Risk Score Model Research Articles

Immune and inflammatory insights in atherosclerosis: development of a risk prediction model through single-cell and bulk transcriptomic analyses.

Investigation into the immune heterogeneity linked with atherosclerosis remains understudied. This knowledge gap hinders the creation of a robust theoretical framework essential for devising personalized immunotherapies aimed at combating this disease. Single-cell RNA sequencing (scRNA-seq) analysis was employed to delineate the immune cell-type landscape within atherosclerotic plaques, followed by assessments of cell-cell interactions and phenotype characteristics using scRNA-seq datasets. Subsequently, pseudotime trajectory analysis was utilized to elucidate the heterogeneity in cell fate and differentiation among macrophages. Through integrated approaches, including single-cell sequencing, Weighted Gene Co-expression Network Analysis (WGCNA), and machine learning techniques, we identified hallmark genes. A risk score model and a corresponding nomogram were developed and validated using these genes, confirmed through Receiver Operating Characteristic (ROC) curve analysis. Additionally, enrichment and immune characteristic analyses were conducted based on the risk score model. The model's applicability was further corroborated by in vitro and in vivo validation of specific genes implicated in atherosclerosis. This comprehensive scRNA-seq analysis has shed new light on the intricate immune landscape and the role of macrophages in atherosclerotic plaques. The presence of diverse immune cell populations, with a particularly enriched macrophage population, was highlighted by the results. Macrophage heterogeneity was intricately characterized, revealing four distinct subtypes with varying functional attributes that underscore their complex roles in atherosclerotic pathology. Intercellular communication analysis revealed robust macrophage interactions with multiple cell types and detailed pathways differing between proximal adjacent and atherosclerotic core groups. Furthermore, pseudotime trajectories charted the developmental course of macrophage subpopulations, offering insights into their differentiation fates within the plaque microenvironment. The use of machine learning identified potential diagnostic markers, culminating in the identification of RNASE1 and CD14. The risk score model based on these biomarkers exhibited high accuracy in diagnosing atherosclerosis. Immune characteristic analysis validated the risk score model's efficacy in defining patient profiles, distinguishing high-risk individuals with pronounced immune cell activities. Finally, experimental validation affirmed RNASE1's involvement in atherosclerotic progression, suggesting its potential as a therapeutic target. Our findings have advanced our understanding of atherosclerosis immunopathology and paved the way for novel diagnostic and therapeutic strategies.

GWAS and polygenic risk score of severe COVID-19 in Eastern Europe.

COVID-19 disease has infected more than 772 million people, leading to 7 million deaths. Although the severe course of COVID-19 can be prevented using appropriate treatments, effective interventions require a thorough research of the genetic factors involved in its pathogenesis. We conducted a genome-wide association study (GWAS) on 7,124 individuals (comprising 6,400 controls who had mild to moderate COVID-19 and 724 cases with severe COVID-19). The inclusion criteria were acute respiratory distress syndrome (ARDS), acute respiratory failure (ARF) requiring respiratory support, or CT scans indicative of severe COVID-19 infection without any competing diseases. We also developed a polygenic risk score (PRS) model to identify individuals at high risk. We identified two genome-wide significant loci (P-value <5 × 10-8) and one locus with approximately genome-wide significance (P-value = 5.92 × 10-8-6.15 × 10-8). The most genome-wide significant variants were located in the leucine zipper transcription factor like 1 (LZTFL1) gene, which has been highlighted in several previous GWAS studies. Our PRS model results indicated that individuals in the top 10% group of the PRS had twice the risk of severe course of the disease compared to those at median risk [odds ratio = 2.18 (1.66, 2.86), P-value = 8.9 × 10-9]. We conducted one of the largest studies to date on the genetics of severe COVID-19 in an Eastern European cohort. Our results are consistent with previous research and will guide further epidemiologic studies on host genetics, as well as for the development of targeted treatments.

Integrating oxidative-stress biomarkers into a precision oncology risk-stratification model for bladder cancer prognosis and therapy.

Bladder cancer is a common malignant tumor with significant heterogeneity, making personalized risk stratification crucial for optimizing treatment and prognosis. This study aimed to develop a prognostic model based on oxidative stress-related genes to guide risk assessment in bladder cancer. Differentially expressed oxidative stress-related genes were identified using the GEO database. Functional enrichment and survival analyses were performed on these genes. A risk-scoring model was built and tested for prognostic value and therapeutic response prediction. Expression of key genes was validated by qRT-PCR in samples from two muscle-invasive and two non-muscle-invasive bladder cancer patients. Several oxidative stress-related genes were identified as significantly associated with survival. The risk-scoring model stratified patients into high- and low-risk groups, accurately predicting prognosis and therapeutic responses. qRT-PCR confirmed the differential expression of key genes in patient samples. The study provides a concise risk stratification model based on oxidative stress-related genes, offering a practical tool for improving personalized treatment in bladder cancer. Further validation is required for broader clinical application.

