Candidate miRNA Regulators of Blood Transcriptional Signatures for Differential Diagnosis of Chronic Lymphocytic Leukemia and Multiple Myeloma: A Comprehensive In Silico Study
Current Issues in Molecular Biology, cilt.48, sa.4, 2026 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 48 Sayı: 4
- Basım Tarihi: 2026
- Doi Numarası: 10.3390/cimb48040352
- Dergi Adı: Current Issues in Molecular Biology
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, EMBASE, Directory of Open Access Journals
- Anahtar Kelimeler: biomarkers, chronic lymphocytic leukemia, miRNA regulators, multiple myeloma
- İstanbul Üniversitesi Adresli: Evet
Özet
Chronic lymphocytic leukemia (CLL) and multiple myeloma (MM) are biologically distinct hematologic malignancies with heterogeneous clinical courses, and minimally invasive molecular biomarkers are needed to support blood-based discrimination. We performed a comprehensive in silico analysis to derive cross-cohort, direction-consistent transcriptomic programs for CLL and MM and to nominate regulatory microRNAs (miRNAs) linked to these signatures. Public gene-expression datasets from the NCBI Gene Expression Omnibus (two cohorts per disease) were processed with a reproducible workflow to define disease-biased consensus gene sets. Experimentally validated miRNA–target interactions from miRTarBase were integrated with consensus genes for miRNA target over-representation analysis, and miRNA–mRNA networks were constructed to prioritize candidate miRNAs by connectivity. A strict intersection strategy yielded a large, direction-consistent CLL consensus program, whereas a vote-based approach produced a smaller MM program due to a weaker signal in one cohort. Enrichment and network analyses identified compact regulatory modules in CLL, including a highly connected candidate miRNA linked to many CLL-up genes. This framework provides reproducible disease-biased gene programs and evidence-anchored miRNA candidates to support targeted experimental validation and the development of hypothesis-driven blood-based biomarker studies for differential diagnosis and monitoring.