Clinical prognosticators and targets in the immune microenvironment of intrahepatic cholangiocarcinoma.

Background: Intrahepatic cholangiocarcinoma (ICC) is a disease with poor prognosis and limited therapeutic options. We investigated the tumor immune microenvironment (TIME) to identify predictors of disease outcome and to explore targets for therapeutic modulation. Methods: Liver tissue samples were collected during 2008-2019 from patients (n = 139) diagnosed with ICC who underwent curative intent surgery without neoadjuvant chemotherapy. Samples from the discovery cohort (n = 86) were immunohistochemically analyzed on tissue microarrays (TMAs) for the expression of CD68, CD3, CD4, CD8, Foxp3, PD-L1, STAT1, and p-STAT1 in tumor core and stroma areas. Results were digitally analyzed using QuPath software and correlated with clinicopathological characteristics. For validation of TIME-related biomarkers, we performed multiplex imaging mass cytometry (IMC) in a validation cohort (n = 53). Results: CD68+ cells were the predominant immune cell type in the TIME of ICC. CD4+high T cell density correlated with better overall survival (OS). Prediction modeling together with validation cohort confirmed relevance of CD4+ cells, PD-L1 expression by immune cells in the stroma and N-stage on overall disease outcome. In turn, IMC analyses revealed that silent CD3+CD4+ clusters inversely impacted survival. Among annotated immune cell clusters, PD-L1 was most relevantly expressed by CD4+FoxP3+ cells. A subset of tumors with high density of immune cells ("hot" cluster) correlated with PD-L1 expression and could identify a group of candidates for immune checkpoint inhibition (ICI). Ultimately, higher levels of STAT1 expression were associated with higher lymphocyte infiltration and PD-L1 expression. Conclusions: These results highlight the importance of CD4+ T cells in immune response against ICC. Secondly, a subset of tumors with "hot" TIME represents potential candidates for ICI, while stimulation of STAT1 pathway could be a potential target to turn "cold" into "hot" TIME in ICC.

The tumor immune microenvironment (TIME) plays a critical role in the immune response In many cancers, including intrahepatic cholangiocarcinoma (ICC). Molecular subtyping of the ICC microenvironment already revealed inter-tumoral heterogeneity with variant profiles of immune cell infiltrates. A recent study created an in-depth immune cell atlas of the TIME in biliary tract cancers and could demonstrate the relevance of specific immune cell subpopulations on patient outcome. We are able to provide a distinctive characterization of TIME, separating tumor epithelial- and stroma areas, in a large and representative ICC cohort using digitalized image analysis on tissue microarrays (TMA) as well as multiplex imaging mass cytometry (IMC). The study was designed for identification of immune cell prognosticators allocating institutional ICC patients into a discovery (2008­15) and a validation (2010­19) cohort. Immune cell subpopulations were correlated with clinicopathological characteristics and patient outcome. Our results highlight: i. The important role of CD4+ T cell infiltration in ICC patients; ii. ICC tumors with high density of immune cells associated with PD-L1 expression identifies a subset of patients with variant tumor biology; iii. Stimulation of STAT1 pathway may be a relevant target to turn "cold" into "hot" tumors.

Research progress on ferroptosis in colorectal cancer.

Ferroptosis is a new form of cell death that differs from traditional forms of death. It is ferroptosis-dependent lipid peroxidation death. Colorectal cancer(CRC) is the most common tumor in the gastrointestinal tract with a long occultation period and a poor five-year prognosis. Exploring effective systemic treatments for CRC remains a great challenge worldwide. Numerous studies have demonstrated that ferroptosis can participate in the biological malignant process of various tumor, including CRC, so understanding the role and regulatory mechanisms of ferroptosis in CRC plays a crucial role in the treatment of CRC. In this paper, we reviews the mechanisms of ferroptosis in CRC, the associated regulatory factors and their interactions with various immune cells in the immune microenvironment. In addition, targeting ferroptosis has emerged as an encouraging strategy for CRC treatment. Finally, to inform subsequent research and clinical diagnosis and treatment, we review therapeutic approaches to CRC radiotherapy, immunotherapy, and herbal therapy targeting ferroptosis.

Identification and validation of ferroptosis-related biomarkers in intervertebral disc degeneration.

