Prognostic Nomogram Research Articles

Prognostic nomograms for young breast cancer: A retrospective study based on the SEER and METABRIC databases.

Young breast cancer (YBC) is a subset of breast cancer that is often more aggressive, but less is known about its prognosis. In this study, we aimed to generate nomograms to predict the overall survival (OS) and breast cancer-specific survival (BCSS) of YBC patients. Data of women diagnosed with YBC between 2010 and 2020 were obtained from the Surveillance, Epidemiology, and End Results (SEER) database. The patients were randomly allocated into a training cohort (n = 15,227) and internal validation cohort (n = 6,526) at a 7:3 ratio. With the Cox regression models, significant prognostic factors were identified and used to construct 3-, 5-, and 10-year nomograms of OS and BCSS. Data from the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) database were used as an external validation cohort (n = 90). We constructed nomograms incorporating 10 prognostic factors for OS and BCSS. These nomograms demonstrated strong predictive accuracy for OS and BCSS in the training cohort, with C-indexes of 0.806 and 0.813, respectively. The calibration curves verified that the nomograms have good prediction accuracy. Decision curve analysis demonstrated their practical clinical value for predicting YBC patient survival rates. Additionally, we provided dynamic nomograms to improve the operability of the results. The risk stratification ability assessment also showed that the OS and BCSS rates of the low-risk group were significantly better than those of the high-risk group. Here, we generated and validated more comprehensive and accurate OS and BCSS nomograms than models previously developed for YBC. These nomograms can help clinicians evaluate patient prognosis and make clinical decisions.

Predicting the prognosis of patients with renal cell carcinoma based on the systemic immune inflammation index and prognostic nutritional index

The aim of the study was to analyze and discuss the value of preoperative systemic immune inflammation index (SII) and prognostic nutritional index (PNI) in predicting the prognosis of patients with renal cell carcinoma (RCC) after operation, and to establish a nomogram prediction model for patients with RCC after operation based on SII and PNI. From January 2014 to December 2018, 210 patients with RCC who underwent surgical treatment at the Xuzhou Central Hospital were selected as the research object. The receiver operating characteristic curve (ROC) was used to determine the optimal cut-off value for preoperative SII, PNI, LMR, PLR, NLR and the patients were divided into groups according to the optimal cutoff values. The survival rate of patients was evaluated. The risk factors that affect the prognosis of patients with RCC were determined by LASSO and Cox regression analysis, and a prognostic nomogram was constructed based on this result. The bootstrap method was used for internal verification of the nomogram model. The prediction efficiency and discrimination of the nomogram model were evaluated by the calibration curve and index of concordance (C-index), respectively. The average overall survival (OS) of all patients was 75.385 months, and the 1-, 2-and 3-year survival rates were 95.5%, 86.6% and 77.2%, respectively. The survival curve showed that the 5-year OS rate of low SII group was significantly higher than that of high SII group (89.0% vs. 64.5%; P < 0.05), and low PNI group was significantly lower than those in high PNI group (43.4% vs. 87.9%; p < 0.05). There were significant differences between preoperative SII and CRP, NLR, PLR, LMR, postoperative recurrence, pathological type and AJCC stage (P < 0.05). There were significant differences between preoperative PNI and BMI, platelet, NLR, PLR, LMR, postoperative recurrence, surgical mode and Fuhrman grade (P < 0.05). The ROC curve analysis showed that the AUC of PNI (AUC = 0.736) was higher than that of other inflammatory indicators, followed by the AUC of SII (0.718), and the difference in AUC area between groups was statistically significant (P < 0.05). The results from multivariate Cox regression analysis showed that SII, PNI, tumor size, tumor necrosis, surgical mode, pathological type, CRP, AJCC stage and Fuhrman grade were independent risk factors for postoperative death of patients with RCC. According to the results of Cox regression analysis, a prediction model for the prognosis of RCC patients was established, and the C-index (0.918) showed that the model had good calibration and discrimination. The subject’s operating characteristic curve indicates that the nomogram has good prediction efficiency (the AUC = 0.953). Preoperative SII and PNI, tumor size, tumor necrosis, surgical mode, pathological type, CRP, AJCC stage and Fuhrman grade are closely related to the postoperative prognosis of patients with renal cell carcinoma. The nomogram model based on SII, PNI, tumor size, tumor necrosis, surgical mode, pathological type, CRP, AJCC stage and Fuhrman grade has good accuracy, discrimination and clinical prediction efficiency.

