Survival Probability Of Patients Research Articles

A novel anoikis-related gene signature identifies LYPD1 as a novel therapy target for bladder cancer

Bladder cancer (BLCA) is a malignant tumor associated with unfavorable outcomes. Studies suggest that anoikis plays a crucial role in tumor progression and cancer cell metastasis. However, its specific role in bladder cancer remains poorly understood. Our objective was to identify anoikis-related genes (ARGs) and subsequently construct a risk model to assess their potential for predicting the prognosis of bladder cancer.The transcriptome data and clinical data of BLCA patients were sourced from The Cancer Genome Atlas and GEO database. We then performed the differential expression analysis to screen differentially expressed ARGs. Subsequently, we conducted non-negative matrix factorization (NMF) clustering analysis to establish molecular subtypes based on the differentially expressed ARGs. The CIBERSORT algorithm was used to estimate the quantification of different cell infiltration in BLCA tumor microenviroment. A prognostic risk model containing 7 ARGs was established using Lasso-Cox regression analysis. The nomogram was built for predicting the survival probability of BLCA patients. To determine the drug sensitivity of each sample from the high- and low-risk groups, the R package “pRRophetic” was performed. Finally, the role of LYPD1 was explored in BLCA cell lines.We identified 90 differential expression ARGs and NMF clustering categorizated the BLCA patientss into two distinct groups (cluster A and B). Patients in cluster A had a better prognosis than those in cluster B. Then, we established a ARGs risk model including CALR, FASN, FOSL1, JUN, LYPD1, MST1R, and SATB1, which was validated in the train and test set. The results suggested overall survival rate was much higher in low risk group than high risk group. The cox regression analysis, ROC curve analysis, and nomogram collectively demonstrated that the risk model served as an independent prognostic factor. The high risk group had a higher level TME scores compared to the low risk group. Furthermore, LYPD1 was low expression in BLCA cells and overexpression of LYPD1 inhibits the prolifearation, migration and invasion.In the current study, we have identified differential expression ARGs and constructed a risk model with the promise for guiding prognostic predictions and provided a therapeutic target for patients with BLCA.

Survival and risk of death in Covid-19 patients admitted to a public hospital in the Legal Amazon

Objective: To evaluate survival and risk of mortality in COVID-19 patients admitted to a public hospital located in the Legal Amazon. Method: A retrospective longitudinal documentary study, in which 196 medical records of patients admitted from April to August 2020, at the General Hospital of Palmas, diagnosed with COVID-19, were assessed. Socioeconomic data, age, sex, comorbidities, and disease outcome were collected from the records. Results: The average age was 58.6±16 years. Male sex was predominant (66.8%) and individuals with comorbidities (77%). It was observed that 76.14% of the patients were between 40 and 80 years of age. The Kaplan-Meier curve demonstrated a decline in survival over time, indicating that the likelihood of a patient surviving for 16 days is around 50%. The Log-rank test showed a statistical difference for the variables age (p = 0.01) and cancer (p = 0.02), suggesting a reduction in the probability of survival in older patients with cancer. The Cox regression identified that the risk of death in patients between 60 and 79 years was approximately 3 times higher than in those aged 40, while in those aged 80 or older, it was 6 times higher. In the same model, the risk of death in patients with obesity was 98% higher, and 2.11 times higher in patients with cancer. Conclusions: The research provided important insights into the behavior of COVID-19 in the Legal Amazon, which will support the response to the virus and other health crises.

Integration of pretreatment tumor markers in a nomogram model for prognostic prediction of FIGO stage I endometrial cancer: A multi-institutional cohort study.