Introduction: Ferroptosis plays a significant role in intervertebral disc degeneration (IDD). Understanding the key genes regulating ferroptosis in IDD could reveal fundamental mechanisms of the disease, potentially leading to new diagnostic and therapeutic targets. Methods: Public datasets (GSE23130 and GSE70362) and the FerrDb database were analyzed to identify ferroptosis-related genes (DE-FRGs) involved in IDD. Single-cell RNA sequencing data (GSE199866) was used to validate the specific roles and expression patterns of these genes. Immunohistochemistry and Western blot analyses were subsequently conducted in both clinical samples and mouse models to assess protein expression levels across different tissues. Results: The analysis identified seven DE-FRGs, including MT1G, CA9, AKR1C1, AKR1C2, DUSP1, CIRBP, and KLHL24, with their expression patterns confirmed by single-cell RNA sequencing. Immunohistochemistry and Western blot analysis further revealed that MT1G, CA9, AKR1C1, AKR1C2, DUSP1, and KLHL24 exhibited differential expression during the progression of IDD. Additionally, the study highlighted the potential immune-modulatory functions of these genes within the IDD microenvironment. Discussion: Our study elucidates the critical role of ferroptosis in IDD and identifies specific genes, such as MT1G and CA9, as potential targets for diagnosis and therapy. These findings offer new insights into the molecular mechanisms underlying IDD and present promising avenues for future research and clinical applications.

Screening of genes co-associated with osteoporosis and chronic HBV infection based on bioinformatics analysis and machine learning.

Objective: To identify HBV-related genes (HRGs) implicated in osteoporosis (OP) pathogenesis and develop a diagnostic model for early OP detection in chronic HBV infection (CBI) patients. Methods: Five public sequencing datasets were collected from the GEO database. Gene differential expression and LASSO analyses identified genes linked to OP and CBI. Machine learning algorithms (random forests, support vector machines, and gradient boosting machines) further filtered these genes. The best diagnostic model was chosen based on accuracy and Kappa values. A nomogram model based on HRGs was constructed and assessed for reliability. OP patients were divided into two chronic HBV-related clusters using non-negative matrix factorization. Differential gene expression analysis, Gene Ontology, and KEGG enrichment analyses explored the roles of these genes in OP progression, using ssGSEA and GSVA. Differences in immune cell infiltration between clusters and the correlation between HRGs and immune cells were examined using ssGSEA and the Pearson method. Results: Differential gene expression analysis of CBI and combined OP dataset identified 822 and 776 differentially expressed genes, respectively, with 43 genes intersecting. Following LASSO analysis and various machine learning recursive feature elimination algorithms, 16 HRGs were identified. The support vector machine emerged as the best predictive model based on accuracy and Kappa values, with AUC values of 0.92, 0.83, 0.74, and 0.7 for the training set, validation set, GSE7429, and GSE7158, respectively. The nomogram model exhibited AUC values of 0.91, 0.79, and 0.68 in the training set, GSE7429, and GSE7158, respectively. Non-negative matrix factorization divided OP patients into two clusters, revealing statistically significant differences in 11 types of immune cell infiltration between clusters. Finally, intersecting the HRGs obtained from LASSO analysis with the HRGs identified three genes. Conclusion: This study successfully identified HRGs and developed an efficient diagnostic model based on HRGs, demonstrating high accuracy and strong predictive performance across multiple datasets. This research not only offers new insights into the complex relationship between OP and CBI but also establishes a foundation for the development of early diagnostic and personalized treatment strategies for chronic HBV-related OP.

Gut microbiota and autoimmune thyroid disease: a bidirectional Mendelian randomization study and mediation analysis.