Prognostic prediction and treatment options for gastric signet ring cell carcinoma: a SEER database analysis.

In recent years, the overall incidence of gastric cancer has decreased. However, the incidence of gastric signet ring cell carcinoma (SRCC) is still increasing year by year. Compared with other subtypes (non-SRCC) such as adenocarcinoma, SRCC usually exhibits a more aggressive biological behavior. Therefore, studying the prognostic differences and factors associated with SRCC is essential to improve the accuracy of diagnosis and prognosis. The purpose of this study was to investigate the prognostic factors influencing the prognosis of patients with SRCC and to develop personalized treatments for different subgroups of patients. The data on gastric SRCC patients and gastric adenocarcinoma (AC) patients from 1992 to 2020 was obtained from the Surveillance, Epidemiology, and End Results (SEER) database. The data of gastric SRCC as the external validation group was reviewed from the First Affiliated Hospital of Soochow University. The overall survival (OS) and cancer specific survival (CSS) at 1 and 2 years were predicted for SRCC patients by constructing prognostic nomograms. A series of validation methods, including Akaike information criterion (AIC), decision curve analysis (DCA), calibration curve analysis, the concordance index (C-index) and the area under the receiver operating characteristic (AUC) curve, were used to verify the accuracy and reliability of the models. In all, 549 patients with SRCC were included after propensity score matching (PSM). Multivariate Cox regression analysis showed that T stage, N stage, M stage and surgical approach were independent risk factors affecting the prognosis of SRCC patients. A prognostic nomogram was constructed and validated as an accurate model for SRCC patients after scoring by receiver operating characteristic curve (ROC) curves and calibration plots. The patients were further divided into high-risk and low-risk groups, and the Kaplan-Meier curves showed that SRCC patients in the low-risk group could receive only surgery without chemotherapy, while chemotherapy plus surgery was a better option for SRCC patients in the high-risk group. The prognosis for SRCC was less favorable than that of AC in terms of CSS. The nomograms were developed and validated to predict OS and CSS in patients with SRCC, helping in developing appropriate individualized treatment schedules.

Dimensional assessment on baseline MRI of soft-tissue sarcomas: longest diameter, sum and product of diameters, and volume-which is the best measurement method to predict patients' outcomes?

The longest diameter (LD) is a strong prognostic factor for patients with soft-tissue sarcoma (STS). Other dimensional assessments, such as the sum of diameters (SoD), product of diameters (PoD), and volume (3D-COG - proposed by the Children Oncology Group), can be rapidly performed; however, their prognostic values have never been compared to LD. Our goal was to investigate their performance in improving patients' prognostication for STS of the lower limbs. All consecutive adults managed with curative intent at our sarcoma reference center for a newly diagnosed STS of the lower limbs between 2000 and 2017, with pre-treatment MRI, were included in this retrospective study. Multivariable Cox regression models were trained to predict metastasis-free survival (MFS) in a Training cohort of 66.7% patients based on LD, PoD, SoD, or 3D-COG (and systematically including age, histologic grade, histotype, radiotherapy, chemotherapy, and surgical margins as covariables). The models were then compared on a validation cohort of 33.3% patients using concordance indices (c-index). The same approach was applied for overall survival (OS) and local relapse-free survival (LFS). Measurement reproducibility among three readers was evaluated with an intraclass correlation coefficient (ICC). 382 patients were included in the survival modeling (72/253 [28.5%] metastatic relapses in Training and 36/129 [27.9%] metastatic relapses in Validation). Higher dimensions were associated with lower MFS (multivariable hazard ratio [HR] = 2.44 and P = 0.0018 for LD; HR = 1.88 and P = 0.0009 for PoD, HR = 1.52 and P = 0.0041 for SoD; and HR = 1.08 and P = 0.0195 for 3D-COG). Higher c-indices were obtained with PoD model in Training (c-index = 0.772) and Validation (c-index = 0.688), but they were not significantly higher thanthose obtained with LD model. None of the measurements was associated with LFS or OS. All measurements demonstrated excellent ICC (> 0.95). Regarding its simplicity and good performance, LD appeared as the best metric to incorporate in prognostic models and nomograms for MFS.

