Low-risk Group Research Articles

Healthy lifestyle reduces cardiovascular risk in women with genetic predisposition to hypertensive disorders of pregnancy

The genetic risk for hypertensive disorders of pregnancy is linked with the development of atherosclerotic cardiovascular disease. However, the effects of lifestyle and metabolic syndrome on atherosclerotic cardiovascular disease have not been evaluated. Here, we assess the long-term association between these factors and atherosclerotic cardiovascular disease in women with genetic risk for hypertensive disorders of pregnancy. We evaluate the genetic risk for hypertensive disorders of pregnancy using a genome-wide polygenic risk score derived from a large-scale GWAS. The incidence of atherosclerotic cardiovascular disease is evaluated according to genetic risk, lifestyle, and metabolic syndrome. Individuals with a very high genetic risk for hypertensive disorders of pregnancy have a 53.0% higher chance of developing atherosclerotic cardiovascular disease than those with a low genetic risk. However, the risk of developing atherosclerotic cardiovascular disease is reduced by up to 64.6% through the maintenance of an ideal metabolic syndrome status and a healthy lifestyle in the high genetic risk group (top 20%), and by up to 65.4% in the low genetic risk group (bottom 20%). These findings emphasize that maintaining a healthy lifestyle in women is equally effective at reducing the risk of atherosclerotic cardiovascular disease independent of genetic risk for hypertensive disorders of pregnancy.

Immune gene features and prognosis in colorectal cancer: insights from ssGSEA typing

BackgroundColorectal cancer (CRC) is a molecularly heterogeneous disease, and its treatment and prognosis vary greatly among subgroups. Therefore, it is necessary to identify prognostic factors associated with the biological heterogeneity of CRC in order to improve patients' survival expectations.MethodsWe obtained and merged RNA-Seq data along with clinical details for colorectal cancer (CRC) from The Cancer Genome Atlas (TCGA) repository, and then performed immunocluster typing on all CRC specimens. We conducted differential expression gene (DEG) analysis, gene set enrichment analysis (GSEA), and tumor microenvironment (TME) analysis on CRC samples that were divided into high and low Immunity categories. Moreover, we pinpointed prognostic genes from immune-related gene (IRGs) sets, developed a prognostic risk model, and executed survival analysis, receiver operating characteristic (ROC) curve analysis, and independent prognostic analysis. Additionally, we assessed the risk for patients categorized into high- and low-risk groups based on the model. Lastly, we created a Nomogram to customize the prediction of survival outcomes in CRC patients.ResultsCRC samples were divided into high and low Immunity groups based on the median value of the immunity score. Between the two groups, a total of 1550 DEGs were identified and 395 differentially expressed immune-related genes (DE-IRGs) were identified by intersection with 2483 IRGs. The DE-IRGs of the high Immunity group were dominated by Cytokine receptor interactions, chemokine signaling pathways and immune cell-mediated cytotoxicity, and molecule function of immune effector process. TME analysis showed that most of the 27 immune cells and functions were highly enriched in high Immunity group, whose Immune Score, Stromal Score and ESTIMATE Score were significantly higher. Subsequently, a prognostic risk model of CRC was constructed based on 12 prognostic genes, and the accuracy and reliability of the model prediction were verified. Finally, Nomogram enabled accurate individual prediction of the survival prognosis of CRC patients.ConclusionsOur study develops an immune-related prognostic model and Nomogram that reliably predicts survival outcomes in CRC patients and enhances understanding of the tumor immunity and molecular mechanisms of CRC.

Identification of Interleukin-Related Genes Signature for Prognosis Prediction in Head and Neck Squamous Cell Carcinoma Patients.

This study focused on identifying the interleukin (IL)-related genes that influence the head and neck squamous cell carcinoma (HNSCC) patients' prognosis and response to anticancer therapy in patients with HNSCC. We developed a risk model that included three gene signatures, IL Enhancer Binding Factor 2 (ILF2), IL 36 alpha (IL36A), and IL10, based on differential expression analysis, survival analysis, Least Absolute Shrinkage and Selection Operator (LASSO) analysis, and Cox regression analysis. We found that the low-risk group was scored with higher immune cell infiltration, higher expression of human leukocyte antigen (HLA) family genes and immune checkpoint genes, higher cytolytic activity (CYT), tertiary lymphoid structures (TLS), and CD8A/PD-L1 ratio. In contrast, the high-risk group was scored with higher tumor immune dysfunction and exclusion (TIDE), which implied worse response to immunotherapy and worse prognosis. The results above indicated that the low-risk group had stronger antitumor immunity and better responsiveness to immunotherapy. We also observed a significantly enriched pattern of cancer-related pathways and immune pathways in the comparison of the high-risk and low-risk groups. Furthermore, the high-risk group had higher sensitivity to chemotherapy drugs, which suggested that they might benefit from chemotherapy treatment. Following the results above, we confirmed in HNSCC cell lines and clinical specimens that the level of ILF2 in tumors was significantly higher than that in adjacent tumor tissues. Besides, in vivo and in vitro results both showed that silencing ILF2 might depress tumor growth, invasion, and migration. This study not only provided novel perspectives into the immunological and molecular mechanisms of HNSCC and uncovered IL-related gene signatures for predicting HNSCC patients' prognosis and response to chemotherapy and immunotherapy, but also preliminarily suggested that ILF2 might be an important target in the treatment of HNSCC.

