Survival Probability Of Patients Research Articles

Predicting pan-cancer immune-checkpoint therapy and prognosis with a chromatin-accessibility-related alternative splicing signature: a retrospective study

Background: This study examines alternative splicing (AS) events in genes linked to chromatin accessibility in various cancers and their relation to the tumor immune microenvironment. Methods: Data from the Cancer Genome Atlas Database (TCGA) were used to identify independent prognostic factors for pan-cancer. We explored the correlation between differentially expressed genes and tumor immunity, including immune checkpoint genes, tumor development, and immune cells. A regulatory network diagram of alternative splicing-splicing factors (AS-SFs) was constructed to find potential immunotherapy targets. Results: IRF5 and E2F8 genes showed significant differential expression in pan-cancer. Age, cancer grade, primary tumor, cancer lymph nodes, and distant metastasis were independent prognostic factors. The risk model achieved good predictive performance, with AUC values of 0.705, 0.746, 0.743, and 0.743 for 1-year, 3-year, 5-year, and 10-year survival predictions, respectively. Positive correlations were found between IRF5/E2F8 and CD274/CTLA4 in certain cancers using TIMER and CIBERSORT software. Conclusions: AS events in chromatin accessibility genes (IRF5 and E2F8) have significant predictive value in pan-cancer prognosis. Our model assesses patient survival probability and highlights the synergistic impact of immune checkpoints and the AS-SF regulatory network on tumor immunotherapy.

The long-term conditional mortality rate in older ICU patients compared to the general population.

Understanding how preexisting comorbidities may interact with a critical illness is important for the assessment of long-term survival probability of older patients admitted to the ICU. The mortality after a first ICU admission in patients ≥ 55years old registered in the Swedish Intensive Care Registry was compared to age- and sex-matched individuals from the general population with a landmark after 1 year. The comparison was adjusted for age, sex, and baseline comorbidity using Cox regression. The 7-year study period included 140 008 patients, of whom 23% were 80 years or older. Patients surviving the first year remained at an increased risk compared to the general population, but much of this difference was attenuated after adjustment for baseline comorbidity (HR, 1.03; 95% CI 1.02-1.04). Excluding cardio-thoracic ICU admissions, the increased risk remained slightly elevated (adjusted HR, 1.15; 95% CI 1.13-1.16). Also, the subgroup ≥ 75years old surviving the first year returned to a mortality rate comparable to the general population (HR, 0.98; 95% CI 0.96-0.99). Stratified by admission diagnosis an increased mortality rate remained beyond the first year for acute-on-chronic respiratory failure (adjusted HR, 1.47; 95% CI 1.36-1.58) but not for other respiratory causes (adjusted HR, 1.03; 95% CI 0.99-1.07) or admission for septic shock (adjusted HR, 1.04; 95% CI 0.95-1.13). No substantial increased mortality rate was notable beyond the first year for other admission diagnoses. Older ICU patients that survive the first year after an ICU admission return to a mortality rate close to that of the general population having similar baseline comorbidity, but variability is seen depending on the ICU admission diagnosis. Trial registration ClinicalTrials.gov ID: NCT06234709, date 02/01/2024.

LW-MorphCNN: a lightweight morphological attention-based subtype classification network for lung cancer

Abstract Lung cancer is generally considered one of the most deadly cancers globally. If it can be identified early and diagnosed correctly, the survival probability of patients can be significantly improved. In this process, histopathological examination is a commonly used method for diagnosing and detecting lung cancer. It is crucial to accurately identify lung cancer subtypes from histopathological images, as this helps doctors formulate effective treatment plans. However, the visual inspection in histopathological diagnosis requires a large amount of time and also depends on the subjective perception of clinicians. Therefore, this paper proposes a lightweight lung cancer subtype classification network based on morphological attention (LW-MorphCNN), which is used to automatically classify the histopathological images of benign lung tumors, ADC (adenocarcinoma), and SCC (squamous cell carcinoma) provided in the public dataset LC25000 (Lung and Colon). This paper takes histopathological images as input and conducts a comparative analysis with classic networks such as VGG16, VGG19, DenseNet121, and ResNet50, as well as existing classification methods proposed in the same work. The network proposed in this paper is superior to other networks in terms of parameter quantity and performance, with an accuracy rate and F1 - score reaching 99.47% and 99.44% respectively. Clinicians can install the provided LW-MorphCNN in the hospital to confirm the diagnosis results.

