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Lymph node metastasis in lung squamous cell carcinoma and identification of metastasis-related genes based on the Cancer Genome Atlas.

Ming DongHao GongTong LiXin LiJinghao LiuHongbing ZhangMinghui LiuGang ChenHongyu LiuJun Chen
Published in: Cancer medicine (2019)
Squamous cell carcinoma (SCC) is a unique clinical and histological category that accounts for about 30% of total lung cancer. To identify risk factors for lymph node metastasis and analyze the molecular features of these metastases in lung SCC, a retrospective study was performed for 170 lung SCC patients who underwent surgical treatment. The overall survival of these patients with or without lymph node metastasis (LM/NLM) was analyzed using the Kaplan-Meier method. We also used the TCGA database to compare the differentially expressed genes (DEGs) in patients with stage T1-2 and T3-4 lung SCC. Data from both our retrospective study and the TCGA database demonstrated a correlation between age and stage T1-T2 LM (P = .002). There were significant differences between the LM and NLM groups in both mean survival time and median survival time for different T-stages (P = .031). There were 176 upregulated and 177 downregulated DEGs between the LM and NLM groups in the stage T1-2 group and 93 upregulated and 34 downregulated DEGs in the stage T3-T4 group. These differentially expressed genes were predicted to participate in five cellular components, five molecular functions, and five biological processes. There were 20 genes, including GCG, CASR, NPY, CGA, TAC1, ALB, APOA1, CRH, CHRH, TRH, and GHSR, located at the core of the protein-protein interaction network in the stage T1-2 group and 11 genes, including F2, CASR, GRM1, GNRHR, GRPR, NTSR1, PROKR2, UTS2D, PTH, ALB, and FGA, in the stage T3-4 group. Overall, LM plays a key role in the treatment response and prognosis of SCC patients. Several risk factors, including age and stage, were identified for LM. There was a previously undiscovered enrichment of significant novel genes in lung SCC between the LM and NLM groups, which may have the potential for predicting prognosis and targeting.
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