scholarly journals A Nomogram-Based Risk Classification System Predicting the Overall Survival of Patients With Newly Diagnosed Stage IVB Cervix Uteri Carcinoma

2021 ◽  
Vol 8 ◽  
Author(s):  
Wenke Yu ◽  
Lu Huang ◽  
Zixing Zhong ◽  
Tao Song ◽  
Hong'en Xu ◽  
...  
Keyword(s):  
Overall Survival ◽  
Classification System ◽  
Cox Regression ◽  
Risk Group ◽  
Training Group ◽  
High Risk Group ◽  
Risk Classification ◽  
Cervix Uteri ◽  
Newly Diagnosed ◽  
Local Surgery

Background: This study constructed and demonstrated a model to predict the overall survival (OS) of newly diagnosed distant metastatic cervical cancer (mCC) patients.Methods: The SEER (Surveillance, Epidemiology, and End Results) database was used to collect the eligible data, which from 2010 to 2016. Then these data were separated into training and validation cohorts (7:3) randomly. Cox regression analyses was used to identify parameters significantly correlated with OS. Harrell's Concordance index (C-index), calibration curves, and decision curve analysis (DCA) were further applied to verify the performance of this model.Results: A total of 2,091 eligible patients were enrolled and randomly split into training (n = 1,467) and validation (n = 624) cohorts. Multivariate analyses revealed that age, histology, T stage, tumor size, metastatic sites, local surgery, chemotherapy, and radiotherapy were independent prognostic parameters and were then used to build a nomogram for predicting 1 and 2-year OS. The C-index of training group and validation group was 0.714 and 0.707, respectively. The calibration curve demonstrated that the actual observation was in good agreement with the predicted results concluded by the nomogram model. Its clinical usefulness was further revealed by the DCAs. Based on the scores from the nomogram, a corresponding risk classification system was constructed. In the overall population, the median OS time was 23.0 months (95% confidence interval [CI], 20.5–25.5), 12.0 months (95% CI, 11.1–12.9), and 5.0 months (95% CI, 4.4–5.6), in the low-risk group, intermediate-risk group, and high-risk group, respectively.Conclusion: A novel nomogram and a risk classification system were established in this study, which purposed to predict the OS time with mCC patients. These tools could be applied to prognostic analysis and should be validated in future studies.

scholarly journals Predicting Prognosis In Primary Pulmonary Sarcoma Using A Clinical Nomogram Based On SEER Database

2022 ◽  
Author(s):  
Piao Shen ◽  
Yuzhen Zheng ◽  
Siyu Zhu ◽  
Xingping Yang ◽  
Jian Tan ◽  
...  
Keyword(s):  
Overall Survival ◽  
Prognostic Factors ◽  
High Risk ◽  
Cox Regression ◽  
Risk Group ◽  
Roc Curves ◽  
Training Group ◽  
High Risk Group ◽  
Seer Database ◽  
Pulmonary Sarcoma

Abstract Background: Primary pulmonary sarcoma (PPS) accounts for less than 1.1% of all pulmonary tumors. Few data outcomes are reported. This study aims to clarify the predictive value of clinicopathologic features on the overall survival (OS) of PPS patients.Methods: Patients with primary pulmonary sarcoma (PPS) were collected from the Surveillance, Epidemiology, and End Results (SEER) database (from 2000 to 2015) and divided randomly into training and validation cohorts at a ratio of 1:1. Univariate Cox analysis and the least absolute shrinkage and selection operator (LASSO) were implemented to identify prognostic factors related to overall survival of primary pulmonary sarcoma patients. Then, we performed multivariate Cox regression to establish a prognostic factors signature. The Kaplan- Meier (K-M) survival curves and time-dependent receiver operating characteristic (ROC) curves were plotted to estimate the prognostic power of the signature. In addition, multivariate Cox regression screened out independent prognostic factors and constructed a nomogram. Results: PPS patients with training group were divided into low- and high-risk group based on risk score, and high-risk group had a shorter survival time. The validation group got the same result. (P<0.001). On multivariate analysis of the training cohort, independent factors for survival were marriage, age, sex, grade, operation, metastasis and tumor size, which were all selected into the nomogram. The calibration curve and ROC plots for probability of 3-year and 5-year survival were in accord with prediction by nomogram and actual observation. And the C-index of the nomogram for predicting survival was 0.77 (95% CI, 0.74 to 0.80, P<0.05), which was statistically significant. Conclusion: We constructed a risk prognosis model based on PPS patients from SEER database. In addition, the construction of nomogram provides one more idea for clinical treatment.

