Risk Score Model Research Articles

Risk of recurrence at the time of withdrawal of short- or long-term methimazole therapy in patients with Graves' hyperthyroidism: a randomized trial and a risk-scoring model.

In Graves' disease, administration of low-dose methimazole for more than 60 months induces higher remission rates compared with the conventional duration of 12-18 months. However, the risk of recurrence and its predictors beyond 48 months of drug withdrawal are not known. The aims of this study were to determine the risk of recurrence during 84 months after withdrawal of short- or long-term methimazole therapy and a risk stratification for recurrence of hyperthyroidism. A total of 258 patients were treated with methimazole for a median of 18 months and randomized to discontinuation of the drug(conventional short-term group; n = 128) or continuation of the treatment up to 60-120 months(long-term group; n = 130). Patients were followed for 84 months after methimazole withdrawal. Cox proportional hazards modeling was performed to identify factors associated with relapse and develop a risk-scoring model at the time of discontinuing the treatment. Hyperthyroidism recurred in 67 of 120(56%) of conventionally-treated patients versus 20 of 118(17%) of those who received long-term methimazole treatment, p < 0.001. Age, sex, goiter grade, triiodothyronine, thyrotropin, and thyrotropin receptor antibodies were significant predictors of recurrence in both "conventional" and "long-term" groups but free thyroxine just in the "long-term" group. The risk-scoring model had a good discrimination power (optimism corrected c-index = 0.78,95%CI = 0.73-0.82) with a range of 0-14 and sensitivity of 86% and specificity of 62% at the risk-score of eight. A relapse-free state was achieved in 83% of patients with Graves' hyperthyroidism 84 months after cessation of long-term methimazole treatment which could be predicted by some significant predictors in a simple risk-scoring system.

Identification of Metabolism-Related Prognostic Biomarkers and Immune Features of Head and Neck Squamous Cell Carcinoma.

We aimed to identify an effective metabolic subtype and risk score to predict survival and immunotherapy response in head and neck squamous cell carcinoma (HNSCC). Data were obtained from an online database. We screened significant prognostic metabolism-related genes between the normal and tumor groups using a series of bioinformatics methods. Based on the selected prognostic genes, we conducted a subtype analysis to identify significantly different subtypes in HNSCC. We then investigated survival, immune features, and hallmark differences among different subtypes. LASSO was utilized to identify optimal genes for the risk score model construction. Finally, distribution of the risk score samples was analyzed for different subtypes. A total of 32 significantly prognostic metabolism-related genes were screened, and all samples were grouped into two subtypes: cluster 1 and cluster 2. Cluster 1 had worse survival. Different immune cell infiltration (CD8 T cells, macrophages, and regulatory T cells) and immune checkpoint gene expression (PD-1 and CLAT-4) were observed between the two clusters. Twelve optimal genes were involved in risk score model, and high-risk group had poorer survival. Cluster 1 contained more high-risk samples (60%). Finally, four genes CAV1, GGT6, PYGL, and HS3ST1 were identified as significantly related to immune cells, and these genes were differentially expressed in the normal oral epithelial cells and HNSCC cells. The subtypes and risk score model in the study provide a promising biomarker for prognosis and immunotherapy response.

Key Cell-in-Cell Related Genes are Identified by Bioinformatics and Experiments in Glioblastoma.

This study aimed to explore the roles of cell-in-cell (CIC)-related genes in glioblastoma (GBM) using bioinformatics and experimental strategies. The ssGSEA algorithm was used to calculate the CIC score for each patient. Subsequently, differentially expressed genes (DEGs) between the CIClow and CIChigh groups and between the tumor and control samples were screened using the limma R package. Key CIC-related genes (CICRGs) were further filtered using univariate Cox and LASSO analyses, followed by the construction of a CIC-related risk score model. The performance of the risk score model in predicting GBM prognosis was evaluated using ROC curves and an external validation cohort. Moreover, their location and differentiation trajectory in GBM were analyzed at the single-cell level using the Seurat R package. Finally, the expression of key CICRGs in clinical samples was examined by qPCR. In the current study, we found that CIC scorelow group had a significantly better survival in the TCGA-GBM cohort, supporting the important role of CICRGs in GBM. Using univariate Cox and LASSO analyses, PTX3, TIMP1, IGFBP2, SNCAIP, LOXL1, SLC47A2, and LGALS3 were identified as key CICRGs. Based on this data, a CIC-related prognostic risk score model was built using the TCGA-GBM cohort and validated in the CGGA-GBM cohort. Further mechanistic analyses showed that the CIC-related risk score is closely related to immune and inflammatory responses. Interestingly, at the single-cell level, key CICRGs were expressed in the neurons and myeloids of tumor tissues and exhibited unique temporal dynamics of expression changes. Finally, the expression of key CICRGs was validated by qPCR using clinical samples from GBM patients. We identified novel CIC-related genes and built a reliable prognostic prediction model for GBM, which will provide further basic clues for studying the exact molecular mechanisms of GBM pathogenesis from a CIC perspective.

