Risk Score For Survival Research Articles

Survival Risk Score for Invasive Nonmetastatic Breast Cancer: A Real-World Analysis.

This study aimed to develop a multivariable, weighted overall survival (OS) risk score (SRS) for nonmetastatic (M0) invasive breast cancer (M0-BC, SRSM0-BC). This study included a training (1,890 patients) and a validation cohort (850 patients) from the Reggio Emilia Cancer Registry (RE-CR). Ten traditional prognostic variables were evaluated. In the training set, all the variables but the human epidermal growth factor receptor were significantly associated with OS at univariable analysis. A multivariable model identified an increased death risk for estrogen receptor (hazard ratio [HR], 2.0 [95% CI, 1.1 to 3.1]; P = .021), tumor stages T2-T3 (HR, 2.4 [95% CI, 1.3 to 4.7]; P = .009) and T4 (HR, 5.1 [95% CI, 2.0 to 13.0]; P < .001), and age >74 years (HR, 5.7 [95% CI, 4.0 to 8.2]; P < .001). By assigning scores according to HRs, four risk categories were generated (P for trend <.001). The HRs of death in the high- (282 patients, 15.6%), intermediate-high (275 patients, 15.2%), and intermediate-risk (349 patients, 19.2%) categories patients were, respectively, 27.3, 12.9, and 3.5 times higher, compared with the low-risk (909 patients, 50%) group. Harrell'C index was 81.1%, and the explained variation in mortality was 66.6. Internal cross-validation performed on the accrual index dates yielded a Harrell'C index ranging from 79.5% to 82.3% and an explained variation in mortality ranging from 60.3% to 69.4%. In the validation set, the same risk categories (P for trend <.001) were devised. The Harrell'C index and the explained variation in mortality were 76.1% and 53.7%, respectively, in the whole cohort, maintaining an elevated percentage according to the two accrual index dates. SRSM0-BC using the real-world RE-CR data set may represent a low-cost, accessible, globally applicable model in daily clinical practice, helping to prognostically stratify patients with invasive M0-BC.

Exploring the Influence of T Cell Marker Gene Expression on the Pathobiology and Clinical Prognostic Outcomes in Intestinal-Type Gastric Carcinoma.

Gastric cancer (GC) poses a significant global health challenge. This study is aimed at elucidating the role of the immune system, particularly T cells and their subtypes, in the pathogenesis and progression of intestinal-type gastric carcinoma (GC), and at evaluating the predictive utility of a T cell marker gene-based risk score for overall survival. We performed an extensive analysis using single-cell RNA sequencing data to map the diversity of immune cells and identify specific T cell marker genes within GC. Pseudotime trajectory analysis was employed to observe the expression patterns of tumor-related pathways and transcription factors (TFs) at various disease stages. We developed a risk score using data from The Cancer Genome Atlas (TCGA) as a training set and validated it with the GSE15459 dataset. Our analysis revealed distinct patterns of T cell marker gene expression associated with different stages of GC. The risk score, based on these markers, successfully stratified patients into high-risk and low-risk groups with significantly different overall survival prospects. High-risk patients exhibited poorer survival outcomes compared to low-risk patients (p < 0.05). Additionally, the risk score was capable of identifying patients across a spectrum from chronic atrophic gastritis to early GC. The findings enhance the understanding of the tumor immune microenvironment in GC and propose new immunotherapeutic targets. The T cell marker gene-based risk score offers a potential tool for gastroenterologists to tailor treatment plans more precisely according to the cancer's severity.

TICRR Overexpression Enhances Disease Aggressiveness and Immune Infiltration of Cutaneous Melanoma.

To investigate the role of the TopBP1 interacting checkpoint and replication regulator (TICRR) in cutaneous melanoma (CM) as a prognostic biomarker and therapeutic target. TICRR expression in tumour samples was explored using the TCGA and the GTEx database. The Kaplan-Meier survival curve, nomogram model and risk score curve were established to evaluate the prognostic role of TICRR in CM. Tissue samples of CM patients were obtained to validate the TICRR expression further. Several experiments in vitro were conducted to investigate the effect of TICRR upon CM aggressiveness and to explore underlying mechanisms. TICRR was overexpressed in CM tissue and was correlated with poor prognosis of CM patients. The knockdown of TICRR decreased the proliferation, migration, and invasion of CM cells, whereas overexpression produced the opposite effect. Furthermore, TICRR suppression substantially attenuated the activation of PI3K/AKT/mTOR signalling, while the PI3K/AKT inhibitor LY294002 could partially reverse the aggressiveness-enhancing effect induced by TICRR overexpression. It was further confirmed that TICRR was closely related to immune cell infiltration activities by using immune infiltration and immunofluorescence analysis. TICRR overexpression may enhance CM aggressiveness by activating the PI3K/Akt/mTOR pathway and promoting immune infiltration. TICRR was verified as a potential prognostic biomarker and therapeutic target for CM.

