High Risk Score Research Articles

Inhibiting the expression of spindle appendix cooled coil protein 1 can suppress tumor cell growth and metastasis and is associated with cancer immune cells in esophageal squamous cell carcinoma.

Inhibiting the expression of spindle appendix cooled coil protein 1 (SPDL1) can slow down disease progression and is related to poor prognosis in patients with esophageal cancer. However, the specific roles and molecular mechanisms of SPDL1 in esophageal squamous cell carcinoma (ESCC) have not been explored yet. The current study aimed to investigate the expression levels of SPDL1 in ESCC via transcriptome analysis using data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus databases. Moreover, the biological roles, molecular mechanisms, and protein networks involved in SPDL1 were identified using machine learning and bioinformatics. The cell counting kit-8 assay, EdU staining, and transwell assay were used to investigate the effects of inhibiting SPDL1 expression on ESCC cell proliferation, migration, and invasion. Finally, the correlation between the SPDL1 expression and cancer immune infiltrating cells was evaluated by analyzing data from the TCGA database. Results showed that SPDL1 was overexpressed in the ESCC tissues. The SPDL1 expression was related to age in patients with ESCC. The SPDL1 co-expressed genes included those involved in cell division, cell cycle, DNA repair and replication, cell aging, and other processes. The high-risk scores of SPDL1-related long non-coding RNAs were significantly correlated with overall survival and cancer progression in patients with ESCC (P < 0.05). Inhibiting the SPDL1 expression was effective in suppressing the proliferation, migration, and invasion of ESCC TE-1 cells (P < 0.05). The overexpression of SPDL1 was positively correlated with the levels of Th2 and T-helper cells, and was negatively correlated with the levels of plasmacytoid dendritic cells and mast cells. In conclusion, SPDL1 was overexpressed in ESCC and was associated with immune cells. Further, inhibiting the SPDL1 expression could effectively slow down cancer cell growth and migration. SPDL1 is a promising biomarker for treating patients with ESCC.

Hub metastatic gene signature and risk score of breast cancer patients with small tumor sizes using WGCNA

BackgroundBreast cancer (BC) is the most common cancer in women and accounts for approximately 15% of all cancer deaths among women globally. The underlying mechanism of BC patients with small tumor size and developing distant metastasis (DM) remains elusive in clinical practices.MethodsWe integrated the gene expression of BCs from ten RNAseq datasets from Gene Expression Omnibus (GEO) database to create a genetic prediction model for distant metastasis-free survival (DMFS) in BC patients with small tumor sizes (≤ 2 cm) using weighted gene co-expression network (WGCNA) analysis and LASSO cox regression.ResultsABHD11, DDX39A, G3BP2, GOLM1, IL1R1, MMP11, PIK3R1, SNRPB2, and VAV3 were hub metastatic genes identified by WGCNA and used to create a risk score using multivariable Cox regression. At the cut-point value of the median risk score, the high-risk score (≥ median risk score) group had a higher risk of DM than the low-risk score group in the training cohort [hazard ratio (HR) 4.51, p < 0.0001] and in the validation cohort (HR 5.48, p = 0.003). The nomogram prediction model of 3-, 5-, and 7-year DMFS shows good prediction results with C-indices of 0.72–0.76. The enriched pathways were immune regulation and cell–cell signaling. EGFR serves as the hub gene for the protein–protein interaction network of PIK3R1, IL1R1, MMP11, GOLM1, and VAV3.ConclusionPrognostic gene signature was predictive of DMFS for BCs with small tumor sizes. The protein–protein interaction network of PIK3R1, IL1R1, MMP11, GOLM1, and VAV3 connected by EGFR merits further experiments for elucidating the underlying mechanisms.Graphical abstract

Development and validation of a practical score to predict 3-year distant metastatic free survival in nasopharyngeal carcinoma incorporating the number of lymph node regions.

The improvement in diagnosis and treatment for nasopharyngeal carcinoma (NPC) has shifted the pattern of failure toward distant metastasis. This study aimed to develop a simplified prognostic scoring model to predict distant metastatic free survival (DMFS) for NPC patients. Patients with non-metastatic NPC were identified from a retrospective cohort diagnosed between 2010 and 2018. Flexible parametric survival analysis was used to identify potential predictors for DMFS and establish a scoring model. The prognostic accuracy between the 8th AJCC system and the scoring model was compared using Harrell's C-index. Of the total 393 patients, the median follow-up time was 85 months. The 3-year DMFS rate was 83.3%. Gender, T-stage, pre-EBV (cut-off 2300 copies/ml), and the number of metastatic lymph node regions were identified as independent risk factors for distant metastasis and were included in the final scoring model. Our established model achieved a high C-index in predicting DMFS (0.79) and was well-calibrated. The score divided patients into two categories: low-risk (score 0-4) and high-risk (score 5-7), corresponding with the predicted 3-year DMFS of 96% and 64.5%, respectively. A feasible and applicative prognostic score was established and validated to discriminate NPC patients into low- and high-risk groups.

