Prediction Of 5-year Survival Research Articles

Tumor mutation burden-related long non-coding RNAs is predictor for prognosis and immune response in pancreatic cancer

BackgroundPancreatic cancer is one of the most common malignant tumors with extremely poor prognosis. It is urgent to identify promising prognostic biomarkers for pancreatic cancer.MethodsA total of 266 patients with pancreatic adenocarcinoma (PAAD) in the Cancer Genome Atlas (TCGA)-PAAD cohort and the PACA-AU cohort were enrolled in this study. Firstly, prognostic tumor mutation burden (TMB)-related long non-coding RNAs (lncRNAs) were identified by DESeq2 and univariate analysis in the TCGA-PAAD cohort. And then, the TCGA-PAAD cohort was randomized into the training set and the testing set. Least absolute shrinkage and selection operator (LASSO) was used to construct the model in the training set. The testing set, the TCGA-PAAD cohort and the PACA-AU cohort was used as validation. The model was evaluated by multiple methods. Finally, functional analysis and immune status analysis were applied to explore the potential mechanism of our model.ResultsA prognostic model based on fourteen TMB-related lncRNAs was established in PAAD. Patients with High risk score was associated with worse prognosis compared to those with low risk score in all four datasets. Besides, the model had great performance in the prediction of 5-year overall survival in four datasets. Multivariate analysis also indicated that the risk score based on our model was independent prognostic factor in PAAD. Additionally, our model had the best predictive efficiency in PAAD compared to typical features and other three published models. And then, our findings also showed that high risk score was also associated with high TMB, microsatellite instability (MSI) and homologous recombination deficiency (HRD) score. Finally, we indicated that high risk score was related to low immune score and less infiltration of immune cells in PAAD.Conclusionwe established a 14 TMB-related lncRNAs prognostic model in PAAD and the model had excellent performance in the prediction of prognosis in PAAD. Our findings provided new strategy for risk stratification and new clues for precision treatment in PAAD.

Identification of m7G Methylation-Related miRNA Signature Associated with Survival and Immune Microenvironment Regulation in Uterine Corpus Endometrial Carcinoma.

N7-methylguanosine (m7G) has been implicated in the development of cancer. The role of m7G-related miRNAs in the survival prediction of UCEC patients has not been investigated. Current research was the first to construct an m7G-related miRNA model to accurately predict the survival of patients with uterine corpus endometrial carcinoma (UCEC) and to explore immune cell infiltration and immune activity in the tumor microenvironment. RNA-seq data and clinical information of UCEC patients were derived from The Cancer Genome Atlas (TCGA) database. Using the TargetScan online database, we predicted miRNAs linked to the m7G-related genes and identified miRNAs which were significantly associated with the survival in UCEC patients and constructed a risk scoring model. The TCGA-UCEC cases were scored according to the risk model, and the high- and low-risk groups were divided by the median risk value. Gene enrichment analysis and immune cell infiltration and immune function analysis were performed using "clusterProfiler" and "GSVA" packages in R. The survival prediction model consisted of 9 miRNAs, namely, hsa-miR-1301, hsa-miR-940, hsa-miR-592, hsa-miR-3170, hsa-miR-876, hsa-miR-215, hsa-miR-934, hsa-miR-3920, and hsa-miR-216b. Survival of UCEC patients in the high-risk group was worse than that in the low-risk group (p < 0.001). The receiver operating characteristic (ROC) curve showed that the model had good predictive performance, and the area under the curve was 0.800, 0.690, and 0.705 for 1-, 3-, and 5-year survival predictions, respectively. There were differences in the degree of immune cell infiltration and immune activity between the low-risk and high-risk groups. The expression levels of the identified differentially expressed genes correlated with the susceptibility to multiple anticancer drugs. The survival prediction model constructed based on 9 m7G-related miRNAs had good predictive performance.

