Prognostic Predictor Research Articles

Development and validation of a predicting model for risk of mortality in heart failure patients with mildly reduced ejection fraction post discharge

Abstract Background Prognostic prediction for heart failure with mildly reduced ejection fraction (HFmrEF) patients remains challenging. We aimed to create and validate a mortality risk prediction model for HFmrEF patients at 6-month, 1-year, and 3-year post discharge. Methods Clinical data of 1691 HFmrEF patients registered in the heart failure registry from 2015 to 2020 at the Heart Center of our hospital were analyzed. Patients were assigned into a training (1183 patients) and validation cohort (508 patients) at 7:3 ratio. Predictive variables for mortality at various period post discharge were identified by the Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis. The predicting model was then established based on these variables. Model performance was evaluated with ROC curve and Decision Curve Analysis (DCA). Results Eight predictors were identified including age, NT-proBNP, hemoglobin levels, use of beta-blockers, ACEI/ARB, invasive ventilation, PCI procedures, and pulmonary artery systolic pressure values. The model achieved a C-index of 0.748 (training set) and 0.755 (validation set). Area Under the Curve (AUC) for training set at 6 months, 1 year, and 3 years were 0.813, 0.784, and 0.774, and AUC for validation set were 0.775, 0.744, and 0.770, respectively. The DCA analysis confirmed the favorable net benefit of the established nomogram model. Conclusion This predicting model might be used for post-discharge risk stratification and individual decision-making of post-discharge management in HFmrEF patients.

Association of R2-CHA2DS2-VASc score and clinical outcomes after bioprosthetic valve replacement: Subanalysis from BPV-AF registry

Abstract Background Although bioprosthetic valve (BPV) replacements are becoming more common, there are limited studies about prognostic prediction method for patients after BPV replacement. R2-CHA2DS2-VASc score is modified CHA2DS2-VASc score by adding reduced estimated glomerular filtration rate (eGFR < 60 mL/min/1.73m2). It is increasingly used for the prediction of cardiovascular (CV) events in patients with atrial fibrillation (AF), device implantation, and acute coronary syndrome. Purpose We aimed to evaluate the predictive value of R2-CHA2DS2-VASc score for future CV events in patients after BPV replacement. Methods BPV-AF, an observational, multicenter, prospective registry, enrolled AF patients that underwent BPV replacement. Patients were stratified into three risk groups according to their R2-CHA2DS2-VASc score as follows; low (scores, 0–1), moderate (scores, 2–4), and high (scores, 5–11). The primary outcome measure was a composite of stroke, systemic embolism, CV events including heart failure hospitalization, and cardiac death. Results A total of 766 patients were included in the analysis. The mean age was 80.3 years and 419 patients (54.7%) were female. Mean R2-CHA2DS2-VASc score was 4.1±1.5. Low, moderate, and high R2-CHA2DS2-VASc score groups consisted of 12 (1.6%), 178 (23.2%), and 576 (75.2%) patients, respectively. Median follow up period was 491 (interquartile range: 393-561) days. Kaplan-Meier analysis showed higher incidence of the composite CV events in high R2-CHA2DS2-VASc score group (Log rank test; p<0.01). Multivariate Cox proportional hazards regression analysis revealed that the R2CHA2DS2-VASc score as a continuous variable was an independent predictor of CV events (hazard ratio 1.33, 95% confidence interval 1.18 to 1.51, p<0.01). Conclusion The R2-CHA2DS2-VASc score is useful for CV risk stratification in AF patients after BPV replacement.Cumulative Incidence for Composite CV Ev

Artificial intelligence assessment of heart failure and mortality after acute stroke

Abstract Background/Introduction The adverse cardiovascular outcomes following a stroke have shown significant associations with post-stroke mortality. However, studies focusing on predicting heart failure and its impact on long-term mortality are scarce. Purpose To address this gap, we employed an commercially approved artificial intelligence/machine learning enabled software as a medical device (AiTiALVSD version 1.00.00) which uses only 12 lead electrocardiogram data, resulting probability score for predicting left ventricular systolic dysfunction. We derived the AiTiALVSD probability score from initial ECGs at stroke admission, and assess its implications for prediction of heart failure and long-term mortality. Methods We collected data from 335 ischemic stroke patients admitted to a cardiovascular center during acute periods between 2013 and 2019. The AiTiALVSD score was employed to predict heart failure with reduced ejection fraction and heart failure with mild reduced ejection fraction, analyzed through logistic regression. Subsequently, we used the Cox Hazard proportional model, incorporating the AiTiALVSD score and clinical factors, to predict long-term mortality. Results The model incorporating the AiTiALVSD score with clinical variables demonstrated a higher AUC for predicting heart failure (0.905 [95% CI, 0.827-0.927] vs. 0.828 [95% CI, 0.749-0.907], p<0.004) than clinical variables alone. Moreover, for long-term mortality prediction, this model outperformed the conventional mortality score (AUC, 0.884 [95% CI, 0.836-0.933] vs. 0.755 [95% CI, 0.690-0.820], p<0.001). Conclusion Multivariable models which combine the AiTiALVSD score with clinical factors, effectively predicts heart failure and long-term mortality. This model could enhance therapeutic strategy tailoring by providing acute phase prognostic predictions for individual stroke patients.Prediction performance for heart failurePrediction performance for mortality