Innovative integration of lung ultrasound and wearable monitoring for predicting pulmonary complications in colorectal surgery: A prospective study.

Postoperative pulmonary complications (PPCs) are common in patients who undergo colorectal surgery. Studies have focused on how to accurately diagnose and reduce the incidence of PPCs. Lung ultrasound has been proven to be useful in preoperative monitoring and postoperative care after cardiopulmonary surgery. However, lung ultrasound has not been studied in abdominal surgeries and has not been used with wearable devices to evaluate the influence of postoperative ambulation on the incidence of PPCs. To investigate the relationship between lung ultrasound scores, PPCs, and postoperative physical activity levels in patients who underwent colorectal surgery. In this prospective observational study conducted from November 1, 2019 to August 1, 2020, patients who underwent colorectal surgery underwent daily bedside ultrasonography from the day before surgery to postoperative day (POD) 5. Lung ultrasound scores and PPCs were recorded and analyzed to investigate their relationship. Pedometer bracelets measured the daily movement distance for 5 days post-surgery, and the correlation between postoperative activity levels and lung ultrasound scores was examined. Thirteen cases of PPCs was observed in the cohort of 101 patients. The mean (standard deviation) peak lung ultrasound score was 5.32 (2.52). Patients with a lung ultrasound score of ≥ 6 constituted the high-risk group. High-risk lung ultrasound scores were associated with an increased incidence of PPCs after colorectal surgery (logistic regression coefficient, 1.715; odds ratio, 5.556). Postoperative movement distance was negatively associated with the lung ultrasound scores [Spearman's rank correlation coefficient (r), -0.356, P < 0.05]. Lung ultrasound effectively evaluates pulmonary condition post-colorectal surgery. Early ambulation and respiratory exercises in the initial two PODs will reduce PPCs and optimize postoperative care in patients undergoing colorectal surgery.

Construction of a NETosis-related gene signature for predicting the prognostic status of sepsis patients

BackgroundSepsis is a common traumatic complication of response disorder of the body to infection. Some studies have found that NETosis may be associated with the progression of sepsis. MethodsData of the sepsis samples were acquired from Gene Expression Omnibus (GEO) database. Gene set enrichment score was calculated using single-sample gene set enrichment analysis (ssGSEA). Weighted gene co-expression network analysis (WGCNA), protein-protein interaction (PPI) networks analysis, and stepwise multivariable regression analysis were performed to identify NETosis-associated genes for sepsis prognosis. To assess the infiltration of immune cells, the ESTIMATE and CIBERPSORT algorithms were used. Functional enrichment analysis was conducted in the clusterProfiler package. ResultsDifferent programmed death pathways were abnormally activated in sepsis patients as compared to normal samples. We screened five important NETosis associated genes, namely, CEACAM8, PGLYRP1, MAPK14, S100A12, and LCN2. These genes were significantly positively correlated with entotic cell death and ferroptosis and negatively correlated with autophagy. A clinical prognostic model based on riskscore was established using the five genes. The ROC curves of the model at 7 days, 14 days and 21 days all had high AUC values, indicating a strong stability of the model. Patients with high riskscore had lower survival rate than those with low riskscore. After the development of a nomogram, calibration curve and decision curve evaluation also showed a strong prediction performance and reliability of the model. As for clinicopathological features, older patients and female patients had a relatively high riskscore. The riskscore was significantly positively correlated with cell cycle-related pathways and significantly negatively correlated with inflammatory pathways. ConclusionWe screened five NETosis-associated genes that affected sepsis prognosis, and then established a riskscore model that can accurately evaluate the prognosis and survival for sepsis patients. Our research may be helpful for the diagnosis and clinical treatment of sepsis.

Machine learning analysis of oxidative stress-related phenotypes for specific gene screening in ovarian cancer.