Conduction and validation of a novel mitotic spindle assembly related signature in hepatocellular carcinoma: prognostic prediction, tumor immune microenvironment and drug susceptibility.

Introduction: We have developed a risk-scoring model using gene expression levels related to mitotic spindle assembly (MSA) to predict the prognosis of liver cancer. Methods and results: Initially, we identified 470 genes related to MSA from public databases. Subsequently, through analysis of sequencing data from liver cancer patient samples in online databases, we identified 7 genes suitable for constructing the risk-scoring model. We validated the predictive accuracy and clinical utility of the model. Through drug sensitivity analysis, we identified SAC3D1 as a gene sensitive to the most common anti-tumor drugs among these 7 genes. We propose SAC3D1 as a significant target for future clinical treatment. Furthermore, we conducted in vivo and in vitro experiments to validate the relevance of SAC3D1 to MSA and found its significant impact on the PI3K/Akt signaling pathway and spindle function. Conclusion: Our research introduces a novel risk-scoring model that accurately predicts liver cancer prognosis. Additionally, our findings suggest SAC3D1 as a promising therapeutic target for hepatocellular carcinoma, potentially revealing new mechanisms underlying liver cancer development.

Construction of a diagnostic model for ischemic stroke based on immune-related genes.

This study aimed to screen immune-related marker genes of ischemic stroke (IS). Two IS-related gene expression datasets were downloaded. The significantly differentially expressed genes (DEGs) and miRNAs (DEMs) between IS and control groups were selected. The differential immune cells were analysed. Weighted gene co-expression network analysis (WGCNA) was applied to analyse immune-related genes, followed by function analysis and interaction network construction. Then, key genes were further screened using optimization algorithm to construct a diagnostic model. Finally, miRNA regulatory network of several key genes was established. In total 321 DEGs and 140 DEMs were obtained. 11 immune cell types were significantly different between IS and control groups. WGCNA identified two key modules, involving 202 differential immune genes. The greenyellow module was enriched in biological processes and pathways associated with T cells, while the midnightblue module was mainly associated with apoptosis, and inflammatory response-related functions and pathways. Protein interaction network identified 10 hub nodes, such as CD8A, ITGAM and TLR4. LASSO regression selected 8 key feature genes, and a risk score model was established. Key model genes were enriched in 63 GO biological processes, such as microglial cell activation, and B cell apoptotic process, and 3 KEGG pathways, such as negative regulation of nuclear cell cycle DNA replication, and hematopoietic cell lineage. Finally, a total of 25 miRNA-target relationship pairs were obtained. This study identified some immune-related marker genes and constructed a diagnostic model based on 8 immune-related genes in IS.

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

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. 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. 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. 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.

Risk score models for urinary tract infection hospitalization.

Annually, urinary tract infections (UTIs) affect over a hundred million people worldwide. Early detection of high-risk individuals can help prevent hospitalization for UTIs, which imposes significant economic and social burden on patients and caregivers. We present two methods to generate risk score models for UTI hospitalization. We utilize a sample of patients from the insurance claims data provided by the Centers for Medicare and Medicaid Services to develop and validate the proposed methods. Our dataset encompasses a wide range of features, such as demographics, medical history, and healthcare utilization of the patients along with provider quality metrics and community-based metrics. The proposed methods scale and round the coefficients of an underlying logistic regression model to create scoring tables. We present computational experiments to evaluate the prediction performance of both models. We also discuss different features of these models with respect to their impact on interpretability. Our findings emphasize the effectiveness of risk score models as practical tools for identifying high-risk patients and provide a quantitative assessment of the significance of various risk factors in UTI hospitalizations such as admission to ICU in the last 3 months, cognitive disorders and low inpatient, outpatient and carrier costs in the last 6 months.

