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PubMed2025

CCDC137 knockdown suppresses bladder cancer progression by downregulating SCD.

BACKGROUND: The Coiled-coil domain-containing (CCDC) family, due to its unique protein structural domain and broad involvement in diverse biological processes, has emerged as a focus in oncology research. Nevertheless, its clinical significance and function in bladder cancer (BLCA) remain poorly defined. METHODS: Machine learning algorithms were employed to identify pivotal CCDC genes in the cancer genome atlas (TCGA), and a prognostic model was subsequently constructed. Multi-omics data encompassing pan-cancer cohorts, single-cell sequencing, and spatial transcriptomics were integrated to characterize the expression patterns and prognostic significance of Coiled-coil domain-containing 137 (CCDC137), a previously uncharacterized CCDC family member in BLCA. Tissue microarray confirmed CCDC137 abnormal expression in bladder carcinoma specimens. The effect of CCDC137 knockdown on BLCA progression was evaluated through CCK8 assay, clonogenic formation, wound healing, Transwell, and subcutaneous xenograft models. RNA sequencing, quantitative RT-PCR, and western blot were utilized to delineate its regulatory network. RESULTS: A prognostic model incorporating 10 CCDC genes was successfully established in the TCGA-BLCA cohort. Then, we found that CCDC137 exhibited pan-cancer overexpression and usually correlation with poor clinical outcomes. Immunohistochemistry further substantiated its dysregulation in bladder carcinoma. Integrated multi-omics analyses suggested associations between CCDC137 expression and a tumor immunosuppressive microenvironment. CCDC137 knockdown significantly suppressed bladder cancer cell proliferation and migratory capacity in vitro. Correspondingly, subcutaneous xenograft tumor growth was inhibited in vivo. Moreover, decreased expression of stearoyl-CoA desaturase (SCD), a key lipid metabolic enzyme, accompanied CCDC137 depletion. These findings collectively suggest a cancer-promoting role for CCDC137 in bladder carcinoma. CONCLUSIONS: This systematic investigation combining multi-omics bioinformatics analyses and experimental validation demonstrates the role of CCDC137 in bladder carcinoma progression, providing novel mechanistic insights into the pathogenesis of BLCA and offering a theoretical foundation for therapeutic targeting of CCDC137 in urothelial malignancies.

Urinary Bladder Neoplasms↗

PubMed2000

External validation of the templeton model for predicting success after IVF.

This study aimed to externally validate the prognostic model presented by Templeton in 1996 for live births resulting from IVF treatment. Data were used from the University Hospital, Nijmegen, The Netherlands, from March 1991 to January 1999. The predictive capacity of the model in our population discriminated between those women with a low probability of success and those with a relatively high probability. Despite these encouraging findings, our data show that implementation of the model in clinical decision-making remains difficult. The Templeton model is not applicable or usable in daily clinical practice, because the model did not give more information about the prognosis for the vast majority of the patients. Therefore, the search for better prognostic factors resulting in better predictive models should continue.

Adult↗

PubMed2003

Comparison of MELD, Child-Pugh, and Emory model for the prediction of survival in patients undergoing transjugular intrahepatic portosystemic shunting.

OBJECTIVES: Recently, new prognostic models (Model for End-Stage Liver Disease [MELD model] and Emory score) were proposed for the prediction of survival in transjugular intrahepatic portosystemic shunt (TIPS) patients. Although the MELD model is considered to be superior and has consecutively been applied to priority listing for liver transplantation, these models have never been directly compared in terms of long-term prognosis. We therefore compared the prognostic accuracy of the different models, including the Child-Pugh score, in an unselected cohort of TIPS patients followed long-term. METHODS: Baseline risk scores for 162 unselected consecutive TIPS patients followed until death (n = 81), liver transplantation, or end of observation (n = 81) (mean follow-up 30.7 +/- 26.4 months) were calculated, and respective concordance- (c-)statistics for the predictive accuracy of 3-, 12-, and 36-month survival for the three models were compared statistically. RESULTS: All three models predicted short-term (3-month) survival with similar accuracy. The MELD model generated the best c-statistics for both 12-month (c-statistic 0.73, 95% CI = 0.64-0.82) and 36-month survival (c-statistic 0.74, 95% CI = 0.64-0.84). The predictive accuracy of the Emory score was significantly lower (c-statistic for 12-month survival: 0.60, 95% CI = 0.52-0.68, p = 0.012 vs MELD). In the statistical comparison of the MELD and the Child-Pugh model, only a trend favoring MELD for the prediction of 1-yr survival in patients with intestinal bleeding could be observed (MELD: c-statistic 0.78, 95% CI = 0.67-0.89; Child-Pugh: c-statistic 0.67, 95% CI = 0.55-0.80, p = 0.059). CONCLUSIONS: The MELD model is superior to the Emory score but only slightly superior to the Child-Pugh classification for the prediction of long-term survival in TIPS patients.

