Prognostic Outcomes In Patients Research Articles

Prediction of outcome in malignant hypertension patients using machine learning: a report from the West Birmingham malignant hypertension registry

Abstract Objectives Malignant hypertension (MHT) is a rare, yet severe condition characterized by high morbidity and mortality rates. This study aimed to assess the potential of machine learning (ML) algorithms in forecasting prognostic outcomes in MHT patients. Methods The dataset from the West Birmingham MHT Registry was used. We evaluated the efficacy of 9 ML algorithms, CatBoost, Decision Tree (DT), Light-Gradient Boosting Machine (LightGBM), K-Nearest Neighbors (KNN), Logistic Regression (LR), Multi-Layer Perceptron (MLP), Random Forest (RF), Support Vector Machine (SVM) and XGBoost, in predicting a composite outcome of all-cause mortality or dialysis. Evaluation metrics included the area under the receiver operating characteristic curve (AUC) and the F1 score. The SHapley Additive exPlanations values were employed to quantify the importance of each feature in the models. Results The study cohort comprised 385 individuals diagnosed with MHT (mean age of 48±13 years, 66% male). During a median follow-up period of 11 years (interquartile range: 3-18 years), 282 patients (73%) experienced the composite outcome. From 24 demographic and clinical variables, 16 were selected into the ML models. Among 9 models, the SVM and LR models exhibited robust predictive performance, achieving AUCs of 0.86 (95% CI: 0.76-0.93) and 0.81 (95% CI: 0.72-0.93), respectively. Furthermore, these models demonstrated high F1 scores (SVM: 0.89, LR: 0.85) and sensitivity (SVM: 0.89, LR: 0.83) (Table). Age, smoking history, systolic blood pressure at follow-up and baseline creatinine levels were commonly identified as primary prognostic features in both SVM and LR models (Figure). Conclusions The application of ML algorithms facilitates effective prediction of prognostic outcomes in patients with MHT, illustrating their potential utility in clinical decision-making through more targeted risk stratification and individualized patient care in these high-risk patients.

Single-cell and bulk transcriptomic datasets enable the development of prognostic models based on dynamic changes in the tumor immune microenvironment in patients with hepatocellular carcinoma and portal vein tumor thrombus

BackgroundHepatocellular carcinoma (HCC) patients exhibiting portal vein tumor thrombosis (PVTT) face a high risk of rapid malignant progression and poor outcomes, with this issue being compounded by a lack of effective treatment options. The integration of bulk RNA-sequencing (RNA-seq) and single-cell RNA-seq (scRNA-seq) datasets focused on samples from HCC patients with PVTT has the potential to yield unprecedented insight into the dynamic changes in the tumor microenvironment (TME) and associated immunological characteristics in these patients, providing an invaluable tool for the reliable prediction of disease progression and treatment responses.MethodsscRNA-seq data from both primary tumor (PT) and PVTT cells were downloaded from the Gene Expression Omnibus (GEO) database, while the International Cancer Genome Consortium (ICGC) and Cancer Genome Atlas (TCGA) databases were used to access bulk RNA-seq datasets. scRNA-seq, clustering, GSVA enrichment, mutational profiling, and predictive immunotherapeutic treatment analyses were conducted using these data with the goal of systematically assessing the heterogeneity of PT and PVTT cells and establishing a model capable of predicting immunotherapeutic and prognostic outcomes in patients with HCC.ResultsThese analyses revealed that PVTT cells exhibited patterns of tumor proliferation, stromal activation, and low levels of immune cell infiltration, presenting with immune desert and immune rejection-like phenotypes. PT cells, in contrast, were found to exhibit a pattern of immunoinflammatory activity. Core PVTT-associated genes were clustered into three patterns consistent with the tumor immune rejection and immune desert phenotypes. An established clustering model was capable of predicting tumor inflammatory stage, subtype, TME stromal activity, and patient outcomes. PVTT signature genes were further used to establish a risk model, with the risk scores derived from this model providing a tool to evaluate patient clinicopathological features including clinical stage, tumor differentiation, histological subtype, microsatellite instability status, and tumor mutational burden. These risk scores were also able to serve as an independent predictor of patient survival outcomes, responses to adjuvant chemotherapy, and responses to immunotherapy. In vitro experiments were used to partially validate the biological prediction results.ConclusionThese results offer new insight into the biological and immunological landscape of PVTT in HCC patients, By utilizing individual patient risk scores, providing an opportunity to guide more effective immunotherapeutic interventional efforts.