Integrated immunogenomic analyses of single-cell and bulk profiling construct a T cell-related signature for predicting prognosis and treatment response in osteosarcoma

PurposesT cells play a crucial role as regulators of anti-tumor activity within the tumor microenvironment (TME) and are closely associated with the progression of osteosarcoma (OS). Nevertheless, the specific role of T cell-related genes (TCRGs) in the pathogenesis of OS remains unclear.MethodsFirst, we processed single-cell RNA sequencing (scRNA-seq) data of OS from the public databases and performed cell annotation. We identified highly variable genes in each cell type using the "FindAllMarkers" function, explored the distribution of different clusters, and investigated inter-cellular communication patterns via the "CellChat" framework. Then, we used multivariate Cox analysis to construct a TCRG and developed a nomogram to predict survival probabilities for OS patients. Finally, we validated the aforementioned results using various cell lines and investigated the immune cell infiltration, expression of immune checkpoints, chemotherapy sensitivity, and the efficacy of targeted therapies across different risk groups.ResultsFrom the scRNA-seq data, we identified 3,000 highly variable genes, presented the top 10 genes, and validated the expression of core genes across different cell lines.Moreover, our analysis delved into interactions between T cells and other cell types. Our analyses constructed a predictive T cell-related signature (TCRS) that incorporated these prognostic TCRGs, showing a clear prognostic separation between the high-risk and low-risk OS patient groups in multiple cohorts. Survival analysis indicated better outcomes for patients classified in the high-risk group. The low-risk group exhibited elevated levels of CD4 memory resting T cells, contrasting with the higher levels of macrophage M0 observed in the high-risk group via the CIBERSORT algorithm. Furthermore, we observed that the low-risk group exhibitedAQ1 significant up-regulation of immune checkpoint genes (ICGs) and lower Tumour Immune Dysfunction and Exclusion (TIDE) scores, suggesting that they may be suitable for immunotherapy. Conversely, the high-risk group appeared more responsive to chemotherapy and targeted therapies, according to our drug sensitivity analysis.ConclusionIn conclusion, our study identified TCRGs, constructed and validated a TCRS for OS, and assessed immune response and drug sensitivity in different risk groups of OS patients. These findings provide novel insights into personalized treatment strategies for OS, potentially guiding more effective therapeutic interventions.

Decoding the Methylation Patterns of ABC Transporters in Colorectal Cancer

Colorectal cancer (CRC) is a significant global health concern, posing a major threat to morbidity and mortality rates. The molecular mechanisms that drive CRC, particularly the DNA methylation patterns in ATP-binding cassette (ABC) genes, have attracted considerable attention because of their potential roles in CRC drug resistance, initiation, and progression. This study aimed to investigate the methylation patterns of ABC genes in CRC using a high-throughput microarray technology. Microarray methylation data from CRC and adjacent normal tissues were subjected to preprocessing, differential methylation analysis, and correlation analysis using the MethSurv tool for a detailed study of methylation patterns. The study revealed global hypomethylation of 43 ABC transporters and two pseudogenes in CRC compared to normal tissues. Receiver operating characteristic curve analysis identified 369 CpG sites as potential biomarkers for CRC diagnosis, with area under the curve values ranging from acceptable to outstanding. Survival analysis demonstrated the correlation between DNA methylation of individual CpG sites and patient survival probability in colon and rectal adenocarcinoma datasets from The Cancer Genome Atlas. The study provides insights into the epigenetic landscape of ABC genes in CRC, highlighting their potential roles in drug resistance, disease initiation, and progression. The findings offer opportunities for developing innovative therapeutic approaches and improving patient outcomes in the fight against CRC. Further research is needed to validate these results and investigate the functional implications of ABC gene methylation in CRC pathogenesis and treatment response.

A 5-Year Mortality Prediction Model for Prostate Cancer Patients Based on the Korean Nationwide Health Insurance Claims Database.

