Prognostic Prediction Model Research Articles

Advancements in prognostic markers and predictive models for intracerebral hemorrhage: from serum biomarkers to artificial intelligence models.

Intracerebral hemorrhage (ICH) is a severe form of stroke with high morbidity and mortality, accounting for 10-15% of all strokes globally. Recent advancements in prognostic biomarkers and predictive models have shown promise in enhancing the prediction and management of ICH outcomes. Serum sestrin2, a stress-responsive protein, has been identified as a significant prognostic marker, correlating with severity indicators such as NIHSS scores and hematoma volume. Its levels predict early neurological deterioration and poor prognosis, offering predictive capabilities comparable to traditional measures. Furthermore, a deep learning-based AI model demonstrated superior performance in predicting early hematoma enlargement, with higher sensitivity and specificity than conventional methods. Additionally, long-term outcome prediction models using CT radiomics and machine learning have achieved high accuracy, particularly with the Random Forest algorithm. These advancements underscore the potential of integrating novel biomarkers and advanced computational techniques to improve prognostication and management of ICH, aiming to enhance patient care and survival rates. The incorporation of serum sestrin2, AI, and machine learning in predictive models represents a significant step forward in the clinical management of ICH, offering new avenues for research and clinical application.

Long Non-Coding H19 in Lymphocytes: Prognostic Value in Acute Ischemic Stroke Patients.

Acute ischemic stroke (AIS) is a cerebrovascular disease that seriously affects the physical and mental health and quality of life of patients. However, there is a lack of reliable prognostic prediction methods. The main objective of this study was to investigate the prognostic value of long non-coding RNA (lncRNA) H19 in lymphocytes of patients with AIS, and to construct a prognostic prediction model for AIS including lncRNA H19 in lymphocytes, which would provide new ideas for the prognostic evaluation of AIS. Poor prognosis was defined when the patient's modified Rankin scale (mRS) score at 3 months after AIS onset was greater than 2. Quantitative real-time polymerase chain reaction (qRT-PCR) was used to measure the level of lncRNA H19 in lymphocytes. Spearman correlation analysis revealed a positive correlation between lncRNA H19 and mRS score at 3 months after AIS onset (r = 0.1977, p = 0.0032), while lncRNA H19 was negatively correlated with white blood cells counts, lymphocytes counts, and neutrophils counts. Logistic regression analysis identified lncRNA H19 as an independent predictor of poor prognosis (OR = 3.062 [1.69-5.548], p < 0.001). Moreover, a nomogram prediction model incorporating lncRNA H19 in lymphocytes demonstrated effective discrimination, calibration, and clinical applicability in predicting AIS outcomes. The findings suggest that lncRNA H19 in lymphocytes could be a valuable prognostic indicator and a potential pharmacological target for AIS patients, and might be a novel pathway for enhanced prognostic evaluation and targeted therapeutic strategies.

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

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

Construction of a nomogram with IrAE and clinic character to predict the survival of advanced G/GEJ adenocarcinoma patients undergoing anti-PD-1 treatment.

