Current Research in Translational Medicine最新文献

Drug repurposing is a promising strategy for managing cardiovascular disease (CVD) in geriatric populations, offering efficient and cost-effective solutions. CVDs are prevalent across all age groups, with a significant increase in prevalence among geriatric populations. The middle-age period (40-65 years) is critical due to factors like obesity, sedentary lifestyle, and psychosocial stress. In individuals aged 65 and older, the incidence of CVDs is highest due to age-related physiological changes and prolonged exposure to risk factors. In this review we find that certain drugs, such as non-cardiovascular drugs like anakinra, probenecid, N-acetyl cysteine, quercetin, resveratrol, rapamycin, colchicine, bisphosphonates, hydroxychloroquine, SGLT-2i drugs, GLP-1Ras drugs and sildenafil are recommended for drug repurposing to achieve cardiovascular benefits in geriatric patients. However, agents such as canakinumab, methotrexate, ivermectin, erythromycin, capecitabine, carglumic acid, chloroquine, and furosemide are constrained in their therapeutic use and warrant meticulous consideration, rendering them less favorable for this specific application. This review emphasizes the importance of exploring alternative therapeutic strategies to improve outcomes in geriatric populations and suggests drug repurposing as a promising avenue to enhance treatment efficacy.

Background: Almost all multiple myeloma (MM) patients will eventually develop disease that has relapsed with or become refractory to current therapeutic regimes. However, the pervious clinical parameters have been proved inaccurate for defining MM relapse, and molecular targets have become the focuses of interests. Prognostic predictions based on molecular targets have been more effective to this day. Our research was performed to demonstrate hub genes involving relapsed MM by bioinformatics and biological experiments.

Methods and results: The integrated bioinformatics analysis in baseline and relapsed MM patients were executed. Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were utilized to analyze biologic functions of up-regulated differentially expressed genes (DEGs). Four hub genes (CENPE, ASPM, TOP2A and FANCI) were adopted for construction of relapsed gene score model (RGS), and RGS model was evaluated in two testing sets. The CENPE inhibitor GSK923295 had anti-myeloma effect, including promoting cell death, cell cycle arrest and DNA damage of MM cell lines.

Conclusion: Through bioinformatics analysis, we found that the four hub genes (CENPE, ASPM, TOP2A and FANCI) were associated to cell cycle, nuclear division, mitosis and spindle. Our research provided proof-of-concept that RGS model could be utilized to estimate recurrence risk and prognosis for patients, and targeting CENPE contributed to developing novel therapeutic pattern for MM.

Cancer immunotherapy, alongside surgery, radiation therapy, and chemotherapy, has emerged as a key treatment modality. Immune checkpoint inhibitors (ICIs) represent a promising immunotherapy that plays a critical role in the management of various solid tumors. However, the limited efficacy of ICI monotherapy and the development of primary or secondary resistance to combination therapy remain a challenge. Consequently, identifying molecular markers for predicting ICI efficacy has become an area of active clinical research. Notably, the correlation between DNA damage repair (DDR) mechanisms and the effectiveness of ICI treatment has been established. This review outlines the two primary pathways of DDR, namely, the homologous recombination repair pathway and the mismatch repair pathway. The relationship between these key genes and ICIs has been discussed and the potential of these genes as molecular markers for predicting ICI efficacy summarized.

This narrative review examines the transformative role of Artificial Intelligence (AI) and Machine Learning (ML) in organ retrieval and transplantation. AI and ML technologies enhance donor-recipient matching by integrating and analyzing complex datasets encompassing clinical, genetic, and demographic information, leading to more precise organ allocation and improved transplant success rates. In surgical planning, AI-driven image analysis automates organ segmentation, identifies critical anatomical features, and predicts surgical outcomes, aiding pre-operative planning and reducing intraoperative risks. Predictive analytics further enable personalized treatment plans by forecasting organ rejection, infection risks, and patient recovery trajectories, thereby supporting early intervention strategies and long-term patient management. AI also optimizes operational efficiency within transplant centers by predicting organ demand, scheduling surgeries efficiently, and managing inventory to minimize wastage, thus streamlining workflows and enhancing resource allocation. Despite these advancements, several challenges hinder the widespread adoption of AI and ML in organ transplantation. These include data privacy concerns, regulatory compliance issues, interoperability across healthcare systems, and the need for rigorous clinical validation of AI models. Addressing these challenges is essential to ensuring the reliable, safe, and ethical use of AI in clinical settings. Future directions for AI and ML in transplantation medicine include integrating genomic data for precision immunosuppression, advancing robotic surgery for minimally invasive procedures, and developing AI-driven remote monitoring systems for continuous post-transplantation care. Collaborative efforts among clinicians, researchers, and policymakers are crucial to harnessing the full potential of AI and ML, ultimately transforming transplantation medicine and improving patient outcomes while enhancing healthcare delivery efficiency.

Background: Stromal cells play a pivotal role in the tumor microenvironment (TME), significantly impacting the progression of acute myeloid leukemia (AML). This study sought to develop a stromal-related prognostic model for AML, aiming to uncover novel prognostic markers and therapeutic targets.

