Validation of the Newly Proposed MD Anderson Prognostic Risk Model for Patients with Myelodysplastic Syndromes

Blood ◽  
2010 ◽  
Vol 116 (21) ◽  
pp. 444-444
Author(s):  
Maria Corrales-Yepez ◽  
Jeffrey E. Lancet ◽  
Alan F. List ◽  
Mohamed A. Kharfan-Dabaja ◽  
Teresa Field ◽  
...  
Keyword(s):  
Overall Survival ◽  
High Risk ◽  
Myelodysplastic Syndromes ◽  
Risk Model ◽  
Cox Regression ◽  
Risk Group ◽  
Risk Groups ◽  
Refractory Anemia ◽  
Kaplan Meier ◽  
Md Anderson

Abstract Abstract 444 Background: The international prognostic scoring system (IPSS) is the most widely used clinical tool for risk stratification and tailoring treatment in myelodysplastic syndromes (MDS). Despite its utility, the IPSS has several limitations. The IPSS was developed using outcomes of untreated primary MDS patients at time of diagnosis, and does not account for patient age, performance, and degree of cytopenia. The recently reported MD Anderson risk model (MDAS) addresses many of the limitations of IPSS (Kantarjian et al, CANCER September 15, 2008 / Volume 113 / Number 6). We validated this new risk model in a large external single institution cohort of patients. Methods: Data were collected retrospectively from Moffitt Cancer Center (MCC) MDS database and chart review of patients with MDS. The primary objective was to validate the new risk model calculated at time of initial presentation MCC. The MDAS was calculated as published based on age, performance status, blast%, degree of thrombocytopenia, cytogenetics, white blood cell count, and prior history if transfusion. Patients were divided into four risk groups: low (0-4 points), int-1 (5-6 points), int-2 (7-8 points), and high risk (≥ 9 points). All analyses were conducted using SPSS version 15.0. (SPSS Inc, Chicago, IL). The Kaplan–Meier method was used to estimate median overall survival. Log rank test was used to compare Kaplan–Meier survival estimates between two groups. Cox regression was used for multivariable analysis. Results: Between January 2001 and December 2009, 844 patients were captured by MCC MDS database. The median age was 69 years, MDS subtypes were coded as Refractory anemia (RA) 98 (12%), refractory anemia with ring sideroblasts (RARS) 76 (9%), MDS with del(5q) 20 (2.4%), refractory cytopenia with multi-lineage dysplasia (RCMD) 96 (11%), refractory anemia with excess blasts (RAEB) 255 (30%), therapy related MDS 22 (2.6%), and MDS-nos 275 (33%). IPSS risk groups were low risk in 158 (18.7%), intermediate-1 (int-1) 362 (42.9%), intermediate-2 (int-2) 168 (19.9%), high risk 45 (5.3%), and missing in 111 (13.2%). Based on the new risk model 169 patients (20%) were low risk, 250 (29.6%) int-1, 182 (21.6%) int-2, 135 (16%) high risk, and 94 (11.1%) were unknown. The median OS for all patients was 36 months (95% CI 31.5–40.5 mo). Age, IPSS risk group, serum ferritin, and RBC transfusion dependence were all significant prognostic factors in univariable analysis. The median OS was 92 mo (95%CI 68.1–115.9 mo), 49 mo (95%CI 40.4–57.6 mo), 26 mo (95%CI 21.2–30.8 mo), and 15 month (95%CI 11.8–42.1 mo) respectively for patients with low, int-1, int-2 and high risk patients according to MDAS. (Figure-1) (P < 0.005). In patients with low/int-1 IPSS risk group the median OS according to MDAS was 92 mo (95%CI 68.3–115.7 mo), 49 mo (95%CI 49.3–58.7 mo), 28 mo (95%CI 20.7–35.3), and 19 mo(95% CI 9.9–28.1 mo) respectively for patients with low, int-1, int-2, high risk MDAS (p<0.005). In patients with int-2/high IPSS risk categories only 4 patients were reclassified as low MDAS risk and the median OS for those patients was 10 month (95% CI 0–38 mo). The median OS was 49 mo (95%CI 23.5–74.5 mo), 23 mo (95%CI 19.4–26.6 mo), 14 mo (95% CI 11.5–16.5 mo). (p<0.005). For all the patients the rate of AML transformation according to MDAS was 5.9%, 16.8%, 36.3%, and 50.4% respectively for low, int-1, int-2, and high risk MDAS groups. (p <0.005). In Cox regression analysis, higher risk MDAS predicted inferior OS (Hazard ratio (HR) 1.54 (95%CI 1.35–1.75) (p <0.005) independent of IPSS risk group (HR 1.25 95%CI 1.1–1.45) (p =0.004). Conclusion: Our data validates the prognostic value of the MDAS risk model which was predictive for overall survival and AML transformation. The MDAS complements the IPSS particularly in low/int-1 risk group by identifying patients with higher risk disease behavior and inferior outcome. The utility of this model as a treatment decision tool should be studied prospectively. Disclosures: No relevant conflicts of interest to declare.

