Identification and Validation of a Novel Six-lncRNA-Based Prognostic Model for Lung Adenocarcinoma

ObjectiveThis study was conducted in order to establish a long non-coding RNA (lncRNA)-based model for predicting overall survival (OS) in patients with lung adenocarcinoma (LUAD).MethodsOriginal RNA-seq data of LUAD samples were extracted from The Cancer Genome Atlas (TCGA) database. Univariate Cox survival analysis was performed to select lncRNAs associated with OS. The least absolute shrinkage and selection operator (LASSO) regression analysis and multivariate Cox analysis were performed for building an OS-associated lncRNA prognostic model. Moreover, receiver operating characteristic (ROC) curves were generated to assess predictive values of the hub lncRNAs. Consequently, qRT-PCR was conducted to validate its prognostic value. The potential roles of these lncRNAs in immunotherapy and anti-angiogenic therapy were also investigated.ResultsThe lncRNA-associated risk score of OS (LARSO) was established based on the LASSO coefficient of six individual lncRNAs, including CTD-2124B20.2, CTD-2168K21.1, DEPDC1-AS1, RP1-290I10.3, RP11-454K7.3, and RP11-95M5.1. Kaplan–Meier analysis revealed that LUAD patients with higher LARSO values had a shorter OS. Furthermore, a new risk score (NRS), including LARSO, stage, and N stage, could better predict the prognosis of LUAD patients compared with LARSO alone. Evaluation of the prognostic model in our cohort demonstrated that patients with higher scores had a worse prognosis. In addition, correlation analysis between these six lncRNAs and immune checkpoints or anti-angiogenic targets suggested that LUAD patients with high LARSO might not be sensitive to immunotherapy or anti-angiogenic therapy.ConclusionsThis robust six-lncRNA prognostic signature may be used as a novel and powerful prognostic biomarker for lung adenocarcinoma.

A Method for Cheating Indication in Unproctored On-Line Exams

COVID-19 has disrupted every field of life and education is not immune to it. Student learning and examinations moved on-line on a few weeks notice, which has created a large workload for academics to grade the assessments and manually detect students’ dishonesty. In this paper, we propose a method to automatically indicate cheating in unproctored on-line exams, when somebody else other than the legitimate student takes the exam. The method is based on the analysis of the student’s on-line traces, which are logged by distance education systems. We work with customized IP geolocation and other data to derive the student’s cheating risk score. We apply the method to approx. 3600 students in 22 courses, where the partial or final on-line exams were unproctored. The found cheating risk scores are presented along with examples of indicated cheatings. The method can be used to select students for knowledge re-validation, or to compare student cheating across courses, age groups, countries, and universities. We compared student cheating risk scores between four academic terms, including two terms of university closure due to COVID-19.

Establishing a Prognostic Signature Based on Epithelial–Mesenchymal Transition-Related Genes for Endometrial Cancer Patients

BackgroundsEpithelial–mesenchymal transition (EMT) is a sequential process where tumor cells develop from the epithelial state to the mesenchymal state. EMT contributes to various tumor functions including initiation, propagating potential, and resistance to therapy, thus affecting the survival time of patients. The aim of this research is to set up an EMT-related prognostic signature for endometrial cancer (EC).MethodsEMT-related gene (ERG) expression and clinical data were acquired from The Cancer Genome Atlas (TCGA). The entire set was randomly divided into two sets, one for contributing the risk model (risk score) and the other for validating. Univariate and multivariate Cox proportional hazards regression analyses were applied to the training set to select the prognostic ERGs. The expression of 10 ERGs was confirmed by qRT-PCR in clinical samples. Then, we developed a nomogram predicting 1-/3-/5-year survival possibility combining the risk score and clinical factors. The entire set was stratified into the high- and low-risk groups, which was used to analyze the immune infiltrating, tumorigenesis pathways, and response to drugs.ResultsA total of 220 genes were screened out from 1,316 ERGs for their differential expression in tumor versus normal. Next, 10 genes were found to be associated with overall survival (OS) in EC, and the expression was validated by qRT-PCR using clinical samples, so we constructed a 10-ERG-based risk score to distinguish high-/low-risk patients and a nomogram to predict survival rate. The calibration plots proved the predictive value of our model. Gene Set Enrichment Analysis (GSEA) discovered that in the low-risk group, immune-related pathways were enriched; in the high-risk group, tumorigenesis pathways were enriched. The low-risk group showed more immune activities, higher tumor mutational burden (TMB), and higher CTAL4/PD1 expression, which was in line with a better response to immune checkpoint inhibitors. Nevertheless, response to chemotherapeutic drugs turned out better in the high-risk group. The high-risk group had higher N6-methyladenosine (m6A) RNA expression, microsatellite instability level, and stemness indices.ConclusionWe constructed the ERG-related signature model to predict the prognosis of EC patients. What is more, it might offer a reference for predicting individualized response to immune checkpoint inhibitors and chemotherapeutic drugs.

