Genetic Profile Research Articles

A Heuristic Approach to Analysis of the Genetic Susceptibility Profile in Patients Affected by Airway Allergies.

Allergic respiratory diseases such as asthma might be considered multifactorial diseases, having a complex pathogenesis that involves environmental factors and the activation of a large set of immune response pathways and mechanisms. In addition, variations in genetic background seem to play a central role. The method developed for the analysis of the complexities, as association rule mining, nowadays may be applied to different research areas including genetic and biological complexities such as atopic airway diseases to identify complex genetic or biological markers and enlighten new diagnostic and therapeutic targets. A total of 308 allergic patients and 205 controls were typed for 13 single nucleotide polymorphisms (SNPs) of cytokine and receptors genes involved in type 1 and type 2 inflammatory response (IL-4 rs2243250 C/T, IL-4R rs1801275A/G, IL-6 rs1800795 G/C, IL-10 rs1800872 A/C and rs1800896 A/G, IL-10RB rs2834167A/G, IL-13 rs1800925 C/T, IL-18 rs187238G/C, IFNγ rs 24030561A/T and IFNγR2 rs2834213G/A), the rs2228137C/T of CD23 receptor gene and rs577912C/T and rs564481C/T of Klotho genes, using KASPar SNP genotyping method. Clinical and laboratory data of patients were analyzed by formal statistic tools and by a data-mining technique-market basket analysis-selecting a minimum threshold of 90% of rule confidence. Formal statistical analyses show that IL-6 rs1800795GG, IL-10RB rs2834167G positive genotypes, IL-13 rs1800925CC, CD23 rs2228137TT Klotho rs564481TT, might be risk factors for allergy. Applying the association rule methodology, we identify 10 genotype combination patterns associated with susceptibility to allergies. Together these data necessitate being confirmed in further studies, indicating that the heuristic approach might be a straightforward and useful tool to find predictive and diagnostic molecular patterns that might be also considered potential therapeutic targets in allergy.

DATOS-CAT: OMOP-Common Data Model for the Standardization, Integration and Analysis of Population-Based Biomedical Data in Catalonia.

Transforming the population based biomedical cohort into the Common Data Model (OMOP-CDM) empowers researchers to access direct sources of information, enabling a deeper understanding of how genetic profiles relate to clinical outcomes and providing new knowledge that can significantly influence health care practices around the world.

Revolutionizing Healthcare: The Emerging Role of Quantum Computing in Enhancing Medical Technology and Treatment.

The healthcare sector faces complex challenges that call for innovative solutions to improve diagnostic accuracy, treatment efficacy, and data management. Quantum computing, with its unique capabilities, holds the potential to revolutionize various aspects of healthcare. This narrative review critically examines the existing literature on the application of quantum computing in healthcare, focusing on its utility in enhancing diagnostics, data processing, and treatment planning. Quantum computing's ability to handle large, complex datasets more efficiently than classical computers can significantly impact domains such as genomics, medical imaging, and personalized medicine. Quantum algorithms can accelerate the identification of genetic markers associated with diseases, facilitate the analysis of medical images, and optimize treatment plans based on individual genetic profiles. Moreover, quantum cryptography offers a robust security solution for safeguarding sensitive patient data, a critical need as healthcare increasingly relies on digital platforms. Despite the promising outlook, the integration of quantum computing into healthcare faces technical, ethical, and regulatory challenges. The delicate nature of quantum hardware, the need for error correction, and the scalability of quantum systems pose barriers to widespread adoption. Additionally, concerns around patient privacy and data security, as well as the need for updated regulatory frameworks, must be addressed. Ongoing research and collaborative efforts involving researchers, healthcare providers, and technology developers are crucial to overcoming these hurdles and realizing the full potential of quantum computing in transforming healthcare. As quantum computing continues to evolve, its impact on the future of healthcare could be profound, leading to earlier disease detection, more personalized treatments, and improved patient outcomes.For instance, quantum computing has already been applied to enhance drug discovery processes, with companies like D-Wave Systems (Burnaby, Canada) demonstrating faster molecular simulations for pharmaceutical research and IBM's (Armonk, USA) quantum systems being used to model chemical reactions for new drug development.