Comprehensive risk factor-based nomogram for predicting one-year mortality in patients with sepsis-associated encephalopathy

Sepsis-associated encephalopathy (SAE) is a frequent and severe complication in septic patients, characterized by diffuse brain dysfunction resulting from systemic inflammation. Accurate prediction of long-term mortality in these patients is critical for improving clinical outcomes and guiding treatment strategies. We conducted a retrospective cohort study using the MIMIC IV database to identify adult patients diagnosed with SAE. Patients were randomly divided into a training set (70%) and a validation set (30%). Least absolute shrinkage and selection operator regression and multivariate logistic regression were employed to identify significant predictors of 1-year mortality, which were then used to develop a prognostic nomogram. The model’s discrimination, calibration, and clinical utility were assessed using the area under the receiver operating characteristic curve (AUC), calibration plots, and decision curve analysis, respectively. A total of 3,882 SAE patients were included in the analysis. The nomogram demonstrated strong predictive performance with AUCs of 0.881 (95% CI: 0.865, 0.896) in the training set and 0.859 (95% CI: 0.830, 0.888) in the validation set. Calibration plots indicated good agreement between predicted and observed 1-year mortality rates. The decision curve analysis showed that the nomogram provided greater net benefit across a range of threshold probabilities compared to traditional scoring systems such as Glasgow Coma Scale and Sequential Organ Failure Assessment. Our study presents a robust and clinically applicable nomogram for predicting 1-year mortality in SAE patients. This tool offers superior predictive performance compared to existing severity scoring systems and has significant potential to enhance clinical decision-making and patient management in critical care settings.

Development of a novel nomogram for patients with SCLC and comparison with other models

BackgroundThough several nomograms have been established to predict the survival probability of patients with small-cell lung cancer (SCLC), none involved enough variables. This study aimed to construct a novel prognostic nomogram and compare its performance with other models.MethodsSeven hundred twenty-two patients were pathologically diagnosed with SCLC in Shanxi Province Cancer Hospital, Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences, Cancer Hospital Affiliated to Shanxi Medical University from January 2016 to December 2018. We input Forty-one factors by reviewing the medical records. The nomogram was constructed based on the variables identified by univariate and multivariate analyses in the training set and validated in the validation set. Then we compared the performance of the models in terms of discrimination, calibration, and clinical net benefit.ResultsThere were eight variables involved in the nomogram: gender, monocyte (MON), neuron-specific enolase (NSE), cytokeratin 19 fragments (Cyfra211), M stage, radiotherapy (RT), chemotherapy cycles (CT cycles), and prophylactic cranial irradiation (PCI). The calibration curve showed a good correlation between the nomogram prediction and actual observation for overall survival (OS). The area under the curve (AUC) of the nomogram was higher, and the Integrated Brier score (IBS) was lower than other models, indicating a more accurate prediction. Decision curve analysis (DCA) showed a significant improvement in the clinical net benefit compared to the other models.ConclusionsWe constructed a novel nomogram to predict OS for patients with SCLC using more comprehensive and objective variables. It performed better than existing models and would assist clinicians in individually estimating risk and making a therapeutic regimen.

Pan-cancer analysis and experimental validation of FPR3 as a prognostic and immune infiltration-related biomarker for glioma.

