Prognostic Risk Score Research Articles

Glycolysis-related genes predict prognosis and indicate immune microenvironment features in gastric cancer.

Gastric cancer (GC) is a major contributor to cancer-related mortality. Glycolysis plays a pivotal role in tumor microenvironment (TME) reprogramming. In this research, the functions of glycolysis-associated genes (GRGs) were evaluated to predict the outcome and reveal the characteristics of the immune microenvironment in individuals with stomach cancer. The Cancer Genome Atlas (TCGA)-stomach adenocarcinoma (STAD) cohort provided gene expression and clinical data for gastric cancer (GC) patients, which were further authenticated using datasets sourced from the Gene Expression Omnibus (GEO). By referencing the Molecular Signatures Database (MSigDB), a total of 326 GRGs were pinpointed. The various subtypes of GC were outlined through consensus clustering, derived from the expression patterns of these GRGs. Utilizing multivariate Cox regression analysis, a multigene risk score model was formulated. Both the CIBERSORT and ESTIMATE algorithms played a pivotal role in assessing the immune microenvironment. To delve into the biological functions of the key genes, wound healing, transwell invasion, and MTT assays were conducted. Based on the expression patterns of GRGs, patients were categorized into two distinct groups: the metabolic subtype, designated as cluster A, and the immune subtype, labeled as cluster B. Patients belonging to cluster B exhibited a poorer prognosis. A prognostic risk score model, formulated upon the expression levels of six key GRGs - ME1, PLOD2, NUP50, CXCR4, SLC35A3, and SRD35A3 - emerged as a viable tool for predicting patient outcomes. The downregulation of CXCR4 notably diminished the glycolytic capacity of gastric cancer (GC) cells, alongside their migratory, invasive, and proliferative capabilities. Intriguingly, despite the adverse prognostic implications associated with both the immune subtype (cluster B) and the high-risk cohort, these groups exhibited a favorable immune microenvironment coupled with elevated expression of immune checkpoint genes. Our investigations revealed a positive correlation between high CXCR4 expression and low ME1 expression with the infiltration of CD8+ T cells, as well as an enhanced responsiveness to treatment with an anti-PD-1 immune checkpoint inhibitor. In this study, we discovered that the expression profiles of GRGs hold the potential to forecast the prognosis of gastric cancer (GC) patients, thereby possibly aiding in clinical treatment decision-making.

Single-cell sequencing combined with spatial transcriptomics reveals that the IRF7 gene in M1 macrophages inhibits the occurrence of pancreatic cancer by regulating lipid metabolism-related mechanisms.

The main focus of this study is to explore the molecular mechanism of IRF7 regulation on RPS18 transcription in M1-type macrophages in pancreatic adenocarcinoma (PAAD) tissue, as well as the transfer of RPS18 by IRF7 via exosomes to PAAD cells and the regulation of ILF3 expression. By utilising single-cell RNA sequencing (scRNA-seq) data and spatial transcriptomics (ST) data from the Gene Expression Omnibus database, we identified distinct cell types with significant expression differences in PAAD tissue. Among these cell types, we identified those closely associated with lipid metabolism. The differentially expressed genes within these cell types were analysed, and target genes relevant to prognosis were identified. Flow cytometry was employed to assess the expression levels of target genes in M1 and M2 macrophages. Cell lines with target gene knockout were constructed using CRISPR/Cas9 editing technology, and cell lines with target gene knockdown and overexpression were established using lentiviral vectors. Additionally, a co-culture model of exosomes derived from M1 macrophages with PAAD cells was developed. The impact of M1 macrophage-derived exosomes on the lipid metabolism of PAAD cells in the model was evaluated through metabolomics analysis. The effects of M1 macrophage-derived exosomes on the viability, proliferation, division, migration and apoptosis of PAAD cells were assessed using MTT assay, flow cytometry, EdU assay, wound healing assay, Transwell assay and TUNEL staining. Furthermore, a mouse PAAD orthotopic implantation model was established, and bioluminescence imaging was utilised to assess the influence of M1 macrophage-derived exosomes on the intratumoural formation capacity of PAAD cells, as well as measuring tumour weight and volume. The expression of proliferation-associated proteins in tumour tissues was examined using immunohistochemistry. Through combined analysis of scRNA-seq and ST technologies, we discovered a close association between M1 macrophages in PAAD samples and lipid metabolism signals, as well as a negative correlation between M1 macrophages and cancer cells. The construction of a prognostic risk score model identified RPS18 and IRF7 as two prognostically relevant genes in M1 macrophages, exhibiting negative and positive correlations, respectively. Mechanistically, it was found that IRF7 in M1 macrophages can inhibit the transcription of RPS18, reducing the transfer of RPS18 to PAAD cells via exosomes, consequently affecting the expression of ILF3 in PAAD cells. IRF7/RPS18 in M1 macrophages can also suppress lipid metabolism, cell viability, proliferation, migration, invasion and intratumoural formation capacity of PAAD cells, while promoting cell apoptosis. Overexpression of IRF7 in M1 macrophages may inhibit RPS18 transcription, reduce the transfer of RPS18 from M1 macrophage-derived exosomes to PAAD cells, thereby suppressing ILF3 expression in PAAD cells, inhibiting the lipid metabolism pathway, and curtailing the viability, proliferation, migration, invasion of PAAD cells, as well as enhancing cell apoptosis, ultimately inhibiting tumour formation in PAAD cells in vivo. Targeting IRF7/RPS18 in M1 macrophages could represent a promising immunotherapeutic approach for PAAD in the future.