Construction and validation of molecular subtype and signature of immune cell-related telomeric genes and prediction of prognosis and immunotherapy efficacy in ovarian cancer patients.

Ovarian cancer (OVC) has emerged as a fatal gynecological malignancy as a result of a lack of reliable methods for early detection, limited biomarkers and few treatment options. Immune cell-related telomeric genes (ICRTGs) show promise as potential biomarkers. ICRTGs were discovered using weighted gene co-expression network analysis (WGCNA). ICRTGs were screened for significant prognosis using one-way Cox regression analysis. Subsequently, molecular subtypes of prognosis-relevant ICRTGs were constructed and validated for OVC, and the immune microenvironment's landscape across subtypes was compared. OVC prognostic models were built and validated using prognosis-relevant ICRTGs. Additionally, chemotherapy susceptibility drugs for OVC patients in the low- and high-risk groups of ICRTGs were screened using genomics of drug susceptibility to cancer (GDSC). Finally, the immunotherapy response in the low- and high-risk groups was detected using the data from GSE78220. We conducted an immune index correlation analysis of ICRTGs with significant prognoses. The MAP3K4 gene, for which the prognostic correlation coefficient is the highest, was validated using tissue microarrays for a prognostic-immune index correlation. WGCNA analysis constructed a gene set of ICRTGs and screened 22 genes with prognostic significance. Unsupervised clustering analysis revealed the best molecular typing for two subtypes. The Gene Set Variation Analysis algorithm was used to calculate telomere scores and validate the molecular subtyping. A prognostic model was constructed using 17 ICRTGs. In the The Cancer Genome Atlas-OVC training set and the Gene Expression Omnibus validation set (GSE30161), the risk score model's predicted risk groups and the actual prognosis were shown to be significantly correlated. GDSC screened Axitinib, Bexarotene, Embelin and the GSE78220 datasets and demonstrated that ICRTGs effectively distinguished the group that responds to immunotherapy from the non-responsive group. Additionally, tissue microarray validation results revealed that MAP3K4 significantly predicted patient prognosis. Furthermore, MAP3K4 exhibited a positive association with PD-L1 and a negative relationship with the M1 macrophage markers CD86 and INOS. ICRTGs may be reliable biomarkers for the molecular typing of patients with OVC, enabling the prediction of prognosis and immunotherapy efficacy.

Development and Validation of the novel Cuproptosis- and Immune-related Signature for Predicting Prognosis in Hepatocellular Carcinoma.

Background: Hepatocellular carcinoma often results in late-stage diagnosis, leading to decreased treatment success. To improve prognosis, this study integrates cuproptosis with immune risk scoring models for HCC patients. Method: We identified differentially expressed genes connected to cuproptosis and immune responses using Pearson correlation. A risk signature was then constructed via LASSO regression, and its robustness was validated in the International Cancer Genome Consortium dataset. Additionally, qPCR confirmed findings in tumor and normal tissues. Results: Eight genes emerged as key prognostic markers from the 110 differentially expressed genes linked to cuproptosis and immunity. A risk-scoring model was developed using gene expression, effectively categorizing patients into low- or high-risk groups. Validated in the ICGC dataset, high-risk patients had significantly reduced survival times. Multivariate Cox regression affirmed the risk signature's independent predictive capability. A clinical nomogram based on the risk signature was generated. Notably, low-risk patients might benefit more from immune checkpoint inhibitors. qPCR and western blotting results substantiated our bioinformatics findings. Conclusions: The genetic risk signature linked to cuproptosis and immunity holds potential as a vital prognostic biomarker for Hepatocellular carcinoma, providing avenues for tailored therapeutic strategies.

Eosinophil-Associated Genes are Potential Biomarkers for Hepatocellular Carcinoma Prognosis.

