Yantaishan Hospital

Colorectal cancer (CRC) remains a leading cause of cancer-related mortality worldwide, with pulmonary metastasis representing a critical determinant of poor prognosis in advanced disease. Although therapeutic strategies have improved, survival outcomes for CRC patients with lung metastases remain limited, underscoring the urgent need for robust prognostic biomarkers and innovative immunotherapeutic approaches. In this study, we employed an integrative computational framework to identify prognostic molecular signatures associated with CRC pulmonary metastasis and to design a rational multi-epitope vaccine (MEV) targeting key chemokines. Differential gene expression analysis of two GEO datasets (GSE181537 and GSE250000) using GEO2R identified five significantly upregulated C-X-C motif chemokines: CXCL1, CXCL3, CXCL5, CXCL8, and CXCL12. Functional enrichment and protein-protein interaction network analyses revealed their central roles in immune regulation, chemotaxis, inflammatory signaling, and cancer progression pathways. Survival and immune infiltration analyses further demonstrated their prognostic relevance and association with the tumor immune microenvironment. Immunoinformatics-based epitope prediction identified high-affinity cytotoxic T lymphocyte (CTL), helper T lymphocyte (HTL), and B-cell epitopes, which were rigorously screened for antigenicity, non-allergenicity, non-toxicity, and interferon-γ induction potential. Selected epitopes were assembled into a chimeric MEV construct using appropriate linkers and immune-stimulatory adjuvants. Structural modeling, molecular docking, and molecular dynamics simulations confirmed stable binding interactions between the vaccine construct and Toll-like receptors TLR2 and TLR4. In silico immune simulations predicted robust humoral and cellular immune responses with the establishment of immunological memory. This integrative bioinformatics and immunoinformatics study identifies immune-associated CXC chemokines as promising prognostic biomarkers in CRC pulmonary metastasis and proposes a rationally designed multi-epitope vaccine candidate for further experimental validation and potential preclinical development in CRC immunotherapy.

Thyroid cancer is the seventh most prevalent cancer worldwide, with a growing incidence and mortality rate. This malignancy encompasses distinct subtypes with diverse biological behaviors, clinical outcomes, and therapeutic vulnerabilities. While most thyroid cancer cases are associated with a favorable prognosis and respond well to conventional therapies, such as surgery and radioactive iodine therapy, advanced, recurrent, or dedifferentiated subtypes have a bad prognosis and limited therapeutic options, necessitating the development of more efficacious therapeutic strategies. Despite the remarkable progress of immunotherapeutic strategies across multiple malignancies, the translation of these approaches into thyroid cancer has been comparatively slow and inconsistent. Except for immune checkpoint inhibitor (ICI) therapy, other types of immunotherapy in thyroid cancer have not yet been advanced beyond early-stage clinical trials. This is attributed to the intrinsic nature of thyroid tumors, which are widely considered cold tumors with low mutational burden and a high immunosuppressive tumor microenvironment (TME). Nonetheless, recent studies indicate that by understanding the immunobiology of different subtypes of thyroid cancers, immunotherapeutic strategies can be adapted to their unique molecular, cellular, and microenvironmental characteristics, thereby improving therapeutic efficacy. This review aims to discuss the progress and pitfalls of various immunotherapy approaches for thyroid cancer, including ICI therapies, adoptive cell therapies (CAR-T cell, TCR-T cell, and TIL therapy), oncolytic virotherapy, and macrophage/myeloid-modulating immunotherapies. A deeper understanding of subtype-specific immunobiology and precise tailoring of immunotherapeutic interventions may ultimately enable more effective and durable clinical responses for patients with advanced thyroid cancer.

