Assessment Of Prognosis In Patients Research Articles

Characteristic alterations of gut microbiota and serum metabolites in patients with chronic tinnitus: a multi-omics analysis.

Chronic tinnitus is a central nervous system disorder. Currently, the effects of gut microbiota on tinnitus remain unexplored. To explore the connection between gut microbiota and tinnitus, we conducted 16S rRNA sequencing of fecal microbiota and serum metabolomic analysis in a cohort of 70 patients with tinnitus and 30 healthy volunteers. We used the weighted gene co-expression network method to analyze the relationship between the gut microbiota and the serum metabolites. The random forest technique was utilized to select metabolites and gut taxa to construct predictive models. A pronounced gut dysbiosis in the tinnitus group, characterized by reduced bacterial diversity, an increased Firmicutes/Bacteroidetes ratio, and some opportunistic bacteria including Aeromonas and Acinetobacter were enriched. In contrast, some beneficial gut probiotics decreased, including Lactobacillales and Lactobacillaceae. In serum metabolomic analysis, serum metabolic disturbances in tinnitus patients and these differential metabolites were enriched in pathways of neuroinflammation, neurotransmitter activity, and synaptic function. The predictive models exhibited great diagnostic performance, achieving 0.94 (95% CI: 0.85-0.98) and 0.96 (95% CI: 0.86-0.99) in the test set. Our study suggests that changes in gut microbiota could potentially influence the occurrence and chronicity of tinnitus, and exert regulatory effects through changes in serum metabolites. Overall, this research provides new perceptions into the potential role of gut microbiota and serum metabolite in the pathogenesis of tinnitus, and proposes the "gut-brain-ear" concept as a pathomechanism underlying tinnitus, with significant clinical diagnostic implications and therapeutic potential.IMPORTANCETinnitus affects millions of people worldwide. Severe cases may lead to sleep disorders, anxiety, and depression, subsequently impacting patients' lives and increasing societal healthcare expenditures. However, tinnitus mechanisms are poorly understood, and effective therapeutic interventions are currently lacking. We discovered the gut microbiota and serum metabolomics changes in patients with tinnitus, and provided the potential pathological mechanisms of dysregulated gut flora in chronic tinnitus. We proposed the innovative concept of the "gut-brain-ear axis," which underscores the exploration of gut microbiota impact on susceptibility to chronic tinnitus through serum metabolic profile modulation. We also reveal novel biomarkers associated with chronic tinnitus, offering a new conceptual framework for further investigations into the susceptibility of patients, potential treatment targets for tinnitus, and assessing patient prognosis. Subsequently, gut microbiota and serum metabolites can be used as molecular markers to assess the susceptibility and prognosis of tinnitus.Furthermore, fecal transplantation may be used to treat tinnitus.

Older age but not comorbidity is associated with worse survival in patients with hepatocellular carcinoma

Age and comorbidity are key factors in assessing patient prognosis and informing stopping rules for cancer screening eligibility, but their impact has not been rigorously evaluated in patients with hepatocellular carcinoma (HCC). We conducted a retrospective cohort study of patients diagnosed with HCC at two health systems between January 2010 and February 2023. We used multivariable logistic regression and Cox proportional hazards models to evaluate the associations between older age (≥65 years) and comorbidity burden (Charlson Comorbidity Index) with early-stage presentation, curative treatment receipt, and overall survival. We performed subgroup analyses in patients with early-stage HCC. We identified 2002 patients with HCC (median age 61 years, 76% male, 21% early-stage), with a median survival of 15.7 months. In adjusted analyses, curative treatment receipt was associated with higher comorbidity but not older age. Conversely, overall survival was significantly associated with older age (HR 1.25, 95%CI 1.06 - 1.47) but not high comorbidity (HR 0.92, 95%CI 0.77 - 1.09). Older age continued to be associated with worse survival among patients with early-stage HCC (HR 1.99, 95%CI 1.45 - 2.73) and those who underwent curative treatment (HR 1.52, 95%CI 1.10 - 2.10). Median survival for younger vs. older individuals was 20 vs 14 months overall, 65 vs 49 months for patients with early-stage HCC, and 113 vs. 60 months for those with curative treatment. Older age but not comorbidity burden is associated with worse survival in patients with early-stage HCC. Further studies are needed to define the role of comorbidity in HCC prognostication.

