scholarly journals Identification and Validation of a Novel Clinical Signature to Predict the Prognosis in Confirmed Coronavirus Disease 2019 Patients

Author(s):  
Shangrong Wu ◽  
Zhiguo Du ◽  
Sanying Shen ◽  
Bo Zhang ◽  
Hong Yang ◽  
...  

Abstract Background Our aim in this study was to identify a prognostic biomarker to predict the disease prognosis and reduce the mortality rate of coronavirus disease 2019 (COVID-19), which has caused a worldwide pandemic. Methods COVID-19 patients were randomly divided into training and test groups. Univariate and multivariate Cox regression analyses were performed to identify the disease prognosis signature, which was selected to establish a risk model in the training group. The disease prognosis signature of COVID-19 was validated in the test group. Results The signature of COVID-19 was combined with the following 5 indicators: neutrophil count, lymphocyte count, procalcitonin, age, and C-reactive protein. The signature stratified patients into high- and low-risk groups with significantly relevant disease prognosis (log-rank test, P < .001) in the training group. The survival analysis indicated that the high-risk group displayed substantially lower survival probability than the low-risk group (log-rank test, P < .001). The area under the receiver operating characteristic (ROC) curve showed that the signature of COVID-19 displayed the highest predictive accuracy regarding disease prognosis, which was 0.955 in the training group and 0.945 in the test group. The ROC analysis of both groups demonstrated that the predictive ability of the signature surpassed the use of each of the 5 indicators alone. Conclusions The signature of COVID-19 presents a novel predictor and prognostic biomarker for closely monitoring patients and providing timely treatment for those who are severely or critically ill.

2020 ◽  
Author(s):  
Ning Wang ◽  
Yanni Li ◽  
Yanfang Zheng ◽  
Huoming Chen ◽  
Xiaolong Wen ◽  
...  

Abstract Background: Previous studies have demonstrated that microRNAs (miRNAs) played a crucial role in various diseases, including cancers. The aim of the study was to evaluate the clinical significance of miR-124 in patients with cholangiocarcinoma (CCA).Methods: The expression pattern of miR-124 was detected in CCA tissues using quantitative reserve transcription polymerase chain reaction (qRT-PCR). The correlation of miR-124 expression with clinicopathological features and overall survival of patients were explored using chi-square test, Kaplan-Meier methods and Cox regression analyses.Results: The miR-124 expression level was strong down-regulated in CCA tissues compared with normal para-cancerous tissues (P<0.001). Moreover, aberrant miR-124 expression was significantly associated with differentiation (P=0.045) and lymph node metastasis (P=0.040). In addition, Kaplan-Meier method and log-rank test revealed that patients with low miR-124 expression has a poorer overall survival compared with those with high miR-124 expression (P=0.002). Furthermore, multivariate analysis confirmed that miR-124 expression (P=0.006; HR=2.006; 95%CI: 1.224-3.289) was an independent prognostic indicator in CCA.Conclusions: Collectively, our results defined miR-124 expression plays important roles in CCA patients. MiR-124 expression might used as a valuable prognostic biomarker for patients with CCA.


2021 ◽  
Vol 11 ◽  
Author(s):  
Xiaoxia Tong ◽  
Xiaofei Qu ◽  
Mengyun Wang

BackgroundCutaneous melanoma (CM) is one of the most aggressive cancers with highly metastatic ability. To make things worse, there are limited effective therapies to treat advanced CM. Our study aimed to investigate new biomarkers for CM prognosis and establish a novel risk score system in CM.MethodsGene expression data of CM from Gene Expression Omnibus (GEO) datasets were downloaded and analyzed to identify differentially expressed genes (DEGs). The overlapped DEGs were then verified for prognosis analysis by univariate and multivariate COX regression in The Cancer Genome Atlas (TCGA) datasets. Based on the gene signature of multiple survival associated DEGs, a risk score model was established, and its prognostic and predictive role was estimated through Kaplan-Meier (K-M) analysis and log-rank test. Furthermore, the correlations between prognosis related genes expression and immune infiltrates were analyzed via Tumor Immune Estimation Resource (TIMER) site.ResultsA total of 103 DEGs were obtained based on GEO cohorts, and four genes were verified in TCGA datasets. Subsequently, four genes (ADAMDEC1, GNLY, HSPA13, and TRIM29) model was developed by univariate and multivariate Cox regression analyses. The K-M plots showed that the high-risk group was associated with shortened survival than that in the low-risk group (P &lt; 0.0001). Multivariate analysis suggested that the model was an independent prognostic factor (high-risk vs. low-risk, HR= 2.06, P &lt; 0.001). Meanwhile, the high-risk group was prone to have larger breslow depth (P&lt; 0.001) and ulceration (P&lt; 0.001).ConclusionsThe four-gene risk score model functions well in predicting the prognosis and treatment response in CM and will be useful for guiding therapeutic strategies for CM patients. Additional clinical trials are needed to verify our findings.


