Nomogram Analysis Research Articles

Plasma concentrations of venetoclax and Pharmacogenetics correlated with drug efficacy in treatment naive leukemia patients: a retrospective study

Venetoclax (VEN) was the only Bcl-2 inhibitor approved yet and showed large differences in clinical efficacy. The aim of the study was to explore the relationships between the plasma concentration and efficacy of VEN, and identify potential influencing factors. A retrospective cohort study was conducted and a total of 76 trough (C0h) and 91 6 h post-dose plasma concentration (C6h) blood concentrations of VEN were collected in 54 patients. C6h/D concentration of VEN was found to be significantly correlated with treatment efficacy (p = 0.006) in leukemia patients with good or intermediate prognosis stratification. A ROC curve was then established and the cut-off value was calculated as 0.2868 μg/ml (AUC = 0.7097, p = 0.1081). Besides, patients co-administered with triazoles or carrying CYP3A5 rs776746 AA/AG genotypes were prone to induce higher VEN plasma concentration regardless of whether VEN dosage was reduced or not. Through LASSO-logistic regression and nomogram analysis, chemotherapy regimens and neutrophil percentages were identified as the critical elements that may predict drug response. Above all, in addition to identify prognostic stratification, AML patients taken with VEN were suggested to test plasma concentration routinely so as to achieve desired efficacy, especially when co-administered with triazoles or carried with CYP3A5 rs776746 AA/AG.

Nomogram Analysis for Predicting Response to Androgen-Receptor-Axis-Targeted Therapies in Patients With Metastatic Castration-Resistant Prostate Cancer.

This study aimed to identify the clinical predictors for the response of patients with mCRPC to ARATs. We retrospectively collected data on consecutive patients who were diagnosed with mCRPC and underwent ARAT treatment during this stage of the disease. Clinical parameters were obtained through medical chart reviews. ARAT failure was defined as a continuous increase in the serum prostate-specific antigen (PSA) level above nadir to > 2 ng/mL, accompanied by radiographic progression. ARAT failure-free survival and overall survival were assessed through Kaplan-Meier survival analysis and Cox regression survival analysis. Nomogram analysis based on significant predictors of ARAT failure-free survival was performed. In total, 319 patients with mCRPC who underwent ARAT were included. Multivariate analysis revealed that age, International Society of Urological Pathology (ISUP) grading, and chemotherapy-naïve status were significant predictors of ARAT failure-free survival. For overall survival, age, ISUP grading, and nadir PSA level during androgen deprivation therapy (ADT) were significant predictors. Through nomogram analysis based on age, ISUP grading, and chemotherapy-naïve status, the likelihood of ARAT duration being more or less than 1 year could be predicted. For mCRPC patients, being older, having ISUP Grade 5 cancer, and having a history of chemotherapy were associated with a shorter duration of response to next-line ARATs. Therefore, other therapeutic agents should be prioritized for such patients. Notably, among the included patients, those who were older, had a higher ISUP grade and a higher nadir PSA level during ADT exhibited worse overall survival.

Residual popliteal vein thrombosis after endovascular treatment of mixed-type lower extremity deep vein thrombosis is relevant to post-thrombotic syndrome

ObjectivePatients with mixed-type lower extremity deep vein thrombosis (LEDVT) have a higher incidence of post-thrombotic syndrome (PTS) following endovascular treatment (EVT). This study aimed to identify risk factors associated with PTS in these patients post-EVT. MethodsThis retrospective study included patients diagnosed with acute mixed-type LEDVT who underwent EVT between January 2020 and December 2022. Patient assessments were conducted using ultrasound and the Villalta scale. Baseline characteristics, management details, and follow-up findings were compared between patients who developed PTS and those who did not at six months after EVT. Cox regression and nomogram analyses were performed to identify risk factors associated with the development of PTS. ResultsThe study enrolled 118 patients, of which 103 completed the follow-up. Among them, 24.3% developed post-thrombotic syndrome (PTS) within six months. Significant differences between the PTS and non-PTS groups were found concerning residual thrombosis in the popliteal, common femoral, and femoral veins. Multivariate Cox regression analysis indicated that residual popliteal vein thrombosis (RPVT) (HR 4.93, 95% CI 1.61-15.11) and preoperative iliac vein stenosis (HR 3.21, 95% CI 1.11-9.33) were significant risk factors for PTS. Additionally, subgroup analysis for preoperative iliac vein stenosis and sensitivity analysis confirmed that RPVT remained a risk factor for PTS (HR 4.48, 95% CI 1.27-15.84). ConclusionOur study demonstrated a positive association between residual popliteal vein thrombosis (RPVT) and PTS in patients with extensive mixed-type LEDVT after EVT. These findings suggest that intensive monitoring and aggressive therapeutic interventions may be required for patients with RPVT to reduce the risk of PTS.