This study aimed to develop and validate a survival prediction model and nomogram to predict survival in patients with advanced gastric or gastroesophageal junction (G/GEJ) adenocarcinoma undergoing treatment with anti-programmed cell death 1 receptor (PD-1). This model incorporates immune-related adverse events (irAEs) alongside common clinical characteristics as predictive factors. A dataset comprising 255 adult patients diagnosed with advanced G/GEJ adenocarcinoma was assembled. The irAEs affecting overall survival (OS) to a significant degree were identified and integrated as a candidate variable, together with 12 other candidate variables. These included gender, age, Eastern cooperative oncology group performance status (ECOG PS) score, tumor stage, human epidermal growth factor receptor 2 (HER2) expression status, presence of peritoneal and liver metastases, year and line of anti-PD-1 treatment, neutrophil-to-lymphocyte ratio (NLR), controlling nutritional status (CONUT) score, and Charlson comorbidity index (CCI). To mitigate timing bias related to irAEs, landmark analysis was employed. Variable selection was performed using the least absolute shrinkage and selection operator (LASSO) regression to pinpoint significant predictors, and the variance inflation factor was applied to address multicollinearity. Subsequently, a Cox regression analysis utilizing the forward likelihood ratio method was conducted to develop a survival prediction model, excluding variables that failed to satisfy the proportional hazards (PH) assumption. The model was developed using the entire dataset, then internally validated through bootstrap resampling and externally validated with a cohort from another Hospital. Furthermore, a nomogram was created to delineate the predictive model. After consolidating irAEs from the skin and endocrine systems into a single protective irAE category and applying landmark analysis, variable selection was conducted for the prognostic prediction model along with other candidate variables. The finalized model comprised seven variables: ECOG PS score, tumor stage, HER2 expression status in tumor tissue, first-line anti-PD-1 treatment, peritoneal metastasis, CONUT score, and protective irAE. The overall concordance index for the model was 0.66. Calibration analysis verified the model's accuracy in aligning predicted outcomes with actual results. Clinical decision curve analysis indicated that utilizing this model for treatment decisions could enhance the net benefit regarding 1- and 2-year survival rates for patients. This study developed a prognostic prediction model by integrating common clinical characteristics of irAEs and G/GEJ adenocarcinoma. This model exhibits good clinical practicality and possesses accurate predictive ability for overall survival OS in patients with advanced G/GEJ adenocarcinoma.

Neutrophil extracellular traps-related lncRNAs prognostic signature for gastric cancer and immune infiltration: potential biomarkers for predicting overall survival and clinical therapy

Gastric cancer (GC) is one of the most common digestive tract malignant tumors in the world. At the time of initial diagnosis, it frequently presents with local or distant metastasis, contributing to poor prognosis in patients. Neutrophil extracellular traps (NETs) constitute a mechanism employed by neutrophils that is intricately associated with tumor progression, prognosis, and response to immunotherapy and chemotherapy. Despite this, the specific involvement of NETs-related long non-coding RNAs (lncRNAs) in gastric cancer remains unclear. A prognostic model for NETs-related lncRNAs was constructed through correlation analysis, COX regression analysis, and least absolute shrinkage and selection operator regression (LASSO) analysis. The predictive performance of the model was assessed using Kaplan–Meier survival curves, receiver operating characteristic (ROC) curves, facilitating the exploration of the relationship between disease onset and prognosis in gastric cancer. Additionally, differences in the tumor microenvironment and response to immunotherapy among gastric cancer patients across high- and low-risk groups were analyzed. Furthermore, a prognostic nomogram integrating the risk score with relevant clinicopathological parameters was developed. The prognostic prediction model for gastric cancer, derived from NETs-related lncRNAs in this study, demonstrates robust prognostic capabilities, serving as a valuable adjunct to traditional tumor staging. This model holds promise in offering novel guidelines for the precise treatment of gastric cancer, thereby potentially improving patient outcomes.

AURKB promotes immunogenicity and immune infiltration in clear cell renal cell carcinoma

BackgroundChromatin regulators (CRs) are capable of causing epigenetic alterations, which are significant features of cancer. However, the function of CRs in controlling Clear Cell Renal Cell Carcinoma (ccRCC) is not well understood. This research aims to discover a CRs prognostic signature in ccRCC and to elucidate the roles of CRs-related genes in tumor microenvironment (TME).MethodsExpression profiles and relevant clinical annotations were retrieved from the Cancer Genome Atlas (TCGA) and UCSC Xena platform for progression-free survival (PFS) data. The R package "limma" was used to identify differentially expressed CRs. A predictive model based on five CRs was developed using LASSO-Cox analysis. The model's predictive power and applicability were validated using K-M curves, ROC curves, nomograms, comparisons with other models, stratified survival analyses, and validation with the ICGC cohort. GO and GSEA analyses were performed to investigate mechanisms differentiating low and high riskScore groups. Immunogenicity was assessed using Tumor Mutational Burden (TMB), immune cell infiltrations were inferred, and immunotherapy was evaluated using immunophenogram analysis and the expression patterns of human leukocyte antigen (HLA) and checkpoint genes. Differentially expressed CRs (DECRs) between low and high riskScore groups were identified using log2|FC|> 1 and FDR < 0.05. AURKB, one of the high-risk DECRs and a component of our prognostic model, was selected for further analysis.ResultsWe constructed a 5 CRs signature, which demonstrated a strong capacity to predict survival and greater applicability in ccRCC. Elevated immunogenicity and immune infiltration in the high riskScore group were associated with poor prognosis. Immunotherapy was more effective in the high riskScore group, and certain chemotherapy medications, including cisplatin, docetaxel, bleomycin, and axitinib, had lower IC50 values. Our research shows that AURKB is critical for the immunogenicity and immune infiltration of the high riskScore group.ConclusionOur study produced a reliable prognostic prediction model using only 5 CRs. We found that AURKB promotes immunogenicity and immune infiltration. This research provides crucial support for the development of prognostic biomarkers and treatment strategies for ccRCC.

