Construction and validation of a prognostic nomogram for primary vulvar melanoma: a SEER population-based study

2020 ◽  
Vol 50 (12) ◽  
pp. 1386-1394
Author(s):  
Hongyu Zhou ◽  
Xuan Zou ◽  
Haoran Li ◽  
Lihua Chen ◽  
Xi Cheng
Keyword(s):  
Overall Survival ◽  
Validation Cohort ◽  
Risk Group ◽  
Curve Analysis ◽  
Lymph Node Status ◽  
Calibration Plot ◽  
Decision Curve Analysis ◽  
Training Cohort ◽  
Clinical Applicability ◽  
Vulvar Melanoma

Abstract Background Primary vulvar melanoma was an aggressive and poorly understood gynecological tumor. Unlike cutaneous melanoma, the incidence of vulvar melanoma was low but the survival was poor. There were no standard staging system and no census on treatment strategies of vulvar melanoma. Therefore, we aimed to conduct and validate a comprehensive prognostic model for predicting overall survival of vulvar melanoma and provide guidance for clinical management. Methods Patients diagnosed with vulvar melanoma between year 2004 and 2015 from Surveillance, Epidemiology, and End Result (SEER) database were randomized to training cohort and validation cohort. Multivariate survival analysis was performed to screen for independent factors of survival. A nomogram was established to predict overall survival of vulvar melanoma. Receiver operating characteristic curve and calibration plot were performed to verify the discrimination and accuracy of the model. The decision curve analysis was performed to verify the clinical applicability of the model. Results Total 737 patients with vulvar melanoma were randomized to the training cohort (n = 517) and the validation cohort (n = 220). Nomogram including age, race, tumor site, depth of tumor invasion, lymph node status, distant metastasis, tumor size, surgery, chemotherapy and radiotherapy was established and validated. The c-indexes for SEER stage, American Joint Committee on Cancer stage and this model were 0.561, 0.635 and 0.826, respectively. The high-risk group scored by this model had worse survival than the low-risk group (P < 0.001). Decision curve analysis revealed this model was superior in predicting survival. Conclusions Our model was deemed to be a useful tool for predicting overall survival of vulvar melanoma with good discrimination and clinical applicability. We hoped this model would assist gynecologists in clinical decision and management of patients diagnosed with vulvar melanoma.

scholarly journals Prediction of 90-day mortality among sepsis patients based on a nomogram integrating diverse clinical indices

2021 ◽  
Author(s):  
Qing-Bo Zeng ◽  
Long-Ping He ◽  
Nian-Qing Zhang ◽  
Qing-Wei Lin ◽  
Lin-Cui Zhong ◽  
...  
Keyword(s):  
Risk Factors ◽  
Intensive Care ◽  
Validation Cohort ◽  
International Normalized Ratio ◽  
Curve Analysis ◽  
Individual Risk ◽  
Decision Curve Analysis ◽  
Training Cohort ◽  
Receiver Operating Characteristic Curves ◽  
Individual Risk Factors

Abstract Background Sepsis is prevalent among intensive care units and is a frequent cause of death. Several studies have identified individual risk factors or potential predictors of sepsis-associated mortality, without defining an integrated predictive model. The present work aimed to define a nomogram for reliably predicting mortality. Methods We carried out a retrospective, single-center study based on 231 patients with sepsis who were admitted to our intensive care unit between May 2018 and October 2020. Patients were randomly split into training and validation cohorts. In the training cohort, multivariate logistic regression and a stepwise algorithm were performed to identify risk factors, which were then integrated into a predictive nomogram. Nomogram performance was assessed against the training and validation cohorts based on the area under receiver operating characteristic curves (AUC), calibration plots and decision curve analysis. Results Among the 161 patients in the training cohort and 70 patients in the validation cohort, 90-day mortality was 31.6%. Older age and higher values for the international normalized ratio, lactate level, and thrombomodulin level were associated with greater risk of 90-day mortality. The nomogram showed an AUC of 0.810 (95% CI 0.739 to 0.881) in the training cohort and 0.813 (95% CI 0.708 to 0.917) in the validation cohort. The nomogram also performed well based on the calibration curve and decision curve analysis. Conclusion This nomogram may help identify sepsis patients at elevated risk of 90-day mortality, which may help clinicians allocate resources appropriately to improve patient outcomes.

