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Results for “Prognostic model”

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PubMed2001

A prognostic model for predicting survival in cirrhosis with ascites.

BACKGROUND/AIMS: Parameters evaluating renal function and systemic hemodynamics are of prognostic significance in cirrhosis with ascites but are rarely used in the evaluation of survival of these patients. The aim of the current study was to develop a prognostic model to estimate survival of patients with cirrhosis and ascites. METHODS: 216 Cirrhotic patients admitted to hospital for the treatment of ascites were evaluated. Thirty-two demographic, clinical and laboratory variables, including parameters assessing liver and renal function and systemic hemodynamics, were analyzed as predictive factors of survival by using a Cox regression model. RESULTS: Four variables had independent prognostic value: renal water excretion, as assessed by measuring diuresis after water load, mean arterial pressure, Child-Pugh class, and serum creatinine. According to these features a prognostic index was calculated that allows to estimate survival in patients with cirrhosis and ascites. The model accurately predicted survival in an independent series of 84 patients with cirrhosis and ascites. CONCLUSION: A prognostic model that uses four easily available variables and predicts prognosis in cirrhotic patients with ascites has been developed. This model may be useful in the evaluation of patients with ascites for liver transplantation.

Ascites↗

PubMed1996

Prognostic models for the probability of achieving an ongoing pregnancy after in-vitro fertilization and the importance of testing their predictive value.

The aim of this study was to create reliable models to predict the probability of achieving an ongoing pregnancy during in-vitro fertilization (IVF) treatment: model A, at the start of the first treatment, model B at the time of embryo transfer, and model C, during the second treatment at the end of the first IVF treatment. Prognostic models were created using data from the University Hospital Nijmegen (n = 757) and applied to the data from the Catharina Hospital Eindhoven (n = 432), The Netherlands, to test their predictive performance. The predictions of model B (made at time of embryo transfer) were fairly good (c = 0.672 in the test population). For instance, 93% of the patients who had a predicted probability of achieving an ongoing pregnancy of < 10% did not achieve an ongoing pregnancy. However, the predictions of the other two models (A and C) for Eindhoven were less reliable. The predictive value of model C was fairly high in Nijmegen (c = 0.673). Its poor performance in the test population may be explained partly by differences in effectiveness of the ovulation stimulation protocols and the decision about when to discontinue the cycle. Thus, before using prognostic models at an IVF centre, their reliability at that specific centre should be tested.

Evaluation Studies as Topic↗

PubMed2004

Prognostic model of pulmonary adenocarcinoma by expression profiling of eight genes as determined by quantitative real-time reverse transcriptase polymerase chain reaction.

PURPOSE: Recently, several expression-profiling experiments have shown that adenocarcinoma can be classified into subgroups that also reflect patient survival. In this study, we examined the expression patterns of 44 genes selected by these studies to test whether their expression patterns were relevant to prognosis in our cohort as well, and to create a prognostic model applicable to clinical practice. PATIENTS AND METHODS: Expression levels were determined in 85 adenocarcinoma patients by quantitative reverse transcriptase polymerase chain reaction. Cluster analysis was performed, and a prognostic model was created by the proportional hazards model using a stepwise method. RESULTS: Hierarchical clustering divided the cases into three major groups, and group B, comprising 21 cases, had significantly poor survival (P =.0297). Next, we tried to identify a smaller number of genes of particular predictive value, and eight genes (PTK7, CIT, SCNN1A, PGES, ERO1L, ZWINT, and two ESTs) were selected. We then calculated a risk index that was defined as a linear combination of gene expression values weighted by their estimated regression coefficients. The risk index was a significant independent prognostic factor (P =.0021) by multivariate analysis. Furthermore, the robustness of this model was confirmed using an independent set of 21 patients (P =.0085). CONCLUSION: By analyzing a reasonably small number of genes, patients with adenocarcinoma could be stratified according to their prognosis. The prognostic model could be applicable to future decisions concerning treatment.

Adenocarcinoma↗

PubMed2004

Construction and validation of a prognostic model across several studies, with an application in superficial bladder cancer.