Validation of the HeartMate 3 survival risk score in a large left ventricular assist device center.

The HeartMate 3 survival risk score was recently validated in the Multicenter study Of MagLev Technology in Patients Undergoing Mechanical Circulatory Support Therapy with HeartMate 3 to predict patient-specific survival in HeartMate 3 left ventricular assist device candidates. The HeartMate 3 survival risk score stratifies individuals into tertiles according to survival probability. We performed a single-center retrospective review of all HeartMate 3 left ventricular assist device recipients between September 2017 and August 2022. Baseline characteristics were collected from the electronic medical records. HeartMate 3 survival risk scores were calculated for all eligible patients. One- and 2-year Kaplan-Meier survival analyses were conducted. A univariate and multivariable Cox regression model was used to identify predictors. A total of 181 patients were included in this final analysis. The median age was 62years, 83% were male, and 26% were Interagency Registry for Mechanically Assisted Circulatory Support Profile 1. The mean HeartMate 3 survival risk score for the entire cohort was 2.66±0.66. Two-year survivals in the high, average, and low survival groups were 93.5%±3.2%, 81.6%±7.4%, and 82.0%±6.6%, respectively. As a continuous variable, the unadjusted HeartMate 3 survival risk score was a significant predictor of mortality (hazard ratio, 2.20; 95% CI, 1.08-4.45; P=.029). The areas under the curve were 0.70 and 0.66 at 1 and 2years, respectively. We were unable to demonstrate the discriminatory ability of the HeartMate 3 survival risk score using the original stratification, but we found significantly increased survival in the high survival group using a binary cutoff (hazard ratio, 4.8; 95% CI, 1.01-20.9; P=.038). The unadjusted HeartMate 3 survival risk score was associated with postimplant survival in patients outside of the Multicenter study Of MagLev Technology in Patients Undergoing Mechanical Circulatory Support Therapy with HeartMate 3 but did not remain an independent predictor after adjusting for ischemic etiology and severe diabetes. The HeartMate 3 survival risk score was able to identify patients at high survival using a binary cutoff, but we were unable to demonstrate its discriminatory ability among the previously published risk tertiles.

Basement membrane-associated gene expression as a predictor of survival in oral cancer

BackgroundThis study sought to investigate the prognostic value of basement membrane (BM)-associated gene expressions in oral cancer.MethodsWe harvested and integrated data on BM-associated genes (BMGs), the oral cancer transcriptome, and clinical information from public repositories. After identifying differentially expressed BMGs, we used Cox and Lasso regression analyses to create a BMG-based risk score for overall survival at various intervals. We then validated this score using the GSE42743 cohort as a validation set. The prognostic potential of the risk scores and their relations to clinical features were assessed. Further, we conducted functional pathway enrichment, immune cell infiltration, and immune checkpoint analyses to elucidate the immunological implications and therapeutic potential of the BMG-based risk score and constituent genes. To confirm the expression levels of the BMG LAMA3 in clinical samples of oral cancer tissue, we performed quantitative real-time PCR (qRT-PCR) and immunohistochemical staining.ResultsThe BMGs LAMA3, MMP14, and GPC2 demonstrated notable prognostic significance, facilitating the construction of a BMG-based risk score. A higher risk score derived from BMGs correlated with a poorer survival prognosis for oral cancer patients. Moreover, the risk-associated BMGs exhibited a significant relationship with immune function variability (P < 0.05), discrepancies in infiltrating immune cell fractions, and immune checkpoint expressions (P < 0.05). The upregulated expression levels of LAMA3 in oral cancer tissues were substantiated through qRT-PCR and immunohistochemical staining.ConclusionThe BMG-based risk score emerged as a reliable prognostic tool for oral cancer, meriting further research for validation and potential clinical application.

Survival outcome prediction of breast carcinomas on whole-slide histopathology images using deep learning.