Traditionally, the prognosis of patients with FIGO stage I endometrial cancer is determined by clinicopathological risk factors. In this study, we assessed the potential contribution of pretreatment carcinoembryonic antigen (CEA) and carbohydrate antigen-125 (CA-125) levels to estimating the prognosis of these patients and aimed to develop and validate a prognostic nomogram. This retrospective study included patients with FIGO stage I endometrial cancer who underwent treatment between January 2009 and December 2021 in the four institutes of Chang Gung Memorial Hospital. To identify optimal cutoff values of CEA and CA-125 for predicting survival, receiver operating characteristic (ROC) curves were generated, the Kaplan-Meier method was used to estimate survival, and a Cox regression model was used to analyze the independent prognostic factors. Finally, a nomogram and calibration curve were constructed to predict patient survival probability. Of the 1559 patients evaluated, the optimal cutoff values of CEA and CA-125 were 1.44 ng/mL (area under the ROC curve [AUC] 0.601) and 39.77 U/mL (AUC 0.503), respectively. Multivariate Cox regression analysis showed that pretreatment CEA (hazard ratio [HR] 2.11, 95% confidence interval [95% CI] 1.35-3.28), CA-125 (HR 2.07, 95% CI 1.31-3.27), age >70 years (HR 12.54, 95% CI 5.05-31.11), myometrial invasion >50% (HR 1.69, 95% CI 1.03-2.73), non-endometrioid histology (HR 1.83, 95% CI 1.14-2.95), high-grade tumor (HR 2.41, 95% CI 1.46-3.97), and lymphovascular space invasion (HR 2.32, 95% CI 1.26-4.25) were significant variables associated with overall survival. These factors were used to construct the nomogram model, which showed good concordance and accuracy. Integration of pretreatment CEA and CA-125 in a prognostic nomogram is feasible. Our prediction model has the potential to assist clinicians in guiding appropriate clinical practice.

MethScore as a new comprehensive DNA methylation-based value refining the prognosis in acute myeloid leukemia.

Changes in DNA methylation are common events in the pathogenesis of acute myeloid leukemia (AML) and have been repeatedly reported as associated with prognosis. However, studies integrating these numerous and potentially prognostically relevant DNA methylation changes are lacking. Therefore, we aimed for an overall evaluation of these epigenetic aberrations to provide a comprehensive NGS-based approach of DNA methylation assessment for AML prognostication. We designed a sequencing panel targeting 239 regions (approx. 573kb of total size) described in the literature as having a prognostic impact or being associated with AML pathogenesis. Diagnostic whole-blood DNA samples of adult AML patients divided into a training (n = 128) and a testing cohort (n = 50) were examined. The libraries were prepared using SeqCap Epi Enrichments System (Roche) and sequenced on MiSeq instrument (Illumina). Altogether, 1935 CpGs affecting the survival (p < 0.05) were revealed in the training cohort. A summarizing value MethScore was then calculated from these significant CpGs. Patients with lower MethScore had markedly longer overall survival (OS) and event-free survival (EFS) than those with higher MethScore (p < 0.001). The predictive ability of MethScore was verified on the independent testing cohort for OS (p = 0.01). Moreover, the proof-of-principle validation was performed using the TCGA dataset. We showed that comprehensive NGS-based approach of DNA methylation assessment revealed a robust epigenetic signature relevant to AML outcome. We called this signature MethScore and showed it might serve as a strong prognostic marker able to refine survival probability of AML patients.

Dynamic prediction of survival using multivariate functional principal component analysis: A strict landmarking approach.

Dynamically predicting patient survival probabilities using longitudinal measurements has become of great importance with routine data collection becoming more common. Many existing models utilize a multi-step landmarking approach for this problem, mostly due to its ease of use and versatility but unfortunately most fail to do so appropriately. In this article we make use of multivariate functional principal component analysis to summarize the available longitudinal information, and employ a Cox proportional hazards model for prediction. Additionally, we consider a centred functional principal component analysis procedure in an attempt to remove the natural variation incurred by the difference in age of the considered subjects. We formalize the difference between a 'relaxed' landmarking approach where only validation data is landmarked and a 'strict' landmarking approach where both the training and validation data are landmarked. We show that a relaxed landmarking approach fails to effectively use the information contained in the longitudinal outcomes, thereby producing substantially worse prediction accuracy than a strict landmarking approach.

