UNASSIGNED: Fetal growth restriction is associated with perinatal morbidity and mortality. Early identification of women having at-risk fetuses can reduce perinatal adverse outcomes.
UNASSIGNED: To assess the predictive performance of existing models predicting fetal growth restriction and birthweight, and if needed, to develop and validate new multivariable models using individual participant data.
UNASSIGNED: Individual participant data meta-analyses of cohorts in International Prediction of Pregnancy Complications network, decision curve analysis and health economics analysis.
UNASSIGNED: Pregnant women at booking. External validation of existing models (9 cohorts, 441,415 pregnancies); International Prediction of Pregnancy Complications model development and validation (4 cohorts, 237,228 pregnancies).
UNASSIGNED: Maternal clinical characteristics, biochemical and ultrasound markers.
UNASSIGNED: fetal growth restriction defined as birthweight <10th centile adjusted for gestational age and with stillbirth, neonatal death or delivery before 32 weeks\' gestation birthweight.
UNASSIGNED: First, we externally validated existing models using individual participant data meta-analysis. If needed, we developed and validated new International Prediction of Pregnancy Complications models using random-intercept regression models with backward elimination for variable selection and undertook internal-external cross-validation. We estimated the study-specific performance (c-statistic, calibration slope, calibration-in-the-large) for each model and pooled using random-effects meta-analysis. Heterogeneity was quantified using τ2 and 95% prediction intervals. We assessed the clinical utility of the fetal growth restriction model using decision curve analysis, and health economics analysis based on National Institute for Health and Care Excellence 2008 model.
UNASSIGNED: Of the 119 published models, one birthweight model (Poon) could be validated. None reported fetal growth restriction using our definition. Across all cohorts, the Poon model had good summary calibration slope of 0.93 (95% confidence interval 0.90 to 0.96) with slight overfitting, and underpredicted birthweight by 90.4 g on average (95% confidence interval 37.9 g to 142.9 g). The newly developed International Prediction of Pregnancy Complications-fetal growth restriction model included maternal age, height, parity, smoking status, ethnicity, and any history of hypertension, pre-eclampsia, previous stillbirth or small for gestational age baby and gestational age at delivery. This allowed predictions conditional on a range of assumed gestational ages at delivery. The pooled apparent c-statistic and calibration were 0.96 (95% confidence interval 0.51 to 1.0), and 0.95 (95% confidence interval 0.67 to 1.23), respectively. The model showed positive net benefit for predicted probability thresholds between 1% and 90%. In addition to the predictors in the International Prediction of Pregnancy Complications-fetal growth restriction model, the International Prediction of Pregnancy Complications-birthweight model included maternal weight, history of diabetes and mode of conception. Average calibration slope across cohorts in the internal-external cross-validation was 1.00 (95% confidence interval 0.78 to 1.23) with no evidence of overfitting. Birthweight was underestimated by 9.7 g on average (95% confidence interval -154.3 g to 173.8 g).
UNASSIGNED: We could not externally validate most of the published models due to variations in the definitions of outcomes. Internal-external cross-validation of our International Prediction of Pregnancy Complications-fetal growth restriction model was limited by the paucity of events in the included cohorts. The economic evaluation using the published National Institute for Health and Care Excellence 2008 model may not reflect current practice, and full economic evaluation was not possible due to paucity of data.
UNASSIGNED: International Prediction of Pregnancy Complications models\' performance needs to be assessed in routine practice, and their impact on decision-making and clinical outcomes needs evaluation.
UNASSIGNED: The International Prediction of Pregnancy Complications-fetal growth restriction and International Prediction of Pregnancy Complications-birthweight models accurately predict fetal growth restriction and birthweight for various assumed gestational ages at delivery. These can be used to stratify the risk status at booking, plan monitoring and management.
UNASSIGNED: This study is registered as PROSPERO CRD42019135045.
UNASSIGNED: This award was funded by the National Institute for Health and Care Research (NIHR) Health Technology Assessment programme (NIHR award ref: 17/148/07) and is published in full in Health Technology Assessment; Vol. 28, No. 14. See the NIHR Funding and Awards website for further award information.
One in ten babies is born small for their age. A third of such small babies are considered to be ‘growth-restricted’ as they have complications such as dying in the womb (stillbirth) or after birth (newborn death), cerebral palsy, or needing long stays in hospital. When growth restriction is suspected in fetuses, they are closely monitored and often delivered early to avoid complications. Hence, it is important that we identify growth-restricted babies early to plan care. Our goal was to provide personalised and accurate estimates of the mother’s chances of having a growth-restricted baby and predict the baby’s weight if delivered at various time points in pregnancy. To do so, first we tested how accurate existing risk calculators (‘prediction models’) were in predicting growth restriction and birthweight. We then developed new risk-calculators and studied their clinical and economic benefits. We did so by accessing the data from individual pregnant women and their babies in our large database library (International Prediction of Pregnancy Complications). Published risk-calculators had various definitions of growth restriction and none predicted the chances of having a growth-restricted baby using our definition. One predicted baby’s birthweight. This risk-calculator performed well, but underpredicted the birthweight by up to 143 g. We developed two new risk-calculators to predict growth-restricted babies (International Prediction of Pregnancy Complications-fetal growth restriction) and birthweight (International Prediction of Pregnancy Complications-birthweight). Both calculators accurately predicted the chances of the baby being born with growth restriction, and its birthweight. The birthweight was underpredicted by <9.7 g. The calculators performed well in both mothers predicted to be low and high risk. Further research is needed to determine the impact of using these calculators in practice, and challenges to implementing them in practice. Both International Prediction of Pregnancy Complications-fetal growth restriction and International Prediction of Pregnancy Complications-birthweight risk calculators will inform healthcare professionals and empower parents make informed decisions on monitoring and timing of delivery.

