Predictor Of Prognosis In Patients Research Articles

Knockdown of integrin β1 inhibits proliferation and promotes apoptosis in bladder cancer cells.

Bladder cancer (BC) is the most common urinary tract malignancy. Identifying biomarkers that predict prognosis and immune function in patients with BC can enhance our understanding of its pathogenesis and provide valuable guidance for diagnosis and treatment. Our findings indicate that increased ITGB1 expression is associated with higher clinical grade and stage, establishing ITGB1 as an independent prognostic risk factor for BC. Enrichment analysis revealed that the function of ITGB1 in BC was linked to the extracellular matrix. The experimental results showed that ITGB1 knockdown in the BC cell lines 5637 and RT112 reduced their proliferation, migration, and invasion. Furthermore, ITGB1 suppression promotes apoptosis in BC cells by inhibiting the PI3K-AKT pathway. A prognostic risk model incorporating CES1, NTNG1, SETBP1, and AIFM3 was developed based on ITGB1, this model can accurately predict patient prognosis based on immunological status. In conclusion, this study shows that knockdown of ITGB1 can restrain the migratory and invasive capabilities of BC cells and accelerate apoptosis, and this role might be associated with PI3K-AKT, highlighting its potential as a diagnostic marker and therapeutic target for BC.

A multi-view prognostic model for diffuse large B-cell lymphoma based on kernel canonical correlation analysis and support vector machine

Background and objectivePositron emission tomography/computed tomography (PET/CT) is recommended as the standard imaging modality for diffuse large B-cell lymphoma (DLBCL) staging. However, many studies have neglected the role of patients' prognostic factors with respect to imaging PET/CT of quantitative features. In this paper, a multi-view learning (MVL) model is established to make full use of both clinical and imaging data to predict the prognosis of DLBCL patients and thereby assist doctors in decision-making.MethodsFeature engineering, including feature extraction, feature screening by recursive feature elimination, and dimensionality reduction by principal component analysis, are successively performed on the clinical data and imaging data of the research subjects to obtain the study data. After dividing the data into training and test sets, an instance weighting method is applied to the training data. Subsequently, kernel mapping is performed on the imaging features and clinical features separately, and this kernel mapping is processed in the new kernel feature space using kernel canonical correlation analysis (KCCA). Lastly, model training is performed on the obtained common kernel subspace using a support vector machine (SVM). The final overall model, named SVM-2view-KCCA (SVM-2 K), was compared with three other multi-view models (Ensemble-SVM, Multi-view maximum entropy discrimination, and canonical correlation analysis). The performance of the model was evaluated on the test data with respect to several dichotomous metrics: accuracy, sensitivity, F1 score, the area under the curve (AUC), and G-mean.ResultsThe SVM model improved AUC by 10.5%, sensitivity by 11.9%, accuracy by 9.8%, F1 score by 9.2%, and G-mean by 7.8% for the DLBCL test data after feature engineering based on dimensionality reduction and instance weighting. In the performance comparison of single-view learning models, the SVM-based integration of clinical and imaging features achieved the best overall performance (AUC = 86.3%, accuracy = 91.6%, sensitivity = 83.2%, F1 = 85.7%, and G-mean = 86.1%). In the comparison of MVL models, SVM-2 K achieved the best overall performance (AUC = 92.1%, accuracy = 96.9%, sensitivity = 90.9%, F1 = 92.8%, and G-mean = 91.4%), and the performance of each MVL model was better than that of the best single-view learning model.ConclusionsMVL models outperformed single-view learning models. Of the MVL models, the proposed SVM-2 K achieved the best overall performance and could accurately predict patient prognosis.

Clinical Frailty Scale Predicts Outcomes After Elective Thoracic Endovascular Aortic Repair: A Single-Center Retrospective Cohort Study.

