Plasma cell neoplasms encompass a spectrum of disorders characterized by the clonal proliferation of plasma cells. Plasmablastic transformation in these neoplasms poses diagnostic and clinical challenges due to its aggressive nature and morphological overlap with other malignancies, including plasmablastic lymphoma (PBL). We report a case of discordant extramedullary plasmablastic transformation in a 55-year-old HIV-negative female presenting with an oral lesion diagnosed as plasmablastic plasmacytoma. Initial imaging indicated localized disease without systemic involvement. Despite undergoing chemotherapy and achieving a partial response, the patient developed osteolytic lesions 2 years later. Subsequent pathology evaluation confirmed mature plasma cell myeloma (PCM) morphology. Whole exome sequencing (WES) and whole RNA expression analysis revealed shared mutations (PTPN13, KRAS, LTK) and a MYC::BMP6 translocation in both lesions, supporting a clonal relationship. Additionally, the oral plasmablastic lesion revealed a KLHL6 mutation and an extra MYC::IGL translocation, which were absent in the tibial lesion. The KLHL6 mutation has not been previously reported in studies of discordant extramedullary plasmacytoma with plasmablastic transformation. This case highlights the diagnostic complexity of plasmablastic plasmacytoma presenting as the initial manifestation of plasma cell myeloma. It underscores the necessity of a thorough evaluation, including bone marrow biopsy, to accurately differentiate plasmablastic transformation from PBL and ensure accurate diagnosis and appropriate management.

This narrative review summarizes objective measures of cancer-related fatigue (CRF), their clinical and research utility, and their value in clinical practice and research. Objective measures are recommended where cognitive and physical CRF are the primary research outcomes of the studies reviewed.

This narrative review was done in two phases. The first, in 2018, evaluated CRF-focused studies published through peer review, and the second, from February 1, 2018, to March 31, 2024, included search results to identify more recent CRF articles. PubMed, Science Direct, and Scopus were used for the search. In both review phases, articles were excluded if they were abstracts only, editorials, or letters to the editor.

The literature searches resulted in 16,332 articles captured, 16,324 excluded, and 8 included. The review and analysis resulted in the discovery of 16 objective CRF assessments, categorized to correspond with their proposed CRF origin: central, peripheral, or both. Each assessment is described and outlined according to: (1) validated populations, (2) practicalities and clinical utility, and (3) applicability in the clinic space or bedside.

This review is the foundation for objective CRF measurement. Recommendations include actigraphy, electrical muscle stimulation, finger tapping test, laboratory measures, positron emission tomography, and sit-to-stand in combination with subjective CRF measures to inform correlations, management, and treatment. An increase in objective measure pathophysiology research will illuminate the "black box" that is CRF.

This study integrates ultrasound Radiomics with clinical data to enhance the diagnostic accuracy of HER-2 expression status in breast cancer, aiming to provide more reliable treatment strategies for this aggressive disease. We included ultrasound images and clinicopathologic data from 210 female breast cancer patients, employing a Generative Adversarial Network (GAN) to enhance image clarity and segment the region of interest (ROI) for Radiomics feature extraction. Features were optimized through Z-score normalization and various statistical methods. We constructed and compared multiple machine learning models, including Linear Regression, Random Forest, and XGBoost, with deep learning models such as CNNs (ResNet101, VGG19) and Transformer technology. The Grad-CAM technique was used to visualize the decision-making process of the deep learning models. The Deep Learning Radiomics (DLR) model integrated Radiomics features with deep learning features, and a combined model further integrated clinical features to predict HER-2 status. The LightGBM and ResNet101 models showed high performance, but the combined model achieved the highest AUC values in both training and testing, demonstrating the effectiveness of integrating diverse data sources. The study successfully demonstrates that the fusion of deep learning with Radiomics analysis significantly improves the prediction accuracy of HER-2 status, offering a new strategy for personalized breast cancer treatment and prognostic assessments.

UDP-glucose: glycoprotein glucosyltransferase 1 (UGGT1), a key component of the endoplasmic reticulum quality control (ERQC) system, has established roles in metabolic/infectious diseases, but its oncogenic potential remains unclear. UGGT1's expression, genetic alterations, methylation patterns, immune function and interacting genes were evaluated through different bioinformatics databases, and the roles of UGGT1 in breast cancer were validated by in vitro experiments. UGGT1 was significantly overexpressed in most cancers, correlating with advanced stage and poor survival. Variations in its promoter methylation and mutation patterns across cancers were associated with patient outcomes. UGGT1 status (expression/mutation) was significantly associated with immune cell infiltration in the tumor microenvironment. Functional enrichment linked UGGT1 co-expressed genes to cell cycle and nucleic acid metabolism. Critically, UGGT1 knockdown inhibited breast cancer cell proliferation/migration in vitro and downregulated key ER stress sensors (IRE1α, ATF6, PERK). This study establishes UGGT1 as a significant pan-cancer prognostic biomarker and reveals its role in tumor progression, highlighting its potential as a therapeutic target.