Prostate cancer is the fourth most common cancer and eighth leading cause of cancer-related mortality worldwide. Its incidence is increasing in South Korea. This study aimed to investigate a predictive model for the 5-year survival probability of prostate cancer patients in a Korean primary care setting. This retrospective study used data from the nationwide insurance claims database. The main outcome was survival probability 5 years after the initial diagnosis of prostate cancer. Potential confounding factors such as age, body mass index (BMI), blood pressure, laboratory results, lifestyle behaviors, household income, and comorbidity index were considered. These variables were available in the national health check-up information. A Cox proportional hazards regression model was used to develop the predictive model. The predictive performance was calculated based on the mean area under the receiver operating characteristic curve (AUC) after 10-fold cross-validation. The mean 5-year survival probability was 82.0%. Age, fasting glucose and gamma-glutamyl transferase levels, current smoking, and multiple comorbidities were positively associated with mortality, whereas BMI, alkaline phosphatase levels, total cholesterol levels, alcohol intake, physical activity, and household income were inversely associated with mortality. The mean AUC after 10-fold cross-validation was 0.71. The 5-year survival probability model showed a moderately good predictive performance. This may be useful in predicting the survival probability of prostate cancer patients in primary care settings. When interpreting these results, potential limitations, such as selection or healthy user biases, should be considered.

Role of neoadjuvant chemotherapy in patients with locally advanced and clinically positive nodes Upper Tract Urothelial Carcinoma treated with Nephroureterectomy: real-world data from the ROBUUST 2.0 Registry.

To assess the impact of neoadjuvant and adjuvant chemotherapy on survival outcomes, within a large multicenter cohort of Upper tract urothelial carcinoma patients treated with Nephroureterectomy. A multicenter retrospective analysis utilizing the Robotic surgery for Upper Tract Urothelial Cancer Study registry was performed. Baseline, preoperative, perioperative, and pathologic variables of three groups of patients receiving surgery only, neoadjuvant or adjuvant chemotherapy were compared. Categorical and continuous variables among the three subgroups were compared with Chi square and ANOVA tests. The impact of perioperative chemotherapy on survival outcomes was assessed with the Kaplan Meier method. Univariable and multivariable Cox regression analyses were performed to identify predictors of survival. Overall, 1,994 patients were included. Overall and Clavien grade ≥3 complications rates were comparable among the three subgroups (p = 0.65 and p = 0.92). At Kaplan Meier analysis, neoadjuvant chemotherapy significantly improved cancer-specific survival (p = 0.03) and overall survival (p = 0.03) probabilities of patients with cT ≥ 3 tumors and of those with positive cN (p = 0.03 and p = 0.02). On multivariable analysis, neoadjuvant chemotherapy was independently associated with an improvement of cancer-specific survival in cT ≥ 3 patients (HR 0.44; p = 0.04), and of both cancer-specific survival (HR 0.50; p = 0.03) and overall survival (HR 0.53; p = 0.02) probabilities in positive cN patients. This large multicenter retrospective analysis suggests significant survival benefit in Upper tract urothelial carcinoma patients with either locally advanced or clinically positive nodes disease receiving neoadjuvant chemotherapy. These findings can be regarded as "hypothesis generating", stimulating future trials focusing on such advanced stages.

Association between lactate/albumin ratio and 28-day all-cause mortality in critically ill patients with acute myocardial infarction

