Coagulation-related Genes Research Articles

Identification of a coagulation-related gene signature for predicting prognosis in recurrent glioma

BackgroundRecurrent gliomas rapidly progress and have high mortality and poor prognosis in the central nervous system. Therefore, further investigation of prognostic and therapeutic markers is needed.MethodsThe mRNA expression, clinical data, and coagulation-related genes (CRGs) associated with recurrent glioma were obtained and calculated from the Chinese Glioma Genome Atlas and Kyoto Encyclopedia of Genes and Genomes databases. The significant CRGs were calculated by Weighted gene co-expression network analysis and PPI network. A prediction model was constructed using the least absolute shrinkage and selection operator regression analysis. Recurrent gliomas were stratified into high and low-risk groups based on the median risk score (RS). The Kaplan–Meier curve was used to analyze the difference in overall survival (OS) between these groups, while the receiver operating characteristic (ROC) curve was used to evaluate the accuracy of the gene model at 1-, 3-, and 5-years. Clinical factors, including age, gender, MGMT methylation status, radiotherapy, chemotherapy, and RS, were included in the univariate and multivariate regression analysis. A prognostic nomogram and calibration curve were established based on these factors.ResultsOverall, seven CRGs associated with the prognosis were identified, including BTK, ITGB1, GNAI3, CFH, LYN, CFI, and F3. OS and survival rates were lower in the high-risk group compared with the low-risk group. The ROC curve demonstrated the area under the curve values >0.65 at 1-, 3-, and 5-years, confirming the reliability of the prognostic model. The univariate regression analysis indicated that tumor grade (grades 2, 3, and 4), histopathology (GBM or not), chemotherapy, IDH mutation, and 1p19q co-deletion status were independent prognostic indicators. Univariate and multivariate regression analyses indicated that RS was an independent prognostic factor for patients with recurrent glioma. Immune analysis revealed low infiltration of resting dendritic cells and high expression of programmed death receptor 1 in the high-risk group.ConclusionThis study comprehensively investigated the correlation between CRGs and recurrent glioma prognosis, offering crucial insights for further research into glioma recurrence mechanisms and treatment strategies.

Identification of coagulation-related genes as potential diagnostic biomarkers for pediatric septic shock

This study aimed to identify important clotting associated genes (CRGs) associated with septic shock in children and explore possible important mechanisms of the disease. Five hub genes with diagnostic performance were identified using GEO database and data from literature. These hub genes have strong correlation with immune cells. ceRNA network was constructed to explore potential pathogenic mechanisms. Ten candidate small molecule compounds were identified. In summary, the hub genes may play an important role in the immunity and disease development of septic shock, providing new ideas and strategies for future diagnosis and mechanism evaluation of children with septic shock.

Coagulation-related genes for thyroid cancer prognosis, immune infltration, staging, and drug sensitivity.

Thyroid cancer (THCA) is the most common endocrine tumor. Coagulation may be associated with the development of cancer, but its role in THCA patients is not yet clear. In this study, we determined the predictive value of coagulation biomarker D-dimer for THCA patient lateral lymph node metastasis (LLNM) through receiver operating characteristics (ROC) analysis and logistic regression analysis. Subsequently, this study used the TCGA database to identify coagulation-related molecular subtypes through consensus clustering analysis and compared their prognosis. We identified coagulation-related genes (CRGs) associated with prognosis in thyroid cancer through gene expression data and clinical information, and constructed a prognostic model by selecting the prognostic CRGs using LASSO regression. Patients were divided into high-risk and low-risk groups based on the median score. Subsequently, prognosis, clinical characteristics, gene mutation occurrence, immune infiltration, function, and drug sensitivity of the two groups were analyzed. We also constructed a nomogram combining the model and clinical features. Finally, the expression of the prognostic CRGs was validated by RT-qPCR. D-dimers had better performance in predicting LLNM(the area under the curve was 0.656 (95% CI 0.580-0.733), with a cut-off value of 0.065 mg/l), and D-dimer>0.065mg/l was an independent predictor of LLNM. Then, we selected 8 prognostic CRGs to construct a predictive model. The prognosis of low-risk group patients was significantly better than that of high-risk group (P<0.001). The results showed significant differences in clinical characteristics, gene mutation occurrence, immune infiltration, function, and drug sensitivity between the high-risk and low-risk groups. We validated by qPCR that these 8 prognostic CRGs were overexpressed in THCA cell lines. Overall, this study provided an in-depth exploration of the potential role of the coagulation in thyroid cancer and its clinical significance, offering a new theoretical basis and research direction for personalized therapy and prognostic evaluation.