Breast cancer (BC) remains a leading cause of cancer-related mortality among women, with therapeutic resistance posing significant challenges. This study explores haloperidol (Halo), a clinically approved antipsychotic drug, for its potential antitumoral effects and ability to induce ferroptosis, a non-apoptotic programmed cell death linked to oxidative stress and lipid peroxidation. Halo's activity, partially mediated by sigma (σ) receptors, may enhance chemotherapy efficacy. This investigation delves into the role of heme oxygenase (HO), which was demonstrated to exhibit dual effects in ferroptosis as it's crucial for the modulation of iron intracellular levels and redox balance. Analysis of main related indicators depict a clear activation of ferroptotic cell death following Halo treatment evidenced by heightened oxidative stress conditions, as indicated by increased lipid peroxidation, elevated reactive oxygen species levels, significant glutathione depletion and mitochondrial membrane potential impairment. Further investigation revealed a protective role of HO-1 and the involvement of ferritinophagic process in MCF-7 BC cells. Additionally, it was evaluated whether Halo effect could be strictly dependent on its activity towards σ receptors and its efficacy in a 3D spheroid model. Data herein reported allow to elucidate Halo triggering of so-called non-canonical ferroptotic pathway suggesting its potential as a candidate for BC treatment.

Tumor-associated macrophages (TAMs) are pivotal immunosuppressive components of the tumor microenvironment (TME) in gastric cancer (GC), yet their heterogeneity and metabolic crosstalk with tumor cells remain poorly understood. Here, we performed single-cell RNA sequencing (scRNA-seq) on 75,743 cells from 11 GC tissues and identified four distinct TAM subsets (TAM-APOE, TAM-IDO1, TAM-SKAP1, TAM-POLB), each exhibiting unique functional signatures. Among these, the TAM-APOE subset, enriched in lipid metabolism and complement pathways, showed the strongest interaction with tumor cells via the MIF-CD74 axis. Functional studies revealed that GC-derived MIF promotes TAM-APOE polarization, upregulating immunosuppressive genes and enhancing tumor progression. In vivo, MIF overexpression in GC cells increases subcutaneous tumor volume, while MIF knockdown decreases tumor weight. Crucially, Milatuzumab, a CD74-targeting antibody, reversed MIF-induced TAM immunosuppression in vitro. Our research provides a comprehensive map of GC-TAM heterogeneity; reveals that the MIF-CD74 axis is a key driver of TAM-mediated immune suppression; and proposes that CD74 blockade is a new therapeutic strategy for GC.

Breast cancer is characterized by the proliferation of abnormal breast cells that eventually turn into malignant tumors. These cancer cells can metastasize to be life-threatening and fatal. An intricate mix of environmental factors and individual genetic composition can lead to the formation of this deadly carcinoma. Improvements in the diagnosis and treatment of cancer are essential given the rising incidence of breast cancer. Over the past few decades, machine learning has helped provide accurate medical diagnosis results. Therefore, this study used diagnostic characteristics of patients and multiple machine learning classifiers to identify breast cancer. Incorporating explainable artificial intelligence techniques revealed the underlying factors for the model predictions, adding a layer of transparency and interpretability. Out of the algorithms, random forest showed the best result, an F1-score of 84%. The stacked ensemble model, which combines the strengths of different models, obtained an F1-score performance of 83%. The research emphasized the results obtained by explainers such as SHAP (SHapley Additive exPlanations), LIME (Local Interpretable Model-agnostic Explanations), ELI5 (Explain Like I'm Five), Anchor and QLattice (Quantum Lattice) to decipher the findings. Interpretable algorithms can be applied in the medical sector to assist practitioners in predicting breast cancer, reducing diagnostic errors, and improving clinical decision-making.

