Search Results (123)

Background: The precise identification of cancer subtypes through the integration of multi-omics data has emerged as a key research direction in bioinformatics. Among existing multi-omics integration methods, similarity graph-based clustering algorithms have attracted widespread interest owing to their capacity to effectively characterize the association patterns between samples. However, the majority of existing methods primarily focus on first-order relationships among samples while ignoring the prevalent high-order neighborhood relationships, and fail to fully exploit the complementary information from different omics. Methods: To address these limitations, we propose an innovative multi-omics integration framework termed MHSGTR, which integrates multi-omics data by combining Motif high-order similarity graphs and tensor regularization to identify cancer subtypes. Specifically, MHSGTR introduces Motif theory to construct a high-order similarity graph and designs a high-order graph learning term to obtain a hybrid similarity that integrates both first-order and high-order information, thereby capturing the latent high-order structural information among samples. For multi-omics data integration, we employ third-order tensor regularization constraints to explore complementary information across multi-omics data, coupled with an attention module to adaptively learn omics-specific weights for constructing a consensus similarity graph. Final clusters are derived via spectral clustering. Results: Comprehensive experiments on eight TCGA cancer datasets and a case study on adrenocortical carcinoma (ACC) demonstrate that MHSGTR achieves superior clustering performance and identifies cancer subtypes with significant biological differences, showcasing its effectiveness in robust multi-omics integration. Full article

Background: Given the higher incidence of adrenocortical carcinoma in peri-menopausal women, this study investigates the associations between sex, age, and ACC prognosis. Methods: We analyzed the National Cancer Database (NCDB) for adult patients with ACC (n = 2384) from 2004 to 2022. Overall survival (OS) was the primary endpoint. Patients were stratified by sex and menopausal status (pre- and post-menopausal age of ≤45 and ≥55, respectively). Data from The Cancer Genome Atlas (TCGA) cohort (n = 92) were analyzed to examine clinical characteristics, genetics, transcriptomics, and methylation profiles of primary ACC samples by age group. Results: The analysis of the NCDB cohort revealed that younger men and pre-menopausal women had significantly longer OS compared to older groups. However, age was the only independent variable associated with OS. Using 50 years as the optimal age cutoff, we found no differences in clinical characteristics, rates of pathogenic driver mutations, methylation or gene expression profiles, or enriched molecular pathways in the TCGA ACC cohort by age group. Conclusions: While age ≥50 years is an independent factor associated with shorter OS in ACC, no differences in clinical characteristics or multi-omic profiles were observed by age. These findings suggest that age-related prognosis may be influenced by other factors such as microenvironmental changes or epigenetics. Full article

Radiopharmaceutical Theranostics defines the combination of molecularly targeted imaging and therapy in two consecutive phases. Targeted theranostic approaches are most established for the management of advanced prostate, thyroid and hepatocellular cancer, as well as neuroendocrine tumors (NETs). Adrenal malignancies present a complex challenge, requiring highly specialized management. The two primary entities addressed by targeted radiotheranostics—pheochromocytoma/paraganglioma (PPGL) and adrenocortical cancer (ACC)—consist of fundamentally distinct molecular targets and, consequently, different radiopharmaceutical agents. While most existing literature focuses on nuclear medicine–driven perspectives, the implications of theranostic advances for surgical decision-making remain underexplored. This narrative review aims to integrate available clinical evidence with multidisciplinary practice considerations, in reshaping the role of surgery in adrenal malignancies. Full article

Adrenocortical carcinomas (ACCs) are rare, aggressive tumors often discovered incidentally. These malignancies may present with abnormal hormone secretion or, as in some cases, as non-functioning masses causing discomfort. We present a case of brain metastasis in a patient with a giant ACC. A 50-year-old man presented with headache and dizziness. A computed tomography (CT) scan showed an intracranial lesion within the parenchyma measuring 73*60*46 mm with left internal temporal involvement, abundant vasogenic edema and compressing the lateral left ventricle. Further imaging investigations identified a large necrotic tissue mass measuring 15 cm, located on both sides of the right diaphragmatic dome, in the middle posterior region. Hormonal workup was conducted and excluded a functional adrenal tumor. A CT-guided biopsy was performed, confirming ACC. Despite medical management, the patient’s condition deteriorated rapidly, with the cerebral metastasis proving fatal. This case underscores the challenges posed by advanced ACC, particularly when associated with atypical metastatic sites. Giant ACC, though rare, presents significant diagnostic and therapeutic challenges. Surgical excision with appropriate oncologic management can lead to favorable outcomes. This report contributes to the limited literature on cerebral metastases in ACC, aiming to enhance awareness among clinicians managing this rare entity. Full article