Adipocytes impact on gastric cancer progression: Prognostic insights and molecular features.

Adipocytes, especially adipocytes within tumor tissue known as cancer-associated adipocytes, have been increasingly recognized for their pivotal role in the tumor microenvironment of gastric cancer (GC). Their influence on tumor progression and patient prognosis has sparked significant interest in recent research. The main objectives of this study were to investigate adipocyte infiltration, assess its correlation with clinical pathological features, develop a prognostic prediction model based on independent prognostic factors, evaluate the impact of adipocytes on immune cell infiltration and tumor invasiveness in GC, and identify and validate genes associated with high adipocyte expression, exploring their potential diagnostic and prognostic value. To explore the relationship between increased adipocytes within tumor tissue and prognosis in GC patients as well as the associated mechanisms and potential biomarkers, using public databases and clinical data. Using mRNA microarray datasets from the Gene Expression Omnibus database and clinical samples from Jiangsu Provincial Hospital, survival and regression analyses were conducted to determine the relevant prognostic factors in GC. Feature gene selection was performed using least absolute shrinkage and selection operator and support vector machine recursive feature elimination algorithms, followed by differential gene expression analysis, gene ontology, pathway analysis, and Gene Set Enrichment Analysis. Immune cell infiltration was analyzed using the CIBERSORT algorithm. Tumor adipocyte infiltration correlated with poor prognosis in GC, leading to the development of a highly accurate and discriminative prognostic prediction model. Key genes, ADH1B, SFRP1, PLAC9, and FABP4, were identified as associated with high adipocyte expression in GC. The diagnostic and prognostic potential of these identified genes was validated using independent datasets. Downregulation of immune cells was observed in GC with high adipocyte expression. GC with high intratumoral adipocyte expression demonstrated aggressive tumor biology and a poorer prognosis. The genes ADH1B, SFRP1, PLAC9, and FABP4 have been identified as holding diagnostic and prognostic significance in GC. These findings strongly support the use of adipocyte expression as a valuable indicator of tumor invasiveness and anticipated patient outcomes in GC.

Gene-age interaction study of breast cancer prognosis based on epigenomic data

Objective: Exploring gene-age interactions associated with breast cancer prognosis based on epigenomic data. Methods: Differential expression analysis of DNA methylation was conducted using multiple independent epigenomic datasets of breast cancer from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). The false discovery rate (FDR) method was used for multiple corrections, retaining differentially methylated sites with q-FDR≤0.05. A three-stage analytic strategy was implemented, using a multivariable Cox proportional hazards regression model to examine gene-age interactions. In the discovery phase, signals with q-FDR ≤ 0.05 were screened out using TCGA-BRCA database. In validation phaseⅠ, the interaction was validated using GSE72245 data, with criteria of P≤0.05 and consistent effect direction. In validation phaseⅡ, the signals were further validated using GSE37754 and GSE75067 data. A prognostic prediction model was constructed by incorporating clinical indicators and interaction signals. Results: The three-stage analytic strategy identified a methylation site (cg16126280EBF1), which interacted with age to jointly affect the overall survival time of patients (interaction HR= 1.001 1,95%CI:1.000 7-1.001 5,P<0.001). Stratified analysis by age showed that the effect of hypermethylation of cg16126280EBF1 was completely opposite in younger patients (HR=0.550 5, 95%CI: 0.383 8-0.789 6, P=0.001) and older patients (HR=2.166 5, 95%CI: 1.285 2-3.652 2, P=0.004). Conclusions: The DNA methylation site cg16126280EBF1 exhibits an interaction with age, jointly influencing the prognosis of breast cancer in a complex association pattern. This finding contributes new population-based evidence for the development of age-specific targeted drugs.