Expression of lipid-metabolism genes is correlated with immune microenvironment and predicts prognosis of hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is one of the most common malignant tumors. This study was aimed to identify a lipid metabolism-related signature associated with the HCC microenvironment to improve the prognostic prediction of HCC patients. Clinical information and expression profile data were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, including the GEO dataset GSE76427. The gene expression profile of lipid metabolism was downloaded from Molecular Signatures Database (MSigDB) database. The infiltrating immune cells were estimated by the Estimation of Stromal and Immune cells in Malignant Tumor tissues using Expression data (ESTIMATE), MCP-counter, and TIMER algorithms. Functional analysis, including Gene ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene set enrichment analysis (GSEA) were performed to elucidate the underlying mechanisms. The prognostic risk model was performed by Least absolute shrinkage and selection operator (LASSO) algorithm and Cox regression analysis. Two distinct subgroups of survival were identified. Better prognosis was associated with high immune score, high abundance of immune infiltrating cells, and high immune status. GO and KEGG analysis showed that differentially expressed genes (DEGs) between the two subgroups were mainly enriched in immune related pathways. GSEA analysis suggested that the expression of lipid metabolism related genes (LMRGs) was related to dysregulation of immune in the high-risk group. Risk models and clinical features based on LMRGs predicted HCC prognosis. This study indicated that the lipid metabolism-related signature was important for the prognosis of HCC. The expression of LMRGs was related to the immune microenvironment of HCC patients and could be used to predict the prognosis of HCC.

Moyamoya disease: insights into the clinical implications of the RNF213 gene

Moyamoya disease (MMD) is a rare cerebrovascular disorder characterized by progressive stenosis of the terminal internal carotid arteries and the formation of compensatory collateral vessels, which appear as a “puff of smoke” on cerebral angiography. It is a significant cause of stroke in East Asia, with an incidence of 0.5 to 1.5 cases per 100,000 people annually. The etiology of MMD remains unclear; however, the identification of the RNF213 gene, particularly the R4810K variant, as a major susceptibility factor among the East Asian population, has provided crucial insights into the disease's pathophysiology and clinical manifestations. MMD typically presents with transient ischemic attacks, ischemic and hemorrhagic strokes, seizures, headaches, and cognitive deficits. Diagnostic criteria have evolved to emphasize advanced imaging techniques. Pathological features include fibrocellular intimal thickening, irregular undulation of the elastic lamina, and the formation of moyamoya vessels. The mutation in the RNF213 gene impairs the degradation of proteins involved in vessel development, leading to abnormal angiogenesis. Genotype-phenotype studies indicate that the RNF213 variant is associated with an earlier onset, transient ischemic attacks, infarctions, and involvement of the posterior cerebral artery, although its effects vary between regions. Additionally, the homozygous RNF213 variant consistently correlates with an earlier age of onset and a higher risk of cerebral infarction. However, further research is necessary to fully understand its long-term impacts and its relationship with revascularization outcomes. Ongoing research is crucial to fully comprehend the pathophysiology and genetics of MMD, improve prognostic predictions, and develop novel therapies.

Left atrial reservoir strain as a prognostic indicator for cardiovascular events after mitral valvuloplasty

