Prognostic Assessment Research Articles

Evaluation of Left Ventricular Remodeling and Prognosis after PCI in Acute Myocardial Infarction Using Real-Time, Three-Dimensional Echocardiography Combined with Layer-Specific Strain Imaging

Aim: This study aimed to evaluate left atrial and left ventricular volumes, strains, and strain rates in patients with acute ST-segment elevation myocardial infarction (STEMI) treated with percutaneous coronary intervention (PCI) using real-time three-dimensional echocardiography and layer-specific strain techniques. The relationships between these parameters and left ventricular remodeling (LVR) and prognosis were also explored. Methods: The study included 217 patients with first-episode STEMI who underwent emergency PCI. Echocardiography and myocardial strain analyses were performed within 24 h post-PCI. Patients were categorized into early and non-early LVR groups based on the increase in left ventricular end-diastolic volume (LVEDV). The occurrence of major cardiovascular adverse events (MACE) was followed up for one-year post-PCI. Differences in clinical data and ultrasound parameters between the groups were compared, and the predictive value of myocardial strain indicators for late LVR was analyzed using a multivariate logistic regression model and receiver operating characteristic (ROC) curves. Results: Early LVR occurred in 54.8% of patients and was characterized by decreased left ventricular systolic function, more segments with abnormal movement, and reduced absolute strain values in the three layers of the left ventricular wall myocardium, with a compensatory increase in left atrial strain rate during the contraction phase. The early LVR group showed a higher incidence of MACE at the one-year follow-up. At 6 months post-PCI, 29.9% of patients developed late LVR, which was not completely related to early LVR. Late LVR was associated with a higher incidence of MACE. The longitudinal strain value of each layer of left ventricular myocardium obtained from layer-specific strain imaging showed predictive value for advanced LVR. Conclusions: More than half of patients with STEMI develop early LVR post-PCI, with a higher incidence of MACE during one-year of follow-up, necessitating attention to early LVR in clinical practice. Late LVR, which develops in some patients after 6 months, is also linked to a higher incidence of MACE. Accurate monitoring of the myocardial deformation function using layer-specific strain imaging is expected to become a reference indicator for clinical diagnosis and treatment. Monitoring the structure and function of the left atrium and mitral valve alongside the LVR is important for prognostic assessment and for formulating diagnostic and treatment plans.

Choosing Wisely Between Radiotherapy Dose-Fractionation Schedules: The Molecular Graded Prognostic Assessment for Elderly Glioblastoma Patients.

This study aimed to develop a graded prognostic assessment (GPA) model integrating genomic characteristics for elderly patients with glioblastoma (eGBM), and to compare the efficacy of different radiotherapy schedules. This multi-institutional retrospective study included patients aged ≥65 years who underwent surgical resection followed by radiotherapy with or without temozolomide (TMZ) for newly diagnosed eGBM. Based on the significant factors identified in the multivariate analysis for overall survival (OS), the molecular GPA for eGBM (eGBM-molGPA) was established. A total of 334 and 239 patients who underwent conventionally fractionated radiotherapy (CFRT) and hypofractionated radiotherapy (HFRT) were included, respectively, with 86% of patients receiving TMZ-based chemoradiation. With a median follow-up of 17.4 months (range, 3.3-149.9), the median OS was 18.7 months for CFRT+TMZ group, 15.1 months for HFRT+TMZ group, and 10.4 months for radiotherapy alone group (CFRT+TMZ vs. HFRT+TMZ: HR 1.52, p<0.001; CFRT+TMZ vs. radiotherapy alone: HR 2.52, p<0.001). In a combined analysis with the NOA-08 and NORDIC trials, CFRT+TMZ group exhibited the highest survival rates among all treatment groups. The eGBM-molGPA, which integrated four clinical and three molecular parameters, stratified patients into low-, intermediate-, and high-risk groups. CFRT+TMZ significantly improved OS compared to HFRT+TMZ or radiotherapy alone in the low-risk (p=0.023) and intermediate-risk groups (p<0.001). However, in the high-risk group, there was no significant difference in OS between treatment options (p=0.770). CFRT+TMZ may be more effective than HFRT+TMZ or radiotherapy alone for selected eGBM patients. The novel eGBM-molGPA model can guide treatment selection for this patient population.

