Breast cancer (BC) is the most frequent neoplasm. As a consequence of the oncologic treatment, there are diverse adverse effects, as the emotional ones. Treatment options for emotional symptoms like anxiety, depression or stress are limited.

To analyze the effectiveness of psychoeducational workshops among women with BC over the anxiety and depression symptoms.

A randomized clinical trial by blocks with 42 BC patients in stages II and III of the disease, distributed into 2 groups of 21 cases (intervention) and 21 controls. There were 9 psychoeducational workshops performed weekly. It was carried out an initial and final measurement according to the Hospital Anxiety and Depression Scale (HADS).

For the intervention group, the median score for anxiety was 9.09 at the beginning of the study and 5.85 at the end (p = 0.0005). In the control group, the median for anxiety was 8.7 and 6.7 at the end (p = 0.0494). According to the depression symptoms, the median for the intervention group was 5 at the beginning and 3.2 at the end (p = 0.0216). For the control group, the median was 4.7 at the beginning and 4.6 at the end (p = 0.6359).

The psychoeducational workshops demonstrated effectiveness to decrease the symptom burden for anxiety and depression. Early education and counselling for recently diagnosed BC patients should be considered part of the treatment to prevent and decrease these symptoms.

Uncontrolled proliferation is not the only factor driving tumor growth; the tumor microenvironment (TME) has a significant impact. Tissue homeostasis and pathogen removal depend on macrophages, which are important innate immune effector cells. Nevertheless, there is growing evidence that tumor-associated macrophages (TAMs) do not consistently suppress cancer and are functionally diverse. Specifically, M2-polarized TAMs (M2-TAMs) build up in multiple solid tumors, where they promote angiogenesis, metastasis, and immunosuppression, hastening the course of the disease. Here, we critically assess the clinical translatability of TAM-targeted treatment approaches, outline the molecular circuits underpinning M2-TAMs-tumor cell interaction, and extensively explore the phenotypic spectrum and functional diversity of macrophages in cancer. Our objective is to offer a theoretical foundation for upcoming immunotherapeutic interventions.

Oncogenic fusion transcription factors (TFs) frequently drive hematopoietic malignancies by altering gene expression in key developmental programs. TCF3::HLF is a fusion TF that characterizes a rare, treatment-resistant subtype of B cell acute lymphoblastic leukemia [t(17;19) TCF3::HLF-positive B-ALL]. Despite its clinical significance, the mechanisms by which TCF3::HLF induces leukemia are unclear. We used HiChIP mapping and genetic interference to analyze TCF3::HLF at the 3D genome level, revealing enhancer-promoter interactions that control gene activation or repression. Notably, TCF3::HLF directly regulates MEF2C expression through its enhancer, as interference disrupted MEF2C transcription and inhibited leukemia propagation. This disruption also diminished embryonal hematopoietic stem cell (HSC) gene signatures and restored mature HSC and B-lymphoid markers. These findings highlight MEF2C as a critical component of the transcriptional network reprogrammed by TCF3::HLF. Our study provides insight into how TCF3::HLF rewires the 3D genome to drive leukemia and serves as a resource for further exploration of the TCF3::HLF regulome.

Checkpoint inhibitors targeting PD-1 and CTLA-4 have transformed cancer therapy. Both are genetically associated with autoimmune disorders. Moreover, certain immune-related adverse events and autoimmune risk variants are linked to the clinical efficacy of checkpoint inhibition. These associations suggest common principles governing successful cancer immunotherapy and autoimmune susceptibility. Here, we show that ablation of the cytosolic DNA exonuclease TREX1 predisposes mice to autoimmunity while promoting robust antitumor immunity. Constitutive TREX1 loss leads to early onset autoimmunity, characterized by multiorgan CD8+ T cell infiltration, myocarditis, and Sjögren's syndrome-like disease. In contrast, induced systemic TREX1 ablation is well tolerated and promotes effective CD8+ T cell-driven antitumor immunity. Detailed phenotypic studies revealed a notable overlap between productive antitumor and pathogenic autoimmune CD8+ T cell responses. Collectively, we provide mechanistic evidence for interrelated mechanisms underlying autoimmunity and successful cancer immunotherapy, uncover key parallels between adaptive T cell and innate immune checkpoints, and suggest that targeting autoimmune risk genes represents a promising future avenue for cancer immunotherapy.