Background: The gut microbiota (GM) plays a pivotal role in influencing various health outcomes, including immune-mediated conditions, but its potential association with autoimmune thyroid disease (AITD) remains underexplored. We aimed to investigate the potentially pathogenic or protective causal impacts of specific GM on two types of AITD, namely Graves' disease and Hashimoto's thyroiditis, and analyzed the mediating effect of 731 immune cell phenotypes. Methods: Leveraging pooled genome-wide association study (GWAS) data of 211 gut microbiota traits, 731 immune cell phenotypes, and two types of AITD (Hashimoto's thyroiditis and Graves' disease), we performed bidirectional Mendelian randomization (MR) analyses to explore the causal relationships between the GM and AITD. Subsequently, we employed a multivariable MR analysis to discover potential mediating immune cell traits. Additionally, sensitivity analyses were utilized to ensure the reliability of the outcomes. Results: Our analysis revealed that a total of 7 GM taxa were positively associated with AITD, and other 14 taxa showed a negative correlation with AITD. Furthermore, we identified several immune cell traits that mediated the effects of GM on AITD. Most notably, Actinobacteria (p) presented protective effects on Hashimoto's thyroiditis via CCR2 on myeloid Dendritic Cell (5.0%), and Bifidobacterium (g) showed facilitating effects on Graves' disease through CD39+ CD4+ T cell %CD4+ T cell (5.0%) and CD14 on CD33+ HLA DR+ CD14dim (12.2%). Conclusion: The current MR study provides evidence supporting the causal relationships between several specific GM taxa and AITD, and further identified potential mediating immunophenotypes.

Impact of Hashimoto's thyroiditis on the tumor microenvironment in papillary thyroid cancer: insights from single-cell analysis.

This study investigates the impact of Hashimoto's thyroiditis (HT), an autoimmune disorder, on the papillary thyroid cancer (PTC) microenvironment using a dataset of 140,456 cells from 11 patients. By comparing PTC cases with and without HT, we identify HT-specific cell populations (HASCs) and their role in creating a TSH-suppressive environment via mTE3, nTE0, and nTE2 thyroid cells. These cells facilitate intricate immune-stromal communication through the MIF-(CD74+CXCR4) axis, emphasizing immune regulation in the TSH context. In the realm of personalized medicine, our HASC-focused analysis within the TCGA-THCA dataset validates the utility of HASC profiling for guiding tailored therapies. Moreover, we introduce a novel, objective method to determine K-means clustering coefficients in copy number variation inference from bulk RNA-seq data, mitigating the arbitrariness in conventional coefficient selection. Collectively, our research presents a detailed single-cell atlas illustrating HT-PTC interactions, deepening our understanding of HT's modulatory effects on PTC microenvironments. It contributes to our understanding of autoimmunity-carcinogenesis dynamics and charts a course for discovering new therapeutic targets in PTC, advancing cancer genomics and immunotherapy research.

Predicting gene signature in breast cancer patients with multiple machine learning models.

AIMS: The aim of this study was to predict gene signatures in breast cancer patients using multiple machine learning models. METHODS: In this study, we first collated and merged the datasets GSE54002 and GSE22820, obtaining a gene expression matrix comprising 16,820 genes (including 593 breast cancer (BC) samples and 26 normal control (NC) samples). Subsequently, we performed enrichment analyses using Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Disease Ontology (DO). RESULTS: We identified 177 differentially expressed genes (DEGs), including 40 up-regulated and 137 down-regulated genes, through differential expression analysis. The GO enrichment results indicated that these genes are primarily involved in extracellular matrix organization, positive regulation of nervous system development, collagen-containing extracellular matrix, heparin binding, glycosaminoglycan binding, and Wnt protein binding, among others. KEGG enrichment analysis revealed that the DEGs were primarily associated with pathways such as focal adhesion, the PI3K-Akt signaling pathway, and human papillomavirus infection. DO enrichment analysis showed that the DEGs play a significant role in regulating diseases such as intestinal disorders, nephritis, and dermatitis. Further, through LASSO regression analysis and SVM-RFE algorithm analysis, we identified 9 key feature DEGs (CF-DEGs): ANGPTL7, TSHZ2, SDPR, CLCA4, PAMR1, MME, CXCL2, ADAMTS5, and KIT. Additionally, ROC curve analysis demonstrated that these CF-DEGs serve as a reliable diagnostic index. Finally, using the CIBERSORT algorithm, we analyzed the infiltration of immune cells and the associations between CF-DEGs and immune cell infiltration across all samples. CONCLUSIONS: Our findings provide new insights into the molecular functions and metabolic pathways involved in breast cancer, potentially aiding in the discovery of new diagnostic and immunotherapeutic biomarkers.

Decoding the possible mechanism of action of Paeoniflorigenone in combating Aflatoxin B1-induced liver cancer: an investigation using network pharmacology and bioinformatics analysis.

