This was the final project (April 2015) from my Data Analytics course from Wayne State University. This presentation discuss diseases that share the breast cancer gene based on data gathered from DisGeNet (disease and gene association database).

1. Analysis of the Breast Cancer
(Familial) Gene and Disease
Association
By: Crystal Thomas

2. What is Breast Cancer?
• Breast cancer is a type of cancer
that affects the breast region.
• It is said to be affecting one in eight
women and being one of the
leading causes of death in women
in the United States (Medline Plus,
2015).
• Even though it is popular in
women, men may also suffer from
breast cancer in rare cases
(National Cancer Institute, 2015).

3. Risks
• Gender (Women more often
than men)
• Age
• Race
• Dense breast tissue
• Family history
• Genetics
(American Cancer Society, 2015).

4. Genetics
• Approximately 5% to 10% of
breast cancers are said to be
caused by family genes passed
on to their children, hence
familial breast cancer (Beast
Cancer.org, 2015).

5. Genetics (Continuted)
• Familial breast cancer contain
several genes that may have
caused breast cancer formation
in people such as: BRCA1,
BRCA2, ATM, TP53, CHEK2,
PTEN, CDH1, PALB2, RINT1,
RAD50, and NBN (Breast
Cancer.org, 2015).
• Those abnormal genes may also
be present in other diseases as
well.

6. Genetics (Continued
• PTEN: cancer of digestive tract,
thyroid, uterus, and ovaries
• CDH1: stomach cancer
• BRCA1 and BRCA2: ovarian
cancer and other cancers
• NBN: Nijmegen Breakage
Syndrome (slow growth during
infancy and childhood)
(Breast Cancer.org, 2015)

7. Hypothesis
• Discovering this has led me to formulate my hypothesis: people with
diseases that have highest amounts of familial breast cancer disease
genes would be more susceptible to having familial breast cancer.

8. DisGeNet
• I was able to collect data for the
familial breast cancer genes and
disease association by utilizing
DisGeNet, a database that would
incorporate information on
gene-disease association
through public data and
literature sources (Pinero et al.,
2015).

9. DisGeNet (Continued)
• DisGeNet has overall 381056
disease and gene associations
(16666 genes and 13172 diseases)
(Pinero et al., 2015).
• Since the number of disease and
gene associations are relatively
high, DisGeNet created a score “in
order to rank the associations
based on the supporting evidence”
(Pinero et al., 2015).
• This database can be examined by
using Browse and Search queries
(Pinero et al., 2015).

10. DisGeNet (Continued)
• In order for me to gather data, I
used the Search query on the
website and selected the
diseases button to type the
specific disease name (Breast
Cancer, Familial).
(DisGeNET Database, 2015)

11. DisGeNet (Continued)
• I have chosen three types of data to work with for my analysis: All
Diseases that Share Genes, Summary of Disease that Share Genes,
and Summary of Associated Genes.

12. (DisGeNET Database, 2015)

13. (DisGeNET
Database, 2015)

14. (DisGeNET
Database, 2015)

15. Excel
• All three data were exported to
Microsoft’s Excel program in
order for me to perform data
manipulations to determine the
validity of my hypothesis.

16. Excel (Continued)
• Since the data contained 3,609
different diseases that contained
familial breast cancer genes, I
decided to create a pivot table
to portray the top 25 diseases
that shared those genes and the
total number of genes that were
included.
• *The table portrayed 26
different diseases since the final
two diseases have the same
number of genes.*
Disease Names Sum of Number Of Shared Genes
Malignant neoplasm breast 107
Breast Carcinoma 105
NEOPLASM MALIGNANT 101
Carcinoma 83
Breast Neoplasms 78
Malignant neoplasm of prostate 76
OVARIAN CARCINOMA 74
Malignant neoplasm of ovary 73
Colorectal Cancer 72
prostate carcinoma 72
Neoplasms 70
Carcinogenesis 70
MALIGNANT LUNG NEOPLASM 67
Neoplasm Metastasis 66
Colorectal Carcinoma 64
Carcinoma, Hepatocellular 62
Melanoma 61
Malignant neoplasm of bladder 60
Carcinoma, Non-Small-Cell Lung 59
Metastatic Neoplasm 59
Adenocarcinoma 59
Ovarian epithelial cancer 58
Tumor Progression 58
Leukemia 57
Colorectal Neoplasms 55
Prostatic Neoplasms 55
Grand Total 1821

17. Summary of Associated Genes
• I was curious to see which
familial breast cancer gene has
the highest score and to see
which diseases were associated
with that particular gene.
• I found that the BRCA genes
(1&2) were the top Genes
according to DisGeNet’s score
and number of resources found
in Pubmed database.

18. Disease ID
• On Microsoft Access, my goal was to perform computations and
further manipulations built from work that I have performed using
Excel.
• The Number of Shared Genes Sorted worksheet was imported
through access to receive an ID number for the disease names.