BACKGROUND: Sepsis poses a critical threat to hospitalized patients, particularly those in the Intensive Care Unit (ICU). Rapid identification of Sepsis is crucial for improving survival rates. Machine learning techniques offer advantages over traditional methods for predicting outcomes. This study aimed to develop a prognostic model using a Stacking-based Meta-Classifier to predict 30-day mortality risks in Sepsis-3 patients from the MIMIC-III database.
METHODS: A cohort of 4,240 Sepsis-3 patients was analyzed, with 783 experiencing 30-day mortality and 3,457 surviving. Fifteen biomarkers were selected using feature ranking methods, including Extreme Gradient Boosting (XGBoost), Random Forest, and Extra Tree, and the Logistic Regression (LR) model was used to assess their individual predictability with a fivefold cross-validation approach for the validation of the prediction. The dataset was balanced using the SMOTE-TOMEK LINK technique, and a stacking-based meta-classifier was used for 30-day mortality prediction. The SHapley Additive explanations analysis was performed to explain the model\'s prediction.
RESULTS: Using the LR classifier, the model achieved an area under the curve or AUC score of 0.99. A nomogram provided clinical insights into the biomarkers\' significance. The stacked meta-learner, LR classifier exhibited the best performance with 95.52% accuracy, 95.79% precision, 95.52% recall, 93.65% specificity, and a 95.60% F1-score.
CONCLUSIONS: In conjunction with the nomogram, the proposed stacking classifier model effectively predicted 30-day mortality in Sepsis patients. This approach holds promise for early intervention and improved outcomes in treating Sepsis cases.

