Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2025 Sep;83(3):3633-3650.
doi: 10.1007/s12013-025-01743-0. Epub 2025 Apr 17.

Gene Expression Profiling Identifies CAV1, CD44, and TFRC as Potential Diagnostic Markers and Therapeutic Targets for Multiple Myeloma

Awais Ali  1 , Syed Luqman Ali  1 , Waseef Ullah  1 , Asifullah Khan  2
Affiliations

Gene Expression Profiling Identifies CAV1, CD44, and TFRC as Potential Diagnostic Markers and Therapeutic Targets for Multiple Myeloma

Awais Ali et al. Cell Biochem Biophys. 2025 Sep.

Abstract

Multiple myeloma (MM) is a highly malignant hematological tumor with a low overall survival rate, making the identification of innovative prognostic markers essential due to its complex and heterogeneous nature. Ferroptosis, an iron-dependent form of cell death driven by lipid peroxidation, is now recognized as crucial in tumor development and progression. Consequently, ferroptosis-related genes (FRGs) are emerging as promising therapeutic targets and prognostic indicators. However, the specific roles and predictive value of FRGs in MM still remain unclear. The current study was therefore conceived to examine the possible involvement of FRGs in MM. FRGs data was obtained from the FerrDb resource. The datasets GSE133346 and GSE166122, sourced from the Gene Expression Omnibus (GEO), provided gene expression data for both healthy and MM individuals. The differentially expressed-FRGs (DE-FRGs) were identified using the limma and DESeq2 packages in R. Functional pathways were analyzed through Gene Ontology (GO) and KEGG enrichment analyses. The miRWalk database was used for miRNA association and enrichment analysis with hub genes. Prognosis-related genes were evaluated using Kaplan-Meier survival analyses. We identified 1400 differentially expressed genes and cross-referenced them with FRGs, ultimately selecting 17 as DE-FRGs or hub genes. GO analysis revealed that the primary enriched functions of these hub genes are sister chromatid segregation, condensed chromosome centromeric region, C-C chemokine receptor activity, and C-C chemokine binding. KEGG pathway analysis showed that these overlapped genes were enriched in several pathways, including cell cycle, viral protein interaction with cytokine and cytokine receptor, as well as breast and prostate cancers involved pathways. Furthermore, significant enrichment was observed in glycolysis, gluconeogenesis, and the citrate cycle pathways based on miRNAs association with the candidate genes. The CAV1, CD44, TFRC, DPP4, and GJA1 are identified as top five significant hub DE-FRGs based on protein-protein interaction (PPI) analysis from multiple resources. Survival analysis eventually identified CAV1, CD44, and TFRC as the top-ranked DE-FRGs associated with overall survival, underscoring their crucial role in MM. This study identifies CAV1, CD44, and TFRC as key FRGs associated with the prognosis of MM, suggesting their potential as valuable prognostic markers and therapeutic targets to improve patient outcomes.

Keywords: Differentially expressed genes; Ferroptosis-related genes; Multiple myeloma.

PubMed Disclaimer

Conflict of interest statement

Compliance with Ethical standards. Conflict of Interest: The authors declare no competing interests.

References

    1. Lohr, J. G., Stojanov, P., Carter, S. L., Cruz-Gordillo, P., Lawrence, M. S., Auclair, D., Sougnez, C., Knoechel, B., Gould, J., & Saksena, G. (2014). Widespread genetic heterogeneity in multiple myeloma: implications for targeted therapy. Cancer Cell, 25, 91–101. - PubMed - PMC - DOI
    1. Marando, L., & Huntly, B. J. P. (2020). Molecular landscape of acute myeloid leukemia: prognostic and therapeutic implications. Current Oncology Reports, 22, 1–9. - DOI
    1. Lynch, H. T., Ferrara, K., Barlogie, B., Coleman, E. A., Lynch, J. F., Weisenburger, D., Sanger, W., Watson, P., Nipper, H., & Witt, V. (2008). Familial myeloma. New England Journal of Medicine, 359, 152–157. - PubMed - DOI
    1. Shah, V., Sherborne, A. L., Walker, B. A., Johnson, D. C., Boyle, E. M., Ellis, S., Begum, D. B., Proszek, P. Z., Jones, J. R., & Pawlyn, C. (2018). Prediction of outcome in newly diagnosed myeloma: a meta-analysis of the molecular profiles of 1905 trial patients. Leukemia, 32, 102–110. - PubMed - DOI
    1. Bolli, N., Avet-Loiseau, H., Wedge, D. C., Van Loo, P., Alexandrov, L. B., Martincorena, I., Dawson, K. J., Iorio, F., Nik-Zainal, S., & Bignell, G. R. (2014). Heterogeneity of genomic evolution and mutational profiles in multiple myeloma. Nature Communications, 5, 2997. - PubMed - DOI

MeSH terms

LinkOut - more resources