Value Of Risk Score Research Articles

Coordinating single-cell and bulk RNA-seq in deciphering the intratumoral immune landscape and prognostic stratification of prostate cancer patients.

Prostate cancer is a common cancer among male population. The aberrant expression of histone modifiers has been identified as a potential driving force in numerous cancer types. However, the mechanism of histone modifiers in the development of prostate cancer remains unknown. Expression profiles and clinical data were obtained from GSE70769, GSE46602, and GSE67980. Seruat R package was utilized to calculate the gene set enrichment of the histone modification pathway and obtain the Histone score. Least absolute shrinkage and selection operator (LASSO) and Cox regression analyses were employed to identify marker genes with prognostic value. Kaplan-Meier survival analysis was conducted to assess the efficacy of the prognostic model. In addition, microenvironment cell populations counter (MCPcounter), single-sample gene set enrichment analysis (ssGSEA), and xCell algorithms were employed for immune infiltration analysis. Drug sensitivity prediction was performed using oncoPredict R package. We screened differentially expressed genes (DEGs) between Histone-high score (Histone-H) and Histone-low score (Histone-L) groups, which were enriched in RNA splicing and DNA-binding transcription factor binding pathways. We retained four prognostic marker genes, including TACC3, YWHAH, TAF1C and TTLL5. The risk model showed significant efficacy in stratification of the prognosis of prostate cancer patients in both internal and external cohorts (p < .0001 and p = .032, respectively). In addition, prognostic gene YWHAH was infiltrated in abundance of fibroblasts and highly correlated with Entinostat_1593 drug sensitivity score and the value of risk score. We innovatively developed a histone modification-related prognostic model with high prognostic potency and identified YWHAH as possible diagnostic and therapeutic biomarkers for prostate cancer. It provides novel insights to address prostate cancer and enhance clinical outcomes, thereby opening up a new avenue for customized treatment alternatives.

The predictive value of the smart risk score in patients referred for coronary angiography

The predictive value of the smart risk score in patients referred for coronary angiography

A Polygenic Risk Score for Idiopathic Pulmonary Fibrosis and Interstitial Lung Abnormalities.

Rationale: In addition to rare genetic variants and the MUC5B locus, common genetic variants contribute to idiopathic pulmonary fibrosis (IPF) risk. The predictive power of common variants outside the MUC5B locus for IPF and interstitial lung abnormalities (ILAs) is unknown. Objectives: We tested the predictive value of IPF polygenic risk scores (PRSs) with and without the MUC5B region on IPF, ILA, and ILA progression. Methods: We developed PRSs that included (PRS-M5B) and excluded (PRS-NO-M5B) the MUC5B region (500-kb window around rs35705950-T) using an IPF genome-wide association study. We assessed PRS associations with area under the receiver operating characteristic curve (AUC) metrics for IPF, ILA, and ILA progression. Measurements and Main Results: We included 14,650 participants (1,970 IPF; 1,068 ILA) from six multi-ancestry population-based and case-control cohorts. In cases excluded from genome-wide association study, the PRS-M5B (odds ratio [OR] per SD of the score, 3.1; P = 7.1 × 10-95) and PRS-NO-M5B (OR per SD, 2.8; P = 2.5 × 10-87) were associated with IPF. Participants in the top PRS-NO-M5B quintile had ∼sevenfold odds for IPF compared with those in the first quintile. A clinical model predicted IPF (AUC, 0.61); rs35705950-T and PRS-NO-M5B demonstrated higher AUCs (0.73 and 0.7, respectively), and adding both genetic predictors to a clinical model yielded the highest performance (AUC, 0.81). The PRS-NO-M5B was associated with ILA (OR, 1.25) and ILA progression (OR, 1.16) in European ancestry participants. Conclusions: A common genetic variant risk score complements the MUC5B variant to identify individuals at high risk of interstitial lung abnormalities and pulmonary fibrosis.

Health Risk and Underweight.

We used routinely collected laboratory parameters from a total of 4087 patients. The risk score was calculated on the basis of electrolytes, blood count, transaminases and LDH. The nine parameters used were summed as zlog-transformed values. Where appropriate, the scales were inverted so that high values represented higher risk. For statistical prediction of the risk score, weight/height and age reference values from the WHO, the CDC (Center of Disease Control) and representative studies of German children and adults (KIGGS and NNS) were used. The score calculated from nine laboratory parameters already shows a convincing relationship with BMI. Among the weight measures used for height and age, the z-score from the CDC reference population emerged as the best estimate, explaining 34% of the variance in health risk measured by the laboratory score. The percentile rank for each age-specific median weight from the KIGGS/NNS still explained more than 31% of the variance. In contrast, percentiles explained less variance than BMI without age correction. The score we used from routine laboratory parameters appears to be an appropriate measure for assessing the health risk associated with underweight, as measured by the quality of the association with BMI. For estimating health risk based on weight, height and age alone, z-scores and percentages of age-specific median weight, as opposed to percentiles, are appropriate parameters. However, the study also shows that existing age-specific BMI reference values do not represent risk optimally. Improved statistical estimation methods would be desirable.