The prognostic significance of Weber and ventilatory CPET classifications in amyloidosis patients

Abstract Background Cardiopulmonary exercise testing (CPET) has emerged as an important tool for risk stratification and prognosis assessment in patients with cardiac diseases. The Weber and Ventilatory Functional Classification have been correlated with outcomes in patients with Heart Failure (HF). Weber classification is based on VO2: Weber class A, peak VO2 >20 mL/kg per minute; class B, peak VO2 16–20 mL/kg per minute; class C, peak VO2 10–16 mL/kg per minute; and class D, peak VO2 ≤10 mL/kg per minute and Ventilatory classification on VE/VCO2 slope I-IV (Figure A). Although VO2 and VE/VCO2 variables have found altered in the amyloidosis population, the applicability of this classification in a cohort of amyloidosis patients and its correlation with all-cause mortality have not been explored. Purpose To evaluate if the Weber and Ventilatory Classification of HF predicts mortality amyloidosis patients. Methods 194 with diagnosis of amyloidosis who underwent CPET from February 2010 to February 2024 were included in this retrospective cohort study. Patients with at least one CPET complete data were included in the analysis. Data on the type of amyloidosis, comorbidities, and echocardiogram were retrieved from medical records. Cohort was divided into two groups according to the Weber Classification: Weber Class A/B and Weber Class C/D. Individuals with CPET studies after heart transplant were excluded from the analysis. Statistical analyses including Fisher’s exact test, chi-square, Tukey-Kramer (TK), Cox regression model analysis, proportional hazards model for time to event, and Kaplan–Meier curves were performed. A p-value <0.05 was considered significant. Results Out of the 194 patients, 94 were classified as Weber A-B and 100 as Weber B-C. The age at stress test was similar between the groups: Weber A/B 62.9 ± 11.7 years and Weber C/D 62.3 ± 10.7 years (p=0.087). Ejection Fraction was similar between groups, 32% and 28.9% in A-B and C-D respectively (p =0.207). Higher levels of NT-proBNP were found in the Weber C/D group. The prevalence of comorbidities was balanced among the groups except chronic kidney disease, which was higher in Weber Class C/D (40%) vs (20.2%) in Weber Class A/B (Table 1). Cox analysis revealed a strong association between higher Weber class and the outcome HR 2.36 (1.18 – 4.71) p=0.014. For Higher Ventilatory Class (Group III-IV) similar results were found HR 2.95 (1.60 – 5.45) p=0.005(Table 2). Mortality was significantly higher during the follow-up in the Weber C/D group (25%) vs (10%) in the Weber A-B group p=0.007(Figure D). Conclusion Weber and ventilatory Classification based on CPET, besides predicting outcomes in patients with cardiac disease, are strong predictors of mortality in patients with amyloidosis. These findings suggest that this patient group benefits from earlier stratification to assess prognosis and the potential requirement for transplant independently from other factors.

Exploring prognostic biomarkers in pathological images of colorectal cancer patients via deep learning.

Hematoxylin and eosin (H&E) whole slide images provide valuable information for predicting prognostic outcomes in colorectal cancer (CRC) patients. However, extracting prognostic indicators from pathological images is challenging due to the subtle complexities of phenotypic information. We trained a weakly supervised deep learning model on data from 640 CRC patients in the prostate, lung, colorectal, and ovarian (PLCO) cancer screening trial dataset and validated it using data from 522 CRC patients in the cancer genome atlas (TCGA) dataset. We created the colorectal cancer risk score (CRCRS) to assess patient prognosis, visualized the pathological phenotype of the risk score using Grad-CAM, and employed multiomics data from the TCGA CRC cohort to investigate the potential biological mechanisms underlying the risk score. The overall survival analysis revealed that the CRCRS served as an independent prognostic indicator for both the PLCO cohort (p < 0.001) and the TCGA cohort (p < 0.001), with its predictive efficacy remaining unaffected by the clinical staging system. Additionally, satisfactory chemotherapeutic benefits were observed in stage II/III CRC patients with high CRCRS but not in those with low CRCRS. A pathomics nomogram constructed by integrating the CRCRS with the tumor-node-metastasis (TNM) staging system enhanced prognostic prediction accuracy compared with using the TNM staging system alone. Noteworthy features of the risk score were identified, such as immature tumor mesenchyme, disorganized gland structures, small clusters of cancer cells associated with unfavorable prognosis, and infiltrating inflammatory cells associated with favorable prognosis. The TCGA multiomics data revealed potential correlations between the CRCRS and the activation of energy production and metabolic pathways, the tumor immune microenvironment, and genetic mutations in APC, SMAD2, EEF1AKMT4, EPG5, and TANC1. In summary, our deep learning algorithm identified the CRCRS as a prognostic indicator in CRC, providing a significant approach for prognostic risk stratification and tailoring precise treatment strategies for individual patients.