Construction and validation of a ubiquitination-related prognostic risk score signature in breast cancer

BackgroundBreast cancer (BC) is a highly common form of cancer that occurs in many parts of the world. However, early -stage BC is curable. Many patients with BC have poor prognostic outcomes owing to ineffective diagnostic and therapeutic tools. The ubiquitination system and associated proteins were found influencing the outcome of individuals with cancer. Therefore, developing a biomarker associated with ubiquitination genes to forecast BC patient outcomes is a feasible strategy. ObjectiveThe primary goal of this work was to develop a novel risk score signature capable of accurately estimate the future outcome of patients with BC by targeting ubiquitinated genes. MethodsUnivariate Cox regression analysis was conducted utilizing the E1, E2, and E3 ubiquitination-related genes in the GSE20685 dataset. Genes with p < 0.01 were screened again using the Non-negative Matrix Factorization (NMF) algorithm, and the resulting hub genes were composed of a risk score signature. Patients were categorized into two risk groups, and the predictive effect was tested using Kaplan-Meier (KM) and Receiver Operating Characteristic (ROC) curves. This risk score signature was later validated using multiple external datasets, namely TCGA-BRAC, GSE1456, GSE16446, GSE20711, GSE58812 and GSE96058. Immuno-microenvironmental, single-cell, and microbial analyses were also performed. ResultsThe selected gene signature comprising six ubiquitination-related genes (ATG5, FBXL20, DTX4, BIRC3, TRIM45, and WDR78) showed good prognostic power in patients with BC. It was validated using multiple externally validated datasets, with KM curves showing significant differences in survival (p < 0.05). The KM curves also demonstrated superior predictive ability compared to traditional clinical indicators. Single-cell analysis revealed that Vd2 gd T cells were less abundantin the low-risk group, whereas patients in the high-risk group lacked myeloid dendritic cells. Tumor microbiological analysis revealed a notable variation in microorganism diversity between the high- and low-risk groups. ConclusionThis study established an risk score signature consisting of six ubiquitination genes, that can accurately forecast the outcome of patients with BC using multiple datasets. It can provide personalized and targeted assistance to provide the evaluation and therapy of individuals having BC.

Open Renal Transplantation in Obese Patients: A Correlation Study between BMI and Early and Late Complications with Implementation of a Prognostic Risk Score.

Obesity is a global epidemic that affects millions worldwide and can be a deterrent to surgical procedures in the population waiting for kidney transplantation. However, the literature on the topic is controversial. This study evaluates the impact of body mass index (BMI) on complications after renal transplantation, and identifies factors associated with major complications to develop a prognostic risk score. A correlation analysis between BMI and early and late complications was first performed, followed by a univariate and multivariate logistic regression analysis. The 302 included patients were divided into obese (BMI ≥ 30 kg/m2) and non-obese (BMI ≤ 30 kg/m2) groups. Correlation analysis showed that delayed graft function (DGF) was the only obesity-associated complication (p = 0.044). Logistic regression analysis identified female sex, age ≥ 57 years, BMI ≥ 25 and ≥30 kg/m2, previous abdominal and/or urinary system surgery, and Charlson morbidity Score ≥ 3 as risk factors for significant complications. Based on the analyzed data, we developed a nomogram and a prognostic risk score. The model's area (AUC) was 0.6457 (95% IC: 0.57; 0.72). The percentage of cases correctly identified by this model retrospectively applied to the entire cohort was 73.61%. A high BMI seems to be associated with an increased risk of DGF, but it does not appear to be a risk factor for other complications. Using an easy-to-use model, identification, and stratification of individualized risk factors could help to identify the need for interventions and, thus, improve patient eligibility and transplant outcomes. This could also contribute to maintaining an approach with high ethical standards.

Predictive Risk Factors and Scoring Systems Associated with the Development of Hepatocellular Carcinoma in Chronic Hepatitis B.

Chronic hepatitis B (CHB) infection constitutes a leading cause of hepatocellular carcinoma (HCC) development. The identification of HCC risk factors and the development of prognostic risk scores are essential for early diagnosis and prognosis. The aim of this observational, retrospective study was to evaluate baseline risk factors associated with HCC in CHB. Six hundred thirty-two consecutive adults with CHB (n = 632) [median age: 46 (IQR: 24)], attending the outpatients' Hepatology clinics between 01/1993-09/2020 were evaluated. Core promoter mutations and cirrhosis-HCC (GAG-HCC), Chinese University-HCC (CU-HCC), risk estimation for hepatocellular carcinoma in chronic hepatitis B (REACH-B), Fibrosis-4 (FIB-4), and Platelet Age Gender-HBV (PAGE-B) prognostic scores were calculated, and receiver operating curves were used to assess their prognostic performance. HCC was developed in 34 (5.38%) patients. In the multivariable Cox regression analysis, advanced age (HR: 1.086, 95% CI: 1.037-1.137), male sex (HR: 7.696, 95% CI: 1.971-30.046), alcohol abuse (HR: 2.903, 95% CI: 1.222-6.987) and cirrhosis (HR: 21.239, 95% CI: 6.001-75.167) at baseline were independently associated with the development of HCC. GAG-HCC and PAGE-B showed the highest performance with c-statistics of 0.895 (95% CI: 0.829-0.961) and 0.857 (95% CI: 0.791-0.924), respectively. In the subgroup of patients with cirrhosis, the performance of all scores declined. When treated and untreated patients were studied separately, the discriminatory ability of the scores differed. In conclusion, HCC development was independently associated with advanced age, male sex, alcohol abuse, and baseline cirrhosis among a diverse population with CHB. GAG-HCC and PAGE-B showed high discriminatory performance to assess the risk of HCC development in these patients, but these performances declined in the subgroup of patients with cirrhosis. Further research to develop scores more specific to certain CHB subgroups is needed.

