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PubMed1991

[Comparative multifactorial analysis of combined administration of injection and peroral forms of an antibiotic with a microbial immunomodulator in experimental anthrax].

Comparative efficacy of the use of injection and oral dosage forms of rifampicin in the subtherapeutic doses in combination with peptidoglycan , an immunomodulator of microbial origin, was studied in respect to experimental anthracic infection with application of multifactorial analysis. It was shown that the antibiotic and immunomodulator had a pronounced synergistic effect. Polynomial statistic models were developed and nomograms or equal level curves defining the survival rate and average life-span (ALS) of the experimental animals within a wide range of the antibiotic and immunomodulator doses and the peptidoglycan dosing time were plotted. The combined use of the injection rifampicin in the subtherapeutic doses and the immunomodulator provided a significant increase in the survival rate and ALS, whereas the use of the oral antibiotic in combination with the immunomodulator increased only the ALS and not the survival rate. Multifactorial analysis proved to be an optimal methodical approach to comparative study of various antibiotic dosage forms used in combination with immunomodulators under experimental conditions.

Adjuvants, Immunologic↗

PubMed2025

Comprehensive investigation identifies CPSF3 as a novel prognostic and oncogenic biomarker in bladder cancer.

BACKGROUND: Bladder cancer (BC) remains a prevalent malignancy worldwide, with rising incidence rates each year. Despite progress in therapeutic strategies, many patients suffer recurrence or progression, emphasizing the urgent need for novel prognostic biomarkers and therapeutic targets. This research evaluated the prognostic relevance and functional role of Cleavage and Polyadenylation Specificity Factor 3 (CPSF3) in BC. METHODS: We analyzed CPSF3 expression using The Cancer Genome Atlas data and immunohistochemistry on a cohort of 203 BC patients. A nomogram incorporating CPSF3 expression was developed based on CPSF3 expression for prediction of overall survival and disease-free survival. Immune infiltration analyses and transcriptome sequencing were performed to explore underlying biological mechanisms. In vitro and in vivo experiments were utilized to examine the results of CPSF3 silencing on bladder cancer cell growth, colony-forming ability and cell cycle transitions. RESULTS: Elevated CPSF3 expression was significantly linked to unfavorable overall survival and disease-free survival both in TCGA datasets and our cohort. The CPSF3-based nomogram outperformed conventional prognostic models. CPSF3 expression was associated with tumor-infiltrating immune cells and immune checkpoint markers. Enrichment analysis revealed CPSF3 enrichment in cell cycle-related pathways. Suppression of CPSF3 expression led to marked reductions in cell proliferation, colony formation, tumor growth in animal models and inhibited G1 to S phase progression. CONCLUSION: CPSF3 is a promising prognostic biomarker for BC and may play a crucial role in BC progression. Incorporating CPSF3 into clinical prognostic models may enhance prediction of patient outcomes. CPSF3 may represent a promising therapeutic target for BC management.

Bladder cancer↗

PubMed1976

Correction of stereological parameters from biased samples on nucleated particle phases. I. Nuclear volume fraction.

Stereologists are aware that the experimental evaluation of component volume fractions and surface-to-volume ratios are subject to systematic errors whenever the requirements for cell identification impose the necessity for component-biased sectioning. Mathematical corrections of biased volume proportion data have recently been published; these corrections assume that the components under analysis are spherical, and that the nucleated particle phase is monodispersed. In this report, general methods for obtaining corrections of biased nuclear volume fraction data are set out for polydispersed phases of nucleated particles, in terms of the relevant shapes and joint size distribution of nucleus and cell; the scope and limitations of these methods are thereby discussed. Explicit corrections of an immediate applicability are obtained, together with their standard errors, for monodispersed phases where nucleus and cell are two dissimilar biaxial ellipsoids (spheroids). When nucleus and cell are two concentric and similar convex bodies of a certain class--to which triaxial ellipsoids belong--the corrections are shown to be very simple. The corrections for the spheroid-spheroid systems are easily accessible with the aid of a small programmable calculator, whereas those for the sphere-spheroid models are directly obtainable from two nomograms.

Cell Nucleus↗

PubMed1991

Nomogram for dosing warfarin at steady state.

