To Evaluate Efficiency of Various Coronary Artery Disease Risk Scores With Traditional Risk Factors in Patients Undergoing Coronary Angiography.

Objective: To analyze and compare various cardiovascular disease risk scores in Western Indian patients undergoing Coronary angiogram (CAG). Methods: In this prospective cross-sectional study, 1213 patients who underwent conventional coronary angiography; clinical risk profile and biochemical investigations were evaluated prior to undergoing CAG. Apart from the demographic information, 10-year absolute risk of having a major cardiovascular event (cardiovascular death, myocardial infarction or stroke) was calculated for each patient using various available Traditional Risk Scores (TRS). The population was divided in low, intermediate and high-risk categories for each of these scores. Results: Traditional cardiovascular risk factors like hypertension (41.8%) and diabetes mellitus-II (26.9%) were the two most prevalent risk factors in our study population. A higher risk value for all these TRS was more likely to be associated with obstructive coronary artery disease (OCAD) on CAG. Patients with high risk (≥20% for 10-year) QRESEARCH (QRISK3) score category had higher number of patients with obstructive CAD (49.6%) as compared to high risk category of risk score for those with high Global Registry of Acute Coronary Events (GRACE) score (46.6%) or risk Framingham (FRS CHD) score (29.2%) and risk atherosclerotic cardiovascular disease (ASCVD) score (30.1%) (P < 0.0001). A higher TRS was more likely to be associated with obstructive CAD, with the highest predictability being with QRISK3 (QRISK3 score 60.9%, GRACE score 54.9%, FRS-CHD score 34% and ASCVD score 42.1% respectively; P < 0.0001). A substantial study population (27.4%) cannot be identified using any of these TRS and hence a need of indigenous or modified risk scores is proposed. Conclusion: QRISK3 score was most efficacious for predicting obstructive CAD in our Indian study population on CAG. A higher risk score also correlated with the number of vessels involved on coronary angiogram. A substantial obstructive CAD patient could not be identified using traditional risk scores hence need for an indigenous or modified score.

An immune-related eleven-RNA signature-drived risk score model for prognosis of osteosarcoma metastasis.

This study aimed to determine an immune-related RNA signature as a prognostic marker, in this study, we developed a risk score model for predicting the prognosis of osteosarcoma metastasis. We first downloaded the clinical information and expression data of osteosarcoma samples from the UCSC Xena and GEO databases, of which the former was the training set and the latter was the validation set. Immune infiltration was assessed using the ssGSEA and ESTIMATE algorithms, and the osteosarcoma samples were divided into the Immunity_L and Immunity_H groups. Then, eleven RNAs were identified as the optimal prognostic RNA signatures using LASSO Cox regression analysis for establishing a risk score (RS) model. Kaplan-Meier approach indicated the high-risk group exhibited a shorter survival. Furthermore, we analyzed the tumor metastasis, age, and RS model status were determined to be independent clinical prognostic factors using Cox regression analysis. Decision curve analysis (DCA) indicated that the prognostic factor + RS model had the best net benefit. Finally, nine tumor-infiltrating immune cells (TIICs) showed significant differences in abundance between high- and low-risk groups via CIBERSORT deconvolution algorithm. In conclusion, the immune-related eleven-RNA signature be could served as a potential prognostic biomarker for osteosarcoma metastasis.

Construction of a prognostic score model for breast cancer based on multi-omics analysis of study on bone metastasis.