Methods: RNA expression data and clinical profiles of AML patients were retrieved from the Cancer Genome Atlas (TCGA). The extent of stromal cell infiltration within the TME was quantified using the ESTIMATE algorithm. Associations between stromal scores and the French-American-British (FAB) classification, overall survival (OS), and the Cancer and Leukemia Group B (CALGB) cytogenetic risk categories were analyzed. Differentially expressed genes (DEGs) were identified, and gene ontology (GO) and protein-protein interaction (PPI) networks were constructed. Prognostic DEGs were selected through LASSO-cox regression analysis. A risk score model was then developed based on these DEGs. A stromal-related prognostic model (SPM) was constructed from the patients' risk scores (RS), and its efficacy was evaluated using Receiver Operating Characteristic (ROC) curves and a nomogram. The association between FAB, CALGB, age, and common mutations and SPM was also assessed. Ultimately, the SPM was validated using an external dataset from 246 patients in the TARGET-AML study.

Results: Kaplan-Meier analysis revealed a significant association between stromal scores and patient survival (p = 0.04). LASSOCox regression identified four genes (MAP7D2, CDRT1, HOXB9, and IRX5) as highly predictive of survival. The prognostic model showed a strong correlation with overall survival, with higher scores indicating poorer outcomes (p = 1.48e-07). Older patients (over 60 years) faced significantly worse prognoses (p = 0.0055). Although no significant association was found between the SPM and the FAB classification (p = 0.063), both poor and intermediate/normal cytogenetic groups had significantly higher SPM risk scores than the favorable group (p = 0.0057 and 0.0026). External validation of the SPM in the TARGET-AML dataset confirmed a significant association with survival (p = 0.00035), with the area under the curve (AUC) for 10-year survival at 75.81 %.

Conclusion: Our research successfully established a stromal-related prognostic model in AML, offering new perspectives for prognostic evaluation and identifying potential targets for therapeutic intervention.

Cancer remains one of the most pressing health challenges worldwide. Recently, chimeric antigen receptor (CAR)-T cell therapy has emerged as a promising approach for treating hematological cancers. However, the translation of CAR-T cell therapy to solid tumors faces formidable obstacles, notably the immunosuppressive tumor microenvironment. Within solid tumors, CAR-T cells encounter a hostile milieu that promotes exhaustion and diminishes their long-term effectiveness against cancer cells. Optimizing the manufacturing process is paramount to ensuring the efficacy of CAR-T cell therapy in solid tumors. A critical aspect of this optimization lies in refining the composition of cell culture media. By supplementing basic culture media with specific additives, researchers aim to improve the behavior and functionality of CAR-T cells, thereby enhancing their therapeutic potential. This review delves into the culture media additives that have been investigated or show promise in modulating CAR-T cell phenotypes and enhancing their anti-tumor efficacy. We explore various types of additives and their mechanisms of action to mitigate exhaustion and augment persistence within the challenging solid tumor microenvironment. By shedding light on the latest advancements in culture media optimization for CAR-T cell therapy, this review aims to provide insights into novel strategies for overcoming the hurdles posed by solid tumors. Ultimately, these insights hold the potential to enhance the effectiveness of CAR-T cell therapy and improve outcomes for cancer patients.

The traditional method of CAR T cell production involves lengthy ex-vivo culture times which can result in the reduction of crucial naïve T cell subsets. Moreover, traditional CAR T cell therapy manufacturing processes can prolong time-to-patient, potentially delaying patient treatment, and contribute to disease progression. In this study, we describe an innovative and semi-automated 24-hour CAR T manufacturing process that yields a higher percentage of naïve/stem-cell like T cells which showed high cytotoxic activity and cytokine release in vitro. The data supports the feasibility of implementing this streamlined manufacturing process in clinics. This approach also has the potential to enhance CAR T therapy efficacy and improve patient access to therapy.

In recent years, chimeric antigen receptor (CAR) T-cell therapy has emerged as a groundbreaking approach in cancer immunotherapy. Particularly in hematologic malignancies, such as B-cell acute lymphoblastic leukemia (B-ALL), B cell lymphomas and multiple myeloma. CAR-T therapy has demonstrated remarkable clinical efficacy, leading to the approval of several CAR-T cell products and offering significant benefits to numerous leukemia patients. Despite these successes, the application of CAR-T cells in solid tumors remains limited due to significant challenges, including immunosuppressive tumor microenvironments, heterogeneous antigen expression, and treatment-associated toxicities. In parallel with CAR-T development, researchers are investigating other immune cell platforms to overcome these obstacles. Among these, invariant natural killer T (iNKT) cells have garnered increasing attention for their unique immunological properties. Unlike conventional T cells, iNKT cells are a subset of T lymphocytes characterized by the expression of a semi-invariant T-cell receptor (TCR) that recognizes lipid antigens presented by CD1d molecules. This distinctive antigen recognition mechanism enables iNKT cells to bridge innate and adaptive immunity, granting them potent antitumor activity and the ability to modulate the tumor microenvironment. Additionally, iNKT cells exhibit intrinsic resistance to exhaustion and an enhanced ability to infiltrate solid tumors compared to traditional T cells. Building on these properties, researchers are leveraging CAR technology to enhance iNKT cell tumor-targeting capabilities, aiming to overcome barriers encountered in solid tumor therapy. This review provides an in-depth discussion of the application and therapeutic potential of CAR-iNKT cells in cancer immunotherapy, with a focus on their advantages over conventional CAR-T cells and their role in addressing the challenges of solid tumor treatment.