Identification of cuproptosis-related molecular subtypes and a novel predictive model of COVID-19 based on machine learning.

To explicate the pathogenic mechanisms of cuproptosis, a newly observed copper induced cell death pattern, in Coronavirus disease 2019 (COVID-19). Cuproptosis-related subtypes were distinguished in COVID-19 patients and associations between subtypes and immune microenvironment were probed. Three machine algorithms, including LASSO, random forest, and support vector machine, were employed to identify differentially expressed genes between subtypes, which were subsequently used for constructing cuproptosis-related risk score model in the GSE157103 cohort to predict the occurrence of COVID-19. The predictive values of the cuproptosis-related risk score were verified in the GSE163151 cohort, GSE152418 cohort and GSE171110 cohort. A nomogram was created to facilitate the clinical use of this risk score, and its validity was validated through a calibration plot. Finally, the model genes were validated using lung proteomics data from COVID-19 cases and single-cell data. Patients with COVID-19 had higher significantly cuproptosis level in blood leukocytes compared to patients without COVID-19. Two cuproptosis clusters were identified by unsupervised clustering approach and cuproptosis cluster A characterized by T cell receptor signaling pathway had a better prognosis than cuproptosis cluster B. We constructed a cuproptosis-related risk score, based on PDHA1, PDHB, MTF1 and CDKN2A, and a nomogram was created, which both showed excellent predictive values for COVID-19. And the results of proteomics showed that the expression levels of PDHA1 and PDHB were significantly increased in COVID-19 patient samples. Our study constructed and validated an cuproptosis-associated risk model and the risk score can be used as a powerful biomarker for predicting the existence of SARS-CoV-2 infection.

Prognostic scoring system based on eosinophil- and basophil-related markers for predicting the prognosis of patients with stage II and stage III colorectal cancer: a retrospective cohort study.

Systemic inflammation is associated with the prognosis of colorectal cancer (CRC). The current study aimed to construct a comprehensively inflammatory prognostic scoring system named risk score (RS) based on eosinophil- and basophil-related markers and assess its prognostic value in patients with stage II and stage III CRC. A total of 3,986 patients were enrolled from January 2007 to December 2013. The last follow-up time was January 2019. They were randomly assigned to the training set and testing set in a 3:2 split ratio. Least absolute shrinkage and selection operator (LASSO)-Cox regression analysis was performed to select the optimal prognostic factors in the construction of RS. The Kaplan-Meier curve, time-dependent receiver operating characteristic (ROC), and Cox analysis were used to evaluate the association between RS and overall survival (OS). In the training set, all inflammatory markers showed certain prognostic values. Based on LASSO-Cox analysis, nine markers were integrated to construct RS. The Kaplan-Meier curve showed that a higher RS (RS > 0) had a significantly worse prognosis (log-rank p< 0.0001). RS (>0) remained an independent prognostic factor for OS (hazard ratio (HR): 1.70, 95% confidence interval (CI), 1.43-2.03, p< 0.001). The prognostic value of RS was validated in the entire cohort. Time-dependent ROC analysis showed that RS had a stable prognostic effect throughout the follow-up times and could enhance the prognostic ability of the stage by combination. Nomogram was established based on RS and clinicopathological factors for predicting OS in the training set and validated in the testing set. The area under the curve (AUC) values of the 3-year OS in the training and testing sets were 0.748 and 0.720, respectively. The nomogram had a satisfactory predictive accuracy and had better clinical application value than the tumor stage alone. RS might be an independent prognostic factor for OS in patients with stage II and III CRC, which is helpful for risk stratification of patients. Additionally, the nomogram might be used for personalized prediction and might contribute to formulating a better clinical treatment plan.

The prognostic value of rapid risk scores among patients with community-acquired pneumonia : Aretrospective cohort study.