Identification of genomic alteration and prognosis using pathomics-based artificial intelligence in oral leukoplakia and head and neck squamous cell carcinoma: A multicenter experimental study.

Loss of chromosome 9p is an important biomarker in the malignant transformation of oral leukoplakia (OLK) to head and neck squamous cell carcinoma (HNSCC), and is associated with the prognosis of HNSCC patients. However, various challenges have prevented 9p loss from being assessed in clinical practice. The objective of this study was to develop a pathomics-based artificial intelligence (AI) model for the rapid and cost-effective prediction of 9p loss (9PLP). 333 OLK cases were retrospectively collected with hematoxylin and eosin (H&E)-stained whole slide images and genomic alteration data from multicenter cohorts to develop the genomic alteration prediction AI model. They were divided into a training dataset (n=217), a validation dataset (n=93), and an external testing dataset (n=23). The latest Transformer method and XGBoost algorithm were combined to develop the 9PLP model. The AI model was further applied and validated in two multicenter HNSCC datasets (n=42, n=365, respectively). Moreover, the combination of 9PLP with clinicopathological parameters was used to develop a nomogram model for assessing HNSCC patient prognosis. 9PLP could predict chromosome 9p loss rapidly and effectively using both OLK and HNSCC images, with the area under the curve achieving 0.890 and 0.825, respectively. Furthermore, the predictive model showed high accuracy in HNSCC patient prognosis assessment (the area under the curve was 0.739 for 1-year prediction, 0.705 for 3-year prediction, and 0.691 for 5-year prediction). To the best of our knowledge, this study developed the first genomic alteration prediction deep learning model in OLK and HNSCC. This novel AI model could predict 9p loss and assess patient prognosis by identifying pathomics features in H&E-stained images with good performance. In the future, the 9PLP model may potentially contribute to better clinical management of OLK and HNSCC.

Deep Learning-Assisted Automatic Diagnosis of Anterior Cruciate Ligament Tear in Knee Magnetic Resonance Images.

Anterior cruciate ligament (ACL) tears are prevalent knee injures, particularly among active individuals. Accurate and timely diagnosis is essential for determining the optimal treatment strategy and assessing patient prognosis. Various previous studies have demonstrated the successful application of deep learning techniques in the field of medical image analysis. This study aimed to develop a deep learning model for detecting ACL tears in knee magnetic resonance Imaging (MRI) to enhance diagnostic accuracy and efficiency. The proposed model consists of three main modules: a Dual-Scale Data Augmentation module (DDA) to enrich the training data on both the spatial and layer scales; a selective group attention module (SG) to capture relationships across the layer, channel, and space scales; and a fusion module to explore the inter-relationships among various perspectives to achieve the final classification. To ensure a fair comparison, the study utilized a public dataset from MRNet, comprising knee MRI scans from 1250 exams, with a focus on three distinct views: axial, coronal, and sagittal. The experimental results demonstrate the superior performance of the proposed model, termed SGNET, in ACL tear detection compared with other comparison models, achieving an accuracy of 0.9250, a sensitivity of 0.9259, a specificity of 0.9242, and an AUC of 0.9747.

Personalized approach to malignant struma ovarii: Insights from a web-based machine learning tool.

Malignant struma ovarii (MSO) is a rare ovarian tumor characterized by mature thyroid tissue. The diverse symptoms and uncommon nature of MSO can create difficulties in its diagnosis and treatment. This study aimed to analyze data and use machine learning methods to understand the prognostic factors and potential management strategies for MSO. In this retrospective cohort, the Surveillance, Epidemiology, and End Results (SEER) database provided the data used for this study's analysis. To identify the prognostic variables, we conducted Cox regression analysis and constructed prognostic models using five machine learning algorithms to predict the 5-year survival. A validation method incorporating the area under the curve of the receiver operating characteristic curve was used to validate the accuracy and reliability of the machine learning models. We also investigated the role of multiple therapeutic options using the Kaplan-Meier survival analysis. The study population comprised 329 patients. Multivariate Cox regression analysis revealed that older age, unmarried status, chemotherapy, and the total number of tumors in patients were poor prognostic factors. Machine learning models revealed that the multilayer perceptron accurately predicted outcomes, followed by the random forest classifier, gradient boosting classifier, K-nearest neighbors, and logistic regression models. The factors that contributed the most were age, marital status, and the total number of tumors in the patients. The present study offers a comprehensive approach for the treatment and prognosis assessment of patients with MSO. The machine learning models we have developed serve as a practical, personalized tool to aid in clinical decision-making processes.

