Multi-Dimensional Fragmentomics Enables Early and Accurate Detection of Colorectal Cancer.

Colorectal cancer (CRC) is frequently diagnosed in advanced stages, highlighting the need for developing approaches for early detection. Liquid biopsy using cell-free DNA (cfDNA) fragmentomics is a promising approach, but the clinical application is hindered by complexity and cost. This study aimed to develop an integrated model using cfDNA fragmentomics for accurate, cost-effective early-stage CRC detection. Plasma cfDNA was extracted and sequenced from a training cohort of 360 participants, including 176 CRC patients and 184 healthy controls. An ensemble stacked model comprising five machine learning models was employed to distinguish CRC patients from healthy controls using five cfDNA fragmentomic features. The model was validated in an independent cohort of 236 participants (117 CRC patients and 119 controls) and a prospective cohort of 242 participants (129 CRC patients and 113 controls). The ensemble stacked model showed remarkable discriminatory power between CRC patients and controls, outperforming all base models and achieving a high area under the ROC curve (AUC) of 0.986 in the validation cohort. It reached 94.88% sensitivity and 98% specificity for detecting CRC in the validation cohort, with sensitivity increasing as cancer progressed. The model also demonstrated consistently high accuracy in within-run and between-run tests and across various conditions in healthy individuals. In the prospective cohort, it achieved 91.47% sensitivity and 95.58% specificity. This integrated model capitalizes on the multiplex nature of cfDNA fragmentomics to achieve high sensitivity and robustness, offering significant promise for early CRC detection and broad patient benefit.

Early detection of uterine corpus endometrial carcinoma utilizing plasma cfDNA fragmentomics.

BACKGROUND: Uterine corpus endometrial carcinoma (UCEC) is a prevalent gynecologic malignancy with a favorable prognosis if detected early. However, there is a lack of accurate and reliable early detection tests for UCEC. This study aims to develop a precise and non-invasive diagnostic method for UCEC using circulating cell-free DNA (cfDNA) fragmentomics. METHODS: Peripheral blood samples were collected from all participants, and cfDNA was extracted for analysis. Low-coverage whole-genome sequencing was performed to obtain cfDNA fragmentomics data. A robust machine learning model was developed using these features to differentiate between UCEC and healthy conditions. RESULTS: The cfDNA fragmentomics-based model showed high predictive power for UCEC detection in training (n = 133; AUC 0.991) and validation cohorts (n = 89; AUC 0.994). The model manifested a specificity of 95.5% and a sensitivity of 98.5% in the training cohort, and a specificity of 95.5% and a sensitivity of 97.8% in the validation cohort. Physiological variables and preanalytical procedures had no significant impact on the classifier's outcomes. In terms of clinical benefit, our model would identify 99% of Chinese UCEC patients at stage I, compared to 21% under standard care, potentially raising the 5-year survival rate from 84 to 95%. CONCLUSION: This study presents a novel approach for the early detection of UCEC using cfDNA fragmentomics and machine learning showing promising sensitivity and specificity. Using this model in clinical practice could significantly improve UCEC management and control, enabling early intervention and better patient outcomes. Further optimization and validation of this approach are warranted to establish its clinical utility.

Modulation of neural gene networks by estradiol in old rhesus macaque females.