Many models for clinical prediction (prognosis or diagnosis) are published in the medical literature every year but few such models find their way into clinical practice. The reason may be that since in most cases models have not been validated in independent data, they lack generality and/or credibility. In this paper we consider the situation in which several compatible, independent data sets relating to a given disease with a time-to-event endpoint are available for analysis. The aim is to construct and evaluate a single prognostic model. Building a multivariable model from the available prognostic factors is accomplished within the Cox proportional hazards framework, stratifying by study. Non-linear relationships with continuous predictors are modelled by using fractional polynomials. To assess the discrimination or separation of a survival model, we use the D statistic of Royston and Sauerbrei. D may be interpreted as the separation (log hazard ratio) between the survival distributions for two independent prognostic groups. To evaluate the generality of a prognostic model across the data sets, we propose 'internal-external cross-validation' on D: each study is omitted in turn, the model parameters are estimated from the remaining studies and D is evaluated in the omitted study. Because the linear predictor of a survival model tells only part of the story, we also suggest a method for investigating heterogeneity in the baseline distribution function across studies which involves fitting completely specified, flexible parametric survival models (Royston and Parmar). Our final models combine the prognostic index (obtained with stratification by study) with the pooled baseline survival distribution (estimated parametrically). By applying this methodology, we construct two prognostic scores in superficial bladder cancer. The simpler of the two scores is more suited to clinical application. We show that a three-group prognostic classification scheme based on either score produces well-separated survival curves for each of the data sets, despite identifiable heterogeneity among the baseline distribution functions and to a lesser extent among the prognostic indexes for the individual studies.

Adult↗

PubMed2006

Early-stage melanoma: staging criteria and prognostic modeling.

Accurate risk assessment is central to the process of making rational surgical and systemic treatment recommendations for melanoma patients and in establishing appropriate clinical trial stratification criteria. The current American Joint Commission on Cancer melanoma staging system incorporated relevant prognostic variables to provide a framework for the estimation of risk for recurrence; however, significant prognostic heterogeneity exists within the stage groupings. In the stage I/II group, survival rates range from 40% to 95% as defined by the combination of tumor thickness and ulceration. The use of novel prognostic factors, such as mitotic rate, sentinel node biopsy, and prognostic modeling using a variety of factors, can minimize this prognostic heterogeneity and provide a more accurate and individualized prognostic profile. Recent modifications in the stage III criteria include the number of positive nodes, whether the nodal disease is microscopic or clinically apparent, and the presence of an ulcerated primary. Through these factors, survival estimates can be provided, but like the stage I/II group, wide ranges in prognosis exist. The complexion of the stage III population is in evolution as a result of increasing numbers of patients being diagnosed as having microscopic sentinel node disease. Contemporary efforts are focused on defining the prognosis and natural history of this group. Through prognostic modeling using the number of nodes involved, ulceration status, and a measure of disease burden--disease in the sentinel node--relatively homogeneous subgroups can be identified. Long-term follow-up of patients staged with PCR molecular techniques on sentinel nodes shows conflicting value in assessing prognosis and therefore cannot be routinely used outside a clinical trial. The combination of genomic profiling using microarray analyses and the development of targeted therapy holds the future promise of individualizing prognosis and therapy.

Humans↗

PubMed2026

Prognostic model based on calcium-related genes predicts prognosis and reveals the immune landscape of acute myeloid leukemia.

Acute myeloid leukemia (AML) exhibits heterogeneous outcomes and lacks reliable prognostic markers. As a critical regulator of cell fate, the prognostic value of calcium signaling in AML requires investigation. This study aimed to construct a calcium-related gene (CRG)-based prognostic model for AML. Differential analysis on RNA-seq data was conducted for AML from The Cancer Genome Atlas and Gene Expression Omnibus (GEO). Intersecting differentially expressed genes and CRGs yielded AML-associated differentially expressed CRGs (DECRGs). A prognostic model was developed using univariate/multivariate Cox regression and least absolute shrinkage and selection operator (LASSO) and validated in a GEO dataset. Bioinformatics analyses explored the links between risk groups and immune characteristics, genomic mutations, and drug sensitivity. Key genes' effects on cell proliferation, apoptosis, and differentiation were verified in vitro using CCK-8 assay, colony formation assay, and flow cytometry. The 13-DECRG-based model distinguished high- and low-risk patients in both training and validation cohorts, with high-risk patients showing a worse prognosis. The risk score was an independent prognostic factor. Immune analysis revealed a unique immune microenvironment for the high-risk group. CAMK2A overexpression inhibited cell proliferation and colony-forming ability, promoted cell apoptosis, and induced an increased proportion of CD11b- and CD14-positive cells. In vitro experiments indicated CAMK2A-induced suppression of AML cells' malignant phenotype by activating the P53 signaling pathway. An AML CRG-based model with favorable risk stratification performance was constructed. In vitro experiments revealed CAMK2A-induced inhibition of the malignant phenotype via suppressing proliferation, promoting apoptosis, and facilitating myeloid differentiation in AML cells. This study provides novel evidence for understanding CRGs in AML as well as the potential functions of CAMK2A.