Community-acquired pneumonia (CAP) is afrequent reason for emergency department (ED) presentations. Various risk scores have been validated in the management of CAP and are recommended for daily practice. The aim of the study was to evaluate the performance of the rapid risk scores (the rapid acute physiology score (RAPS), the rapid emergency medicine score (REMS), the Worthing physiological scoring system (WPS), CURB-65 and CRB-65) among patients with CAP. This retrospective cohort study was conducted in the ED of atertiary hospital between 1 January 2019 and 31 December 2019. Patients aged ≥ 18years and diagnosed with CAP were included. Patients who were transferred from another center or with missing records were excluded. Demographic information, vital signs, level of consciousness, laboratory results, and outcomes were recorded. Atotal of 2057 patients were included in the final analysis. The 30-day mortality of the patients was 15.2% (n = 312). The WPS achieved the most successful results for all three outcomes, 30-day mortality, intensive care unit (ICU) admission and mechanical ventilation (MV) needs (area under the curve, AUC 0.810, 0.918, and 0.910, respectively; p < 0.001). In the prediction of mortality, RAPS, REMS, CURB-65, and CRB-65 had a moderate overall performance (AUC 0.648, 0.752, 0.778, and 0.739, respectively). In the prediction of ICU admission and MV needs, RAPS, REMS, CURB-65, and CRB-65 had moderate to good overall performance (AUC at ICU admission 0.793, 0.873, 0.829, and 0.810; AUC for MV needs 0.759, 0.892, 0.754, and 0.738, respectively). Advanced age, lower levels of mean arterial pressure and peripheral oxygen saturation, presence of active malignancy and cerebrovascular disease, and ICU admission were associated with mortality (p < 0.05). The WPS outperformed other risk scores in patients with CAP and can be used safely. The CRB-65 can be used to discriminate critically ill patients with CAP due to its high specificity. The overall performances of the scores were satisfactory for all three outcomes.

Establishment and Evaluation of Exosomes-Related Gene Risk Model in Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) is a challenging disease to evaluate in terms of prognosis, requiring close attention to the prognosis of HCC patients. Exosomes have been shown to play an important role in HCC development and have significant potential in managing HCC patient prognosis, as they are detectable in patients' blood. By using small extracellular vesicular RNA, liquid biopsies can reflect the underlying physiological and pathological status of the originating cells, providing a valuable assessment of human health. No study has explored the diagnostic value of mRNA expression changes in exosomes for liver cancer. The present study investigated establishing a risk prognosis model based on mRNA expression levels in exosomes from blood samples of liver cancer patients and evaluated its diagnostic and prognostic value, providing new targets for liver cancer detection. We obtained mRNA data from HCC patients and normal controls from the TCGA and exoRBase 2.0 databases and established a risk prognostic assessment model using exosomes-related risk genes selected through prognostic analysis and Lasso Cox analysis. The patients were divided into high-risk and low-risk groups based on median risk score values to validate the independence and evaluability of the risk score. The clinical value of the model was further analyzed using a nomograph model, and the efficacy of immunotherapy and cell-origin types of prognostic risk genes were further assessed in the high- and low-risk groups by immune checkpoint and single-cell sequencing. A total of 44 genes were found to be significantly associated with the prognosis of HCC patients. From this group, we selected six genes (CLEC3B, CYP2C9, GNA14, NQO1, NT5DC2, and S100A9) as exosomal risk genes and used them as a basis for the risk prognosis model. The clinical information of HCC patients from the TCGA and ICGC databases demonstrated that the risk prognostic score of the model established in this study was an independent prognostic factor with good robustness. When pathological stage and risk prognostic score were incorporated into the model to predict clinical outcomes, the nomograph model had the best clinical benefit. Furthermore, immune checkpoint assays and single-cell sequencing analysis suggested that exosomal risk genes were derived from different cell types and that immunotherapy in the high-risk groups could be beneficial. Our study demonstrated that the prognostic scoring model based on exosomal mRNA was highly effective. The six genes selected using the scoring model have been previously reported to be associated with the occurrence and development of liver cancer. However, this study is the first to confirm that these related genes existed in the blood exosomes, which could be used for liquid biopsy of patients with liver cancer, thereby avoiding the need for puncture diagnosis. This approach has a high value in clinical application. Through single-cell sequencing, we found that the six genes in the risk model originate from multiple cell types. This finding suggests that the exosomal characteristic molecules secreted by different types of cells in the microenvironment of liver cancer may serve as diagnostic markers.

Prognostic role of intermountain risk score (IMRS) in intensive care unit patients with a diagnosis of COVID-19.

In this study, we aimed to assess the predictive value of Intermountain Risk Score (IMRS) in intensive care unit (ICU) patients with COVID-19. Our retrospective study included the data of 194 patients who were admitted to the COVID-19 ICU of a tertiary care center. COVID-19 diagnoses were made by a positive result from a real-time reverse-transcriptase (RT) polymerase chain reaction (PCR) assay of nasal and pharyngeal swab specimens. Patients who had negative RT-PCR results or who were not admitted to ICU and patients under 18 years old were excluded from the study. Complete blood count, biochemistry panel, and blood gas analysis results were gathered and compiled. 194 ICU patients with COVID-19 (PCR positive) were included in the study. The patients were divided into two groups according to IMRS (if IMRS was <15 in women and <17 in men, patients were included in the non-high-risk group, while patients with IMRS ≥15 in women and ≥17 in men were defined as a high-risk group). Multivariate regression analysis was performed to predict in-hospital mortality. The IMRS [OR: 1.17 (1.08-1.27) p<0.001)] was found to predict in-hospital mortality. In this study, we showed that the IMRS score at admission can predict in-hospital mortality in intensive care unit patients with a diagnosis of COVID-19.