OBJECTIVE: To ascertain the connection between cuproptosis-related genes (CRGs) and the prognosis of hepatocellular carcinoma (HCC) via single-cell RNA sequencing (scRNA-seq) and RNA sequencing (RNA-seq) data, relevant data were downloaded from the GEO and TCGA databases. The differentially expressed CRGs (DE-CRGs) were filtered by the overlaps in differentially expressed genes (DEGs) between HCC patients and normal controls (NCs) in the scRNA-seq database, DE-CRGs between high- and low-CRG-activity cells, and DEGs between HCC patients and NCs in the TCGA database.
RESULTS: Thirty-three DE-CRGs in HCC were identified. A prognostic model (PM) was created employing six survival-related genes (SRGs) (NDRG2, CYB5A, SOX4, MYC, TM4SF1, and IFI27) via univariate Cox regression analysis and LASSO. The predictive ability of the model was validated via a nomogram and receiver operating characteristic curves. Research has employed tumor immune dysfunction and exclusion as a means to examine the influence of PM on immunological heterogeneity. Macrophage M0 levels were significantly different between the high-risk group (HRG) and the low-risk group (LRG), and a greater macrophage level was linked to a more unfavorable prognosis. The drug sensitivity data indicated a substantial difference in the half-maximal drug-suppressive concentrations of idarubicin and rapamycin between the HRG and the LRG. The model was verified by employing public datasets and our cohort at both the protein and mRNA levels.
CONCLUSIONS: A PM using 6 SRGs (NDRG2, CYB5A, SOX4, MYC, TM4SF1, and IFI27) was developed via bioinformatics research. This model might provide a fresh perspective for assessing and managing HCC.

Background: There is an association between LUAD and TB, and TB increases the risk of lung adenocarcinogenesis. However, the role of TB in the development of lung adenocarcinoma has not been clarified. Methods: DEGs from TB and LUAD lung samples were obtained to identify TB-LUAD-shared DEGs. Consensus Clustering was performed on the TCGA cohort to characterize unique changes in TB transcriptome-derived lung adenocarcinoma subtypes. Prognostic models were constructed based on TB signatures to explore the characterization of subgroups. Finally, experimental validation and single-cell analysis of potential markers were performed. Results: We characterized three molecular subtypes with unique clinical features, cellular infiltration, and pathway change manifestations. We constructed and validated TB-related Signature in six cohorts. TB-related Signature has characteristic alterations, and can be used as an effective predictor of immunotherapy response. Prognostically relevant novel markers KRT80, C1QTNF6, and TRPA1 were validated by RT-qPCR. The association between KRT80 and lung adenocarcinoma disease progression was verified in Bulk transcriptome and single-cell transcriptome. Conclusion: For the first time, a comprehensive bioinformatics analysis of tuberculosis signatures was used to identify subtypes of lung adenocarcinoma. The TB-related Signature predicted prognosis and identified potential markers. This result reveals a potential pathogenic association of tuberculosis in the progression of lung adenocarcinoma.

UNASSIGNED: The tumor microenvironment (TME) of colorectal cancer (CRC) mainly comprises immune cells, stromal cells, tumor cells, as well as the extracellular matrix (ECM), which holds a pivotal position. The ECM affects cancer progression, but its regulatory roles and predictive potential in CRC are not fully understood.
UNASSIGNED: We analyzed transcriptomes from CRC tumors and paired normal tissues to study ECM features. Up-regulated ECM components were examined through functional enrichment analysis, and single-cell sequencing identified cell types producing collagen, regulators, and secreted factors. Transcription factor analysis and cell-cell interaction studies were conducted to identify potential regulators of ECM changes. Additionally, a prognostic model was developed using TCGA-CRC cohort data, focusing on up-regulated core ECM components.
UNASSIGNED: Bulk RNA-seq analysis revealed a unique ECM pattern in tumors, with ECM abundance and composition significantly related to patient survival. Up-regulated ECM components were linked to various cancer-related pathways. Fibroblasts and non-fibroblasts interactions were crucial in forming the TME. Key potential regulators identified included ZNF469, PRRX2, TWIST1, and AEBP1. A prognostic model based on five ECM genes (THBS3, LAMB3, ESM1, SPRX, COL9A3) demonstrated strong associations with immune suppression and tumor angiogenesis.
UNASSIGNED: The ECM components were involved in various cell-cell interactions and correlated with tumor development and poor survival outcomes. The ECM prognostic model components could be potential targets for novel therapeutic interventions in colorectal cancer.