Predictive value of the new ESGO-ESTRO-ESP endometrial cancer risk classification on survival and recurrence in the Danish population

2021 ◽  
pp. ijgc-2021-002582
Author(s):  
Gitte Ortoft ◽  
Claus Høgdall ◽  
Estrid Stæhr Hansen ◽  
Margit Dueholm
Keyword(s):  
Endometrial Cancer ◽  
High Risk ◽  
Recurrence Rate ◽  
European Society ◽  
Classification System ◽  
Risk Group ◽  
High Risk Group ◽  
Risk Classification ◽  
Recurrence Rates ◽  
Disease Specific

ObjectiveTo compare the performance of the new ESGO-ESTRO-ESP (European Society of Gynecological Oncology-European Society for Radiotherapy & Oncology-European Society for Pathology) 2020 risk classification system with the previous 2016 risk classification in predicting survival and patterns of recurrence in the Danish endometrial cancer population.MethodsThis Danish national cohort study included 4516 patients with endometrial cancer treated between 2005 and 2012. Five-year Kaplan–Meier adjusted and unadjusted survival estimates and actuarial recurrence rates were calculated for the previous and the new classification systems.ResultsIn the 2020 risk classification system, 81.0% of patients were allocated to low, intermediate, or high-intermediate risk compared with 69.1% in the 2016 risk classification system, mainly due to reclassification of 44.5% of patients previously classified as high risk to either intermediate or especially high-intermediate risk. The survival of the 2020 high-risk group was significantly lower, and the recurrence rate, especially the non-local recurrence rate, was significantly higher than in the 2016 high risk group (2020/2016, overall survival 59%/66%; disease specific 69%/76%; recurrence 40.5%/32.3%, non-local 34.5%/25.8%). Survival and recurrence rates in the other risk groups and the decline in overall and disease-specific survival rates from the low risk to the higher risk groups were similar in patients classified according to the 2016 and 2020 systems.ConclusionThe new ESGO-ESTRO-ESP 2020 risk classification system allocated fewer patients to the high risk group than the previous risk classification system. The main differences were lower overall and disease-specific survival and a higher recurrence rate in the 2020 high risk group. The introduction of the new 2020 risk classification will potentially result in fewer patients at high risk and allocation to the new high risk group will predict lower survival, potentially allowing more specific selection for postoperative adjuvant therapy.

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 Identification of a Pyroptosis-Related Gene Signature for Predicting Overall Survival and Response to Immunotherapy in Hepatocellular Carcinoma

2021 ◽  
Vol 12 ◽  
Author(s):  
Susu Zheng ◽  
Xiaoying Xie ◽  
Xinkun Guo ◽  
Yanfang Wu ◽  
Guobin Chen ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Overall Survival ◽  
High Risk ◽  
Regression Model ◽  
Risk Score ◽  
Cox Regression ◽  
Risk Group ◽  
Gene Signature ◽  
High Risk Group ◽  
Related Gene

Pyroptosis is a novel kind of cellular necrosis and shown to be involved in cancer progression. However, the diverse expression, prognosis and associations with immune status of pyroptosis-related genes in Hepatocellular carcinoma (HCC) have yet to be analyzed. Herein, the expression profiles and corresponding clinical characteristics of HCC samples were collected from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Then a pyroptosis-related gene signature was built by applying the least absolute shrinkage and selection operator (LASSO) Cox regression model from the TCGA cohort, while the GEO datasets were applied for verification. Twenty-four pyroptosis-related genes were found to be differentially expressed between HCC and normal samples. A five pyroptosis-related gene signature (GSDME, CASP8, SCAF11, NOD2, CASP6) was constructed according to LASSO Cox regression model. Patients in the low-risk group had better survival rates than those in the high-risk group. The risk score was proved to be an independent prognostic factor for overall survival (OS). The risk score correlated with immune infiltrations and immunotherapy responses. GSEA indicated that endocytosis, ubiquitin mediated proteolysis and regulation of autophagy were enriched in the high-risk group, while drug metabolism cytochrome P450 and tryptophan metabolism were enriched in the low-risk group. In conclusion, our pyroptosis-related gene signature can be used for survival prediction and may also predict the response of immunotherapy.

