Median Risk Score Research Articles

The value of inflammation-related indicators in chemotherapy efficacy and disease-free survival of triple-negative breast cancer.

Systemic inflammation is closely correlated with the progression of cancer. Inflammation-related indicators has been recognized as outcome predictors in triple-negative breast cancer (TNBC). Our study aimed to investigate the predictive value of several inflammation-related indicators in TNBC patients underwent chemotherapy (NAC). 100 TNBC patients were enrolled in the study. Levels of interleukin-6 (IL-6) were detected by enzyme-linked immunosorbent assays (ELISAs). Platelet-to-lymphocyte ratio (PLR) and immune inflammatory index (SII) were obtained from blood routine. Levels of Ki-67 expression were detected by immunohistochemistry (IHC). Mentioned indicators were divided into two groups based on their median values. The correlation between these indicators and NAC efficacy was analyzed using t tests. Prognostic risk scores were calculated by univariate Cox regression analysis and Lasso-penalized Cox regression. The patients were divided into low- and high-risk groups based on the median risk score. Survival curves were obtained by Kaplan-Meier method. Models for univariate and multivariate Cox regression analyses were performed to determine the independent risk factors. A nomogram was used for the prediction of 1-, 2-, and 3-year disease-free survival (DFS). Accuracy of the prognostic model was validated by receiver operating characteristic curve (ROC). IL-6, PLR, SII, and Ki-67 levels were associated with neoadjuvant efficacy. IL-6, PLR, SII, Ki-67, and lymphocyte count were associated with DFS. The risk score for each TNBC patient was obtained using LASSO regression analysis to construct a prognostic model. In the prognostic model, patients in the high-risk score group showed worse DFS than those in the low-risk group. Risk score and tumor size were independently correlated with outcomes in multivariate Cox regression analysis. A nomogram was constructed using IL-6, PLR, SII, Ki-67, and Miller-Payne (MP) scores. Calibration curves demonstrated good consistency between the actual and predictive values of the nomogram. A prognostic model was established by combining four prognosis-related indicators in TNBC patients who underwent NAC.

Single-cell transcriptomics analysis reveals dynamic changes and prognostic signature in tumor microenvironment of PDAC

Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal malignant tumor characterized by a complex tumor microenvironment (TME) with significant heterogeneity, posing immense challenges for devising effective therapeutic strategies. This study aims to elucidate the dynamic changes in the TME during PDAC progression and develop a prognostic model using single-cell RNA sequencing (scRNA-seq) data. We utilized a previously published comprehensive dataset comprising 31 samples (including 8 PDAC I, 9 PDAC II, 6 PDAC III, and 8 PDAC IV) to characterize the changes in TME composition with PDAC progression through advanced scRNA-seq analysis. We found that as cancer progresses, immune cells gradually become a predominant component in late-stage PDAC. We defined a novel Treg and exhausted T cell signature gene, TNFRSF4. Additionally, we identified a prognostic gene set (RPS10, MIF, MT-ATP6, CSTB, IFI30, NPC2, BTG1, CTSD, FCGR2A, SEC61G, IER3, HSPB1, HMOX1, and ZFP36L1) and differentiated high-risk from low-risk PDAC patients based on median risk score threshold. Based on these findings, we developed a novel prognostic model that identifies poorer prognosis in high-risk groups. Furthermore, our analysis revealed significant interactions between cells at different stages of PDAC and identified three promising therapeutic agents (XR-11576, Ixabepilone, and AMONAFIDE) based on correlated genes. Finally, molecular docking studies validated their potential by confirming stable binding with key protein targets. This study not only provides insights into the evolving TME of PDAC but also offers a new prognostic model and potential therapeutic strategies, contributing to improved management and treatment of this aggressive cancer.

Mitral annulus calcification-derived cardiac computed tomography: a marker for cardiovascular prognosis after aortic valve replacement

Abstract Introduction Calcific aortic stenosis (AS) is the most common valvular heart disease in developed countries, with incidence rising due to an aging population. Although transthoracic and transesophageal echocardiography are the primary imagistic tools for patients with AS, cardiac computed tomography (CCT) is essential in the pre-procedural planning of aortic valve replacement (AVR), crucially informing management strategies. Furthermore, CCT provides detailed imaging of cardiovascular calcification, such as mitral annular calcification (MAC) presence and extension for these patients. However, the implications of CCT-derived MAC for patient outcomes, particularly regarding major adverse cardiovascular events (MACE) remain unclear. Purpose This study aimed to determine if the presence and extent of CCT-derived MAC affect all-cause, non-cardiovascular mortality, and MACE in patients undergoing AVR. Methods Several clinical cardiovascular risk factors were collected retrospectively and CCT scans of 313 patients with AS were assessed semi-quantitatively. CCT was conducted in spiral acquisition mode with retrospectively electrocardiogram-gated reconstruction and a contrast agent was injected. This study categorizes MAC severity using Guerrero et al.'s recently validated CCT-derived MAC score. Patients were followed for 60 months post-AVR to evaluate MACE (composite of cardiovascular mortality, major arrhythmias, heart failure, and stroke), all-cause, and non-cardiovascular-related deaths. Cox univariate models were used to study the role of MAC as a predictor of the aforementioned outcomes. Results The population's median age was 81 years (78-84) and most of them were females (60.3%). Arterial hypertension was present in 85.9% of the patients, 30.6% presented diabetes mellitus, 12.1% presented chronic kidney disease, and 36% had previous myocardial revascularization. The median left ventricular ejection fraction was 58% (55-63), the mean index aortic valve area was 0.4 cm2/m2 (0.3-0.5) and most of them presented a tricuspid aortic valve (94.8%). The median STS risk score of mortality was 4.14 % (2.85-6.29), while the EuroScore II was 2.83 (1.70-4.77) points. In univariate Cox models, the lowest and intermediate MAC severity categories did not show any significant associations with MACE, all-cause, and non-cardiovascular mortality (all p values >0.05). However, the MAC score for the highest category (3 vs. 0 points) was significantly associated with MACE (HR 2.32, 95% CI [1.13-4.77], p=0.02), all-cause (HR 2.37, 95% CI [1.36-4.16], p< 0.0.001), and non-cardiovascular mortality (HR 2.09, 95% CI [1.05-4.16], p=0.03). Conclusion In the present study, severe MAC was identified as an independent predictor of cardiovascular outcomes in patients with AS undergoing AVR. These results could be translated into a more practical, largely available, clinical tool to ensure closer monitoring of this population