Journal Article↗

PubMed1999

Optimal timing of liver transplantation for patients with primary biliary cirrhosis: use of prognostic modelling.

BACKGROUND/AIMS: Liver transplantation remains the only definitive treatment for patients with end-stage primary biliary cirrhosis, although the optimal timing of the procedure remains uncertain. The aim of the study was to use prognostic modelling to determine the optimal timing of transplantation for patients with primary biliary cirrhosis. METHODS: A prognostic model for predicting the survival of patients after transplantation was generated using the Cox regression model with data from 312 patients transplanted for primary biliary cirrhosis at the Queen Elizabeth Hospital, Birmingham. The prognosis after transplantation was compared to that without transplantation (using a previously published prognostic index for non-transplantation) both in these patients and in 98 non-transplanted primary biliary cirrhosis patients dying from the liver disease, in order to establish at what stage the prognosis with transplantation was better than without transplantation. RESULTS: The prognostic index for transplantation included the following significant prognostic variables: serum bilirubin, serum albumin, age, year of transplantation, and the presence of ascites or treatment with diuretics. Comparison of prognosis with and without transplantation showed that the predicted gain in survival after transplantation becomes increasingly positive when the 6-month survival probability in the absence of transplantation falls below 0.85. In the non-transplanted patients this occurs on average about 8 months before death. CONCLUSIONS: Comparison of the prognosis with and without transplantation provides a rational method for determining the optimum timing of the procedure which occurs approximately when the predicted 6-month survival probability without transplantation falls below 0.85.

Female↗

PubMed2025

Construction of a prognostic model for gastric cancer based on immune infiltration and microenvironment, and exploration of MEF2C gene function.

BACKGROUND: Advanced gastric cancer (GC) exhibits a high recurrence rate and a dismal prognosis. Myocyte enhancer factor 2c (MEF2C) was found to contribute to the development of various types of cancer. Therefore, our aim is to develop a prognostic model that predicts the prognosis of GC patients and initially explore the role of MEF2C in immunotherapy for GC. METHODS: Transcriptome sequence data of GC was obtained from The Cancer Genome Atlas (TCGA), the Gene Expression Omnibus (GEO) and PRJEB25780 cohort for subsequent immune infiltration analysis, immune microenvironment analysis, consensus clustering analysis and feature selection for definition and classification of gene M and N. Principal component analysis (PCA) modeling was performed based on gene M and N for the calculation of immune checkpoint inhibitor (ICI) Score. Then, a Nomogram was constructed and evaluated for predicting the prognosis of GC patients, based on univariate and multivariate Cox regression. Functional enrichment analysis was performed to initially investigate the potential biological mechanisms. Through Genomics of Drug Sensitivity in Cancer (GDSC) dataset, the estimated IC50 values of several chemotherapeutic drugs were calculated. Tumor-related transcription factors (TFs) were retrieved from the Cistrome Cancer database and utilized our model to screen these TFs, and weighted correlation network analysis (WGCNA) was performed to identify transcription factors strongly associated with immunotherapy in GC. Finally, 10 patients with advanced GC were enrolled from Sun Yat-sen University Cancer Center, including paired tumor tissues, paracancerous tissues and peritoneal metastases, for preparing sequencing library, in order to perform external validation. RESULTS: Lower ICI Score was correlated with improved prognosis in both the training and validation cohorts. First, lower mutant-allele tumor heterogeneity (MATH) was associated with lower ICI Score, and those GC patients with lower MATH and lower ICI Score had the best prognosis. Second, regardless of the T or N staging, the low ICI Score group had significantly higher overall survival (OS) compared to the high ICI Score group. For its mechanisms, consistently, for Camptothecin, Doxorubicin, Mitomycin, Docetaxel, Cisplatin, Vinblastine, Sorafenib and Paclitaxel, all of the IC50 values were significantly lower in the low ICI Score group compared to the high ICI Score group. As a result, based on univariate and multivariate Cox regression, ICI Score was considered to be an independent prognostic factor for GC. And our Nomogram showed good agreement between predicted and actual probabilities. Based on CIBERSORT deconvolution analysis, there was difference of immune cell composition found between high and low ICI Score groups, probably affecting the efficacy of immunotherapy. Then, MEF2C, a tumor-related transcription factor, was screened out by WGCNA analysis. Higher MEF2C expression is significantly correlated with a worse OS. Moreover, its higher expression is also negatively correlated with tumor mutation burden (TMB) and microsatellite instability (MSI), but positively correlated with several immunosuppressive molecules, indicating MEF2C may exert its influence on tumor development by upregulating immunosuppressive molecules. Finally, based on transcriptome sequencing data on 10 paired tumor tissues from Sun Yat-sen University Cancer Center, MEF2C expression was significantly lower in paracancerous tissues compared to tumor tissues and peritoneal metastases, and it was also lower in tumor tissues compared to peritoneal metastases, indicating a potential positive association between MEF2C expression and tumor invasiveness. CONCLUSIONS: Our prognostic model can effectively predict outcomes and facilitate stratification GC patients, offering valuable insights for clinical decision-making. The identified transcription factor MEF2C can serve as a biomarker for assessing the efficacy of immunotherapy for GC.