Background: Adrenocortical carcinoma (ACC) is a rare and aggressive endocrine malignancy, for which population-level evidence regarding prognostic factors and survival conditions is limited. The available data mostly represent single-institution series, limiting their applicability. This study, therefore, assesses clinicopathological features and determines independent predictive variables of overall survival (OS) in patients with ACC using a population-based cohort. Methods: This retrospective observational cohort study used data from the Surveillance, Epidemiology, and End Results (SEER) Program between 2000 and 2022, initially identifying 1176 patients with ACC. Adult patients (≥18 years) with histologically confirmed ACC were identified using ICD-O-3 histology code 8370/3 and primary site code C74.0. Cases with zero-month survival, missing survival data, or identified only through autopsy or death certificate were excluded. To ensure dataset harmonization, patients with missing or indeterminate tumor grade and unknown stage were also excluded. After applying these inclusion and exclusion criteria, the final analytic cohort consisted of 267 patients. Data on demographic factors, stage of the disease, and treatment (surgery, chemotherapy, radiotherapy) were extracted. OS was evaluated using the Kaplan–Meier method, and independent prognostic factors were identified using Cox proportional hazards regression analysis. Results: The median OS was 54 months [95% confidence intervals (CI): 36–85]. The estimated 1-, 3-, and 5-year OS rates were 77%, 57%, and 48%, respectively. Survival differed significantly according to tumor grade, stage, and surgical treatment. In multivariable Cox regression analysis, increasing age [Hazard ratio (HR): 1.03, 95% CI: 1.02–1.04; p < 0.001], high tumor grade (HR: 2.21, 95% CI: 1.43–3.41; p < 0.001), and distant-stage disease (HR: 3.24, 95% CI: 1.95–5.38; p < 0.001) were independently associated with an increased risk of mortality, whereas surgical treatment was associated with improved survival (HR 0.53, 95% CI 0.30–0.93; p = 0.028). Chemotherapy and radiotherapy were not significantly associated with mortality. Conclusions: In this SEER-based cohort of patients with adrenocortical carcinoma, older age, high tumor grade, and distant-stage disease were independently associated with worse OS, whereas documented receipt of surgery was associated with longer OS. Treatment-related associations should be interpreted cautiously in view of the inherent limitations of registry-based data. Further prospective multicenter studies are needed to confirm these findings. Full article

Adrenocortical carcinoma (ACC) is a rare, aggressive endocrine malignancy with poor survival outcomes and high recurrence rates. Whilst surgical resection is the cornerstone of curative treatment, the role of lymphadenectomy remains debated. “Nodes of contention” in ACC management center on balancing accurate staging and potential oncologic benefit against added operative time, complexity, and morbidity. We reviewed the available published literature over the last 20 years, including retrospective series, to evaluate the prognostic and therapeutic significance of lymphadenectomy in ACC. Though systematic lymph node dissection improves staging accuracy and may identify patients at higher risk who could benefit from adjuvant therapy, evidence demonstrating a survival benefit is inconsistent. This is largely due to the rarity of the condition, heterogeneity in surgical approaches, and lack of standardized nodal templates. Concerns regarding increased operative morbidity further limit widespread adoption. This review synthesizes current evidence on nodal assessment in ACC and highlights gaps in prospective data. While nodal involvement is a strong prognostic factor, the therapeutic impact of lymphadenectomy remains unclear. Prospective, multicenter trials are urgently needed to define its role in ACC management. Full article