Validation of the Lower Risk MD Anderson Prognostic Scoring System for Patients with Myelodysplastic Syndromes

Blood ◽  
2012 ◽  
Vol 120 (21) ◽  
pp. 3826-3826 ◽  
Author(s):  
Rami S. Komrokji ◽  
Maria Corrales-Yepez ◽  
Najla H Al Ali ◽  
Eric Padron ◽  
Ling Zhang ◽  
...  
Keyword(s):  
Overall Survival ◽  
Risk Stratification ◽  
Lower Risk ◽  
Risk Model ◽  
Cox Regression ◽  
Risk Group ◽  
Refractory Anemia ◽  
Kaplan Meier ◽  
Disease Behavior ◽  
Md Anderson

Abstract Abstract 3826 Background: The International Prognostic Scoring System (IPSS) is the most widely used clinical tool for risk stratification and tailoring treatment in myelodysplastic syndromes (MDS). Outcome of patients stratified as lower risk MDS by IPSS is variable, with a subset of patients experiencing inferior than expected outcomes. Identifying patients with higher risk disease behavior is indispensible for proper implantation of disease altering therapy. The Lower Risk MD Anderson Risk Model (LR-MDAS) is a recently proposed model provides prognostic refinement to identify such patients (Garcia-Manero et al, Leukemia 2008). To validate this model, we tested the new risk model in large external single institution cohort of patients. Methods: Data were collected retrospectively from the Moffitt Cancer Center (MCC) MDS database and chart review. The primary objective was to validate the new risk model when applied at time of initial presentation to MCC. The LR-MDAS was calculated as published using the sum of points generated from unfavorable (non-del(5q), non–diploid) cytogenetics, hemoglobin (hgb) <10g/dl, platelet count (plt) <50 k/uL or 50–200k/uL, bone marrow blast % >= 4, and age>= 60 years. Patients were divided into 3 prognostic categories. All analyses were conducted using SPSS version 15.0. (SPSS Inc, Chicago, IL). The Kaplan–Meier method was used to estimate median overall survival. Log rank test was used to compare Kaplan–Meier survival estimates between the groups. Cox regression was used for multivariable analysis. Results: Between January 2001 and December 2009, 479 patients with low or int-1 risk IPSS were captured by MCC MDS database. The median age was 69 years, MDS subtypes were coded as Refractory anemia (RA) 113 (24%), refractory anemia with ring sideroblasts (RARS) 73 (15%), MDS with del(5q) 19 (4%), refractory cytopenia with multi-lineage dysplasia (RCMD) 109 (23%), refractory anemia with excess blasts (RAEB) 147 (31%), and MDS-unclassified (U) 18 (4%). IPSS risk groups were low risk in 145 (30%), and intermediate-1 (int-1) 334 (70%). Only 31 patients (7%) had a poor risk karyotype by IPSS. Red blood cell transfusion dependence was documented in 42% (n=202), 22% had elevated serum ferritin ≥ 1000 ng/ml, and 