The intratumoral microbiota biomarkers for predicting survival and efficacy of immunotherapy in patients with ovarian serous cystadenocarcinoma

BackgroundOvarian serous cystadenocarcinoma, accounting for about 90% of ovarian cancers, is frequently diagnosed at advanced stages, leading to suboptimal treatment outcomes. Given the malignant nature of the disease, effective biomarkers for accurate prediction and personalized treatment remain an urgent clinical need.MethodsIn this study, we analyzed the microbial contents of 453 ovarian serous cystadenocarcinoma and 68 adjacent non-cancerous samples. A univariate Cox regression model was used to identify microorganisms significantly associated with survival and a prognostic risk score model constructed using LASSO Cox regression analysis. Patients were subsequently categorized into high-risk and low-risk groups based on their risk scores.ResultsSurvival analysis revealed that patients in the low-risk group had a higher overall survival rate. A nomogram was constructed for easy visualization of the prognostic model. Analysis of immune cell infiltration and immune checkpoint gene expression in both groups showed that both parameters were positively correlated with the risk level, indicating an increased immune response in higher risk groups.ConclusionOur findings suggest that microbial profiles in ovarian serous cystadenocarcinoma may serve as viable clinical prognostic indicators. This study provides novel insights into the potential impact of intratumoral microbial communities on disease prognosis and opens avenues for future therapeutic interventions targeting these microorganisms.

Construction of a novel prognostic scoring model for HBV-ACLF liver failure based on dynamic data

Early prognostic assessment of patients with hepatitis B virus-related acute-on-chronic liver failure (HBV-ACLF) is important for guiding clinical management and reducing mortality. The aim of this study was to dynamically monitor the clinical characteristics of HBV-ACLF patients, thereby allowing the construction of a novel prognostic scoring model to predict the outcome of HBV-ACLF patients. Clinical data was prospectively collected for 518 patients with HBV-ACLF and randomly divided into training and validation sets. We constructed day-1, day-2, and day-(1 + 3) prognostic score models based on dynamic time points. The prognostic risk score constructed for day-3 was found to have the best predictive ability. The factors included in this scoring system, referred to as DSM-ACLF-D3, were age, hepatic encephalopathy, alkaline phosphatase, total bilirubin, triglycerides, very low-density lipoprotein, blood glucose, neutrophil count, fibrin, and INR. ROC analysis revealed the area under the curve predicted by DSM-ACLF-D3 for 28-day and 90-day mortality (0.901 and 0.889, respectively) was significantly better than those of five other scoring systems: COSSH-ACLF IIs (0.882 and 0.836), COSSH-ACLFs (0.863 and 0.832), CLIF-C ACLF (0.838 and 0.766), MELD (0.782 and 0.762) and MELD-Na (0.756 and 0.731). Dynamic monitoring of the changes in clinical factors can therefore significantly improve the accuracy of scoring models. Evaluation of the probability density function and risk stratification by DSM-ACLF-D3 also resulted in the best predictive values for mortality. The novel DSM-ACLF-D3 prognostic scoring model based on dynamic data can improve early warning, prediction and clinical management of HBV-ACLF patients.

A prognostic model for use before elective surgery to estimate the risk of postoperative pulmonary complications (GSU-Pulmonary Score): a development and validation study in three international cohorts

