Fanconi anemia (FA) is a genetically heterogeneous rare autosomal recessive disorder characterized by congenital malformations, hematological problems and predisposition to malignancies. The genes that have been found to be mutated in FA patients are called FANC. To date 16 distinct FANC genes have been reported. Among these, mutations in FANCA are the most frequent among FA patients worldwide which account for 60- 65%. In this study, a nine years old male child was brought to our hospital one year ago for opinion and advice. He was the third child born to consanguineous parents. The mutation analyses were performed for proband, parents, elder sibling and the relatives [maternal aunt and maternal aunt’s son (cousin)]. Molecular genetic testing [targeted next-generation sequencing (MiSeq, Illumina method)] was performed by mutation analysis in 15 genes involved. Entire coding exons and their flanking regions of the genes were analysed. Sanger sequencing [(ABI 3730 analyzer by Applied Biosystems)] was performed using primers specific for 43 coding exons of the FANCA gene. A novel splice site mutation, c.3066 + 1G > T, (IVS31 + 1G > T), homozygote was detected by sequencing in the patient. The above sequence variant was identified in heterozygous state in his parents. Further, the above sequence variant was not identified in other family members (elder sibling, maternal aunt and cousin). It is concluded that genetic study should be done if possible in all the cases of suspected FA, including siblings, parents and close blood relatives. It will help us to plan appropriate treatment and also to select suitable donor for hematopoietic stem cell transplantation and to plan for genetic counseling. In addition to the case report, the main focus of this manuscript was to review literature on role of FANCA gene in FA since large number of FANCA mutations and polymorphisms have been identified.

Von Hippel-Lindau (VHL) disease is a rare hereditary tumor syndrome caused by VHL gene mutations that is characterized by heterogeneous phenotypes such as benign/malignant tumors of the central nervous system, retina, kidney, adrenal gland, and pancreas. The genotype-phenotype correlation has not been well characterized in the Korean population so far. Therefore, this study aimed to evaluate the VHL mutation spectrum and genotype-phenotype correlations in Korean VHL patients. Thirteen unrelated subjects with VHL mutations were included. Direct sequencing and multiplex ligation-dependent probe amplification were performed. Consequently, the clinical manifestations and family histories of the subjects were evaluated. We identified 10 different VHL mutations. The c.160_161delAT frameshift mutation was novel. Missense mutations clustered in 2 domains (α domain in exon 1; β domain in exon 3). The most frequently observed mutation was c.208G > A (p.Glu70Lys). Milder phenotypes were observed in subjects with de novo mutations. Age-specific risk for CNS hemangioblastoma was significantly higher in subjects carrying missense mutations within the HIF-α binding site (P < 0.05). This study provides insight into the genotype-phenotype correlation in that amino acid substitutions in the HIF-α binding site may predispose patients to age-related risks of CNS hemangioblastoma.

Namhee Kim, M.D., Sun-Young Kong, M.D., Ph.D., Jongha Yoo, M.D., Ph.D., Do-Hoon Kim, M.D., Ph.D., Soo Hyun Seo, M.D., and Jieun Kim, M.D., Ph.D.. Ann Lab Med 2022;42:314-20. https://doi.org/10.3343/alm.2022.42.3.314

Park Y, et al. J Gastric Cancer. 2023 Jan;23:e5. https://doi.org/10.5230/jgc.2023.23.e5

The early and accurate detection of skin cancer can reduce mortality rates and improve patient outcomes, but requires advanced diagnostics. The integration of artificial intelligence (AI) into healthcare enables the precise and timely detection of skin cancer. However, significant challenges remain including the difficulty in differentiating visually similar skin conditions and the limitations of diverse, representative datasets. In this study, we proposed DCAN-Net, a novel deep-learning framework designed for the early detection of skin cancer. The model leverages an efficient backbone architecture optimized for capturing diverse skin patterns, utilizing carefully tuned parameters to enhance the discrimination capabilities and refine the extracted features using modified attention modules, thereby prioritizing relevant foreground information while minimizing background noise. Furthermore, the Grad-CAM explainable AI method was employed, highlighting the most salient features within dermatoscopic images. The fused optimal feature representations significantly enhanced the dermatoscopic image analysis. When evaluated on the HAM10000 dataset, DCAN-Net achieved a precision, recall, F1-score, and accuracy of 97.00%, 97.57%, 97.10%, and 97.57%, respectively. Moreover, the application of advanced data augmentation techniques mitigated data imbalance issues and reduced false-positive and false-negative rates across the original and augmented datasets. These findings demonstrate the potential of DCAN-Net for improving clinical outcomes and advancing AI-driven skin cancer diagnostics.