The Clinical Frailty Scale (CFS), used to define frail patients, is significantly associated with clinical outcomes. The CFS can predict postoperative prognosis after cardiovascular procedures. However, no reports exist on the relationship between frailty defined by the CFS and thoracic endovascular aortic repair (TEVAR) outcomes. We analyzed data obtained from patients who underwent TEVAR between January 2011 and December 2021. Frailty was assessed using the 9-point CFS and was defined as a score of ≥5 points. The primary outcome was the 5-year overall survival rate. The secondary outcomes included the rate of freedom from aneurysm-related death at 5 years, postoperative complications, length of stay, rate of nonhome discharge, need for mobility assistance, 30-day mortality rate, and re-intervention rate. Of 331 patients who underwent TEVAR, 186 were included. The mean age of frail (n=36) and nonfrail (n=150) patients was 73.2 ± 7.3 years and 70.9 ± 9.0 years, respectively (p=0.15). The length of stay (17 [9-39] vs 11 [10-16] days; p=0.09) and re-intervention rate (8.3% vs 13.3%; p=0.58) were not significantly different, although frail patients had a higher rate of nonhome discharge (33.3% vs 4.7%; p<0.001), need for mobility assistance (38.9% vs 6.0%; p<0.001), and 30-day mortality (11.1% vs 0.7%; p=0.005) than nonfrail patients. The 5-year overall survival rate after TEVAR was 6.2 ± 5.5 and 84.5 ± 3.4% in frail and nonfrail patients (p<0.001). The median survival time was 22 (6-40) and 136 (87-138&x41; months, the number of recorded deaths in 5 years was 28 (77.8%) and 18 (12.0%; p<0.001), and the rate of freedom from aneurysm-related death at 5 years was 80.7%±11.2% and 96.9%±1.5% (p=0.01) in frail and nonfrail patients, respectively. The mean follow-up time was 53.3 ± 2.7 months. Multivariate Cox regression indicated that the CFS (hazard ratio, 10.14; 95% confidence interval, 5.06-20.32) was significantly associated with overall survival. The CFS is a valuable prognosis predictor, and TEVAR for frail patients with a high surgical risk could not improve the overall survival. Thoracic endovascular aortic repair in frail patients should be approached cautiously. The Clinical Frailty Scale (CFS) could be a useful predictor of prognosis in patients undergoing thoracic endovascular aortic repair (TEVAR). A significant difference was observed between frail and nonfrail patients in the 5-year overall survival rate following TEVAR. Thoracic endovascular aortic repair for frail patients (CFS was ≥5 points) could not improve overall survival because their death was attributed to their comorbidities. Thus, TEVAR in frail patients should be approached cautiously.

Inflammation Biomarker-Driven Vertical Visualization Model for Predicting Long-Term Prognosis in Unstable Angina Pectoris Patients with Angiographically Intermediate Coronary Lesions.

Angina, a prevalent manifestation of coronary artery disease, is primarily associated with inflammation, an established contributor to the pathogenesis of atherosclerosis and acute coronary syndromes (ACS). Various inflammatory markers are employed in clinical practice to predict patient prognosis and optimize clinical decision-making in the management of ACS. This study investigated the prognostic significance of integrating commonly used, easily repeatable inflammatory biomarkers within a multimodal preoperative prediction model in patients presenting with unstable Angina Pectoris (UAP) and intermediate coronary lesions. This retrospective analysis included patients diagnosed with UAP and intermediate coronary lesions (50%-70% stenosis) who underwent coronary angiography at our hospital between January 2019 and June 2021. The assessed outcome was the occurrence of major adverse cardiac and cerebrovascular events (MACCEs). The Boruta algorithm was applied to identify potential risk factors and develop a prognostic multimodal model. A total of 773 patients were enrolled and divided into a training cohort (n=463) and validation cohort (n=310). A nomogram was constructed to predict the probability of MACCE-free survival based on five clinical features: diabetes mellitus, current smoking, history of myocardial infarction, neutrophil-to-lymphocyte ratio, and fasting blood glucose. In the training cohort, the area under the curve values for the nomogram at 24, 32, and 40 months were 0.669, 0.707, and 0.718, respectively, while those in the validation cohort were 0.613, 0.612 and 0.630, respectively. The model demonstrated good calibration in both cohorts with predicted outcomes aligning well with actual results at all time points up to 40 months. Furthermore, decision curve analysis showed significant clinical utility of the model across the specified time intervals. The developed preoperative prognostic model visually illustrates the association among inflammation, blood glucose level, established risk factors, and long-term MACCEs in UAP patients with intermediate coronary lesions.

Fibroblast growth factor receptor risk signature predicts patient prognosis and immunotherapy resistance in colorectal cancer

Fibroblast growth factor receptor risk signature predicts patient prognosis and immunotherapy resistance in colorectal cancer

Prognostic relevance of Exercise Pulmonary Hypertension: Results of the multi-center PEX-NET Clinical Research Collaboration.