Effects of ergonomic training and equipment on musculoskeletal system disorders and quality of life in healthcare professionals

Objective: To determine the association of musculoskeletal disorders with ergonomic training and equipment with respect to the quality of life of nurses working in the intensive care unit. Method: The experimental study was conducted at Eskisehir Osmangazi University Health, Practice and Research Hospital from November 2018 to December 2019, and comprised adult intensive care unit nurses. The nurses were provided with ergonomic equipment (rollboards) to facilitate the transfer patients from bed to stretcher and from stretcher to bed. The nurses were trained on the use of rollboards for a month. Data was collected at baseline and after the training using a personal data collection form, Cornell Musculoskeletal Discomfort Questionnaire, Rapid Entire Body Assessment, and Short Form-36. Data was analysed using SPSS 21. Results: Of the 103 nurses, 71(58.9%) were females and 32(31.1%) were males. There were 30(29.1%) subjects aged 25-29 years, and body mass index was <25kg/m2 in 62(60.2%). Median risk score for lower back was 3.50 (interquartile range: 1.50-14.00; Z=-2.58, p=0.01) and for risky working posture it was 5.00 (interquartile range: 5.00-7.00; Z=-8.39, p<0.001) of nurses which decreased after training and the use of equipment. The sub-dimension scores of the quality of life for the physical function, mental health, social function, general health perception and pain decreased post-intervention, but the difference was not significant (p>0.05). Conclusion: Nurses' musculoskeletal symptoms decreased as a result of ergonomic training and equipment use. Although ergonomic training and equipment use did not affect nurses' quality of life, the ergonomic workplace increased their awareness of ergonomic risk assessment. Key Words: Education, Ergonomics, Musculoskeletal system, Nurses, Quality of life.

Association Between Cardiothoracic Fellows and Clinical and Financial Outcomes in Coronary Surgery.

Mounting financial pressures on academic institutions highlight the need to understand the effect on outcomes from trainee involvement in cardiac surgery. The purpose of this study is to examine the association between cardiothoracic fellows and clinical and financial outcomes in coronary artery bypass grafting (CABG). Data for all patients from 2017 to 2022 at a single institution who underwent nonemergent, isolated, open CABG were included in the study, with patients grouped by whether there was fellow operative participation. Financial and clinical outcomes were compared between the two groups using multivariable regression and generalized estimating equations to control for variation in patient characteristics and attending cardiac surgeon practices. A total of 1997 patients met criteria for study inclusion, with 632 (31.5%) cases that had fellow participation and 1365 (68.4%) that did not. Patients in the fellow participation group had a 0.8% higher median preoperative risk score than those that did not have fellow participation. Fellow participation was associated with significantly longer total case length (61mins) but no differences in postoperative clinical outcomes. There was an increased median total hospitalization cost of approximately $2200 in cases with fellow participation, likely attributable to increased intraoperative costs from longer case times. While CT surgery fellow participation was associated with longer operative times and a small increase in hospitalization cost in CABG, there were no significant differences in clinical outcomes. Continuing to train cardiothoracic surgery fellows in this procedure is safe and would not be expected to significantly impact reimbursement under bundled payment models.

Efferocytosis-related gene IL33 predicts prognosis and immune response and mediates proliferation and migration in vitro and in vivo of breast cancer.