45% (n=217) received azanucleoside treatment. Based on the LR-MDAS, 52 patients (11%) were category 1, 188 (39%) category 2, 232 (48%) category 3, and 7 (2%) were unknown. The median OS from time of referral to MCC for all patients was 32 months (95% CI 27–37 mo), Age, IPSS risk group, serum ferritin, and RBC transfusion dependence were all significant prognostic factors in univariate analysis. The median OS for the corresponding categories was, 1 - not reached (NR), 2– 50 mo (95%CI 33–68 mo), and 3 – 22 mo (95%CI 16–27 mo), from time of MCC referral, respectively. (Figure-1) (P < 0.005). Among 142 patients classified as low risk by IPSS, 25 patients (18%) were category 1 LR-MDAS, 81 (57%) category 2, and 36 (25%) category 3 with corresponding median OS of, NR, 62 month, and 35 month respectively (p=0.002). Among 330 patients risk stratified as int-1 IPSS group, 27 patients where category 1 LR-MDAS, 107 category 2, and 196 category 3 where median OS was NR, 28 months and 20 months, respectively. (p< 0.001) (Figure-2). When we applied IPSS risk stratification among each category of LR-MDAS to assess if IPSS can further refine prognosis within LR-MDAS categories, only in patients classified as Category 2 LR-MDAS the median OS was different among low and int-1 risk IPSS (62 month versus 28 month). (p <0.005). The rate of AML transformation according to LR-MDAS was 4%, 12%, and 20% for category 1,2, and 3, respectively. (p<0.02). In Cox regression analysis higher risk LR-MDAS predicted inferior OS (Hazard ratio (HR) 1.8 (95%CI 1.4–2.3) (p <0.005) independent of IPSS risk group (HR 2 95%CI 1.4–2.8) (p <0.005). Conclusion: Our data validates the prognostic value of the proposed LR-MDAS risk model, demonstrating predictive power for overall survival and AML transformation among low/int-1 risk IPSS. The LR-MDAS is complementary to the IPSS, offering further discrimination to identifying those patients with aggressive disease behavior that merit disease altering therapy. The utility of the model as treatment decision tool should be studied prospectively. Disclosures: No relevant conflicts of interest to declare.

scholarly journals Prognostic Significance of Severe Thrombocytopenia in Overall Survival of Patients with Myelodysplastic Syndromes Treated with Azacytidine. a Multicenter Study By the Hellenic MDS Study Group

Blood ◽  
2018 ◽  
Vol 132 (Supplement 1) ◽  
pp. 1822-1822
Author(s):  
Athanasios Galanopoulos ◽  
Evdoxia Kamouza ◽  
Christos K. Kontos ◽  
Argiris Symeonidis ◽  
Vassiliki Pappa ◽  
...  
Keyword(s):  
Overall Survival ◽  
Regression Analysis ◽  
High Risk ◽  
Myelodysplastic Syndromes ◽  
Cox Regression ◽  
Risk Group ◽  
Prognostic Significance ◽  
High Risk Group ◽  
Severe Thrombocytopenia ◽  
Kaplan Meier

