文献类型：期刊文献

英文题名：Prognostic values of intracellular cell-related genes in esophageal cancer and their regulatory mechanisms

作者：Cao, Wei[1,2,3];Jin, Dacheng[2,3];Min, Weirun[1,2,3];Li, Haochi[1,2,3];Wang, Rong[1];Zhang, Jinlong[1,2,3];Gou, Yunjiu[2,3]

第一作者：Cao, Wei

通信作者：Gou, YJ[1];Gou, YJ[2]

机构：[1]Gansu Univ Chinese Med, Clin Med Sch 1, Lanzhou, Peoples R China;[2]Gansu Prov Peoples Hosp, Chest Clin Ctr, Lanzhou, Peoples R China;[3]Gansu Prov Peoples Hosp, Dept Thorac Surg 1, Lanzhou, Peoples R China

第一机构：甘肃中医药大学

通信机构：[1]corresponding author), Gansu Prov Peoples Hosp, Chest Clin Ctr, Lanzhou, Peoples R China;[2]corresponding author), Gansu Prov Peoples Hosp, Dept Thorac Surg 1, Lanzhou, Peoples R China.

年份：2025

卷号：25

期号：1

起止页码：105

外文期刊名：BMC CANCER

收录：;Scopus(收录号：2-s2.0-85216440057);WOS:【SCI-EXPANDED(收录号:WOS:001400878000002)】；

基金：Not Applicable.

语种：英文

外文关键词：Esophageal cancer; Bioinformatics; Cell-in-cell; Molecular docking; Prognostic risk model

摘要：Esophageal cancer is a grave malignant condition. While radiotherapy, often in conjunction with chemotherapy, serves as a cornerstone in the management of locally advanced or metastatic cases, patient tolerance and treatment resistance frequently hinder its efficacy. Cell-in-cell structures, prevalent in various tumors, have been linked to prognosis. Hence, investigating the prognostic significance and regulatory mechanisms of genes related to these intracellular structures in esophageal cancer is imperative. The Cancer Genome Atlas (TCGA) Esophageal Cancer (ESCA) dataset served as the training set for the analysis. Differentially expressed genes (DEGs) in ESCA samples were identified, with those related to intercellular structures designated cell-in-cell-related differential expression genes (CIC-related DEGs). Cox regression analysis was employed to identify prognostic genes, categorizing samples into high- and low-risk groups based on median risk scores. Validation was conducted using the GSE53624 risk model. Established methodologies included morphological mapping, enrichment analysis, immune infiltration analysis, prognostic gene expression validation, molecular docking, and Reverse Transcription Polymerase Chain Reaction (RT-PCR) validation. Thirty-eight intersecting genes were identified between the disease and normal groups in ESCA samples. Stepwise multivariate Cox analysis pinpointed three prognostic genes: androgen receptor (AR), C-X-C motif chemokine ligand 8 (CXCL8), and epidermal growth factor receptor (EGFR). The risk model's applicability was confirmed in the GSE53624 dataset, revealing eight significantly different immune-related gene sets. Prognostic gene expression validation demonstrated significant differences between the disease and normal groups in both datasets. The proteins corresponding to the three prognostic genes interacted with gefitinib and osimertinib. RT-PCR results corroborated the differential expression of prognostic genes in esophageal cancer tissues. This study identified AR, CXCL8, and EGFR as prognostic genes and demonstrated their molecular interactions with gefitinib and osimertinib, providing a foundation for ESCA diagnosis and treatment.