Breast cancer (BRCA) has a high incidence among women, with poor prognosis and high mortality, which is increasing year by year. Efferocytosis is a process of phagocytosis of abnormal cells and is of great value in tumor research. Our study seeks to create a predictive model for BRCA using efferocytosis-related genes (ERGs) to explore the significance of efferocytosis in this disease. In this research, Differential analysis, and univariate Cox regression were employed to identify genes linked to prognosis in BRCA patients. Then the BRCA patients were categorized into distinct groups using consensus clustering based on prognosis genes. Survival analysis, PCA, and t-SNE were performed to verify these groups. The enrichment of metabolic pathways within the detected clusters was evaluated using gene set variation analysis (GSVA) and gene set enrichment analysis (GSEA). Additionally, single-sample GSEA (ssGSEA) was used to examine changes in immune infiltration and enrichment. A risk prognostic model was constructed utilizing multivariable Cox regression and Least Absolute Shrinkage and Selection Operator (LASSO) analyses, and subsequently validated its predictive accuracy by stratifying patients according to the median risk score. Ultimately, some crucial independent prognostic genes were pinpointed and their expression, roles, and immune characteristics were explored in both laboratory and live models. Findings revealed 52 differentially expressed genes (DEGs), of which 21 were significantly linked to BRCA outcomes. These 21 genes were utilized for consensus clustering to categorize BRCA patients into two subtypes. Subtype B was linked to a worse prognosis compared to Subtype A, though both subtypes were distinguishable. The enriched pathways were mainly concentrated in Subtype A and were actively expressed in this group. Following this, a prognostic risk model was constructed using five risk genes, which was proven to possess significant predictive value. A significant link was identified between the immune microenvironment and the risk-associated genes and scores. IL33 was identified as an independent prognostic gene with important research value. Its in vivo expression results aligned with the data analysis findings, showing low expression in BRCA. Furthermore, overexpression of IL33 significantly inhibited BRCA growth and motility in vitro and in vivo, while also enhancing their vulnerability to destruction by activated CD8+ T cells. The ERG-based risk model effectively predicts the prognosis of BRCA patients and shows a strong link with the immune microenvironment. IL33 stands out as a significant prognostic marker, crucial in the onset and advancement of BRCA. This highlights the necessity for additional studies and indicates that IL33 might be a potential target for BRCA treatment.

Novel pyroptosis-immune-related lncRNA signature exhibits a distinct immune cell infiltration landscape in breast cancer.

This study investigated pyroptosis- and immunity-related long non-coding RNAs (lncRNAs) to identify promising therapeutic targets for breast cancer (BC), and constructed lncRNA signatures to determine the prognosis and immunotherapy responses of BC patients. Pearson's correlation coefficient was used to identify pyroptosis- and immune-related differentially expressed lncRNAs (DE-pyrolncRNAs and DE-ImmlncRNAs, respectively). The Cancer Genome Atlas dataset was allocated to training and testing subsets. Prognostic lncRNA signatures were derived based on the training subset using univariate Cox regression analysis and Least Absolute Shrinkage and Selection Operator methods. Stepwise Cox regression was used to refine these signatures and to select the optimal lncRNA signature. The median risk score of the training subset was applied as a threshold to divide patients into high-risk (HR) and low-risk (LR) groups. The Wilcoxon test was used to reveal differences in immune scores, cell types, functions, and checkpoint genes between these groups. Single-cell sequencing data from GSE176078 were used to validate the immune cell infiltration landscape of the identified lncRNA signatures. We identified a six-lncRNA pyroptosis-immune signature comprising MAPT.AS1, CTA.384, D8.34, RP11.561, I11.3, HID1.AS1, AC097713.3, and USP2.AS1. Patients in the HR group demonstrated inferior prognoses in the training, testing, and full datasets (P=3.622e-07, P=3.736e-03, and P=1.151e-08, respectively). Immune scores were significantly enhanced in the LR group, whereas tumor purity was elevated in the HR group. Fifty-eight immune scores showed significant differences between the groups (P<0.05). Immune function (APC coinhibition, CCR, and checkpoints) more significantly impaired in the HR group. Expression levels of 38 immune checkpoint genes, including KIR2DS4, KIR3DL2, CD40LG, KIR3DL1, and PDCD1, were significantly higher in the LR group. Conversely, the TDO2, PVR, and CD276 levels were elevated in the HR group. Single-cell sequencing data from GSE176078 showed sparse T cell, B cell, myeloid, and plasmablast clusters in the HR group, whereas the LR group displayed significant clustering of B cells, myeloids, and plasmablasts. The six-lncRNA pyroptosis-immune signature effectively predicted BC prognosis and highlighted distinct immune cell infiltration patterns. This holds promise for evaluating immunotherapy responses and guiding therapeutic target identification in BC.

Prognostic values of intracellular cell-related genes in esophageal cancer and their regulatory mechanisms

Esophageal cancer is a grave malignant condition. While radiotherapy, often in conjunction with chemotherapy, serves as a cornerstone in the management of locally advanced or metastatic cases, patient tolerance and treatment resistance frequently hinder its efficacy. Cell-in-cell structures, prevalent in various tumors, have been linked to prognosis. Hence, investigating the prognostic significance and regulatory mechanisms of genes related to these intracellular structures in esophageal cancer is imperative. The Cancer Genome Atlas (TCGA) Esophageal Cancer (ESCA) dataset served as the training set for the analysis. Differentially expressed genes (DEGs) in ESCA samples were identified, with those related to intercellular structures designated cell-in-cell-related differential expression genes (CIC-related DEGs). Cox regression analysis was employed to identify prognostic genes, categorizing samples into high- and low-risk groups based on median risk scores. Validation was conducted using the GSE53624 risk model. Established methodologies included morphological mapping, enrichment analysis, immune infiltration analysis, prognostic gene expression validation, molecular docking, and Reverse Transcription Polymerase Chain Reaction (RT-PCR) validation. Thirty-eight intersecting genes were identified between the disease and normal groups in ESCA samples. Stepwise multivariate Cox analysis pinpointed three prognostic genes: androgen receptor (AR), C-X-C motif chemokine ligand 8 (CXCL8), and epidermal growth factor receptor (EGFR). The risk model’s applicability was confirmed in the GSE53624 dataset, revealing eight significantly different immune-related gene sets. Prognostic gene expression validation demonstrated significant differences between the disease and normal groups in both datasets. The proteins corresponding to the three prognostic genes interacted with gefitinib and osimertinib. RT-PCR results corroborated the differential expression of prognostic genes in esophageal cancer tissues. This study identified AR, CXCL8, and EGFR as prognostic genes and demonstrated their molecular interactions with gefitinib and osimertinib, providing a foundation for ESCA diagnosis and treatment.