Accuracy of Medical Oncology Prognosis for Metastatic Cancer Patients Evaluated for Enrollment Onto an Ongoing Randomized Clinical Trial.

For patients with metastatic cancer, a key aspect of interdisciplinary care has involved the overall prognosis provided by Medical Oncology. This study represents prospective evaluation of Medical Oncology prognosis accuracy for patients considered for enrollment onto an ongoing randomized controlled trial. The Spine Patient Optimal Radiosurgery Treatment for Symptomatic Metastatic Neoplasms (SPORTSMEN) phase 2 randomized clinical trial examines optimal radiation therapy treatment of symptomatic spinal metastases with a primary end point of pain freedom at 3 months post-treatment. A key eligibility criterion for trial enrollment is overall prognosis exceeding 3 months, typically provided by Medical Oncology. During the first year of trial enrollment, Medical Oncology prognosis for patients considered for SPORTSMEN inclusion was prospectively assessed for accuracy. Twenty-seven patients with documented Medical Oncology prognosis were considered for SPORTSMEN enrollment. The prognosis administered by Medical Oncology exceeded 3 months in 26 patients, and <3 months in 1 patient. The overall accuracy of Medical Oncology prognosis was correct for 15 of 27 patients (56%), significantly worse for inpatients than outpatients ( P =0.0381). In patients with metastatic spine disease, the estimated prognosis provided by Medical Oncology is often optimistic, as nearly half of patients assigned a prognosis of >3 months failed to reach this threshold before experiencing death or hospice. These findings indicate that a more heuristic approach to assessing patient prognosis may be necessary to avoid unwarranted prognostic optimism, particularly for inpatients. Such an approach could potentially provide a more compassionate and cost-effective management of these patients' remaining lifespan thereby optimizing quality of life.

Impact of surgical types on overall survival in patients with ductal carcinoma in situ: an analysis based on the SEER database.

Breast cancer, as one of the most common malignancies among women globally, presents a concerning incidence rate, underscoring the importance of addressing the treatment of its precursor lesion, ductal carcinoma in situ (DCIS). Treatment decisions for DCIS, involving the balance between breast-conserving surgery (BCS) and mastectomy, remain an area requiring further investigation. This study aimed to compare influence of different surgical types on overall survival (OS) of patients with DCIS and identify specific subgroups with improved OS to develop an effective survival nomogram for patients. Patient data from the Surveillance, Epidemiology, and End Results (SEER) database for DCIS cohort from 2010 to 2020 were retrieved. Kaplan-Meier (K-M) survival curves were utilized to compare prognostic OS of patients with different surgical methods. Cox regression analysis was employed to determine prognostic factors and establish a nomogram to predict 3-, 5-, and 10-year survival rates. The model was confirmed by Concordance Index (C-index), calibration curves, and receiver operating characteristic (ROC) curves. A total of 71,675 patients with DCIS were included. Patients who underwent subcutaneous mastectomy (SM) demonstrated the best OS compared to other surgical types. Additionally, adjuvant radiotherapy or chemotherapy in combination with surgery significantly improved OS compared to surgery alone. Among DCIS patients aged ≤74 years, those who underwent SM benefited the most in terms of OS, while in the age group of 63-74 years, patients who underwent BCS had significantly higher OS than those who underwent total (simple) mastectomy (TM)/modified radical mastectomy (MRM). Multiple factors were associated with improved OS in DCIS patients, and these factors were integrated into the nomogram to establish OS predictions. The C-index, calibration curves, and ROC curves indicated that the nomogram was suitable for assessing patient prognosis. This study demonstrated that SM treatment yielded the best survival rates for DCIS patients, providing important guidance for future surgical decision-making. Moreover, identifying multiple independent factors related to survival and establishing reliable survival nomograms can assist physicians in developing personalized treatment plans and prolonging patient survival.

Unplanned hemodialysis initiation: A retrospective analysis of patient characteristics and prognosis in an emergency hospital.