Unique genetic and risk-factor profiles in clusters of major depressive disorder-related multimorbidity trajectories

The heterogeneity and complexity of symptom presentation, comorbidities and genetic factors pose challenges to the identification of biological mechanisms underlying complex diseases. Current approaches used to identify biological subtypes of major depressive disorder (MDD) mainly focus on clinical characteristics that cannot be linked to specific biological models. Here, we examined multimorbidities to identify MDD subtypes with distinct genetic and non-genetic factors. We leveraged dynamic Bayesian network approaches to determine a minimal set of multimorbidities relevant to MDD and identified seven clusters of disease-burden trajectories throughout the lifespan among 1.2 million participants from cohorts in the UK, Finland, and Spain. The clusters had clear protective- and risk-factor profiles as well as age-specific clinical courses mainly driven by inflammatory processes, and a comprehensive map of heritability and genetic correlations among these clusters was revealed. Our results can guide the development of personalized treatments for MDD based on the unique genetic, clinical and non-genetic risk-factor profiles of patients.

Statistics in Personalized Medicine: Challenges and Innovations

Personalized medicine represents a paradigm shift in healthcare, aiming to tailor medical treatments to individual patients based on their unique genetic profiles, lifestyle factors, and environmental influences. At the forefront of this transformation lies statistics, which plays a pivotal role in integrating diverse data sources, identifying biomarkers, and developing predictive models that guide personalized treatment decisions. However, statistics in personalized medicine face challenges such as data integration complexities, small sample sizes, and ethical considerations. Despite these challenges, innovative statistical approaches including machine learning, Bayesian inference, and multi-omics integration are driving advancements. The future of statistics in personalized medicine lies in integrating multi-omics data, adopting artificial intelligence for predictive modeling, enhancing quantitative pharmacology, leveraging real-world evidence, and addressing ethical and regulatory frameworks. By advancing these fronts, statistics holds the promise to optimize treatment outcomes, improve patient care, and redefine the landscape of healthcare delivery in the 21st century. Keywords: Personalized medicine, Statistics, Data integration, Machine learning, Ethical considerations

Neurosurgical Management of Rubinstein-Taybi Syndrome: An Institutional Experience

Introduction: Rubinstein-Taybi syndrome (RTS) is a rare genetic condition with a distinctive set of physical features. This case series reports a single institutional experience of RTS cases, highlighting the role of neurosurgery in the comprehensive management of RTS patients. Methods: A retrospective review of patients with genetically confirmed RTS presenting between 2010 and 2023 at Children’s Hospital of Pittsburgh was performed. Patient demographics, genetic profile, clinical symptoms, radiographic characteristics, and neurosurgical management were recorded for all patients. Results: Twenty-one patients (13 females, 8 males) aged 0 to 22 years presented for formal genetic counseling and diagnosis. Twenty patients (95%) had CREBBP pathogenic variants (RTS type 1), and 1 patient (5%) had EP300 pathogenic variants (RTS type 2). Ten patients (48%) had a low-lying conus medullaris, and 3 patients (30%) underwent subsequent spinal cord detethering. Four patients (19%) had a Chiari malformation, and three (75%) underwent Chiari decompression surgeries. One patient (5%) had Chiari-associated syringomyelia. Conclusions: RTS patients have an increased rate of tethered cord syndrome requiring detethering. The incidence of symptomatic Chiari I malformation requiring decompression has not been previously reported. The RTS series presented here demonstrates a high incidence of symptomatic Chiari I malformation in addition to tethered cord syndrome.

Genetic and molecular profiling in bone sarcomas: advancing personalized treatment approaches

Genetic and molecular profiling in bone sarcomas: advancing personalized treatment approaches

Comprehensive molecular profiling of multiple myeloma identifies refined copy number and expression subtypes

Multiple myeloma is a treatable, but currently incurable, hematological malignancy of plasma cells characterized by diverse and complex tumor genetics for which precision medicine approaches to treatment are lacking. The Multiple Myeloma Research Foundation’s Relating Clinical Outcomes in Multiple Myeloma to Personal Assessment of Genetic Profile study (NCT01454297) is a longitudinal, observational clinical study of newly diagnosed patients with multiple myeloma (n = 1,143) where tumor samples are characterized using whole-genome sequencing, whole-exome sequencing and RNA sequencing at diagnosis and progression, and clinical data are collected every 3 months. Analyses of the baseline cohort identified genes that are the target of recurrent gain-of-function and loss-of-function events. Consensus clustering identified 8 and 12 unique copy number and expression subtypes of myeloma, respectively, identifying high-risk genetic subtypes and elucidating many of the molecular underpinnings of these unique biological groups. Analysis of serial samples showed that 25.5% of patients transition to a high-risk expression subtype at progression. We observed robust expression of immunotherapy targets in this subtype, suggesting a potential therapeutic option.