Construction of the bromodomain-containing protein-associated prognostic model in triple-negative breast cancer

BackgroundBromodomain-containing protein (BRD) play a pivotal role in the development and progression of malignant tumours. This study aims to identify prognostic genes linked to BRD-related genes (BRDRGs) in patients with triple-negative breast cancer (TNBC) and to construct a novel prognostic model.MethodsData from TCGA-TNBC, GSE135565, and GSE161529 were retrieved from public databases. GSE161529 was used to identify key cell types. The BRDRGs score in TCGA-TNBC was calculated using single-sample Gene Set Enrichment Analysis (ssGSEA). Differential expression analysis was performed to identify differentially expressed genes (DEGs): DEGs1 in key cells, DEGs2 between tumours and controls and DEGs3 in high and low BRDRGs score subgroups in TCGA-TNBC. Differentially expressed BRDRGs (DE-BRDRGs) were determined by overlapping DEGs1, DEGs2 and DEGs3. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, and protein-protein interaction (PPI) network analysis were conducted to investigate active pathways and molecular interactions. Prognostic genes were selected through univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression analyses to construct a risk model and calculate risk scores. TNBC samples from TCGA-TNBC were classified into high and low-risk groups based on the median risk score. Additionally, correlations with clinical characteristics, Gene Set Enrichment Analysis (GSEA), immune analysis, and pseudotime analysis were performed.ResultsA total of 120 DE-BRDRGs were identified by overlapping 605 DEGs1 from four key cell types, 10,776 DEGs2, and 4,497 DEGs3. GO analysis revealed enriched terms such as ‘apoptotic process,’ ‘immune response,’ and ‘regulation of the cell cycle,’ while 56 KEGG pathways, including the ‘MAPK signaling pathway,’ were associated with DE-BRDRGs. A risk model comprising six prognostic genes (KRT6A, PGF, ABCA1, EDNRB, CTSD and GJA4) was constructed. A nomogram based on independent prognostic factors was also developed. Immune cell abundance was significantly higher in high-risk group. In both risk groups, TP53 exhibited the highest mutation frequency. The expression of KRT6A, ABCA1, EDNRB, and CTSD went decreased progressively in pseudotime.ConclusionA novel prognostic model for TNBC associated with BRDRGs was developed and validated, providing fresh insights into the relationship between BRD and TNBC.

Exploring prognosis and therapeutic strategies for HBV-HCC patients based on disulfidptosis-related genes.

Hepatocellular carcinoma (HCC) accounts for over 80% of primary liver cancers and is the third leading cause of cancer-related deaths worldwide. Hepatitis B virus (HBV) infection is the primary etiological factor. Disulfidptosis is a newly discovered form of regulated cell death. This study aims to develop a novel HBV-HCC prognostic signature related to disulfidptosis and explore potential therapeutic approaches through risk stratification based on disulfidptosis. Transcriptomic data from HBV-HCC patients were analyzed to identify BHDRGs. A prognostic model was established and validated using machine learning, with internal datasets and external datasets for verification. We then performed immune cell infiltration analysis, tumor microenvironment (TME) analysis, and immunotherapy-related analysis based on the prognostic signature. Besides, RT-qPCR and immunohistochemistry were conducted. A prognostic model was constructed using five genes (DLAT, STC2, POF1B, S100A9, and CPS1). A corresponding prognostic nomogram was developed based on riskScores, age, stage. Stratification by median risk score revealed a significant correlation between the prognostic signature and TME, tumor immune cell infiltration, immunotherapy efficacy, and drug sensitivity. The results of the experiments indicate that DLAT expression is higher in tumor tissues compared to adjacent tissues. DLAT expression is higher in HBV-HCC tumor tissues compared to normal tissues. This study stratifies HBV-HCC patients into distinct subgroups based on BHDRGs, establishing a prognostic model with significant implications for prognosis assessment, TME remodeling, and personalized therapy in HBV-HCC patients.

Identification and validation of mitochondria-related LncRNA signatures as a novel prognostic model for glioma.