Author(s):  
JinQun Jiang ◽  
HongYan Xu ◽  
PingShen Zhao ◽  
Hai Lu

Cervical cancer is a common malignancy in women and has a poor prognosis.More and more studies have shown that autophagy disorder is closely related to the occurrence of tumors. However, the prognostic role of autophagy gene in cervical cancer is still unclear. In this study, we constructed the risk signatures of autophagy related genes to predict the prognosis of cervical cancer. The expression profiles and clinical information of autophagy gene sets were downloaded from the TCGA and GES52903 queues as training sets and validation sets. The cervical normal tissue expression profile data from UCSC XENA website is GTEx data as a supplement to TCGA normal cervical tissue. Univariate COX regression analysis of 17 different autophagy genes with the Consensus approach tumor samples from the TCGA is divided into six subtypes, and the clinical traits in the six subtypes have different distribution, with further then absolute shrinkage and selection operator (LASSO) and multiariable COX regression method finally got seven autophagy genetic risk model is constructed, in the training set, the survival rate of high risk group is lower than the low risk group (p &lt; 0.0001), the validation set,The AUC area of the receiver operating characteristic (ROC) curve, the training set is 0.894, and the verification set is 0.736. We find that the high and low risk score is closely related to the TMN stage (All P is less than 0.05).The nomogram shows that the risk score combined with other indicators such as age, G,T,M, and N better predicts 1-year, 2-year, 3-year survival, and the DCA curve shows that the risk model combined with other indicators produces better clinical efficacy.Then immune cells in 28 in the enrichment score, there were statistically significant differences, high and low risk most GSEA enrichment analysis, the main enrichment in G2 / M checkpoint high-risk score, Genes defining epithelial and mesenchymal transition, raised in response to the low oxygen levels (hypoxia) gene, gene is important to the mitotic spindle assembly, these are closely related with the occurrence of tumor . In conclusion, our constructed autophagy risk signature may be a prognostic tool for cervical cancer.


2020 ◽  
Vol 2020 ◽  
pp. 1-13 ◽  
Author(s):  
Mi Zhou ◽  
Weihua Shao ◽  
Haiyun Dai ◽  
Xin Zhu

Objective. To construct a predictive signature based on autophagy-associated lncRNAs for predicting prognosis in lung adenocarcinoma (LUAD). Materials and Methods. Differentially expressed autophagy genes (DEAGs) and differentially expressed lncRNAs (DElncRNAs) were screened between normal and LUAD samples at thresholds of ∣log2Fold Change∣>1 and P value < 0.05. Univariate Cox regression analysis was conducted to identify overall survival- (OS-) associated DElncRNAs. The total cohort was randomly divided into a training group (n=229) and a validation group (n=228) at a ratio of 1 : 1. Multivariate Cox regression analysis was used to build prognostic models in the training group that were further validated by the area under curve (AUC) values of the receiver operating characteristic (ROC) curves in both the validation and total cohorts. Results. A total of 30 DEAGs and 2997 DElncRNAs were identified between 497 LUAD tissues and 54 normal tissues; however, only 1183 DElncRNAs were related to the 30 DEAGs. A signature consisting of 13 DElncRNAs was built to predict OS in lung adenocarcinoma, and the survival analysis indicated a significant OS advantage of the low-risk group over the high-risk group in the training group, with a 5-year OS AUC of 0.854. In the validation group, survival analysis also indicated a significantly favorable OS for the low-risk group over the high-risk group, with a 5-year OS AUC of 0.737. Univariate and multivariate Cox regression analyses indicated that only positive surgical margin (vs negative surgical margin) and high-risk group (vs low-risk group) based on the predictive signature were independent risk factors predictive of overall mortality in LUAD. Conclusions. This study investigated the association between autophagy-associated lncRNAs and prognosis in LUAD and built a robust predictive signature of 13 lncRNAs to predict OS.