1070 Background: Breast cancer is the most common cancer among women with 2 million new cases and 627,000 deaths in 2020. Early diagnosis and effective treatment are crucial for improved outcomes. Prognosis mainly depends on histopathological features, among them grade. As whole slide histopathology image (WSI) of tumor tissue contains a huge amount of hidden morphological features unexploited by pathologists, we investigate the potential of Artificial Intelligence (AI)-based analysis on WSI to predict prognosis in terms of 5-year overall survival in breast cancer patients. Methods: We used a novel deep neural network (DNN), DiaDeepBreastPRS, designed specifically for predicting a survival risk score in breast cancer patients based on H&amp;E-stained whole slide images (WSI) of tumor tissues. The model incorporates two distinct viewpoints, one capturing cellular details and the other the tissue-level information. DiaDeepBreastPRS was trained and evaluated on a multi-centric discovery cohort of 1,027 patients (1,095 H&amp;E WSI) from the TCGA-BRCA dataset using a cross-testing and cross-validation technique. It was evaluated on an external cohort, comprising 232 patients (247 H&amp;E WSI). A statistical analysis with a multivariate Cox regression model was carried out on clinico-pathological data. AI scores and the concordance index (c-index) serves as the metric for assessing the performance. The predicted risk scores were used for effective risk stratification of breast carcinomas. Results: On the TCGA-BRCA dataset, the model achieved an average c-index of 67%, which reaches 78% by adding the pTNM stage and age at diagnosis. On the external dataset, the model achieved a c-index of 66% and 75% when including some histopathological prognosis factors (pTNM stage, age at diagnosis, HER2 and HR status and mitosis). The AI score was independently associated with the survival of breast cancer patients with the highest hazard ratio (HR 2.46, p&lt;0.005). Furthermore, the model was able to significantly discriminate between the 2 groups of patients, with a good and a poor prognosis in terms of overall survival (p&lt;0.005). Conclusions: In this study, we showcased that the algorithm was able to instantly extract prognostic morphological features from H&amp;E whole slide images (WSI) and could be included in the pathology report. This could potentially enhance clinical decision-making, elevating the standard of care. Compared to commonly used molecular signatures, the AI algorithm enables a reduction in response time and cost savings. However, further investigations using additional independent cohorts are essential to consolidate the algorithm's performance and allow its generalizability, establish its superiority over existing prognostic markers, and provide insights into its interpretability.

Risk score stratification of cutaneous melanomas based on whole slide images using deep learning.

e21573 Background: Predictive and prognostic biomarkers represent the cornerstone of clinical oncology. Improving the prognostication of melanoma is needed to select patients for effective adjuvant therapies. Taking into account that tumor tissue contains a large amount of clinically relevant hidden information that is not fully exploited, we applied a weakly-supervised deep-learning approach on H&amp;E-stained whole-slide image (WSI) to directly predict 5-year survival outcomes in melanoma patients. Methods: We designed a deep neural (DL) network that extracts features from no-padding patches of WSI (tiles of size 512x512 pixels) using a self-supervised learning framework as well as from the tumor region. These features are then fed along with survival information into the deep learning fusion network assigning a survival risk score to each WSI. The model was trained and validated on 195 cases of primary melanoma diagnosed between 2015 and 2017 originating from the IHP Group and the French RICMel melanoma database. Performance was evaluated using a cross-validation and cross-testing framework as well as on an external set of 238 cases from the TCGA database. Concordance index (c-index) was used as a metric to assess the performance of the proposed algorithm. Results: For the prediction of survival, the proposed pipeline yielded c-index of 0.76 and 0.66 for the IHP Group cohort and TCGA dataset respectively. Furthermore, the model is able to significantly discriminate between the 2 groups of patients, with a good and a poor prognosis in terms of overall survival (p-value &lt; 0.001 for IHP and p-value = 0.01 for TCGA). Also, the proposed score is statistically significant when added to a multivariate cox model incorporating the classic prognosis factors (p-value &lt; 0.005) and allows to reach a c-index of 0.81 over the discovery cohort. Conclusions: This infrequent weakly-supervised deep-learning approach to HE images analysis has the advantage of leaving the feature selection entirely to the deep network, and so discover new morphological biomarkers. By this way, it could have in the near future the potential to accelerate and improve the clinical decision-making process in the field of melanoma. Also, these findings will undoubtedly open room for substantial applications with economic and treatment delay fallouts.

A prognostic model for hepatocellular carcinoma patients based on polyunsaturated fatty acid-related genes.

