Comprehensive analysis of the functions, prognostic and diagnostic values of RNA binding proteins in head and neck squamous cell carcinoma.

BACKGROUND: Accumulating evidence has suggested that RNA binding protein (RBP) dysregulation plays an essential role during tumorigenesis. Here, we sought to explore the potential biological functions and clinical significance of RBP and develop diagnostic and prognostic signatures based on RBP in patients with head and neck squamous cell carcinoma (HNSCC). METHODS: The differently expressed RBPs between HNSCC samples and their normal counterparts were identified using the Limma package. The immunohistochemistry (IHC) images of several RBPs were collected from the Human Protein Atlas database. The diagnostic signature based on RBP was built by LASSO-logistic regression and random forest. The prognostic signature based on RBP was constructed by LASSO and stepwise Cox regression analysis in the training cohort and validated in the validation cohort. RESULTS: Eighty-four aberrantly expressed RBPs were obtained, comprising 41 up-regulated and 43 down-regulated RBPs. Seven RBP genes (CPEB3, PDCD4, ENDOU, PARP12, DNMT3B, IGF2BP1, EXO1) were identified as diagnostic-related hub genes. They were used to establish a diagnostic RBP signature risk score (DRBPS) model by the coefficients in least absolute shrinkage and selection operator (LASSO)-logistic regression analysis and showed high specificity and sensitivity in the training (area under the receiver operating characteristic curve (AUC) = 0.998), and in all validation cohorts (AUC > 0.95 for all). Similarly, seven RBP genes (MKRN3, ZC3H12D, EIF5A2, AFF3, SIDT1, RBM24, and NR0B1) were identified as prognosis-associated hub genes by LASSO and stepwise multiple Cox regression analyses and were used to construct the prognostic model named as PRBPS. The AUC of the time-dependent receiver operator characteristic curve of the prognostic model was 0.664 at 3 years and 0.635 at 5 years in the training cohort and 0.720, 0.777 in the validation cohort, showing a favorable predictive efficacy for prognosis in HNSCC. CONCLUSIONS: Our results demonstrate the value of consideration of RBP in the diagnosis and prognosis for HNSCC and provide a novel insight into understanding the potential role of dysregulated RBP in HNSCC.

Establishment of the Diagnostic Signature of Ferroptosis Genes in Multiple Sclerosis.

Ferroptosis is a novel form of membrane-dependent cell death that differs from other cell death modalities such as necrosis, apoptosis, and autophagy. Multiple sclerosis (MS) is a chronic inflammatory disease of the central nervous system primarily affecting brain and spinal cord neurons. Although the pathogenesis of these two conditions may seem unrelated, recent studies have indicated a connection between ferroptosis and multiple sclerosis. In fact, ferroptosis plays a significant role in the development of MS, as evidenced by the presence of elevated iron levels and iron metabolism abnormalities in the brains, spinal cords, and other neurons of MS patients. These abnormalities disrupt iron homeostasis within cells, leading to the occurrence of ferroptosis. However, there is currently a lack of research on the diagnostic value of ferroptosis-related genes in multiple sclerosis. In this study, we employed bioinformatics methods to identify ferroptosis-related genes (ATM, GSK3B, HMGCR, KLF2, MAPK1, NFE2L1, NRAS, PCBP1, PIK3CA, RPL8, VDAC3) associated with the diagnosis of multiple sclerosis and constructed a diagnostic model. The results demonstrated that the diagnostic model accurately identified the patients' condition. Subsequently, subgroup analysis was performed based on the expression levels of ferroptosis-related genes, dividing patients into high and low expression groups. The results showed differences in immune function and immune cell infiltration between the two groups. Our study not only confirms the correlation between ferroptosis and multiple sclerosis but also demonstrates the diagnostic value of ferroptosis-related genes in the disease. This provides guidance for clinical practice and direction for further mechanistic research.

FPR1, as a Potential Biomarker of Diagnosis and Infliximab Therapy Responses for Crohn's Disease, is Related to Disease Activity, Inflammation and Macrophage Polarization.

Purpose: Crohn's disease (CD) represents a multifaceted inflammatory gastrointestinal condition, with a profound significance placed on unraveling its molecular pathways to enhance both diagnostic capabilities and therapeutic interventions. This study focused on identifying a robust macrophage-related signatures (MacroSig) for diagnosing CD, emphasizing the role of FPR1 in macrophage polarization and its implications in CD. Patients and Methods: Expression profiles from intestinal biopsies and macrophages of 1804 CD patients were retrieved from the Gene Expression Omnibus (GEO). Utilizing CIBERSORTx, differential expression analysis, and weighted correlation network analysis to to identify macrophage-related genes (MRGs). By unsupervised clustering, distinct clusters of CD were identified. Potential biomarkers were identified via using four machine learning algorithms, leading to the establishment of MacroSig which combines insights from 12 machine learning algorithms. Furthermore, the expression of FPR1 was verified in intestinal biopsies of CD patients and two murine experimental colitis models. Finally, we further explored the role of FPR1 in macrophage polarization through single-cell analysis as well as through the study of RAW264.7 cells and peritoneal macrophages. Results: Two distinct clusters with differential levels of macrophage infiltration and inflammation were identified. The MacroSig, which included FPR1 and LILRB2, exhibited high diagnostic accuracy and outperformed existing biomarkers and signatures. Clinical analysis demonstrated a strong correlation of FPR1 with disease activity, endoscopic inflammation status, and response to infliximab treatment. The expression levels of FPR1 were validated in our CD cohort by immunohistochemistry and confirmed in two colitis mouse models. Single-cell analysis indicated that FPR1 is predominantly expressed in macrophages and monocytes. In vitro studies demonstrated that FPR1 was upregulated in M1 macrophages, and its activation promoted M1 polarization. Conclusion: We developed a promising diagnostic signature for CD, and targeting FPR1 to modulate macrophage polarization may represent a novel therapeutic strategy.