The postmenopausal decrease in circulating estradiol (E2) levels has been shown to contribute to several adverse physiological and psychiatric effects. To elucidate the molecular effects of E2 on the brain, we examined differential gene expression and DNA methylation (DNAm) patterns in the nonhuman primate brain following ovariectomy (Ov) and subsequent subcutaneous bioidentical E2 chronic treatment. We identified several dysregulated molecular networks, including MAPK signaling and dopaminergic synapse response, that are associated with ovariectomy and shared across two different brain areas, the occipital cortex (OC) and prefrontal cortex (PFC). The finding that hypomethylation (p = 1.6 × 10-51) and upregulation (p = 3.8 × 10-3) of UBE2M across both brain regions provide strong evidence for molecular differences in the brain induced by E2 depletion. Additionally, differential expression (p = 1.9 × 10-4; interaction p = 3.5 × 10-2) of LTBR in the PFC provides further support for the role E2 plays in the brain, by demonstrating that the regulation of some genes that are altered by ovariectomy may also be modulated by Ov followed by hormone replacement therapy (HRT). These results present real opportunities to understand the specific biological mechanisms that are altered with depleted E2. Given E2's potential role in cognitive decline and neuroinflammation, our findings could lead to the discovery of novel therapeutics to slow cognitive decline. Together, this work represents a major step toward understanding molecular changes in the brain that are caused by ovariectomy and how E2 treatment may revert or protect against the negative neuro-related consequences caused by a depletion in estrogen as women approach menopause.

Association of follow-up neutrophil-to-lymphocyte ratio and systemic inflammation response index with stroke-associated pneumonia and functional outcomes in cerebral hemorrhage patients: a case-controlled study.

BACKGROUND: The neutrophil-to-lymphocyte ratio (NLR) and systemic inflammation response index (SIRI) at admission are independent diagnostic biomarkers in stroke-associated pneumonia (SAP). Our study aimed to investigate the association between NLR, SIRI, specifically follow-up NLR and SIRI, and SAP, as well as their relationship with functional outcomes. PATIENTS AND METHODS: We retrospectively included 451 consecutive intracerebral hemorrhage patients from May 2017 to May 2019. We conducted univariate and multivariable analyses to identify the factors independently associated with SAP and poor functional outcomes. RESULTS: Compared to 127 (28.16%) patients diagnosed with SAP, those without SAP had both lower baseline and follow-up NLR and SIRI values ( P <0.001). After adjustments, we found that baseline NLR [OR, 1.039 (95% CI, 1.003-1.077); P =0.036] and follow-up NLR [OR, 1.054 (95% CI, 1.011-1.098); P =0.012] were independently associated with SAP. The follow-up NLR was also associated with a higher mRS [OR, 1.124 (95% CI, 1.025-1.233); P =0.013] and lower ADL-MBI score [OR, 1.167 (95% CI, 1.057-1.289); P =0.002] at discharge. Multivariable analysis indicated that advanced age and nasogastric tube feeding were independently associated with SAP ( P <0.05). We constructed a dynamic nomogram to identify SAP risk. Further subgroup analysis revealed that baseline NLR [OR, 1.062 (95% CI, 1.007-1.120); P =0.026] is independently associated with SAP in the nasogastric feeding group, while follow-up NLR [OR, 1.080 (95% CI, 1.024-1.139); P =0.005] was associated with the occurrence of SAP in non-nasogastric feeding patients. CONCLUSIONS: We found elevated baseline and follow-up NLR values were associated with SAP occurrence, and increasing follow-up NLR indicated poor functional outcomes. Inflammatory markers at different stages may offer individualized guidance for patients receiving various treatments.

Multimodal radiomics based on 18F-Prostate-specific membrane antigen-1007 PET/CT and multiparametric MRI for prostate cancer extracapsular extension prediction.

OBJECTIVES: To compare the performance of the multiparametric magnetic resonance imaging (mpMRI) radiomics and 18F-Prostate-specific membrane antigen (PSMA)-1007 PET/CT radiomics model in diagnosing extracapsular extension (EPE) in prostate cancer (PCa), and to evaluate the performance of a multimodal radiomics model combining mpMRI and PET/CT in predicting EPE. METHODS: We included 197 patients with PCa who underwent preoperative mpMRI and PET/CT before surgery. mpMRI and PET/CT images were segmented to delineate the regions of interest and extract radiomics features. PET/CT, mpMRI, and multimodal radiomics models were constructed based on maximum correlation, minimum redundancy, and logistic regression analyses. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC) and indices derived from the confusion matrix. RESULTS: AUC values for the mpMRI, PET/CT, and multimodal radiomics models were 0.85 (95% CI, 0.78-0.90), 0.73 (0.64-0.80), and 0.83 (0.75-0.89), respectively, in the training cohort and 0.74 (0.61-0.85), 0.62 (0.48-0.74), and 0.77 (0.64-0.87), respectively, in the testing cohort. The net reclassification improvement demonstrated that the mpMRI radiomics model outperformed the PET/CT one in predicting EPE, with better clinical benefits. The multimodal radiomics model performed better than the single PET/CT radiomics model (P < .05). CONCLUSION: The mpMRI and 18F-PSMA-PET/CT combination enhanced the predictive power of EPE in patients with PCa. The multimodal radiomics model will become a reliable and robust tool to assist urologists and radiologists in making preoperative decisions. ADVANCES IN KNOWLEDGE: This study presents the first application of multimodal radiomics based on PET/CT and MRI for predicting EPE.

