Breast Cancer: Current Research
Open Access

Triple-negative breast cancer (TNBC) represents a distinct subtype characterized by the absence of estrogen receptor, progesterone receptor, and human epidermal growth factor receptor 2 expression. Patients diagnosed with TNBC often face a more aggressive clinical course and limited treatment options compared to other breast cancer subtypes. Identifying reliable biomarkers associated with TNBC progression is crucial for refining prognostic stratification and developing targeted therapeutic strategies.

Leucine-rich repeat-containing G protein-coupled receptor 5 (LGR5), known for its role as a marker of stem cells in various tissues, has gained attention in cancer research. Previous studies have implicated LGR5 in promoting tumorigenesis and cancer stem cell properties in different malignancies. However, the specific relationship between LGR5 expression and clinicopathological features in TNBC remains poorly understood. This study aims to elucidate the association between LGR5 expression and clinicopathological characteristics in TNBC. Through immunohistochemical analysis of a well-characterized cohort of TNBC patient samples, we seek to identify potential correlations between LGR5 expression levels and parameters such as tumor size, grade, lymph node involvement, and patient survival. Understanding the prognostic significance of LGR5 in TNBC may pave the way for novel therapeutic interventions and contribute to more personalized and effective management strategies for patients with this aggressive breast cancer subtype [1-5].

Conclusion

In conclusion, our study sheds light on the association between LGR5 expression and clinicopathological features in triple-negative breast cancer (TNBC). The findings underscore the potential significance of LGR5 as a biomarker in TNBC, providing valuable insights into the molecular landscape of this aggressive subtype. Further research and validation studies are warranted to elucidate the underlying mechanisms and establish the clinical relevance of LGR5 expression in TNBC. This exploration may pave the way for the development of targeted therapeutic strategies and personalized treatment approaches for patients with triple-negative breast cancer. As we continue to unravel the intricate molecular characteristics of TNBC, the identification of LGR5 as a potential prognostic factor opens new avenues for improving patient outcomes and advancing our understanding of the disease.

Acknowledgment

None

Conflict of Interest

None

1. Schnorrenberg F (1996) Comparison of Manual and Computer-Aided Breast Cancer Biopsy Grading. Conf Proc IEEE Eng Med Biol Soc 3: 1166-1167.

Indexed at, Google Scholar, Crossref

2. Chekkoury A, Khurd P, Ni J, Bahlmann C, Kamen A, et al. (2012) Automated Malignancy Detection in Breast Histopathological Images. SPIE Medical Imaging 8315.

Indexed at, Google Scholar, Crossref

3. Robertson S, Azizpour H, Smith K, Hartman J (2018) Digital Image Analysis in Breast Pathology-from Image Processing Techniques to Artificial Intelligence. Transl Res 194: 19-35.

Indexed at, Google Scholar, Crossref

4. Williams BJ, DaCosta P, Goacher E, Treanor D (2017) A Systematic Analysis of Discordant Diagnoses in Digital Pathology Compared with Light Microscopy. Arch Pathol Lab Med 141: 1712-1718.

Indexed at, Google Scholar, Crossref

5. Janowczyk A, Madabhushi A (2016) Deep Learning for Digital Pathology Image Analysis: A Comprehensive Tutorial with Selected Use Cases. J Pathol Inform 7: 29.

Indexed at, Google Scholar, Crossref