A potential coagulation-related diagnostic model associated with immune infiltration for acute myocardial infarction.

The production of pro-coagulation factors can affect the development and prognosis of acute myocardial infarction (AMI). The clinical value of coagulation-related genes (CRGs) was investigated to discover new targets for diagnosing and treating AMI. We screened 335 differentially expressed genes (DEGs) between AMI and healthy individuals based on the GSE66360 dataset. We took the intersection of the obtained DEGs with 139 CRGs. Finally, 10 differentially expressed CEGs were screened out. The random forest algorithm was constructed to identify 6 signature CRGs (THBS1, SERPINA1, THBD, MMP9, MAFF, and PLAU). Subsequently, the established predictive model was found to have good diagnostic accuracy (AUC = 0.9694 in the training cohort [GSE66360 dataset] and 0.9076 in the external validation cohort [GSE48060 dataset]). Consensus clustering identified the CRG clusters, and the accuracy of the grouping was verified. We found that AMI patients can be divided into two distinct subgroups based on the differentially expressed CRGs. Immune cell infiltration level was consistent with the expression levels of CRGs based on single sample gene set enrichment analysis. These findings reveal the potential role of CRGs in AMI. Characterizing the coagulation features of AMI patients can help in the risk stratification of patients and provide personalized treatment strategies.

Comprehensive Analysis of a Platelet- and Coagulation-Related Prognostic Gene Signature Identifies CYP19A1 as a Key Tumorigenic Driver of Colorectal Cancer.

The intricate interplay between the platelet-coagulation system and the progression of malignant tumors has profound therapeutic implications. However, a thorough examination of platelet and coagulation markers specific to colorectal cancer (CRC) is conspicuously absent in the current literature. Consequently, there is an urgent need for further exploration into the mechanistic underpinnings of these markers and their potential clinical applications. By integrating RNA-seq data and clinicopathological information from patients with CRC in the cancer genome atlas, we identified genes related to the platelet-coagulation system using weighted gene co-expression networks and univariate Cox analysis. We established a prognostic risk model based on platelet- and coagulation-related genes using Lasso Cox regression analysis and validated the model in two independent CRC cohorts. We explored potential biological functional disparities between high-risk and low-risk groups through comprehensive bioinformatics analysis. Our findings indicate that colorectal cancer patients classified as high-risk generally exhibit poorer prognoses. Moreover, the model's risk scores were associated with the differential composition of the immune tumor microenvironment, suggesting its applicability to infer immunotherapy responsiveness. Cellular functional experiments and animal experiments indicated that CYP19A1 expression in CRC influences malignant phenotype and platelet activation. In summary, we present a novel platelet- and coagulation-related risk model for prognostic assessment of patients with CRC and confirm the important role of CYP19A1 in promoting malignant progression of CRC.