Exercise pulmonary hypertension (exercise PH) was defined by a mean pulmonary arterial pressure (mPAP)/cardiac output (CO) slope >3 mmHg·L-1·min-1 between rest and exercise in the 2022 ESC/ERS PH guidelines. However, large, multi-center studies on the prognostic relevance of exercise hemodynamics and its added value to resting hemodynamics are missing. The PEX-NET (Pulmonary Hemodynamics during Exercise Network) registry enrolled patients who underwent clinically indicated right heart catheterizations both at rest and ergometer exercise from 23 PH-centers worldwide. In this retrospective analysis we included subjects with resting mPAP<25 mmHg and complete hemodynamic data at rest and exercise in the same body position. Mixed effects Cox proportional hazard models with random effect center were applied to identify independent markers of prognosis among the hemodynamic parameters. We included n=764 patients (64% females, age: 59yrs (IQR:46-69), mPAP: 17 mmHg (IQR:14-20)). Median observation time was 6.8yrs (range:0.1-15.9) and 87 patients (11%) died during follow-up. After adjustment for age, sex, hemoglobin level, and resting hemodynamics, CO (p=0.001; HR: 0.85 (95%CI:0.77-0.93)) and trans-pulmonary gradient (p=0.044; HR: 1.04 (95%CI:1.00-1.08)) at peak exercise and the mPAP/CO slope (p<0.001; HR: 1.12 (95%CI:1.06-1.18)) were the only independent predictors of prognosis. Patients with mPAP/CO slope >3 mmHg·L-1·min-1 had significantly worse survival compared to those with ≤3 mmHg·L-1·min-1 (p=0.013; HR: 2.04 (95%CI:1.16-3.58)). The mPAP/CO slope is a robust and independent predictor of prognosis in patients with normal or mildly elevated resting pulmonary arterial pressure that provides prognostic information beyond resting hemodynamics and appears suitable to define exercise PH.

Constructing methylation-driven ceRNA networks unveil tumor heterogeneity and predict patient prognosis.

Cancer development involves a complex interplay between genetic and epigenetic factors, with emerging evidence highlighting the pivotal role of competitive endogenous RNA (ceRNA) networks in regulating gene expression. However, the influence of ceRNA networks by aberrant DNA methylation remains incompletely understood. In our study, we proposed DMceNet, a computational method to characterize the effects of DNA methylation on ceRNA regulatory mechanisms and apply it across eight prevalent cancers. By integrating methylation and transcriptomic data, we constructed methylation-driven ceRNA networks and identified a dominant role of lncRNAs within these networks in two key ways: (i) 17 cancer-shared differential methylation lncRNAs (DMlncs), including PVT1 and CASC2, form a Common Cancer Network (CCN) affecting key pathways such as the G2/M checkpoint, and (ii) 24 cancer-specific DMlncs construct unique ceRNA networks for each cancer type. For instance, in LUAD and STAD, hypomethylation drives DMlncs like PCAT6 and MINCR, disrupting the Wnt signaling pathway and apoptosis. We further investigated the characteristics of these methylation-driven ceRNA networks at the cellular level, revealing how methylation-driven dysregulation varies across distinct cell populations within the tumor microenvironment. Our findings also demonstrate the prognostic potential of cancer-specific ceRNA relationships, highlighting their relevance in predicting patient survival outcomes. This integrated transcriptomic and epigenomic analysis provides new insights into cancer biology and regulatory mechanisms.

Establishment and Validation of Diagnostic and Prognostic Prediction Models for Liver Metastasis in Patients with Rectal Cancer: A SEER-based Study