Oxidative stress serves a crucial role in tumor development. However, the relationship between ovarian cancer and oxidative stress remains unknown. We aimed to create an oxidative stress-related prognostic signature to enhance the prognosis prediction of CC patients using bioinformatics. The genes differentially expressed and associated with oxidative stress were extracted with the help of "limma" packages. The model for prognosis was created using Multivariate Cox regression analysis to determine the risk related to the genes related to oxidative stress. Patients were categorized as low-risk or high-risk based on the median score. The receiver operation characteristic (ROC) and survival curves were used to evaluate the predictive effect of the prognostic signature. We utilized quantitative real-time PCR to assess the expression levels of key genes associated with oxidative stress in ovarian cancer cell lines (SKOV3, OVCAR3, and HeyA8) and normal ovarian epithelial cells (HOSEpiC). A signature comprising seven genes associated with oxidative stress was developed to prognosticate patients with ovarian cancer. Overall survival (OS) of the patient having CC was determined using Kaplan-Meier analysis. It was found that patient with a higher risk score had lower OS than the low-risk score. The signature of genes associated with oxidative stress was found to be independently prognostic for 1, 2, and 3 years. Further research found that the expression levels of nine hub genes had a strong association with patient outcomes. Our analysis revealed a higher expression of CX3CR1 in ovarian cancer cell lines compared with normal cells. To deploy a novel oxidative stress-related prognostic signature as an independent biomarker in cervical cancer, we developed and validated it.

Enhancing the Management of Deployment-Limiting Medical Conditions in the U.S. Department of Navy Leveraging Electronic Health Records and Predictive Analytics Methods.

Deployment-limiting medical conditions (DLMCs) such as debilitating injuries and conditions may interfere with the ability of military service members (SMs) to deploy. SMs in the United States (U.S.) Department of the Navy (DoN) with DLMCs who are not deployable should be placed in the medically restricted status of limited duty (LIMDU) or referred to the Physical Evaluation Board (PEB) for Service retention determination. It is critical to identify SMs correctly and promptly with DLMCs and predict their return-to-duty (RTD) to ensure the combat readiness of the U.S. Military. In this study, an algorithmic approach was developed to identify DoN SMs with previously unidentified DLMCs and predict whether SMs on LIMDU will be able to RTD. Five years of historical data (2016-2022) were obtained from inpatient and outpatient datasets across direct and purchased care from the Military Health System (MHS) Data Repository (MDR). Key fields included International Classification of Diseases diagnosis and procedure codes, Current Procedure Terminology codes, prescription medications, and demographics information such as age, rank, gender, and service. The data consisted of 44,580,668 medical encounters across 1,065,224 SMs. To identify SMs with unidentified DLMCs, we developed an ensemble model combining outputs from multiple machine learning (ML) algorithms. When the ML ensemble model predicted a SM to have high risk scores, despite appearing healthy on administrative reports, their case was reviewed by expert clinicians to investigate for previously unidentified DLMCs; and such feedback served to validate the developed algorithms. In addition, leveraging 1,735,422 encounters (60,433 SMs) from LIMDU periods, we developed four separate ML models to estimate RTD probabilities for SMs after each medical encounter and predict the final LIMDU outcome. The ensemble model had 0.91 area under the receiver operating characteristic curve (AUROC). Out of 236 (round one) and 314 (round two) SMs reviewed by clinicians, 127 (54%) and 208 (66%) SMs were identified with a previously unidentified or undocumented DLMC, respectively. Regarding predicting RTD for SMs placed on LIMDU, the best performing ML model achieved 0.76 AUROC, 68% sensitivity, and 71% specificity. Our research highlighted potential benefits of using predictive analytics in a medical assessment to identify SMs with DLMCs and to predict RTD outcomes once placed on LIMDU. This capability is being deployed for real-time clinical decision support to enhance health care provider's deployability assessment capability, improve accuracy of the DLMC population, and enhance combat readiness of the U.S Military.

Integration of Bioinformatics and Machine Learning to Identify CD8+ T Cell-Related Prognostic Signature to Predict Clinical Outcomes and Treatment Response in Breast Cancer Patients.

We assessed the abundance of CD8+ T cells in the TCGA and METABRIC datasets and obtained CTRGs through WGCNA. Subsequently, a prognostic signature (CTR score) was constructed from CTRGs screened by seven machine learning algorithms, and the relationship between the CTR score and TME, immunotherapy, and drug sensitivity was analyzed. Additionally, CTRGs' expression in different cells within TME was identified through single-cell analysis and spatial transcriptomics. Finally, the expression of CTRGs in clinical tissues was verified via RT-PCR. The CD8+ T cell-related prognostic signature consists of two CTRGs. In the TCGA and METABRIC datasets, the CTR score appeared to be negatively linked to the abundance of CD8+ T cells, and BC patients with higher risk score show a worse prognosis. The low CTR score group exhibits higher immune infiltration levels, closely associated with inhibiting the tumor microenvironment. Compared with the high CTR score group, the low CTR score group shows better responses to chemotherapy and immune checkpoint therapy. Single-cell analysis and spatial transcriptomics reveal the heterogeneity of two CTRGs in different cells. Compared with the adjacent tissues, CD163L1 and KLRB1 mRNA are downregulated in tumor tissues. This study establishes a robust CD8+ T cell-related prognostic signature, providing new insights for predicting the clinical outcomes and treatment responses of breast cancer patients.