The predictive performance of a nomogram for dosing warfarin was compared with that of a computer program. The nomogram and the computer program were developed from the log-linear model describing warfarin pharmacodynamics at steady state. The nomogram's dose-response curves were generated by using previously reported pharmacodynamic and pharmacokinetic values for an outpatient population receiving warfarin. The series of dose-response curves were plotted by altering the pharmacodynamic values over a range of 3 standard deviations. The ability of the nomogram to predict the steady-state prothrombin time ratio (PTR) after an adjustment in the dosage of warfarin was evaluated, and the results were compared with those of a commercially available program involving Bayesian regression. Data for 65 outpatients were evaluated. The mean +/- S.D. nomogram-predicted, computer-predicted, and measured PTRs were 1.63 +/- 0.27, 1.64 +/- 0.24, and 1.66 +/- 0.23, respectively. The mean prediction errors for the nomogram and the computer program were -0.037 and -0.026, respectively, and the mean percent absolute prediction errors were 11.6% and 11.0%, respectively. Neither method was biased, and differences between the results for the two methods were not significant. The predictive performance of the warfarin dosing nomogram was comparable to that of the computer program.

Bayes Theorem↗

PubMed2024

The prognostic significance of ubiquitination-related genes in multiple myeloma by bioinformatics analysis.

BACKGROUND: Immunoregulatory drugs regulate the ubiquitin-proteasome system, which is the main treatment for multiple myeloma (MM) at present. In this study, bioinformatics analysis was used to construct the risk model and evaluate the prognostic value of ubiquitination-related genes in MM. METHODS AND RESULTS: The data on ubiquitination-related genes and MM samples were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. The consistent cluster analysis and ESTIMATE algorithm were used to create distinct clusters. The MM prognostic risk model was constructed through single-factor and multiple-factor analysis. The ROC curve was plotted to compare the survival difference between high- and low-risk groups. The nomogram was used to validate the predictive capability of the risk model. A total of 87 ubiquitination-related genes were obtained, with 47 genes showing high expression in the MM group. According to the consistent cluster analysis, 4 clusters were determined. The immune infiltration, survival, and prognosis differed significantly among the 4 clusters. The tumor purity was higher in clusters 1 and 3 than in clusters 2 and 4, while the immune score and stromal score were lower in clusters 1 and 3. The proportion of B cells memory, plasma cells, and T cells CD4 naïve was the lowest in cluster 4. The model genes KLHL24, HERC6, USP3, TNIP1, and CISH were highly expressed in the high-risk group. AICAr and BMS.754,807 exhibited higher drug sensitivity in the low-risk group, whereas Bleomycin showed higher drug sensitivity in the high-risk group. The nomogram of the risk model demonstrated good efficacy in predicting the survival of MM patients using TCGA and GEO datasets. CONCLUSIONS: The risk model constructed by ubiquitination-related genes can be effectively used to predict the prognosis of MM patients. KLHL24, HERC6, USP3, TNIP1, and CISH genes in MM warrant further investigation as therapeutic targets and to combat drug resistance.

Humans↗

PubMed1991

Estimation of vulnerable zones due to accidental release of toxic materials resulting in dense gas clouds.

Heavy gas dispersion models have been developed at IIT (hereinafter referred as IIT heavy gas models I and II) with a view to estimate vulnerable zones due to accidental (both instantaneous and continuous, respectively) release of dense toxic material in the atmosphere. The results obtained from IIT heavy gas models have been compared with those obtained from the DEGADIS model [Dense Gas Dispersion Model, developed by Havens and Spicer (1985) for the U.S. Coast Guard] as well as with the observed data collected during the Burro Series, Maplin Sands, and Thorney Island field trials. Both of these models include relevant features of dense gas dispersion, viz., gravity slumping, air entrainment, cloud heating, and transition to the passive phase, etc. The DEGADIS model has been considered for comparing the performance of IIT heavy gas models in this study because it incorporates most of the physical processes of dense gas dispersion in an elaborate manner, and has also been satisfactorily tested against field observations. The predictions from IIT heavy gas models indicate a fairly similar trend to the observed values from Thorney Island, Burro Series, and Maplin experiments with a tendency toward overprediction. There is a good agreement between the prediction of IIT Heavy Gas models I and II with those from DEGADIS, except for the simulations of IIT heavy gas model-I pertaining to very large release quantities under highly stable atmospheric conditions. In summary, the performance of IIT heavy gas models have been found to be reasonably good both with respect to the limited field data available and various simulations (selected on the basis of relevant storages in the industries and prevalent meteorological conditions performed with DEGADIS). However, there is a scope of improvement in the IIT heavy gas models (viz., better formulation for entrainment, modification of coefficients, transition criteria, etc.). Further, isotons (nomograms) have been prepared by using IIT heavy gas models for chlorine, which provide safe distance for various storage amounts for 24 meteorological scenarios prevalent in the entire year. These nomograms are prepared such that a nonspecialist can use them easily for control and management in case of an emergency requiring the evacuation of people in the affected region. These results can also be useful for siting and limiting the storage quantities.