Background: Rectal cancer is one of the most common gastrointestinal tumors, among which the liver is the most common site of distant metastasis and liver metastasis that leads to poor prognosis. This study aimed to develop and validate a diagnostic nomogram to predict the occurrence of rectal cancer with liver metastasis (RCLM) and a prognostic nomogram to predict cancer-specific survival (CSS) in RCML patients. Methods: Data on patients with rectal cancer diagnosed between 2010 and 2013 were collected from the Surveillance, Epidemiology, and End Results (SEER) database. Univariate logistic regression and multivariate logistic regression were used to determine the independent risk factors of RCLM. Univariate Cox proportional hazards regression and multivariate Cox proportional hazards regression were used to identify independent prognostic factors for RCLM. This study then developed two novel nomograms, and the results were evaluated by receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). Results: A total of 29367 patients with rectal cancer were included. Among them, 3403 patients (11.59%) had liver metastases at the time of diagnosis. The independent risk factors of RCLM included sex, race, tumor grade, AJCC N, CEA, marital status, tumor size, total number of primary tumors, and histological type. Age, chemotherapy, total number of primary tumors, surgery sites, and histological type were independent prognostic factors of patients with RCLM. The results of ROC curves, calibration curves, and DCA in the development, validation, and testing sets confirmed that the diagnostic nomogram can precisely predict the occurrence of RCLM. The results of ROC curves, calibration curves, DCA, C-indexes, and Kaplan–Meier (K-M) survival curves in the development, validation, and testing sets confirmed that the prognostic nomogram could precisely predict the prognosis of RCLM. Conclusion: The two nomograms are expected to be effective tools for predicting the risk of liver metastasis for patients with rectal cancer and personalized prognosis prediction for patients with RCLM, which may benefit clinical decision-making.

Identification of inflammation related gene signatures for bladder cancer prognosis prediction

Early diagnosis and treatment of bladder cancer are crucial, and since inflammation plays a role in all stages of bladder cancer, this study aims to develop a model based on inflammation-related genes to accurately predict patient prognosis. The data were initially processed through differential analysis and prognostic correlation analysis, then a Least absolute shrinkage and selection operator (LASSO) regression model was constructed by M-cohort and a nomogram was designed to increase the model readability. The T-cohort was used for internal validation, with the GSE32894 and Imvigor210 cohorts used as external data to verify the model’s accuracy. The model’s predictive ability was verified for the prognosis of patients of different ages, gender, tumor stage, and tumour grade. The GSE3167, GSE13507 and GeneExpression Profiling Interactive Analysis (GEPIA) datasets and Human Protein Atlas (HPA) database were used to verify the expression of the inflammation-related genes, which were confirmed by real-time Polymerase Chain Reaction (PCR). A comprehensive analysis of the model’s inflammation-related genes, Gene Set Enrichment Analysis (GSEA), Gene Set Variation Analysis (GSVA) enrichment analysis, and immune-related analysis were also performed. Both internal and external data validations confirmed that the developed model can accurately predict the prognosis across different patient populations. Hierarchical validation results demonstrated that the model’s predictive power is reliable for various patient stratifications. The expression of inflammation-related genes was consistent across The Cancer Genome Atlas (TCGA) database, GSE3167 dataset, GSE13507 dataset, Gene Expression Profiling Interactive Analysis (GEPIA) database, and the Human Protein Atlas (HPA) database, and was further validated by real-time Polymerase Chain Reaction (PCR). Pathway enrichment analysis indicated that patients in the high-risk (H-risk) group exhibited a variety of tumors. Meanwhile, patients in the low-risk (L-risk) group may be candidates for immunotherapy, whereas those in the high-risk group are more likely to benefit from chemotherapy. The model of inflammation-related genes can accurately predict bladder cancer patient prognosis, with MEST, FASN, KRT6B, and RGS2 anticipated to become new prognostic bladder cancer markers.

Prognostic value of the cholesterol-dependent nutritional prognostic index in patients receiving adjuvant chemotherapy after radical esophageal cancer treatment

BackgroundEsophageal cancer is the 6th most common cancer in terms of incidence and the 4th most common cause of mortality in China. Given the limited survival outcomes observed in patients with locally advanced disease managed exclusively with surgical intervention, postoperative treatment is critically important. This study sought to assess the prognostic value of total cholesterol (TC) levels and the prognostic nutritional index (PNI) in patients undergoing postoperative chemotherapy for esophageal cancer.MethodsA total of 101 patients who underwent postoesophagectomy chemotherapy were included in this retrospective analysis. PNI values for prechemotherapy were calculated for each patient [PNI = albumin + 5 × lymphocyte count] and TC. The optimal cutoff values for these indices were calculated from the receiver operating characteristic (ROC) curve. Patients were stratified into three groups on the basis of their PNI and TC levels. A novel nutritional prognostic index, termed the cholesterol-dependent nutritional prognostic index (CPNI), was developed on the basis of the PNI and TC. Univariate and multivariate Cox analyses were employed to determine the associations between each indicator and clinical outcomes.ResultsThe prechemotherapy PNI, TC level, and TNM stage became independent risk factors for OS (p < 0.05). Patients in the high PNI-high TC group had significantly improved DFS and OS compared with those in the low PNI-low TC group (p < 0.001) and had a lower early recurrence rate (P = 0.008). In contrast, patients with a high CPNI had a higher mortality rate.ConclusionThe prechemotherapy PNI combined with TC is an accurate and useful predictor of patient prognosis.