Polyunsaturated fatty acids (PUFAs) have attracted increasing attention for their role in liver cancer development. The objective of this study is to develop a prognosis prediction model for patients with liver cancer based on PUFA-related metabolic gene characteristics. Transcriptome data and clinical data were obtained from public databases, while gene sets related to PUFAs were acquired from the gene set enrichment analysis (GSEA) database. Univariate Cox analysis was conducted on the training set, followed by LASSO logistic regression and multivariate Cox analysis on genes with p < .05. Subsequently, the stepwise Akaike information criterion method was employed to construct the model. The high- and low-risk groups were divided based on the median score, and the model's survival prediction ability, diagnostic efficiency, and risk score distribution of clinical features were validated. The above procedures were also validated in the validation set. Immune infiltration levels were evaluated using four algorithms, and the immunotherapeutic potential of different groups was explored. Significant enrichment pathways among different groups were selected based on the GSEA algorithm, and mutation analyses were conducted. Nomogram prognostic models were constructed by incorporating clinical factors and risk scores using univariate and multivariate Cox regression analysis, validated through calibration curves and clinical decision curves. Additionally, sensitivity analysis of drugs was performed to screen potential targeted drugs. We constructed a prognostic model comprising eight genes (PLA2G12A, CYP2C8, ABCCI, CD74, CCR7, P2RY4, P2RY6, and YY1). Validation across multiple datasets indicated the model's favorable prognostic prediction ability and diagnostic efficiency, with poorer grading and staging observed in the high-risk group. Variations in mutation status and pathway enrichment were noted among different groups. Incorporating Stage, Grade, T.Stage, and RiskScore into the nomogram prognostic model demonstrated good accuracy and clinical decision benefits. Multiple immune analyses suggested greater benefits from immunotherapy in the low-risk group. We predicted multiple targeted drugs, providing a basis for drug development. Our study's multifactorial prognostic model across multiple datasets demonstrates good applicability, offering a reliable tool for personalized therapy. Immunological and mutation-related analyses provide theoretical foundations for further research. Drug predictions offer important insights for future drug development and treatment strategies. Overall, this study provides comprehensive insights into tumor prognosis assessment and personalized treatment planning.

Systematic literature review with meta-analysis on essential oil interventions for agitated behavior in dementia patients

Introduction Essential oil treatment has been explored as a potential alternative medicine, as these therapies can induce calming and sedative effects by acting on the neuro-limbic system and upregulating neurotransmitter synthesis. Objectives This paper aims to assess the impact of essential oil interventions on aggressive behavior in people with dementia. Methods The literature search was carried out through six databases. The outcomes of interest were the survival risk and post-treatment score of the Cohen-Mansfield Agitation Inventory (CMAI), Neuropsychiatric Inventory (NPI), and Quality of Life (QoL). Quality appraisal was done using RoB 2.0, while meta-analysis was done using RevMan 5.4. This systematic review has been registered to the PROSPERO database (CRD42023476844). Results We analyzed 11 included studies with 1,418 patients. RoB 2.0 analysis resulted in eight low-risk-of-bias and three high-risk-of-bias studies. This study suggests that essential oil interventions lead to statistically significant improvements in survival risk with moderate heterogeneity [OR = 0.63 (95% CI: 0.41, 0.98, p = 0.04; I2 = 74%)]. This study also found a significant improvement in NPI scores following the use of essential oils [SMD = -2.97 (95% CI = -5.61, -0.32, p = 0.03; I2 = 98%)], but no statistically significant improvements were found in CMAI scores [SMD = 0.17 (95% CI = 0.37, 0.71, p = 0.53; I2 = 74%)]. The QoL assessment showed a trend favoring the control group after implementing essential oil treatments [SMD = 4.89 (95% CI = 1.51, 8.27, p = 0.005; I2 = 98%)]. Conclusions Essential oil is a potential approach in alleviating the agitated behavior of dementia patients considering its survival risk and some parameters, such as NPI, CMAI, and QoL score. However, more studies regarding essential oils on these parameters should be done, especially studies with specific main ingredients of the essential oil.

Risk Score for Long-Term Survival and Major Adverse Cardiovascular and Cerebrovascular Events After Coronary Artery Bypass Grafting Surgery