Accidents, Occupational↗

PubMed2026

scRNA-seq and bulk RNA-seq reveal the characteristics of macrophage copper metabolism and establish a risk signature in hepatocellular carcinoma.

BACKGROUND: Hepatocellular carcinoma (HCC) is a prevalent malignancy with an urgent need for improved prognostic stratification and treatment-response prediction. This study aimed to explore a macrophage copper metabolism-associated prognostic model and to investigate the relationship between this risk model and the tumor immune microenvironment. METHODS: The FindClusters function was used to analyze cell clusters, and CellChat and CellPhoneDB/LIANA were employed for cell-cell communication analysis. Copper metabolism-related genes were sourced from the MSigDB database. A prognostic risk model was established using least absolute shrinkage and selection operator (LASSO) analysis and multivariate Cox regression analysis, and a nomogram was constructed by integrating the prognostic model with clinicopathological factors. Additional analyses were performed to map the seven model genes in single-cell data, assess model uncertainty and robustness, evaluate macrophage/copper/cuproptosis-related transcriptional programs, and examine the correlations between risk score, immune infiltration and predicted drug sensitivity. RESULTS: Using single-cell RNA sequencing (scRNA-seq) data, we identified four macrophage subpopulations. Macrophages with high SPP1 expression showed close interaction with T cell populations and were associated with copper ion metabolism. By incorporating 141 copper metabolism-related genes and using The Cancer Genome Atlas Liver Hepatocellular Carcinoma (TCGA-LIHC) cohort, we constructed a seven-gene risk prediction model. Additional single-cell mapping showed that the model genes were detectable in the HCC single-cell dataset and showed a macrophage-associated expression pattern. The model showed moderate prognostic discrimination in TCGA-LIHC, whereas its external performance was heterogeneous and remained evaluable across external cohorts, with performance varying among datasets. Immune and mechanism-related analyses suggested that the risk signature was associated with macrophage-related infiltration, copper metabolism and cuproptosis-related transcriptional programs. Drug sensitivity analysis nominated Daporinad as a computationally predicted candidate compound, supporting Daporinad as a pharmacogenomic candidate for follow-up investigation. CONCLUSIONS: By integrating scRNA-seq and bulk RNA sequencing (RNA-seq) data, we constructed a macrophage copper metabolism-associated prognostic signature for HCC. The risk score was associated with survival, immune microenvironment features and predicted drug response, providing a transcriptomic framework for risk stratification and therapeutic hypothesis generation.

Hepatocellular carcinoma (HCC)↗

PubMed2026

Development and validation of a comprehensive prognostic model for 28-day ICU mortality in non-traumatic subarachnoid hemorrhage: an analysis based on the MIMIC-IV database.