Background: The differentiation of pheochromocytoma (PCC) from other adrenal lesions, particularly in incidentalomas with non-benign radiological characteristics (size > 4 cm or density > 10 HU), remains a clinical challenge. The study aimed to develop and validate an interpretable machine learning (ML) model for pairwise differentiation of PCC from adrenocortical carcinomas (ACCs) and non-functioning adrenal adenomas (NAAs) and to identify the most important clinical features. Methods: We analyzed a dataset of 50 clinical, laboratory, and radiological parameters from 123 patients with histologically verified adrenal tumors (63 PCC, 30 ACC, 30 NAA). Four classifiers—Logistic Regression (LR), Random Forest (RF), Linear Discriminant Analysis (LDA), and Extreme Gradient Boosting (XGBoost)—were trained for binary classification tasks (PCC vs. ACC, PCC vs. NAA, ACC vs. NAA) using a robust nested stratified cross-validation pipeline to ensure generalizability and avoid overfitting. Results: All four models showed strong predictive performance, with discrimination (AUC) more than 0.8. Our analysis, based on the interpretable LR model, identified the key discriminators differentiated PCC from both ACC and NAA: maximum systolic blood pressure, grade 3 hypertension, headache, palpitation, tachycardia, male sex, and concomitant gastric and duodenal ulcers. In contrast, lower back pain and general weakness were strong signs of lower probability of PCC. The tumor density specifically differentiated PCC from NAA, whereas tumor size was an important marker for distinguishing PCC and ACC. Conclusions: We developed robust ML models capable of accurately differentiating PCC from other adrenal tumors in complex cases. The models provide a clinically actionable tool for pre-surgical decision support. Furthermore, the identification of key discriminative features enhances the clinical understanding of PCC and facilitates its differential diagnosis prior to histological verification. Full article

Artificial intelligence (AI) and machine learning (ML) are reshaping cancer research and care. In pediatric oncology, early evidence—most robust in imaging—suggests value for diagnosis, risk stratification, and assessment of treatment response. Pediatric endocrine tumors are rare and heterogeneous, including intra- and extra-adrenal paraganglioma (PGL), adrenocortical tumors (ACT), differentiated and medullary thyroid carcinoma (DTC/MTC), and gastroenteropancreatic neuroendocrine neoplasms (GEP-NEN). Here, we provide a pediatric-first, entity-structured synthesis of AI/ML applications in endocrine tumors, paired with a methods-for-clinicians primer and a pediatric endocrine tumor guardrails checklist mapped to contemporary reporting/evaluation standards. We also outline a realistic EU-anchored roadmap for translation that leverages existing infrastructures (EXPeRT, ERN PaedCan). We find promising—yet preliminary—signals for early non-remission/recurrence modeling in pediatric DTC and interpretable survival prediction in pediatric ACT. For PGL and GEP-NEN, evidence remains adult-led (biochemical ML screening scores; CT/PET radiomics for metastatic risk or peptide receptor radionuclide therapy response) and serves primarily as methodological scaffolding for pediatrics. Cross-cutting insights include the centrality of calibration and validation hierarchy and the current limits of explainability (radiomics texture semantics; saliency ≠ mechanism). Translation is constrained by small datasets, domain shift across age groups and sites, limited external validation, and evolving regulatory expectations. We close with pragmatic, clinically anchored steps—benchmarks, multi-site pediatric validation, genotype-aware evaluation, and equity monitoring—to accelerate safe, equitable adoption in pediatric endocrine oncology. Full article

This study examined the long-term transcriptomic reprogramming in peripheral blood mononuclear cells (PBMCs) of severe COVID-19 patients and its effects for cancer development. RNA sequencing was conducted on PBMCs obtained from healthy controls, COVID-19 patients without pneumonia, and COVID-19 patients exhibiting severe pneumonia one year post-infection. Differential gene expression analysis identified a sustained pro-oncogenic molecular signature, especially among severe COVID-19 patients. Functional enrichment analysis revealed a substantial enrichment of cancer-associated pathways, encompassing apoptosis, viral carcinogenesis, and transcriptional dysregulation. Notably, the autophagy-related gene SQSTM1/P62 was recognized as a distinctive hub gene within the severe COVID-19 patients, interacting with pivotal genes associated with inflammation, apoptosis, and cancer advancement. Survival analysis demonstrated that elevated expression of COVID-19-associated hub genes correlated with unfavorable prognosis in various cancer types, including adrenocortical carcinoma, bladder urothelial carcinoma, and brain lower-grade glioma. These findings indicate that severe COVID-19 infection may establish a systemic milieu favorable to cancer development or recurrence, highlighting the necessity of prolonged oncological monitoring in these patients. Finding specific molecular targets and pathways can help us understand how COVID-19 might be linked to a higher risk of cancer. Full article