scholarly journals Nomogram predicts the overall survival of patients with Glioma

2021 ◽  
Author(s):  
Junming Xu ◽  
Honglin Li ◽  
Yuanyuan Zou ◽  
Chunjiao Yu
Keyword(s):  
Overall Survival ◽  
Logistic Regression Model ◽  
Training Group ◽  
Curve Analysis ◽  
Clinical Use ◽  
Seer Database ◽  
Discriminative Ability ◽  
Decision Curve Analysis ◽  
Training Cohort ◽  
Discrimination Ability

Abstract Aim Our study aimed to establish a nomogram to predict the cancer-specific surviva (CSS) of patients with Glioma. Patients and methods Patients diagnosed with glioma between 2004 and 2016 were collected from the SEER database. On the basis of the logistic regression model, the nomogram was established, the C-index was used to evaluate the accuracy of the nomogram, and the Decision Curve Analysis was used to evaluate the clinical use of the nomogram. Results 2626 eligible patients were randomly divided into training group (n=1864) and verification group (n=762). Nomogram had better discrimination ability, the C index of the training cohort was 0.74, and the C index of the verification cohort was 0.736. This new predictive model has shown better discriminative ability and greater benefits in both training and validation cohorts to predict CSS in patients with Glioma.Conclusion A nomogram was constructed to predict the CSS of Glioma patients at 1, 3, and 5 years. The verification showed that the nomogram had better discrimination and calibration ability, indicating that the nomogram can be used to predict the CSS of Glioma patients and guide the treatment of Glioma patients.

scholarly journals Development and Validation of a Prognostic Nomogram in Patients with Bladder Cancer after Radical Cystectomy: A Study Based on the Chinese Population

2021 ◽  
Author(s):  
Xun Lu ◽  
Yiduo Wang ◽  
Qi Chen ◽  
Di Xia ◽  
Hanyu Zhang ◽  
...  
Keyword(s):  
Overall Survival ◽  
Bladder Cancer ◽  
Radical Cystectomy ◽  
Validation Cohort ◽  
Disease Free Survival ◽  
Calibration Plot ◽  
Concordance Index ◽  
Free Survival ◽  
Training Cohort ◽  
Disease Free

Abstract PurposeTo develop and validate a prognostic nomogram in patients with bladder cancer who underwent radical cystectomy based on the Chinese population.MethodsThe nomogram was built on a retrospective study included 191 patients with bladder cancer who underwent radical cystectomy between January 2010 to December 2019 at the authors’ hospital. The primary cohort was divided into the training cohort and the validation cohort randomly. The endpoints in the study were disease-free survival and overall survival. The ability of distinguishing and predicting of the prognostic nomogram were determined by calibration plot and concordance index in the training cohort. Moreover, the results were also verified in the validation cohort internally.ResultsMultivariate analysis of the training cohort showed that hydronephrosis, Stage_T, Stage_N, PNI and EGFR were significantly associated with overall survival. Meanwhile, Stage_T, Stage_N, PNI and EGFR were independent risk factors for disease-free survival. The calibration plot agreed well between prediction and actual observation in survival possibility. The concordance index of the nomogram in the training cohort of overall survival and disease-free survival were 0.834 (95%CI: 0.785-0.833) and 0.823 (95%CI: 0.772-0.873), respectively. In the validation cohort, the nomogram also showed high predictive accuracy.ConclusionThe proposed nomogram showed high accuracy in predicting survival for bladder cancer patients after radical cystectomy.

scholarly journals A nomogram based on a gene signature for predicting the prognosis of patients with head and neck squamous cell carcinoma

2019 ◽  
Vol 34 (3) ◽  
pp. 309-317
Author(s):  
Bowen Yang ◽  
Lingyu Fu ◽  
Shan Xu ◽  
Jiawen Xiao ◽  
Zhi Li ◽  
...  
Keyword(s):  
Gene Expression ◽  
Overall Survival ◽  
Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Head And Neck ◽  
Predictive Model ◽  
Validation Cohort ◽  
Gene Signature ◽  
Curve Analysis ◽  
Decision Curve Analysis