Association of various RAS codon mutations and prognostic outcomes of patients with colorectal liver metastases after hepatectomy.

The prognostic and predictive value of RAS mutations in patients with colorectal liver metastases (CRLM) who have undergone hepatectomy holds substantial importance. The present study aimed to investigate the impact of different RAS codon mutations on long-term survival in CRLM patients. A retrospective analysis was conducted on clinicopathological data from 399 CRLM patients with RAS mutations who underwent hepatectomy between January 2000 and December 2020. The RAS mutation gene status was assessed in KRAS codons (G12, G13, Q61, and A146) and NRAS codons (G12, G13, and Q61). Survival curves were generated using the Kaplan-Meier plotter and compared using the log-rank test. Univariate and multivariate analyses were performed to analyze the clinicopathological data. In the entire cohort, patients with KRAS G12 mutations exhibited the most favorable prognosis (p = 0.018). Comparatively, patients harboring KRAS Q61 mutations experienced poorer overall survival (OS) with a median of 15 months versus 33 months (p = 0.011) when compared to those with KRAS G12 mutations. Moreover, patients with NRAS Q61 mutations also showed decreased OS with a median of 26 months versus 33 months (p = 0.020) in comparison to KRAS G12 mutation patients. The results of multivariate analysis showed that both KRAS Q61 mutation (HR 2.130; 95% CI 1.088-4.168; p = 0.027) and NRAS Q61 mutation (HR 2.877; 95% CI 1.398-5.922; p = 0.004) were independent influencing factors of OS. Based on all identified risk factors, patients with RAS mutation were divided into high-risk and low-risk groups. Notably, in the high-risk group, the incorporation of postoperative chemotherapy was associated with longer OS, while it did not improve the survival of patients in the low-risk group. KRAS Q61 and NRAS Q61 mutations are promising predictors for OS in CRLM patients after hepatectomy. Postoperative chemotherapy may significantly benefit CRLM patients with RAS mutations, particularly those identified as high-risk.

Identification of immune patterns in idiopathic pulmonary fibrosis patients driven by PLA2G7-positive macrophages using an integrated machine learning survival framework

Patients with advanced idiopathic pulmonary fibrosis (IPF), a complex and incurable lung disease with an elusive pathology, are nearly exclusive candidates for lung transplantation. Improved identification of patient subtypes can enhance early diagnosis and intervention, ultimately leading to better prognostic outcomes for patients. The goal of this study is to identify new immune patterns and biomarkers in patients. Immune subtypes in IPF patients were identified using single-sample gene set enrichment analysis, and immune subtype-related genes were explored using the weighted correlation network analysis algorithm. A machine learning integration framework was used to establish the optimal prognostic model, known as the immune-related risk score (IRS). Single-cell sequencing was conducted to investigate the major role of macrophage-derived PLA2G7 in the immune microenvironment. We assessed the stability of celecoxib in targeting PLA2G7 through molecular docking and surface plasmon resonance. IPF patients present two distinct immune subtypes, one characterized by immune activation and inflammation, and the other by immune suppression. IRS can predict the immune status and prognosis of IPF patients. Furthermore, multi-cohort analysis and single-cell sequencing analysis demonstrated the diagnostic and prognostic value of PLA2G7 derived from macrophages and its role in shaping the inflammatory immune microenvironment in IPF patients. Celecoxib could effectively and stably bind with PLA2G7. PLA2G7, as identified through IRS, demonstrates marked stability in diagnosing and predicting the prognosis of IPF patients as well as predicting their immune status. It can serve as a novel biomarker for IPF patients.

Machine Learning-Based Predictions of Mortality and Readmission in Type 2 Diabetes Patients in the ICU