Automatic delineation and prognostic assessment of head and neck tumor lesion in multi-modality positron emission tomography / computed tomography images based on deep learning: A survey

Accurately segmenting and staging tumor lesions in cancer patients presents a significant challenge for radiologists, but it is essential for devising effective treatment plans including radiation therapy, personalized medicine, and surgical options. The integration of artificial intelligence (AI), particularly deep learning (DL), has become a useful tool for radiologists, enhancing their ability to understand tumor biology and deliver personalized care to patients with H&N tumors. Segmenting H&N tumor lesions using Positron Emission Tomography/Computed Tomography (PET/CT) images has gained significant attention. However, the diverse shapes and sizes of tumors, along with indistinct boundaries between malignant and normal tissues, present significant challenges in effectively fusing PET and CT images. To overcome these challenges, various DL-based models have been developed for segmenting tumor lesions in PET/CT images. This article reviews multimodality (PET/CT) based H&N tumor lesions segmentation methods. We firstly discuss the strengths and limitations of PET/CT imaging and the importance of DL-based models in H&N tumor lesion segmentation. Second, we examine the current state-of-the-art DL models for H&N tumor segmentation, categorizing them into UNet, VNet, Vision Transformer, and miscellaneous models based on their architectures. Third, we explore the annotation and evaluation processes, addressing challenges in segmentation annotation and discussing the metrics used to assess model performance. Finally, we discuss several open challenges and provide some avenues for future research in H&N tumor lesion segmentation.

Shared decision-making in end-of-life care for end-stage renal disease patients: nephrologists’ views and attitudes

BackgroundThe term end-stage renal disease (ESRD) refers to the final stage of chronic kidney disease. Not all ESRD patients are suitable for dialysis treatment, which despite its advantages, is not without risks. Shared nephrologist-patient decision-making could be beneficial at this stage, yet little is known about such practices in Israel. This study aimed at examining the practice of shared decision-making (SDM) between nephrologists and ESRD patients in Israel, while exploring related conflicts, ethical dilemmas, and considerations.MethodsThe descriptive-quantitative approach applied in this study included a validated questionnaire for nephrologists, based on Emanual and Emanual (1992). The survey, which was distributed via social-media platforms and snowball sampling, was completed by 169 nephrologists. Data analysis included t-tests for independent samples, f-tests for analysis of variance, and t-tests and f-tests for independence. Descriptive analysis examined attitudes towards SDM in end-of-life care for ESRD patients.ResultsThe findings show that the research sample did not include nephrologists who typically act according to the paternalistic decision-making style. Rather, 53% of the respondents were found to act in line with the informative decision-making style, while 47% act according to the interpretive decision-making style. Almost 70% of all respondents reported their discussing quality-of-life with patients; 63.4% provide prognostic assessments; 61.5% inquire about the patient’s desired place of death; 58.6% ask about advance directives or power-of-attorney; and 57.4% inquire about cultural and religious beliefs in end-of-life treatment. Additionally, informative nephrologists tend to promote the patients’ autonomy over their health (P < 0.001); they are also in favor of conservative treatment, compared to paternalistic and interpretive nephrologists, and use less invasive methods than other nephrologists (P = 0.02).ConclusionsNephrologists in Israel only partially pursue an SDM model, which has the potential to improve quality-of-care for ESRD patients and their families. SDM programs should be developed and implemented for increasing such practices among nephrologists, thereby expanding the possibilities for providing conservative care at end-of-life.

Preoperative measures of pain at rest and movement-evoked pain in knee arthroplasty: Associations with pain and function outcome trajectories from a prospective multicentre longitudinal cohort study.

The study of pain at rest (PAR) and movement-evoked pain (MEP) in persons with musculoskeletal pain has received substantial attention. Despite strong interest, relatively little attention has been directed to the psychometric development of these constructs. Our purpose was to explore the relationship between PAR and MEP and to examine the prognostic utility of these measures in persons with knee arthroplasty. We used prospectively collected data from persons scheduled for knee arthroplasty who had moderate to high levels of pain catastrophizing. Preoperative latent variables for PAR and MEP were developed and used to determine if they were associated with a binary latent variable of good versus poor pain and function outcome trajectories. Factor correlations were used to determine the extent to which the variance for PAR and MEP was overlapping. PAR and MEP are significant predictors of good versus poor pain and function classes. Odds ratios ranged from 1.21 to 1.64 (p < 0.001) indicating a significant increase in the likelihood of poor outcome. Correlation between PAR and MEP latent variables was high (r = 0.89; 95% CI: 0.86-0.92) indicating substantially overlapping variance. PAR and MEP, as defined in our study, can be used to make prognostic judgements regarding risk of poor postoperative outcome trajectory following knee arthroplasty. However, PAR and MEP showed substantially overlapping variance indicating that measurements of both are not necessary when making prognostic assessments. Preoperative PAR and MEP latent variables, as defined in our study, had prognostic significance for 1 year pain and function outcome trajectories. PAR and MEP latent variables had substantially overlapping variance which suggested that only one is needed to make prognostic judgements. The prognostic significance of PAR and MEP as well as their substantially overlapping variance is new to the field prognostic research in knee arthroplasty.