Abstract INTRODUCTION: The hypomethylating agents 5-azacitidine (5-AZA) and decitabine are recently considered the most preferable treatment option for patients with intermediate-2 and high-risk myelodysplastic syndromes (MDS), by International Prognostic Scoring System (IPSS). 5-AZA responders experience improved survival both in clinical trials (AZA 001) and in the real-life setting. Thrombocytopenia is a common event in MDS, during the course of the disease; recently, severe thrombocytopenia (≤30,000 platelets/μL) has been suggested as an important factor regarding the survival of MDS patients. In the present study, we examined the potential prognostic significance of severe thrombocytopenia, in intermediate-2- and high-risk MDS patients, being treated with 5-AZA, during the first 3 years of treatment. METHODS: This retrospective study included 850 higher-risk patients (intermediate-2- and high-risk), registered in the the Hellenic MDS Registry, treated with 5-AZA from 2010 to 2018 and were followed up for a time period up to 3 years. Complete patient data were available for 225 patients. Biostatistical analysis performed in this study included Kaplan-Meier survival analysis and Cox regression. The level of statistical significance was set at a probability value of less than 0.050 (P<0.050). RESULTS: The current study included 225 patients (159 male and 66 women) with intermediate-2- or high-risk MDS treated with 5-AZA, with a median age of 74 years (range: 47 - 89). WHO diagnosis included 1 (0.4%) case of RCUD, 8 (3.6%) cases of RCMD, 3 (1.3%) cases of RCMD-RS, 43 (19.1%) cases of RAEB-1, and 170 (75.6%) cases of RAEB-2. According to IPSS, 174 (77.3%) patients were classified in the intermediate-2 risk group and 51 (22.7%) patients in the high-risk group. In addition, according to IPSS-R, 24 (10.7%) patients were categorized in the intermediate risk group, 106 (47.1%) patients in the high-risk group, and 95 (42.2%) patients in the very-high risk group. All patients were evaluated regarding response to 5-AZA treatment. The initial response at 6 months was: complete remission (CR) in 40 (18.4%) patients, partial remission (PR) in 24 (11.1%) patients, hematological improvement (HI) in 35 (16.1%) patients; therefore, the initial overall response rate (CR, PR, and HI) was 45.6%. Stable disease (SD) was achieved by 56 (25.8%) MDS patients, while 62 (28.5%) patients showed progression of disease (PD) or treatment failure. Severe thrombocytopenia was not predictive of response, as shown using logistic regression analysis. However, severe thrombocytopenia predicted poor overall survival (OS) in the first 3 years of treatment with 5-AZA, as shown by the Kaplan-Meier analysis (Figure 1; P=0.016). Regarding AML-free survival, a strong trend was observed for thy unfavorable prognostic role of this severe cytopenia (P=0.096). Univariate Cox regression analysis for OS revealed a statistically significant hazard ratio (HR) of 1.6 for MDS patients with severe thrombocytopenia (HR=1.6, 95% CI=1.08, P=0.019). CONCLUSIONS: Our study showed that severe thrombocytopenia (≤ 30,000 platelets/μL) in intermediate-2- and high-risk MDS patients, treated with 5-AZA, predicts lower OS rates during the first 3 years of treatment. Figure 1. Figure 1. Disclosures No relevant conflicts of interest to declare.

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.

The Combination of SKY92 and ISS Provides a Powerful Tool to Identify Both High Risk and Low Risk Multiple Myeloma Cases, Validation in Two Independent Cohorts

Blood ◽  
2015 ◽  
Vol 126 (23) ◽  
pp. 2970-2970 ◽  
Author(s):  
Martin van Vliet ◽  
Joske Ubels ◽  
Leonie de Best ◽  
Erik van Beers ◽  
Pieter Sonneveld
Keyword(s):  
Overall Survival ◽  
High Risk ◽  
Research Funding ◽  
Prognostic Markers ◽  
Risk Group ◽  
Risk Groups ◽  
High Risk Group ◽  
Low Risk ◽  
Kaplan Meier ◽  
Standard Risk

