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Mining cancer-specific disease comorbidities from a large observational health database.

Chen Y, Xu R - Cancer Inform (2014)

Bottom Line: We stratified 3,354,043 patients based on age and gender, and developed a network-based approach to extract comorbidity patterns from each patient group.We applied our comorbidity mining approach on colorectal cancer and detected its comorbid associations with metabolic syndrome components, diabetes, and osteoporosis.Our results not only confirmed known cancer comorbidities but also generated novel hypotheses, which can illuminate the common pathophysiology between cancers and their co-occurring diseases.

View Article: PubMed Central - PubMed

Affiliation: Division of Medical Informatics, Case Western Reserve University, Cleveland, OH, USA.

ABSTRACT
Cancer comorbidities often reflect the complex pathogenesis of cancers and provide valuable clues to discover the underlying genetic mechanisms of cancers. In this study, we systematically mine and analyze cancer-specific comorbidity from the FDA Adverse Event Reporting System. We stratified 3,354,043 patients based on age and gender, and developed a network-based approach to extract comorbidity patterns from each patient group. We compared the comorbidity patterns among different patient groups and investigated the effect of age and gender on cancer comorbidity patterns. The results demonstrated that the comorbidity relationships between cancers and non-cancer diseases largely depend on age and gender. A few exceptions are depression, anxiety, and metabolic syndrome, whose comorbidity relationships with cancers are relatively stable among all patients. Literature evidences demonstrate that these stable cancer comorbidities reflect the pathogenesis of cancers. We applied our comorbidity mining approach on colorectal cancer and detected its comorbid associations with metabolic syndrome components, diabetes, and osteoporosis. Our results not only confirmed known cancer comorbidities but also generated novel hypotheses, which can illuminate the common pathophysiology between cancers and their co-occurring diseases.

No MeSH data available.


Related in: MedlinePlus

Compare the relevance scores for six disease classes between the two gender groups.Notes: Disease class 1: cardiovascular diseases; 2: respiration disorders; 3: digestive system diseases; 4: nervous system diseases; 5: endocrine, nutritional and metabolic diseases; 6: autoimmune diseases.
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f4-cin-suppl.1-2014-037: Compare the relevance scores for six disease classes between the two gender groups.Notes: Disease class 1: cardiovascular diseases; 2: respiration disorders; 3: digestive system diseases; 4: nervous system diseases; 5: endocrine, nutritional and metabolic diseases; 6: autoimmune diseases.

Mentions: We repeated the analysis between cancers and non-cancer diseases among the two gender groups. The results show that gender has little impact on most disease classes except for cardiovascular diseases, which are more common among male cancer patients, and digestive system diseases, which have a stronger association with cancers among female (Fig. 4).


Mining cancer-specific disease comorbidities from a large observational health database.

Chen Y, Xu R - Cancer Inform (2014)

Compare the relevance scores for six disease classes between the two gender groups.Notes: Disease class 1: cardiovascular diseases; 2: respiration disorders; 3: digestive system diseases; 4: nervous system diseases; 5: endocrine, nutritional and metabolic diseases; 6: autoimmune diseases.
© Copyright Policy - open-access
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC4216041&req=5

f4-cin-suppl.1-2014-037: Compare the relevance scores for six disease classes between the two gender groups.Notes: Disease class 1: cardiovascular diseases; 2: respiration disorders; 3: digestive system diseases; 4: nervous system diseases; 5: endocrine, nutritional and metabolic diseases; 6: autoimmune diseases.
Mentions: We repeated the analysis between cancers and non-cancer diseases among the two gender groups. The results show that gender has little impact on most disease classes except for cardiovascular diseases, which are more common among male cancer patients, and digestive system diseases, which have a stronger association with cancers among female (Fig. 4).

Bottom Line: We stratified 3,354,043 patients based on age and gender, and developed a network-based approach to extract comorbidity patterns from each patient group.We applied our comorbidity mining approach on colorectal cancer and detected its comorbid associations with metabolic syndrome components, diabetes, and osteoporosis.Our results not only confirmed known cancer comorbidities but also generated novel hypotheses, which can illuminate the common pathophysiology between cancers and their co-occurring diseases.

View Article: PubMed Central - PubMed

Affiliation: Division of Medical Informatics, Case Western Reserve University, Cleveland, OH, USA.

ABSTRACT
Cancer comorbidities often reflect the complex pathogenesis of cancers and provide valuable clues to discover the underlying genetic mechanisms of cancers. In this study, we systematically mine and analyze cancer-specific comorbidity from the FDA Adverse Event Reporting System. We stratified 3,354,043 patients based on age and gender, and developed a network-based approach to extract comorbidity patterns from each patient group. We compared the comorbidity patterns among different patient groups and investigated the effect of age and gender on cancer comorbidity patterns. The results demonstrated that the comorbidity relationships between cancers and non-cancer diseases largely depend on age and gender. A few exceptions are depression, anxiety, and metabolic syndrome, whose comorbidity relationships with cancers are relatively stable among all patients. Literature evidences demonstrate that these stable cancer comorbidities reflect the pathogenesis of cancers. We applied our comorbidity mining approach on colorectal cancer and detected its comorbid associations with metabolic syndrome components, diabetes, and osteoporosis. Our results not only confirmed known cancer comorbidities but also generated novel hypotheses, which can illuminate the common pathophysiology between cancers and their co-occurring diseases.

No MeSH data available.


Related in: MedlinePlus