Escalating Surgical Treatment for Left Ventricular Assist Device Infection and Expected Mortality: Clinical Risk Prediction Score.

Left ventricular assist devices (LVAD) improve survival for patients with cardiac failure, but LVAD-specific infection (VSI) remains a challenge with poorly understood predictive risk factors. The indications and use of escalating medical treatment to surgical debridement and potential flap reconstruction are not well characterized. A retrospective review of consecutive patients undergoing primary LVAD implantation at a tertiary academic center was performed. The primary outcome measures were 90-day and overall mortality after VSI. Cox proportional hazards regression was used to generate a risk prediction score for mortality. Of the 760 patients undergoing primary LVAD implantation, 255 (34%) developed VSI; of these patients, 91 (36%) were managed medically, 134 (52%) with surgical debridement, and 30 (12%) with surgical debridement and flap reconstruction. One-year survival after infection was 85% with median survival of 2.40 years. Factors independently associated with increased mortality were diabetes (hazard ratio [HR] 1.44, p = 0.04), MRSA infection (HR 1.64, p = 0.03), deep space (pump pocket or outflow cannula) involvement (HR 2.26, p < 0.001), and extracorporeal membrane oxygenation after LVAD (HR 2.52, p < 0.01). Factors independently associated with decreased mortality were flap reconstruction (HR 0.49, p = 0.02) and methicillin-sensitive Staphylococcus aureus infection (HR 0.63, p = 0.03). A clinical risk prediction score was developed using these factors and showed significant differences in median survival, which was 5.67 years for low-risk (score 0 to 1), 3.62 years for intermediate-risk (score 2), and 1.48 years for high-risk (score ≥3; p < 0.001) patients. We developed a clinical risk prediction score to stratify patients with VSI. In selected cases, escalating surgical treatment was associated with increased survival. Future work is needed to determine whether early surgical debridement and flap reconstruction can alter outcomes in select cases of VSI.

Do risk measure scores and diagnoses predict evaluator opinions in sexually violent predator cases? It depends on the evaluator.

Field research increasingly reveals that forensic evaluators are not interchangeable. Instead, they tend to differ in their patterns of forensic opinions, in ways that likely reflect something about themselves, not just the persons evaluated. This study used data from sexually violent predator (SVP) evaluations to examine whether evaluator differences in making intermediate decisions (e.g., instrument scoring, assigning diagnoses) might explain their different patterns of final opinions. Although this study was generally exploratory and not strongly hypothesis driven, we expected that there might be evidence for a simple form of bias in which some evaluators would be more likely than others to consistently "find" indications of SVP status (i.e., consistently assigning higher risk scores and more SVP-relevant diagnoses) and, therefore, be more likely to find behavioral abnormality, the legal construct qualifying someone for commitment as an SVP. The study used data from 745 SVP evaluations conducted by 10 different evaluators who were assigned cases from the same referral stream. Potential evaluator difference variables included behavioral abnormality opinions, paraphilia and antisocial personality disorder diagnoses, and Psychopathy Checklist-Revised and Static-99 scores. Evaluator differences explained a statistically significant (p < .001) amount of variance in behavioral abnormality opinions (17%), paraphilia diagnoses (7%), and Psychopathy Checklist-Revised scores (16%). Contrary to our expectation of a simple tendency for some evaluators to find all indicators of SVP status more often than others, evaluators differed in the ways that underlying diagnoses and scores corresponded with their conclusions. The overall pattern was one in which different evaluators appeared to base their final opinions on different factors. Findings reveal further evidence of substantial forensic evaluator differences in patterns of assigning instrument scores and reaching forensic conclusions. But these findings are the first to also reveal wide variability in their patterns of reaching forensic conclusions. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

A comprehensive meta-analysis of tissue resident memory T cell shaping non-small-cell lung cancer immune microenvironment and patient prognosis.