Functional decline occurs during dialysis initiation, particularly in unplanned cases. To prevent unplanned hemodialysis, we aimed to identify associated factors from the first referral to the nephrology department to hemodialysis initiation and assess patient prognosis post-unplanned hemodialysis initiation. This retrospective study involved 257 Japanese patients initiating hemodialysis and compared patient characteristics based on whether hemodialysis was planned or unplanned at a single center. Patient outcomes were evaluated in collaboration with maintenance hemodialysis centers. Unplanned hemodialysis initiation correlated with heart failure history (p < 0.05) and infections like pneumonia (p < 0.001). Patients with unplanned hemodialysis initiation had a worse prognosis than those with planned initiation (p < 0.001), and multivariable Cox regression showed it as an independent risk factor for death (p < 0.05). Hygiene and careful attention to heart failure may reduce unplanned hemodialysis and improve patient well-being and healthcare efficiency. This retrospective analysis highlights crucial considerations for optimizing the initiation of hemodialysis.

Analysis of cancer-specific survival in patients with metastatic colorectal cancer: A evidence-based medicine study.

Metastatic colorectal cancer (mCRC) is a common malignancy whose treatment has been a clinical challenge. Cancer-specific survival (CSS) plays a crucial role in assessing patient prognosis and treatment outcomes. However, there is still limited research on the factors affecting CSS in mCRC patients and their correlation. To predict CSS, we developed a new nomogram model and risk grading system to classify risk levels in patients with mCRC. Data were extracted from the United States Surveillance, Epidemiology, and End Results database from 2018 to 2023. All eligible patients were randomly divided into a training cohort and a validation cohort. The Cox proportional hazards model was used to investigate the independent risk factors for CSS. A new nomogram model was developed to predict CSS and was evaluated through internal and external validation. A multivariate Cox proportional risk model was used to identify independent risk factors for CSS. Then, new CSS columns were developed based on these factors. The consistency index (C-index) of the histogram was 0.718 (95%CI: 0.712-0.725), and that of the validation cohort was 0.722 (95%CI: 0.711-0.732), indicating good discrimination ability and better performance than tumor-node-metastasis staging (C-index: 0.712-0.732). For the training set, 0.533, 95%CI: 0.525-0.540; for the verification set, 0.524, 95%CI: 0.513-0.535. The calibration map and clinical decision curve showed good agreement and good potential clinical validity. The risk grading system divided all patients into three groups, and the Kaplan-Meier curve showed good stratification and differentiation of CSS between different groups. The median CSS times in the low-risk, medium-risk, and high-risk groups were 36 months (95%CI: 34.987-37.013), 18 months (95%CI: 17.273-18.727), and 5 months (95%CI: 4.503-5.497), respectively. Our study developed a new nomogram model to predict CSS in patients with synchronous mCRC. In addition, the risk-grading system helps to accurately assess patient prognosis and guide treatment.

Pan-immune-inflammation value: a new prognostic index in operative laryngeal and pharyngeal carcinomas.

This study aimed to further evaluate the potential value of Pan-Immune-Inflammation Value (PIV) as a prognostic marker in patients with laryngeal and pharyngeal tumors. A total of 545 patients with laryngeal and pharyngeal tumors who underwent surgery at Qilu Hospital of Shandong University were included. We determined the optimal cutoff of PIV and divided the patients into two groups. The relationship between PIV and clinicopathological features was explored by the chi-square test and the Mann-Whitney U test. Survival analysis and Cox regression analysis were used to evaluate the relationship between PIV and overall survival (OS) and disease-free survival (DFS). We also compared the prognostic predictive value of PIV with other inflammation-related markers. Finally, we developed a simple scoring prediction model based on several independent prognostic parameters. We found that PIV was statistically associated with clinicopathological features such as tumor stage (p < 0.001), node stage (p = 0.001), postoperative chemotherapy (p = 0.026), and vascular thrombosis (p = 0.027). Survival analysis demonstrated a significant correlation between elevated PIV and reduced OS and DFS (p < 0.0001). Multivariate Cox regression analysis further confirmed PIV as a prognostic indicator (HR 2.507; 95% CI 1.343-4.681; p = 0.004), which is superior to SII, NLR, MLR and PLR. Three of the independent prognostic factors screened by multivariate Cox regression analysis were selected to be used to create a scoring system with a concordance index of 0.756. Elevated PIV is associated with poor prognosis in patients with laryngeal and pharyngeal tumors, suggesting that PIV may be an important adjunctive indicator for assessing patient prognosis. Registration number: KYLL-202307-001, date: July 2023.

