SERPINE2

Last update 08 May 2025

Basic Info

Synonyms

GDN, Glia-derived nexin, nexin

+ [11]

Introduction

Serine protease inhibitor with activity toward thrombin, trypsin, and urokinase. Promotes neurite extension by inhibiting thrombin. Binds heparin.

100 Clinical Results associated with SERPINE2

100 Translational Medicine associated with SERPINE2

0 Patents (Medical) associated with SERPINE2

970

Literatures (Medical) associated with SERPINE2

31 Dec 2025·Annals of Medicine

Predictive value of three nutritional indexes for disease activity in patients with inflammatory bowel disease

Article

Author: Zhang, Xiangting ; Ying, Kanglei ; Wu, Xiao ; Song, Xian ; Zeng, Yuan ; Zhou, Ruoru ; Yu, Fujun ; Xu, Qian ; Chen, Yuhao ; Cai, Weimin ; Zhao, Luying ; Xu, Jun ; Chen, Zhuoyan ; Zeng, Liuwei ; Ying, Huiya

BACKGROUNDMalnutrition is prevalent in patients with inflammatory bowel disease (IBD); however, its ability to predict the disease activity in IBD remains unexplored. Therefore, this study aimed to explore the association between malnutrition and disease activity in IBD.METHODSIn this retrospective study, we enrolled 1006 patients diagnosed with IBD from the First Affiliated Hospital of Wenzhou Medical University from 2011 to 2022. Malnutrition was assessed based on the prognostic nutritional index (PNI), geriatric nutritional risk index (GNRI), and controlling nutritional status (CONUT) scores. Logistic regression analyses were performed to identify predictors for disease activity. Restricted cubic spline analysis was performed to evaluate the possible nonlinear relations, and subgroup analysis was performed to explore potential interactions. Additionally, prediction performances were compared through receiver operating characteristic curves, net reclassification improvement, and integrated discrimination improvement.RESULTSThe prevalence of malnutrition calculated by the PNI, GNRI, and CONUT scores in IBD was 16.9%, 72.1%, and 75.6%, respectively and significant correlations were observed among them. Multivariate logistic regression analysis showed that PNI, GNRI, and CONUT were independent risk factors for disease activity, and no significant nonlinear relationship was observed between disease activity and all three indexes. No statistically significant interactive effect was found in nearly all the subgroups. GNRI showed the highest predictive value compared with PNI and CONUT. Additionally, combining any of the three indexes improved the ability of C-reactive protein to predict IBD activity.CONCLUSIONSAll three nutritional indexes evaluated malnutrition to be an independent risk factor for IBD activity.

01 May 2025·Academic Radiology

CT Multidimensional Radiomics Combined with Inflammatory Immune Score For Preoperative Prediction of Pathological Grade in Esophageal Squamous Cell Carcinoma

Article

Author: Wang, Fei ; Chen, Jun ; Ren, Anwei ; He, Jiqiang ; Long, Weili ; Zheng, Shaokun ; Zhang, Xiaojiao ; Yang, Ming

RATIONALE AND OBJECTIVESInflammation and immune biomarkers can promote angiogenesis and proliferation and metastasis of esophageal squamous cell carcinoma (ESCC). The degree of pathological grade reflects the tumor heterogeneity of ESCC. The purpose is to develop and validate a nomogram based on enhanced CT multidimensional radiomics combined with inflammatory immune score (IIS) for predicting poorly differentiated ESCC.MATERIALS AND METHODSA total of 266 ESCC patients from the retrospective study were included and randomly divided into a training set (N=186) and a validation set (N=80), and a complete data set (N=266), and overall survival was determined to follow up after surgery. The tumor imaging was segmented to form intratumoral and peritumoral 3 mm areas of 3D volume of interest (VOI) on CT arterial and venous phases, and 3404 radiomics features were extracted. Finally, the radiomics scores were calculated for arterial phase intratumoral (aInRads), peritumoral 3 mm (aPeriRads3), and venous phase intratumoral (vInRads), peritumoral 3 mm (vPeriRads3). Logistic regression was used to fuse the four cohorts of scores to form a Stacking. Additionally, sixteen inflammatory-immune biomarkers were analyzed, including aspartate aminotransferase to lymphocyte ratio (ALRI), aspartate aminotransferase to alanine aminotransferase ratio (AAR), neutrophil times gamma-glutamyl transpeptidase to lymphocyte ratio (NγLR), and albumin plus 5 times lymphocyte sum (PNI), etc. Finally, IIS was constructed using ALRI, AAR, NγLR and PNI. Model performance was evaluated by area under receiver operating characteristic curve (AUC), calibration curve, and decision curve analyse (DCA).RESULTSStacking and IIS were independent risk factors for predicting poorly differentiated ESCC (P<0.05). Ultimately, three models of the IIS, Stacking, and nomogram were developed. Compared with the Stacking and IIS models, nomogram achieved better diagnostic performance for predicting poorly differentiated ESCC in the training set (0.881vs 0.835 vs 0.750), validation set (0.808 vs 0.796 vs 0.595), and complete data set (0.857 vs 0.823 vs 0.703). The nomogram achieved an AUC of 0.881(95%CI 0.826-0.924) in the training set, and was well verified in the validation set (AUC: 0.808[95%CI 0.705-0.888]) and the complete data set (AUC: 0.857[95%CI 0.809-0.897]). Moreover, calibration curve and DCA showed that nomogram achieved good calibration and owned more clinical net benefits in the three cohorts. KaplanMeier survival curves indicated that nomogram achieved excellent stratification for ESCC grade status (P<0.0001).CONCLUSIONThe nomogram that integrates preoperative inflammatory-immune biomarkers, intratumoral and peritumoral CT radiomics achieves a high and stable diagnostic performance for predicting poorly differentiated ESCC, and may be promising for individualized surgical selection and management.AVAILABILITY OF DATA AND MATERIALSThe original manuscript contained in the research is included in the article. Further inquiries can be made directly to the corresponding author.