A novel risk stratification approach and molecular subgroup characterization based on coagulation related genes in colon adenocarcinoma

Colon adenocarcinoma (COAD) represents a significant health concern within the population. Advancing our understanding of COAD is imperative for early detection, enabling personalized treatment interventions, and facilitating the development of effective preventive measures. The coagulation system plays a role in tumor-related pathological processes; however, its specific involvement in COAD and potential contributors remain unclear. This study aimed to establish a novel risk stratification approach by analyzing coagulation related genes (CRGs) associated with COAD. Through a comprehensive bioinformatics analysis of data from public databases, we screened COAD associated CRGs and characterized the associated molecular subtypes. After a comprehensive analysis of the characteristics of each subtype, we applied differentially expressed genes in CRG subtypes to establish a new risk stratification method. Clinical subgroup analysis, immunoinfiltration analysis, therapeutic reactivity prediction and other analytical methods suggest the potential clinical value of the established risk stratification method. As one of the selected targets, the effect of MS4A4A on the proliferation and invasion of COAD was confirmed by in vitro experiments, which partially verified the reliability of bioinformatics results. Our findings delineate CRGs potentially implicated in COAD pathogenesis and offer fresh insights into the influence of the coagulation process on tumorigenesis and progression.

Integrated pan-cancer and scRNA-seq analyses identify a prognostic coagulation-related gene signature associated with tumor microenvironment in lower-grade glioma

Cancer-associated thrombosis is a significant complication in cancer patients, leading to increased morbidity and mortality. The expression of coagulation/fibrinolysis genes, termed the "coagulome", plays a critical role in this process. Using the single-sample gene set enrichment analysis (ssGSEA), we identified seven cancer types with significantly activated coagulation pathways, focusing on lower-grade glioma (LGG) and stomach adenocarcinoma due to their predictive value for overall survival. Through 1000 iterations of the Least Absolute Shrinkage and Selection Operator (LASSO), we selected prognostic genes and constructed effective Cox regression models, particularly for LGG. Incorporating clinical characteristics, we constructed a nomogram for LGG, achieving an impressive area under the curve (AUCs) of 0.79, 0.82, and 0.81 at 1, 3, and 5 years in the test dataset, indicating strong potential for clinical application. Functional enrichment analysis between high-risk and low-risk LGG groups revealed significant enrichment of genes involved in the inflammatory response, interferon-gamma response, and epithelial-mesenchymal transition pathways. Combined with CIBERSORT and single-cell RNA sequencing analysis of LGG, our results demonstrated that the interplay between coagulation and the tumor microenvironment, particularly involving gliomas and myeloid cells, significantly influences tumor progression and patient outcomes.

Identification of coagulation diagnostic biomarkers related to the severity of spinal cord injury

BackgroundBlood always shows coagulation changes after spinal cord injury (SCI), and identifying these blood changes may be helpful for diagnosis and treatment of SCI. Nevertheless, studies to date on blood coagulation changes after SCI in humans are not comprehensive. Therefore, this study aims to identify blood coagulation diagnostic biomarkers and immune changes related to SCI and its severity levels. MethodsHuman blood sequencing datasets were obtained from public databases. Differentially expressed coagulation-related genes were analyzed (DECRGs). Enrichment analysis and assessment of immune changes were conducted. Weighted gene co-expression network analysis, least absolute shrinkage and selection operator logistic regression were used to identify biomarkers. Validation for these biomarkers was performed. The correlation between biomarkers and immune cells was evaluated. Transcription factors, miRNA, lncRNA, and drugs that can regulate biomarkers were analyzed. ResultsDECRGs associated with SCI and its different grades were identified, showing enrichment in altered coagulation and immune-related signaling pathways. ADAM9, CD55, and STAT4 were identified as coagulation diagnostic biomarkers for SCI. IRF4 and PABPC4 were identified as coagulation diagnostic biomarkers for American Spinal Injury Association Impairment Scale (AIS) A grade of SCI. GP9 was designated as a diagnostic biomarker for AIS D grade of SCI. Immune changes in blood of SCI and its different grades were observed. Correlation between diagnostic biomarkers and immune cells were identified. Transcription factors, miRNA, lncRNA, and drugs that can regulate diagnostic biomarker expression were discovered. ConclusionTherefore, detecting the expression of these putative diagnostic biomarkers and related immune changes may be helpful for predicting the severity of SCI. Uncovering potential regulatory mechanisms for biomarkers may be beneficial for further research.

