scholarly journals Identification of a five-miRNA signature as a novel potential prognostic biomarker in patients with nasopharyngeal carcinoma

Hereditas ◽  
2022 ◽  
Vol 159 (1) ◽  
Author(s):  
Bo Tu ◽  
Ling Ye ◽  
Qingsong Cao ◽  
Sisi Gong ◽  
Miaohua Jiang ◽  
...  
Keyword(s):  
High Risk ◽  
Nasopharyngeal Carcinoma ◽  
Prognostic Model ◽  
Target Genes ◽  
Cox Regression ◽  
External Validation ◽  
Risk Groups ◽  
Low Risk ◽  
Mirna Signature ◽  
Cox Regression Analysis

Abstract Background MicroRNAs (miRNAs) are involved in the prognosis of nasopharyngeal carcinoma (NPC). This study used clinical data and expression data of miRNAs to develop a prognostic survival signature for NPC patients to detect high-risk subject. Results We identified 160 differentially expressed miRNAs using RNA-Seq data from the GEO database. Cox regression model consisting of hsa-miR-26a, hsa-let-7e, hsa-miR-647, hsa-miR-30e, and hsa-miR-93 was constructed by the least absolute contraction and selection operator (LASSO) in the training set. All the patients were classified into high-risk or low-risk groups by the optimal cutoff value of the 5-miRNA signature risk score, and the two risk groups demonstrated significant different survival. The 5-miRNA signature showed high predictive and prognostic accuracies. The results were further confirmed in validation and external validation set. Results from multivariate Cox regression analysis validated 5-miRNA signature as an independent prognostic factor. A total of 13 target genes were predicted to be the target genes of miRNA target genes. Both PPI analysis and KEGG analysis networks were closely related to tumor signaling pathways. The prognostic model of mRNAs constructed using data from the dataset GSE102349 had higher AUCs of the target genes and higher immune infiltration scores of the low-risk groups. The mRNA prognostic model also performed well on the independent immunotherapy dataset Imvigor210. Conclusions This study constructed a novel 5-miRNA signature for prognostic prediction of the survival of NPC patients and may be useful for individualized treatment of NPC patients.

scholarly journals Development and Validation of an Mesenchymal-Related Long Non-Coding RNA Prognostic Model in Glioma

2021 ◽  
Vol 11 ◽  
Author(s):  
Kebing Huang ◽  
Xiaoyu Yue ◽  
Yinfei Zheng ◽  
Zhengwei Zhang ◽  
Meng Cheng ◽  
...  
Keyword(s):  
High Risk ◽  
Prognostic Model ◽  
Cox Regression ◽  
Risk Groups ◽  
Low Risk ◽  
Gene Set Enrichment Analysis ◽  
Migration And Invasion ◽  
Aberrant Expression ◽  
Cox Regression Analysis ◽  
Genome Atlas

Glioma is well known as the most aggressive and prevalent primary malignant tumor in the central nervous system. Molecular subtypes and prognosis biomarkers remain a promising research area of gliomas. Notably, the aberrant expression of mesenchymal (MES) subtype related long non-coding RNAs (lncRNAs) is significantly associated with the prognosis of glioma patients. In this study, MES-related genes were obtained from The Cancer Genome Atlas (TCGA) and the Ivy Glioblastoma Atlas Project (Ivy GAP) data sets of glioma, and MES-related lncRNAs were acquired by performing co-expression analysis of these genes. Next, Cox regression analysis was used to establish a prognostic model, that integrated ten MES-related lncRNAs. Glioma patients in TCGA were divided into high-risk and low-risk groups based on the median risk score; compared with the low-risk groups, patients in the high-risk group had shorter survival times. Additionally, we measured the specificity and sensitivity of our model with the ROC curve. Univariate and multivariate Cox analyses showed that the prognostic model was an independent prognostic factor for glioma. To verify the predictive power of these candidate lncRNAs, the corresponding RNA-seq data were downloaded from the Chinese Glioma Genome Atlas (CGGA), and similar results were obtained. Next, we performed the immune cell infiltration profile of patients between two risk groups, and gene set enrichment analysis (GSEA) was performed to detect functional annotation. Finally, the protective factors DGCR10 and HAR1B, and risk factor SNHG18 were selected for functional verification. Knockdown of DGCR10 and HAR1B promoted, whereas knockdown of SNHG18 inhibited the migration and invasion of gliomas. Collectively, we successfully constructed a prognostic model based on a ten MES-related lncRNAs signature, which provides a novel target for predicting the prognosis for glioma patients.