BACKGROUND: Due to the complex pathophysiology of non-traumatic subarachnoid hemorrhage (SAH), accurate risk prediction remains a challenge. Our aim is to develop and validate a comprehensive prognostic model that integrates demographic characteristics, vital signs, laboratory parameters, and more, to provide clinical decision-making support in real-world practice. METHODS: We conducted a retrospective cohort study of 785 Non-traumatic subarachnoid hemorrhage patients. The cohort was randomly divided into a training set (n = 549) and a validation set (n = 236). Feature selection was performed using LASSO regression, followed by backward stepwise Cox regression for optimization. A nomogram was constructed based on independent predictive factors, and model performance was assessed using discrimination, calibration, and decision curve analysis. To prevent immortal-time bias, all predictors were anchored to a fixed early (first-24-hour) measurement window, treatment variables were modelled as binary indicators rather than cumulative exposures, and a five-model sensitivity analysis with baseline-severity adjustment was performed. RESULTS: The development of our model followed a systematic approach: first, 15 potential predictive factors were selected via LASSO regression, which were then refined to 12 independent predictors using backward stepwise Cox regression. The final predictive factors included: Ventilation, AHT, Nimodipine 60 mg, Age, SAPS.II, Input amount, Calcium total, Platelet count, White blood cells, Anion gap, pH, and Chloride. The integrated model demonstrated excellent predictive ability for 7-day, 14-day, and 21-day mortality in both the training set (AUC: 0.972, 0.934, 0.898) and the validation set (AUC: 0.968, 0.948, 0.911). Calibration curves and decision curve analysis confirmed the model's reliability and clinical utility across different time points. We constructed a nomogram for individualized risk prediction. Univariate Kaplan-Meier survival analysis demonstrated significant stratification of survival outcomes by each predictor, while restricted cubic spline analysis revealed non-linear relationships between continuous variables and mortality risk. Random survival forest analysis identified the top three predictive factors (Nimodipine 60 mg, Ventilation, AHT) and compared them with our full 12-variable model, confirming superior performance of the integrated model at all time points. At the 28-day primary endpoint, the model achieved a time-dependent AUC of 0.898 (training) and 0.904 (validation); after restricting predictors to the early baseline window, the leakage-controlled model retained good discrimination (validation C-index 0.803). CONCLUSIONS: Our ICU 28-day mortality prognosis model demonstrated robust performance in predicting ICU 28-day mortality in non-traumatic subarachnoid hemorrhage. The model, through the nomogram, provides individualized risk assessment, aiding clinical decision-making and patient stratification.

Humans↗

PubMed2026

Crucial role of telomere maintenance-related genes in survival prediction and subtype identification in colorectal cancer.

BACKGROUND: Telomere maintenance-related genes (TMRGs) are implicated in Colorectal cancer (CRC) development, but their prognostic value and clinical relevance remain insufficiently explored. This study aims to develop a TMRG-based prognostic model and elucidate its clinical utility in CRC management. METHODS: The Cancer Genome Atlas database was utilized to download RNA-seq data from 638 CRC and 51 control samples. Differential expressed genes were screened and intersected with 2086 TMRGs, resulting in the identification of 976 TMRGs. Through univariate and multivariate Cox regression analysis, a prognostic model comprising three telomere maintenance-related biomarkers (PDE1B, TFAP2B, and HSPA1A) was developed and validated using an external dataset. By integrating the model risk score with clinical features, a nomogram was constructed to predict the survival outcomes of CRC patients. Additionally, an in-depth investigation of the immuno-infiltration, functional variation and drug sensitivity analysis were performed in two risk subgroups defined by the prognostic model. Finally, the functional significance of PDE1B in CRC cell lines was investigated through MTT assays, cell colony formation assays, transwell assays and flow cytometry. RESULTS: A total of 976 DE-TMRGs were enriched in telomere/DNA replication pathways. A three-gene signature (PDE1B, TFAP2B, and HSPA1A) stratified patients into high- and low-risk groups with divergent survival (AUC >0.60, validated externally). High-risk patients had advanced N/M stages, elevated M0/M2 macrophages, reduced CD4+ memory T cells, and upregulated immune checkpoints. Nomogram integrating risk score, age, and N/M stage accurately predicted 1-/3-/5-year survival. Low-risk patients showed greater 5-fluorouracil sensitivity. PDE1B expression was significantly reduced in CRC tissues and correlated with advanced stages. Functional assays confirmed PDE1B overexpression suppressed proliferation, migration, invasion, and induced apoptosis in CRC cells. CONCLUSION: This study identifies a moderately predictive telomere maintenance-related gene signature as an independent prognostic predictor in CRC. The risk stratification model effectively discriminates patients with distinct survival patterns, tumor microenvironments, and therapeutic responses, while the integrated nomogram offers additional reference information for survival analysis, albeit with only moderate predictive accuracy. These findings indicate telomere maintenance-related gene signature could serve as a preliminary auxiliary risk stratification tool for postoperative CRC patients, PDE1B may also serve as a potential epithelial tumor-suppressor target for future preclinical studies.