Abstract Background In patients after mitral valvuloplasty (MVP), assessing valve function and heart failure (HF) is crucial for prognostic predictions. Left ventricular diastolic function assessed by transthoracic echocardiography (TTE) plays an important role in the evaluation of HF in general practice. However, post-mitral valve surgery patients are excluded from the left ventricular diastolic function assessment guidelines proposed by the American Society of Echocardiography in 2016 due to their low correlation with left ventricular diastolic function indices. On the contrary, the left atrial reservoir strain (LARS) before MVP is considered as a prognostic factor, reflecting left atrial dysfunction caused by mitral regurgitation. Nevertheless, there are limited reports on the relationship between LARS and cardiovascular (CV) events after MVP. Purpose The study aims to investigate the utility of LARS in predicting CV events after MVP. Methods The study included 249 patients who underwent TTE between January 2011 and January 2023 after MVP at our hospital. Patient background data, including NYHA classification, medical history, medications, blood tests, and TTE data, were collected at the time of follow-up TTE. LARS, left ventricular global longitudinal strain (LV-GLS), and right ventricular free wall strain (RV-FWSL) analysis were performed using a vender-independent external strain analysis software for all patients in the cohort. The primary endpoints were cardiovascular death and HF rehospitalization. The predictor of cardiovascular events was assessed using the Cox proportional hazards model and Kaplan-Meier curve (KMC). Results The mean age was 61±14 years, and 159 patients (64%) were male. The median time from surgery to TTE was 4.8 (1.0-6.8) years, 32 patients (13%) had events within 8.5 (3.7-10.8) years after TTE. The event groups had higher age, NYHA classification, and NT-pro BNP compared to the non-event group. TTE parameters such as left ventricular mass index, left ventricular end-diastolic volume and end-systolic volume were larger, and LV-GLS and LARS were significantly lower in the event group. In multivariate Cox proportional hazards models showed that LARS was also a prognostic indicator even after adjustment for several variables (Table 1). The KMC showed that LARS was associated with events regardless of left atrial volume index (Log-rank p <0.001, chi-square:31.03) (Figure 1). Conclusion LARS was strongly associated with CV events after MVP. We consider LARS to be a useful prognostic indicator after MVP.

Identification of lncRNA dual targeting PD-L1 and PD-L2 as a novel prognostic predictor for gastric cancer.

Although breakthroughs have been achieved in gastric cancer (GC) therapy with immune checkpoint inhibitors (ICIs) targeting programmed death-1 (PD-1) and programmed death-ligand 1 (PD-L1), the acquisition of high response rate remains a huge challenge for clinicians. It is imperative to identify novel biomarkers for predicting response to immunotherapy and explore alternative therapeutic strategy for GC. The transcriptomic profiles and clinical information of GC patients from The Cancer Genome Atlas (TCGA)-stomach adenocarcinoma (STAD) database was used to screen differentially expressed lncRNAs between the tumor specimens and the paracancerous tissues. The TargetScan, miRDB and miRcode database were then utilized to construct competing endogenous RNA (ceRNA) networks and identify pivotal lncRNAs. An independent dataset from GEO (GSE70880) and 23 pairs of GC specimens of our cohort were subsequently performed for external validity. The relationship between clinical variables and gene expression were evaluated by Kruskal-wallis test and Wilcoxon signed-rank. The prognostic value of the candidate genes was assessed using Kaplan-Meier analysis and Cox regression models. CIBERSORT and Gene set enrichment analysis (GSEA) were used to determine immune cell infiltration. Gastric adenocarcinoma AGS cells and human embryonic kidney 293T (HEK293T) cells with knockdown of LINC01094 were generated by siRNA transfection, followed by detecting the alteration of the target miRNA and PD-L1/PD-L2 by RT-qPCR. Besides, the interaction between lncRNA and the miRNA-PD-L1/PD-L2 axis were verified by dual luciferase reporter assay. Twenty-two intersecting lncRNAs were identified to be PD-L1/PD-L2-related lncRNAs and LINC01094-miR-17-5p-PD-L1/PD-L2 was constructed as a potential ceRNA network. LINC01094 was increased in tumor specimens than adjacent normal samples and was positively associated with advanced tumor stages and EBV and MSI status. Furthermore, LINC01094 expression was an independent risk factor for poor overall survival (OS) in GC patients. CD8+ T cell exhaustion-related genes were enriched in high-LINC01094 tissues and high-PD-L2 group. A strong positive association of LINC01094 expression was established with M2 macrophages, IL-10+ TAM, as well as PD-L1 and PD-L2 levels, therefore a LINC01094-miR-17-5p-IL-10 network was proposed in macrophages. Using the exoRBase database, LINC01094 was assumed in blood exosomes of GC patients The results of knockdown experiments and luciferase reporter assays revealed that LINC01094 interacted with miR-17-5p and served as a miRNA sponge to regulate the expression of PD-L1 and PD-L2. LINC01094 dually regulates the expression of PD-L1 and PD-L2 and shapes the immunosuppressive tumor microenvironment via sponging miR-17-5p. LINC01094 may serve as a potential prognostic predictor and therapeutic target in GC.

Characterizing adipocytokine-related signatures for prognosis prediction in prostate cancer.