Protein tyrosine phosphatase non-receptor II: A possible biomarker of poor prognosis and mediator of immune evasion in hepatocellular carcinoma.

The incidence of primary liver cancer is increasing year by year. In 2022 alone, more than 900000 people were diagnosed with liver cancer worldwide, with hepatocellular carcinoma (HCC) accounting for 75%-85% of cases. HCC is the most common primary liver cancer. China has the highest incidence and mortality rate of HCC in the world, and it is one of the malignant tumors that seriously threaten the health of Chinese people. The onset of liver cancer is occult, the early cases lack typical clinical symptoms, and most of the patients are already in the middle and late stage when diagnosed. Therefore, it is very important to find new markers for the early detection and diagnosis of liver cancer, improve the therapeutic effect, and improve the prognosis of patients. Protein tyrosine phosphatase non-receptor 2 (PTPN2) has been shown to be associated with colorectal cancer, triple-negative breast cancer, non-small cell lung cancer, and prostate cancer, but its biological role and function in tumors remain to be further studied. To combine the results of relevant data obtained from The Cancer Genome Atlas (TCGA) to provide the first in-depth analysis of the biological role of PTPN2 in HCC. The expression of PTPN2 in HCC was first analyzed based on the TCGA database, and the findings were then verified by immunohistochemical staining, quantitative real-time polymerase chain reaction (qRT-PCR), and immunoblotting. The value of PTPN2 in predicting the survival of patients with HCC was assessed by analyzing the relationship between PTPN2 expression in HCC tissues and clinicopathological features. Finally, the potential of PTPN2 affecting immune escape of liver cancer was evaluated by tumor immune dysfunction and exclusion and immunohistochemical staining. The results of immunohistochemical staining, qRT-PCR, and immunoblotting in combination with TCGA database analysis showed that PTPN2 was highly expressed and associated with a poor prognosis in HCC patients. Kyoto Encyclopedia of Genes and Genomes enrichment analysis showed that PTPN2 was associated with various pathways, including cancer-related pathways, the Notch signaling pathway, and the MAPK signaling pathway. Gene Set Enrichment Analysis showed that PTPN2 was highly expressed in various immune-related pathways, such as the epithelial mesenchymal transition process. A risk model score based on PTPN2 showed that immune escape was significantly enhanced in the high-risk group compared with the low-risk group. This study investigated PTPN2 from multiple biological perspectives, revealing that PTPN2 can function as a biomarker of poor prognosis and mediate immune evasion in HCC.

Unveiling the impact of cell death-related genes and immune dynamics on drug resistance in lung adenocarcinoma: a risk score model and functional insights

Lung adenocarcinoma (LUAD), characterized by its heterogeneity and complex pathogenesis, is the focus of this study which investigates the association between cell death-related genes and LUAD. Through machine learning, a risk score model was developed using the Coxboost rsf algorithm, demonstrating strong prognostic accuracy in both validation (GSE30219, GSE31210, GSE72094) and training (TCGA-LUAD) datasets with C-indices of 0.93, 0.67, 0.68, and 0.64, respectively. The study reveals that the expression of Keratin 18 (KRT18), a key cytoskeletal protein, varies across LUAD cell lines (DV-90, PC-9, A549) compared to normal bronchial epithelial cells (BEAS-2B), suggesting its potential role in LUAD's pathogenesis. Kaplan–Meier survival curves further validate the model, indicating longer survival in the low-risk group. A comprehensive analysis of gene expression, functional differences, immune infiltration, and mutations underscores significant variations between risk groups, highlighting the high-risk group's immunological dysfunction. This points to a more intricate tumor immune environment and the possibility of alternative therapeutic strategies. The study also delves into drug sensitivity, showing distinct responses between risk groups, underscoring the importance of risk stratification in treatment decisions for LUAD patients. Additionally, it explores KRT18's epigenetic regulation and its correlation with immune cell infiltration and immune regulatory molecules, suggesting KRT18's significant role in the tumor immune landscape. This research not only offers a valuable prognostic tool for LUAD but also illuminates the complex interplay between cell death-related genes, drug sensitivity, and immune infiltration, positioning KRT18 as a potential therapeutic or prognostic target to improve patient outcomes by personalizing LUAD treatment strategies.

