In this study, we developed a rapid strategy to screen a serum-free medium for culturing the anchorage-dependent PC-3 prostate cancer cells, which was going to be prepared in large scale to generate GM-CSF/TNFα-surface-modified whole cell prostate cancer vaccine. Automated real-time cellular analysis as a rapid and non-invasive technology was used to monitor the growth of PC-3 cells in 16-well plates. At the same time, Plackett-Burman design was employed to identify the most influential formulation by integrating relevant information statistically. The effects of the 16 selected factors were evaluated during exponential cell growth and three medium constituents (EGF, FGF and linoleic acid) were identified to have significant effects on the cell growth. Subsequently, the response surface methodology with central composite design was applied to determine the interactions among the three factors so that these factors were optimized to improve cell growth. Finally, the prediction of the best combination was made under the maximal response to optimize cell growth by Design-Expert software 7.0. A total of 20 experiments were conducted to construct a quadratic model and a second-order polynomial equation. With the optimized combination validated by the stability test of serial passaging PC-3 cells, the serum-free medium had similar cell density and cell viability to the original serum medium. In summary, this high-throughput scheme minimized the screening time and may thus provide a new platform to efficiently develop the serum-free media for adherent cells.
BackgroundThis study aimed to investigate the tumor-related infiltrating lymphocytes (TILs) affecting the response of trastuzumab and identify potential biomarkers based on immune-related genes to improve prognosis and clinical outcomes of targeted therapies in breast cancer.MethodsEstimation of stromal and immune cells in malignant tumors using expression data (ESTIMATE) was adopted to infer the fraction of stromal and immune cells through utilizing gene expression signatures in breast tumor samples. Cell-type identification by estimating relative subsets of RNA transcript (CIBERSORT) algorithm was applied to characterize cell composition of 22 lymphocytes from breast cancer tissues using their gene expression profiles. Immune-related genes were collected from the Immunology Database and Analysis (ImmPort). Univariate and multivariate Cox regression analyses were performed to identify the significant independent risk factors associated with poor overall survival (OS) and breast cancer-specific survival (BCSS) of breast cancer patients. Hub genes were identified based on the protein–protein interaction (PPI) network analysis.ResultsBased on the ESTIMATE algorithm, a significant reduction of stromal scores was observed in tumor tissues and pretreated tumor tissues compared with nontumor and posttreated tumor tissues, respectively, while immune scores failed to present notably statistical differences between both groups. However, from the results of the univariate Cox regression analysis, the immune score was identified to be remarkably associated with the poor OS for breast cancer patients. Subsequently, the infiltrating lymphocytes were evaluated in tumor tissues based on the CIBERSORT algorithm. Furthermore, significance analysis identified 1,244 differentially expressed genes (DEGs) from the GSE114082 dataset, and then 91 overlapping immune-related DEGs were screened between GSE114082 and ImmPort datasets. Subsequently, 10 top hub genes were identified and five (IGF1, ADIPOQ, PPARG, LEP, and NR3C1) significantly correlated with worse OS and BCSS on response to trastuzumab in breast cancer patients.ConclusionsThis study provided an insight into the immune score based on the tumor-related infiltrating lymphocytes in breast cancer tissues and demonstrates the benefits of immune infiltration on the treatment of trastuzumab. Meanwhile, the study established a novel five immune-related gene signature to predict the OS and BCSS of breast cancer treated by trastuzumab.
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