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.

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 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.

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.

scholarly journals A transcription factor signature predicts prognosis of patients with adrenocortical carcinoma

2020 ◽  
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. 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. 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. Conclusions Taken together, we identified a 13-TF prognostic marker to predict overall survival in ACC patients.

scholarly journals Identification of Seven-Gene Hypoxia Signature for Predicting Overall Survival of Hepatocellular Carcinoma

2020 ◽  
Author(s):  
YuPing Bai ◽  
Wenbo Qi ◽  
Le Liu ◽  
Jing Zhang ◽  
Lan Pang ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Overall Survival ◽  
High Risk ◽  
Cox Regression ◽  
Risk Group ◽  
Enrichment Analysis ◽  
High Risk Group ◽  
Prognostic Models ◽  
Cox Regression Analysis ◽  
Significant Difference

Abstract Background: Hepatocellular carcinoma is ranked fifth among the most common cancer worldwide. Hypoxia can induce tumor growth, but the relationship with HCC prognosis remains unclear. Our study aims to construct a hypoxia-related multigene model to predict the prognosis of HCC. Methods: RNA-seq expression data and related clinical information were download from TCGA database and ICGC database, respectively. Univariate/multivariate Cox regression analysis was used to construct prognostic models. KM curve analysis, and ROC curve were used to evaluate the prognostic models, which were further verified in the clinical traits and ICGC database. GSEA analyzed pathway enrichment in high-risk groups. Nomogram was constructed to predict the personalized treatment of patients. Finally, real-time fluorescence quantitative PCR(RT-qPCR) was used to detect the expressions of KDELR3 and SCARB1 in normal hepatocytes and 4 hepatocellular carcinoma cells. Results: Through a series of analyses, 7 prognostic markers related to HCC survival were constructed. HCC patients were divided into the high and low risk group, and the results of KM curve showed that there was a significant difference between the two groups. Stratified analysis，found that there were significant differences in risk values of different ages, genders, stages and grades, which could be used as independent predictors. In addition, we assessed the risk value in the clinical traits analysis and found that it could accelerate the progression of cancer, while the results of GSEA enrichment analysis showed that the high-risk group patients were mainly distributed in the cell cycle and other pathways. Then, Nomogram was constructed to predict the overall survival of patients. Finally, RT-qPCR showed that KDELR3 and SCARB1 were highly expressed in HepG2 and L02, respectively. Conclusion: This study provides a potential diagnostic indicator for HCC patients, and help clinicians to deepen the comprehension in HCC pathogenesis so as to make personalized medical decisions.

scholarly journals Identification of prognostic immune-related lncRNA signature predicting the overall survival for colorectal cancer

2021 ◽  
Author(s):  
Jianxin Li ◽  
Ting Han ◽  
Xin Wang ◽  
Yinchun Wang ◽  
Qingqiang Yang
Keyword(s):  
Colorectal Cancer ◽  
Overall Survival ◽  
High Risk ◽  
Cox Regression ◽  
Risk Group ◽  
Enrichment Analysis ◽  
High Risk Group ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Regression Analyses

Abstract Background Long non-coding RNA (lncRNA) is an important regulator of gene expression and serves fundamental role in immune regulation. The present study aimed to develop a novel immune-related lncRNA signature to accurately assess the prognosis of patients with colorectal cancer (CRC). Methods Transcriptome data and clinical information of patients with CRC were downloaded from The Cancer Genome Atlas (TCGA), and the immune-related mRNAs were extracted from immunomodulatory gene datasets IMMUNE RESPONSE and IMMUNE SYSTEM PROCESS based on the Molecular Signatures Database (MSigDB). Then, the immune-related lncRNAs were identified by a correlation analysis between immune-related mRNAs and lncRNAs. Subsequently, univariate, lasso and multivariate Cox regression were used to identify an immune-related lncRNA signature in training cohort, and the predict ability of the signature was further confirmed in the testing cohort and the entire TCGA cohort. Finally, the lncRNA-mRNA co-expression network was established to explore the biological role of the immune-related lncRNA signature. Results In total, 272 Immune-related lncRNAs were identified, five of which were applied to construct an immune-related lncRNA signature based on univariate, lasso and multivariate Cox regression analyses. The signature divided patients with CRC into low- and high-risk groups, and patients with CRC in high-risk group had poorer overall survival than those in low-risk group. Univariate and multivariate Cox regression analyses confirmed that the signature could be an independent prognostic factor in human CRC. Furthermore, functional enrichment analysis revealed that the immune-related lncRNA signature was significantly enriched in immune process and tumor classical pathways. Conclusions The present study revealed that the novel immune-related lncRNA signature could be exploited as underlying molecular biomarkers and therapeutic targets for the patients with CRC.

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.

scholarly journals A Prognostic Model for Hepatocellular Carcinoma Patients Based on Signature Ferroptosis-related Genes

2021 ◽  
Author(s):  
Sizhe Wan ◽  
Yiming Lei ◽  
Mingkai Li ◽  
Bin Wu
Keyword(s):  
Overall Survival ◽  
T Cells ◽  
High Risk ◽  
Risk Score ◽  
Cox Regression ◽  
Risk Group ◽  
High Risk Group ◽  
Low Risk ◽  
Tnm Stage ◽  
Functional Enrichment

Abstract BackgroundWith the increasing number of HCC patients, it is necessary to accurately predicting the prognosis of these patients. Ferroptosis has been confirmed to be closely related to HCC progression. However, there is still a challenge in predicting the survival of HCC patients through ferroptosis-related genes.MethodThe RNA-seq data and corresponding clinical data of HCC from TCGA database were downloaded to establish a prognosis model, and data of ICGC and GSE14520 as the validation set. The risk score was constructed with 5 genes identified by univariate and LASSO Cox regression analysis. Then, risk score, TNM stage and cirrhosis were included to construct a nomogram, through univariate and multivariate Cox regression analysis.Results5 genes were identified from 70 ferroptosis-related DEGs to construct a gene signature to predict HCC patient survival from TCGA cohort. PCA and heatmap results show that there are obvious differences in patients with different score groups. Then, we included risk score, TNM stage and cirrhosis to construct a nomogram to further predict the overall survival of the patients. Survival analysis indicates that overall survival of the low- risk group is significantly higher than that of the high-risk group. Similarly, the data in the GSE14520 cohort also confirmed good performance for the nomogram. Furthermore, KEGG and GO functional enrichment analyses indicates the difference in overall survival between groups is closely related to immune-related pathways. Finally, through analyzing the immune status of all patients, we found that compared with patients in the low-risk group, “Macrophages M0”, “T cells CD8”, and “T cells regulatory” of the high-risk group were significantly higher.ConclusionThe nomogram based on ferroptosis-related genes has a good performance for the prognosis of HCC patients. The model may provide a reference for evaluation of HCC patients by targeting ferroptosis.

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