Chenguang Ji, Hongmei Cai, Xiaoxu Jin, Kaige Yin, Dongqiang Zhao, Zhijie Feng, Li Liu
{"title":"识别早期胃癌进展中与免疫浸润细胞相关的生物标记物","authors":"Chenguang Ji, Hongmei Cai, Xiaoxu Jin, Kaige Yin, Dongqiang Zhao, Zhijie Feng, Li Liu","doi":"10.1177/15330338241262724","DOIUrl":null,"url":null,"abstract":"<p><strong>Objectives: </strong>Gastric cancer (GC) is one of the most prevalent malignancies worldwide, and early detection is crucial for improving patient survival rates. We aimed to identify immune infiltrating cell-related biomarkers in early gastric cancer (EGC) progression.</p><p><strong>Methods: </strong>The GSE55696 and GSE130823 datasets with low-grade intraepithelial neoplasia (LGIN), high-grade intraepithelial neoplasia (HGIN), and EGC samples were downloaded from the Gene Expression Omnibus database to perform an observational study. Immune infiltration analysis was performed by single sample gene set enrichment analysis and Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data. Weighted gene co-expression network analysis was used to explore the co-expression modules and genes, and further enrichment analysis was performed on these genes. A protein-protein interaction (PPI) network of these genes was constructed to identify biomarkers associated with EGC progression. Screened hub genes were validated by the rank sum test and reverse transcription quantitative polymerase chain reaction.</p><p><strong>Results: </strong>Immune scores were significantly elevated in EGC samples compared to LGIN and HGIN samples. The green-yellow module exhibited the strongest correlation with both immune score and disease progression. The 87 genes within this module were associated with the chemokine signaling pathways, the PI3K-Akt signaling pathways, leukocyte transendothelial migration, and Ras signaling pathways. Through PPI network analysis, the hub genes identified were protein tyrosine phosphatase receptor-type C (PTPRC), pleckstrin, CD53, CD48, lymphocyte cytosolic protein 1 (LCP1), hematopoietic cell-specific Lyn substrate 1, IKAROS Family Zinc Finger 1, Bruton tyrosine kinase, and Vav guanine nucleotide exchange factor 1. Notably, CD48, LCP1, and PTPRC showed high expression levels in EGC samples, with the remaining hub genes demonstrating a similar expression trend.</p><p><strong>Conclusion: </strong>This study identified 9 immune cell-related biomarkers that may be actively involved in the progression of EGC and serve as potential targets for GC diagnosis and treatment.</p>","PeriodicalId":22203,"journal":{"name":"Technology in Cancer Research & Treatment","volume":"23 ","pages":"15330338241262724"},"PeriodicalIF":2.7000,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11168250/pdf/","citationCount":"0","resultStr":"{\"title\":\"Identification of Immune Infiltrating Cell-Related Biomarkers in Early Gastric Cancer Progression.\",\"authors\":\"Chenguang Ji, Hongmei Cai, Xiaoxu Jin, Kaige Yin, Dongqiang Zhao, Zhijie Feng, Li Liu\",\"doi\":\"10.1177/15330338241262724\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Objectives: </strong>Gastric cancer (GC) is one of the most prevalent malignancies worldwide, and early detection is crucial for improving patient survival rates. We aimed to identify immune infiltrating cell-related biomarkers in early gastric cancer (EGC) progression.</p><p><strong>Methods: </strong>The GSE55696 and GSE130823 datasets with low-grade intraepithelial neoplasia (LGIN), high-grade intraepithelial neoplasia (HGIN), and EGC samples were downloaded from the Gene Expression Omnibus database to perform an observational study. Immune infiltration analysis was performed by single sample gene set enrichment analysis and Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data. Weighted gene co-expression network analysis was used to explore the co-expression modules and genes, and further enrichment analysis was performed on these genes. A protein-protein interaction (PPI) network of these genes was constructed to identify biomarkers associated with EGC progression. Screened hub genes were validated by the rank sum test and reverse transcription quantitative polymerase chain reaction.