Prognostic outcomes for patients with type 2 diabetes in the intensive care unit (ICU), including mortality and readmission rates, are critical for informed clinical decision-making. Although existing research has established a link between type 2 diabetes and adverse outcomes in the ICU, the potential of machine learning techniques for enhancing predictive accuracy has not been fully realized. This study seeks to develop and validate predictive models employing machine learning algorithms to forecast mortality and 30-day post-discharge readmission rates among ICU type 2 diabetes patients, thereby enhancing predictive accuracy and supporting clinical decision-making. Data were extracted and preprocessed from the MIMIC-III database, focusing on 14,222 patients with type 2 diabetes and their corresponding ICU admission records. Comprehensive information, including vital signs, laboratory results, and demographic characteristics, was utilized. Six machine learning algorithms—bagging, AdaBoost, GaussianNB, logistic regression, MLP, and SVC—were developed and evaluated using 10-fold cross-validation to predict mortality at 3 days, 30 days, and 365 days, as well as 30-day post-discharge readmission rates. The machine learning models demonstrated strong predictive performance for both mortality and readmission rates. Notably, the bagging and AdaBoost models showed superior performance in predicting mortality across various time intervals, achieving AUC values up to 0.8112 and an accuracy of 0.8832. In predicting 30-day readmission rates, the MLP and AdaBoost models yielded the highest performance, with AUC values reaching 0.8487 and accuracy rates of 0.9249. The integration of electronic health record data with advanced machine learning techniques significantly enhances the accuracy of mortality and readmission predictions in ICU type 2 diabetes patients. These models facilitate the identification of high-risk patients, enabling timely interventions, improving patient outcomes, and demonstrating the significant potential of machine learning in clinical prediction and decision support.

Transcatheter aortic valve implantation for severe aortic regurgitation using the J-Valve system: A midterm follow-up study.

Transcatheter aortic valve implantation (TAVI) is a well-established intervention for severe aortic valve stenosis. However, its application for severe aortic regurgitation (AR) is still under evaluation. This study aims to present the 3-year follow-up outcomes of the J-Valve system in managing severe AR. The aim of this study was to evaluate the mid-term efficacy and durability of the J-Valve system in the treatment of severe AR and to provide new information on this intervention. In this retrospective, single-center study, we evaluated the prognostic outcomes of patients with AR, who underwent treatment with the J-Valve system at Nanjing Drum Tower Hospital. Consecutive patients who were treated with the J-Valve were included in the analysis. The study focused on the echocardiographic follow-up to assess the effectiveness and durability of the J-Valve system in managing AR. From January 2018 to December 2022, 36 high-risk AR patients treated with the J-Valve system had a procedural success rate of 97.2%, with one case requiring open-heart surgery due to valve displacement. Significant improvements were observed in left ventricular diameter (from 63.50 [58.75-69.50] mm to 56.50 [53.00-60.50] mm, p < 0.001) and left atrial diameter (from 44.00 [40.00-45.25] mm to 39.00 [36.75-41.00] mm, p = 0.003) postsurgery. All patients completed the 1-year follow-up, with an overall mortality rate of 2 out of 36 (5.6%). Among the surviving patients, there was one case of III° atrioventricular block and one case of stroke, both occurring within 90 days postsurgery. After a 3-year follow-up, 15.0% of patients had mild or moderate valvular regurgitation, with no cases of moderate or severe paravalvular leak. Additionally, 89.5% of patients were classified as New York Heart Association class I or II, showing significantly enhanced cardiac function. The J-Valve system has shown positive therapeutic outcomes in treating AR, with notable effectiveness in managing the condition and significant improvements in heart failure symptoms and cardiac remodeling. However, due to the limited sample size and partial follow-up data, it is important to emphasize the need for further research with comprehensive long-term follow-up, to fully validate these results.

A Prognostic Risk Signature of Two Autophagy-Related Genes for Predicting Triple-Negative Breast Cancer Outcomes.

Triple-negative breast cancer (TNBC) is recognized as the most aggressive molecular subtype of breast cancer. Recent studies have highlighted the complex role of autophagy in the pathogenesis of TNBC. In this study, we evaluated 18,330 genes, including 1111 autophagy-related genes, (ARGs), across 579 TNBC samples from online databases. Differentially expressed ARGs in TNBC were identified using high-throughput RNA-seq data from the Cancer Genome Atlas (TCGA). Prognostic factors were examined through Cox regression and multivariate Cox analyses, with predictive efficacy assessed using receiver operating characteristic (ROC) curves. A nomogram integrating the risk signature with clinicopathological factors, such as TNM stage, was developed. Immunohistochemical analysis of clinical samples was also conducted. EIF4EBP1 and NPAS3 were significantly correlated with prognostic outcomes in patients with TNBC. Multivariate Cox regression analysis demonstrated that the expression levels of these two genes were accurate predictors of disease progression in TNBC samples from TCGA and the GSE31519 dataset. The efficacy of this predictive model was validated using ROC curve analysis and calibration plots, confirming its ability to accurately estimate the 1-, 2-, and 3-year survival rates for individuals with TNBC. Additionally, EIF4EBP1 and NPAS3 expression influenced drug sensitivity in TNBC cell lines, with notably lower NPAS3 expression in TNBC tissues, particularly in Stage III cases. This study is the first to report NPAS3 expression in patients with TNBC. The autophagy-related genes EIF4EBP1 and NPAS3 may serve as independent prognostic factors for individuals with TNBC.