scholarly journals Identification and validation of a pyroptosis-related prognostic signature for thyroid cancer

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Pu Wu ◽  
Jinyuan Shi ◽  
Wei Sun ◽  
Hao Zhang
Keyword(s):  
Thyroid Cancer ◽  
Risk Score ◽  
Prognostic Model ◽  
Immune Cell ◽  
Cox Regression ◽  
Checkpoint Inhibitors ◽  
Risk Groups ◽  
Low Risk ◽  
Prognostic Signature ◽  
Cox Regression Analysis

Abstract Background Pyroptosis is a form of programmed cell death triggered by inflammasomes. However, the roles of pyroptosis-related genes in thyroid cancer (THCA) remain still unclear. Objective This study aimed to construct a pyroptosis-related signature that could effectively predict THCA prognosis and survival. Methods A LASSO Cox regression analysis was performed to build a prognostic model based on the expression profile of each pyroptosis-related gene. The predictive value of the prognostic model was validated in the internal cohort. Results A pyroptosis-related signature consisting of four genes was constructed to predict THCA prognosis and all patients were classified into high- and low-risk groups. Patients with a high-risk score had a poorer overall survival (OS) than those in the low-risk group. The area under the curve (AUC) of the receiver operator characteristic (ROC) curves assessed and verified the predictive performance of this signature. Multivariate analysis showed the risk score was an independent prognostic factor. Tumor immune cell infiltration and immune status were significantly higher in low-risk groups, which indicated a better response to immune checkpoint inhibitors (ICIs). Of the four pyroptosis-related genes in the prognostic signature, qRT-PCR detected three of them with significantly differential expression in THCA tissues. Conclusion In summary, our pyroptosis-related risk signature may have an effective predictive and prognostic capability in THCA. Our results provide a potential foundation for future studies of the relationship between pyroptosis and the immunotherapy response.

scholarly journals Development and Validation of a Novel Hypoxia-Related Long Noncoding RNA Model With Regard to Prognosis and Immune Features in Breast Cancer

2021 ◽  
Vol 9 ◽  
Author(s):  
Peng Gu ◽  
Lei Zhang ◽  
Ruitao Wang ◽  
Wentao Ding ◽  
Wei Wang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
High Risk ◽  
Long Noncoding Rna ◽  
Noncoding Rna ◽  
Prognostic Model ◽  
Cox Regression ◽  
Risk Group ◽  
High Risk Group ◽  
Low Risk ◽  
Cox Regression Analysis

Background: Female breast cancer is currently the most frequently diagnosed cancer in the world. This study aimed to develop and validate a novel hypoxia-related long noncoding RNA (HRL) prognostic model for predicting the overall survival (OS) of patients with breast cancer.Methods: The gene expression profiles were downloaded from The Cancer Genome Atlas (TCGA) database. A total of 200 hypoxia-related mRNAs were obtained from the Molecular Signatures Database. The co-expression analysis between differentially expressed hypoxia-related mRNAs and lncRNAs based on Spearman’s rank correlation was performed to screen out 166 HRLs. Based on univariate Cox regression and least absolute shrinkage and selection operator Cox regression analysis in the training set, we filtered out 12 optimal prognostic hypoxia-related lncRNAs (PHRLs) to develop a prognostic model. Kaplan–Meier survival analysis, receiver operating characteristic curves, area under the curve, and univariate and multivariate Cox regression analyses were used to test the predictive ability of the risk model in the training, testing, and total sets.Results: A 12-HRL prognostic model was developed to predict the survival outcome of patients with breast cancer. Patients in the high-risk group had significantly shorter median OS, DFS (disease-free survival), and predicted lower chemosensitivity (paclitaxel, docetaxel) compared with those in the low-risk group. Also, the risk score based on the expression of the 12 HRLs acted as an independent prognostic factor. The immune cell infiltration analysis revealed that the immune scores of patients in the high-risk group were lower than those of the patients in the low-risk group. RT-qPCR assays were conducted to verify the expression of the 12 PHRLs in breast cancer tissues and cell lines.Conclusion: Our study uncovered dozens of potential prognostic biomarkers and therapeutic targets related to the hypoxia signaling pathway in breast cancer.