The role of folate receptor-positive circulating tumor cells in the prognosis analysis of esophageal squamous cell carcinomas.

e15026 Background: Patients with ESCC always be discovered at an advanced stage and have a poor prognosis. The Circulating Tumor Cells (CTCs) were investigated for use as a biomarker for diagnosis and assessing the effectiveness of treatment. This study aims to investigate the predictive value of the baseline level of folate receptor-positive circulating tumor cells (FR+CTCs) for disease-free survival (DFS) and overall survival (OS) in patients with esophageal squamous cell carcinomas (ESCC). Methods: A retrospective observational study was conducted from January 2018 to February 2021 and followed up to June 2022 with ESCC patients in Sichuan Cancer Hospital &amp; Institute Esophageal Cancer Case Management Database (SCCH-ECCM Database). Three ml of peripheral blood were obtained from each patient, and FR+CTCs were quantified using ligand-targeted polymerase chain reaction (LT-PCR). Traditional techniques were employed to measure other serum markers. Clinicopathological features were collected from the hospital medical record system, and survival data (DFS and OS) were obtained by outpatient follow-up or phone calls. The correlation between clinicopathological characteristics, survival data, and FR+CTCs was analyzed. Cox regression analysis was used to assess risk factors that might have an impact on DFS and OS. Results: Analysis was conducted on 123 ESCC patients. Of them, 86% were men, and their average age was 62.0. FR+CTCs did not significantly correlate with patient age, sex, albumin, pre-albumin, C-reactive protein (CRP), ferritin, tumor size, grade of differentiation, metastasis of lymph nodes, perineural invasion/vessel invasion, or tumor size (all P&gt;0.05). But for DFS (HR 3.29; 95% CI 1.52-7.15, P=0.003) and OS (HR 3.85; 95% CI 1.53-9.72, P=0.004), preoperative FR+CTCs were an independent prognostic factor. Patients with post-operative FR+CTCs ≥17.42 FU/3ml had a substantially shorter DFS than patients with FR+CTCs &lt; 17.42 FU/3ml (P=0.0012). Patients with FR+CTCs ≥17.42 FU/3ml had a shorter OS than patients with FR+CTCs &lt;17.42 FU/3ml, although the difference was not statistically significant (P=0.51). Conclusions: Patients with ESCC who have elevated FR+CTCs typically have a poorer prognosis. We anticipate that FR+CTCs will be able to guide clinical treatment, assess patient prognosis, and monitor cancer recurrence and metastasis in real-time.

Establishment of a prognostic nomogram for elderly patients with limited-stage small cell lung cancer receiving radiotherapy

The present study explored the risk factors associated with radiotherapy in seniors diagnosed with limited-stage small cell lung cancer (LS-SCLC) to construct and validate a prognostic nomogram. The study retrospectively included 137 elderly patients with LS-SCLC who previously received radiotherapy. Univariate and multivariate COX analyses were conducted to identify independent risk factors and determine optimal cut-off values. Kaplan–Meier survival curves and nomograms were constructed to predict survival. Calibration and receiver operating characteristic (ROC) curves were used to evaluate the accuracy and consistency of the nomogram. Illness rating scale-geriatric (CIRS-G) score, treatment strategy, lymphocyte-to-monocyte ratio (LMR), white blood cell-to-monocyte ratio (WMR), and prognostic nutritional index (PNI) were discovered to be independent prognostic factors. Based on the findings of our multivariate analysis, a risk nomogram was developed to assess patient prognosis. Internal bootstrap resampling was utilized to validate the model, and while the accuracy of the AUC curve at 1 year was modest at 0.657 (95% CI 0.458–0.856), good results were achieved in predicting 3- and 5 year survival with AUCs of 0.757 (95% CI 0.670–0.843) and 0.768 (95% CI 0.643–0.893), respectively. Calibration curves for 1-, 3-, and 5 year overall survival probabilities demonstrated good cocsistency between expected and actual outcomes. Patients with concurrent chemoradiotherapy, CIRS-G score > 5 points and low PNI, WMR and LMR correlated with poor prognosis. The nomogram model developed based on these factors demonstrated good predictive performance and provides a simple, accessible, and practical tool for clinicians to guide clinical decision-making and study design.

Global longitudinal active strain energy density (GLASED): a powerful prognostic marker in a community-based cohort.