Prostate cancer (PCa) is a prevalent malignant tumor in males, with a significant incidence of biochemical recurrence (BCR) despite advancements in treatment. Adipose tissue surrounding the prostate, known as periprostatic adipose tissue (PPAT), contributes to PCa invasion through adipocytokine production. However, the relationship between adipocytokine-related genes and PCa prognosis remains understudied. This study was conducted to provide a theoretical basis and serve as a reference for the use of adipocytokine-related genes as prognostic markers in PCa. Transcriptome and survival data of PCa patients from The Cancer Genome Atlas (TCGA) database were analyzed. Differential gene expression analysis was conducted using the DESeq2 and limma packages. Prognostic genes were identified through univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression. A prognostic model was developed and validated utilizing receiver operating characteristic (ROC) and Kaplan-Meier (K-M) curves. Assessments of immune cell infiltration and drug sensitivity were also carried out. Subsequently, the function of BNIP3L gene in PCa was verified. A total of 47 adipocytokine-related differentially expressed genes (DEGs) were identified. Five genes (PPARGC1A, APOE, BNIP3L, STEAP4, and C1QTNF3) were selected as prognostic markers. The prognostic model demonstrated significant predictive accuracy in both training and validation cohorts. Patients with higher risk scores exhibited poorer survival outcomes. Immune cell infiltration analysis revealed that the high-risk group had increased immune and ESTIMATE scores, while the low-risk group had higher tumor purity. In vitro experiments confirmed the suppressive effects of BNIP3L on PCa cell proliferation, migration, and invasion. The prognostic model independently predicts the survival of patients with PCa, aiding in prognostic prediction and therapeutic efficacy. It expands the study of adipocytokine-related genes in PCa, presenting novel targets for treatment.

Serous Ovarian Carcinoma: Detailed Analysis of Clinico-Pathological Characteristics as Prognostic Factors.

Serous ovarian carcinoma (SOC) is the most common subtype of epithelial ovarian cancer, with high-grade (HGSOC) and low-grade (LGSOC) subtypes presenting distinct clinical behaviours. This study aimed to evaluate histopathologic features in SOC, correlating these with prognostic outcomes, and explore the potential clinical implications. We analysed 51 SOC cases for lymphovascular space invasion (LVSI), tumour border configuration (TBC), microvessel density (MVD), tumour budding (TB), the tumour-stroma ratio (TSR), the stromal type, tumour-infiltrating lymphocytes (TILs), and tertiary lymphoid structures (TLSs). A validation cohort of 54 SOC cases from The Cancer Genome Atlas (TCGA) was used for comparison. In the discovery set, significant predictors of aggressive behaviour included LVSI, high MVD, high TB, and low TILs. These findings were validated in the validation set where the absence of TLSs, lower peritumoural TILs, immature stromal type, and low TSR were associated with worse survival outcomes. The stromal type was identified as an independent prognostic predictor in SOC across both datasets. Inter-observer variability analysis demonstrated substantial to almost perfect agreement for these features, ensuring the reproducibility of the findings. The histopathological evaluation of immune and stromal features, such as TILs, TLSs, TB, TSR, and stromal type, provides critical prognostic information for SOC. Incorporating these markers into routine pathological assessments could enhance risk stratification and guide treatment, offering practical utility, particularly in low-resource settings when molecular testing is not feasible.

Impact of TTF-1 Expression on the Prognostic Prediction of Patients with NSCLC with PD-L1 Expression Levels of 1% to 49%, Treated with Chemotherapy vs Chemoimmunotherapy: A Multicenter, Retrospective Study.

Thyroid transcription factor 1 (TTF-1) expression is a useful predictor of treatment efficacy in advanced non-squamous non-small-cell lung cancer (NSCLC). This study aimed to evaluate whether TTF-1 could predict the effectiveness of chemotherapy versus chemoimmunotherapy in patients with non-squamous NSCLC with programmed death ligand-1 (PD-L1) expression between 1% and 49%. We conducted a retrospective study of patients with NSCLC who were treated with chemotherapy or chemoimmunotherapy between March 2016 and May 2023. The patients had histologically confirmed NSCLC, stage III-IV or postoperative recurrence, TTF-1 measurements, and PD-L1 expression levels between 1% and 49%. Clinical data were analyzed to evaluate the effect of TTF-1 expression on treatment efficacy. This study included 283 of 624 patients. TTF-1-positive patients showed longer progression-free survival (PFS) and overall survival (OS) (PFS: 6.4 months [95% confidence interval (CI): 5.0-9.4] vs 4.1 months [95% CI: 2.7-6.1], p=0.03, OS: 17.9 months [95% CI: 15.2-28.1] vs 9.4 months [95% CI: 6.3-17.0], p<0.01) in the chemotherapy cohorts (n=93). In the chemoimmunotherapy cohort (n=190), there was no significant difference in PFS and OS between TTF-1-positive and negative groups (PFS: 7.6 months [95% CI: 6.4-11.0] vs 6.0 months [95% CI: 3.6-12.6], p=0.59, OS: 25.0 months [95% CI: 18.0-49.2] vs 21.3 months [95% CI: 9.8-28.8], p=0.09). In patients with NSCLC with PD-L1 expression between 1% and 49%, TTF-1 expression was a predictor of chemotherapeutic, but not chemoimmunotherapeutic, efficacy.