210 Background: Tissue-resident memory T cells (TRM) are a specialized subset of long-lived memory T cells that reside in peripheral tissues. However, the impact of TRM-related immunosurveillance on the tumor-immune microenvironment (TIME) and tumor progression across various non-small-cell lung cancer (NSCLC) patient populations is yet to be elucidated. Methods: Our comprehensive analysis of multiple independent single-cell and bulk RNA-seq datasets of patient NSCLC samples generated reliable, unique TRM signatures, through which we inferred the abundance of TRM in NSCLC. A machine learning model was developed to prognosticate survival, tested in multiple independent NSCLC cohorts. Model performance was corroborated through Kaplan-Meier survival plots, receiver operating characteristic (ROC) curves, principal component analysis, and t-SNE analyses. Results: We discovered that TRM abundance is consistently positively correlated with CD4+ T helper 1 cells, M1 macrophages, and resting dendritic cells in the TIME. In addition, TRM signatures are strongly associated with immune checkpoint genes and the prognosis of NSCLC patients. A TRM-based machine learning model to predict patient survival was validated and an 18-gene risk score was further developed to effectively stratify patients into low-risk and high-risk categories, wherein patients with high-risk scores had significantly lower overall survival than patients with low-risk. The prognostic value of the risk score was independently validated by the TCGA dataset and multiple independent NSCLC patient datasets. Notably, low-risk NSCLC patients with higher TRM infiltration exhibited enhanced T-cell immunity, nature killer cell activation, and other TIME immune responses related pathways, indicating a more active immune profile benefitting from immunotherapy. However, the TRM signature revealed low TRM abundance and a lack of prognostic association among lung squamous cell carcinoma patients in contrast to adenocarcinoma, indicating that the two NSCLC subtypes are driven by distinct TIMEs. Conclusions: Altogether, this study provides valuable insights into the complex interactions between TRM and TIME and their impact on NSCLC patient prognosis. The development of a simplified 18-gene risk score provides a practical prognostic marker for risk stratification. Keywords: Tissue resident memory T cell, non-small-cell lung cancer, prognosis, tumor immune microenvironment, machine learning.

Predicting prognosis and drug sensitivity in bladder cancer: an insight into Pan-programmed cell death patterns regulated by M6A modifications

The team aimed to explore the possible functional significance of M6A regulation in Pan-programmed cell death (PCD) among patients with bladder cancer (BLCA). In BLCA patients, the analysis was conducted on the13 patterns of programmed cell death (PCD) and the regulation of M6A. Transcriptome, genomics, and clinical data were collected from TCGA-BLCA, GEO32548, and IMvigor210. Consensus clustering analysis, functional enrichment analysis, and other prognostic tools were used to validate the Pan-PCD. Finally, in vitro experiments and transcription sequencing were performed to understand the potential influence of the PI3K pathway on Pan-PCD in BLCA patients. Diverse PCD patterns were simultaneously activated, and M6A regulators exhibited significant variability in bladder malignant tissues. The machine learning algorithm established an 8-gene M6A-related Pan-PCD signature. This signature was validated in three independent datasets, and BLCA patients with higher risk scores had worse prognosis. An unsupervised clustering approach identified activated and suppressed Pan-PCD subgroups of BLCA patients, with distinct responses to immunotherapy and drug sensitivity. In addition, the PI3K pathway was identified as a key mechanism for various forms of programmed cell death, encompassing apoptosis, pyroptosis, autophagy, and cell death dependent on lysosomes. This research revealed that the Pan-PCD model was a more promising approach for BLCA patients under M6A regulation. A new signature from M6A-related Pan-PCD was proposed, with prognostic value for survival or drug sensitivity. The PI3K pathway was a key mechanism for multiple PCDs in BLCA patients.

EMT-related gene classifications predict the prognosis, immune infiltration, and therapeutic response of osteosarcoma.

Osteosarcoma (OS), a bone tumor with high ability of invasion and metastasis, has seriously affected the health of children and adolescents. Many studies have suggested a connection between OS and the epithelial-mesenchymal transition (EMT). We aimed to integrate EMT-Related genes (EMT-RGs) to predict the prognosis, immune infiltration, and therapeutic response of patients with OS. We used consensus clustering to identify potential EMT-Related OS molecular subtypes. Somatic mutation, tumor immune microenvironment, and functional enrichment analyses were performed for each subtype. We next constructed an EMT-Related risk signature and evaluated it by Kaplan-Meier (K-M) analysis survival and receiver operating characteristic (ROC) curves. Moreover, we constructed a nomogram to more accurately predict OS patients' clinical outcomes. Response effects of immunotherapy in OS patients was analyzed by Tumor Immune Dysfunction and Exclusion (TIDE) analysis, while sensitivity for chemotherapeutic agents was analyzed using oncoPredict. Finally, the expression patterns of hub genes were investigated by single-cell RNA sequencing (scRNA-seq) data analysis. A total of 53 EMT-RDGs related to prognosis were identified, separating OS samples into two separate subgroups. The EMT-high subgroup showed favourable overall survival and more active immune response. Significant correlations were found between EMT-Related DEGs and functions as well as pathways linked to the development of OS. Additionally, a risk signature was established and OS patients were divided into two categories based on the risk scores. The signature presented a good predictive performance and could be recognized as an independent predictive factor for OS. Furthermore, patients with higher risk scores exhibited better sensitivity for five drugs, while no significant difference existed in immunotherapy response between the two risk subgroups. scRNA-seq data analysis displayed different expression patterns of the hub genes. We developed a novel EMT-Related risk signature that can be considered as an independent predictor for OS, which may help improve clinical outcome prediction and guide personalized treatments for patients with OS.