Revealing curcumin therapeutic targets on SRC, PPARG, MAPK8 and HSP90 as liver cirrhosis therapy based on comprehensive bioinformatic study

Cirrhosis naturally progresses through three stages: compensated, decompensated, and late decompensated, which carry an elevated risk of death. Although curcumin’s anti-cirrhosis effects have been studied, underlying mechanism in preventing cirrhosis progression and the correlation between curcumin’s action with upregulated genes remains insufficiently explored. In this study, we employed network pharmacology approach to construct a drug-target-disease network through bioinformatics and validate the findings with molecular docking and dynamic simulation. The curcumin-targeted liver cirrhosis network encompassed 54 nodes with 282 edges in protein-protein interactions (PPI) network. By utilizing network centrality analysis, we identified eight crucial genes. KEGG enrichment pathway revealed that these crucial genes are involved in pathway of cancer, endocrine resistance, estrogen signaling, chemical carcinogenesis-receptor activation, lipid metabolism, and atherosclerosis. Notably, these eight genes predominantly participate in cancer-related pathways. Further investigation revealed upregulation of four genes and downregulation of four others in hepatocellular carcinoma patients. These upregulated genes—MAPK8, SRC, PPARG, and HSP90AA1—strongly correlated with reduced survival probability in liver hepatocellular carcinoma patients with survival times approximately under 4000 days (∼11 years). Molecular docking and molecular dynamic results exhibited curcumin’s superior binding affinities and stability compared to native ligands of MAPK8, SRC, PPARG, and HSP90AA1 within 50 ns simulations. Moreover, MM-GBSA analysis showed stronger binding energy of curcumin to MAPK8, SRC, and HSP90AA1 than native ligand. In conclusion, this study provides valuable insights into curcumin’s potential mechanisms in preventing liver cirrhosis progression, specifically in HCC. These findings offer a theoretical basis for further pharmacological research into anti-HCC effect of curcumin. Communicated by Ramaswamy H. Sarma

Prognostic nomograms for breast cancer with lung metastasis: a SEER-based population study

BackgroundLung metastasis is a significant adverse predictor of prognosis in patients with breast cancer. Accurate estimation for the prognosis of patients with lung metastasis and population-based validation for the models are lacking. In the present study, we aimed to establish the nomogram to identify prognostic factors correlated with lung metastases and evaluate individualized survival in patients with lung metastasis based on SEER (Surveillance, Epidemiology, and End Results) database.MethodsWe selected 1197 patients diagnosed with breast cancer with lung metastasis (BCLM) from the SEER database and randomly assigned them to the training group (n = 837) and the testing group (n = 360). Based on univariate and multivariate Cox regression analysis, we evaluated the effects of multiple variables on survival in the training group and constructed a nomogram to predict the 1-, 2-, and 3-year survival probability of patients. The nomogram were verified internally and externally by Concordance index (C-index), Net Reclassification (NRI), Integrated Discrimination Improvement (IDI), Decision Curve Analysis (DCA), and calibration plots.ResultsAccording to the results of multi-factor Cox regression analysis, age, histopathology, grade, marital status, bone metastasis, brain metastasis, liver metastasis, human epidermal growth factor receptor 2 (HER2), estrogen receptor (ER), progesterone receptor (PR), surgery, neoadjuvant therapy and chemotherapy were considered as independent prognostic factors for patients with BCLM. The C-index in the training group was 0.719 and the testing group was 0.695, respectively. The AUC values of the 1-, 2-, and 3-year prognostic nomogram in the training group were 0.798, 0.790 and 0.793, and the corresponding AUC values in the testing group were 0.765, 0.761 and 0.722. The calculation results of IDI and NRI were shown. The nomograms significantly improved the risk reclassification for 1-, 2-, and 3-year overall mortality prediction compared with the AJCC 7th staging system. According to the calibration plot, nomograms showed good consistency between predicted and actual overall survival (OS) values for the patients with BCLM. DCA showed that nomograms had better net benefits at different threshold probabilities at different time points compared with the AJCC 7th staging system.ConclusionsNomograms that predicted 1-, 2-, and 3-year OS for patients with BCLM were successfully constructed and validated to help physicians in evaluating the high risk of mortality in breast cancer patients.