Risk factors for venous thromboembolism in glioma patients

Background: Ranking third in the cause of death structure, venous thromboembolism (VTE) is a frequent complication in patients with central nervous system tumors.Objective: To assess the incidence of venous thrombosis and its risk factors in glioma patients based on retrospective data.Methods: Our retrospective study included 186 glioma patients from the Neurosurgery Department of Almazov National Medical Research Centre.Results: The VTE incidence was 8% in patients with brain neoplasms. Comparative analysis of 2 groups to identify VTE predictors showed that the probability of thrombosis increases with factors leading to the patient immobilization: altered mental status (40% [n = 6] in the VTE group vs 18% [n = 30] in the non-VTE group, OR: 3.1 [95% СI: 0.8–10.6], P = .080), neurological deficit (67% [n = 50] vs 29% [n = 10], OR: 4.8 [95% СI: 1.4–18.7], P = .007), and bed rest for more than 3 days (33% vs 4%, OR: 13.1 [95% СI: 2.7–62.8], P &lt; .001). Based on the validation results, Caprini and IMPROVEDD risk scores have high negative predictive values: 0.99 [95% СI: 0.93–1.00] vs 0.97 [95% СI: 0.93–0.99] (P = .317). Caprini risk score has a specificity of 48.2% and a sensitivity of 93.3% (AUC = 78.98); the threshold value for high-risk VTE detection is 5.5 points. IMPROVEDD risk score has a specificity of 82.5% and a sensitivity of 73.3% (AUC = 81.1); the threshold value for high-risk VTE detection is 4.5 points.Conclusion: Our study revealed VTE risk factors in glioma patients, such as neurological deficit, prolonged bed rest (more than 3 days), and a high-grade tumor. We demonstrated high negative predictive values of Caprini and IMPROVEDD risk scores and determined their threshold values to be validated in a further prospective study. Due to the high incidence of VTE and risks of hemorrhage in patients with central nervous system tumors, personalized venous thrombosis risk calculators should be developed, providing for features of thrombosis pathogenesis in this patient group. Received 12 December 2022. Revised 7 April 2023. Accepted 31 May 2023. Funding: The study was supported by Ministry of Science and Higher Education of Russian Federation (No. 075-15-2022-301). Conflict of interest: The authors declare no conflict of interest. Contribution of the authorsConception and study design: K.A. Pishchulov, M.A. SimakovaData collection and analysis: K.A. Pishchulov, S.I. ParkhomenkoStatistical analysis: V.L. LukinovDrafting the article: K.A. Pishchulov, M.A. Simakova, V.L. Lukinov, S.I. Parkhomenko, N.E. Voinov, S.S. Sklyar, O.M. MoiseevaCritical revision of the article: K.A. Pishchulov, M.A. Simakova, V.L. Lukinov, S.I. Parkhomenko, N.E. Voinov, S.S. Sklyar, O.M. MoiseevaFinal approval of the version to be published: K.A. Pishchulov, M.A. Simakova, V.L. Lukinov, S.I. Parkhomenko, N.E. Voinov, S.S. Sklyar, O.M. Moiseeva

Development and Validation of PET/CT-Based Nomogram for Preoperative Prediction of Lymph Node Status in Esophageal Squamous Cell Carcinoma.

This study was conducted to predict the lymph node status and survival of esophageal squamous cell carcinoma before treatment by PET-CT-related parameters. From January 2013 to July 2018, patients with pathologically diagnosed ESCC at our hospital were retrospectively enrolled. Completed esophagectomy and two- or three-field lymph node dissections were conducted. Those with neoadjuvant therapy were excluded. The first 65% of patients in each year were regarded as the training set and the last 35% as the test set. Nomogram was constructed by the "rms" package. Five-year, overall survival was analyzed based on the best cutoff value of risk score determined by the "survivalROC" package. Ultimately, 311 patients were included with 209 in the training set and 102 in the test set. The positive rate of the lymph node in the training set was 36.8% and that in the test set was 32.4%. The C-index of the training set was 0.763 and the test set was 0.766. The decision curve analysis showed that it was superior to the previous methods based on lymph node uptake or long/short axis diameter or axial ratio. Risk score > 0.20 was significantly associated with 5-year, overall survival (p = 0.0015) in all patients. The nomogram constructed from PET-CT parameters including primary tumor metabolic length and thickness can accurately predict the risk of lymph node metastasis in ESCC. The risk score calculated by our model accurately predicts the patient's 5-year overall survival.

Diagnostic value of carotid intima-media thickness and clinical risk scores in determining etiology of ischemic stroke.