Host Response in Critically Ill Patients Aged 65 Years or Older: A Prospective Study.

The host response plays a critical role in the progression of all critical illnesses, especially in the aging population. With aging becoming a global phenomenon, understanding changes in the host response among elderly patients can provide valuable insights for diagnosis and treatment in the ICU. This study included all patients aged 65 and older admitted to our geriatric intensive care unit (GICU). Demographic, clinical, and medication data were extracted from electronic medical records. The primary outcome was in-hospital mortality, while secondary outcomes included hospital length of stay (LOS) and ICU stay duration. We employed the generalized additive mixed model for analysis and utilized nomogram analysis to build a predictive mortality model. A total of 1204 patients, with a median age of 75 years and a maximum age of 110 years, were admitted to the GICU. Host response biomarkers were notably lower in patients over 85 years. White blood cell (WBC) count, lactate dehydrogenase (LDH), interleukin-10 (IL-10), and tumor necrosis factor-α (TNF-α) were positively associated with mortality, while a higher platelet-to-lymphocyte ratio (PLR) was inversely related to mortality. Lymphocyte count was identified as a significant risk factor for mortality (RR = 1.2181). Elevated host response biomarkers were inversely associated with both hospital and ICU LOS. The predictive model integrating these biomarkers exhibited strong predictive performance for mortality. Our findings underscore the significant impact of aging on host response in critically ill patients. Older patients, particularly those over 85, exhibited lower biomarker levels and higher mortality rates. The predictive model developed from inflammatory, immune, and coagulation markers demonstrated robust prognostic utility, aiding in the evaluation of critically ill elderly patients.

Nomogram based on burn characteristics and the national early warning score to predict survival in severely burned patients

BackgroundExtensive burns are associated with a high mortality rate. Early prediction and action can reduce mortality. The National Early Warning Score (NEWS) is considered the best early warning score for predicting mortality. However, there has been no assessment conducted on the clinical prognostic significance of NEWS in individuals suffering from severe burns. The objective of this research was to establish a nomogram based on burn characteristics and the NEWS to predict survival in severely burned patients. MethodsA retrospective analysis was performed on 335 patients diagnosed with extensive burns from 2005 to 2021 in the Department of Burn Surgery of XX Hospital. Univariate and multivariate analyses were used to determine independent prognostic factors. A nomogram was developed using these prognostic factors and its internal validity was assessed through bootstrap resampling. ResultsThe results of multivariate analysis showed that the independent factors affecting the prognosis of severe burn patients were age, full-thickness burn, creatinine, absorptive injury, tracheotomy, and the NEWS, all of which were identified to create the nomogram. The Akaike Information Criterion and Bayesian Information Criterion values of the nomogram demonstrated superior goodness-of-fit in predicting severe burns compared to NEWS, with lower scores (195.21 vs. 201.24; 221.91 vs. 224.12, respectively). The bootstrap-adjusted concordance index (C-index) of the nomogram yielded a higher value of 0.923 (95% CI 0.892–0.953), compared to NEWS which had a C-index of 0.699 (95% CI 0.628–0.770). The calibration curves demonstrated excellent agreement between predicted probabilities and observed outcomes in the nomogram analysis. Furthermore, decision curve analysis indicated promising clinical utility for the proposed nomogram model. By applying an appropriate cutoff value derived from receiver operating characteristics curve analysis, it was observed that the high-risk group identified by the nomogram exhibited a significantly higher mortality rate than the low-risk group. ConclusionThis study introduces an innovative nomogram that predicts the survival rate of individuals with severe burn injuries by combining clinical attributes and laboratory examinations, demonstrating superior efficacy compared to conventional NEWS systems.

A predictive model of computed tomography and clinical features of EGFR gene mutation in lung adenocarcinoma.