Identifying the imaging method that best predicts all-cause mortality, cardiovascular adverse events, and heart failure risk is crucial for tailoring optimal management. Potential prognostic markers include left ventricular (LV) myocardial mass, ejection fraction, myocardial strain, stroke work, contraction fraction, pressure-strain product, and a new measurement called global longitudinal active strain density (GLASED). This study sought to compare the utility of 23 potential LV prognostic markers of structure and contractile function in a community-based cohort. The impact of cardiovascular magnetic resonance image-derived markers extracted by machine learning algorithms was compared with the future risk of adverse events in a group of 44 957 UK Biobank participants. Most markers, including the LV ejection fraction, have limited prognostic value. GLASED was significantly associated with all-cause mortality and major adverse cardiovascular events, with the largest hazard ratio, highest ranking, and differentiated risk in all three tertiles (P ≤ 0.0003). GLASED predicted all-cause mortality and major cardiovascular adverse events better than conventional markers of risk and is recommended for assessing patient prognosis.

Glycometabolism and lipid metabolism related genes predict the prognosis of endometrial carcinoma and their effects on tumor cells

BackgroundGlycometabolism and lipid metabolism are critical in cancer metabolic reprogramming. The primary aim of this study was to develop a prognostic model incorporating glycometabolism and lipid metabolism-related genes (GLRGs) for accurate prognosis assessment in patients with endometrial carcinoma (EC).MethodsData on gene expression and clinical details were obtained from publicly accessible databases. GLRGs were obtained from the Genecards database. Through nonnegative matrix factorization (NMF) clustering, molecular groupings with various GLRG expression patterns were identified. LASSO Cox regression analysis was employed to create a prognostic model. Use rich algorithms such as GSEA, GSVA, xCELL ssGSEA, EPIC,CIBERSORT, MCPcounter, ESTIMATE, TIMER, TIDE, and Oncoppredict to analyze functional pathway characteristics of the forecast signal, immune status, anti-tumor therapy, etc. The expression was assessed using Western blot and quantitative real-time PCR techniques. A total of 113 algorithm combinations were combined to screen out the most significant GLRGs in the signature for in vitro experimental verification, such as colony formation, EdU cell proliferation, wound healing, apoptosis, and Transwell assays.ResultsA total of 714 GLRGs were found, and 227 of them were identified as prognostic-related genes. And ten GLRGs (AUP1, ESR1, ERLIN2, ASS1, OGDH, BCKDHB, SLC16A1, HK2, LPCAT1 and PGR-AS1) were identified to construct the prognostic model of patients with EC. Based on GLRGs, the risk model’s prognosis and independent prognostic value were established. The signature of GLRGs exhibited a robust correlation with the infiltration of immune cells and the sensitivity to drugs. In cytological experiments, we selected HK2 as candidate gene to verify its value in the occurrence and development of EC. Western blot and qRT-PCR revealed that HK2 was substantially expressed in EC cells. According to in vitro experiments, HK2 knockdown can increase EC cell apoptosis while suppressing EC cell migration, invasion, and proliferation.ConclusionThe GLRGs signature constructed in this study demonstrated significant prognostic value for patients with endometrial carcinoma, thereby providing valuable guidance for treatment decisions.

Fibroblast growth factor 21 (FGF21): diagnostic, prognostic and therapeutic aspects in heart failure. A review

Chronic heart failure (CHF) is a global medical, social and economic problem. Currently, the search and study of new biomarkers that can provide early diagnosis of CHF, serve as a laboratory tool for assessing the effectiveness of treatment, or be used as prognostic markers and risk stratification criteria are ongoing. Scientists' interest is focused, in particular, on studying the role of fibroblast growth factor 21 (FGF21) in CHF. There is increasing evidence highlighting the value of FGF21 as a new marker for the diagnosis and assessment of prognosis in patients with CHF. The role of FGF21 in CHF is very interesting due to its cardioprotective aspects. Final confirmation of the diagnostic, prognostic and therapeutic roles of FGF21 will come from future studies.

Impact of Changes in Rectus Femoris Cross-Sectional Area Measured by Ultrasound on the Prognosis of Patients With Acute Heart Failure.

