Prediction Of Clinical Prognosis Research Articles

Single-cell transcriptomics identify a novel macrophage population associated with bone invasion in pituitary neuroendocrine tumors

BackgroundBone-invasive Pituitary Neuroendocrine Tumors (BI PitNETs) epitomize an aggressive subtype of pituitary tumors characterized by bone invasion, culminating in extensive skull base bone destruction and fragmentation. This infiltration poses a significant surgical risk due to potential damage to vital nerves and arteries. However, the mechanisms underlying bone invasion caused by PitNETs remain elusive, and effective interventions for PitNET-induced bone invasion are lacking in clinical practice.MethodsIn this study, we performed single-cell (n = 87,287) RNA sequencing on 10 cases of bone-invasive PitNETs and 5 cases of non-bone-invasion PitNETs (Non-BI PitNETs). We identified various cell types and determined their interactions through cell-cell communication analysis, which was further validated experimentally.ResultsWe identified a novel TNF-α+ TAM macrophage subset. BI PitNETs showed increased IL-34 secretion, impacting TNF-α+ TAMs via the IL34/CSF1R axis, leading to TNF-α production. TNF-α+ TAMs, in turn, communicate with CD14+ monocytes to promote their differentiation into osteoclasts and leading to bone invasion. In addition, we defined a gene signature for TNF-α+ TAM to guide the clinical prognosis prediction of BI PitNETs.ConclusionsOur study elucidates the tumor microenvironment changes in bone invasion and identifies the critical role of TNF-α+ TAMs in promoting bone invasion of PitNETs, laying a foundation for developing new molecular markers or therapeutic agents targeting BI PitNETs.

Prediction of clinical prognosis and drug sensitivity in hepatocellular carcinoma through the combination of multiple cell death pathways.

Hepatocellular carcinoma (HCC) is the sixth most common malignant tumor, highlighting a significant need for reliable predictive models to assess clinical prognosis, disease progression, and drug sensitivity. Recent studies have highlighted the critical role of various programmed cell death pathways, including apoptosis, necroptosis, pyroptosis, ferroptosis, cuproptosis, entotic cell death, NETotic cell death, parthanatos, lysosome-dependent cell death, autophagy-dependent cell death, alkaliptosis, oxeiptosis, and disulfidptosis, in tumor development. Therefore, by investigating these pathways, we aimed to develop a predictive model for HCC prognosis and drug sensitivity. We analyzed transcriptome, single-cell transcriptome, genomic, and clinical information using data from the TCGA-LIHC, GSE14520, GSE45436, and GSE166635 datasets. Machine learning algorithms were used to establish a cell death index (CDI) with seven gene signatures, which was validated across three independent datasets, showing that high CDI correlates with poorer prognosis. Unsupervised clustering revealed three molecular subtypes of HCC with distinct biological processes. Furthermore, a nomogram integrating CDI and clinical information demonstrated good predictive performance. CDI was associated with immune checkpoint genes and tumor microenvironment components using single-cell transcriptome analysis. Drug sensitivity analysis indicated that patients with high CDI may be resistant to oxaliplatin and cisplatin but sensitive to axitinib and sorafenib. In summary, our model offers a precise prediction of clinical outcomes and drug sensitivity for patients with HCC, providing valuable insights for personalized treatment strategies.

Lactylation Modification as a Promoter of Bladder Cancer: Insights from Multi-Omics Analysis.

Bladder cancer (BLAC) is a malignant tumor with high morbidity and mortality. The establishment of a prognostic model for BLAC is of great significance for clinical prognosis prediction and treatment guidance. Lactylation modification is a newly discovered post-transcriptional modification of proteins, which is closely related to the occurrence and development of tumors. Multiple omics data of BLAC were obtained from the GEO database and TCGA database. The Lasso algorithm was used to establish a prognostic model related to lactylation modification, and its predictive ability was tested with a validation cohort. Functional enrichment analysis, tumor microenvironment analysis, and treatment response evaluation were performed on the high- and low-risk groups. Single-cell and spatial transcriptome data were used to analyze the distribution characteristics of model genes and their changes during epithelial carcinogenesis. A prognostic model consisting of 12 genes was constructed. The survival rate of the high-risk group was significantly lower than that of the low-risk group. The multiple ROC curve showed that the prediction efficiency of the model was higher than that of the traditional clinical tumor grading. Functional enrichment analysis showed that glycolysis and hypoxia pathways were significantly upregulated in the high-risk group. The high-risk group was more sensitive to most first-line chemotherapy drugs, while the low-risk group had a better response to immunotherapy. Single-cell sequencing analysis revealed the dynamic changes of model genes during the transition of epithelial cells to squamous-differentiated cells. Spatial transcriptome analysis showed the spatial distribution characteristics of the model genes. The lactylation-related models have a satisfactory predictive ability and the potential to guide the clinical treatment of BLAC. This model has significant biological implications at the single-cell level as well as at the spatial level.