Pulmonary complications are the most common cause of death after surgery. This study aimed to derive and externally validate a novel prognostic model that can be used before elective surgery to estimate the risk of postoperative pulmonary complications and to support resource allocation and prioritisation during pandemic recovery. Data from an international, prospective cohort study were used to develop a novel prognostic risk model for pulmonary complications after elective surgery in adult patients (aged ≥18 years) across all operation and disease types. The primary outcome measure was postoperative pulmonary complications at 30 days after surgery, which was a composite of pneumonia, acute respiratory distress syndrome, and unexpected mechanical ventilation. Model development with candidate predictor variables was done in the GlobalSurg-CovidSurg Week dataset (global; October, 2020). Two structured machine learning techniques were explored (XGBoost and the least absolute shrinkage and selection operator [LASSO]), and the model with the best performance (GSU-Pulmonary Score) underwent internal validation using bootstrap resampling. The discrimination and calibration of the score were externally validated in two further prospective cohorts: CovidSurg-Cancer (worldwide; February to August, 2020, during the COVID-19 pandemic) and RECON (UK and Australasia; January to October, 2019, before the COVID-19 pandemic). The model was deployed as an online web application. The GlobalSurg-CovidSurg Week and CovidSurg-Cancer studies were registered with ClinicalTrials.gov, NCT04509986 and NCT04384926. Prognostic models were developed from 13 candidate predictor variables in data from 86 231 patients (1158 hospitals in 114 countries). External validation included 30 492 patients from CovidSurg-Cancer (726 hospitals in 75 countries) and 6789 from RECON (150 hospitals in three countries). The overall rates of pulmonary complications were 2·0% in derivation data, and 3·9% (CovidSurg-Cancer) and 4·7% (RECON) in the validation datasets. Penalised regression using LASSO had similar discrimination to XGBoost (area under the receiver operating curve [AUROC] 0·786, 95% CI 0·774-0·798 vs 0·785, 0·772-0·797), was more explainable, and required fewer covariables. The final GSU-Pulmonary Score included ten predictor variables and showed good discrimination and calibration upon internal validation (AUROC 0·773, 95% CI 0·751-0·795; Brier score 0·020, calibration in the large [CITL] 0·034, slope 0·954). The model performance was acceptable on external validation in CovidSurg-Cancer (AUROC 0·746, 95% CI 0·733-0·760; Brier score 0·036, CITL 0·109, slope 1·056), but with some miscalibration in RECON data (AUROC 0·716, 95% CI 0·689-0·744; Brier score 0·045, CITL 1·040, slope 1·009). This novel prognostic risk score uses simple predictor variables available at the time of a decision for elective surgery that can accurately stratify patients' risk of postoperative pulmonary complications, including during SARS-CoV-2 outbreaks. It could inform surgical consent, resource allocation, and hospital-level prioritisation as elective surgery is upscaled to address global backlogs. National Institute for Health Research.

A five-gene prognosis model based on lysine β-hydroxybutyrylation site genes to predict the survival and therapy response in pancreatic adenocarcinoma

BackgroundPancreatic adenocarcinoma (PAAD) is one of the most malignancy diseases. Lysine β-hydroxybutyrylation (Kbhb) has been reported to involve various metabolism and cancer progression. MethodsData from online databases (TCGA and GEO) were retrieved for the selection of differential expressed Kbhb site genes (DTRGs). Univariate cox and LASSO analysis were performed to identify the prognostic DTRGs. Based on the optimal DTRGs, a prognostic risk score model was established. Kaplan-Meier and Receiver operator characteristic analysis were conducted to evaluate the predicting ability of the prognosis model. Generated with clinical data, independent analysis and nomogram model were performed. Finally, the differences of survival, immune cell levels, immunotherapy response, drug sensitivity between high- and low-risk groups were explored. ResultsA total of 63 DTRGs were identified in PAAD, and these genes were related to cell division and apoptosis biological functions. Through univariate cox regression and LASSO analysis, 30 DTRGs were selected to be related to prognosis and five (KRT18, ANLN, ECT2, RBM5, and RBM6) were identified as the optimal DTRGs in PAAD. Based on the five optimal DTRGs, a prognostic risk score model was constructed, with promising predictive ability in PAAD survival (AUC >0.70). High-risk group showed lower survival rate (P < 0.05). Moreover, based on the risk score, a nomogram model was also established, which possessed perfect stability. Finally, lower risk score was related to higher immune cell levels, indicating an immune activation in low-risk status, which maybe the reason for the better survival in low-risk group. Furthermore, the immunotherapy response and drug sensitivity were all higher than that in low-risk groups (P < 0.05). ConclusionA five-gene prognosis risk model which exhibit promising predictive ability in survival is constructed for patients with PAAD.

Identification of a prognosis-related phagocytosis regulator gene signature in medulloblastoma

ObjectivesThe aims of this study were to screen for phagocytosis regulator-related genes in tissue samples from children with medulloblastoma (MB) and to construct a prognostic model based on those genes. MethodsDifferentially expressed genes between the MB and control groups were identified using the GSE50161 dataset from the Gene Expression Omnibus database. Prognosis-related phagocytosis regulator genes were selected from the GSE85217 dataset. Intersecting genes of the two datasets (differentially expressed prognosis-related phagocytosis regulator genes) were submitted to unsupervised cluster analysis to identify disease subtypes, after which the association between the subtypes and the immune microenvironment was analyzed. A prognostic risk score model was constructed, and functional, immune-related, and drug sensitivity analyses were performed. ResultsIn total, 23 differentially expressed prognosis-related phagocytosis regulator genes were identified, from which two disease subtypes (clusters 1 and 2) were classified. The prognoses of the patients in cluster 2 were significantly worse than those of the patients in cluster 1. The immune microenvironment differed significantly between the two subtypes. Finally, 10 genes (FAM81A, EZR, NDUFB9, RCOR1, FOXO4, NHLRC2, KIF23, PTPN6, SMAGP, and MED13) were selected to establish the prognostic risk score model. The prognosis in the low-risk group was better than that in the high-risk group. The model genes NDUFB9 and PTPN6 were positively correlated with M2 macrophages. ConclusionTen key phagocytosis regulator genes were screened to construct a prognostic model for MB. These genes may serve as key biomarkers for predicting the prognosis of patients with this type of brain cancer.