Humans↗

PubMed2024

Construction and Analysis of a Mitochondrial Metabolism-Related Prognostic Model for Breast Cancer to Evaluate Survival and Immunotherapy.

As one of the most prevalent malignancies among women, breast cancer (BC) is tightly linked to metabolic dysfunction. However, the correlation between mitochondrial metabolism-related genes (MMRGs) and BC remains unclear. The training and validation datasets for BC were obtained from The Cancer Genome Atlas and Gene Expression Omnibus databases, respectively. MMRG-related data were obtained from the Molecular Signatures Database. A risk score prognostic model incorporating MMRGs was established based on univariate, LASSO, and multivariate Cox regression analyses. Independent factors affecting BC prognosis were identified through regression analysis and presented in a nomogram. Single-sample gene set enrichment analysis was employed to assess the immune levels of high-risk (HR) and low-risk (LR) groups. The sensitivity of BC patients in the two groups to common anti-tumor drugs was evaluated by utilizing the Genomics of Drug Sensitivity in Cancer database. 12 MMRGs significantly associated with survival were selected from 1234 MMRGs. A 12-gene risk score prognostic model was built. In the multivariate regression analysis incorporating classical clinical factors, the MMRG-related risk score remained an independent prognostic factor. As revealed by tumor immune microenvironment analysis, the LR group with higher survival rates had elevated immune levels. The drug sensitivity results unmasked that the LR group demonstrated higher sensitivity to Irinotecan, Nilotinib, and Oxaliplatin, while the HR group demonstrated higher sensitivity to Lapatinib. The development of MMRG characteristics provides a comprehensive understanding of mitochondrial metabolism in BC, aiding in the prediction of prognosis and tumor microenvironment, and offering promising therapeutic choices for BC patients with different MMRG risk scores.

Humans↗

PubMed1989

Prognosis of aggressive lymphomas: a study of five prognostic models with patients included in the LNH-84 regimen.

Four prognostic models described for aggressive malignant lymphomas and the classical Ann Arbor staging system were used to compare the survival of 737 patients treated with the LNH-84 regimen. The aim of the study was to determine the optimal prognostic system at the time of diagnosis. Three institutions have described these models after multivariate analyses: the Dana Farber Cancer Institute (DFCI1 and DFCI2), the MD Anderson Hospital (MDAH), and the Memorial Sloan-Kettering Cancer Center (MSKCC). The models were constructed with the following variables: performance status, LDH level, and tumor extension. The latter is the most difficult to assess: it was considered as the number of extranodal sites and the diameter of the largest mass in DFCI1, stage and the diameter of the largest mass in DFCI2, the number of extranodal and extensive nodal sites in MDAH, and the number of nodal sites and their localization in MSKCC. Univariate studies with LNH-84 regimen patients showed all these variables to have major prognostic significance (logrank tests: P less than 10(-4)). All five prognostic systems divided patients into three subgroups: good, intermediate, and poor prognosis. Logrank analyses of survival showed highly significant differences (X2 greater than 90 and P less than 10(-6)) between the subgroups. No gross difference was found between the models, and none was better than the others. A new, internationally accepted prognostic system for the expression and comparison of treatment results in aggressive malignant lymphomas should include major univariate prognostic parameters and must be reliable and easy to use in clinical practice. Until such time, stage or LDH level are the best alternatives.