Establishment of a chemokine-based prognostic model and identification of CXCL10+ M1 macrophages as predictors of neoadjuvant therapy efficacy in colorectal cancer.

Although neoadjuvant therapy has brought numerous benefits to patients, not all patients can benefit from it. Chemokines play a crucial role in the tumor microenvironment and are closely associated with the prognosis and treatment of colorectal cancer. Therefore, constructing a prognostic model based on chemokines will help risk stratification and providing a reference for the personalized treatment. Employing LASSO-Cox predictive modeling, a chemokine-based prognostic model was formulated, harnessing the data from TCGA and GEO databases. Then, our exploration focused on the correlation between the chemokine signature and elements such as the immune landscape, somatic mutations, copy number variations, and drug sensitivity. CXCL10+M1 macrophages identified via scRNA-seq. Monocle2 showed cell pseudotime trajectories, CellChat characterized intercellular communication. CytoTRACE analyzed neoadjuvant therapy stemness, SCENIC detected cell type-specific regulation. Lastly, validation was performed through multiplex immunofluorescence experiments. A model based on 15 chemokines was constructed and validated. High-risk scores correlated with poorer prognosis and advanced TNM and clinical stages. Individuals presenting elevated risk scores demonstrated an increased propensity towards the development of chemotherapy resistance. Subsequent scRNA-seq data analysis indicated that patients with higher presence of CXCL10+ M1 macrophages in tumor tissues are more likely to benefit from neoadjuvant therapy. We developed a chemokine-based prognostic model by integrating both single-cell and bulk RNA-seq data. Furthermore, we revealed epithelial cell heterogeneity in neoadjuvant outcomes and identified CXCL10+ M1 macrophages as potential therapy response predictors. These findings could significantly contribute to risk stratification and serve as a key guide for the advancement of personalized therapeutic approaches.

Exploration of bacterial lipopolysaccharide-related genes signature based on T cells for predicting prognosis in colorectal cancer.

The intratumoral microorganisms participates in the progression and immunotherapy of colorectal cancer (CRC). However, due to technical limitations, the impact of microorganisms on CRC has not been fully understood. Therefore, we conducted a systematic analysis of relationship between bacterial lipopolysaccharide (LPS)-associated genes and immune cells to explore new biomarkers for predicting the prognosis of CRC. The single-cell RNA sequencing data and the Comparative Toxicogenomics Database were used to screen T cells-associated LPS-related genes (TALRGs). Then, we established and validated the TALRGs risk signature in The Cancer Genome Atlas Colon Adenocarcinoma (TCGA-COAD) cohort and GSE39582 cohort. Besides, we compared the differences in tumor-infiltrating immune cell types, immunotherapeutic response, somatic mutation profiles, and tumor mutation burden (TMB) between high-risk group and low-risk group. In addition, the immunotherapeutic cohort (Imvigor210) treated with an anti-PD-L1 agent was performed to explore the potential value of the TALRGs signature on immunotherapy. Five prognostic TALRGs were identified and selected to build the prognostic model. The high-risk group had poor prognosis in both TCGA-COAD cohort (P < 0.0001) and GSE39582 cohort (P = 0.00019). The areas under the curves (AUCs) of TALRGs signature were calculated (TCGA-COAD cohort: 0.624 at 1 years, 0.639 at 3 years, 0.648 at 5 years; anti-PD-L1 cohort was 0.59). The high-risk group had advanced pathological stages and higher TMN stages in both TCGA-COAD cohort and GSE39582 cohort. The high-risk group had the higher infiltration of immunosuppressive cells, the expressions of immune checkpoint molecules, the IC50 values of chemotherapy drugs, and TP53 mutation rate (P < 0.05). In addition, patients with high TMB had worse prognosis (P < 0.05). Furthermore, the Imvigor210 also showed patients with high-risk scores had poor prognosis (platinum-treated cohort: P = 0.0032; non-platinum-treated cohort: P = 0.00017). Microorganisms are closely related to the tumor microenvironment to influence the progression and immune response of CRC via stimulating T cells through LPS-related genes. The TALRGs signature contributed to predict the prognosis and immunotherapy of CRC, and became new therapeutic targets and biomarkers of CRC.

Changes in Atherosclerotic Cardiovascular Disease Risk Scores in a Predominantly Black Cohort with HIV and Associated Comorbidities: A Preliminary Study.