Identification and validation of a novel 17 coagulation-related genes signature for predicting prognostic risk in colorectal cancer

BackgroundPatients with colorectal cancer commonly experience disturbances in coagulation homeostasis. Activation of the coagulation system contributes to cancer-associated thrombosis as the second risk factor for death in cancer patients. This study intended to discover coagulation-related genes and construct a risk model for colorectal cancer patients' prognosis. MethodsCoagulation-related genes were identified by searching coagulation-related pathways in the Molecular Signatures Database. Transcriptomic data and clinical data were downloaded from the Cancer Genome Atlas and Gene Expression Omnibus datasets. Univariate Cox and backward stepwise regression were utilized to identify prognosis-related genes and construct a predictive risk model for the training cohort. Next, survival analysis determines the risk model's predictive power, correlation with clinicopathological characteristics, and nomogram. Additionally, we characterized the variances in immune cell infiltration, somatic mutations, immune checkpoint molecules, biological functions, and drug sensitivity between the high- and low-score patients. ResultEight hundred forty-five genes were obtained by searching the theme term "coagulation" after de-duplication. After univariate regression analysis, 69 genes correlated with prognosis were obtained from the Cancer Genome Atlas dataset. A signature consisting of 17 coagulation-related genes was established through backward stepwise regression. The Kaplan-Meier curve indicated a worse prognosis for high-score patients. Time-dependent receiver operating characteristic curve analysis demonstrated high accuracy in predicting overall survival. Further, the results were validated by two independent datasets (GSE39582 and GSE17536). Combined with clinicopathological characteristics, the risk model was proven to be an independent prognostic factor to predict poor pathological status and worse prognosis. Furthermore, high-score patients had significantly higher stromal cell infiltration. Low-score patients were associated with high infiltration of resting memory CD4+ T cells, activated CD4+ T cells, and T follicular helper cells. The low-score patients exhibited increased expression of immune checkpoint genes, and this might be relevant to their better prognosis. High-score patients exhibited lower IC50 values of Paclitaxel, Rapamycin, Temozolomide, Cyclophosphamide, etc. The differential signaling pathways mainly involve the calcium signaling pathway and the neuroactive ligand-receptor interaction. Lastly, a nomogram was constructed and showed a good prediction. ConclusionThe prognostic signature of 17 coagulation-related genes had significant prognostic value for colorectal cancer patients. We expect to improve treatment modalities and benefit more patients through research on molecular features.

Molecular characterization of positively selected genes contributing aquatic adaptation in marine mammals.

Marine mammals, which have evolved independently into three distinct lineages, share common physiological features that contribute to their adaptation to the marine environment. To identify positively selected genes (PSGs) for adaptation to the marine environment using available genomic data from three taxonomic orders: cetaceans, pinnipeds, and sirenians. Based on the genomes within each group of Artiodactyla, Carnivora and Afrotheria, we performed selection analysis using the branch-site model in CODEML. Based on the branch-site model, 460, 614, and 359 PSGs were predicted for the cetaceans, pinnipeds, and sirenians, respectively. Functional enrichment analysis indicated that genes associated with hemostasis were positively selected across all lineages of marine mammals. We observed positive selection signals for the hemostasis and coagulation-related genes plasminogen activator, urokinase (PLAU), multimerin 1 (MMRN1), gamma-glutamyl carboxylase (GGCX), and platelet endothelial aggregation receptor 1 (PEAR1). Additionally, we found out that the sodium voltage-gated channel alpha subunit 9 (SCN9A), serine/arginine repetitive matrix 4 (SRRM4), and Ki-ras-induced actin-interacting protein (KRAP) are under positive selection pressure and are associated with cognition, neurite outgrowth, and IP3-mediated Ca2 + release, respectively. This study will contribute to our understanding of the adaptive evolution of marine mammals by providing information on a group of candidate genes that are predicted to influence adaptation to aquatic environments, as well as their functional characteristics.