An Online Dynamic Radiomics-Clinical Nomogram to Predict Recurrence in Patients with Spontaneous Intracerebral Hemorrhage.

OBJECTIVE: Radiomics can reflect the heterogeneity within the focus. We aim to explore whether radiomics can predict recurrent intracerebral hemorrhage (RICH) and develop an online dynamic nomogram to predict it. METHODS: This retrospective study collected the clinical and radiomics features of patients with spontaneous intracerebral hemorrhage seen in our hospital from October 2013 to October 2016. We used the minimum redundancy maximum relevancy and the least absolute shrinkage and selection operator methods to screen radiomics features and calculate the Rad-score. We use the univariate and multivariate analyses to screen clinical predictors. Optimal clinical features and Rad-score were used to construct different logistics regression models called the clinical model, radiomics model, and combined-logistic regression model. DeLong testing was performed to compare performance among different models. The model with the best predictive performance was used to construct an online dynamic nomogram. RESULTS: Overall, 304 patients with intracerebral hemorrhage were enrolled in this study. Fourteen radiomics features were selected to calculate the Rad-score. The patients with RICH had a significantly higher Rad-score than those without (0.5 vs. -0.8; P< 0.001). The predictive performance of the combined-logistic regression model with Rad-score was better than that of the clinical model for both the training (area under the receiver operating curve, 0.81 vs. 0.71; P = 0.02) and testing (area under the receiver operating curve, 0.65 vs. 0.58; P = 0.04) cohorts statistically. CONCLUSIONS: Radiomics features were determined related to RICH. Adding Rad-score into conventional clinical models significantly improves the prediction efficiency. We developed an online dynamic nomogram to accurately and conveniently evaluate RICH.

Modulation of neural gene networks by estradiol in old rhesus macaque females.

The postmenopausal decrease in circulating estradiol (E2) levels has been shown to contribute to several adverse physiological and psychiatric effects. To elucidate the molecular effects of E2 on the brain, we examined differential gene expression and DNA methylation (DNAm) patterns in the nonhuman primate brain following ovariectomy (Ov) and subsequent E2 treatment. We identified several dysregulated molecular networks, including MAPK signaling and dopaminergic synapse response, that are associated with ovariectomy and shared across two different brain areas, the occipital cortex (OC) and prefrontal cortex (PFC). The finding that hypomethylation (p=1.6×10-51) and upregulation (p=3.8×10-3) of UBE2M across both brain regions, provide strong evidence for molecular differences in the brain induced by E2 depletion. Additionally, differential expression (p=1.9×10-4; interaction p=3.5×10-2) of LTBR in the PFC, provides further support for the role E2 plays in the brain, by demonstrating that the regulation of some genes that are altered by ovariectomy may also be modulated by Ov followed by hormone replacement therapy (HRT). These results present real opportunities to understand the specific biological mechanisms that are altered with depleted E2. Given E2's potential role in cognitive decline and neuroinflammation, our findings could lead to the discovery of novel therapeutics to slow cognitive decline. Together, this work represents a major step towards understanding molecular changes in the brain that are caused by ovariectomy and how E2 treatment may revert or protect against the negative neuro-related consequences caused by a depletion in estrogen as women approach menopause.