Elucidating prognosis in cervical squamous cell carcinoma and endocervical adenocarcinoma: a novel anoikis-related gene signature model.

Cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) are among the most prevalent gynecologic malignancies globally. The prognosis is abysmal once cervical cancer progresses to lymphatic metastasis. Anoikis, a specialized form of apoptosis induced by loss of cell adhesion to the extracellular matrix, plays a critical role. The prediction model based on anoikis-related genes (ARGs) expression and clinical data could greatly aid clinical decision-making. However, the relationship between ARGs and CESC remains unclear. ARGs curated from the GeneCards and Harmonizome portals were instrumental in delineating CESC subtypes and in developing a prognostic framework for patients afflicted with this condition. We further delved into the intricacies of the immune microenvironment and pathway enrichment across the identified subtypes. Finally, our efforts culminated in the creation of an innovative nomogram that integrates ARGs. The utility of this prognostic tool was underscored by Decision Curve Analysis (DCA), which illuminate its prospective benefits in guiding clinical interventions. In our study, We discerned a set of 17 survival-pertinent, anoikis-related differentially expressed genes (DEGs) in CESC, from which nine were meticulously selected for the construction of prognostic models. The derived prognostic risk score was subsequently validated as an autonomous prognostic determinant. Through comprehensive functional analyses, we observed distinctimmune profiles and drug response patterns among divergent prognosticstratifications. Further, we integrated the risk scores with the clinicopathological characteristics of CESC to develop a robust nomogram. DCA corroborated the utility of our model, demonstrating its potential to enhance patient outcomes through tailored clinical treatment strategies. The predictive signature, encompassing nine pivotal genes, alongside the meticulously constructed nomogram developed in this research, furnishes clinicians with a sophisticated tool for tailoring treatment strategies to individual patients diagnosed with CESC.

Identification of a novel monocyte/macrophage-related gene signature for predicting survival and immune response in acute myeloid leukemia

Acute myeloid leukemia (AML) is a heterogeneous hematological tumor with poor immunotherapy effect. This study was to develop a monocyte/macrophage-related prognostic risk score (MMrisk) and identify new therapeutic biomarkers for AML. We utilized differentially expressed genes (DEGs) in combination with single-cell RNA sequencing to identify monocyte/macrophage-related genes (MMGs). Eight genes were selected for the construction of a MMrisk model using univariate Cox regression analysis and LASSO regression analysis. We then validated the MMrisk on two GEO datasets. Lastly, we investigated the immunologic characteristics and advantages of immunotherapy and potential targeted drugs for MMrisk groups. Our study identified that the MMrisk is composed of eight MMGs, including HOPX, CSTB, MAP3K1, LGALS1, CFD, MXD1, CASP1 and BCL2A1. The low MMrisk group survived longer than high MMrisk group (P < 0.001). The high MMrisk group was positively correlated with B cells, plasma cells, CD4 memory cells, Mast cells, CAFs, monocytes, M2 macrophages, Endothelial, tumor mutation, and most immune checkpoints (PD1, Tim-3, CTLA4, LAG3). Furthermore, drug sensitivity analysis showed that AZD.2281, Axitinib, AUY922, ABT.888, and ATRA were effective in high-risk MM patients. Our research shows that MMrisk is a potential biomarker which is helpful to identify the molecular characteristics of AML immunology.

A retrospective validation of the effective and safe treatment of patients on general care wards with high velocity nasal insufflation therapy utilizing prognostic risk scores during COVID-19

Background Non-invasive positive pressure ventilation (NIPPV) has been a traditional therapy for acute respiratory failure (ARF). However, the use of NIPPV during the COVID-19 pandemic was challenging, while the use of invasive mechanical ventilation produced poor outcomes. An alternative to NIPPV, high velocity nasal insufflation (HVNI) has shown promise in treating ARF effectively. Objective This study evaluated whether HVNI can be used to treat ARF safely on the general care ward (GCW) during COVID-19 pandemic surges. Methods After introducing HVNI therapy to the facility, an evidence-based scoring system, Modified Early Warning Score (MEWS), was used to risk stratify patients and assist in assigning care level. Initial settings, demographic data, patient outcomes, and health care worker (HCW) virus conversion were measured throughout the study. Treatment failure was defined as the need for invasive mechanical ventilation (IMV) or NIPPV after HVNI therapy. MEWS and ROX index were compared retrospectively using the Pearson product-moment correlation coefficient to identify trends. The Welch two sample t-test (desired power of 90% with alpha=0.05) was used for demographic and outcome analysis. Results Two hundred thirty-four patients were treated with HVNI. The GCW failure rate of 18.56% (n=31/167) was lower than the ICU failure rate of 37.31% (n=25/67) but not statistically significant (P 0.175). No elevated risk to patients or HCW was observed. Respiratory rate (GCW 24.85 vs. ICU 30.14; P &lt;0.001), MEWS (GCW 2.34 vs. ICU 3.09; p=0.002), and ROX index (GCW 5.49 vs. ICU 4.68; P 0.002) assessments appear to be adequate predictors of HVNI failure. The Pearson product-moment coefficient comparing MEWS and ROX index identified a moderate negative correlation (-0.434; P &lt;0.001). Discussion HVNI therapy is an effective alternative to NIPPV for treating patients with COVID-19 associated ARF. Using measures such as MEWS and/or ROX, strict patient monitoring, and HCW surveillance, HVNI can be safely utilized on the GCW. This has a direct impact when dealing with patient surges where ICU beds and resources are limited. Additional studies are needed to further delineate these concepts. Keywords: COVID-19, Non-invasive Positive Pressure Ventilation, Acute Respiratory Failure, High Flow Nasal Oxygen, High Velocity Nasal Insufflation, ICU Admission, General Care Ward, MEWS, ROX

