scholarly journals A risk stratification model based on four novel biomarkers predicts prognosis for patients with renal cell carcinoma

2020 ◽  
Author(s):  
Shigehisa Kubota ◽  
Tetsuya Yoshida ◽  
Susumu Kageyama ◽  
Takahiro Isono ◽  
Takeshi Yuasa ◽  
...  
Keyword(s):  
Prognostic Model ◽  
Cox Regression ◽  
Treatment Strategies ◽  
Immunohistochemical Analysis ◽  
Cancer Specific Survival ◽  
Clinical Prognosis ◽  
Cox Regression Analysis ◽  
Model Based ◽  
Novel Biomarkers ◽  
Metastatic Rcc

Abstract Background: Accurate prediction of the prognosis of RCC using a single biomarker is challenging due to the genetic heterogeneity of the disease. However, it is essential to develop an accurate system to allow better patient selection for optimal treatment strategies. ARL4C, ECT2, SOD2 and STEAP3 are novel molecular biomarkers identified in earlier studies as survival-related genes by comprehensive analyses of 43 primary RCC tissues and RCC cell lines. Methods: To develop a prognostic model based on these multiple biomarkers, the expression of four biomarkers ARL4C, ECT2, SOD2, and STEAP3 in primary RCC tissue were semi-quantitatively investigated by immunohistochemical analysis in an independent cohort of 97 patients who underwent nephrectomy, and the clinical significance of these biomarkers were analyzed by survival analysis using Kaplan-Meier curves. The prognostic model was constructed by calculation of the contribution score to prognosis of each biomarker on Cox regression analysis, and its prognostic performance was validated.Results: Patients whose tumors had high expression of the individual biomarkers had shorter cancer-specific survival (CSS) from the time of primary nephrectomy. The prognostic model based on four biomarkers segregated the patients into a high- and low-risk scored group according to defined cut-off value. This approach was more robust in predicting CSS compared to each single biomarker alone in the total of 97 patients with RCC. Especially in the 36 metastatic RCC patients, our prognostic model could more accurately predict early events within 2 years of diagnosis of metastasis. In addition, high risk-scored patients with particular strong SOD2 expression had a much worse prognosis in 25 patients with metastatic RCC who were treated with molecular targeting agents. Conclusions: Our findings indicate that a prognostic model based on four novel biomarkers provides valuable data for prediction of clinical prognosis and useful information for considering the follow-up conditions and therapeutic strategies for patients with primary and metastatic RCC.

scholarly journals A risk stratification model based on four novel biomarkers predicts prognosis for patients with renal cell carcinoma

2020 ◽  
Vol 18 (1) ◽  
Author(s):  
Shigehisa Kubota ◽  
Tetsuya Yoshida ◽  
Susumu Kageyama ◽  
Takahiro Isono ◽  
Takeshi Yuasa ◽  
...  
Keyword(s):  
Prognostic Model ◽  
Cox Regression ◽  
Treatment Strategies ◽  
Immunohistochemical Analysis ◽  
Cancer Specific Survival ◽  
Clinical Prognosis ◽  
Cox Regression Analysis ◽  
Model Based ◽  
Novel Biomarkers ◽  
Metastatic Rcc

Abstract Background Accurate prediction of the prognosis of RCC using a single biomarker is challenging due to the genetic heterogeneity of the disease. However, it is essential to develop an accurate system to allow better patient selection for optimal treatment strategies. ARL4C, ECT2, SOD2, and STEAP3 are novel molecular biomarkers identified in earlier studies as survival-related genes by comprehensive analyses of 43 primary RCC tissues and RCC cell lines. Methods To develop a prognostic model based on these multiple biomarkers, the expression of four biomarkers ARL4C, ECT2, SOD2, and STEAP3 in primary RCC tissue were semi-quantitatively investigated by immunohistochemical analysis in an independent cohort of 97 patients who underwent nephrectomy, and the clinical significance of these biomarkers were analyzed by survival analysis using Kaplan-Meier curves. The prognostic model was constructed by calculation of the contribution score to prognosis of each biomarker on Cox regression analysis, and its prognostic performance was validated. Results Patients whose tumors had high expression of the individual biomarkers had shorter cancer-specific survival (CSS) from the time of primary nephrectomy. The prognostic model based on four biomarkers segregated the patients into a high- and low-risk scored group according to defined cut-off value. This approach was more robust in predicting CSS compared to each single biomarker alone in the total of 97 patients with RCC. Especially in the 36 metastatic RCC patients, our prognostic model could more accurately predict early events within 2 years of diagnosis of metastasis. In addition, high risk-scored patients with particular strong SOD2 expression had a much worse prognosis in 25 patients with metastatic RCC who were treated with molecular targeting agents. Conclusions Our findings indicate that a prognostic model based on four novel biomarkers provides valuable data for prediction of clinical prognosis and useful information for considering the follow-up conditions and therapeutic strategies for patients with primary and metastatic RCC.

scholarly journals A risk stratification model based on four novel biomarkers predicts prognosis for patients with renal cell carcinoma.