2021 ◽  
Vol 8 ◽  
Author(s):  
Shenglan Huang ◽  
Jian Zhang ◽  
Xiaolan Lai ◽  
Lingling Zhuang ◽  
Jianbing Wu

Introduction: Hepatocellular carcinoma (HCC) is one of the most common malignant tumors with poor prognosis. The tumor microenvironment (TME) plays a vital role in HCC progression. Thus, this research was designed to analyze the correlation between the TME and the prognosis of HCC patients and to construct a TME-related long noncoding RNA (lncRNA) signature to determine HCC patients’ prognosis and response to immunotherapy.Methods: We assessed the stromal–immune–estimate scores within the HCC microenvironment using the ESTIMATE (Estimation of Stromal and Immune Cells in Malignant Tumor Tissues Using Expression Data) algorithm based on The Cancer Genome Atlas database, and their associations with survival and clinicopathological parameters were also analyzed. Thereafter, differentially expressed lncRNAs were filtered out according to the immune and stromal scores. Cox regression analysis was performed to build a TME-related lncRNA risk signature. Kaplan–Meier analysis was used to explore the prognostic value of the risk signature. Furthermore, we explored the biological functions and immune microenvironment features in the high- and low-risk groups. Lastly, we probed the association of the risk model with treatment responses to immune checkpoint inhibitors (ICIs) in HCC.Results: The stromal, immune, and estimate scores were obtained utilizing the ESTIMATE algorithm for patients with HCC. Kaplan–Meier analysis showed that high scores were significantly correlated with better prognosis in HCC patients. Six TME-related lncRNAs were screened to construct the prognostic model. The Kaplan–Meier curves suggested that HCC patients with low risk had better prognosis than those with high risk. Receiver operating characteristic (ROC) curve and Cox regression analyses indicated that the risk model could predict HCC survival exactly and independently. Functional enrichment analysis revealed that some tumor- and immune-related pathways were activated in the high-risk group. We also revealed that some immune cells, which were important in enhancing immune responses toward cancer, were significantly increased in the low-risk group. In addition, there was a close correlation between ICIs and the risk signature, which can be used to predict the treatment responses of HCC patients.Conclusion: We analyzed the influence of the stromal, immune, and estimate scores on the prognosis of HCC patients. A novel TME-related lncRNA risk model was established, which could be effectively applied as an independent prognostic biomarker and predictor of ICIs for HCC patients.


2021 ◽  
Vol 11 ◽  
Author(s):  
Feng Xu ◽  
Huan Xu ◽  
Zixiong Li ◽  
Yuanyuan Huang ◽  
Xiaoling Huang ◽  
...  

While increased glycolysis has been identified as a cancer marker and attracted much attention in thyroid cancer (THCA), the prognostic role of it remains to be further elucidated. Here we aimed to determine a specific glycolysis-associated risk model to predict THCA patients' survival. We also explored the interaction between this signature and tumor immune microenvironment and performed drug screening to identify specific drugs targeting the glycolysis-associated signature. Six genes (CHST6, POM121C, PPFIA4, STC1, TGFBI, and FBP2) comprised the specific model, which was an independent prognostic indicator in THCA patients determined by univariate, LASSO and multivariate Cox regression analyses. The receiver operating characteristic (ROC) curve analysis confirmed the excellent clinical performance of the prognostic signature. According to the specific gene signature, patients were categorized into high- and low-risk subgroups. The high-risk group was characterized by decreased immune score and elevated tumor purity, as well as worser survival prognosis compared to the low-risk group. We also validated the expression of these genes in clinical samples and in-vitro experiments. Lastly, we identified potential drugs targeting the glycolysis-associated signature. The derived glycolysis-related signature is an independent prognostic biomarker for THCA patients and might be used as an efficacy of biomarker for drug-sensitivity prediction.


Blood ◽  
2020 ◽  
Vol 136 (Supplement 1) ◽  
pp. 51-52
Author(s):  
Marie-Claude Pelland-Marcotte ◽  
Ketan Kulkarni ◽  
Uma Athale ◽  
Jason Pole ◽  
Leonardo R. Brandao ◽  
...  