Prognosis and immunotherapeutic implications of molecular classification of cervical cancer based on immunophenoscore-related genes

The immunophenoscore (IPS) is an important indicator for evaluating immunotherapy response. This work was designed to establish a prognostic model based on IPS-related genes in cervical cancer. Weighted correlation network analysis (WGCNA) was utilized to identify key modules related to IPS in cervical cancer data from The Cancer Genome Atlas (TCGA). The results show that the yellow module (158 genes) had a high correlation with both IPS_CTLA4_blocker and IPS_CTLA4_and PC1/PDL1/PDL2 blocker. Univariate cox regression analysis and LASSO regression analysis were performed based on 158 genes, and 9 characteristic genes were finally identified to construct the model. According to the differentially expressed genes, cervical cancer samples were divided into high-risk and low-risk groups and cluster 1.2.3. Higher risk scores associated with poorer prognosis. cluster2 and cluster3 were identified as cervical cancer subtypes with significant survival differences. cluster2 had higher immune cell infiltration levels and better prognosis, with greater sensitivity to Cyclopamine, Imatinib, MG-13, Paclitaxel, PHA-665752, Rapamycin, Sorafenib, Sunitinib, and VX-680. In contrast, cluster3 had higher TTN and PIK3CA mutations and greater sensitivity to AZ628, Dasatinib, Doxorubicin, HG-6-64-1, JQ12, Midostaurin, PF-562271, TAE684, and WH-4-023. In conclusion, we developed a feasible risk score model based on IPS-related genes for cervical cancer prognosis and identified potential drugs for different cervical cancer subtypes.

Validation of endoplasmic reticulum stress-related gene signature to predict prognosis and immune landscape of patients with non-small cell lung cancer.

Lung cancer is one of the most common cancers worldwide, with the incidence increasing each year. It is crucial to improve the prognosis of patients who have lung cancer. Non-Small Cell Lung Cancer (NSCLC) accounts for the majority of lung cancer. Though its prognostic significance in NSCLC has not been often documented, Endoplasmic Reticulum (ER) stress has been identified to be implicated in tumour malignant behaviours and resistance to treatment. This work aimed to develop a gene profile linked to ER stress that could be applied to predictive and risk assessment for non-small cell lung cancer. Data from 1014 NSCLC patients were sourced from The Cancer Genome Atlas (TCGA) database, integrating clinical and Ribonucleic Acid (RNA) information. Diverse analytical techniques were utilized to identify ERS-associated genes associated with patients' prognoses. These techniques included Kaplan-Meier analysis, univariate Cox regression, Least Absolute Shrinkage and Selection Operator regression analysis (LASSO) regression, and Pearson correlation analysis. Using a risk score model obtained from multivariate Cox analysis, a nomogram was created and validated to classify patients into high- and low-risk groups. The study employed the CIBERSORT algorithm and Single-Sample Gene Set Eenrichment Analysis (ssGSEA) to investigate the tumour immune microenvironment. We used the Genomics of Drug Sensitivity in Cancer (GDSC) database and R tools to identify medicines that could be responsive. Four genes - FABP5, C5AR1, CTSL, and LTA4H - were chosen to create the risk model. Overall Survival (OS) was considerably lower (P< 0.05) in the high-risk group. When it came to predictive accuracy, the risk model outperformed clinical considerations. Several medication types that are sensitive to high-risk groups were chosen. Our study has produced a gene signature associated with ER stress that may be employed to forecast the prognosis and therapeutic response of non-small cell lung cancer patients.

Exploring extrahepatic metastasis of hepatocellular carcinoma based on methylation driver genes and establishing a prognostic model for hepatocellular carcinoma