Acute myocardial infarction (AMI) is a leading cause of morbidity and mortality worldwide. Early identification of high-risk patients is crucial for timely interventions and improved outcomes. The lactate/albumin ratio (LAR) has been suggest as a significant correlate for assessing the risk of mortality in critically ill patients. This study aimed to utilize the American eICU Collaborative Research Database to explore the association between baseline LAR and all-cause mortality within 28 days in ICU of critically ill patients diagnosed with AMI. We conducted a retrospective cohort study of 989 AMI patients from the eICU Collaborative Research Database. Patients were included based on ICD-9 code 410 and the universal definition of AMI. LAR was calculated as the ratio of baseline lactate to albumin levels within the first 24 h of ICU admission. The outcome was all-cause mortality within 28 days after ICU admission. Multivariable logistic regression models were used to evaluate the independent association between LAR and the risk of death, adjusting for potential confounders including demographics, comorbidities, vital signs, and laboratory parameters. Subgroup analyses and nonlinear modeling were performed to further explore the relationship. Of the 989 AMI patients, 171 (17.3%) died within 28 days after ICU admission. Patients who died had significantly higher LAR compared to survivors (1.66 vs. 0.96, p < 0.001). Multivariable analysis showed that each unit increase in LAR was associated with a 2.15-fold higher risk of all-cause mortality within 28 days after ICU admission (95% CI: 1.64–2.83, p < 0.001). Subgroup analyses confirmed the consistent association across different patient characteristics. Nonlinear modeling revealed a threshold effect, where LAR above 2.15 was no longer significantly associated with mortality. Kaplan-Meier survival analysis demonstrated lower survival probabilities for patients with higher LAR(1.0526–5.8235). The findings suggest that a higher LAR was associated with an increased risk of 28-day all-cause mortality for critically ill patients with AMI after ICU admission.

Development of a novel nomogram for patients with SCLC and comparison with other models

BackgroundThough several nomograms have been established to predict the survival probability of patients with small-cell lung cancer (SCLC), none involved enough variables. This study aimed to construct a novel prognostic nomogram and compare its performance with other models.MethodsSeven hundred twenty-two patients were pathologically diagnosed with SCLC in Shanxi Province Cancer Hospital, Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences, Cancer Hospital Affiliated to Shanxi Medical University from January 2016 to December 2018. We input Forty-one factors by reviewing the medical records. The nomogram was constructed based on the variables identified by univariate and multivariate analyses in the training set and validated in the validation set. Then we compared the performance of the models in terms of discrimination, calibration, and clinical net benefit.ResultsThere were eight variables involved in the nomogram: gender, monocyte (MON), neuron-specific enolase (NSE), cytokeratin 19 fragments (Cyfra211), M stage, radiotherapy (RT), chemotherapy cycles (CT cycles), and prophylactic cranial irradiation (PCI). The calibration curve showed a good correlation between the nomogram prediction and actual observation for overall survival (OS). The area under the curve (AUC) of the nomogram was higher, and the Integrated Brier score (IBS) was lower than other models, indicating a more accurate prediction. Decision curve analysis (DCA) showed a significant improvement in the clinical net benefit compared to the other models.ConclusionsWe constructed a novel nomogram to predict OS for patients with SCLC using more comprehensive and objective variables. It performed better than existing models and would assist clinicians in individually estimating risk and making a therapeutic regimen.

Stratification of Wilms tumor patients using physicochemical properties of the adaptive immune receptor polypeptides, IGL and TRG

Wilms tumor (WT) is the most common pediatric renal malignancy. Current guidelines that stratify WT risk and determine treatment courses are inadequate, as over 60% of WT survivors develop treatment-related complications. Recently, numerous advances in establishing patient sub-groups with different clinical features have been realized by evaluating the adaptive immune receptor (IR) complementarity determining region-3 (CDR3) amino acid (AA) sequences, a reasonable series of successes, given the prominent role of the CDR3 in antigen binding, including tumor antigen binding. However, the possibility that adaptive IR chemical variability correlates with distinct survival outcomes for WT has not yet been explored. The goal of this study was to isolate the T-cell receptor and B-cell receptor recombination, sequencing reads from WT RNAseq files, representing the actual tumor tissue, translate the sequences to AAs, identify the adaptive IR CDR3 domains, and determine whether the physicochemical properties of those CDR3 AA sequences correlated with survival probability distinctions. WT RNA-seq files were mined to obtain the CDR3 AAs for various adaptive IRs. The physicochemical properties of these CDR3s were examined for trends in how those properties correlated with survival probabilities for WT patients, using a Kaplan-Meier analyses, verified via several approaches. The above processes indicated the association of the (a) IGL CDR3s' instability index and the (b) TRG CDR3s' fraction disorder promoting features with better outcomes. Additionally, the IGL CDR3 data were assessed using the Predictor of Natural Disordered Regions web tool, which strengthened the evidence for the association with the IGL CDR3 instability index with a better outcome. The approaches described here indicate that greater adaptive IR CDR3 instability and flexibility may serve as prognostic indicators; and may indicate the flexibility of CDR3 domains provides for greater opportunity to bind tumor antigens. Further exploration and development of these approaches and findings may lead to new guidelines for more precise treatment regimens, or even watchful waiting periods, that could thereby decrease the lifetime occurrence of adverse events.