Introduction: Thromboembolism (TE) is a well-known complication of cancer and its treatments. The impact of TE on survival outcomes remains unclear, especially in children. We assessed whether TE development was associated with overall survival (OS) and event-free survival (EFS) in children with acute lymphoblastic leukemia (ALL). Methods: We performed a population-based retrospective cohort study using the national registry Cancer in Young People Canada (CYP-C). Children 0-&lt;15 years of age diagnosed with ALL (2000-2018) and treated at one of 12 Canadian pediatric centers outside of Ontario were included. OS was defined as the time between the date of cancer diagnosis and death and, EFS, as the time between the date of cancer diagnosis and the date of relapse, subsequent malignancy or death (whichever came first). Patients were categorized as to whether they experienced a radiologically-confirmed TE during treatment graded 3, 4 or 5 as per the Common Terminology Criteria for Adverse Events v.4 (i.e. requiring medical treatment, life-threatening or fatal). Only TEs that occurred before relapse or subsequent malignancy were considered. The Kaplan-Meier survival method estimated the 5-year OS and EFS of children with TE compared to those without TE. Univariate and multivariable Cox regression models were used to estimate the hazard ratio (HR) and 95% confidence interval (CI) of death or an event between groups, adjusted for age, sex, and leukemia risk group. A sub-analysis stratified the analysis by leukemia risk group. Results: The study included 2,208 children (median age: 4 years [interquartile range: 2-7 years], 54.9% male). Precursor B-cell ALL was the most common diagnosis (1,789, 89.1%). Patients were stratified as standard/low risk ALL in 58.0% of cases, and high/very high risk ALL in 42.0%. Of these, 121 (6.0%) developed a TE, at a median time of 100 days (interquartile range: 30-183 days) after cancer diagnosis. Eight patients (0.4%) had a life-threatening or fatal TE. Patients with TE were more likely to be aged 10 years or older, to present with T-cell ALL, and to have high risk leukemia. The 5-year OS (95% CI) of patients with and without TE was 80.2% (72.9-87.5%) and 93.7% (92.5-94.9%) respectively (log-rank test: p&lt;0.001, Figure 1). The adjusted HR (95% CI) of death in children with TE was 2.09 (1.33-3.27, p=0.001). Similarly, as shown in Figure 2, the 5-year EFS (95% CI) of patients with and without TE was 68.7% (59.7-77.7%) and 88.6% (87.1-90.1%), respectively (log-rank test: p&lt;0.001). The adjusted HR (95% CI) of an event was 2.01 (1.39-2.90, p&lt;0.001). When stratified by leukemia risk group, no statistically significant difference was seen in standard/low risk ALL for both OS and EFS but TE was associated with a significantly lower OS and EFS in children with high/very high risk ALL (Table 1). In this group, the increased risk of death was attributable to both deaths following relapsed disease (HR [95% CI]: 2.37 [1.39-4.04]) and death not following relapse (HR [95% CI]: 2.93 [1.35-6.35]). Sensitivity analyses in which 1) patients with very high risk ALL were removed and 2) only grade 3 or 4 TE were considered showed similar results. Conclusions: Clinically relevant TE led to a statistically significant reduction in OS and EFS in children with high risk/very high risk leukemia. Further research is needed to assess whether TE prevention may improve anti-cancer outcomes. Disclosures Brandao: Boehringer Ingelheim: Other: Member of a paediatric expert working group.


2021 ◽  
Author(s):  
Fang Wen ◽  
Xiaoxue Chen ◽  
Wenjie Huang ◽  
Shuai Ruan ◽  
Suping Gu ◽  
...  

Abstract Background: The diagnosis rate and mortality of gastric cancer (GC) are among the highest in the global, so it is of great significance to predict the survival time of GC patients. Ferroptosis and iron-metabolism make a critical impact on tumor development and are closely linked to the treatment of cancer and the prognosis of patients. However, the predictive value of the genes involved in ferroptosis and iron-metabolism in GC and their effects on immune microenvironment remain to be further clarified.Methods: In this study, the RNA sequence information and general clinical indicators of GC patients were acquired from the public databases. We first systematically screen out 134 DEGs and 13 PRGs related to ferroptosis and iron-metabolism. Then, we identified six PRDEGs (GLS2, MTF1, SLC1A5, SP1, NOX4, and ZFP36) based on the LASSO-penalized Cox regression analysis. The 6-gene prognostic risk model was established in the TCGA cohort and the GC patients were separated into the high- and the low-risk groups through the risk score median value. GEO cohort was used for verification. The expression of PRDEGs was verified by quantitative QPCR.Results: Our study demonstrated that patients in the low-risk group had a higher survival probability compared with those in high-risk group. In addition, univariate and multivariate Cox regression analyses confirmed that the risk score was an independent prediction parameter. The ROC curve analysis and nomogram manifested that the risk model had the high predictive ability and was more sensitive than general clinical features. Furthermore, compared with the high-risk group, the low-risk group had higher TMB and a longer 5-year survival period. In the immune microenvironment of GC, there were also differences in immune function and highly infiltrated immune cells between the two risk groups.Conclusions: The prognostic risk model based on the six genes associated with ferroptosis and iron-metabolism has a good performance for predicting the prognosis of patients with GC. The treatment of cancer by inducing tumor ferroptosis or mediating tumor iron-metabolism, especially combined with immunotherapy, provides a new possibility for individualized treatment of GC patients.