Background: Head and neck squamous cell carcinoma (HNSCC) is one of the most common malignant tumors. The purpose of this study was to establish and validate a gene-expression-based prognostic signature in non-metastatic patients with HNSCC. Materials and methods: All the patients were retrieved from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. We randomly divided the GSE65858 samples into 70% (training cohort, n = 190) and 30% (internal validation cohort, n = 72). A total of 36 samples collected from the TCGA HNSCC databases were selected as an independent external validation cohort. The oligo package in R was used to normalize the raw data before analysis. Data characteristics were extracted, and a gene signature was built via the least absolute shrinkage and selection operator regression model. The predictive model was developed by multivariable Cox regression analysis. T stage, N stage, human papilloma virus status, and the gene signature were incorporated in this predictive model, which was shown as a nomogram. Calibration and discrimination were performed to assess the performance of the nomogram. The clinical utility of this nomogram was assessed by the decision curve analysis. Results: Overall, 2001 significant messenger RNAs in HNSCC samples were identified compared with normal samples. The gene signature contained seven genes and significantly correlated with overall survival. The gene signature was also significant in subgroup analysis of the primary cohort. The calibration was plotted in the external cohort (C-index 0.90, 95% CI 0.85, 0.95) compared with the training (C-index 0.76, 95% CI 0.73, 0.79) and internal (C-index 0.71, 95% CI 0.66, 0.77) cohorts. In clinic, a decision curve analysis demonstrated that the model including the prognostic gene signature score status was better than that without it. Conclusion: This study developed and validated a predictive model, which can promote the individualized prediction of overall survival in non-metastatic patients with HNSCC.

scholarly journals Prediction of 90-Day Mortality among Sepsis Patients Based on a Nomogram Integrating Diverse Clinical Indices

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Qingbo Zeng ◽  
Longping He ◽  
Nianqing Zhang ◽  
Qingwei Lin ◽  
Lincui Zhong ◽  
...  
Keyword(s):  
Risk Factors ◽  
Intensive Care ◽  
Validation Cohort ◽  
International Normalized Ratio ◽  
Curve Analysis ◽  
Individual Risk ◽  
Decision Curve Analysis ◽  
Training Cohort ◽  
Receiver Operating Characteristic Curves ◽  
Individual Risk Factors

Background. Sepsis is prevalent among intensive care units and is a frequent cause of death. Several studies have identified individual risk factors or potential predictors of sepsis-associated mortality, without defining an integrated predictive model. The present work was aimed at defining a nomogram for reliably predicting mortality. Methods. We carried out a retrospective, single-center study based on 231 patients with sepsis who were admitted to our intensive care unit between May 2018 and October 2020. Patients were randomly split into training and validation cohorts. In the training cohort, multivariate logistic regression and a stepwise algorithm were performed to identify risk factors, which were then integrated into a predictive nomogram. Nomogram performance was assessed against the training and validation cohorts based on the area under receiver operating characteristic curves (AUC), calibration plots, and decision curve analysis. Results. Among the 161 patients in the training cohort and 70 patients in the validation cohort, 90-day mortality was 31.6%. Older age and higher values for the international normalized ratio, lactate level, and thrombomodulin level were associated with greater risk of 90-day mortality. The nomogram showed an AUC of 0.810 (95% CI 0.739 to 0.881) in the training cohort and 0.813 (95% CI 0.708 to 0.917) in the validation cohort. The nomogram also performed well based on the calibration curve and decision curve analysis. Conclusion. This nomogram may help identify sepsis patients at elevated risk of 90-day mortality, which may help clinicians allocate resources appropriately to improve patient outcomes.

scholarly journals Development and External Validation of a Prognostic Nomogram for Angioimmunoblastic T-Cell Lymphoma

Blood ◽  
2021 ◽  
Vol 138 (Supplement 1) ◽  
pp. 1440-1440
Author(s):  
Hua Wang ◽  
Bibo Fu ◽  
Guanjun Chen
Keyword(s):  
T Cell ◽  
Validation Cohort ◽  
Cell Lymphoma ◽  
External Validation ◽  
Prognostic Index ◽  
Curve Analysis ◽  
T Cell Lymphoma ◽  
Survival Prediction ◽  
Decision Curve Analysis ◽  
Training Cohort

