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Establishment and Evaluation of a <em>Candida albicans</em> Water-Soluble Extract-Induced Murine Model of Kawasaki Disease-Associated Coronary Arteritis

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The Affiliated Huai'an No.1 People's Hospital of Nanjing Medical University

Esophageal squamous cell carcinoma (ESCC) is a highly aggressive malignancy and treatment failure is largely due to metastasis and invasion. Aberrant tumor cell adhesion is often associated with tumor progression and metastasis. However, the exact details of cell adhesion in ESCC progression have yet to be determined. In our study, the clinical relevance of Pax2 transactivation domain-interacting protein (PTIP/PAXIP1) was analyzed by immunohistochemistry of ESCC tissues. We found that low expression of PTIP was associated with lymph node metastasis in ESCC, and loss-of-function approaches showed that depletion of PTIP promoted ESCC cell migration and invasion both  and . Analysis integrating RNA-seq and ChIP-seq data revealed that PTIP directly regulated ephrin type-A receptor 2 (EphA2) expression in ESCC cells. Moreover, PTIP inhibited EphA2 expression by competing with Fosl2, which attenuated the invasion ability of ESCC cells. These results collectively suggest that PTIP regulates ESCC invasion through modulation of EphA2 expression and hence presents a potential therapeutic target for its treatment.

PTIP Inhibits Cell Invasion in Esophageal Squamous Cell Carcinoma  Modulation of EphA2 Expression.

Oral cancer (OC) is a common and dangerous malignant tumor with a low survival rate. However, the micro level mechanism has not been explained in detail.

Identification of latent biomarkers in connection with progression and prognosis in oral cancer by comprehensive bioinformatics analysis.

At present, the treatment of esophageal cancer (EC) is mainly surgical and drug treatment. However, due to drug resistance, these therapies can not effectively improve the prognosis of patients with the EC. Therefore, a multigene prognostic risk scoring system was constructed by bioinformatics analysis method to provide a theoretical basis for the prognosis and treatment decision of EC. The gene expression profiles and clinical data of esophageal cancer patients were gathered from the Cancer Genome Atlas TCGA database, and the differentially expressed genes (DEGs) were screened by R software. Genes with prognostic value were screened by Kaplan Meier analysis, followed by functional enrichment analysis. A cox regression model was used to construct the prognostic risk score model of DEGs. ROC curve and survival curve were utilized to evaluate the performance of the model. Univariate and multivariate Cox regression analysis was used to evaluate whether the model has an independent prognostic value. Network tool mirdip was used to find miRNAs that may regulate risk genes, and Cytoscape software was used to construct gene miRNA regulatory network. GSCA platform is used to analyze the relationship between gene expression and drug sensitivity. 41 DEGs related to prognosis were pre-liminarily screened by survival analysis. A prognostic risk scoring model composed of 8 DEGs (APOA2, COX6A2, CLCNKB, BHLHA15, HIST1H1E, FABP3, UBE2C and ERO1B) was built by Cox regression analysis. In this model, the prognosis of the high-risk score group was poor (P < 0.001). The ROC curve showed that (AUC = 0.862) the model had a good performance in predicting prognosis. In Cox regression analysis, the comprehensive risk score can be employed as an independent prognostic factor of the EC. HIST1H1E, UBE2C and ERO1B interacted with differentially expressed miRNAs. High expression of HIST1H1E was resistant to trametinib, selumetinib, RDEA119, docetaxel and 17-AAG, High expression of UBE2C was resistant to masitinib, and Low expression of ERO1B made the EC more sensitive to FK866. We constructed an EC risk score model composed of 8 DEGs and gene resistance analysis, which can provide reference for prognosis prediction, diagnosis and treatment of the EC patients.

Establishment of prognostic risk model and drug sensitivity based on prognostic related genes of esophageal cancer.