scholarly journals A Transcription Factor Signature Predicts Prognosis of Patients with Adrenocortical Carcinoma

2021 ◽  
Author(s):  
Jianyu Zhao ◽  
Bo Liu ◽  
Xiaoping Li
Keyword(s):  
Overall Survival ◽  
High Risk ◽  
Risk Score ◽  
Adrenocortical Carcinoma ◽  
Cox Regression ◽  
Risk Group ◽  
High Risk Group ◽  
Prognostic Signature ◽  
Abdominal Masses ◽  
Cox Analysis

Abstract Background: Adrenocortical carcinoma (ACC) is a rare endocrine cancer that manifests as abdominal masses and excessive steroid hormone levels. Transcription factors (TFs) deregulation is found to be involved in adrenocortical tumorigenesis and cancer progression. This study aimed to construct a TF-based prognostic signature for prediction of survival of ACC patients.Methods: The gene expression profile for ACC patients were downloaded from TCGA and GEO datasets. The univariate Cox analysis was applied to identify survival-related TFs and the LASSO Cox regression was conducted to construct the TF signature. The multivariate analysis was used to reveal the independent prognostic factors.Results: We identified a 13-TF prognostic signature comprised of CREB3L3, NR0B1, CENPA, FOXM1, E2F2, MYBL2, HOXC11, ZIC2, ZNF282, DNMT1, TCF3, ELK4, and KLF6 using the univariate Cox analysis and LASSO Cox regression. The risk score based on the TF-signature could classify patients into low- and high-risk group. Kaplan-Meier analyses showed that patients in the high-risk group had significantly shorter overall survival compared to the low-risk patients. ROC curves showed that the prognostic signature predicted the overall survival of ACC patients with good sensitivity and specificity. Furthermore, the TF-risk score was an independent prognostic factor.Conclusion: Taken together, we identified a 13-TF prognostic marker to predict overall survival in ACC patients.

scholarly journals A Transcription Factor Signature Predicts Prognosis of Patients with Adrenocortical Carcinoma

2021 ◽  
Author(s):  
Jianyu Zhao ◽  
Bo Liu ◽  
Xiaoping Li
Keyword(s):  
Overall Survival ◽  
High Risk ◽  
Cancer Progression ◽  
Risk Score ◽  
Adrenocortical Carcinoma ◽  
Cox Regression ◽  
Risk Group ◽  
High Risk Group ◽  
Prognostic Signature ◽  
Abdominal Masses

Abstract Background: Adrenocortical carcinoma (ACC) is a rare endocrine cancer that manifests as abdominal masses and excessive steroid hormone levels. Transcription factors (TFs) deregulation is found to be involved in adrenocortical tumorigenesis and cancer progression. This study aimed to construct a TF-based prognostic signature for prediction of survival of ACC patients.Results: We identified a 13-TF prognostic signature comprised of CREB3L3, NR0B1, CENPA, FOXM1, E2F2, MYBL2, HOXC11, ZIC2, ZNF282, DNMT1, TCF3, ELK4, and KLF6 using the univariate Cox analysis and LASSO Cox regression. The risk score based on the TF-signature could classify patients into low- and high-risk group. Kaplan-Meier analyses showed that patients in the high-risk group had significantly shorter overall survival compared to the low-risk patients. ROC curves showed that the prognostic signature predicted the overall survival of ACC patients with good sensitivity and specificity. Furthermore, the TF-risk score was an independent prognostic factor.Conclusion: Taken together, we identified a 13-TF prognostic marker to predict overall survival in ACC patients.