Incorporation of Chest Computed Tomography Quantification to Predict Outcomes for Patients on Hemodialysis with COVID-19

Introduction: Patients undergoing maintenance hemodialysis are vulnerable to coronavirus disease 2019 (COVID-19), exhibiting a high risk of hospitalization and mortality. Thus, early identification and intervention are important to prevent disease progression in these patients. Methods: This was a two-center retrospective observational study of patients on hemodialysis diagnosed with COVID-19 at the Lingang and Xuhui campuses of Shanghai Sixth People’s Hospital. Patients were randomized into the training (130) and validation cohorts (54), while 59 additional patients served as an independent external validation cohort. Artificial intelligence-based parameters of chest computed tomography (CT) were quantified, and a nomogram for patient outcomes at 14 and 28 days was created by screening quantitative CT measures, clinical data, and laboratory examination items, using univariate and multivariate Cox regression models. Results: The median dialysis duration was 48 (interquartile range, 24–96) months. Age, diabetes mellitus, serum phosphorus level, lymphocyte count, and chest CT score were identified as independent prognostic indicators and included in the nomogram. The concordance index values were 0.865, 0.914, and 0.885 in the training, internal validation, and external validation cohorts, respectively. Calibration plots showed good agreement between the expected and actual outcomes. Conclusion: This is the first study in which a reliable nomogram was developed to predict short-term outcomes and survival probabilities in patients with COVID-19 on hemodialysis. This model may be helpful to clinicians in treating COVID-19, managing serum phosphorus, and adjusting the dialysis strategies for these vulnerable patients to prevent disease progression in the context of COVID-19 and continuous emergence of novel viruses.

Development of prognostic models for advanced multiple hepatocellular carcinoma based on Cox regression, deep learning and machine learning algorithms.

Most patients with multiple hepatocellular carcinoma (MHCC) are at advanced stage once diagnosed, so that clinical treatment and decision-making are quite tricky. The AJCC-TNM system cannot accurately determine prognosis, our study aimed to identify prognostic factors for MHCC and to develop a prognostic model to quantify the risk and survival probability of patients. Eligible patients with HCC were obtained from the Surveillance, Epidemiology, and End Results (SEER) database, and then prognostic models were built using Cox regression, machine learning (ML), and deep learning (DL) algorithms. The model's performance was evaluated using C-index, receiver operating characteristic curve, Brier score and decision curve analysis, respectively, and the best model was interpreted using SHapley additive explanations (SHAP) interpretability technique. A total of eight variables were included in the follow-up study, our analysis identified that the gradient boosted machine (GBM) model was the best prognostic model for advanced MHCC. In particular, the GBM model in the training cohort had a C-index of 0.73, a Brier score of 0.124, with area under the curve (AUC) values above 0.78 at the first, third, and fifth year. Importantly, the model also performed well in test cohort. The Kaplan-Meier (K-M) survival analysis demonstrated that the newly developed risk stratification system could well differentiate the prognosis of patients. Of the ML models, GBM model could predict the prognosis of advanced MHCC patients most accurately.

A metabolic-inflammatory-nutritional score (MINS) is associated with lymph node metastasis and prognostic stratification for endometrial cancer patients.

Objective: This study aims to propose a personalized cancer prediction model based on the metabolic-inflammatory-nutritional score (MINS) for predicting lymph node metastasis (LNM) in endometrial cancer (EC) and validated prediction of survival probability in patients with a family history of Lynch syndrome-associated cancers (LSAC). Methods: A total of 676 patients diagnosed with EC were enrolled in this study. We calculated the optimal cutoff value using restricted cubic splines (RCS) analysis or the mean value. Our feature selection process for constructing the MINS involved using the LASSO regression model. MINS were evaluated for LNM using logistic regression analysis. To assess the prognostic value of the MINS, we generated survival curves using the Kaplan-Meier method with a log-rank test. Furthermore, we constructed a nomogram to validate the prognostic significance of the MINS. The predictive accuracy of nomogram was evaluated using the concordance index (C-index) and calibration plot. Results: LNM risk was associated with family history of LSAC and MINS group (all adjusted p<0.05). Patients in the high-risk MINS group or patients with a family history of LSAC exhibited poorer overall survival (p=0.038, p=0.001, respectively). Additionally, a nomogram was demonstrated effective predictive performance with a C-index of 0.778 (95% CI: 0.725-0.832). Conclusion: Preoperative MINS has been determined to be associated with the risk of LNM in EC patients. Utilizing MINS as a basis, the development of a prognostic nomogram holds promise as an effective tool for risk stratification in clinical settings among EC patients with a family history of LSAC.