A predictive model for long-term survival is needed, and mitochondrial dysfunction is a key feature of cancer metabolism, though its link to glioma is not well understood. The aim of this study was to identify the molecular characteristics associated with glioma prognosis and explore its potential function. We analyzed RNA-seq data from The Cancer Genome Atlas and identified differentially expressed mitochondrial long noncoding RNAs (lncRNAs) using R's 'limma' package. A prognostic model was developed using 10 selected lncRNAs and validated with Cox regression and least absolute shrinkage and selection operator algorithm. The model's efficacy was assessed using Kaplan-Meier and receiver operating characteristic curve analyses, and its correlation with immune cell profiles and drug sensitivity was explored. A 10-mitochondria-related LncRNA signature was generated. The median risk score values are used to classify glioma samples into low-risk and high-risk groups. In breast patients, the signature-based risk score demonstrated a more potent ability to predict survival than conventional clinicopathological features. Furthermore, we noted a substantial disparity in the number of immune cells, including B cells, CD8+T cells, and macrophages, between the two groups. In addition, the high-risk group exhibited lower half-maximal inhibitory concentration values for specific chemotherapy medications, including bortezomib, luminespib, rapamycin, and 5-fluorouracil. Our study elucidates the diagnostic and prognostic value of mitochondria-related-lncRNAs in the promotion, suppression, and treatment of glioma.

Red cell distribution width as a cardiovascular risk predictor in adults with hypertension in sub-Saharan Africa.

Red cell distribution width (RDW) quantifies the degree of variation in erythrocyte size, is identified as a potential marker of adverse cardiovascular events, and may be a surrogate marker for assessing cardiovascular disease (CVD) risk in low-resource settings. We evaluated RDW as a predictor of CVD risk compared to the World Health Organization (WHO) CVD risk score among adults with hypertension attending primary healthcare centers (PHCs) in Ghana and Nigeria. Adults with hypertension attending selected PHCs in Ghana and Nigeria participated in a cross-sectional study. Each participant underwent blood pressure (BP) measurement and laboratory evaluation (RDW, total cholesterol, and fasting blood sugar) following standard methods. We recruited 319 adults aged 40-74 years from the study sites. The mean (standard deviation) RDW was 13.96 (1.1%). The median CVD risk score was 8.11% [interquartile range (IQR) 4.00 to 11.00]. For participants with hemoglobin (Hb) levels ≥ 12 g/dL, RDW showed positive correlations with age (r = 0.136; p = 0.042); systolic BP (r = 0.183; p = 0.006), diastolic BP (r = 0.206, p = 0.002) and WHO CVD risk scores (r = 0.166, p = 0.013). Multiple linear regression showed an independent association between RDW and WHO CVD risk scores with an upward gradient, and was most significant at 3rd quartiles. Using receiver operating characteristic curve, the C-statistic was 0.673 (95% confidence interval: 0.618 to 0.724), p = 0.031. With a cut-off of >14, the RDW demonstrated a sensitivity of 81.82% and specificity of 55.84%. This study shows that at Hb levels ≥ 12 g/dL, RDW modestly predicted CVD risk in adults with hypertension in sub-Saharan Africa.

A novel mitochondrial-related risk model for predicting prognosis and immune checkpoint blockade therapy response in uterine corpus endometrial carcinoma

Uterine Corpus Endometrial Carcinoma (UCEC) represents a common malignant neoplasm in women, with its prognosis being intricately associated with available therapeutic interventions. In the past few decades, there has been a burgeoning interest in the role of mitochondria within the context of UCEC. Nevertheless, the development and application of prognostic models predicated on mitochondrial-related genes (MRGs) in UCEC remains in the exploratory stages. This study utilized RNA sequencing data and clinical information from the TCGA database to identify differentially expressed MRGs (DEMRGs) between UCEC and normal groups that are associated with overall survival (OS). Patients were randomly assigned to training and testing cohorts in a 1:1 ratio. In the training cohort, a risk model based on DEMRGs was developed using Lasso Cox regression analysis. Subsequently, patients in both cohorts were stratified into high-risk and low-risk groups based on their median risk scores. The prognostic performance of the model was validated through Kaplan-Meier survival analysis, ROC curves, and nomograms. Additionally, further analyses including functional enrichment, immune landscape assessment, prediction of response to ICB therapy, mutation profiling, and drug sensitivity analysis elucidated biological distinctions between the identified risk groups. We established a risk model incorporating eight MRGs. Patients classified within he high-risk group exhibited significantly poorer prognoses relative to those in the low-risk group. Functional enrichment analysis identified substantial differences in biological processes and signaling pathways between the high-risk and low-risk cohorts. Immune landscape analysis showed that patients with elevated risk scores exhibited significant immunosuppressive and immune evasion mechanisms. Conversely, low-risk patients exhibited higher expression of human leukocyte antigen (HLA) family members and immune checkpoint genes (ICGs) compared to their high-risk counterparts.Consequently, low-risk patients showed greater responsiveness to immunotherapy and potential small molecule drugs, whereas high-risk patients were more susceptible to chemotherapy. The mitochondrial-related risk model formulated in this study demonstrates efficacy in predicting both prognosis and response to immunotherapy in patients with UCEC, thereby providing a scientific basis for personalized treatment strategies. Future research endeavors should focus on further validating the clinical utility of this model and investigate the specific mechanisms of the identified MRGs in UCEC.