Pathomics signatures and cuproptosis-related genes signatures for prediction of prognosis in patients with hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is a common malignant tumor with high heterogeneity and poor prognosis, so early prediction and treatment are still difficult. Cuproptosis is a newly discovered type of programmed cell death that has been shown to be closely related to the occurrence and progression of HCC. Cancer morphology is influenced by genetic drivers, and computational pathology methods typically use tissue images such as entire slide images as input to predict clinical or genetic features. Therefore, the comprehensive analysis of pathological features and genomic data provides a feasible way to explore the potential mechanism of the tumor. The objective of this study was to develop a prediction model for HCC prognosis based on the pathomics signatures (PS) and the genomics signatures (GS). A dataset comprising 315 HCC patients was randomly divided into a training set (n=200) and a validation set (n=115). Prognostic models related to PS and GS were constructed by univariate and multivariate Cox regression analyses and least absolute shrinkage and selection operator (LASSO) regression analysis. Kaplan-Meier survival analysis, receiver operating characteristic (ROC) curve, univariate and multivariate Cox analyses, and nomogram were used to evaluate the predictive performance of the prognostic model. The prognostic value of the model was internally validated. A prognostic model incorporating clinical features, PS, and GS was developed using Cox regression analysis and LASSO regression analyses. Kaplan-Meier survival analysis revealed statistically significant differences in survival time between high-risk and low-risk subgroups in both the training and validation datasets (PS: P=0.003 and <0.001, respectively; GS: P=0.008 and 0.004, respectively). The time-dependent ROC curve showed favorable predictive value for survival in both the training and validation sets. The area under the ROC curves at 1, 3, and 5 years was 0.750, 0.830, and 0.870 in the training set, and 0.780, 0.810, and 0.760 in the validation set, respectively. A nomogram model based on the risk model score could effectively predict the survival probability of HCC patients. The calibration curves further demonstrated the good predictive capability of the nomogram model. The prognostic model incorporating PS and GS could effectively predict the prognosis of HCC patients.

Divergent autoantibody and cytokine levels in COVID-19 sepsis patients influence survival.

Studies have pointed to a decisive role of autoantibodies in the context of sepsis and severe Coronavirus disease 2019 (COVID-19), which itself often fulfills the criteria for sepsis, including dysregulated immune responses and organ dysfunction. To directly compare and further analyze the autoantibody profiles of sepsis patients with and without COVID-19, the luciferase immunoprecipitation systems (LIPS) assay was used to measure the levels of autoantibodies against a variety of clinically relevant cytokines, lung-associated proteins, other autoantigens, and antibodies against the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In addition, cytokine titers were measured with the LEGENDplex™ Human Antivirus Response Panel. We observed significantly increased levels of autoantibodies in 59% of the COVID-19-Sepsis group compared to 48% of the Sepsis group. Significant differences were identified between the groups for the levels of autoantibodies against gATPase. The cytokine levels of interferon (IFN)-λ1 and IP-10 were higher in the COVID-19-Sepsis groupcompared to the Sepsis group. Additional correlations between autoantibodies, cytokines and 30-day survival could be demonstrated, suggesting varied underlying pathological mechanisms. Elevated levels of cytokines and autoantibodies may serve as prognostic indicators for the survival probability of sepsis patients, highlighting the intricate relationship between immune responses and patient outcomes in the context of both sepsis and COVID-19.