People with HIV (PWH) have an increased risk of atherosclerotic cardiovascular disease (ASCVD) compared to non-PWH, but the reasons for this increased risk remain elusive. We investigated the change in ASCVD risk scores over 4 years to identify clinical factors associated with change in risk scores or high-risk scores. We conducted a preliminary study using retrospective analysis of PWH, between 40 and 75 years old, seen at the Evelyn Jordan Center with at least two routine HIV visits. We collected clinical and demographic data and calculated the ASCVD risk scores using the Pooled Cohort Equation. Exploratory analyses examined change in risk score categories over time. Final adjusted analysis examined factors associated with change in continuous risk scores over time. Our sample included 187 PWH; 166 were black/African American and 79 were female. We found no significant change in ASCVD risk score over time. The risk score was significantly higher in PWH with hepatitis C (7.34%; 95% CI: 2.59, 12.09; p = 0.003) and trended higher in those with dual hepatitis B/C and hepatitis B compared to those without hepatitis (p = 0.07). We found that ASCVD risk did not change over a 4-year period among predominantly black young PWH, but infection with hepatitis C and dual hepatitis B/C were associated with higher ASCVD risk scores. Our findings illustrate the need for further longitudinal studies evaluating change in cardiovascular disease (CVD) risk and investigating viral hepatitis as an added potential contributor to increased CVD risk in high-risk, vulnerable populations.

Development of prognostic model incorporating a ferroptosis/cuproptosis-related signature and mutational landscape analysis in muscle-invasive bladder cancer

BackgroundMuscle-invasive bladder cancer (MIBC) is a prevalent and aggressive malignancy. Ferroptosis and cuproptosis are recently discovered forms of programmed cell death (PCD) that have attracted much attention. However, their interactions and impacts on MIBC overall survival (OS) and treatment outcomes remain unclear.MethodsData from the TCGA-BLCA project (as the training set), cBioPortal database, and GEO datasets (GSE13507 and GSE32894, as the test sets) were utilized to identify hub ferroptosis/cuproptosis-related genes (FRGs and CRGs) and develop a prognostic signature. Differential expression analysis (DEA) was conducted, followed by univariate and multivariate Cox’s regression analyses and multiple machine learning (ML) techniques to select genetic features. The performance of the ferroptosis/cuproptosis-related signature was evaluated using Kaplan–Meier (K–M) survival analysis and receiver-operating characteristics (ROC) curves. Mutational and tumour immune microenvironment landscapes were also explored. Real-time quantitative reverse transcription polymerase chain reaction (RT-qPCR) experiments confirmed the expression patterns of the hub genes, and functional assays assessed the effects of SCD knockdown on cell viability, proliferation, and migration.ResultsDEA revealed dysregulated FRGs and CRGs in the TCGA MIBC cohort. SCD, DDR2, and MT1A were identified as hub genes. A prognostic signature based on the sum of the weighted expression of these genes demonstrated strong predictive efficacy in the training and test sets. Nomogram incorporating this signature accurately predicted 1-, 3-, and 5-year survival probabilities in the TCGA cohort and GSE13507 dataset. Copy number variation (CNV) and tumour immune microenvironment analysis revealed that high risk score level groups were associated with immunosuppression and lower tumour purity. The associations of risk scores with immunotherapy and chemical drugs were also explored, indicating their potential for guiding treatment for MIBC patients. The dysregulated expression patterns of three hub genes were validated by RT-qPCR experiments.ConclusionsTargeting hub FRGs and CRGs could be a promising therapeutic approach for MIBC. Our prognostic model offers a new framework for MIBC subtyping and can inform personalized therapeutic strategies.

Impact of postoperative necrotizing enterocolitis after neonatal cardiac surgery on neurodevelopmental outcome at 1 year of age.

We analyzed the impact of postoperative necrotizing enterocolitis (NEC) after cardiac surgery in neonatal age on neurodevelopmental (ND) outcome at 1 year of age. Using data from the Swiss Neurodevelopmental Outcome Registry for Children with Congenital Heart Disease (ORCHID), we analyzed perioperative variables including postoperative NEC (Bell's stage ≥2) and 1-year ND outcome (Bayley III). The included patients (n = 101) had congenital heart disease (CHD), categorized as follows: 77 underwent biventricular repair for CHD with two functional chambers, 22 underwent staged palliation until the Fontan procedure for CHD with single ventricle physiology (n = 22), or 4 underwent single ventricle palliation or biventricular repair for borderline CHD (n = 4). Neonatal cardiopulmonary bypass (CBP) surgery was performed at a median age (IQR) of 8 (6) days. NEC occurred in 16 patients. Intensive care unit (ICU) length of stay (LOS) and the total duration of the hospitalization were longer in children with NEC than those in others (14 with vs. 8 days without NEC, p < 0.05; 49 with vs. 32 days without NEC, p < 0.05). The Bayley III scores of the analyzed patients determined at an age of 11.5 ± 1.5 months showed cognitive (CCS) (102.2 ± 15.0) and language scores (LCS) (93.8 ± 13.1) in the normal range and motor composite scores (MCS) (88.7 ± 15.9) in the low-normal range. After adjusting for socioeconomic status and CHD type, patients with NEC had lower CCS scores [β = -11.2 (SE 5.6), p = 0.049]. Using a cumulative risk score including NEC, we found a higher risk score to be associated with both lower CCS [β = -2.8 (SE 1.3), p = 0.030] and lower MCS [β = -3.20 (SE 1.3), p = 0.016]. Postoperative NEC is associated with longer ICU and hospital LOS and contributes together with other complications to impaired ND outcome at 1 year of age. In the future, national and international patient registries may provide the opportunity to analyze large cohorts and better identify the impact of modifiable perioperative risk factors on ND outcome. ClinicalTrials.gov identifier: NCT05996211.