Identification of a novel immune checkpoint-related gene signature predicts prognosis and immunotherapy in breast cancer and experiment verification

Breast cancer (BRCA) is one of the pivotal causes of female death worldwide. And the morbidity and mortality of breast cancer have increased rapidly. Immune checkpoints are important to maintain immune tolerance and are regarded as important therapeutic targets. However, research for BRCA were limited to single immune checkpoint-related gene (ICG) and few studies have systematically explored expression profile of Immune checkpoint-related genes or attempted to construct a prognostic gene risk model based on immune checkpoint-related genes. We identified immune checkpoint-related differentially expressed genes (DEGs) in BRCA and normal breast tissues from TCGA database. A 7-gene signature was created by utilizing the univariate Cox regression model with least absolute shrinkage and selection operator (LASSO) Cox regression method. In addition, we conducted a nomogram to predict the prognostic significance. This tool enables quantitative prediction of patient prognosis, serving as a valuable reference for clinical decision-making, thereby improving patient outcomes. Relationships between our risk model and clinical indicators, TME (Tumor Microenvironment), immune cell infiltration, immune response and drug susceptibility were investigated. A set of in vitro cell assays was conducted to decipher the relationship between MAP2K6 and proliferation, invasion, migration, colony formation and apoptosis rate of breast cancer cells. As a result, we established a prognostic model composed of seven ICGs in BRCA. Based on the median risk score, BRCA patients were equally assigned into two groups of high- and low-risk. High-risk BRCA patients have poorer OS (overall survival) than low-risk patients. In addition, there were remarkable differences between these two groups in clinicopathological features, TME, immune cell infiltration, immune response and drug susceptibility. The results of GO and KEGG analyses indicated that DEGs between the high- and low-risk groups were involved in immune-related biological processes and pathways. GSEA analysis also showed that a number of immune-related pathways were notably enriched in the low-risk group. Finally, results of cell-based assays indicated that MAP2K6 may play a pivotal role in the initiation and progression of breast cancer as a tumor suppressor gene. In conclusion, we created a novel ICG signature that has the potential to predict the survival and drug sensitivity of BRCA patients. Furthermore, this study indicated that MAP2K6 may serve as a novel target for BRCA therapy.

Machine learning-based identification of histone deacetylase-associated prognostic factors and prognostic modeling for low-grade glioma.

Low-grade glioma (LGG) is a slow-growing but invasive tumor that affects brain function. Histone deacetylases (HDACs) play a critical role in gene regulation and tumor progression. This study aims to develop a prognostic model based on HDAC-related genes to aid in risk stratification and predict therapeutic responses. Expression data from The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) were analyzed to identify an optimal HDAC-related risk signature from 73 genes using 10 machine learning algorithms. Patients were stratified into high- and low-risk groups based on the median risk score. Prognostic accuracy was evaluated using Kaplan-Meier survival analysis and receiver operating characteristic (ROC) curves. Functional enrichment analyses, including Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA), were performed to explore pathways linked to the gene signature. Immune infiltration and tumor microenvironment characteristics were assessed using Single Sample Gene Set Enrichment Analysis (ssGSEA) and ESTIMATE algorithm. SubMap was applied to predict responsiveness to immune checkpoint inhibitors, and chemotherapeutic sensitivity was analyzed via the Genomics of Drug Sensitivity in Cancer (GDSC) database. A prognostic model consisting of four HDAC-related genes-SP140, BAZ1B, SP100, and SIRT1-was identified. This signature displayed strong prognostic accuracy, achieving a C-index of 0.945. Individuals with LGG were systematically divided into high-risk and low-risk cohorts based on the median risk value, enabling more precise risk stratification. The survival prognosis was significantly worse in the high-risk cohort compared to the low-risk group, highlighting distinct survival trajectories. Notably, the two cohorts exhibited marked shifts in immune checkpoint gene transcriptional profiles and immune cell infiltration maps, underscoring fundamental biological differences that contribute to these differing prognoses. We developed an HDAC-related four-gene prognostic model that correlates with survival, immune landscape, and therapeutic response in LGG patients. This model may guide personalized treatment strategies and improve prognostic accuracy, warranting further validation in clinical settings.

Development and validation of disulfidptosis-related genes signature for patients with glioma

BackgroundDisulfidptosis has recently emerged as a novel form of regulated cell death (RCD). Evasion of cell death is a hallmark of cancer, and the resistance of many tumors to apoptosis-inducing therapies has heightened interest in exploring alternative RCD mechanisms.MethodsTranscriptomic and clinical data were obtained from The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), and Chinese Glioma Genome Atlas (CGGA). Glioma samples were classified using non-negative matrix factorization (NMF). A predictive model was constructed using Lasso regression analysis, and its performance was evaluated through receiver operating characteristic (ROC) and Kaplan–Meier survival analyses. The relationship between the model and the tumor immune microenvironment (TIME) as well as treatment sensitivity was also assessed. Finally, we validated the expression of key signature genes in glioma.ResultsGlioma samples were categorized into two distinct subtypes based on disulfidptosis-related genes, showing significant differences in overall survival (OS) and progression-free survival (PFS) between the subtypes. A genetic risk score model was then developed using these genes. A nomogram predicting OS was constructed using the risk score and clinical variables. Patients were stratified into low- and high-risk groups based on the median risk score from the TCGA cohort. Low-risk patients had significantly better outcomes compared to high-risk patients (TCGA cohort, OS: p < 0.001; PFS: p < 0.001; CGGA cohort, OS: p < 0.001). The risk score was associated with HLA expression, immune checkpoint genes, immune cell infiltration, immune function, tumor mutation burden, tumor stemness score, and drug sensitivity. Lastly, the expression of 11 signature genes was confirmed in glioma tissues.ConclusionsThe disulfidptosis-related gene-based risk score model effectively predicted glioma outcomes and highlighted the role of disulfidptosis-related genes in tumor immunity. This study offers potential new avenues for glioma treatment by targeting disulfidptosis.