T cell exhaustion is a major barrier to effective antitumor immunity, yet the tumor-intrinsic mechanisms remain poorly defined. Through single-cell and spatial proteomics analyses of esophageal squamous cell carcinoma (ESCC), we uncover two infection-like CD8⁺ T cell trajectories, acute-like and chronic-like responses, whose fates are dictated by the tumor cell subtypes they encounter. This concept links tumor heterogeneity to the shaping of local immune niches. Mechanistically, we identify CDC28 protein kinase regulatory subunit 1B (CKS1B) as a tumor-intrinsic inducer of chronic-like exhaustion. CKS1B forms a complex with S-phase kinase-associated protein to promote interferon regulatory factor 3 (IRF3) ubiquitination and degradation, thereby suppressing type I interferon signaling and antigen presentation. This impairs tumor cell elimination and drives progressive CD8⁺ T cell stimulation and exhaustion. Pharmacological blockade of the CKS1B-IRF3 interaction with 14i restores CD8⁺ T cell function and synergizes with immune checkpoint blockade. The tumor-intrinsic oncogenic-immune axis, which connects cancer cell signaling to immune dysfunction, is conserved across multiple malignancies, establishing a conceptual and therapeutic framework for overcoming tumor-driven T cell exhaustion.

Breast cancer is the leading cause of cancer mortality among women globally, and drug resistance complicates treatment. Garlic-derived organosulfur compounds exhibit anticancer potential, but their multi-target activity against key breast cancer biomarkers remains unclear. This study utilized AutoDock Vina for molecular docking, OpenBabel for post-docking energy minimization, and employs SWISS-ADME and PreADMET platforms for ADMET profiling to assess six garlic compounds (Z-ajoene, allyl-methyl trisulfide, diallyl disulfide, diallyl sulfide, diallyl trisulfide, and S-allyl-L-cysteine) against clinically relevant breast cancer targets. Z-ajoene showed strong binding to Bcl-2, Topoisomerase II, and CDK-2, while S-allyl-L-cysteine targets five biomarkers. All compounds complied with Lipinski's rule of five, indicating good oral bioavailability, and display favorable ADMET properties with no mutagenic or tumorigenic risks. Most compounds were predicted to inhibit P-glycoprotein, while only Z-ajoene showed potential inhibition of CYP2C9, suggesting possible drug-drug interactions. Despite moderate affinities, these compounds may serve as potential promising multi-target agents in breast cancer therapy. Our computational findings provide preliminary evidence that garlic-derived compounds warrant further in vitro and in vivo evaluation, particularly in the context of drug-resistant breast cancer.

The rising incidence of thyroid cancer presents a growing diagnostic and therapeutic challenge. Various risk stratification systems have sought to integrate clinical, ultrasonographic, and, in some cases, cytological features to aid malignancy prognostication. This systematic review aims to critically evaluate risk stratification tools (RSTs) for patients with thyroid nodules, which incorporate multimodal inputs to assess their diagnostic performance and clinical utility in supporting surgical decision-making.

PubMed, Embase, and Cochrane databases were searched from inception to 04/13/2026, identifying studies evaluating multivariable risk prediction models for adult patients undergoing assessment of thyroid nodules. Studies were excluded if the proposed tool failed to incorporate clinical features, ultrasound findings, and cytology results or was not validated with histology. Data extraction encompassed methodology of model development, performance metrics, and approaches to validation. Risk of bias was assessed using the PROBAST+AI tool.

Seven studies describing five distinct RSTs met inclusion criteria Thyroid Nodule App (TNAPP), the McGill Thyroid Nodule Score (MTNS), CUT Score, Memorial Sloan Kettering Cancer Centre (MSKCC) nomogram, and Thyroid Prediction Score (TiPS). TiPS demonstrated the highest sensitivity (96.2%) and specificity (97.5%) with area under the curve (AUC) >0.9. The CUT score also showed strong performance (AUC >0.9), particularly in low-to-intermediate risk nodules. TNAPP underperformed (accuracy 50.5%; specificity 27.5%) despite broad clinical inputs. The MTNS and MSKCC, although promising for indeterminate cytology, lacked robust validation. Most models were derived from single-center, retrospective cohorts, limiting generalizability.

RSTs integrating multimodal data may improve thyroid nodule risk stratification, particularly in cases of indeterminate cytology. However, methodological limitations and lack of external validation currently restrict clinical utility. Prospective evaluation in diverse populations is required to identify the most effective and generalizable tools. Until then, RSTs should be used as adjuncts to, not replacements for, clinical judgment and shared decision-making in thyroid nodule assessment.