In the general population, carotid intima-media thickness (CIMT) is associated with atherosclerosis as well as atrial fibrillation (AF). However, the extent to which CIMT might be of diagnostic value in clarifying stroke etiology is currently unclear. In this retrospective cohort study, we included 800 consecutive patients with acute ischemic stroke. We compared CIMT-values between stroke etiologies. The association between CIMT and cardioembolic stroke was investigated via logistic regression analysis adjusting for vascular risk factors. Receiver operating characteristic analyses were conducted to investigate the diagnostic value of CIMT in comparison to vascular risk factors and clinical AF risk scores (CHA2DS2VASc, HAVOC, and AS5F). CIMT-values were highest in patients with cardioembolic or atherosclerotic stroke origin. CIMT was associated with newly diagnosed AF compared against cryptogenic strokes (crude odds ratio (OR) per 0.1 mm-increase of CIMT: 1.26 (95% confidence interval (CI): 1.13-1.41)). After adjustment for vascular risk factors, the effect of CIMT on AF-diagnosis, however, was weakened (adjusted OR: 1.10 (95% CI: 0.97-1.25)). The diagnostic value of CIMT for detection of AF (AUC: 0.60, 95% CI: 0.54-0.65) was outperformed by AF risk scores. Among the scores investigated, the AS5F-score yielded best accuracy and calibration to predict newly diagnosed AF (AUC: 0.71, 95% CI: 0.65-0.78). CIMT may help in the diagnosis of stroke etiology. However, compared with vascular risk factors or clinical AF risk scores, CIMT does not provide substantial additional information on the risk of newly detected AF. Thus, stratification of AF risk based on scores, such as the AS5F, is advisable.

Predictive value of ASCVD risk score for mortality and major adverse cardiovascular events in the year following a COVID-19 infection among US Veterans

BackgroundMorbidity and mortality following COVID-19 infection may be influenced by baseline atherosclerotic cardiovascular disease (ASCVD) risk, yet limited data are available to identify those at highest risk. We examined the association between baseline ASCVD risk with mortality and major adverse cardiovascular events (MACE) in the year following COVID-19 infection. MethodsWe evaluated a nationwide retrospective cohort of US Veterans free of ASCVD who were tested for COVID-19. The primary outcome was absolute risk of all-cause mortality in the year following a COVID-19 test among those hospitalized vs. not stratified by baseline VA-ASCVD risk scores. Secondarily, risk of MACE was examined. ResultsThere were 393,683 Veterans tested for COVID-19 and 72,840 tested positive. Mean age was 57 years, 86% were male, and 68% were white. Within 30 days following infection, hospitalized Veterans with VA-ASCVD scores >20% had an absolute risk of death of 24.6% vs. 9.7% (P ≤0.0001) for those who tested positive and negative for COVID-19 respectively. In the year following infection, risk of mortality attenuated with no difference in risk after 60 days. The absolute risk of MACE was similar for Veterans who tested positive or negative for COVID-19. ConclusionsVeterans without clinical ASCVD experienced an increased absolute risk of death within 30 days of a COVID-19 infection compared to Veterans with the same VA-ASCVD risk score who tested negative, but this risk attenuated after 60 days. Whether cardiovascular preventive medications can lower the risk of mortality and MACE in the acute period following COVID-19 infection should be evaluated.

Predicting 5-Year Clinical Outcomes After Transcatheter or Surgical Aortic Valve Replacement (a Risk Score from the SURTAVI Trial)

Risk prediction scores for long-term outcomes after transcatheter aortic valve implantation (TAVI) or surgical aortic valve replacement (SAVR) are lacking. This study aimed to develop preprocedural risk scores for 5-year clinical outcomes after TAVI or SAVR. This analysis included 1,660 patients at an intermediate surgical risk with severe aortic stenosis randomly assigned to TAVI (n=864) or SAVR (n=796) from the SURTAVI (Surgical Replacement and Transcatheter Aortic Valve Implantation) trial. The primary end point was a composite of all-cause mortality or disabling stroke at 5 years. The secondary end point was a composite of cardiovascular mortality or hospitalizations for valve disease or worsening heart failure at 5 years. Preprocedural multivariable predictors of clinical outcomes were used to calculate a simple risk score for both procedures. At 5 years, the primary end point occurred in 31.3% of the patients with TAVI and 30.8% of the patients with SAVR. Preprocedural predictors differed between TAVI and SAVR. Baseline anticoagulant use was a common predictor for events in both procedures, whereas male sex and a left ventricular ejection fraction <60% were significant predictors for events in patients with TAVI and SAVR, respectively. A total of 4 simple scoring systems were created based on these multivariable predictors. The C-statistics of all models were modest but performed better than the contemporary risk scores. In conclusion, preprocedural predictors of events differ between TAVI and SAVR, necessitating separate risk models. Despite the modest predictive value of the SURTAVI risk scores, they appeared superior to other contemporary scores. Further research is needed to strengthen and validate our risk scores, possibly by including biomarker and echocardiographic parameters.