Construction of a novel copper-induced-cell-death-related gene signature for prognosis in colon cancer, with focus on KIF7

BackgroundColon cancer (CC) is a leading cause of cancer-related mortality worldwide. Accurate prognostic markers are essential for patient risk stratification and personalized treatment. Copper-induced cell-death-related genes (CRG) have emerged as potential players in cancer prognosis, yet their role in CC remains unclear.MethodsThis study aimed to comprehensively evaluate the expression of CRG and their roles in CC using gene expression and clinical data from TCGA and GEO databases. Univariate Cox regression and least absolute shrinkage and selection operator (LASSO) analyses identified prognostic genes, leading to the construction of a CRG prognostic signature. The signature’s predictive accuracy was validated using Kaplan-Meier survival curves, Receiver Operating Characteristic (ROC) curves, and a nomogram model. Additionally, we conducted experiments including immunofluorescence staining and cellular assays to validate the key genes’ biological functions.ResultsA 12-gene CRG signature was significantly associated with overall survival in CC patients. The high-risk group, as classified by median risk score, exhibited significantly shorter survival times compared to the low-risk group. The signature’s predictive accuracy was further confirmed with Area Under the Curve (AUC) scores exceeding 0.75 in TCGA and GSE17536 cohorts. Notably, the risk score was significantly correlated with immune checkpoints, chemotherapy sensitivity, and tumor microenvironment. Furthermore, the risk score showed a strong association with immunotherapy response in patients from GSE78220 and GSE39688 cohorts. Bioinformatics analysis of KIF7, a key gene within the signature, revealed its upregulation in CC and significant associations with tumor mutation burden, microsatellite instability, and immune cell infiltration across various cancers. Experiments confirmed that KIF7 was upregulated in CC and its knockdown reduced cell proliferation, migration, and invasion.ConclusionThe CRG prognostic signature can effectively predict overall survival, immune microenvironment and chemotherapy response in CC. KIF7, as a potential prognostic marker, has significant potential for the prediction and treatment of CC.

Pan-cancer single cell and spatial transcriptomics analysis deciphers the molecular landscapes of senescence related cancer-associated fibroblasts and reveals its predictive value in neuroblastoma via integrated multi-omics analysis and machine learning.

Cancer-associated fibroblasts (CAFs) are a diverse group of cells that significantly contribute to reshaping the tumor microenvironment (TME), and no research has systematically explored the molecular landscapes of senescence related CAFs (senes CAF) in NB. We utilized pan-cancer single cell and spatial transcriptomics analysis to identify the subpopulation of senes CAFs via senescence related genes, exploring its spatial distribution characteristics. Harnessing the maker genes with prognostic significance, we delineated the molecular landscapes of senes CAFs in bulk-seq data. We established the senes CAFs related signature (SCRS) by amalgamating 12 and 10 distinct machine learning (ML) algorithms to precisely diagnose stage 4 NB and to predict prognosis in NB. Based on risk scores calculated by prognostic SCRS, patients were categorized into high and low risk groups according to median risk score. We conducted comprehensive analysis between two risk groups, in terms of clinical applications, immune microenvironment, somatic mutations, immunotherapy, chemotherapy and single cell level. Ultimately, we explore the biological function of the hub gene JAK1 in pan-cancer multi-omics landscape. Through integrated analysis of pan-cancer spatial and single-cell transcriptomics data, we identified distinct functional subgroups of CAFs and characterized their spatial distribution patterns. With marker genes of senes CAF and leave-one-out cross-validation, we selected RF algorithm to establish diagnostic SCRS, and SuperPC algorithm to develop prognostic SCRS. SCRS demonstrated a stable predictive capability, outperforming the previously published NB signatures and clinic variables. We stratified NB patients into high and low risk group, which showed the low-risk group with a superior survival outcome, an abundant immune infiltration, a different mutation landscape, and an enhanced sensitivity to immunotherapy. Single cell analysis reveals biologically cellular variations underlying model genes of SCRS. Spatial transcriptomics delineated the molecular variant expressions of hub gene JAK1 in malignant cells across cancers, while immunohistochemistry validated the differential protein levels of JAK1 in NB. Based on multi-omics analysis and ML algorithms, we successfully developed the SCRS to enable accurate diagnosis and prognostic stratification in NB, which shed light on molecular landscapes of senes CAF and clinical utilization of SCRS.

Comprehensive analysis of tumor immune-related gene signature for predicting prognosis, immunotherapy, and drug sensitivity in bladder urothelial carcinoma.

