YunQiang Zhang1, MingYang Tang2, Qiang Guo3, HaoQiang Xu4, ZhiYong Yang5, Dan Li6. 1. Department of Thoracic Surgery, Beilun District people's Hospital of Ningbo, Ningbo, Zhejiang 315800, China. 2. Department of General Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430022, China. 3. Department of Thoracic Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430022, China. 4. Department of Neurology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430022, China. 5. Department of Oncology, Huanggang Central Hospital, Huanggang, Hubei 438000, China. Electronic address: 8099798@qq.com. 6. Department of Oncology, Huanggang Central Hospital, Huanggang, Hubei 438000, China. Electronic address: 804180423@qq.com.
Abstract
OBJECTIVE: Erlotinib was found to be an effective treatment for metastatic kidney renal cell carcinoma (KIRC). This study employed bioinformatics to explore the value of erlotinib's target molecules in KIRC. METHODS: We screened GSE25698 dataset for differentially expressed genes (DEGs) following erlotinib treatment, followed by analyzing their underlying functional mechanisms. The value of DEGs was identified in TCGA database to construct risk model and nomogram, and possible mechanisms underlying model factors and their relationship with KIRC immune infiltration were analyzed. RESULTS: Following erlotinib treatment, DEGs were involved in antigen binding, myeloid leukocyte activation, JAK-STAT signaling pathway, etc. COL11A1, EMCN, GLYATL1, HHLA2, IGFN1, LIPA, LRRC19, PANK1, PRAME, and TNFSF14 were independent factors influencing poor prognosis in KIRC patients. Age, grade, and risk score were independent risk factors influencing poor prognosis of KIRC patients. The risk score was associated with immune cells such as T cells regulatory, T cells follicular helper, macrophages M0, etc., and participated signaling mechanisms such as ERBB, insulin, mTOR, PPAR, apoptosis, MAPK, T cell receptor, etc. CONCLUSIONS: The expression levels of COL11A1, EMCN, GLYATL1, HHLA2, IGFN1 LIPA, LRRC19, PANK1, PRAME, and TNFSF14 were associated with KIRC prognosis and immune cell infiltration. The risk model and nomogram based on erlotinib's target molecules were expected to be a tool for evaluating the prognosis of KIRC patients.
OBJECTIVE: Erlotinib was found to be an effective treatment for metastatic kidney renal cell carcinoma (KIRC). This study employed bioinformatics to explore the value of erlotinib's target molecules in KIRC. METHODS: We screened GSE25698 dataset for differentially expressed genes (DEGs) following erlotinib treatment, followed by analyzing their underlying functional mechanisms. The value of DEGs was identified in TCGA database to construct risk model and nomogram, and possible mechanisms underlying model factors and their relationship with KIRC immune infiltration were analyzed. RESULTS: Following erlotinib treatment, DEGs were involved in antigen binding, myeloid leukocyte activation, JAK-STAT signaling pathway, etc. COL11A1, EMCN, GLYATL1, HHLA2, IGFN1, LIPA, LRRC19, PANK1, PRAME, and TNFSF14 were independent factors influencing poor prognosis in KIRC patients. Age, grade, and risk score were independent risk factors influencing poor prognosis of KIRC patients. The risk score was associated with immune cells such as T cells regulatory, T cells follicular helper, macrophages M0, etc., and participated signaling mechanisms such as ERBB, insulin, mTOR, PPAR, apoptosis, MAPK, T cell receptor, etc. CONCLUSIONS: The expression levels of COL11A1, EMCN, GLYATL1, HHLA2, IGFN1 LIPA, LRRC19, PANK1, PRAME, and TNFSF14 were associated with KIRC prognosis and immune cell infiltration. The risk model and nomogram based on erlotinib's target molecules were expected to be a tool for evaluating the prognosis of KIRC patients.
Authors: Dan Li; Xiaoli Liu; Ni Jiang; Di Ke; Qiang Guo; Kui Zhai; Hao Han; Xue Xiao; Tengyang Fan Journal: Am J Cancer Res Date: 2022-07-15 Impact factor: 5.942
Authors: Dan Li; Kai Li; Wei Zhang; Kong-Wu Yang; De-An Mu; Guo-Jun Jiang; Rong-Shu Shi; Di Ke Journal: Front Immunol Date: 2022-06-27 Impact factor: 8.786