Bioinformatics Analysis and Experimental Verification Define Different Angiogenesis Subtypes in Endometrial Carcinoma and Identify a Prognostic Signature.

Increasing evidence indicates that peripheral blood vessels play a pivotal role in regulating tumor growth with the presence of new blood vessels facilitating tumor growth and metastasis. Nevertheless, the impact of specific molecule-mediated angiogenesis on the tumor immune microenvironment (TIME) and individual prognosis of uterine corpus endometrial carcinoma (UCEC) remains uncertain. The transcriptome information on 217 prognostic angiogenesis-related genes was integrated, and the angiogenesis patterns of 506 UCEC patients in The Cancer Genome Atlas (TCGA) cohort were comprehensively evaluated. We identified five angiogenic subtypes, namely, EC1, EC2, EC3, EC4, and EC5, which differed significantly in terms of prognosis, clinicopathological features, cancer hallmarks, genomic mutations, TIME patterns, and immunotherapy responses. Additionally, an angiogenesis-related prognostic risk score (APRS) was constructed to enable an individualized comprehensive evaluation. In multiple cohorts, APRS demonstrated a powerful predictive ability for the prognosis of UCEC patients. Likewise, APRS was confirmed to be associated with clinicopathological features, genomic mutations, cancer hallmarks, and TIME patterns in UCEC patients. The predictability of APRS for immune checkpoint inhibitor (ICI) therapy was also salient. Subsequently, the expression levels of four angiogenesis-related hub genes were verified by qRT-PCR, immunohistochemistry, and single-cell sequencing data analysis. The effects of four representative genes on angiogenesis were validated by Wound-Healing and Transwell assays, tube formation assay in vitro, and tumor xenograft model in vivo. This study proffered a new classification of UCEC patients based on angiogenesis. The established APRS may contribute to individualized prognosis prediction and immunotherapy selections that are better suited for UCEC patients.

Predictors and Risk Score for Immune Checkpoint-Inhibitor-Associated Myocarditis Severity.

Immune-checkpoint inhibitors (ICI) are associated with life-threatening myocarditis but milder presentations are increasingly recognized. The same autoimmune process that causes ICI-myocarditis can manifest concurrent generalized myositis, myasthenia-like syndrome, and respiratory muscle failure. Prognostic factors for this "cardiomyotoxicity" are lacking. A multicenter registry collected data retrospectively from 17 countries between 2014-2023. A multivariable cox regression model (hazard-ratio(HR), [95%confidence-interval]) was used to determine risk factors for the primary composite outcome: severe arrhythmia, heart failure, respiratory muscle failure, and/or cardiomyotoxicity-related death. Covariates included demographics, comorbidities, cardio-muscular symptoms, diagnostics, and treatments. Time-dependent covariates were used and missing data were imputed. A point-based prognostic risk score was derived and externally validated. In 748 patients (67% male, age 23-94), 30-days incidence of the primary composite outcome, cardiomyotoxic death, and overall death were 33%, 13%, and 17% respectively. By multivariable analysis, the primary composite outcome was associated with active thymoma (HR=3.60[1.93-6.72]), presence of cardio-muscular symptoms (HR=2.60 [1.58-4.28]), low QRS-voltage on presenting electrocardiogram (HR for ≤0.5mV versus >1mV=2.08[1.31-3.30]), left ventricular ejection fraction (LVEF) <50% (HR=1.78[1.22-2.60]), and incremental troponin elevation (HR=1.86 [1.44-2.39], 2.99[1.91-4.65], 4.80[2.54-9.08], for 20, 200 and 2000-fold above upper reference limit, respectively). A prognostic risk score developed using these parameters showed good performance; 30-days primary outcome incidence increased gradually from 3.9%(risk-score=0) to 81.3%(risk-score≥4). This risk-score was externally validated in two independent French and US cohorts. This risk score was used prospectively in the external French cohort to identify low risk patients who were managed with no immunosuppression resulting in no cardio-myotoxic events. ICI-myocarditis can manifest with high morbidity and mortality. Myocarditis severity is associated with magnitude of troponin, thymoma, low-QRS voltage, depressed LVEF, and cardio-muscular symptoms. A risk-score incorporating these features performed well. NCT04294771 and NCT05454527.

Breast Cancer Index re-stratifies 21-gene assay risk groups for late distant recurrence and extended endocrine therapy benefit: Results from the BCI Registry Study.