Background: Breast cancer (BRCA) is the most common type of cancer and the second leading cause of cancer-related death in women all over the world. Metastasis to bone is an indicator of poor prognosis in BRCA patients. This study aimed to develop a prognostic score model for predicting bone metastasis in patients with BRCA. Methods: BRCA-related RNA sequencing datasets and corresponding clinical information were downloaded from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA). Differentially expressed genes (DEGs) were screened using Limma package of R software. A risk score based predictive model was constructed based on the key genes identified through univariate Cox regression and the least absolute shrinkage and selection operator (LASSO) Cox regression. The gene expression profiles in BRCA patients were analyzed by gene set variation analysis (GSVA) and gene set enrichment analysis (GSEA). Random survival forest (RSF) analysis of BRCA patients with bone metastasis was conducted to identify the key DEGs. Results: Based on DEG analysis, a total of 677 genes were identified as genes related to bone metastasis in BRCA. By univariate Cox regression and LASSO regression, 28 DEGs were identified as signature genes to develop the prognostic model. A risk score for each patient was created by incorporating the expression values of each specific gene and weighting them with the corresponding estimated regression coefficients. Patients were divided into a low-risk and a high-risk group based on the median risk score. Overall survival (OS) was significantly lower in the high-risk group. The receiver operating characteristic (ROC) curve and multi-omics analysis indicated that the model had high training/testing accuracy and a good clinical predictive value. We used extra data from GEO database to verify the robustness of the prognostic model, and the lower OS in high-risk group and area under the curve (AUC) value indicated the model had strong predictive efficacy for prognosis of BRCA. Conclusions: A prognostic prediction model was constructed based on 28 key DEGs identified through multi-omics analysis of studies on bone metastasis. The model may provide a promising method for distinguishing the high-risk BRCA patients and help on decision making in addition to prognosis prediction for BRCA patients.

Construction of oxidative phosphorylation-related prognostic risk score model in uveal melanoma.

BACKGROUND: Uveal melanoma (UVM) is a malignant intraocular tumor in adults. Targeting genes related to oxidative phosphorylation (OXPHOS) may play a role in anti-tumor therapy. However, the clinical significance of oxidative phosphorylation in UVM is unclear. METHOD: The 134 OXPHOS-related genes were obtained from the KEGG pathway, the TCGA UVM dataset contained 80 samples, served as the training set, while GSE22138 and GSE39717 was used as the validation set. LASSO regression was carried out to identify OXPHOS-related prognostic genes. The coefficients obtained from Cox multivariate regression analysis were used to calculate a risk score, which facilitated the construction of a prognostic model. Kaplan-Meier survival analysis, logrank test and ROC curve using the time "timeROC" package were conducted. The immune cell frequency in low- and high-risk group was analyzed through Cibersort tool. The specific genomic alterations were analyzed by "maftools" R package. The differential expressed genes between low- or high-risk group were analyzed and performed Gene Ontology (GO) and GSEA. Finally, we verified the function of CYC1 in UVM by gene silencing in vitro. RESULTS: A total of 9 OXPHOS-related prognostic genes were identified, including NDUFB1, NDUFB8, ATP12A, NDUFA3, CYC1, COX6B1, ATP6V1G2, ATP4B and NDUFB4. The UVM prognostic risk model was constructed based on the 9 OXPHOS-related prognostic genes. The prognosis of patients in the high-risk group was poorer than low-risk group. Besides, the ROC curve demonstrated that the area under the curve of the model for predicting the 1 to 5-year survival rate of UVM patients were all more than 0.88. External validation in GSE22138 and GSE39717 dataset revealed that these 9 genes could also be utilized to evaluate and predict the overall survival of patients with UVM. The risk score levels related to immune cell frequency and specific genomic alterations. The DEGs between the low- and high- risk group were enriched in tumor OXPHOS and immune related pathway. In vitro experiments, CYC1 silencing significantly inhibited UVM cell proliferation and invasion, induced cell apoptosis. CONCLUSION: In sum, a prognostic risk score model based on oxidative phosphorylation-related genes in UVM was developed to enhance understanding of the disease. This prognostic risk score model may help to find potential therapeutic targets for UVM patients. CYC1 acts as an oncogene role in UVM.

Development of a Prognostic Risk Model Based on Oxidative StressRelated Genes for Platinum-Resistant Ovarian Cancer Patients.