Impact of comorbidities and inflammatory markers on mortality of COVID-19 patients.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus that causes coronavirus disease 2019 (COVID-19) is a serious global health concern. The severity of the disease can be determined by serologic indicators such as C-reactive protein, lactate dehydrogenase, D-dimer, ferritin, and interleukin-6. (IL-6). Patients with preexisting conditions such as respiratory, cardiovascular, and pulmonary disease could be at risk of adverse outcomes. It is crucial to provide adequate medical care to manage these patients and increase their chances of survival. The study examined the impact of comorbidity and inflammatory markers on the severity and mortality of hospitalised COVID-19 patients. This retrospective study included 101 COVID-19 patients who had comorbidities and were hospitalised from April 2021 to April 2022. Patients with a severe COVID-19 infection could be anticipated to have higher levels of inflammatory markers in their blood. Patients with chronic kidney and coronary artery disease have a worse prognosis than those with other comorbidities (P value <0.001). However, tuberculosis had no statistically significant effect on mortality and showed a minimal chance of death (P value = 0.303). In addition, tocilizumab performed poorly and was ineffective against the COVID-19 treatment. However, ivermectin exhibited a statistically significant probability of survival in COVID-19 patients. The inflammatory markers D-dimer, ferritin, and IL-6 were identified as valuable indicators of disease severity. Further, chronic kidney disease and coronary artery disease were identified as risk factors for mortality, while tuberculosis showed potential protective effects. The study showed that higher neutrophil levels were linked to mortality in tocilizumab-treated patients, while ivermectin showed promise in increasing survival rates.

Integrative analysis of cancer multimodality data identifying COPS5 as a novel biomarker of diffuse large B-cell lymphoma.

Preventing, diagnosing, and treating diseases requires accurate clinical biomarkers, which remains challenging. Recently, advanced computational approaches have accelerated the discovery of promising biomarkers from high-dimensional multimodal data. Although machine-learning methods have greatly contributed to the research fields, handling data sparseness, which is not unusual in research settings, is still an issue as it leads to limited interpretability and performance in the presence of missing information. Here, we propose a novel pipeline integrating joint non-negative matrix factorization (JNMF), identifying key features within sparse high-dimensional heterogeneous data, and a biological pathway analysis, interpreting the functionality of features by detecting activated signaling pathways. By applying our pipeline to large-scale public cancer datasets, we identified sets of genomic features relevant to specific cancer types as common pattern modules (CPMs) of JNMF. We further detected COPS5 as a potential upstream regulator of pathways associated with diffuse large B-cell lymphoma (DLBCL). COPS5 exhibited co-overexpression with MYC, TP53, and BCL2, known DLBCL marker genes, and its high expression was correlated with a lower survival probability of DLBCL patients. Using the CRISPR-Cas9 system, we confirmed the tumor growth effect of COPS5, which suggests it as a novel prognostic biomarker for DLBCL. Our results highlight that integrating multiple high-dimensional data and effectively decomposing them to interpretable dimensions unravels hidden biological importance, which enhances the discovery of clinical biomarkers.

Deep learning-assisted survival prognosis in renal cancer: A CT scan-based personalized approach

This paper presents a deep learning (DL) approach for predicting survival probabilities of renal cancer patients based solely on preoperative CT imaging. The proposed approach consists of two networks: a classifier- and a survival- network. The classifier attempts to extract features from 3D CT scans to predict the ISUP grade of Renal cell carcinoma (RCC) tumors, as defined by the International Society of Urological Pathology (ISUP). Our classifier is a 3D convolutional neural network to avoid losing crucial information on the interconnection of slides in 3D images. We employ multiple procedures, including image augmentation, preprocessing, and concatenation, to improve the performance of the classifier. Given the strong correlation between ISUP grading and renal cancer prognosis in the clinical context, we use the ISUP grading features extracted by the classifier as the input to the survival network. By leveraging this clinical association and the classifier network, we are able to model our survival analysis using a simple DL-based network. We adopt a discrete LogisticHazard-based loss to extract intrinsic survival characteristics of RCC tumors from CT images. This allows us to build a completely parametric survival model that varies with patients' tumor characteristics and predicts non-proportional survival probability curves for different patients. Our results demonstrated that the proposed method could predict the future course of renal cancer with reasonable accuracy from the CT scans. The proposed method obtained an average concordance index of 0.72, an integrated Brier score of 0.15, and an area under the curve value of 0.71 on the test cohorts.