To screen key biomarkers of esophageal cancer (ESCA) by bioinformatics and analyze the correlation between key genes and immune infiltration. Expression profile data of ESCA was downloaded from TCGA database, and DEGs in ESCA were screened with R software. After the RNA binding proteins (RBPs) in DEGs were screened, the protein interaction network was constructed using tools such as STRING and Cytoscape and the key genes (HENMT1) were screened. Survival analysis of HENMT1 was performed by Kaplan-Meier method. Functional enrichment analysis of HENMT1 interacting proteins was performed using the DAVID website, and GSEA predicted the signal pathways involved by HENMT1. CIBERSORT algorithm was used to analyze the infiltration of immune cells in ESCA. The expression of HENMT1 in ESCA was detected by immunohistochemistry. A total of 105 RNA binding proteins (RBPs) were differentially expressed in ESCA, and a PPI network was constructed to screen the key gene HENMT1. The expression level of hemmt1 gene was closely related to the infiltration of B cells naive, T cells regulatory (Tregs), neutrophils, T cells CD4 memory activated, master cells resting and dendritic cells resting in ESCA tissues (P < .05). Immunohistochemical results showed that HENMT1 was highly expressed in ESCA tissues and was positively correlated with the expression of MKI67. HENMT1 is related to the occurrence and prognosis of ESCA, and is also related to the infiltration of immune cells in ESCA tissue, which may provide a new idea for the targeted treatment of ESCA.

Screening of potential key genes in esophageal cancer based on RBP and expression verification of HENMT1.

Esophageal Squamous Cell Carcinoma (ESCC) poses a significant health challenge in China due to its high incidence and mortality. Single-cell transcriptomics has transformed the approach to cancer research, allowing detailed analysis of cellular and molecular heterogeneity. The interaction between the transcription factor STAT1 and the tumor suppressor LHPP is crucial, potentially influencing cancer progression and therapeutic outcomes.

Single-Cell Transcriptomics Reveals STAT1 Drives LHPP Downregulation in Esophageal Squamous Cell Carcinoma Progression.

To analyze the expression and mechanism of prognosis related differentially expressed genes (DEGs) in hepatocellular carcinoma (HCC), and establish a prognosis risk model of prognosis related DEGs.

Establishment of prognosis model of hepatocellular carcinoma based on prognosis related gene analysis and study on gene regulation mechanism of model.

Collagen, a primary protein component of the extracellular matrix (ECM), undergoes a notable series of alterations concomitant with the growth of the tumor. Procollagen-lysine,2-oxoglutarate 5-dioxygenase 3 (PLOD3) is involved in the synthesis of collagen and has been associated with a variety of cancers. However, it is unclear how PLOD3 functions in esophageal squamous cell carcinoma (ESCC).

RBM15 increase tumor-infiltrating CD4+ T cell in ESCC  modulating of PLOD3.

Esophageal squamous-cell carcinoma (ESCC) is a highly aggressive cancer, predominantly affecting populations in Eastern Asia and parts of Africa. Its pathogenesis is influenced by both genetic and environmental factors. Despite recent therapeutic advances, survival rates remain dismal, underscoring an urgent need for novel therapeutic targets.

ArticleTotal : 3	Year
Publication title	2025
Publication title	2025
Publication title	2025

Article	Year
PTIP Inhibits Cell Invasion in Esophageal Squamous Cell Carcinoma Modulation of EphA2 Expression. Frontiers in oncology\| PubMed ID: 33833989	2021
Identification of latent biomarkers in connection with progression and prognosis in oral cancer by comprehensive bioinformatics analysis. World journal of surgical oncology\| PubMed ID: 34384424	2021
Establishment of prognostic risk model and drug sensitivity based on prognostic related genes of esophageal cancer. Scientific reports\| PubMed ID: 35568702	2022
Screening of potential key genes in esophageal cancer based on RBP and expression verification of HENMT1. Medicine\| PubMed ID: 38065897	2023
Single-Cell Transcriptomics Reveals STAT1 Drives LHPP Downregulation in Esophageal Squamous Cell Carcinoma Progression. Technology in cancer research & treatment\| PubMed ID: 39497541	2024
Establishment of prognosis model of hepatocellular carcinoma based on prognosis related gene analysis and study on gene regulation mechanism of model. Heliyon\| PubMed ID: 39512465	2024
RBM15 increase tumor-infiltrating CD4+ T cell in ESCC modulating of PLOD3. American journal of cancer research\| PubMed ID: 39659928	2024
Hypoxia-inducible factor 1-alpha and lactate dehydrogenase-A axis in metabolic changes and aggression in esophageal squamous-cell carcinoma. World journal of gastrointestinal oncology\| PubMed ID: 40092940	2025

Wubi Zhou

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