Genetic diversity, virulence profiles, and antimicrobial resistance of Salmonella enterica serovar Typhi isolated from typhoid fever patients in Baghdad, Iraq

Typhoid fever is an important health issue in developing countries, and the pathogenicity of Salmonella enterica serovar Typhi (S. ser. Typhi) depends on the presence of different virulence factors. Therefore, this study aimed to investigate the spread of virulence genes among S. Typhi isolates from patients with typhoid fever in Baghdad, Iraq. Sixty S. Typhi isolates were collected from several hospitals in Baghdad and identified using VITEK-II and confirmed by polymerase chain reaction (PCR) to detect the 16S rRNA gene. After testing their susceptibility to different antimicrobials (via the disk diffusion method), we found the highest resistance rates (100 %) were to ampicillin, piperacillin, cefotaxime, and ceftriaxone. The highest sensitivity rates (100 %) were to ertapenem, imipenem, meropenem, and sulfamethoxazole/trimethoprim. The presence of genes encoding for virulence in S. Typhi isolates was tested by conventional PCR. The results showed that out of 60 isolates, 59 (98.3 %), 59 (98.3 %), 58 (96.7 %), and 60 (100 %) were positive for viaB, staA, cdtB, and orfL genes, respectively. The sequencing of PCR products (viaB, staA, cdtB, and orfL genes) was carried out at the Macrogen Company (Seoul, Korea). The sequences were compared with nucleotide sequences in the BLAST GenBank database, and data obtained from the sequencing of these virulence genes were submitted to GenBank under different accession numbers. A phylogenetic analysis of the 16S rRNA gene sequence found a high similarity between local sequences and the closely related sequences of genes in GenBank. The presence of the viaB, staA, cdtB, and orfL virulence genes in nearly all of the isolates under examination suggests that they play an important role in the pathogenicity of local isolates.

The Influence of Betulin and Its Derivatives on Selected Colorectal Cancer Cell Lines' Viability and Their Antioxidant Systems.

Oxidative stress is considered one of the main reasons for the development of colorectal cancer (CRC). Depending on the stage of the disease, variable activity of the main antioxidant enzymes, i.e., superoxide dismutase (SOD), catalase (CAT) and glutathione peroxidase (GPx), is observed. Due to limited treatment methods for CRC, new substances with potential antitumor activity targeting pathways related to oxidative stress are currently being sought, with substances of natural origin, including betulin, leading the way. The betulin molecule is chemically modified to obtain new derivatives with improved pharmacokinetic properties and higher biological activity. The aim of this study was to evaluate the effects of betulin and its new derivatives on viability and major antioxidant systems in colorectal cancer cell lines. The study showed that betulin and its derivative EB5 affect the antioxidant enzyme activity to varying degrees at both the protein and mRNA levels. The SW1116 cell line is more resistant to the tested compounds than RKO, which may be due to differences in the genetic and epigenetic profiles of these lines.

Genetic profiles of multiple system atrophy revealed by exome sequencing, long-read sequencing and spinocerebellar ataxia repeat expansion analysis.

Multiple system atrophy (MSA) is a progressive, adult-onset neurodegenerative disorder clinically characterized by combinations of autonomic failure, parkinsonism, cerebellar ataxia and pyramidal signs. Although a few genetic factors have been reported to contribute to the disease, its mutational profiles have not been systemically studied. To address the genetic profiles of clinically diagnosed MSA patients, exome sequencing and triplet repeat detection was conducted in 205 MSA patients, including one familial case. The pathogenicity of variants was determined according to the American College of Medical Genetics and Genomics and the Association for Molecular Pathology guidelines. In the familial patient, a novel heterozygous COQ2 pathogenic variant (p.Ala351Thr) was identified in the MSA pedigree. In the sporadic patients, 29 pathogenic variants were revealed in 21 genes, and the PARK7 p.Ala104Thr variant was significantly associated with MSA (p = 0.0018). Moreover, burden tests demonstrated that the pathogenic variants were enriched in cerebellar ataxia-related genes in patients. Furthermore, repeat expansion analyses revealed that two patients carried the pathogenic CAG repeat expansion in the CACNA1A gene (SCA6), one patient carried the (ACAGG)exp/(ACAGG)exp expansion in RFC1 and one carried the GAA-pure expansion in FGF14 gene. In conclusion, a novel COQ2 pathogenic variant was identified in a familial MSA patient, and repeat expansions in CACNA1A, RFC1 and FGF14 gene were detected in four sporadic patients. Moreover, a PARK7 variant and the burden of pathogenic variants in cerebellar ataxia-related genes were associated with MSA.