Abstract Introduction: Heterogeneity exists in prognosis of Angioimmunoblastic T-cell lymphoma (AITL) patients. Thus, a personalized prognostic model is crucial for survival prediction for each AITL patient. Nomogram is a powerful mathematical tool to predict survival. In this study, we aimed to develop a prognostic nomogram for AITL based on data from a large clinical database and validate it in an independent external cohort. In addition, we compared the accuracy of the nomogram with previous prognostic models used in AITL including International Prognostic Index (IPI) and Prognostic Index of T-cell lymphoma (PIT) model. Methods: Totally, 1071 patients were retrieved from the Surveillance, Epidemiology, and End Results (SEER) database as the training cohort; 156 patients diagnosed from 2009-2021 in Sun Yat-Sen University Cancer Center or The First Affiliated Hospital of Guangzhou Medical University were recruited as the external validation cohort. 105 patients with IPI information in the training cohort were used to compare the nomogram and IPI. 156 patients in the external cohort were used to compare the nomogram and IPI or PIT. The Prognostic risk factors in the nomogram were identified by cox proportional hazards model. Concordance index (C-index) and calibration curves were used for internal and external validation. C-index and decision curve analysis (DCA) curves were used to compare the models. Results: Age, sex, systematic symptoms, Ann Arbor stage and chemotherapy were risk factors finally included to develop the nomogram. C-indexes of the nomogram were 0.676 and 0.652 in the training cohort and the validation cohort. Favorable agreement of nomogram-predicted and actual probability of overall survival (OS) was detected by calibration curves in both training and validation cohorts. In the cohort of 105 patients, C-indexes of the nomogram and IPI were 0.696 vs 0.616 (P<0.05); in the cohort of 156 patients, C-indexes were 0.652 vs 0.597 (P=0.08) of the nomogram and IPI while 0.652 vs 0.616 (P=0.176) of the nomogram and PIT. Decision curve analysis (DCA) showed superiority of the nomogram as compared with the IPI or the PIT model in both 105 and 156 patients' cohorts. A web calculator was published for convenient clinical application. Based on the prognostic scores calculated by the nomogram, three cut points were identified by X-tile program to establish a classification system that could significantly distinguish patients in four risk groups. Conclusion: We establish a nomogram for survival prediction of AITL, which may assist in treatment strategy making and survival consultation. Figure 1 Figure 1. Disclosures No relevant conflicts of interest to declare.

scholarly journals A Prediction Model Based on Noninvasive Indicators to Predict the 8-Year Incidence of Type 2 Diabetes in Patients with Nonalcoholic Fatty Liver Disease: A Population-Based Retrospective Cohort Study

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Xintian Cai ◽  
Qing Zhu ◽  
Yuanyuan Cao ◽  
Shasha Liu ◽  
Mengru Wang ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Cohort Study ◽  
Fatty Liver Disease ◽  
Validation Cohort ◽  
Japanese Patients ◽  
Curve Analysis ◽  
Decision Curve Analysis ◽  
Training Cohort ◽  
Nonalcoholic Fatty Liver

Background. The prevention of type 2 diabetes (T2D) and its associated complications has become a major priority of global public health. In addition, there is growing evidence that nonalcoholic fatty liver disease (NAFLD) is associated with an increased risk of diabetes. Therefore, the purpose of this study was to develop and validate a nomogram based on independent predictors to better assess the 8-year risk of T2D in Japanese patients with NAFLD. Methods. This is a historical cohort study from a collection of databases that included 2741 Japanese participants with NAFLD without T2D at baseline. All participants were randomized to a training cohort ( n = 2058 ) and a validation cohort ( n = 683 ). The data of the training cohort were analyzed using the least absolute shrinkage and selection operator method to screen the suitable and effective risk factors for Japanese patients with NAFLD. A cox regression analysis was applied to build a nomogram incorporating the selected features. The C-index, receiver operating characteristic curve (ROC), calibration plot, decision curve analysis, and Kaplan-Meier analysis were used to validate the discrimination, calibration, and clinical usefulness of the model. The results were reevaluated by internal validation in the validation cohort. Results. We developed a simple nomogram that predicts the risk of T2D for Japanese patients with NAFLD by using the parameters of smoking status, waist circumference, hemoglobin A1c, and fasting blood glucose. For the prediction model, the C-index of training cohort and validation cohort was 0.839 (95% confidence interval (CI), 0.804-0.874) and 0.822 (95% CI, 0.777-0.868), respectively. The pooled area under the ROC of 8-year T2D risk in the training cohort and validation cohort was 0.811 and 0.805, respectively. The calibration curve indicated a good agreement between the probability predicted by the nomogram and the actual probability. The decision curve analysis demonstrated that the nomogram was clinically useful. Conclusions. We developed and validated a nomogram for the 8-year risk of incident T2D among Japanese patients with NAFLD. Our nomogram can effectively predict the 8-year incidence of T2D in Japanese patients with NAFLD and helps to identify people at high risk of T2D early, thus contributing to effective prevention programs for T2D.