An Individualized Algorithm to Predict Mortality in COVID-19 Pneumonia: a Machine Learning Based Study

IntroductionIdentifying SARS-CoV-2 patients at higher risk of mortality is crucial in the management of a pandemic. Artificial intelligence techniques allow to analyze big amount of data to find hidden patterns. We aimed to develop and validate a mortality score at admission for COVID-19 based on high-level machine learning.Material and methodsWe conducted a retrospective cohort study on hospitalized adults COVID-19 patients between March and December 2020. The primary outcome was in-hospital mortality. A machine learning approach on vital parameters, laboratory values, and demographic features was applied to develop different models. Then, a feature importance analysis was performed to reduce the number of variables included in the model, to develop a risk score with good overall performance, that was finally evaluated in terms of discrimination and calibration capabilities. All results underwent cross-validation.Results1,135 consecutive patients (median age 70 years, 64% males) were enrolled, 48 patients were excluded, the cohort was randomly divided in training (760) and test (327). During hospitalization, 251 (22%) patients died. After feature selection, the best performing classifier was random forest (AUC 0.88±0.03). Based on the relative importance of each variable, a pragmatic score was developed, showing good performances (AUC 0.85, ±0.025), and three levels were defined that correlated well with in-hospital mortality.ConclusionsMachine learning techniques were applied in order to develop an accurate in-hospital mortality risk score for COVID-19 based on ten variables. The application of the proposed score has utility in clinical settings to guide the management and prognostication of COVID-19 patients.

The impact of cardiopulmonary exercise-derived scoring on prediction of cardio-cerebral outcome in hypertrophic cardiomyopathy

Background Sudden cardiac death (SCD) and stroke-related events accompanied by atrial fibrillation (AF) can affect morbidity and mortality in hypertrophic cardiomyopathy (HCM). This study sought to evaluate a scoring system predicting cardio-cerebral events in HCM patients using cardiopulmonary exercise testing (CPET). Methods We investigated the role of a previous prediction model based on CPET, the HYPertrophic Exercise-derived Risk score for Heart Failure-related events (HyperHF), which is derived from peak circulatory power ventilatory efficiency and left atrial diameter (LAD), for predicting a composite of SCD-related (SCD, serious ventricular arrhythmia, death from cardiac cause, heart failure admission) and stroke-related (new-onset AF, acute stroke) events. The Novel HyperHF risk model using left atrial volume index (LAVI) instead of LAD was proposed and compared with the previous HCM Risk-SCD model. Results A total of 295 consecutive HCM patients (age 59.9±13.2, 71.2% male) who underwent CPET was included in the present study. During a median follow-up of 742 days (interquartile range 384–1047 days), 29 patients (9.8%) experienced an event (SCD-related event: 14 patients (4.7%); stroke-related event: 17 patients (5.8%)). The previous model for SCD risk score showed fair prediction ability (AUC of HCM Risk-SCD 0.670, p = 0.002; AUC of HyperHF 0.691, p = 0.001). However, the prediction power of Novel HyperHF showed the highest value among the models (AUC of Novel HyperHF 0.717, p<0.001). Conclusions Both conventional HCM Risk-SCD score and CPET-derived HyperHF score were useful for prediction of overall risk of SCD-related and stroke-related events in HCM. Novel HyperHF score using LAVI could be utilized for a better prediction power.