Evaluation Studies as Topic↗

PubMed2006

Relationship between VOC and NOx emissions and chemical production of tropospheric ozone in the Aburrá Valley (Colombia).

Relationship between volatile organic compounds (VOC) and nitrogen oxides (NOx) emissions and the chemical production of tropospheric ozone is studied through mathematical simulation. The study is applied to the Aburrá Valley, in the Colombian Andes, which is a practically unknown area from the point of view of ozone formation. The model used for this application is the European modelling of atmospheric constituents (EUMAC) zooming model (EZM) which consists of a mesoscale prognostic model (MEMO, mesoscale meteorological model) and a chemical reaction model (MUSE, multiscale for the atmospheric dispersion of reactive species), coupled to the chemical mechanism EMEP (European monitoring and evaluation program). The analysis is performed for a real episode that was characterized by high ozone production and that happened during the 23rd and 24th December, 1999 in Medellín (Colombia). From this real scenario, a sensitivity analysis has been carried out in order to assess the influence of VOC and NOx amounts on ozone production and to extract some conclusions for future ozone abatement policies in Andean regions. As far as ozone air quality is concerned, it is shown that in order to keep current levels the emphasis must be put to avoid increasing NOx emissions, or alternatively, to augment VOC emissions in order to have a high VOC/NOx ratio.

Air Pollutants↗

PubMed2026

Exploration and experimental verification of triaptosis-related prognostic genes and cells in gastric cancer.

BACKGROUND: Triaptosis is a recently characterized form of programmed cell death with unclear implications in cancer. This study aimed to investigate the prognostic significance and biological relevance of triaptosis in gastric cancer (GC). METHODS: Transcriptomic and clinical data from TCGA-STAD and GSE62254, and single-cell RNA sequencing data from GSE183904 were analyzed. Triaptosis-related gene (TRG) scores were calculated using single-sample gene set enrichment analysis. Differentially expressed genes identified in TRG-score and GC-versus-normal comparisons underwent functional enrichment, Cox regression, and least absolute shrinkage and selection operator regression to develop an externally validated signature. Immune profiles, pathway activity, somatic mutations, tumor mutational burden (TMB), predicted drug sensitivity, and clinical features were compared by risk group. Single-cell analyses assessed TRG activity, prognostic gene expression, cell-cell communication, and pseudotime. Reverse transcription-quantitative PCR and Western blotting assessed mRNA expression and protein levels, respectively. RESULTS: A TRG-based prognostic model comprising ASPN, GRB14, and VTN was developed and externally validated, effectively distinguishing patients into two distinct risk groups with notably different survival outcomes. mRNA expression of all three genes and their protein levels were significantly higher in SGC-7901 cells than in GES-1 cells. High-risk patients had higher stromal scores and distinct immune profiles; 15 immune cell types differed between groups. Single-cell analysis revealed fibroblasts and pericytes among high-TRG-active cell types. Prognostic genes were significantly overexpressed in fibroblasts, which also showed high TRG activity. Fibroblasts demonstrated enhanced communication with pericytes, whereas tumor-derived fibroblasts showed weaker communication with macrophages, indicating immune microenvironment remodeling. CONCLUSION: The three-gene prognostic signature predicted GC prognosis and was associated with distinct immune and genomic features, suggesting potential value for risk stratification and personalized treatment.