scholarly journals Establishment and validation of prognosis model for patients with cerebral contusion

BMC Neurology ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Yufeng Zhu ◽  
Xiaoqing Jin ◽  
Lulu Xu ◽  
Pei Han ◽  
Shengwu Lin ◽  
...  
Keyword(s):  
Prediction Model ◽  
Validation Cohort ◽  
Characteristic Curve ◽  
Third Ventricle ◽  
Curve Analysis ◽  
Imaging Features ◽  
Lasso Regression ◽  
Cerebral Contusion ◽  
Decision Curve Analysis ◽  
Training Cohort

Abstract Background And Objective Cerebral Contusion (CC) is one of the most serious injury types in patients with traumatic brain injury (TBI). In this study, the baseline data, imaging features and laboratory examinations of patients with CC were summarized and analyzed to develop and validate a prediction model of nomogram to evaluate the clinical outcomes of patients. Methods A total of 426 patients with cerebral contusion (CC) admitted to the People’s Hospital of Qinghai Province and Affiliated Hospital of Qingdao University from January 2018 to January 2021 were included in this study, We randomly divided the cohort into a training cohort (n = 284) and a validation cohort (n = 142) with a ratio of 2:1.At Least absolute shrinkage and selection operator (Lasso) regression were used for screening high-risk factors affecting patient prognosis and development of the predictive model. The identification ability and clinical application value of the prediction model were analyzed through the analysis of receiver operating characteristic curve (ROC), calibration curve, and decision curve analysis (DCA). Results Twelve independent prognostic factors, including age, Glasgow Coma Score (GCS), Basal cistern status, Midline shift (MLS), Third ventricle status, intracranial pressure (ICP) and CT grade of cerebral edema,etc., were selected by Lasso regression analysis and included in the nomogram. The model showed good predictive performance, with a C index of (0.87, 95% CI, 0.026–0.952) in the training cohort and (0.93, 95% CI, 0.032–0.965) in the validation cohort. Clinical decision curve analysis (DCA) also showed that the model brought high clinical benefits to patients. Conclusion This study established a high accuracy of nomogram model to predict the prognosis of patients with CC, its low cost, easy to promote, is especially applicable in the acute environment, at the same time, CSF-glucose/lactate ratio(C-G/L), volume of contusion, and mean CT values of edema zone, which were included for the first time in this study, were independent predictors of poor prognosis in patients with CC. However, this model still has some limitations and deficiencies, which require large sample and multi-center prospective studies to verify and improve our results.

scholarly journals Dynamic nomograms combining N classification with ratio-based nodal classifications to predict long-term survival for patients with lung adenocarcinoma after surgery: a SEER population-based study

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Suyu Wang ◽  
Yue Yu ◽  
Wenting Xu ◽  
Xin Lv ◽  
Yufeng Zhang ◽  
...  
Keyword(s):  
Overall Survival ◽  
Lung Adenocarcinoma ◽  
External Validation ◽  
Predictive Performance ◽  
Information Criterion ◽  
Curve Analysis ◽  
Tnm Stage ◽  
Concordance Index ◽  
Cancer Specific Survival ◽  
Decision Curve Analysis