To construct and validate a risk score model of angiogenesis-related genes to predict the prognosis of hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is one of the most common malignant tumors with high morbidity and mortality worldwide. Angiogenesis is essential for HCC progression and metastasis. Some angiogenesis-related genes promote this process, whereas other antiangiogenic genes inhibit HCC growth and metastasis. Therefore, finding new potential biomarkers for HCC prognosis prediction and treatment is essential. Public RNAseq and clinical data from TCGA and GEO database, download angiogenesis-related genes from the GeneCards, MSigDB database, through the single factor analysis of Cox, LASSO build risk score—Cox regression analysis model and external validation verified from the GEO. Cox regression analysis, Kaplan Meier (KM) curve, ROC curve, and decision-curve analysis will be used to evaluate and examine the risk score prediction effect of the model. GSVA analysis was used to assess the variation of gene sets between groups, and ClBERSOFT, ESTIMATE, and TIMER databases were used to analyze the immune infiltration in the single-cell level analysis of gene expression differences between cells. Finally, in the three pairs of HCC tissues and tissue adjacent to carcinoma by real-time fluorescent quantitative PCR (qRT_PCR) and western blotting (WB) to evaluate angiogenesis-related genes (ATP2A3 AEBP1 PNMA1, PLAT) expression level in HCC, and AEBP1 was knocked out in HCCLM3 cells, which is to study AEBP1 biological function in HCC. We established a prognostic risk assessment model based on 13 significant genes associated with HCC prognosis by Cox analysis and LASSO-Cox regression analysis. The median was used to divide these patients into high-risk and low-risk groups, and the prognosis of the high-risk group was worse than that of the low-risk group. Through the multivariate Cox regression analysis, it was found that the risk score was an independent predictor of overall survival (OS). The GSVA analysis suggested that the predicted high-risk population showed higher activity in the purine, pyrimidine, and riboflavin metabolic pathways. Compared with the low-risk group, the tumor microenvironment in the high-risk group showed a reduction in the number of cells promoting anti-tumor immunity and an increase in the number of cells inhibiting anti-tumor immunity, as well as a reduction in overall immune infiltration and matrix components. On the single-cell level, it was confirmed that the key genes (AEBP1, ATP2A3, PLAT, and PNMA1) expressed differently between liver cancer and adjacent tissue cell groups. Finally, qRT_PCR and WB results showed that ATP2A3, AEBP1, PNMA1, and PLAT were highly expressed in liver cancer tissue compared to adjacent tissue, and the proliferation, migration, and invasion of HCCLM3 cells were inhibited after knocking out AEBP1. We constructed novel risk score models as prognostic biomarkers for HCC, which has the potential to guide the development of more personalized treatment strategies for HCC patients. In addition, AEBP1 is a potential therapeutic target for HCC.

Machine learning model using immune indicators to predict outcomes in early liver cancer

BACKGROUND Patients with early-stage hepatocellular carcinoma (HCC) generally have good survival rates following surgical resection. However, a subset of these patients experience recurrence within five years post-surgery. AIM To develop predictive models utilizing machine learning (ML) methods to detect early-stage patients at a high risk of mortality. METHODS Eight hundred and eight patients with HCC at Beijing Ditan Hospital were randomly allocated to training and validation cohorts in a 2:1 ratio. Prognostic models were generated using random survival forests and artificial neural networks (ANNs). These ML models were compared with other classic HCC scoring systems. A decision-tree model was established to validate the contribution of immune-inflammatory indicators to the long-term outlook of patients with early-stage HCC. RESULTS Immune-inflammatory markers, albumin-bilirubin scores, alpha-fetoprotein, tumor size, and International Normalized Ratio were closely associated with the 5-year survival rates. Among various predictive models, the ANN model generated using these indicators through ML algorithms exhibited superior performance, with a 5-year area under the curve (AUC) of 0.85 (95%CI: 0.82-0.88). In the validation cohort, the 5-year AUC was 0.82 (95%CI: 0.74-0.85). According to the ANN model, patients were classified into high-risk and low-risk groups, with an overall survival hazard ratio of 7.98 (95%CI: 5.85-10.93, P < 0.0001) between the two cohorts. CONCLUSION A non-invasive, cost-effective ML-based model was developed to assist clinicians in identifying high-risk early-stage HCC patients with poor postoperative prognosis following surgical resection.