scholarly journals Nomogram for Predicting Overall Survival in Patients With Invasive Micropapillary Carcinoma After Breast-Conserving Surgery: A Population‐Based Analysis

2021 ◽  
Author(s):  
Yuting Zhao ◽  
Shouyu Li ◽  
Lutong Yan ◽  
Zejian Yang ◽  
Na Chai ◽  
...  
Keyword(s):  
Overall Survival ◽  
Cox Regression ◽  
Risk Group ◽  
Breast Conserving Surgery ◽  
High Risk Group ◽  
Good Prediction ◽  
Invasive Micropapillary Carcinoma ◽  
Operating Characteristics ◽  
Micropapillary Carcinoma ◽  
Calibration Curves

Abstract Background: Due to the rarity of invasive micropapillary carcinoma (IMPC) of the breast, no randomized trial has investigated the prediction of overall survival (OS) for patients with IMPC after breast-conserving surgery (BCS). This study aimed to construct a nomogram for predicting OS in IMPC patients after BCS. Methods: Using the Surveillance, Epidemiology, and End Results (SEER) database, 481 eligible cases diagnosed with IMPC were collected. OS in IMPC patients after BCS were assessed through multivariable Cox analyses, Harrell’s concordance indexes (C-indexes), receiver operating characteristics (ROCs) curves, calibration curves, decision curve analyses (DCA), and survival analyses. Results: 336 patients were randomly assigned into training cohort and 145 cases in validation cohort. The multivariate Cox regression analyses revealed that age at diagnosis, American Joint Committee on Cancer (AJCC) stage, marital status, hormone receptor status and chemotherapy were significant prognostic factors for OS in conservatively operated IMPC patients. The nomogram had a good prediction performance with the C-indices 0.771 (95%CI, 0.712-0.830) and 0.715 (95%CI, 0.603-0.827) in training and validation cohorts, respectively, and good consistency between the predicted and observed probability, with calibration curves plotted and the slope was close to 1. Based on calculation of the model, participants in low-risk group had a better OS in comparison with those in high-risk group (P < 0.001). Conclusions: A nomogram was developed to predict individualized risk of OS for IMPC patients after BCS. By risk stratification, this model is expected to guide treatment decision making in improving long-term follow-up strategies for IMPC patients.

scholarly journals An autophagy-related long non-coding RNA signature for patients with colorectal cancer

2021 ◽  
Author(s):  
Dongyan Zhao ◽  
Xizhen Sun ◽  
Sidan Long ◽  
Shukun Yao
Keyword(s):  
Colorectal Cancer ◽  
Overall Survival ◽  
High Risk ◽  
Cox Regression ◽  
Risk Group ◽  
Expression Profiles ◽  
High Risk Group ◽  
Low Risk ◽  
Gene Set Enrichment Analysis ◽  
Cox Regression Analysis

AbstractAimLong non-coding RNAs (lncRNAs) have been identified to regulate cancers by controlling the process of autophagy and by mediating the post-transcriptional and transcriptional regulation of autophagy-related genes. This study aimed to investigate the potential prognostic role of autophagy-associated lncRNAs in colorectal cancer (CRC) patients.MethodsLncRNA expression profiles and the corresponding clinical information of CRC patients were collected from The Cancer Genome Atlas (TCGA) database. Based on the TCGA dataset, autophagy-related lncRNAs were identified by Pearson correlation test. Univariate Cox regression analysis and the least absolute shrinkage and selection operator analysis (LASSO) Cox regression model were performed to construct the prognostic gene signature. Gene set enrichment analysis (GSEA) was used to further clarify the underlying molecular mechanisms.ResultsWe obtained 210 autophagy-related genes from the whole dataset and found 1187 lncRNAs that were correlated with the autophagy-related genes. Using Univariate and LASSO Cox regression analyses, eight lncRNAs were screened to establish an eight-lncRNA signature, based on which patients were divided into the low-risk and high-risk group. Patients’ overall survival was found to be significantly worse in the high-risk group compared to that in the low-risk group (log-rank p = 2.731E-06). ROC analysis showed that this signature had better prognostic accuracy than TNM stage, as indicated by the area under the curve. Furthermore, GSEA demonstrated that this signature was involved in many cancer-related pathways, including TGF-β, p53, mTOR and WNT signaling pathway.ConclusionsOur study constructed a novel signature from eight autophagy-related lncRNAs to predict the overall survival of CRC, which could assistant clinicians in making individualized treatment.