19. Number of Shared Genes Sorted
Disease ID Disease Names Sum of Number Of Shared Genes
1 Malignant neoplasm breast 107
2 Breast Carcinoma 105
3 NEOPLASM MALIGNANT 101
4 Carcinoma 83
5 Breast Neoplasms 78
6 Malignant neoplasm of prostate 76
7 OVARIAN CARCINOMA 74
8 Malignant neoplasm of ovary 73
9 Colorectal Cancer 72
10 prostate carcinoma 72
11 Neoplasms 70
12 Carcinogenesis 70
13 MALIGNANT LUNG NEOPLASM 67
14 Neoplasm Metastasis 66
15 Colorectal Carcinoma 64
16 Carcinoma, Hepatocellular 62
17 Melanoma 61
18 Malignant neoplasm of bladder 60
19 Carcinoma, Non-Small-Cell Lung 59
20 Metastatic Neoplasm 59
21 Adenocarcinoma 59
22 Ovarian epithelial cancer 58
23 Tumor Progression 58
24 Leukemia 57
25 Colorectal Neoplasms 55
26 Prostatic Neoplasms 55

20. Gene ID
• Gene ID was sorted in
alphabetical order.

21. Bridge Table
• The Bridge Table contained both
Disease and Gene ID’s.

22. Relationships
• Gene and Bridge Tables were
imported into Access from Excel.
• In order to see whether or not
those tables were interrelated,
relationships were formed.

23. Query
• Since the tables are aware that
they are interrelated, it allowed
me to be able to create a query
to see whether or not I have
accurately integrated the
content from those tables.

24. Network Analysis
• In order to view familial breast
cancer gene-disease interaction,
I decided to perform a network
analysis with the top 25 diseases
and familial breast cancer genes
by using Pajek network tool.

25. Network Analysis

26. 2 Degree Centrality
• I noticed that I have lost
one of the vertices, which
was the LHFP gene from
Neoplasm Malignant
disease. This means that
LHFP gene was the only
gene that has less than 2
degree centrality.

27. VOSviewer
• To provide another visualization
for this analysis, I decided to
import what I have done from
Pajek network program to
VOSviewer.
• The diseases and genes were
ranked according to color. Red
symbolizes the largest node (i.e.
Malignant Neoplasm Breast due
to having most amount of
familial breast cancer genes).

28. Results
• This table shows that malignant
neoplasm of breast (breast
cancer) has the most amount of
familial breast cancer genes out
of 3,609 diseases.
• According to American Cancer
Society (2015), if a person has
had breast cancer (or malignant
neoplasm of breast) in the past,
that person would have a
“greater chance of getting
another breast cancer.”
Disease Names Sum of Number Of Shared Genes
Malignant neoplasm breast 107
Breast Carcinoma 105
NEOPLASM MALIGNANT 101
Carcinoma 83
Breast Neoplasms 78
Malignant neoplasm of prostate 76
OVARIAN CARCINOMA 74
Malignant neoplasm of ovary 73
Colorectal Cancer 72
prostate carcinoma 72
Neoplasms 70
Carcinogenesis 70
MALIGNANT LUNG NEOPLASM 67
Neoplasm Metastasis 66
Colorectal Carcinoma 64
Carcinoma, Hepatocellular 62
Melanoma 61
Malignant neoplasm of bladder 60
Carcinoma, Non-Small-Cell Lung 59
Metastatic Neoplasm 59
Adenocarcinoma 59
Ovarian epithelial cancer 58
Tumor Progression 58
Leukemia 57
Colorectal Neoplasms 55
Prostatic Neoplasms 55
Grand Total 1821
(DisGeNet Database,
2015)

29. Results
• The table also revealed that all of
the top 25 diseases are cancer-
related.
• In regards to this result, Dr. Michael
Naughton of Washington School of
Medicine theory was “because the
body’s immune system was
vulnerable to the development of
the first cancer, it may be more
susceptible to the development of
a second cancer” (National
Comprehensive Cancer Network,
2015).
Disease Names Sum of Number Of Shared Genes
Malignant neoplasm breast 107
Breast Carcinoma 105
NEOPLASM MALIGNANT 101
Carcinoma 83
Breast Neoplasms 78
Malignant neoplasm of prostate 76
OVARIAN CARCINOMA 74
Malignant neoplasm of ovary 73
Colorectal Cancer 72
prostate carcinoma 72
Neoplasms 70
Carcinogenesis 70
MALIGNANT LUNG NEOPLASM 67
Neoplasm Metastasis 66
Colorectal Carcinoma 64
Carcinoma, Hepatocellular 62
Melanoma 61
Malignant neoplasm of bladder 60
Carcinoma, Non-Small-Cell Lung 59
Metastatic Neoplasm 59
Adenocarcinoma 59
Ovarian epithelial cancer 58
Tumor Progression 58
Leukemia 57
Colorectal Neoplasms 55
Prostatic Neoplasms 55
Grand Total 1821
(DisGeNet Database,
2015)

30. 59
105
78
70
83
62
59
72
64
55
57
67
107
60
73
76
61
59
101
66
70
74
58
72
55
58
Number of Shared Genes in the Top 25 Diseases
Total
(DisGeNet Database,
2015)

