Genetic Analysis Identifies Potential Biomarkers to Help Diagnose AS | 40 AS Patients Genetically Sequenced to Identify New Biomarkers

 Genetic Analysis Identifies Potential Biomarkers to Help Diagnose AS |  40 AS Patients Genetically Sequenced to Identify New Biomarkers

An analysis of genetic sequencing data has identified potential new biomarkers for diagnosing ankylosing spondylitis (AS).

Specifically, scientists analyzed mRNAs, or the templates used to generate a protein from DNA, and found that the levels of three — Cxcr6, IL17RA, and Lrrfip1 — were associated with disease severity and could thus offer a way of predicting symptoms.

Other mRNAs were found at abnormal levels in AS patients and might also serve as biomarkers, but more analyzes are needed, researchers noted.

“Our findings provide a framework for identifying the key mRNAs in whole blood of AS that is conducive for the development of novel diagnostic markers for AS,” the researchers wrote.

“These mRNAs may function via involvement in various pathways of AS, especially in immune-related pathways,” they noted. “Exploration of their function in AS pathology may be beneficial for the diagnosis of AS.”

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The study, “Identification of diagnostic mRNA biomarkers in whole blood for ankylosing spondylitis using WGCNA and machine learning feature selection,” was published in Frontiers in Immunology.

No single test can be used currently to diagnose AS, which shares many symptoms with other diseases, complicating and often delaying the diagnostic process. Thus, patients may wait several years after their symptoms emerge before they’re diagnosed with AS.

Human leukocyte antigen B27 (HLA-B27) is the most significant genetic risk factor contributing to AS, and is considered a biomarker of the disease.

However, HLA-B27 isn’t specific to AS and is also found in other inflammatory diseases, as are the two inflammatory proteins C-reactive protein and matrix metalloproteinase 3.

“To facilitate early diagnosis and assess AS activity, finding novel biomarkers with satisfactory sensitivity and specificity by exploring the molecular mechanisms of AS is crucial,” the researchers wrote.

To search for new biomarkers, the team analyzed genetic sequencing data from 40 AS patients, with a mean age of 41.2, and 40 age-matched healthy adults, using a number of analytical steps.

Study identifies genes with different activity levels in patients vs. healthy controls

The goal was to identify genes that had different expression, or activity, levels in AS versus healthy samples. As a way of looking at gene expression, levels of the gene’s mRNA were measured. The genetic code in DNA is transcribed into mRNA before a final protein is produced.

Promising mRNAs were narrowed down in a series of steps. A first approach involved identifying three groups of 300–500 mRNAs associated with AS.

One of these groups housed mRNAs mostly involved in immune cell movement and inflammatory responses, “which involved active inflammatory and immune responses in AS patients’ blood,” the researchers wrote. The other groups weren’t associated with pathways known to be affected in AS, but “the possibility of their synergism with the immune response cannot be ruled out and needs to be further explored.”

In a second approach, the researchers compared individual mRNAs that had different expression in the AS group compared with the healthy group. This analysis yielded 1,116 mRNAs—491 of which were at higher levels in AS and 625 at lower levels.

Overlapping the two approaches, 296 feature mRNAs for AS were identified, which were then narrowed down to 63 hub mRNAs using another analytical technique.

While all 63 could be AS biomarkers, “there is still much redundant information in them,” the researchers wrote, noting that this limits the feasibility of using them in clinical practice.

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To address that limitation, the researchers used machine learning, a type of artificial intelligence, to predict which ones would have the strongest link to AS. Of 13 mRNAs identified by their algorithm, eight were confirmed to have different levels in the blood of AS patients compared with healthy people: IL17RA, Sqstm1, Picalm, Eif4e, Srrt, Lrrfip1, Synj1, and Cxcr6.

Cxcr6, IL17RA, and Lrrfip1 were correlated with disease severity, as measured by the Bath Ankylosing Spondylitis Disease Activity Index. Specifically, higher levels of Cxcr6 and IL17RA, and lower levels of Lrrfip1 were predictive of greater disease activity.

Of note, IL17RA, a molecule involved in driving inflammation, has been previously linked to AS, as has Sqstm1, but the other identified mRNAs are newly linked to the disease.

“But this does not mean that they are unqualified to serve as biomarkers,” the researchers wrote. “Their correlations with AS need further investigation to be elucidated in the future.”