External Validation of the Simple Postoperative Acute Kidney Injury Risk Index in Patients Admitted to the Intensive Care Unit After Noncardiac Surgery.

The Simple Postoperative AKI Risk (SPARK) index is a novel model for predicting risk of postoperative acute kidney injury (PO-AKI) among patients after noncardiac surgery. However, the performance of the index has been inconsistent partly due to heterogeneity in case mix and effects of the involved clinical features. To clarify potential reasons for poor performance, we tested the SPARK index in a cohort of high-risk patients requiring intensive care unit (ICU) care after noncardiac surgery and examined whether model modification by refitting coefficients of clinical features could optimize model performance. This was a single-center prospective cohort study. Preoperative variables of the SPARK index were extracted from electronic medical records. PO-AKI was defined by an increase in sCr ≥26.5 mmol/L within 48 hours or 150% compared with the preoperative baseline value within 7 days after surgery, whereas critical AKI was defined as AKI stage 2 or greater and/or any AKI connected to postoperative death or requiring renal replacement therapy during the hospital stay. Discrimination was evaluated by the area under the receiver operating characteristic curve (AUC), and calibration was evaluated by the Hosmer-Lemeshow χ2 test and calibration plot. Model modification was performed by rebuilding the model with the original variables of the SPARK index through proportional odds logistic regression among participants in the earlier study period and was validated in the later one. A total of 973 patients were enrolled, among whom 79 (8.1%) PO-AKI cases and 14 (1.4%) critical AKI cases occurred. Our study participants demonstrated a higher SPARK risk score than the SPARK discovery cohort (eg, 8.02% vs 1.20% allocated in the highest risk group), and the incidence of both outcomes increased through the classes of the score (incidence proportion of PO-AKI increased from 2.56% in the lowest risk group to 25.64% in the highest risk group). The AUCs for PO-AKI and critical AKI were 0.703 (95% confidence interval [CI], 0.641-0.765) and 0.699 (95% CI, 0.550-0.848), respectively. The sensitivity, specificity, negative predictive value and positive predictive value were 68.35%, 57.49%, 95.36%, and 12.44%, respectively, when using 10% of predicted probability of PO-AKI as threshold. Calibration plots suggested acceptable consistency between the predicted and actual risk. After model modification, external validation demonstrated a significantly improved AUC for PO-AKI. The SPARK index showed fair discrimination and calibration among patients admitted to the ICU after noncardiac surgery. Modification of the model improved the performance of the model in terms of predicting PO-AKI.

Long-Term Fine Particulate Matter Exposure on Lung Cancer Incidence and Mortality in Chinese Nonsmokers.

Rationale: The association between fine particulate matter (PM2.5) and lung cancer incidence in non-smokers (LCINS) remains inconsistent. Objectives: To investigate the association between long-term PM2.5 exposure and LCINS in a Chinese population and to assess the modifying effect of genetic factors. Methods: Time-dependent Cox proportional hazard models were employed to evaluate the hazard ratios (HR) and 95% confidence interval (CI) of PM2.5 with LCINS risk and LCINS-related mortality. The polygenic risk score (PRS) was constructed to further explore the interactions between genetic risk and PM2.5 exposure. Additionally, the population attributable fraction (PAF) of PM2.5 to lung cancer risk and mortality was calculated. Measurements and Main Results: The results demonstrated significant associations between PM2.5 exposure and LCINS incidence (HR: 1.10, 95% confidence interval [CI]: 1.04-1.17, per 10 µg/m3) and mortality (HR: 1.17, 95% CI: 1.08-1.27, per 10 µg/m3). Compared to the lowest-risk group, individuals exposed to the high PM2.5 level (≥50.9 µg/m3) and high genetic risk (top 30%) exhibited the highest LCINS incidence (HR: 2.01, 95% CI: 1.39-2.87) and mortality (HR: 2.30, 95% CI: 1.38-3.82). A significant additive interaction between PM2.5 and genetic risk on LCINS incidence was observed. Approximately 33.6% of LCINS cases and 48.5% of LCINS-related deaths in China could be prevented if PM2.5 concentrations were reduced to meet WHO guidelines. Conclusion: Long-term exposure to outdoor PM2.5 increases LCINS risk and LCINS-related mortality, especially in populations with high genetic risk. Strengthening air pollution control measures in China has the potential to significantly reduce the burden of LCINS.