Clinical characteristics and genetic profile of children with WDR72-associated distal renal tubular acidosis: a nationwide experience.

Limited data, primarily from small case series, exist regarding the clinical profile, genetic variants, and outcomes of WDR72-associated distal renal tubular acidosis (WDR72-dRTA). Our study enrolled children diagnosed with WDR72-dRTA below 18years of age from 9 Indian centers and analyzed their clinical characteristics, genetic profiles, and outcomes. Potential genotype-phenotype correlations were explored. We report 22 patients (59% female) with WDR72-dRTA who were diagnosed at a median age of 5.3 (3, 8) years with polyuria (n = 17; 77.3%), poor growth (16; 72.7%), and rickets (9; 40.9%). Amelogenesis imperfecta was present in 21 (95.5%) cases. At presentation, all patients had normal anion gap metabolic acidosis; hypokalemia and nephrocalcinosis were seen in 17 (77.3%) patients each. Seven (31.8%) patients had concomitant proximal tubular dysfunction. Genetic analysis identified biallelic nonsense variants in 18 (81.8%) patients, including novel variants in 6 cases. A previously reported variant, c.88C > T, and a novel variant, c.655C > T, were the most frequent variants, accounting for 10 (45.5%) cases. Over a median follow-up of 1.3 (1, 8) years, the height velocity improved by 0.74 (0.2, 1.2) standard deviation scores, while 3 children (13.6%) progressed to chronic kidney disease (CKD) stage 2, with eGFR ranging from 67 to 76mL/min/1.73 m2, respectively, after 11.3-16years of follow-up. No specific genotype-phenotype correlation could be established. WDR72-dRTA should be considered in children with typical features of amelogenesis imperfecta and dRTA. Biallelic nonsense variants are common in Asians. While most patients respond well to treatment with improved growth and preserved eGFR, on long-term follow-up, a decline in eGFR may occur.

Genetic Characterization, Antibiotic Resistance, and Virulence Genes Profiling of Bacillus cereus Strains from Various Foods in Japan.

Bacillus cereus sensu stricto is a foodborne pathogen that causes food poisoning. Their spore and biofilm-forming abilities persist in various environments and foods. This study investigated the prevalence, virulence, antibiotic resistance, and genetic diversity of B. cereus s. s. strains isolated from various food samples. Of 179 samples, 22.34% were positive for B. cereus s. s., with significantly high detection rates in milk products and raw chicken meat. Forty strains were isolated from positive samples. Matrix-assisted laser desorption ionization/time of flight mass spectrometry analysis revealed nine distinct clusters and multi-locus sequence typing revealed 34 sequence types including 23 novel sequences, demonstrating high genetic diversity among the isolates. PCR analysis revealed that all the strains contained at least one toxin gene, but none contained the cytK gene. Antibiotic resistance tests revealed that all isolates were classified as multidrug-resistant, with high resistance levels, particularly to β-lactam antibiotics and vancomycin, but were susceptible to gentamicin. All isolates showed variations in biofilm formation. This study highlights the significant public health risk due to B. cereus s. s. and underscores the need for stringent monitoring and control measures in food production to manage antimicrobial resistance and ensure food safety.