Humans↗

PubMed1997

The future of biostatistics: expecting the unexpected.

The paper explores the future of (clinical) biostatistics. In the first part of the paper important trends in biostatistics are reviewed: new applications and more complex data; causal models for observational data; cross-validation-based model building; graphical chain and random effect models; faster computing and new algorithms as Markov chain Monte Carlo; generalized estimating equations and pseudo-likelihood; pooled data sets for meta-analysis and prognostic modelling. In the second part some dreams and nightmares of the author are sketched. Dreams are: implementation of prognostic and diagnostic models in the clinic; an instantaneous numeric integrator; much more and better organized follow-up data; disease mapping in space and time. 'Nightmares', that is, issues which it is hoped will go out of use in the future, are: P-value; rank tests; exact methods; meta-analysis; matched case-control studies.

Biometry↗

PubMed2025

Biomarker-Based Nomogram to Predict Neoadjuvant Chemotherapy Response in Muscle-Invasive Bladder Cancer.

Background/Objectives: The aim of this study was to identify response prediction and prognostic biomarkers in muscle-invasive bladder cancer (MIBC) patients undergoing neoadjuvant chemotherapy (NAC). Methods: A retrospective multicentre study including 191 patients with MIBC who received NAC previous to radical cystectomy (RC) between 1996 and 2013. Gene expression patterns were analysed in 34 samples from transurethral resection of the bladder (TURB) using Illumina microarrays. The expression levels of 45 selected differentially expressed genes between responders and non-responders to NAC were validated by quantitative PCR in an independent cohort of 157 patients. Regression analysis was used to identify predictors of downstaging and relapse. A nomogram for predicting downstaging and relapse-including clinicopathological and gene expression variables-was developed. Results: The expression levels of 1352 transcripts differed between responders and non-responders to NAC. A nomogram based on the most predictive clinical variables (age, Tis (in situ), gender, history of NMIBC, and lymphadenopathy) and genes selected following the Akaike information criterion (AIC) (CBTB16, CHMP6, DDX54, CASP8, LOR, and PLEC) was then created. In addition, a three-gene expression prognostic model to predict tumour relapse was generated. This model was able to discriminate between two groups of patients with a significantly different probability of tumour relapse (HR: 2.11; CI: 1.16-3.83, p = 0.01). Conclusions: Our nomogram based on gene expression and clinical data is a useful tool to predict downstaging and tumour relapse after NAC in MIBC patients. Further validation is warranted.

bladder cancer↗

PubMed1992

Prognostic factors of thyroid cancers in Ramathibodi Hospital.

The need to create different prognostic models for different populations is obvious after the findings that significant prognostic factors can change from one population to another. The purpose of this study is to fit a linear logistic model to a data set of Thai patients with thyroid cancers to find important prognostic factors and their effects on short-term mortality. Among the 89 cases selected, 20 died within 4 years after initial diagnosis and 69 survived more than 4 years. Age at diagnosis, sex, histological type and differentiation, microscopic vascular invasion, and extent of tumor spreading were included as independent variables. These variables were selected into the model by step-down selection procedure. Age at diagnosis was found to be the most significant prognostic factor followed by differentiation, vascular invasion and extent of spreading, respectively. However, vascular invasion appeared to exert the strongest impact on the prognosis with the risk of death increasing 62 fold for those showing microscopic evidence of this parameter. The odds ratios for the extent of spreading, age at diagnosis and differentiation were 7.5, 1.1 and 0.02, respectively.

Adult↗

PubMed2006

Prognosis following severe head injury: Development and validation of a model for prediction of death, disability, and functional recovery.