Integrated single-cell and bulk RNA sequencing analyses identify an immunotherapy nonresponse-related fibroblast signature in gastric cancer.

Factors that determine nonresponse to immune checkpoint inhibitor (ICI) remain unclear. The protumor activities of cancer-associated fibroblasts (CAFs) suggest that they are potential therapeutic targets for cancer treatment. There is, however, a lack of CAF-related signature in predicting response to immunotherapy in gastric cancer (GC). Single-cell RNA sequencing (scRNA-seq) and RNA sequencing (RNA-seq) data of GC immunotherapy were downloaded from the Gene Expression Omnibus database. Bulk RNA-seq data were obtained from The Cancer Genome Atlas. The R package 'Seurat' was used for scRNA-seq data processing. Cellular infiltration, receptor-ligand interactions, and evolutionary trajectory analysis were further explored. Differentially expressed genes affecting overall survival were obtained using the limma package. Weighted Gene Correlation Network Analysis was used to identify key modules of immunotherapy nonresponder. Prognostic model was constructed by univariate Cox and least absolute contraction and selection operator analysis using the intersection of activated fibroblast genes (AFGs) with key module genes. The differences in clinicopathological features, immune microenvironment, immunotherapy prediction, and sensitivity to small molecule agents between the high- and low-risk groups were further investigated. Based on scRNA-seq, we finally identified 20 AFGs associations with the prognosis of GC patients. AFGs' high expression levels were correlated with both poor prognosis and tumor progression. Three genes ( FRZB , SPARC , and FKBP10 ) were identified as immunotherapy nonresponse-related fibroblast genes and used to construct the prognostic signature. This signature is an independent significant risk factor affecting the clinical outcomes of GC patients. Remarkably, there were more CD4 memory T cells, resting mast cells, and M2 macrophages infiltrating in the high-risk group, which was characterized by higher tumor immune exclusion. Moreover, patients with higher risk scores were more prone to not respond to immunotherapy but were more sensitive to various small molecule agents, such as memantine. In conclusion, this study constructed a fibroblast-associated ICI nonresponse gene signature, which could predict the response to immunotherapy. This study potentially revealed a novel way to overcome immune resistance in GC.

A high hospital frailty risk score indicates an increased risk for complications following surgical treatment of proximal humerus fractures

BackgroundApproximately 70 % of proximal humerus fractures (PHF) occur after the age of 60. High complication rates have been described in correlation with the treatment of PHF. Major risk factors for the outcome might be frailty, mobility and comorbidities of patients at the time of hospital admission. The aim of this study was to create risk adjusted quality indicators for surgical treatment of proximal humerus fractures based on German claims data and to evaluate the impact of the Hospital Frailty Risk Score (HFRS) on risk adjustment. MethodsRetrospective claims data (2015–2021) were used to create risk adjusted quality indicators for eight outcomes by clustered multivariable logistic regression. The comparison of different risk adjustment model performances was done by ROC-AUC and Standardized Mortality/Morbidity Ratios. ResultsIn total, N = 34,912 patients (median age 75 years, 80.3 % female) were included. The most common surgical procedure was open reduction and internal fixation with plate osteosynthesis with 39.7 %, followed by reverse shoulder arthroplasty with 25.3 %. The most influential risk factor for all outcomes was a high HFRS with an Odds Ratio of 2.0 (95 %-Confidence Interval 1.8–2.3) for any secondary surgery (365 days) up to an Odds Ratio of 17.6 (95 %-Confidence Interval 14.9–20.8) for general complications during the index stay. ConclusionComparative quality reporting for the surgical treatment of PHF appears feasible with the developed models for risk adjustment using claims data. Preoperative evaluation of HFRS in PHF can contribute to risk assessment, and individual patient management. It therefore enables personalized treatment decisions.