Establishment of a nomogram to predict the overall survival of patients with collecting duct renal cell carcinoma

BackgroundCollecting duct carcinoma (CDC) is a rare histological type of renal cell carcinoma that lacks a prognostic prediction model. In this study, we developed a nomogram to predict the prognosis of CDC patients.MethodsData for patients (n = 247) diagnosed with CDC from 2004 to 2015 were obtained from the Surveillance, Epidemiology, and End Results (SEER) database, and the patients were randomized into training (n = 165) and validation (n = 82) cohorts. Survival outcomes were evaluated by the Kaplan–Meier method. Significant variables determined by univariate and multivariate Cox regression analyses were used to construct the nomogram. C-indexes and calibration plots were applied to evaluate the performance of the nomogram.ResultsCDC patients had a median overall survival (OS) of 18.0 months (95% confidence interval: 13.7–22.3); 1-year, 3-year, and 5-year OS rates were 58.7%, 34.2%, and 29.4%, respectively. Independent prognostic factors, including age at diagnosis, tumor size, tumor grade, T stage, N stage, M stage, and surgery information, were identified by multivariate analysis. The nomogram was constructed based on significant factors in the training cohort. The C-indexes were 0.769 (training cohort) and 0.767 (validation cohort). The calibration curves for survival rates showed that the predicted and observed values were consistent.ConclusionsThis study constructed a nomogram to predict prognosis in patients with CDC. The nomogram performed well in predicting the 1-year, 3-year, and 5-year OS, which can help doctors actively monitor and follow up patients.

Adaptability of prognostic prediction models for patients with acute coronary syndrome during the COVID-19 pandemic

BackgroundThe detrimental repercussions of the COVID-19 pandemic on the quality of care and clinical outcomes for patients with acute coronary syndrome (ACS) necessitate a rigorous re-evaluation of prognostic prediction models...

Alternative Splicing in Glioblastoma and its Clinical Implication in Outcome Prediction.

Alternative splicing (AS) offers an important mechanism to form protein polymorphism. A growing body of evidence indicates the correlation between splicing abnormality and carcinoma. Nevertheless, an overall analysis of AS signatures in glioblastoma (GBM) is absent and urgently needed. TCGA SpliceSea data was used to evaluate the AS profiles and further classified into different AS events. The survival analysis was based on these AS events, and AS-related genes were identified and performed with enrichment analysis. At last, the splicing factor-AS regulatory network was established in Cytoscape. Eight hundred forty-two splicing events were confirmed as prognostic molecular events in GBM. Furthermore, the final prognostic signature constructed by seven AS events gave good result with an area under the curve (AUC) of receiver operating characteristic (ROC) curve up to 0.935 for five years, showing high potency in predicting patients' outcome. We built the splicing regulatory network to show the internal relationship of splicing events in GBM. PC4 and SFRS1 interacting protein 1 (PSIP1) and histone H4 acetylation may play a significant part in the prognosis induced by splicing events. In our study, a high-efficiency prognostic prediction model was built for GBM patients based on AS events, which could become potential prognostic biomarkers for GBM. Meanwhile, PSIP1 may be a critical target for pharmaceutical treatment.

Prognostic prediction model for salivary gland carcinoma based on machine learning

Although rare overall, salivary gland carcinomas (SGCs) are among the most common oral and maxillofacial malignancies. The aim of this study was to develop a machine learning-based model to predict the survival of patients with SGC. Patients in whom SGC was confirmed by histological testing and who underwent primary extirpation at the authors’ institution between 1963 and 2014 were identified. Demographic and clinicopathological data with complete follow-up information were collected for analysis. Feature selection methods were used to determine the correlation between prognosis-related factors and survival in the collected patient data. The collected clinicopathological data and multiple machine learning algorithms were used to develop a survival prediction model. Three machine learning algorithms were applied to construct the prediction models. The area under the receiver operating characteristic curve (AUC) and accuracy were used to measure model performance. The best classification performance was achieved with a LightGBM algorithm (AUC = 0.83, accuracy = 0.91). This model enabled prognostic prediction of patient survival. The model may be useful in developing personalized diagnostic and treatment strategies and formulating individualized follow-up plans, as well as assisting in the communication between doctors and patients, facilitating a better understanding of and compliance with treatment.