A case series on endometrial cancer in multipara and their prognostication based on IHC markers

Endometrial carcinoma is one of the leading gynecological malignancies across the globe. With advent of molecular analysis, establishing an accurate treatment plan and improving patient outcome has become eminent. In resource limited settings like India, surrogate IHC markers like hormonal receptor expression are congruous substitutes that aid to accurately predict patient prognosis. In this case series, expression of hormonal receptors in multiparous women and it’s association with patient outcome seen as disease free survival at a span of 2 years was compared. This study opines inclusion of hormonal receptors in evaluation of endometrial carcinoma patients to accurately comment on patient outcome.

Neutrophil-Lymphocyte Ratio as Predictor of Prognosis In Patient with Heart Failure with Reduced Ejection Fraction that Treated With Angiotensin Converting Enzyme Inhibitor or Angiotensin Receptor Neprilysin Inhibitor: A Prospective Cohort Study in Aceh,

Introduction: Heart failure (HF) and inflammation are related. Angiotensin Converting Enzyme Inhibitors (ACE-I) and Angiotensin Receptor Neprilysin Inhibitors (ARNI) can reduce inflammation by lowering neutrophil counts. The neutrophil-lymphocyte ratio (NLR) is a simple marker of inflammation. There is a lack of studies showing that NLR can predict prognosis in patients with HFrEF, and further investigation is needed.Objectives: Investigating whether NLR could be a prognostic indicator for mortality and rehospitalization in patients with heart failure and reduced ejection fraction receiving treatment with ACE-I or ARNI.Methods: A prospective observational cohort study with double-blind methods was conducted at Zainal Abidin General Hospital, Banda Aceh, Indonesia. Two groups, ACE-I and ARNI, each comprised 40 participants. Initially, blood sampling was performed to determine NLR. Participants were observed weekly for three months for prognosis outcomes (death and rehospitalization). After three months, the remaining participants were invited for re-evaluation of the NLR.Results: This study found that pre-treatment NLR had a hazard ratio (HR) of 1.454 (95% CI: 1.266-1.670, p &lt; 0.001). The risk ratio of abnormal pre-treatment NLR for rehospitalization was 6.56 in the ACE-I group and 8.33 in the ARNI group. Nevertheless, both ACE-I (p = 0.014) and ARNI (p = 0.032) decreased NLR levels.Conclusions: High NLR could be as a predictor of prognosis in patients with heart failure with a decrease in left ventricular ejection fraction that treated with ACE-I or ARNI, also ACE-I and ARNI could reduce NLR levels.

Upregulation of multiple key molecules is correlated with poor prognosis and immune infiltrates in hepatocellular carcinoma by bulk and single-cell RNA-seq.

Recent discoveries in hepatocellular carcinoma (HCC) unveil key molecules. However, due to liver cancer's high heterogeneity, predicting patient prognosis is challenging. This study aims to construct a model for predicting HCC prognosis using multiple key genes. TCGA provided RNA expression and clinical data, differentially analyzed by DESeq2, edgeR, and Limma. The hub gene was pinpointed via CytoHubba's degree algorithm in Cytoscape. GO and KEGG analyses illuminated potential pathways. Single-cell sequencing detailed key gene expression in diverse cell types. The LASSO regression model predicted patient prognosis. In the RNA-seq analysis using three R packages, we identified 762 differentially expressed genes, with Cytoscape revealing ten key genes showing significant prognostic value (P < 0.05). GO and KEGG analyses highlighted key biological processes and pathways. IHC confirmed higher expression in cancer tissues. Reduced immune cell infiltration was observed in HCC tissues, and immune checkpoint analysis showed a strong correlation between PD1, CTLA4, and hub genes. Single-cell sequencing indicated higher expression of key genes in immune cells than hepatocytes. Cox analysis validated the riskScore as a reliable, independent prognostic marker (HR = 4.498, 95% CI: 2.526-8.007). The results from differential analysis using three R packages are robust, revealing genes closely linked to immune cell infiltration in the tumor microenvironment. Additionally, a validated prognostic model for liver cancer was established based on key genes.