Pretreatment Cytogenetic Abnormalities Are Predictive of Induction Success, Cumulative Incidence of Relapse and Overall Survival in Patients >60 Years of Age with Newly Diagnosed Acute Myeloid Leukemia.

Blood ◽  
2005 ◽  
Vol 106 (11) ◽  
pp. 3293-3293
Author(s):  
Richard F. Schlenk ◽  
Sabine Kayser ◽  
Martina Morhardt ◽  
Konstanze Döhner ◽  
Hartmut Döhner ◽  
...  
Keyword(s):  
Overall Survival ◽  
High Risk ◽  
Myeloid Leukemia ◽  
Risk Group ◽  
Normal Karyotype ◽  
Risk Groups ◽  
High Risk Group ◽  
Newly Diagnosed ◽  
Balanced Translocations ◽  
Acute Myeloid

Abstract Purpose: Karyotype at diagnosis provides the most important prognostic information in younger adults with acute myeloid leukemia (AML). However, there are few data available looking in particular at patients (pts.) above 60 years of age. We prospectively analyzed 361 elderly pts. with newly diagnosed AML. All pts. were treated within the AMLHD98B treatment trial and received intensive induction and consolidation therapy. Pts. exhibiting a t(15;17) received an age-adjusted AIDA-regimen. Median follow-up time was 48 months. The median age was 67 years (range 60–85 years). Results: 160 pts. had a normal karyotype (44%); 48 pts. (13%) exhibited the balanced translocations t(8;21) (n=12), inv(16) (n=14), t(15;17) (n=11), or t(11q23) (n=11); in the absence of these balanced translocations, 73 pts. exhibited a single aberration, 179 pts. two aberrations, and 61 pts. a complex karyotype (≥3 aberrations; including 44 pts. with 5 or more aberrations). Analyses were normalized to the complete remission (CR) rate (52%), cumulative incidence of relapse (CIR) (77%) and overall survival (OS) (13%) after 4 years of pts. with normal karyotype. Pts. exhibiting a t(15;17) showed a significantly better CIR (29%) and OS (55%), whereas pts. with the other balanced translocations [t(8;21), inv(16)/t(16;16) and t(11q23)] did not differ from pts. with normal karyotype. The limited backward selected Cox-model for OS [t(15;17) excluded] revealed two risk groups: standard-risk [normal karyotype, t(8;21), inv(16), t(11q23), +8 and +11 in absence of a complex karyoytpe] and high-risk [all other aberrations]. The CR rates were 56% and 18%, and the OS-rates after 4 years for the standard- (n=223) and the high-risk group (n=127) were 15% and 0%, respectively. The MRC risk classification system for patients &gt;55 years applied to our patients revealed CR- and OS-rates after 4 years of 73% and 19%, 47% and 12%, as well as 7% and 0% for the low (n=26), intermediate (n=282) and high risk groups (n=44), respectively [t(15;17) excluded]. In conclusion, our risk classification system identified a large high-risk group (36%) of elderly patients with AML who did not benefit from intensive chemotherapy.

scholarly journals A Robust Signature Based on Autophagy-Associated LncRNAs for Predicting Prognosis in Lung Adenocarcinoma

2020 ◽  
Vol 2020 ◽  
pp. 1-13 ◽  
Author(s):  
Mi Zhou ◽  
Weihua Shao ◽  
Haiyun Dai ◽  
Xin Zhu
Keyword(s):  
High Risk ◽  
Lung Adenocarcinoma ◽  
Cox Regression ◽  
Risk Group ◽  
Surgical Margin ◽  
Training Group ◽  
High Risk Group ◽  
Low Risk ◽  
Validation Group ◽  
Cox Regression Analysis

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

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