To develop risk scoring models predicting long-term survival and major adverse cardiovascular and cerebrovascular events (MACCEs), including myocardial infarction and stroke after coronary artery bypass grafting (CABG). All 4,821 consecutive patients who underwent isolated CABG at Lankenau between January 2005 and July 2021 were included. MACCE was defined as all-cause mortality + myocardial infarction + stroke. Variable selection for both outcomes was obtained using a double-selection logit least absolute shrinkage and selection operator with adaptive selection. Model performance was internally evaluated by calibration and accuracy using bootstrap cross-validation. Mortality and MACCEs were compared in patients split into 3 groups based on the predicted risk scores for all-cause mortality and MACCEs. An external validation of our database was performed with 665 patients from the University of Brescia, Italy. Preoperative risk predictors were found to be predictors for all-cause mortality and MACCEs. In addition, being of African-American ethnicity is a significant predictor for MACCEs after isolated CABG. The areas under the curve (AUCs), which measures the discrimination of the models, were 80.4%, 79.1%, 81.3%, and 79.2% for mortality at 1, 2, 3, and 5 years follow-up. The AUCs for MACCEs were 75%, 72.5%, 73.8%, and 72.7% at 1, 2, 3, and 5 years follow-up. For external validation, the AUCs for all-cause mortality and MACCEs at 1, 2, 3, and 5 years were 73.7%, 70.8%, 68.7%, and 72.2% and 72.3%, 68.2%, 65.6%, and 69.6%, respectively. The Advanced (AD) Coronary Risk Score for All-Cause Mortality and MACCE provide good discrimination of long-term mortality and MACCEs after isolated CABG. External validation observed a more AUCs greater than 70%.

Revising model for end-stage liver disease from calendar-time cross-sections with correction for selection bias

BackgroundEurotransplant liver transplant candidates are prioritized by Model for End-stage Liver Disease (MELD), a 90-day waitlist survival risk score based on the INR, creatinine and bilirubin. Several studies revised the original MELD score, UNOS-MELD, with transplant candidate data by modelling 90-day waitlist mortality from waitlist registration, censoring patients at delisting or transplantation. This approach ignores biomarkers reported after registration, and ignores informative censoring by transplantation and delisting.MethodsWe study how MELD revision is affected by revision from calendar-time cross-sections and correction for informative censoring with inverse probability censoring weighting (IPCW). For this, we revised UNOS-MELD on patients with chronic liver cirrhosis on the Eurotransplant waitlist between 2007 and 2019 (n = 13,274) with Cox models with as endpoints 90-day survival (a) from registration and (b) from weekly drawn calendar-time cross-sections. We refer to the revised score from cross-section with IPCW as DynReMELD, and compare DynReMELD to UNOS-MELD and ReMELD, a prior revision of UNOS-MELD for Eurotransplant, in geographical validation.ResultsRevising MELD from calendar-time cross-sections leads to significantly different MELD coefficients. IPCW increases estimates of absolute 90-day waitlist mortality risks by approximately 10 percentage points. DynReMELD has improved discrimination over UNOS-MELD (delta c-index: 0.0040, p < 0.001) and ReMELD (delta c-index: 0.0015, p < 0.01), with differences comparable in magnitude to the addition of an extra biomarker to MELD (delta c-index: ± 0.0030).ConclusionCorrecting for selection bias by transplantation/delisting does not improve discrimination of revised MELD scores, but substantially increases estimated absolute 90-day mortality risks. Revision from cross-section uses waitlist data more efficiently, and improves discrimination compared to revision of MELD exclusively based on information available at listing.

Polo-like kinase 1 as a biomarker predicts the prognosis and immunotherapy of breast invasive carcinoma patients.

Invasive breast carcinoma (BRCA) is associated with poor prognosis and high risk of mortality. Therefore, it is critical to identify novel biomarkers for the prognostic assessment of BRCA. The expression data of polo-like kinase 1 (PLK1) in BRCA and the corresponding clinical information were extracted from TCGA and GEO databases. PLK1 expression was validated in diverse breast cancer cell lines by quantitative real-time polymerase chain reaction (qRT-PCR) and western blotting. Single sample gene set enrichment analysis (ssGSEA) was performed to evaluate immune infiltration in the BRCA microenvironment, and the random forest (RF) and support vector machine (SVM) algorithms were used to screen for the hub infiltrating cells and calculate the immunophenoscore (IPS). The RF algorithm and COX regression model were applied to calculate survival risk scores based on the PLK1 expression and immune cell infiltration. Finally, a prognostic nomogram was constructed with the risk score and pathological stage, and its clinical potential was evaluated by plotting calibration charts and DCA curves. The application of the nomogram was further validated in an immunotherapy cohort. PLK1 expression was significantly higher in the tumor samples in TCGA-BRCA cohort. Furthermore, PLK1 expression level, age and stage were identified as independent prognostic factors of BRCA. While the IPS was unaffected by PLK1 expression, the TMB and MATH scores were higher in the PLK1-high group, and the TIDE scores were higher for the PLK1-low patients. We also identified 6 immune cell types with high infiltration, along with 11 immune cell types with low infiltration in the PLK1-high tumors. A risk score was devised using PLK1 expression and hub immune cells, which predicted the prognosis of BRCA patients. In addition, a nomogram was constructed based on the risk score and pathological staging, and showed good predictive performance. PLK1 expression and immune cell infiltration can predict post-immunotherapy prognosis of BRCA patients.

