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To Treat or Not to Treat: Navigating Early-Stage CLL in the Era of Targeted Therapy.

Chronic lymphocytic leukemia (CLL) is most frequently diagnosed at early, asymptomatic stages (Rai 0/Binet A), in which a watch-and-wait strategy remains the standard of care, based on historical trials demonstrating no overall survival benefit from early treatment. Over the past two decades, however, substantial advances in genomic profiling-including immunoglobulin heavy-chain variable region (IGHV) mutational status, TP53 disruption, recurrent gene mutations, and complex karyotype-have uncovered marked biological heterogeneity among early-stage patients and substantially improved prediction of disease progression. In parallel, targeted therapies such as Bruton tyrosine kinase (BTK) inhibitors and venetoclax-based combinations have transformed the management of symptomatic CLL, raising renewed interest in whether early intervention might favorably alter the natural history of biologically high-risk disease. In this review, we critically examine the evolution of prognostication in early-stage CLL, integrate contemporary molecular and clinical risk models, and summarize evidence from both historical chemotherapy-era studies and modern early-intervention trials. We discuss key unresolved controversies, including reliance on surrogate endpoints, the risks of overtreatment, and the persistent absence of an overall survival benefit across all early-treatment strategies. Finally, we outline future research priorities, including refined genomic stratification, minimal residual disease-driven (MRD)-driven approaches, and combination targeted therapies currently under investigation. Despite renewed interest in preemptive treatment, available evidence supports continued observation for asymptomatic patients outside clinical trials.

Humans

Effect of Semaglutide on the Inflammatory Biomarker High-Sensitivity CRP in Patients With Established Cardiovascular Disease and Overweight or Obesity in SELECT: A Prespecified Secondary Analysis.

BACKGROUND: In SELECT (Semaglutide Effects on Heart Disease and Stroke in Patients With Overweight or Obesity), among 17&#x2009;604 patients with known atherosclerotic cardiovascular disease and overweight or obesity, but not diabetes, randomization to the glucagon-like peptide-1 receptor agonist semaglutide significantly reduced the primary outcome of major adverse cardiovascular events (MACE; cardiovascular death, nonfatal myocardial infarction, or nonfatal stroke) compared with placebo (mean follow-up, 39.8 months). Inflammation, as indicated by plasma hsCRP (high-sensitivity C-reactive protein) level, is implicated as a biomarker predicting cardiovascular risk in obesity and atherosclerotic cardiovascular disease. SELECT provides a unique opportunity to study the relationship among hsCRP, obesity, weight loss, and MACE outcomes in semaglutide versus placebo groups. METHODS: In this prespecified SELECT substudy, we evaluated whether baseline hsCRP levels predicted MACE risk and examined the relationships between changes in hsCRP levels and time to first MACE, baseline body weight, weight loss, and other clinical measures among treatment groups over time (104, 208 weeks) using multiple approaches, including Cox modeling. RESULTS: Baseline hsCRP level, which was similar in the semaglutide (geometric mean 1.96 mg/L) and placebo (geometric mean 1.91 mg/L) groups, was prognostic of future MACE. The risk of MACE increased across baseline hsCRP level <2, 2-<10, and &#x2265;10 mg/L subgroups, including significant associations with cardiovascular and all-cause death. Semaglutide reduced hsCRP levels (-37.8% [104 weeks]) and risk of MACE across all hsCRP subgroups. Greater reductions in ratio-to-baseline hsCRP with semaglutide were associated with greater weight loss, but preceded major weight loss, evident by 4 and 8 weeks, and occurred among those without weight loss. Semaglutide-associated changes in hsCRP were independent of low-density lipoprotein cholesterol levels, statin use, and atherosclerotic cardiovascular disease entry criteria. hsCRP reductions were found to be prognostic of decreased risk of MACE. Modeling suggests decreased inflammation as contributing in part to the benefits seen with semaglutide in SELECT. CONCLUSIONS: In SELECT, hsCRP data at baseline and in response to treatment with semaglutide support inflammation as a potential prognostic factor associated with cardiovascular risk in these generally well-treated patients with atherosclerotic cardiovascular disease and overweight or obesity but not diabetes. These findings suggest that the MACE reduction observed with semaglutide versus placebo in SELECT may have partially involved a decrease in inflammation. REGISTRATION: URL: https://www.clinicaltrials.gov; Unique identifier: NCT03574597.

Humans

Diagnostic and Predictive Value of Circulating and Exosomal microRNAs in Ferroptosis-Associated Neurological Conditions: A Systematic Review and Meta-analysis.