Ferroptosis-Related lncRNA Signature Predicts Prognosis and Immunotherapy Efficacy in Cutaneous Melanoma

Ferroptosis-related lncRNAs are promising biomarkers for predicting the prognosis of many cancers. However, a ferroptosis-related signature to predict the prognosis of cutaneous melanoma (CM) has not been identified. The purpose of our study was to construct a ferroptosis-related lncRNA signature to predict prognosis and immunotherapy efficacy in CM. Ferroptosis-related differentially expressed genes (FDEGs) and lncRNAs (FDELs) were identified using TCGA, GTEx, and FerrDb datasets. We performed Cox and LASSO regressions to identify key FDELs, and constructed a risk score to stratify patients into high- and low-risk groups. A nomogram was developed for clinical use. We performed gene set enrichment analyses (GSEA) to identify significantly enriched pathways. Differences in the tumor microenvironment (TME) between the 2 groups were assessed using 7 algorithms. To predict the efficacy of immune checkpoint inhibitors (ICI), we analyzed the association between PD1 and CTLA4 expression and the risk score. Finally, differences in Tumor Mutational Burden (TMB) and molecular drugs Sensitivity between the 2 groups were performed. Here, we identified 5 lncRNAs (AATBC, AC145423.2, LINC01871, AC125807.2, and AC245041.1) to construct the risk score. The AUC of the lncRNA signature was 0.743 in the training cohort and was validated in the testing and entire cohorts. Kaplan-Meier analyses revealed that the high-risk group had poorer prognosis. Multivariate Cox regression showed that the lncRNA signature was an independent predictor of OS with higher accuracy than traditional clinicopathological features. The 1-, 3-, and 5-year survival probabilities for CM patients were 92.7%, 57.2%, and 40.2% with an AUC of 0.804, indicating a good accuracy and reliability of the nomogram. GSEA showed that the high-risk group had lower ferroptosis and immune response. TME analyses confirmed that the high-risk group had lower immune cell infiltration (e.g., CD8+ T cells, CD4+ memory-activated T cells, and M1 macrophages) and lower immune functions (e.g., immune checkpoint activation). Low-risk patients whose disease expressed PD1 or CTLA4 were likely to respond better to ICIs. The analysis demonstrated that the TMB had significantly difference between low- and high- risk groups. Chemotherapy drugs, such as sorafenib, Imatinib, ABT.888 (Veliparib), Docetaxel, and Paclitaxel showed Significant differences in the estimated IC50 between the two risk groups. Overall, our novel ferroptosis-related lncRNA signature was able to accurately predict the prognosis and ICI outcomes of CM patients. These ferroptosis-related lncRNAs might be potential biomarkers and therapeutic targets for CM.

Development and validation of Pyroptosis‑related lncRNAs prediction model for bladder cancer

Bladder cancer is one of the highly heterogeneous disorders accompanied by a poor prognosis. This study aimed to construct a model based on pyroptosis‑related lncRNA to evaluate the potential prognostic application in bladder cancer. The mRNA expression profiles of bladder cancer patients and corresponding clinical data were downloaded from the public database from The Cancer Genome Atlas (TCGA). Pyroptosis‑related lncRNAs were identified by utilizing a co-expression network of Pyroptosis‑related genes and lncRNAs. The lncRNA was further screened by univariate Cox regression analysis. Finally, 8 pyroptosis-related lncRNA markers were established using Lasso regression and multivariate Cox regression analysis. Patients were separated into high and low-risk groups based on the performance value of the median risk score. Patients in the high-risk group had significantly poorer overall survival (OS) than those in the low-risk group (p &lt; 0.001), and In multivariate Cox regression analysis, the risk score was an independent predictive factor of OS ( HR&gt;1, P&lt;0.01). The area under the curve (AUC) of the 3- and 5-year OS in the receiver operating characteristic (ROC) curve were 0.742 and 0.739 respectively. In conclusion, these 8 pyroptosis-related lncRNA and their markers may be potential molecular markers and therapeutic targets for bladder cancer patients.