Integrated analysis of necroptosis related gene signature to predict clinical outcomes, immune status and drug sensitivity in lower grade Glioma

BackgroundThe treatment of lower grade gliomas (LGG) is currently the most challenging dilemma in the management of intracranial tumors. Necroptosis is a type of programmed cell death that is closely associated with tumor progression, However, the role of necroptosis related genes in LGG is not yet well elucidated. MethodsOnline databases were used to obtain gene expression and clinical information. After gene differential expression analysis, a risk score model based on prognostic differentially expressed necroptosis-related genes (DENGs) were constructed to predict prognosis for LGG patients. The validity of the risk score model was then assessed with Kaplan-Meier survival curve. The prognostic DENGs included in the risk score model were then subjected to gene expression analysis, functional enrichment analysis, consensus clustering analysis, and single cell sequencing analysis. Finally, we investigated the correlation of the risk score and immune infiltration in LGG tumor microenvironment and drug sensitivity for LGG patients in different risk groups. ResultsA survival risk score model was constructed based on seven prognostic DENGs, which demonstrated satisfactory performance in predicting the prognosis of LGG patients. According to functional enrichment analyses, these seven DENGs may play a regulatory role in LGG tumorigenesis through several immune and metabolic pathways. LGG patients could be categorized into two clusters with distinct prognosis and clinicopathologic characteristics based on the expression of seven DENGs. Single-cell sequencing analysis demonstrated that the DENG signature was differentially expressed in various types of cells in LGG and may play a vital role in oncogenesis. Additionally, drug sensitivity analysis suggested that the seven-gene signature could guide clinical medication for LGG patients. ConclusionOur study developed a reliable necroptosis-related signature to predict the prognosis of LGG patients. This gene signature may also help estimate immune status and anti-cancer drug sensitivity in LGG patients. Our findings may pave the way to enhance our understanding of necroptosis in LGG.

Validation of the Minnesota Pectoralis Risk Score to predict mortality in the HeartMate 3 population

The Minnesota Pectoralis Risk Score (MPRS) utilizes computed tomography-quantified thoracic muscle and clinical variables to predict survival after left ventricular assist device (LVAD) implantation. The model has not been prospectively tested in HeartMate 3 recipients. A single-center HeartMate 3 cohort from July 2016 to July 2021 (n=108) was utilized for this analysis. Cohort subjects with complete covariates for MPRS calculation (pectoralis muscle measures, Black race, creatinine, total bilirubin, body mass index, bridge to transplant status, and presence/absence of contrast) implanted after MPRS development were included. MPRS were calculated on each subject. Receiver operating characteristic curves were generated to test model discrimination at 30-day, 90-day, and 1-year mortality post-LVAD. Next, the performance of the 1-year post-LVAD outcome was compared to the HeartMate 3 survival risk score (HM3RS). The mean age was 58 (15years), 80% (86/108) were male, and 26% (28/108) were destination therapy. The area under the curve (AUC) for the MPRS model to predict post-LVAD mortality was 0.73 at 30days, 0.78 at 90days, and 0.81 at 1year. The AUC for the HM3RS for the 1-year outcome was 0.693. Each 1-unit point of the MPRS was associated with a significant increase in the hazard rate of death after LVAD (hazard ratio 2.1, 95% confidence interval 1.5-3.0, p<0.0001). The MPRS had high performance in this prospective validation, particularly with respect to 90-day and 1-year post-LVAD mortality. Such a tool can provide additional information regarding risk stratification to aid informed decision-making.

Machine learning reveals PANoptosis as a potential reporter and prognostic revealer of tumour microenvironment in lung adenocarcinoma.