Purpose: This study aims to develop a predictive model for epidermal growth factor receptor (EGFR) mutations in lung adenocarcinoma by integrating computed tomography (CT) imaging features with clinical characteristics. Methods: A retrospective analysis was conducted using electronic medical records from 194 patients diagnosed with lung adenocarcinoma between January 2016 and December 2020, with approval from the institutional review board. Features were selected using LASSO regression, and predictive models were built using logistic regression, support vector machine, and random forest methods. Individual models were created for clinical features, CT imaging features, and a combined model to predict EGFR mutations. Results: The training set revealed that alcohol consumption, intrapulmonary metastasis, and pleural effusion were statistically significant in distinguishing between wild-type and mutation groups (p < 0.05). In the testing set, hilar and mediastinal lymphadenopathy showed statistical significance (p < 0.05). The combined model outperformed the individual clinical and CT imaging feature models. In the testing set, the logistic regression model achieved the highest AUC of 0.827, with sensitivity, specificity, and accuracy of 0.714, 0.712, and 0.712, respectively. Nomogram analysis identified lobulation as an important feature, with a predicted probability of up to 0.9. The decision curve analysis showed that the CT imaging feature model provided a higher net benefit compared to both the clinical feature model and the combined model. Conclusion: In summary, while the combined model outperformed the individual feature models in the testing set, the CT imaging feature model demonstrated the greatest clinical net benefit. Lobulation was identified as an important predictor of EGFR mutations in lung adenocarcinoma.

Development and validation of a clinical prediction model for osteonecrosis of the jaw in patients receiving zoledronic acid using FAERS and canadian databases.

Osteonecrosis of the jaw (ONJ) stands as a severe complication linked to the use of bisphosphonates, particularly zoledronic acid, which is widely prescribed for managing conditions like osteoporosis and bone metastasis. This study is geared towards the development and validation of a clinical prediction model for ONJ in patients undergoing zoledronic acid treatment. We harnessed data from the FDA Adverse Event Reporting System (FAERS) as our training dataset, while the Canada Vigilance Adverse Reaction (CVAR) database served as the testing dataset. The study encompassed patients treated with zoledronic acid and subsequently diagnosed with ONJ. We analysed a range of predictive factors, including breast cancer, bone metastasis, osteoporosis, vitamin D and calcium levels, comorbidities, the number of concomitant medications, dosage, age, weight, and gender. Logistic regression and nomogram analysis were the chosen methodologies for constructing the predictive model. To evaluate the model's performance, we utilized receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). The study encompassed a total of 2,126 patients in the training cohort, 911 patients in the internal test cohort from the FAERS database, and 121 patients in the external test cohort from the CVAR database. Notable predictors for ONJ included bone metastasis (OR: 1.65, 95% CI: 1.22-2.24), osteoporosis (OR: 0.33, 95% CI: 0.21-0.52), the number of concomitant medications (OR: 1.07, 95% CI: 1.05-1.09), and the dosage of zoledronic acid (OR: 1.24, 95% CI: 1.10-1.39). The nomogram exhibited robust discriminatory power, evidenced by an area under the curve (AUC) of 0.77 in the training cohort, 0.76 in the internal test cohort, and 0.90 in the external test cohort. Calibration plots demonstrated a strong alignment between observed and predicted probabilities. Furthermore, DCA highlighted the prediction model's significant net benefit across various threshold probabilities. By leveraging data from both the FAERS and Canadian databases, this study has successfully developed and validated a clinical prediction model for ONJ in patients receiving zoledronic acid. This model stands as a valuable tool for clinicians, enabling them to pinpoint high-risk patients and make evidence-based treatment decisions to minimize the risk of ONJ.

Clinical model to predict the risk of nonalcoholic fatty liver disease: A secondary analysis of data from a cross-sectional study.

This study aimed to develop and validate a clinical model for predicting the risk of nonalcoholic fatty liver disease (NAFLD) by using data from a cross-sectional study. This investigation utilized data from the Dryad database and employed multivariable logistic regression analysis, restricted cubic spline, and nomogram analysis to achieve comprehensive insights. The discrimination and calibration of the nomogram were evaluated using the receiver operating characteristic curve and calibration plot. A total of 1072 patients were included in the study, including 456 with non-NAFLD and 616 with NAFLD. Significant differences were observed in terms of sex, body mass index (BMI), tobacco, hypertension, diabetes, alanine aminotransferase (ALT), aspartate aminotransferase (AST), ALT/AST ratio, uric acid (UA), fasting blood glucose (FBG), triglyceride (TG), high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, systolic blood pressure, and diastolic blood pressure (P < .05 for all comparisons). Multivariable logistic regression analysis indicated that sex, BMI, diabetes, ALT/AST ratio, UA, FBG, and TG were associated with an increased risk of NAFLD. Restricted cubic spline indicated a nonlinear relationship between the risk of NAFLD and variables including ALT/AST ratio, FPG, TG, and UA (P for nonlinearity < .01). The variables in the nomogram included BMI, diabetes, ALT/AST ratio, UA, FBG, and TG. The value of area under the curve was 0.790, indicating that the nomogram prediction model exhibited significant discriminatory accuracy. A reliable clinical model for predicting the risk of NAFLD was developed using readily available clinical data. The model can assist clinicians in identifying individuals with an increased risk of NAFLD, enabling early interventions for preventing and managing this prevalent liver disease.