PDE1B↗

PubMed1976

Maximal oxygen consumption in patients with lung disease.

A theoretical model for oxygen transport assuming a series linkage of ventilation, diffusion, oxygen uptake by erythrocytes, cardiac output, and oxygen release was used to calculate expected values for maximal oxygen intake (VO2max) of patients with various pulmonary disorders 22 patients with either restrictive or obstructive ventilatory impairment were studied at rest and maximal exercise. When exercise measurements of maximal pulmonary blood flow (QCmax), oxygen capacity, membrane diffusing capacity for CO, pulmonary capillary blood volume, alveolar ventilation, and mixed venous oxygen saturation were employed as input values, predictions of VO2max from the model correlated closely with measured values (r = 0.978). Measured VO2max was 976+/-389 ml/min (45.3+/-13% of predicted normal), and VO2max predicted from the model was 1,111+/-427 ml/min. The discrepancy may in part reflect uneven matching of alveolar ventilation, pulmonary capillary blood flow, and membrane diffusing capacity for CO within the lung; uniform matching is assumed in the model so that mismatching will impair gas exchange beyond our predictions. Although QCmax was less than predicted in most patients (63.6+/-19.6% of predicted) the model suggests that raising QCmax to normal could have raised VO2max only 11.6+/-8.8% in the face of existent impairment of intrapulmonary gas exchange. Since pulmonary functions measured at rest correlated well with exercise parameters needed in the model to predict VO2max we developed a nomogram for predicting VO2max from resting CO diffusing capacity, the forced one second expired volume, and the resting ratio of dead space to tidal volume. The correlation coefficient between measured and predicted VO2max, by using this nomogram, was 0.942.

Adolescent↗

PubMed1976

Errors involved in instantaneous intravascular input assumptions.

A comparative evaluation of a zero-order input and the generally accepted instantaneous intravascular input assumption is made for two- and three-compartment open model systems. Equations are derived and a nomogram is prepared to calculate the magnitude of error involved in instantaneous input assumptions. It is suggested that all intravascular administrations be considered as zero-order inputs.

Injections, Intra-Arterial↗

PubMed1976

Calculations for pH during CO2 and O2 exchange with blood.

New mathematical formulas are presented to calculate directly the change in blood pH during CO2, and O2 exchange with blood. pH changes are calculated from changes in blood PCO2 and saturation values or from changes in blood PCO2 and blood PO2 values. Computational results agree well with information obtained from a Dill nomogram. These new formulas have applicability to the modelling of gas exchange in blood oxygenators as well as to estimating blood acid-base parameters following the mixing of blood samples.

Blood↗

PubMed1991

[Multifactorial experiment on the combined effect of rifampicin and microbial polysaccharide in experimental plague infection].

Multifactorial analysis was applied to the study of the combined effect of rifampicin and a microbial polysaccharide in experimental plague infection. The effect of the antibiotic and immunomodulator was shown to be synergistic. On the basis of the study results polynomial statistic models of the second order were designed and nomograms or equal level lines were plotted which provided optimization of the combined chemo- and immunotherapy.

Animals↗

PubMed2026

Screening of molecular biomarkers ASPN and LBH and construction of a prediction nomogram for the progression of esophagogastric junction adenocarcinoma.