BackgroundHepatocellular carcinoma (HCC), theseventh most common cancer worldwide, is characterized by a high mortality rate, advanced diagnosis, and susceptibility to extrahepatic metastasis. Numerous studies have shown that DNA methylation is a crucial factor in epigenetic modifications and regulation of carcinogenesis. MethodsHCC patient data were sourced from the TCGA dataset as a training set, while GSE116174 was used as an external validation set for verification. Differential methylation and expression analyses were performed on HCC samples with and without extrahepatic metastasis. In the intersecting genes, the relationship between methylation and expression levels of the intersecting genes was analyzed. Genes with a correlation coefficient≥|0.30| and P<0.05 were identified as methylation driver genes. Cox regression analysis was conducted to identify genes associated with HCC prognosis and establish a risk score. Subsequently, a prognostic model was established and validated using Cox regression analysis incorporating the risk score and other clinical factors. Using immunohistochemistry to evaluate the expression of DHX58 and EIF5A2 in HCC tissues with and without extrahepatic metastasis. Immunoinfiltration analysis was performed on the HCC samples using CIBERSORT. ResultsOur research identified eight methylation driver genes for HCC extrahepatic metastasis, of which two genes (DHX58 and EIF5A2) were associated with HCC patient prognosis. And the study further constructed and validated the risk score and prognostic model. Immunoinfiltration analysis showed that M0 macrophage abundance was correlated with the prognosis of HCC patients. Immunohistochemistry revealed differences in DHX58 and EIF5A2 expression between HCC tissues with and without extrahepatic metastasis, consistent with our bioinformatics findings.

Mammographic breast density and cardiovascular disease in women. A literature review

The world is searching for new simple and economically available gender-specific markers to improve cardiovascular risk stratification in women. The aim of this review was to analyze the association of mammographic density (MD) with cardiovascular disease (CVD). In low MD, i.e., high relative mammary gland fat content, there is a higher incidence of the main risk factors for CVD: hypertension, hyperlipidemia, hyperglycemia, excess body weight, as well as an increase in the volume of fat depots, visceral and ectopic fat. Low MD is associated with a higher 10-year risk of adverse cardiovascular events such as coronary artery disease, stroke, peripheral arterial disease, revascularization, and heart failure, and may serve as a predictor of their development. Including MD in the Framingham Risk Score model improves its accuracy. Identification of low MD, as a marker of high cardiovascular risk, allows the use of mammography for early detection and prevention of the two most dangerous diseases among the female population — breast cancer and CVD.

A useful mTORC1 signaling-related RiskScore model for the personalized treatment of osteosarcoma patients by using the bulk RNA-seq analysis

AimsThis research developed a prognostic model for OS patients based on the Mechanistic Target of Rapamycin Complex 1 (mTORC1) signature.BackgroundThe mTORC1 signaling pathway has a critical role in the maintenance of cellular homeostasis and tumorigenesis and development through the regulation of cell growth, metabolism and autophagy. However, the mechanism of action of this signaling pathway in Osteosarcoma (OS) remains unclear.ObjectiveThe datasets including the TARGET-OS and GSE39058, and 200 mTORC1 genes were collected.MethodsThe mTORC1 signaling-related genes were obtained based on the Molecular Signatures Database (MSigDB) database, and the single sample gene set enrichment analysis (ssGSEA) algorithm was utilized in order to calculate the mTORC1 score. Then, the WGCNA were performed for the mTORC1-correlated gene module, the un/multivariate and lasso Cox regression analysis were conducted for the RiskScore model. The immune infiltration analysis was performed by using the ssGSEA method, ESTIMATE tool and MCP-Count algorithm. KM survival and Receiver Operating Characteristic (ROC) Curve analysis were performed by using the survival and timeROC package.ResultsThe mTORC1 score and WGCNA with β = 5 screened the mTORC1 positively correlated skyblue2 module that included 67 genes, which are also associated with the metabolism and hypoxia pathways. Further narrowing of candidate genes and calculating the regression coefficient, we developed a useful and reliable RiskScore model, which can classify the patients in the training and validation set into high and low-risk groups based on the median value of RiskScore as an independent and robust prognostic factor. High-risk patients had a significantly poor prognosis, lower immune infiltration level of multiple immune cells and prone to cancer metastasis. Finally, we a nomogram model incorporating the metastasis features and RiskScore showed excellent prediction accuracy and clinical practicability.ConclusionWe developed a useful and reliable risk prognosis model based on the mTORC1 signaling signature.