Individual dynamic prediction and prognostic analysis for long-term allograft survival after kidney transplantation

BackgroundPredicting allograft survival is vital for efficient transplant success. With dynamic changes in patient conditions, clinical indicators may change longitudinally, and doctors’ judgments may be highly variable. It is necessary to establish a dynamic model to precisely predict the individual risk/survival of new allografts.MethodsThe follow-up data of 407 patients were obtained from a renal allograft failure study. We introduced a landmarking-based dynamic Cox model that incorporated baseline values (age at transplantation, sex, weight) and longitudinal changes (glomerular filtration rate, proteinuria, hematocrit). Model performance was evaluated using Harrell’s C-index and the Brier score.ResultsSix predictors were included in our analysis. The Kaplan–Meier estimates of survival at baseline showed an overall 5-year survival rate of 87.2%. The dynamic Cox model showed the individual survival prediction with more accuracy at different time points (for the 5-year survival prediction, the C-index = 0.789 and Brier score = 0.065 for the average of all time points) than the static Cox model at baseline (C-index = 0.558, Brier score = 0.095). Longitudinal covariate prognostic analysis (with time-varying effects) was performed.ConclusionsThe dynamic Cox model can utilize clinical follow-up data, including longitudinal patient information. Dynamic prediction and prognostic analysis can be used to provide evidence and a reference to better guide clinical decision-making for applying early treatment to patients at high risk.

Development of a 4-miRNA prognostic signature for endometrial cancer.

To develop an effective uterine corpus endometrial carcinoma (UCEC) risk assessment tool to monitor treatment outcomes. Limma package was used to analyze differentially expressed microRNAs (miRNAs) between UCEC tissues and normal tissues in the TCGA database. According to univariate Cox risk regression, least absolute shrinkage, and selection operator (LASSO) Cox analysis were performed to screen prognostic miRNAs and construct a risk scoring model. The prognostic performance of signature was evaluated by Kaplan–Meier and receiver operating characteristic. Multivariate Cox regression analysis was used to determine the independent prognostic factors of UCEC. Nomogram was constructed according to age, clinical stage, and risk score. A 4-miRNA signature based on miR-31-5p, miR-34a-5p, miR-26a-1-3p and miR-4772-3p was established. Risk scores of each patient were calculated by the 4-miRNA signature. After z-score, the patients were divided into high- and low-risk groups. The overall survival of high-risk patients was significantly shorter than that of low-risk patients, pointing to the high performance and independence of the 4-miRNA signature in predicting UCEC prognosis. The nomogram showed a high accuracy in predicting overall survival of UCEC patients. We developed a 4-miRNA signature that could effectively predict the prognosis of UCEC.

Potential mechanisms and prognostic model of eRNAs-regulated genes in stomach adenocarcinoma

Gastric Carcinoma is the fourth leading cause of cancer deaths worldwide, in which stomach adenocarcinoma (STAD) is the most common histological type. A growing amount of evidence has suggested the importance of enhancer RNAs (eRNAs) in the cancer. However, the potential mechanism of eRNAs in STAD remains unclear. The eRNAs-regulated genes (eRRGs) were identified through four different enhancer resources. The differentially expressed eRRGs were obtained by ‘DESeq2’ R package. The prognosis prediction model was constructed by Cox and Lasso regression analysis. The ‘ChAMP’ R package and ‘maftools’ R package were used to investigate the multi-omics characters. In this study, combining the concept of contact domain, a total of 9014 eRRGs including 4926 PCGs and 4088 lncRNAs were identified and these eRRGs showed higher and more stable expression. Besides, the functions of these genes were mainly associated with tumor-related biological processes. Then, a prognostic prediction model was constructed and the AUC values of the 1-, 3- and 5-year survival prediction reached 0.76, 0.84 and 0.84, respectively, indicating that this model has a high accuracy. Finally, the difference between high-risk group and low-risk group were investigated using multi-omics data including gene expression, DNA methylation and somatic mutations. Our study provides significant clues for the elucidation of eRNAs in STAD and may help improve the overall survival for STAD patients.

Development and validation of a survival prediction model for 113,239 patients with colon cancer: a retrospective cohort study.