Poor-prognosis nasopharyngeal carcinoma as defined by a molecularly distinct subgroup and prediction by a miRNA expression signature.

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. 6056-6056
Author(s):  
Lan Zhao ◽  
Feng Gao ◽  
Wang Wei ◽  
Xin Duan ◽  
Yuchen Zhang ◽  
...  
Keyword(s):  
High Risk ◽  
Nasopharyngeal Carcinoma ◽  
Clinical Outcomes ◽  
Distant Metastasis ◽  
Mirna Expression ◽  
Poor Prognosis ◽  
Prognostic Model ◽  
Cox Regression ◽  
Risk Groups ◽  
Training Cohort

6056 Background: Nasopharyngeal carcinoma (NPC) is a highly invasive and metastatic cancer, with diverse molecular characteristics and clinical outcomes. Our aim in this study is to dissect the molecular heterogeneity of NPC, followed by construction of a prognostic model for prediction of distant metastasis. Methods: For molecular subtyping of NPC using miRNA expression data, we selected 86 stage II (AJCC 7th Edition) NPC patients from GSE32960 as training cohort. The remaining 226 NPC patients from GSE32960 and 246 NPC patients from GSE70970 were used as two validation cohorts. Consensus clustering was employed for unsupervised classification of the training cohort. Classifier was built using support vector machine (SVM), and was validated in the two validation cohorts. Univariate and multivariate Cox regression analyses were employed for feature selection and constructing a prognostic model for predicting high-risk distant metastasis, respectively. Results: We identified three NPC subtypes (NPC1, 2, and 3) that are molecularly distinct and clinically relevant. NPC1 (~45%) is enriched for cell cycle related pathways, and patients classified to NPC1 have an intermediate survival; NPC3 (~19%) is enriched for immune related pathways, and has good clinical outcomes. More importantly, NPC2 (~36%) is associated with poor prognosis, and is characterized by upregulation of epithelial-mesenchymal transition (EMT). Out of the total 25 differentially expressed miRNAs in NPC2, miR-142, miR-26a, miR-141 and let-7i have significant prognostic power (p < 0.05), as determined by univariate Cox regression analysis. For identification of high-risk distant metastasis, we built a multivariate Cox regression model using the selected 4 miRNAs. Our model can robustly stratify NPC patients into high- and low- risk groups both in GSE32960 (HR 3.1, 95% CI 1.8-5.4, p = 1.2e-05) and GSE70970 (HR 2.2, 95% CI 1.1-4.5, p = 0.022) cohorts. Conclusions: We proposed for the first time that NPC can be stratified into three subtypes. Using a panel of 4 miRNAs, we established a prognostic model that can robustly stratify NPC patients into high- and low- risk groups of distant metastasis.

Construction and Validation of Prognostic Markers of Liver Cancer Based on Autophagy Genes

2021 ◽  
Vol 21 ◽  
Author(s):  
Dawei Zhou ◽  
Junchen Wan ◽  
Jiang Luo ◽  
Yuhao Tao
Keyword(s):  
Regression Analysis ◽  
High Risk ◽  
Liver Cancer ◽  
Cancer Patients ◽  
Prognostic Model ◽  
Cox Regression ◽  
Risk Group ◽  
Primary Liver Cancer ◽  
Risk Groups ◽  
Cox Regression Analysis