Identification of a novel necroptosis-related LncRNA signature for prognostic prediction and immune response in oral squamous cell carcinoma.

Necroptosis is a caspase-independent regulated necrotic cell death modality that elicits strong adaptive immune responses, and has the potential to activate antitumor immunity. Long non-coding RNAs (lncRNAs) have critical effects on oral squamous cell carcinoma (OSCC), which are closely associated with the prognosis and immune regulation of OSCC patients. This study aimed to identify a novel necroptosis-related lncRNAs signature to predict the prognosis and immune response of OSCC patients and provide patients with anti-tumor drug selection through bioinformatics analysis and in vitro experiments. A series of analyses, including differential lncRNA screening, survival analysis, Cox regression analysis, ROC analysis, nomogram prediction, enrichment analysis, tumor-infiltrating immune cells, drug sensitivity analysis, and consensus cluster analysis, were performed to determine and validate the prognostic value of necroptosis-associated lncRNAs signature in OSCC. And real-time quantitative polymerase chain reaction (RT-qPCR) was used to determine the expression levels of these lncRNAs. This signature including 5 lncRNAs (AC099850.3, StarD4-AS1, AC011978.1, LINC01503, CDKN2A-DT) in OSCC associated with necroptosis were established and verified by bioinformatics. Further, ROC, K-M, univariate/multivariate Cox regression, and nomogram analysis were used to evaluate the model's features for OSCC prognosis. Using multiple bioinformatics techniques, the levels of tumor-infiltrating immune cells, immune checkpoints and semi-inhibitory concentrations showed significant differences across risk subtypes. By consensus cluster analysis, there were significant differences between clusters in survival, immune checkpoint expression, clinicopathological correlation, and tumor immunity. RT-qPCR showed that AC099850.3, AC011978.1, LINC01503 were up-regulated, STARD4-AS1 and CDKN2A-DT were down-regulated in OSCC cell lines compared with human normal oral keratinoid cell line. We established 5-NRLs markers, which is useful for assessing OSCC immune response and prognosis, recommending personalized antitumor drugs. The expression level of 5-NRLs in OSCC was identified in vitro, and the results preliminarily verified this model. And this study would generate new insights for future experimental research.

Predictors of prolonged supratherapeutic serum lithium concentrations: a retrospective chart review

Introduction The Extracorporeal Treatments in Poisoning (EXTRIP) workgroup suggests hemodialysis in severe lithium poisoning if specific criteria are met. One criterion is if the expected time to obtain a lithium concentration <1.0 mEq/L with optimal management is >36 h. There are a lack of data regarding which patient characteristics are associated with the rate at which patients achieve a lithium concentration <1.0 mEq/L. Methods We conducted a retrospective chart review analyzing hospital electronic medical records. Inclusion criteria consisted of a lithium concentration >1.2 mEq/L during hospitalization. We excluded patients who received extracorporeal treatment before 36 h elapsed from time of initial lithium concentration >1.2 mEq/L. The primary analysis consisted of a Cox regression and a secondary analysis evaluated the nomogram method described by Buckley and colleagues for predicting prolonged supratherapeutic lithium concentration. Results One hundred and one patients were included in the study. The median time to reach a lithium concentration <1.0 mEq/L was 42.5 h (IQR: 33.8–51.1). Older patients, patients taking a thiazide, angiotensin-converting enzyme inhibitor or angiotensin receptor blocker, patients with a higher initial lithium concentration, and patients with higher sodium concentrations achieved a lithium concentration <1 mEq/L at a slower rate. For the nomogram analysis, sensitivity (61.5%) and specificity (54.5%) were moderate, the positive predictive value (16.7%) was poor, and the negative predictive value (90.6%) was excellent. Discussion The results from our primary analysis suggest that identifying higher serum sodium concentration and use of certain antihypertensives that decrease glomerular filtration rate as predictors of an increased time to reach a therapeutic lithium concentration may help identify patients who meet the Extracorporeal Treatments in Poisoning criteria for hemodialysis. The nomogram method performed similarly to prior validation studies. Conclusions In this retrospective chart review of patients with supratherapeutic lithium concentrations, we identified several risk factors for prolonged supratherapeutic lithium concentrations.