INTRODUCTION: Ovarian Cancer (OC) is a heterogeneous malignancy with poor outcomes. Oxidative stress plays a crucial role in developing drug resistance. However, the relationships between Oxidative Stress-related Genes (OSRGs) and the prognosis of platinum-resistant OC remain unclear. This study aimed to develop an OSRGs-based prognostic risk model for platinum-resistant OC patients. METHODS: Gene Set Enrichment Analysis (GSEA) was performed to determine the expression difference of OSRGs between platinum-resistant and -sensitive OC patients. Cox regression analyses were used to identify the prognostic OSRGs and establish a risk score model. The model was validated by using an external dataset. Machine learning was used to determine the prognostic OSRGs associated with platinum resistance. Finally, the biological functions of selected OSRG were determined via in vitro cellular experiments. RESULTS: Three gene sets associated with oxidative stress-related pathways were enriched (p < 0.05), and 105 OSRGs were found to be differentially expressed between platinum-resistant and - sensitive OC (p < 0.05). Twenty prognosis-associated OSRGs were identified (HR: 0:562-5.437; 95% CI: 0.319-20.148; p < 0.005), and seven independent OSRGs were used to construct a prognostic risk score model, which accurately predicted the survival of OC patients (1-, 3-, and 5-year AUC=0.69, 0.75, and 0.67, respectively). The prognostic potential of this model was confirmed in the validation cohort. Machine learning showed five prognostic OSRGs (SPHK1, PXDNL, C1QA, WRN, and SETX) to be strongly correlated with platinum resistance in OC patients. Cellular experiments showed that WRN significantly promoted the malignancy and platinum resistance of OC cells. CONCLUSION: The OSRGs-based risk score model can efficiently predict the prognosis and platinum resistance of OC patients. This model may improve the risk stratification of OC patients in the clinic.

A prognostic signature established based on genes related to tumor microenvironment for patients with hepatocellular carcinoma.

BACKGROUND: Complex cellular signaling network in the tumor microenvironment (TME) could serve as an indicator for the prognostic classification of hepatocellular carcinoma (HCC) patients. METHODS: Univariate Cox regression analysis was performed to screen prognosis-related TME-related genes (TRGs), based on which HCC samples were clustered by running non-negative matrix factorization (NMF) algorithm. Furthermore, the correlation between different molecular HCC subtypes and immune cell infiltration level was analyzed. Finally, a risk score (RS) model was established by LASSO and Cox regression analyses (CRA) using these TRGs. Functional enrichment analysis was performed using gene set enrichment analysis (GSEA). RESULTS: HCC patients were divided into three molecular subtypes (C1, C2, and C3) based on 704 prognosis-related TRGs. HCC subtype C1 had significantly better OS than C2 and C3. We selected 13 TRGs to construct the RS model. Univariate and multivariate CRA showed that the RS could independently predict patients' prognosis. A nomogram integrating the RS and clinicopathologic features of the patients was further created. We also validated the reliability of the model according to the area under the receiver operating characteristic (ROC) curve value, concordance index (C-index), and decision curve analysis. The current findings demonstrated that the RS was significantly correlated with CD8+ T cells, monocytic lineage, and myeloid dendritic cells. CONCLUSION: This study provided TRGs to help classify patients with HCC and predict their prognoses, contributing to personalized treatments for patients with HCC.

AAT resistance-related AC007405.2 and AL354989.1 as novel diagnostic and prognostic markers in prostate cancer.

OBJECTIVE: Prostate cancer (PCa) is the second disease threatening men's health, and anti-androgen therapy (AAT) is a primary approach for treating this condition. Increasing evidence suggests that long non-coding RNAs (lncRNAs) play crucial roles in the development of PCa and the process of AAT resistance. The objective of this study is to utilize bioinformatics methods to excavate lncRNAs association with AAT resistance and investigate their biological functions. METHODS: AAT resistance-related risk score model (ARR-RSM) was established by multivariate Cox analysis. Paired clinical tissue samples of 36 PCa patients and 42 blood samples from patients with PSA over 4 ng/ml were collected to verify the ARR-RSM. In vitro, RT-qPCR, CCK-8 and clone formation assays were displayed to verify the expression and function of AL354989.1 and AC007405.2. RESULTS: Pearson correlation analysis identified 996 lncRNAs were associated with AAT resistance (ARR-LncRs). ARR-RSM was established using multivariate Cox regression analysis, and PCa patients were divided into high-risk and low-risk groups. High-risk patients showed increased expression of AL354989.1 and AC007405.2 had poorer prognoses. The high-risk score correlated with advanced T-stage and N-stage. The AUC of ARR-RSM outperformed tPSA in diagnosing PCa. Silencing of AC007405.2 and AL354989.1 inhibited PCa cells proliferation and AAT resistance. CONCLUSIONS: In this study, we have discovered the clinical significance of AC007405.2 and AL354989.1 in predicting the prognosis and diagnosing PCa patients. Furthermore, we have confirmed their correlation with various clinical features. These findings provide potential targets for PCa treatment and a novel diagnostic and predictive indicator for precise PCa diagnosis.