BACKGROUND: Esophagogastric junction adenocarcinoma (EGJA) is an aggressive malignancy of the digestive system with poor prognosis. Early diagnosis and accurate prediction of tumor progression remain major clinical challenges. This study aimed to identify and validate molecular biomarkers and construct a precise diagnostic model, providing a scientific basis for individualized treatment. METHODS: Differentially expressed genes (DEGs) associated with EGJA were identified using The Cancer Genome Atlas (TCGA) database. Quantitative real-time polymerase chain reaction (qRT-PCR) was then performed for further screening. The protein expression levels of ASPN and LBH were validated by immunohistochemistry in both tumor and adjacent non-tumor tissues. A nomogram was constructed by integrating clinical and pathological features, and its performance and clinical utility were assessed using receiver operating characteristic (ROC) curves and decision curve analysis (DCA). RESULTS: Immunohistochemistry demonstrated that the protein expression of ASPN was significantly upregulated in tumor tissues, with expression levels increasing with tumor stage. Conversely, LBH was downregulated in tumor tissues and decreased with advancing stages. The predictive model achieved an area under the curve (AUC) value of 0.977, indicating excellent diagnostic and prognostic performance. DCA confirmed the clinical net benefit of the model. CONCLUSIONS: ASPN and LBH are critical molecular biomarkers for EGJA. The nomogram combining these two markers enables accurate distinction between early and advanced-stage tumors, offering significant support for early diagnosis of EGJA.

ASPN↗

PubMed2026

An individualized nomogram for predicting progression-free survival in systemic anaplastic large cell lymphoma: a multicenter, retrospective, and internally validated study.

OBJECTIVES: To develop an individualized nomogram for predicting disease progression risk in systemic anaplastic large cell lymphoma (sALCL). METHODS: Independent predictors of progression-free survival (PFS) were identified using Cox regression in a multicenter retrospective cohort of 109 sALCL patients (2010-2022). These were incorporated into a three-factor nomogram, evaluated via bootstrapped internal validation (1000 resamples), ROC analysis, C-index, decision curve analysis (DCA), and clinical impact curve (CIC). RESULTS: A total of 29 PFS events occurred during a median follow-up of 31 months. Multivariable modelling selected serum β2-microglobulin elevation, extranodal disease, and front-line chemotherapy choice (CHOP versus CHOPE or BV+CHP) as autonomous progression drivers. Upon internal bootstrap validation, the nomogram yielded strong prognostic accuracy, achieving AUCs of 0.81, 0.85 and 0.87 for 1-, 3- and 5-year progression-free survival, alongside a corrected C-index of 0.779 (95% CI: 0.699 - 0.861). Calibration plots showed close agreement between predicted and observed outcomes, while DCA confirmed superior net clinical benefit versus conventional IPI or Ann Arbor stratification across multiple decision thresholds. CONCLUSION: This first sALCL-specific nomogram integrates clinical and treatment variables to provide personalized PFS risk estimation. While internally validated, this exploratory, observation-based tool requires external validation and recalibration in prospective cohorts before clinical implementation.

Humans↗

PubMed2026

Identification of a novel human gut microbes and microbial metabolites related genes signature for prognostic implication in head and neck squamous carcinomas.

BACKGROUND: The gut microbiota acts as a critical driver influencing the pathogenesis, therapeutic response, and clinical outcomes across various cancer types. This study aimed to investigate the prognostic value of human gut microbes and microbial metabolites related genes (HGMMMRGs) in head and neck squamous cell carcinoma (HNSCC). METHODS: We constructed a prognostic risk model comprising 19 core HGMMMRGs using LASSO penalized regression and a multivariate Cox proportional hazards model. The predictive performance of the model was evaluated through Kaplan-Meier analysis, receiver operating characteristic (ROC) curves, nomograms, and concordance index. In addition, functional enrichment analysis was performed on the differentially expressed risk genes. Furthermore, the relationship between the immune microenvironment of HNSCC and the risk diagnostic model was examined. Western blot analysis was used to assess the expression levels of IL10 in both HNSCC tissues and adjacent normal tissues. Finally, the correlation between IL10 and the gut microbiota was explored. RESULTS: This study developed a risk score model integrating 19 HGMMMRG genes, which can serve as a tool to guide prognosis and immune microenvironment assessment in HNSCC patients. Survival analysis showed that patients in the high-risk group had significantly worse outcomes (P < 0.05). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis revealed significant enrichment of differentially expressed genes (DRLs) and immune-related pathways. Western blot analysis further confirmed that IL10 was highly expressed in HNSCC, and the abundance of Faecalibacterium prausnitzii and Enterococcus durans colonies was correlated with IL10 expression. CONCLUSION: We developed a prognostic model for HGMMMRGs that can be effectively used to predict OS in patients with HNSCC. Second, Faecalibacterium prausnitzii and Enterococcus durans can influence the prognosis of patients with HNSCC by mediating the expression IL10 and thereby affecting the prognosis of HNSCC patients. Thus, human gut microbes and microbial metabolite-related genes may be another promising strategy for the treatment of patients with HNSCC.