Abstract Background The prognostic roles of three lymph node classifications, number of positive lymph nodes (NPLN), log odds of positive lymph nodes (LODDS), and lymph node ratio (LNR) in lung adenocarcinoma are unclear. We aim to find the classification with the strongest predictive power and combine it with the American Joint Committee on Cancer (AJCC) 8th TNM stage to establish an optimal prognostic nomogram. Methods 25,005 patients with T1-4N0–2M0 lung adenocarcinoma after surgery between 2004 to 2016 from the Surveillance, Epidemiology, and End Results database were included. The study cohort was divided into training cohort (13,551 patients) and external validation cohort (11,454 patients) according to different geographic region. Univariate and multivariate Cox regression analyses were performed on the training cohort to evaluate the predictive performance of NPLN (Model 1), LODDS (Model 2), LNR (Model 3) or LODDS+LNR (Model 4) respectively for cancer-specific survival and overall survival. Likelihood-ratio χ2 test, Akaike Information Criterion, Harrell concordance index, integrated discrimination improvement (IDI) and net reclassification improvement (NRI) were used to evaluate the predictive performance of the models. Nomograms were established according to the optimal models. They’re put into internal validation using bootstrapping technique and external validation using calibration curves. Nomograms were compared with AJCC 8th TNM stage using decision curve analysis. Results NPLN, LODDS and LNR were independent prognostic factors for cancer-specific survival and overall survival. LODDS+LNR (Model 4) demonstrated the highest Likelihood-ratio χ2 test, highest Harrell concordance index, and lowest Akaike Information Criterion, and IDI and NRI values suggested Model 4 had better prediction accuracy than other models. Internal and external validations showed that the nomograms combining TNM stage with LODDS+LNR were convincingly precise. Decision curve analysis suggested the nomograms performed better than AJCC 8th TNM stage in clinical practicability. Conclusions We constructed online nomograms for cancer-specific survival and overall survival of lung adenocarcinoma patients after surgery, which may facilitate doctors to provide highly individualized therapy.

Identifying an immune-related gene-pair for prognosis prediction of metastatic colorectal cancer.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e15565-e15565
Author(s):  
Qiqi Zhu ◽  
Du Cai ◽  
Wei Wang ◽  
Min-Er Zhong ◽  
Dejun Fan ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
High Risk ◽  
Prognostic Value ◽  
Validation Cohort ◽  
Risk Group ◽  
High Risk Group ◽  
Related Gene ◽  
Training Cohort ◽  
Gene Pairs ◽  
Immune Related Gene

e15565 Background: Few robust predictive biomarkers have been applied in clinical practice due to the heterogeneity of metastatic colorectal cancer (mCRC) . Using the gene pair method, the absolute expression value of genes can be converted into the relative order of genes, which can minimize the influence of the sequencing platform difference and batch effects, and improve the robustness of the model. The main objective of this study was to establish an immune-related gene pairs signature (IRGPs) and evaluate the impact of the IRGPs in predicting the prognosis in mCRC. Methods: A total of 205 mCRC patients containing overall survival (OS) information from the training cohort ( n = 119) and validation cohort ( n = 86) were enrolled in this study. LASSO algorithm was used to select prognosis related gene pairs. Univariate and multivariate analyses were used to validate the prognostic value of the IRGPs. Gene sets enrichment analysis (GSEA) and immune infiltration analysis were used to explore the underlying biological mechanism. Results: An IRGPs signature containing 22 gene pairs was constructed, which could significantly separate patients of the training cohort ( n = 119) and validation cohort ( n = 86) into the low-risk and high-risk group with different outcomes. Multivariate analysis with clinical factors confirmed the independent prognostic value of IRGPs that higher IRGPs was associated with worse prognosis (training cohort: hazard ratio (HR) = 10.54[4.99-22.32], P < 0.001; validation cohort: HR = 3.53[1.24-10.08], P = 0.012). GSEA showed that several metastasis and immune-related pathway including angiogenesis, TGF-β-signaling, epithelial-mesenchymal transition and inflammatory response were enriched in the high-risk group. Through further analysis of the immune factors, we found that the proportions of CD4+ memory T cell, regulatory T cell, and Myeloid dendritic cell were significantly higher in the low-risk group, while the infiltrations of the Macrophage (M0) and Neutrophil were significantly higher in the high-risk group. Conclusions: The IRGPs signature could predict the prognosis of mCRC patients. Further prospective validations are needed to confirm the clinical utility of IRGPs in the treatment decision.

Sign in / Sign up

Export Citation Format

Share Document