NecroGlobalGCN: Integrating micronecrosis information in HCC prognosis prediction via graph convolutional neural networks

Background and ObjectiveHepatocellular carcinoma (HCC) ranks fourth in cancer mortality, underscoring the importance of accurate prognostic predictions to improve postoperative survival rates in patients. Although micronecrosis has been shown to have high prognostic value in HCC, its application in clinical prognosis prediction requires specialized knowledge and complex calculations, which poses challenges for clinicians. It would be of interest to develop a model to help clinicians make full use of micronecrosis to assess patient survival. MethodsTo address these challenges, we propose a HCC prognosis prediction model that integrates pathological micronecrosis information through Graph Convolutional Neural Networks (GCN). This approach enables GCN to utilize micronecrosis, which has been shown to be highly correlated with prognosis, thereby significantly enhancing prognostic stratification quality. We developed our model using 3622 slides from 752 patients with primary HCC from the FAH-ZJUMS dataset and conducted internal and external validations on the FAH-ZJUMS and TCGA-LIHC datasets, respectively. ResultsOur method outperformed the baseline by 8.18% in internal validation and 9.02% in external validations. Overall, this paper presents a deep learning research paradigm that integrates HCC micronecrosis, enhancing both the accuracy and interpretability of prognostic predictions, with potential applicability to other pathological prognostic markers. ConclusionsThis study proposes a composite GCN prognostic model that integrates information on HCC micronecrosis, collecting large dataset of HCC histopathological images. This approach could assist clinicians in analyzing HCC patient survival and precisely locating and visualizing necrotic tissues that affect prognosis. Following the research paradigm outlined in this paper, other prognostic biomarker integration models with GCN could be developed, significantly enhancing the predictive performance and interpretability of prognostic model.

Comprehensive analysis of Epha10 as a predictor of clinical prognosis and immune checkpoint therapy efficacy in non-small cell lung cancer

The EphA family belongs to a large group of membrane receptor tyrosine kinases. Emerging evidence indicates that the EphA family participates in tumour occurrence and progression. Nonetheless, the expression patterns and prognostic values of the nine EphAs in non-small cell lung cancer (NSCLC) have rarely been studied before. In the current study, we comprehensively analysed the expression and prognostic role of EphA family members by different means. The Cancer Genome Atlas and Gene Expression Profiling Interactive Analysis databases were used to investigate the expression of EphAs in NSCLC. The cBioPortal database was applied to analyse the prognostic values and genetic mutations of EphAs.We discovered that the expression of EphA10 was significantly higher in NSCLC tissues than in adjacent noncancerous tissues, and survival analyses showed that a higher level of EphA10 predicted poor prognosis. Further exploration into the role of EphA10 by ESTIMATE, CIBERSORT, and ssGSEA analysis found that it was also related to immune infiltration and higher expression of targets of ICI targets. In conclusion, this study revealed that among the EphA family members, EphA10 played an oncogenic role and was a promising biomarker for poor prognosis and better immunotherapy response in NSCLC.

Immune infiltration and prognosis in gastric cancer: role of NAD+ metabolism-related markers.

This study endeavored to develop a nicotinamide adenine dinucleotide (NAD+) metabolism-related biomarkers in gastric cancer (GC), which could provide a theoretical foundation for prognosis and therapy of GC patients. In this study, differentially expressed genes (DEGs1) between GC and paraneoplastic tissues were overlapped with NAD+ metabolism-related genes (NMRGs) to identify differentially expressed NMRGs (DE-NMRGs). Then, GC patients were divided into high and low score groups by gene set variation analysis (GSVA) algorithm for differential expression analysis to obtain DEGs2, which was overlapped with DEGs1 for identification of intersection genes. These genes were further analyzed using univariate Cox and least absolute shrinkage and selection operator (LASSO) regression analyses to obtain prognostic genes for constructing a risk model. Enrichment and immune infiltration analyses further investigated investigate the different risk groups, and qRT-PCR validated the prognostic genes. Initially, we identified DE-NMRGs involved in NAD biosynthesis, with seven (DNAJB13, CST2, THPO, CIDEA, ONECUT1, UPK1B and SNCG) showing prognostic significance in GC. Subsequent, a prognostic model was constructed in which the risk score, derived from the expression profiles of these genes, along with gender, emerged as robust independent predictors of patient outcomes in GC. Enrichment analysis linked high-risk patients to synaptic membrane pathways and low-risk to the CMG complex pathway. Tumor immune infiltration analysis revealed correlations between risk scores and immune cell abundance, suggesting a relationship between NAD+ metabolism and immune response in GC. The prognostic significance of our identified genes was validated by qRT-PCR, which confirmed their upregulated expression in GC tissue samples. In this study, seven NAD+ metabolism-related markers were established, which is of great significance for the development of prognostic molecular biomarkers and clinical prognosis prediction for gastric cancer patients.