Colon cancer (CC) is the third most commonly diagnosed malignant tumor and remains the second leading cause of cancer-related deaths worldwide. However, the risk assessment of poor prognosis of CC is limited in previous studies. This study aimed to develop a predictive nomogram for the survival of CC patients. In this retrospective cohort study, 113,239 CC patients from the Surveillance, Epidemiology, and End Results (SEER) database were randomly divided into training (n=56,619) and testing (n=56,620) sets with a ratio of 1:1. Demographic, clinical data and survival status of patients were extracted. The outcomes were 3- and 5-year survival of CC. Univariate and multivariate Cox regression analyses were used to screen the predictors to develop the predictive nomogram. Internal validation and stratified analyses were further assessed the nomogram. The C-index and area under the curve (AUC) were calculated to estimate the model's predictive capacity, and calibration curves were adopted to estimate the model fit. Totally 38,522 (34.02%) patients died during the 5-year follow-up. The nomogram incorporated variables associated with the prognosis of CC patients, including age, gender, marital status, insurance status, tumor grade, stage (T/N/M), surgery, and number of nodes examined, with a C-index of 0.775 in the training set and 0.774 in the testing set. The AUCs of the nomogram for the 3- and 5-year survival prediction in the training set were 0.817 and 0.808, with the sensitivity of 0.688 and 0.716, and the specificity of 0.785 and 0.740, respectively. Similar results were found in the testing set. The C-index of the predictive nomogram for male, female, White, Black, and other races was 0.769, 0.779, 0.773, 0.770, and 0.770, respectively. The calibration curves for the nomogram in the above five cohorts showed a good agreement between actual and predicted values. The nomogram may exhibit a certain predictive performance based on the SEER database, which may provide individual survival predictions for CC patients.

370. EXTERNAL VALIDATION OF A NOMOGRAM PREDICTING CONDITIONAL SURVIVAL AFTER CURATIVE TREATMENT OF ESOPHAGEAL CANCER

Abstract To predict 5-year overall survival for esophageal cancer patients after neoadjuvant treatment followed by esophagectomy, recently a conditional survival nomogram was developed. This nomogram includes cN-stage, ypT-stage, ypN-stage, cardiac comorbidity, chyle leakage and pulmonary complications as independent predictors of survival. The aim of this study was to externally validate the conditional survival nomogram in a cohort of patients from another European high volume esophageal cancer center. We included consecutive patients with a resectable (cT1-4a, N0–3, M0) adeno- or squamous cell esophageal carcinoma between January 2004 and January 2016 who received neoadjuvant treatment followed by an esophagectomy in a tertiary referral center for esophageal cancer. Standard prognostic factors and nomogram specific data were collected. The discriminative ability of the nomogram for the prediction of 5-year overall survival was quantified by Harrell’s C-statistics, directly after surgery and given survival for 1, 2, 3 and 4 years already survived. Calibration of the conditional survival nomogram was visualized by plotting actual 5 years survival against predicted probabilities. 296 patients were included. The median overall survival was 48.1 months (95%CI:37.5–58.7). The probability to achieve 5-year overall survival directly after surgery was 45% and increased to 57%, 68%, 78% and 89% for each additional year survived. Prediction of 5-year overall survival differed from the observed survival with a calibration slope of 0.54, 0.55, 0.59, 0.73 and 1.09, directly after surgery and given 1, 2, 3, and 4 years already survived, respectively. The discriminative ability of the nomogram for 5-year survival was moderate with a C-statistics of 0.65, compared to a value of 0.70 in the original study. This study externally validated a model for conditional survival after neoadjuvant chemoradiotherapy and surgery for esophageal cancer. The proposed nomogram had a moderate predictive discrimination and accuracy for the derivation cohort, and therefore it should be updated for or used with caution in external cohorts for the prediction of conditional 5-year survival.