BACKGROUND: A prognostic model for head trauma patients is useful only if it predicts clinically relevant outcomes accurately on new subjects in various settings. Most existing models consider only dichotomous outcome and have not been tested externally. We developed and validated a rule for prediction of three functional outcome states after severe head injury, using information from day 1. METHODS: The model was developed in a cohort of 304 adults who were admitted to a Dutch trauma center and had survived and remained comatose for >24 hours following severe head injury. We used ordinal logistic regression analysis to predict the extended Glasgow Outcome Scale after > or =12 months, merged into three categories. We preselected five known predictors of outcome and used bootstrapping techniques to avoid statistical overfitting. The performance of the model was subsequently tested in a cohort of 122 patients from an unrelated hospital. RESULTS: The model contained age (p < 0.0001), best motor response on day 1 (p = 0.002), pupil response after resuscitation (p = 0.005), computed tomography findings (p = 0.004), and presence of arterial hypotension (p = 0.37) as predictor variables. In the external validation cohort, the model showed adequate agreement between observed and predicted outcome probabilities (calibration). The model had a good ability to discriminate patients with different outcomes (c-statistic 0.808). The predictive accuracy was 66% when the model was used to classify patients across the three outcome categories. CONCLUSIONS: We have developed a practical model for predicting the probability of death, survival with major disability, and functional recovery in patients who are comatose 24 hours after severe head injury. The model performed well in an external setting, indicating that measures to avoid statistical overfitting were successful.

Adult↗

PubMed2026

Lactate Dehydrogenase and Outcomes in Patients With HF and Reduced Ejection Fraction: Insights From GALACTIC-HF.

BACKGROUND: Lactate dehydrogenase (LDH) is a cytoplasmic enzyme found in most cells. Increased LDH levels are a nonspecific measure of cellular injury and may be prognostically important in heart failure (HF). OBJECTIVES: This study aims to assess the relationship between LDH and clinical characteristics and outcomes in heart failure and reduced ejection fraction (HFrEF). METHODS: Using data from GALACTIC-HF, a phase 3, randomized, placebo-controlled trial evaluating the efficacy and safety of omecamtiv mecarbil (OM) in patients with HFrEF, the relationship between LDH and clinical outcomes was analyzed. The incremental value of LDH added to a validated prognostic model (PREDICT-HF) was also calculated using Harrell's C statistic, integrated discrimination index (IDI), and net reclassification index (NRI). RESULTS: In GALACTIC-HF, baseline LDH data were available for 8,179 patients, including 6,138 outpatients. Patients with higher LDH were more frequently female and had worse HF status. They were also more likely to have elevated serum creatinine, liver enzymes, creatine kinase, NT-proBNP, and high-sensitivity troponin I. Compared with patients in the lowest LDH (Q1: 155 U/L [25th-75th percentile: 144-163 U/L]), the HRs for the primary outcome (first HF event or cardiovascular death) were Q2: 183 U/L (25th-75th percentile: 177-188 U/L); HR: 1.15 [95% CI: 1.02-1.31]; Q3: 207 U/L (25th-75th percentile: 201-215 U/L); HR: 1.39 [95% CI: 1.23-1.58]; and Q4: 253 U/L (25th-75th percentile: 236-280 U/L); HR: 1.84 [95% CI: 1.62-2.08], respectively. Even after adjustment, elevated LDH remained independently associated with higher HR. When added to the PREDICT-HF risk model, baseline LDH improved Harrell's C statistic, IDI, and NRI for the primary outcome. CONCLUSIONS: In GALACTIC-HF, higher LDH levels were independently associated with a higher risk of clinical outcomes in HFrEF. (Global Approach to Lowering Adverse Cardiac Outcomes Through Improving Contractility in Heart Failure [GALACTIC-HF]; NCT02929329; EudraCT number: 2016-002299-28).

Humans↗

PubMed1998

Use of the international prognostic index and the tumor score to detect poor-risk patients with primary mediastinal large B-cell lymphoma: a study of 37 previously untreated patients.

We tested two prognostic models devised for intermediate-grade lymphomas, the age-adjusted international prognostic index and the tumor score, in 37 consecutive untreated patients treated for a diagnosis of primary mediastinal large B-cell lymphoma (PMLCL). Neither model selected for a group of patients with statistically significant differences in rates of complete response, failure-free survival (FFS) and overall survival (OS). Because the level of beta microglobulin (beta2m) is consistently low in the serum of patients with PMLCL despite bulky disease, we tested the median value of this continuous variable in the 37 patients and found it to be statistically significant for predicting FFS. A hypothetical tumor score model using the adjusted value for beta2m improved the prognostic accuracy for achievement of complete response (93% vs. 60%; P = 0.02), FFS (73% vs. 35%; P = 0.02), and OS (80% vs. 55%; P = 0.05). This hypothetical model merits further testing in a larger population of patients with PMLCL.

