By grouping patients based on gene activity, scientists show that common molecular pathways, especially immune, help to explain why some diseases are overlapping and others diverge, offering evidence of treatment and prevention.
Study: Patient stratification reveals the molecular base of sick cases. Credit Picture: Nobeastsofierce/Shutterstock.com
The use of genomic and transcriptional data has greatly improved the understanding of multiple aspects of human physiology. A new paper in the Pnas Reports on the molecular level correlations of co -existing diseases identified by their RNA expression.
The researchers went one step further, categorizing participants with their gene expression standards. This has revealed more groups of diseases, both known and potential, offering opportunities to systematically discover relationships between molecular diseases. This could enhance treatment approaches to such co -institutions.
Import
Coordinatorness refers to the occurrence of two or more conditions of illness in the same patient or a set of patients. Special diseases give a higher risk for some other situations. These co-appearance standards contribute to the prediction of the course and prognosis of diseases, as well as the likelihood of developing specific secondary diseases as a result of the status of the index.
Common genes associated with the disease can explain these co-consultations and can be identified using network analysis. The authors of this document have previously shown how gene expression profiles predicted the similarity of diseases of diseases, revealing well -known co -institutions.
However, previous network studies have failed to identify many well -known co -institutions. The current study has used RNA sequence data available in the common RNA sequence, which provide greater sensitivity and reproduction from previous methods.
The researchers created a network of similarity of diseases, which reproduced and added to correlations between a much higher percentage of known co -institutionality. They then took advantage of differential gene expression data to create a straightened network of similarity, grouping patients with their gene expression profile.
Study findings
Networks have identified immediate and reverse co -institutions, that is, conditions that occur more or less often than expected. Most importantly, the layered network reminds ~ 64% of epidemiologically known co -ordinance pairs by analyzing the subgroups of patients with a similar expression profile. The results are associated with those of epidemiological studies, validating the methodological health of analysis.
Identified compounds include those of irritable bowel disease (IBD) and lung or liver or infection by Kaposi’s HIV infection. Some less obvious compounds were also identified, such as kaposi sarcoma and immunological diseases such as IBD.
Again, Kinesini streets were enriched in cancer, but were lower than expected in Huntington’s disease. Huntington’s disease has increased activation of signaling and supplementation Th1/IL-12, while these paths are underxiety in various cancers, depicting opposing immunological tendencies.
Co -conservatives associated with the intestine had the highest accuracy of 66.4%. Neoplasms have shown the lowest precision, while mental disorders tend to have a lower recall. Specifically, 95.2% of DSN interactions that match epidemiology share one or more over -defined immune pathways. More than 90% metabolic or extracellular uterus.
Common mechanisms
The study denotes common underlying biological explanations for co -operation with a strong immune component and reveals multiple deeper relationships between diseases.
Thus, the usual underlying mechanisms can be three types: Both diseases share the same path, a condition changes the paths, causing the second condition or a third condition causes changes that increase the risk of the other two.
Multiple combinations may also occur, especially with chronic medical conditions.
Therefore, it is not all correlations between diseases to reflect real risk increases. Some mirror similarities in path dysfunctions. Others correspond to co -institutions that have not been widely recognized, such as breast cancer with colon cancer or thyroid and thyroid cancer with ulcers due to radiation.
For example, the metabolic syndrome is due to the evolution of the metabolic trajectory involving obesity, insulin resistance, diabetes, cardiovascular disease and cancer.
Subtypes and co -institutions of diseases
The patterns and subtypes of the disease also modify the incidence of compounds, as they include separate gene expression standards. The current study suggests that some patients with breast cancer are more likely to have autism and bipolar disorder, although evidence is mixed or not significant in some cases.
Down syndrome was also associated with a higher risk of childhood leukemia and multiple autoimmune diseases, especially celiac disease, with six times higher incidence. This is related to widespread changes in the immune system.
Conclusions
The study is based on Networks of Similarity of Disease based on gene expression profiles that provided correlations between co -operatives on an unprecedented scale. Networks indicate that “Coordinators have a powerful molecular ingredient that is best recorded with gene expression profile than with other molecular sources“And provides”A systematic framework for translating the co -existence of diseases into molecular standards”.
The study clarifies the biological processes involved, helping to explain how these conditions occur and why they coexist, with great emphasis on immune pathways. It could perhaps guide the efforts to revise drugs and drug development.
Methodology has exceeded previous systematic restrictions, such as a biased and inadequate knowledge of genes related to disease and disease interactions. Using evenly processed RNA-SEQ with adapting the study effect improved sensitivity and reproducibility. The links were further intersected with epidemiology and literature.
The use of stratification of the patient with gene expression “phenotypes” has excluded non -significant changes in the road. Finally, it determines both positive and negative (reverse) correlations. Due to data limitations, only positive bonds could be systematically compared to epidemiology.
Further research is needed to ratify negative correlations, obtaining generalized epidemiological data and the correlation of data related to demographic and treatment with gene expression differences. Larger sample sizes would help achieve these goals.
