Background: Extracellular vesicles (EVs) derived from tumor cells are implicated in the progression of malignancies through the transfer of molecular cargo microRNAs (miRNAs or miRs). We aimed to explore the role of EVs derived from breast cancer cells carrying miR-182-5p in the occurrence and development of breast cancer. Methods: Differentially expressed miRNAs and their downstream target genes related to breast cancer were screened through GEO and TCGA databases. miR-182-5p expression was examined in cancer tissues and adjacent normal tissues from patients with breast cancer. EVs were isolated from breast cancer cell line MDA-MB-231 cells and identified. The gain- and loss-of function approaches of miR-182-5p and CKLF-like MARVEL transmembrane domain-containing 7 (CMTM7) were performed in MDA-MB-231 cells and the isolated EVs. Human umbilical vein endothelial cells (HUVECs) were subjected to co-culture with MDA-MB-231 cell-derived EVs and biological behaviors were detected by CCK-8 assay, flow cytometry, immunohistochemical staining, Transwell assay and vessel-like tube formation in vitro. A xenograft mouse model in nude mice was established to observe the tumorigenesis and metastasis of breast cancer cells in vivo. Results: miR-182-5p was highly expressed in breast cancer tissues and cells, and this high expression was associated with poor prognosis of breast cancer patients. miR-182-5p overexpression was shown to promote tumor angiogenesis in breast cancer. Moreover, our data indicated that miR-182-5p was highly enriched in EVs from MDA-MD-231 cells and then ultimately enhanced the proliferation, migration, and angiogenesis of HUVECs in vitro and in vivo. Moreover, we found that CMTM7 is a target of miR-182-5p. EVs-miR-182-5p promotes tumorigenesis and metastasis of breast cancer cells by regulating the CMTM7/EGFR/AKT signaling axis. Conclusions: Taken altogether, our findings demonstrates that EVs secreted by breast cancer cells could carry miR-182-5p to aggravate breast cancer through downregulating CMTM7 expression and activating the EGFR/AKT signaling pathway.

Background: Isthmic papillary thyroid carcinoma (IPTC) is an aggressive thyroid cancer associated with a poor prognosis. Guidelines elaborating on the extent of surgery for IPTC are yet to be developed. This study aims to construct and validate a model to predict central lymph node metastasis (CLNM) in patients with IPTC, which could be used as a risk stratification tool to determine the best surgical approach for patients.Methods: Electronic medical records for patients diagnosed with isolated papillary thyroid carcinoma who underwent surgery at Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, from January 2010 to December 2021 were reviewed. All patients who underwent thyroidectomy with central neck dissection (CND) for isolated IPTC were included. We conducted univariate and multivariate logistic regression analyses to assess risk factors for ipsilateral and contralateral CLNM and the number of CLNM in IPTC patients. Based on the analysis, the nomogram construction and internal validations were performed.Results: A total of 147 patients with isolated IPTC were included. The occurrence of CLNM was 53.7% in the patients. We identified three predictors of ipsilateral CLNM, including age, gender, and size. For contralateral CLNM, three identified predictors were age, gender, and capsular invasion. Predictors for the number of CLNM included age, gender, capsular invasion, tumor size, and chronic lymphocytic thyroiditis (CLT). The concordance index(C-index) of the models predicting ipsilateral CLNM, contralateral CLNM, 1-4 CLNM, and ≥5 CLNM was 0.779 (95%CI, 0.704, to 0.854), 0.779 (95%CI, 0.703 to 0.855), 0.724 (95%CI, 0.629 to 0.818), and 0.932 (95%CI, 0.884 to 0.980), respectively. The corresponding indices for the internal validation were 0.756 (95%CI, 0.753 to 0.758), 0.753 (95%CI, 0.750 to 0.756), 0.706 (95%CI, 0.702 to 0.708), and 0.920 (95%CI, 0.918 to 0.922). Receiver operating characteristic (ROC) curves, calibration, and decision curve analysis (DCA) results confirmed that the three nomograms could precisely predict CLNM in patients with isolated IPTC.Conclusion: We constructed predictive nomograms for CLNM in IPTC patients. A risk stratification scheme and corresponding surgical treatment recommendations were provided accordingly. Our predictive models can be used as a risk stratification tool to help clinicians make individualized surgical plans for their patients.