A pyroptosis-related lncRNA risk model for the prediction of prognosis and immunotherapy response in head and neck squamous cell carcinoma.

Recent research has highlighted pyroptosis as a key factor in cancer progression. This study aims to explore the association between pyroptosis-related signatures and overall survival (OS) in head and neck squamous cell carcinoma (HNSC) and develop a pyroptosis-related long non-coding RNA (lncRNA) risk model to predict prognosis and response to immunotherapy in HNSC. We extracted expression data for 18 pyroptosis-related genes and identified lncRNA probes specific to HNSC by using datasets from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA). Consensus clustering was performed to categorize HNSC patients into distinct subtypes. A six-lncRNA risk score model was constructed through univariate and least absolute shrinkage and selection operator (LASSO) Cox regression analyses. We evaluated the predictive ability of the lncRNA model for patients' survival and immunotherapy response. Gene expression was evaluated using immunohistochemistry (IHC) and Reverse Transcription Quantitative Polymerase Chain Reaction (RT-qPCR). Our analysis revealed two distinct pyroptosis-related subtypes in HNSC patients, Cluster A and Cluster B. Notably, patients in Cluster B exhibited significantly poorer overall survival compared to those in Cluster A. Through differential expression analysis, we identified six lncRNAs (AC002331.1, CTA-384D8.35, RP11-291B21.2, AC006262.5, RP1-27K12.2, and RP11-54H7.4) that were differentially expressed between these clusters. A 6-lncRNA risk score model was developed, which successfully stratified patients into high- and low-risk groups with distinct overall survival outcomes. Validation using RT-qPCR confirmed the differential expression of these six lncRNAs in HNSC tumor tissues compared to adjacent normal tissues, we found that the expression of CTA-384D8.35 was significantly increased in the tumor group (t=-6.203, P<0.001). Furthermore, the 6-lncRNA risk score model demonstrated a significant association with patient response to immunotherapy, with the low-risk group exhibiting a higher objective response rate to immune checkpoint blockade (ICB) therapy and longer survival compared to the high-risk group. Our study underscores the role of pyroptosis signatures in HNSC prognosis and identifies two distinct pyroptosis subtypes with differing survival outcomes. The six-lncRNA risk score model offers a valuable tool for predicting patient prognosis and potential benefits from ICB therapy. These findings highlight the importance of pyroptosis and associated lncRNAs in the tumor microenvironment, paving the way for novel targeted therapies in HNSC.

Construction and Validation of a Novel T/NK-Cell Prognostic Signature for Pancreatic Cancer Based on Single-Cell RNA Sequencing

Background Evidence with regards to the distinction between primary and metastatic tumors in pancreatic cancer and driving factors for metastases remains limited. Methods Single-cell RNA sequencing (scRNA-seq) was conducted on metastatic pancreatic cancer. Bioinformatics analysis on relevant sequencing data was used to construct a risk model to predict patient prognosis. Furthermore, immune infiltration and metabolic differences were assessed. The biological function of key differential genes was evaluated. Results Paired primary and metastatic tumor tissues from 3 pancreatic cancer patients were collected and conducted scRNA-seq. Subsequently, the T/NK cell subgroup was the most different cell type between primary tumors and liver metastases and was selected for further analysis. Eventually, 6 specifically expressed genes of T/NK cells (B2M, ZFP36L2, ANXA1, ARL4C, TSPYL2, FYN) were used constructing the prognostic model. The stability of this model was validated by an external cohort. Meanwhile, different immune infiltration abundances occurred between high and low risk groups stratified by the model. The high-risk group had a stronger metabolic capability. Conclusions A novel prognostic T/NK-cell signature for pancreatic cancer was constructed based on scRNA-seq data and externally validated. The involved key genes may play a role in multiple metabolic pathways of metastasis and affect the tumor immune microenvironment.

Combined fibrinogen concentration and neutrophil-to-lymphocyte ratio, an integrative model of the inflammatory response and coagulation cascades, for predicting prognosis in patients with upper tract urothelial carcinoma.

