Cancer Biology's Functional Implications of Tetraspanin Proteins

Seeram Ramakrishna^*
*Correspondence: Seeram Ramakrishna, Department of Clinical Research, National University of Singapore, Queenstown, Singapore, Email:

Keywords

Tetraspanins • Stroma • Membrane protein

Introduction

The 33 proteins that make up the tetraspanin family in the human proteome are distinguished by their distinctive structural characteristics. These proteins have two Extra Cellular (EC) loops and four transmembrane domains with short intracytosolic N- and C-terminal regions. The EC1 loop is short and lies between the first and second transmembrane domains. The EC2 loop is long and lies between the third and fourth transmembrane domains. It has more than 100 amino acids and a few unique characteristics, such as a conserved CCG motif with no known function and a number of conserved cysteines that are shared by all of the family members. The moderated buildups grant the ID of a protein signature, and the EC2 structure recognizes something like three tetraspanin subgroups in view of their different collapsing qualities. The hydrophobic transmembrane districts additionally contain monitored polar deposits. It is possible that the brief region at the C-terminus will serve as a link to molecules involved in signaling within cells.

Literature Review

A colon carcinoma model with two cell lines derived from metastasis and one from primary tumors has been used to investigate tetraspanin expression. The proteins related with not set in stone by a proteomic approach. In this framework 32 proteins were recognized, including integrins, proteins with immunoglobulin areas, CD44, epithelial cell bond atom, film proteases (ADAM10, TADG-15 and CD26/dipeptidyl peptidase IV) and flagging proteins (heterotrimeric G proteins). The Co-029 tetraspanin antigen in metastasis, which was almost absent in primary tumors but very high in normal colon, was one of the differences that was also found. CD82 is highly expressed in benign goiters and thyroid tumors, but expression at the RNA and protein levels was significantly lower in carcinomas and even lower in metastasizing cells. The expression of CD63 in these tumors remained unchanged. CD9 and CD53 are downregulated in some prostate carcinomas with high levels of CD151. CD9 and CD53 are upregulated in one more gathering of prostate carcinomas. In these prostate carcinomas, CD9 and CD53 articulation is by all accounts emphatically planned, which is something contrary to what has been distinguished in other cell types [1,2].

Discussion

Lower CD151 levels were associated with a higher survival rate in lowgrade prostate cancer, and CD151 had a higher predictive value than histological (Gleason) grade. Through DARC, murine prostate cancer cells are able to attach to vascular endothelial cells. This interaction causes the expression of T-box 2 (TBX)2 and p21, which stops proliferation and causes senescence. In DARC knockout mice, CD82's function as a metastasis suppressor is compromised. All of these data suggest that the metastasis-suppressor function of CD82 depends on the interaction between DARC and CD82. It would be interesting to know if other tetraspanins that may also act as metastasis suppressors exhibit a similar interaction. Due to its features, bladder cancer is a distinct entity. An antigen that seals the bladder epithelium, uroplakin, can be used to determine the type and severity of a tumor. Uroplakin II can be used to track metastasis and circulating cancer cells in transitional bladder carcinomas, but not in squamous cell carcinomas [3,4].

The expression of the most common tetraspanins in this type of tumor has not been characterized. Regardless of the distributed information on tetraspanin proteins corresponding to disease, and that they have been for the most part concentrated on in a singular setting as opposed to as edifices almost certainly, examples of articulation of these proteins will influence the way of behaving of cancer cells concerning announcing development factors, cell motility and aversion to treatment, which will be distinguished when these examinations are done. As they modulate cell signaling by associated growth factor receptors, palmitoylation and gangliosides regulatory roles need to be better understood [5,6].

Conclusion

It is normal that later on these proteins will draw in additional consideration and be concentrated on in the legitimate setting inside growth science, as they are probably going to assume a significant part in presenting explicitness to numerous natural impacts. Although the fundamental processes in which tetraspanin proteins participate have already been described, a methodical approach that takes into account the numerous components, relative levels, and localization of these components simultaneously to generate a specific cell behavior is necessary for tetraspanin proteins specific participation in these processes and in various cell types.

Acknowledgement

None.

Conflict of Interest

No potential conflict of interest was reported by the authors.

References

1. Nevala-Plagemann, Christopher, Manuel Hidalgo and Ignacio Garrido-Laguna. "From state-of-the-art treatments to novel therapies for advanced-stage pancreatic cancer." Nat Rev Clin Oncol 17 (2020): 108-123.

Google Scholar, Crossref

2. Apte, M. V., S. Park, P. A. Phillips and N. Santucci, et al. "Desmoplastic reaction in pancreatic cancer: role of pancreatic stellate cells."Pancreas29 (2004): 179-187.

Google Scholar

3. Zeng, Jiaming, Michael F. Gensheimer, Daniel L. Rubin and Susan Athey, et al. "Uncovering interpretable potential confounders in electronic medical records."Nat Commun13 (2022): 1014.

Google Scholar, Crossref, Indexed at

4. Cui, Sijia, Shuai Ming, Yi Lin and Fanghong Chen, et al. "Development and clinical application of deep learning model for lung nodules screening on CT images."Sci Rep 10 (2020): 13657.

Google Scholar, Crossref

5. Ahmad, Omer F., Antonio S. Soares, Evangelos Mazomenos and Patrick Brandao, et al. "Artificial intelligence and computer-aided diagnosis in colonoscopy: current evidence and future directions."Lancet Gastroenterol Hepatol4 (2019): 71-80.

Google Scholar, Crossref, Indexed at

6. Bera, Kaustav, Kurt A. Schalper, David L. Rimm and Vamsidhar Velcheti, et al. "Artificial intelligence in digital pathology—New tools for diagnosis and precision oncology."Nat Rev Clin Oncol16 (2019): 703-715.

Google Scholar, Crossref, Indexed at