Moutan cortex has demonstrated antitumor properties attributed to its bioactive compound Paeoniflorigenone (PA). Nevertheless, there is limited research on the efficacy of PA in the prevention and treatment of hepatocellular carcinoma (HCC). We aimed to investigate the potential pharmacological mechanisms of PA in the treatment of Aflatoxin B1 (AFB1)-induced hepatocarcinogenesis using network pharmacology and bioinformatics analysis approaches. Through various databases and bioinformatics analysis approaches, 34 shared targets were identified as potential candidate genes for PA in fighting liver cancer caused by AFB1. Pathway analysis revealed involvement in cell cycle, HIF-1, and Rap1 pathways. A risk assessment model was developed using LASSO regression, showing an association between the identified genes and the tumor immune microenvironment. The genes within the risk model were found to be linked to the immune response in liver cancer. Molecular docking studies indicated that PA interacts with its targets through hydrogen bonding and hydrophobic interactions. This study provides insights into the possible mechanisms of PA in liver cancer treatment and offers a predictive model for assessing the risk level of individuals with liver cancer. These findings have significant implications for the therapeutic strategies in managing liver cancer patients.

Immune cell traits and causal relationships with cholecystitis: a mendelian randomization analysis.

Cholecystitis, characterized by inflammation of the gallbladder, is intricately linked to immune cells and the cytokines they produce. Despite this association, the specific contributions of immune cells to the onset and progression of cholecystitis remain to be fully understood. To delineate this relationship, we utilized the Mendelian randomization (MR) method to scrutinize the causal connections between 731 immune cell phenotypes and cholecystitis. By conducting MR analysis on 731 immune cell markers from public datasets, this study seeks to understand their potential impact on the risk of cholecystitis. It aims to elucidate the interactions between immune phenotypes and the disease, aiming to lay the groundwork for advancing precision medicine and developing effective treatment strategies for cholecystitis. Taking immune cell phenotypes as the exposure factor and cholecystitis as the outcome event, this study used single nucleotide polymorphisms (SNPs) closely associated with both immune cell phenotypes and cholecystitis as genetic instrumental variables. We conducted a two-sample MR analysis on genome-wide association studies (GWAS) data. Our research thoroughly examined 731 immune cell markers, to determine potential causal relationships with susceptibility to cholecystitis. Sensitivity analyses were performed to ensure the robustness of our findings, excluding the potential impacts of heterogeneity and pleiotropy. To avoid reverse causality, we conducted reverse MR analyses with cholecystitis as the exposure factor and immune cell phenotypes as the outcome event. Among the 731 immune phenotypes, our study identified 21 phenotypes with a causal relationship to cholecystitis (P < 0.05). Of these, eight immune phenotypes exhibited a protective effect against cholecystitis (odds ratio (OR) < 1), while the other 13 immune phenotypes were associated with an increased risk of developing cholecystitis (OR > 1). Additionally, employing the false discovery rate (FDR) method at a significance level of 0.2, no significant causal relationship was found between cholecystitis and immune phenotypes. Our research has uncovered a significant causal relationship between immune cell phenotypes and cholecystitis. This discovery not only enhances our understanding of the role of immune cells in the onset and progression of cholecystitis but also establishes a foundation for developing more precise biomarkers and targeted therapeutic strategies. It provides a scientific basis for more effective and personalized treatments in the future. These findings are expected to substantially improve the quality of life for patients with cholecystitis and mitigate the impact of the disease on patients and their families.

ERBB2 is a potential diagnostic and prognostic biomarker in renal clear cell carcinoma.