Predicting the 28-day prognosis of acute-on-chronic liver failure patients based on machine learning

BackgroundWe aimed to establish a prognostic predictive model based on machine learning (ML) methods to predict the 28-day mortality of acute-on-chronic liver failure (ACLF) patients, and to evaluate treatment effectiveness. MethodsACLF patients from six tertiary hospitals were included for analysis. Features for ML models’ development were selected by LASSO regression. Models’ performance was evaluated by area under the curve (AUC) and accuracy. Shapley additive explanation was used to explain the ML model. ResultsOf the 736 included patients, 587 were assigned to a training set and 149 to an external validation set. Features selected included age, hepatic encephalopathy, total bilirubin, PTA, and creatinine. The eXtreme Gradient Boosting (XGB) model outperformed other ML models in the prognostic prediction of ACLF patients, with the highest AUC and accuracy. Delong's test demonstrated that the XGB model outperformed Child-Pugh score, MELD score, CLIF-SOFA, CLIF-C OF, and CLIF-C ACLF. Sequential assessments at baseline, day 3, day 7, and day 14 improved the predictive performance of the XGB-ML model and can help clinicians evaluate the effectiveness of medical treatment. ConclusionsWe established an XGB-ML model to predict the 28-day mortality of ACLF patients as well as to evaluate the treatment effectiveness.

Analysis of methylation-driven genes for predicting the prognosis of patients with oral squamous cell carcinoma.

Oral squamous cell carcinoma (OSCC) is a highly aggressive malignancy that is characterized by early distant metastasis and poor prognosis. DNA methylation plays an important role in the etiology and pathogenesis of OSCC. This study aimed to identify methylation-driven genes through bioinformatics analysis as potential biomarkers for early diagnosis and prognostic assessment of OSCC. Methylation data, RNA sequencing (RNA-seq) data and clinical prognosis information of OSCC patients were retrieved from The Cancer Genome Atlas (TCGA) database. The R packages MethylMix were employed to analyze the correlation between methylation status and corresponding gene expression in tumor and normal tissues to obtain methylation-driven genes. Univariate Cox regression analysis was developed to further screen methylation-driven genes associated with the prognosis of OSCC patients. Subsequently, multivariate Cox regression analysis was utilized to construct a linear prognostic risk prediction model. Furthermore, a combined survival analysis integrating methylation and gene expression was performed to investigate the prognostic value. A total of 374 differentially expressed methylation-driven genes were identified. Seven methylation-driven genes (BST2, KRT15, ZNF134, NT5E, GSTA7P, NAPRT, and GOLPH3L) were found to be significantly associated with patient prognosis. Additionally, four methylation-driven genes (BST2, KRT15, ZNF134 and NAPRT) were used to construct a linear prognostic risk prediction model for OSCC patients. Furthermore, a combined Kaplan-Meier survival analysis revealed that three methylation-driven genes (ZKSCAN7, MFF, ZNF134) alone can be used as independent prognostic markers or drug targets. Our findings facilitate a better understanding of molecular mechanisms of OSCC and provide potential biomarkers of early diagnosis, precision treatment and prognosis evaluation.

Development and validation of a prognostic immunoinflammatory index for patients with gastric cancer