Study on the prediction model of liver cancer based on chronic liver disease and the related molecular mechanism

Introduction and objectiveDue to the high heterogeneity of HCC, which leads to poor prognostic outcomes for patients, there is a need to develop a novel predictive model for accurate classification of HCC in order to improve patient survival rates. Materials and methodsThe data of the HCV, cirrhosis, and HCC were obtained from TCGA and GEO databases. Multivariable Cox regression analysis and survival analysis was conducted to assess the prognostic relevance of these differentially expressed genes. Single-cell sequencing was used to explore the intercellular interaction patterns and identify relevant signaling pathways. Drug sensitivity analysis was conducted to determine personalized treatment strategies for patients. ResultsIn this study, we conducted integrated analysis of hepatitis, cirrhosis, and hepatocellular carcinoma datasets and identified 10 liver disease progression genes associated with prognosis. These genes exhibited significant downregulation in expression as the disease advanced, suggesting their crucial involvement in HCC development. By performing multivariable Cox analysis, we established a prognostic model for liver disease progression to predict the prognosis of HCC patients. The model was validated using ROC analysis, demonstrating good accuracy and stability in prognostic evaluation. Single-cell sequencing analysis revealed that these genes primarily exert their effects through the MIF signaling pathway during HCC progression. Furthermore, we observed that patients in the low-risk group exhibited higher sensitivity to TACE treatment, while patients in the high-risk group showed better response to sorafenib treatment. ConclusionsIn summary, we have elucidated the key genes involved in the progression of liver diseases and established a precise prognostic model for assessing the prognosis of HCC patients. Our study provides novel insights and strategies for the treatment of HCC.

The relationship between imaging-based body composition abnormalities and long-term mortality in patients with liver cirrhosis

BackgroundEmerging evidence on cirrhosis suggests a close correlation between abnormality in body composition characteristics and poor prognosis. This study aimed to evaluate the impact of dynamic changes in body composition on the prognostic outcomes in patients with cirrhosis. MethodsThis retrospective analysis included 158 patients diagnosed as cirrhosis from January 2018 to August 2023. Skeletal muscle mass, muscle quality, visceral and subcutaneous adiposity were evaluated using computed tomography (CT) imaging at the third lumbar vertebra level. Competing risk model was performed four different body composition status (i.e., normal, only sarcopenia, only myosteatosis, and combined status) for liver-related mortality. We also explored the relationship between the dynamic change in body composition and long-term prognosis by applying Gray’s test. ResultsOf the 158 cirrhotic patients (mean [SD] age, 57.1 [12.6] years), sarcopenia was present in 85 (60.1 %) patients, while 22 (13.9 %) patients had sarcopenic obesity and 68 (43.0 %) had myosteatosis. Patients solely diagnosed with sarcopenia exhibited a higher mortality rate compared to those with normal body composition (Gray’s test, P=0.006), while patients solely diagnosed with myosteatosis or with a combination of sarcopenia and myosteatosis did not reach statistical significance (Gray’s test, P=0.076; P=0.140). Multivariable analysis also revealed that VSR (HR=1.10 [1.01∼1.20]; P=0.028), sarcopenia (HR=2.73 [1.20∼6.22], P=0.017) and myosteatosis (HR=2.39 [1.10∼5.18], P=0.028) were significant independent predictors of liver-related deaths. Otherwise, patients exhibiting aggravating body composition during follow-up period were associated with a significantly higher mortality risk compared to those with normal or remission body composition status (HR=7.63 [1.12∼51.14]; P=0.036). ConclusionProgressive alterations in body composition status appears to be associated with liver-related mortality in individuals with liver cirrhosis. Focusing on the management of skeletal muscle, along with visceral and subcutaneous adiposity, may contribute to improving the prognosis of cirrhotic patients.