Inflammation and coagulation cascades are closely correlated with cancer occurrence and progression. This study investigated the prognostic value of the combination of plasma fibrinogen level and neutrophil-to-lymphocyte ratio (F-NLR) in patients with upper tract urothelial carcinoma (UTUC). The predictive ability of the F-NLR for overall survival (OS), cancer-specific survival (CSS), and progression-free survival (PFS) was initially established and then further validated in patients who underwent radical nephroureterectomy for UTUC. As a result, patients were divided into three groups following the establishment of cut-off values for the NLR (≥2.53 vs. <2.53) and fibrinogen (≥4.55 vs. <4.55) through receiver operating characteristic (ROC) curve analysis: F-NLR score 0 (low fibrinogen and low NLR), 2 (high fibrinogen and high NLR), or 1 (remaining patients). The F-NLR score was then identified as an independent risk factor for OS, CSS, and PFS (all P-value <0.05) by multivariate regression analysis in both the training and validation cohorts. In addition, F-NLR-based nomograms for OS, CSS, and PFS were developed and evaluated using the concordance index (C-index) and calibration curves. The integration of the F-NLR into existing nomograms improved predictive accuracy compared to the use of nomograms without the F-NLR score. This suggests that the addition of F-NLR is beneficial for enhancing the accuracy of prognosis prediction in patients with UTUC. The F-NLR score may serve as a powerful predictor for patients with UTUC.

The Demographic and Clinical Characteristics, Prognostic Factors, and Survival Outcomes of Head and Neck Carcinosarcoma: A SEER Database Analysis.

Head and neck carcinosarcoma (HNCS) is a rare and highly aggressive malignancy with limited research, resulting in an incomplete understanding of disease progression and a lack of reliable prognostic tools. This study aimed to retrospectively analyze the clinical characteristics and outcomes of HNCS patients using data from the Surveillance, Epidemiology, and End Results (SEER) database and to develop a nomogram to predict overall survival (OS) and cancer-specific survival (CSS). Patients diagnosed with HNCS from 1975 to 2020 were identified in the SEER database. Univariate and multivariate Cox regression analyses were conducted to identify independent prognostic indicators, with the optimal model selected using the minimal Akaike Information Criterion (AIC). The identified prognostic factors were incorporated into nomograms to predict OS and CSS. Model performance was assessed using the concordance index (C-index), area under the curve (AUC), calibration curves, and decision curve analysis (DCA). Survival curves were generated using Kaplan-Meier analysis and compared via the log-rank test. A total of 152 HNCS patients were included, with 108 assigned to the training cohort and 44 to the validation cohort in a 7:3 ratio. Prognostic factors including age, primary tumor site, marital status, radiotherapy, chemotherapy, tumor size, pathological grade, and tumor stage were incorporated into the nomogram models. The models demonstrated strong predictive performance, with C-index values for OS and CSS of 0.757 and 0.779 in the training group, and 0.777 and 0.776 in the validation group, respectively. AUC values for predicting 3-, 5-, and 10-year OS were 0.662, 0.713, and 0.761, and for CSS the values were 0.726, 0.703, and 0.693. Kaplan-Meier analysis indicated significantly improved survival for patients with lower risk scores. The 3-, 5-, and 10-year OS rates for the entire cohort were 54.1%, 45.6%, and 35.1%, respectively, and the CSS rates were 62.9%, 57.5%, and 52.2%, respectively. This study provides validated nomograms for predicting OS and CSS in HNCS patients, offering a reliable tool to support clinical decision-making for this challenging malignancy. These nomograms enhance the ability to predict patient prognosis and personalize treatment strategies.

Computed tomography radiomics-based prognosis prediction of patients with chronic obstructive pulmonary disease

