Hepatocellular carcinoma (HCC) is a leading cause of cancer mortality in the world, with most cases being diagnosed in an advanced stage of the disease. Small non-coding microRNAs (miRNAs), which play a crucial role in regulating gene expression, have emerged as potential diagnostic, prognostic, and therapeutic targets for HCC. This systematic review aims to review the current evidence on the diagnostic and prognostic significance of miRNAs in HCC METHODS: A comprehensive literature search by using PubMed, Scopus, Web of Science, and Google Scholar was conducted, considering papers published between January 2013 and June 2025. The review followed PRISMA 2020. The studies that fit the criteria were human studies and focused on the effects of miRNAs on the diagnosis, prognosis, or response to treatment of HCC. Data extraction was performed independently by 2 reviewers, and the quality of included studies was assessed with use of the Newcastle-Ottawa Scale. Narrative synthesis was performed, and sensitivity, specificity, and AUC measurements were extracted as appropriate.

We aimed to summarize the tumor spectrum and clinical features of multiple endocrine neoplasia type 5 (MEN5) caused by heterozygous inactivating germline MYC-associated factor X (MAX) mutations.

Cognitive outcomes following brain insult are shaped by a range of factors, including genetic predispositions. Emerging evidence indicates that specific genetic variants may affect the susceptibility to cognitive impairment in individual patients. In this systematic review we summarize the evidence for genetic variants on cognitive outcomes following brain insults.

BACKGROUND: The insulin-like growth factor (IGF) family plays a critical role in cancer progression, with the insulin-like growth factor-1 receptor IGF-1Rsignificantly regulating cellular growth through specific signaling pathways. These pathways may enhance cell proliferation and colorectal cancer (CRC) advancement. Some studies suggest an association between serum IGF levels and reduced CRC risk. To further elucidate this relationship, we conducted a systematic literature review and meta-analysis investigating the association between blood IGF levels and CRC onset. METHODS: A comprehensive search was conducted in PubMed, Scopus and Web of Science databases for studies published until February 15, 2025. A total of 71 eligible studies, comprising 7360 CRC cases and 15982 controls, were included. Gene expression data for IGF-1, IGF-1R, insulin receptor substrates (IRS1), IGF-2, and insulin-like growth factor-binding protein 3 (IGFBP3) were analyzed. The pooled effect estimates with 95% confidence intervals (CIs) were calculated to assess relationships between gene expression levels and CRC susceptibility. Statistical analyses were performed using STATA version 14.0 and R, version 4.4.1. RESULTS: The meta-analysis demonstrated a significant association between elevated circulating IGF-1 levels and an increased risk of colorectal cancer (WMD = 11.24; 95% CI: 4.18–18.30; p = 0.002) as well as precancerous colorectal lesions (WMD = 21.35; 99% CI: 3.37–34.89; p = 0.002). Conversely, reduced IGFBP-3 levels were significantly associated with a higher CRC risk (WMD = − 138.20; 95% CI: −231.77 to − 44.64; p = 0.004). No significant association was observed for IGF-2 (WMD = − 5.40; 95% CI: −35.11 to 24.31. CONCLUSIONS: Elevated IGF1 levels and reduced IGFBP3 levels are correlated with increased CRC risk, suggesting a potential role of these factors in CRC development. These findings provide insight into CRC pathogenesis and could inform future diagnostic and therapeutic approaches.

Matrix metalloproteinases (MMPs) are a group of genes that play a crucial role in cancer progression. In this study, we conducted a systematic review and meta-analysis to evaluate the association between gelatinase polymorphisms (specifically MMP2 and MMP9) and lung cancer (LC) susceptibility.

This study aimed to identify the main directions and recent advancements in personalised approaches to cancer diagnosis and treatment, based on a comprehensive analysis of current clinical and technological practices.

Circulating tumor DNA (ctDNA) has emerged as a promising biomarker for minimal residual disease (MRD) detection in resected non-small cell lung cancer (NSCLC), offering potential for risk stratification and treatment optimization in the postoperative setting.

This systematic review aimed to summarize the clinicopathological characteristics, molecular profile, treatment, and outcomes of head and neck spindle cell carcinoma (HNSpCC).