Circulating microRNAs (miRNAs) have emerged as potential non-invasive markers for intracranial pathology, yet their diagnostic accuracy and relationship with ferroptosis-mediated neuronal damage remain poorly defined. The primary objective of this study was to evaluate the diagnostic and predictive potential of circulating and exosomal miRNAs across ferroptosis-associated neurological conditions and to explore their associations with ferroptosis-related pathways. Following PRISMA-DTA guidelines, a systematic literature search was conducted across PubMed, Scopus, Cochrane, and ScienceDirect, identifying 205 records. After screening for human clinical cohort validation, 7 studies were included in the qualitative synthesis and 5 in the quantitative meta-analysis. Pooled Area-under-the-Curve (AUC) was calculated using a random-effects inverse-variance model, while prognostic correlation coefficients (r) were synthesized using Fisher's Z-transformation. Methodological quality was assessed via QUADAS-2. Analysis of 7 clinical cohorts provided heterogeneous evidence on the diagnostic and prognostic potential of miRNAs. Random-effects pooling of the two eligible diagnostic AUC estimates yielded an exploratory pooled AUC of 0.87 (95% CI, 0.79-0.94; I2&#x2009;.90%). Prognostic synthesis of Group 2 identified an exploratory association between miRNA levels and clinical severity scales (exploratory pooled correlation coefficient of 0.67 (95% CI: 0.56-0.76; I2&#x2009;.714.4%). Selected miRNAs were mapped to ferroptosis-associated regulators, including SLC7A11, ABCB8, and SLC40A1. Exosomal miRNAs hold potential to indicate disease-associated molecular information, although comparative clinical evidence remains yet to be explored. Circulating and exosomal miRNAs show promising diagnostic and prognostic potential across selected neurological conditions. These findings highlight a potential mechanistic association between miRNA expression and ferroptosis-mediated neuronal injury.

Humans

The role of artificial intelligence in the diagnosis and prognosis of traumatic brain injury based on brain CT scans: a systematic review.

Traumatic brain injury (TBI) is a leading cause of emergency department visits and a major contributor to injury-related mortality and long-term neurological disability. Non-contrast computed tomography (CT) is the gold-standard imaging modality for the rapid diagnosis of TBI. Clinical outcomes depend strongly on early detection and prompt acute management. Artificial intelligence (AI)-based models may support faster automated identification of traumatic findings and early prediction of patient prognosis.&#xa0;A systematic literature search was conducted in PubMed/MEDLINE, Scopus, IEEE Xplore, ACM Digital Library, and the Cochrane Library in accordance with PRISMA 2020 guidelines to evaluate AI-based models for automated detection of TBI-related findings on CT and for prediction of clinical outcomes. Risk of bias and applicability were assessed using QUADAS-2 for diagnostic accuracy studies and PROBAST&#x2009;+&#x2009;AI for prediction model studies.&#xa0;Twenty-two studies were included. Sixteen studies evaluated diagnostic tasks and 10 evaluated prognostic outcomes, with four studies contributing to both categories. Diagnostic performance was generally high, with many studies reporting AUC values approaching or exceeding 0.90, particularly for larger lesion volumes.Prognostic performance was more variable, with moderate to high discrimination and substantial heterogeneity. Only 9 studies incorporated independent external validation, and performance was frequently lower in external cohorts. All prognostic model studies were judged to be at high overall risk of bias using PROBAST&#x2009;+&#x2009;AI, and most diagnostic accuracy studies also demonstrated high or unclear risk of bias in at least one QUADAS-2 domain, most frequently in patient selection.&#xa0;AI-based models applied to brain CT demonstrate strong technical performance for both diagnostic and prognostic tasks in TBI. However, most studies relied on retrospective designs and lacked independent external validation which limits models generalizability and raises concern for potential overfitting. Prospective, multicenter studies with standardized methodologies and rigorous external validation are required before widespread clinical implementation.

Humans

Externally validated risk prediction models for gestational diabetes mellitus: A systematic review and meta-analysis.