Identification of early coagulation changes associated with survival outcomes post severe burns from multiple perspectives

Coagulation alterations manifest early after severe burns and are closely linked to mortality outcomes. Nevertheless, the precise characterization of coagulation changes associated with early mortality remains elusive. We examined alterations in indicators linked to mortality outcomes at both the transcriptomic and clinical characteristic levels. At the transcriptomic level, we pinpointed 28 differentially expressed coagulation-related genes (DECRGs) following burn injuries and endeavored to validate their causal relationships through Mendelian randomization. DECRGs tied to survival exhibit a significant association with neutrophil function, wherein the expression of CYP4F2 and P2RX1 serves as robust predictors of fatal outcomes. In terms of clinical indicators, early levels of D-dimer and alterations in serum calcium show a strong correlation with mortality outcomes. Coagulation depletion and fibrinolytic activation, stemming from the hyperactivation of coagulation pathways post-severe burns, are strongly linked to patient mortality. Monitoring these early coagulation markers with predictive value can effectively identify individuals necessitating priority critical care.

Unraveling breast cancer prognosis: a novel model based on coagulation-related genes.

Breast cancer is highly heterogeneous, presenting challenges in prognostic assessment. Developing a universally applicable prognostic model could simplify clinical decision-making. This study aims to develop and validate a novel breast cancer prognosis model using coagulation-related genes with broad clinical applicability. A total of 203 genes related to coagulation were obtained from the KEGG database, and the mRNA data of 1,099 tumor tissue samples and 572 samples of normal tissue were retrieved from the TCGA-BRCA cohort and GTEx databases. The R package "limma" was utilized to detect variations in gene expression related to coagulation between the malignancies and normal tissue. A model was constructed in the TCGA cohort through a multivariable Cox regression analysis, followed by validation using the GSE42568 dataset as the testing set. Constructing a nomogram incorporating clinical factors to enhance the predictive capacity of the model. Utilizing the ESTIMATE algorithm to investigate the immune infiltration levels in groups with deferent risk. Performing drug sensitivity analysis using the "oncoPredict" package. A risk model consisting of six coagulation-associated genes (SERPINA1, SERPINF2, C1S, CFB, RASGRP1, and TLN2) was created and successfully tested for validation. Identified were 6 genes that serve as protective factors in the model's development. Kaplan-Meier curves revealed a worse prognosis in the high-risk group compared to the low-risk group. The ROC analysis showed that the model accurately forecasted the overall survival (OS) of breast cancer patients at 1, 3, and 5 years. Nomogram accompanied by calibration curves can also provide better guidance for clinical decision-making. The low-risk group is more likely to respond well to immunotherapy, whereas the high-risk group may show improved responses to Gemcitabine treatment. Furthermore, individuals in distinct risk categories displayed different responses to various medications within the identical therapeutic category. We established a breast cancer prognostic model incorporating six coagulation-associated genes and explored its clinical utility. This model offers valuable insights for clinical decision-making and drug selection in breast cancer patients, contributing to personalized and precise treatment advancements.

Exploring the role of coagulation-related genes in renal cell carcinoma: Implications for tumor microenvironment and prognostic biomarkers