A 68-year-old man with a history of diffuse large B-cell lymphoma (DLBCL) with cutaneous involvement presented with a new, rapidly growing, exophytic, bleeding tumor. Histopathologic and immunophenotypic characterization was consistent with a diagnosis of histiocytic sarcoma (HS). A subsequent lymph node biopsy also yielded a diagnosis of HS. Genetic analysis of this patient's original DLBCL bone marrow specimen and subsequent lymph node HS specimen identified identical p.G13D KRAS mutations. This case highlights, to our knowledge, the first case of systemic DLBCL with cutaneous involvement linked to transdifferentiated cutaneous HS by identical KRAS mutations. We also include a systematic review of cutaneous HS cases, identifying only two cases with underlying hematologic transdifferentiation. The distinct clinical morphology, histopathologic characteristics, and immunohistochemical markers associated with each of these entities highlight a very rare and unique example of histiocytic transdifferentiation in the context of a hematologic malignancy.

Breast cancer remains the leading cause of morbidity and mortality for women worldwide. This study focuses on the ability of DNA-based hybrid materials, including DNA-Gold Nano Particles (AuNP) conjugates and DNA nanostructures, to enhance the early detection and diagnosis of breast cancer. By utilizing the emerging technology of DNA nanotechnology, this review identifies improvements in the sensitivity and specificity for the detection of biomarkers critical to breast cancer, such as TP53, BRCA1, BRCA2, PIK3CA, and ESR1. These biomarkers can now be measured through highly developed sequencing technologies combined with polymerase chain reactions. This research also delves into mutation detection, technological methods, and various stages of breast cancer, offering a comprehensive approach to understanding and managing the disease. Complementing these advancements, the proposed HER2Classifier, integrating a ResNet101 backbone with an attention mechanism, demonstrates robust performance in classifying HER2 status using the Zenodo breast cancer dataset. The classifier's two-step classification strategy effectively addresses class imbalance, enhancing the detection of HER2 High samples. By generating visual attention maps, the model offers interpretability, which is crucial for clinical applications. The integration of mixed precision training and dynamic memory management ensures scalability and memory efficiency, paving the way for potential clinical deployment. The synergy between DNA-based technologies and the HER2Classifier highlights the need for further development to fully harness the potential of these technologies in personalizing care and enhancing outcomes in breast cancer treatment.

Background: Diffuse large B-cell lymphoma (DLBCL) is a biologically heterogeneous malignancy, with various outcomes despite significant advances in therapeutic options. Current conventional prognostic tools, e.g., the International Prognostic Index (IPI), lack sufficient precision at an individual patient level. However, artificial intelligence (AI), including machine learning (ML) and deep learning (DL), can enable specialists to navigate complex datasets, with the final aim of improving prognostic models for DLBCL. Objectives: This scoping review aims to systematically map the current literature regarding the use of AI/ML techniques in DLBCL outcome prediction and risk stratification. We categorized studies by data modality and computational approach to identify key trends, knowledge gaps, and opportunities for their translation into current practice. Methods: We conducted a structured search of the PubMed/MEDLINE, Scopus, and Cochrane Library databases through July 2025 using terms related to DLBCL, prognosis, and AI/ML. Eligible studies included original papers applying AI/ML to predict survival outcomes, classify risk groups, or identify prognostic subtypes. Studies were categorized based on input modality: clinical, positron emission tomography/computed tomography (PET/CT) imaging, histopathology, transcriptomics, genomics, circulating tumor DNA (ctDNA), and multi-omics data. Narrative synthesis was performed in line with PRISMA-ScR guidelines. Results: From the 215 records screened, 91 studies met the inclusion criteria. Group-wise we report the following categories: clinical risk features (n = 8), PET/CT imaging (n = 30), CT (n = 1), digital pathology (n = 3), conventional histopathology (n = 2), gene expression profiling (n = 19), specific mutational signatures (n = 18), ctDNA (n = 3), microRNA (n = 2), and multi-omics integration (n = 5). The most common techniques reported amongst the papers included ensemble learning, convolutional neural networks (CNNs), and LASSO-based Cox models. Several AI techniques demonstrated superior predictive performance over IPI, with area under the curve (AUC) values frequently exceeding 0.80. Multi-omics models and ctDNA-based predictors showed strong potential for clinical translation, a perspective worth considering in further studies. Conclusions: AI/ML methods are increasingly used in DLBCL to improve prognostic accuracy by leveraging data types with diverse inputs. These approaches allow an enhanced stratification, superior to traditional indices, and support the early identification of high-risk patients, earlier guidance for therapy tailoring, and early trial enrollment for flagged cases. Future investigations should focus on external validation and improvement of model interpretability, with tangible perspectives of integration into real-world workflows and translation from bench to bedside.