HNSCC↗

PubMed2026

Sodium Overload-Related Molecular Subtypes and a Four-Gene Prognostic Signature Predict Survival, Immune Landscape, and Therapeutic Response in Acute Myeloid Leukemia.

Sodium overload has recently emerged as a critical metabolic stressor involved in cancer progression; however, its molecular characteristics and clinical relevance in acute myeloid leukemia (AML) remain unexplored. RNA-seq data sets, clinical annotations, and mutational profiles of AML patients were annotations from The Cancer Genome Atlas and integrated with Genotype-Tissue Expression normal samples. Sodium overload-related genes (SORGs) were obtained from GeneCards. Differentially expressed SORGs (DESORGs) screened by applying the limma statistical model, followed by univariate Cox proportional hazards regression, consensus clustering, functional enrichment, immune infiltration analysis, and pathway evaluation. A prognostic signature was developed through least absolute shrinkage and selection operator regression followed by multivariate Cox modeling. The model's performance was further verified in two external GEO data sets (GSE71014 and GSE37642). Nomogram construction, subgroup analysis, tumor mutational burden (TMB) assessment, drug sensitivity prediction, transcription factor (TF) analysis, and competing endogenous RNA (ceRNA) network analyses were also performed. A total of 57 DESORGs were identified, and 2 sodium overload-related molecular subtypes exhibited distinct survival, immune infiltration, and inflammatory pathway activation. A robust four-gene signature (DOCK1, GABRE, HTR7, ACSM1) stratified patients into high- and low-risk categories with significantly different survival across training and validation cohorts. High-risk patients displayed increased immune infiltration, higher TMB, reduced sensitivity to multiple chemotherapeutic drugs, and inferior predicted response to PD-L1 blockade. TF and ceRNA networks revealed multilayered transcriptional and post-transcriptional regulation of the signature genes. This study identifies sodium overload-related molecular heterogeneity in AML and establishes a validated four-gene prognostic signature that integrates genomic, immunologic, and therapeutic features, offering potential utility for personalized risk assessment and treatment optimization.

Humans↗

PubMed2025

Multi‑omics identification of a novel signature for serous ovarian carcinoma in the context of 3P medicine and based on twelve programmed cell death patterns: a multi-cohort machine learning study.

BACKGROUND: Predictive, preventive, and personalized medicine (PPPM/3PM) is a strategy aimed at improving the prognosis of cancer, and programmed cell death (PCD) is increasingly recognized as a potential target in cancer therapy and prognosis. However, a PCD-based predictive model for serous ovarian carcinoma (SOC) is lacking. In the present study, we aimed to establish a cell death index (CDI)-based model using PCD-related genes. METHODS: We included 1254 genes from 12 PCD patterns in our analysis. Differentially expressed genes (DEGs) from the Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) were screened. Subsequently, 14 PCD-related genes were included in the PCD-gene-based CDI model. Genomics, single-cell transcriptomes, bulk transcriptomes, spatial transcriptomes, and clinical information from TCGA-OV, GSE26193, GSE63885, and GSE140082 were collected and analyzed to verify the prediction model. RESULTS: The CDI was recognized as an independent prognostic risk factor for patients with SOC. Patients with SOC and a high CDI had lower survival rates and poorer prognoses than those with a low CDI. Specific clinical parameters and the CDI were combined to establish a nomogram that accurately assessed patient survival. We used the PCD-genes model to observe differences between high and low CDI groups. The results showed that patients with SOC and a high CDI showed immunosuppression and hardly benefited from immunotherapy; therefore, trametinib_1372 and BMS-754807 may be potential therapeutic agents for these patients. CONCLUSIONS: The CDI-based model, which was established using 14 PCD-related genes, accurately predicted the tumor microenvironment, immunotherapy response, and drug sensitivity of patients with SOC. Thus this model may help improve the diagnostic and therapeutic efficacy of PPPM.

Humans↗