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.

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.

A Novel Risk Classification Model Predicts Overall Survival and Locoregional Surgery Benefit in Colorectal Patients with Distant Metastasis at the Initial Diagnosis

2020 ◽  
Author(s):  
Mo Chen ◽  
Tian-en Li ◽  
Pei-zhun Du ◽  
Junjie Pan ◽  
Zheng Wang ◽  
...  
Keyword(s):  
Overall Survival ◽  
High Risk ◽  
Distant Metastasis ◽  
Cox Regression ◽  
Risk Group ◽  
Risk Groups ◽  
Risk Classification ◽  
Low Risk ◽  
Classification Model ◽  
Intermediate Risk

Abstract Background and aims: In this research, we aimed to construct a risk classification model to predict overall survival (OS) and locoregional surgery benefit in colorectal cancer (CRC) patients with distant metastasis.Methods: We selected a cohort consisting of 12741 CRC patients diagnosed with distant metastasis between 2010 and 2014, from the Surveillance, Epidemiology and End Results (SEER) database. Patients were randomly assigned into training group and validation group at the ratio of 2:1. Univariable and multivariable Cox regression models were applied to screen independent prognostic factors. A nomogram was constructed and assessed by the Harrell’s concordance index (C-index) and calibration plots. A novel risk classification model was further established based on the nomogram.Results: Ultimately 12 independent risk factors including race, age, marriage, tumor site, tumor size, grade, T stage, N stage, bone metastasis, brain metastasis, lung metastasis and liver metastasis were identified and adopted in the nomogram. The C-indexes of training and validation groups were 0.77 (95% confidence interval [CI] 0.73-0.81) and 0.75 (95% CI 0.72-0.78), respectively. The risk classification model stratified patients into three risk groups (low-, intermediate- and high-risk) with divergent median OS (low-risk: 36.0 months, 95% CI 34.1-37.9; intermediate-risk: 18.0 months, 95% CI 17.4-18.6; high-risk: 6.0 months, 95% CI 5.3-6.7). Locoregional therapies including surgery and radiotherapy could prognostically benefit patients in the low-risk group (surgery: hazard ratio [HR] 0.59, 95% CI 0.50-0.71; radiotherapy: HR 0.84, 95% CI 0.72-0.98) and intermediate risk group (surgery: HR 0.61, 95% CI 0.54-0.68; radiotherapy: HR 0.86, 95% CI 0.77-0.95), but not in the high-risk group (surgery: HR 1.03, 95% CI 0.82-1.29; radiotherapy: HR 1.03, 95% CI 0.81-1.31). And all risk groups could benefit from systemic therapy (low-risk: HR 0.68, 95% CI 0.58-0.80; intermediate-risk: HR 0.50, 95% CI 0.47-0.54; high-risk: HR 0.46, 95% CI 0.40-0.53).Conclusion: A novel risk classification model predicting prognosis and locoregional surgery benefit of CRC patients with distant metastasis was established and validated. This predictive model could be further utilized by physicians and be of great significance for medical practice.

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.

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 Detection of expression of microRNAs in serum of loco-regionally advanced nasopharyngeal carcinoma patients: A potential marker for prognosis prediction of nasopharyngeal carcinoma treated with concurrent chemoradiotherapy screening

2019 ◽  
Author(s):  
Zhimin Zhang ◽  
Ge Wang ◽  
Feng Jin ◽  
Jijun Zheng ◽  
He Xiao ◽  
...  
Keyword(s):  
High Risk ◽  
Nasopharyngeal Carcinoma ◽  
Regression Model ◽  
Risk Score ◽  
Distant Metastasis ◽  
Concurrent Chemoradiotherapy ◽  
Cox Regression ◽  
Cox Regression Model ◽  
Serum Mirna ◽  
Before And After

Abstract Background Serum miRNA signature has recently been found as potential disease fingerprints to predict survival. Therefore we investigated the role of serum miRNA in predicting prognosis in patients with loco-regionally advanced nasopharyngeal carcinoma (NPC) treated with concurrent chemoradiotherapy (CCRT). Methods This study included two phases: (i) We enrolled 3 NPC patients with recurrence or distant metastasis (experimental group, EG) and 3 NPC patients in clinical remission (control group, CG), who were treated with CCRT within 5 years. The paired serum was collected before and after treatment and biomarkers were discovered by TaqMan Human MiRNA Arrays. (ii) we used the bioinformatic analysis, marker selection and an independent validation by qRT-PCR to analyse the serums of 29 NPC patients with recurrent disease or distant metastasis and 19 NPC patients treated with CCRT. We used the Kaplan-Meier method, log-rank test and Cox regression model to estimate the accuracy of the miRNAs to predict PFS and OS, and identified factors significantly associated with prognosis, respectively. Results Using fold change≥2.0 or ≤0.5 and p≤0.05 as a cutoff level, we identified 1 up-regulated and 9 down-regulated miRNAs, 1 up-regulated and 6 down-regulated miRNAs in EG versus CG before and after CCRT, respectively. After significantly down-regulated miRNA from EG versus CG before and after CCRT were removed, only 9 different miRNAs were significantly reduced. In an independent set of serum samples, the expression of miR-26b, miR-29a and miR-125b showed no significant difference in 48 NPC patients before CCRT. The expression of miR-143 and miR-29b showed no significantly difference between the two groups after CCRT. We calculated a risk score from the expression of miR-26b、miR-29a、miR-125b、miR-29b、miR-143 and then classified patients as with high or low risk. Compared to patients with low-risk score, high-risk patients had shorter PFS and OS. Cox regression model suggested that combining serum miR-29a and miR-125b before CCRT with miR-26b after CCRT was independent prognostic factors for PFS, whereas combining the former two is independent for OS. Conclusions Combined expression of serum miR-29a, miR-125b and miR-26b might provide prognostic value in loco-regionally advanced NPC patients treated with CCRT, especially for high-risk progression patients.