Etiological stratification and prognostic assessment of haemophagocytic lymphohistiocytosis by machine learning on onco-mNGS data and clinical data.

Hemophagocytic lymphohistiocytosis (HLH) is a rare, complicated and life threatening hyperinflammatory syndrome that maybe triggered by various infectious agents, malignancies and rheumatologic disorders. Early diagnosis and identification of the cause is essential to initiate appropriate treatment and improve the quality of life and survival of patients. The recently developed Onco-mNGS technology can be successfully used for simultaneous detection of infections and tumors. In the present study, 92 patients with clinically confirmed HLH were etiologically subtyped for infection, tumor and autoimmunity based on CNV and microbial data generated by Onco-mNGS technology, and a predictive model was developed and validated for the differential diagnosis of the underlying disease leading to secondary HLH. Furthermore, the treatment outcomes of patients with HLH triggered by EBV infection and non-EBV infection were evaluated, respectively. The current study demonstrated that the novel Onco-mNGS can identify the infection and malignancy- related triggers among patients with secondary HLH. A random forest classification model based on CNV profile, infectious pathogen spectrum and blood microbial community was developed to better identify the different HLH subtypes and determine the underlying triggers. The prognosis for treatment of HLH patients is not only associated with CNV, but also with the presence of pathogens and non- pathogens in peripheral blood. Higher CNV burden along with frequent deletions on chromosome 19, higher pathogen burden and lower non-pathogenic microbes were prognosis factors that significantly related with unfavorable treatment outcomes. Our study provided comprehensive knowledge in the triggers and prognostic predictors of patients with secondary HLH, which may help early diagnosis and appropriate targeted therapy, thus improving the survival and prognosis of the patients.

Integrative radiopathomics model for predicting progression-free survival in patients with nonmetastatic nasopharyngeal carcinoma

PurposeTo construct an integrative radiopathomics model for predicting progression-free survival (PFS) in nonmetastatic nasopharyngeal carcinoma (NPC) patients.Methods357 NPC patients who underwent pretreatment MRI and pathological whole-slide imaging (WSI) were included in this study and randomly divided into two groups: a training set (n = 250) and validation set (n = 107). Radiomic features extracted from MRI were selected using the minimum redundancy maximum relevance and least absolute shrinkage and selection operator methods. The pathomics signature based on WSI was constructed using a deep learning architecture, the Swin Transformer. The radiopathomics model was constructed by incorporating three feature sets: the radiomics signature, pathomics signature, and independent clinical factors. The prognostic efficacy of the model was assessed using the concordance index (C-index). Kaplan-Meier curves for the stratified risk groups were tested by the log-rank test.ResultsThe radiopathomics model exhibited superior predictive performance with C-indexes of 0.791 (95% confidence interval [CI]: 0.724–0.871) in the training set and 0.785 (95% CI: 0.716–0.875) in the validation set compared to any single-modality model (radiomics: 0.619, 95% CI: 0.553–0.706; pathomics: 0.732, 95% CI: 0.662–0.802; clinical model: 0.655, 95% CI: 0.581–0.728) (all, P < 0.05). The radiopathomics model effectively stratified patients into high- and low-risk groups in both the training and validation sets (P < 0.001).ConclusionThe developed radiopathomics model demonstrated its reliability in predicting PFS for NPC patients. It effectively stratified individual patients into distinct risk groups, providing valuable insights for prognostic assessment.

Prognostic Value of the Lactate/Albumin Ratio for 28-Day Mortality in Pediatric Septic Shock: A Prospective Cohort Study.