Ovarian cancer (OC) is a highly fatal gynecological malignancy primarily attributable to late-stage detection and restricted treatment options. Aberrant glycolysis, exemplified by the Warburg effect, facilitates tumor development, immunological evasion, and alteration of the microenvironment. Identifying glycolysis-related biomarkers could provide novel insights into prognosis and potential therapeutic targets for OC.The transcriptomic and clinical information of OC patients were obtained from The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), and Gene Expression Omnibus (GEO) databases. Differentially expressed glycolysis-related genes (GRGs) were identified and analyzed for their prognostic significance. Consensus clustering was employed to identify glycolysis subtypes, followed by pathway enrichment and immune infiltration analyses. A ten-gene GRG signature was developed with LASSO-Cox regression and verified in various cohorts. Single-cell RNA sequence and drug susceptibility analysis were performed to explore tumor microenvironment heterogeneity and potential therapeutic agents.A total of 457 differentially expressed GRGs were discovered, of which 30 were substantially linked with OC prognosis. Three molecular subtypes were characterized, with cluster C exhibiting the worst prognosis and activation of tumor-associated pathways. A prognostic model comprising ten genes (LMCD1, L1CAM, MYCN, GALT, IDO1, RPL18, XBP1, LPAR3, RUNX3, PLCG1) was developed and validated, demonstrating robust predictive efficacy across various cohorts. Immune analysis revealed substantial disparities in immune infiltration among risk groups, whereas single-cell analysis identified several critical genes essential for metabolism, proliferation, and interactions within the tumor microenvironment.This work highlights the prognostic and therapeutic significance of GRGs in OC. The ten-gene GRG signature serves as a reliable framework for risk assessment and the formulation of individualized treatment regimens. Nonetheless, further experimental validation and extensive clinical research are necessary to enable the application of these findings in clinical practice. These results highlight the potential of targeting glycolytic pathways as a promising approach to improve the management and treatment outcomes of OC.

Residual material from fecal immunochemical test (FIT) tubes, commonly used in colorectal cancer screening programs, offers a valuable resource for large-scale gut microbiome studies. With recent advances in sequencing technologies, sequencing the full-length bacterial 16S ribosomal gene is now feasible. In this study, we evaluated the impact of pre-analytical handling conditions on microbiome profiling using FIT samples. Stool samples from eight healthy adults were subjected to various short-term (+ 20 °C) and long-term (-18 °C or -80 °C) storage conditions prior to DNA extraction. We also investigated the effects of sampling variation and the presence of buffer medium. Full-length 16S rRNA gene amplicons were generated and sequenced using Oxford Nanopore Technology to characterize the microbiome composition. Despite variations in sampling and storage conditions, microbiome richness, Shannon diversity, and individual characteristics were preserved, demonstrating the robustness of microbiomes extracted from FIT tubes. However, some variations were noted, such as increased amounts of collagenase-producing bacteria from 0.2 to 0.6% to 1.7-2.6% in samples stored at +20 °C for 4-10 days. Despite unsupervised and varying sampling and storage conditions, the fecal 16S rRNA microbiomes remained representative and robust. These findings support the usability of FIT samples for large-scale population microbiome research.

Although triple-negative breast cancers are still challenging to treat, the development of novel neoadjuvant chemotherapy combined with immune checkpoint antibodies is promising. Our group developed the small compound-based anti-PD-1/PD-L1 inhibitor SCL-1 and reported its potent anti-tumor effects on various syngeneic mouse tumors. We herein investigated the efficacy of SCL-1 using an in vivo humanized NOG mouse system. We established a humanized mouse system using double major histocompatibility complex-knockout NOG mice transplanted with MDA-MB231 breast cancer cells and HLA-matched human PBMCs. Tumor-infiltrating lymphocytes (TILs) were analyzed using flow cytometry and real-time PCR. An RNA-sequencing analysis (RNA-seq) of SCL-1-treated MDA-MB231 tumors was performed to identify differentially expressed genes. Orally administered SCL-1 exerted potent anti-tumor effects with > 50% reduction in tumor sizes, which were dependent on PD-L1 expression and T-cell infiltration. Its effects were significantly stronger than those of nivolumab or atezolizumab. A TIL analysis revealed effector CD8⁺ T cells expressing cytotoxic markers and exhausted markers as well as increases in NK cells and B cells. RNA-seq showed the up-regulated expression of tumor-specific long non-coding (lnc) RNAs in SCL-1-treated tumor tissues, some of which exhibited high HLA-binding activity. SCL-1 exerted strong tumor growth inhibitory effects that were mediated by effector T-cell induction inside tumors and the up-regulated expression of lncRNAs as neoantigens leading to CTL activation. The up-regulated expression of lncRNAs in SCL-1-treated MDA-MB231 tumors is a novel result and may be one of the mechanisms responsible for the anti-tumor efficacy of SCL-1.