Nomogram-Based Prognostic Evaluation of Gastric Cancer Patients with Low Counts of Examined Lymph Nodes Outperforms the Predictive Ability of the 7th and 8th Editions of the American Joint Committee on Cancer Staging System

BackgroundThe American Joint Committee on Cancer (AJCC) staging system has limited accuracy in predicting survival of gastric cancer patients with inadequate counts of evaluated lymph nodes (LNs). We therefore aimed to develop a prognostic nomogram suitable for clinical applications in such cases. MethodsA total of 1511 noncardia gastric cancer patients treated between 1990 and 2010 in the academic surgical center were reviewed to compare the 7th and 8th editions of the AJCC staging system. A nomogram was developed for the prediction of 5-year survival in patients with less than 16 LNs evaluated (n = 546). External validation was performed using datasets derived from the Polish Gastric Cancer Study Group (n = 668) and the SEER database (n = 11,225). ResultsThe 8th edition of AJCC staging showed better overall discriminatory power compared to the previous version, but no improvement was found for patients with < 16 evaluated LNs. The developed nomogram had better concordance index (0.695) than the former (0.682) or latest (0.680) staging editions, including patients subject to neoadjuvant treatment, and calibration curves showed excellent agreement between the nomogram-predicted and actual survival. High discriminatory power was also demonstrated for both validation cohorts. Subsequently, the nomogram showed the best accuracy for the prediction of 5-year survival through the time-dependent ROC curve analysis in the training and validation cohorts. ConclusionsA clinically relevant nomogram was built for the prediction of 5-year survival in patients with inadequate numbers of LNs evaluated in surgical specimens. The predictive accuracy of the nomogram was validated in two Western populations.

Prediction of 5-year progression-free survival in advanced nasopharyngeal carcinoma with pretreatment PET/CT using multi-modality deep learning-based radiomics.

ObjectiveDeep learning-based radiomics (DLR) has achieved great success in medical image analysis and has been considered a replacement for conventional radiomics that relies on handcrafted features. In this study, we aimed to explore the capability of DLR for the prediction of 5-year progression-free survival (PFS) in advanced nasopharyngeal carcinoma (NPC) using pretreatment PET/CT images.MethodsA total of 257 patients (170/87 patients in internal/external cohorts) with advanced NPC (TNM stage III or IVa) were enrolled. We developed an end-to-end multi-modality DLR model, in which a 3D convolutional neural network was optimized to extract deep features from pretreatment PET/CT images and predict the probability of 5-year PFS. The TNM stage, as a high-level clinical feature, could be integrated into our DLR model to further improve the prognostic performance. For a comparison between conventional radiomics and DLR, 1,456 handcrafted features were extracted, and optimal conventional radiomics methods were selected from 54 cross-combinations of six feature selection methods and nine classification methods. In addition, risk group stratification was performed with clinical signature, conventional radiomics signature, and DLR signature.ResultsOur multi-modality DLR model using both PET and CT achieved higher prognostic performance (area under the receiver operating characteristic curve (AUC) = 0.842 ± 0.034 and 0.823 ± 0.012 for the internal and external cohorts) than the optimal conventional radiomics method (AUC = 0.796 ± 0.033 and 0.782 ± 0.012). Furthermore, the multi-modality DLR model outperformed single-modality DLR models using only PET (AUC = 0.818 ± 0.029 and 0.796 ± 0.009) or only CT (AUC = 0.657 ± 0.055 and 0.645 ± 0.021). For risk group stratification, the conventional radiomics signature and DLR signature enabled significant difference between the high- and low-risk patient groups in both the internal and external cohorts (p < 0.001), while the clinical signature failed in the external cohort (p = 0.177).ConclusionOur study identified potential prognostic tools for survival prediction in advanced NPC, which suggests that DLR could provide complementary values to the current TNM staging.

Identifying a Novel Endoplasmic Reticulum-Related Prognostic Model for Hepatocellular Carcinomas.