The genomic and immunogenomic landscape of mechanics pathway informs clinical prognosis and response to mechanotherapy.

Mechanics shape cell and tissue plasticity and maintain their homeostasis. In cancers, mechanical signals regulate cancer hallmarks via mechanotransduction pathways, such as proliferation, metastasis and metabolic reprogramming. However, comprehensive characterization of mechanotransduction pathway genes and their clinical relevance across different cancer types remains untouched. Herein, we systematically portrayed the alterations of mechanotransduction pathway genes across 31 cancer types using The Cancer Genome Atlas (TCGA) databases. All the cancer types could be categorized into 6 subtypes based upon the transcriptional pattern of mechanics pathway genes. Each subtype has its own unique molecular expression pattern, mutation landscapes, immune infiltrates, and patient clinical outcome. We further found that the responses of two subtypes of cancers, one with the optimal outcome and the other with the worst prognosis, to a classical mechanotherapeutic agent (Fasudil, RhoA/ROCK inhibitor) were totally different, indicating that our cancer stratification system based upon mechanotransduction pathway genes could inform clinical responses of patients to mechanotherapeutic agents. Collectively, our study provides a novel pan-cancer landscape of the mechanotransduction pathways and underscores its potential clinical significance in the prediction of clinical prognosis and therapeutic responses to mechanotherapy among cancer patients.

Comparison of Cox regression and generalized Cox regression models to machine learning in predicting survival of children with diffuse large B-cell lymphoma.

The incidence of diffuse large B-cell lymphoma (DLBCL) in children is increasing globally. Due to the immature immune system in children, the prognosis of DLBCL is quite different from that of adults. We aim to use the multicenter large retrospective analysis for prognosis study of the disease. For our retrospective analysis, we retrieved data from the Surveillance, Epidemiology and End Results (SEER) database that included 836 DLBCL patients under 18 years old who were treated at 22 central institutions between 2000 and 2019. The patients were randomly divided into a modeling group and a validation group based on the ratio of 7:3. Cox stepwise regression, generalized Cox regression and eXtreme Gradient Boosting (XGBoost) were used to screen all variables. The selected prognostic variables were used to construct a nomogram through Cox stepwise regression. The importance of variables was ranked using XGBoost. The predictive performance of the model was assessed by using C-index, area under the curve (AUC) of receiver operating characteristic (ROC) curve, sensitivity and specificity. The consistency of the model was evaluated by using a calibration curve. The clinical practicality of the model was verified through decision curve analysis (DCA). ROC curve demonstrated that all models except the non-proportional hazards and non-log linearity (NPHNLL) model, achieved AUC values above 0.7, indicating high accuracy. The calibration curve and DCA further confirmed strong predictive performance and clinical practicability. In this study, we successfully constructed a machine learning model by combining XGBoost with Cox and generalized Cox regression models. This integrated approach accurately predicts the prognosis of children with DLBCL from multiple dimensions. These findings provide a scientific basis for accurate clinical prognosis prediction.

Radiomic score for lung nodules as a prognostic biomarker in locally advanced rectal cancer patients: A bi-institutional study.