</p><p><strong>Results: </strong>Immune scores were significantly elevated in EGC samples compared to LGIN and HGIN samples. The green-yellow module exhibited the strongest correlation with both immune score and disease progression. The 87 genes within this module were associated with the chemokine signaling pathways, the PI3K-Akt signaling pathways, leukocyte transendothelial migration, and Ras signaling pathways. Through PPI network analysis, the hub genes identified were protein tyrosine phosphatase receptor-type C (PTPRC), pleckstrin, CD53, CD48, lymphocyte cytosolic protein 1 (LCP1), hematopoietic cell-specific Lyn substrate 1, IKAROS Family Zinc Finger 1, Bruton tyrosine kinase, and Vav guanine nucleotide exchange factor 1. Notably, CD48, LCP1, and PTPRC showed high expression levels in EGC samples, with the remaining hub genes demonstrating a similar expression trend.</p><p><strong>Conclusion: </strong>This study identified 9 immune cell-related biomarkers that may be actively involved in the progression of EGC and serve as potential targets for GC diagnosis and treatment.</p>\",\"PeriodicalId\":22203,\"journal\":{\"name\":\"Technology in Cancer Research & Treatment\",\"volume\":\"23 \",\"pages\":\"15330338241262724\"},\"PeriodicalIF\":2.7000,\"publicationDate\":\"2024-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11168250/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Technology in Cancer Research & Treatment\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1177/15330338241262724\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"ONCOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Technology in Cancer Research & Treatment","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1177/15330338241262724","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ONCOLOGY","Score":null,"Total":0}
Identification of Immune Infiltrating Cell-Related Biomarkers in Early Gastric Cancer Progression.
Objectives: Gastric cancer (GC) is one of the most prevalent malignancies worldwide, and early detection is crucial for improving patient survival rates. We aimed to identify immune infiltrating cell-related biomarkers in early gastric cancer (EGC) progression.
Methods: The GSE55696 and GSE130823 datasets with low-grade intraepithelial neoplasia (LGIN), high-grade intraepithelial neoplasia (HGIN), and EGC samples were downloaded from the Gene Expression Omnibus database to perform an observational study. Immune infiltration analysis was performed by single sample gene set enrichment analysis and Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data. Weighted gene co-expression network analysis was used to explore the co-expression modules and genes, and further enrichment analysis was performed on these genes. A protein-protein interaction (PPI) network of these genes was constructed to identify biomarkers associated with EGC progression. Screened hub genes were validated by the rank sum test and reverse transcription quantitative polymerase chain reaction.
Results: Immune scores were significantly elevated in EGC samples compared to LGIN and HGIN samples. The green-yellow module exhibited the strongest correlation with both immune score and disease progression. The 87 genes within this module were associated with the chemokine signaling pathways, the PI3K-Akt signaling pathways, leukocyte transendothelial migration, and Ras signaling pathways. Through PPI network analysis, the hub genes identified were protein tyrosine phosphatase receptor-type C (PTPRC), pleckstrin, CD53, CD48, lymphocyte cytosolic protein 1 (LCP1), hematopoietic cell-specific Lyn substrate 1, IKAROS Family Zinc Finger 1, Bruton tyrosine kinase, and Vav guanine nucleotide exchange factor 1. Notably, CD48, LCP1, and PTPRC showed high expression levels in EGC samples, with the remaining hub genes demonstrating a similar expression trend.
Conclusion: This study identified 9 immune cell-related biomarkers that may be actively involved in the progression of EGC and serve as potential targets for GC diagnosis and treatment.
期刊介绍:
Technology in Cancer Research & Treatment (TCRT) is a JCR-ranked, broad-spectrum, open access, peer-reviewed publication whose aim is to provide researchers and clinicians with a platform to share and discuss developments in the prevention, diagnosis, treatment, and monitoring of cancer.