PurposeClear cell renal cell carcinoma (ccRCC) frequently progresses to advanced stages due to tumor thrombus (TTs) formation. In this study, we aimed to investigate the role of coagulation-related pathway activation in the progression of ccRCC. MethodsConsensus clustering was used to identify coagulation-related molecular clusters of ccRCC patients from The Cancer Genome Atlas Program (TCGA) database. The function of coagulation and its correlation with the immune microenvironment were investigated. Protein-protein interactions and differential expression analysis were used to identify the key gene, which was verified by external experiments. The coagulation-associated risk score was constructed by cox proportional hazards regression. ResultsNotable disparities were detected in immune characteristics, prognostic differentiation and drug sensitivity between two coagulation-related clusters. Through the integration of clinical stage significance and protein-protein interactions, the key gene MMP9 was screened and it was significantly correlated with CD4+T cells, CD8+T cells and Treg cells. A coagulation-related risk score prognostic model was developed in the Cancer Genome Atlas (TCGA) cohort for risk stratification and prognosis prediction. The prognostic predictive values of the coagulation-related risk score were further authenticated in both TCGA-KIRC and E-MTAB-1980 cohorts. ConclusionThere is an obvious correlation between the coagulation and the tumor microenvironment in ccRCC. As a key coagulation-related gene, MMP9 may promote the progression of renal cell carcinoma by influencing immune infiltration of CD8+T cells and Treg cells. Additionally, the risk score could be used as a durable prognostic biomarker, which could assist in clinical decision making for ccRCC patients.

C3AR1 is a regulatory factor associated with coagulation cascade and inflammation in sepsis.

Sepsis is a leading cause of mortality in intensive care units. Sepsis is associated with activation of the coagulation cascade and inflammation. The aim of this study was to identify coagulation-related genes in sepsis that may provide translational potential therapeutic targets. The datasets GSE28750, GSE95233, and GSE65682 were downloaded from the gene expression omnibus database. Consensus-weighted gene co-expression network analysis (WGCNA) was used to identify sepsis modules. Gene set enrichment analysis was used to identify genes enriched in the coagulation cascade. The value of hub-gene in immunological analysis was tested in the validation sets (GSE95233). The value of hub-gene in clinical prognosis was tested in the validation sets (GSE65582). One thousand one hundred seventy-six genes with high connectivity in the clinically significant module were identified as hub genes. Ten genes were found to be enriched in coagulation-related signaling pathways. C3AR1 was selected for further analysis. The immune infiltration analysis showed that lower expression of C3AR1 was associated with immune response in sepsis and could be an independent predictor of survival status in sepsis patients. Meanwhile, univariate and multivariate Cox analysis showed that C3AR1 had a significant correlation with survival. C3AR1 may become an effective biomarker for worse outcomes in sepsis patients associated with immune and coagulation cascade.

Prognostic gene landscapes and therapeutic insights in sepsis-induced coagulopathy

BackgroundSepsis is a common and critical condition encountered in clinical practice that can lead to multi-organ dysfunction. Sepsis-induced coagulopathy (SIC) significantly affects patient outcomes. However, the precise mechanisms remain unclear, making the identification of effective prognostic and therapeutic targets imperative. MethodsThe analysis of transcriptome data from the whole blood of sepsis patients, facilitated the identification of key genes implicated in coagulation. Then we developed a prognostic model and a nomogram to predict patient survival. Consensus clustering classified sepsis patients into three subgroups for comparative analysis of immune function and immune cell infiltration. Single-cell sequencing elucidated alterations in intercellular communication between platelets and immune cells in sepsis, as well as the role of the coagulation-related gene FYN. Real-time quantitative PCR determined the mRNA levels of critical coagulation genes in septic rats' blood. Finally, administration of a FYN agonist to septic rats was observed for its effects on coagulation functions and survival. ResultsThis study identified four pivotal genes—CFD, FYN, ITGAM, and VSIG4—as significant predictors of survival in patients with sepsis. Among them, CFD, FYN, and ITGAM were underexpressed, while VSIG4 was upregulated in patients with sepsis. Moreover, a nomogram that incorporates the coagulation-related genes (CoRGs) risk score with clinical features of patients accurately predicted survival probabilities. Subgroup analysis of CoRGs expression delineated three molecular sepsis subtypes, each with distinct prognoses and immune profiles. Single-cell sequencing shed light on heightened communication between platelets and monocytes, T cells, and plasmacytoid dendritic cells, alongside reduced interactions with neutrophils in sepsis. The collagen signaling pathway was found to be essential in this dynamic. FYN may affect platelet function by modulating factors such as ELF1, PTCRA, and RASGRP2. The administration of the FYN agonist can effectively improve coagulation dysfunction and survival in septic rats. ConclusionsThe research identifies CoRGs as crucial prognostic markers for sepsis, highlighting the FYN gene's central role in coagulation disorders associated with the condition and suggesting novel therapeutic intervention strategies.