Anoikis-related mRNA-lncRNA and DNA methylation profiles for overall survival prediction in breast cancer patients.

As a type of programmed cell death, anoikis resistance plays an essential role in tumor metastasis, allowing cancer cells to survive in the systemic circulation and as a key pathway for regulating critical biological processes. We conducted an exploratory analysis to improve risk stratification and optimize adjuvant treatment choices for patients with breast cancer, and identify multigene features in mRNA and lncRNA transcriptome profiles associated with anoikis. First, the variance selection method filters low information content genes in RNA sequence and then extracts the mRNA and lncRNA expression data base on annotation files. Then, the top ten key mRNAs are screened out through the PPI network. Pearson analysis has been employed to identify lncRNAs related to anoikis, and the prognosis-related lncRNAs are selected using Univariate Cox regression and machine learning. Finally, we identified a group of RNAs (including ten mRNAs and six lncRNAs) and integrated the expression data of 16 genes to construct a risk-scoring system for BRCA prognosis and drug sensitivity analysis. The risk score's validity has been evaluated with the ROC curve, Kaplan-Meier survival curve analysis and decision curve analysis (DCA). For the methylation data, we have obtained 169 anoikis-related prognostic methylation sites, integrated these sites with 16 RNA features and further used the deep learning model to evaluate and predict the survival risk of patients. The developed anoikis feature is demonstrated a consistency index (C-index) of 0.778, indicating its potential to predict the survival probability of breast cancer patients using deep learning methods.

Naples Prognostic Score: A Novel Predictor of Survival in Patients with Triple-Negative Breast Cancer.

This study investigated the correlation between the Naples prognostic score (NPS), clinicopathological traits, and the postoperative prognoses of patients with triple-negative breast cancer (TNBC). Based on NPS, a predictive nomogram was developed to estimate the long-term survival probabilities of patients with TNBC post-surgery. We retrospectively examined the clinical records of 223 women with TNBC treated at Ningbo Medical Center, Lihuili Hospital between January 1, 2016 and December 31, 2020. Blood tests and biochemical analyses were conducted before surgery. The prognostic nutritional index (PNI), controlling nutritional status (CONUT), neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, and NPS were determined based on blood-related markers. A Kaplan-Meier survival analysis assessed the association between NPS, PNI, CONUT score, overall survival (OS), and breast cancer-specific survival (BCSS). Predictive accuracy was evaluated using the area under the receiver operating characteristic curve (AUC) and C index. The patients were randomly divided into the training and the validation group (6:4 ratio). A nomogram prediction model was developed and evaluated using the R Software for Statistical Computing (RMS) package. NPS outperformed other scores in predicting inflammation outcomes. Patients with an elevated NPS had a poorer prognosis (P<0.001). Lymph node ratio (LNR), surgical method, postoperative chemotherapy, and NPS independently predicted OS, whereas M stage, LNR, and NPS independently predicted BCSS outcome. The OS and BCSS predicted by the nomogram model aligned well with the actual OS and BCSS. The decision curve analysis showed significant clinical utility for the nomogram model. In this study, NPS was an important prognostic indicator for patients with TNBC. The nomogram prognostic model based on NPS outperformed other prognostic scores for predicting patient prognosis. The model demonstrated a clear stratification ability for patient prognosis, which emphasized the potential benefits of early intervention for high-risk patients.

An Oxidative Stress-Related Prognostic Signature Predicts Treatment Response and Outcomes for Hepatocellular Carcinoma After Transarterial Chemoembolization.