Can tip-apex distance and calcar reference tip-apex distance predict lag screw cut-out in elderly patients with trochanteric femoral fractures treated with gamma3 nails

Background: The most common complication of cephalomedullary nail fixation for femoral fractures is lag screw cut-out. The significance of the tip-apex distance (TAD) in predicting lag screw cut-out is still debated. There has been no study conducted on the calcar reference tip-apex distance (CalTAD) regarding its impact on lag screw cut-out. To compare the average TAD and CalTAD between groups with and without lag screw cut-out and to determine the TAD and CalTAD values associated with the highest risk of lag screw cut-out. Methods: This was a prognostic prediction study utilizing a retrospective cohort of 756 patients with trochanteric femoral fractures treated with Gamma3 nails. This study compared the differences in TAD and CalTAD values between the two groups. Results: The group without lag screw cut-out had lower TAD and CalTAD values compared to the group with lag screw cut-out (TAD; 20.3±5.3 mm vs. 22.1±5.7 mm; p=0.044); (CalTAD 22.3±4.4 mm vs. 25.2±5.3 mm; p&lt;0.001). The cutoff points of TAD &gt;23 mm and CalTAD &gt;25 mm were associated with the highest risk of lag screw cut-out (TAD: OR, 2.91; 95% CI, 1.47-5.77; p=0.002); (CalTAD: OR, 3.43; 95% CI, 1.73-6.80; p&lt;0.001). Conclusions: The lag screw cut-out group had higher TAD and CalTAD values. TAD values greater than 23 mm and CalTAD values greater than 25 mm were associated with the highest risk of lag screw cut-out.

Comprehensive assessment of the association between tumor-infiltrating immune cells and the prognosis of renal cell carcinoma.

According to current statistics, renal cancer accounts for 3% of all cancers worldwide. Renal cell carcinoma (RCC) is the most common solid lesion in the kidney and accounts for approximately 90% of all renal malignancies. Increasing evidence has shown an association between immune infiltration in RCC and clinical outcomes. To discover possible targets for the immune system, we investigated the link between tumor-infiltrating immune cells (TIICs) and the prognosis of RCC. To investigate the effects of 22 TIICs on the prognosis of RCC patients and identify potential therapeutic targets for RCC immunotherapy. The CIBERSORT algorithm partitioned the 22 TIICs from the Cancer Genome Atlas cohort into proportions. Cox regression analysis was employed to evaluate the impact of 22 TIICs on the probability of developing RCC. A predictive model for immunological risk was developed by analyzing the statistical relationship between the subpopulations of TIICs and survival outcomes. Furthermore, multivariate Cox regression analysis was used to investigate independent factors for the prognostic prediction of RCC. A value of P < 0.05 was regarded as statistically significant. Compared to normal tissues, RCC tissues exhibited a distinct infiltration of immune cells. An immune risk score model was established and univariate Cox regression analysis revealed a significant association between four immune cell types and the survival risk connected to RCC. High-risk individuals were correlated to poorer outcomes according to the Kaplan-Meier survival curve (P = 1E-05). The immunological risk score model was demonstrated to be a dependable predictor of survival risk (area under the curve = 0.747) via the receiver operating characteristic curve. According to multivariate Cox regression analysis, the immune risk score model independently predicted RCC patients' prognosis (hazard ratio = 1.550, 95%CI: 1.342-1.791; P < 0.001). Finally, we established a nomogram that accurately and comprehensively forecast the survival of patients with RCC. TIICs play various roles in RCC prognosis. The immunological risk score is an independent predictor of poor survival in kidney cancer cases.

The Geriatric Nutrition Risk Index Is Not a Prognostic Predictor for Postoperative Morbidity in Extremely Elderly Patients Undergoing Surgery for Proximal Femur Fractures.