Renal clear cell carcinoma (ccRCC) is a common parenchymal tumor of the kidney, and the discovery of biomarkers for early and effective diagnosis of ccRCC can improve the early diagnosis of patients and thus improve long-term survival. Erb-b2 receptor tyrosine kinase 2 (ERBB2) mediates the processes of cell proliferation, differentiation, and apoptosis inhibition. The purpose of this study was to investigate the diagnostic and prognostic role of ERBB2 in ccRCC. We analyzed the expression levels of ERBB2 in various cancers from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. RNA-seq data were analyzed using R packages to identify differentially expressed genes between the high and low ERBB2 expression groups in the TCGA-KIRC dataset. Spearman correlation analysis was performed to determine the correlation between ERBB2 expression and immune cell infiltration, immune checkpoint expression, and PTEN expression. DNA methylation changes and genetic alterations in ERBB2 were assessed using the MethSurv and cBioPortal databases. Logistic regression analysis was performed to determine the correlation between ERBB2 expression and the clinicopathological characteristics of ccRCC patients. The diagnostic and prognostic value of ERBB2 was assessed using Kaplan‒Meier (K‒M) survival curves, diagnostic ROC curves, time-dependent ROC curves, nomogram models, and Cox regression models. The expression level of ERBB2 is lower in tumor tissues of ccRCC patients than in the corresponding control tissues. Differentially expressed genes associated with ERBB2 were significantly enriched in the pathways "BMP2WNT4FOXO1 pathway in primary endometrial stromal cell differentiation" and "AMAN pathway". In ccRCC tissues, ERBB2 expression levels were associated with immune cell infiltration, immune checkpoints, and PTEN. The DNA methylation status of 10 CpG islands in the ERBB2 gene was associated with the prognosis of ccRCC. ERBB2 expression levels in ccRCC tissues were associated with race, sex, T stage, M stage, histological grade, and pathological stage. Cox regression analysis showed that ERBB2 was a potential independent predictor of overall survival (OS), disease-specific survival (DSS), and progression-free interval (PFI) in ccRCC patients. ROC curve analysis showed that the expression level of ERBB2 could accurately distinguish between ccRCC tissue and adjacent normal renal tissue. Our study showed that ERBB2 expression in ccRCC tissues can be of clinical importance as a potential diagnostic and prognostic biomarker.

Pan-Cancer Analysis Confirms the Prognostic and Immunological Implications of the 1,25-Dihydroxy Vitamin D3 Receptor in Cervical Squamous Cell Carcinoma.

Vitamin D receptor (VDR), specifically the 1,25-dihydroxy form, holds significant importance in various types of cancer, including cervical squamous cell carcinoma (CESC), which poses a significant public health challenge. A pan-cancer analysis was conducted on VDR in CESC, with a focus on its expression and relationship with immune infiltration and genetic alterations. Bioinformatics databases, including TIMER, GEPIA, UALCAN, cBioportal, and Kaplan-Meier Plotter, have been utilized. VDR expression in CESC has been validated using publicly available data. Results were significantly upregulated (P=0.05) in THCA, BRCA, KICH, LUAD, LIHC, STAD, UCEC, CESC, CHOL, ESCA, and HNSC samples. We analyzed the correlation between VDR expression and various clinicopathological factors such as age, race, and cancer stage. VDR expression was significantly upregulated across all age groups, with the highest levels observed in older adults followed by young and middle-aged adults. VDR gene expression was significantly elevated across all races, including Caucasians, African-Americans, and Asians, compared to that in the normal group. Furthermore, VDR expression was significantly upregulated in cancer stages 1, 2, 3, and 4, with the highest increase observed in stage 3 compared to that in normal individuals. We analyzed the expression of the VDR in relation to immune cell type and tumor cell purity in CESC. Our results indicated that VDR expression was positively correlated with neutrophils and dendritic cells and negatively correlated with tumor cell purity in CESC patients. There was no significant correlation between VDR expression and the abundance of B cells, CD8+ T cells, CD4+ T cells, and macrophages. Our study found no significant effect of VDR expression on patient prognosis, although it was positively correlated with CD4+ T cells. The Cox proportional hazards model indicated that age and immune cells did not significantly affect prognosis. Most VDR mutations are concentrated in diffuse large B-cell lymphoma, with an amplification frequency of 4% and a deep deletion frequency of 2.2%. GEO confirmed VDR expression in CESC, identifying 1515 upregulated and 1877 downregulated genes, with volcano plots showing CESC downregulation in patients.

The Aging Immune System: A Critical Attack on Ischemic Stroke.