A scoring system to predict the technical difficulty of endoscopic resection for cardial submucosal tumors.

BACKGROUND AND AIM: Endoscopic resection has been successfully used for the removal of digestive submucosal tumors (SMTs). However, the cardia has been considered a challenging location for endoscopic resection due to its narrow lumen and sharp angle. The objective of this study was to establish a clinical scoring model to grade the technical difficulty of endoscopic resection for cardial SMTs. METHODS: A total of 246 patients who suffered cardial SMTs and received endoscopic resection were included in this retrospective study. All of them were randomized into the training cohort (n = 123) or internal validation cohort (n = 123). Potential predictors were analyzed using univariate analysis. Then, covariates with P < 0.05 were selected for the multivariate logistic regression model. The ß coefficients from the logistic regression model were used to create a scoring system for technical difficulty prediction by rounding the score to the nearest integer of the absolute ß coefficient value. RESULTS: The clinical score consisted of the following factors: male gender (2 points), extraluminal growth (3 points), and maximum diameter ≥3 cm (3 points). The scoring model demonstrated good discriminatory power, with an area under the receiver operating characteristic curve of 0.860 and a 95% confidence interval of 0.763-0.958. The model also showed a good goodness of fit in the Hosmer-Lemeshow test (P = 0.979). In the training cohort, the probability of encountering technical difficulty in the easy (score = 0), intermediate (score = 1-3), difficult (score = 4-6), and very difficult (score >6) categories was 0, 6.8%, 33.3%, and 100.0%, respectively; similarly, in the validation cohort, it was 0, 5.6%, 22.2%, and 50.0%, respectively. CONCLUSIONS: This scoring system could serve as a valuable tool for clinicians in predicting the technical difficulty of endoscopic resection for cardial SMTs.

Predicting One-Year Mortality after Discharge Using Acute Heart Failure Score (AHFS).

Background: Acute heart failure (AHF) represents a leading cause of unscheduled hospital stays, frequent rehospitalisations, and mortality worldwide. The aim of our study was to develop a bedside prognostic tool, a multivariable predictive risk score, that is useful in daily practice, thus providing an early prognostic evaluation at admission and an accurate risk stratification after discharge in patients with AHF. Methods: This study is a subanalysis of the STADE HF study, which is a single-centre, prospective, randomised controlled trial enrolling 123 patients admitted to hospital for AHF. Here, 117 patients were included in the analysis, due to data exhaustivity. Regression analysis was performed to determine predictive variables for one-year mortality and/or rehospitalisation after discharge. Results: During the first year after discharge, 23 patients died. After modellisation, the variables considered to be of prognostic relevance in terms of mortality were (1) non-ischaemic aetiology of HF, (2) elevated creatinine levels at admission, (3) moderate/severe mitral regurgitation, and (4) prior HF hospitalisation. We designed a linear model based on these four independent predictive variables, and it showed a good ability to score and predict patient mortality with an AUC of 0.84 (95%CI: 0.76-0.92), thus denoting a high discriminative ability. A risk score equation was developed. During the first year after discharge, we observed as well that 41 patients died or were rehospitalised; hence, while searching for a model that could predict worsening health conditions (i.e., death and/or rehospitalisation), only two predictive variables were identified: non-ischaemic HF aetiology and previous HF hospitalisation (also included in the one-year mortality model). This second modellisation showed a more discrete discriminative ability with an AUC of 0.67 (95% C.I. 0.59-0.77). Conclusions: The proposed risk score and model, based on readily available predictive variables, are promising and useful tools to assess, respectively, the one-year mortality risk and the one-year mortality and/or rehospitalisations in patients hospitalised for AHF and to assist clinicians in the management of patients with HF aiming at improving their prognosis.

Creation of signatures and identification of molecular subtypes based on disulfidptosis-related genes for glioblastoma patients' prognosis and immunological activity.