Decoding the immune microenvironment: unveiling CD8 + T cell-related biomarkers and developing a prognostic signature for personalized glioma treatment

BackgroundGliomas are aggressive brain tumors with poor prognosis. Understanding the tumor immune microenvironment (TIME) in gliomas is essential for developing effective immunotherapies. This study aimed to identify TIME-related biomarkers in glioma using bioinformatic analysis of RNA-seq data.MethodsIn this study, we employed weighted gene co-expression network analysis (WGCNA) on bulk RNA-seq data to identify TIME-related genes. To identify prognostic genes, we performed univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression analyses. Based on these genes, we constructed a prognostic signature and delineated risk groups. To validate the prognostic signature, external validation was conducted.ResultsCD8 + T cell infiltration was strongly correlated with glioma patient prognosis. We identified 115 CD8 + T cell-related genes through integrative analysis of bulk-seq data. CDCA5, KIF11, and KIF4A were found to be significant immune-related genes (IRGs) associated with overall survival in glioma patients and served as independent prognostic factors. We developed a prognostic nomogram that incorporated these genes, age, gender, and grade, providing a reliable tool for clinicians to predict patient survival probabilities. The nomogram’s predictions were supported by calibration plots, further validating its accuracy.ConclusionIn conclusion, our study identifies CD8 + T cell infiltration as a strong predictor of glioma patient outcomes and highlights the prognostic value of genes. The developed prognostic nomogram, incorporating these genes along with clinical factors, provides a reliable tool for predicting patient survival probabilities and has important implications for personalized treatment decisions in glioma.

Dynamic estimates of survival in patients with follicular thyroid cancer: a retrospective cohort study.

Few studies have been conducted on the dynamic survival rates of follicular thyroid cancer (FTC). This study aimed to ascertain how the survival probability of patients with FTC changes over time. In this retrospective analysis, 10,617 patients diagnosed with FTC between 2000 and 2019 from the Surveillance, Epidemiology, and End Results (SEER) database were included. Actuarial disease-specific survival (DSS) was estimated using the Kaplan-Meier method, and the log-rank test was used for comparisons. The annual hazard of mortality was determined using the hazard function, and the conditional survival (CS) was calculated using the life table method. A total of 459 (4.3%) patients died of FTC, and the 5-year and 10-year DSS rates were 96.6 ± 0.2% and 94.6 ± 0.3%, respectively. There was a statistically significant difference in the DSS rate between patients with different SEER combined summary stages (P < 0.001). The annual hazard curve for cancer mortality in the entire study cohort displayed a steep downward trend with a slight peak at 2.5 years after diagnosis, followed by a gradual decline. Patients with distant metastases exhibited a higher mortality hazard curve and more notable declining trend. CS demonstrated an upward trend across the entire study population, with the most pronounced trend in patients with distant metastases. Prognosis improved over time in a stage-dependent manner in patients with FTC after diagnosis. The most significant improvement was observed in the patients with distant metastases. Notably, dynamic survival estimations, such as death hazard and conditional survival analysis, provide more precise survival projections than traditional survival analysis for FTC survivors.

Association of urinary albumin excretion with all-cause and cardiovascular mortality among patients with rheumatoid arthritis: a national prospective study.

Rheumatoid arthritis (RA) patients suffering from chronic renal insufficiency tend to exhibit subtle manifestations at the beginning. Urine albumin to creatinine ratio (ACR) is a sensitive indicator for early assessment of renal function. However, it is unclear whether it serves as an independent risk factor influencing the prognosis of RA patients. National Health and Nutrition Examination Survey (NHANES) data from 2009-2018 were included. Kaplan-Meier (K-M) curves were plotted to compare the cumulative survival probability of RA patients with different urinary albumin excretion. The association of ACR with mortality among RA patients was investigated with Cox regression model, restricted cubic spline (RCS) and stratified analyses. The prognostic efficacy of ACR and estimated glomerular filtration rate (eGFR) was evaluated by receiver operating characteristic (ROC) curves. The Cox regression model adjusted with covariates showed a 53% (HR 1.53, 95% CI 1.06-2.21) increase in all-cause mortality and a statistically non-significant increase in cardiovascular disease (CVD) mortality in RA patients with microalbuminuria (30mg/g ≤ACR<300mg/g). ACR≥300mg/g was associated with an increase in all-cause mortality (HR 2.62, 95% CI 1.55-4.45) and CVD mortality (HR 5.67, 95% CI 1.96-16.39). RCS demonstrated a nonlinear correlation between ACR and all-cause mortality in RA patients with microalbuminuria. Subgroup analysis showed that CVD mortality was higher in RA patients with microalbuminuria characterized by the following features: female, other ethnicity, eGFR≥60 ml/min/1.73 m2, hypertension or hyperlipidemia. Compared with eGFR, ACR provided better prognostic efficacy than eGFR with higher values of the area under the curve (AUC) for all-cause mortality (AUC=0.683, 95% CI 0.613-0.754) and CVD mortality (AUC=0.681, 95% CI 0.541-0.820). ACR is an independent risk factor affecting the prognosis of RA patients. The all-cause mortality was increased in RA patients with albuminuria. There was an upward trend in the CVD mortality of those with macroalbuminuria when ACR increased.

