Pub Date : 2023-08-12eCollection Date: 2023-01-01DOI: 10.1155/2023/3360310
Lu Sun, Lufei Wang, Bethany B Moore, Shaoping Zhang, Peng Xiao, Ann M Decker, Hom-Lay Wang
The biological role of interleukin 17 (IL-17) has been explored during recent decades and identified as a pivotal player in coordinating innate and adaptive immune responses. Notably, IL-17 functions as a double-edged sword with both destructive and protective immunological roles. While substantial progress has implicated unrestrained IL-17 in a variety of infectious diseases or autoimmune conditions, IL-17 plays an important role in protecting the host against pathogens and maintaining physiological homeostasis. In this review, we describe canonical IL-17 signaling mechanisms promoting neutrophils recruitment, antimicrobial peptide production, and maintaining the epithelium barrier integrity, as well as some noncanonical mechanisms involving IL-17 that elicit protective immunity.
{"title":"IL-17: Balancing Protective Immunity and Pathogenesis.","authors":"Lu Sun, Lufei Wang, Bethany B Moore, Shaoping Zhang, Peng Xiao, Ann M Decker, Hom-Lay Wang","doi":"10.1155/2023/3360310","DOIUrl":"10.1155/2023/3360310","url":null,"abstract":"<p><p>The biological role of interleukin 17 (IL-17) has been explored during recent decades and identified as a pivotal player in coordinating innate and adaptive immune responses. Notably, IL-17 functions as a double-edged sword with both destructive and protective immunological roles. While substantial progress has implicated unrestrained IL-17 in a variety of infectious diseases or autoimmune conditions, IL-17 plays an important role in protecting the host against pathogens and maintaining physiological homeostasis. In this review, we describe canonical IL-17 signaling mechanisms promoting neutrophils recruitment, antimicrobial peptide production, and maintaining the epithelium barrier integrity, as well as some noncanonical mechanisms involving IL-17 that elicit protective immunity.</p>","PeriodicalId":15952,"journal":{"name":"Journal of Immunology Research","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2023-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10439834/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10275274","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Xiuhong Guo, H. Cao, Meichun Hu, Yuening Wu, Jingxiang Li
KIF22, also known as kinesin-like DNA-binding protein (Kid), is a member of the Kinesin superfamily proteins (KIFs). Available evidence indicated that KIF22 was associated with cancer occurrence and development. However, the functions and underlying mechanisms of KIF22 in carcinogenesis and cancer progression remain largely unknown. In this study, we examined the expression profile and methylation status of KIF22 in different cancers, as well as its associations with prognosis, tumor stemness, genomic heterogeneity, immune evasion, immune infiltration, and therapeutic response in various tumor types. The results demonstrated that the expression level of KIF22 was higher in tumors than nontumor tissues and had strong relationships with prognosis, genomic heterogeneity, tumor stemness, neoantigen, ESTIMATE, and immune infiltration. KIF22 methylation status showed strong relationships with immunomodulators and chemokines. KIF22 had a significant relevance with drug susceptibility and could be a useful biomarker for forecasting survival probability and therapeutic reaction. Furthermore, KIF22 interaction and coexpression networks were mainly involved in cell division, cell cycle, DNA repair, and antigen processing and presentation. KIF22 could be used as a pan-cancer biomarker for clinical diagnosis, therapeutic schedule, prognosis, and cancer monitoring.
{"title":"KIF22 in the Prognosis and Immune Biomarking of Pan-Cancer","authors":"Xiuhong Guo, H. Cao, Meichun Hu, Yuening Wu, Jingxiang Li","doi":"10.1155/2023/9542311","DOIUrl":"https://doi.org/10.1155/2023/9542311","url":null,"abstract":"KIF22, also known as kinesin-like DNA-binding protein (Kid), is a member of the Kinesin superfamily proteins (KIFs). Available evidence indicated that KIF22 was associated with cancer occurrence and development. However, the functions and underlying mechanisms of KIF22 in carcinogenesis and cancer progression remain largely unknown. In this study, we examined the expression profile and methylation status of KIF22 in different cancers, as well as its associations with prognosis, tumor stemness, genomic heterogeneity, immune evasion, immune infiltration, and therapeutic response in various tumor types. The results demonstrated that the expression level of KIF22 was higher in tumors than nontumor tissues and had strong relationships with prognosis, genomic heterogeneity, tumor stemness, neoantigen, ESTIMATE, and immune infiltration. KIF22 methylation status showed strong relationships with immunomodulators and chemokines. KIF22 had a significant relevance with drug susceptibility and could be a useful biomarker for forecasting survival probability and therapeutic reaction. Furthermore, KIF22 interaction and coexpression networks were mainly involved in cell division, cell cycle, DNA repair, and antigen processing and presentation. KIF22 could be used as a pan-cancer biomarker for clinical diagnosis, therapeutic schedule, prognosis, and cancer monitoring.","PeriodicalId":15952,"journal":{"name":"Journal of Immunology Research","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2023-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80907880","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jianxin Li, T. Han, Xin Wang, Yinchun Wang, Xuan Chen, Wangsheng Chen, Qingqiang Yang