BACKGROUND: In recent times, disulfidptosis, an intricate form of cellular demise, has garnered attention due to its impact on prognosis, tumor progression and treatment response. Nevertheless, the exact significance of disulfidptosis-related genes (DisRGs) in glioblastoma (GBM) remains enigmatic. METHODS: The GEO and TCGA databases provided transcriptional and clinically relevant data on tumor samples, while the GTEx database provided data on healthy tissues. Disulfidptosis-related genes (DisRGs) were procured from previous scholarly investigations. The expression profile of DisRGs was initially scrutinized among patients diagnosed with GBM, subsequent to which their prognostic value was explored. Through consensus clustering, we constructed DisRGs-related clusters and gene subtypes. Our results established that the DisRG-related clusters had differentially expressed genes, resulting in a DisulfidptosisScore model, which had a positive prognostic value. RESULTS: The differential expression profile of 24 DisRGs between GBM samples and healthy samples was acquired. Through consensus cluster analysis, two distinct disulfidptosis subtypes, namely DisRGcluster A and DisRGcluster B, were identified. Then, the DisulfidptosisScore model including 4 characteristic genes was constructed.Notably, patients with GBM assigned with lower score demonstrated a considerably longer overall survival (OS) compared to those with higher score. CONCLUSION: We have effectively devised a prognostic model associated with disulfidptosis, presenting autonomous prognostic predictions for patients with GBM. These findings serve as a valuable addition to the current comprehension of disulfidptosis and offer fresh theoretical substantiation for the development of enhanced treatment strategies.

Identification of macrophage-related molecular subgroups and risk signature in colorectal cancer based on a bioinformatics analysis.

Macrophages play a crucial role in tumor initiation and progression, while macrophage-associated gene signature in colorectal cancer (CRC) patients has not been investigated. Our study aimed to identify macrophage-related molecular subgroups and develop a macrophage-related risk model to predict CRC prognosis. The mRNA expression profile and clinical information of CRC patients were obtained from TCGA and GEO databases. CRC patients from TCGA were divided into high and low macrophage subgroups based on the median macrophage score. The ESTIMATE and CIBERSORT algorithms were used to assess immune cell infiltration between subgroups. GSVA and GSEA analyses were performed to investigate differences in enriched pathways between subgroups. Univariate and LASSO Cox regression were used to build a prognostic risk model, which was further validated in the GSE39582 dataset. A high macrophage score subgroup was associated with poor prognosis, highly activated immune-related pathways and an immune-active microenvironment. A total of 547 differentially expressed macrophage-related genes (DEMRGs) were identified, among which seven genes (including RIMKLB, UST, PCOLCE2, ZNF829, TMEM59L, CILP2, DTNA) were identified by COX regression analyses and used to build a risk score model. The risk model shows good predictive and diagnostic values for CRC patients in both TCGA and GSE39852 datasets. Furthermore, multivariate Cox regression analysis showed that the risk score was an independent risk factor for overall survival in CRC patients. Our findings provided a novel insight into macrophage heterogeneity and its immunological role in CRC. This risk score model may serve as an effective prognostic tool and contribute to personalised clinical management of CRC patients.

Identification of Transcriptomic Signatures of Pancreatic Ductal Adenocarcinoma-Derived Exosomes That Promote Macrophage M2 Polarization and Predict Prognosis: S100A9 Reveals Tumor Progression.