Oxidative stress plays a critical role in promoting tumor resistance to hypoxia and chemotherapeutic drugs. However, the prognostic role of oxidative stress-related genes (OSRGs) in hepatocellular carcinoma (HCC) treated with transarterial chemoembolization (TACE) has not been fully explored. We used transcriptome data from the GSE104580 cohort containing patients marked as responders or nonresponders to TACE therapy to identify differentially expressed OSRGs associated with TACE response (TR-OSRGs). We created a TR-OSRG prognostic signature based on TR-OSRGs using least absolute shrinkage and selection operator Cox and stepwise Cox regression analyses in a training cohort of patients with HCC (TCGA-LIHC). We verified this prognostic signature in two external cohorts of patients who received TACE for HCC (GSE14520-TACE and ZS-TACE-37). Finally, we constructed a prognostic nomogram model for predicting survival probability of patients with HCC based on Cox regression analysis. The TR-OSRG prognostic signature was created and shown to be a robust independent prognostic factor for treatment response and outcomes for HCC after TACE therapy. Risk scores based on this signature correlated with tumor stage and grade. Tumor samples from patients with higher risk scores exhibited more infiltration of immune cells and significantly increased expression of immune checkpoint genes. We also developed a nomogram for patients with HCC based on the TR-OSRG prognostic signature and clinical parameters; this nomogram was a useful quantitative analysis tool for predicting patient survival. The TR-OSRGs signature exhibited good performance in predicting treatment response and outcomes in patients with HCC treated with TACE.

A comprehensive analysis of essential meiotic endonuclease 1 to prognosis and immune infiltration in kidney renal clear cell carcinoma.

Clear cell renal cell carcinoma (ccRCC) is the most common type of cancer, and its molecular pathogenesis is unclear. In this study, we investigated the prognostic value of essential meiotic endonuclease 1 (EME1) in kidney renal clear cell carcinoma (KIRC). We downloaded the RNA-Seq expression of 526 KIRC tissues and 72 normal tissues from the TCGA database and the corresponding clinical data. The gene expression profiles associated with four clear cell renal cell carcinomas were downloaded from the GEO database for analysis. The expression of EME1 in clear renal cell carcinoma and its correlation with the clinical baseline data were analyzed. Kaplan-Meier survival curve analysis was performed to assess the relationship between EME1 and patient survival. Enrichment analysis was performed to elucidate the possible functions of EME1. We also analyzed the relationship between the EME1 expression and immune infiltration through TIMER2.0 and TISIDB online databases as well as the relationship between EME1 and common immune checkpoints. EME1 was identified as a risk factor for overall survival in clear cell renal cell carcinoma with a hazard ratio of 3.201 (95% confidence interval: 2.430-4.215; p < 0.001). EME1 was highly expressed in KIRC compared to that in normal tissues (p < 0.001) and in the worse TNM stages and late stages (stage 3/4) (p < 0.001). High EME1 expression was strongly associated with the advanced T stage (p = 0.003), advanced N stage (p = 0.002), and advanced M stage (p = 0.006). Research data on KIRC were simultaneously collected and analyzed from the GEO database, including GSE40435, GSE53000, GSE68417, and GSE53757. EME1 predicted the survival status in KIRC patients (AUC = 0.62). We further established a nomogram including the correlation between the high and low EME1 expression, and EME1 was found to contribute to the prediction of the probability of patient survival with a c-index = 0.796. Kaplan-Meier analysis revealed a lower likelihood of survival with a high EME1 expression (p < 0.001). In addition, further bioinformatics analysis suggested that EME1 may be associated with the extent of immune infiltration in KIRC. An increased expression of EME1 in KIRC is thus associated with advanced clinicopathological features, possibly acting as a potential biomarker of poor prognosis in KIRC.

Association of Serum Potassium Variability With 60-day Mortality and Cardiovascular Events after COVID-19 Infection in Maintenance Hemodialysis Patients.