RGS4 inhibits glioma cells sensitivity to radiotherapy and temozolomide by regulating ferroptosis

Background Chemoradiotherapy is the major means in the treatment of gliomas followed by surgery. Ferroptosis has been shown to play an important role in carcinogenesis by many studies. However, its underlying effect on chemoradiotherapy sensitivity in gliomas remains unclear. Methods The genetic and clinical information and ferroptosis-related genes were downloaded from The Cancer Genome Atlas (TCGA) database. Gene Expression Profiling Interactive Analysis (GEPIA) was used to perform hub gene expression and survival analysis. Cell Counting Kit 8 (CCK-8), colony formation, 5-Ethynyl-2′-Deoxyuridine (EdU), Transwell and chemoradiotherapy sensitivity experiments were performed to confirm the biological function of RGS4 in glioma cells. The molecular mechanism of RGS4 on ferroptosis in gliomas was explored in vitro. Results 385 ferroptosis-related genes were identified via bioinformatics analysis. 16 differential expressed genes (DEGs) were identified as radiation-related genes. Among them, RGS4, HSPA5, and SLC40A1 had prognostic values in further analysis. The calculated risk score could significantly distinguish the high-risk population. Moreover, RGS4 expression was closely related with immune infiltration and regulators. RGS4 knockdown could inhibit the proliferation and migration of glioma cells. Down-regulation of RGS4 expression induced ferroptosis to promote cancer sensitivity to chemoradiotherapy. Conclusions A three-gene signature was developed in a risk-score model, which could be used to predict the prognosis of glioma patients. RGS4 is dysregulated in many types of cancers, and is a candidate prognostic biomarker for many types of cancers. Moreover, RGS4 may be a target for predicting and enhancing the chemoradiotherapy sensitivity of gliomas.

Prediction of immediate bleeding after cold snare polypectomy: A prospective observational study.

The risk factors for immediate post-polypectomy bleeding (IPPB) after cold snare polypectomy (CSP) are not well-known. We sought to define such risk factors and develop a predictive risk-scoring model. This prospective observational study included 161 polyps (4-9 mm in diameter) that were removed via CSP from 118 patients during the period from June to September 2019 in 2 tertiary hospitals. IPPB was defined as post-polypectomy bleeding within 24 hours or grade 3 or 4 intraprocedural bleeding requiring endoscopic hemostasis. IPPB incidences according to grade were 13.0% (21/161) (grade 3) and 0% (grade 4). Univariate analysis showed that the polyp size and morphology, as well as iatrogenic ulcer size and shape, were significantly associated with IPPB. Multivariate analysis showed that polyp size [6-9 mm vs 4-5 mm, odds ratio (OR) 3.72, 95% confidence interval (CI) 1.28-10.79], polyp morphology (polypoid vs non-polypoid, OR: 3.93, 95% CI: 1.22-12.64), and iatrogenic ulcer size (≥10 vs ≤ 9 mm, OR: 3.12, 95% CI: 1.04-9.38) were significantly associated with IPPB. We created a four-marker risk-scoring model to predict IPPB after CSP; we summed the points assigned for the 4 factors. At a cutoff of 2, the sensitivity was 85.7% and the specificity was 65.0%; at a cutoff of 3, the sensitivity was 65% and the specificity was 90.0%. Polyp size and morphology, as well as iatrogenic ulcer size and shape, were associated with IPPB after CSP. The four-marker risk-scoring model appears to effectively predict IPPB after CSP (Clinical Research Information Service: KCT0004375).

Integrative analysis of bulk and single-cell RNA sequencing reveals sphingolipid metabolism and immune landscape in clear cell renal cell carcinoma.

Clear cell renal cell carcinoma (ccRCC) is characterized by its aggressive behavior and complex molecular heterogeneity, posing significant challenges for treatment and prognostication. This study offers a comprehensive analysis of ccRCC by leveraging both bulk and single-cell RNA sequencing data, with a specific aim to unravel the complexities of sphingolipid metabolism and the intricate dynamics within the tumor microenvironment (TME). By examining ccRCC samples sourced from public databases, our investigation delves deep into the genetic and transcriptomic landscape of this cancer type. Employing advanced analytical techniques, we have identified pivotal patterns in gene expression and cellular heterogeneity, with a special focus on the roles and interactions of various immune cells within the TME. Significantly, our research has unearthed insights into the dynamics of sphingolipid metabolism in ccRCC, shedding light on its potential implications for tumor progression and strategies for immune evasion. A novel aspect of this study is the development of a risk score model designed to enhance prognostic predictions for ccRCC patients, which is currently pending external validation to ascertain its clinical utility. Despite its contributions, the study is mindful of its limitations, including a reliance on observational data from public sources and a primary focus on RNA sequencing data, which may constrain the depth and generalizability of the findings. The study does not encompass critical aspects, such as protein expression, posttranslational modifications, and comprehensive metabolic profiles. Moreover, its retrospective design underscores the necessity for future prospective studies to solidify these preliminary conclusions. Our findings illuminate the intricate interplay between genetic alterations, sphingolipid metabolism, and immune responses in ccRCC. This research not only enhances our understanding of the molecular foundations of ccRCC but also paves the way for the development of targeted therapies and personalized treatment modalities. The study underlines the importance of cautious interpretation of results and champions ongoing research using diverse methodologies to thoroughly comprehend and effectively combat this formidable cancer type.