The increasing global incidence of thyroid cancer highlights the importance of accurately assessing risk factors, particularly those related to family history. Although having affected family members is widely recognized as a risk factor for thyroid cancer, the exact degree of risk and its variation across types of familial relationships, parental gender, and geographic regions remain unclear. This systematic review and meta-analysis aimed to clarify the association between family history and thyroid cancer risk. We conducted a comprehensive literature search of PubMed, Web of Science, and Embase following PRISMA guidelines, identifying 13 studies from 503 initially screened. Statistical analyses were performed using random-effects models to estimate pooled odds ratios and risk ratios, with subgroup analyses to assess variations across population and relationship types. Our findings showed an approximately 4.5-fold higher risk of thyroid cancer in individuals with affected family members. Individuals with affected siblings were more likely to develop thyroid cancer while the risks associated with maternal and paternal family history were comparable in magnitude, with no statistical difference between them. Socioeconomic, educational, and lifestyle differences did not significantly influence risk, and geographic variations in familial risk could not be statistically confirmed by the subgroup analysis, in the context of high between-study heterogeneity. These results suggest that family history is a substantial risk factor for thyroid cancer, reinforcing the need for enhanced surveillance and screening strategies for those with a familial predisposition.

BRCA1 and BRCA2 are tumor suppressor genes essential for DNA repair. Mutations in these genes significantly increase breast (BC) and ovarian cancer (OC) risk, with BRCA1-positive facing a 70% BC and 40% OC lifetime risk. While guidelines for BRCA-positive are well established, recommendations for BC surveillance in BRCA-patients already diagnosed with OC remain limited. This meta-analysis evaluates BC risk post-OC in BRCA-mutated women.

Hematological malignancies, including leukemia, lymphoma, and multiple myeloma, are among the most aggressive cancers, with high mortality rates and limited early diagnostic tools. Exosomes, nano-sized extracellular vesicles secreted by numerous cells including tumor cells, have emerged as promising biomarkers due to their stability, non-invasive isolation, and disease-specific molecular cargo, particularly non-coding RNAs (ncRNAs).

OBJECTIVE: Noncoding RNAs (ncRNAs), including microRNAs (miRNAs), long noncoding RNAs (lncRNAs), and circular RNAs (circRNAs), play critical epigenetic roles in regulating gene expression and cancer development. Dysregulation of these molecules is closely associated with breast cancer initiation, progression, metastasis, and therapeutic resistance. Another major epigenetic mechanism, DNA methylation, also contributes to cancer susceptibility. METHODS: Following PRISMA guidelines, a systematic search was conducted in PubMed, Scopus, IranMedex, and Magiran for studies published between 2019 and 2024. Eligible studies investigated the roles of ncRNAs and/or DNA methylation in breast cancer. Data extraction and study selection were performed according to predefined inclusion criteria. RESULTS: The study identified several dysregulated ncRNAs, including miR-155, miR-605, ZEB2-AS1, MALAT1, AK058003, SGO-AS1, and circ_0005046, that regulate key cellular processes including apoptosis, proliferation, invasion, and therapy resistance. Additionally, aberrant DNA methylation of genes such as PGR, ISL1, and MGMT was associated with altered gene expression and an increased risk of breast cancer. CONCLUSION: Both ncRNAs and DNA methylation demonstrate strong potential as diagnostic and prognostic biomarkers with clinical relevance in breast cancer. To our knowledge, this is the first systematic review to integrate findings from both international and Iranian studies, providing a comprehensive overview of epigenetic regulation in breast cancer.