A new signature associated with anoikis predicts the outcome and immune infiltration in nasopharyngeal carcinoma

BackgroundPrevious studies have confirmed the phenomenon of anoikis resistance in nasopharyngeal carcinoma (NPC). Nevertheless, the prognostic significance of anoikis-related genes (ARGs) in NPC remains incompletely understood. This study aimed to create a predictive risk score using an ARGs signature for NPC patients and to investigate how this score relates to clinicopathologic features and immune infiltration in the tumor microenvironment.MethodsBy using data from the Gene Expression Omnibus (GEO) database, we employed machine learning methods to discover prognostic ARGs and create a risk score. Key gene expression levels were validated through real-time PCR and immunohistochemical staining.ResultsThree differentially expressed ARGs (CDC25C, E2F1 and RBL2) with prognostic value were identified by the intersection of multiple machine learning algorithms. A risk score based on t 3-ARG feature was developed to stratify NPC patients into two distinct risk groups using the optimal model, Random Survival Forest. NPC patients with high-risk scores experienced notably shorter progression-free survival in comparison to those with low-risk scores. Multivariate Cox regression analysis indicated that the risk score served as an independent prognostic factor. The time-dependent ROC and decision curve analyses demonstrated the risk model's strong predictive accuracy and clinical utility. The low-risk score group exhibited features indicative of early clinical stage, immune activation, high immune checkpoint gene's expression, and low Epstein-Barr virus gene's expression. Functional analysis revealed enrichment of immune-related pathways in the low-risk group. Patients with high-risk scores were discovered to be unlikely to benefit from immune checkpoint inhibitor treatment. Moreover, the expression of RBL2, E2F1, and CDC25C were significantly correlated with the expression of caspase family genes. Finally, the lower mRNA and protein expression of RBL2 were validated in NPC cell lines and tissues.ConclusionsOur ARGs-based signature model shows promising results in predicting the prognosis of NPC patients and might be associated with immune cell infiltration.

Exploring the prognostic role of microbial and genetic markers in lung squamous cell carcinoma

Despite advances in diagnostic and therapeutic strategies, the prognosis of lung squamous cell carcinoma (LUSC) patients remains poor, and the potential of microbiome-based prognostic biomarkers and therapeutic targets remains largely unexplored. LUSC patient data from The Cancer Genome Atlas (TCGA), including microbial genus level abundance data and RNA sequencing (RNA-Seq) data, were used as a training dataset. Two other independent datasets GSE19188 and GSE157009 serve as validation datasets. A microbiome-based risk score (RS) model was constructed by univariate Cox regression analysis combined with the least absolute contraction and selection operator (LASSO) regression. 18 microbial genera were found to be significantly associated with RFS in LUSC patients. The microbial signature built with these microbial genera, exhibited robust predictive accuracy in both the training and validation datasets. Furthermore, hub mRNA between high- and low-risk groups were selected by XGBOOST and intersect with mRNAs screened by univariate Cox regression analysis, finally identifying four mRNA significantly associated with LUSC prognosis. This study reveals a complex interplay between the lung microbiome and genetic biomarkers, and identifies specific microbial-based and mRNA associated with prognosis in LUSC. These findings provide a basis for future studies aimed to elucidate the mechanisms underlying these associations and provide potential biomarkers for guiding treatment decisions and improving patient outcomes.