Comprehensive Analysis of Copy Number Variation, Nucleotide Mutation, and Transcription Level of PPAR Pathway-Related Genes in Endometrial Cancer

Endometrial cancer is a common malignant tumor in gynecology, and the prognosis of advanced patients is dismal. Recently, many studies on the peroxisome proliferator-activated receptor pathway have elucidated its crucial involvement in endometrial cancer. Copy number variation (CNA) and nucleotide mutations often occur in tumor tissues, leading to abnormal protein expression and changes in protein structure. We analyzed the exon sequencing data of endometrial cancer patients in the TCGA database and found that somatic changes in PPAR pathway-related genes (PPAR-related-gene) often occur in UCEC patients. Patients with CNA or mutation changes in the exon region of the PPAR-related-gene usually have different prognostic outcomes. Furthermore, we found that the mRNA transcription and protein translation levels of PPAR-related-gene in UCEC are significantly different from that of adjacent tissues/normal uterus. The transcription level of some PPAR-related-gene (DBI, CPT1A, CYP27A1, and ME1) is significantly linked to the prognosis of UCEC patients. We further constructed a prognostic predicting tool called PPAR Risk score, a prognostic prediction tool that is a strong independent risk factor for the overall survival rate of UCEC patients. Comparing to the typical TNM classification system, this tool has higher prediction accuracy. We created a nomogram by combining PPAR Risk score with clinical characteristics of patients in order to increase prediction accuracy and promote clinical use. In summary, our study demonstrated that PPAR-related-gene in UCEC had significant alterations in CNA, nucleotide mutations, and mRNA transcription levels. These findings can provide a fresh perspective for postoperative survival prediction and individualized therapy of UCEC patients.

Novel Risk Model to Predict Emergency Department Associated Mortality for Patients Supported With a Ventricular Assist Device: The Emergency Department–Ventricular Assist Device Risk Score

Background The past decade has seen tremendous growth in patients with ambulatory ventricular assist devices. We sought to identify patients that present to the emergency department (ED) at the highest risk of death. Methods and Results This retrospective analysis of ED encounters from the Nationwide Emergency Department Sample includes 2010 to 2017. Using a random sampling of patient encounters, 80% were assigned to development and 20% to validation cohorts. A risk model was derived from independent predictors of mortality. Each patient encounter was assigned to 1 of 3 groups based on risk score. A total of 44 042 ED ventricular assist device patient encounters were included. The majority of patients were male (73.6%), <65 years old (60.1%), and 29% presented with bleeding, stroke, or device complication. Independent predictors of mortality during the ED visit or subsequent admission included age ≥65 years (odds ratio [OR], 1.8; 95% CI, 1.3–4.6), primary diagnoses (stroke [OR, 19.4; 95% CI, 13.1–28.8], device complication [OR, 10.1; 95% CI, 6.5–16.7], cardiac [OR, 4.0; 95% CI, 2.7–6.1], infection [OR, 5.8; 95% CI, 3.5–8.9]), and blood transfusion (OR, 2.6; 95% CI, 1.8–4.0), whereas history of hypertension was protective (OR, 0.69; 95% CI, 0.5–0.9). The risk score predicted mortality areas under the curve of 0.78 and 0.71 for development and validation. Encounters in the highest risk score strata had a 16‐fold higher mortality compared with the lowest risk group (15.8% versus 1.0%). Conclusions We present a novel risk score and its validation for predicting mortality of patients with ED ventricular assist devices, a high‐risk, and growing, population.