Background: Liver cancer is one of the most common diseases in the world. At present, the mechanism of autophagy genes in liver cancer is not very clear. Therefore, it is meaningful to study the role and prognostic value of autophagy genes in liver cancer. Objective: The purpose of this study is to conduct a bioinformatics analysis of autophagy genes related to primary liver cancer to establish a prognostic model of primary liver cancer based on autophagy genes. Results: Through difference analysis, 31 differential autophagy genes were screened out and then analyzed by GO and KEGG analysis. At the same time, we built a PPI network. To optimize the evaluation of the prognosis of liver cancer patients, we integrated multiple autophagy genes to establish a prognostic model. By using univariate cox regression analysis, 15 autophagy genes related to prognosis were screened out. Then we included these 15 genes into the Least Absolute Shrinkage and Selection Operator (LASSO), and performed multi-factor cox regression analysis on the 9 selected genes to construct a prognostic model. The risk score of each patient was calculated based on 4 genes(BIRC5, HSP8, SQSTM1, and TMEM74) which participated in the establishing of the model, then the patients were divided into high-risk groups and low-risk groups. In the multivariate cox regression analysis, the risk score was the independent prognostic factors (HR=1.872, 95%CI=1.544-2.196, P<0.001). Survival analysis showed that the survival time of the low-risk group was significantly longer than that of the high-risk group. Combining clinical characteristics and autophagy genes, we constructed a nomogram for predicting prognosis. The external dataset GSE14520 proved that the nomogram has a good prediction for individual patients with primary liver cancer. Conclusion: This study provided potential autophagy-related markers for liver cancer patients to predict their prognosis and revealed part of the molecular mechanism of liver cancer autophagy. At the same time, the certain gene pathways and protein pathways related to autophagy may provide some inspiration for the development of anticancer drugs.

scholarly journals Construction of a prognostic model for lung squamous cell carcinoma based on seven N6-methylandenosine-related autophagy genes

2021 ◽  
Vol 18 (5) ◽  
pp. 6709-6723
Author(s):  
Xin Yu ◽  
◽  
Jun Liu ◽  
Ruiwen Xie ◽  
Mengling Chang ◽  
...  
Keyword(s):  
Squamous Cell Carcinoma ◽  
Prognostic Model ◽  
Immune Cell ◽  
Cox Regression ◽  
Risk Group ◽  
Lung Squamous Cell Carcinoma ◽  
Risk Groups ◽  
Low Risk ◽  
Immune Cell Infiltration ◽  
Cox Regression Analysis

<abstract> <sec><title>Objective</title><p>We aimed to construct a novel prognostic model based on N6-methyladenosine (m6A)-related autophagy genes for predicting the prognosis of lung squamous cell carcinoma (LUSC).</p> </sec> <sec><title>Methods</title><p>Gene expression profiles and clinical information of Patients with LUSC were downloaded from The Cancer Genome Atlas (TCGA) database. In addition, m6A- and autophagy-related gene profiles were obtained from TCGA and Human Autophagy Database, respectively. Pearson correlation analysis was performed to identify the m6A-related autophagy genes, and univariate Cox regression analysis was conducted to screen for genes associated with prognosis. Based on these genes, LASSO Cox regression analysis was used to construct a prognostic model. The corresponding prognostic score (PS) was calculated, and patients with LUSC were assigned to low- and high-risk groups according to the median PS value. An independent dataset (GSE37745) was used to validate the prognostic ability of the model. CIBERSORT was used to calculate the differences in immune cell infiltration between the high- and low-risk groups.</p> </sec> <sec><title>Results</title><p>Seven m6A-related autophagy genes were screened to construct a prognostic model: <italic>CASP4</italic>, <italic>CDKN1A</italic>, <italic>DLC1</italic>, <italic>ITGB1</italic>, <italic>PINK1</italic>, <italic>TP63</italic>, and <italic>EIF4EBP1</italic>. In the training and validation sets, patients in the high-risk group had worse survival times than those in the low-risk group; the areas under the receiver operating characteristic curves were 0.958 and 0.759, respectively. There were differences in m6A levels and immune cell infiltration between the high- and low-risk groups.</p> </sec> <sec><title>Conclusions</title><p>Our prognostic model of the seven m6A-related autophagy genes had significant predictive value for LUSC; thus, these genes may serve as autophagy-related therapeutic targets in clinical practice.</p> </sec> </abstract>