31. 0
20
40
60
80
100
120
Adenocarcinoma
Breast Carcinoma
Breast Neoplasms
Carcinogenesis
Carcinoma
Carcinoma, Hepatocellular
Carcinoma, Non-Small-Cell Lung
Colorectal Cancer
Colorectal Carcinoma
Colorectal Neoplasms
Leukemia
MALIGNANT LUNG NEOPLASM
Malignant neoplasm breastMalignant neoplasm of bladder
Malignant neoplasm of ovary
Malignant neoplasm of prostate
Melanoma
Metastatic Neoplasm
NEOPLASM MALIGNANT
Neoplasm Metastasis
Neoplasms
OVARIAN CARCINOMA
Ovarian epithelial cancer
prostate carcinoma
Tumor Progression
Number of Genes in Top 25 Diseases
(DisGeNet Database,
2015)

32. Results (Continued)
• According to the Initial_from_DisGeNet worksheet from Excel, there
were many diseases that were not within the top 25 that are
considered non-cancerous (i.e. Anorexia Nervosa (1 familial breast
cancer genes), Abdominal Obesity Metabolic Syndrome (1 familial
breast cancer genes), Cognition Disorder (1 familial breast cancer
genes), and a mental disorder Asperger’s Syndrome (1 familial breast
cancer genes).

33. (DisGeNet Database,
2015)

34. • However, majority of the diseases were listed on that table were still
cancer-related. In regards to this result, Dr. Michael Naughton of
Washington School of Medicine theory was “because the body’s
immune system was vulnerable to the development of the first
cancer, it may be more susceptible to the development of a second
cancer” (National Comprehensive Cancer Network, 2015). This led
me to believe that specific genes that were involved in each disease
may increase one’s chance of suffering from familial breast cancer.

35. Results (continued)
• The results from Table 2 portray
the genes that have all of the top
25 diseases. The results network
analysis from Pajek network tool
showed that all of those genes
have the highest value of 26.
The lowest value was 1, the
LHFP gene, which was taken out
of the network due to it having a
degree of centrality less than 2.
Genes Number of Diseases
FHIT 26
PTEN 26
PCNA 26
PARP1 26
MYC 26
MDM2 26
ATM 26
IGF1 26
RASSF1 26
HIF1A 26
MLH1 26
ESR1 26
ERBB2 26
EGFR 26
CHEK2 26
CCND1 26
CASP8 26
BRCA2 26
BRCA1 26
HRAS 26
XRCC1 26
VEGFA 26
TP53 26

36. Conclusion
• The results of the analysis suggested that the diseases that have most
amount of familial breast cancer genes may cause one to be more
susceptible to having familial breast cancer. However, it also helped me to
develop another theory: there are some diseases on the initial list that are
not cancerous but may put someone at risk due to a specific gene with the
highest score according to DisGeNet Database. For instance, some
diseases, like obesity, may not be cancer-related, but it does have the
BRCA1 gene (and 41 other familial breast cancer genes) according to
DisGeNet’s database which I have found to be particularly shocking (shown
in the appendix section). One would have never thought that a disease
like obesity to have a gene that was particularly well-known for breast
cancer diagnosis. I believe that there needs to be more research regarding
this surprise discovery.

37. Conclusion (Continued)
• Ultimately, genes may only account for 5% to 10% of breast cancer
cases (American Cancer Society, 2015). When one decides to perform
research on genetic factors of breast cancer, they should be aware
that there are other risk factors to consider such as lifestyle and
environment, which may trigger the mutation of the familial breast
cancer genes. They also should take into consideration that if
someone has familial breast cancer genes or other risk factors, it does
not necessarily mean that they are guaranteed to suffer from such a
deadly disease sometime in their lives.

38. Works Cited
• American Cancer Society. (2015). What are the risk factors for breast cancer. Retrieved from:
http://www.cancer.org/cancer/breastcancer/detailedguide/breast-cancer-risk-factors
• BreastCancer.Org. (2015). Genetics. Retrieved from: http://www.breastcancer.org/risk/factors/genetics
• Centers for Disease Control and Prevention. (2015). Cancer prevention. Retrieved from:
http://www.cdc.gov/cancer/dcpc/prevention/index.htm
• DisGeNET Database. (2015). All diseases that share genes [data file]. Available from www.disgenet.org
• DisGeNET Database. (2015). Summary of associated genes [data file]. Available from www.disgenet.org
• DisGeNET Database. (2015). Summary of disease that share genes [data file]. Available from www.disgenet.org
• MedlinePlus. (2015). Breast cancer. Retrieved from: http://www.nlm.nih.gov/medlineplus/breastcancer.html
• National Cancer Institute. (2015). BRCA1 and BRCA2: Cancer risk and genetic testing. Retrieved from:
http://www.cancer.gov/cancertopics/causes-prevention/genetics/brca-fact-sheet
• National Comprehensive Cancer Network. (2015). Understanding your risk of developing secondary cancers. Retrieved from:
http://www.nccn.org/patients/resources/life_after_cancer/understanding.aspx
• Pinero, J. et al. (2015). DisGeNET: a discovery platform for the dynamical exploration of human diseases and their genes. Database
(2015) Vol. 2015: article ID bav028; doi:10.1093/database/bav028