Background: Eosinophils, a type of white blood cell originating from the bone marrow, are widely believed to play a crucial role in inflammatory processes, including allergic reactions and parasitic infections. However, the relationship between eosinophils and liver cancer is not well understood. Methods: Tumor immune infiltration scores were calculated using single-sample Gene Set Enrichment Analysis (ssGSEA). Key modules and hub genes associated with eosinophils were screened using Weighted Gene Co-expression Network Analysis (WGCNA). Univariate and multivariate Cox analyses, along with LASSO regression, were used to identify prognostic genes and create a risk model. The Tumor Immune Dysfunction and Exclusion (TIDE) score was used to evaluate the immunotherapeutic significance of the eosinophil-associated gene risk score (ERS) model. Experiments such as flow cytometry, immunohistochemical analysis, real-time quantitative PCR (RT-qPCR), and Western blotting were used to determine gene expression levels and the status of eosinophil infiltration in tumors. Results: A risk trait model including 4 eosinophil-associated genes (RAMP3, G6PD, SSRP1, PLOD2) was developed by univariate Cox analysis and Lasso screening. Pathologic grading (p < 0.001) and model risk scores (p < 0.001) were found to be independent predictors of hepatocellular carcinoma (HCC) patient survival. Western blotting revealed higher levels of eosinophil peroxidase (EPX) in HCC tissues compared to adjacent normal tissues. Immunohistochemistry showed that eosinophils mainly infiltrated the connective tissue around HCC. The HCC samples showed low expression of RAMP3 and high expression of G6PD, SSRP1, and PLOD2, as detected by IHC and RT-qPCR analysis. The in vivo mouse experiments showed that IL-33 treatment induced the recruitment of eosinophils and reduced the number of intrahepatic tumor nodules. Conclusion: Overall, eosinophil infiltration in HCC is significantly correlated with patient survival. The risk assessment model based on eosinophil-related genes serves as a reliable clinical prognostic indicator and provides insights for precise treatment of HCC.

A Comprehensive Prognostic Model for Colon Adenocarcinoma Depending on Nuclear-Mitochondrial-Related Genes.

Background: Colon adenocarcinoma (COAD) has increasing incidence and is one of the most common malignant tumors. The mitochondria involved in cell energy metabolism, oxygen free radical generation, and cell apoptosis play important roles in tumorigenesis and progression. The relationship between mitochondrial genes and COAD remains largely unknown. Methods: COAD data including 512 samples were set out from the UCSC Xena database. The nuclear mitochondrial-related genes (NMRGs)-related risk prognostic model and prognostic nomogram were constructed, and NMRGs-related gene mutation and the immune environment were analyzed using bioinformatics methods. Then, a liver metastasis model of colorectal cancer was constructed and protein expression was detected using Western blot assay. Results: A prognostic model for COAD was constructed. Comparing the prognostic model dataset and the validation dataset showed considerable correlation in both risk grouping and prognosis. Based on the risk score (RS) model, the samples of the prognostic dataset were divided into high risk group and low risk group. Moreover, pathologic N and T stage and tumor recurrence in the two risk groups were significantly different. The four prognostic factors, including age and pathologic T stage in the nomogram survival model also showed excellent predictive performance. An optimal combination of nine differentially expressed NMRGs was finally obtained, including LARS2, PARS2, ETHE1, LRPPRC, TMEM70, AARS2, ACAD9, VARS2, and ATP8A2. The high-RS group had more inflamed immune features, including T and CD4+ memory cell activation. Besides, mitochondria-associated LRPPRC and LARS2 expression levels were increased in vivo xenograft construction and liver metastases assays. Conclusion: This study established a comprehensive prognostic model for COAD, incorporating nine genes associated with nuclear-mitochondrial functions. This model demonstrates superior predictive performance across four prognostic factors: age, pathological T stage, tumor recurrence, and overall prognosis. It is anticipated to be an effective model for enhancing the prognosis and treatment of COAD.

Development of a cell adhesion-based prognostic model for multiple myeloma: Insights into chemotherapy response and potential reversal of adhesion effects.