Lung adenocarcinoma (LUAD), a prominent lung cancer subtype, has an underexplored relationship with PANoptosis, a recently discovered mode of tumour cell death. This study incorporated iron death, copper death, scorch death, necrotizing apoptosis and bisulfide death into a pan-death gene set (PANoptosis) and conducted single-cell analysis of scRNA-seq data from 11 LUAD samples. Differentially expressed genes were identified, and Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses were performed. Univariate COX regression and least absolute shrinkage and selection operator (LASSO) regression were used to screen PANoptosis key genes for constructing an LUAD risk model. The model's prognostic performance was evaluated using survival curves, risk scores and validation in the Gene Expression Omnibus database. The study also explored the correlation between risk scores, tumour biological function, immunotherapy, drug sensitivity and immune infiltration. The SMS gene in the PANoptosis model was silenced in two LUAD cell lines for cellular validation. Single-cell analysis revealed eight major cell types and several PANoptosis genes significantly associated with LUAD survival. The risk model demonstrated strong prognostic performance and association with immune infiltration, suggesting PANoptosis involvement in LUAD tumour immunity. Cellular validation further supported these findings. The PANoptosis key risk genes are believed to be closely related to the tumour microenvironment and immune regulation of LUAD, potentially providing valuable insights for early diagnosis and clinical treatment, and broader applications in other tumours and complex diseases.

Spinster homolog 2 reduces malignancies of glioblastoma via PTEN/PI3K/AKT pathway.

The molecular mechanisms of glioblastoma (GBM) are unclear, and the prognosis is poor. Spinster homolog 2 (SPNS2) is reportedly involved in pathological processes such as immune response, vascular development, and cancer. However, the biological function and molecular role of SPNS2 in GBM are unclear. SPNS2 is aberrantly low expressed in glioma. Survival curves, risk scores, prognostic nomograms, and univariate and multifactorial Cox regression analyses showed that SPNS2 is an independent prognostic indicator significantly associated with glioma progression and prognosis. Cell function assays and in vivo xenograft transplantation were performed that downregulation of SPNS2 promoted GBM cell growth, migration, invasion, epithelial-mesenchymal transition (EMT), anti-apoptosis, drug resistance, and stemness, while overexpression of SPNS2 had the opposite effect. Meanwhile, the functional enrichment and signaling pathways of SPNS2 in the Cancer Genome Atlas (TCGA), Chinese Glioma Genome Atlas (CGGA), and RNA sequencing were analyzed by Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene set enrichment analysis (GSEA). The above results were related to the inhibition of the PTEN/PI3K/AKT pathway by SPNS2. In addition, we predicted that SPNS2 is closely associated with immune infiltration in the tumor microenvironment by four immune algorithms, ESTIMATE, TIMER, CIBERSORT, and QUANTISEQ. In particular, SPNS2 was negatively correlated with the infiltration of most immune cells, immunomodulators, and chemokines. Finally, single-cell sequencing analysis also revealed that SPNS2 was remarkably correlated with macrophages, and downregulation of SPNS2 promotes the expression of M2-like macrophages. This study provides new evidence that SPNS2 inhibits malignant progression, stemness, and immune infiltration of GBM cells through PTEN/PI3K/AKT pathway. SPNS2 may become a new diagnostic indicator and potential immunotherapeutic target for glioma.

CARC9: Validation of HeartMate 3 Risk Score as a Survival Predictor Calculator

Purpose: Recently, a patient-specific HeartMate 3 Survival Risk Score (HM3RS) was developed to predict 1-year and 2-year survival after implantation based on age, prior cardiac surgery, sodium level, blood urea nitrogen (BUN) level, left ventricular end diastolic diameter, and right atrial pressure/pulmonary capillary wedge pressure (RAP/PCWP) ratio. We sought to validate the HM3RS using recent data from our referral center. Methods: Through a retrospective review of 101 HM3 patients implanted between 2017-2021, baseline characteristics, complete hemodynamic profiles, and significant outcomes were collected. Patients were stratified into 3 groups by HM3RS: higher-than-average expected survival (HM3RS <2.41), average expected survival (2.41-2.97), and lower-than-average expected survival (>2.97). Kaplan Meier (KM) curves stratified by HM3RS grouping were plotted and survival was compared with log-rank tests. Time-varying receiver operating characteristic (ROC) curves were also constructed to evaluate survival predictions. Results: Patients with lower-than-average expected survival (n=43) were older (mean age 65±7.6 years), had higher BUN before LVAD (45.3±20.1 mg/dl), were more likely to have had prior coronary artery bypass surgery (41.8%), and RAP/PCWP ratio > 0.6 (56%) (all P < 0.05) (table 1). KM plots showed lower survival in this group at 1-year, 2-years, and during the entire follow-up of 2±1.2 years censored at time of transplant, death or pump exchange (figure 1). However, these results did not reach statistical significance, possibly owing to small sample size. Area under the ROC curve was 0.622 and 0.676 at 1 and 2 year follow-up, respectively (figure 2). Summary: In this study, observed survival was consistent with predictions derived from HM3RS score. The HM3RS appears to be a valid tool to predict survival after LVAD implantation in the real-world and to aid in decision-making. Larger prospective trials are needed to independently validate this tool in clinical practice.Figure 1. Kaplan Meier curves for survival by HM3RS groups through (A) entire follow-up, (B) 1-year of follow-up, (C) 2-years of follow-up.Figure 2. Time dependent receiver operating curve (ROC) to determine survival prediction (A) at 1 year, and (B) at 2 years.