Abstract Introduction Multiple Myeloma (MM) is a heterogeneous disease with a strong need for robust markers for prognosis. Frequently occurring chromosomal abnormalities, such as t(4;14), gain(1q), and del(17p) etc. have some prognostic power, but lack robustness across different cohorts. Alternatively, gene expression profiling (GEP) studies have developed specific high risk signatures such as the SKY92 (EMC92, Kuiper et al. Leukemia 2012), which has shown to be a robust prognostic factor across five different clinical datasets. Moreover, studies comparing prognostic markers have indicated that the SKY92 signature outperforms all other markers for identifying high risk patients, both in single and multivariate analyses. Similarly, when assessing the prognostic value of combinations of various prognostic markers, the SKY92 combined with ISS was the top performer, and also enables detection of a low risk group (Kuiper et al. ASH 2014). Here, we present a further validation of the low and high risk groups identified by the SKY92 signature in combination with ISS on two additional cohorts of patients with diverse treatment backgrounds, containing newly diagnosed, previously treated, and relapsed/refractory MM patients. Materials and Methods The SKY92 signature was applied to two independent datasets. Firstly, the dataset from the Total Therapy 6 (TT6) trial, which is a phase 2 trial for symptomatic MM patients who have received 1 or more prior lines of treatment. The TT6 treatment regime consists of VTD-PACE induction, double transplant with Melphalan + VRD-PACE, followed by alternating VRD/VMD maintenance. Affymetrix HG-U133 Plus 2.0 chips were performed at baseline for n=55 patients, and OS was made available previously (Gene Expression Omnibus identifier: GSE57317). However, ISS was not available for this dataset. Secondly, a dataset of patients enrolled at two hospitals in the Czech Republic, and one in Slovakia (Kryukov et al. Leuk&Lymph 2013). Patients of all ages, and from first line up to seventh line of treatment were included (treatments incl Bort, Len, Dex). For n=73 patients Affymetrix Human Gene ST 1.0 array, OS (n=66), and ISS (n=58) was made available previously (ArrayExpress accession number: E-MTAB-1038). Both datasets were processed from .CEL files by MAS5 (TT6), and RMA (Czech), followed by mean variance normalization per probeset across the patients. The SKY92 was applied as previously described (Kuiper et al. Leukemia 2012), and identifies a High Risk and Standard Risk group. In conjunction with ISS, the SKY92 Standard Risk group is then further stratified into low and intermediate risk groups (Kuiper et al. ASH 2014). Kaplan-Meier plots were created, and the Cox proportional hazards model was used to calculate Hazard Ratios (HR), and associated 1-sided p-values that assess whether the SKY92 High Risk group has worse survival than SKY92 Standard Risk group (i.e. HR>1). Results Figure 1 shows the Kaplan Meier plots of the SKY92 High Risk and Standard Risk groups on the TT6 and Czech cohorts. On the TT6 dataset, the SKY92 signature identifies 11 out of 55 patients (20%) as High Risk. In both datasets, the SKY92 High Risk group has significantly worse overall survival, HR=10.3, p=7.4 * 10-6 (TT6), and HR=2.6, p=2.2 * 10-2 (Czech). In addition, the combination of SKY92 with ISS on the Czech dataset identifies a low risk group of 14 out of 61 patients (23%), with a five year overall survival estimate of 100% versus 28.7% in the SKY92 High Risk group (HR=inf). Robustness of the SKY92 signature is further demonstrated by the fact that it validates on both datasets, despite different microarray platforms being used. Conclusions The SKY92 high risk signature has been successfully validated on two independent datasets generated using different microarray platforms. In addition, on the Czech data, the low risk group (SKY92 Standard Risk combined with ISS 1) has been successfully validated. Together, this signifies the robust nature of the SKY92 signature for high and low risk prediction, across treatments, and with applicability in newly diagnosed, treated, and relapsed/refractory MM patients. Figure 1. Kaplan-Meier plots showing a significantly poorer overall survival in patients identified as SKY92 High Risk (red curves), relative to SKY92 Standard Risk, on both the TT6 (left), and Czech (middle) datasets, as well as a low risk group by SKY92 & ISS1 on the Czech dataset (green curve, right). Figure 1. Kaplan-Meier plots showing a significantly poorer overall survival in patients identified as SKY92 High Risk (red curves), relative to SKY92 Standard Risk, on both the TT6 (left), and Czech (middle) datasets, as well as a low risk group by SKY92 & ISS1 on the Czech dataset (green curve, right). Disclosures van Vliet: SkylineDx: Employment. Ubels:SkylineDx: Employment. de Best:SkylineDx: Employment. van Beers:SkylineDx: Employment. Sonneveld:Celgene: Honoraria, Research Funding; Amgen: Honoraria, Research Funding; Karyopharm: Research Funding; SkylineDx: Membership on an entity's Board of Directors or advisory committees; Janssen: Honoraria, Research Funding.