Outcomes of Glaucoma Referrals in Adults Aged 18 to 40 Years

While early detection of glaucoma is vital to prevent irreversible vision loss, there are sparse data on the effectiveness of glaucoma referrals and methods to establish evidence-based referral guidelines in large, diverse populations. To assess the prevalence and risk factors for diagnosed glaucoma and loss to follow-up among adult patients aged 18 to 40 years after a new diagnosis of referable glaucoma (ie, with glaucoma or suspected glaucoma). This retrospective cohort study included patients aged 18 to 40 years presenting to Kaiser Permanente Southern California, a large managed health care system, for first-time eye examinations between January 1, 2013, and December 31, 2018. Data analysis occurred between September 2022 and August 2024. The primary outcome was glaucoma diagnosed within 2 years of the first eye examination. The secondary outcome was the loss to follow-up, defined as failure to receive a glaucoma evaluation with visual field or optical coherence tomography testing within 2 years. The cohort included 292 453 patients aged 18 to 40 years who underwent first-time eye evaluations (mean [SD] age, 29.8 [6.4] years). Among 12 050 identified patients with referable glaucoma (52.3% female), 6827 (56.7%) completed glaucoma evaluations, of whom 563 (8.2%) were diagnosed with glaucoma (344 [61.1%] with open angle, 28 [5.0%] with angle closure, 84 [14.9%] with secondary glaucoma, and 107 [19.0%] with unspecified glaucoma). On multivariable analysis, male sex (odds ratio [OR], 1.55 [95% CI, 1.07-2.27]), higher intraocular pressure (IOP) (OR, 1.19 [95% CI, 1.15-1.23] per 1 mm Hg), and greater cup-disc ratio (CDR) (OR, 1.53 [95% CI, 1.34-1.75] per 0.1 unit) were associated with greater odds of glaucoma. Dichotomized age, IOP, and CDR models stratified 51 of 1613 patients (3.2%) into the low-risk group and 202 of 1477 patients (13.7%) into the high-risk group. Being younger than 32 years and having an IOP less than 18 mm Hg and a CDR less than 0.7 yielded a negative predictive value of 98.2% for a glaucoma diagnosis. In this cohort study, the diagnostic yield of glaucoma referrals was low among adults aged 18 to 40 years with first-time eye examinations. A simple risk-stratification strategy could help identify individuals with low and high risks of developing glaucoma, and adoption of evidence-based risk stratification and referral guidelines by health care systems and clinicians could improve equity of glaucoma care and use of eye-care resources.

Predicting appropriateness of antibiotic treatment among ICU patients with hospital-acquired infection

Antimicrobial resistance is a rising global health threat, leading to ineffective treatments, increased mortality and rising healthcare costs. In ICUs, inappropriate empiric antibiotic therapy is often given due to treatment urgency, causing poor outcomes. This study developed a machine learning model to predict the appropriateness of empiric antibiotics for ICU-acquired bloodstream infections, using data from the MIMIC-III database. To address missing values and dataset imbalances, novel computational methods were introduced. The model achieved an AUROC of 77.3% and AUPRC of 40.4% on validation, with similar results on external datasets from MIMIC-IV and Rambam Hospital. The model also predicted mortality risk, identifying a 30% mortality rate in high-risk patients versus 16.8% in low-risk groups. External validation on the eICU database showed a comparable gap, with mortality rates at 24% for high-risk and 7.7% for low-risk groups. Our study demonstrates the potential of machine learning models to predict inappropriate empiric antibiotic treatment.

Classifying problematic gaming using a latent profile approach based on personality traits in Chinese young adolescent

BackgroundInternet gaming has gained widespread popularity in China, yet the classification of problematic gaming subtypes based on personality traits remains limited. This study aimed to employ latent profile analysis (LPA) to identify distinct groups of online gamers and compare key variables across these groups.MethodsAn online survey was conducted within 5593 internet gaming users, including a demographic questionnaire, queries on internet gaming usage, the Video Gaming Dependency Scale, and the Chinese Big Five Inventory-brief version. LPA was applied to identify distinct user groups, followed by an examination of associations between profile membership and auxiliary variables.ResultsLPA identified three gamer categories for problematic gaming: “high-risk” (64.78%), “medium-risk” (3.22%), and “low-risk” (32%). High-risk gamers allocated more time and financial resources to gaming for escapism and leisure purposes. The medium-risk group sought enjoyable experiences, exhibiting traits that were intermediate between the high- and low-risk groups. High-risk gamers demonstrated elevated levels of neuroticism, accompanied by lower scores in other Big Five personality traits. In contrast, medium-risk gamers scored low across all Big Five dimensions, while low-risk gamers achieved higher scores in all traits except neuroticism. Notably, the low-risk group reported forming the fewest new online friendships, despite sharing similar social motivations with the other groups.ConclusionTraits such as low neuroticism and high conscientiousness serve as protective factors against gaming addiction, while being unmarried or an only child provides additional safeguards. Conversely, increased time and financial investment in gaming activities are associated with a heightened risk of addiction. These findings are crucial for identifying high-risk gamers and informing the development of targeted interventions.

Long-term Outcomes and Prognostic Impact of Residual Cancer Burden After Intensified Neoadjuvant Therapy in High-risk Prostate Cancer.