Bladder urothelial carcinoma (BLCA) is globally recognized as a prevalent malignancy. Its treatment remains challenging due to the extensive morbidity, high mortality rates, and compromised quality of life from postoperative complications and the lack of specific molecular targets. Our aim was to establish a prognostic model to evaluate the prognostic significance, assess immunotherapy responses, and determine drug susceptibility in patients with BLCA. From The Cancer Genome Atlas (TCGA) datasets, we obtained BLCA clinical details and expression data of immune-related genes. These data were analyzed using R and related packages. Differential expression analysis, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses, weighted gene co-expression network analysis (WGCNA), univariate and multivariate Cox regression analysis, prognostic analysis, model establishment and evaluation, gene set variation analysis (GSVA), immune function and checkpoint analysis, immunotherapy response prediction, and prediction of drug sensitivity were conducted. A total of 11 differentially expressed immune genes (DEIGs) were selected to establish the bladder carcinoma immune-related gene signature for BLCA prognosis prediction. In both the training and testing groups, the high-risk cohort showed a lower overall survival (OS) than the low-risk cohort. The area under the receiver operating characteristic curve (AUC) was 0.712 in the training group and 0.631 in the testing group, highlighting its predictive capacity. In the external validation datasets GSE39281 and IMvigor210, the OS of the high-risk group was significantly lower than that of the low-risk group, with AUC values of 0.609 and 0.563, respectively. Patients in the training group were categorized into low- and high-risk groups based on the bladder carcinoma immune gene signature (BCIGS) median risk score. GSVA showed 21 KEGG pathways positively correlated with model risk scores. The high-risk group presented with elevated stromal score, immune score, ESTIMATE score, and T cell exclusion score level. Conversely, the low-risk group displayed heightened cytotoxic T-lymphocyte antigen 4 (CTLA4) expression, indicative of a better response to immune checkpoint inhibitors (ICIs). Notably, significant disparities were found in immune subtypes, immune-related function, and immune-related survival between the two risk groups. The AUC values of our model are 0.765 and 0.660, respectively, surpassing those of other models, such as the tumor inflammation signature (TIS), tumor immune dysfunction and exclusion (TIDE), and various clinical factors. We also presented a nomogram, with the AUCs for predicting 1-, 2-, and 3-year OS at 0.727, 0.772, and 0.765 respectively, suggesting the signature's robust predictive power. Finally, 20 small molecular compounds were identified, with the TW.37 drug's half maximum inhibitory concentration (IC50) value difference being the most pronounced between the high- and low-risk patient groups, indicating its potential as a treatment option. Our constructed immune-related gene signature model forecasts BLCA patient prognosis and potentially guides individualized immunotherapy and chemotherapeutic drug choices.

Development and experimental validation of a senescence-related long non-coding RNA signature for prognostic prediction and immune microenvironment characterization in gastric cancer patients.

Cellular senescence is considered a new marker of cancer. It has been suggested that long non-coding RNA (lncRNA) can be used to predict the prognosis of cancers. However, it remains to be seen whether the lncRNAs associated with cellular senescence can be used to predict the prognosis of gastric cancer (GC). The present study aimed to develop a novel senescence-related lncRNA signature (SenLncSig) to predict GC prognosis. The SenLncSig model holds promise for enhancing patient stratification, enabling more precise prognostic predictions and facilitating immunotherapy strategies. Senescence-associated lncRNAs were identified from RNA expression profiles in The Cancer Genome Atlas (TCGA) database through the construction of a co-expression network linking senescence genes and lncRNAs. A prognostic signature for GC (334 patients from TCGA-STAD data set), comprising the senescence-related lncRNAs, was developed through univariate and multivariate Cox proportional hazards regression analyses. By using the median SenLncSig risk score, the GC patients were categorized into high- and low-risk groups. A Kaplan-Meier analysis and gene set enrichment analysis were conducted, and immune infiltration, the tumor mutation burden (TMB), and pharmacological treatments were compared between the high- and low-risk groups. We used an independent GC cohort (an external cohort of 30 pairs of tumor and non-tumor tissues from the GC patients) and three GC cell lines to conduct a quantitative reverse-transcription polymerase chain reaction (qRT-PCR) analysis to validate the results. We established a SenLncSig, a prognostic risk model comprising the following five senescence-associated lncRNAs; AP000695.2, LINC02381, AC005586.1, AP003392.1, and AP001528.2. According to the SenLncSig, high-risk scores were associated with poor overall survival (multivariate Cox proportional hazard ratio: 1.498, 95% confidence interval: 1.294-1.735; P<0.001). The time-dependent receiver operating characteristic curve indicated that the model performed (area under the curve: 0.711). We developed a nomogram incorporating age, gender, grade, stage, T stage, M stage, N stage, and SenLncSig risk score to estimate 1-year, 3-year, and 5-year survival rates. Further, according to the results of the mutation analysis, patients with a high TMB in the high-risk group had the worst prognosis. Interestingly, the high-risk group had a stronger infiltration of regulatory T cells (P<0.001) and M2 macrophage cells (P<0.001), as well as higher tumor immune dysfunction and exclusion scores than the low-risk group. These results might explain why the high-risk group had a worse prognosis. Finally, the qRT-PCR validation revealed that the AP000695.2 and AP003392.1 expression levels were significantly higher in the tumor tissues and GC cell lines than the normal tissues and normal human gastric epithelial cell line, whereas the opposite pattern was found for LINC02381. The development of the SenLncSig provided a potential tool for improving patient prognosis predictions and offered preliminary insights into predicting the efficacy of GC immunotherapy.