Background Septic shock remains a leading cause of mortality in children. The lactate/albumin ratio (LAR) has emerged as a potential prognostic marker for mortality in septic shock, yet most existing research focuses on adults, with limited data available for pediatric populations, particularly in Vietnam. Objectives This study aims to evaluate the prognostic utility of the LAR in predicting 28-day mortality among children aged two months to 15 years with septic shock in Vietnam. Methods We conducted a prospective cohort study involving children diagnosed with septic shock at the largest pediatric intensive care unit (PICU) in the Mekong Delta, Vietnam, from July 2022 to June 2024. Clinical and laboratory parameters, including lactate and albumin levels, were measured at the time of septic shock diagnosis. Patients were followed for 28 days, with outcomes categorized as either survival or mortality. The prognostic performance of LAR was assessed through its discrimination and calibration capabilities. Results The 28-day mortality rate was 63.4%. LAR was significantly higher in non-survivors compared to survivors (p < 0.001). The area under the receiver operating characteristic curve (AUROC) for LAR was 0.91, indicating superior discriminatory power compared to lactate alone and comparable to albumin. Using a Youden index-derived cut-off of 1.84, LAR demonstrated a sensitivity of 84.6% and a specificity of 80%. Kaplan-Meier analysis and log-rank testing revealed significantly lower survival probabilities in children with LAR ≥1.84 (p < 0.05). The Hosmer-Lemeshow test confirmed good calibration of LAR in mortality prediction (p > 0.05). Conclusion The lactate/albumin ratio exhibits excellent discriminatory and calibration properties, making it a valuable tool for predicting 28-day mortality in pediatric septic shock. This ratio should be considered for routine use in clinical practice to improve prognostic assessments in this vulnerable population.

AxonFinder: Automated segmentation of tumor innervating neuronal fibers.

Neurosignaling is increasingly recognized as a critical factor in cancer progression, where neuronal innervation of primary tumors contributes to the disease's advancement. This study focuses on segmenting individual axons within the prostate tumor microenvironment, which have been challenging to detect and analyze due to their irregular morphologies. We present a novel deep learning-based approach for the automated segmentation of axons, AxonFinder, leveraging a U-Net model with a ResNet-101 encoder, based on a multiplexed imaging approach. Utilizing a dataset of whole-slide images from low-, intermediate-, and high-risk prostate cancer patients, we manually annotated axons to train our model, achieving significant accuracy in detecting axonal structures that were previously hard to segment. Our analysis includes a comprehensive assessment of axon density and morphological features across different CAPRA-S prostate cancer risk categories, providing insights into the correlation between tumor innervation and cancer progression. Our paper suggests the potential utility of neuronal markers in the prognostic assessment of prostate cancer in aiding the pathologist's assessment of tumor sections and advancing our understanding of neurosignaling in the tumor microenvironment.

Optimized biomarker evaluation and molecular testing in the era of breast cancer precision medicine

ABSTRACT Ground breaking advances in medicine, driven in part by major technologic developments in molecular biology have led us to a new model for cancer care that has been termed personalized, or precision medicine. Precision medicine is a model for making medical decisions that employs an innovative clinical approach and advanced tumor testing methods that are tailored to understanding an individual patient’s tumor biology and the molecular drivers of their disease. This medical model includes a combination of diagnostic testing and specific treatment options that can be offered to patients at presentation and in theory throughout the course of their disease as new mutations arise with the development of disease recurrence. Although the precision medicine model offers incredible potential to transform cancer care, these advances are only meaningful when they reach the correct patients. The evolving paradigm of precision medicine is changing the practice of pathology, and the pathology community needs to be mindful of these changes because every tissue specimen represents a patient’s life, and those patients are depending on the pathology community to handle their tissue correctly. The diagnostic tests performed in the pathology laboratory for precision medicine are increasingly complex, and pathologists along with the entire laboratory and clinical communities need to take steps to ensure that the right diagnosis is given to the right patient to inform the right treatment options, at the right time, along every step of the continuum of care for cancer patients. While hormone receptors and human epidermal growth factor receptor 2 (HER2) overexpression and/or amplification have been the mainstay for risk-stratification, and treatment decision making in breast cancer since the early 2000’s, the seminal work on gene expression by Perou and colleagues in the early 2000’s opened the door for molecular testing in the prognostic and predictive assessment of breast cancer. Molecular testing is now part of the standard of care in the precision medicine model for breast cancer care. In this article, the reader will gain a better understanding of how the lack of standardization of pre-analytic factors has the potential to negatively impact the quality of the tissue specimen for downstream biomarker and molecular testing, which ultimately can negatively affect patient care. The reader will also gain insight into the current climate surrounding molecular testing in breast cancer.

