938. Lower LCDS values were significantly associated with elevated immune scores and increased CD4+ and CD8+ T-cell infiltration, indicative of enhanced antitumor-immune responses. Higher LCDS scores correlated with activation of hypoxia, peroxisome proliferator-activated receptor (PPAR), and Toll-like receptor (TLR) signaling pathways, as well as reduced DNA damage repair pathway scores. Our study presents a novel, machine learning-derived peripheral blood transcriptomic biomarker panel with potential applications in early lung cancer diagnosis. The LCDS model not only demonstrates high accuracy in distinguishing lung cancer patients from healthy individuals but also offers valuable insights into tumor-immune interactions and underlying cancer biology. This approach may facilitate early lung cancer detection and contribute to a deeper understanding of the molecular and cellular mechanisms underlying tumor-immune crosstalk. Furthermore, our findings on the relationship between LCDS and immune infiltration patterns may have implications for future research on therapeutic strategies targeting the immune system in lung cancer.

Mitochondrial dysfunction may precipitate intestinal dysfunction, while inflammatory bowel disease manifests as a chronic inflammatory ailment affecting the gastrointestinal tract. This condition disrupts the barrier function of the intestinal epithelium and alters metabolic products. Increasing mitochondrial adenosine triphosphate (ATP) synthesis in intestinal epithelial cells presents a promising avenue for colitis treatments. Nevertheless, the impact of cedrol on ATP and the intestinal barrier remains unexplored. Hence, this study is dedicated to examining the cedrol's protective effect on an inflammatory cocktail (IC)–induced intestinal epithelial barrier dysfunction in Caco-2 cells. The finding reveals that cedrol enhances ATP content and the transepithelial electrical resistance value in the intestinal epithelial barrier. Moreover, cedrol mitigates the IC-induced decrease in the messenger ribonucleic acid (mRNA) expression of tight junction proteins (ZO-1, Occludin, and Claudin-1), thereby ameliorating intestinal epithelial barrier dysfunction. Furthermore, nuclear magnetic resonance (NMR)–based metabolomic analysis indicated that IC-exposed Caco-2 cells are restored by cedrol treatments. Notably, cedrol elevates metabolites such as amino acids, thereby enhancing the intestinal barrier. In conclusion, cedrol alleviates IC-induced intestinal epithelial barrier dysfunction by promoting ATP-dependent proliferation of Caco-2 cells and bolstering amino acid levels to sustain tight junction messenger ribonucleic acid expression.

To explore the potential mechanisms which O-linked-N-acetylglucosaminylation (O-GlcNAcylation) regulates osteogenesis, a publicly RNA-seq dataset was re-analyzed with literature-mining and showed the primary targets of O-GlcNAcylation in osteoblasts are mitochondria/cytoskeleton. Although the O-GlcNAcylation-regulated mitochondria/cytoskeleton has been extensively studied, its specific role during osteogenesis remains unclear. To address this, we knocked out Ogt (Ogt-KO) in MC3T3-E1 osteoblastic cells. Then, significantly reduced osteoblast differentiation, motility, proliferation, mitochondria–endoplasmic reticulum (Mito–ER) coupling, volume of ER, nuclear tubulins, and oxygen metabolism were observed in Ogt-KO cells. Through artificial intelligence (AI)-predicted cellular structures, the time-lapse live cells imaging with reactive-oxygen-species/hypoxia staining showed that lower cell proliferation and altered oxygen metabolism in the Ogt-KO cells were correlated with the Mito–ER coupling. Bioinformatics analysis, combined with correlated mRNA and protein expression, suggested that Ezh2 and its downstream targets (Opa1, Gsk3a, Wnt3a, Hif1a, and Hspa9) may be involved in O-GlcNAcylation-regulated Mito–ER coupling, ultimately impacting osteoblast differentiation. In conclusion, our findings indicate that O-GlcNAcylation-regulated osteoblast differentiation is linked to morphological changes in mitochondria, cytoskeleton, and ER, with Ezh2 potentially playing a crucial role.