Value of ATRIA stroke risk score in predicting atrial high-rate episodes: A comparison of six different risk scores.

Atrial high-rate episodes (AHRE) are asymptomatic atrial tachy-arrhythmias detected through continuous monitoring with a cardiac implantable electronic device (CIED). AHRE's have been associated with increased risks of developing clinically manifested atrial fibrillation (AF), thromboembolism, cardiovascular events, and mortality. Several variables has been researched and identified to predict AHRE development. The aim of this study, which compared the six frequently-used scoring systems for thromboembolic risk in AF (CHA2 DS2 -VASc, mC2 HEST, HAT2 CH2 , R2 -CHADS2 , R2 -CHA2 DS2 -VASc, and ATRIA) in terms of their prognostic power in predicting AHRE. This retrospective study included 174 patients with CIED's. The study population was divided into two groups according to presence of AHRE: patients with AHRE (+) and patients without AHRE (-). Thereafter, patients baseline characteristics and scoring systems were analyzed for prediction of AHRE. The distribution of patients' baseline characteristics and scoring systems according to presence of AHRE was evaluated. Furthermore, ROC curve analyses of the stroke risk scoring systems have been investigated in terms of predicting the development of AHREs. ATRIA, which predicted AHRE with a specificity of 92% and sensitivity of 37.5% for ATRIA values of>6, performed better than other scoring systems in predicting AHRE (AUC: 0.700, 0.626-0.767 95% confidence interval (CI), p=.004) CONCLUSION: AHRE is common in patients with a CIED. In this context, several risk scoring systems have been used to predict the development of AHRE in patients with a CIED. This study's findings revealed that The ATRIA stroke risk scoring system performed better than other commonly used risk scoring systems in predicting AHRE.

Characterization of metabolism-associated molecular patterns in prostate cancer

BackgroundMetabolism is a hallmark of cancer and it involves in resistance to antitumor treatment. Therefore, the purposes of this study are to classify metabolism-related molecular pattern and to explore the molecular and tumor microenvironment characteristics for prognosis predicting in prostate cancer.MethodsThe mRNA expression profiles and the corresponding clinical information for prostate cancer patients from TCGA, cBioPortal, and GEO databases. Samples were classified using unsupervised non-negative matrix factorization (NMF) clustering based on differentially expressed metabolism-related genes (MAGs). The characteristics of disease-free survival (DFS), clinicopathological characteristics, pathways, TME, immune cell infiltration, response to immunotherapy, and sensitivity to chemotherapy between subclusters were explored. A prognostic signature was constructed by LASSO cox regression analysis based on differentially expressed MAGs and followed by the development for prognostic prediction.ResultsA total of 76 MAGs between prostate cancer samples and non-tumorous samples were found, then 489 patients were divided into two metabolism-related subclusters for prostate cancer. The significant differences in clinical characteristics (age, T/N stage, Gleason) and DFS between two subclusters. Cluster 1 was associated with cell cycle and metabolism-related pathways, and epithelial-mesenchymal transition (EMT), etc., involved in cluster 2. Moreover, lower ESTIMATE/immune/stromal scores, lower expression of HLAs and immune checkpoint-related genes, and lower half-maximal inhibitory concentration (IC50) values in cluster 1 compared with cluster 2. The 10 MAG signature was identified and constructed a risk model for DFS predicting. The patients with high-risk scores showed poorer DFS. The area under the curve (AUC) values for 1-, 3-, 5-year DFS were 0.744, 0.731, 0.735 in TCGA-PRAD dataset, and 0.668, 0.712, 0.809 in GSE70768 dataset, 0.763, 0.802, 0.772 in GSE70769 dataset. Besides, risk score and Gleason score were identified as independent factors for DFS predicting, and the AUC values of risk score and Gleason score were respectively 0.743 and 0.738. The nomogram showed a favorable performance in DFS predicting.ConclusionOur data identified two metabolism-related molecular subclusters for prostate cancer that were distinctly characterized in prostate cancer. Metabolism-related risk profiles were also constructed for prognostic prediction.

A novel signature incorporating lipid metabolism- and immune-related genes to predict the prognosis and immune landscape in hepatocellular carcinoma.