Formyl peptide receptor 3 (FPR3) is known to have implications in the progression of various cancer types. Despite this, its biological significance within pan-cancer datasets has yet to be investigated. In this investigation, we scrutinized FPR3's expression profiles, genetic alterations, prognostic significance, immune-related characteristics, methylation status, tumor mutation burden (TMB), and microsatellite instability (MSI) across different types of cancer. We utilized TISCH's single-cell data to identify immune cells closely associated with FPR3. The predictive significance of FPR3 was evaluated independently in gliomas using data from TCGA and CGGA datasets, leading to the development of a prognostic nomogram. Immunohistochemistry and Western blot analysis confirmed FPR3 expression in gliomas. Lastly, the CCK-8 and wound-healing assays were employed to assess the impact of FPR3 on the proliferation and metastasis of GBM cell lines. In numerous cancer types, heightened FPR3 expression correlated with adverse outcomes, immune cell infiltration, immune checkpoints, TMB, and MSI. In glioma, FPR3 emerged as a notable risk factor, with the prognostic model effectively forecasting patient results. The potential biological relevance of FPR3 was confirmed in glioma, and it was shown to have significant involvement in the processes of glioma growth, immune infiltration, and metastasis. Our results imply a potential association of FPR3 with tumor immunity, indicating its viability as a prognostic indicator in glioma.

Establishment and Validation of Prognostic Nomograms for Nonmetastatic Melanoma of the Limbs—A SEER-Based Study

Background Malignant melanoma, a highly aggressive skin cancer, has remarkable incidence and mortality nowadays. This study aims to explore prognostic factors associated with nonmetastatic cutaneous melanoma of the limbs and to develop nomograms for predicting overall survival (OS) and cancer-specific survival (CSS). Methods The study cohort was derived from the Surveillance, Epidemiology, and End Results database. Univariate Cox regression, Lasso regression, and multivariate Cox regression analyses were conducted to identify prognostic factors and construct nomograms. The receiver operating characteristic (ROC) curve, time-dependent C-index, calibration curve, decision curve analysis (DCA) and Kaplan-Meier method were used to evaluate the accuracy and clinical applicability of the nomograms. Results A total of 15,606 patients were enrolled. Multivariate analysis identified several prognostic factors for OS and CSS including age, sex, histologic type, N stage, tumor thickness, depth of invasion, mitotic rate, ulceration, surgery of primary site, systemic therapy, race, and number of lymph nodes examined. A nomogram incorporating 12 independent predictors for OS was developed, with a C-index of 0.866 (95% confidence interval [CI]: 0.858–0.874) in the training cohort and 0.853 (95% CI: 0.839–0.867) in validation. For CSS, 10 independent predictors and one related factor were included, yielding a C-index of 0.913 (95% CI: 0.903–0.923) in the training cohort and 0.922 (95% CI: 0.908–0.936) in validation. The ROC curve, time-dependent C-index, calibration curve, DCA, and K-M plot demonstrated favorable discrimination, calibration, and clinical utility. Conclusion The developed nomograms provide a precise and personalized predictive tool for risk management of patients with nonmetastatic limb melanoma.

Nomogram to Predict Nodal Recurrence-Free Survival in Early Oral Squamous Cell Carcinoma.

This study aimed to develop and internally validate a prognostic nomogram for predicting nodal recurrence-free survival (NRFS) in patients with early-stage oral squamous cell carcinoma (OSCC) with clinically negative neck lymph nodes. The management of early-stage oral cancer patients with clinically negative neck lymph nodes (cN0) remains controversial, especially concerning the need for elective neck dissection. Data from a single institution spanning 2010 to 2020 were utilized to develop and evaluate the nomogram. The nomogram was constructed using multivariable Cox regression and LASSO regression analyses to identify independent risk factors for lymph node metastasis. Internal validation was performed using bootstrap resampling to assess the nomogram's predictive accuracy. A total of 930 cN0 patients with T1 and T2 stage OSCC were randomly divided into training and validation cohorts (8:2 ratio). Independent risk factors for lymph node metastasis included tumor pathological grade (well: reference, moderate/poor: OR 1.69), cT (cT1: reference, cT2: OR 2.01), history of drinking (never: reference, current/former: OR 1.72), and depth of invasion (0 mm < DOI ≤ 5 mm: reference, 5 mm < DOI ≤ 10 mm: OR 1.31). The nomogram, incorporating these variables, demonstrated good predictive accuracy with a C-index of 0.67 (95% CI: 0.58-0.76) in the validation set. In both training and validation groups, the nomogram effectively stratified patients into low-risk and high-risk groups for occult cervical nodal metastases (p < 0.05). The nomogram enables risk stratification and improved identification of occult cervical nodal metastases in clinically node-negative OSCC patients by incorporating tumor-specific and patient-specific risk factors.