scholarly journals Identification of Novel Tumor Microenvironment-Related Long Noncoding RNAs to Determine the Prognosis and Response to Immunotherapy of Hepatocellular Carcinoma Patients

2021 ◽  
Vol 8 ◽  
Author(s):  
Shenglan Huang ◽  
Jian Zhang ◽  
Xiaolan Lai ◽  
Lingling Zhuang ◽  
Jianbing Wu
Keyword(s):  
Hepatocellular Carcinoma ◽  
High Risk ◽  
Tumor Microenvironment ◽  
Immune Cells ◽  
Risk Model ◽  
Cox Regression ◽  
Risk Group ◽  
Low Risk ◽  
Kaplan Meier ◽  
Treatment Responses

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.

Construction of a Prognostic Model Related to Ferroptosis and Iron-Metabolism and the Relationship with the Immune Microenvironment of Gastric Cancer

2021 ◽  
Author(s):  
Fang Wen ◽  
Xiaoxue Chen ◽  
Wenjie Huang ◽  
Shuai Ruan ◽  
Suping Gu ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
High Risk ◽  
Iron Metabolism ◽  
Risk Model ◽  
Cox Regression ◽  
Risk Group ◽  
Risk Groups ◽  
High Risk Group ◽  
Low Risk ◽  
Immune Microenvironment

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.

scholarly journals Identification of a Seven-Gene Signature and Establishment of a Nomogram Predicting Overall Survival in Head and Neck Squamous Cell Carcinoma

2020 ◽  
Author(s):  
Haige Zheng ◽  
Xiangkun Wu ◽  
Huixian Liu ◽  
Yumin Lu ◽  
Hengguo Li
Keyword(s):  
Overall Survival ◽  
Squamous Cell Carcinoma ◽  
Regression Analysis ◽  
High Risk ◽  
Cell Carcinoma ◽  
Cox Regression ◽  
Risk Groups ◽  
Gene Signature ◽  
Cox Regression Analysis ◽  
Kaplan Meier

Abstract Background: Head and neck squamous cell carcinoma (HNSCC) is a highly heterogeneous tumor with high incidence and poor prognosis. Therefore, effective predictive models are needed to evaluate patient outcomes and optimize treatment. Methods: Ten gene microarray datasets were obtained from the gene expression omnibus (GEO) database. Level 3 mRNA expression and clinical data were obtained in The Cancer Genome Atlas (TCGA) database. We identified highly robust differentially-expressed genes (DEGs) between HNSCC and normal tissue in nine GEO and TCGA datasets using Robust Rank Aggregation (RRA) method. Univariate Cox regression analysis and lasso Cox regression analysis were performed to identify DEGs related to the Overall-survival (OS) and to construct a prognostic gene signature. External validation was performed using GSE65858. Moreover, gene set enrichment analyses (GSEA) analysis was used to analyze significantly rich pathways in high-risk and low-risk groups, and tumor immunoassays were used to clarify immune correlation of the prognostic gene. Finally, integrate multiple forecast indicators were used to build a nomogram using the TCGA-HNSCC dataset. Kaplan–Meier analysis, receiver operating characteristic (ROC), a calibration plot, Harrell’s concordance index (C-index), and decision curve analysis (DCA) were used to test the predictive capability of the seven genetic signals and the nomogram. Results: A novel seven-gene signature (including SLURP1, SCARA5, CLDN10, MYH11, CXCL13, HLF, and ITGA3) was established to predict overall survival in HNSCC patients. ROC curve performed well in the training and validation data sets. Kaplan–Meier analysis demonstrated that low-risk groups had a longer survival time. The nomogram containing seven genetic markers and clinical prognostic factors was a good predictor of HNSCC survival and showed a certain net clinical benefit through the DCA curve. Further research demonstrated that the infiltration degree of CD8 + T cells, B cells, neutrophils, and NK cells were significantly lower in the high-risk group.Conclusion: Our analysis established a seven-gene model and nomogram to accurately predict the prognosis status of HNSCC patients, immune relevance was also described, which may provide a new possibility for individual treatment and medical decision-making.