Undetermined lung nodules are common in locally advanced rectal cancer (LARC) and lack precise risk stratification. This study aimed to develop a radiomic-based score (Rad-score) to distinguish metastasis and predict overall survival (OS) in patients with LARC and lung nodules. Retrospective data from two institutions (July 10, 2006-September 24, 2015) was used to develop and validate the Rad-score for distinguishing lung nodule malignancy. The prognostic value of the Rad-score was investigated in LARC cohorts, leading to the construction and validation of a clinical and radiomic score (Cli-Rad-score) that incorporates both clinical and radiomic information for the purpose of improving personalized clinical prognosis prediction. Descriptive statistics, survival analysis, and model comparison were performed to assess the results. The Rad-score demonstrated great performance in distinguishing malignancy, with C-index values of 0.793 [95% CI: 0.729-0.856] in the training set and 0.730 [95% CI: 0.666-0.874] in the validation set. In independent LARC cohorts, Rad-score validation achieved C-index values of 0.794 [95% CI: 0.737-0.851] and 0.747 [95% CI: 0.615-0.879]. Regarding prognostic prediction, Rad-score effectively stratified patients. Cli-Rad-score outperformed the clinicopathological information alone in risk stratification, as evidenced by significantly higher C-index values (0.735 vs. 0.695 in the internal set and 0.618 vs. 0.595 in the external set). CT-based radiomics could serve as a reliable and powerful tool for lung nodule malignancy distinction and prognostic prediction in LARC patients. Rad-score predicts prognosis independently. Incorporation of Cli-Rad-score significantly enhances the persionalized clinical prognostic capacity in LARC patients with lung nodules.

Clinical features and outcomes of patients with follicular lymphoma grade 3A: A real-world study in single center.

e19058 Background: Follicular lymphoma (FL) was classified into three grades based on the number of centroblasts (FL1-2, FL3A, and FL3B). Guidelines suggest that FL1-2 should be treated according to the treatment recommendations for classical FL. FL3B is usually treated as diffuse large B-cell lymphoma (DLBCL).There is no consensus yet on the management of FL3A. In order to explore the relationship between clinical features and outcomes of FL3A, we conducted this study. Methods: We conducted a retrospective study of newly diagnosed FL patients who were admitted to our hospital from January 2013 to December 2022. The data were collected from the hospital electronic medical record system, including but not limited to: demographic characteristics, pathological information, clinical features at diagnosis, treatment, and efficacy evaluation. The survival curve was calculated through Kaplan-Meier method and compared with Log Rank test.The prognostic factors of PFS was compared by COX regression analysis. Results: There were 253 FL3A patients admitted, accounting for 27.55% of all FL patients. After screening, a total of 223 FL3A patients were included in this study. Among 223 patients, the male to female ratio was close to 1:1; The median age is 57 years old. At diagnosed, 33 patients in early stage and 190 in advanced stage. Among advanced patients, 144 had no treatment indications according to the GELF criteria. 86.1% (192/223) of patients received systemic treatment, with the majority of rituximab contained regimens. The overall response rate (ORR) was 96%, and 62.9% of patients who responded to chemotherapy received maintenance therapy with CD20 monoclonal antibody. A median follow-up of 41 months, with an estimated 5-year OS of 91.3% and 5-year PFS of 73%. The PFS of advanced FL patients treated with RCHOP regimen (rituximab, cyclophosphamide, doxorubicin, vincristine, prednisone) was significantly longer than that of non-RCHOP regimen patients (P<0.05). The PFS of patients receiving maintenance treatment was better than that of those not (P<0.05). Conduct prognostic analysis on advanced FL3A patients. Univariate analysis showed that treatment plan selection, maintenance of treatment, PET scan of FDG metabolic maximum uptake value (SUVmax), Ki-67, lactate dehydrogenase (LDH), white blood cell count (WBC), IgA, IgM, all significantly affected PFS; Multivariate analysis shows SUVmax ≥ 15 and platelet count<100 × 109/L and LDH>210U/L are all correlated with poor PFS. Conclusions: This study found that FL3A accounts for 27.55% of all FLs, with a median age of 57 years old and mostly in the advanced stage. FL3A has a long survival period, with a 5-year PFS of 73%. COX regression analysis showed that SUVmax, platelet count, and LDH were all associated with PFS in advanced FL3A patients.Those may explore new clinical prognosis prediction models.

