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The Use of Reverse Phase Protein Arrays RPPA to Explore Protein Expression Variation within Individual Renal Cell Cancers
Published on: January 22, 2013
The value of erlotinib related target molecules in kidney renal cell carcinoma via bioinformatics analysis
YunQiang Zhang1, MingYang Tang2, Qiang Guo3
1Department of Thoracic Surgery, Beilun District people's Hospital of Ningbo, Ningbo, Zhejiang 315800, China.
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.
Insights
Erlotinib shows promise for metastatic kidney renal cell carcinoma (KIRC). Bioinformatics identified key genes (COL11A1, EMCN, etc.) linked to KIRC prognosis and immune infiltration, aiding in risk assessment.
Area of Science:
- Oncology
- Bioinformatics
- Genomics
Background:
- Erlotinib is an effective treatment for metastatic kidney renal cell carcinoma (KIRC).
- Understanding erlotinib's molecular targets is crucial for improving KIRC treatment strategies.
- Bioinformatic approaches can identify novel biomarkers for cancer prognosis.
Purpose of the Study:
- To explore the prognostic value of erlotinib's target molecules in kidney renal cell carcinoma (KIRC).
- To identify key genes associated with patient outcomes and immune infiltration in KIRC.
- To develop a predictive model for KIRC prognosis based on erlotinib's molecular targets.
Main Methods:
- Differential gene expression analysis of GSE25698 dataset after erlotinib treatment.
- Functional enrichment analysis of differentially expressed genes (DEGs).
- Construction of a prognostic risk model and nomogram using TCGA database and DEG analysis.
Main Results:
- DEGs after erlotinib treatment were involved in antigen binding, myeloid cell activation, and JAK-STAT signaling.
- Ten genes (COL11A1, EMCN, GLYATL1, HHLA2, IGFN1, LIPA, LRRC19, PANK1, PRAME, TNFSF14) were independent predictors of poor KIRC prognosis.
- The risk score correlated with immune cell infiltration (e.g., regulatory T cells, M0 macrophages) and various signaling pathways (e.g., MAPK, apoptosis).
Conclusions:
- The identified genes (COL11A1, EMCN, etc.) are significantly associated with KIRC prognosis and immune infiltration.
- A risk model and nomogram incorporating erlotinib's target molecules can aid in evaluating KIRC patient prognosis.
- These findings provide a basis for personalized treatment strategies in KIRC.
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