Objectivethe objective of this study was to develop a predictive model for pulmonary ventilation function in patients with chronic obstructive pulmonary disease (COPD) utilizing computed tomography (CT) and pulmonary function parameters. Material and methoda retrospective analysis was conducted on 90 patients with COPD from our hospital, who were divided into Group I (AG, normal pulmonary function), Group II (BG, mild ventilation dysfunction), Group III (CG, moderate ventilation dysfunction), Group IV (DG, severe ventilation dysfunction), Group V (EG, very severe restrictive ventilation dysfunction), and Group VI (FG, very severe mixed ventilation dysfunction) according to pulmonary function. The imaging and pulmonary function indicators of the subjects were analyzed. Resultas lung dysfunction worsened, structural changes in the lungs on CT images also progressively worsened, manifesting as an enlargement of emphysematous areas, a decrease in lung field homogeneity, changes in lung tissue density and structure, and a reduction in lung field volume. Maximal expiratory flow (MEF) when 75%, 50% of the forced vital capacity (FVC) remained in the lung, i.e. MEF75%, MEF50%, and maximum mid-expiratory flow (MMEF75%-25%) were most highly correlated with ventilation dysfunction. Conclusionthe severity of pulmonary ventilation dysfunction in patients with COPD can be better predicted by CT imaging and pulmonary function examination indicators.

Construction and validation of a prognostic signature based on microvascular invasion and immune-related genes in hepatocellular carcinoma

Background: Microvascular invasion (MVI) is an independent risk factor of poor prognosis in hepatocellular carcinoma (HCC) and can be used to guide the diagnosis and treatment of HCC. The immune system serves as an integral role in the incidence and progression of HCC. However, the molecular biology correlation between MVI and tumor immunity and the value of combining the two parameters to predict patient prognosis and HCC response to treatment remain to be evaluated. Results: In this study, we used univariate Cox regression analysis and least absolute shrinkage and selection operator (LASSO) Cox analysis to establish the MVI and immune-related gene index (MIRGPI) including eight genes. We demonstrated that the MIRGPI was an independent risk factor in predicting the prognosis of HCC. Subsequently, our research established a nomogram model combining pathologic characteristics and verified its good clinical application value. In addition, our study found that the TP53 gene had a higher mutation frequency and a lower degree of immune infiltration in the high-risk group. The low-risk group had higher sensitivity to immunotherapy, sorafenib, and TACE treatment, and the high-risk group had higher sensitivity to common chemotherapeutic agents. Finally, SEMA3C was found to facilitate the proliferation, migration and invasive ability of HCC by in vitro and in vivo experiments, and its mechanism may be associated with the activation of the NF-Κb/EMT signaling pathway. Conclusions: In summary, the MIRGPI signature we developed is a reliable marker for the prediction of prognosis and treatment response, and is important for the prognostic assessment and individualized treatment of HCC.

Development and Validation of a Novel Classification System and Prognostic Model for Open Traumatic Brain Injury: A Multicenter Retrospective Study.

Open traumatic brain injury (OTBI) is associated with high mortality and morbidity; however, the classification of these injuries and the determination of patient prognosis remain uncertain, hindering the selection of optimal treatment strategies. This study aimed to develop and validate a novel OTBI classification system and a prognostic model for poor prognosis. This retrospective study included patients with isolated OTBI who received treatment at three large medical centersin China between January 2020 and June 2022 as the training set. Data on patients with OTBI collected at the Fuzong Clinical Medical College of Fujian Medical University between July 2022 and June 2023 were used as the validation set. Clinical parameters, including clinical data at admission, radiological and laboratory findings, details of surgical methods, and prognosis were collected. Prognosis was assessed through a dichotomized Glasgow Outcome Scale (GOS). A novel OTBI classification was proposed, categorizing patients based on a combination of intracranial hematoma and midline shift observed on imaging, and logistic regression analyses were performed to identify risk factors associated with poor prognosis and to investigate the association between the novel OTBI classification and prognosis. Finally, a nomogram suitable for clinical application was established and validated. Multivariable logistic regression analysis identified OTBI classification type C (p<0.001), a Glasgow Coma Scale score (GCS)≤8 (p<0.001), subarachnoid hemorrhage (SAH) (p=0.004), subdural hematoma (SDH) (p=0.011), and coagulopathy (p=0.020) as independent risk factors for poor prognosis. The addition of the OTBI classification to a model containing all the other identified prognostic factors improved the predictive ability of the model (Z=1.983; p=0.047). In the validation set, the model achieved an area under the curve (AUC) of 0.917 [95% confidence interval (CI)=0.864-0.970]. The calibration curve closely approximated the ideal curve, indicating strong predictive performance of the model. The implementation of our proposed OTBI classification system and its use alongside the other prognostic factors identified here may improve the prediction of patient prognosis and aid in the selection of the most suitable treatment strategies.