A Novel Prognostic Model Based on Tumor Microenvironment Biology in Relapse Biopsies Predicts Post-Autologous Stem Cell Transplantation Outcomes in Classical Hodgkin Lymphoma

Blood ◽  
2016 ◽  
Vol 128 (22) ◽  
pp. 1093-1093
Author(s):  
Fong Chun Chan ◽  
Anja Mottok ◽  
Alina S Gerrie ◽  
Maryse M Power ◽  
Kerry J Savage ◽  
...  
Keyword(s):  
Gene Expression ◽  
High Risk ◽  
Research Funding ◽  
Prognostic Model ◽  
Cox Regression ◽  
Inverse Correlation ◽  
Risk Groups ◽  
Low Risk ◽  
Classical Hodgkin Lymphoma ◽  
First Line

Abstract Introduction: Disease progression remains a significant clinical burden in classical Hodgkin lymphoma (CHL) with 25-30% of patients relapsing after first-line treatment. The current standard of care for relapsed CHL is high dose chemotherapy followed by autologous stem cell transplantation (ASCT). This secondary therapy only cures approximately 50% of patients, with virtually no reliable biomarkers to identify the patients in which this salvage treatment regimen fails. The specific aim of this study was to establish the extent of changes in tumor microenvironment (TME) composition between matched primary and relapse samples, and to build and validate a prognostic model for post-ASCT outcomes using relapse samples. Materials & Methods:NanoString digital gene expression profiling was used to ascertain the gene expression of 784 genes of interest from 245 biopsies sampled from 174 CHL patients. This cohort included 90 patients with single biopsies performed at first diagnosis (primary), 13 patients with single biopsies taken at relapse, and 71 patients with paired biopsies taken at first diagnosis and at relapse. All patients received ABVD as first-line treatment, and 151 patients went on to receive ASCT. The 784 genes of interest were selected based on previously reported associations with outcome in CHL and/or components of the TME. Spearman statistics were used for pairwise correlations and log-rank tests were used to assess survival differences. Bootstrap aggregation with concordance statistics (C-stat) was used to calculate the post-ASCT prognostic properties and a two-sample t-test was used to compare primary and relapse samples. Penalized elastic-net multivariate cox regression was used for model construction. An independent, similarly treated, cohort of 31 relapse biopsies was used for model validation. Results: Comparative gene expression analysis revealed that 17 of the 71 patients (24%) exhibited poor correlations between their paired primary and relapse samples (r2 < 0.75) - indicative of significant differences in their TME compositions. Amongst these differences was a striking inverse correlation between macrophage and B-cell gene expression pattern changes (r2 = -0.809). We validated these findings by using CD20 and CD163 immunohistochemistry confirming this inverse correlation of relative changes in macrophage and B cell content in the TME (r2= -0.645). Patients who exhibited poor gene expression correlations had inferior post-ASCT failure-free survival (FFS) (3-year: 38.5%) compared to patients with high gene expression correlations (3-year: 77%; P = 0.005). A comparative C-stat analysis of prognostic properties between primary and relapse samples demonstrated that relapse samples contain superior prognostic features for prediction of post-ASCT outcomes (relapse C-stat 0.785 ± 0.073 vs. primary C-stat 0.594 ± 0.079, P < 0.001). To this end, we developed a gene expression prognostic model using penalized Cox regression that was based on gene expression measurements in relapse samples (RHL30). RHL30 was able to risk stratify patients according to post-ASCT outcomes in the training cohort (5-year post-ASCT FFS: 23.8% in high-risk vs. 77.5% in low-risk; 5-year post-ASCT overall survival [OS]: 30.9% in high-risk vs. 85.4% in low-risk). This was validated in an independent cohort of 31 patients with relapsed CHL with a 5-year post-ASCT FFS of 37.5% in the high-risk vs. 70.1% in the low-risk groups (P = 0.017) and 5-year post-ASCT OS of 37.5% in the high-risk vs. 71.6% in the low-risk groups (P = 0.006). Conclusions: The TME gene expression profile differs significantly between matched primary and relapse CHL samples in a subset of patients with relapsed CHL. Gene expression measurements derived from relapse samples contain superior predictive properties for response to ASCT. To this end, we have developed a novel clinically applicable prognostic model (RHL30), derived from relapse samples, that identifies patients who have a high likelihood to benefit from ASCT (low-risk), and conversely a subgroup of patients who may benefit from additional or alternative therapeutic approaches (high-risk). Disclosures Connors: Seattle Genetics: Research Funding; Bristol Myers Squib: Research Funding; Millennium Takeda: Research Funding; NanoString Technologies: Research Funding; F Hoffmann-La Roche: Research Funding. Scott:NanoString Technologies: Patents & Royalties: named inventor on a patent for molecular subtyping of DLBCL that has been licensed to NanoString Technologies.