Low muscle mass in patients with acute heart failure (AHF) is associated with poor prognosis; however, this is based on a single baseline measurement, with little information on changes in muscle mass during hospitalization and their clinical implications. This study investigated the relationship between changes in rectus femoris cross-sectional area (RFCSA) on ultrasound and the prognosis of patients with AHF. This is a retrospective evaluation of 284 AHF patients (mean [±SD] age 79.1±11.9 years; 116 female). RFCSA assessments at admission (pre-RFCSA), ∆RFCSA (i.e., the percentage change in RFCSA from admission to 2 weeks), and composite prognosis (all-cause death and heart failure-related readmission) within 1 year were determined. Patients were divided into 4 groups according to their median pre-RFCSA and ∆RFCSA after sex stratification: Group A, higher pre-RFCSA/better ∆RFCSA; Group B, higher pre-RFCSA/worse ∆RFCSA; Group C, lower pre-RFCSA/better ∆RFCSA; Group D, lower pre-RFCSA/worse ∆RFCSA. In the Cox regression analysis, with Group A as the reference, the cumulative event rate of Group C (hazard ratio [HR] 3.39; 95% confidence interval [CI] 0.71-16.09; P=0.124) did not differ significantly; however, the cumulative event rates of Group B (HR 7.93; 95% CI 1.99-31.60; P=0.003) and Group D (HR 9.24; 95% CI 2.57-33.26; P<0.001) were significantly higher. ∆RFCSA during hospitalization is useful for risk assessment of prognosis in patients with AHF.

Integrating bulk and single-cell sequencing data to construct a Scissor+ dendritic cells prognostic model for predicting prognosis and immune responses in ESCC

Esophageal squamous cell carcinoma (ESCC) is characterized by molecular heterogeneity with various immune cell infiltration patterns, which have been associated with therapeutic sensitivity and resistance. In particular, dendritic cells (DCs) are recently discovered to be associated with prognosis and survival in cancer. However, how DCs differ among ESCC patients has not been fully comprehended. Recently, the advance of single-cell RNA sequencing (scRNA-seq) enables us to profile the cell types, states, and lineages in the heterogeneous ESCC tissues. Here, we dissect the ESCC tumor microenvironment at high resolution by integrating 192,078 single cells from 60 patients, including 4379 DCs. We then used Scissor, a method that identifies cell subpopulations from single-cell data that are associated bulk samples with genomic and clinical information, to stratify DCs into Scissorhi and Scissorlow subtypes. We applied the Scissorhi gene signature to stratify ESCC scRNAseq patient, and we found that PD-L1, TIGIT, PVR and IL6 ligand-receptor-mediated cell interactions existed mainly in Scissorhi patients. Finally, based on the Scissor results, we successfully developed a validated prognostic risk model for ESCC and further validated the reliability of the risk prediction model by recruiting 40 ESCC clinical patients. This information highlights the importance of these genes in assessing patient prognosis and may help in the development of targeted or personalized therapies for ESCC.

Integrating iron metabolism-related gene signature to evaluate prognosis and immune infiltration in nasopharyngeal carcinoma

BackgroundDysregulation of iron metabolism has been shown to have significant implications for cancer development. We aimed to investigate the prognostic and immunological significance of iron metabolism-related genes (IMRGs) in nasopharyngeal carcinoma (NPC).MethodsMultiple Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) datasets were analyzed to identify key IMRGs associated with prognosis. Additionally, the immunological significance of IMRGs was explored.ResultsA novel risk model was established using the LASSO regression algorithm, incorporating three genes (TFRC, SLC39A14, and ATP6V0D1).This model categorized patients into low and high-risk groups, and Kaplan–Meier analysis revealed significantly shorter progression-free survival for the high-risk group (P < 0.0001). The prognostic model’s accuracy was additionally confirmed by employing time-dependent Receiver Operating Characteristic (ROC) curves and conducting Decision Curve Analysis (DCA). High-risk patients were found to correlate with advanced clinical stages, specific tumor microenvironment subtypes, and distinct morphologies. ESTIMATE analysis demonstrated a significant inverse relationship between increased immune, stromal, and ESTIMATE scores and lowered risk score. Immune analysis indicated a negative correlation between high-risk score and the abundance of most tumor-infiltrating immune cells, including dendritic cells, CD8+ T cells, CD4+ T cells, and B cells. This correlation extended to immune checkpoint genes such as PDCD1, CTLA4, TIGIT, LAG3, and BTLA. The protein expression patterns of selected genes in clinical NPC samples were validated through immunohistochemistry.ConclusionThis study presents a prognostic model utilizing IMRGs in NPC, which could assist in assessing patient prognosis and provide insights into new therapeutic targets for NPC.