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Abstract：
OBJECTIVE: 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.
CONCLUSIONS: 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.
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.
CONCLUSIONS: 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.
摘要：
目的：胃癌（GC）是全球最常见的恶性肿瘤之一，早期检测对提高患者生存率至关重要。我们旨在确定早期胃癌（EGC）进展中免疫浸润细胞相关的生物标志物。
方法：低度上皮内瘤变（LGIN）的GSE55696和GSE130823数据集，高级别上皮内瘤变（HGIN），并从基因表达综合数据库下载EGC样本进行观察性研究。通过单样品基因集富集分析进行免疫浸润分析,并使用表达数据估计MAlignant肿瘤组织中的STromal和免疫细胞。加权基因共表达网络分析用于探索共表达模块和基因，并对这些基因进行了进一步的富集分析。构建了这些基因的蛋白质-蛋白质相互作用（PPI）网络以鉴定与EGC进展相关的生物标志物。筛选的hub基因通过秩和检验和逆转录定量聚合酶链反应进行验证。
结果：与LGIN和HGIN样品相比，EGC样品中的免疫评分显著升高。绿黄色模块显示出与免疫评分和疾病进展两者最强的相关性。该模块中的87个基因与趋化因子信号通路相关，PI3K-Akt信号通路，白细胞跨内皮迁移，和Ras信号通路。通过PPI网络分析，鉴定的hub基因是蛋白酪氨酸磷酸酶受体C型（PTPRC），Pleckstrin,CD53，CD48，淋巴细胞胞浆蛋白1（LCP1），造血细胞特异性Lyn底物1，IKAROS家族锌指1，布鲁顿酪氨酸激酶，和Vav鸟嘌呤核苷酸交换因子1。值得注意的是,CD48，LCP1和PTPRC在EGC样品中显示出高表达水平，其余的hub基因表现出相似的表达趋势。
结论：本研究确定了9种免疫细胞相关生物标志物，它们可能与EGC的进展密切相关，并可作为GC诊断和治疗的潜在靶标。
方法：低度上皮内瘤变（LGIN）的GSE55696和GSE130823数据集，高级别上皮内瘤变（HGIN），并从基因表达综合数据库下载EGC样本进行观察性研究。通过单样品基因集富集分析进行免疫浸润分析,并使用表达数据估计MAlignant肿瘤组织中的STromal和免疫细胞。加权基因共表达网络分析用于探索共表达模块和基因，并对这些基因进行了进一步的富集分析。构建了这些基因的蛋白质-蛋白质相互作用（PPI）网络以鉴定与EGC进展相关的生物标志物。筛选的hub基因通过秩和检验和逆转录定量聚合酶链反应进行验证。
结果：与LGIN和HGIN样品相比，EGC样品中的免疫评分显著升高。绿黄色模块显示出与免疫评分和疾病进展两者最强的相关性。该模块中的87个基因与趋化因子信号通路相关，PI3K-Akt信号通路，白细胞跨内皮迁移，和Ras信号通路。通过PPI网络分析，鉴定的hub基因是蛋白酪氨酸磷酸酶受体C型（PTPRC），Pleckstrin,CD53，CD48，淋巴细胞胞浆蛋白1（LCP1），造血细胞特异性Lyn底物1，IKAROS家族锌指1，布鲁顿酪氨酸激酶，和Vav鸟嘌呤核苷酸交换因子1。值得注意的是,CD48，LCP1和PTPRC在EGC样品中显示出高表达水平，其余的hub基因表现出相似的表达趋势。
结论：本研究确定了9种免疫细胞相关生物标志物，它们可能与EGC的进展密切相关，并可作为GC诊断和治疗的潜在靶标。
