Genetic Testing and Molecular Biomarkers

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Research article
Restricted accessResearch articleFirst published May, 2026pp. 107-123
Identification and Validation of Transcriptomic Signatures in Inflammatory Bowel Disease with Metabolic Syndrome via Bioinformatics and Machine Learning
Yu-zhe YinChao-sheng YanLing Wu
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Abstract
Background:
Inflammatory bowel disease (IBD) is a chronic, nonspecific inflammatory disorder affecting the gastrointestinal tract. The condition’s pathology not only involves the digestive system but also can impact various organs and tissues throughout the body. Metabolic syndrome is a clinical syndrome characterized by obesity, insulin resistance, hypertension, and hyperlipidemia. Extensive research suggests a potential association between IBD and metabolic syndrome.
Aim:
To seek biomarkers related to the diagnosis and treatment of IBD and metabolic syndrome.
Methods:
In this study, we utilized bioinformatics, integrated transcriptome analysis, and machine learning to develop a diagnostic model for the co-occurrence of IBD and metabolic syndrome. We applied two machine learning algorithms to select relevant features Least Absolute Shrinkage and Selection Operator and Random Forest (RF). Moreover, through external datasets and quantitative real-time polymerase chain reaction (qRT-PCR) experiments, we validated our findings.
Results:
We identified nine candidate gene-expression-based biomarkers (CCR6, CST7, LIMK2, TCF4, THBS3, CARS2, IFI27, LETM1, LOXL1) associated with immune and metabolic regulation that exhibited discriminatory potential for IBD complicated by metabolic syndrome. Furthermore, a column chart for diagnosing IBD in metabolic syndrome patients is provided.
Conclusion:
Our study demonstrates that integrated bioinformatics and machine learning approaches can identify transcriptomic signatures associated with IBD and metabolic syndrome, providing candidate biomarkers for further validation.
Review article
Restricted accessReview articleFirst published May, 2026pp. 124-132
Circulating Alu Elements as Biomarkers for Radiation-Induced Toxicity in Cancer
Mansi PatelCharmi JyotishiSuresh Prajapati
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Abstract
Radiation therapy is a key treatment for many cancers, but patient responses vary due to differences in genomic stability and DNA repair capacity. Increasing evidence highlights the role of Alu elements, the most abundant short interspersed nuclear elements in the human genome, as regulators of cellular stress responses. This review summarizes current knowledge on Alu biology, including their structure, retrotransposition, methylation, and expression, with a focus on their activation by ionizing radiation and their influence on DNA damage, repair pathways, gene regulation, and immune signaling. Radiation exposure has been shown to activate Alu elements, resulting in genomic destabilization, impaired repair, and pro-inflammatory responses. Consistent with this, Alu-associated mutations and microsatellite instability were observed at loci linked to key cancer genes (e.g., TP53, BRCA2), aligning with increased loss of heterozygosity and exon mutational burden. In addition, Alu hypomethylation further promotes microsatellite instability, recombination, defective repair, and is associated with therapeutic resistance. In parallel, circulating cell-free DNA fragments containing Alu sequences have also been correlated with delivered radiation dose and clinical toxicity, underscoring their potential as minimally invasive biomarkers. Taken together, these findings position Alu activation as both a mechanistic driver of radio-response heterogeneity and a minimally invasive biomarker for biodosimetry and toxicity risk stratification.
Research article
Restricted accessResearch articleFirst published May, 2026pp. 133-137
MTHFR C677T and A1298C Gene Variants in Patients with Turner Syndrome and Their Mothers
Izabel Maryalexandra Rios-FloresORCIDLucía Flores-ContrerasLucina Bobadilla-Morales
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Abstract
Background:
It has been proposed that there is a relationship between chromosomal nondisjunction and folate metabolism. Previous studies in mothers of girls with Turner syndrome (TS) and TS patients have suggested that 5,10-methylentetrahydrofolate reductase (MTHFR) C677T and A1298C gene variants might contribute to the risk of TS. However, data in different populations have yielded contradictory results.
Aim:
Here, we design a case-control study to evaluate the MTHFR C677T and A1298C gene variants in TS patients and their mothers as potential risk factors for TS.
Materials and Methods:
Using the TaqMan allelic discrimination assay, the frequency of the MTHFR C677T and A1298C gene variants was compared in two study groups, one of 54 girls with TS versus 93 control girls and another of 49 mothers of girls with TS versus 97 control mothers.
Results:
The allele and genotype frequencies of the MTHFR C677T and A1298C variants were not significantly different nor showed association among TS patients compared with control girls, nor among TS mothers compared with control mothers.
Conclusions:
The risk of having a child with TS did not appear to be associated with the MTHFR C677T and A1298C variants in the studied population.

Volume 30, Issue 5, May 2026

Identification and Validation of Transcriptomic Signatures in Inflammatory Bowel Disease with Metabolic Syndrome via Bioinformatics and Machine Learning

Circulating Alu Elements as Biomarkers for Radiation-Induced Toxicity in Cancer

MTHFR C677T and A1298C Gene Variants in Patients with Turner Syndrome and Their Mothers