Zhili Chu

Objective: To systematically compare the differences in immune characteristics among three populations (normal group, high-risk group, and esophageal cancer [EC] patients group) and construct a predictive model based on immune metrics, thereby exploring its value in EC risk assessment and early diagnosis. Methods: A total of 440 participants were enrolled, including 173 normal individuals, 162 high-risk individuals, and 105 EC patients. Peripheral blood samples were collected, and 75 immune metrics were detected using flow cytometry. First, 21 highly correlated indicators were eliminated through the Pearson correlation matrix (|r|≥0.7), leaving 54 indicators for further analysis. Multivariate analysis of variance (MANOVA) was used to assess the effect of EC grouping on immune metrics, adjusting for gender and age. Significant indicators were analyzed using analysis of covariance (ANCOVA) and the false discovery rate (FDR). Stratified sensitivity analysis was conducted to verify the robustness of the results. Finally, a predictive model was built using 12 key immune metrics, and 10 covariates. The model performance was evaluated by 10-fold cross-validation, neural network algorithm, accuracy, Kappa value, and area under the receiver operating characteristic curve (AUC). Results: MANOVA showed that EC grouping had a significant overall impact on the 54-immune metrics (Pillai = 0.585, P < 2.2×10 ^{^−16} ). After ANCOVA and FDR correction, 12 immune metrics with significant inter-group differences were identified ( P′ < 0.05), including Tcm CD4 ⁺ T cells, Naive CD8 ⁺ T cells, Tem CD8 ⁺ T cells, and FB CD8 ⁺ T cells. Stratified sensitivity analysis confirmed stable differences in key indicators (FB CD8 ⁺ T cells and Act NK cells) remained stable among subgroups. The neural network predictive model exhibited excellent discriminative efficacy: the overall cross-validation accuracy was 81.2%, the Kappa value was 0.709, and the AUC values for pairwise comparisons among the three populations were all higher than 0.90 (0.905 to 0.927). Conclusion: This study demonstrates distinct and stable immune profiles across EC risk groups. The immune-based machine learning model effectively differentiates normal, high-risk, and cancer populations, offering a promising non-invasive tool for early risk assessment and screening of esophageal cancer.

[This corrects the article DOI: 10.3389/fvets.2024.1410371.].

Background: Gonadotrophin-releasing hormone (GnRH) administration significantly decreases the pregnancy rate of recipient ewes after embryo transfer, possibly because GnRH affects endometrial epithelial cell function. Therefore, this study investigated the effect of GnRH on endometrial epithelial cells.Methods: Transcriptome sequencing was used to determine the regulatory effect of GnRH on the ewe endometrium, and the S100A4 gene, which showed altered transcription, was screened as a candidate regulator of this effect. Endometrial epithelial cells were further isolated, the S100A4 protein was immunoprecipitated, and host proteins that interacted with S100A4 were identified by mass spectrometry. We further verified the effects of S100A4 and GNAI2 on the proliferation of endometrial epithelial cells via overexpression/knockdown experiments and subsequent CCK-8 and EdU assays. The effect of S100A4 deletion in endometrial cells on reproduction was verified in mice with S100A4 knockout.Results: Our results showed that S100A4 gene transcription in endometrial cells was significantly inhibited after GnRH administration. GNAI2 was identified as a downstream interacting protein of S100A4, and S100A4 was confirmed to activate the MAPK signaling pathway to promote cell proliferation by targeting GNAI2.Conclusion: GnRH can suppress the expression of S100A4 in the endometrium, consequently inhibiting the proliferation of endometrial cells through the S100A4/GNAI2/MAPK signaling pathway. These findings suggest a potential explanation for the limited efficacy of GnRH in promoting embryo implantation.

Background: Newcastle disease virus (NDV) is an oncolytic virus that can inhibit cancer cell proliferation and kill cancer cells. The NDV nonstructural V protein can regulate viral replication; however, whether the V protein contributes to NDV oncolysis is unclear. Results: This study revealed that NDV inhibited tumor cell proliferation and that V protein expression promoted the proliferation of HepG2 cells, as determined at the single-cell level. In addition, to identify the regulatory mechanism of the V protein in HepG2 cells, transcriptome sequencing was performed and indicated that the expression/activation of multiple cell proliferation-related genes/signaling pathways were changed in cells overexpressing the V protein. Hence, the MAPK and WNT signaling pathways were selected for verification, and after blocking these two signaling pathways with inhibitors, the V protein promotion of cell proliferation was found to be attenuated. Conclusions: The results showed that the V protein regulated the proliferation of cancer cells through multiple signaling pathways, providing valuable references for future studies on the mechanism by which the V protein regulates cancer cell proliferation.

A method to synthesize benzofurylselenocyanates, benzothienylselenocyanates and indolylselenocyanates via electrophilic selenocyanogen cyclization was established. This sequential process was conducted under mild conditions in a short time. This protocol was successfully applied to late‐stage functionalization of bioactive molecules. Notablely, the selenocyanate can be converted into other valuable Se‐containing compounds and showed antitumor activity to human hepatoma cells HepG2. magnified image