Anoikis-related gene signatures in colorectal cancer: implications for cell differentiation, immune infiltration, and prognostic prediction

Colorectal cancer (CRC) is a malignant tumor originating from epithelial cells of the colon or rectum, and its invasion and metastasis could be regulated by anoikis. However, the key genes and pathways regulating anoikis in CRC are still unclear and require further research. The single cell transcriptome dataset GSE221575 of GEO database was downloaded and applied to cell subpopulation type identification, intercellular communication, pseudo time cell trajectory analysis, and receptor ligand expression analysis of CRC. Meanwhile, the RNA transcriptome dataset of TCGA, the GSE39582, GSE17536, and GSE17537 datasets of GEO were downloaded and merged into one bulk transcriptome dataset. The differentially expressed genes (DEGs) related to anoikis were extracted from these data sets, and key marker genes were obtained after feature selection. A clinical prognosis prediction model was constructed based on the marker genes and the predictive effect was analyzed. Subsequently, gene pathway analysis, immune infiltration analysis, immunosuppressive point analysis, drug sensitivity analysis, and immunotherapy efficacy based on the key marker genes were conducted for the model. In this study, we used single cell datasets to determine the anoikis activity of cells and analyzed the DEGs of cells based on the score to identify the genes involved in anoikis and extracted DEGs related to the disease from the transcriptome dataset. After dimensionality reduction selection, 7 marker genes were obtained, including TIMP1, VEGFA, MYC, MSLN, EPHA2, ABHD2, and CD24. The prognostic risk model scoring system built by these 7 genes, along with patient clinical data (age, tumor stage, grade), were incorporated to create a nomogram, which predicted the 1-, 3-, and 5-years survival of CRC with accuracy of 0.818, 0.821, and 0.824. By using the scoring system, the CRC samples were divided into high/low anoikis-related prognosis risk groups, there are significant differences in immune infiltration, distribution of immune checkpoints, sensitivity to chemotherapy drugs, and efficacy of immunotherapy between these two risk groups. Anoikis genes participate in the differentiation of colorectal cancer tumor cells, promote tumor development, and could predict the prognosis of colorectal cancer.

A novel defined programmed cell death related gene signature for predicting the prognosis of serous ovarian cancer.

This study aims to explore the contribution of differentially expressed programmed cell death genes (DEPCDGs) to the heterogeneity of serous ovarian cancer (SOC) through single-cell RNA sequencing (scRNA-seq) and assess their potential as predictors for clinical prognosis. SOC scRNA-seq data were extracted from the Gene Expression Omnibus database, and the principal component analysis was used for cell clustering. Bulk RNA-seq data were employed to analyze SOC-associated immune cell subsets key genes. CIBERSORT and single-sample gene set enrichment analysis (ssGSEA) were utilized to calculate immune cell scores. Prognostic models and nomograms were developed through univariate and multivariate Cox analyses. Our analysis revealed that 48 DEPCDGs are significantly correlated with apoptotic signaling and oxidative stress pathways and identified seven key DEPCDGs (CASP3, GADD45B, GNA15, GZMB, IL1B, ISG20, and RHOB) through survival analysis. Furthermore, eight distinct cell subtypes were characterized using scRNA-seq. It was found that G protein subunit alpha 15 (GNA15) exhibited low expression across these subtypes and a strong association with immune cells. Based on the DEGs identified by the GNA15 high- and low-expression groups, a prognostic model comprising eight genes with significant prognostic value was constructed, effectively predicting patient overall survival. Additionally, a nomogram incorporating the RS signature, age, grade, and stage was developed and validated using two large SOC datasets. GNA15 emerged as an independent and excellent prognostic marker for SOC patients. This study provides valuable insights into the prognostic potential of DEPCDGs in SOC, presenting new avenues for personalized treatment strategies.

Prognostic Biomarkers for Hepatocellular Carcinoma Based on Serine and Glycine Metabolism-related Genes.

Targeted therapy and immunotherapy have emerged as treatment options for hepatocellular carcinoma (HCC) in recent years. The significance of serine and glycine metabolism in various cancers is widely acknowledged. This study aims to investigate their correlation with the prognosis and tumor immune microenvironment (TIME) of HCC. Based on the public database, different subtypes were identified by cluster analysis, and the prognostic model was constructed through regression analysis. The gene expression omnibus (GEO) data set was used as the validation set to verify the performance of the model. The survival curve evaluated prognostic ability. CIBERSORT was used to evaluate the level of immune cell infiltration, and maftools analyzed the mutations. DsigDB screened small molecule compounds related to prognostic genes. HCC was found to have two distinct subtypes. Subsequently, we constructed a risk score prognostic model through regression analysis based on serine and glycine metabolism-related genes (SGMGs). A nomogram was constructed based on risk scores and other clinical factors. HCC patients with a higher risk score showed a poor prognosis, and there were significant differences in immune cell infiltration between the high- and low-risk groups. In addition, three potential drugs associated with prognostic genes, streptozocin, norfloxacin, and hydrocotarnine, were identified. This study investigated the expression patterns of SGMGs and their relationship with tumor characteristics, resulting in the development of a novel model for predicting the prognosis of HCC patients. The study provides a reference for clinical prognosis prediction and treatment of HCC patients.

