Objective: We aimed to evaluate the prognostic value of C-C motif chemokine receptor type 5 (CCR5) expression level for patients with ovarian cancer and to establish a radiomics model that can predict CCR5 expression level using The Cancer Imaging Archive (TCIA) and The Cancer Genome Atlas (TCGA) database.Methods: A total of 343 cases of ovarian cancer from the TCGA were used for the gene-based prognostic analysis. Fifty seven cases had preoperative computed tomography (CT) images stored in TCIA with genomic data in TCGA were used for radiomics feature extraction and model construction. 89 cases with both TCGA and TCIA clinical data were used for radiomics model evaluation. After feature extraction, a radiomics signature was constructed using the least absolute shrinkage and selection operator (LASSO) regression analysis. A prognostic scoring system incorporating radiomics signature based on CCR5 expression level and clinicopathologic risk factors was proposed for survival prediction.Results: CCR5 was identified as a differentially expressed prognosis-related gene in tumor and normal sample, which were involved in the regulation of immune response and tumor invasion and metastasis. Four optimal radiomics features were selected to predict overall survival. The performance of the radiomics model for predicting the CCR5 expression level with 10-fold cross- validation achieved Area Under Curve (AUCs) of 0.770 and of 0.726, respectively, in the training and validation sets. A predictive nomogram was generated based on the total risk score of each patient, the AUCs of the time-dependent receiver operating characteristic (ROC) curve of the model was 0.8, 0.673 and 0.792 for 1-year, 3-year and 5-year, respectively. Along with clinical features, important imaging biomarkers could improve the overall survival accuracy of the prediction model.Conclusion: The expression levels of CCR5 can affect the prognosis of patients with ovarian cancer. CT-based radiomics could serve as a new tool for prognosis prediction.

Background: The occurrence of chronic inflammation resulting from infection with human papillomaviruses is an important factor in the development of cervical cancer (CC); thus, deciphering the crosstalk between the tumor microenvironment and innate immune cells during the establishment of immune tolerance is vital for identifying potential treatment strategies. Methods: Single-cell RNA sequencing data and primary tumor samples from patients with CC were used to evaluate the functional role of Siglec-10 on dendritic cells (DCs). Patient-derived tumor fragment platforms were used to examine the ability of Siglec-10 blockade to reinvigorate DC-mediate T-cell activation and tumor clearance. Results: Here, we demonstrated that Siglec-10 is a prominent inhibitory checkpoint for DCs infiltrated in CC. CC epithelial cells use their aberrant surface sialylated structures to induce the transformation of conventional DCs into phenotypes characterized by low immunogenicity and high immunotolerance. Additionally, Siglec-10⁺ DCs suppress the function of adaptive T cells via galectin-9 signaling to strengthen the immunosuppressive CC microenvironment. Disturbance of Siglec-10 signaling restored the DC-mediated tumoricidal response and increased adaptive T cells sensitivity to programmed cell death protein 1 inhibition. Conclusion: Our study confirms the checkpoint role of Siglec-10 on DCs and proposes that targeting Siglec-10 may be a promising avenue for immunotherapy against CC.

Purpose: Our aim was to construct a signature that accurately predicted the prognostic and immune response of melanoma.Methods: First, the weighted co-expression network analysis (WGCNA) algorithm was used to identify the hub genes related to clinical phenotypes of melanoma in the cancer genome atlas (TCGA) database. Nest, the least absolute shrinkage and selection operator (LASSO) analysis was used to dimensionality reduction of these hub genes and constructed a prognostic signature to predict the prognosis and immunosuppressive response of melanoma.Result: Through in-depth analysis, we constructed a 5-mRNA prognostic signature and verified its prognostic value in internal (TCGA-SKCM, n = 452) and external independent datasets (GSE53118, n = 79). Based on this signature, the tumor immune microenvironment (TME) of melanoma was characterized, and the result was found that patients in the high-risk group had lower CD8 T cell infiltration and immune checkpoint expression (PD-1, PD-L1, CTLA4), as well as higher M0/M2 macrophage infiltration. Our results also found the risk score based on a 5-mRNA signature was significantly associated with tumor mutational burden (TMB) and tumor stem cell markers (CD20, CD38, ABCB5, CD44, etc.). Lastly, we built a nomogram for clinician prediction for the prognosis of patients with melanoma.Conclusion: Our findings indicated that the 5-mRNA signature has an important predictive value for the overall survival of melanoma. By analyzing the tumor immune microenvironment and tumor stem cell marker between different groups, a new method is provided for the stratified diagnosis and treatment of melanoma.

Background. In the past few years, the immune system and tumor immune microenvironment are becoming increasingly popular as more work has been accomplished in this field. However, nomograms based on immune‐related characteristics for prognosis prediction of cervical cancer have not been fully explored to our knowledge. We constructed a novel immune score‐based nomogram to predict patients with high risk and poor prognosis. Materials and Methods. 198 patients with cervical cancer from The Cancer Genome Atlas (TCGA) database were included in our study. Immune scores were generated with Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE) algorithm, and clinic‐pathological characteristics were also included for subsequent analysis. Cox proportional hazards regression models were performed for univariate and multivariate analyses to screen the significant factors, and a prognostic nomogram was built. Bootstrap resampling analysis was used for internal validation. The calibration curve and concordance index (C‐index) were used to assess the predictive performance of the nomogram. Results. Patients were split into three subgroups based on immune scores. We found that patients with high immune scores conferred significantly better overall survival (OS) compared with those with medium and low immune scores (hazard ratio (HR), 0.305; 95% confidence interval (CI), 0.108‐0.869). A nomogram with a C‐index of 0.720 had a favorable performance for predicting survival rate for clinical use by combining immune scores with other clinical features. The calibration curves at 3 and 5 years suggested a good consistency between the predicted OS and the actual OS probability. Conclusions. Our work highlights the potential clinical application significance of immune score‐based nomogram in predicting the OS of cervical cancer patients.

Objective. In this study, we utilized gonadotropin-releasing hormone analogue-modified indocyanine green (GnRHa-ICG) to improve the accuracy of intraoperative recognition and resection of endometriotic lesions. Methods. Gonadotropin-releasing hormone receptor (GnRHR) expression was detected in endometriosis tissues and cell lines via immunohistochemistry and western blotting. The in vitro binding capacities of GnRHa, GnRHa-ICG, and ICG were determined using fluorescence microscopy and flow cytometry. In vivo imaging was performed in mouse models of endometriosis using a near-infrared fluorescence (NIRF) imaging system and fluorescence navigation system. The ex vivo binding capacity was determined using confocal fluorescence microscopy. Results. GnRHa-ICG exhibited a significantly stronger binding capacity to endometriotic cells and tissues than ICG. In mice with endometriosis, GnRHa-ICG specifically imaged endometriotic tissues (EMTs) after intraperitoneal administration, whereas ICG exhibited signals in the intestine. GnRHa-ICG showed the highest fluorescence signals in the EMTs at 2h and a good signal-to-noise ratio at 48h postadministration. Compared with traditional surgery under white light, targeted NIRF imaging-guided surgery completely resected endometriotic lesions with a sensitivity of 97.3 and specificity of 77.8. No obvious toxicity was observed in routine blood tests, serum biochemicals, or histopathology in mice. Conclusions. GnRHa-ICG specifically recognized and localized endometriotic lesions and guided complete resection of lesions with high accuracy.