Background: Exosomes play a role in intercellular communication and participate in the interaction between pancreatic ductal adenocarcinoma (PDAC) cells and immune cells. Macrophages can receive tumor cell-derived exosomes to polarize into M2-type macrophages, which can enhance the invasion and metastasis of pancreatic cancer, leading to poor prognosis. However, the mechanism by which pancreatic cancer cell-derived exosomes promote M2-type macrophages is still unclear. Methods: M2 macrophage-associated exosome-derived key module genes were identified by differentially expressed genes (DEGs) and weighted gene co-expression network analysis (WGCNA) analysis using exoRbase 2.0, The Cancer Genome Atlas (TCGA), and The International Cancer Genome Consortium (ICGC) databases. Multivariate Cox regression analysis was used to identify key prognostic genes and obtain regression coefficients to establish prognostic signature. Immune infiltration, tumor mutations, and GSEA among different risk groups were compared. exoRbase 2.0, Gene Expression Profiling Interactive Analysis 2 (GEPIA2), HPA, and TISCH2 databases were used to further analyze the expression pattern of S100A9 in pancreatic cancer. In vitro experiments, cell-derived exosome isolation, quantitative polymerase chain reaction (qPCR), western blot, flow cytometry analysis, cell transfection, transwell assay, and CCK-8 assay were applied to investigate the roles of S100A9 in macrophage M2 polarization and tumor progression. Results: The key genes of PDAC-derived exosomes promoting M2-type macrophage polarization were identified, and a risk score model was established. The risk score is related to the expression of common immune checkpoints, immune score, and stromal score, and the tumor mutational burden and biological function of high- and low-risk groups were also different. S100A9 was positively correlated with M2-type macrophage marker. In addition, scRNA-seq data from the TISCH2 database revealed that S100A9 is predominantly expressed in pancreatic cancer cells and mono/macrophage cells, suggesting that S100A9 in pancreatic cancer cells could be received by macrophages, thereby inducing macrophage polarization. In vitro, we used exosomes from BxPC-3 cell lines to coculture macrophages and found that macrophages were mainly polarized toward M2 type, which further promoted the proliferation and metastasis of PDAC. Conclusions: Our study established a reliable risk score model for PDAC-derived exosomes and M2 macrophages, identified the important role of S100A9 in macrophage M2 polarization, which provides a new strategy for the diagnosis and treatment of PDAC, and strengthened the understanding of the mechanism of tumor development and metastasis.

Comprehensive prognostic assessment in kidney cancer: A multidimensional approach analyzing Fufang Sanling granules, genetic variants, and immune infiltration.

This study delves into the potential therapeutic benefits of Fufang Sanling Granules for kidney cancer, focusing on their active components and the underlying mechanisms of their interaction with cancer-related targets. By constructing a drug-active component-target network based on eight herbs, key active compounds such as kaempferol, quercetin, and linolenic acid were identified, suggesting their pivotal roles in modulating immune responses and cellular signaling pathways relevant to cancer progression. The research further identified 51 central drug-disease genes through comprehensive bioinformatics analyses, implicating their involvement in crucial biological processes and pathways. A novel risk score model, encompassing six genes with significant prognostic value for renal cancer, was established and validated, showcasing its effectiveness in predicting patient outcomes through mutation analysis and survival studies. The model's predictive power was further confirmed by its ability to stratify patients into distinct risk groups with significant survival differences, highlighting its potential as a prognostic tool. Additionally, the study explored the relationship between gene expression within the identified black module and the risk score, uncovering significant associations with the extracellular matrix and immune infiltration patterns. This reveals the complex interplay between the tumor microenvironment and cancer progression. The integration of the risk score with clinical parameters through a nomogram significantly improved the model's predictive accuracy, offering a more comprehensive tool for predicting kidney cancer prognosis. In summary, by combining detailed molecular analyses with clinical insights, this study presents a robust framework for understanding the therapeutic potential of Fufang Sanling Granules in kidney cancer. It not only sheds light on the active components and their interactions with cancer-related genes but also introduces a reliable risk score model, paving the way for personalized treatment strategies and improved patient management in the future.

TBX15 and SDHB expression changes in colorectal cancer serve as potential prognostic biomarkers.

Alterations in the expression of certain genes could be associated with both patient mortality rates and drug resistance. This study aimed to identify genes in colorectal cancer (CRC) that potentially serve as hub genes influencing patient survival rates. RNA-Seq data were downloaded from the cancer genome atlas database, and differential expression analysis was performed between tumors and healthy controls. Through the utilization of univariate and multivariate Cox regression analyses, in combination with the MCODE clustering module, the genes whose expression changes were related to survival rate and the hub genes related to them were identified. The mortality risk model was computed using the hub genes. CRC samples and the RT-qPCR method were utilized to confirm the outcomes. PharmacoGx data were employed to link the expression of potential genes to medication resistance and sensitivity. The results revealed the discovery of seven hub genes, which emerged as independent prognostic markers. These included HOXC6, HOXC13, HOXC8, and TBX15, which were associated with poor prognosis and overexpression, as well as SDHB, COX5A, and UQCRC1, linked to favorable prognosis and downregulation. Applying the risk model developed with the mentioned genes revealed a markedly higher incidence of deceased patients in the high-risk group compared to the low-risk group. RT-qPCR results indicated a decrease in SDHB expression and an elevation in TBX15 levels in cancer samples relative to adjacent healthy tissue. Also, PharmacoGx data indicated that the expression level of SDHB was correlated with drug sensitivity to Crizotinib and Dovitinib. Our findings highlight the potential association between alterations in the expression of genes such as HOXC6, HOXC13, HOXC8, TBX15, SDHB, COX5A, and UQCRC1 and increased mortality rates in CRC patients. As revealed by the PPI network, these genes exhibited the most connections with other genes linked to survival.