Grading carcinoembryonic antigen levels can enhance the effectiveness of prognostic stratification in patients with colorectal cancer: a single-centre retrospective study.

This study developed a refined carcinoembryonic antigen (CEA) grading system using CEA cut-off points of 5, 20 and 50 ng/mL and to explore the prognostic value of CEA grading in predicting the progression-free survival (PFS) and overall survival (OS) of colorectal cancer (CRC) patients. A retrospective cohort study. First Affiliated Hospital of Guangxi Medical University. 1107 CRC patients who received surgical treatment. Survival analysis was conducted using the Kaplan-Meier method and compared using the log-rank test. A Cox regression model with a 95% CI was used to evaluate the independent prognostic risk factors for CRC. Prognostic nomograms were constructed to predict the 1-5-year PFS/OS. Elevated serum CEA levels are often indicative of recurrence and death in CRC patients. Higher CEA levels were significantly associated with more aggressive tumour phenotypes. The CEA grading system was an independent predictor of prognosis in CRC patients and effectively stratified PFS (62.0% vs 51.2% vs 33.7% vs 20.2%, p<0.001) and OS (64.7% vs 54.4% vs 36.6% vs 22.5%, p<0.001) in CRC patients. As the CEA grade increased, the risk of poor prognosis gradually increased in a gradient manner, with an approximately 10% difference in risk grade between each CEA grade. The internal validation cohort further confirmed that CEA grade remains an effective prognostic factor for the survival of CRC patients. Prognostic nomograms, which integrate individual characteristics, tumour features and CEA grading, provide a more comprehensive prognostic evaluation for CRC patients. The CEA grading system is an independent predictor of prognosis for CRC patients and can effectively stratify PFS and OS.

Short-Term Prognostic Risk Factors and Nomogram Predictive Model Construction for Acute Ischaemic Stroke after Endovascular Treatment.

To investigate the factors influencing the severity of acute ischaemic stroke (AIS) and short-term prognosis after endovascular treatment. Observational study. Place and Duration of the Study: Department of Cerebrovascular Diseases, The First People's Hospital of Qujing City, Yunnan Province, China, from June 2020 to December 2022. An analysis was conducted on 160 AIS patients undergoing endovascular treatment, classified into atherosclerosis subtype (AS) and cardioembolic subtype (CE) based on the Trial of ORG 10172 in Acute Stroke Treatment (TOAST) classification, each comprising 80 cases. The association among baseline and endovascular treatment parameters, clinical characteristics, postoperative complications, and short-term outcomes (risks of mortality and poor prognosis) was tested. Univariate analysis revealed significant positive correlations between increased fasting glucose, diastolic pressure, and the number of thrombectomy procedures with the frequency of cerebral haemorrhage in CE-type stroke. The time from femoral artery puncture to vessel opening was positively associated with the degree of brain oedema. In the AS subtype, univariate regression analysis demonstrated a significant association between heart rate, fasting glucose, cholesterol, time from femoral artery puncture to vessel opening, and degree of brain oedema with short-term prognosis. Fasting glucose and the number of thrombectomy procedures were significantly linked to short-term prognosis in CE-type AIS. A predictive model using line charts was developed for factors associated with postoperative complications and short-term prognosis, achieving predictive accuracies of 95.5% for the risk of death and 92.7% for poor prognosis (mRS >2), notably surpassing traditional prediction methods. Clinical characteristics and endovascular treatment-related factors are important for the short-term prognosis of AIS patients. Development of predictive models can efficiently identify high-risk patients at an early stage. Acute ischaemic stroke, Atherosclerosis, Cardioembolic, Severity, Prognosis.

Development of a CT-assessed adiposity nomogram for predicting outcome in localized ccRCC.