From the standpoint of the ER (endoplasmic reticulum), we were interested in identifying hub genes that impact clinical prognosis for HCC (hepatocellular carcinoma) patients and developing an ER-related prognostic model. Using TCGA-LIHC (The Cancer Genome Atlas-Liver Hepatocellular Carcinoma) and GSE14520 datasets, we conducted a series of analyses, which included differential gene screening, clinical prognostic analysis, Lasso regression, nomogram prediction, tumour clustering, gene functional enrichment, and tumour infiltration of immune cells. Following our screening for ER-related genes (n = 1975), we conducted a Lasso regression model to obtain five hub genes, KPNA2, FMO3, SPP1, KIF2C, and LPCAT1, using TCGA-LIHC as a training set. According to risk scores, HCC samples within either the TCGG-LIHC or GSE14520 cohort were categorized into high- and low-risk groups. Compared to the high-risk group of HCC patients, patients in the low-risk group had a better prognosis of OS (overall survival) or RFS (relapse-free survival). For TCGA-LIHC training set, with the factors of risk score, stage, age, and sex, we plotted a nomogram for 1-, 3-, and 5-year survival predictions. Our model demonstrated better clinical validity in both TCGA-LIHC and GSE14520 cohorts. Additionally, events related to biological enzyme activity, biological metabolic processes, or the cell cycle were associated with the prognostic risk of ER. Furthermore, two HCC prognosis-associated tumour clusters were identified by ER hub gene-based consensus clustering. Our findings indicated a link between ER prognostic signature-related high/low risk and tumour infiltration levels of several immune cells, such as “macrophages M2/M0” and “regulatory T cells (Tregs).” Overall, we developed a novel ER-related clinical prognostic model for HCC patients.

Prediction of All-Cause Mortality Based on Stress/Rest Myocardial Perfusion Imaging (MPI) Using Deep Learning: A Comparison between Image and Frequency Spectra as Input.

Background: Cardiovascular management and risk stratification of patients is an important issue in clinics. Patients who have experienced an adverse cardiac event are concerned for their future and want to know the survival probability. Methods: We trained eight state-of-the-art CNN models using polar maps of myocardial perfusion imaging (MPI), gender, lung/heart ratio, and patient age for 5-year survival prediction after an adverse cardiac event based on a cohort of 862 patients who had experienced adverse cardiac events and stress/rest MPIs. The CNN model outcome is to predict a patient’s survival 5 years after a cardiac event, i.e., two classes, either yes or no. Results: The best accuracy of all the CNN prediction models was 0.70 (median value), which resulted from ResNet-50V2, using image as the input in the baseline experiment. All the CNN models had better performance after using frequency spectra as the input. The accuracy increment was about 7~9%. Conclusions: This is the first trial to use pure rest/stress MPI polar maps and limited clinical data to predict patients’ 5-year survival based on CNN models and deep learning. The study shows the feasibility of using frequency spectra rather than images, which might increase the performance of CNNs.

A multiomic ferroptosis-associated prognostic signature incorporating epigenetic and transcriptional biomarkers for hepatocellular carcinoma.

BackgroundFerroptosis was reported to have tremendous promise in the treatment and prognosis of hepatocellular carcinoma (HCC). Here, we identified a novel ferroptosis-related prognostic signature incorporating epigenetic and transcriptional biomarkers could help predicting survival of patients with HCC.MethodsWe employed multi-omics and clinical data from The Cancer Genome Atlas (TCGA) database to identify the ferroptosis-associated methylation CpG sites associated with HCC survival using sure independence screening (SIS). Then we utilized Kaplan-Meier curves to evaluate the prognostic significance of gene expression and DNA methylation. Receiver operating characteristic (ROC) curve was used predicting the 3- and 5-year survival. Mediation analysis of ferroptosis-related methylation and transcriptional score was performed.ResultsWe firstly identified 114 significant CpG sites under the criteria of false discovery rate (FDR) <0.05 in training set. Then we screened out 5 candidate CpG sites in validation set for multivariate screening and stepwise regression. We found that the high-risk group had significantly shorter survival time than the low-risk group in the prognostic signature combined with epigenetic and transcriptional scores (HR =2.72 95% CI: 2.01–3.68, P=8.75E-11). And the predictive model involving clinical information, gene expression, and methylation data performed best for 3-year survival prediction (AUC =0.672) and 5-year survival prediction (AUC =0.742).ConclusionsOur results suggested a signature combining clinical information, ferroptosis-related gene expression, and methylation presented a superior ability for prognostic prediction in HCC, which may bring us novel tool and targets in the treatment of HCC.