Evaluation of the A2B Score for Prediction of Survival in Patients With Heart Failure in a Nationwide Cohort in Japan.

BACKGROUND: Although a tool for sharing patient prognosis among all medical staff is desirable in heart failure (HF) cases, only a few simple HF prognostic scores are available. We previously presented the A2B score, a simple user-friendly HF risk score, and validated it in a small single-center cohort. In the present study, we validated it in a larger nationwide cohort. METHODS AND RESULTS: We examined the 2-year mortality in relation to the A2B scores in 3483 patients from a Japanese nationwide cohort and attempted to stratify their prognoses according to the scores. The A2B score was determined by assigning points for age, anemia, and brain natriuretic peptide (BNP) level at discharge: age (<65 years, 0; 65-74 years, 1; ≥75 years, 2), anemia (hemoglobin ≥12 g/dL, 0; 10-11.9 g/dL, 1; <10 g/dL, 2), and BNP (<200 pg/mL, 0; 200-499 pg/mL, 1; ≥500 pg/mL, 2). Hemoglobin and BNP levels were applied to the data at discharge. The 2-year survival rates for A2B scores 1, 2, 3, 4, 5, and 6 were 94.1%, 83.2%, 74.1%, 63.5%, 51.6%, and 41.5%, respectively; the mortality rate increased by ≈10% for each point increase (c-index, 0.702). The A2B score was applicable in HF cases with reduced or preserved ejection fraction and remained useful when BNP was substituted with N-terminal proBNP (c-index, 0.749, 0.676, and 0.682, respectively). CONCLUSIONS: The A2B score showed a good prognostic value for HF in a large population even when BNP was replaced with N-terminal proBNP.

Characterization of immunogenic cell death regulators predicts survival and immunotherapy response in lung adenocarcinoma.

Lung adenocarcinoma (LUAD) is highly lethal tumor; understanding immune response is crucial for current effective treatment. Research investigated immunogenic cell death (ICD) impact on LUAD through 75 ICD-related genes which encompass cell damage, endoplasmic reticulum stress, microenvironment, and immunity. Transcriptome data and clinical info were analyzed, revealing two ICD-related clusters: B, an immune osmotic subgroup, had better prognosis, stronger immune signaling, and higher infiltration, while A represented an immune-deficient subgroup. Univariate Cox analysis identified six prognostic genes (AGER, CD69, CD83, CLEC9A, CTLA4, and NT5E), forming a validated risk score model. It was validated across datasets, showing predictive performance. High-risk group had unfavorable prognosis, lower immune infiltration, and higher chemotherapy sensitivity. Conversely, low-risk group had better prognosis, higher immune infiltration, and favorable immunotherapy response. The key gene NT5E was examined via immunohistochemistry, with higher expression linked to poorer prognosis. NT5E was predominantly expressed in B cells, fibroblasts, and endothelial cells, correlated with immune checkpoints. These outcomes suggest that NT5E can serve as a LUAD therapeutic target. The study highlights gene predictive value, offers an efficient tumor assessment tool, guides clinical treatment strategies, and identifies NT5E as therapeutic target for LUAD.

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

Background: The 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. Methods: Online 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. Results: A 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. Conclusion: Our 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.

Impacts of difficulties with emotion regulation on the longitudinal relationship between peer victimization and changes in adolescent nonsuicidal self-injury.