This study aimed to develop and validate a computed tomography-based nomogram assessing visceral and subcutaneous adiposity for predicting outcomes in localized clear cell renal cell carcinoma (ccRCC). A cohort of 364 patients with pathologically confirmed ccRCC participated in this retrospective study, with 254 patients assigned to the training set and 110 to the validation set (a 7:3 distribution ratio). The adipose score (AS) was generated using the least absolute shrinkage and selection operator Cox regression. Subsequently, a nomogram was constructed by integrating the clinical independent predictor with the AS to predict disease-free survival (DFS) in localized ccRCC after surgery. The performance of the nomogram was compared with the University of California, Los Angeles, Integrated Staging System (UISS), and the Stage, Size, Grade, and Necrosis (SSIGN) score. In both the training and validation cohorts, the nomogram exhibited superior discrimination compared to SSIGN and UISS (C-index: 0.897 vs. 0.781 vs. 0.776 in the training cohort, and 0.752 vs. 0.596 vs. 0.686 in the validation cohort; 5year AUC: 0.907 vs. 0.805 vs. 0.820 in the training cohort, and 0.832 vs. 0.577 vs. 0.726 in the validation cohort). Decision curve analysis (DCA) revealed a superior net benefit across a wider range of threshold probabilities for predicting 5year DFS compared to UISS and SSIGN scores. The developed prognostic nomogram demonstrated high accuracy and overall superior performance compared to existing prognostic models.

Transcription Factor Forkhead Box Protein 3 (FOXP3) as a Prognostic Indicator for Postoperative Outcomes in Patients with Breast Cancer: Establishment of a Prognostic Nomogram.

The current investigation is to assess FOXP3 expression in breast cancer patients and evaluate the predictive significance of FOXP3. A cohort of 313 cases between January 2015 and November 2015 were enrolled this research. Immunohistochemistry (IHC) assay was utilized to detect the expression levels of FOXP3 in primary breast carcinoma specimens. These patients were separated into two groups by semiquantitative scoring approach. Chi-square test and Fisher's exact test were conducted to investigate the correlations between FOXP3 expression in tumors and clinicopathological variables. Kaplan-Meier method and Log rank test were utilized to generate survival curves for disease-free survival (DFS) and overall survival (OS). The independent factors were examined using Cox regression analysis. Nomogram models were created for assessing DFS and OS rates. Depending on the levels of FOXP3 expression in tumors, these patients were categorized into two groups: low FOXP3 expression (174 cases) and high FOXP3 expression (139 cases). The patients exhibiting low levels of FOXP3 expression in tumors demonstrated a longer survival duration contrasted with those with high expression (DFS: 88.75 vs 65.87 months, χ2=36.1100, P<0.0001; OS: 89.70 vs 78.37 months, χ2=32.4900, P<0.0001). Multivariate analysis revealed that FOXP3 was a significant prognostic factor [DFS: hazard ratio (HR): 2.822, 95% CI: 1.595-4.992, P<0.0001; OS: HR: 3.232, 95% CI: 1.812-5.763, P<0.0001]. The good predictive clinical utility of FOXP3-based nomograms within the threshold probability range for different survival rates was demonstrated by calibration curve and decision curve analyses. FOXP3 expression serves as a crucial prognostic indicator in breast cancer patients, and may aid preoperative evaluation in clinical practice.

Decoding the immune microenvironment: unveiling CD8 + T cell-related biomarkers and developing a prognostic signature for personalized glioma treatment

BackgroundGliomas are aggressive brain tumors with poor prognosis. Understanding the tumor immune microenvironment (TIME) in gliomas is essential for developing effective immunotherapies. This study aimed to identify TIME-related biomarkers in glioma using bioinformatic analysis of RNA-seq data.MethodsIn this study, we employed weighted gene co-expression network analysis (WGCNA) on bulk RNA-seq data to identify TIME-related genes. To identify prognostic genes, we performed univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression analyses. Based on these genes, we constructed a prognostic signature and delineated risk groups. To validate the prognostic signature, external validation was conducted.ResultsCD8 + T cell infiltration was strongly correlated with glioma patient prognosis. We identified 115 CD8 + T cell-related genes through integrative analysis of bulk-seq data. CDCA5, KIF11, and KIF4A were found to be significant immune-related genes (IRGs) associated with overall survival in glioma patients and served as independent prognostic factors. We developed a prognostic nomogram that incorporated these genes, age, gender, and grade, providing a reliable tool for clinicians to predict patient survival probabilities. The nomogram’s predictions were supported by calibration plots, further validating its accuracy.ConclusionIn conclusion, our study identifies CD8 + T cell infiltration as a strong predictor of glioma patient outcomes and highlights the prognostic value of genes. The developed prognostic nomogram, incorporating these genes along with clinical factors, provides a reliable tool for predicting patient survival probabilities and has important implications for personalized treatment decisions in glioma.