High expression of COPZ2 is associated with poor prognosis and cancer progression in glioma.

Coatomer protein complex zeta 2 (COPZ2) is a member of heptameric coatomer protein complex I and has been reported to be involved in various tumors. However, COPZ2's potential involvement in glioma remains to be explored. The COPZ2 expression and related clinical data were obtained from The Cancer Genome Atlas (TCGA). TIMER2.0 and the Ualcan database were utilized to assess the COPZ2 expression in various tumors. Univariable, multivariate Cox regression, Kaplan-Meier methods, nomogram analysis, and ROC curve analysis were carried out to assess the relationship of COPZ2 and other prognostic factors with glioma. The LinkedOmics database was used to predict the potential biological mechanism of COPZ2 in glioma. We also conducted in vitro experiments to evaluate the functional role and mechanism of COPZ2 in glioma cell lines. We found that COPZ2 was highly expressed in glioma and it was associated with age and WHO grades. Kaplan-Meier survival curves, Cox analysis, nomogram analysis, and ROC curve showed that COPZ2 was a disadvantageous factor in poor glioma prognosis. The functions of COPZ2 and co-expression genes were significantly associated with neutrophil-mediated immunity, granulocyte activation, and response to interferon-gamma. In addition, COPZ2 knockdown significantly inhibited the proliferation, migration, and invasion of glioblastoma cells. Mechanistically, COPZ2 suppressed tumor development by participating in the regulation of the PI3K-AKT signaling pathway. Our results demonstrated that the elevation of COPZ2 was associated with the prognosis and progression of glioma, and it might be a potential diagnostic and prognostic biomarker for glioma.

Risk prediction model of uterine corpus endometrial carcinoma based on immune-related genes

BackgroundGiven the significant role of immune-related genes in uterine corpus endometrial carcinoma (UCEC) and the long-term outcomes of patients, our objective was to develop a prognostic risk prediction model using immune-related genes to improve the accuracy of UCEC prognosis prediction.MethodsThe Limma, ESTIMATE, and CIBERSORT methods were used for cluster analysis, immune score calculation, and estimation of immune cell proportions. Univariate and multivariate analyses were utilized to develop a prognostic risk model for UCEC. Risk model scores and nomograms were used to evaluate the models. String constructs a protein-protein interaction (PPI) network of genes. The qRT-PCR, immunofluorescence, and immunohistochemistry (IHC) all confirmed the genes.ResultsCluster analysis divided the immune-related genes into four subtypes. 33 immune-related genes were used to independently predict the prognosis of UCEC and construct the prognosis model and risk score. The analysis of the survival nomogram indicated that the model has excellent predictive ability and strong reliability for predicting the survival of patients with UCEC. The protein-protein interaction network analysis of key genes indicates that four genes play a pivotal role in interactions: GZMK, IL7, GIMAP, and UBD. The quantitative real-time polymerase chain reaction (qRT-PCR), immunofluorescence, and immunohistochemistry (IHC) all confirmed the expression of the aforementioned genes and their correlation with immune cell levels. This further revealed that GZMK, IL7, GIMAP, and UBD could potentially serve as biomarkers associated with immune levels in endometrial cancer.ConclusionThe study identified genes related to immune response in UCEC, including GZMK, IL7, GIMAP, and UBD, which may serve as new biomarkers and therapeutic targets for evaluating immune levels in the future.

Identification of key genes associated with cervical cancer based on bioinformatics analysis

BackgroundCervical cancer has extremely high morbidity and mortality, and its pathogenesis is still in the exploratory stage. This study aimed to screen and identify differentially expressed genes (DEGs) related to cervical cancer through bioinformatics analysis.MethodsGSE63514 and GSE67522 were selected from the GEO database to screen DEGs. Then GO and KEGG analysis were performed on DEGs. PPI network of DEGs was constructed through STRING website, and the hub genes were found through 12 algorithms of Cytoscape software. Meanwhile, GSE30656 was selected from the GEO database to screen DEMs. Target genes of DEMs were screened through TagetScan, miRTarBase and miRDB. Next, the hub genes screened from DEGs were merged with the target genes screened from DEMs. Finally, ROC curve and nomogram analysis were performed to assess the predictive capabilities of the hub genes. The expression of these hub genes were verified through TCGA, GEPIA, qRT-PCR, and immunohistochemistry.ResultsSix hub genes, TOP2A, AURKA, CCNA2, IVL, KRT1, and IGFBP5, were mined through the protein-protein interaction network. The expression of these hub genes were verified through TCGA, GEPIA, qRT-PCR, and immunohistochemistry, and it was found that TOP2A, AURKA as well as CCNA2 were overexpressed and IGFBP5 was low expression in cervical cancer.ConclusionsThis study showed that TOP2A, AURKA, CCNA2 and IGFBP5 screened through bioinformatics analysis were significantly differentially expressed in cervical cancer samples compared with normal samples, which might be biomarkers of cervical cancer.