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.

scholarly journals Development and validation of the immune signature to predict distant metastasis in patients with nasopharyngeal carcinoma

2020 ◽  
Vol 8 (1) ◽  
pp. e000205 ◽  
Author(s):  
Sai-Lan Liu ◽  
Li-Juan Bian ◽  
Ze-Xian Liu ◽  
Qiu-Yan Chen ◽  
Xue-Song Sun ◽  
...  
Keyword(s):  
High Risk ◽  
Nasopharyngeal Carcinoma ◽  
Distant Metastasis ◽  
Low Risk ◽  
Reliable Estimate ◽  
Free Survival ◽  
Training Cohort ◽  
Immune Signature ◽  
High Risk Patients ◽  
Risk Patients

BackgroundThe tumor immune microenvironment has clinicopathological significance in predicting prognosis and therapeutic efficacy. We aimed to develop an immune signature to predict distant metastasis in patients with nasopharyngeal carcinoma (NPC).MethodsUsing multiplexed quantitative fluorescence, we detected 17 immune biomarkers in a primary screening cohort of 54 NPC tissues presenting with/without distant metastasis following radical therapy. The LASSO (least absolute shrinkage and selection operator) logistic regression model used statistically significant survival markers in the training cohort (n=194) to build an immune signature. The prognostic and predictive accuracy of it was validated in an external independent group of 304 patients.ResultsEight statistically significant markers were identified in the screening cohort. The immune signature consisting of four immune markers (PD-L1+ CD163+, CXCR5, CD117) in intratumor was adopted to classify patients into high and low risk in the training cohort and it showed a high level of reproducibility between different batches of samples (r=0.988 for intratumor; p<0.0001). High-risk patients had shorter distant metastasis-free survival (HR 5.608, 95% CI 2.619 to 12.006; p<0.0001) and progression-free survival (HR 2.798, 95% CI 1.498 to 5.266; p=0·001). The C-indexes which reflected the predictive capacity in training and validation cohort were 0.703 and 0.636, respectively. Low-risk patients benefited from induction chemotherapy plus concurrent chemoradiotherapy (IC+CCRT) (HR 0.355, 95% CI 0.147 to 0.857; p=0·021), while high-risk patients did not (HR 1.329, 95% CI 0.543 to 3.253; p=0·533). To predict the individual risk of distant metastasis, nomograms with the integration of both immune signature and clinicopathological risk factors were developed.ConclusionsThe immune signature provided a reliable estimate of distant metastasis risk in patients with NPC and might be applied to identify the cohort which benefit from IC+CCRT.

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.

scholarly journals A Novel Seven Gene Signature-Based Prognostic Model to Predict Distant Metastasis of Lymph Node-Negative Triple-Negative Breast Cancer

2021 ◽  
Vol 11 ◽  
Author(s):  
Wenting Peng ◽  
Caijin Lin ◽  
Shanshan Jing ◽  
Guanhua Su ◽  
Xi Jin ◽  
...  
Keyword(s):  
Lymph Node ◽  
High Risk ◽  
Distant Metastasis ◽  
Prognostic Model ◽  
Cox Regression ◽  
Gene Signature ◽  
Training Set ◽  
Node Negative ◽  
Auc Value ◽  
Validation Set

BackgroundThe prognosis of lymph node-negative triple-negative breast cancer (TNBC) is still worse than that of other subtypes despite adjuvant chemotherapy. Reliable prognostic biomarkers are required to identify lymph node-negative TNBC patients at a high risk of distant metastasis and optimize individual treatment.MethodsWe analyzed the RNA sequencing data of primary tumor tissue and the clinicopathological data of 202 lymph node-negative TNBC patients. The cohort was randomly divided into training and validation sets. Least absolute shrinkage and selection operator Cox regression and multivariate Cox regression were used to construct the prognostic model.ResultsA clinical prognostic model, seven-gene signature, and combined model were constructed using the training set and validated using the validation set. The seven-gene signature was established based on the genomic variables associated with distant metastasis after shrinkage correction. The difference in the risk of distant metastasis between the low- and high-risk groups was statistically significant using the seven-gene signature (training set: P &lt; 0.001; validation set: P = 0.039). The combined model showed significance in the training set (P &lt; 0.001) and trended toward significance in the validation set (P = 0.071). The seven-gene signature showed improved prognostic accuracy relative to the clinical signature in the training data (AUC value of 4-year ROC, 0.879 vs. 0.699, P = 0.046). Moreover, the composite clinical and gene signature also showed improved prognostic accuracy relative to the clinical signature (AUC value of 4-year ROC: 0.888 vs. 0.699, P = 0.029; AUC value of 5-year ROC: 0.882 vs. 0.693, P = 0.038). A nomogram model was constructed with the seven-gene signature, patient age, and tumor size.ConclusionsThe proposed signature may improve the risk stratification of lymph node-negative TNBC patients. High-risk lymph node-negative TNBC patients may benefit from treatment escalation.

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