BACKGROUND Studies have demonstrated the influence of immunity and inflammation on the development of tumors. Although single biomarkers of immunity and inflammation have been shown to be clinically predictive, the use of biomarkers integrating both to predict prognosis in patients with gastric cancer remains to be investigated. AIM To investigate the prognostic and clinical significance of inflammatory biomarkers and lymphocytes in patients undergoing surgical treatment for gastric cancer. METHODS Univariate COX regression analysis was performed to identify potential prognostic factors for patients with gastric cancer undergoing surgical treatment. Least absolute shrinkage and selection operator-COX (LASSO-COX) regression analysis was performed to integrate these factors and formulate a new prognostic immunoinflammatory index (PII). The correlation between PII and clinical characteristics was statistically analyzed. Nomograms incorporating the PII score were devised and validated based on the time-dependent area under the curve and decision curve analysis. RESULTS Patients exhibiting elevated neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, and systemic immune inflammatory index displayed inferior progression-free survival (PFS) and overall survival (OS). Conversely, low levels of CD3(+), CD3(+) CD8(+), CD4(+)CD8(+), and CD3(+)CD16(+)CD56(+) T lymphocytes were associated with improved PFS and OS, while high CD19(+) T lymphocyte levels were linked to worse PFS and OS. The PII score demonstrated associations with tumor characteristics (primary tumor site and tumor size), establishing itself as an independent prognostic factor for both PFS and OS. Time-dependent area under the curve and decision curve analysis affirmed the effectiveness of the PII-based nomogram as a robust prognostic predictive model. CONCLUSION PII may be a reliable predictor of prognosis in patients with gastric cancer undergoing surgical treatment, and it offers insights into cancer-related immune-inflammatory responses, with potential significance in clinical practice.

Preoperative Prediction of Long-Term Survival After Surgery in Patients with Resectable Pancreatic Ductal Adenocarcinoma

BackgroundAlthough some clinical trials have demonstrated the benefits of neoadjuvant therapy for resectable pancreatic ductal adenocarcinoma (PDAC), its optimal candidate has not been clarified. This study aimed to detect predictive prognostic factors for resectable PDAC patients who underwent upfront surgery and identify patient cohorts with long-term survival without neoadjuvant therapy.Patients and MethodsA total of 232 patients with resectable PDAC who underwent upfront surgery between January 2008 and December 2019 were evaluated.ResultsThe median overall survival (OS) time and 5-year OS rate of resectable PDAC with upfront surgery was 31.5 months and 33.3%, respectively. Multivariate analyses identified tumor diameter in computed tomography (CT) ≤ 19 mm [hazard ratio (HR) 0.40, p < 0.001], span-1 within the normal range (HR 0.54, p = 0.023), prognostic nutritional index (PNI) ≥ 44.31 (HR 0.51, p < 0.001), and lymphocyte-to-monocyte ratio (LMR) ≥ 3.79 (HR 0.51, p < 0.001) as prognostic factors that influence favorable prognoses after upfront surgery. According to the prognostic prediction model based on these four factors, patients with four favorable prognostic factors had a better prognosis with a 5-year OS rate of 82.4% compared to others (p < 0.001). These patients had a high R0 resection rate and a low frequency of tumor recurrence after upfront surgery.ConclusionsWe identified patients with long-term survival after upfront surgery by prognostic prediction model consisting of tumor diameter in CT, span-1, PNI, and LMR. Evaluation of anatomical, biological, nutritional, and inflammatory factors may be valuable to introduce an optimal treatment strategy for resectable PDAC.

Candidate prognostic biomarkers and prediction models for high-grade serous ovarian cancer from urinary proteomics

High-grade serous ovarian cancer (HGSOC) is one of the most common histologic types of ovarian cancer. The purpose of this study was to identify potential prognostic biomarkers in urine specimens from patients with HGSOC. First, 56 urine samples with information on relapse-free survival (RFS) months were collected and classified into good prognosis (RFS ≥ 12 months) and poor prognosis (RFS < 12 months) groups. Next, data-independent acquisition (DIA)-based mass spectrometry (MS) analysis was combined with MSFragger-DIA workflow to identify potential prognostic biomarkers in a discovery set (n = 31). With the aid of parallel reaction monitoring (PRM) analysis, four candidate biomarkers (ANXA1, G6PI, SPB3, and SPRR3) were finally validated in both the discovery set and an independent validation set (n = 25). Subsequent RFS and Cox regression analyses confirmed the utility of these candidate biomarkers as independent prognostic factors affecting RFS in patients with HGSOC. Regression models were constructed to predict the 12-month RFS rate, with area under the receiver operating characteristic curve (AUC) values ranging from 0.847 to 0.905. Overall, candidate prognostic biomarkers were identified in urine specimens from patients with HGSOC and prediction models for the 12-month RFS rate constructed. SignificanceOC is one of the leading causes of death due to gynecological malignancies. HGSOC constitutes one of the most common histologic types of OC with aggressive characteristics, accounting for the majority of advanced cases. In cases where patients with advanced HGSOC potentially face high risk of unfavorable prognosis or disease advancement within a 12-month period, intensive medical monitoring is necessary. In the era of precision cancer medicine, accurate prediction of prognosis or 12-month RFS rate is critical for distinguishing patient groups requiring heightened surveillance. Patients could significantly benefit from timely modifications to treatment regimens based on the outcomes of clinical monitoring. Urine is an ideal resource for disease surveillance purposes due to its easy accessibility. Furthermore, molecules excreted in urine are less complex and more stable than those in other liquid samples. In the current study, we identified candidate prognostic biomarkers in urine specimens from patients with HGSOC and constructed prediction models for the 12-month RFS rate.