Incremental Value of Radiomics in 5-Year Overall Survival Prediction for Stage II-III Rectal Cancer.

Although rectal cancer comprises up to one-third of colorectal cancer cases and several prognosis nomograms have been established for colon cancer, statistical tools for predicting long-term survival in rectal cancer are lacking. In addition, previous prognostic studies did not include much imaging findings, qualitatively or quantitatively. Therefore, we include multiparametric MRI information from both radiologists’ readings and quantitative radiomics signatures to construct a prognostic model that allows 5-year overall survival (OS) prediction for advance-staged rectal cancer patients. The result suggested that the model combined with quantitative imaging findings might outperform that of conventional TNM staging or other clinical prognostic factors. It was noteworthy that the identified radiomics signature consisted of three from dynamic contrast-enhanced (DCE)-MRI, four from anatomical MRI, and one from functional diffusion-weighted imaging (DWI). This highlighted the importance of multiparametric MRI to address the issue of long-term survival estimation in rectal cancer. Additionally, the constructed radiomics signature demonstrated value to the conventional prognostic factors in predicting 5-year OS for stage II–III rectal cancer. The presented nomogram also provides a practical example of individualized prognosis estimation and may potentially impact treatment strategies.

Incorporation of functional status, frailty, comorbidities and comedication in prediction models for colorectal cancer survival.

Limitations in functional status, frailty, multiple comorbidities and comedications are common among older colorectal cancer (CRC) patients. We investigated whether adding these factors could improve the predictive value of a reference model containing age, sex, tumor stage and location for prediction of 5-year overall survival (OS), disease-free survival (DFS), disease-specific survival (DSS), recurrence-free survival (RFS) and nondisease-specific survival (nDSS) for all CRC patients as well as for younger (<65 years) and older patients (≥65 years). Overall, 3410 CRC patients from the DACHS study were analyzed and area under receiver operating characteristic curves (AUC) and net reclassification improvements (NRI) were assessed. In prediction of OS, the reference model plus functional status was identified as the best model among all CRC patients (AUC: 0.762) and younger CRC patients (AUC: 0.820). In older CRC patients, comorbidity should additionally be added (AUC: 0.747). For nDSS, the reference model plus comorbidity and frailty had the best predictive performance in all CRC patients (AUC: 0.776). For the outcomes DFS (AUC: 0.727), DSS (AUC: 0.838) and RFS (AUC: 0.784), the reference model was already the best model in all CRC patients because no significant NRIs were observed. The pattern "The less CRC-specific the survival outcome and the older the CRC patients, the more relevant the inclusion of functional status, comorbidity, and frailty in CRC prognostic scores is" was observed. Thus, different nomograms for younger and older CRC patients for 1-, 3- and 5-year OS prognosis estimation are being suggested.

A Novel Overall Survival Nomogram Prediction of Secondary Primary Malignancies after Hypopharyngeal Cancer: A Population-Based Study.

Objectives We aimed to construct a nomogram for predicting the overall survival (OS) of patients with secondary primary malignancies (SPMs) after hypopharyngeal cancer (HPC). Methods 613 HPC patients were included in the Surveillance, Epidemiology, and End Results (SEER) database between 2000 and 2018, which were divided into training and validation cohorts. The least absolute shrinkage and selection operation (LASSO) and stepwise Cox regression were used to determine the variables by which a nomogram model was established. Results After the LASSO and stepwise Cox regression analysis, the age, year of diagnosis, sites of SPMs, SEER stage of SPMs, surgery for SPMs, and radiotherapy for SPMs were included for model establishment. The ROC curve showed good discrimination for the 3- and 5-year AUC values in the training (0.774 and 0.779, respectively) and validation (0.758 and 0.763, respectively) cohorts. The calibration curve indicated good prognostic accuracy, especially in the 5-year survival prediction for this model. The DCA also demonstrated clinical efficacy over a wide range of threshold probabilities. Lastly, the risk group classified by the individual nomogram values showed significantly different survival outcomes in both training and validation cohorts. Conclusions We constructed a nomogram to predict the OS of SPMs after HPC with good clinical values.

