Integrated bioinformatics data analysis reveals a risk signature and PKD1 induced progression in endometrial cancer patients with postmenopausal status

The influence of menopause age on gynecologic cancer risk: a comprehensive analysis using NHANES data

Background

Menopause, a natural transition, affects women's health risks, including gynecologic cancers. Early menopause, linked to lower estrogen, may increase cancer susceptibility. This study analyzed NHANES data from 1999 to 2020 for 8,219 postmenopausal women to explore the relationship between menopausal age and gynecologic cancers. We used regression models and RCS models to assess the risk.

Methods

This study utilized data from the NHANES spanning 1999 to 2020, focusing on 8,219 postmenopausal women selected through stratified sampling. Variables including socioeconomic factors, health behaviors, nutritional status, and medical history were assessed in relation to participants' menopausal age and gynecologic cancer prevalence. We analyzed the relationship between menopausal age and gynecologic cancers (cervical, ovarian, and uterine) using multiple regression models. Additionally, we employed RCS models to evaluate nonlinear relationships between menopausal age and gynecologic cancer risk.

Results

Our findings indicate a significant inverse association between menopausal age and the risk of gynecologic cancers. After controlling for confounding factors such as age, race, BMI, and lifestyle variables, a later age at menopause was associated with a reduced risk of cervical, ovarian, and uterine cancers. The RCS model revealed a non-linear, low-L-shaped relationship, particularly highlighting increased cancer risks at younger menopausal ages. Subgroup analyses demonstrated consistent results across demographic and lifestyle factors, confirming the robustness of the observed associations.

Conclusion

This study reveals the link between menopausal age and gynecologic cancer prevalence. Early menopause is a significant risk factor for cervical, ovarian, and uterine cancers. Our findings support tailored cancer screening based on menopausal age, potentially improving preventive care for postmenopausal women.

Machine learning combined with single-cell analysis reveals predictive capacity and immunotherapy response of T cell exhaustion-associated lncRNAs in uterine corpus endometrial carcinoma

BACKGROUND

The exhaustion of T-cells is a primary factor contributing to immune dysfunction in cancer. Long non-coding RNAs (lncRNAs) play a significant role in the advancement, survival, and treatment of Uterine Corpus Endometrial Carcinoma (UCEC). Nevertheless, there has been no investigation into the involvement of lncRNAs associated with T-cell exhaustion (TEXLs) in UCEC. The goal of this work is to establish predictive models for TEXLs in UCEC and study their related immune features.

METHODS

Using transcriptome and single-cell sequencing data from The Cancer Genome Atlas and Gene Expression Omnibus databases, we employed co-expression analysis and univariate Cox regression to identify prognostic-associated TEXLs (pTEXLs). The prognostic model was developed using the Least Absolute Contraction and Selection Operator. The immunotherapy characteristics of the prognostic model risk score were studied. Then molecular subgroups were identified through non-negative Matrix Factorization based on pTEXLs. The identification of co-expressed genes was done using a weighted correlation network analysis. Subsequently, a diagnostic model for UCEC was created. In-depth investigations, both in vitro and in vivo, were carried out to elucidate the molecular mechanism of the key gene within the diagnostic model.

RESULTS

Receiver operating characteristic curve, calibration curve, and decision curve analysis proved the validity of the predictive models established according to pTEXLs. The subgroup with lower risk scores in the prognostic model has better responses to blocking immune checkpoint therapy. Single-cell analysis suggests that the expression level of MIEN1 is relatively high in immune cells among diagnostic genes. Furthermore, the targeted suppression of MIEN1 via sh-MIEN1 diminishes the proliferative, migratory, and invasive capacities of UCEC cells, potentially associated with CD8+ T cell exhaustion.

CONCLUSIONS

The association between TEXLs and UCEC was methodically elucidated by our investigation. A stable pTEXLs risk prediction model and a diagnosis model for UCEC were also established.