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Introduction

FOXO1 has been observed to be associated with obesity and cancer.

Kamei, et. al. connected FOXO1 upregulation and muscle degeneration in mice.
Kamei, et. al. postulated that a similar interaction may occur in human skeletal muscle.
Lu and Huang connected FOXO1 with apoptosis and cell cycle arrest.

Still, not much is known about FOXO1’s exact regulatory mechanisms.
This work draws a novel connection between FOXO1’s association with cancer and its role in skeletal muscle.

Fig. 1: Image from Pleitrope, https://www.rcsb.org/structure/FOXO1

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Introduction

I aimed to find genes which are both

Negatively regulated by FOXO1 in skeletal muscle
Significantly associated with prognosis, transcription, and co-expression in breast cancer.

I hypothesized that no genes would fit such a description

Fig. 2: My own work, created using BioRender, biorender.com

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Methods

1: I began by finding genes related to muscular processes through the MSigDB (Liberzon, et al. 2011).

I cast a wide net, choosing genes also associated with pathways like MAPK and Wnt-b Catenin to examine FOXO1’s regulatory mechanisms.
I primarily investigated muscular growth.

2: I then analyzed the gene sets through GSEA, selecting genes of interest from significantly enriched gene sets (p≤.05) (Subramanian, et. al., 2005)

GSEA finds genes that are regulated in both the gene set and in breast cancer by FOXO1.
Genes of interest are those which FOXO1 negatively regulates in muscular contexts.

3: Using these genes of interest, I conducted further analysis through GEPIA-2 (Tang, et. al. 2019).

Kaplan-Meier Survival Analysis: Shows which genes are significantly associated with prognosis (p ≤.05)
Box Plot Expression Analysis: Shows which genes are significantly more transcribed in breast cancer (p ≤.05)
Spearman Correlation Analysis: Shows which genes are significantly co-expressed (p ≤.05).

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Results

Out of the 14 gene sets analyzed, 5 were significantly enriched.
Of the 659 genes in them, 143 were targeted as genes of interest.
Of the 143 genes of interest, 10 had significant associations with breast cancer prognosis.
Of those 10 genes, 3 were significantly more expressed in breast cancer cells vs. non-cancerous cells.
All 3 genes were significantly co-expressed with each other.

These three genes are WNT7B, EZR, and SFRP1.

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Fig 4 – 8: My own work, created using GSEA and GEPIA-2.

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Fig 3: Canonical Wnt Signaling Pathway, accessed via MSigDB

WNT7B

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Fig 10 - 14: My own work, created using GSEA and GEPIA-2.

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Fig 9: Negative Regulation of MAPK Cascade, accessed via MSigDB

Fig. 9

Fig. 14

EZR

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Fig 15 - 17: My own work, created using GSEA and GEPIA-2

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Fig 3: Canonical Wnt Signaling Pathway, accessed via MSigDB

SFRP1

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Discussion

My results indicate that the genes that are both negatively regulated by FOXO1 and significantly associated with breast cancer are all significantly co-expressed.

However, the r-scores (r ≤ .7) that WNT7B, EZR, and SFRP1 don’t necessarily impact each other’s transcription.

This tells us that FOXO1’s regulatory mechanisms in skeletal muscle are multifaceted and not simple enough to pin down into one or two functions.
WNT7B, EZR, and SFRP1 all have different functions.

WNT7B is known to induce cell proliferation (NCBI).
EZR is known to play a role in cell migration (NCBI).
SFRP1 is known to act as a cell growth regulator

(NCBI).

Data	Analyzed	Sig. Results
Gene Sets	14	5
GSEA Genes	659	143
Kaplan-Meier	143	10
Box Plot	10	3
Spearman	5 combinations	5 combinations

Fig. 18: My own work, table summary of results

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Conclusion

I rejected my hypothesis.

WNT7B, EZR, and SFRP1 are all negatively regulated by FOXO1 and significantly associated with breast cancer.

WNT7B and EZR are associated with cell growth and cellular proliferation.

Their regulation by FOXO1 fits in with our current understanding.
Their upregulation in breast cancer is expected.

SFRP1 is associated with growth regulation.

Its regulation by FOXO1 implies that FOXO1 may be promoting growth factor independence entirely.
Its downregulation in breast cancer is expected.

These three genes should be investigated further to understand FOXO1’s regulatory role.

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References

- Kamei, Y., Miura, S., Suzuki, M., Kai, Y., Mizukami, J., Taniguchi, T., Mochida,

K., Hata, T., Matsuda, J., Aburatani, H., Nishino, I., & Ezaki, O. (2004).

Skeletal muscle foxo1 (FKHR) transgenic mice have less skeletal muscle

mass, down-regulated type I (Slow twitch/red muscle) fiber genes, and

impaired glycemic control. Journal of Biological Chemistry, 279(39),

41114-41123. https://doi.org/10.1074/jbc.M400674200

- Liberzon, A., Subramanian, A., Pinchback, R., Thorvaldsdóttir, H., Tamayo, P.,

& Mesirov, J. P. (2011). Molecular signatures database (MSigDB) 3.0.

Bioinformatics (Oxford, England), 27(12), 1739–1740. https://doi.org/

10.1093/bioinformatics/btr260

- Lu, H., & Huang, H. (2011). FOXO1: a potential target for human diseases.

Current drug targets, 12(9), 1235–1244. https://doi.org/10.2174/

138945011796150280

- Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L.,

Gillette, M. A., Paulovich, A., Pomeroy, S. L., Golub, T. R., Lander, E.

S., & Mesirov, J. P. (2005). Gene set enrichment analysis: A

knowledge-based approach for interpreting genome-wide expression profiles. Proceedings of the National Academy of Sciences, 102(43), 15545-15550. https://doi.org/10.1073/pnas.0506580102

- Tang, Z., Kang, B., Li, C., Chen, T., & Zhang, Z. (2019). GEPIA2: an enhanced web server for large-scale expression profiling and interactive analysis. Nucleic acids research, 47(W1), W556–W560. https://doi.org/10.1093/nar/gkz430