scholarly journals A Novel Ferroptosis-related Gene Signature for Prognosis Prediction in Glioma

2021 ◽  
Author(s):  
Tian Lan ◽  
Die Wu ◽  
Wei Quan ◽  
Donghu Yu ◽  
Sheng Li ◽  
...  
Keyword(s):  
Overall Survival ◽  
High Risk ◽  
Cox Regression ◽  
Risk Group ◽  
Risk Groups ◽  
Gene Signature ◽  
High Risk Group ◽  
Differential Analysis ◽  
Related Gene ◽  
Cox Regression Analysis

Abstract Background: Glioma is a fatal brain tumor characterized by invasive nature, rapidly proliferation and tumor recurrence. Despite aggressive surgical resection followed by concurrent radiotherapy and chemotherapy, the overall survival (OS) of Glioma patients remains poor. Ferroptosis is a unique modality to regulate programmed cell death and associated with multiple steps of tumorigenesis of a variety of tumors.Methods: In this study, ferroptosis-related genes model was identified by differential analysis and Cox regression analysis. GO, KEGG and GSVA analysis were used to detect the potential biological functions and signaling pathway. The infiltration of immune cells was quantified by Cibersort.Results: The patients’ samples are stratified into two risk groups based on 4-gene signature. High-risk group has poorer overall survival. The results of functional analysis indicated that the extracellular matrix-related biologic functions and pathways were enriched in high-risk group, and that the infiltration of immunocytes is different in two groups.Conclusion: In summary, a novel ferroptosis-related gene signature can be used for prognostic prediction in glioma. The filtered genes related to ferroptosis in clinical could be a potential extra method to assess glioma patients’ prognosis and therapeutic.

scholarly journals Construction of a Prognostic Immune-Related LncRNA Risk Model for Lung Adenocarcinoma

2021 ◽  
Vol 9 ◽  
Author(s):  
Yue Li ◽  
Ruoyi Shen ◽  
Anqi Wang ◽  
Jian Zhao ◽  
Jieqi Zhou ◽  
...  
Keyword(s):  
High Risk ◽  
Differentially Expressed Genes ◽  
Risk Model ◽  
Cox Regression ◽  
Risk Group ◽  
Enrichment Analysis ◽  
Risk Groups ◽  
Low Risk ◽  
Differentially Expressed ◽  
Cox Regression Analysis