Antineoplastic Combined Chemotherapy Protocols↗

PubMed1997

High-dose cyclophosphamide, carmustine, and etoposide with autologous transplantation in Hodgkin's disease: a prognostic model for treatment outcomes.

PURPOSE: To identify clinical factors predictive of treatment outcome after high-dose chemotherapy (HDC) for Hodgkin's disease and to develop a prognostic model for progression-free and overall survival. PATIENTS AND METHODS: 102 patients with relapsed or refractory Hodgkin's disease were treated with high-dose cyclophosphamide, carmustine, and etoposide and autologous marrow and/or peripheral blood progenitor cell support. Median follow-up of survivors is 4.1 years (1.8-7.5 years). Factors potentially important for treatment outcome were examined in univariate analysis, and Cox regression with forward selection was performed. A prognostic model was developed. RESULTS: Poorer progression-free and overall survival were associated with nodular sclerosis histology, abnormal performance status, progressive disease at HDC, more than one extranodal site of disease, and shorter time from initial diagnosis to HDC. These factors and the presence of B symptoms at relapse also predicted for decreased overall survival. Progressive disease immediately prior to HDC, more than one extranodal disease site, and abnormal performance status retained significance for both progression-free and overall survival in multivariate analysis. Progression-free and overall survival are 42% (95% confidence interval, CI, 34 to 53) and 65% (95% CI 54 to 73) at three years. A model based on number of risk factors present divides patients into low, intermediate, and high risk groups with three-year actuarial survival of 82%, 56%, and 19% respectively. Treatment outcome for patients treated with HDC at first chemotherapy relapse was not significantly different from that of the group overall (p > 0.3). CONCLUSIONS: Asymptomatic patients with Hodgkin's disease involving at most one extranodal site whose disease is controlled by conventional dose chemotherapy or radiation therapy at the time of HDC have good outcomes after this therapy. Presence of increasing numbers of risk factors are associated with poorer outcomes. Results of HDC compare favorably to those of standard dose salvage therapy. These data can be used to estimate likely outcomes in patients undergoing HDC for Hodgkin's disease, to identify potential candidates for innovative therapies, and to evaluate strategies for the optimal use of HDC in Hodgkin's disease.

Adult↗

PubMed1997

Prognostic models--what is their future?

Estimation of prognosis for an individual patient is a valuable tool for the clinician. During the last decade, there has been an increasing number of prognostic models developed. Such models identify variables which have a statistical correlation with a clinically useful end-point (such as death). However, confidence limits of estimates are often wide, and over-reliance on prognostic models may be inappropriate. Ideally, models should be useful, intuitive and simple to use.

Humans↗

PubMed1997

Prognostic modelling in peritonitis. Peritonitis Study Group of the Surgical Infection Society Europe.

OBJECTIVE: To develop and to evaluate a new score to aid management in peritonitis. DESIGN: Prospective, multicentre study. SETTING: 18 departments of surgery in Germany. Austria, and Switzerland. SUBJECTS: 355 patients with peritonitis confirmed at laparotomy. INTERVENTIONS: Computation of four different prognostic systems: APACHE II; APACHE II and successful operation; APACHE II, successful operation and Goris score on the first postoperative day: and multivariate analysis. Predictions were evaluated according to the following criteria: specificity with a fixed sensitivity at 80%, receiver operating characteristic (ROC-) curve, and predictive value. MAIN OUTCOME MEASURE: The ability to predict hospital death and infective complications. RESULTS: Multivariate analysis was superior to APACHE II: APACHE II and successful operation: and APACHE II, successful operation, and Goris score. From the analysis a new prognostic model was derived from which it was possible to identify patients early in the postoperative period who are at high risk of developing further complications (prognostic peritonitis model: PPM). CONCLUSIONS: None of the existing scores was of particular use for therapeutic decision making in peritonitis. The new prognostic model should be the focus of further trials in the management of peritonitis.

