Background and Objectives: Head and neck squamous cell carcinoma (HNSCC) is a heterogeneous malignancy influenced by various genetic and environmental factors. Mast cells (MCs), typically associated with allergic responses, have recently emerged as key regulators of the HNSCC tumor microenvironment (TME). This systematic review explores the role of MCs in HNSCC pathogenesis and their potential as prognostic markers and therapeutic targets. Materials and Methods: A systematic search was conducted in the PubMed, Scopus and ClinicalTrials.gov databases until 31 December 2023, using \"Mast cells\" AND \"Head and neck squamous cell carcinoma\" as search terms. Studies in English which reported on MCs and HNSCC were included. Screening, data extraction and analysis followed PRISMA guidelines. No new experiments were conducted. Results: Out of 201 articles, 52 studies met the inclusion criteria, 43 of which were published between 2020 and 2023. A total of 28821 HNSCC and 9570 non-cancerous tissue samples had been examined. MC density and activation varied among normal tissues and HNSCC. Genetic alterations associated with MCs were identified, with specific gene expressions correlating with prognosis. Prognostic gene signatures associated with MC density were established. Conclusions: MCs have arisen as multifaceted TME modulators, impacting various aspects of HNSCC development and progression. Possible site-specific or HPV-related differences in MC density and activation should be further elucidated. Despite conflicting findings on their prognostic role, MCs represent promising targets for novel therapeutic strategies, necessitating further research and clinical validation for personalized HNSCC treatment.

Cancer-associated thrombosis is a significant complication in cancer patients, leading to increased morbidity and mortality. The expression of coagulation/fibrinolysis genes, termed the \"coagulome\", plays a critical role in this process. Using the single-sample gene set enrichment analysis (ssGSEA), we identified seven cancer types with significantly activated coagulation pathways, focusing on lower-grade glioma (LGG) and stomach adenocarcinoma due to their predictive value for overall survival. Through 1000 iterations of the Least Absolute Shrinkage and Selection Operator (LASSO), we selected prognostic genes and constructed effective Cox regression models, particularly for LGG. Incorporating clinical characteristics, we constructed a nomogram for LGG, achieving an impressive area under the curve (AUCs) of 0.79, 0.82, and 0.81 at 1, 3, and 5 years in the test dataset, indicating strong potential for clinical application. Functional enrichment analysis between high-risk and low-risk LGG groups revealed significant enrichment of genes involved in the inflammatory response, interferon-gamma response, and epithelial-mesenchymal transition pathways. Combined with CIBERSORT and single-cell RNA sequencing analysis of LGG, our results demonstrated that the interplay between coagulation and the tumor microenvironment, particularly involving gliomas and myeloid cells, significantly influences tumor progression and patient outcomes.

Disulfidptosis, an innovative type of controlled cellular death linked to metabolic dysfunction, has garnered attention. However, there is limited knowledge regarding the involvement of disulfidptosisrelated lnRNAs (DRlncRNAs) in laryngeal squamous cell carcinoma (LSCC). The objective of our team in this study seeks to establish a DRlncRNAs signature, investigate their prognostic value in LSCC, and explore their associations with immune cell subpopulations, biological signaling pathways, and exploring implications for drug sensitivity. We accessed LSCC patients\' RNA-seq data and pertinent clinical data for subsequent further analysis from The Cancer Genome Atlas (TCGA) portal. A literature search was conducted focusing on disulfidptosis-related genes. Pearson correlation coefficients were calculated to identify DRlncRNAs. Differential expression analysis of lncRNAs was performed. Utilizing univariate Cox regression analysis, we identified disulfidptosis-associated prognostic lncRNAs. The LASSO-Cox regression analysis was employed to refine this set of lncRNAs and construct a disulfidptosis-related lncRNAs signature. Various statistical techniques were employed to appraise model predictive performance. Subsequently, risk groups were stratified based on the risk score derived from the DRlncRNAs signature. The superiority of the risk score in prognostication over traditional clinicopathological features in LSCC patients was demonstrated. Evident distinctions emerged between risk groups, particularly in immune cell subpopulations like activated mast cells, eosinophils, and activated NK cells. Finally, the low-risk group demonstrated reduced IC50 values for specific chemotherapeutics like cisplatin and gemcitabine. The in vitro experiments indicated differential behavior of our DRlncRNAs. The DRlncRNAs signature can serve as a robust biomarker with the ability to predict both prognosis and therapeutic responses among patients with LSCC.