Identification of angiogenesis-related subtypes and risk models for predicting the prognosis of gastric cancer patients

Gastric cancer (GC) is a leading cause of cancer-related mortality and is characterized by significant heterogeneity, highlighting the need for further studies aimed at personalized treatment strategies. Tumor angiogenesis is critical for tumor development and metastasis, yet its role in molecular subtyping and prognosis prediction remains underexplored. This study aims to identify angiogenesis-related subtypes and develop a prognostic model for GC patients. Using data from The Cancer Genome Atlas (TCGA), we performed consensus cluster analysis on differentially expressed angiogenesis-related genes (ARGs), identifying two patient subtypes with distinct survival outcomes. Differentially expressed genes between the subtypes were analyzed via Cox and LASSO regression, leading to the establishment of a subtype-based prognostic model using a machine learning algorithm. Patients were classified into high- and low-risk groups based on the risk score. Validation was performed using independent datasets (ICGC and GSE15459). We utilized a deconvolution algorithm to investigate the tumor immune microenvironment in different risk groups and conducted analyses on genetic profiling, sensitivity and combination of anti-tumor drug. Our study identified ten prognostic signature genes, enabling the calculation of a risk score to predict prognosis and overall survival. This provides critical data for stratified diagnosis and treatment upon patient admission, monitoring disease progression throughout the entire course, evaluating immunotherapy efficacy, and selecting personalized medications for GC patients.

Comprehensive genetic profiling and molecularly guided treatment for patients with primary CNS tumors

Despite major advances in molecular profiling and classification of primary brain tumors, personalized treatment remains limited for most patients. Here, we explored the feasibility of individual molecular profiling and the efficacy of biomarker-guided therapy for adult patients with primary brain cancers in the real-world setting within the molecular tumor board Freiburg, Germany. We analyzed genetic profiles, personalized treatment recommendations, and clinical outcomes of 102 patients with 21 brain tumor types. Alterations in the cell cycle, BRAF, and mTOR pathways most frequently led to personalized treatment recommendations. Molecularly informed therapies were recommended in 71% and implemented in 32% of patients with completed molecular diagnostics. The disease control rate following targeted treatment was 50% and the overall response rate was 30%, with a progression-free survival 2/1 ratio of at least 1.3 in 31% of patients. This study highlights the efficacy of molecularly guided treatment and the need for biomarker-stratified trials in brain cancers.

SHEDDING LIGHT ON GENETIC RELATIONSHIPS, PLASMID PROFILES AND ANTIBIOTIC RESISTANCE PATTERNS OF CLINICAL ESCHERICHIA COLI STRAINS: PULSED-FIELD GEL ELECTROPHORESIS PERSPECTIVE

In the present work, 47 Escherichia coli isolates collected from various clinical samples were elucidated with regard to genetic relationship, antibiotic resistance and plasmid profile. The genetic diversity of the isolates was defined by the pulsed field gel electrophoresis (PFGE) method. With a degree of similarity of 80%, the results from PFGE separated the E. coli strains into 39 different groups representing four subtypes. Antibiotic resistance patterns and extended spectrum of beta lactamase (ESBL) producing properties of the strains were determined by using a disk diffusion and double disc synergy method, respectively. According to the susceptibility test results, 36 distinct resistance profiles (resisto type) were observed among clinical strains. The strains were mostly resistant to ampicillin (100%) and this has been followed by cephalothin and ticarcillin/clavulanic acid with the ratio of 89.36%, cefuroxime with the ratio of 87.24%, tetracycline and cefotaxime with the ratio of 80.85%. It was observed that 24 of the strains (51.06%) were defined as ESBL positive. When E. coli strains were evaluated according to plasmid size, it was determined that 27 of 47 strains (57.44%) carried plasmids and the sizes of the determined plasmids were ranging between 77.1 kb and 1.6 kb. It was concluded that plasmids of E. coli strains are randomly distributed and no significant correlation was found between antibiotic resistance patterns and plasmids.

Endometrial carcinoma: molecular classification in routine pathology

The molecular classification of endometrial carcinoma defines four main groups: polymerase‑ɛ(PolE) gene mutated, microsatellite unstable (MSI), p53 abnormal tumors and tumors with no specific molecular profile (NSMP). This classification provides significant insights into the prognosis and therapeutic decisions. Each group exhibits unique genetic profiles identified through immunohistochemistry and molecular diagnostics, enabling personalized treatment. The identification of these molecular signatures necessitates precise analytical methods, selected based on the local circumstances at each site. The approach to molecular classification highlights the critical role of pathology in the diagnosis and emphasizes the necessity of collaboration between the clinic and pathology.

Personalized Nutrition: Tailoring Dietary Recommendations through Genetic Insights.