Multiple myeloma (MM) is a hematologic malignancy notorious for its high relapse rate and development of drug resistance, in which cell adhesion-mediated drug resistance plays a critical role. This study integrated four RNA sequencing datasets (CoMMpass, GSE136337, GSE9782, and GSE2658) and focused on analyzing 1706 adhesion-related genes. Rigorous univariate Cox regression analysis identified 18 key prognosis-related genes, including KIF14, TROAP, FLNA, MSN, LGALS1, PECAM1, and ALCAM, which demonstrated the strongest associations with poor overall survival (OS) in MM patients. To comprehensively evaluate the impact of cell adhesion on MM prognosis, an adhesion-related risk score (ARRS) model was constructed using Lasso Cox regression analysis. The ARRS model emerged as an independent prognostic factor for predicting OS. Furthermore, our findings revealed that a heightened cell adhesion effect correlated with tumor resistance to DNA-damaging drugs, protein kinase inhibitors, and drugs targeting the PI3K/Akt/mTOR signaling pathway. Nevertheless, we identified promising drug candidates, such as tirofiban, pirenzepine, erlotinib, and bosutinib, which exhibit potential in reversing this resistance. In vitro, experiments employing NCIH929, RPMI8226, and AMO1 cell lines confirmed that MM cell lines with high ARRS exhibited poor sensitivity to the aforementioned candidate drugs. By employing siRNA-mediated knockdown of the key ARRS model gene KIF14, we observed suppressed proliferation of NCIH929 cells, along with decreased adhesion to BMSCs and fibronectin. This study presents compelling evidence establishing cell adhesion as a significant prognostic factor in MM. Additionally, potential molecular mechanisms underlying adhesion-related resistance are proposed, along with viable strategies to overcome such resistance. These findings provide a solid scientific foundation for facilitating clinically stratified treatment of MM.

High Fibrinogen Levels with Diabetes Mellitus are Associated with All-Cause and Cardiovascular Mortality in Patients with End-Stage Renal Disease and Acute Coronary Syndrome.

As a biomarker of inflammation and a core component in the coagulation pathway, fibrinogen contributes to atherosclerosis and subsequent adverse cardiovascular events and is modified by the occurrence of diabetes mellitus. However, the association between fibrinogen, diabetes status, and mortality in patients with end-stage renal disease (ESRD) and acute coronary syndrome (ACS) remains scarce. A multi-center cohort study enrolled 1079 patients with ESRD and ACS between January 2015 and June 2021. Patients were classified into three groups based on fibrinogen tertiles and were further categorized by diabetes status. The primary outcome was all-cause mortality, while the secondary outcome was cardiovascular mortality. During a median 21.5 months of follow-up, 386 cases of all-cause mortality were recorded, including 262 cases of cardiovascular mortality. Multivariable Cox regression model revealed that patients with the third tertile of fibrinogen and those with diabetes experienced a significantly increased risk of all-cause mortality (fibrinogen: hazard ratio [HR], 1.70; 95% confidence interval [CI], 1.32-2.19; diabetes: HR, 1.36; 95% CI, 1.10-1.68). When patients were stratified by both fibrinogen levels and diabetes status, patients in the third fibrinogen tertile with diabetes had the highest risk of all-cause mortality (HR 2.43, 95% CI 1.69-3.48) compared to those in the first fibrinogen tertile without diabetes. Similar associations were observed for cardiovascular mortality. Notably, incorporating the combined fibrinogen and diabetes status into the Global Registry of Acute Coronary Events (GRACE) score or baseline risk model led to significant improvements in the C-statistics for predicting mortality, surpassing the advancements achieved with any single biomarker. In patients with ESRD and ACS, elevated fibrinogen and diabetes were associated with an increased risk of all-cause and cardiovascular mortality. Categorizing patients based on fibrinogen levels and diabetes status could provide valuable information for risk stratification of these patients.

987 Prospective validation of an automated Peripartum Risk Score Model to predict severe maternal morbidity (SMM)

987 Prospective validation of an automated Peripartum Risk Score Model to predict severe maternal morbidity (SMM)

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.

A Novel Necroptosis-Related Signature Can Predict Prognosis and Chemotherapy Sensitivity in Multiple Myeloma.

Necroptosis is an inflammatory cell death mode, and its association with multiple myeloma (MM) remains unclear. This prospective study first analyzed the association between necroptosis-related signature as well as prognosis and chemotherapy sensitivity in MM using the necroptosis score. Consensus clustering was used to identify necroptosis-related molecular clusters. Least absolute shrinkage and selection operator analysis and multivariate Cox regression analysis were performed to establish the prognostic model of necroptosis-related genes (NRGs). A high necroptosis score was associated with poor prognosis and abundant immune infiltration. Two molecular clusters (clusters A and B) significantly differed in terms of prognosis and tumor microenvironment. Cluster B had a worse prognosis and higher tumor marker pathway activity than cluster A. The risk score model based on four NRGs can accurately predict the prognosis of patients with MM, which was validated in two validation cohorts. Receiver operating characteristic curve analysis showed that the area under the curves of the risk score in predicting the 1-, 3-, and 5-year survival rates were 0.710, 0.758, and 0.834, respectively. Further, the activity of pathways related to proliferation and genetic regulation in the high-risk group significantly increased. The drug prediction results showed that the low-risk score group was more sensitive to bortezomib, cytarabine, and doxorubicin than the high-risk score group. Meanwhile, the high-risk score group was more sensitive to lenalidomide and vinblastine than the low-risk score group. Finally, the upregulation of model genes CHMP1A, FAS, JAK3, and HSP90AA1 in clinical samples collected from patients with MM was validated via real-time polymerase chain reaction. A systematic analysis of NRGs can help identify potential necroptosis-related mechanisms and provide novel biomarkers for MM prognosis prediction, tumor microenvironment evaluation, and personalized treatment planning.