Immune cell infiltration and tumor-related immune molecules play key roles in tumorigenesis and tumor progression. The influence of immune interactions on the molecular characteristics and prognosis of clear cell renal cell carcinoma (ccRCC) remains unclear. A machine learning algorithm was applied to the transcriptome data from The Cancer Genome Atlas database to determine the immunophenotypic and immunological characteristics of ccRCC patients. These algorithms included single-sample gene set enrichment analyses and cell type identification. Using bioinformatics techniques, we examined the prognostic potential and regulatory networks of immune-related genes (IRGs) involved in ccRCC immune interactions. Fifteen IRGs (CCL7, CHGA, CMA1, CRABP2, IFNE, ISG15, NPR3, PDIA2, PGLYRP2, PLA2G2A, SAA1, TEK, TGFA, TNFSF14, and UCN2) were identified as prognostic IRGs associated with overall survival and were used to construct a prognostic model. The area under the receiver operating characteristic curve at 1 year was 0.927; 3 years, 0.822; and 5 years, 0.717, indicating good predictive accuracy. Molecular regulatory networks were found to govern immune interactions in ccRCC. Additionally, we developed a nomogram containing the model and clinical characteristics with high prognostic potential. By systematically examining the sophisticated regulatory mechanisms, molecular characteristics, and prognostic potential of ccRCC immune interactions, we provided an important framework for understanding the molecular mechanisms of ccRCC and identifying new prognostic markers and therapeutic targets for future research.

Dysregulated super-enhancer (SE) results in aberrant transcription that drives cancer initiation and progression. SEs have been demonstrated as novel promising diagnostic/prognostic biomarkers and therapeutic targets across multiple human cancers. Here, we sought to develop a novel prognostic signature derived from SE-associated genes for head and neck squamous cell carcinoma (HNSCC). SE was identified from H3K27ac ChIP-seq datasets in HNSCC cell lines by ROSE algorithm and SE-associated genes were further mapped and functionally annotated. A total number of 133 SE-associated genes with mRNA upregulation and prognostic significance was screened via differentially-expressed genes (DEGs) and Cox regression analyses. These candidates were subjected for prognostic model constructions by machine learning approaches using three independent HNSCC cohorts (TCGA-HNSC dataset as training cohort, GSE41613 and GSE42743 as validation cohorts). Among dozens of prognostic models, the random survival forest algorithm (RSF) stood out with the best performance as evidenced by the highest average concordance index (C-index). A prognostic nomogram integrating this SE-associated gene signature (SEAGS) plus tumor size demonstrated satisfactory predictive power and excellent calibration and discrimination. Moreover, WNT7A from SEARG was validated as a putative oncogene with transcriptional activation by SE to promote malignant phenotypes. Pharmacological disruption of SE functions by BRD4 or EP300 inhibitor significantly impaired tumor growth and diminished WNT7A expression in a HNSCC patient-derived xenograft model. Taken together, our results establish a novel, robust SE-derived prognostic model for HNSCC and suggest the translational potentials of SEs as promising therapeutic targets for HNSCC.

BACKGROUND: CCA has a poor prognosis. Different anatomical subtypes are characterized by distinct clinical features, surgical options, and prognoses, which can potentially impact survival outcomes following radical resection. In addition to the malignancy of CCA itself, clinical staging and treatment methods are the main factors that can affect survival. This study aims to update a more reliable prediction model for the prognosis of CCA based on different anatomical locations.
METHODS: A total of 1172 CCA patients (305 iCCA, 467 pCCA, and 400 dCCA) who underwent surgical resection between 2015 and 2022 were included in the analysis. The covariates included in the analysis were age, sex, tumor diameter, differentiation grade, T stage, N stage, M stage, neural invasion, cancer thrombus, history of hepatitis B or biliary calculi, and receipt of adjuvant chemotherapy. The data were randomly divided into training (80 %) and validation cohort (20 %).
RESULTS: We developed a nomogram of the sensitive model and calculated concordance indices of different constructed prognostic survival models. Meanwhile, we validated the effectiveness of the nomogram model and compared it with the TNM system through decision curve analysis (DCA) and internal cohort validation. The nomogram model had a better net benefit than the TNM system at any given threshold for iCCA, pCCA, and dCCA, regardless of their location.
CONCLUSIONS: We have updated the prognostic model for OS in CCA patients who underwent radical resection according to the different tumor locations. This model can effectively predict OS and has the potential to facilitate individual clinical decision-making.

