A SERPINE1-Based Immune Gene Signature Predicts Prognosis and Immunotherapy Response in Gastric Cancer

Xu, Xiang; Zhang, Lipeng; Yan, Qi; Fang, Qian; Xiao, Yongbiao; Chen, Guizeng; Cai, Guojing; Abula, Alimujiang; Wang, Zhao; Zhai, Ertao; Chen, Jianhui; Cai, Shirong; Wu, Hui

doi:10.3390/ph15111401

Cited by 4 publications

(5 citation statements)

References 49 publications

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“…The SERPINEs were significantly related to patient survival of some cancers. Besides, SERPINE1 was a detrimental factor in HNSC, STAD, LUSC, CESC, SARC, PAAD, KIRP, LUAD, which was consistent in different databases and was consistent with most previous studies (Guo et al, 2022;Hong et al, 2022;Minemura et al, 2022;Xu et al, 2022;Zhou et al, 2022;Wang et al, 2023;Zhai et al, 2023). We also found that the overexpression of SERPINE2 was significantly associated with worse OS in MESO, BLCA, and LIHC.…”

Section: Discussionsupporting

confidence: 92%

“…Pei et al developed a hypoxia risk score model based on SERPINE1 revealing the correlation between hypoxia and tumor immune microenvironment ( Pei et al, 2021 ). In addition, numerous studies published in the past few decades reported that SERPINE1 is one of the most reliable biomarkers and prognostic markers in many cancer types, including breast cancer ( Harbeck et al, 2004 ; Palmirotta et al, 2009 ; Duffy et al, 2014 ; Duffy et al, 2016 ; Jevrić et al, 2019 ), ovarian cancer ( Mashiko et al, 2015 ; Nakatsuka et al, 2017 ), renal cell carcinoma ( Ahluwalia et al, 2021 ), head and neck squamous cell carcinoma ( Pavón et al, 2015 ; Guo et al, 2022 ; Zhou et al, 2022 ; Li et al, 2023 ), bladder cancer ( Chan et al, 2017 ), colorectal cancer ( Sakakibara et al, 2005 ), gastric cancer ( Khan et al, 2022 ; Xu et al, 2022 ) and non-small cell lung cancers ( Sotiropoulos et al, 2019 ). Previous studies also demonstrated that SERPINE1 can promote cancer progression and metastasis as it is promoting the tumor migration, invasion, and angiogenesis ( Dellas and Loskutoff, 2005 ; Li et al, 2018 ; Kubala and DeClerck, 2019 ).…”

Section: Introductionmentioning

confidence: 99%

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Pan-cancer analysis of SERPINE family genes as biomarkers of cancer prognosis and response to therapy

Liu,

Li,

Chen

et al. 2024

Front. Mol. Biosci.

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Background: Serine protease inhibitor E (SERPINE) family genes participate in the tumor growth, cancer cell survival and metastasis. However, the SERPINE family members role in the prognosis and their clinical therapeutic potentials in various human cancer types have not been elaborately explored.Methods: We preliminarily analyzed expression levels and prognostic values of SERPINE family genes, and investigated the correlation between SERPINEs expression and tumor microenvironment (TME), Stemness score, clinical characteristic, immune infiltration, tumor mutational burden (TMB), immune subtype, and drug sensitivity in pan-cancer, which based on updated public databases and integrated some bioinformatics analysis methods. In addition, we conducted the enrichment analysis of SERPINEs from DAVID and KOBAS databases.Results: SERPINE1, SERPINE2, and SERPINE3 expression were upregulated in nine cancers, twelve cancers, and six cancers, respectively. The expression of SERPINE family genes was associated with the prognosis in several cancers from The Cancer Genome Atlas (TCGA). Furthermore, SERPINE family genes expression also had a significant relation to stromal and immune scores, and RNA stemness score and DNA stemness score in pan-cancer. SERPINE1 and SERPINE2 expression significantly increased in tumor advanced stage in colon adenocarcinoma (COAD). Results showed that SERPINE1 and SERPINE2 expression were negatively related with B cells and Monocytes, respectively. SERPINE2 expression had a significantly positive relation with B cells and Macrophages. In terms of TMB, SERPINE1, SERPINE2, and SERPINE3 were found to associated with TMB in seven cancers, fourteen cancers, and four cancers, respectively. Moreover, all SERPINE gene family members were significantly correlated with immune subtypes. SERPINE1 expression had a significantly positive or negative correlation with drug sensitivity.Conclusion: The study indicated the great potential of SERPINE family genes as biomarkers for prognosis and provided valuable strategies for further investigation of SERPINE family genes as potential targets in cancer.

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Section: Discussionsupporting

confidence: 92%

Section: Introductionmentioning

confidence: 99%

Pan-cancer analysis of SERPINE family genes as biomarkers of cancer prognosis and response to therapy

Liu,

Li,

Chen

et al. 2024

Front. Mol. Biosci.