Immune-Related Long Non-Coding RNA Signature Determines Prognosis and Immunotherapeutic Coherence in Esophageal Cancer.

Aim of this study was to explore the immune-related lncRNAs having prognostic role and establishing risk score model for better prognosis and immunotherapeutic coherence for esophageal cancer (EC) patients. To determine the role of immune-related lncRNAs in EC, we analyzed the RNA-seq expression data of 162 EC patients and 11 non-cancerous individuals and their clinically relevant information from the cancer genome atlas (TCGA) database. Bioinformatic and statistical analysis such as Differential expression analysis, co-expression analysis, Kaplan Meier survival analysis, Cox proportional hazards model, ROC analysis of risk model was employed. Utilizing a cutoff criterion (log2FC > 1 + log2FC < -1 and FDR < 0.01), we identified 3737 RNAs were significantly differentially expressed in EC patients. Among these, 2222 genes were classified as significantly differentially expressed mRNAs (demRNAs), and 966 were significantly differentially expressed lncRNAs (delncRNA). Through Pearson correlation analysis between differentially expressed lncRNAs and immune related-mRNAs, we identified 12 immune-related lncRNAs as prognostic signatures for EC. Notably, through Kaplan-Meier analysis on these lncRNAs, we found the low-risk group patients showed significantly improved survival compared to the high-risk group. Moreover, this prognostic signature has consistent performance across training, testing and entire validation cohort sets. Using ESTIMATE and CIBERSORT algorithm we further observed significant enriched infiltration of naive B cells, regulatory T cells resting CD4+ memory T cells, and, plasma cells in the low-risk group compared to high-risk EC patients group. On the contrary, tumor-associated M2 macrophages were highly enriched in high-risk patients. Additionally, we confirmed immune-related biological functions and pathways such as inflammatory, cytokines, chemokines response and natural killer cell-mediated cytotoxicity, toll-like receptor signaling pathways, JAK-STAT signaling pathways, chemokine signaling pathways significantly associated with identified IRlncRNA signature and their co-expressed immune genes. Furthermore, we assessed the predictive potential of the lncRNA signature in immune checkpoint inhibitors; we found that programed cell death ligand 1 (PD-L1; P-value = .048), programed cell death ligand 2 (PD-L2; P-value = .002), and T cell immunoglobulin and mucin-domain containing-3 (TIM-3; P-value = .045) expression levels were significantly higher in low-risk patients compared to high-risk patients. We believe this study will contribute to better prognosis prediction and targeted treatment of EC in the future.

A novel basement membrane-related gene signature predicts prognosis and immunotherapy response in hepatocellular carcinoma.

Basement membranes (BMs) have recently emerged as significant players in cancer progression and metastasis, rendering them promising targets for potential anti-cancer therapies. Here, we aimed to develop a novel signature of basement membrane-related genes (BMRGs) for the prediction of clinical prognosis and tumor microenvironment in hepatocellular carcinoma (HCC). The differentially expressed BMRGs were subjected to univariate Cox regression analysis to identify BMRGs with prognostic significance. A six-BMRGs risk score model was constructed using Least Absolute Shrinkage Selection Operator (LASSO) Cox regression. Furthermore, a nomogram incorporating the BMRGs score and other clinicopathological features was developed for accurate prediction of survival rate in patients with HCC. A total of 121 differentially expressed BMRGs were screened from the TCGA HCC cohort. The functions of these BMRGs were significantly enriched in the extracellular matrix structure and signal transduction. The six-BMRGs risk score, comprising CD151, CTSA, MMP1, ROBO3, ADAMTS5 and MEP1A, was established for the prediction of clinical prognosis, tumor microenvironment characteristics, and immunotherapy response in HCC. Kaplan-Meier analysis revealed that the BMRGs score-high group showed a significantly shorter overall survival than BMRGs score-low group. A nomogram showed that the BMRGs score could be used as a new effective clinical predictor and can be combined with other clinical variables to improve the prognosis of patients with HCC. Furthermore, the high BMRGs score subgroup exhibited an immunosuppressive state characterized by infiltration of macrophages and T-regulatory cells, elevated tumor immune dysfunction and exclusion (TIDE) score, as well as enhanced expression of immune checkpoints including PD-1, PD-L1, CTLA4, PD-L2, HAVCR2, and TIGIT. Finally, a multi-step analysis was conducted to identify two pivotal hub genes, PKM and ITGA3, in the high-scoring group of BMRGs, which exhibited significant associations with an unfavorable prognosis in HCC. Our study suggests that the BMRGs score can serve as a robust biomarker for predicting clinical outcomes and evaluating the tumor microenvironment in patients with HCC, thereby facilitating more effective clinical implementation of immunotherapy.