Association between haemoglobin, albumin, lymphocytes, and platelets and mortality in patients with heart failure.

The combination of haemoglobin, albumin, lymphocytes, and platelets (HALP) is a new metric used to assess patient prognosis in many diseases. This study aimed to assess the relationship between HALP and short- and long-term mortality in patients with heart failure. This retrospective cohort study included adult patients with heart failure who were hospitalized between 2019 and 2021. The primary outcomes were 1-month mortality and 1-year mortality. The multivariable logistic regression analysis was used to evaluate the association between HALP and the risk of mortality. Stratified analyses were conducted based on New York Heart Association functional classification (NYHA) stage (II/III, IV) and left ventricular ejection fraction (LVEF, <50%, ≥50%). The area under the receiver operating characteristic curve (AUC) was used to evaluate the ability of HALP, prognostic nutritional index (PNI), C-reactive protein (CRP), and the Meta-Analysis Global Group in Chronic Heart Failure (MAGGIC-HF) risk score in predicting mortality in patients with heart failure. A total of 730 patients with heart failure were included, of whom 61 (8.36%) died within 1month and 77 (10.55%) died within 1year. High HALP scores were associated with a reduced risk of 1-month mortality (odds ratio (OR)=0.978, 95% confidence interval (CI): 0.963-0.992, P=0.003) and 1-year mortality (OR=0.987, 95% CI: 0.977-0.997, P=0.009) in patients with heart failure. In patients with different NYHA stages or LVEF levels, high HALP scores were correlated with a reduced risk of 1-year mortality in patients with NYHA stage II/III (OR=0.978, 95% CI: 0.957-1.000, P=0.045) or LVEF ≥50% (OR=0.970, 95% CI: 0.945-0.996, P=0.024). The AUC for HALP, PNI, CRP, and MAGGIC-HF to predict 1-year mortality in patients with heart failure were 0.677 (95% CI: 0.619-0.735), 0.666 (95% CI: 0.608-0.723), 0.638 (95% CI: 0.572-0.704), and 0.654 (95% CI: 0.591-0.717), respectively. HALP may be a potential marker for predicting mortality in patients with heart failure. Further exploration based on HALP may yield better clinical predictors of prognosis in patients with heart failure.

Clinical predictors of prognosis in stroke patients after endovascular therapy

Endovascular therapy (EVT) is effective in the treatment of large vascular occlusive stroke. However, many factors are associated with the outcomes of acute ischemic stroke (AIS) after EVT. This study aimed to identify the main factors related to the prognosis of AIS patients after EVT. We analyzed the clinical data of AIS patients in the neurology department of our medical center from June 2017 to August 2021 following treatment with EVT. The data included the patients’ blood pressure upon admission, blood glucose concentration, National Institutes of Health Stroke Scale (NIHSS) score, 90-day modified Rankin scale (mRs) score follow-up data, and time from LKN to the successful groin puncture (GP). A good outcome was defined as a 90-day mRs score of 0–2, and a poor outcome was defined as a 90-day mRs score of 3–6. A total of 144 patients were included in the study. Admission, smoking, and LKN-to-GP time, NIHSS score of 6–12 was found to be relevant to the prognosis. The results of multivariate analysis showed that prognosis was significantly influenced by baseline NIHSS (odds ratio = 3.02; 95% confidence interval, 2.878–4.252; P = 0.001), LKN-to-GP time (odds ratio = 2.17; 95% confidence interval, 1.341–2.625; P = 0.003), and time stratification (6–12 h) (odds ratio = 4.22; 95% confidence interval, 2.519–5.561; P = 0.001). Our study indicated that smoking, baseline NIHSS score, and LKN-to-GP time were the risk factors for a poor outcome in stroke patients following an EVT. Quitting smoking and shortening LKN time to GP should improve the outcome of AIS after EVT.

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.

Multi-omics identification of GPCR gene features in lung adenocarcinoma based on multiple machine learning combinations.