A prognostic framework for predicting lung signet ring cell carcinoma via a machine learning based cox proportional hazard model

PurposeSignet ring cell carcinoma (SRCC) is a rare type of lung cancer. The conventional survival nomogram used to predict lung cancer performs poorly for SRCC. Therefore, a novel nomogram specifically for studying SRCC is highly required.MethodsBaseline characteristics of lung signet ring cell carcinoma were obtained from the Surveillance, Epidemiology, and End Results (SEER) database. Univariate and multivariate Cox regression and random forest analysis were performed on the training group data, respectively. Subsequently, we compared results from these two types of analyses. A nomogram model was developed to predict 1-year, 3-year, and 5-year overall survival (OS) for patients, and receiver operating characteristic (ROC) curves and calibration curves were used to assess the prediction accuracy. Decision curve analysis (DCA) was used to assess the clinical applicability of the proposed model. For treatment modalities, Kaplan-Meier curves were adopted to analyze condition-specific effects.ResultsWe obtained 731 patients diagnosed with lung signet ring cell carcinoma (LSRCC) in the SEER database and randomized the patients into a training group (551) and a validation group (220) with a ratio of 7:3. Eight factors including age, primary site, T, N, and M.Stage, surgery, chemotherapy, and radiation were included in the nomogram analysis. Results suggested that treatment methods (like surgery, chemotherapy, and radiation) and T-Stage factors had significant prognostic effects. The results of ROC curves, calibration curves, and DCA in the training and validation groups demonstrated that the nomogram we constructed could precisely predict survival and prognosis in LSRCC patients. Through deep verification, we found the constructed model had a high C-index, indicating that the model had a strong predictive power. Further, we found that all surgical interventions had good effects on OS and cancer-specific survival (CSS). The survival curves showed a relatively favorable prognosis for T0 patients overall, regardless of the treatment modality.ConclusionsOur nomogram is demonstrated to be clinically beneficial for the prognosis of LSRCC patients. The surgical intervention was successful regardless of the tumor stage, and the Cox proportional hazard (CPH) model had better performance than the machine learning model in terms of effectiveness.

Development and Validation of an Immune Prognostic Index Related to Infiltration of CD4+ and CD8+ T Cells in Colorectal Cancer.

Colorectal cancer (CRC) is a highly prevalent cancer worldwide, but treatment outcomes can vary significantly among patients with similar clinical or historical stages. This study aimed to investigate the differences in immune cell abundance associated with malignant progression in CRC patients. We utilized data from patients with CRC obtained from The Cancer Genome Atlas as our training set. To assess immune cell infiltration levels, an immune cell risk score (ICRS) was calculated. Furthermore, we performed network analysis to identify effective T cell-related genes (ETRGs) and subsequently constructed an effective T cell prognostic index (ETPI). The performance of the ETPI was evaluated through external validation using four Gene Expression Omnibus datasets. Additionally, a nomogram analysis and drug sensitivity analysis were conducted to explore the clinical utility of the ETRGs. We also examined the expression of ETRGs in clinical samples. Based on the ICRS, we identified activated CD4+ and CD8+ T cells as protective factors in terms of prognosis. Six ETRGs were identified to develop the ETPI, which exhibited remarkable prognostic performance. In the external validation of immunotherapy, the low ETPI group demonstrated a significantly lower recurrence rate. To optimize therapeutic strategies, we developed a nomogram. Notably, patients with different ETPI values exhibited varying responses to tumor pathway inhibitors. Finally, we observed higher protein expression of certain ETRGs in normal tissues compared to tumors. Our findings suggest that the ETPI may contribute to the precise selection of patients based on tumor microenvironment and key genomic landscape interactions, thereby optimizing drug benefits and informing clinical strategies in future.

Development of a major amputation prediction model and nomogram in patients with diabetic foot.