Clinical characteristics, survival and prognostic nomogram for patients with esophageal mucinous adenocarcinoma: a SEER population-based analysis

Clinical characteristics, survival and prognostic nomogram for patients with esophageal mucinous adenocarcinoma: a SEER population-based analysis

Development and validation of a dynamic nomogram for short-term survival in acute heart failure patients with acute kidney injury upon ICU admission

ObjectiveThe objective of this study is to develop and validate an effective prognostic nomogram for predicting the short-term survival rate of patients with acute heart failure (AHF) complicated by acute kidney injury (AKI) who are admitted to the intensive care unit (ICU). Patients and methodsWe conducted an analysis of data from patients of AHF with AKI spanning the period from 2008 to 2019, utilizing the MIMIC-IV database. Patients were randomly divided into training and validation sets. The training set employed the least absolute shrinkage and selection operator regression model to identify predictors of AKI. Subsequently, a dynamic nomogram was constructed using multivariate Cox regression analysis within the training set and was subsequently validated using the validation set. The nomogram's predictive accuracy, calibration, and clinical utility were evaluated through the concordance index (C-index), calibration plots, and decision curve analysis (DCA). ResultsA total of 978 AHF patients with AKI were analyzed. Multivariate analysis identified serum creatinine, race, age, use of human albumin, use of vasoactive drug, and hemoglobin as independent predictors significantly influencing the short-term prognosis of AHF patients with AKI upon ICU admission. The C-index for the training and validation sets were 0.81 (95%CI: 0.74–0.87) and 0.80 (95 % CI: 0.67–0.92), respectively. The calibration plot of the nomogram demonstrated a close alignment between predicted and observed probabilities. Furthermore, the DCA confirmed the clinical utility of the nomogram. ConclusionsThis study presents a dynamic nomogram that incorporates clinical risk factors and can be conveniently utilized to predict short-term prognosis for AHF patients with AKI upon ICU admission.

Development and validation of a prognostic nomogram for elderly-onset pancreatic neuroendocrine carcinoma: a prospective cohort study from the SEER database

Development and validation of a prognostic nomogram for elderly-onset pancreatic neuroendocrine carcinoma: a prospective cohort study from the SEER database

Breast cancer promotes the expression of neurotransmitter receptor related gene groups and image simulation of prognosis model

Breast cancer (BC), a prevalent and severe malignancy, detrimentally affects women globally. Its prognostic implications are profoundly influenced by gene expression patterns. This study retrieved 509 BC-associated oncogenes and 1,012 neurotransmitter receptor-related genes from the GSEA and KEGG databases, intersecting to identify 98 relevant genes. Clinical and transcriptomic expression data related to BC were downloaded from the TCGA, and differential genes were identified based on an FDR value <0.05 & |log2FC| ≥ 0.585. Univariate analysis of these genes revealed that high expression of NSF and low expression of HRAS, KIF17, and RPS6KA1 are closely associated with BC survival prognosis. A prognostic model constructed for these four genes demonstrated significant prognostic relevance for BC-TCGA patients (P < 0.001). Subsequently, an immunofunctional analysis of the BC oncogene-neurotransmitter receptor-related gene cluster revealed the involvement of immune cells such as T cells CD8, T cells CD4 memory resting, and Macrophages M2. Further analysis indicated that immune functions were primarily concentrated in APC_co_inhibition, APC_co_stimulation, CCR, and Check-point, among others. Lastly, a prognostic nomogram model was established, and ROC curve analysis revealed that the nomogram is a vital indicator for assessing BC prognosis, with 1-year, 3-year, and 5-year survival rates of 0.981, 0.897, and 0.802, respectively. This model demonstrates high calibration, clinical utility, and predictive capability, promising to offer an effective preliminary tool for clinical diagnostics.