Exploration of the prognostic value of methylation regulators related to m5C in papillary thyroid carcinoma.

The incidence of papillary thyroid carcinoma (PTC) has increased significantly in recent years, and for patients with metastatic and recurrent PTC, the options for treatment currently available are insufficient. To date, the exact molecular mechanism underlying PTC is still not fully understood. 5-Methylcytosine (m5C) RNA methylation is associated with the prognosis of a variety of tumors. However, the molecular mechanisms and biomarkers associated with m5C in the diagnosis, treatment, and prognosis of this disease have not been fully elucidated. Ten m5C regulators with significantly different expression levels were included in this study. Immune infiltration analysis revealed significant negative correlations between most of these regulators and regulatory T cells. TRDMT1, NSUN5, and NSUN6 had high weights and strong correlations in the protein-protein interaction network. Using gene ontology, Kyoto Encyclopedia of Genes and Genomes, and gene set enrichment analysis, 1489 differentially expressed genes were screened from The Cancer Genome Atlas messenger RNA matrix, indicating that these differentially expressed genes were significantly enriched in various pathways and functions related to cancers. Four m5C regulators, NSUN2, NSUN4, NSUN6, and DNMT3B, were screened as prognostic markers by least absolute shrinkage and selection operator regression analysis, and NSUN2 and NSUN6 were identified as risk factors for poor prognosis. We found that the prognostic prediction model constructed using the m5C regulators NSUN2, NSUN4, NSUN6, and DNMT3B showed good prognostic prediction ability and diagnostic ability. This model was applied to predict the survival probability of patients with PTC, the prediction ability of 5-year survival was the best. The multi-factor prognostic prediction model combined with the tumor node metastasis stage and risk score grouping showed better prognostic predictive power.

Prognostic prediction model for esophageal cancer based on probability membrane systems

Prognostic prediction model for esophageal cancer based on probability membrane systems

The suboptimal clinical applicability of prognostic prediction models for severe postpartum hemorrhage: a meta-epidemiological study

ObjectivesTo systematically investigate clinical applicability of the current prognostic prediction models for severe postpartum hemorrhage (SPPH). Study Design and SettingA meta-epidemiological study of prognostic prediction models was conducted for SPPH. A pre-designed structured questionnaire was adopted to extract the study characteristics, predictors and the outcome, modeling methods, predictive performance, the classification ability for high-risk individuals, and clinical use scenarios. The risk of bias among studies was assessed by the Prediction model Risk Of Bias ASsessment Tool (PROBAST). ResultsTwenty-two studies containing 27 prediction models were included. The number of predictors in the final models varied from 3 to 53. However, one-third of the models (11) did not clearly specify the timing of predictor measurement. Calibration was found to be lacking in 10 (37.0%) models. Among the 20 models with an incidence rate of predicted outcomes below 15.0%, none of the models estimated the area under the precision-recall curve, and all reported positive predictive values were below 40.0%. Only two (7.4%) models specified the target clinical setting, while seven (25.9%) models clarified the intended timing of model use. Lastly, all 22 studies were deemed to be at high risk of bias. ConclusionCurrent SPPH prediction models have limited clinical applicability due to methodological flaws, including unclear predictor measurement, inadequate calibration assessment, and insufficient evaluation of classification ability. Additionally, there is a lack of clarity regarding the timing for model use, target users, and clinical settings. These limitations raise concerns about the reliability and usefulness of these models in real-world clinical practice.