Molecular subtypes and nomogram for predicting the prognosis of cervical cancer based on a matrix-immune signature

Cervical cancer is a kind of tumor related to chronic HPV infection. Currently, the treatment of cervical cancer is guided mainly by clinicopathological factors. The role of tumor microenvironment in the prognosis and treatment of cervical cancer has been ignored. We aimed to use bioinformatics to identify the molecular subtypes in cervical cancer and construct a predictive nomogram combining a matrix-immune signature (MIS) and clinicopathological factors to support treatment decisions. Two cervical cancer subtypes with different prognoses were identified based on matrix- and immune-genes in TCGA-CESC. The MIS was developed using Cox regression and Lasso algorithm and verified in the Cancer Genome Characterization Initiative (CGCI) using time-dependent receiver operating characteristic (ROC) curve analysis. Multivariable analysis identified lymph node metastases, lymphovascular space invasion, and the MIS as independent prognostic factors, which were used to construct the predictive nomogram. The areas under the ROC curve of the model were 0.872, 0.879, and 0.803 for the 1-, 3-, and 5-year periods, respectively. The C-index was 0.845. Calibration curves confirmed the excellent prognosis prediction of the nomogram. The nomogram indicted a 3-year survival rate of > 90% in patients with a total score > 110.1. The constructed predictive nomogram has significant implications for prognostic assessment and treatment selection in cervical cancer.

Echocardiographic assessment of right ventricular function and right ventriculoarterial coupling in tricuspid regurgitation.

Echocardiographic evaluation of the cardiopulmonary unit is difficult in case of tricuspid regurgitation (TR) and combined echocardiographic parameters could be useful. This study aimed to assess the variation of simple and combined echocardiographic parameters analysing the cardiopulmonary unit according to the severity of TR. TR was graded according to Hahn's classification in 179 patients. Classical morphological, function and load parameters analysing right ventricle were assessed. Combined parameters of function and load; morphology and load; and morphology-load-function index were calculated. We used ROC curve analysis to analyze the diagnostic value of echocardiographic parameters to predict potential high or low surgical risk of mortality according to TRISCORE in 82 patients. Simple parameters were significatively different among groups with a nonlinear progression between the 5 levels of severity of TR. Combined parameters were also significatively different among groups. Among them, myomechanical index (MMI = RV-RA mean pressure gradient x RVFWS/indexed RAED area × 10-2) and morphology-load-function index (MLF = RVED length/area x TR TVI x RVFWS) had a linear progression between the 5 groups and had the best predictive value for TRISCORE high and low risk. Combined parameters are relevant to evaluate cardiopulmonary unit in patients with various degrees of TR, especially when combining morphology, function, and load parameters, and are potentially interesting in their prognostic assessment, as shown by the good predictive value for TRISCORE risk.

Clinical Utility of Serum C-peptide Concentration for Hospitalized Patients with Hyperglycemia.

Serum C-peptide concentration is often utilized for diagnostic, prognostic, or therapeutic assessment in diabetes mellitus. However, there is limited clinical data regarding diagnostic and predictive value of C-peptide measured during hospitalizations for hyperglycemia. Adults admitted to Mayo Clinic inpatient facilities due to an acute hyperglycemic emergency between January 2017 and November 2022 were included in our study. Predictive capacity of C-peptide for discontinuation of therapeutic insulin was examined in the entire cohort and the subgroup of non-autoimmune non-pancreatitis diabetes (NANP-DM). We included 187 patients (63 women) in our study. During hospitalization, patients with type 1 diabetes antibodies displayed diminished serum C-peptide concentration (p=0.014), correlating inversely with subsequent HbA1c% [r= (-0.22), p=0.005]. Initial C-peptide concentrations did not differ between patients requiring insulin therapy during follow-up and those who did not (p=0.16). C-peptide concentrations showed limited predictive capacity for achieving glycemic control. Subgroup analyses in NANP-DM exhibited similar limited capacity for anticipating therapeutic insulin needs and achieving glycemic controls. C-peptide concentration did not exhibit a robust predictive capability for future need of insulin therapy and achieving glycemic control, limiting its utility in clinical practice within inpatient settings.

Constructing machine learning models based on non-contrast CT radiomics to predict hemorrhagic transformation after stoke: a two-center study.