Background/Objectives: The geriatric nutrition risk index (GnRI) has been regarded as a useful predictor of morbidity and mortality in elderly patients. This study aimed to determine the use of the GnRI as a prognostic predictor in extremely elderly patients undergoing proximal femur fracture surgery and the usefulness of evaluation tools related to a patient's underlying disease and functional capacity in predicting the prognosis of extremely elderly patients. Methods: We analyzed 548 patients who had undergone surgery for proximal femur fracture caused by trauma, with an age of ≥80 years, without other accompanying trauma. Results: Body mass index (BMI) (OR, 1.077; 95% CI, 1.010-1.149; p = 0.023), serum albumin levels (0.389; 0.223-0.678; p = 0.001), and Charlson comorbidity index (CCI) (1.170; 1.014-1.349; p = 0.031) were determined to be predictors of morbidity in a multivariable regression analysis. The area under the curve (AUC) in the receiver operating characteristic curve of BMI was 0.565 (95% CI, 0.493-0.637; p = 0.065), and the optimal cut-off value could not be determined. The AUC of serum albumin was 0.647 (0.576-0.717; p < 0.001), and the optimal cut-off value was 3.65 g/dL (sensitivity, 72.2%; specificity, 52.7%). The AUC of the CCI was 0.648 (0.580-0.715; p < 0.001), and the optimal cut-off value was 6.5 (sensitivity, 63.3%; specificity, 61.4%). Conclusions: The GnRI was not a predictive factor for patient prognosis after proximal femur fractures in extreme elderly patients. Rather, serum albumin level and CCI, which reflect the patient's underlying comorbid conditions, were more useful in predicting in-hospital morbidity after proximal femur surgery in extremely elderly patients.

Association between BNP and all-cause mortality in critically ill children: a cohort study.

There is evidence that a high level of BNP is associated with poorer outcomes in patients with cardiac diseases, but few data are available concerning BNP and all-cause mortality in pediatric population. Using the 2010-2018 pediatric intensive care database, we conducted a retrospective study on patients aged 28 days to 18 years, analyzing post-admission BNP measurements. Through two-piecewise regression to identify inflection points, and multivariable logistic regression, we investigated BNP's association with all-cause mortality. We also developed a multivariable-adjusted restricted cubic spline model to explore BNP's non-linear correlation with mortality. In a study of 3220 patients, the overall all-cause mortality rate was 6.7%, with rates across BNP quartiles (Q1-Q4) significantly differing, highlighting a notable increase in mortality at higher BNP levels (P < 0.001). Specifically, patients with BNP ≥ 10,170 pg/ml had an adjusted mortality odds ratio (OR) of 2.017 (95% CI 1.265-3.217; P = 0.0032). Analysis confirmed a non-linear relationship between BNP levels and mortality, with log2 BNP associated with increased risk (OR1.28, 95% CI 1.19-1.38; P < 0.001). Subgroup analyses further revealed that very high BNP levels, especially in infants, with lactate ≥2.0 mmol/L, or CKMB ≥ 45 μ/L. BNP level was associated with all-cause mortality, especially for the patients with BNP ≥ 10,170 pg/ml. This study explored the non-linear association between BNP levels and all-cause mortality in the PICU, finding a significant association among patients with BNP levels above 10,170 pg/ml. The study revealed that higher BNP levels are associated with increased mortality in critically ill children, including those with non-cardiac diseases. This research provides new data on a Southern Chinese population, previously unstudied, enriching the existing body of knowledge. While most studies have focused on adult cardiac patients, this research highlights the importance of BNP as a prognostic tool in the PICU, including non-cardiac cases, adding to the literature. This study furnishes novel clinical evidence supporting the monitoring of BNP concentrations within the PICU, aiding in prognostic predictions and the development of tailored treatment plans for patients.

Comprehensive single-cell and bulk transcriptomic analyses to develop an NK cell-derived gene signature for prognostic assessment and precision medicine in breast cancer.

Natural killer (NK) cells play crucial roles in mediating anti-cancer activity in breast cancer (BRCA). However, the potential of NK cell-related molecules in predicting BRCA outcomes and guiding personalized therapy remains largely unexplored. This study focused on developing a prognostic and therapeutic prediction model for BRCA by incorporating NK cell-related genes. The data analyzed primarily originated from the TCGA and GEO databases. The prognostic role of NK cells was evaluated, and marker genes of NK cells were identified via single-cell analysis. Module genes closely associated with immunotherapy resistance were identified by bulk transcriptome-based weighted correlation network analysis (WGCNA). Following taking intersection and LASSO regression, NK-related genes (NKRGs) relevant to BRCA prognosis were screened, and the NK-related prognostic signature was subsequently constructed. Analyses were further expanded to clinicopathological relevance, GSEA, tumor microenvironment (TME) analysis, immune function, immunotherapy responsiveness, and chemotherapeutics. Key NKRGs were screened by machine learning and validated by spatial transcriptomics (ST) and immunohistochemistry (IHC). Tumor-infiltrating NK cells are a favorable prognostic factor in BRCA. By combining scRNA-seq and bulk transcriptomic analyses, we identified 7 NK-related prognostic NKRGs (CCL5, EFHD2, KLRB1, C1S, SOCS3, IRF1, and CCND2) and developed an NK-related risk scoring (NKRS) system. The prognostic reliability of NKRS was verified through survival and clinical relevance analyses across multiple cohorts. NKRS also demonstrated robust predictive power in various aspects, including TME landscape, immune functions, immunotherapy responses, and chemotherapeutic sensitivity. Additionally, KLRB1 and CCND2 emerged as key prognostic NKRGs identified through machine learning and external validation, with their expression correlation with NK cells confirmed in BRCA specimens by ST and IHC. We developed a novel NK-related gene signature that has proven valuable for evaluating prognosis and treatment response in BRCA, expecting to advance precision medicine of BRCA.