UNASSIGNED: At the beginning of December 2022, the Chinese government made major adjustments to the epidemic prevention and control measures. The epidemic infection data and laboratory makers for infected patients based on this period may help with the management and prognostication of COVID-19 patients.
UNASSIGNED: The COVID-19 patients hospitalized during December 2022 were enrolled. Logistic regression analysis was used to screen significant factors associated with mortality in patients with COVID-19. Candidate variables were screened by LASSO and stepwise logistic regression methods and were used to construct logistic regression as the prognostic model. The performance of the models was evaluated by discrimination, calibration, and net benefit.
UNASSIGNED: 888 patients were eligible, consisting of 715 survivors and 173 all-cause deaths. Factors significantly associated with mortality in COVID-19 patients were: lactate dehydrogenase (LDH), albumin (ALB), procalcitonin (PCT), age, smoking history, malignancy history, high density lipoprotein cholesterol (HDL-C), lactate, vaccine status and urea. 335 of the 888 eligible patients were defined as ICU cases. Seven predictors, including neutrophil to lymphocyte ratio, D-dimer, PCT, C-reactive protein, ALB, bicarbonate, and LDH, were finally selected to establish the prognostic model and generate a nomogram. The area under the curve of the receiver operating curve in the training and validation cohorts were respectively 0.842 and 0.853. In terms of calibration, predicted probabilities and observed proportions displayed high agreements. Decision curve analysis showed high clinical net benefit in the risk threshold of 0.10-0.85. A cutoff value of 81.220 was determined to predict the outcome of COVID-19 patients via this nomogram.
UNASSIGNED: The laboratory model established in this study showed high discrimination, calibration, and net benefit. It may be used for early identification of severe patients with COVID-19.

BACKGROUND: SEPT9 is a pivotal cytoskeletal GTPase that regulates diverse biological processes encompassing mitosis and cytokinesis. While previous studies have implicated SEPT9 in tumorigenesis and development; comprehensive pan-cancer analyses have not been performed. This study aims to systematically explore its role in cancer screening, prognosis, and treatment, addressing this critical gap.
METHODS: Gene and protein expression data containing clinical information were obtained from public databases for pan-cancer analyses. Additionally, clinical samples from 90 patients with lung squamous cell carcinoma (LUSC) were used to further experimentally validate the clinical significance of SEPT9. In addition, the molecular docking tool was used to analyze the affinities between SEPT9 protein and drugs.
RESULTS: SEPT9 is highly expressed in various cancers, and its aberrant expression correlates with genetic alternations and epigenetic modifications, leading to adverse clinical outcomes. Take LUSC as an example, additional dataset analyses and immunohistochemical experiments further confirm the diagnostic and prognostic values as well as the clinical relevance of the SEPT9 gene and protein. Functional enrichment, single-cell expression, and immune infiltration analyses revealed that SEPT9 promotes malignant tumor progression and modulates the immune microenvironments, enabling patients to benefit from immunotherapy. Moreover, drug sensitivity and molecular docking analyses showed that SEPT9 is associated with the sensitivity and resistance of multiple drugs and has stable binding activity with them, including Vorinostat and OTS-964. To harness its prognostic and therapeutic potential in LUSC, a mitotic spindle-associated prognostic model including SEPT9, HSF1, ARAP3, KIF20B, FAM83D, TUBB8, and several clinical characteristics, was developed. This model not only improves clinical outcome predictions but also reshapes the immune microenvironment, making immunotherapy more effective for LUSC patients.
CONCLUSIONS: This is the first study to systematically analyze the role of SEPT9 in cancers and innovatively apply the mitotic spindle-associated model to LUSC, fully demonstrating its potential as a valuable biomarker for cancer screening and prognosis, and highlighting its application value in promoting immunotherapy and chemotherapy, particularly for LUSC.