A Predictive Nomogram for Atypical Meningioma Based On Preoperative Magnetic Resonance Imaging and Routine Blood Tests

The objective of the study was to establish a 5-year progression-free survival prediction nomogram using preoperative routine blood tests and magnetic resonance imaging to guide postoperative treatment. Our study was a retrospective analysis of patients with atypical meningioma admitted into our facility from January 31, 2010, to January 31, 2016. We used single-factor logistic analysis to extract valuable indicators from preoperative blood test results and 3D Slicer software to extract radiomic features from magnetic resonance imaging. The radiomics score was calculated by least absolute shrinkage and selection operator logistic regression analysis. We then combined blood indicators and radiomic signatures to construct a radiomic nomogram image. The performance of the model was evaluated comprehensively using the following three aspects: recognition ability, accuracy, and clinical value. Six significant radiological features were selected through least absolute shrinkage and selection operator logistic regression analysis. The radiometric label established by these six features has satisfactory predictive performance. The area under the curve in the training group was 0.885 (95% confidence interval, 0.8037-0.9659), and the area under the curve in the validation set was 0.789 (95% confidence interval, 0.6092-0.9686). We used the combined image tags and preoperative leukocyte and neutrophil count to construct a 5-year progression-free survival prediction nomogram. The analysis results of the calibration curve and the decision curve show that the nomogram constructed by combining radiomics and preoperative blood tests has a good predictive value for 5-year progression-free survival in atypical meningioma and can provide a reference for selecting postoperative treatment options.

Identification and construction of a 13-gene risk model for prognosis prediction in hepatocellular carcinoma patients.

We attempted to screen out the feature genes associated with the prognosis of hepatocellular carcinoma (HCC) patients through bioinformatics methods, to generate a risk model to predict the survival rate of patients. Gene expression information of HCC was accessed from GEO database, and differentially expressed genes (DEGs) were obtained through the joint analysis of multi‐chip. Functional and pathway enrichment analyses of DEGs indicated that the enrichment was mainly displayed in biological processes such as nuclear division. Based on TCGA‐LIHC data set, univariate, LASSO, and multivariate Cox regression analyses were conducted on the DEGs. Then, 13 feature genes were screened for the risk model. Also, the hub genes were examined in our collected clinical samples and GEPIA database. The performance of the risk model was validated by Kaplan–Meier survival analysis and receiver operation characteristic (ROC) curves. While its universality was verified in GSE76427 and ICGC (LIRI‐JP) validation cohorts. Besides, through combining patients’ clinical features (age, gender, T staging, and stage) and risk scores, univariate and multivariate Cox regression analyses revealed that the risk score was an effective independent prognostic factor. Finally, a nomogram was implemented for 3‐year and 5‐year overall survival prediction of patients. Our findings aid precision prediction for prognosis of HCC patients.

Postoperative Adjuvant Imatinib Therapy-Associated Nomogram to Predict Overall Survival of Gastrointestinal Stromal Tumor.