APACHE↗

PubMed2001

A prognostic model for ovarian cancer.

About 6000 women in the United Kingdom develop ovarian cancer each year and about two-thirds of the women will die from the disease. Establishing the prognosis of a woman with ovarian cancer is an important part of her evaluation and treatment. Prognostic models and indices in ovarian cancer should be developed using large databases and, ideally, with complete information on both prognostic indicators and long-term outcome. We developed a prognostic model using Cox regression and multiple imputation from 1189 primary cases of epithelial ovarian cancer (with median follow-up of 4.6 years). We found that the significant (P< or = 0.05) prognostic factors for overall survival were age at diagnosis, FIGO stage, grade of tumour, histology (mixed mesodermal, clear cell and endometrioid versus serous papillary), the presence or absence of ascites, albumin, alkaline phosphatase, performance status on the ZUBROD-ECOG-WHO scale, and debulking of the tumour. This model is consistent with other models in the ovarian cancer literature; it has better predictive ability and, after simplification and validation, could be used in clinical practice.

Adolescent↗

PubMed2005

Derivation and validation of a prognostic model for pulmonary embolism.

RATIONALE: An objective and simple prognostic model for patients with pulmonary embolism could be helpful in guiding initial intensity of treatment. OBJECTIVES: To develop a clinical prediction rule that accurately classifies patients with pulmonary embolism into categories of increasing risk of mortality and other adverse medical outcomes. METHODS: We randomly allocated 15,531 inpatient discharges with pulmonary embolism from 186 Pennsylvania hospitals to derivation (67%) and internal validation (33%) samples. We derived our prediction rule using logistic regression with 30-day mortality as the primary outcome, and patient demographic and clinical data routinely available at presentation as potential predictor variables. We externally validated the rule in 221 inpatients with pulmonary embolism from Switzerland and France. MEASUREMENTS: We compared mortality and nonfatal adverse medical outcomes across the derivation and two validation samples. MAIN RESULTS: The prediction rule is based on 11 simple patient characteristics that were independently associated with mortality and stratifies patients with pulmonary embolism into five severity classes, with 30-day mortality rates of 0-1.6% in class I, 1.7-3.5% in class II, 3.2-7.1% in class III, 4.0-11.4% in class IV, and 10.0-24.5% in class V across the derivation and validation samples. Inpatient death and nonfatal complications were <or= 1.1% among patients in class I and <or= 1.9% among patients in class II. CONCLUSIONS: Our rule accurately classifies patients with pulmonary embolism into classes of increasing risk of mortality and other adverse medical outcomes. Further validation of the rule is important before its implementation as a decision aid to guide the initial management of patients with pulmonary embolism.

Acute Disease↗

PubMed2001

On prognostic models, artificial intelligence and censored observations.

The development of prognostic models for assisting medical practitioners with decision making is not a trivial task. Models need to possess a number of desirable characteristics and few, if any, current modelling approaches based on statistical or artificial intelligence can produce models that display all these characteristics. The inability of modelling techniques to provide truly useful models has led to interest in these models being purely academic in nature. This in turn has resulted in only a very small percentage of models that have been developed being deployed in practice. On the other hand, new modelling paradigms are being proposed continuously within the machine learning and statistical community and claims, often based on inadequate evaluation, being made on their superiority over traditional modelling methods. We believe that for new modelling approaches to deliver true net benefits over traditional techniques, an evaluation centric approach to their development is essential. In this paper we present such an evaluation centric approach to developing extensions to the basic k-nearest neighbour (k-NN) paradigm. We use standard statistical techniques to enhance the distance metric used and a framework based on evidence theory to obtain a prediction for the target example from the outcome of the retrieved exemplars. We refer to this new k-NN algorithm as Censored k-NN (Ck-NN). This reflects the enhancements made to k-NN that are aimed at providing a means for handling censored observations within k-NN.

Algorithms↗

PubMed2026

Non-small cell lung cancer and tumor-educated platelets: screening of biomarkers and construction of a prognostic model.