Adult↗

PubMed1999

Shoulder disorders: a state-of-the-art review.

This paper provides an up-to-date overview of the occurrence, diagnosis, risk factors, prognostic indicators and outcome of shoulder disorder (SD), and of the validity and reproducibility of diagnostic classifications and diagnostic imaging techniques for SD. Furthermore, the available evidence on the effectiveness of non-steroidal anti-inflammatory drugs (NSAIDs), corticosteroid injections and physiotherapy for SD is summarized on the basis of randomized controlled trials with an acceptable quality of their methods. The annual incidence of SD is estimated at about 7%, its 1-year period prevalence at about 51% and its lifetime prevalence at about 10%. While approximately 50% of all patients with SD seek medical care, about 95% are treated in primary health care. Of all new episodes of SD presenting to primary care, approximately 50% seem to resolve within 6 months, while about 40% seem to persist for up to 12 months. Several prognostic indicators for a favourable or a poor outcome of SD have been identified, but a comprehensive prognostic model is not available. While evidence for the prognostic validity of popular diagnostic classifications of SD is lacking, their reproducibility has been shown to be poor. The accuracy and clinical usefulness of diagnostic imaging techniques appear to be sufficiently verified for SD in secondary care, while their clinical usefulness in primary care and prognostic validity are not. NSAIDs and steroid injections for SD have been shown to be effective within 6 weeks, but their effect on long-term outcome remains unclear. There is very limited evidence for the effectiveness in SD of physiotherapy, including exercise therapy, ultrasound, electrotherapy, laser, mobilization and manipulation.

Electric Stimulation Therapy↗

PubMed1999

Shoulder disorders: a state-of-the-art review

Journal Article↗

PubMed1996

Prognostic factors in agnogenic myeloid metaplasia: a report on 195 cases with a new scoring system.

We studied the survival of 195 patients with agnogenic myeloid metaplasia (AMM) diagnosed between 1962 and 1992 in an attempt to stratify patients into risk groups. Median survival was 42 months. Adverse prognostic factors for survival were age > 60 years, hepatomegaly, weight loss, low hemoglobin level (Hb), low or very high leukocyte count (WBC), high percentage of circulating blasts, male sex, and low platelet count. A new scoring system based on two adverse prognostic factors, namely Hb < 10 g/dL and WBC < 4 or > 30 x 10(9)/L, was able to separate patients in three groups with low (0 factor), intermediate (1 factor), and high (2 factors) risks, associated with a median survival of 93, 26, and 13 months, respectively. An abnormal karyotype (32 cases of 94 tested patients) was associated with a short survival, especially in the low-risk group (median survival of 50 v 112 months in patients with normal karyotype). The prognostic factors for acute conversion were WBC > 30 x 10(9)/L and abnormal karyotype. Thus, hemoglobin level and leukocyte count provide a simple prognostic model for survival in AMM, and the adverse prognostic value of abnormal karyotype may be related to a higher rate of acute conversion.

Adult↗

PubMed1996

[The prognostic criteria in the primary myelodysplastic syndrome].

Basing on computer processing of 126 primary clinical and laboratory parameters obtained from 92 patients with myelodysplasia and using multifactorial regression analysis, the authors have developed prognostic models of life span and probability of transformation into acute leukemia. The model of life span enabled recognition of 3 prognostic groups of myelodysplasia patients: of high (median 10 months), moderate (median 22 months) and low (median 35 months) risk. This makes it possible to prognosticate the disease and assume optimal therapeutic policy.

Adult↗

PubMed1997

Sequential versus standard neural networks for pattern recognition: an example using the domain of coronary heart disease.