Background: Lung adenocarcinoma is a common malignant tumor that ranks second in the world and has a high mortality rate. G protein-coupled receptors (GPCRs) have been reported to play an important role in cancer; however, G protein-coupled receptor-associated features have not been adequately investigated. Methods: In this study, GPCR-related genes were screened at single-cell and bulk transcriptome levels based on AUcell, single-sample gene set enrichment analysis (ssGSEA) and weighted gene co-expression network (WGCNA) analysis. And a new machine learning framework containing 10 machine learning algorithms and their multiple combinations was used to construct a consensus G protein-coupled receptor-related signature (GPCRRS). GPCRRS was validated in the training set and external validation set. We constructed GPCRRS-integrated nomogram clinical prognosis prediction tools. Multi-omics analyses included genomics, single-cell transcriptomics, and bulk transcriptomics to gain a more comprehensive understanding of prognostic features. We assessed the response of risk subgroups to immunotherapy and screened for personalized drugs targeting specific risk subgroups. Finally, the expression of key GPCRRS genes was verified by RT-qPCR. Results: In this study, we identified 10 GPCR-associated genes that were significantly associated with the prognosis of lung adenocarcinoma by single-cell transcriptome and bulk transcriptome. Univariate and multivariate showed that the survival rate was higher in low risk than in high risk, which also suggested that the model was an independent prognostic factor for LUAD. In addition, we observed significant differences in biological function, mutational landscape, and immune cell infiltration in the tumor microenvironment between high and low risk groups. Notably, immunotherapy was also relevant in the high and low risk groups. In addition, potential drugs targeting specific risk subgroups were identified. Conclusion: In this study, we constructed and validated a lung adenocarcinoma G protein-coupled receptor-related signature, which has an important role in predicting the prognosis of lung adenocarcinoma and the effect of immunotherapy. It is hypothesized that LDHA, GPX3 and DOCK4 are new potential targets for lung adenocarcinoma, which can achieve breakthroughs in prognosis prediction, targeted prevention and treatment of lung adenocarcinoma and provide important guidance for anti-tumor.

Bioinformatics-Based Identification of Key Prognostic Genes in Neuroblastoma with a Focus on Immune Cell Infiltration and Diagnostic Potential of VGF.

This study aims to identify differentially expressed genes (DEGs) in neuroblastoma (NB) through comprehensive bioinformatics analysis and machine learning techniques. We seek to elucidate these DEGs' biological functions and associated signaling pathways. Furthermore, our objective extends to predicting upstream microRNAs (miRNAs) and relevant transcription factors of pivotal genes, with the ultimate goal of guiding clinical diagnostics and informing future treatment strategies for Neuroblastoma. In this study, we sourced datasets GSE49710 and TARGET from the GEO and UCSC-XENA databases, respectively. Differentially expressed genes (DEGs) were identified using the R language "limma" package. Subsequent Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of these DEGs were conducted using the "clusterProfiler" package. We employed Weighted Gene Co-expression Network Analysis (WGCNA) to isolate the most significant modules associated with death and MYCN amplification, specifically MEpink and MEbrown modules. These modules were then cross-referenced with the DEGs for further GO and KEGG pathway analyses. LASSO regression analysis, facilitated by the "glmnet" package, was utilized to pinpoint three hub genes. We performed differential analysis on these genes and constructed Receiver Operating Characteristic (ROC) curves for disease diagnosis purposes. Immune infiltration analysis was conducted using the "GSVA" package's ssGSEA function. Additionally, single-gene Gene Set Enrichment Analysis (GSEA) on the hub gene was carried out based on Reactome and KEGG databases. Upstream miRNA and transcription factors associated with the hub gene were predicted using RegNetwork, with visual representations created in Cytoscape. Furthermore, to validate the three identified markers in neuroblastoma tissues, quantitative Real-Time Polymerase Chain Reaction (qRT-PCR) analysis was conducted. We identified 483 differentially expressed genes (DEGs) in neuroblastoma. These genes predominantly function in protein translation, membrane composition, and RNA transcription regulation, and are implicated in multiple signaling pathways relevant to neurodegenerative diseases. Utilizing LASSO regression analysis, we pinpointed three hub genes: VGF, DGKD, and C19orf52. The Receiver Operating Characteristic (ROC) curve analysis yielded Area Under Curve (AUC) values of 0.751 and 0.722 for VGF, 0.79 and 0.656 for DGKD, and 0.8 and 0.753 for C19orf52, respectively. Our immune infiltration analysis revealed significant correlations among monocytes, follicular helper T cells, and CD4+ T cells. Notably, in the death group, we observed heightened infiltration levels of activated CD4+ T cells, macrophages, and Th2 cells. C19orf52 exhibited a close association with the infiltration of monocytes, CD4+ T cells, and Th2 cells, with P-values less than 0.05. Furthermore, qRT-PCR analysis corroborated the upregulation of VGF in neuroblastoma tissues, further validating our findings. The hub genes (VGF, DGKD, and C19orf52) of neuroblastoma are screened. VGF, one of the hub genes, may have a high diagnostic value and is involved in the immune cell infiltration in neuroblastoma tissue, which may be used as a biomarker for the diagnosis of neuroblastoma and provides a new direction for clinical prognosis prediction and management improvement.

