Differentially expressed and survival-related proteins of lung adenocarcinoma with bone metastasis.
Mengdi YangYi SunJing SunZhiyu WangYiyi ZhouGuangyu YaoYifeng GuHuizhen ZhangHui ZhaoPublished in: Cancer medicine (2018)
Despite recent advances in targeted and immune-based therapies, the poor prognosis of lung adenocarcinoma (LUAD) with bone metastasis (BM) remains a challenge. First, two-dimensional gel electrophoresis (2-DE) was used to identify proteins that were differentially expressed in LUAD with BM, and then matrix-assisted laser desorption/ionization time of flight mass spectrometry (MALDI-TOF-MS) was used to identify these proteins. Second, the Cancer Genome Atlas (TCGA) was used to identify mutations in these differentially expressed proteins and Kaplan-Meier plotter (KM Plotter) was used to generate survival curves for the analyzed cases. Immunohistochemistry (IHC) was used to check the expression of proteins in 28 patients with BM and nine patients with LUAD. Lastly, the results were analyzed with respect to clinical features and patient's follow-up. We identified a number of matched proteins from 2-DE. High expression of enolase 1 (ENO1) (HR = 1.67, logrank P = 1.9E-05), ribosomal protein lateral stalk subunit P2 (RPLP2) (HR = 1.77, logrank P = 2.9e-06), and NME/NM23 nucleoside diphosphate kinase 2 (NME1-NME2) (HR = 2.65, logrank P = 3.9E-15) was all significantly associated with poor survival (P < 0.05). Further, ENO1 was upregulated (P = 0.0004) and calcyphosine (CAPS1) was downregulated (P = 5.34E-07) in TCGA LUAD RNA-seq expression data. IHC revealed that prominent ENO1 staining (OR = 7.5, P = 0.034) and low levels of CAPS1 (OR = 0.01, P < 0.0001) staining were associated with BM incidence. Finally, we found that LUAD patients with high expression of ENO1 and RPLP2 had worse overall survival. This is the first instance where the genes ENO1, RPLP2, NME1-NME2 and CAPS1 were associated with disease severity and progression in LUAD patients with BM. Thus, with this study, we have identified potential biomarkers and therapeutic targets for this disease.
Keyphrases
- poor prognosis
- long non coding rna
- rna seq
- single cell
- binding protein
- free survival
- risk factors
- mass spectrometry
- bone mineral density
- squamous cell carcinoma
- drug delivery
- electronic health record
- machine learning
- genome wide
- case report
- cancer therapy
- small molecule
- protein kinase
- soft tissue
- bone loss
- flow cytometry
- data analysis
- hyaluronic acid
- squamous cell