INTRODUCTION: Risk prediction models for gestational diabetes mellitus (GDM) offer potential for early identification and targeted prevention. External validation is crucial to assess model performance across diverse populations. Despite the availability of numerous GDM prediction models, limited evidence exists on their external validation frequency, methodological quality, and clinical applicability. This systematic review evaluated externally validated GDM prediction models, focusing on methodological rigor, reporting standards, and clinical relevance to inform future research and implementation. MATERIAL AND METHODS: Databases including Ovid MEDLINE, Embase, Scopus, Emcare, and CINAHL were searched up to May 1, 2025. Studies reporting external validation of GDM risk prediction models were included. Two reviewers independently screened studies. Data were extracted using the CHARMS framework, and risk of bias and applicability were assessed using PROBAST+AI. The study protocol was registered in the International Prospective Register of Systematic Reviews (PROSPERO; CRD420251125758). RESULTS: Twenty-six studies validated 33 models, with validation sample sizes ranging from 50 to 75&#x2009;161. Over half used the IADPSG criteria to define GDM. Discrimination metrics were commonly reported, but calibration, overall performance, and clinical utility were often lacking. Meta-analysis was feasible for only four models: Teede et&#xa0;al., Nanda et&#xa0;al., Naylor et&#xa0;al., and Van Leeuwen et&#xa0;al., each showing fair discrimination. The Teede et&#xa0;al. model was the most widely validated, with 11 external validations across six continents and a pooled AUC of 0.72 (95% CI: 0.67-0.76). Despite fewer validations, the Nanda et&#xa0;al. model achieved the highest pooled discrimination (5 validations; pooled AUC 0.77, 95% CI: 0.74-0.80). The Naylor et&#xa0;al. and van Leeuwen et&#xa0;al. models also underwent meta-analysis, as sufficient external validation studies were available to support comparative performance assessment. Notably, 69.23% of studies had a high risk of bias. CONCLUSIONS: While many models showed acceptable predictive performance, most validations were methodologically weak. Future studies should follow best-practice guidelines and promote scalable validation strategies, such as algorithm sharing, to enhance clinical utility.

Humans

PGR expression as a pharmacogenomic companion biomarker to GENE70-derived genomic risk in ER-positive/HER2-negative breast cancer.

BACKGROUND: The biology of the estrogen receptor-positive (ER+) and human epidermal growth factor receptor 2-negative (HER2-) breast cancers is heterogeneous even when they are categorized by their risk via genomics. Transcriptomic PGR expression reflects endocrine pathway activity and may provide complementary biological information within established GENE70-derived genomic-risk categories. Whether this molecular marker improves the biological interpretation of genomic-risk stratification beyond conventional clinicopathological assessment remains uncertain. OBJECTIVES: The aim of this study was to determine whether transcriptomic PGR expression provides complementary biological and prognostic information within reconstructed GENE70-derived genomic-risk categories and refines the characterization of endocrine-related tumour biology in ER-positive/HER2-negative breast cancer. METHODS: This study analysed publicly available transcriptomic and clinical data from three cohorts: METABRIC (discovery cohort), GSE96058/SCAN-B cohort (validation cohort) and TCGA-BRCA cohort (molecular validation cohort). The GENE70-derived genomic-risk score was reconstructed for each cohort using matched genes. Cox regression, Kaplan-Meier analysis and subgroup comparisons were used to assess relationships between PGR expression, clinicopathologic variables, molecular features and survival outcomes. RESULTS: Across the three independent cohorts, low transcriptomic PGR expression was consistently associated with higher GENE70-derived genomic risk, increased MKI67 expression, reduced ESR1 expression and enrichment of the Luminal B subtype. Survival findings differed between cohorts. In the discovery METABRIC cohort, transcriptomic PGR expression showed heterogeneous associations with survival, particularly within GENE70-derived high-risk subgroups, whereas the external GSE96058/SCAN-B validation cohort demonstrated consistent associations between low PGR expression and poorer overall survival in both the overall ER-positive/HER2-negative population and GENE70-derived high-risk subgroups. CONCLUSION: These findings suggest that transcriptomic PGR provides complementary biological and prognostic information within GENE70-derived genomic-risk categories. However, because treatment response was not evaluated in the present study, the findings should not be interpreted as evidence of predictive or pharmacogenomic utility and prospective studies incorporating treatment-response analyses are required before such applications can be established.

Humans

Association of time-averaged systemic immune-inflammation indices with in-hospital mortality after intracerebral hemorrhage: a retrospective study.

BACKGROUND: Systemic inflammation plays a central role in secondary brain injury following intracerebral hemorrhage (ICH). Although inflammatory indices such as the neutrophil-to-lymphocyte ratio (NLR), systemic immune-inflammation index (SII), and systemic inflammation response index (SIRI) are linked to poor outcomes, their associations with mortality are commonly assumed to be linear, potentially overlooking nonlinear patterns where mortality risk rises steeply at higher levels. METHODS: We conducted a retrospective study using the MIMIC-IV database, including 440 patients with non-traumatic ICH who were alive and remained in the ICU for at least 72&#xa0;h after admission. Mean NLR, SII, and SIRI were calculated from measurements obtained during this period. Multivariable logistic regression and restricted cubic spline (RCS) analyses were applied to assess their independent and nonlinear associations with in-hospital mortality. Model discrimination and calibration were internally validated using 1,000 bootstrap resamples. RESULTS: The in-hospital mortality rate was 26.1%. After multivariable adjustment, NLR and SIRI remained independently associated with mortality. Patients in the highest SIRI quartile had the highest risk of death (aOR&#xa0;=&#xa0;5.12; 95% CI: 2.57-12.24; p&#xa0;<&#xa0;0.001). RCS analysis revealed a significant nonlinear association between SIRI and mortality (p-nonlinearity&#xa0;<&#xa0;0.05), showing a steep risk increase at higher SIRI levels. Adding SIRI to the base model provided a modest improvement in discrimination (AUC 0.762 to 0.785, p&#xa0;=&#xa0;0.045) and significantly improved risk reclassification (cNRI&#xa0;=&#xa0;0.4778, p&#xa0;<&#xa0;0.001; IDI&#xa0;=&#xa0;0.0240, p&#xa0;=&#xa0;0.0151). CONCLUSIONS: Among patients with ICH who met the 72-hour eligibility criterion, higher 72-hour average SIRI was independently associated with in-hospital mortality. As a time-averaged measure, SIRI should be interpreted as a dynamic marker integrating the initial inflammatory state and the early clinical course rather than as a purely baseline prognostic factor. Although adding SIRI to the base model modestly improved discrimination and risk reclassification, it should be considered a candidate prognostic marker requiring external validation before clinical application.