Personalized nutrition (PN) represents a transformative approach in dietary science, where individual genetic profiles guide tailored dietary recommendations, thereby optimizing health outcomes and managing chronic diseases more effectively. This review synthesizes key aspects of PN, emphasizing the genetic basis of dietary responses, contemporary research, and practical applications. We explore how individual genetic differences influence dietary metabolisms, thus underscoring the importance of nutrigenomics in developing personalized dietary guidelines. Current research in PN highlights significant gene-diet interactions that affect various conditions, including obesity and diabetes, suggesting that dietary interventions could be more precise and beneficial if they are customized to genetic profiles. Moreover, we discuss practical implementations of PN, including technological advancements in genetic testing that enable real-time dietary customization. Looking forward, this review identifies the robust integration of bioinformatics and genomics as critical for advancing PN. We advocate for multidisciplinary research to overcome current challenges, such as data privacy and ethical concerns associated with genetic testing. The future of PN lies in broader adoption across health and wellness sectors, promising significant advancements in public health and personalized medicine.

Classification of Cannabis Strains Based on their Chemical Fingerprint-A Broad Analysis of Chemovars in the German Market.

Introduction: Cannabis cultivars were usually categorized based on their genetic profile as sativa, indica, or hybrid types. However, these three criteria do not allow sufficient differentiation between the numerous varieties of cannabis strains. Furthermore, this classification is based on morphological and bio-geographical properties of the plants and does not represent the chemical composition of different cultivars. The concentration of cannabinoids and terpenes are crucial for the pharmacological effect, not only because of the known entourage effect, and therefore needs to be considered by categorization. Materials and Methods: A total of 140 medicinal cannabis flowers available on the German market were analyzed regarding their individual terpene profile using GC-MS analysis. Statistical evaluation was performed to investigate correlations and data relations as well as for clustering. Results: Multivariate analysis showed correlations between individual terpenes. However, there was no statistical correlation between terpene profiles and their respective genetic profile. Terpene profiles of sativa, indica, and hybrid strains are quite heterogenous and clearly showed that there is no relation between terpenes and the estimated pharmacological effect. As a result, we suggest a new classification system based on individual terpene profiles to faster a comprehensive understanding of the expected medical effect. Discussion: Considering main terpenes, we established a concept of six clusters with various terpene profiles being attributed to different medicinal applications. We excluded tetrahydrocannabinol (THC) and cannabidiol (CBD) content from clustering as most of the strains were THC dominant and therefore distort the results. Our pattern of strains with similar terpene profiles might refine the existing classes of chemotypes with different THC:CBD content. Conclusion: The categorization of cannabis strains based on their terpene profiles allows a clearer, finer and, above all, more meaningful classification than the existing sativa/indica classification. Due to the entourage effect and the interactions between cannabinoids and terpenes, this group of substances is also given the necessary consideration when selecting the right medicine for the individual. Within the next steps, further studies are needed with the aim of mapping clinical validated effects to our chemovars. If it is possible to correlate therapy of symptoms to specific chemical profiles personalized cannabinoid therapy will be possible.

Loss of clear cell characteristics in aggressive clear cell odontogenic carcinoma: a case report

BackgroundClear cell odontogenic carcinoma (CCOC) is an odontogenic carcinoma characterized by sheets and islands of vacuolated and clear cells. The diagnosis of atypical CCOC can pose a challenge when tumor cells deviate from their characteristic clear morphology, even with the aid of genetic profiling for CCOC identification.Case presentationIn this manuscript, we detailed the inaugural instance of a recurrently recurring clear cell odontogenic carcinoma (CCOC) with pronounced squamous differentiation in a 64-year-old male. The primary tumor in this individual initially displayed a biphasic clear cell phenotype. However, subsequent to the third recurrence, the clear tumor cells were entirely supplanted by epidermoid cells characterized by eosinophilic cytoplasm, vesicular chromatin, and prominent nucleoli. Notable aggressive attributes such as necrosis, conspicuous cytological malignancy, perineural dissemination, and vascular invasion were noted. Additionally, the tumor progressed to manifest lung metastases. The tumor cells exhibited positive immunoreactivity for AE1/AE3, KRT19, Pan-CK, EMA, P40, P63, CK34βE12, and P53, while they tested negative for CK35βH11, KRT7, S-100, and neuroendocrine markers. The Ki-67 proliferation index was calculated at an average of 15%. Furthermore, FISH analysis unveiled the presence of the EWSR1::ATF1 gene fusion.ConclusionsThis case illustrated a rare and aggressive case of CCOC characterized by significant squamous differentiation upon recurrence of the tumor.