Background: Laryngeal cancer has the highest mortality rate among head and neck tumours. RNA N6-methyladenosine (m6A) is the most plentiful and variable in mammalian mRNA. Yet, the m6A regulatory mechanism underlying the carcinogenesis or progression of LSCC remains poorly understood. Methods: The m6A RNA methylation quantification kit was used to detect tissue methylation levels. m6A microarray analysis, mRNA transcriptomic sequencing (mRNA-seq), and proteomics were used to determine RBM15, TMBIM6, and IGF2BP3. Immunohistochemical (IHC), quantitative real-time PCR (qRT-PCR) and Western blot were used to investigate RBM15, TMBIM6, and IGF2BP3 expression in tissue samples and cell lines. The biological effects of RBM15 were detected both in vitro and in vivo. The combination relationship between RBM15/IGF2BP3 and TMBIM6 was verified by RNA immunoprecipitation (RIP) assay, Methylated RNA immunoprecipitation sequencing (MeRIP-seq), RNase Mazf, and luciferase report assay. RNase Mazf was used to determine the methylation site on TMBIM6 mRNA. Hoechst staining assay was used to confirm the apoptotic changes. The actinomycin D verified TMBIM6 stability. Results: The global mRNA m6A methylation level significantly increased in LSCC patients. RBM15, as a “writer” of methyltransferase, was significantly increased in LSCC and was associated with unfavorable prognosis. The knockdown of RBM15 reduced the proliferation, invasion, migration, and apoptosis of LSCC both in vitro and in vivo. The results were reversed after overexpressing RBM15. Mechanically, TMBIM6 acted as a downstream target of RBM15-mediated m6A modification. Furthermore, RBM15-mediated m6A modification of TMBIM6 mRNA enhanced TMBIM6 stability through IGF2BP3-dependent. Conclusion: Our results revealed the essential roles of RBM15 and IGF2BP3 in m6A methylation modification in LSCC, thus identifying a novel RNA regulatory mechanism.

Translation machinery associated 7 homolog (TMA7) is closely related to proliferation-related diseases. However, the function and regulatory mechanism of TMA7 in laryngeal squamous cell carcinoma (LSCC) remain unclear. The present study aimed to investigate the effect of TMA7 on the occurrence and development of LSCC and to study the mechanism of TMA7. TMA7 is upregulated in LSCC tissues and associated with poor prognosis. After TMA7 downregulation, the autophagy level was increased, and the proliferation, migration, and invasion of LSCC cells were inhibited. The m6A methylated reader IGF2BP3 enhanced the stability of TMA7 and reduced the level of autophagy. TMA7 interacted directly with UBA2. Furthermore, the activation of the IGF2BP3-regulated TMA7-UBA2-PI3K pathway is the primary mechanism by which TMA7 inhibits autophagy and promotes the progression of LSCC. The current study revealed that IGF2BP3-mediated TMA7 m6A modification promotes LSCC progression and cisplatin-resistance through UBA2-PI3K pathway, providing new insights into the autophagy-related mechanism, potential biomarkers, and therapeutic targets for LSCC.

Sanguisorba officinalis L. (S. officinalis) has been widely distributed in various regions of China and holds significant potential for various applications. However, limited studies have focused on S. officinalis polysaccharides (SOPs) and their biological activities. Utilizing response surface methodology (RSM), the process of ultrasonic-assisted extraction of SOPs was optimized. The optimal conditions were 270.2 W, 42.4 mL/g, and 47.7 min. Through separation and purification, a neutral polysaccharide named SOUP-1 was obtained. SOUP-1 (17.1 kDa) mainly includes glucose and a few galactose, mannose, and arabinose. The main chain of SOUP-1 is primarily → 4)-α-D-Glcp-(1 → 4,6)-α-D-Glcp-(1 → substituted at C6 with branch chain → 6)-α-D-Glcp-(1 → . Furthermore, SOUP-1 showed antioxidant activity, including scavenging activities of hydroxyl radicals and DPPH. It also enhances macrophage phagocytic activity and modulates the expression of inflammatory cytokines such as TNF-α and IL-6. Additionally, this study first discovered the hemostatic activity of SOUP-1. This study provides theoretical support for the potential applications and further research of SOPs. It provides a basis for the development of SOPs as novel natural-source immunomodulators in the food and pharmaceutical industries.