Adrenocortical carcinoma (ACC) is a rare and aggressive cancer with limited treatment options, commonly managed with mitotane, which can cause serious side effects, including central hypothyroidism and dyslipidemia. This study aimed to evaluate the incidence, clinical features, and relationship between mitotane-induced central hypothyroidism and dyslipidemia in ACC patients, as well as to investigate mitotane’s direct toxic effects on thyroid cells. Thirty-eight ACC patients treated with mitotane for at least six months were monitored for thyroid function and lipid profiles. Central hypothyroidism developed in 50% of patients with normal baseline thyroid function, mostly women, who were at higher risk. Dyslipidemia occurred in 40% of patients, more frequently in men, and appeared earlier than hypothyroidism. In vitro experiments on rat thyroid cells demonstrated a dose-dependent toxic effect of mitotane on cell viability. No significant link was found between hypothyroidism and dyslipidemia risk. These findings reveal sex-specific susceptibilities to mitotane toxicity and provide novel evidence of direct mitotane-induced thyroid cell damage. This insight supports the need for careful thyroid and lipid profile monitoring during mitotane treatment and may inform the development of safer therapies for ACC. Full article

Hereditary predisposition substantially shapes prevention and management across urologic oncology. This narrative review synthesizes contemporary, practice-oriented guidance on whom to test, what to test, how to act on results, and how to implement care equitably for hereditary forms of prostate cancer, renal cell carcinoma (RCC), urothelial carcinoma, pheochromocytoma/paraganglioma (PPGL), and adrenocortical carcinoma (ACC). We delineate between forms of indication-driven germline testing (e.g., universal testing in metastatic prostate cancer; early-onset, bilateral/multifocal, or syndromic RCC; reflex tumor mismatch repair (MMR)/microsatellite instability (MSI) screening in upper-tract urothelial carcinoma (UTUC); universal testing in PPGL; universal TP53 testing in ACC) and pair these strategies with minimum actionable gene sets and syndrome-specific surveillance frameworks. Key points include targeted prostate-specific antigen screening in BRCA2 carriers and the impact of BRCA/ATM variants on reclassification during active surveillance; major hereditary RCC syndromes with genotype-tailored surveillance and pathway-directed therapy (e.g., HIF-2α inhibition for von Hippel–Lindau disease); UTUC/bladder cancer in Lynch syndrome with tumor MMR/MSI screening, annual urinalysis (selective cytology), and immunotherapy opportunities in deficient MMR disease/MSI-H; PPGL management emphasizing universal germline testing, intensified surveillance for SDHB, cortical-sparing adrenalectomy, and emerging HIF-2α inhibition; and ACC care modified by Li–Fraumeni syndrome (minimization of radiation/genotoxic therapy with whole-body imaging surveillance). Testicular germ cell tumor remains largely polygenic, with no routine germline testing in typical presentations. Finally, we provide pre-/post-test genetic-counseling checklists and mainstreamed workflows with equity metrics to operationalize precision care and close real-world access gaps. Full article

The AP-2 family is a group of key regulators in cancer, yet their relationship with intratumoral microbes remains undefined. The present pan-cancer workflow leveraged TCGA transcriptomic data to correlate expression of AP-2 representatives with bacterial abundance on the genus and species level, followed by cohort-specific survival modeling, clinical profiling, differential expression, weighted co-expression analysis, and chromatin proximity tests with AP-2 enrichment. Significant correlations between microbiota and AP-2 were observed in 18 of 33 analyzed tumor types; TFAP2E-AS1 was most recurrent among AP-2 members, and Halomonas was most recurrent among genera. Further species-level verification and prognostic importance nominated three promising pairs: Paraburkholderia fungorum–TFAP2E in adrenocortical carcinoma (ACC), Actinomyces oris–TFAP2E in diffuse large B-cell lymphoma (DLBC), and Cutibacterium granulosum–TFAP2B in stomach adenocarcinoma (STAD). An attempt to define a consensus expression signature driven by microbiota and AP-2, yet independent of the specific species or family member, revealed genes regulating various biological processes and pathways. ACC and DLBC shared a consensus expression program, whereas STAD diverged; chromatin analysis showed AP-2 motifs near microbe-responsive genes in ACC and DLBC but not STAD, supporting cohort-specific regulation. Collectively, AP-2 family members emerge as plausible mediators of tumor microbiota–host interplay, warranting further mechanistic and translational research. Full article