Ischemic stroke caused by cerebrovascular embolism is an age-related disease with high rates of disability and mortality. Although the mechanisms of immune and inflammatory development after stroke have been of great interest, most studies have neglected the critical and unavoidable factor of age. As the global aging trend intensifies, the number of stroke patients is constantly increasing, emphasizing the urgency of finding effective measures to address the needs of elderly stroke patients. The concept of "immunosenescence" appears to explain the worse stroke outcomes in older individuals. Immune remodeling due to aging involves dynamic changes at all levels of the immune system, and the overall consequences of central (brain-resident) and peripheral (non-brain-resident) immune cells in stroke vary according to the age of the individual. Lastly, the review outlines recent strategies aimed at immunosenescence to improve stroke prognosis.

A novel tumor-derived exosomal gene signature predicts prognosis in patients with pancreatic cancer.

Background: Pancreatic cancer is a devastating disease with poor prognosis. Accumulating evidence has shown that exosomes and their cargo have the potential to mediate the progression of pancreatic cancer and are promising non-invasive biomarkers for the early detection and prognosis of this malignancy. This study aimed to construct a gene signature from tumor-derived exosomes with high prognostic capacity for pancreatic cancer using bioinformatics analysis. Methods: Gene expression data of solid pancreatic cancer tumors and blood-derived exosome tissues were downloaded from The Cancer Genome Atlas (TCGA) and ExoRBase 2.0. Overlapping differentially expressed genes (DEGs) in the two datasets were analyzed, followed by functional enrichment analysis, protein-protein interaction networks, and weighted gene co-expression network analysis (WGCNA). Using the least absolute shrinkage and selection operator (LASSO) regression of prognosis-related exosomal DEGs, a tumor-derived exosomal gene signature was constructed based on the TCGA dataset, which was validated by an external validation dataset, GSE62452. The prognostic power of this gene signature and its relationship with various pathways and immune cell infiltration were analyzed. Results: A total of 166 overlapping DEGs were identified from the two datasets, which were markedly enriched in functions and pathways associated with the cell cycle. Two key modules and corresponding 70 exosomal DEGs were identified using WGCNA. Using LASSO Cox regression of prognosis-related exosomal DEGs, a tumor-derived exosomal gene signature was built using six exosomal DEGs (ARNTL2, FHL2, KRT19, MMP1, CDCA5, and KIF11), which showed high predictive performance for prognosis in both the training and validation datasets. In addition, this prognostic signature is associated with the differential activation of several pathways, such as the cell cycle, and the infiltration of some immune cells, such as Tregs and CD8+ T cells. Conclusions: This study established a six-exosome gene signature that can accurately predict the prognosis of pancreatic cancer.

Identification of novel mitophagy-related biomarkers for Kawasaki disease by integrated bioinformatics and machine-learning algorithms.

Background: Kawasaki disease (KD) is a systemic vasculitis primarily affecting the coronary arteries in children. Despite growing attention to its symptoms and pathogenesis, the exact mechanisms of KD remain unclear. Mitophagy plays a critical role in inflammation regulation, however, its significance in KD has only been minimally explored. This study sought to identify crucial mitophagy-related biomarkers and their mechanisms in KD, focusing on their association with immune cells in peripheral blood. Methods: This research used four datasets from the Gene Expression Omnibus (GEO) database that were categorized as the merged and validation datasets. Screening for differentially expressed mitophagy-related genes (DE-MRGs) was conducted, followed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. A weighted gene co-expression network analysis (WGCNA) identified the hub module, while machine-learning algorithms [random forest-recursive feature elimination (RF-RFE) and support vector machine-recursive feature elimination (SVM-RFE)] pinpointed the hub genes. Receiver operating characteristic (ROC) curves were generated for these genes. Additionally, the CIBERSORT algorithm was used to assess the infiltration of 22 immune cell types to explore their correlations with hub genes. Interactions between transcription factors (TFs), genes, and Gene-microRNAs (miRNAs) of hub genes were mapped using the NetworkAnalyst platform. The expression difference of the hub genes was validated using quantitative reverse transcriptase polymerase chain reaction (qRT-PCR). Results: Initially, 306 DE-MRGs were identified between the KD patients and healthy controls. The enrichment analysis linked these MRGs to autophagy, mitochondrial function, and inflammation. The WGCNA revealed a hub module of 47 KD-associated DE-MRGs. The machine-learning algorithms identified cytoskeleton-associated protein 4 (CKAP4) and serine-arginine protein kinase 1 (SRPK1) as critical hub genes. In the merged dataset, the area under the curve (AUC) values for CKAP4 and SRPK1 were 0.933 [95% confidence interval (CI): 0.901 to 0.964] and 0.936 (95% CI: 0.906 to 0.966), respectively, indicating high diagnostic potential. The validation dataset results corroborated these findings with AUC values of 0.872 (95% CI: 0.741 to 1.000) for CKAP4 and 0.878 (95% CI: 0.750 to 1.000) for SRPK1. The CIBERSORT analysis connected CKAP4 and SRPK1 with specific immune cells, including activated cluster of differentiation 4 (CD4) memory T cells. TFs such as MAZ, SAP30, PHF8, KDM5B, miRNAs like hsa-mir-7-5p play essential roles in regulating these hub genes. The qRT-PCR results confirmed the differential expression of these genes between the KD patients and healthy controls. Conclusions: CKAP4 and SRPK1 emerged as promising diagnostic biomarkers for KD. These genes potentially influence the progression of KD through mitophagy regulation.