BackgroundAdjuvant imatinib therapy has been shown to improve overall survival (OS) of gastrointestinal stromal tumor (GIST) significantly. Few nomograms combining the use of adjuvant imatinib and clinicopathological characteristics estimate the outcome of patients. We aimed to establish a more comprehensive nomogram for predicting OS in patients with GIST.MethodsIn total, 1310 GIST patients undergoing curative resection at four high-volume medical centers between 2001 and 2015 were enrolled. Independent prognostic factors were identified by multivariate Cox analysis. Eligible patients were randomly assigned in a ratio of 7:3 into a training set (916 cases) and a validation set (394 cases). A nomogram was established by R software and its predictive power compared with that of the modified National Institutes of Health (NIH) classification using time-dependent receiver operating characteristic (ROC) curves and calibration plot.ResultsAge, tumor site, tumor size, mitotic index, postoperative imatinib and diagnostic delay were identified as independent prognostic parameters and used to construct a nomogram. Of note, diagnostic delay was for the first time included in a prognostic model for GIST. The calibrated nomogram resulted in predicted survival rates consistent with observed ones. And the decision curve analysis suggested that the nomogram prognostic model was clinically useful. Furthermore, time-dependent ROC curves showed the nomogram exhibited greater discrimination power than the modified NIH classification in 3- and 5-year survival predictions for both training and validation sets (all P < 0.05).ConclusionsPostoperative adjuvant imatinib therapy improved the survival of GIST patients. We developed and validated a more comprehensive prognostic nomogram for GIST patients, and it could have important clinical utility in improving individualized predictions of survival risks and treatment decision-making.

BAR Score Performance in Predicting Survival after Living Donor Liver Transplantation: A Single-Center Retrospective Study.

Methods 146 adult liver transplant recipients were included. Univariate and multivariate analyses were used to determine the independent predictors of survival at 3 months, 1 year, and 5 years. The receiver operating characteristic (ROC) curve for the BAR score was plotted, and the area under the ROC curve (AUROC) was calculated. Kaplan–Meier curve and log-rank test were used to compare survival above and below the best cutoff values. Results The mean age was 52.45 ± 8.54 years, and 59.6% were males. The survival rates were 89, 78.8, and 72% at 3 months, 1 year, and 5 years, respectively. The BAR score demonstrated a clinically significant value in the prediction of 3-month (AUROC = 0.89), 1-year (AUROC = 0.76), and 5-year survival (AUROC = 0.71). Among the investigated factors associated with survival, BAR score <10 points was the only independent predictor of 3-month (OR 7.34, p < 0.0001), 1-year (OR 3.37, p=0.001), and 5-year survival (OR 2.83, p=0.044). Conclusions BAR is a simple and easily applicable scoring system that could significantly predict short- and long-term survival after LDLT. A large multicenter study is warranted to validate our results in the Egyptian population.

A Novel Glycolysis and Hypoxia Combined Gene Signature Predicts the Prognosis and Affects Immune Infiltration of Patients with Colon Cancer.

PurposeWe aimed to characterize the expression patterns of glycolysis and hypoxia genes in colon cancers as well as their value in prognosis and immune microenvironment.MethodsThe expression profiles were acquired from the Cancer Genome Atlas database. Enrichment of hypoxia and glycolysis gene sets in colon cancer was identified by gene set enrichment analysis. Then, a prognostic signature was built up after Cox regression analyses, and overall survival analysis validated the predictive ability. Immune status and infiltration in cancer tissues were explored using the single sample gene set enrichment analysis and CIBERSORT algorithm. A nomogram model integrating clinical variables and the gene signature was established and assessed.ResultsAltogether, 378 cancer and 39 control cases were enrolled. Three glycolysis gene sets and two hypoxia gene sets were enriched in colon cancer (P < 0.05). Five independent genes (ENO3, GPC1, P4HA1, SPAG4, and STC2) were significantly correlated with prognosis of colon cancer patients. Patients with higher risks had significantly better prognosis than those with lower risks (P = 0.002 and AUC = 0.750), which was also observed in the elderly, female and stage I–II subgroups (P < 0.05). In high-risk cases, proportion of NK cells resting increased (P < 0.05) while that of dendritic cells activated (P < 0.05), dendritic cells resting (P < 0.01) and monocytes (P < 0.01) decreased. Besides, expressions of 22 checkpoint genes were found abnormal in groups with different risks (P < 0.05). The predictive nomogram presented satisfactory performance with C-index of 0.771 (0.712–0.830). The area under ROC curve was 0.796 and 0.803 for 3- and 5-year survival prediction, respectively.ConclusionA glycolysis and hypoxia combined gene signature was a promising method to evaluate the prognosis and immune infiltration of colon cancer patients, which may provide a new tool for cancer management.