Objective Secondary lower limb lymphedema (LLL) is a long-term side effect following surgery or radiotherapy in survivors with gynecological cancer. This systematic review aims to systematically evaluate the effectiveness of physical therapy interventions for LLL patients with gynecological cancer, providing evidence-based support for clinical practice. Methods We searched the Medline, Web of Science, Scopus, and Cochrane Library databases for literature published up to 27 December 2025. We include randomized controlled trials (RCTs) focusing on physical therapy as the main interventions for LLL in gynecological cancer survivors. Literature screening and data extraction were performed using the Rayyan platform, and the methodological quality of included studies was assessed with the Cochrane Risk of Bias 2 (ROB 2) tool. This systematic review has been registered in PROSPERO (Registration number: CRD420251274284). Results Six RCTs involving a total of 289 eligible patients were ultimately included. None of the included RCTs were judged to have a high risk of bias. Results showed that physical therapy interventions resulted in significant reductions in lower limb volume or circumference across studies. Additionally, they alleviated symptoms such as pain and heaviness, and improved physical functions including muscle strength and gait, as well as quality of life. Evidence indicates that the multimodal physical therapy strategy showed a superior trend in improving the most outcomes compared to a single therapy mode. All intervention-related adverse events were mild, with no serious adverse events reported. Conclusion Overall, this systematic review confirms that both multimodal and single physical therapy are safe and effective for secondary lower limb lymphedema in gynecologic cancer survivors. A combination of different physical therapy modalities may be a better option. The results of this study may provide an evidence-based reference for clinical decision-making about LLL intervention strategies in this specific population. Systematic review registration https://www.crd.york.ac.uk/PROSPERO/view/CRD420251274284 , identifier CRD420251274284.

Glioblastoma (GB), the most aggressive primary brain tumor, is characterized by profound inter- and intratumoral heterogeneity and a highly immunosuppressive tumor microenvironment (TME), both of which contribute to its poor prognosis and resistance to conventional therapies. The dynamic interplay between malignant cells and diverse TME constituents including immune cells, neural elements, and extracellular matrix components drives tumor progression, clonal evolution, and therapeutic failure. Traditional treatment modalities such as surgery, radiotherapy, and chemotherapy often fall short due to their inability to address the spatial and temporal complexity of the TME. Recent advances in artificial intelligence (AI) and cellular MRI profiling offer promising avenues for decoding the GB microenvironment at unprecedented resolution. By integrating AI-driven analysis of cellular MRI signatures, researchers can identify distinct microenvironmental niches and resistant subclones, enabling the development of targeted therapies that simultaneously disrupt tumor cells and their supportive ecosystems. This approach holds potential to overcome current therapeutic limitations and pave the way for personalized, microenvironment-informed interventions in GB management.

High rates of migration and invasiveness are crucial factors contributing to the elevated mortality associated with liver cancer. Peptidyl-prolyl isomerase A (PPIA) has emerged as a key player in the progression of various human cancers, although its specific role in the advancement of liver cancer has not been fully elucidated. Previous research revealed that PPIA dictated nuclear factor E2-related factor 2 (Nrf2) stability to promote cancer progression. To clarify this by exploring the biological effects of PPIA and Nrf2 in liver cancer. First, PPIA and Nrf2 expression in normal hepatocyte cells and human hepatoma cells were quantitatively analyzed using RT-qPCR and western blot. Then, to assess effects of PPIA on migration and invasion of hepatoma cells, the study employed the scratch assay and the transwell invasion assay. Further, PPIA and Nrf2 were knock-downed, and ferroptotic cell death was assessed in erastin-treated hepatoma cells to clarify effect mechanism of PPIA in liver cancer progression. The interaction between PPIA and Nrf2 by conducting Co-IP experiments. The findings of our research indicate that PPIA is overexpressed, and promotes proliferation, migration and invasion in human hepatoma cells. The knockdown of PPIA and Nrf2 significantly enhances ferroptosis, which subsequently leads to a marked decrease in both migration and invasion of human hepatoma cells. Furthermore, our investigation revealed that PPIA interacts with Nrf2 in hepatoma cells, suggesting a complex relationship between these two proteins in the progression of liver cancer. This study highlights the potential role of PPIA as a critical oncogenic driver in liver cancer, suggesting that targeting PPIA could offer therapeutic advantages in the treatment of this malignancy.