529 Background: Patients with hormone receptor-positive (HR+) breast cancer face a prolonged risk for late distant recurrence (DR), even after 5 years of adjuvant endocrine therapy. The BCI (Breast Cancer Index, Biotheranostics) is a genomic assay that provides the risk of late DR and predicts the likelihood of benefit from extended endocrine therapy (EET) based on the H/I (HOXB13/IL-17BR) ratio. The 21-gene RS (Recurrence Score; Oncotype DX, Exact Biosciences) test provides the 9-year risk of DR and the benefit from adjuvant chemotherapy (CT) in HR+/HER2− breast cancer. However, the RxPONDER trial showed that RS was not predictive of CT benefit in premenopausal women with N1 disease. Here, we investigated the concordance between BCI prognostic score and H/I versus RS in HR+/HER2- patients from the BCI Registry study. Methods: The BCI Registry study is a prospective, multi-institutional study to evaluate the long-term clinical outcome, decision impact and medication adherence in early-stage HR+ breast cancer patients. Based on TAILORx and RxPONDER results, RS stratified patients into four risk groups: Age ≤ 50, No CT benefit (RS ≤ 15); Age ≤ 50, CT benefit (RS&gt;15); Age &gt; 50, No CT benefit (RS ≤ 25); and Age &gt; 50, CT benefit (RS &gt; 25). Kendall’s correlation coefficient (R) was used to estimate the correlation between BCI prognostic score, BCI (H/I) and RS as continuous variables. Results: BCI and RS results were reported for 847 patients (76% T1; 59% grade II; 85% N0). H/I classified 559 patients (66%) as H/I-Low and 288 (34%) as H/I-High, respectively, while BCI prognostic risk scores classified 434 patients (51%) as low-risk and 413 (49%) as high-risk for late DR. RS classified 30 patients (4%) as Age ≤ 50, No CT benefit, 38 (5%) as Age ≤ 50, CT benefit, 665 (78%) as Age &gt; 50, No CT benefit and 114 (13%) as Age &gt; 50, CT benefit. When analyzed as continuous variables, BCI H/I and prognostic score showed a weak correlation with RS with R=0.18 and R=0.2, respectively. Based on categorical groups, H/I had low concordance with RS for younger women (Age ≤ 50) but showed some correlation with RS for older women (Age &gt; 50). Among those older than Age 50, H/I identified 31% of those with no CT benefit as likely to benefit from EET while 39% of those with CT benefit as not likely to benefit from EET. Conclusions: BCI H/I and prognostic risk scores exhibited low concordance with RS risk categories. H/I consistently re-stratified RS risk categories into distinct H/I-Low and -High groups, further substantiating that prediction of CT benefit does not equate to prediction of EET benefit. [Table: see text]

Development and validation of a clinical risk score for postoperative outcome in newly diagnosed glioblastoma: A report of the RANO Resect group.

2060 Background: Following resection or biopsy, patients with newly diagnosed glioblastoma frequently enter clinical trials. To standardize terminology for extent of resection, we proposed the RANO classification acknowledging four prognostic resection classes reflecting different amounts of residual tumor. Here, we aim to make use of our classification to (I) analyze the interactive effects of residual tumor with clinical and molecular factors on outcome (II) to define a prognostic postoperative risk score. Methods: The RANO resect group retrospectively compiled an international, seven-center training cohort of newly diagnosed glioblastoma. Predictors of outcome were identified by uni- and multivariate Cox’s proportional hazard regression. The combined effects of residual tumor with prognostic molecular and clinical factors on survival were assessed by recursive partitioning analysis (significance level: p≤ 0.05). Terminal regression tree nodes were combined into risk classes guided by Kaplan-Meier survival analyses and log-rank tests. The risk classes were converted into a numerical score which was prognostically verified in an external validation cohort. Results: Our training cohort compromised 1003 patients with newly diagnosed IDH-wildtype glioblastoma, including 744 patients who underwent adjuvant radiochemotherapy per EORTC-NCIC (TMZ/RT→TMZ). Residual tumor per RANO classification, MGMT promotor methylation status, age (as continuous variable; optimal cutoff: ≤65 years), and KPS (as continuous variable; optimal cutoff: ≥80) were prognostic of overall survival and forwarded into regression tree analysis. By combining eleven terminal nodes and individually weighting the prognostic factors, an additive scale (range, 0-9 points) integrating these four variables distinguished patients with low (0-2 points), intermediate (3-5 points), and high risk (6-9 points) for poor postoperative outcome (median overall survival: 24 vs. 16 vs. 6 months; p &lt; 0.01). Adjustment of the regression tree for adjuvant therapy was applied, and the presence of the three risk groups was confirmed in the subgroups of patients treated with or without RT/TMZ→TMZ. The prognostic value of the risk score was verified in a external single-center validation cohort of newly diagnosed glioblastoma ( n = 231, p &lt; 0.01). When we compared our risk score to previously postulated risk models, our score was superior in concordance between predicted with observed survival (c-index; development cohort: 0.7, validation cohorts: 0.6). Conclusions: The novel RANO risk score integrates molecular and clinical factors to serve as an easy-to-use, yet highly prognostic tool to stratify patients after resection or biopsy of newly diagnosed glioblastoma. The score may serve to guide patient management and reduce imbalances between study arms in prospective interventional trials.

