Toward the precision breast cancer survival prediction utilizing combined whole genome-wide expression and somatic mutation analysis

FOXD1 Regulates the Sensitivity of Cetuximab by Regulating the Expression of EGFR in Head and Neck Squamous Cell Cancer

Objectives

Previous experiments have shown that growth factor receptors play important role in tumor proliferation, metastasis, and therapeutic effect of chemotherapeutic drugs. At the same time, forkhead box D1 (FOXD1) plays an important role in a variety of signal transmission, but its expression profile was known little about head and neck cancer. The purpose of this experiment was to explore the regulation of FOXD1 on the tumor progression of head and neck cancer and to explore the correlation of FOXD1 on the expression of growth factor receptors (EGFR).

Methods

The bioinformatics online database analyzed the expression of FOXD1 and EGFR in tumor tissues and nontumor tissues. Real-time quantitative PCR was used to detect the FOXD1 and EGFR expression in 45 tumor tissues and 15 nontumor tissues. The plasmid was used to construct FOXD1 overexpressing head and neck squamous cell cancer lines and observe the clonal formation and invasion of tumor cells under the intervention of EGFR-specific antibody—cetuximab.

Results

The expression of FOXD1 and EGFR in tumor tissues was higher than that in nontumor tissues. The higher expression of FOXD1 and EGFR was not conducive to the prognosis of patients. The expression of FOXD1 and EGFR was positively correlated, and immunohistochemical analysis showed the high expression of FOXD1 and EGFR has close relation to the advanced stage of the tumor. In vitro cell experiments proved that overexpression of FOXD1 can partially offset the cloning ability of cetuximab on head and neck tumor cells.

Conclusion

FOXD1 has an important regulatory role in the progression of head and neck cancer, and its abnormally high expression was not conducive to the prognosis of cancer patients. FOXD1 can regulate the expression of growth factor receptors in head and neck cancer, which provides a new idea for the better use of tumor growth factor receptor-specific antibodies for collaborative therapy.

The Impact of Pathway Database Choice on Statistical Enrichment Analysis and Predictive Modeling

Pathway-centric approaches are widely used to interpret and contextualize -omics data. However, databases contain different representations of the same biological pathway, which may lead to different results of statistical enrichment analysis and predictive models in the context of precision medicine. We have performed an in-depth benchmarking of the impact of pathway database choice on statistical enrichment analysis and predictive modeling. We analyzed five cancer datasets using three major pathway databases and developed an approach to merge several databases into a single integrative one: MPath. Our results show that equivalent pathways from different databases yield disparate results in statistical enrichment analysis. Moreover, we observed a significant dataset-dependent impact on the performance of machine learning models on different prediction tasks. In some cases, MPath significantly improved prediction performance and also reduced the variance of prediction performances. Furthermore, MPath yielded more consistent and biologically plausible results in statistical enrichment analyses. In summary, this benchmarking study demonstrates that pathway database choice can influence the results of statistical enrichment analysis and predictive modeling. Therefore, we recommend the use of multiple pathway databases or integrative ones.

The Impact of Pathway Database Choice on Statistical Enrichment Analysis and Predictive Modeling

Pathway-centric approaches are widely used to interpret and contextualize -omics data. However, databases contain different representations of the same biological pathway, which may lead to different results of statistical enrichment analysis and predictive models in the context of precision medicine. We have performed an in-depth benchmarking of the impact of pathway database choice on statistical enrichment analysis and predictive modeling. We analyzed five cancer datasets using three major pathway databases and developed an approach to merge several databases into a single integrative one: MPath. Our results show that equivalent pathways from different databases yield disparate results in statistical enrichment analysis. Moreover, we observed a significant dataset-dependent impact on the performance of machine learning models on different prediction tasks. In some cases, MPath significantly improved prediction performance and also reduced the variance of prediction performances. Furthermore, MPath yielded more consistent and biologically plausible results in statistical enrichment analyses. In summary, this benchmarking study demonstrates that pathway database choice can influence the results of statistical enrichment analysis and predictive modeling. Therefore, we recommend the use of multiple pathway databases or integrative ones.