Oncogene-addicted non-small cell lung cancer (NSCLC) encompasses distinct molecular phenotypes, each associated with specific clinical behavior and variable sensitivity to targeted therapies. Therapeutic advancements in the field enhanced clinical outcomes and overall prognostic improvement. Beyond the molecular profile, disease burden serves as a predictive marker for treatment response and overall prognostic outcomes. The current disease staging categorizes patients based on the number and sites of metastatic involvement. In this context, the addition of local ablative therapies (LAT) is candidate to enhance or prolong the effectiveness of standard systemic therapies. Our systematic review aims to summarize current evidence dealing with the outcomes of LAT in the context of oncogene addicted NSCLC. Over response results, we also reported side effects whenever available.

Decisions regarding endometrial cancer (EC) adjuvant therapy can be challenging due to the need to balance the benefits of recurrence reduction with treatment toxicity. Circulating tumor DNA (ctDNA) has the potential to guide adjuvant therapy decisions by stratifying patients based on their risk of recurrence.

To investigate the prognostic value of the B-type Raf kinase (BRAF) V600E mutation in papillary thyroid carcinoma.

The impact of epigenetic modifier gene mutations (EMMs) on the prognosis of patients with acute lymphoblastic leukemia (ALL) is controversial, which is unlike AML. A meta-analysis is needed to evaluate the prognostic value of EMMs in ALL. Three databases, including PubMed, EMBase and Web of Science, were retrieved to find out studies exploring the association of EMMs and survival outcomes in ALL. Pooled hazard ratios (HRs) and 95% confidential interval (95%CI) were used to assess the impact of EMMs. Nineteen studies were included in our meta-analysis. DNMT3A mutation was an adverse prognostic factor in overall survival (OS) in patients with ALL (HR, 4.143; P < 0.001) as well as adult T-ALL patients (HR, 3.746; P < 0.001) and those with early T-ALL (HR, 3.523; P = 0.001). IDH mutation also had an unfavorable impact on OS in ALL (HR, 3.583; P < 0.001) and adult T-ALL cohort (HR, 3.562; P < 0.001). For pediatric patients, mutant PHF6 was significantly associated with worse OS in both B-ALL (HR, 3.194; P = 0.026) and T-ALL (HR, 2.125; P = 0.033), while PHF6 mutation had no prognostic impact on the survival of adult T-ALL patients. In addition, patients with KMT2A mutation had shorter OS compared to those with wild type (HR, 4.605; P = 0.045), whereas other EMMs had no impact on prognosis in any type of ALL. Mutations in DNMT3A, IDH, PHF6 and KMT2A showed a significant prognostic effect in ALL or in its specific subtypes, which might contribute to risk stratification and treatment guidance in the management of ALL patients.

BackgroundMYCN gene amplification is associated with poor prognosis in neuroblastoma (NB) patients; however, its detection relies on the invasive fluorescence in situ hybridization technique. Radiomics can non-invasively predict MYCN gene amplification by extracting high-dimensional features from medical images.PurposeTo systematically review and meta-analyze the performance of radiomics models in predicting MYCN gene amplification status in NB patients.Material and MethodsAs of 18 March 2025, a systematic search was performed for original literature on the prediction of MYCN amplification in NB patients using radiomics models in the following databases: PubMed, Embase, Web of Science, and Cochrane Library. The quality of the literature was assessed using the QUADAS-2 and Radiomics Quality Score (RQS) tools. The meta-analysis was performed using the random-effects model.ResultsThis research ultimately included nine articles (899 patients), from which data could be extracted for both radiomics-only models and combined models that integrate radiomic features with other predictors. The radiomics-only model demonstrated pooled sensitivity of 0.85 (95% confidence interval [CI] = 0.77-0.91) and specificity of 0.86 (95% CI = 0.79-0.90), while the combined model showed a sensitivity of 0.81 (95% CI = 0.75-0.87) and specificity of 0.92 (95% CI = 0.87-0.95). Summary receiver operating characteristic (SROC) curve yielded an area under ROC curve of 0.92 ± 0.02 for the radiomics-only model and 0.94 ± 0.02 for the combined model. No evidence of publication bias was found.ConclusionsRadiomics might be one promising approach for predicting MYCN gene amplification in patients with NB.