Single-cell RNA sequencing: an emerging tool revealing dysregulated innate and adaptive immune response at single cell level in Kawasaki disease.

INTRODUCTION: Kawasaki disease [KD] is a systemic disorder characterized by acute febrile illness due to widespread medium-vessel vasculitis, mainly affecting children. Despite the ongoing advanced research into the disease pathophysiology and molecular mechanisms, the exact etiopathogenesis of KD is still an enigma. Recently, single-cell RNA sequencing [scRNA-seq], has been utilized to elucidate the pathophysiology of KD at a resolution higher than that of previous methods. AREA COVERED: In the present article, we re-emphasize the pivotal role of this high-resolution technique, scRNA-seq, in the characterization of immune cell transcriptomic profile and signaling/response pathways in KD and explore the diagnostic, prognostic, and therapeutic potential of this new technique in KD. Using combinations of the search phrases 'KD, scRNA-seq, CAA, childhood vasculitis' a literature search was carried out on Scopus, Google Scholar, and PubMed until the beginning of 2024. EXPERT OPINION: scRNA-seq presents a transformative tool for dissecting KD at the cellular level. By revealing rare cell populations, gene expression alterations, and disease-specific pathways, scRNA-seq aids in understanding the intricacies of KD pathogenesis. This review will provide new insights into pathogenesis of KD and the field of applications of scRNA-seq in personalized therapeutics for KD in the future.

MCT4 is an independent prognostic factor and affects immune cell infiltration in patients with colorectal liver oligometastases.

BACKGROUND: Monocarboxylate transporter 4 (MCT4) is a novel biomarker related to the level of immune cell infiltration, but its impact on tumor immune microenvironment (TIME) of colorectal liver oligometastases (CLO) remains unclear. The aim of this study was to assess MCT4 expression in primary tumor and liver oligometastases, investigate its impact on immune cell infiltration and its prognostic value for CLO patients undergoing liver resection. METHODS: We retrospectively selected 135 CLO patients who underwent curative liver resection between June 1999 and December 2016, and samples included 74 primary tumor tissues and 122 liver metastases. Immunohistochemistry (IHC) was performed to detect MCT4 expression in paraffin-embedded specimens and tyramine signal amplification (TSA) was used to detect the density of tumor-infiltrating lymphocytes, including CD3 + , CD8 + and Foxp3 + . Recurrence-free survival (RFS) and overall survival (OS) were analyzed using the Kaplan-Meier method and log-rank test, and independent prognostic factors were identified with Cox regression modeling. RESULTS: Survival analysis indicated that CLO patients with low MCT4 expression had better 3-year RFS and 3-year OS rates than those with high MCT4 expression. Multivariate analysis indicated that high MCT4 expression was independently associated with poor RFS and OS. High MCT4 expression was associated with a lower number of intratumoral CD3 + /CD8 + T cells and was associated with higher Foxp3 + T cells infiltration. Patients with low MCT4 expression and high levels of differential immune infiltration had longer survival. CONCLUSIONS: MCT4 overexpression was associated with an unfavorable prognosis in patients with CLO and MCT4 expression level had an impact on intratumoral immune infiltration degree. A novel parameter that combined MCT4 expression level and differential immune infiltration level was constructed to stratify patients with CLO into different risk groups.