Analyzing the network properties of cancer biomarkers within protein-protein interaction (PPI) networks is valuable for discovering novel biomarker candidates. Therefore, we constructed PPI networks using breast cancer (BC)-associated gene sets and performed 12 distinct centrality analyses to characterize the topological features of clinically validated biomarkers. Our reference set of biomarkers comprised genes from five clinical genetic testing panels-MammaPrint, Oncotype DX, PAM50, EndoPredict, and the BC Index-that were also present in the STRING database. The PPI networks were constructed from the top 2,000 BC-associated genes, ranked by disease score from the DISEASES database. These networks were then subjected to centrality analysis using five local and seven global measures. The top 5% centrality rankings were evaluated, demonstrating that maximum clique centrality (MCC) identified the highest proportion of known biomarkers, with an inclusion rate of approximately 36%. Furthermore, MCC generated a unique biomarker-ranking pattern, exhibiting a Spearman's rank correlation coefficient below 0.8 when compared with all other metrics. Consequently, a high MCC score is a key topological feature of many validated biomarkers. Genes with the highest MCC scores (top 5%) were significantly enriched for gene-ontology terms related to the cell cycle and fibroblast growth factor receptor signaling pathway. Additionally, biomarkers with high MCC scores exhibited significantly greater evolutionary conservation and potential for protein complex formation. Collectively, our findings indicate that many effective BC biomarkers are components of large, evolutionarily conserved cliques within cell-cycle-associated regions of the PPI network. Finally, based on this MCC-centric approach, we identified 11 novel candidate biomarkers.

Deep learning (DL) algorithms for digital breast tomosynthesis (DBT) have proliferated, demonstrating emerging potential in enhancing lesion detection and classification.

This study aimed to compare the diagnostic performance of DL algorithms for DBT with that of radiologists of varying experience and assess the clinical impact of DL assistance.

A systematic search of PubMed, Embase, Web of Science, and the Cochrane Library was conducted up to November 8, 2025. Included studies compared the performance of stand-alone DL algorithms for DBT, radiologist interpretation alone, and DL-assisted diagnosis. Study quality was assessed using the Prediction Model Risk of Bias Assessment Tool+Artificial Intelligence (PROBAST+AI). Performance metrics were pooled using bivariate random effects and generalized linear mixed models.

A total of 13 studies with 38,565 patients were included in the final analysis. Stand-alone DL algorithms achieved a pooled sensitivity of 0.88 (95% CI 0.80-0.93), specificity of 0.74 (95% CI 0.59-0.85), and area under the receiver operating characteristic curve (AUC) of 0.89 (95% CI 0.86-0.92). While DL performance showed no statistically significant difference compared to all radiologists (AUC=0.89 vs 0.88; P=.64) or senior radiologists (AUC=0.89 vs 0.90; P=.48), DL demonstrated significantly superior sensitivity compared to junior radiologists (0.88 vs 0.76; P=.03). Notably, DL assistance did not statistically improve diagnostic metrics for radiologists across any experience level. Meta-regression identified validation methods as a significant source of heterogeneity.

DL algorithms for DBT exhibited strong diagnostic proficiency and showed higher sensitivity than junior radiologists, suggesting their potential utility as adjunctive tools to help reduce oversight in less experienced settings. However, given that DL assistance did not significantly elevate overall human performance, current models act primarily as supplementary aids rather than definitive clinical tools. Future prospective multimodal studies are warranted to validate these findings and optimize clinical integration.

Numerous proteins display pleiotropic functions in different clinical contexts. However, the molecular mechanism underlying such effects is rarely understood. Speckle-type POZ protein (SPOP) is a typical example, exhibiting tumor-suppressing or tumor-promoting effects in different tumor types in accordance with different amino acid changes; specifically, two distinct sets of variants in SPOP are commonly found in subsets of prostate cancer and endometrial cancer patients. To comprehensively characterize the functional landscape of SPOP alteration, we performed a deep mutational screening (DMS), elucidating the functionality of 7,933 out of 8,228 possible single amino acid changes (96.4% coverage). Leveraging the observation that overexpression of human SPOP leads to yeast growth arrest, we assessed the functionality of each variant using a yeast proliferation assay. In addition, our approach combined long-read and short-read sequencing. Finally, our DMS model enables a clear distinction of likely-loss-of-function variants that are enriched in prostate cancers and reveals their differential characteristics in both protein structure and genetic assessments. These results demonstrate the utility of our approach in high-resolution mapping and amino acid-level interpretation of protein function.