A risk score based on polyamine metabolism and chemotherapy-related genes predicts prognosis and immune cells infiltration of lung adenocarcinoma.

We aimed to explore whether the genes associated with both platinum-based therapy and polyamine metabolism could predict the prognosis of LUAD. We searched for the differential expression genes (DEGs) associated with platinum-based therapy, then we interacted them with polyamine metabolism-related genes to obtain hub genes. Subsequently, we analysed the main immune cell populations in LUAD using the scRNA-seq data, and evaluated the activity of polyamine metabolism of different cell subpopulations. The DEGs between high and low activity groups were screened to identify key DEGs to establish prognostic risk score model. We further elucidated the landscape of immune cells, mutation and drug sensitivity analysis in different risk groups. Finally, we got 10 hub genes associated with both platinum-based chemotherapy and polyamine metabolism, and found that these hub genes mainly affected signalling transduction pathways. B cells and mast cells with highest polyamine metabolism activity, while NK cells were found with lowest polyamine metabolism activity based on scRNA-seq data. DEGs between high and low polyamine metabolism activity groups were identified, then 6 key genes were screened out to build risk score, which showed a good predictive power. The risk score showed a universal negative correlation with immunotherapy checkpoint genes and the cytotoxic T cells infiltration. The mutation rates of EGFR in low-risk group was significantly higher than that of high-risk group. In conclusion, we developed a risk score based on key genes associated with platinum-based therapy and polyamine metabolism, which provide a new perspective for prognosis prediction of LUAD.

Identification and Validation of Immune Implication of R-Spondin 1 and an R-Spondin 1-Related Prognostic Signature in Esophagus Cancer.

R-spondin 1 (RSPO1), which encodes a secretory-activating protein, is a promising therapeutic target for various tumors. The aim of this study was to establish a robust RSPO1-related signature specific to esophageal cancer (ESCA). Our comprehensive study involved meticulous analysis of RSPO1 expression in ESCA tissues and validation across ESCA cell lines and clinical samples using The Cancer Genome Atlas (TCGA) and GTEx databases. Using TCGA-ESCA dataset, we employed single-sample gene set enrichment analysis (ssGSEA) to elucidate the complex interaction between RSPO1 expression and the abundance of 22 specific immune cell types infiltrating ESCA. The biological significance of RSPO1 was further elucidated using KEGG, GO, and GSEA, demonstrating its relevance to pivotal tumor and immune pathways. This study culminated in the construction of prognostic nomograms enriched by calibration curves, facilitating the projection of individual survival probabilities at intervals of one, three, and five years. A substantial decrease in RSPO1 expression was observed within ESCA tissues and cell lines compared to their normal esophageal counterparts, and a significant decrease in the proportion of activated dendritic cells was evident within ESCA, accompanied by an augmented presence of macrophages and naive B cells relative to normal tissue. GSEA and KEGG analyses showed that RSPO1 was associated with tumor and immune pathways. Additionally, an independent prognostic risk score based on the RSPO1-related gene signature was developed and validated for patients with ESCA. Finally, RT-qPCR and western blotting were performed to confirm RSPO1 expression in normal and ESCA cell lines and tissue samples. In summary, our investigation underscores the pivotal role of RSPO1 in orchestrating tumor immunity and proposes RSPO1 as a prospective target for immunotherapeutic interventions in ESCA. Furthermore, the intricate profile of the two RSPO1-related genes has emerged as a promising predictive biomarker with notable potential for application in ESCA.

Deciphering fatty acid biosynthesis-driven molecular subtypes in pancreatic ductal adenocarcinoma with prognostic insights.

Pancreatic ductal adenocarcinoma (PDAC) poses a significant challenge due to its high heterogeneity and aggressiveness. Recognizing the urgency to delineate molecular subtypes, our study focused on the emerging field of lipid metabolism remodeling in PDAC, particularly exploring the prognostic potential and molecular classification associated with fatty acid biosynthesis. Gene set variation analysis (GSVA) and single-sample gene set enrichment analysis (ssGSEA) were performed to evaluate the dysregulation of lipid metabolism in PDAC. Univariate cox analysis and the LASSO module were used to build a prognostic risk score signature. The distinction of gene expression in different risk groups was explored by the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis and Weighted Gene Co-expression Network Analysis (WGCNA). The biological function of Acyl-CoA Synthetase Long Chain Family Member 5 (ACSL5), a pivotal gene within 7-hub gene signature panel, was validated through in vitro assays. Our study identified a 7-hub gene signature associated with fatty acid biosynthesis-related genes (FRGs), providing a robust tool for prognosis prediction. The high-FRGs score group displayed a poorer prognosis, decreased immune cell infiltration, and a higher tumor mutation burden. Interestingly, this group exhibited enhanced responsiveness to various compounds according to the Genomics of Drug Sensitivity in Cancer (GDSC) database. Notably, ACSL5 was upregulated in PDAC and essential for tumor progression. In conclusion, our research defined two novel fatty acid biosynthesis-based subtypes in PDAC, characterized by distinct transcriptional profiles. These subtypes not only served as prognostic indicator, but also offered valuable insights into their metastatic propensity and therapeutic potential.