Immune Gene Networks from Lung Cancer Patients Treated with Immune Checkpoint Inhibitors.

The association between immune checkpoint inhibitors (ICIs) and immune gene networks in squamous lung cancer (LUSC) and lung adenocarcinoma (LUAD) was studied. Immune gene networks were constructed using RNA-seq data from the gene expression omnibus (GEO) database. Datasets with more than 10 samples of normal control and tumor tissues were selected; of these, GSE87340, GSE120622, and GSE111907 were suitable for analysis. Gene set enrichment for pathway analysis was performed. For immune gene network construction, 998 unique immune genes were selected from 21 pathways in the Kyoto Encyclopedia of Genes and Genomes (KEGG). Gene function annotation was performed based on the KEGG, Gene Ontology, and Reactome databases. Tumor tissues showed decreased coagulation, hematopoiesis, and innate immune pathways, whereas complement- and coagulation-related genes were prominent in the tumor immune gene network. The average numbers of neighbors, clustering coefficients, network diameters, path lengths, densities, and heterogeneities were highest for normal tissue, followed by LUAD and LUSC. Decreased coagulation genes, which were prominent in tumor immune networks, imply functional attenuation. LUAD was deviated from normal tissue, based on network parameters. Tumor tissues showed decreased immune function, and the deviation of LUSC from normal tissue might explain LUSC's better therapeutic response to ICI treatment.

A novel prognostic signature of coagulation-related genes leveraged by machine learning algorithms for lung squamous cell carcinoma

Coagulation-related genes (CRGs) have been demonstrated to be essential for the development of certain tumors; however, little is known about CRGs in lung squamous cell carcinoma (LUSC). In this study, we adopted CRGs to construct a coagulation-related gene prognostic signature (CRGPS) using machine learning algorithms. Using a set of 92 machine learning integrated algorithms, the CRGPS was determined to be the optimal prognostic signature (median C-index = 0.600) for predicting the prognosis of an LUSC patient. The CRGPS was not only superior to traditional clinical parameters (e.g., T stage, age, and gender) and its commutative genes but also outperformed 19 preexisting prognostic signatures for LUSC on predictive accuracy. The CRGPS score was positively correlated with poor prognoses in patients with LUSC (hazard ratio > 1, p < 0.05), indicating its suitability as a prognostic marker for this disease. The CRGPS was observed to be inversely correlated with the degree of infiltration of natural killer cells. For some tumors, patients with lower CRGPS scores are more likely to experience enhanced immunotherapy effects (area under the curve = 0.70), which implies that the CRGPS can potentially predict immunotherapy efficacy. A high CRGPS score is predictive of an LUSC patient being sensitive to several drugs. Collectively, these findings indicate that the CRGPS may be a reliable indicator of the prognoses of patients with LUSC and may be useful for the clinical management of such patients.

Establishment and analysis of artificial neural network diagnosis model for coagulation-related molecular subgroups in coronary artery disease.

Background: Coronary artery disease (CAD) is the most common type of cardiovascular disease and cause significant morbidity and mortality. Abnormal coagulation cascade is one of the high-risk factors in CAD patients, but the molecular mechanism of coagulation in CAD is still limited. Methods: We clustered and categorized 352 CAD paitents based on the expression patterns of coagulation-related genes (CRGs), and then we explored the molecular and immunological variations across the subgroups to reveal the underlying biological characteristics of CAD patients. The feature genes between CRG-subgroups were further identified using a random forest model (RF) and least absolute shrinkage and selection operator (LASSO) regression, and an artificial neural network prediction model was constructed. Results: CAD patients could be divided into the C1 and C2 CRG-subgroups, with the C1 subgroup highly enriched in immune-related signaling pathways. The differential expressed genes between the two CRG-subgroups (DE-CRGs) were primarily enriched in signaling pathways connected to signal transduction and energy metabolism. Subsequently, 10 feature DE-CRGs were identified by RF and LASSO. We constructed a novel artificial neural network model using these 10 genes and evaluated and validated its diagnostic performance on a public dataset. Conclusion: Diverse molecular subgroups of CAD patients may each have a unique gene expression pattern. We may identify subgroups using a few feature genes, providing a theoretical basis for the precise treatment of CAD patients with different molecular subgroups.