Characteristics of Oxidative Phosphorylation-Related Subtypes and Construction of a Prognostic Signature in Ovarian Cancer.

Ovarian cancer is associated with a high mortality rate. Oxidative Phosphorylation (OXPHOS) is an active metabolic pathway in cancer; nevertheless, its role in ovarian cancer continues to be ambiguous. Therefore, the objective of this study was to identify the prognostic value of OXPHOS-related genes and the immune landscape in ovarian cancer. We obtained public ovarian cancer-related datasets from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases and recognized OXPHOS-related genes from the GeneCards database and literature. Cox regression analyses were conducted to identify prognostic OXPHOS-related genes and develop a prognostic nomogram based on the OXPHOS score and clinicopathological features of patients. Functional enrichment analyses were employed to identify related processes. A 12-gene signature was identified to classify the ovarian cancer patients into high- and low-risk groups. The Immunophenoscore (IPS) was higher in the OXPHOS score-high group than in the OXPHOS score-low group, suggesting a better response to immune checkpoint inhibitors. Functional enrichment analyses unveiled that OXPHOS-related genes were considerably abundant in a series of immune processes. The calibration curves of the constructed prognostic nomograms at 1, 2, and 3 years exhibited strong concordance between the anticipated and observed survival probabilities of ovarian cancer patients. We have constructed a prognostic model containing 12 OXPHOS-related genes and demonstrated its strong predictive value in ovarian cancer patients. OXPHOS has been found to be closely linked to immune infiltration and the reaction to immunotherapy, which may contribute to improving individualized treatment and prognostic evaluation in ovarian cancer.

TNM 8 staging system beyond p16: Double HPV/p16 status is superior to p16 alone in predicting outcome in oropharyngeal squamous cell carcinoma

PurposeThe assessment of p16INK4a (p16) in oropharyngeal squamous cell carcinoma (OPSCC) has been incorporated into tumor classification, as p16 has been shown to impact survival probability. However, a recent study demonstrated that human papillomavirus (HPV) status in addition to p16 may have a better discriminatory effect on survival probability. This study aims to determine the impact of combined evaluation of p16 and HPV on prognosis. MethodsThis was a multicenter, multinational analysis including retrospective and prospective cohorts of patients treated for primary OPSCC with curative intent, based on the data of the HNCIG-EPIC study. The primary outcome was to determine how the combined assessment of HPV and p16 status predicts prognosis of patients with OPSCC compared to p16 assessment alone. We employed multivariable analyses models to compute hazard ratios regarding survival. Analyses were stratified by stage, smoking status, and sub-anatomical region. ResultsThe study included 7654 patients, with approximately half of the tumors being p16-negative (50.3 %, n = 3849). A total of 9.2 % of patients had discordant p16 and HPV status (n = 704). HPV status significantly impacted overall survival and disease-free survival regardless of p16 status and across both UICC 8th stage I-II and III-IVb cancers. p16-positive/HPV-positive OPSCC patients exhibited the best survival probability. ConclusionThe detection of HPV had a significant impact on survival probability for OPSCC patients with both p16-positive and p16-negative tumors. HPV testing should be integrated in cancer staging, especially in regions of low attributable fraction, alongside p16 evaluation to ensure a comprehensive assessment of prognosis.

