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Gene expression profile of A549 cells from tissue of 4D model predicts poor prognosis in lung cancer patients

ABSTRACT: Tumor microenvironment plays an important role in regulating cell growth and metastasis. Recently we developed an ex vivo lung cancer model (4D) that forms perfusable tumor nodules on a lung matrix that mimics human lung cancer histopathology and protease secretion pattern. We compared the gene expression profile (Human OneArray v5 chip) of A549 cells, a human lung cancer cell line, grown on petri dish (2D), and of the same cells grown in the matrix of our ex vivo model (4D). Furthermore, we obtained gene expression data of A549 cells grown on petri dish (2D) and matrigel (3D) from a previous study and compared the 3D expression profile with that of 4D. Expression array analysis showed 2954 genes differentially expressed between 2D and 4D. Gene Ontology (GO) analysis showed up-regulation of several genes associated with extracellular matrix, polarity, and cell fate and development. Moreover, expression array analysis of 2D versus 3D showed 269 genes that were most differentially expressed, with only 35 genes (13%) having similar expression patterns as observed between 2D and 4D. Finally, the differential gene expression signature of 4D cells (versus 2D) correlated significantly with poor survival in patients with lung cancer (n=1492), while the expression signature of 3D versus 2D correlated with better survival in lung cancer patients. Since patients with larger tumors tend to have worse survival, the ex vivo 4D model may offer additional features over the 3D model, to better mimic of natural progression of tumor growth in lung cancer patients. We compared the gene expression profile (Human OneArray v5 chip) of A549 cells, human lung cancer cell line, grown on petri dish (2D) and same cells grown in the matrix of our ex vivo model (4D).

ORGANISM(S): Homo sapiens

SUBMITTER: Mishra DK

PROVIDER: S-ECPF-GEOD-45142 | biostudies-other | 2014 Feb

REPOSITORIES: biostudies-other

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Project description:BackgroundInterleukin 6 (IL6) is both a pleiotropic cytokine and an immune-related gene. Interleukin 6 receptor (IL6R) is the receptor for IL6. It may be closely connected to the development of lung cancer. This research aims to explore the prognostic value of IL6R and prevent overtreatment of patients with lung adenocarcinoma (LUAD).MethodsIn this study, the expression of IL6R in tumor tissues and surrounding tissues was first analyzed by immunohistochemistry in the Affiliated Hospital of Nantong University (NTU) cohort. Secondly, we downloaded information from The Cancer Genome Atlas (TCGA) for the TCGA cohort and used this information to explore the messenger RNA (mRNA) level of IL6R. We then used Kaplan-Meier survival analyses, univariate and multivariate Cox analyses, nomogram models, and decision curve analyses to assess the prognostic value of IL6R. In addition, we also analyzed immune cell infiltration and the signaling pathways related to IL6R through Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA).ResultsThrough the data analysis of the NTU cohort and the TCGA cohort, it was found that the expression of IL6R in normal tissues around the tumor was higher than that in tumor tissue, and was positively correlated with the overall survival (OS) of LUAD patients. Additionally, low expression of IL6R was found to be an independent predictor of poor prognosis among the patients in these two research cohorts. Next, using GO, KEGG, and GSEA analyses, we found that partially infiltrated tumor immune cells might be related to earlier staging and better prognosis of patients with LUAD. Finally, the study of the 3-5-year survival rate of LUAD patients through the nomogram showed that the expression of IL6R could improve the accuracy of prediction to prevent the overtreatment of some LUAD patients.ConclusionsIn summary, our study indicated that the low expression of IL6R was associated with poor prognosis among LUAD patients and that low expression of IL6R is a potential independent risk factor that could provide a basis for strengthening postoperative classification management of such patients.

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Gene expression profile of A549 cells from tissue of 4D model predicts poor prognosis in lung cancer patients

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