BACKGROUND: Gastric cancer is a life-threatening disease that poses a serious risk to human health. Although there are numerous molecular targets for gastric cancer in clinical practice, they often exhibit low specificity and sensitivity. Consequently, this can result in a low early diagnosis rate, delayed treatment, and poor prognosis for patients with gastric cancer. Hence, it remains crucial to identify more precise diagnostic markers for this disease.
METHODS: This study utilized ceRNA chips and bioinformatics methods to investigate the key genes and mechanisms involved in matrine intervention in gastric cancer cells.
RESULTS: ADAM12 and PDGFRB are the key genes that are down-regulated after matrine intervention in gastric cancer cells. By conducting bioinformatics analysis, two ceRNA regulatory axes were identified, which are associated with the prognosis of gastric cancer. These axes are lncRNA DGCR5/hsa-miR-206/ADAM12 and circRNA ITGA3/hsa-miR-24-3p/PDGFRB.
CONCLUSIONS: The low expression of ADAM12 may weaken the digestion of extracellular matrix (ECM) molecules, which can result in the invasion and metastasis of tumor cells. This occurs without the catalysis of ECM proteases, thereby impacting the invasion and metastasis of gastric cancer cells. Additionally, the analysis of immune infiltration suggests that ADAM12 and PDGFRB may influence changes in the tumor immune microenvironment, thereby affecting the occurrence and development of gastric cancer. This study contributes to a deeper understanding of the role of the matrine-related ceRNA network in gastric cancer, providing a reference for clinical diagnosis and treatment. It holds significant importance in discovering new drug treatment targets.

Lung adenocarcinoma (LUAD) is the most prevalent subtype of non-small cell lung cancer. Additionally, disulfidptosis, a newly discovered type of cell death, has been found to be closely associated with the onset and progression of tumors.
The study first identified genes related to disulfidptosis through correlation analysis. These genes were then screened using univariate cox regression and LASSO regression, and a prognostic model was constructed through multivariate cox regression. A nomogram was also created to predict the prognosis of LUAD. The model was validated in three independent data sets: GSE72094, GSE31210, and GSE37745. Next, patients were grouped based on their median risk score, and differentially expressed genes between the two groups were analyzed. Enrichment analysis, immune infiltration analysis, and drug sensitivity evaluation were also conducted.
In this study, we examined 21 genes related to disulfidptosis and developed a gene signature that was found to be associated with a poorer prognosis in LUAD. Our model was validated using three independent datasets and showed AUC values greater than 0.5 at 1, 3, and 5 years. Enrichment analysis revealed that the disulfidptosis-related genes signature had a multifaceted impact on LUAD, particularly in relation to tumor development, proliferation, and metastasis. Patients in the high-risk group exhibited higher tumor purity and lower stromal score, ESTIMATE score, and Immune score.
This study constructed a gene signature related to disulfidptosis in lung adenocarcinoma and analyzed its impact on the disease and its association with the tumor microenvironment. The findings of this research provide valuable insights into the understanding of lung adenocarcinoma and could potentially lead to the development of new treatment strategies.

Background: Colon cancer (CC) is a prevalent malignant tumor that affects people all around the world. In this study, N6-methylandenosine-related long non-coding RNAs (m6A-related lncRNAs) in 473 colon cancers and 41 adjacent tissues of CC patients from The Cancer Genome Atlas (TCGA) were investigated. Method: The Pearson correlation analysis was conducted to examine the m6A-related lncRNAs, and the univariate Cox regression analysis was performed to screen 38 prognostic m6A-related lncRNAs. The least absolute shrinkage and selection operator (LASSO) regression analysis were carried out on 38 prognostic lncRNAs to develop a 14 m6A-related lncRNAs prognostic signature (m6A-LPS) in CC. The availability of the m6A-LPS was evaluated using the Kaplan-Meier and Receiver Operating Characteristic (ROC) curves. Results: Three m6A modification patterns with significantly different N stages, survival time, and immune landscapes were identified. It has been discovered that the m6A-LPS, which is based on 14 m6A-related lncRNAs (TNFRSF10A-AS1, AC245041.1, AL513550.1, UTAT33, SNHG26, AC092944.1, ITGB1-DT, AL138921.1, AC099850.3, NCBP2-AS1, AL137782.1, AC073896.3, AP006621.2, AC147651.1), may represent a new, promising biomarker with great potential. It was re-evaluated in terms of survival rate, clinical features, tumor infiltration immune cells, biomarkers related to Immune Checkpoint Inhibitors (ICIs), and chemotherapeutic drug efficacy. The m6A-LPS has been revealed to be a novel potential and promising predictor for evaluating the prognosis of CC patients. Conclusion: This study revealed that the risk signature is a promising predictive indicator that may provide more accurate clinical applications in CC therapeutics and enable effective therapy strategies for clinicians.