<title>Abstract</title> Objective To construct various radiomics models based on CT venous phase images for the quantitative assessment of gastric cancer (GC) invasion depth into the gastric wall, and to compare the diagnostic performance of different models in distinguishing early (T1-T2 stage) from advanced (T3-T4 stage) GC, aiming to identify the optimal model and evaluate its potential for clinical application. Methods In this retrospective study, 223 pathologically confirmed GC patients (66 early-stage, 157 advanced-stage) treated between January 2022 and May 2025 were enrolled. Patients were allocated into a training set (n = 156) and an independent test set (n = 67) through stratified random sampling at a 7:3 ratio. All patients underwent enhanced CT within one week prior to surgery. Venous phase images were selected, and three-dimensional regions of interest (ROIs) encompassing the tumor were manually delineated using 3D Slicer for radiomics feature extraction. In the training set, feature selection was performed using Least Absolute Shrinkage and Selection Operator (LASSO) regression. Four machine learning models were subsequently constructed: Logistic Regression (LR), Random Forest (RF), XGBoost (XGB), and Support Vector Machine (SVM). Model optimization was conducted via 5-fold cross-validation. Performance was evaluated using receiver operating characteristic (ROC) curves, precision-recall (PR) curves, confusion matrices, and decision curve analysis (DCA). Results A total of 148 radiomics features were extracted, from which 11 key features were selected by LASSO. In the training set under 5-fold cross-validation, the LR, RF, SVM, and XGB models demonstrated strong discriminatory ability, with AUCs of 0.912, 0.910, 0.899, and 0.880, respectively. In the independent test set, the LR model exhibited the best overall performance: AUC 0.912 (95% CI: 0.853–0.948), accuracy 0.791, sensitivity 0.787, specificity 0.800, F1-score 0.841, and average precision (AP) 0.966. DeLong's test revealed statistically significant differences in AUC among models (all P &lt; 0.001), though the difference between LR and RF was minimal (0.0021, 95% CI: − 0.0459–0.0502). All models showed good calibration (Hosmer–Lemeshow test P &gt; 0.05). DCA indicated that the LR model provided higher net clinical benefit within a threshold probability range of 0.3–0.8. Conclusion Among the multiple radiomics models constructed based on CT venous phase images, the LR model demonstrated the best performance in distinguishing early from advanced GC T stages, showing favorable diagnostic efficacy and clinical utility. It can serve as a quantitative auxiliary tool for preoperative staging and treatment decision-making, providing an objective basis for precise GC diagnosis and treatment.

ObjectiveTo investigate the changes in the global attributes of structural connectivity (SC) and functional connectivity (FC) graph theory, as well as the coupling topological structure of the whole brain SC-FC in patients with noise-induced hearing loss (NIHL).Methods57 NIHL patients and 55 health controls (HCs) were included; resting-state functional magnetic resonance imaging, diffusion tensor imaging, clinical data (scales, laboratory indicators) were collected. The graph theory network analysis of SC and FC, the whole-brain SC-FC coupling were performed, and a correlation analysis was employed to analyze the results in relation to the clinical data.ResultsFor FC, there was no significant difference in the global indices directly between groups (P > 0.05, FDR corrected). For SC, the normalized clustering coefficient (γ) and small-worldness (σ) of the NIHL were higher than those of the HCs (P < 0.05, FDR corrected). There was no significant difference in the SC-FC coupling strength of whole brain between two groups (P > 0.05). The graph attribute indices and coupling values of NIHL were correlated to varying degrees with the results of anxiety scale, coagulation, inflammation and biochemical indices (P < 0.05).ConclusionThe brain network topology structure of NIHL patients is abnormal, but the SC-FC coupling strength does not change significantly. This may provide a basis for understanding the theoretical mechanism of brain neural function remodeling and for future more detailed and diverse studies.