Long-term outcomes and the prognostic impact of the extent of residual disease after neoadjuvant therapy (NAT) with androgen deprivation therapy (ADT) and an androgen receptor pathway inhibitor (ARPI) before radical prostatectomy (RP) for high-risk localized prostate cancer (HRLPC) are not known. We analyzed data for patients treated in five trials evaluating 6 mo of ARPI NAT for HRLPC at our institution between 2006 and 2018. Residual cancer burden (RCB) was quantitated as the calculated tumor volume adjusted for cellularity in the primary tumor. The primary outcome was metastasis-free survival (MFS) according to conventional imaging. We explored RCB categories using the Contal-O'Quigley method to distinguish high- and low-risk groups for MFS. Among 218 eligible patients, median prostate-specific antigen at diagnosis was 8ng/ml, 42 (20%) had cT3-4 disease, and 154 (71%) had a biopsy Gleason score of 8-10. At RP, 24 (11%) had a pathologic complete response and median RCB was 0.05 cm3 (interquartile range 0.00-0.32). By median follow-up of 5 yr, 45 patients had developed metastases and 11 died; the 5-yr MFS rate was 83% (95% confidence interval [CI] 77-88%). On multivariable analysis, higher RCB was associated with poorer MFS (hazard ratio 1.21, 95% CI 1.01-1.47). The 5-yr MFS rates were 100%, 90% (95% CI 72-97%), 82% (95% CI 73-88%), and 63% (95% CI 40-79%) for patients with RCB-0 (a pathologic complete response or no residual disease), RCB-1 (<0.003cm3), RCB-2 (0.003-0.672cm3), and RCB-3 (≥0.672cm3), respectively. The key limitation is lack of a validation cohort. The 5-yr MFS rate for patients treated with ARPI NAT before RP for HRLPC was 83%. The depth of pathologic response, evaluated as the RCB, was highly prognostic for MFS. RCB could be used to guide NAT and post-NAT adjuvant trials in HRLPC.

Multiple machine learning-based integrations of multi-omics data to identify molecular subtypes and construct a prognostic model for HNSCC

BackgroundImmunotherapy has introduced new breakthroughs in improving the survival of head and neck squamous cell carcinoma (HNSCC) patients, yet drug resistance remains a critical challenge. Developing personalized treatment strategies based on the molecular heterogeneity of HNSCC is essential to enhance therapeutic efficacy and prognosis.MethodsWe integrated four HNSCC datasets (TCGA-HNSCC, GSE27020, GSE41613, and GSE65858) from TCGA and GEO databases. Using 10 multi-omics consensus clustering algorithms via the MOVICS package, we identified two molecular subtypes (CS1 and CS2) and validated their stability. A machine learning-driven prognostic signature was constructed by combining 101 algorithms, ultimately selecting 30 prognosis-related genes (PRGs) with the Elastic Net model. This signature was further linked to immune infiltration, functional pathways, and therapeutic sensitivity.ResultsCS1 exhibited superior survival outcomes in both TCGA and META-HNSCC cohorts. The PRG-based signature stratified patients into low- and high-risk groups, with the low-risk group showing prolonged survival, enhanced immune cell infiltration (B cells, T cells, monocytes), and activated immune functions (cytolytic activity, T cell co-stimulation). High-risk patients were more sensitive to radiotherapy and chemotherapy (e.g., Cisplatin, 5-Fluorouracil), while low-risk patients responded better to immunotherapy and targeted therapies.ConclusionOur study delineates two molecular subtypes of HNSCC and establishes a robust prognostic model using multi-omics data and machine learning. These findings provide a framework for personalized treatment selection, offering clinical insights to optimize therapeutic strategies for HNSCC patients.

Construction and evaluation of a prognostic model for cervical cancer based on dynamic hematological and clinical features

ObjectivesThis study aims to investigate the prognostic significance of dynamic hematological and clinical characteristics and to construct nomograms to predict overall survival (OS) in patients with cervical carcinoma who underwent radical radiotherapy.MethodsThe study analyzed patients with cervical cancer who underwent radical radiotherapy at The University of Hong Kong-Shenzhen Hospital between January 2015 and June 2022 and were staged as IB1 to IVA according to the International Federation of Gynecology and Obstetrics (FIGO) 2018 staging system. We identified predictive factors through univariate and multivariate cox regression analyses. Two multivariate analyses integrating different groups of variables were conducted independently. Concordance index (C-index), receiver operating characteristic (ROC), and Kaplan-Meier curves were used to evaluate the nomograms. Bootstrap validation was performed to determine the accuracy of the nomogram using 1000 resamples. The performances of proposed nomograms and FIGO 2018 staging system were compared to assess the prognostic value of hematological and inflammatory markers.ResultsOne hundred fifty-nine patients were included in this retrospective analysis. The median follow-up time was 41.37 months, and the 3-year OS rate was 82.6%. The first multivariate analysis of pre-treatment clinical factors and all hematological variables showed that FIGO2018 staging, and pre-treatment albumin levels were associated with 3-year OS. The final multivariate analysis incorporating all clinical factors, hematological variables, and inflammatory markers identified the following prognostic factors: FIGO2018 staging, rate of tumor shrinkage before brachytherapy, pre-treatment albumin levels, treatment times, minimum neutrophils during treatment, concurrent chemotherapy cycles, and lymphopenia grade. Calibration plots showed agreement between the OS predicted by the nomograms and actual OS. Kaplan–Meier curves demonstrated that patients in the high-risk group had shorter OS than those in the low-risk group (P ≤ 0.001). The C-index for the two nomograms was superior to that of the current FIGO2018 staging system, with values of 0.709 (95% Confidence Interval [CI], 0.622–0.795) and 0.803 (95% CI, 0.729–0.877), compared to 0.593 (95% CI, 0.508–0.678) for the FIGO system.ConclusionWe developed and validated nomograms to predict OS in cervical cancer patients staged IB1 to IVA who underwent radical radiotherapy RT. The prognostic significance of dynamic changes in blood and inflammatory markers has been confirmed. The proposed nomogram exhibits robust predictive capabilities for estimating OS in these patients, facilitating risk stratification and individualized treatment.