Humans

Risk Factors for Long-Term Health-Related Quality-of-Life and Mental Health Outcomes in Traumatic Brain Injury: A Systematic Review and Meta-Analysis.

Traumatic brain injury (TBI) often leads to long-term disability, including persistent mental health issues and lower health-related quality of life (HRQoL). Early interventions can improve recovery, but because resources limit routine monitoring of all patients, trauma care remains largely symptom-driven. The combination of long-term disability and limited capacity for routine follow-up highlights the need for risk-stratified follow-up care and reliable evidence on early prognostic factors. However, the existing literature is sparse and methodologically heterogeneous, limiting the clinical applicability of findings. We therefore conducted a systematic review and meta-analysis to identify early risk factors for poorer long-term mental health and HRQoL outcomes. A systematic search of seven electronic databases identified studies of adult patients with TBI, with outcomes assessed at least 6 months postdischarge. Two authors independently screened the studies, assessed the risk of bias, and extracted the data. We pooled effect estimates using a random-effects meta-analysis and calculated 95% prediction intervals. A narrative synthesis was applied when meta-analysis was not feasible. The review was registered with PROSPERO (CRD42024576912) and reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Of the 8,104 articles screened, 64 studies met the inclusion criteria (n = 334,672). Most studies (58%) had a low risk of bias. Female sex, socioeconomic disadvantage, psychiatric history, assaultive-related injuries, and previous TBI were consistently associated with worse long-term outcomes. Across meta-analyses, assault-related injuries more than doubled the odds of post-traumatic stress disorder (odds ratio [OR] = 2.72; 95% confidence interval [CI]: 2.01-3.66, I2 = 0%). Higher odds were also observed among females (OR = 1.33; 95% CI: 1.11-1.59, I2 = 0%), individuals with prior TBI (OR = 1.56; 95% CI: 1.07-2.27, I2 = 0%), and those with psychiatric history (OR = 2.38; 95% CI: 1.83-3.10, I2 = 48%). We found that female sex (OR = 1.72; 95% CI: 1.38-2.16, I2 = 58%), prior TBI (OR = 1.52; 95% CI: 1.25-1.85, I2 = 0%), and psychiatric history (OR = 3.25; 95%CI: 1.86-5.69, I2 = 98%) were associated with higher odds of depression. Furthermore, higher pooled anxiety scores were observed in females and in individuals with a psychiatric history. The study identified several readily available factors present before or at discharge that are associated with poor long-term HRQoL and mental health outcomes. Leveraging these factors in follow-up protocols, prediction modeling, and clinical decision support systems may facilitate risk-stratified postdischarge care for TBI patients.

Humans

Role of Polygenic Risk Scores in Predicting Cognitive Functioning after Mild Traumatic Brain Injury: A TRACK-TBI Study.

Patients with traumatic brain injury (TBI) and Glasgow Coma Scale scores of 13-15 (historically called mild TBI [mTBI]) commonly experience changes in cognitive functioning, including processing speed, memory, and executive functioning. In a prospective sample (N = 523) of individuals of European descent who had been treated in a U.S. level 1 trauma center for mTBI, we examined the prognostic value of four polygenic risk scores (PRS) for cognitive outcomes at 6-months postinjury. To estimate the impact of mTBI on cognition, primary cognitive outcomes were scaled as z-scores reflecting changes in performance relative to predicted preinjury performance. The PRS examined were previously developed and validated to predict cognition-related outcomes of educational attainment (Education-PRS), intelligence (Intelligence-PRS), and Alzheimer's disease (AD-mild traumatic brain injury (APOE)-PRS and AD + APOE-PRS). Both the Education-PRS and Intelligence-PRS displayed bivariate associations with all four cognitive outcomes (&#x3b2; = 0.19-0.32), whereas neither Alzheimer's disease PRS was significantly associated with any outcome. After controlling for other factors known to predict cognitive outcomes of TBI (e.g., sex, education, mTBI severity defined by a combination of Glasgow Coma Scale scores and the presence/absence of acute intracranial findings on clinical neuroimaging), the Education-PRS and Intelligence-PRS remained independently predictive of verbal episodic memory (&#x3b2; = 0.10-0.16), whereas their associations with processing speed and executive functioning were mostly nonsignificant and were mediated through educational attainment. Looking across primary z-score and secondary raw score outcomes, cognitive outcomes 6 months post-mTBI were good on average, and PRS made small independent contributions to outcome prediction. The mediation model findings may support theories of cognitive reserve, which propose that individuals with stronger preinjury cognitive processing abilities (often estimated by educational history) can better compensate for TBI. Moreover, findings indicate that PRS may contribute modestly to multivariable models predicting cognitive function after TBI.