Diabetes mellitus, as one of the world's fastest-growing diseases, is a chronic metabolic disease that has now become a public health problem worldwide. The purpose of this research was to develop a predictive nomogram model to demonstrate the risk of major amputation in patients with diabetic foot. A total of 634 Type 2 Diabetes Mellitus (T2DM) patients with diabetic foot ulcer hospitalized at the Air Force Medical Center between January 2018 and December 2023 were included in our retrospective study. There were 468 males (73.82%) and 166 females (26.18%) with an average age of 61.64 ± 11.27years and average body mass index of 24.45 ± 3.56kg/m2. The predictive factors were evaluated by single factor logistic regression and multiple logistic regression and the predictive nomogram was established with these features. Receiver operating characteristic (subject working characteristic curve) and their area under the curve, calibration curve, and decision curve analysis of this major amputation nomogram were assessed. Model validation was performed by the internal validation set, and the receiver operating characteristic curve, calibration curve, and decision curve analysis were used to further evaluate the nomogram model performance and clinical usefulness. Predictors contained in this predictive model included body mass index, ulcer sites, hemoglobin, neutrophil-to-lymphocyte ratio, blood uric acid (BUA), and ejection fraction. Good discrimination with a C-index of 0.957 (95% CI, 0.931-0.983) in the training group and a C-index of 0.987 (95% CI, 0.969-1.000) in the validation cohort were showed with this predictive model. Good calibration were displayed. The decision curve analysis showed that using the nomogram prediction model in the training cohort and validation cohort would respectively have clinical benefits. This new nomogram incorporating body mass index, ulcer sites, hemoglobin, neutrophil-to-lymphocyte ratio, BUA, and ejection fraction has good accuracy and good predictive value for predicting the risk of major amputation in patients with diabetic foot.

Exploring and clinical validation of prognostic significance and therapeutic implications of copper homeostasis-related gene dysregulation in acute myeloid leukemia.

The patterns and biological functions of copper homeostasis-related genes (CHRGs) in acute myeloid leukemia (AML) remain unclear. We explored the patterns and biological functions of CHRGs in AML. Using independent cohorts, including TCGA-GTEx, GSE114868, GSE37642, and clinical samples, we identified 826 common differentially expressed genes. Specifically, 12 cuproptosis-related genes (e.g., ATP7A, ATP7B) were upregulated, while 17 cuproplasia-associated genes (e.g., ATOX1, ATP7A) were downregulated in AML. We used LASSO-Cox, Kaplan-Meier, and Nomogram analyses to establish prognostic risk models, effectively stratifying patients with AML into high- and low-risk groups. Subgroup analysis revealed that high-risk patients exhibited poorer overall survival and involvement in fatty acid metabolism, apoptosis, and glycolysis. Immune infiltration analysis indicated differences in immune cell composition, with notable increases in B cells, cytotoxic T cells, and memory T cells in the low-risk group, and increased monocytes and neutrophils in the high-risk group. Single-cell sequencing analysis corroborated the expression characteristics of critical CHRGs, such as MAPK1 and ATOX1, associated with the function of T, B, and NK cells. Drug sensitivity analysis suggested potential therapeutic agents targeting copper homeostasis, including Bicalutamide and Sorafenib. PCR validation confirmed the differential expression of 4 cuproptosis-related genes (LIPT1, SLC31A1, GCSH, and PDHA1) and 9 cuproplasia-associated genes (ATOX1, CCS, CP, MAPK1, SOD1, COA6, PDK1, DBH, and PDE3B) in AML cell line. Importantly, these genes serve as potential biomarkers for patient stratification and treatment. In conclusion, we shed light on the expression patterns and biological functions of CHRGs in AML. The developed risk models provided prognostic implications for patient survival, offering valuable information on the regulatory characteristics of CHRGs and potential avenues for personalized treatment in AML.

Analysis of cell death-related genes to evaluate the prognostic and immunotherapeutic value in bladder cancer

To enhance therapeutic approaches, we created a distinctive pattern utilizing the cell demise indicator (CDI) to predict the effectiveness of immunotherapy in individuals with bladder carcinoma (BLCA). Hub prognostic CDIs were identified from the TCGA database using differential gene expression and survival analysis, encompassing 763 genes across 13 death modes. The subtype assessment was employed to evaluate the impact of these genes on the prognosis and immunotherapeutic outcomes in patients with BLCA. The LASSO regression method was used to identify significant CDIs, while Cox regression and nomogram analyses were conducted to explore the impact of CDIs on prognosis. CHMP4C and GSDMB were selected as the hub genes for the following research. Subsequently, These two central genes underwent further investigation to explore their association with immunotherapy, followed by an analysis of their potential regulatory network. Subtype analysis showed that these CDIs were significantly associated with the prognosis and immunotherapy of BLCA patients. The regulatory network in BLCA was evaluated through the establishment of the lncRNA XIST/NEAT1-CDIs-miR-146a-5p/miR-429 axis. Immunohistochemical analysis revealed a significant up-regulation of CHMP4C in bladder cancer tissues, which was strongly associated with an unfavorable prognosis for BLCA patients. Moreover, our findings provide compelling evidence that CHMP4C plays a pivotal role in promoting BLCA progression through the activation of the epithelial-mesenchymal transition (EMT) pathway. These findings highlight the negative impact of CHMP4C on BLCA patient prognosis, while also providing insights into the oncogenic mechanisms and immunotherapy in which CHMP4C may be involved.