Machine learning (ML) models were constructed according to non-contrast computed tomography (NCCT) images as well as clinical and laboratory information to assess risk stratification for the occurrence of hemorrhagic transformation (HT) in acute ischemic stroke (AIS) patients. A retrospective cohort was constructed with 180 AIS patients who were diagnosed at two centers between January 2019 and October 2023 and were followed for HT outcomes. Patients were analyzed for clinical risk factors for developing HT, infarct texture features were extracted from NCCT images, and the radiomics score (Rad-score) was calculated. Then, five ML models were established and evaluated, and the optimal ML algorithm was used to construct the clinical, radiomics, and clinical-radiomics models. Receiver operating characteristic (ROC) curves were used to compare the performance of the three models in predicting HT. Based on the outcomes of the AIS patients, 104 developed HT, and the remaining 76 had no HT. The HT group consisted of 27 hemorrhagic infarction (HI) and 77 parenchymal-hemorrhage (PH). Patients with HT had a greater neutrophil-to-lymphocyte ratio (NLR), baseline National Institutes of Health Stroke Scale (NIHSS) score, infarct volume, and Rad-score and lower Alberta stroke program early CT score (ASPECTS) (all p < 0.01) than patients without HT. The best ML algorithm for building the model was logistic regression. In the training and validation cohorts, the AUC values for the clinical, radiomics, and clinical-radiomics models for predicting HT were 0.829 and 0.876, 0.813 and 0.898, and 0.876 and 0.957, respectively. In subgroup analyses with different treatment modalities, different infarct sizes, and different stroke time windows, the assessment accuracy of the clinical-radiomics model was not statistically meaningful (all p > 0.05), with an overall accuracy of 79.5%. Moreover, this model performed reliably in predicting the PH and HI subcategories, with accuracies of 82.9 and 92.9%, respectively. ML models based on clinical and NCCT radiomics characteristics can be used for early risk evaluation of HT development in AIS patients and show great potential for clinical precision in treatment and prognostic assessment.

Enhanced machine learning models for predicting one-year mortality in individuals suffering from type A aortic dissection

ObjectiveThe study objective was to develop and validate an interpretable machine learning model to predict 1-year mortality in patients with type A aortic dissection, improving risk classification and aiding clinical decision-making. MethodsWe enrolled 289 patients with type A aortic dissection, dividing them into a training cohort (202 patients) and a validation cohort (87 patients). The Least Absolute Shrinkage and Selection Operator method with 10-fold cross-validation identified 8 key factors related to 1-year mortality. The Treebag model's performance was assessed using accuracy, F1-Score, Brier score, area under the curve, and area under the precision-recall curve with calibration and clinical utility evaluated through decision curves. Shapley Additive Explanations analysis determined the most influential predictors. ResultsThe Treebag model outperformed others, achieving a Brier score of 0.128 and an area under the curve of 0.91. Key risk factors included older age and elevated white blood cell count, whereas higher systolic blood pressure, lymphocyte, carbon dioxide combining power, eosinophil, β-receptor blocker use, and surgical intervention were protective. A web-based application, TAAD One-Year Prognostic Risk Assessment Web, was developed for clinical use (available at https://taad-1year-mortality-predictor.streamlit.app/). This platform allows for the prediction of 1-year mortality in patients with type A aortic dissection based on the identified predictive factors, facilitating clinical decision-making and patient management. ConclusionsThe Treebag machine learning model effectively predicts 1-year mortality in patients with type A aortic dissection, stratifying risk profiles. Key factors for enhancing survival include surgical intervention, β-blocker administration, and management of systolic blood pressure, lymphocyte, carbon dioxide combining power, eosinophil, and white blood cell levels, offering a valuable tool for improving patient outcomes.

Liquid-liquid phase separation-related signature predicts prognosis and therapeutic response in esophageal adenocarcinoma

BackgroundEsophageal adenocarcinoma is a leading cause of mortality worldwide. New evidence indicates that liquid-liquid phase separation is related to malignancies. The current study aims at exploring the functions of liquid-liquid phase separation within esophageal adenocarcinoma. Patients within the TCGA dataset were classified using liquid–liquid phase separation-related genes. Significantly differentially expressed genes and prognostic factors for overall survival have been screened by Cox regression. Based on the liquid-liquid phase separation score, the construction of a prognostic model and liquid-liquid phase separation signature was constructed. Tumor mutation burden and drug sensitivity were analyzed in two groups: high liquid-liquid phase separation scores, and low liquid-liquid phase separation scores. According to liquid-liquid phase separation, some small-molecule compounds targeting esophageal adenocarcinoma were screened. The results were verified in vitro with an external cohort. Results87 samples are involved, and 61 liquid-liquid phase separation-related genes may influence esophageal adenocarcinoma by changing DNA conformation and metabolism. Meanwhile, based on a high liquid-liquid phase separation score and low score group including 43 patients, it is found that the result significantly lowered the 5-year overall survival to 32.6 %, compared to 64.8 % in the low-score group of 44 patients with p < 0.001. The high score group had an average TIDE score of 0.27 versus 0.14 in the low-score group, with p = 0.003. The median tumor mutation burden was 9.1 mutations/Mb in the high-score group versus 6.4 mutations/Mb in the low-score group, with p = 0.011. The predictive model worked very well, with area under the curve values of 0.82, 0.79, and 0.76 for 1-, 3-, and 5-year survival, respectively. Liquid-liquid phase separation has been validated as an effective prognostic biomarker and drug sensitivity predictor. SignificanceLiquid-liquid phase separation is potentially implicated in esophageal adenocarcinoma and works as a prognostic biomarker assessment of vulnerability to LLPS, which could help develop individualized therapies by showing how one is situated about various medications where responses vary across the body.

