Objectives: The antibodies interact with the “Human Leukocyte Antigen (HLA) antigens” at specific epitopes. “Epitopes” are present on a single HLA or shared by multiple antigens. In this study, we aim to determine the frequency of prevalent epitopes common in the Turkish population. Methods: Non-related 644 healthy volunteers were recruited, and The “HLA-A, -B, -C, -DR -DQ’s” were typed using the “Next Generation Sequencing”. The provisional and confirmed epitopes were identified using the “HLA Epitope Registry databases, HLA Epitopia Maps and Immucor Epitope databases” dated 07.02.2018. Epitope frequencies were calculated by counting the shared epitopes in the total number of shared HLA Class epitopes in our sample database. Results: Class I HLA’s had 298 epitopes that repeated a total of 158,117 times with frequencies ranging between 0.0006 and 2.03%, and the most frequent epitope was 170RY found on 119 different alleles. Class II HLA’s had 193 epitopes that repeated a total of 93,082 times with frequencies ranging between 0.002 and 1.36%, and the most frequent epitope was 108P found on 42 different alleles. Conclusions: Our findings summarize both the provisional, and confirmed epitope frequencies in the Turkish population and may help clinicians and immunogeneticists develop a better understanding of HLA epitope mismatches.

Introduction: Epidemio logic studies on pediatric acute lymphoblastic leukemias (ALL) have been conducted to evaluate the possible risk factors including genetic, infectious and environmental factors with the objective of idenfying the etiology. Mannose-binding lectin 2 (MBL2) plays an important role in first-line immune defense. HLA DRB1 alleles play a role in presentation of peptides to T cells and in activation of the adaptive immune response. Objective: In our study, we aimed to investigate both the MBL2 gene variant and HLA-DRB1 alleles in pediatric ALL patients. Materials: In this study, 86 high-risk ALL patients and 100 controls were included. Polymerase Chain Reaction (PCR)-Restriction Fragment Length Polymorphism (PCR-RFLP) and PCRsequence specific primer (SSP) methods were used for detection of polymorphism of the MBL2 and HLA-DRB1 alleles, respectively. Results: The frequency of the MBL2 AB genotype was lower in female ALL patients, compared to male ALL patients (p = 0.034). An association was found between the MBL2 BB genotype and DRB1*07 and among patients with the MBL2 BB genotype; those who also carried the DRB1*07 and *04 alleles were significantly higher than those without the DRB1*07 and *04 alleles. (p = 0.048, p = 0.022, respectively). Conclusion: This is the first study suggesting that the MBL2 BB genotype in association with the DRB1*07 or co-inheritance of the HLA-DRB1*04 and HLA DRB1*07 may have an impact on the etiopathogenesis of the disease. (c) 2023 Associa & ccedil;& atilde;o Brasileira de Hematologia, Hemoterapia e Terapia Celular. Published by Elsevier Espa & ntilde;a, S.L.U. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Background/Objectives: The mTOR serine/threonine kinase coordinates protein translation, cell growth, and metabolism, and its dysregulation promotes tumorigenesis. We present a reproducible, pan-cancer, network-aware framework that integrates curated resources with genomics to move beyond pathway curation, yielding falsifiable hypotheses and prioritized candidates for mTOR axis biomarker validation. Materials and Methods: We assembled MTOR-related genes and interactions from GeneCards, KEGG, STRING, UniProt, and PathCards and harmonized identifiers. We formulated a concise working model linking genotype → pathway architecture (mTORC1/2) → expression-level rewiring → phenotype. Three analyses operationalized this model: (i) pan-cancer alteration mapping to separate widely shared drivers from tumor-specific nodes; (ii) expression-based activity scoring to quantify translational/nutrient-sensing modules; and (iii) topology-aware network propagation (personalized PageRank/Random Walk with Restart on a high-confidence STRING graph) to nominate functionally proximal neighbors. Reproducibility was supported by degree-normalized diffusion, predefined statistical thresholds, and sensitivity analyses. Results: Gene ontology analysis demonstrated significant enrichment for mTOR-related processes (TOR/TORC1 signaling and cellular responses to amino acids). Database synthesis corroborated disease associations involving MTOR and its partners (e.g., TSC2, RICTOR, RPTOR, MLST8, AKT1 across selected carcinomas). Across cohorts, our framework distinguishes broadly shared upstream drivers (PTEN, PIK3CA) from lineage-enriched nodes (e.g., RICTOR-linked components) and prioritizes non-mutated, network-proximal candidates that align with mTOR activity signatures. Conclusions: This study delivers a transparent, pan-cancer framework that unifies curated biology, genomics, and network topology to produce testable predictions about the mTOR axis. By distinguishing shared drivers from tumor-specific nodes and elevating non-mutated, topology-inferred candidates, the approach refines biomarker discovery and suggests architecture-aware therapeutic strategies. The analysis is reproducible and extensible, supporting prospective validation of prioritized candidates and the design of correlative studies that align pathway activity with clinical response.

Objectives Sequence variants in cytokine genes are related to affect cytokine gene levels. In this study, it was aimed to examine eight single nucleotide polymorphisms (SNPs) in five cytokine genes (TNF-α, INF-γ, IL-6, IL10, TGF-β) for the Turkish population living in Marmara region and to reveal the genetic distance between the study group and other populations. Methods In this study, three-hundred unrelated healthy individuals were involved and all genotyping were performed by using sequence-specific primers PCR (PCR-SSP) method. The SNP data were analyzed for Hardy Weinberg equilibrium fit by calculating expected genotype frequencies and comparing them to the observed values using Arlequin software version 3.1. The genetic distances between the study group and other populations were calculated and a neighbor-joining tree was constructed by PHYLIP. Results The observed genotypes of TNF-α (−308), IFN-γ (+874), TGF-β (codon 10), and TGF-β (codon 25) of the subjects were found to be similar with other populations investigated in this study. However, there is a significant frequency difference for IL-6 and IL-10 genotypes between populations. Conclusions The current population study provided more reference values for these polymorphisms and generated a control group to be used in further association studies especially for transplantation, GVHD, autoimmune and malign disease.