Biomarker Research最新文献

There are significant stress factors within the tumor microenvironment (TME), such as hypoxia, oxidative stress, and nutrient deprivation. These disrupt endoplasmic reticulum (ER) function in cancer cells, as well as the infiltrating immune cells, leading to activation of the unfolded protein response (UPR) signaling, which the tumor uses to mitigate stress and survive. There are three canonical UPR pathways that are regulated by respective ER-resident transmembrane sensors: inositol-requiring protein 1α (IRE1α), PKR-like ER kinase (PERK), and activating transcription factor 6 (ATF6); activation of these pathways results in expression of cognate transcription factors that regulate gene expression to mitigate ER stress. Persistent UPR activation in the TME has been linked to aberrant tumor growth, progression, metastasis, angiogenesis, and therapy resistance in different cancer types. In addition, modulation of UPR activity significantly impacts immune cell function at different levels further impacting its role on the TME. Therefore, there is now significant interest to design novel therapies that target the UPR to kill cancer cells and simultaneously enhance protective anti-tumor immunity. Here we summarize recent findings as to how targeting UPR signaling can induce tumor regression and at the same time galvanize the immune response. We discuss the potential of integrating UPR targeting with other therapies, such as immune checkpoint inhibition, highlighting emerging strategies to improve therapeutic efficacy and overcome resistance. These recent insights underscore the importance of UPR as a novel therapeutic target for cancer treatment.

Background: Spinal ependymoma prognosis is closely correlated with tumor malignancy and biomarker levels, such as Ki-67 and p53, which reflect cellular proliferation and genetic instability. Despite their clinical significance, current methods to assess these biomarkers rely on invasive postoperative immunohistochemistry (IHC), delaying critical treatment decisions and limiting preoperative planning. While deep learning have revolutionized biomarker prediction in brain tumors, their application to spinal ependymomas remains underexplored due to the rarity of these tumors, insufficient datasets, and the technical challenges of analyzing spinal cord MRI. We used a deep learning model to predict molecular markers for spinal ependymomas using preoperative magnetic resonance imaging (MRI) scans and clinical information to predict biomarkers for spinal ependymoma.

Methods: This study enrolled 352 patients with preoperative MRI, confirmed histological diagnoses of spinal ependymomas, and Ki-67 and p53 status assessed via IHC. Cross-validation and external testing strategies ensured the generalizability of the results. We harnessed multimodal information by integrating the sagittal and transverse MRI phases with clinical data. MRI scans were automatically segmented to extract high-quality features. These features were used to train an ensemble neural network model, Light Gradient Boosting Machine Net (LGBMNet), which predicted the expression of Ki-67 and p53 biomarkers. To validate model architecture and input choice, we conducted ablation and comparison experiments across multiple classifiers and feature subsets.

Results: High-precision automatic image segmentation was achieved using the SegFormer model. LGBMNet showed superior predictive power in cross-validation for Ki-67 and p53, with area under the receiver operating characteristic curves (AUCs) of 0.8904 and 0.8948, and externally validated with AUCs of 0.8348 and 0.8521, respectively. The full multimodal LGBMNet model consistently outperformed reduced and classical variants, highlighting the added value of neural-enhanced fusion.

Conclusions: This study developed a deep learning framework for non-invasive prediction of Ki-67 and p53 in spinal ependymomas, integrating multimodal MRI and clinical data. The SegFormer model achieved high-precision segmentation, ensuring robust feature extraction. LGBMNet, combining Multilayer Perceptron and Light Gradient Boosting Machine, demonstrated strong predictive performance. Our results confirm that deep learning can effectively predict tumor biomarkers preoperatively, aiding precision neurosurgery.

Background: Alloreactive T cells mediate graft-versus-leukemia (GvL) reactions and acute graft-versus-host disease (aGvHD) in AML patients following allogeneic hematopoietic stem cell transplantation.

Methods: To investigate biomarkers that identify alloreactive T cells associated with either beneficial GvL or detrimental aGvHD, we collected graft samples and two post-transplant follow-up blood samples (day 30 and day 100) of ten AML patients undergoing hematopoietic stem cell transplantation and profiled over 777,000 CD45⁺ leukocytes in total by combinatorial barcoding-based mega-scale single-cell RNA sequencing.

Results: Using immune receptor sequences as intrinsic clonal barcodes, we observed that especially CD8⁺ graft-derived T cells persisted and displayed enhanced proliferation, clonal expansion, and likely alloreactivity. Notably, patient-derived peripheral leukocytes that survived the conditioning, as identified by sex-chromosome-related genes, were primarily CD4⁺ T helper cells. MDGA1 expression on T cells and NK cells emerged as a novel biomarker potentially associated with aGvHD. Additionally, we observed a significant deficiency of ADGRG1 expression, a marker of alloreactive cytotoxic T cells, by αβ and γδ T cells from relapsed patients.