BACKGROUND: Lung cancer is a leading cause of cancer-related mortality worldwide, emphasizing the urgent need for effective early detection strategies. Traditional Chinese medicine (TCM) provides a unique perspective on tumor pathogenesis, focusing on concepts such as "long-term stasis leading to accumulation". Tumor-educated platelets (TEPs) offer potential as biomarkers due to their ability to reflect cancer heterogeneity and facilitate less invasive diagnostic approaches. This study aims to identify TEP-related prognostic biomarkers for non-small cell lung cancer (NSCLC) and to construct and validate a multigene prognostic model by integrating platelet transcriptomic data with tumor tissue datasets. METHODS: We performed comprehensive analysis of gene expression datasets obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) repositories to characterize transcriptomic differences among lung cancer specimens, normal tissue samples, and TEPs. Using R software, we identified Differentially expressed genes (DEGs) and subsequently applied a multi-stage analytical pipeline to TEP-associated DEGs, incorporating univariate Cox proportional hazards regression, least absolute shrinkage and selection operator (LASSO) regression, multivariate Cox regression, and stepwise regression modeling to pinpoint genes with prognostic significance. These prognostically relevant genes served as the foundation for developing a risk stratification model. We computed individual risk scores across both training and validation cohorts, enabling patient stratification into high- and low-risk categories. Model robustness was assessed through internal cross-validation and external validation procedures, while predictive performance was quantified using risk calibration metrics and receiver operating characteristic (ROC) curve analysis. RESULTS: Through systematic bioinformatics screening, we identified a four-gene prognostic signature comprising NELL2, C4orf48, PRAM1, and KLHL35, which served as the foundation for developing our risk stratification algorithm. Rigorous internal cross-validation and external cohort validation substantiated the moderate predictive performance of this signature. Comprehensive clinicopathological correlation analysis revealed that elevated risk indices, advanced pathological staging (stage III-IV), increased primary tumor dimensions, regional lymph node metastasis, and distant organ dissemination each demonstrated statistically significant associations with diminished overall survival (OS) outcomes in lung cancer patients. The clinical nomogram exhibited acceptable calibration, with calibration plots showing reasonable concordance between predicted and observed survival probabilities across all time points. Discriminative capacity assessment via time-dependent ROC analysis yielded area under the curve (AUC) values consistently surpassing 0.6, confirming moderate prognostic discrimination. Furthermore, decision curve analysis (DCA) demonstrated that our integrated multi-gene model conferred potential net clinical benefit compared to individual prognostic variables across the full spectrum of clinically relevant threshold probabilities (0-1 range), thereby establishing its potential utility for risk-informed clinical decision-making. CONCLUSIONS: This study identified NELL2, C4orf48, PRAM1, and KLHL35 as candidate TEP-related prognostic biomarkers for non-small cell lung cancer (NSCLC). The developed prognostic model shows preliminary potential for patient stratification, but its clinical application, particularly as a platelet-based liquid biopsy tool, requires further validation in independent TEP-based cohorts.

Tumor-educated platelets (TEPs)↗

PubMed2000

Prognostic models for diffuse large B-cell lymphoma.

Prognosis of DLCL patients is variable and associated with well-defined risk factors. In the past decade several pretreatment variables have been incorporated into prognostic models to predict the death risk of individual patients. The International Prognostic Index (IPI), developed in an international consensus study, has been one of the most widely accepted of these models. In our study we applied some of the major prognostic models proposed for DLCLs in a cohort of 111 patients uniformly treated with a CHOP-like regimen in order to compare their sensitivity and specificity. We also evaluated the possibility of improving the IPI with the inclusion, from among the variables analysed, of serum beta-2 microglobulin level (beta-2M). The sensitivity, reflecting the ability to predict all failures in the cohort of patients as a whole, has improved from 45 to 73 per cent when the beta-2M-IPI model is compared with IPI, without a significant loss of specificity. Based on these results, the beta-2M-IPI may be useful for identifying the subset of patients with very poor prognoses. Therefore, the use of the serum beta-2M value in addition to the IPI may help in selection of the patients with DLCL at higher risk for treatment failure, and identification of those who may require specifically tailored therapeutic approaches.

Adult↗