Enhancing outcomes in extensive-stage small cell lung cancer brain metastases: a retrospective study on the synergistic effects of immune checkpoint inhibitor, brain radiotherapy, and chemotherapy.

Brain metastasis is a frequent complication in small cell lung cancer (SCLC), and there is an urgent need for new treatment modalities, given the limited success of traditional approaches. This study evaluates the combined efficacy and safety of brain radiotherapy (BRT), chemotherapy, and immune checkpoint inhibitors (ICIs) in the treatment of brain metastases in patients with extensive-stage SCLC (ES-SCLC). Additionally, it seeks to identify prognostic factors in these cases. A retrospective analysis was performed on 187 patients with ES-SCLC and brain metastases treated at Zhejiang Cancer Hospital from January 2017 to October 2023. Patients were divided into three groups based on their initial treatment: BRT alone, ICI alone, and a combined ICI + BRT approach, with chemotherapy included in all regimens. Variables such as age, number of brain metastases, symptoms, comorbidities, Karnofsky Performance Status (KPS) scores, smoking history, Graded Prognostic Assessment (GPA) scores, survival time, and treatment-related adverse events (TRAEs), including hematologic and hepatic toxicities were evaluated. Prognostic factors were assessed using univariate and multivariate analyses via Cox's proportional hazards model. The study also compared outcomes and TRAEs between patients undergoing synchronous treatment (ICI and BRT within four weeks) versus those with asynchronous therapy (more than four weeks apart). Median overall survival (OS) times differed significantly across the groups: 11.6 months for BRT, 11.6 months for ICI, and 20.9 months for ICI + BRT (P<0.001). The ICI + BRT group also exhibited notably better progression-free survival (PFS) and intracranial PFS (iPFS), with medians of 12.6 and 14.9 months, respectively (P<0.001). This group demonstrated significantly improved systemic and intracranial objective response rates (ORR) and disease control rates (DCR). No significant differences in acute radiation injury rates were observed between the BRT and ICI + BRT groups. Multivariate analysis identified several factors influencing OS, including treatment regimen, number of chemotherapy and ICI cycles, presence of bone and multiple brain metastases, and antiangiogenesis therapies and extracranial radiotherapy. Both atezolizumab and serplulimab ICIs, in combination with various radiotherapy regimens [whole BRT (WBRT), WBRT with boost], were effective. Notably, asynchronous ICI and BRT treatment demonstrated advantages in PFS and iPFS over concurrent therapy, with no significant differences in other therapeutic indices or TRAE incidence rates. For ES-SCLC patients with synchronous brain metastases, combined ICI and BRT, alongside chemotherapy, surpasses the efficacy of either treatment alone with manageable TRAEs. Importantly, asynchronous ICI and BRT therapy showed superior outcomes compared to synchronous treatment modalities.

Identifying CDCA3 as a pivotal biomarker for predicting outcomes and immunotherapy efficacy in pan-renal cell carcinoma.

Renal cell carcinoma (RCC) is a heterogeneous disease. Identifying effective biomarkers is crucial for improving prognostic accuracy and therapy outcomes. This study investigates cell division cycle-associated 3 (CDCA3) as a novel biomarker for prognostic assessment and immunotherapy response in RCC. This study analyzed multi-omics data from The Cancer Genome Atlas (TCGA) for kidney renal clear cell carcinoma (KIRC), kidney renal papillary cell carcinoma (KIRP), and kidney chromophobe (KICH) subtypes to evaluate CDCA3 expression and its clinical implications. Functional enrichment and immune infiltration analyses were performed using bioinformatics tools gene set enrichment analysis (GSEA) and xCell. The prognostic value of CDCA3 was assessed through Cox regression and Kaplan-Meier survival analysis. Immunohistochemistry (IHC) was employed to confirm CDCA3 expression at the protein level in RCC samples. Higher CDCA3 expression correlated with poor survival outcomes and reduced response to programmed cell death protein 1 (PD-1) blockade therapies. Statistical analysis indicated that CDCA3 was an independent prognostic factor for poor survival in RCC. CDCA3 was consistently overexpressed in RCC tissues compared to normal tissues and was associated with adverse clinical features, including high Th2 cell infiltration and suppression of immune pathways. CDCA3 is a promising biomarker for RCC, offering insights into the tumor's prognosis and potential response to immunotherapy. The strong association between CDCA3 expression and poor therapeutic outcomes suggests that CDCA3 could be targeted in future therapeutic strategies.