RNA-binding protein LSM7 facilitates breast cancer metastasis through mediating alternative splicing of CD44

AimsThe RNA-binding protein LSM7 is essential for RNA splicing, acting as a key component of the spliceosome complex; however, its specific role in breast cancer (BC) has not been extensively investigated. Materials and methodsLSM7 expression in BC samples was evaluated through bioinformatics analysis and immunohistochemistry. The impact of LSM7 on promoting metastatic tumor characteristics was examined using transwell and wound healing assays, as well as an orthotopic xenograft model. Additionally, the involvement of LSM7 in alternative splicing of CD44 was explored via RNA immunoprecipitation and third-generation sequencing. The regulatory role of TCF3 in modulating LSM7 gene expression was further elucidated using luciferase reporter assays and chromatin immunoprecipitation. Key findingsOur findings demonstrate that LSM7 was significantly overexpressed in metastatic BC tissues and was associated with poor prognostic outcomes in patients with BC. LSM7 overexpression markedly increased the migratory and invasive capabilities of BC cells in vitro and significantly promoted spontaneous lung metastasis in vivo. Furthermore, RIP-seq analysis revealed that LSM7 binded to CD44 RNA, enhancing the expression of its alternatively spliced isoform CD44s, thereby driving BC metastasis and invasion. Additionally, the transcription factor TCF3 was found to activate LSM7 transcription by directly binding to its promoter. SignificanceIn summary, this study highlights the pivotal role of LSM7 in the production of the CD44s isoform and the promotion of breast cancer metastasis. Targeting the TCF3/LSM7/CD44s axis may offer a promising therapeutic strategy for breast cancer treatment.

Survival Outcome Superiority of Total Pharyngolaryngectomy Compared with Chemoradiotherapy for T4aM0 Hypopharyngeal Squamous Cell Carcinoma: A Nationwide Database Study of Japan.

The purpose of this study is to elucidate whether total pharyngolaryngectomy (TPL) or chemoradiotherapy (CRT) provides a better prognostic outcome in patients with T4aM0 hypopharyngeal carcinoma (HPSCC) using a nationwide database. All data were obtained from the Head and Neck Cancer Registry of Japan, and information from patients who were newly diagnosed with T4aM0 HPSCC between 2011 and 2015 was extracted. The primary endpoint was disease-specific survival (DSS), and the secondary endpoint was overall survival (OS). The inverse probability of treatment weighting (IPTW) adjustments was used for survival analyses. Our cohort included 1143 patients. The TPL and CRT groups included 724 and 419 patients, respectively. Following IPTW adjustments, both the OS and DSS of the TPL group were significantly longer than those of the CRT group (P = .02 and P = .002, respectively). Survival superiority was demonstrated for patients with T4aM0 HPSCC treated with TPL compared with those treated with CRT.

Blood pressure variability and prognostic significance in traumatic brain injury: analysis of the eICU-CRD database

BackgroundPreliminary evidence demonstrates that visit-to-visit systolic blood pressure (SBP) variability is a prognostic factor of TBI. However, literature regarding the impact of initial blood pressure management on the outcomes of TBI patients is limited. We aimed to further validate the clinical significance of BPV on the prognostic outcomes of patients with TBI.MethodsWe performed the analysis by using individual patient-level data acquired from the eICU-CRD, which collected 200,859 ICU admissions of 139,367 patients in 2014 and 2015 from 208 US hospitals. Adult patients with traumatic intraparenchymal hemorrhage or contusion were included. The primary outcome was in-hospital mortality and the secondary outcome was discharge-home rate. Blood pressure variability (BPV) was calculated according to standard criteria: at least six measurements were taken in the first 24 h (hyperacute group) and 36 over days 2–7 (acute group). We estimated the associations between BPV and outcomes with logistic and proportional odds regression models. The key parameter for BPV was standard deviation (SD) of SBP, categorized into quintiles. We also calculated the average real variability (ARV), as well as maximum, minimum, and mean SBP for comparison in our analysis.ResultsWe studied 1486 patients in the hyperacute group and 857 in the acute group. SD of SBP had a significant association with the in-hospital mortality for both the hyperacute group (highest quintile adjusted OR 2.28 95% CI 1.18–4.42; ptrend<0.001) and the acute group (highest quintile adjusted OR 2.17, 95% CI 1.08–4.36; ptrend<0.001). The strongest predictors of primary outcome were SD of SBP in the hyperacute phase and minimum SBP in the acute phase. Associations were similar for the discharge-home rate (for the hyperacute group, highest quintile adjusted OR 0.58, 95% CI 0.37–0.89; ptrend<0.001; for the acute group OR 0.55, 95% CI 0.32–0.95; ptrend<0.001).ConclusionSystolic BPV seems to predict a poor outcome in patients with TBI. The benefits of early treatment to maintain appropriate SBP level might be enhanced by smooth and sustained control.