The Role of Microsurgery and Fluorescent-reporter Genes in Establishing Mouse Models for Real-Time Imaging of Metastatic Cancer-Cell Trafficking and Colony Formation: A Revolutionary and Disruptive Technology for Metastasis Research.

The field of experimental microsurgery was pioneered by the great microsurgeon Sun Lee, who developed the foundation of transplant surgery in the clinic. Dr Lee also played a seminal role in introducing microsurgery to establish mouse models of cancer. In 1990, at the age of 70, Dr Lee demonstrated microsurgery techniques to the mouse-model team at AntiCancer Inc., leading to the development of the surgical orthotopic implant (SOI) technique and the first orthotopic mouse models of cancer that metastasized in a pattern similar to clinical cancer. At the beginning of the present century, one of us (NY) from Kanazawa University School of Medicine became a visiting scientist at AntiCancer to learn SOI and develop mouse models of cancer using cancer cells expressing fluorescent reporter genes, such as green fluorescent protein (GFP) and red fluorescent protein (RFP), in order to image metastatic cancer cells trafficking in real time. Since then, a total of eight young surgeons from Kanazawa University have been visiting researchers at AntiCancer, developing SOI mouse models of cancer to visualize cancer cells in vivo, tracking all stages of metastasis in real time. The present perspective review summarizes this seminal work, which has revolutionized the field of metastasis research.

Elucidating the Molecular Interactions and Immune Modulation of Bisphenols Exposure in the Pathogenesis of Polycystic Ovary Syndrome.

Bisphenols (BPs) are known endocrine disruptors potentially contributing to the pathogenesis of Polycystic Ovary Syndrome (PCOS). This study aims to elucidate the molecular interactions between BPs and PCOS-related genes and their combined effects on PCOS development. We identified common genes associated with BPs and PCOS using the CTD. Differential expression analysis was performed on three GEO datasets, leading to the identification of differentially expressed genes (DEGs). Protein-Protein Interaction (PPI) network construction, enrichment analysis, single-gene Gene Set Enrichment Analysis (GSEA), and immune cell infiltration analysis were carried out. A nomogram was developed for PCOS risk prediction, and molecular docking studies were performed using AutoDock Vina, with interaction visualizations via PyMOL. We identified 139 common genes between BPs exposure and PCOS, enrichment analysis highlighted pathways related to hormone metabolism, ovarian steroidogenesis, and p53 signaling. Four hub DEGs (PBK, CCNE2, LPCAT2, S100P) were identified, and a nomogram incorporating these genes demonstrated excellent predictive accuracy. GSEA revealed roles in cell adhesion, immune response, and metabolism. ssGSEA analysis showed significant differences in immune cell infiltration between PCOS and control groups, with notable correlations between hub DEGs and immune cells. Molecular docking indicated strong binding affinities between the hub DEGs and BPAF, suggesting potential disruptions induced by BPs. BPs exposure is associated with significant molecular and immunological changes in PCOS, impacting genes involved in hormone regulation, immune response, and metabolic pathways. The strong binding affinities between BPs and key PCOS-related genes reveal their potential role in exacerbating PCOS, providing insights for targeted therapeutic strategies.

Low CD3 level is a risk factor for amyotrophic lateral sclerosis: a Mendelian randomization study.

Amyotrophic lateral sclerosis (ALS) is a progressive and fatal disease characterized by neuronal degeneration of the spinal cord and brain and believed to be related to the immune system. In this study, our aim is to use Mendelian randomization (MR) to search for immune markers related to ALS. A total of 731 immune cell traits were included in this study. MR analysis was used to identify the causality between 731 immune cell traits (with 3,757 Europeans) and ALS (with 138,086 Europeans). Colocalization analysis was used to verify the found causality, protein-protein interaction prediction was used to look for the interacting proteins that are known to be involved in ALS. We found low expression levels of CD3 on central memory CD8+ T cell is risk factor for ALS (OR = 0.90, 95% CI: 0.86-0.95, P = 0.0000303). CD3 can interact with three ALS-related proteins: VCP, HLA-DRA and HLA-DRB5, which are associated with adaptive immune response. Our study reported for the first time that low-level CD3 is a risk factor for ALS and the possible mechanism, which could provide a potential strategy for ALS diagnosis and therapy.