Exploring the immune microenvironment of osteosarcoma through T cell exhaustion-associated gene expression: a study on prognosis prediction.

Osteosarcoma is a highly aggressive type of bone cancer with a poor prognosis. In the tumor immune microenvironment, T-cell exhaustion can occur due to various factors, leading to reduced tumor-killing ability. The purpose of this study was to construct a prognostic model based on T-cell exhaustion-associated genes in osteosarcoma. Patient data for osteosarcoma were retrieved from the TARGET and GEO databases. Consensus clustering was employed to identify two novel molecular subgroups. The dissimilarities in the tumor immune microenvironment between these subgroups were evaluated using the "xCell" algorithm. GO and KEGG analyses were conducted to elucidate the underlying mechanisms of gene expression. Predictive risk models were constructed using the least absolute shrinkage and selection operator algorithm and Cox regression analysis. To validate the prognostic significance of the risk gene expression model at the protein level, immunohistochemistry assays were performed on osteosarcoma patient samples. Subsequently, functional analysis of the key risk gene was carried out through in vitro experimentation. Four gene expression signatures (PLEKHO2, GBP2, MPP1, and VSIG4) linked to osteosarcoma prognosis were identified within the TARGET-osteosarcoma cohort, categorizing patients into two subgroups. The resulting prognostic model showed strong predictive capability, with area under the receiver operating characteristic curve (AUC) values of 0.728/0.740, 0.781/0.658, and 0.788/0.642 for 1, 3, and 5-year survival in both training and validation datasets. Notably, patients in the low-risk group had significantly higher stromal, immune, and ESTIMATE scores compared to high-risk counterparts. Additionally, a nomogram was developed, exhibiting high accuracy in predicting the survival outcome of osteosarcoma patients. Immunohistochemistry, Kaplan-Meier, and time-dependent AUC analyses consistently supported the prognostic value of the risk model within our osteosarcoma patient cohort. In vitro experiments provided additional validation by demonstrating that the downregulation of GBP2 promoted the proliferation, migration, and invasion of osteosarcoma cells while inhibiting apoptosis. The current study established a prognostic signature associated with TEX-related genes and elucidated the impact of the pivotal gene GBP2 on osteosarcoma cells via in vitro experiments. Consequently, it introduces a fresh outlook for clinical prognosis prediction and sets the groundwork for targeted therapy investigations in osteosarcoma.

A quantitative review of competing learning slope metrics: effects of age, sex, and clinical diagnosis

ABSTRACT Introduction In learning and memory tests that involve multiple presentations of the same material, learning slope refers to the degree to which examinees improve performances over successive learning trials. We aimed to quantitatively review the traditional raw learning slope (RLS), and the newly created learning ratio (LR) to understand the effects of demographic variables and clinical diagnoses on learning slope (e.g., limited improvement over multiple trials), and to develop demographically sensitive norms. Method A systematic literature search was conducted to evaluate the potential for these aims to be examined across the most popular contemporary multi-trial learning tests. Two databases were searched. Following this, hierarchical linear modeling was used to examine how demographic variables predict learning slope indices. These results were in turn used to contrast the performance of clinical groups with the predicted performance of demographically similar healthy controls. Finally, preliminary normative estimates for learning slope indices were presented. Results A total of 82 studies met criteria for inclusion in this study. However, the Rey Auditory Verbal Learning Test (RAVLT) was the only test to have sufficient trial-level learning and demographic data. Fifty-eight samples from 19 studies were quantitatively examined. Hierarchical linear models provided evidence of sex differences and a curvilinear decline in learning slope with age, with strongest and most consistent effects for LR relative to RLS. Regression-based norms for demographically corrected RLS and LR scores for the RAVLT are presented. The effect of clinical diagnoses was consistently stronger for LR, and Alzheimer’s disease had the strongest effect, followed by invalid performances, severe traumatic brain injury, and seizures/epilepsy. Conclusion Overall, LR enjoys both conceptual and demonstrated psychometric advantages over RLS. Replication of these findings can be completed by reanalyzing existing datasets. Further work may focus on the utility of using LR in diagnosis and prediction of clinical prognosis.