The cytoskeleton has been described as a regulator of adrenal physiology and tumour behaviour. In the adrenal cortex, both cytoskeletal filaments, by mediating cholesterol transfer to mitochondria, and their binding proteins, such as cofilin and diaphanous-related formin 1 (DIAPH1), have been implicated in modulating steroidogenic processes. Beyond hormone production, the cytoskeleton participates in oncogenic signalling and contributes to the acquisition of malignant behaviour in adrenocortical carcinoma (ACC). Cytoskeleton-associated proteins such as filamin A (FLNA), fascin-1 (FSCN1), RASSF1A, and the guanine nucleotide exchange factor VAV2 are involved in signal transduction, cell cycle regulation, and cytoskeletal remodelling. In ACC, dysregulation of the expression or activity of these proteins correlates with ACC aggressiveness, including increased proliferation, motility, and invasion as well as poor prognosis, making them attractive candidates for targeted therapeutic strategies. To date, no review has systematically addressed the role of cytoskeleton and its binding partners in both adrenal physiological regulation and pathological context. This review is the first to provide a comprehensive overview of cytoskeletal involvement in adrenal cortex function and cancer, highlighting emerging molecular players and their possible therapeutic implications. Full article

Background/Objectives: Adrenocortical carcinoma (ACC) is a rare, aggressive malignancy with poor prognosis, particularly in metastatic cases. The Ki-67 proliferation index is a recognized marker of tumor aggressiveness, yet its role in guiding diagnostic imaging and surgical decision-making remains underexplored. This study evaluates Ki-67’s predictive value for metastasis at diagnosis, leveraging artificial intelligence (AI) to inform personalized, minimally invasive strategies for ACC management. Methods: We retrospectively analyzed 53 patients with histologically confirmed ACC from the Adrenal-ACC-Ki67-Seg dataset in The Cancer Imaging Archive. All patients had Ki-67 indices from surgical specimens and preoperative contrast-enhanced CT scans. Descriptive statistics, t-tests, ANOVA, and multivariable logistic regression evaluated associations between Ki-67, tumor size, age, and metastasis. Random Forest classifiers—with and without the Synthetic Minority Oversampling Technique (SMOTE)—were developed to predict metastasis. A Ki-67-only model served as a baseline comparator. Model performance was assessed using the area under the curve (AUC) and DeLong’s test. Results: Patients with metastatic disease had significantly higher Ki-67 indices (mean 39.4% vs. 21.6%, p < 0.05). Logistic regression identified Ki-67 as the sole significant predictor (OR = 1.06, 95% CI: 1.01–1.12). The Ki-67-only model achieved an AUC of 0.637, while the SMOTE-enhanced Random Forest achieved an AUC of 0.994, significantly outperforming all others (p < 0.001). Conclusions: Ki-67 is significantly associated with metastasis at ACC diagnosis and demonstrates independent predictive value in regression analysis. However, integration with machine learning models incorporating tumor size and age significantly improves overall predictive accuracy, supporting AI-assisted risk stratification and precision imaging strategies in adrenal cancer care. Full article

Adrenocortical carcinoma (ACC) is an aggressive cancer with a poor prognosis. Mitotane, the only FDA-approved treatment for ACC, targets adrenocortical cells and reduces cortisol levels. Although it remains the cornerstone of systemic therapy, its overall impact on long-term outcomes is still a matter of ongoing clinical debate. Drug repurposing is a cost-effective way to identify new therapies, and defactinib, currently in clinical trials as part of combination therapies for various solid tumours, may enhance ACC treatment. We aimed to assess its efficacy in combination with mitotane. We tested the combination of mitotane and defactinib in H295R, SW13, and mitotane-sensitive and -resistant HAC15 cells, using functional assays, transcriptomic profiling, 2D and 3D cultures, bioprinted tissues, and xenografts. We assessed drug interactions with NMR and toxicity in vivo, as mitotane and defactinib have never been previously administered together. Genomic data from 228 human ACC and 158 normal adrenal samples were also analysed. Transcriptomic analysis revealed dysregulation of focal adhesion along with mitotane-related pathways. Focal adhesion kinase (FAK) signalling was enhanced in ACC compared to normal adrenal glands, with PTK2 (encoding FAK) upregulated in 44% of tumour samples due to copy number alterations. High FAK signature scores correlated with worse survival outcomes. FAK inhibition by defactinib, both alone and in combination with mitotane, showed effective anti-tumour activity in vitro. No toxicity or drug—drug interactions were observed in vivo. Combination treatment significantly reduced tumour volume and the number of macrometastases compared to those in the mitotane and control groups, with defactinib-treated tumours showing increased necrosis in xenografts. Defactinib combined with conventionally used mitotane shows promise as a novel combination therapy for ACC and warrants further investigation. Full article