TMSB4Y restrains sphingomyelin synthesis via de novo purine synthesis to exert a tumor suppressor function in male esophageal squamous cell carcinoma.

Y chromosome genes play a vital role in sex difference of cancer. The dysregulation and functional implications of Y chromosome genes in esophageal squamous cell carcinoma (ESCC) remains elusive. Here, we analyze the Y chromosome gene signature and identify TMSB4Y as an emerging prognostic predictor in male ESCC. Functional analyses show that TMSB4Y inhibits the proliferation, invasion and metastasis of male ESCC cells. Mechanistically, we demonstrate that TMSB4Y interacts with PAICS, wherein TMSB4Y disrupts the formation of the PAICS octamer to inhibit purine de novo synthesis, leading to a decrease in the AMP/ATP ratio, subsequently impeding AMPK phosphorylation. Furthermore, we uncover a regulatory cascade orchestrated by the TMSB4Y/PAICS-AMPK axis, which exerts a suppressive effect on sphingomyelin metabolism by inhibiting the expression of sphingomyelin synthases (SMSs). Notably, Malabaricone C, an inhibitor of SMS1 and SMS2, effectively suppresses male ESCC cell proliferation and xenograft tumor growth. Collectively, these findings reveal the regulation of sphingomyelin metabolism by TMSB4Y/PAICS-AMPK axis and underscore the potential of targeting SMSs as a promising therapeutic approach for the treatment of male ESCC.

Immune regulation and prognostic prediction model establishment and validation of PSMB6 in lung adenocarcinoma.

Lung cancer is one of the most common malignant tumors, and patients are often diagnosed at an advanced stage, posing a substantial risk to human health, so it is crucial to establish a model to forecast the prognosis of patients with lung cancer. Recent research has indicated that proteasome 20S subunit 6 (PSMB6) may be closely associated with anti-apoptotic pathways, and proliferation transduction signals in tumor cells of different tumors. However, the precise role of PSMB6 in the immunoregulatory processes within lung adenocarcinoma (LUAD) is yet to be elucidated. By analyzing the TCGA database, we discovered a positive correlation between the expression of PSMB6 and tumor growth trends, and lung adenocarcinoma patients with elevated PSMB6 expression levels had a worse prognosis. Our findings suggest a close correlation between PSMB6 expression levels, immune cell infiltration and immune checkpoint gene expression, which suggests that PSMB6 may become a new independent prognostic indicator. In addition, we developed a prognostic model of PSMB6-regulated immune infiltration-associated genes by analyzing the link between PSMB6 and the immune microenvironment. This model can not only predict the prognosis of lung adenocarcinoma but also forecasts the patient's reaction to immunotherapy. The validity of this research outcome has been confirmed by the GSE31210 and IMvigor210 cohorts. Analysis of the Kaplan-Meier Plotter database indicates that individuals with elevated levels of PSMB6 expression exhibit a poorer prognosis. Additionally, in vitro experiments demonstrated that knockdown of PSMB6 inhibits the proliferation, migration, and invasion of lung adenocarcinoma cells while promoting their apoptosis. Overall, our findings suggest that PSMB6 could remarkably influence the management and treatment of lung adenocarcinoma, opening new avenues for targeted immunotherapeutic strategies.

Development and validation of a novel prognostic prediction system based on GLIM-defined malnutrition for colorectal cancer patients post-radical surgery.

Malnutrition often occurs in patients with colorectal cancer. This study aims to develop a predictive model based on GLIM criteria for patients with colorectal cancer who underwent radical surgery. From December 2015 to May 2021, patients with colorectal cancer who underwent radical surgery at our center were recruited for this study. We prospectively collected data on GLIM-defined malnutrition and other clinicopathological characteristics. Using Cox regeneration, we developed a novel nomogram for prognostic prediction, which was validated and compared to traditional nutritional factors for predictive accuracy. Among the 983 patients enrolled in this study, malnutrition was identified in 233 (23.70%) patients. Multivariate analysis indicated that GLIM-defined malnutrition is the independent risk factor for overall survival (HR = 1.793, 95% CI = 1.390-2.313 for moderate malnutrition and HR = 3.485, 95% CI = 2.087-5.818 for severe malnutrition). The novel nomogram based on the GLIM criteria demonstrated a better performance than existing criteria, with AUC of 0.729, 0.703, and 0.683 for 1-year, 3-year, and 5-year OS, respectively, in the validation cohort. In addition, the risk score determined by this system exhibited significantly poorer short-term and long-term clinical outcomes in high-risk groups in both malnourished and well-nourished patients. Combining handgrip strength, serum albumin level, and TNM stage would help improve the predictive effect of GLIM criteria for colorectal cancer patients post-radical surgery and benefit the individual prognostic prediction of colorectal cancer.