Background. The long-term prognosis of gastric cancer (GC) remains poor due to postoperative recurrence and metastasis. The increasing evidence show that the lymph node ratio (LNR) serves as an independent prognostic factor in patients with GC. In this study, we aimed to develop a prognostic signature for GC based on LNR. Methods. Survival analysis was conducted by comparing low- and high-LNR groups according to the optimal cutoff value of LNR, which was identified by receiver operating characteristic (ROC) curve analysis. Then, we identified the differentially expressed genes (DEGs) related to LNR in the training cohort of GC. Univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression were performed to construct the risk score signature. We then evaluated the risk score signature from the viewpoints of survival, clinic-pathological characteristics, tumor microenvironment (TME), tumor mutation burden (TMB), and immunotherapeutic and chemotherapeutic efficacy. Results. High LNR was significantly correlated with poorer prognosis and was an independent predictor of recurrence in patients with GC. Then, an eleven-gene signature that could predict the prognosis of GC patients was developed based on LNR-related DEGs in the training cohort, and the results were further confirmed in external independent cohort. In addition, the high-risk group showed aggressive clinicopathological characteristics, specific TME status, low TMB, and low immunotherapeutic sensitivity. Conclusions. The present study constructed an eleven-gene prognostic signature based on LNR to predict the prognosis of patients with GC and facilitate the development of individualized treatment strategy.
{"title":"Development and Validation of an Immunotherapy-Related Prognostic Signature Based on Lymph Node Ratio for Gastric Cancer","authors":"Jianxin Li, T. Han, Xin Wang, Yinchun Wang, Xuan Chen, Wangsheng Chen, Qingqiang Yang","doi":"10.1155/2023/6562422","DOIUrl":"https://doi.org/10.1155/2023/6562422","url":null,"abstract":"Background. The long-term prognosis of gastric cancer (GC) remains poor due to postoperative recurrence and metastasis. The increasing evidence show that the lymph node ratio (LNR) serves as an independent prognostic factor in patients with GC. In this study, we aimed to develop a prognostic signature for GC based on LNR. Methods. Survival analysis was conducted by comparing low- and high-LNR groups according to the optimal cutoff value of LNR, which was identified by receiver operating characteristic (ROC) curve analysis. Then, we identified the differentially expressed genes (DEGs) related to LNR in the training cohort of GC. Univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression were performed to construct the risk score signature. We then evaluated the risk score signature from the viewpoints of survival, clinic-pathological characteristics, tumor microenvironment (TME), tumor mutation burden (TMB), and immunotherapeutic and chemotherapeutic efficacy. Results. High LNR was significantly correlated with poorer prognosis and was an independent predictor of recurrence in patients with GC. Then, an eleven-gene signature that could predict the prognosis of GC patients was developed based on LNR-related DEGs in the training cohort, and the results were further confirmed in external independent cohort. In addition, the high-risk group showed aggressive clinicopathological characteristics, specific TME status, low TMB, and low immunotherapeutic sensitivity. Conclusions. The present study constructed an eleven-gene prognostic signature based on LNR to predict the prognosis of patients with GC and facilitate the development of individualized treatment strategy.","PeriodicalId":15952,"journal":{"name":"Journal of Immunology Research","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2023-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82582969","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Background. Kawasaki disease (KD) is characterized by a disordered inflammation response of unknown etiology. Immune cells are closely associated with its onset, although the immune-related genes’ expression and possibly involved immune regulatory mechanisms are little known. This study aims to identify KD-implicated significant immune- and inflammation-related biomarkers and pathways and their association with immune cell infiltration. Patients and Methods. Gene microarray data were collected from the Gene Expression Omnibus database. Differential expression analysis, weighted gene coexpression network analysis (WGCNA), least absolute shrinkage and selection operator (LASSO) regression, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and gene set enrichment analysis (GSEA) were used to find KD hub markers. GSEA was used to assess the infiltration by 28 immune cell types and their connections to essential gene markers. Receiver operating characteristic (ROC) curves were used to examine hub markers’ diagnostic effectiveness. Finally, hub genes’ expressions were validated in Chinese KD patients by reverse transcription-quantitative polymerase chain reaction (RT-qPCR). Results. One hundred and fifty-one unique genes were found. Among 10 coexpression modules at WGCNA, one hub module exhibited the strongest association with KD. Thirty-six overlapping genes were identified. Six hub genes were potential biomarkers according to LASSO analysis. Immune infiltration revealed connections among activated and effector memory CD4+ T cells, neutrophils, activated dendritic cells, and macrophages. The six hub genes’ diagnostic value was shown by ROC curve analysis. Hub genes were enriched in immunological and inflammatory pathways. RT-qPCR verification results of FCGR1B ( P <