INTRODUCTION: Research has found that peer victimization is associated with adolescent nonsuicidal self-injury (NSSI) behavior; however, most of these studies ignored the association between these constructs at the within-person level. Additionally, the association between peer victimization and NSSI may vary among adolescents with different personal characteristics. With a longitudinal design, this study investigated whether and how emotion regulation (ER) difficulties moderate the relationship between peer victimization and changes in NSSI, with particular attention given to the unique moderating role of different dimensions of ER difficulties. METHODS: The study sample comprised 3,561 adolescents aged between 10 and 17 years old (Mage = 13.22, SD = 0.85; 56.9% males). Self-report assessments were administered in December 2021 and June 2022 in Shanxi province, China. RESULTS: The latent change score model showed that the adolescent NSSI increased during our assessments, with peer victimization as a significant predictor. ER difficulties moderated the association between peer victimization and NSSI changes, but interestingly, in an unexpected pattern. Specifically, peer victimization significantly predicted NSSI changes among adolescents with low ER difficulties but not for those with high ER difficulties. Moreover, among the multiple dimensions of ER difficulties, only nonacceptance of emotional responses and limited access to emotion regulation strategies interacted with peer victimization to predict NSSI changes and showed interaction patterns similar to those at the overall level of ER difficulties. CONCLUSIONS: The current study revealed the moderating role of ER difficulties in the relationship between peer victimization and changes in NSSI. These findings provide intervention implications for adolescents who engage in NSSI.

Clinical features that predict the mortality risk in older patients with Omicron pneumonia: the MLWAP score.

In December 2022, the Chinese suffered widespread Omicron of SARS-CoV-2 with variable symptom severity and outcome. We wanted to develop a scoring model to predict the mortality risk of older Omicron pneumonia patients by analyzing admission data. We enrolled 227 Omicron pneumonia patients aged 60 years and older, admitted to our hospital from December 15, 2022, to January 16, 2023, and divided them randomly into a 70% training set and a 30% test set. The former were used to identify predictors and develop a model, the latter to verify the model, using the area under the receiver operating characteristic curve (AUC), the Hosmer-Lemeshow goodness-of-fit test, a calibration curve to test its performance and comparing it to the existing scores. The MLWAP score was calculated based on a multivariate logistic regression model to predict mortality with a weighted score that included immunosuppression, lactate ≥ 2.4, white blood cell count ≥ 6.70 × 109/L, age ≥ 77 years, and PaO2/FiO2 ≤ 211. The AUC for the model in the training and test sets was 0.852 (95% CI, 0.792-0.912) and 0.875 (95% CI, 0.789-0.961), respectively. The calibration curves showed a good fit. We grouped the risk scores into low (score 0-7 points), medium (8-10 points), and high (11-13 points). This model had a sensitivity of 0.849, specificity of 0.714, and better predictive ability than the CURB-65 and PSI scores (AUROC = 0.859 vs. 0.788 vs. 0.801, respectively). The MLWAP-mortality score may help clinicians to stratify hospitalized older Omicron pneumonia patients into relevant risk categories, rationally allocate medical resources, and reduce the mortality.

Developmental interactions between peer victimization and depressive symptoms in adolescents: A latent change score analysis.

Peer victimization and depressive symptoms are highly relevant risks during adolescence. Understanding the dynamic patterns of interactions between peer victimization and depressive symptoms as well as gender differences in these variables can improve intervention strategies for adolescents navigating this critical transition period. In the present study, a large sample of Chinese adolescents reported peer victimization and depressive symptoms in four survey waves at six-month intervals. A total of 2534 adolescents (51.9% boys, M = 12.98 ± 0.60 years) were included in the latent change score (LCS) analysis. The results supported the reciprocal effects model obtained in the full sample. Changes in peer victimization were influenced by prior changes in depressive symptoms over time, and changes in depressive symptoms were influenced by prior levels of peer victimization. There were also gender differences, with boys exhibiting depressive symptom-driven effects on peer victimization, while girls exhibiting peer victimization-induced depressive symptoms. The dynamic relationships between peer victimization and depressive symptoms that promote and constrain each other in adolescents are elucidated in this study. Differentiating effects on boys and girls is crucial for enhancing the effectiveness of practical interventions.