A tool to identify patients with embolic stroke of undetermined source at high recurrence risk

Neurology ◽  
2019 ◽  
Vol 93 (23) ◽  
pp. e2094-e2104 ◽  
Author(s):  
George Ntaios ◽  
Georgios Georgiopoulos ◽  
Kalliopi Perlepe ◽  
Gaia Sirimarco ◽  
Davide Strambo ◽  
...  
Keyword(s):  
High Risk ◽  
Cox Regression ◽  
External Validation ◽  
Low Risk ◽  
Embolic Stroke ◽  
Stroke Recurrence ◽  
Intermediate Risk ◽  
Cox Regression Analysis ◽  
Derivation Cohort ◽  
Risk Patients

ObjectiveA tool to stratify the risk of stroke recurrence in patients with embolic stroke of undetermined source (ESUS) could be useful in research and clinical practice. We aimed to determine whether a score can be developed and externally validated for the identification of patients with ESUS at high risk for stroke recurrence.MethodsWe pooled the data of all consecutive patients with ESUS from 11 prospective stroke registries. We performed multivariable Cox regression analysis to identify predictors of stroke recurrence. Based on the coefficient of each covariate of the fitted multivariable model, we generated an integer-based point scoring system. We validated the score externally assessing its discrimination and calibration.ResultsIn 3 registries (884 patients) that were used as the derivation cohort, age, leukoaraiosis, and multiterritorial infarct were identified as independent predictors of stroke recurrence and were included in the final score, which assigns 1 point per every decade after 35 years of age, 2 points for leukoaraiosis, and 3 points for multiterritorial infarcts (acute or old nonlacunar). The rate of stroke recurrence was 2.1 per 100 patient-years (95% confidence interval [CI] 1.44–3.06) in patients with a score of 0–4 (low risk), 3.74 (95% CI 2.77–5.04) in patients with a score of 5–6 (intermediate risk), and 8.23 (95% CI 5.99–11.3) in patients with a score of 7–12 (high risk). Compared to low-risk patients, the risk of stroke recurrence was significantly higher in intermediate-risk (hazard ratio [HR] 1.78, 95% CI 1.1–2.88) and high-risk patients (HR 4.67, 95% CI 2.83–7.7). The score was well-calibrated in both derivation and external validation cohorts (8 registries, 820 patients) (Hosmer-Lemeshow test χ2: 12.1 [p = 0.357] and χ2: 21.7 [p = 0.753], respectively). The area under the curve of the score was 0.63 (95% CI 0.58–0.68) and 0.60 (95% CI 0.54–0.66), respectively.ConclusionsThe proposed score can assist in the identification of patients with ESUS at high risk for stroke recurrence.

scholarly journals Construction and Validation of a Ferroptosis-Related Prognostic Model for Gastric Cancer