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“…SERPINE1 gene has been studied in a variety of human cancers, including gastric cancer. One group reported a SERPINE1-based immune gene signature, which can predict immunotherapy response and patient prognosis in gastric cancer 60 . Another group uncovered that SERPINE1 enhanced malignant progression and associated with poor prognosis in gastric cancer patients 61 .…”

Section: Discussionmentioning

confidence: 99%

Integrated bioinformatics analysis of noncoding RNAs with tumor immune microenvironment in gastric cancer

Xu,

Hu,

Chen

et al. 2023

Sci Rep

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In recent years, molecular and genetic research hotspots of gastric cancer have been investigated, including microRNAs, long noncoding RNAs (lncRNAs) and messenger RNA (mRNAs). The study on the role of lncRNAs may help to develop personalized treatment and identify potential prognostic biomarkers in gastric cancer. The RNA-seq and miRNA-seq data of gastric cancer were downloaded from the TCGA database. Differential analysis of RNA expression between gastric cancer samples and normal samples was performed using the edgeR package. The ceRNA regulatory network was visualized using Cytoscape. KEGG pathway analysis of mRNAs in the ceRNA network was performed using the clusterProfiler package. CIBERSORT was used to distinguish 22 immune cell types and the prognosis-related genes and immune cells were determined using Kaplan-Meier and Cox proportional hazard analyses. To estimate these nomograms, we used receiver operating characteristic and calibration curve studies. The ceRNA regulation network of gastric cancer was built in this study, and the genes in the network were analyzed for prognosis. A total of 980 lncRNAs were differentially expressed, of which 774 were upregulated and 206 were downregulated. A survival study identified 15 genes associated with gastric cancer prognosis, including VCAN-AS1, SERPINE1, AL139002.1, LINC00326, AC018781.1, C15orf54, hsa-miR-145. Monocytes and Neutrophils were associated with the survival rate of gastric cancer. Our research uncovers new ceRNA network for the detection, treatment, and monitoring of gastric cancer.

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“…Although direct links between SNV mutations in these risk genes and GC progression warrant further independent exploration, our findings suggest their plausible roles in GC advancement. Notably, recent investigations have constructed gene signatures derived from genomic instability, with SERPINE1, among others, predicting GC prognosis [31]. Further correlational analyses between these five genes and 39 pathways unveiled distinct pathway signatures for protective and risk genes.…”

Section: Discussionmentioning

confidence: 99%

Profiling Cancer-Associated Fibroblasts (CAFs) in Gastric Cancer and Development of a CAF-Based Prognostic Signature Using Integrated Single-Cell RNA Sequencing and TCGA Analysis

Sun,

Gao,

et al. 2024

Preprint

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Background: Cancer-associated fibroblasts (CAFs) play critical roles in tumor growth, angiogenesis, metastasis, and therapy resistance. This study aimed to investigate the characteristics of CAFs in gastric cancer (GC) and develop a CAF-based risk signature for predicting the prognosis of GC patients. Methods: Single-cell RNA sequencing (scRNA-seq) data were obtained from the GEO database. The data related to the survival prognosis of gastric cancer patients is derived from TCGA. The scRNA-seq data were analyzed using the Seurat R package, resulting in the identification of CAF clusters based on characteristic markers. Subsequent differential expression analysis was performed on the TCGA dataset to identify genes exhibiting differential expression (DEGs) between normal and tumor samples. Pearson correlation analysis was then utilized to uncover DEGs correlated with CAF clusters, followed by univariate Cox regression analysis to identify prognostic genes associated with CAFs. Lasso regression was applied to construct a risk signature based on these prognostic CAF-related genes. Lastly, a comprehensive scoring model, integrating the risk signature and clinicopathological characteristics, was meticulously developed. Results: From the scRNA-seq data of gastric cancer (GC), six distinct clusters of cancer-associated fibroblasts (CAFs) were delineated, among which five clusters exhibited significant associations with GC prognosis. A total of 557 differentially expressed genes (DEGs) were identified that showed strong correlations with these CAF clusters. From this set, a refined risk signature comprising six key genes was derived. These pivotal genes were predominantly implicated in 39 biological pathways, encompassing crucial processes such as angiogenesis, apoptosis, and hypoxia. Notably, the risk signature demonstrated notable correlations with stromal and immune scores, as well as specific immune cell types. Furthermore, multivariate analysis underscored the independent prognostic role of the risk signature in GC, showcasing its potential for predicting outcomes of immunotherapy. The integration of the stage with the CAF-based risk signature yielded a novel scoring model with robust predictive performance and reliability in prognosticating GC outcomes. Conclusion: The CAF-derived risk signature emerges as a potent prognostic tool for GC, offering valuable insights into the intricate landscape of CAFs within the tumor microenvironment. Such comprehensive profiling may hold promise in guiding personalized immunotherapeutic strategies and refining treatment modalities for GC.

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A SERPINE1-Based Immune Gene Signature Predicts Prognosis and Immunotherapy Response in Gastric Cancer

Cited by 4 publications

References 49 publications

Pan-cancer analysis of SERPINE family genes as biomarkers of cancer prognosis and response to therapy

Pan-cancer analysis of SERPINE family genes as biomarkers of cancer prognosis and response to therapy

Integrated bioinformatics analysis of noncoding RNAs with tumor immune microenvironment in gastric cancer

Profiling Cancer-Associated Fibroblasts (CAFs) in Gastric Cancer and Development of a CAF-Based Prognostic Signature Using Integrated Single-Cell RNA Sequencing and TCGA Analysis

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