Humans

Artificial intelligence in genitourinary oncology: publication trends and systematic review.

OBJECTIVE: To conduct an analysis of publication trends and a systematic review of randomized controlled trials (RCTs) to characterize the current state of artificial intelligence (AI) use in genitourinary (GU) oncology, as AI has emerged as a transformative tool in healthcare with potential applications in diagnostics, treatment planning, and prognostication. METHODS: We searched the Medical Literature Analysis and Retrieval System Online (MEDLINE), Excerpta Medica dataBASE (EMBASE; Ovid), and Cumulative Index to Nursing and Allied Health Literature (CINAHL) Ultimate for studies related to AI and GU oncology, excluding non-English papers, non-human studies, review articles, and articles using AI solely for manuscript writing. Publication trends were analysed from 2013 to 2023 and categorized by study design and cancer type. RCTs were evaluated through systematic review using Covidence (Veritas Health Innovation Ltd, Melbourne, Victoria, Australia) for screening and data extraction. Two reviewers independently assessed all studies, with risk of bias (RoB) evaluated using the Cochrane RoB 2.0 tool. RESULTS: Of 2409 articles identified, 1220 met inclusion criteria. These included 962 retrospective articles, 175 prospective studies, 79 studies with combined retrospective/prospective methods, and four RCTs. Studies most commonly addressed prostate (n&#x2009;=&#x2009;923), renal (n&#x2009;=&#x2009;274), and urothelial (n&#x2009;=&#x2009;194) cancers. Publications grew from 14 in 2013 to 362 in 2023, with substantial acceleration in 2019. Four RCTs were identified - one in urothelial cancer and three in prostate cancer. Two RCTs evaluated AI-based diagnostics, demonstrating improved performance over conventional methods; the remaining two RCTs evaluated AI in prognostication and treatment planning, showing improved gains in imaging interpretation and operational efficiency. RoB varied across studies, primarily related to randomisation and deviations from intended interventions. CONCLUSIONS: Artificial intelligence research in GU oncology has grown, although high-level evidence from RCTs remains limited. Existing trials underscore AI's promise in diagnostics, prognostication, and treatment planning, and the rapidly evolving nature of this field warrants continued prospective investigation.

Humans

Manual, digital, and AI tumour-infiltrating lymphocyte scoring: a secondary analysis of the APHINITY randomised trial.