Machine learning-random forest model was used to construct gene signature associated with cuproptosis to predict the prognosis of gastric cancer

Gastric cancer (GC) is one of the most common tumors; one of the reasons for its poor prognosis is that GC cells can resist normal cell death process and therefore develop distant metastasis. Cuproptosis is a novel type of cell death and a limited number of studies have been conducted on the relationship between cuproptosis-related genes (CRGs) in GC. The purpose of the present study was to establish a prognostic model of CRGs and provide directions for the diagnosis and treatment of GC. Transcriptome and clinical data of patients with GC were collected from The Cancer Genome Atlas and Gene Expression Omnibus datasets. Single sample gene set enrichment analysis (GSEA) and the randomized forest method were used to establish the prognostic model. Kaplan-Meier survival curve, receiver operating characteristics diagram and a nomogram were used to evaluate the reliability of the model. GSEA and gene set variation analysis (GSVA) were used to examine enrichment pathways between high and low risk groups. Finally, immunohistochemical analysis was used to examine ephrin 4 (EFNA4) expression in GC samples and determine the prognosis of patients with GC based on the expression pattern of EFNA4. A group of 7 predictive models (RTKN2, INO80B, EFNA4, ELF2, MUSTN, KRTAP4, and ARHGEF40) was established which were correlated with CRGs. This model can be used as an independent prognostic factor to predict the prognosis of patients with GC. GSEA and GSVA results indicated that high risk patients with GC were mainly associated with the enrichment of ANGIOGENESIS and TGF_BETA_SIGNALING pathways. Finally, EFNA4 expression in GC was significantly higher than that in normal tissues, and patients with GC and high EFNA4 expression exhibited improved prognosis. In conclusion, the prognosis model based on CRGs could be used as the basis for predicting the potential prognosis of patients with GC and provide new insights for the treatment of GC.

The value of inflammation-related indicators in chemotherapy efficacy and disease-free survival of triple-negative breast cancer.

Systemic inflammation is closely correlated with the progression of cancer. Inflammation-related indicators has been recognized as outcome predictors in triple-negative breast cancer (TNBC). Our study aimed to investigate the predictive value of several inflammation-related indicators in TNBC patients underwent chemotherapy (NAC). 100 TNBC patients were enrolled in the study. Levels of interleukin-6 (IL-6) were detected by enzyme-linked immunosorbent assays (ELISAs). Platelet-to-lymphocyte ratio (PLR) and immune inflammatory index (SII) were obtained from blood routine. Levels of Ki-67 expression were detected by immunohistochemistry (IHC). Mentioned indicators were divided into two groups based on their median values. The correlation between these indicators and NAC efficacy was analyzed using t tests. Prognostic risk scores were calculated by univariate Cox regression analysis and Lasso-penalized Cox regression. The patients were divided into low- and high-risk groups based on the median risk score. Survival curves were obtained by Kaplan-Meier method. Models for univariate and multivariate Cox regression analyses were performed to determine the independent risk factors. A nomogram was used for the prediction of 1-, 2-, and 3-year disease-free survival (DFS). Accuracy of the prognostic model was validated by receiver operating characteristic curve (ROC). IL-6, PLR, SII, and Ki-67 levels were associated with neoadjuvant efficacy. IL-6, PLR, SII, Ki-67, and lymphocyte count were associated with DFS. The risk score for each TNBC patient was obtained using LASSO regression analysis to construct a prognostic model. In the prognostic model, patients in the high-risk score group showed worse DFS than those in the low-risk group. Risk score and tumor size were independently correlated with outcomes in multivariate Cox regression analysis. A nomogram was constructed using IL-6, PLR, SII, Ki-67, and Miller-Payne (MP) scores. Calibration curves demonstrated good consistency between the actual and predictive values of the nomogram. A prognostic model was established by combining four prognosis-related indicators in TNBC patients who underwent NAC.