BackgroundLung adenocarcinoma (LUAD) originates mainly from the mucous epithelium and glandular epithelium of the bronchi. It is the most common pathologic subtype of non-small cell lung cancer (NSCLC). At present, there is still a lack of clear criteria to predict the efficacy of immunotherapy. The 5-year survival rate for LUAD patients remains low.MethodsAll data were downloaded from The Cancer Genome Atlas (TCGA) database. We used Gene Set Enrichment Analysis (GSEA) database to obtain immune-related mRNAs. Immune-related lncRNAs were acquired by using the correlation test of the immune-related genes with R version 3.6.3 (Pearson correlation coefficient cor = 0.5, P &lt; 0.05). The TCGA-LUAD dataset was divided into the testing set and the training set randomly. Based on the training set to perform univariate and multivariate Cox regression analyses, we screened prognostic immune-related lncRNAs and given a risk score to each sample. Samples were divided into the high-risk group and the low-risk group according to the median risk score. By the combination of Kaplan–Meier (KM) survival curve, the receiver operating characteristic (ROC) (AUC) curve, the independent risk factor analysis, and the clinical data of the samples, we assessed the accuracy of the risk model. Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were performed on the differentially expressed mRNAs between the high-risk group and the low-risk group. The differentially expressed genes related to immune response between two risk groups were analyzed to evaluate the role of the model in predicting the efficacy and effects of immunotherapy. In order to explain the internal mechanism of the risk model in predicting the efficacy of immunotherapy, we analyzed the differentially expressed genes related to epithelial-mesenchymal transition (EMT) between two risk groups. We extracted RNA from normal bronchial epithelial cell and LUAD cells and verified the expression level of lncRNAs in the risk model by a quantitative real-time polymerase chain reaction (qRT-PCR) test. We compared our risk model with other published prognostic signatures with data from an independent cohort. We transfected LUAD cell with siRNA-LINC0253. Western blot analysis was performed to observed change of EMT-related marker in protein level.ResultsThrough univariate Cox regression analysis, 24 immune-related lncRNAs were found to be strongly associated with the survival of the TCGA-LUAD dataset. Utilizing multivariate Cox regression analysis, 10 lncRNAs were selected to establish the risk model. The K-M survival curves and the ROC (AUC) curves proved that the risk model has a fine predictive effect. The GO enrichment analysis indicated that the effect of the differentially expressed genes between high-risk and low-risk groups is mainly involved in immune response and intercellular interaction. The KEGG enrichment analysis indicated that the differentially expressed genes between high-risk and low-risk groups are mainly involved in endocytosis and the MAPK signaling pathway. The expression of genes related to the efficacy of immunotherapy was significantly different between the two groups. A qRT-PCR test verified the expression level of lncRNAs in LUAD cells in the risk model. The AUC of ROC of 5 years in the independent validation dataset showed that this model had superior accuracy. Western blot analysis verified the change of EMT-related marker in protein level.ConclusionThe immune lncRNA risk model established by us could better predict the prognosis of patients with LUAD.

scholarly journals Identification of an Epithelial-Mesenchymal Transition-Related Long Non-coding RNA Prognostic Signature for Hepatocellular Carcinoma

2021 ◽  
Author(s):  
Shenglan Huang ◽  
Dan Li ◽  
LingLing Zhuang ◽  
Jian Zhang ◽  
Jianbing Wu
Keyword(s):  
Hepatocellular Carcinoma ◽  
High Risk ◽  
Cancer Progression ◽  
Risk Model ◽  
Cox Regression ◽  
Epithelial Mesenchymal Transition ◽  
Risk Group ◽  
High Risk Group ◽  
Mesenchymal Transition ◽  
Kaplan Meier

Abstract Introduction: Hepatocellular carcinoma (HCC) is one of the most common malignant tumors with poor prognosis. Epithelial–mesenchymal transition (EMT) is crucial for cancer progression and associates with a worse prognosis. Thus, we aimed to construct an EMT-related lncRNAs signature for predicting the prognosis of HCC patients.Methods: We built an EMT-related lncRNA risk signature in the training set by using Cox regression and LASSO regression based on TCGA database. Kaplan-Meier survival analysis was conducted to compare the overall survival (OS) in different risk groups. Cox regression was performed to explore whether the signature could be used as an independent factor. A nomogram was built involving the risk score and clinicopathological features. Furthermore, we explored the biological functions and immune states in two groups.Results: 12 EMT-related lncRNAs were obtained for constructing the prognosis model in HCC. The Kaplan-Meier curve analysis revealed that patients in the high-risk group had worse survival than low-risk group. Time-dependent ROC and Cox regression analyses suggested that the signature could predict HCC survival exactly and independently. The prognostic value of the risk model was confirmed in the validation group. The nomogram was built and could accurately predict survival of HCC patients. GSEA results showed that in high-risk group cancer-related pathways were enriched, and exhibited more cell division activity suggested by Gene Ontology (GO) analysis.Conclusions: We established a novel EMT-related prognostic risk signature including 12 lncRNAs and constructed a nomogram to predict the prognosis in HCC patients, which may improve prognostic predictive accuracy for HCC patients and guide the individualized treatment methods for the patients with HCC.

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