The proliferation, metastasis, and drug resistance of cancer cells pose significant challenges to the treatment of lung squamous cell carcinoma (LUSC). However, there is a lack of optimal predictive models that can accurately forecast patient prognosis and guide the selection of targeted therapies. The extensive multi-omic data obtained from multi-level molecular biology provides a unique perspective for understanding the underlying biological characteristics of cancer, offering potential prognostic indicators and drug sensitivity biomarkers for LUSC patients. We integrated diverse datasets encompassing gene expression, DNA methylation, genomic mutations, and clinical data from LUSC patients to achieve consensus clustering using a suite of 10 multi-omics integration algorithms. Subsequently, we employed 10 commonly used machine learning algorithms, combining them into 101 unique configurations to design an optimal performing model. We then explored the characteristics of high- and low-risk LUSC patient groups in terms of the tumor microenvironment and response to immunotherapy, ultimately validating the functional roles of the model genes through in vitro experiments. Through the application of 10 clustering algorithms, we identified two prognostically relevant subtypes, with CS1 exhibiting a more favorable prognosis. We then constructed a subtype-specific machine learning model, LUSC multi-omics signature (LMS) based on seven key hub genes. Compared to previously published LUSC biomarkers, our LMS score demonstrated superior predictive performance. Patients with lower LMS scores had higher overall survival rates and better responses to immunotherapy. Notably, the high LMS group was more inclined toward “cold” tumors, characterized by immune suppression and exclusion, but drugs like dasatinib may represent promising therapeutic options for these patients. Notably, we also validated the model gene SERPINB13 through cell experiments, confirming its role as a potential oncogene influencing the progression of LUSC and as a promising therapeutic target. Our research provides new insights into refining the molecular classification of LUSC and further optimizing immunotherapy strategies.

Paclitaxel (PTX)-induced peripheral neuropathy (PIPN) is a disabling side effect of PTX, which adversely affects the life quality of cancer patients. Flavonoids such as hesperidin methyl chalcone (HMC) and taxifolin (TAX) can alleviate neuropathic pain via their anti-inflammatory, antioxidant, neuroprotective, and antinociceptive properties. The current study aimed to assess the efficacy of HMC and TAX in preventing PIPN individually or in combination. Pretreatment with HMC and TAX mitigated PTX-induced mechanical allodynia and hyperalgesia, cold allodynia, and thermal hyperalgesia as well as restore the normal histological architecture. Remarkably, neuropathic pain was relieved by suppression of nerve growth factor (NGF), p38 mitogen-activated protein kinase (p38 MAPK), and transient receptor potential vanilloid type-1 (TRPV1), which ultimately lead to reduced calcitonin gene-related peptide (CGRP). Furthermore, both HMC or TAX enhanced nuclear factor erythroid 2–related factor 2 (Nrf2), leading to elevated glutathione (GSH) and total antioxidant capacity (TAC) along with lowered malondialdehyde (MDA), which in turn, downregulated nuclear factor kappa B P65 (NF-κB P65) and its phosphorylated form and eventually reduced tumor necrosis factor alpha (TNF-α) and interleukin-1 beta (IL-1β) then lowered the apoptotic indices. Promisingly, the combination of both agents was superior to each drug alone through targeting more diverse signaling pathways and achieving synergistic and comprehensive therapeutic effects. In conclusion, pretreatment with HMC and TAX separately or in combination alleviated PIPN via modulating NGF/p38 MAPK/NF-κB P65/TRPV1/CGRP pathway.

Indoor air pollution is a recognized emerging threat, claiming millions of lives annually. People are constantly exposed to ambient and indoor air pollution. The latest research shows that people in developed countries spend up to 90% of their time indoors and almost 70% at home. Although impaired Indoor Air Quality (IAQ) represents a significant health risk, it affects people differently, and specific populations are more vulnerable: children, the elderly, and people with respiratory illnesses are more sensitive to these environmental risks. Despite rather extensive research on IAQ, most of the current understanding about the subject, which includes pollution sources, indoor–outdoor relationships, and ventilation/filtration, is still quite limited, mainly because air quality monitoring in the EU is primarily focused on ambient air quality and regulatory requirements are lacking for indoor environments. Therefore, the EDIAQI project aims to improve guidelines and awareness for advancing the IAQ in Europe and beyond by allowing user-friendly access to information about indoor air pollution exposures, sources, and related risk factors. The solution proposed with EDIAQI consists of conducting a characterization of sources and routes of exposure and dispersion of chemical, biological, and emerging indoor air pollution in multiple cities in the EU. The project will deploy cost-effective/user-friendly monitoring solutions to create new knowledge on sources, exposure routes, and indoor multipollutant body burdens. The EDIAQI project brings together 18 organizations from 11 different European countries that provide interdisciplinary skills and expertise in various fields, including environmental science and technology, medicine, and toxicology, as well as policy design and public engagement.