Liver hepatocellular carcinoma (LIHC) is a highly malignant tumor with high metastasis and recurrence rates. Due to the relation between lipid metabolism and the tumor immune microenvironment is constantly being elucidated, this work is carried out to produce a new prognostic gene signature that incorporates immune profiles and lipid metabolism of LIHC patients. We used the "DEseq2" R package and the "Venn" R package to identify differentially expressed genes related to lipid metabolism (LRDGs) in LIHC. Additionally, we performed unsupervised clustering of LIHC patients based on LRDGs to identify their subgroups and immuno-infiltration and Gene Ontology (GO) enrichment analysis on the subgroups. Next, we employed multivariate, LASSO and univariate Cox regression analyses to determine variables and to create a prognostic profile on the basis of immune- and lipid metabolism-related differential genes (IRDGs and LRDGs). We separated patients into low- and high-risk groups in accordance with the best cut-off value of risk score. We conducted Decision Curve Analysis (DCA), Receiver Operating Characteristic curve analysis as a function of time as well as Survival Analysis to evaluate this signature's prognostic value. We incorporated the clinical characteristics of patients into the risk model to obtain a nomogram prognostic model. GEO14520 and ICGC-LIRI JP datasets were employed to externally confirm the accuracy and robustness of signature. The gene set variation analysis (GSVA) and gene set enrichment analysis (GSEA) were applied for investigating the underlying mechanisms. Immune infiltration analysis was implemented to examine the differences in immune between both risk groups. Single-cell RNA sequencing (scRNA-SEQ) was utilized to characterize the genes that were involved in the distribution of signature and expression characteristics of different LIHC cell types. The patients' sensitivity in both risk groups to commonly used chemotherapeutic agents and semi-inhibitory concentrations (IC50) of the drugs was assessed using the GDSC database. On the basis of the differentially expressed genes (DEGs) in the two groups, the CMAP database was adopted for the prediction of potential small-molecule compounds. Small-molecule compounds were molecularly docked with prognostic markers. Lastly, we investigated the prognostic gene expression levels in normal and LIHC tissues with immunohistochemistry (IHC) and quantitative reverse transcription polymerase chain reaction(qRT-PCR). We built and verified a prognostic signature with seven genes that incorporated immune profiles and lipid metabolism. Patients were classified as low- and high-risk groups depending on their prognostic profiles. The overall survival (OS) was markedly lower in the high-risk group as compared to low-risk group. Time-dependent ROC curves more precisely predicted patients' survival at 1, 3 and 5 years; the area under the ROC curve was 0.81 (1 year), 0.75 (3 years) and 0.77 (5 years). The DCA curves showed the value of the prognostic genes in this signature for clinical applications. We included the patients' clinical characteristics in the risk model for both multivariate and univariate Cox regression analyses, and the findings revealed that the risk model represents an independent factor that influences OS in LIHC patients. With immune analysis, GSVA and GSEA, we identified that there are remarkable differences between the two risk groups in immune pathways, lipid metabolism, tumor development, immune cell infiltration and immune microenvironment, response to immunotherapy, and sensitivity to chemotherapy. Moreover, those with higher risk scores presented greater sensitivity to the chemotherapeutic agents. Experiments in vitro further elucidated the roles of SPP1 and FLT3 in the LIHC immune microenvironment. Furthermore, four small-molecule drugs that could target LIHC were screened. In vitro qRT-PCR , IHC revealed that the SPP1,KIF18A expressions were raised in LIHC in tumor samples, whereas FLT3,SOCS2 showed the opposite trend. We developed and verified a new signature comprising immune- and lipid metabolism-associated markers and to assess the prognosis and the immune status of LIHC patients. This signature can be applied to survival prediction, individualized chemotherapy, and immunotherapeutic guidance for patients with liver cancer. This study also provides potential targeted therapeutics and novel ideas for the immune evasion and progression of LIHC.

Assessing of programmed cell death gene signature for predicting ovarian cancer prognosis and treatment response.

Programmed cell death (PCD) is an overwhelming factor affecting tumor cell metastasis, but the mechanism of PCD in ovarian cancer (OV) is still uncertain. To define the molecular subtypes of OV, we performed unsupervised clustering based on the expression level of prognosis related PCD genes in the Cancer Genome Atlas (TCGA)-OV. COX and least absolute shrinkage and selection operator (LASSO) COX analysis were used to identify the OV prognostic related PCD genes, and the genes identified according to the minimum Akaike information criterion (AIC) were the OV prognostic characteristic genes. According to the regression coefficient in the multivariate COX analysis and gene expression data, the Risk Score of OV prognosis was constructed. Kaplan-Meier analysis was conducted to assess the prognostic status of OV patients, and receiver operating characteristic (ROC) curves were conducted to assess the clinical value of Risk Score. Moreover, RNA-Seq date of OV patient derived from Gene Expression Omnibus (GEO, GSE32062) and the International Cancer Genome Consortium (ICGC) database (ICGC-AU), verifying the robustness of the Risk Score via Kaplan-Meier and ROC analysis.Pathway features were performed by gene set enrichment analysis and single sample gene set enrichment analysis. Finally, Risk Score in terms of chemotherapy drug sensitivity and immunotherapy suitability was also evaluated in different groups. 9-gene composition Risk Score system was finally determined by COX and LASSO COX analysis. Patients in the low Risk Score group possessed improved prognostic status, immune activity. PI3K pathway activity was increased in the high Risk Score group. In the chemotherapy drug sensitivity analysis, we found that the high Risk Score group might be more suitable for treatment with PI3K inhibitors Taselisib and Pictilisib. In addition, we found that patients in the low-risk group responded better to immunotherapy. Risk Score of 9-gene composition of PCD signature possesses promising clinical potential in OV prognosis, immunotherapy, immune microenvironment activity, and chemotherapeutic drug selection, and our study provides the basis for an in-depth investigation of the PCD mechanism in OV.