Prognostic value of interim [68Ga]Ga-DOTA-TOC PET/CT in patients with neuroendocrine tumour who underwent peptide receptor radionuclide therapy.

This study evaluated the prognostic value of basal and interim [68Ga]Ga-DOTA-TOC PET/CT in patients with locally advanced or metastatic neuroendocrine tumour (NET) who received peptide receptor radionuclide therapy (PRRT). Patients with NET who received PRRT with [177Lu]Lu-DOTA-TATE at our institution were retrospectively reviewed. Among them, patients who underwent both basal and interim (after two cycles of PRRT) [68Ga]Ga-DOTA-TOC PET/CT were included. Alongside clinicopathologic parameters, PET parameters of maximum standardised uptake value (SUVmax), tumour-to-liver ratio (TLR), whole tumour volume (WTV) and total receptor expression (TRE: WTV multiplied by mean standardised uptake value) were obtained from basal and interim [68Ga]Ga-DOTA-TOC PET/CT, and their proportional changes (∆) were assessed for associations with progression-free survival (PFS) using Kaplan-Meier analysis, log-rank tests, and a Cox proportional-hazards regression model. Twenty-four patients were finally included (10 men and 14 women, median age of 56.5 years, age range 32-74 years). Among them, 16 patients (66.7%) experienced disease progression. In univariate analysis, high ∆WTV (≥ -10%, hazard ratio [HR] = 3.053 [1.003-9.289], p = 0.049) and high ∆TRE (≥ -21%, HR = 3.567 [1.144-11.122], p = 0.028) were significantly associated with shorter PFS. In multivariate analyses adjusted for WHO grade, high ∆WTV (HR = 3.345 [1.055-10.601], p = 0.043) and high ∆TRE (HR = 3.894 [1.194-12.695], p = 0.024) were significant predictors of shorter PFS. The study demonstrates that basal and interim [68Ga]Ga-DOTA-TOC PET/CT scans, through proportional changes in WTV and TRE, effectively predict PFS in neuroendocrine tumour patients receiving PRRT. Question Peptide receptor radionuclide therapy is utilised for patients with somatostatin receptor-positive well-differentiated neuroendocrine tumours; however, prognostic predictors are not well established. Findings Progression-free survival was significantly associated with the proportional change in whole tumour volume and total receptor expression between basal and interim [68Ga]Ga-DOTA-TOC PET/CT. Clinical relevance Interim [68Ga]Ga-DOTA-TOC PET/CT can serve as a valuable imaging method to predict prognosis of peptide receptor radionuclide therapy.

Psoas Muscle Index and Density as Prognostic Predictors in Patients Hospitalized with Acute Pancreatitis.

Background: Early prognostication of acute pancreatitis (AP) patients for those at high risk of complications during hospitalization can facilitate clinical decision-making. Sarcopenia has been proven to be a risk factor for poor prognosis in patients with AP. We aimed to evaluate the association between the muscle parameters measured in computed tomography (CT) and the clinical outcomes of hospitalized patients with AP. Methods: A total of 132 consecutive patients hospitalized between 1 January 2015 and 31 December 2021 for AP with a valid CT scan were analyzed. The first CT conducted during hospitalization was analyzed for psoas muscle area (PMA), index (PMI), and density (PMD) at the L3 vertebral level. The main adverse outcomes indicating a worse prognosis were the development of extrapancreatic complications, infections, ICU transfer, in-hospital mortality, and hospitalization length. Results: The lowest tertile of PMI, as a surrogate for sarcopenia, was significantly correlated with increased rates of extrapancreatic complications, infections, and longer hospitalizations. It was additionally correlated with a worse CT severity index. The results for PMA and PMD also showed worse outcomes, largely mirroring the results for PMI. Although in-hospital mortality was relatively low, none of the patients died in the highest tertile of PMI. A clear cutoff with sufficient predictive capability could not be found. Conclusions: A low psoas muscle index can serve as an additional potential predictive marker for more severe disease and worse outcomes in hospitalized acute pancreatitis patients. More studies are needed to determine its combination with existing prediction tools.