Integrative prognostic modeling of ovarian cancer: incorporating genetic, clinical, and immunological markers

Ovarian cancer has a high mortality rate, primarily due to late diagnosis and complex pathogenesis. This study develops an integrative prognostic model combining genetic, clinical, and immunological data to predict outcomes in ovarian cancer patients. Utilizing data from The Cancer Genome Atlas (TCGA), we identified significant prognostic genes through differential expression and survival analysis, integrating these with clinical features and immune landscape assessments including immune cell infiltration and checkpoint expression. The risk score effectively predicted patient survival, distinguishing between high and low-risk groups with significant outcome differences. High-risk patients demonstrated poor prognosis, greater immune checkpoint expression, and higher tumor mutational burdens (TMB), suggesting potential responsiveness to immunotherapy. The model's predictive capacity was validated across multiple cohorts, showing consistent performance in survival prediction and treatment response. Calibration curves and decision curve analysis confirmed the model's clinical utility. This study highlights the potential of an integrated approach to enhance personalized treatment strategies in ovarian cancer, aiming to improve patient management and outcomes.

Cognitive Risk Stratification Score in Middle-aged and Older Adults with Type 2 Diabetes: a Cross-Sectional Study.

Cognitive impairment (CI) affects approximately 45% of middle-aged and older adults with Type 2 Diabetes Mellitus (T2DM) globally. Although formal comprehensive neuropsychological tests are the gold standard for diagnosing CI, they are often time-intensive and may not be feasible in primary care. This study aimed to develop and validate a novel Risk Stratification Score (RSS) to rapidly and comprehensively predict CI risk among middle-aged and older adults with T2DM, offering a streamlined alternative in clinical practice. A cross-sectional study was conducted from July 2023 to February 2024 in a primary care polyclinic in Singapore's western region. Participants aged between 40 and 85 diagnosed with T2DM (n=150) were included in a convenience sampling. The primary outcome was CI status and assessed using formal neuropsychological tests, which including Montreal Cognitive Assessment (MoCA). CI was identified in 49.3% of participants (n=74). The RSS, incorporating the MoCA, Diastolic Blood Pressure (DBP), and Short Physical Performance Battery (SPPB), demonstrated excellent discrimination, achieving an area under the ROC curve of 0.802 (p < 0.001). With an optimal cutoff of 0.3, the model showed a sensitivity of 63.5% and specificity of 86.8%, effectively differentiating high- and low-risk CI groups. RSS in clinical practice, exemplified by the Integrated Metabolic Cognitive Risk Stratification Pathway (imCRSP), is a promising tool for rapid CI risk assessment in primary care. Its robust predictive accuracy and ease of use support its application for early intervention in middle-aged and older adults with T2DM. Future studies should validate its use longitudinally and across diverse populations to enhance generalizability.

Hospital malnutrition and stroke: a study of nutritional risk and patient outcomes.

Introduction: Hospital malnutrition is a prevalent issue among stroke patients, with significant impacts on immune function and clinical outcomes. Malnutrition is associated with poor outcomes such as increased complications, prolonged recovery, and higher mortality. This study aims to assess the prevalence of malnutrition using the Malnutrition Screening Tool (MST) and its association with clinical outcomes, including length of stay (LOS), inflammatory markers, and mortality. Methods: A retrospective cohort study was conducted, including 230 stroke patients who were admitted between January 2022 and January 2024. Nutritional status was assessed using the MST, with key outcomes including LOS, Total Lymphocyte Count (TLC), Neutrophil-to-Lymphocyte Ratio (NLR), serum albumin, Prognostic Nutritional Index (PNI), and mortality. Statistical analyses were performed using chi-square tests for categorical variables and t-tests or Mann-Whitney U tests for continuous variables, with a p-value of &lt;0.05 considered statistically significant. Result: The study found that 26.5% of patients had an MST score of 2 or higher, indicating a high risk of malnutrition. Patients with high MST scores had significantly lower TLC (p = 0.017), indicating a weakened immune response. No significant differences were observed in LOS (p = 0.63), mortality (p = 0.404), or other inflammatory markers such as NLR, albumin, and PNI between the high-risk and low-risk groups. Conclusion: Malnutrition is common among stroke patients and is associated with impaired immune function, as evidenced by lower TLC in malnourished patients. Although no significant differences were observed in LOS or mortality, the findings underscore the importance of routine nutritional screening and timely intervention to improve patient outcomes.