01 May 2025·Neurobiology of Disease

Joint analysis of single-cell RNA sequencing and bulk transcriptome reveals the heterogeneity of the urea cycle of astrocytes in glioblastoma

Article

Author: Chen, Huahui ; Wan, Xing ; Tong, Minfeng ; Tu, Qi ; Xu, Zhijian ; Wang, Lude

BACKGROUNDGlioblastoma (GB) is incurable with a dismal prognosis. Single-cell RNA sequencing (scRNA-seq) is a pivotal tool for studying tumor heterogeneity. The dysregulation of the urea cycle (UC) frequently occurs in tumors, but its characteristics in GB have not been illuminated. This study integrated scRNA-seq UC scores and bulk RNA-seq data to build a GB prognostic model.METHODSSamples from 3 pairs of GB patients were collected for scRNA-seq analysis. GB-mRNA expression data, clinical data, and SNV mutation data were sourced from the Cancer Genome Atlas (TCGA). GB-mRNA expression data and clinical data were downloaded from the Chinese Glioma Genome Atlas (CGGA). GB RNA-seq data and clinical data were obtained from Gene Expression Omnibus (GEO) database. The R package Seurat was applied for scRNA-seq data processing. UMAP and TSNE were used for dimensionality reduction. UCell enrichment method was employed to score each astrocyte. Monocle algorithm was applied for pseudotime trajectory analysis. CellChat R package was applied for cell communication analysis. Cell labeling was performed on the results of the nine subclusters of astrocytes. The GSE138794 dataset was used to validate the results of single-cell classification. For bulk RNA-seq, univariate Cox and LASSO analyses were undertaken to screen prognostic genes, while multivariate Cox regression analysis was applied to set up a prognostic model. The differences between high-risk (HR) and low-risk (LR) groups were studied in terms of immune infiltration, sensitivity to anti-tumor drugs, etc. We verified the effect of the marker gene on the function of GB cells at the cellular level.RESULTSThe analysis of scRNA-seq data yielded 7 core cell types. Further clustering of the largest proportion of astrocytes resulted in 9 subclusters. UC score and pseudotime analysis revealed the heterogeneity and differentiation process among subclusters. Subcluster 8 was annotated as an immature astrocyte (marker: CXCL8), and this cell cluster had a higher UC score. The results were validated in the GSE138794 dataset. Combining UC scores, we performed univariate Cox and LASSO to select prognostic genes on bulk RNA-seq data. A prognostic model based on 5 feature genes (RGS4, CTSB, SERPINE2, ID1, and CALD1) was established through multivariate Cox analysis. In addition, patients in the HR group had higher immune infiltration and immune function. Final cell experiments demonstrated that 5 feature genes were highly expressed in GB cells. CALD1 promoted the malignant phenotype of GB cells.CONCLUSIONWe set up a novel prognostic model for predicting the survival of GB patients by integrating bulk RNA-seq and scRNA-seq data. The risk score was closely correlated with immune infiltration and drug sensitivity, pinpointing it as a promising independent prognostic factor.

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