Bioinformatics analysis of the prognostic and clinical value of senescence-related gene signature in papillary thyroid cancer.

Cellular senescence can both inhibit and promote the occurrence of tumors, so how to apply cellular senescence therapy is of great importance. However, it is worth to be analyzed from multiple perspectives by researchers, especially for tumors with a high incidence like papillary thyroid cancer (PTC). We obtained senescence-related differentially expressed genes (SRGs) from The Cancer Genome Atlas (TCGA) and gene expression omnibus database. Enrichment analysis of SRGs was performed via gene ontology and Kyoto Encyclopedia of Genes and Genomes. Prognostic model was constructed by univariate and multivariate Cox regression analysis. Evaluation of clinical value was analyzed via Receiver operating characteristic curve, Kaplan-Meier curve and Cox regression. Immune infiltrates were investigated through ESTIMATE and single-sample gene set enrichment analysis. Immunohistochemical images were obtained from The Human Protein Atlas. Twenty-seven SRGs from TCGA cohort and gene expression omnibus datasets were found. These genes are mainly concentrated in senescence-related terms and pathways, including "DNA damage response, signal transduction by p53 class mediator," "signal transduction in response to DNA damage," "p53 signaling pathway" and "Endocrine resistance." Based on SRGs, prognostic model was constructed by E2F transcription factor 1, snail family transcriptional repressor 1 and phospholipase A2 receptor 1. PTC patients were divided into a low-risk group and a high-risk group according to the median value (cutoff point = 0.969) of risk score in TCGA cohort. The diagnostic efficiency of this model is good (area under curve = 0.803, 0.809, and 0.877 at 1, 2, and 3 years in TCGA; area under curve = 0.964, 0.813 in GPL570 and GPL96), particularly advanced grade, state and tumor mutation burden, such as Stage III - IV, T3 - 4, H-tumor mutation burden. Furthermore, High-risk group was significantly associated with poor prognosis and more immune infiltration. Our prognostic model has a good diagnostic and prognostic efficacy, and there is a certain clinical application value. In addition, we provide the first new insight into the genesis, diagnosis, prognosis and treatment of PTC based on senescence-related genes.

Predictive value of clinical risk scores for determination of left atrial low voltage area and arrhythmia recurrences in patients undergoing atrial fibrillation ablation

Abstract Funding Acknowledgements Type of funding sources: None. Background Presence of left atrial low voltage area (LVA) is an independent predictor of atrial tachyarhythmia (ATa) recurrences after atrial fibrillation (AF) ablation. Thus identifying patients with LVA by using non-invasive methods is an important endeavour. Purpose In this study; we investigated the predictors of LVA and postablation recurrences especially focusing on clinical risk scores. Methods We enrolled 328 consecutive patients who underwent initial AF ablation procedure using high density mapping. LVAs were assessed and segmental distribution was noted in each patient. CHA2DS2-VASc, HATCH, APPLE and SPEED scores were calculated. Predictive value and arrhythmia recurrences were evaluated. Results Two hundred and twenty one patients (67.37%) were paroxysmal AF and 107 (32.62%) patients were non-paroxysmal AF at the time of ablation. Mean CHA2DS2-VASc score was 1.88±1.66, mean APPLE score was 1.20±1.20, HATCH score was 0.93±1.00 and SPEED score was 1.54±1.27 in the whole study group. LVA was detected in 131 patients (39.93%). Female gender [OR:2.94, CI: 1.34-6.43, p=0.007), non-paroxsymal AF (OR: 2.49 CI:1.13-5.46, p=0.023), APPLE score ≥1 (OR:1.69 CI:1.12-2.55, p=0.012) and SPEED score ≥ 1 (OR: 1.47 CI:1.00-2.14, p=0.045] were independent predictors for the presence of LVA in multivariate analysis. Presence of LVA (OR:1.98; CI:1.13-4.15; p=0.017) and HT (OR:2.00; CI:1.19-3.38; p=0.009) were the independent predictors of recurrences. Conclusion Higher clinical scores are associated with more LVA which is associated with ATa recurrence. However the predictive value of these risk scores is limited. Better risk scoring systems are needed to precisely identify the underlying atrial substrate in this patient group.