A modified GLIM criteria-based nomogram for the survival prediction of gastric cancer patients undergoing surgical resection

BackgroundThis study aimed to develop a comprehensive model based on five GLIM variables to predict the individual survival and provide more appropriate patient counseling.MethodsThis retrospective cohort study included 301 gastric cancer (GC) patients undergoing radical resection. C-reactive protein (CRP) as an inflammatory marker was included in GLIM criteria and a nomogram for predicting 5-year overall survival (OS) in GC patients was established. The Bootstrap repeated sampling for 1000 times was used for internal validation.ResultsOf the total 301 patients, 20 (6.64%) died within 5 years. CRP improved the sensitivity and accuracy of the survival prediction model (AUC = 0.782, 0.694 to 0.869 for the model without CRP; AUC = 0.880, 0.809 to 0.950 for the model adding CRP). Besides, a GLIM-based nomogram was established with an AUC of 0.889. The C-index for predicting OS was 0.878 (95% CI: 0.823 to 0.934), and the calibration curve fitted well. Decision curve analysis (DCA) showed the clinical utility of the nomogram based on GLIM.ConclusionThe addition of CRP improved the sensitivity and accuracy of the survival prediction model. The 5-year survival probability of GC patients undergoing radical resection can be reliably predicted by the nomogram presented in this study.

FTO diversely influences sensitivity of neuroblastoma cells to various chemotherapeutic drugs.

Chemotherapy resistance is a significant factor in treatment failure in patients with neuroblastoma (NB), and it directly affects patient prognosis. Therefore, identifying novel therapeutic targets to enhance chemosensitivity is essential to improve the cure rate and prognosis of patients with NB. In this study, we investigated the role of FTO in chemosensitivity of NB cells to various chemotherapeutic drugs. Our results showed that high FTO expression was positively correlated with increased survival probability and favorable prognostic factors in patients with NB. FTO overexpression inhibited cell proliferation, whereas FTO knockdown promoted cell proliferation in NB cells. FTO expression alteration had contrasting effects on NB cells' sensitivity to etoposide but had no significant impact on sensitivity to cisplatin. Downregulation of FTO reduced the sensitivity of NB cells to paclitaxel, whereas upregulation of FTO enhanced its sensitivity. Additionally, the sensitivities between patients with lower and higher FTO expression to various chemotherapeutic drugs or small-molecule inhibitors were different. Thus, FTO affects the sensitivities of NB cells differently depending on the different chemotherapeutic drugs and small-molecule inhibitors. This finding may guide physicians and patients choose the appropriate chemotherapeutic drugs or small-molecule inhibitors for treatment.

Incidence, Timing and Social Correlates of the Development of Opioid Use Disorder Among Clients Seeking Treatment for an Alcohol Use Problem: Changes Over the Three Waves of the Opioid Epidemic.

Opioid use disorder (OUD) and opioid overdose (OD) have shown to be strongly associated with alcohol use disorder (AUD). As a potential target population for secondary prevention, we examined the incidence and timing of OUD/OD among clients seeking treatment for alcohol problems and how this has changed over the three waves of the opioid epidemic corresponding to the primary opioid involved in fatal ODs, prescription painkillers (2007-2009), heroin (2010-2012), and fentanyl (2013-2016). We also examined social determinants of health as predictors of OUD/OD. Clients (N = 59,186) presenting for a first treatment for alcohol use problems were extracted from the Client Data System (CDS) of the New York State Office of Addiction Services and Support (OASAS) and New York State (NYS) Medicaid Data Warehouse. Using this cohort, we employed the Kaplan-Meier method to determine the survival probabilities for patients admitted in each of the three waves of the epidemic. Patients in Cohort 3 (2013-2016) were diagnosed with OUD/OD more rapidly than patients in Cohort 1 (2007-2009) or Cohort 2 (2010-2012), although the overall estimated OUD/OD rate was comparable across the three cohorts. These findings provide a useful estimate of the incidence and the expected time frame of an opioid use disorder in clients with an alcohol use problem. Moreover, it suggests that as the opioid epidemic progressed, OUD/OD developed more rapidly but the overall prevalence did not increase.