Conclusions: In conclusion, mega-scale single-cell monitoring of graft and hematopoietic immune cell reconstitution allowed us to demonstrate that MDGA1 and ADGRG1 may function as complementary biomarkers expressed by distinct circulating T cells that are associated with divergent outcomes in AML patients, enabling precise risk stratification of alloHSCT outcomes and presenting potential therapeutic targets.

Alternative splicing (AS) is a crucial post-transcriptional regulatory mechanism that is frequently disrupted in cancer, leading to the generation of tumor-specific splice variants. These aberrant splicing events, often driven by mutations in splice sites or splicing factors (SFs), produce abnormal mRNA transcripts and protein isoforms that contribute to tumor initiation, progression, and immune evasion. Recent advancements in cancer immunotherapy have positioned AS-derived neoantigens as a novel and promising class of tumor-specific targets. These neoepitopes significantly expand the pool of immunogenic antigens for mRNA vaccines and adoptive cell transfer therapies, triggering robust and targeted anti-tumor immune responses. This review offers a comprehensive overview of the molecular mechanisms driving the generation of AS-derived neoantigens, their tumorigenic and immunological properties, and the antigen processing and presentation pathways involved. Additionally, we discuss emerging therapeutic strategies that exploit these neoantigens, such as splicing modulation and personalized immunotherapies, while also addressing current challenges and future prospects for translating AS-derived neoantigens into precision cancer immunotherapy.

Cancer remains a leading cause of mortality worldwide, with early detection being critical for improving survival rates. Traditional diagnostic methods, such as tissue biopsies and imaging, face limitations in invasiveness, cost, and accessibility, making liquid biopsy a compelling non-invasive alternative. Among liquid biopsy approaches, circulating cell-free DNA (cfDNA) analysis has gained prominence for its ability to capture tumor-derived genetic and epigenetic alterations. This review summarizes key cfDNA biomarkers, including gene mutations, copy number variations (CNVs), DNA methylation, fragmentation patterns, and end motifs (EMs), and highlights their utility in cancer detection and monitoring. By integrating these multi-modal cfDNA biomarkers, feature fusion approaches have not only enhanced the performance of cancer classification models but also stabilized low-abundance signals, thus ensuring more reliable cancer detection and monitoring. Furthermore, the diagnostic power of cfDNA analysis has been further amplified by machine learning (ML), with both traditional ML and deep learning (DL) methods demonstrating strong predictive performance in routine clinical liquid biopsy applications. However, challenges remain, including tumor heterogeneity, standardization of data processing, model explainability, and cost constraints. Future advancements should focus on refining multi-modal feature integration, developing explainable AI (XAI) models, and optimizing cost-effective strategies to enhance clinical applicability. As computational methodologies advance, the integration of cfDNA biomarkers with ML frameworks holds great promise to reshape non-invasive cancer detection by enabling earlier diagnostics, more accurate prognostic evaluation and personalized treatment strategies.

Solid pseudopapillary neoplasm (SPN) of the pancreas is a rare but distinct disease that remains poorly understood, especially at proteome level. We report comprehensive mass spectrometry-based proteomic analyses of SPN (n = 13) and characterize differences from other pancreatic neoplasms, pancreatic ductal adenocarcinoma (n = 11) and neuroendocrine tumor (n = 10). We discovered that the SPN proteome is uniquely distinct from that of other pancreatic neoplasms. Lysosome-related proteins are enriched and upstream lysosomal processes transcriptional regulators, MITF and TFE3, are overexpressed in SPN. MITF protein expression is more specific for SPN than TFE3, previously considered the most specific immunohistochemical marker. Since lysosomal-related processes are connected to biological energy generation processes, we profiled metabolic pathways and found that SPN is characterized by higher fatty acid oxidation and lower glycolysis than PDAC and high proteasome pathway activity with many proteasomal proteins upregulated, suggesting a possible link to metabolic adaptation mechanisms in low-nutrient environments. Proteomics characterizes SPN as an immune-cold tumor with low MHC class I expression. Proteome-based receptor tyrosine kinase (RTK) pathway profiling suggests PDGFRA and ERBB2 (HER2) as potential candidates for targeted therapy. Our results provide unique proteomic contribution to the understanding of SPN biology and highlight differences from other pancreatic tumors.