2021 ◽  
Author(s):  
Ziyan Chen ◽  
Haitao Yu ◽  
Lijun Wu ◽  
Sina Zhang ◽  
Zhihui Lin ◽  
...  

Introduction: Selecting the hub genes associated with hepatocellular carcinoma (HCC) to construct a COX regression model for predicting prognosis in HCC patients. Methods: Using HCC patient data from the ICGC and TCGA databases, screened for 40 core genes highly correlated with histological grade of HCC. Univariate and multivariate COX regression analysis were performed on the genes highly associated with HCC prognosis and the model was established. The expression of those genes was measured by immunohistochemistry in 110 HCC patients who underwent the surgery in The First Affiliated Hospital of Wenzhou Medical University. The survival of HCC patients was analyzed by the Kaplan-Meier method. Results: Eight genes (CDC45, CENPA, MCM10, MELK, CDC20, ASF1B, FANCD2 and NCAPH) were correlated with prognosis, and the same result was observed in 110 HCC patients. Using the regression model, the HCC patients in the training set were classified as high- and low-risk groups. The overall survival (OS) of patients in the high-risk group was shorter than that in the low-risk group, the same results were obtained in verification set. Conclusion: This study found that the risk model according to these eight genes can be used as a predictor of prognosis in HCC. These genes may become alternative biomarkers and therapeutic targets and provide new therapeutic strategies for HCC.


2018 ◽  
Vol 36 (6_suppl) ◽  
pp. 84-84 ◽  
Author(s):  
Amar Upadhyaya Kishan ◽  
Alan J. Katz ◽  
Constantine Mantz ◽  
Fang-I Chu ◽  
Limor Appelbaum ◽  
...  

84 Background: While a growing body of evidence supports the use of stereotactic body radiotherapy (SBRT) for the treatment of low- and intermediate-risk prostate adenocarcinoma (PCa), some trepidation exists regarding its long-term efficacy and safety. Methods: Men with low- and intermediate-risk PCa, as defined per the National Comprehensive Cancer Network guidelines, who were enrolled on various institutional phase II trials of SBRT between 2000-2012 were included in a multi-institutional consortium. Biochemical relapse (BCR) was defined as PSA > “nadir +2” or initiation of androgen deprivation therapy (ADT). Toxicity data were scored according to the CTCAE v 3.0 or Radiation Therapy Oncology Group scoring systems. Results: A total of 1644 men were eligible for analysis, with a median followup of 7.2 years. 297 patients (18.1%) had at least 9 years of followup. Fractionation schemes ranged from 33.50-40 Gy in 4-5 fractions. 892 patients had low-risk disease and 752 had intermediate-risk disease. 59 patients (3.6%) received short-term ADT. 100 patients (6.0%) experienced BCR, and 7 (0.4%) experienced distant metastases. No patients died of PCa. By Kaplan-Meier analysis, 5- and 10-year BCR-free survival rates were 98% and 94% in the low-risk group and 96% and 90% in the intermediate-risk group (p < 0.05 by log-rank test). 5- and 10-year overall survival rates were 93% and 86% in the low-risk group and 95% and 91% in the intermediate-risk group (p > 0.05 by log-rank test). Five patients (0.3%) experienced grade 3 acute genitourinary (GU) toxicities, including urinary retention, hematuria, and frequency. 30 (2%) experienced grade 3 late GU toxicity, including urinary strictures, hematuria, and retention. One late grade 4 GU toxicity (hemorrhagic urethritis) and one late grade 4 gastrointestinal toxicity (fistula-in-ano) were seen. Conclusions: To the best of our knowledge, this is the largest analysis of long-term outcomes following SBRT for PCa. The results indicate that SBRT has an efficacy and toxicity profile that compares favorably to more widespread forms of treatment, such as conventionally-fractionated external beam radiotherapy and brachytherapy.


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