Objective: This study aimed to investigate the association between serum potassium variability and 60-day mortality and cardiovascular disease (CVD) in maintenance hemodialysis (MHD) patients following the coronavirus disease 2019 (COVID-19) infection. Methods: We conducted a retrospective study on MHD patients treated at the affiliated hospital of Qingdao University hemodialysis center who were infected with the novel coronavirus between December 1, 2022, and January 31, 2023. Baseline characteristics of patients were collected from electronic medical records. Kaplan-Meier survival analysis was used to obtain patient survival probabilities, and multivariate Cox hazard regression models and binary Logistic regression models were used to obtain hazard ratios (HR), odds ratios (OR), and 95% confidence intervals (95% CI) between exposure and outcomes. Results: A total of 296 patients were included in this study, with a mean age of 57.2±16.3 years, and 59.8% were male. The 60-day mortality rate was 10.8%, and the incidence of CVD was 32.8%. Kaplan-Meier curves showed that a higher potassium variability coefficient was associated with higher all-cause mortality (P = 0.024). After adjusting for potential confounders, multivariate Cox regression analysis showed that the HR for 60-day mortality in the Q4 group compared to the Q1 group was 2.06 (95% CI = 1.03-4.09, P = 0.040), and binary Logistic regression analysis showed that the OR for 60-day CVD in the Q4 group compared to the Q1 group was 4.09 (95% CI = 1.52-10.97, P = 0.005). Conclusion: Increased serum potassium variability in MHD patients after COVID-19 infection significantly increased the likelihood of 60-day mortality and CVD.

Outcomes of COVID-19 patients undergoing extracorporeal membrane oxygenation: A systematic review and meta-Analysis.

The Coronavirus Disease 2019 (COVID-19) pandemic has been ongoing for over 3years, during which numerous clinical and experimental studies have been conducted. The objective of this systematic review and meta-analysis was to assess the survival probability and complications of COVID-19 patients receiving extracorporeal membrane oxygenation (ECMO). We searched the databases by using Population-Intervention-Comparison-Outcome-Study Design (PICOS). We conducted a search of the PubMed, Web of Science, and EMBASE databases to retrieve studies published until December 10, 2022. A random-effects meta-analysis, subgroup analysis, and assessed the studies using the Newcastle-Ottawa Scale score. The results were presented as pooled morbidity with 95% confidence intervals. The study was conducted on 19 studies that enrolled a total of 1494 patients, and the results showed a pooled survival probability of 66.0%. The pooled morbidity for intracranial hemorrhage was 8.7%, intracranial thrombosis 7.0%, pneumothorax 9.0%, pulmonary embolism 11.0%, pulmonary hemorrhage 9.0%, heart failure 14.0%, liver failure 13.0%, renal injury 44.0%, gastrointestinal hemorrhage 6.0%, gastrointestinal ischemia 6.0% and venous thrombosis 31.0%. This systematic review and meta-analysis of observational studies focused on the survival probability and complications of COVID-19 patients undergoing ECMO, which are significant in evaluating the use of ECMO in COVID-19 patients and provide a basis for further research. Our study was registered on PROSPERO with registration number CRD42022382555.

Development and Validation of Potential Molecular Subtypes and Signatures of Thyroid Carcinoma Based on Aging-related Gene Analysis.

Thyroid carcinoma (THCA) is a cancer of the endocrine system that most commonly affects women. Aging-associated genes play a critical role in various cancers. Therefore, we aimed to gain insight into the molecular subtypes of thyroid cancer and whether senescence-related genes can predict the overall prognosis of THCA patients. Thyroid carcinoma (THCA) transcriptome-related expression profiles were obtained from The Cancer Genome Atlas (TCGA) database. These profiles were randomly divided into training and validation subsets at a ratio of 1:1. Unsupervised clustering algorithms were used to compare differences between the two subtypes; prognosis-related senescence genes were used to further construct our prognostic models by univariate and multivariate Cox analyses and construct a nomogram to predict the 1-, 3-, and 5-year overall survival probability of THCA patients. In addition, we performed gene set enrichment analysis (GSEA) to predict the immune microenvironment and somatic mutations between the different risk groups. Finally, real-time PCR was used to verify the expression levels of key model genes. The 'ConsensusClusterPlus' R package was used to cluster thyroid cancer into two categories (Cluster1 and Cluster2) on the basis of 46 differentially expressed aging-related genes (DE-ARGs); patients in Cluster1 demonstrated a better prognosis than those in Cluster2. Cox analysis was used to screen six prognosis-related DE-ARGs. Finally, our real-time PCR results confirmed our hypothesis. Differences exist between the two subtypes of thyroid cancer that help guide treatment decisions. The six DE-ARG genes have a high predictive value for risk stratifying THCA patients.