(793) Validation of the Heartmate 3 Survival Risk Score in a Large Left Ventricular Assist Device Center

(793) Validation of the Heartmate 3 Survival Risk Score in a Large Left Ventricular Assist Device Center

A novel prognostic model based on pyroptosis-related genes for multiple myeloma

BackgroundMultiple myeloma (MM) is an incurable and relapse-prone disease with apparently prognostic heterogeneity. At present, the risk stratification of myeloma is still incomplete. Pyroptosis, a type of programmed cell death, has been shown to regulate tumor growth and may have potential prognostic value. However, the role of pyroptosis-related genes (PRGs) in MM remains undetermined. The aims of this study were to identify potential prognostic biomarkers and to construct a predictive model related to PRGs.MethodsSequencing and clinical data were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Non-negative matrix factorization (NMF) was performed to identify molecular subtype screening. LASSO regression was used to screen for prognostic markers, and then a risk score model was constructed. The Maxstat package was utilized to calculate the optimal cutoff value, according to which patients were divided into a high-risk group and a low-risk group, and the survival curves were plotted using the Kaplan-Meier (K-M) method. Nomograms and calibration curves were established using the rms package.ResultsA total of 33 PRGs were extracted from the TCGA database underlying which 4 MM molecular subtypes were defined. Patients in cluster 1 had poorer survival than those in cluster 2 (p = 0.035). A total of 9 PRGs were screened out as prognostic markers, and the predictive ability of the 9-gene risk score for 3-year survival was best (AUC = 0.658). Patients in the high-risk group had worse survival than those in the low-risk group (p < 0.001), which was consistent with the results verified by the GSE2658 dataset. The nomogram constructed by gender, age, International Staging System (ISS) stage, and risk score had the best prognostic predictive performance with a c-index of 0.721.ConclusionOur model could enhance the predictive ability of ISS staging and give a reference for clinical decision-making. The new, prognostic, and pyroptosis-related markers screened out by us may facilitate the development of novel risk stratification for MM.Clinical trial registrationNot applicable.

The prognostic value of the GPAT/AGPAT gene family in hepatocellular carcinoma and its role in the tumor immune microenvironment.

Liver cancer is the sixth most commonly diagnosed cancer and the third leading cause of cancer-related death worldwide. Hepatocellular carcinoma accounts for an estimated 90% of all liver cancers. Many enzymes of the GPAT/AGPAT family are required for the synthesis of triacylglycerol. Expression of AGPAT isoenzymes has been reported to be associated with an increased risk of tumorigenesis or development of aggressive phenotypes in a variety of cancers. However, whether members of the GPAT/AGPAT gene family also influence the pathophysiology of HCC is unknown. Hepatocellular carcinoma datasets were obtained from the TCGA and ICGC databases. Predictive models related to the GPAT/AGPAT gene family were constructed based on LASSO-Cox regression using the ICGC-LIRI dataset as an external validation cohort. Seven immune cell infiltration algorithms were used to analyze immune cell infiltration patterns in different risk groups. IHC, CCK-8, Transwell assay, and Western blotting were used for in vitro validation. Compared with low-risk patients, high-risk patients had shorter survival and higher risk scores. Multivariate Cox regression analysis showed that risk score was a significant independent predictor of overall survival (OS) after adjustment for confounding clinical factors (p < 0.001). The established nomogram combined risk score and TNM staging to accurately predict survival at 1, 3, and 5 years in patients with HCC with AUC values of 0.807, 0.806, and 0.795, respectively. This risk score improved the reliability of the nomogram and guided clinical decision-making. In addition, we comprehensively analyzed immune cell infiltration (using seven algorithms), response to immune checkpoint blockade, clinical relevance, survival, mutations, mRNA expression-based stemness index, signaling pathways, and interacting proteins related to the three core genes of the prognostic model (AGPAT5, LCLAT1, and LPCAT1). We also performed preliminary validation of the differential expression, oncological phenotype, and potential downstream pathways of the three core genes by IHC, CCK-8, Transwell assay, and Western blotting. These results improve our understanding of the function of GPAT/AGPAT gene family members and provide a reference for prognostic biomarker research and individualized treatment of HCC.