Identification of STAT1 as a biomarker for lymph node metastasis in esophageal squamous cell carcinoma

Lymph node metastasis in esophageal squamous cell carcinoma (ESCC) is associated with poor prognosis, and screening for potential biomarkers of metastasis is significant for improving the prognosis of ESCC. After batch effect removal and integration of two ESCC transcriptome datasets (GSE157804, GSE118493), differentially expressioned gene analysis was performed. The intersection of differentially expressed genes was obtained to identify characteristic genes associated with lymph node metastasis in ESCC. GO/KEGG enrichment analyses were conducted for the characteristic genes. The top 10 pathways and pathway genes based on q-value were selected to construct a PPI network. Hub genes were identified and intersected with the characteristic genes to determine key genes. GO/KEGG enrichment, expression level analysis, and immune infiltration analysis were performed for the key genes. Immunohistochemistry was used to validate the expression levels of key genes in clinical samples. A cell model with regulated expression of key genes was established to verify their impact on the invasive and migratory capabilities of ESCC cells and epithelial-mesenchymal transition (EMT), as well as ECM‑related gene expression. A total of 542 characteristic genes associated with lymph node metastasis in ESCC were identified through differential gene analysis, which were mainly enriched in extracellular matrix-related pathways. PPI network analysis identified 63 hub genes, and the intersection with characteristic genes resulted in 20 key genes, which were also primarily enriched in extracellular matrix-related pathways. Expression level analysis showed that key genes such as STAT1, STAT2, IFIT1, and IFIT3 were highly expressed in metastatic ESCC tissues compared to primary sites and adjacent non-tumor tissues. Immune infiltration analysis revealed a significant positive correlation between IFIT3, STAT1, STAT2, and M1 macrophage polarization. Immunohistochemical analysis indicated high expression of STAT1 in ESCC patient samples with lymph node metastasis. Upregulation of STAT1 in ESCC cells enhanced cell invasion, migration, EMT levels and ECM‑related gene expression, while downregulation of STAT1 expression suppressed these processes. Consistently, an additional gain‑of‑function experiment further confirmed these promoting effects. STAT1 serves as a biomarker for lymph node metastasis in ESCC. The expression level of STAT1 is positively correlated with the degree of lymph node metastasis in ESCC clinical samples. Overexpression of STAT1 promotes the invasion, migration, EMT and ECM‑related gene expression of ESCC cells, while the downregulation of STAT1 inhibits these effects.

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Publication Details

Journal
Journal of Molecular Histology
Published
2026-09-05
DOI
https://doi.org/10.1007/s10735-026-10914-z
Primary Topic
Esophageal Cancer Research and Treatment
Type
article
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article

Identification of STAT1 as a biomarker for lymph node metastasis in esophageal squamous cell carcinoma

Guoli Lv, Haoyu Li, Wei Zhao, Youming Lei et al.
Journal of Molecular Histology
Esophageal Cancer Research and Treatment
article

Identification of STAT1 as a biomarker for lymph node metastasis in esophageal squamous cell carcinoma

Guoli Lv, Haoyu Li, Wei Zhao, Youming Lei, Fanghao Liu, Jungao Peng
article en

Abstract

Lymph node metastasis in esophageal squamous cell carcinoma (ESCC) is associated with poor prognosis, and screening for potential biomarkers of metastasis is significant for improving the prognosis of ESCC. After batch effect removal and integration of two ESCC transcriptome datasets (GSE157804, GSE118493), differentially expressioned gene analysis was performed. The intersection of differentially expressed genes was obtained to identify characteristic genes associated with lymph node metastasis in ESCC. GO/KEGG enrichment analyses were conducted for the characteristic genes. The top 10 pathways and pathway genes based on q-value were selected to construct a PPI network. Hub genes were identified and intersected with the characteristic genes to determine key genes. GO/KEGG enrichment, expression level analysis, and immune infiltration analysis were performed for the key genes. Immunohistochemistry was used to validate the expression levels of key genes in clinical samples. A cell model with regulated expression of key genes was established to verify their impact on the invasive and migratory capabilities of ESCC cells and epithelial-mesenchymal transition (EMT), as well as ECM‑related gene expression. A total of 542 characteristic genes associated with lymph node metastasis in ESCC were identified through differential gene analysis, which were mainly enriched in extracellular matrix-related pathways. PPI network analysis identified 63 hub genes, and the intersection with characteristic genes resulted in 20 key genes, which were also primarily enriched in extracellular matrix-related pathways. Expression level analysis showed that key genes such as STAT1, STAT2, IFIT1, and IFIT3 were highly expressed in metastatic ESCC tissues compared to primary sites and adjacent non-tumor tissues. Immune infiltration analysis revealed a significant positive correlation between IFIT3, STAT1, STAT2, and M1 macrophage polarization. Immunohistochemical analysis indicated high expression of STAT1 in ESCC patient samples with lymph node metastasis. Upregulation of STAT1 in ESCC cells enhanced cell invasion, migration, EMT levels and ECM‑related gene expression, while downregulation of STAT1 expression suppressed these processes. Consistently, an additional gain‑of‑function experiment further confirmed these promoting effects. STAT1 serves as a biomarker for lymph node metastasis in ESCC. The expression level of STAT1 is positively correlated with the degree of lymph node metastasis in ESCC clinical samples. Overexpression of STAT1 promotes the invasion, migration, EMT and ECM‑related gene expression of ESCC cells, while the downregulation of STAT1 inhibits these effects.

Journal of Molecular HistologyVol. 57(5)
Kunming Medical University (CN), First Affiliated Hospital of Kunming Medical University (CN)
No poverty
Openalex Percentile: Top 8%
Esophageal Cancer Research and Treatment
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