Elafin is related to immune infiltration and could predict the poor prognosis in ovarian cancer

BACKGROUND

Ovarian cancer (OC) is the most lethal gynecologic malignancy, yet the clinical results for OC patients are still variable. Therefore, we examined how elafin expression affects the patients' prognoses and immunotherapy responses in OC, which may facilitate treatment selection and improve prognosis.

METHODS

The elafin mRNA expression profile was downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus. Elafin's prognostic potential and its relationship with clinical variables were investigated using Kaplan-Meier survival curves, time-dependent receiver operating characteristic curves as well as univariate and multivariate Cox regression models. As validation, protein expression in the tumor and adjacent tissues of OC patients was investigated by using immunohistochemistry (IHC). Comprehensive analyses were then conducted to explore the correlation between immune infiltration and elafin expression.

RESULTS

A higher mRNA expression of elafin was associated with an unfavorable prognosis in TCGA cohort and was validated in GSE31245 and IHC. Moreover, elafin was indicated as an independent risk factor for OC. A significantly higher protein expression of elafin was detected in the adjacent tissues of OC patients with shorter overall survival (OS). The immune-related pathways were mainly enriched in the high-elafin-mRNA-expression group. However, the mRNA expression of elafin was favorably correlated with indicators of the immune filtration and immunotherapy response, which also proved better immunotherapy outcomes.

CONCLUSION

The high elafin expression was associated with an unfavorable OS, while it also indicated better immunotherapy responses. Thus, the detection of elafin is beneficial to diagnosis and treatment selection.

Morphology-Based Deep Learning Approach for Predicting Osteogenic Differentiation

Early, high-throughput, and accurate recognition of osteogenic differentiation of stem cells is urgently required in stem cell therapy, tissue engineering, and regenerative medicine. In this study, we established an automatic deep learning algorithm, i.e., osteogenic convolutional neural network (OCNN), to quantitatively measure the osteogenic differentiation of rat bone marrow mesenchymal stem cells (rBMSCs). rBMSCs stained with F-actin and DAPI during early differentiation (day 0, 1, 4, and 7) were captured using laser confocal scanning microscopy to train OCNN. As a result, OCNN successfully distinguished differentiated cells at a very early stage (24 h) with a high area under the curve (AUC) (0.94 ± 0.04) and correlated with conventional biochemical markers. Meanwhile, OCNN exhibited better prediction performance compared with the single morphological parameters and support vector machine. Furthermore, OCNN successfully predicted the dose-dependent effects of small-molecule osteogenic drugs and a cytokine. OCNN-based online learning models can further recognize the osteogenic differentiation of rBMSCs cultured on several material surfaces. Hence, this study initially demonstrated the foreground of OCNN in osteogenic drug and biomaterial screening for next-generation tissue engineering and stem cell research.

Racial/Ethnic Disparities on the Risk of Second Malignant Neoplasm Among Hodgkin Lymphoma Survivors

Background

Hodgkin lymphoma survivors are at risk for second malignant neoplasm (SMN). How race/ethnicity affects the risk remains unclear.

Methods

This retrospective cohort study included 22,415 patients diagnosed with primary Hodgkin lymphoma from January 1992 to December 2015 in 13 Surveillance, Epidemiology, and End Results-based registries and divided patients into four groups: non-Hispanic whites, non-Hispanic blacks, Hispanics, and Asian/others. Taking non-Hispanic whites as a reference, both the proportional subdistribution hazard (PSH) and the cause-specific hazard (CSH) methods were used to calculate the SMN hazard ratio for other racial/ethnic groups with and without considering the competing mortality risk.

Results

1,778 patients developed SMN with a median follow-up of 11.63 years. In the adjusted PSH model, Hispanic, Asian/others, and non-Hispanic black patients had 26% (PSH, 0.74; 95% CI, 0.63–0.87), 20% (PSH, 0.80; 95% CI, 0.64–1.01), and 12% (PSH, 0.88; 95% CI, 0.75–1.03) decreased overall SMN hazard, respectively. Moreover, the PSH method revealed the racial/ethnic difference in the SMN risk in the skin, the respiratory system, and the endocrine system. These hazards were slightly higher and different with the use of the CSH approach. In addition to the aforementioned overall SMN and subtypes, adjusted CSH analysis also revealed the racial/ethnic disparities in the risk of subsequent female breast cancer, digestive cancer, and non-Hodgkin lymphoma.

Conclusions

The subtype and SMN risk among Hodgkin lymphoma survivors varied by race/ethnicity. The use of CSH and PSH provides a dynamic view of racial/ethnic effects on SMN risk in Hodgkin lymphoma survivors.

The PNA mouse may be the best animal model of polycystic ovary syndrome

Polycystic ovary syndrome (PCOS) exerts negative effects on females of childbearing age. It is important to identify more suitable models for fundamental research on PCOS. We evaluated animal models from a novel perspective with the aim of helping researchers select the best model for PCOS. RNA sequencing was performed to investigate the mRNA expression profiles in the ovarian tissues of mice with dehydroepiandrosterone (DHEA) plus high-fat diet (HFD)-induced PCOS. Meanwhile, 14 datasets were obtained from the Gene Expression Omnibus (GEO), including eight studies on humans, three on rats and three on mice, and genes associated with PCOS were obtained from the PCOSKB website. We compared the consistency of each animal model and human PCOS in terms of DEGs and pathway enrichment analysis results. There were 239 DEGs shared between prenatally androgenized (PNA) mice and PCOS patients. Moreover, 1113 genes associated with PCOS from the PCOSKB website were identified among the DEGs of PNA mice. A total of 134 GO and KEGG pathways were shared between PNA mice and PCOS patients. These findings suggest that the PNA mouse model is the best animal model to simulate PCOS.