Enhancing early detection of pancreatic cancer by integrating AI with advanced imaging techniques

Pancreatic cancer remains one of the most lethal malignancies, with a five-year survival rate of less than 10%, primarily due to late-stage diagnosis and rapid disease progression. Early detection is critical for improving patient outcomes, yet current diagnostic methods lack the sensitivity and specificity needed for effective screening. This review explores the integration of advanced imaging techniques with artificial intelligence (AI) to enhance the early detection of pancreatic cancer. Emphasizing a biological approach, we examine the underlying molecular and cellular mechanisms that contribute to the pathogenesis of pancreatic cancer and how they manifest in imaging data. Key imaging modalities, including high-resolution magnetic resonance imaging (MRI), computed tomography (CT), and positron emission tomography (PET), are evaluated for their efficacy in visualizing pancreatic abnormalities. AI algorithms, particularly machine learning and deep learning, are discussed in the context of their ability to analyze complex imaging datasets, identify subtle biomarkers, and predict disease onset with high accuracy. We delve into the biological markers that AI algorithms can detect, such as changes in the tumor microenvironment, alterations in tissue architecture, and specific molecular signatures of pancreatic ductal adenocarcinoma (PDAC). Furthermore, the integration of AI with molecular imaging techniques, such as positron emission tomography-magnetic resonance imaging (PET-MRI) and optical coherence tomography (OCT), is explored to provide a multi-faceted approach to early diagnosis. The review also highlights the potential of combining AI-driven imaging with liquid biopsies and genomics to create a comprehensive diagnostic framework. By leveraging the power of AI to interpret complex biological data, we propose a novel paradigm for the early detection of pancreatic cancer, aiming to improve screening protocols, enable timely therapeutic interventions, and ultimately enhance patient survival rates. In supposition, the integration of AI with advanced imaging techniques holds significant promise for revolutionizing the early detection of pancreatic cancer. Continued research and clinical validation are essential to translate these technological advancements into routine clinical practice, offering hope for better prognostic outcomes in patients with this devastating disease.

Predictive value of serum magnesium levels for prognosis in patients with non-small cell lung cancer undergoing EGFR-TKI therapy.

The aim of this study is to assess the impact of serum magnesium (Mg) levels on prognostic outcomes in patients with non-small cell lung cancer (NSCLC) undergoing treatment with epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKI). A cohort comprising 91 patients with NSCLC with epidermal growth factor receptor mutations received EGFR-TKI therapy. Assessments of liver and kidney function and electrolyte levels were conducted before treatment initiation and after completing two cycles of EGFR-TKI therapy. Data on variables such as age, gender, presence of distant metastasis, smoking history, other therapeutic interventions, and the specific TKI used were collected for analysis. Cox regression analysis revealed that patients with higher Mg levels prior to EGFR-TKI therapy had significantly longer progression-free survival (PFS) and overall survival (OS). Elevated Mg levels remained predictive of PFS and OS after two cycles of EGFR-TKI therapy. Multiple regression analysis confirmed these findings. Additionally, it was observed that smokers might represent a unique population, demonstrating a correlation between OS and Mg levels. Our findings indicate that serum Mg level is a prognostic factor in patients with NSCLC undergoing EGFR-TKI therapy. This may provide new insights into the underlying mechanisms of EGFR-TKI therapy related to electrolyte balance.

Decreased levels of PTCSC3 promote the deterioration of prostate cancer and affect the prognostic outcome of patients through sponge miR-182-5p

BackgroundProstate cancer, characterized by its insidious onset and short overall survival, and has seen a rise in incidence over recent decades. This study aims to investigate the expression and molecular mechanism of lncRNA PTCSC3 (PTCSC3) in prostate cancer in order to develop new prognostic and therapeutic biomarkers.MethodsThe level of PTCSC3 in serum and cell samples of prostate cancer was quantitatively measured using RT-qPCR assays. The correlation between the variation in PTCSC3 levels and clinical indicators of patients was evaluated. The survival status of the prostate cancer patients included in the study was evaluated using Kaplan-Meier curve and multivariable Cox analysis. The impact of PTCSC3 overexpression on cell growth and activity was revealed by CCK-8 and Transwell assays. The targeting relationship between PTCSC3 and miR-182-5p was determined by bioinformatics prediction and luciferase activity.ResultsPTCSC3 was found to be downregulated in prostate cancer, and its low levels were associated with short overall survival in patients. It influenced the progression of prostate cancer by targeting miR-182-5p. Increasing PTCSC3 levels suppressed the proliferation, migration and invasion levels of cells, and miR-182-5p mimic counteracted PTCSC3’s effects on cells.ConclusionsAs a potential prognostic biological factor for prostate cancer, PTCSC3 may regulate the progression of prostate cancer by sponging miR-182-5p and affect the prognosis of patients.