Prognosis, risk factors, and scoring model of patients resuscitated from traumatic cardiac arrest: A multicenter observational study in Japan.

A subset of patients with traumatic cardiac arrest is salvageable when the reversible causes of cardiac arrest are promptly treated. However, prognosis and risk factors of survivors upon hospital admission after traumatic cardiac arrest remain unclear. We aimed to describe the outcomes, identify risk factors, and develop a simple risk-scoring model for patients resuscitated from traumatic cardiac arrest. This observational multicenter study analyzed data from the Japan Trauma Data Bank from January 1, 2019, to December 31, 2021. Patients who underwent cardiopulmonary resuscitation in the emergency department and survived to the day after hospital admission for traumatic cardiac arrest were included. Factors associated with survival to hospital discharge were determined using mixed-effects multivariable logistic regression analysis. A simple scoring model was developed to stratify the probability of survival to hospital discharge. In total, 452 patients from 119 hospitals (median age, 64 years; 334 [74.4%] men) were included in the analysis. Of these, 130 (28.8%) survived until discharge. Penetrating injury, signs of life upon hospital arrival, and Injury Severity Score were significantly associated with survival at hospital discharge. A scoring model that assigned 1 point each for penetrating injury and signs of life upon hospital arrival effectively stratified the probability of survival to hospital discharge, with scores of 0, 1, and 2 corresponding to survival probabilities of 12.2%, 35.2%, and 83.3%, respectively. This study described the outcomes and risk factors of patients resuscitated from traumatic cardiac arrest. Our simple scoring model effectively stratified the likelihood of survival to hospital discharge. Therapeutic/Care Management; Level III.

Validity of predictive score model for microvascular invasion in hepatocellular carcinoma.

Objective: To validate a risk score model to predict MVI using Total tumor volume (TTV) and AFP. Study Design: Cross-sectional. Setting: Department of Gastroenterology, Liaquat National Hospital, Karachi, Pakistan. Period: June 2022 to December, 2023. Methods: Patients of either gender, older than 18 years and above with confirmed diagnosis of HCC were enrolled. When there were several HCC tumors, the largest tumor's axis was measured and used as the representative HCC diameter. When microscopic tumor invasion was detected in the portal, hepatic vein, or biliary vein of the surrounding liver tissue that was adjacent to the tumor, it was referred to as micro vascular invasion. Results: Total 302 patients were studied with mean age of 62.1 ± 9.2 years. Majority of patients were males (64.2%). Around one-fifth patients had more than one tumor (19.9%). Out of 302 patients, MVI was seen in 30.1% patients. In multivariable analysis number of tumors (aOR=220.65, 95% CI: 128.91-922.67), tumor volume (aOR=1.01, 95% CI: 1.01-1.02), and AFP (aOR=1.01, 95% CI: 1-1.02) were found to be significant predictors of MVI. Conclusion: This study validated a prediction model for forecasting of MVI in HCC patients with highly significant variables including number of tumors, tumor volume and alpha feto protein. Timely decision making could be done using this model.

Comprehensive analysis of cuproptosis-related genes involved in prognosis and tumor microenvironment infiltration of colorectal cancer.

Colorectal cancer (CRC) is a common malignancy, with high incidence and high mortality rates. Cuproptosis, a novel form of copper-induced programmed cell death, contributes to tumor progression. However, whether cuproptosis-related genes (CRGs) play a role in CRC remains unclear. This study aims to elucidate the role of CRGs in CRC development, patient prognosis, and immune response. We performed bioinformatics analysis of the differential expression of CRGs between CRC and normal tissues. Least absolute shrinkage and selection operator (LASSO), and univariate and multivariate Cox analyses were employed to identify risk factors, which were used to construct a risk score model. Patients with CRC were categorized into high- and low-risk groups based on their median risk scores. Receiver operating characteristic curve analysis was used to verify the predictive accuracy of the risk model. A nomogram was developed for CRC through univariate and multivariate Cox regression analyses. The chemotherapeutic drug sensitivity was compared between patients with high and low CDKN2A/DLAT expression using the Wilcoxon rank-sum test. Spearman's correlation and TISIDB database analyses were conducted to determine relationships between CDKN2A or DLAT and immune cell infiltration. Eight of ten identified CRGs exhibited significant differential expression between CRC and normal tissues. Among the eight significant differential expression CRGs, CDKN2A and DLAT were identified as independent risk factors for predicting overall survival (OS) in CRC. Patients with CRC in the low-risk group had longer OS than those in the high-risk group. The risk score model had good predictive accuracy for OS. Based on CDKN2A, DLAT and some clinical characteristics, a prognostic nomogram was developed to predict OS for CRC patients and showed good predictive ability. CDKN2A and DLAT expressions were significantly associated with chemotherapeutic drug sensitivity and immune cell infiltration in CRC, and the molecular subtypes and immune subtypes differed between CDKN2A and DLAT. Our research revealed the prognostic value of CRGs, particularly CDKN2A and DLAT, in CRC and demonstrated the relationship between CDKN2A/DLAT and immune infiltration in CRC, thereby contributing to the outcome evaluation of patients with CRC and identifying novel targets for CRC immunotherapy.

