Stratification and prognostic evaluation of breast cancer subtypes defined by obesity-associated genes.

OBJECTIVE: Breast cancer was the most common type of cancer among women worldwide, significantly impacting their quality of life and survival rates. And obesity has been widely accepted as an important risk factor for breast cancer. However, the specific mechanisms by which obesity affects breast cancer were still unclear. Therefore, studying the impact mechanisms of obesity as a risk factor for breast cancer was of utmost importance. METHODS: This study was based on TCGA breast cancer RNA transcriptomic data and the GeneCard obesity gene set. Through single and multiple factor Cox analysis and LASSO coefficient screening, seven hub genes were identified. The independent mechanisms of these seven hub genes were evaluated from various aspects, including survival data, genetic mutation data, single-cell sequencing data, and immune cell data. Additionally, the risk prognosis model and the neural network diagnostic model were established to further investigate these seven hub genes. In order to achieve precision treatment for breast cancer (BRCA), based on the RNA transcriptomic data of the seven genes, 1226 BRCA patients were divided into two subtypes: BRCA subtype 1 and BRCA subtype 2. By studying and comparing the immune microenvironment, investigating the mechanisms of differential gene expression, and exploring the mechanisms of subnetworks, we aim to explore the clinical differences in the presentation of BRCA subtypes and achieve precision treatment for BRCA. Finally, qRT-PCR experiments were conducted to validate the conclusions of the bioinformatics analysis. RESULTS: The 7 hub genes showed good diagnostic independence and can serve as excellent biomarkers for molecular diagnosis. However, they do not perform well as independent prognostic molecular markers for BRCA patients. When predicting the survival of BRCA patients, their AUC values at 1 year, 3 years, and 5 years are mostly below 0.5. Nevertheless, through the establishment of the risk prognosis model considering the combined effect of the seven hub genes, it was found that the survival prediction of BRCA patients can be significantly improved. The risk prognosis model, compared to the independent use of the seven hub genes as prognostic markers, achieved higher timeROC AUC values at 1 year, 3 years, and 5 years, with values of 0.651, 0.669, and 0.641 respectively. Additionally, the neural network diagnostic model constructed from the 7 genes performs well in diagnosing BRCA, with an AUC value of 0.94, accurately identifying BRCA patients. The two subtypes identified by the seven hub genes exhibited significant differences in survival period, with subtype 1 having a poor prognosis. The differential mechanisms between the two subtypes mainly originate from regulatory differences in the immune microenvironment. Finally, the results of this study's bioinformatics analysis were validated through qRT-PCR experiments. CONCLUSION: 7 hub genes serve as excellent independent biomarkers for molecular diagnosis, and the neural network diagnostic model can accurately distinguish BRCA patients. In addition, based on the expression levels of these seven genes in BRCA patients, two subtypes can be reliably identified: BRCA subtype 1 and BRCA subtype 2, and these two subtypes showed significant differences in BRCA patient survival prognosis, proportion of immune cells, and expression levels of immune cells. Among them, patients with subtype 1 of BRCA had a poor prognosis.

Uterine arteries pulsatility index by Doppler ultrasound in the prediction of preeclampsia: an updated systematic review and meta-analysis.

BACKGROUND: Preeclampsia is a common pregnancy complication with serious potential risks for maternal and neonatal health. Early prediction of preeclampsia is crucial for timely prevention, surveillance, and treatment to improve maternal and neonatal outcomes. This systematic review aimed to summarize the available evidence on the prediction of preeclampsia based on Doppler ultrasound of uterine arteries at different gestational ages. METHODS: A systematic literature search and meta-analysis were conducted to evaluate the sensitivity and specificity of the pulsatility index of Doppler ultrasound of uterine arteries for predicting preeclampsia. The timing of ultrasound scans within and beyond 20 weeks of gestational age was compared to assess its effect on the sensitivity and specificity of the pulsatility index. RESULTS: This meta-analysis included 27 studies and 81,673 subjects (3309 preeclampsia patients and 78,364 controls). The pulsatility index had moderate sensitivity (0.586) and high specificity for predicting preeclampsia (0.879) (summary point: sensitivity 0.59; 1-specificity 0.12). Subgroup analysis revealed that ultrasound scans performed within 20 weeks of gestational age did not significantly affect the sensitivity and specificity for predicting preeclampsia. The summary receiver operator characteristic curve showed the pulsatility index's optimal range of sensitivity and specificity. CONCLUSIONS: The uterine arteries pulsatility index measured by Doppler ultrasound is useful and effective for predicting preeclampsia and should be implemented in the clinical practice. The timing of ultrasound scans at different gestational age ranges does not significantly affect the sensitivity and specificity.

A nine-long non-coding RNA signature for prognosis prediction of patients with lung squamous cell carcinoma.

BACKGROUND: Lung squamous cell carcinoma (LUSC) is malignant disease with poor therapeutic response and unfavourable prognosis. OBJECTIVE: This study aims to develop a long non-coding RNA (lncRNA) signature for survival prediction in patients with LUSC. METHODS: We obtained lncRNA expression profiles of 493 LUSC cases from The Cancer Genome Atlas, and randomly divided the samples into a training set (n= 296) and a testing set (n= 197). Univariate Cox regression and random survival forest algorithm were performed to select optimum survival-related lncRNAs. RESULTS: A lncRNA-focused risk score model was then constructed for prognosis prediction in the training set and further validated in the testing set and the entire set. Finally, bioinformatics analysis was carried out to explore the potential signaling pathways associated with the prognostic lncRNAs. A set of 9 lncRNAs were found to be strongly correlated with overall survival of LUSC patients. These 9 lncRNAs were integrated into a prognostic signature, which could separate patients into high- and low-risk groups with significantly different survival times in the training set (median: 30.5 vs. 80.5 months, log-rank P< 0.001). This signature was also confirmed in the testing set and the entire set. Besides, the prognostic value of the 9-lncRNA signature was independent of clinical features and maintained stable in stratified analyses. Functional enrichment study suggested that the 9 lncRNAs may be mainly involved in metabolism-related pathways, phosphatidylinositol signaling system, p53 signaling pathway, and notch signaling pathway. CONCLUSIONS: Our study demonstrated the potential clinical implication of the 9-lncRNA signature for survival prediction of LUSC patients.