Background: Recent research has highlighted pyroptosis as a key factor in cancer progression. This study aims to explore the association between pyroptosis-related signatures and overall survival (OS) in head and neck squamous cell carcinoma (HNSC) and develop a pyroptosis-related long non-coding RNA (lncRNA) risk model to predict prognosis and response to immunotherapy in HNSC.Methods: We extracted expression data for 18 pyroptosis-related genes and identified lncRNA probes specific to HNSC by using datasets from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA). Consensus clustering was performed to categorize HNSC patients into distinct subtypes. A six-lncRNA risk score model was constructed through univariate and least absolute shrinkage and selection operator (LASSO) Cox regression analyses. We evaluated the predictive ability of the lncRNA model for patients’ survival and immunotherapy response. Gene expression was evaluated using immunohistochemistry (IHC) and Reverse Transcription Quantitative Polymerase Chain Reaction (RT-qPCR).Results: Our analysis revealed two distinct pyroptosis-related subtypes in HNSC patients, Cluster A and Cluster B. Notably, patients in Cluster B exhibited significantly poorer overall survival compared to those in Cluster A. Through differential expression analysis, we identified six lncRNAs (AC002331.1, CTA-384D8.35, RP11-291B21.2, AC006262.5, RP1-27K12.2, and RP11-54H7.4) that were differentially expressed between these clusters. A 6-lncRNA risk score model was developed, which successfully stratified patients into high- and low-risk groups with distinct overall survival outcomes. Validation using RT-qPCR confirmed the differential expression of these six lncRNAs in HNSC tumor tissues compared to adjacent normal tissues, we found that the expression of CTA-384D8.35 was significantly increased in the tumor group (t=-6.203, P<0.001). Furthermore, the 6-lncRNA risk score model demonstrated a significant association with patient response to immunotherapy, with the low-risk group exhibiting a higher objective response rate to immune checkpoint blockade (ICB) therapy and longer survival compared to the high-risk group.Conclusion: Our study underscores the role of pyroptosis signatures in HNSC prognosis and identifies two distinct pyroptosis subtypes with differing survival outcomes. The six-lncRNA risk score model offers a valuable tool for predicting patient prognosis and potential benefits from ICB therapy. These findings highlight the importance of pyroptosis and associated lncRNAs in the tumor microenvironment, paving the way for novel targeted therapies in HNSC.

Laryngeal cancer (LC) is a malignant tumor that occurs in the larynx. N6-methyladenosine (m6A) RNA methylation, a pivotal and prevalent epigenetic modification in eukaryotic mRNA, intricately intertwines with ferroptosis, and together, they play a crucial role in the development of LC. Accordingly, further research on related molecular mechanisms and pathology of LC is necessary. Weighted gene co-expression network analysis and correlation analysis were used to identify differentially expressed m6A-related ferroptosis genes in LC. The TCGA-HNSC and GSE65858 datasets were obtained from public databases. The TCGA-HNSC dataset consisted of 110 primary tumor oropharynx samples and 12 control oropharynx samples, while the GSE65858 dataset contained forty-eight primary tumor oropharynx samples. Univariate Cox and least absolute shrinkage and selection operator (LASSO) regression were utilized for feature selection and risk model construction in the TCGA-HNSC dataset. The risk model was validated in the GSE65858 dataset. Then, a nomogram was built based on the independent prognostic factor identified using univariate and multivariate Cox regression in the TCGA-HNSC dataset. Mutation analysis, immune-related analysis, and drug sensitivity prediction were applied to analyze the utility of the risk model in the TCGA-HNSC dataset. Additionally, qRT-PCR and western blot were performed to detect the TFRC, RGS4, and FTH1 expression. Three biomarkers were identified to build a risk model using the univariate Cox and LASSO regression algorithms. Receiver operating characteristic (ROC) analysis verified the accuracy of the risk model. Tumor Immune Dysfunction and Exclusion (TIDE) and Estimation of STromal and Immune cells in MAlignant Tumors using the Expression data (ESTIMATE) algorithm showed a positive relationship between risk score and TIDE or ESTIMATE score. Furthermore, drug sensitivity prediction found that 19 chemotherapy drugs were strongly correlated with a risk score. TFRC, RGS4, and FTH1 exhibited high expression levels in 30 laryngeal carcinoma tissues and cell lines. Notably, TFRC and FTH1 expression levels were significantly associated with patient prognosis. In Conclusion, TFRC, RGS4, and FTH1, were identified as m6A-regulated ferroptosis biomarkers in LC, providing insights into LC treatment and prognosis.

Chronic rhinosinusitis with nasal polyps (CRSwNP) is a prevalent inflammatory disease where immunomodulation plays a pivotal role. However, immuno-transcriptomic characteristics and its clinical relevance remains largely known. We analyzed transcriptome data of 48 patients with CRSwNP and 34 healthy control subjects from different cohorts and investigated the immuno-transcriptomic characteristics. Differential immune-related genes (DIRGs) were identified and subjected to enrichment analysis. Protein–protein interaction (PPI) networks were constructed to identify hub genes. The least absolute shrinkage and selection operator (LASSO) regression model and multivariate support vector machine recursive feature elimination (mSVM-RFE) were used to identify potential biomarkers, which were validated using the real time quantitative polymerase chain reaction (RT-PCR) and immunohistochemistry (IHC). Infiltration abundance of immune cells in the microenvironment were estimated using CIBERSORT algorithm. Our study identified a total of 660 differentially expressed genes (DEGs) and 81 differentially immune-related genes (DIRGs) in CRSwNP compared to controls. Functional enrichment analysis revealed that the DIRGs were primarily associated with cell chemotaxis and leukocyte migration, and cytokine-cytokine receptor interaction. Through machine learning, we further identified five candidate genes, CXCR1, CCL13, CCR3, PPBP, and MMP9. These five potential CRSwNP biomarkers were experimentally verified in our in-house cohort. Analysis of immune cell infiltration landscape revealed significant variations in the abundance of macrophages and mast cells between CRSwNP and healthy control. Our findings illuminate the significance of immune characteristics in CRSwNP pathogenesis. Future studies focusing on these candidate genes can help elucidate the underlying mechanisms and identify potential therapeutic targets for CRSwNP.