Identification and validation of a novel robust glioblastoma prognosis model based on bioinformatics

BackgroundGlioblastoma (GBM) is a very common primary malignant tumor of the central nervous system (CNS). Aging, macrophage, autophagy, and methylation related genes are hypothesized to be crucial to its pathogenesis. In this study, we aimed to explore the role of these genes in the prognosis of GBM. MethodsThe RNA sequence (RNA-seq) and clinical information were downloaded from The Cancer Genome Atlas database (TCGA) and the Chinese Glioma Genome Atlas database (CGGA). We performed univariate and least absolute shrinkage and selection operator (LASSO) multivariate Cox regression analysis to identify risk signatures related to overall survival (OS). We further developed a nomogram to predict individual outcomes. In addition, the immune microenvironment was analyzed by CIBERSORT. Results256 differentially expressed genes (DEGs) were obtained based on aging, macrophage, autophagy, and methylation related genes between GBM samples and normal tissues in TCGA-GBM cohort. We identified five optimal risk signatures with prognostic values in TCGA-GBM cohort and established a prognostic risk score model. The validity of the model was verified in the CGGA cohort and Huanhu cohort. Finally, we constructed a nomogram for clinical application by combining age, O(6)-methylguanine-DNA methyltransferase (MGMT) promoter methylation status, and risk score. Activated NK cells and resting mast cells were highly expressed and memory B cells, plasma cells, resting NK cells, M1 macrophages, and neutrophils exhibited low expression in the high-risk score group. GBM patients with a low-risk score had a higher Tumor Immune Dysfunction and Exclusion (TIDE) score. The risk score of hot tumors was higher than that of the cold tumors. Additionally, 29 genes involved in glucose and lipid metabolism were highly expressed with a high-risk score. 31 metabolism-related pathways were significantly different between high-risk and low-risk groups. ConclusionsWe constructed and validated a novel prognostic model for GBM. Aging, macrophage, autophagy, and methylation related genes may serve as prognostic and therapeutic biomarkers. The model developed may assist in guiding treatment for GBM patients. Our research had great significance in accurately predicting the prognosis of GBM and may offer reference for immunotherapy decision for GBM patients.

Deciphering Dormant Cells of Lung Adenocarcinoma: Prognostic Insights from O-glycosylation-Related Tumor Dormancy Genes Using Machine Learning.

Lung adenocarcinoma (LUAD) poses significant challenges due to its complex biological characteristics and high recurrence rate. The high recurrence rate of LUAD is closely associated with cellular dormancy, which enhances resistance to chemotherapy and evasion of immune cell destruction. Using single-cell RNA sequencing (scRNA-seq) data from LUAD patients, we categorized the cells into two subclusters: dormant and active cells. Utilizing high-density Weighted Gene Co-expression Network Analysis (hdWGCNA) and pseudo-time cell trajectory, aberrant expression of genes involved in protein O-glycosylation was detected in dormant cells, suggesting a crucial role for O-glycosylation in maintaining the dormant state. Intercellular communication analysis highlighted the interaction between fibroblasts and dormant cells, where the Insulin-like Growth Factor (IGF) signaling pathway regulated by O-glycosylation was crucial. By employing Gene Set Variation Analysis (GSVA) and machine learning, a risk score model was developed using hub genes, which showed high accuracy in determining LUAD prognosis. The model also demonstrated robust performance on the training dataset and excellent predictive capability, providing a reliable basis for predicting patient clinical outcomes. The group with a higher risk score exhibited a propensity for adverse outcomes in the tumor microenvironment (TME) and tumor mutational burden (TMB). Additionally, the 50% inhibitory concentration (IC50) values for chemotherapy exhibited significant variations among the different risk groups. In vitro experiments demonstrated that EFNB2, PTTG1IP, and TNFRSF11A were upregulated in dormant tumor cells, which also contributed greatly to the diagnosis of LUAD. In conclusion, this study highlighted the crucial role of O-glycosylation in the dormancy state of LUAD tumors and developed a predictive model for the prognosis of LUAD patients.

