Construction of a CCL20-centered circadian-signature based prognostic model in cervical cancer.

BACKGROUND: Rather low vaccination rates for Human papillomavirus (HPV) and pre-existing cervical cancer patients with limited therapeutic strategies ask for more precise prognostic model development. On the other side, the clinical significance of circadian clock signatures in cervical cancer lacks investigation. METHODS: Subtypes classification based upon eight circadian clock core genes were implemented in TCGA-CESC through k-means clustering methods. Afterwards, KEGG, GO and GSEA analysis were conducted upon differentially expressed genes (DEGs) between high and low-risk groups, and tumor microenvironment (TME) investigation by CIBERSORT and ESTIMATE. Furthermore, a prognostic model was developed by cox and lasso regression methods, and verified in GSE44001 by time-dependent receiver-operating characteristic curve (ROC) analysis. Lastly, FISH and IHC were used for validation of CCL20 expression in patients' specimens and U14 subcutaneous tumor models were built for TME composition. RESULTS: We successfully classified cervical patients into high-risk and low-risk groups based upon circadian-oscillation-signatures. Afterwards, we built a prognostic risk model composed of GJB2, CCL20 and KRT24 with excellent predictive value on patients' overall survival (OS). We then proposed metabolism unbalance, especially for glycolysis, and immune related pathways to be major enriched signatures between the high-risk and low-risk groups. Then, we proposed an 'immune-desert'-like suppressive myeloid cells infiltration pattern in high-risk group TME and verified its resistance to immunotherapies. Finally, CCL20 was proved positively correlated with real-world patients' stages and induced significant less CD8+ T cells and more M2 macrophages infiltration in mouse model. CONCLUSIONS: We unraveled a prognostic risk model based upon circadian oscillation and verified its solidity. Specifically, we unveiled distinct TME immune signatures in high-risk groups.

Daily variations and levels of human chorionic gonadotropin before methotrexate treatment as predictors of treatment success.

AIM: The aim of this study is to investigate the role of human chorionic gonadotropin (hCG) daily variations and levels prior to methotrexate treatment as predictors for treatment outcome. METHODS: This retrospective study included patients who had a sonographically confirmed ectopic pregnancy at the International Peace Maternity and Child Health Hospital between November 2015 and June 2020. The associations of hCG levels and daily variations with the treatment success were evaluated by multivariable logistic regression and receiver operator characteristic (ROC) curve. Establish a nomogram that predicts how methotrexate (MTX) therapy will turn out. The performance of the model was assessed utilizing concordance index, receiver operating characteristic curves, and calibration plots. RESULTS: The median serum hCG levels before treatment and hCG daily variation in the failure group were higher than those in the success group (487.8 vs. 270.7 IU/L, -1.86% vs. 7.29%, both p < 0.01). According to the ROC curve analysis, the cutoff values of serum hCG level before treatment and daily variations were 617.35 IU/L and 1.76%/day. By multivariable logistic regression analysis, serum hCG levels before treatment (odds ratio [OR]: 1.001, 95% confidence interval [CI]: 1.000 ~ 1.001) and hCG daily variations were independently associated with the treatment success (OR: 1.033, 95% CI: 1.015 ~ 1.052). The nomogram was effective at predicting the outcome of MTX treatment with a receiver operating characteristic area under the curve of 0.717 (p < 0.001). The nomogram's calibration curve was almost parallel to the ideal diagonal line. CONCLUSION: We successfully created a nomogram based on serum hCG levels before treatment and hCG daily changes to anticipate the result of MTX therapy, which could assist medical professionals in selecting therapeutic schedule for patients with tubal pregnancies.

A novel cervix carcinoma biomarker: Pathological-epigenomics, integrated analysis of MethylMix algorithm and pathology for predicting response to cancer immunotherapy.

Herein, A non-invasive pathomics approach was developed to reveal the methylation status in patients with cervical squamous cell carcinoma and predict clinical outcomes and treatment response. Using the MethylMix algorithm, 14 methylation-driven genes were selected for further analysis. We confirmed that methylation-driven genes were differentially expressed in immune, stromal, and tumor cells. In addition, we constructed a methylation-driven model and explored the alterations in immunocyte infiltration between the different models. The methylation-driven subtypes identified in our investigation could effectively predict the clinical outcomes of cervical cancer. To further evaluate the level of methylation-driven patterns, we constructed a risk model with four genes. Significant correlations were observed between the score and immune response markers, including PD1 and CTLA4. Multiple immune infiltration algorithms evaluated the level of immunocyte infiltration between the high- and low-risk groups, while the components of anti-tumor immunocytes in the low-risk group were significantly increased. Subsequently, a total of 205 acquired whole-slide imaging (WSI) images were processed to capture image signatures, and the pathological algorithm was employed to construct an image prediction model based on the risk score classification. The model achieved an area under the curve (AUC) of 0.737 and 0.582 for the training and test datasets, respectively. Moreover, we conducted vitro assays for validation of hub risk gene. The proposed prediction model is a non-invasive method that combines pathomics features and genomic profiles and shows satisfactory performance in predicting patient survival and treatment response. More interdisciplinary fields combining medicine and electronics should be explored in the future.