Neuroblastoma is a childhood neurological tumor which affects hundreds of thousands of children worldwide, and information about its prognosis can be pivotal for patients, their families, and clinicians. One of the main goals in the related bioinformatics analyses is to provide stable genetic signatures able to include genes whose expression levels can be effective to predict the prognosis of the patients. In this study, we collected the prognostic signatures for neuroblastoma published in the biomedical literature, and noticed that the most frequent genes present among them were three: AHCY, DPYLS3, and NME1. We therefore investigated the prognostic power of these three genes by performing a survival analysis and a binary classification on multiple gene expression datasets of different groups of patients diagnosed with neuroblastoma. Finally, we discussed the main studies in the literature associating these three genes with neuroblastoma. Our results, in each of these three steps of validation, confirm the prognostic capability of AHCY, DPYLS3, and NME1, and highlight their key role in neuroblastoma prognosis. Our results can have an impact on neuroblastoma genetics research: biologists and medical researchers can pay more attention to the regulation and expression of these three genes in patients having neuroblastoma, and therefore can develop better cures and treatments which can save patients\' lives.

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Understanding if divergent molecular profiles of DNA damage and repair (DDR) pathway activity, a biomarker of disease progression, exist in prostate tumors with favorable-risk features is an unmet need, which this study aim to unearth.
This was a multicenter registry genome-wide expression profiling study of prospectively collected radical prostatectomy (RP) tumor samples from 2014 to 2016. DDR activity was calculated from average expression of 372 DDR genes. Consensus hierarchical clustering was used to arrive at a robust clustering solution based on DDR gene expression patterns. Genome-wide differential expression between clusters was performed, and outcomes were evaluated across expression patterns.
Of 5239 patients from the prospective registry, 376 had favorable-risk disease (Grade group [GG] 1 to 2, PSA prior to RP <10ng/ml, pT2 or less). DDR activity score was correlated with prognostic genomic signatures that predict for metastatic risk (r = 0.37, P < 2e-16) and high grade groups (P < .001). High DDR activity (top-quartile) was observed in 28% of patients with favorable-risk disease. In favorable-risk disease, 3 distinct clusters with varied DDR activity emerged with consensus clustering. Cluster I (compared with cluster II-III and GG3-GG5 disease) had the highest expression of all DDR sub-pathways, MYC, PAPR1, AR, and AR activity (P < .001 for all). Furthermore, cluster I was associated with poorer metastasis-free survival (MFS) and Overall survival (OS) compared with other clusters (MFS; HR: 2.43, 95%CI, [1.22-4.83], P = .01; OS; HR: 2.77, 95%CI, [1.18-6.5], P = .01).
Cluster I is a novel subgroup of favorable-risk disease with high DDR activity, AR activity, PARP1 and chr8q/MYC expression, and poorer MFS and OS.

Necroptosis is a regulated form of cell necroptotic process, playing a pivotal role in tumors. In renal cell cancer (RCC), inhibiting necroptosis could promote the proliferation of tumor cells. However, the molecular mechanisms and prognosis prediction of necroptotic-process-related genes in RCC are still unclear. In this study, we first identified the necroptotic process prognosis-related genes (NPRGss) by analyzing the kidney renal clear cell carcinoma (KIRC) data in The Cancer Genome Atlas (TCGA, n=607). We systematically analyzed the expression alteration, clinical relevance, and molecular mechanisms of NPRGss in renal clear cell carcinoma. We constructed an NPRGs risk signature utilizing the least absolute shrinkage and selection operator (LASSO) Cox regression analysis on the basis of the expression of seven NPRGss. We discovered that the overall survival (OS) of KIRC patients differed significantly in high- or low-NPRGs-risk groups. The univariate/multivariate Cox regression revealed that the NPRGs risk signature was an independent prognosis factor in RCC. The gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA) were used to explore the molecular mechanisms of NPRGss. Immune-/metabolism-related pathways showed differential enrichment in high-/low-NPRGs-risk groups. The E-MTAB-1980, TCGA-KIRP, GSE78220, the cohort of Alexandra et al., and IMvigor210 cohort datasets were respectively used as independent validation cohorts of NPRGs risk signature. The patients in high- or low-NPRGs-risk groups showed different drug sensitivity, immune checkpoint expression, and immune therapy response. Finally, we established a nomogram based on the NPRGs risk signature, stage, grade, and age for eventual clinical translation; the nomogram possesses an accurate and stable prediction effect. The signature could predict patients\' prognosis and therapy response, which provides the foundation for further clinical therapeutic strategies for RCC patients.