Constructing and validating pan-apoptosis-related features for predicting prognosis and immunotherapy response in hepatocellular carcinoma

The study aimed to develop a prognostic model for Hepatocellular Carcinoma (HCC) based on pan-apoptosis-related genes, a novel inflammatory programmed cell death form intricately linked to HCC progression. Utilizing transcriptome sequencing and clinical data from the TCGA database, we identified six crucial pan-apoptosis-related genes through statistical analyses. These genes were then employed to construct a prognostic model that accurately predicts overall survival rates in HCC patients. Our findings revealed a strong correlation between the model's risk scores and tumor microenvironment (TME) status, immune cell infiltration, and immune checkpoint expression. Furthermore, we screened for drugs with potential therapeutic efficacy in high- and low-risk HCC groups. Notably, PPP2R5B gene knockdown was found to inhibit HCC cell proliferation and clonogenic capacity, suggesting its role in HCC progression. In conclusion, this study presents a novel pan-apoptosis gene-based prognostic risk model for HCC, providing valuable insights into patient TME status and guiding the selection of targeted therapies and immunotherapies.

Neutrophil estimation and prognosis analysis based on existing lung squamous cell carcinoma datasets: the development and validation of a prognosis prediction model.

Notwithstanding the rapid developments in precision medicine in recent years, lung cancer still has a low survival rate, especially lung squamous cell cancer (LUSC). The tumor microenvironment (TME) plays an important role in the progression of lung cancer, in which high neutrophil levels are correlated with poor prognosis, potentially due to their interactions with tumor cells via pro-inflammatory cytokines and chemokines. However, the precise mechanisms of how neutrophils influence lung cancer remain unclear. This study aims to explore these mechanisms and develop a prognosis predictive model in LUSC, addressing the knowledge gap in neutrophil-related cancer pathogenesis. LUSC datasets from the Xena Hub and Gene Expression Omnibus (GEO) databases were used, comprising 473 tumor samples and 195 tumor samples, respectively. Neutrophil contents in these samples were estimated using CIBERSORT, xCell, and microenvironment cell populations (MCP) counter tools. Differentially expressed genes (DEGs) were identified using DEseq2, and a weighted gene co-expression network analysis (WGCNA) was performed to identify neutrophil-related genes. A least absolute shrinkage and selection operator (LASSO) Cox regression model was constructed for prognosis prediction, and the model's accuracy was validated using Kaplan-Meier survival curves and time-dependent receiver operating characteristic (ROC) curves. Additionally, genomic changes, immune correlations, drug sensitivity, and immunotherapy response were analyzed to further validate the model's predictive power. Neutrophil content was significantly higher in adjacent normal tissue compared to LUSC tissue (P<0.001). High neutrophil content was associated with worse overall survival (OS) (P=0.02), disease-free survival (DFS) (P=0.02), and progression-free survival (PFS) (P=0.03) using different software estimates. Nine gene modules were identified, with blue and yellow modules showing strong correlations with neutrophil prognosis (P<0.001). Eight genes were selected for the prognostic model, which accurately predicted 1-, 3-, and 5-year survival in both the training set [area under the curve (AUC) value =0.60, 0.63, 0.66, respectively] and validation set (AUC value =0.58, 0.58, 0.59, respectively), with significant prognosis differences between high- and low-risk groups (P<0.001). The model's independent prognostic factors included risk group, pathologic M stage, and tumor stage (P<0.05). A further molecular mechanism analysis revealed differences between risk groups were revealed in immune checkpoint and human leukocyte antigen (HLA) gene expression, hallmark pathways, drug sensitivity, and immunotherapy responses. This study established a risk-score model that effectively predicts the prognosis of LUSC patients and sheds light on the molecular mechanisms involved. The findings enhance the understanding of neutrophil-tumor interactions, offering potential targets for personalized treatments. However, further experimental validation and clinical studies are required to confirm these findings and address study limitations, including reliance on public databases and focus on a specific lung cancer subtype.