In silico Design of a Quadruplex RNA Aptamer Against Carcinoembryonic Antigen, and Evaluating its Potential as a Drug Delivery Vehicle

Background: Colorectal cancer is the third most common cancer worldwide, with carcinoembryonic antigen (CEA) being a key biomarker for tumor prognostic assessment. This study focused on the in silico development of an RNA aptamer capable of efficiently targeting the CEA biomarker, and evaluating its potential to act as a drug delivery vehicle of chemotherapeutic anticancer drugs. Methods: In this study, five G-quadruplex RNA aptamers were manually designed and docked with the CEA protein. The aptamer sequence with the best docking score was optimized through various mutations, and these mutant sequences underwent drug-aptamer docking using AutoDock vina, followed by further docking experiments with CEA, with and without the drug, using the Hdock server. The best aptamer-drug configuration and the entire (aptamers-drug)-CEA complex were then analyzed using molecular dynamics (MD) simulation. Results: Irinotecan exhibited the highest binding affinity with three mutant sequences compared to 5-FU and Raltitrexed, with the seq 3-5/Irinotecan complex showing the best configuration (score: -11.6). This complex also demonstrated a good binding affinity with the target CEA (HDOCK score: -393.07). Throughout 31.6 ns simulation, the RMSD plot of the (aptamer-drug)-CEA complex showed an average of 8.7 A°. The residues of interacting amino acids had lower RMSF than 1.4 Å, except for the ALA A0 (RMSF:1.74 Å). Rg plot showed minimal fluctuation, which were maintained between 19.2 Å and 20.2 Å. The mean SASA was in the range of 112.8–125.8 nm2. H-bonds were observed with an average of 7.3 hydrogen bonds. MD simulation results indicated that the (aptamers-drug)-CEA complex maintained a stable, low-flexibility, and relatively compact structure throughout the simulation. Conclusion: The study concludes that the designed quadruplex RNA aptamer can potentially target the CEA biomarker with good binding affinity and serves as an effective carrier for Irinotecan. However, in vitro and in vivo experiments are necessary for further validation.

Development of an immunogenic cell death-related lncRNAs signature for prognostic risk assessment in gastric cancer.

Immunogenic cell death (ICD) is a functionally specialized form of apoptosis induced by endoplasmic reticulum (ER) stress and is associated with a variety of cancers, including gastric cancer (GC). In recent years, long non-coding RNAs (lncRNAs) have been shown to be important mediators in the regulation of ICD. However, the specific role and prognostic value of ICD-related lncRNAs in GC remain unclear. This study aims to develop an ICD-related lncRNAs signature for prognostic risk assessment in GC. The ICD-related lncRNAs signature (ICDlncSig) of GC was constructed by univariate Cox regression analysis, least absolute shrinkage, and selection operator (LASSO) regression model and multivariate Cox regression analysis, and the signature was correlated with immune infiltration. The potential response of GC patients to immunotherapy was predicted by the tumor immune dysfunction and rejection (TIDE) algorithm. In vitro functional experiments were conducted to assess the impact of lncRNAs on the proliferation, migration, and invasion capabilities of GC cells. We constructed a novel ICDlncSig and found that this signature could be used as a prognostic risk model to predict survival of GC patients by validating it in the training cohort, testing cohort and entire cohort. The robust predictive power of the signature was demonstrated by building a Nomogram based on ICDlncSig scores and clinical characteristics. Furthermore, immune cell subpopulations, expression of immune checkpoint genes, and response to chemotherapy and immunotherapy differed significantly between the high- and low-risk groups. The in vitro functional experiments revealed that AP002954.1 and AP000695.1 can promote the proliferation, migration, and invasion of GC cells. In conclusion, our ICDlncSig model has significant predictive value for the prognosis of GC patients and may provide clinical guidance for individualized immunotherapy.