The added value of an AI-based body composition analysis in a lung cancer screening population: preliminary results

Background and aimsBody composition has been linked with clinical and prognostic outcomes in patients with cancer and cardiovascular diseases. Body composition analysis in lung cancer screening (LCS) is very limited. This study aimed at assessing the association of subcutaneous fat volume (SFV) and subcutaneous fat density (SFD), measured on chest ultra-low dose computed tomography (ultra-LDCT) images by a fully automated artificial intelligence (AI)-based software, with clinical and anthropometric characteristics in a LCS population. Methods and resultsDemographic, clinical, and dietary data were obtained from the written questionnaire completed by each participant at the first visit, when anthropometric measurements, blood sample collection and chest ultra-LDCT were performed. Images were analyzed for automated 3D segmentation of subcutaneous fat and muscle.The analysis included 938 volunteers (372 females); men with a smoking history of ≥40 pack-years had higher SFV (p = 0.0009), while former smokers had lower SFD (p = 0.0019). In female participants, SFV and SFD differed significantly according to age. SFV increased with rising BMI, waist circumference, waist-hip ratio, and CRP levels ≥2 mg/L (p < 0.0001), whereas SFD decreased with rising BMI, waist circumference, waist-hip ratio, and CRP levels ≥2 mg/L (p < 0.001) in both sexes. SFV was associated with glycemia and triglycerides levels (p = 0.0067 and p=<0.0001 in males, p = 0.0074 and p < 0.0001 in females, respectively), while SFD with triglycerides levels (p < 0.0001). ConclusionWe observed different associations of SFV and SFD with age and smoking history between men and women, whereas the association with anthropometric data, CRP, glycemia and triglycerides levels was similar in the two sexes.

Emerging Predictors by Non-HDL-C/HDL-C Ratio and Novel Biomarkers for Coronary Slow Flow Phenomenon.

The coronary slow-flow (CSF) phenomenon is a condition characterized by delayed coronary opacification during diagnostic angiography without the presence of epicardial coronary artery disease. This mini-review explores various emerging predictors and biomarkers associated with CSF, aiming to address the potential diagnostic tools. A comprehensive analysis of recent studies has investigated different biomarkers, including growth differentiation factor 15, galectin 3, microRNA (miRNA)-22, miRNA-155, interleukin 34, soluble vascular cell adhesion molecule-1, long non-coding RNA, plasma choline, adropin, and lipid markers non-high-density lipoprotein cholesterol (HDL-C)/HDL-C ratio to enhance understanding and predict CSF. Additionally, we have summarizes the major findings and significant limitations observed in various studies on CSF biomarkers. The implications of these findings suggest significant advancements in personalized treatment strategies and improved prognostic outcomes for patients exhibiting CSF.

Development and validation of an inflammatory biomarkers model to predict gastric cancer prognosis: a multi-center cohort study in China

BackgroundInflammatory factors have increasingly become a more cost-effective prognostic indicator for gastric cancer (GC). The goal of this study was to develop a prognostic score system for gastric cancer patients based on inflammatory indicators.MethodsPatients’ baseline characteristics and anthropometric measures were used as predictors, and independently screened by multiple machine learning(ML) algorithms. We constructed risk scores to predict overall survival in the training cohort and tested risk scores in the validation. The predictors selected by the model were used in multivariate Cox regression analysis and developed a nomogram to predict the individual survival of GC patients.ResultsA 13-variable adaptive boost machine (ADA) model mainly comprising tumor stage and inflammation indices was selected in a wide variety of machine learning models. The ADA model performed well in predicting survival in the validation set (AUC = 0.751; 95% CI: 0.698, 0.803). Patients in the study were split into two sets – “high-risk” and “low-risk” based on 0.42, the cut-off value of the risk score. We plotted the survival curves using Kaplan-Meier analysis.ConclusionThe proposed model performed well in predicting the prognosis of GC patients and could help clinicians apply management strategies for better prognostic outcomes for patients.