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Xiaotao Jiang ◽  
Qiaofeng Yan ◽  
Linling Xie ◽  
Shijie Xu ◽  
Kailin Jiang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gastric Cancer ◽  
High Risk ◽  
Risk Score ◽  
Prognostic Model ◽  
Cox Regression ◽  
Risk Groups ◽  
Low Risk ◽  
Gene Set Enrichment Analysis ◽  
Functional Enrichment

Background. Gastric cancer (GC), an extremely aggressive tumor with a very different prognosis, is the third leading cause of cancer-related mortality. We aimed to construct a ferroptosis-related prognostic model that can be distinguished prognostically. Methods. The gene expression and the clinical data of GC patients were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus database (GEO). The ferroptosis-related genes were obtained from the FerrDb. Using the “limma” R package and univariate Cox analysis, ferroptosis-related genes with differential expression and prognostic value were identified in the TCGA cohort. Last absolute shrinkage and selection operator (LASSO) Cox regression was applied to shrink ferroptosis-related predictors and construct a prognostic model. Functional enrichment, ESTIMATE algorithm, and single-sample gene set enrichment analysis (ssGSEA) were applied for exploring the potential mechanism. GC patients from the GEO cohort were used for validation. Results. An 8-gene prognostic model was constructed and stratified GC patients from TCGA and meta-GEO cohort into high-risk groups or low-risk groups. GC patients in high-risk groups have significantly poorer OS compared with those in low-risk groups. The risk score was identified as an independent predictor for OS. Functional analysis revealed that the risk score was mainly associated with the biological function of extracellular matrix (ECM) organization and tumor immunity. Conclusion. In conclusion, the ferroptosis-related model can be utilized for the clinical prognostic prediction in GC.

scholarly journals Development of a Nomogram Model for Treatment of Elderly Patients with Locoregionally Advanced Nasopharyngeal Carcinoma

2021 ◽  
Vol 11 (11) ◽  
pp. 1065
Author(s):  
Jia Kou ◽  
Lu-Lu Zhang ◽  
Xing-Li Yang ◽  
Dan-Wan Wen ◽  
Guan-Qun Zhou ◽  
...  
Keyword(s):  
High Risk ◽  
Nasopharyngeal Carcinoma ◽  
Risk Stratification ◽  
Elderly Patients ◽  
Cox Regression ◽  
Risk Group ◽  
Disease Free Survival ◽  
Risk Groups ◽  
Rank Test ◽  
Cox Regression Analysis

(1) Purpose: This study aims to explore risk-adapted treatment for elderly patients with locoregionally advanced nasopharyngeal carcinoma (LA-NPC) according to their pretreatment risk stratification and the degree of comorbidity. (2) Methods: A total of 583 elderly LA-NPC patients diagnosed from January 2011 to January 2018 are retrospectively studied. A nomogram for disease-free survival (DFS) is constructed based on multivariate Cox regression analysis. The performance of the model is evaluated by using the area under the curve (AUC) of the receiver operating characteristic curve and Harrell concordance index (C-index). Then, the entire cohort is divided into different risk groups according to the nomogram cutoff value determined by X-tile analysis. The degree of comorbidities is assessed by the Charlson Comorbidity Index (CCI). Finally, survival rates are estimated and compared by the Kaplan–Meier method and the log-rank test. (3) Results: A nomogram for DFS is constructed with T/N classification, Epstein-Barr virus DNA and albumin. The nomogram shows well prognostic performance and significantly outperformed the tumor-node-metastasis staging system for estimating DFS (AUC, 0.710 vs. 0.607; C-index, 0.668 vs. 0.585; both p < 0.001). The high-risk group generated by nomogram has significantly poorer survival compared with the low-risk group (3-year DFS, 76.7% vs. 44.6%, p < 0.001). For high-risk patients with fewer comorbidities (CCI = 2), chemotherapy combined with radiotherapy is associated with significantly better survival (p < 0.05) than radiotherapy alone. (4) Conclusion: A prognostic nomogram for DFS is constructed with generating two risk groups. Combining risk stratification and the degree of comorbidities can guide risk-adapted treatment for elderly LA-NPC patients.

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