Related Experiment Video
Updated: Feb 13, 2026
07:41
Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
Published on: May 17, 2019
9.6K
Immunohistochemical Analysis Of Breast Cancer Subtypes And Their Correlation With Ki 67 Index
Shagufta Naeem1, Shabana Naz1, Anila Riyaz1
1Department of Pathology, Ayub Medical College, Abbottabad, Pakistan.
Journal of Ayub Medical College, Abbottabad : JAMC
|March 6, 2018
Summary
In Pakistan, Luminal A is the most common breast cancer subtype. This study analyzed 50 breast cancer cases using immunohistochemistry to determine subtype frequency and ki 67 index correlation.
Area of Science:
- Oncology
- Pathology
- Immunohistochemistry
Background:
- Breast cancer is a significant health concern for females in Pakistan.
- Immunohistochemistry has transformed breast cancer management.
- This study investigated breast cancer subtypes and their relation to ki 67 index.
Purpose of the Study:
- To determine the frequency of breast cancer subtypes using immunohistochemical staining.
- To correlate these subtypes with the ki 67 index.
- To provide insights into breast cancer characteristics in the Pakistani population.
Main Methods:
- A cross-sectional study included 50 consecutive breast cancer cases.
- Immunohistochemistry was performed for Oestrogen receptor (ER), Progesterone receptor (PgR), and Her2neu.
- Ki 67 index was assessed to evaluate tumor proliferation.
Main Results:
- Luminal A (ER/PgR positive) was the most frequent subtype.
- 68% of cases were positive for ER and PgR.
- Her2neu positive, triple positive, and triple negative subtypes were observed in 12%, 12%, and 8% of cases, respectively.
Conclusions:
- Low-risk Luminal A represents the most common breast cancer category in this cohort.
- Understanding subtype distribution is crucial for targeted therapy and prognosis.
- Immunohistochemistry is vital for accurate breast cancer subtyping.
Related Concept Videos
02:20Correlations
36.6K
Correlation means that there is a relationship between two or more variables (such as ice cream consumption and crime), but this relationship does not necessarily imply cause and effect. When two variables are correlated, it simply means that as one variable changes, so does the other. We can measure correlation by calculating a statistic known as a correlation coefficient. A correlation coefficient is a number from -1 to +1 that indicates the strength and direction of the relationship between...
36.6K
01:27Correlation and Causation
43.0K
Statistical tests can calculate whether there is a relationship, or correlation, between independent and dependent variables. An indirect relationship of the variables signifies a correlation, while a direct relationship shows causation. If it is determined that no connection exists between the variables, then the correlation is a coincidence.
Correlation versus Causation
If the dependent variable increases or decreases when the independent variable increases, there is a positive or negative...
Correlation versus Causation
If the dependent variable increases or decreases when the independent variable increases, there is a positive or negative...
43.0K
01:09Correlation
15.2K
In statistics, two variables are said to be correlated if the values of one variable are associated with the other variable. Depending on the relationship between two variables, correlation can be of three types– positive correlation, negative correlation, and zero correlation.
Two variables, for example, a and b, are said to be positively correlated if both variables move in the same direction. In other words, a positive correlation exists between two variables, a and b, if:
Two variables, for example, a and b, are said to be positively correlated if both variables move in the same direction. In other words, a positive correlation exists between two variables, a and b, if:
15.2K
01:21Cancer Survival Analysis
774
Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
774
01:31Adrenergic Receptors: ɑ Subtype
3.0K
Adrenoceptors are classified into α and ꞵ classes based on their potencies to catecholamine agonists. α-adrenoceptors show the following order of catecholamine potency:
Adrenaline ≥ Noradrenaline >> Isoprenaline
α-adrenoceptors are further divided into α1 and α2-adrenoceptors.
α1-Adrenoceptors: These receptors are located postsynaptically on the effector organs and cause constriction of smooth muscle mediated by activation of phospholipase...
Adrenaline ≥ Noradrenaline >> Isoprenaline
α-adrenoceptors are further divided into α1 and α2-adrenoceptors.
α1-Adrenoceptors: These receptors are located postsynaptically on the effector organs and cause constriction of smooth muscle mediated by activation of phospholipase...
3.0K
01:26Adrenergic Receptors: β Subtype
3.9K
β-adrenoceptors have varied sensitivities towards adrenaline, noradrenaline, and isoprenaline. The order of agonist potency is as follows:
Isoprenaline > Adrenaline > Noradrenaline
Neurotransmitter binding to these receptors causes activation of adenylyl cyclase resulting in increased concentrations of cAMP and modulation of calcium ion channels within the cell. They are further classified into β1, β2, and β3 subtypes.
β1-adrenoceptors: β1-adrenoceptors...
Isoprenaline > Adrenaline > Noradrenaline
Neurotransmitter binding to these receptors causes activation of adenylyl cyclase resulting in increased concentrations of cAMP and modulation of calcium ion channels within the cell. They are further classified into β1, β2, and β3 subtypes.
β1-adrenoceptors: β1-adrenoceptors...
3.9K