Magnetic nanoparticles (MNPs) are temperature sensitive, making them useful as probes for measuring cellular temperature. In this paper, we propose a remote non-invasive thermometer that replaces a traditional magnetic sensor with linearly polarized light as the transmission medium, utilizing the Faraday rotation of MNPs. This method not only overcomes the limitations of distance on temperature measurement accuracy but also offers strong anti-interference capabilities. Using this thermometer, we measured the cooling process of MNPs within a temperature range of 310–316 K, achieving an estimated temperature root mean square error (RMSE) of 0.14 K. This approach shows promise for integration with microscopy systems for temperature imaging of cells or biological tissues, potentially revealing new biological insights.

Background: This study aimed to develop predictive models based on preoperative clinicopathological and imaging features to accurately assess the individual risk of contralateral occult thyroid carcinoma (OTC) and determine the number of central lymph node metastasis (CLNM) in patients with unilateral papillary thyroid carcinoma, thereby providing actionable guidance for surgical planning.Methods: Seven widely-used machine learning algorithms were employed to develop predictive models. Hyperparameter tuning was performed via cross-validation in combination with grid search. The models were subsequently trained and evaluated by using the optimal hyperparameter combinations. To facilitate comparative analysis, ROC curves, calibration curves were generated and DCA was performed. The optimal model was then selected on the basis of this comprehensive evaluation. Furthermore, a clinical prediction model was constructed utilizing the significant predictors identified.Results: The logistic regression model was identified to be the optimal predictive model. For the clinical prediction model of OTC, the following independent variables were incorporated: body mass index, and ultrasonographic findings, including capsular disruption, number of malignant nodules within a unilateral lobe, sum of the longest diameter (SLD) of tumors, and the presence of isthmic malignant nodule(s). This model yielded an area under the ROC curve (AUC) of 0.74 and 0.70 in the training and validation cohorts, respectively. For the clinical prediction model of ≥5 CLNM, the incorporated independent variables included: age, sex, chronic lymphocytic thyroiditis, and ultrasonographic features covering malignant nodules located near the isthmus, SLD, capsular disruption, and calcification. This model produced an AUC of 0.75 and 0.71 in the training and validation cohorts, respectively. Decision curve analysis indicated that clinical interventions guided by the two models could provide net benefit within threshold probability ranges of 10% to 90% and 10% to 70% for patients with PTC. And the calibration curves demonstrated a good agreement between model predictions and actual observations.Conclusion: This study developed and validated clinical prediction models to estimate the risk of contralateral OTC and the presence of ≥5 CLNM in patients with unilateral PTC. These models were designed to prevent overtreatment in low-risk patients while providing evidence-based guidance for decision-making about treatment choice in high-risk patients.

Dysfunction of cytotoxic T cells (CTL) remains a major cause of tumor immune evasion and is correlated with poor cancer survival. Here, we found that increased soluble form of ICOSL (sICOSL) induced CTL dysfunction and was associated with shorter survival of patients with breast cancer. sICOSL emerged as a formidable adversary to CTLs, by directly triggering ICOS internalization and subsequent degradation—a critical blow to the co-stimulatory machinery essential for CTL activation. Our research shows that dipeptidyl peptidase-4 (DPP4) mainly breaks down sICOSL. Notably, certain chemotherapeutic drugs activate the histone methyltransferase Enhancer of zeste homolog 2 (EZH2), which in turn suppresses DPP4 expression. To address this issue, we have developed nanobody-DPP4 fusion proteins that can specifically degrade sICOSL, achieving substrate selectivity and tumor targeting. Overall, This work unveils that sICOSL orchestrates CTL dysfunction, and establishs targeted degradation of sICOSL as a new strategy for immunotherapy.