The goal of this study was to compare standard and sequential neural network models for recognition of patterns of disease progression. Medical researchers who perform prognostic modeling usually oversimplify the problem by choosing a single point in time to predict outcomes (e.g. death in 5 years). This approach not only fails to differentiate patterns of disease progression, but also wastes important information that is usually available in time-oriented research data bases. The adequate use of sequential neural networks can improve the performance of prognostic systems if the interdependencies among prognoses at different intervals of time are explicitly modeled. In such models, predictions for a certain interval of time (e.g. death within 1 year) are influenced by predictions made for other intervals, and prognostic survival curves that provide consistent estimates for several points in time can be produced. We developed a system of neural network models that makes use of time-oriented data to predict development of coronary heart disease (CHD), using a set of 2594 patients. The output of the neural network system was a prognostic curve representing survival without CHD, and the inputs were the values of demographic, clinical, and laboratory variables. The system of neural networks was trained by backpropagation and its results were evaluated in test sets of previously unseen cases. We showed that, by explicitly modeling time in the neural network architecture, the performance of the prognostic index, measured by the area under the receiver operating characteristic (ROC) curve, was significantly improved (p < 0.05).

Adult↗

PubMed1999

Is serum bilirubin concentration the only valid prognostic marker in primary biliary cirrhosis?

From the many prognostic models for primary biliary cirrhosis (PBC) patients based on Cox's regression analysis, the Mayo model has gained the most popularity and was successfully validated in some centers. The aim of our study was to validate the Mayo survival model for Polish PBC patients and, in case of its inapplicability, to select prognostic variables and to create time-fixed and time-dependent survival models for the patients. We used database information on patients from 6 medical centers in Poland, fulfilling clinical, serological, and/or pathological criteria of PBC. The Mayo model was validated using data from 116 PBC patients. The time-fixed and time-dependent models were created using data on clinical and biochemical variables used in the Mayo model from 162 and 208 patients, respectively. The Mayo model validation was performed graphically and by one-sample log-rank tests after dividing the study sample into 3 groups of high, medium, and low risk. The survival analysis was performed using Cox's proportional hazards regression method on clinical and biochemical variables used in the Mayo model. Treatment with ursodeoxycholic acid (UDCA) was included in the time-dependent analysis. Validation showed that the Mayo model overestimated death risk in Polish PBC patients. Of the variables used in the Mayo model, serum bilirubin concentration appeared to be the only variable of prognostic importance. The analysis shows that serum bilirubin concentration holds most of the prognostic information for our PBC patients irrespective of prior treatment with UDCA.

Bilirubin↗

PubMed2026

Multimodal artificial intelligence and machine learning in oncology: from data integration to precision cancer care.

Cancer remains a major global health burden, with approximately 20 million new cases and 9.7 million cancer-related deaths reported globally in 2022. While advances in radiological imaging, molecular profiling, and clinical data have enhanced the interpretation of disease progression, the availability of multiple such modalities still does not meet the needs of a large patient population. This narrative review focuses on the role of multimodal artificial intelligence and machine learning in bridging the gap in interpreting heterogeneous modalities to improve risk prediction, prognostic assessment, and treatment decision-making in precision oncology. Multimodal frameworks such as Pathomic Fusion illustrate how complementary histopathological and genomic information can be integrated for cancer diagnosis and prognostic modeling. Multimodal models have demonstrated potential in virtual biopsy, cancer screening, prognostic prediction, radiotherapy planning, intraoperative guidance, and clinical-trial design using digital twins and synthetic control arms. The major limitations of incorporating multimodal artificial intelligence and machine learning in oncology include data heterogeneity, demographic or institutional biases, and reproducibility challenges that hinder translation. Accordingly, appropriate data-governance strategies, fairness audits, and privacy-preserving approaches such as federated learning should be considered where appropriate. Future progress will depend on the development of standardized benchmarking datasets, robust external validation, seamless integration with electronic health records and picture archiving and communication systems, and the implementation of explainable, secure, and clinically validated multimodal artificial intelligence frameworks that support precision oncology in routine clinical practice.

deep learning↗