Clinical significance, molecular characterization, and immune microenvironment analysis of coagulation-related genes in clear cell renal cell carcinoma.

Numerous studies have revealed a tight connection between tumor development and the coagulation system. However, the effects of coagulation on the prognosis and tumor microenvironment (TME) of clear cell renal cell carcinoma (ccRCC) remain poorly understood. We employed the consensus clustering method to characterize distinct molecular subtypes associated with coagulation patterns. Subsequently, we examined variations in the overall survival (OS), genomic profiles, and TME characteristics between these subtypes. To develop a prognostic coagulation-related risk score (CRRS) model, we utilized the least absolute shrinkage and selection operator Cox regression and stepwise multivariate Cox regression analyses. We also created a nomogram to aid in the clinical application of the risk score, evaluating the relationships between the CRRS and the immune microenvironment, responsiveness to immunotherapy, and targeted treatment. The clinical significance of PLAUR and its biological function in ccRCC were also further analyzed. There were significant differences in clinical features, prognostic stratification, genomic variation, and TME characteristics between the two coagulation-related subtypes. We established and validated a CRRS using six coagulation-related genes that can be employed as an effective indicator of risk stratification and prognosis estimation for ccRCC patients. Significant variations in survival outcomes were observed between the high- and low-risk groups. The nomogram was proficient in predicting the 1-, 3-, and 5-year OS. Additionally, the CRRS emerged as a novel tool for evaluating the clinical effectiveness of immunotherapy and targeted treatments in ccRCC. Moreover, we confirmed upregulated PLAUR expression in ccRCC samples that was significantly correlated with poor patient prognosis. PLAUR knockdown notably inhibited ccRCC cell proliferation and migration. Our data suggested that CRRS may be employed as a reliable predictive biomarker that can provide therapeutic benefits for immunotherapy and targeted therapy in ccRCC.

Genes Differentially Expressed Across Major Arteries Are Enriched in Endothelial Dysfunction-Related Gene Sets: Implications for Relative Inter-artery Atherosclerosis Risk.

Atherosclerosis differs across major arteries. Although the biological basis is not fully understood, limited evidence of genetic differences has been documented. This study, therefore, was aimed to identify differentially expressed genes between clinically relevant major arteries and investigate their enrichment in endothelial dysfunction-related gene sets. A bioinformatic analysis of publicly available gene-level read counts for coronary, aortic, and tibial arteries was performed. Differential gene expression was conducted with DeSeq2 at a false discovery rate of 0.05. Differentially expressed genes were then subjected to over-representation analysis and active-subnetwork-oriented enrichment analysis, both at a false discovery rate of 0.005. Enriched terms common to both analyses were categorized for each contrast into immunity/inflammation-, membrane biology-, lipid metabolism-, and coagulation-related terms, and the top differentially expressed genes validated against Swiss Institute of Bioinformatics' Bgee database. There was mostly upregulation of differentially expressed genes for the coronary/tibial and aorta/tibial contrasts, but milder changes for the coronary/aorta contrast. Transcriptomic differences between coronary or aortic versus tibial samples largely involved immunity/inflammation-, membrane biology-, lipid metabolism-, and coagulation-related genes, suggesting potential to modulate endothelial dysfunction and atherosclerosis. These results imply atheroprone coronary and aortic environments compared with tibial artery tissue, which may explain observed relative inter-artery atherosclerosis risk.