BACKGROUND: Stromal tumour-infiltrating lymphocytes (sTILs) are prognostic in early-stage HER2-positive breast cancer, but their role in the context of dual HER2 blockade remains undefined. We evaluated manual, digital, and artificial intelligence (AI)-based sTIL quantification, together with AI-derived spatial metrics, for prognostic and treatment-benefit stratification using tumour samples from the phase 3 APHINITY trial. METHODS: In the APHINITY trial, 4805 patients were randomly assigned to receive chemotherapy plus trastuzumab with pertuzumab or chemotherapy plus trastuzumab with placebo. Median follow-up was 74&#xb7;1 months (IQR 68&#xb7;3-75&#xb7;4). We analysed 4262 haematoxylin and eosin-stained images using manual assessment, an automated digital approach, AI-based lymphocyte quantification (AI percentage lymphocytes), and two AI-derived spatial features (AI-TIL and immune hotspot). Interobserver reproducibility was assessed in 262 randomly chosen tumour samples scored independently by five pathologists. Multivariable Cox models were used to assess associations between TIL levels and invasive disease-free survival (primary outcome in APHINITY), distant recurrence-free interval, and overall survival. The heterogeneity of pertuzumab benefit was evaluated using subgroup analyses, subpopulation treatment effect pattern plot analyses, and nested Cox models with treatment-by-biomarker interaction terms. FINDINGS: Manual scoring showed high interobserver reproducibility (intraclass correlation coefficient 0&#xb7;84 [95% CI 0&#xb7;79-0&#xb7;88]). Concordance between manual and automated methods was modest. AI-based scoring (AI percentage lymphocytes) reclassified 120 (11&#xb7;6%) of 1035 node-positive tumours from immune-low (by manual scoring) to immune-high; this subgroup of patients showed greater separation of 5-year invasive disease-free survival curves between pertuzumab and placebo groups compared with patients whose tumours were concordantly classified as immune-low by both manual and AI-based approaches. Higher levels of TILs were associated with improved invasive disease-free survival for all sTIL measurement approaches and spatial measurements (hazard ratios [HRs] 0&#xb7;41-0&#xb7;93). Pertuzumab was associated with improved invasive disease-free survival at higher sTIL levels across all measurement approaches (HRs 0&#xb7;36-0&#xb7;48), but was not associated with higher values of spatial measures. The largest 6-year absolute improvements with pertuzumab were observed in patients with node-positive disease whose tumours scored in the highest level of immune infiltration of manual sTIL scoring (&#x2265;70&#xb7;0%; mean absolute improvement 12&#xb7;1 percentage points [SD 2&#xb7;8]). In nested prognostic and predictive models, AI-based immune hotspot scores provided the most consistent additional information when combined with any sTIL measurement (all p<0&#xb7;010). INTERPRETATION: Standardised manual sTIL scoring was reproducible, and digital and AI-based methods showed consistent prognostic stratification and potential for treatment-benefit stratification despite only modest correlation between platforms. AI spatial metrics provided complementary information beyond sTIL density and could support more scalable immune assessment. Future studies are needed to validate these approaches in independent cohorts and to clarify their clinical utility for stratifying contemporary HER2-directed therapies. FUNDING: None.

Humans

Characterization of ZIC5 expression in esophageal squamous cell carcinoma and its association with patient survival.

Esophageal squamous cell carcinoma (ESCC) is a prevalent malignancy known for its aggressive nature and poor prognosis. The present study aimed to investigate the expression levels and clinical importance of the Zic family member 5 (ZIC5) gene in ESCC. Gene expression data and survival information obtained from The Cancer Genome Atlas and Gene Expression Omnibus were utilized. In 176 patients with surgically resected ESCC, immunohistochemical analysis was conducted to validate the expression of ZIC5 protein in cancerous and adjacent tissues. The findings of the present study revealed a significant upregulation of ZIC5 in ESCC compared with normal tissues (P<0.05), which was further corroborated by immunohistochemistry exhibiting a notable association between ZIC5 expression and clinical parameters such as tumor size, invasion depth, lymph node metastasis and TNM staging (P<0.05). Survival analysis further indicated that high ZIC5 expression was an independent prognostic factor for poor outcomes in patients with ESCC (hazard ratio=1.519; 95% CI: 1.017-2.269; P<0.05). In addition, bioinformatic analyses predicted that hsa-microRNA-212-5p may regulate ZIC5 mRNA and gene enrichment analysis suggested that ZIC5 may facilitate ESCC progression through involvement in the cell cycle and DNA repair pathways. In conclusion, ZIC5 is highly expressed in ESCC and associated with a poor prognosis, indicating its potential as a therapeutic target and biomarker for ESCC management. Further studies are warranted to elucidate the precise mechanisms underlying the role of ZIC5 in ESCC progression.

ESCC

Ictal electroencephalography and heart rate as treatment criteria in electroconvulsive therapy: a systematic review of the literature.

BACKGROUND: Decades before the emergence of precision medicine, psychiatrists raised the question of whether specific seizure characteristics could help optimize electroconvulsive therapy (ECT), as relationships between some of these characteristics and better outcomes were found. From 1990 onward, researchers focused on electroencephalography (EEG) and cardiovascular markers, which were broadly adopted by guidelines worldwide. However, the prognostic value of these markers is still controversial. Here, we provide a systematic summary of the studies on this topic. METHODS: We conducted a literature review on the use of ictal EEG and heart rate as outcome predictors in ECT using the PubMed, EMBASE, Cochrane and PsycINFO databases. RESULTS: Thirty-seven studies addressing more than 100 quality markers fulfilled our inclusion criteria. Single EEG markers were assigned to five categories (postictal inhibition, amplitude, coherence, regularity, and seizure duration). Heart rate and composite markers were considered separately. In contrast to single EEG markers, heart rate and composite markers could be consistently linked to better outcomes in patients with depression. Only a few studies on schizophrenia could be retrieved. CONCLUSION: Multiparametric markers outperformed single markers. Furthermore, changes in heart rate during seizures were related to better outcomes. Although clinical assessment remains the cornerstone of treatment guidance decisions, EEG and cardiac monitoring could help prevent insufficient seizures during the period preceding clinical improvement. Evidence on schizophrenia remains limited. More randomized trials are needed to analyze the role of composite markers as prognostic tools.