Bioinformatics analysis and experimental validation of m6A and cuproptosis-related lncRNA NFE4 in clear cell renal cell carcinoma

PurposeThis study aimed to construct an m6A and cuproptosis-related long non-coding RNAs (lncRNAs) signature to accurately predict the prognosis of kidney clear cell carcinoma (KIRC) patients using the information acquired from The Cancer Genome Atlas (TCGA) database.MethodsFirst, the co-expression analysis was performed to identify lncRNAs linked with N6-methyladenosine (m6A) and cuproptosis in ccRCC. Then, a model encompassing four candidate lncRNAs was constructed via univariate, least absolute shrinkage together with selection operator (LASSO), and multivariate regression analyses. Furthermore, Kaplan–Meier, principal component, functional enrichment annotation, and nomogram analyses were performed to develop a risk model that could effectively assess medical outcomes for ccRCC cases. Moreover, the cellular function of NFE4 in Caki-1/OS-RC-2 cultures was elucidated through CCK-8/EdU assessments and Transwell experiments. Dataset outcomes indicated that NFE4 can have possible implications in m6A and cuproptosis, and may promote ccRCC progression.ResultsWe constructed a panel of m6A and cuproptosis-related lncRNAs to construct a prognostic prediction model. The Kaplan–Meier and ROC curves showed that the feature had acceptable predictive validity in the TCGA training, test, and complete groups. Furthermore, the m6A and cuproptosis-related lncRNA model indicated higher diagnostic efficiency than other clinical features. Moreover, the NFE4 function analysis indicated a gene associated with m6A and cuproptosis-related lncRNAs in ccRCC. It was also revealed that the proliferation and migration of Caki-1 /OS-RC-2 cells were inhibited in the NFE4 knockdown group.ConclusionOverall, this study indicated that NFE4 and our constructed risk signature could predict outcomes and have potential clinical value.

Characterization of alternative splicing events and prognostic signatures in gastric cancer

BackgroundAccumulating evidences indicate that the specific alternative splicing (AS) events are linked to the occurrence and prognosis of gastric cancer (GC). Nevertheless, the impact of AS is still unclear and needed to further elucidation.MethodsThe expression profile of GC and normal samples were downloaded from TCGA. AS events were achieved from SpliceSeq database. Cox regression together with LASSO analysis were employed to identify survival-associated AS events (SASEs) and calculate risk scores. PPI and pathway enrichment analysis were implemented to determine the function and pathways of these genes. Kaplan-Meier (K-M) analysis and Receiver Operating Characteristic Curves were used to evaluate the clinical significance of genes of SASEs. Q-PCR were applied to validate the hub genes on the survival prognosis in 47 GC samples. Drug sensitivity and immune cell infiltration analysis were conducted.ResultsIn total, 48 140 AS events in 10 610 genes from 361 GC and 31 normal samples were analyzed. Through univariate Cox regression, 855 SASEs in 763 genes were screened out. Further, these SASEs were analyzed by PPI and 17 hub genes were identified. Meanwhile, using Lasso and multivariate Cox regression analysis, 135 SASEs in 132 genes related to 7 AS forms were further screened and a GC prognostic model was constructed. K-M curves indicates that high-risk group has poorer prognosis. And the nomogram analysis on the basis of the multivariate Cox analysis was disclosed the interrelationships between 7 AS forms and clinical parameters in the model. Five key genes were then screened out by PPI analysis and Differential Expression Gene analysis based on TCGA and Combined-dataset, namely STAT3, RAD51B, SOCS2, POLE2 and TSR1. The expression levels of AS in STAT3, RAD51B, SOCS2, POLE2 and TSR1 were all significantly correlated with survival by qPCR verification. Nineteen drugs were sensitized to high-risk patients and eight immune cells showed significantly different infiltration between the STAD and normal groups.ConclusionsIn this research, the prognostic model constructed by SASEs can be applied to predict the prognosis of GC patients and the selected key genes are expected to become new biomarkers and therapeutical targets for GC treatment.