TM9SF1 offers utility as an efficient predictor of clinical severity and mortality among acute respiratory distress syndrome patients.

Acute respiratory distress syndrome (ARDS) is a major cause of death among critically ill patients in intensive care settings, underscoring the need to identify biomarkers capable of predicting ARDS patient clinical status and prognosis at an early time point. This study specifically sought to explore the utility and clinical relevance of TM9SF1 as a biomarker for the early prediction of disease severity and prognostic outcomes in patients with ARDS. This study enrolled 123 patients with severe ARDS and 116 patients with non-severe ARDS for whom follow-up information was available. The mRNA levels of TM9SF1 and cytokines in peripheral blood mononuclear cells from these patients were evaluated by qPCR. The predictive performance of TM9SF1 and other clinical indicators was evaluated using received operating characteristic (ROC) curves. A predictive nomogram was developed based on TM9SF1 expression and evaluated for its ability in the early prediction of severe disease and mortality in patients with ARDS. TM9SF1 mRNA expression was found to be significantly increased in patients with severe ARDS relative to those with non-severe disease or healthy controls. ARDS severity increased in correspondence with the level of TM9SF1 expression (odds ratio [OR] = 2.43, 95% confidence interval [CI] = 2.15-3.72, P = 0.005), and high TM9SF1 levels were associated with a greater risk of mortality (hazard ratio [HR] = 2.27, 95% CI = 2.20-4.39, P = 0.001). ROC curves demonstrated that relative to other clinical indicators, TM9SF1 offered superior performance in the prediction of ARDS severity and mortality. A novel nomogram incorporating TM9SF1 expression together with age, D-dimer levels, and C-reactive protein (CRP) levels was developed and was used to predict ARDS severity (AUC = 0.887, 95% CI = 0.715-0.943). A separate model incorporating TM9SF1 expression, age, neutrophil-lymphocyte ratio (NLR), and D-dimer levels (C-index = 0.890, 95% CI = 0.627-0.957) was also developed for predicting mortality. Increases in ARDS severity and patient mortality were observed with rising levels of TM9SF1 expression. TM9SF1 may thus offer utility as a novel biomarker for the early prediction of ARDS patient disease status and clinical outcomes.

Prognostic value of FGFR2 alterations in patients with iCCA undergoing surgery or systemic treatments: A meta-analysis.

Over recent years, there has been a notable rise in the incidence of intrahepatic cholangiocarcinoma (iCCA), which presents a significant challenge in treatment due to its complex disease characteristics and prognosis. Notably, the identification of fibroblast growth factor receptor 2 (FGFR2) fusion/rearrangement, a potential oncogenic driver primarily observed in iCCA, raises questions about its impact on the prognostic outcomes of patients undergoing surgical intervention or other therapeutic approaches. A comprehensive search from inception to July 2023 was conducted across PubMed, Embase, Web of Science, and the Cochrane Library databases. The objective was to identify relevant publications comparing the prognosis of FGFR2 alterations and no FGFR2 alterations groups among patients with iCCA undergoing surgical resection or other systemic therapies. The primary outcome indicators, specifically Overall Survival (OS) and Disease-Free Survival (DFS), were estimated using Hazard Ratios (HRs) with 95% confidence intervals (CIs), and statistical significance was defined as p < .05. Study quality was assessed using the Newcastle-Ottawa Quality Assessment Scale. Statistical analyses were performed using Review Manager 5.4 software and Stata, version 12.0. Six studies, involving 1314 patients (FGFR2 alterations group n = 173 and no FGFR2 alterations group n = 1141), were included in the meta-analysis. The analysis revealed that the FGFR2 alterations group exhibited a significantly better OS prognosis compared to the no FGFR2 alterations group, with a fixed-effects combined effect size HR = 1.31, 95%CI = 1.001-1.715, p = .049. Furthermore, meta-regression and subgroup analysis showed that the length of the follow-up period did not introduce heterogeneity into the results. This finding indicates the stability and reliability of the study outcomes. The current study provides compelling evidence that FGFR2 alterations are frequently associated with improved survival outcomes for patients with iCCA undergoing surgical resection or other systemic treatments. Additionally, the study suggests that FGFR2 holds promise as a safe and dependable therapeutic target for managing metastatic, locally advanced or unresectable iCCA. This study offers a novel perspective in the realm of targeted therapy for iCCA, presenting a new and innovative approach to its treatment.