Construction and Validation of Novel Ferroptosis-related Risk Score Signature and Prognostic Prediction Nomogram for Patients with Colorectal Cancer.

Background: Colorectal cancer (CRC) has a high morbidity and mortality. Ferroptosis is a phenomenon in which metabolism and cell death are closely related. The role of ferroptosis-related genes in the progression of CRC is still not clear. Therefore, we screened and validated the ferroptosis-related genes which could determine the prevalence, risk and prognosis of patients with CRC. Methods: We firstly screened differentially expressed ferroptosis-related genes by The Cancer Genome Atlas (TCGA) database. Then, these genes were used to construct a risk-score model using the least absolute shrinkage and selection operator (LASSO) regression algorithm. The function and prognosis of the ferroptosis-related genes were confirmed using multi-omics analysis. The gene expression results were validated using publicly available databases and qPCR. We also used publicly available data and ferroptosis-related genes to construct a prognostic prediction nomogram. Results: A total of 24 differential expressed genes associated with ferroptosis were screened in this study. A three-gene risk score model was then established based on these 24 genes and GPX3, CDKN2A and SLC7A11 were selected. The significant prognostic value of this novel three-gene signature was also assessed. Furthermore, we conducted RT-qPCR analysis on cell lines and tissues, and validated the high expression of CDKN2A, GPX3 and low expression of SLC7A11 in CRC cells. The observed mRNA expression of GPX3, CDKN2A and SLC7A11 was consistent with the predicted outcomes. Besides, eight variables including selected ferroptosis related genes were included to establish the prognostic prediction nomogram for patients with CRC. The calibration plots showed favorable consistency between the prediction of the nomogram and actual observations. Also, the time-dependent AUC (>0.7) indicated satisfactory discriminative ability of the nomogram. Conclusions: The present study constructed and validated a novel ferroptosis-related three-gene risk score signature and a prognostic prediction nomogram for patients with CRC. Also, we screened and validated the ferroptosis-related genes GPX3, CDKN2A, and SLC7A11 which could serve as novel biomarkers for patients with CRC.

Identification and validation of a novel apoptosis-related prognostic risk score model for lung adenocarcinoma.

The prognostic roles of apoptosis-related genes (ARGs) in lung adenocarcinoma (LUAD) have not been fully elucidated. In this study, differentially expressed genes (DEGs) associated with apoptosis and the hub genes were further identified. The prognostic values of the ARGs were evaluated using the LASSO Cox regression method. Prognostic values were determined using Kaplan-Meier (K-M) curves and receiver operating characteristic (ROC) curves in the TCGA and GEO datasets. The correlations, mutation data, and protein expression of the 10 ARGs predictive models were also analyzed. We identified 130 differentially expressed ARGs. DEGs were used to split LUAD cases into two subtypes whose overall survival (OS) were significantly different (P = 0.025). We developed a novel 10-gene signature using LASSO Cox regression. In both TCGA and GEO datasets, the results of the K-M curve and log-rank test showed significant difference in the survival rate of patients in the high-risk group and low-risk group (P < 0.0001). According to the GO and KEGG analyses, ARGs were enriched in cancer-related terms. In both cohorts, the immune status of the high-risk group was significantly lower than that of the low-risk group. Based on the differential expression of the ARGs, we established a new risk model to predict the prognosis of patients with LUAD.