Humans

Genetic regulation of CPEB3-mediated alternative polyadenylation associated with survival of patients with hepatocellular carcinoma.

BACKGROUND: Alternative polyadenylation (APA) is a key post-transcriptional mechanism that regulates gene expression by modulating 3'UTR length, its dysregulation has been implicated in carcinogenesis. How genetic variants influence APA to affect hepatocellular carcinoma (HCC) prognosis remains unclear. METHODS: Prognosis-APA quantitative trait loci (apaQTL) were performed using genotype and APA profiling from TCGA data. A two-stage survival analysis in 848 Chinese and 369 TCGA LIHC patients and functional validation were used to identify prognostic apaQTL in HCC progression. RESULTS: A total of 2,025 and 817 significant APA events were identified in Chinese and TCGA cohort, respectively. Besides, 859 events were associated with poor prognosis in HCC and enriched in RNA splicing / metabolism pathways. We detected 32,034 significant apaQTLs, predominantly enriched in 3'UTRs and RBP-binding regions. CPEB3 was prioritized as a key APA regulator RBP; its low expression correlated with poor patient survival and promoted proliferation, migration, and invasion in HCC cells. Notably, a functional apaQTL variant rs2037547, located in GSK3B and mediated by CPEB3, demonstrated a poor survival of HCC patients in both cohort (pooled HR=1.29, p=0.016). Mechanistically, rs2037547 promoted aberrant APA at proximal poly(A) sites of GSK3B through CPEB3, leading to increased expression of short 3'UTR isoform. This regulatory alteration enhanced HCC cell proliferation, invasion, and migration, and contributed to HCC progression. CONCLUSION: These findings elucidated the distinct role of apaQTL-mediated APA dysregulation in HCC prognosis, providing insights for prognostic stratification and potential targets for personalized therapy in HCC.

RNA-binding proteins

Unraveling the c-Myc-CASC19/HDAC1-NPM1 epigenetic axis: A novel regulatory circuitry and therapeutic target in gastric carcinogenesis.

Mounting evidence implicates long non-coding RNA cancer susceptibility candidate 19 (CASC19) in the pathogenesis of diverse malignancies. However, its functional role and molecular mechanisms in gastric cancer (GC) remain elusive. Herein, we identified a novel 717-bp transcript isoform of CASC19 in GC cells. This study aimed to delineate the biological functions and underlying mechanisms of this novel CASC19 transcript in GC pathogenesis. CASC19 was significantly upregulated in GC tissues and cell lines, correlating with adverse clinicopathological features and poor prognosis in GC patients. Functional investigations demonstrated that CASC19 overexpression potentiated GC cell proliferation, metastasis, and epithelial-mesenchymal transition, whereas CASC19 knockdown attenuated these malignant phenotypes and suppressed tumorigenesis in xenograft models. Mechanistically, CASC19 functioned as a molecular scaffold by recruiting histone deacetylase 1 (HDAC1) to the nucleophosmin 1 (NPM1) promoter. This recruitment sustained H3K27 deacetylation, thereby transcriptionally repressing NPM1 promoter activity and accelerating gastric carcinogenesis. Crucially, Depletion of HDAC1 or NPM1 partial rescued CASC19-mediated oncogenic effects. Intriguingly, the transcription factor c-Myc was found to transcriptionally activate CASC19 through direct binding to its promoter region. Collectively, our findings indicate that the c-Myc-CASC19/HDAC1-NPM1 axis acts as a potential prognostic biomarker candidate for GC and may represent a therapeutic vulnerability worthy of future investigation.

Humans

Plasma proteomics reveal SERPINA1 and CD59 as candidate biomarkers for COVID-19 severity stratification and prognosis prediction.

BACKGROUND: COVID-19 has been closely associated with coagulation abnormalities. However, existing biomarkers, including D-dimer and fibrin degradation products (FDP), exhibit limited accuracy in stratifying disease severity and predicting long-term clinical outcomes. OBJECTIVES: This study aimed to use proteomic analysis to identify plasma biomarkers associated with COVID-19 severity and prognosis, and validate their predictive utility for mortality and thromboembolic complications. METHODS: Plasma proteomic profiles were analyzed across three COVID-19 severity classes. Differential expression analysis and functional analysis were performed. Clustering analysis was used to identify proteins correlated with disease severity. Candidate biomarkers were validated in an independent cohort. Predictive performance of the biomarkers for mortality, sepsis and venous thromboembolism was evaluated using bootstrap-corrected ROC analyses and multivariable regression analyses. RESULTS: Proteomic analysis revealed progressive involvement of the coagulation and complement pathway with increasing disease severity. SERPINA1 and CD59 were identified as candidate biomarkers and exhibited significantly higher plasma levels in severe cases. Bootstrap-corrected ROC analyses demonstrated strong predictive performance: SERPINA1 achieved AUCs of 0.775 and 0.924 for 30-day and 12-month mortality, and CD59 achieved AUCs of 0.720 for sepsis; the combined model further improved prediction of 12-month mortality (AUC 0.946) and sepsis (AUC 0.904), outperforming D-dimer and FDP. Multivariable regression confirmed their independent prognostic value. CONCLUSION: This exploratory study identifies SERPINA1 and CD59 as candidate prognostic biomarkers in COVID-19, highlighting the role of coagulation and complement-related pathways in disease severity and warranting further prospective validation.