Genome-Wide DNA Methylation in Early-Onset-Dementia Patients Brain Tissue and Lymphoblastoid Cell Lines.

Epigenetics, a potential underlying pathogenic mechanism of neurodegenerative diseases, has been in the scope of several studies performed so far. However, there is a gap in regard to analyzing different forms of early-onset dementia and the use of Lymphoblastoid cell lines (LCLs). We performed a genome-wide DNA methylation analysis on sixty-four samples (from the prefrontal cortex and LCLs) including those taken from patients with early-onset forms of Alzheimer's disease (AD) and frontotemporal dementia (FTD) and healthy controls. A beta regression model and adjusted p-values were used to obtain differentially methylated positions (DMPs) via pairwise comparisons. A correlation analysis of DMP levels with Clariom D array gene expression data from the same cohort was also performed. The results showed hypermethylation as the most frequent finding in both tissues studied in the patient groups. Biological significance analysis revealed common pathways altered in AD and FTD patients, affecting neuron development, metabolism, signal transduction, and immune system pathways. These alterations were also found in LCL samples, suggesting the epigenetic changes might not be limited to the central nervous system. In the brain, CpG methylation presented an inverse correlation with gene expression, while in LCLs, we observed mainly a positive correlation. This study enhances our understanding of the biological pathways that are associated with neurodegeneration, describes differential methylation patterns, and suggests LCLs are a potential cell model for studying neurodegenerative diseases in earlier clinical phases than brain tissue.

hdWGCNA and Cellular Communication Identify Active NK Cell Subtypes in Alzheimer's Disease and Screen for Diagnostic Markers through Machine Learning.

BACKGROUND: Alzheimer's disease (AD) is a recognized complex and severe neurodegenerative disorder, presenting a significant challenge to global health. Its hallmark pathological features include the deposition of ß-amyloid plaques and the formation of neurofibrillary tangles. Given this context, it becomes imperative to develop an early and accurate biomarker model for AD diagnosis, employing machine learning and bioinformatics analysis. METHODS: In this study, single-cell data analysis was employed to identify cellular subtypes that exhibited significant differences between the diseased and control groups. Following the identification of NK cells, hdWGCNA analysis and cellular communication analysis were conducted to pinpoint NK cell subset with the most robust communication effects. Subsequently, three machine learning algorithms-LASSO, Random Forest, and SVM-RFE-were employed to jointly screen for NK cell subset modular genes highly associated with AD. A logistic regression diagnostic model was then designed based on these characterized genes. Additionally, a protein-protein interaction (PPI) networks of model genes was established. Furthermore, unsupervised cluster analysis was conducted to classify AD subtypes based on the model genes, followed by the analysis of immune infiltration in the different subtypes. Finally, Spearman correlation coefficient analysis was utilized to explore the correlation between model genes and immune cells, as well as inflammatory factors. RESULTS: We have successfully identified three genes (RPLP2, RPSA, and RPL18A) that exhibit a high association with AD. The nomogram based on these genes provides practical assistance in diagnosing and predicting patients' outcomes. The interconnected genes screened through PPI are intricately linked to ribosome metabolism and the COVID-19 pathway. Utilizing the expression of modular genes, unsupervised cluster analysis unveiled three distinct AD subtypes. Particularly noteworthy is subtype C3, characterized by high expression, which correlates with immune cell infiltration and elevated levels of inflammatory factors. Hence, it can be inferred that the establishment of an immune environment in AD patients is closely intertwined with the heightened expression of model genes. CONCLUSION: This study has not only established a valuable diagnostic model for AD patients but has also delved deeply into the pivotal role of model genes in shaping the immune environment of individuals with AD. These findings offer crucial insights into early AD diagnosis and patient management strategies.

Multi-Omics Characteristics of Ferroptosis Associated with Colon Adenocarcinoma Typing and Survival.

BACKGROUND: Ferroptosis, an iron-dependent form of cell death, plays a crucial role in the progression of various cancers, including colon adenocarcinoma (COAD). However, the multi-omics signatures relevant to ferroptosis regulation in COAD diagnosis remain to be elucidated. METHODS: The transcriptomic, miRNAomic, and methylomic profiles of COAD patients were acquired from the Cancer Genome Atlas (TCGA). Ferroptosis activity in these patients was determined, represented by a ferroptosis score (FS), using single-sample gene set enrichment analysis (ssGSEA) based on the expression of ferroptosis-related genes. RESULTS: Results showed that the COAD patients with high-FS displayed favorable survival outcomes and heightened drug sensitivity. They also exhibited an up-regulation of genes involved in immune-related pathways (e.g., tumor necrosis factor signaling pathway), suggesting a correlation between immunity and ferroptosis in COAD progression. Furthermore, three survival prediction models were established based on 10 CpGs, 12 long non-coding RNAs (lncRNAs), and 14 microRNAs (miRNAs), respectively. These models demonstrated high accuracy in predicting COAD survival, achieving areas under the curve (AUC) >0.7. The variables used in the three models also showed strong correlations at different omics levels and were effective at discriminating between high-FS and low-FS COAD patients (AUC >0.7). CONCLUSIONS: This study identified different DNA methylation (DNAm), lncRNA, and miRNA characteristics between COAD patients with high and low ferroptosis activity. Furthermore, ferroptosis-related multi-omics signatures were established for COAD prognosis and classification. These insights present new opportunities for improving the efficacy of COAD therapy.