High expression of ARHGEF5 predicts unfavorable prognosis in acute myeloid leukemia

Acute myeloid leukemia (AML) is a highly heterogeneous hematological neoplasm, highlighting the need for new molecular markers to improve prognosis prediction and therapeutic strategies. While Rho guanine nucleotide exchange factor 5 (ARHGEF5) is known to be overexpressed in various cancers, its role in AML is not well understood. This study investigates the correlation between ARHGEF5 expression and AML using data from the Cancer Genome Atlas (TCGA). ARHGEF5 expression levels in AML patients and normal samples were compared using the Wilcoxon rank-sum test. The Kaplan–Meier method and Cox regression analysis (CRA) assessed the association between ARHGEF5 expression and patient survival. A prognostic nomogram was constructed using CRA, incorporating patient age and cytogenetic risk.Our findings indicate significant overexpression of ARHGEF5 in AML compared to normal samples. Elevated ARHGEF5 levels were associated with poor prognosis, particularly in patients ≤ 60 years, those with NPM1 mutations, FLT3 mutation-positive, and wild-type RAS (P < 0.05). CRA confirmed that high ARHGEF5 expression independently predicts poor prognosis. Additionally, 412 differentially expressed genes (DEGs) were identified between high and low ARHGEF5 expression groups, with 216 genes upregulated and 196 downregulated. Pathway enrichment analyses using GO and KEGG, along with protein–protein interaction network and single sample gene set enrichment analyses, revealed key pathways and immune cell associations linked to ARHGEF5. These findings suggest that ARHGEF5 overexpression could serve as a biomarker for unfavorable outcomes in AML, providing insights into the underlying mechanisms of AML onset and progression.

Effect of aberrant fructose metabolism following SARS-CoV-2 infection on colorectal cancer patients' poor prognosis.

Most COVID-19 patients have a positive prognosis, but patients with additional underlying diseases are more likely to have severe illness and increased fatality rates. Numerous studies indicate that cancer patients are more prone to contract SARS-CoV-2 and develop severe COVID-19 or even dying. In the recent transcriptome investigations, it is demonstrated that the fructose metabolism is altered in patients with SARS-CoV-2 infection. However, cancer cells can use fructose as an extra source of energy for growth and metastasis. Furthermore, enhanced living conditions have resulted in a notable rise in fructose consumption in individuals' daily dietary habits. We therefore hypothesize that the poor prognosis of cancer patients caused by SARS-CoV-2 may therefore be mediated through fructose metabolism. Using CRC cases from four distinct cohorts, we built and validated a predictive model based on SARS-CoV-2 producing fructose metabolic anomalies by coupling Cox univariate regression and lasso regression feature selection algorithms to identify hallmark genes in colorectal cancer. We also developed a composite prognostic nomogram to improve clinical practice by integrating the characteristics of aberrant fructose metabolism produced by this novel coronavirus with age and tumor stage. To obtain the genes with the greatest potential prognostic values, LASSO regression analysis was performed, In the TCGA training cohort, patients were randomly separated into training and validation sets in the ratio of 4: 1, and the best risk score value for each sample was acquired by lasso regression analysis for further analysis, and the fifteen genes CLEC4A, FDFT1, CTNNB1, GPI, PMM2, PTPRD, IL7, ALDH3B1, AASS, AOC3, SEPINE1, PFKFB1, FTCD, TIMP1 and GATM were finally selected. In order to validate the model's accuracy, ROC curve analysis was performed on an external dataset, and the results indicated that the model had a high predictive power for the prognosis prediction of patients. Our study provides a theoretical foundation for the future targeted regulation of fructose metabolism in colorectal cancer patients, while simultaneously optimizing dietary guidance and therapeutic care for colorectal cancer patients in the context of the COVID-19 pandemic.