Humans

Validation of the newly introduced Deauville score 5a for patients treated for advanced-stage classic Hodgkin lymphoma.

The Lugano Imaging Committee recently refined the Deauville score (DS), subdividing DS5 into DS5a (>2&#xd7; liver uptake without new lesions) and DS5b (new lesions). We investigated whether this improves prognostic discrimination at interim positron emission tomography (PET) after 2 cycles (PET-2) in patients with advanced-stage classical Hodgkin lymphoma (AS-cHL) treated in recent German Hodgkin Study Group randomized phase 3 trials. The primary analysis cohort was HD18 postamendment standard arms (uniform treatment with 6 cycles of escalated doses of bleomycin, etoposide, doxorubicin, cyclophosphamide, vincristine, procarbazine, and prednisone [eBEACOPP]); sensitivity cohorts were HD18 intention-to-treat and HD21 eBEACOPP and brentuximab vedotin, etoposide, cyclophosphamide, doxorubicin, dacarbazine, and dexamethasone arms. Progression-free survival (PFS) was analyzed by landmark Cox models starting at PET-2. DS5a was infrequent (4%-6% across cohorts; 39/639, 67/1745, 33/568, and 29/560). In the primary cohort, DS5 was associated with inferior PFS vs DS1 to DS3 (hazard ratio [HR], 3.00; 95% confidence interval [CI], 1.25-7.23) and vs DS1 to DS4 (HR, 2.35; 95% CI, 1.01-5.50). Across sensitivity cohorts, DS5a remained adverse compared with DS1 to DS4 (HR range, 2.57-5.47), whereas DS4 according to the new definition did not consistently separate from DS1 to DS3, which is likely a result of PET-adapted treatment. Overall survival trends were concordant, but interpretation is limited by few events. To our knowledge, this is the first prognostic validation of the refined DS in prospectively randomized trial populations. The newly introduced DS5a isolates a small high-risk AS-cHL, which further supports risk assessment and adaptation using quantitative biomarkers from PET. The HD18 and HD21 trials were registered at www.clinicaltrials.gov as NCT00515554 and NCT02661503, respectively.

Humans

Spironolactone, early acute eGFR changes, and clinical outcomes in patients with heart failure with preserved ejection fraction: insights from TOPCAT Americas.

AIMS: Early acute changes in estimated glomerular filtration rate (eGFR) have been well described with renin-angiotensin system inhibitors and sodium-glucose cotransporter-2 inhibitors, but less is known about the frequency, prognostic relevance, and implications of these changes after mineralocorticoid receptor antagonist (MRA) initiation in patients with heart failure with preserved ejection fraction (HFpEF). METHODS: We performed a post-hoc analysis of 1648 patients enrolled in the TOPCAT trial (Americas regional subgroup), defining an early eGFR dip as a &#x2265;15% decrease in eGFR between baseline and week 4. Landmark analyses assessed the association of eGFR changes, treatment, and the primary composite endpoint (cardiovascular death, HF hospitalization, or aborted cardiac arrest). RESULTS: Within 4 weeks of treatment initiation, 431 (26%) patients experienced acute eGFR decrease with a higher proportion of patients assigned to spironolactone [269 (33%)] compared with placebo [162 (20%)] (odds ratio 1.97; 95% confidence interval 1.58-2.47). An acute eGFR decrease was independently associated with higher risk of subsequent cardiovascular outcomes, irrespective of treatment arm. However, treatment with spironolactone appeared beneficial in reducing the primary cardiovascular outcome irrespective of the presence [hazard ratio 0.75 (0.53-1.08)] or absence [0.80 (0.64-1.00)] of early eGFR decrease (Pinteraction = .81). At any given magnitude of eGFR decline, risk of the primary endpoint was consistently lower with spironolactone compared with placebo (Pinteraction = .64). CONCLUSIONS: Early acute eGFR changes were common and adversely prognostic in patients with HFpEF. Spironolactone treatment was beneficial in improving cardiovascular outcomes, despite a modest increase in the likelihood of acute eGFR decrease. An acute eGFR decrease early after MRA initiation should not automatically prompt treatment discontinuation. TRIAL REGISTRATION: ClinicalTrials.gov NCT00094302.

Humans