2020 ◽  
Author(s):  
Shigehisa Kubota ◽  
Tetsuya Yoshida ◽  
Susumu Kageyama ◽  
Takahiro Isono ◽  
Takeshi Yuasa ◽  
...  
Keyword(s):  
Prognostic Model ◽  
Cox Regression ◽  
Treatment Strategies ◽  
Immunohistochemical Analysis ◽  
Cancer Specific Survival ◽  
Clinical Prognosis ◽  
Cox Regression Analysis ◽  
Model Based ◽  
Novel Biomarkers ◽  
Metastatic Rcc

Abstract Background: Accurate prediction of the prognosis of RCC using a single biomarker is challenging due to the genetic heterogeneity of the disease. However, it is essential to develop an accurate system to allow better patient selection for optimal treatment strategies. ARL4C, ECT2, SOD2 and STEAP3 are novel molecular biomarkers identified in earlier studies as survival-related genes by comprehensive analyses of 43 primary RCC tissues and RCC cell lines. Methods: To develop a prognostic model based on these multiple biomarkers, the expression of four biomarkers ARL4C, ECT2, SOD2, and STEAP3 in primary RCC tissue were semi-quantitatively investigated by immunohistochemical analysis in an independent cohort of 97 patients who underwent nephrectomy, and the clinical significance of these biomarkers were analyzed by survival analysis using Kaplan-Meier curves. The prognostic model was constructed by calculation of the contribution score to prognosis of each biomarker on Cox regression analysis, and its prognostic performance was validated.Results: Patients whose tumors had high expression of the individual biomarkers had shorter cancer-specific survival (CSS) from the time of primary nephrectomy. The prognostic model based on four biomarkers segregated the patients into a high- and low-risk scored group according to defined cut-off value. This approach was more robust in predicting CSS compared to each single biomarker alone in the total of 97 patients with RCC. Especially in the 36 metastatic RCC patients, our prognostic model could more accurately predict early events within 2 years of diagnosis of metastasis. In addition, high risk-scored patients with particular strong SOD2 expression had a much worse prognosis in 25 patients with metastatic RCC who were treated with molecular targeting agents. Conclusions: Our findings indicate that a prognostic model based on four novel biomarkers provides valuable data for prediction of clinical prognosis and useful information for considering the follow-up conditions and therapeutic strategies for patients with primary and metastatic RCC.

scholarly journals Exploration of Various Roles of Hypoxia Genes in Osteosarcoma

2021 ◽  
Author(s):  
Jimin Ma ◽  
Yakun Zhu ◽  
Ziming Guo ◽  
Xuefei Yang ◽  
Haitao Fan
Keyword(s):  
High Risk ◽  
Immune Cells ◽  
Prognostic Model ◽  
Cox Regression ◽  
High Expression ◽  
Cox Regression Analysis ◽  
Model Based ◽  
Survival Difference ◽  
High Expression Group ◽  
Low Expression

Abstract Background: Osteosarcoma is a primary malignant tumor that often metastasizes in orthopedic diseases. Although multi-drug chemotherapy and surgical treatment have significantly improved the survival and prognosis of patients with osteosarcoma, the survival rate is still very low due to frequent metastases in patients with osteosarcoma. In-depth exploration of the relationship between various influencing factors of osteosarcoma is very important for screening promising therapeutic targets. Methods: This study used multivariate COX regression analysis to select the hypoxia genes SLC2A1 and FBP1 in patients with osteosarcoma, and used the expression of these two genes to divide the patients with osteosarcoma into high-risk and low-risk groups. Then, we first constructed a prognostic model based on the patient's risk value, and compared the survival difference between the high expression group and the low expression group. Second, in the high expression group and the low expression group, compare the differences in tumor invasion and inflammatory gene expression between the two groups of immune cells. Finally, the ferroptosis-related genes with differences between the high expression group and the low expression group were screened, and the correlation between these genes was analyzed. Results: In the high-risk group, immune cells with higher tumor invasiveness, macrophages M0 and immune cells with lower invasiveness included: mast cell resting, regulatory T cells (Tregs) and monocytes. Finally, among genes related to ferroptosis, we found AKR1C2, AKR1C1 and ALOX15 that may be related to hypoxia. These ferroptosis-related genes were discovered for the first time in osteosarcoma. Among them, the hypoxia gene FBP1 is positively correlated with the ferroptosis genes AKR1C1 and ALOX15, and the hypoxia gene SLC2A1 is negatively correlated with the ferroptosis genes AKR1C2, AKR1C1 and ALOX15. Conclusion: This study constructed a prognostic model based on hypoxia-related genes SLC2A1 and FBP1 in patients with osteosarcoma, and explored their correlation with immune cells, inflammatory markers and ferroptosis-related genes. This indicates that SLC2A1 and FBP1 are promising targets for osteosarcoma research.

scholarly journals An Independent Prognostic Model Based on Ten Autophagy-Related Genes in Pancreatic Cancer

2021 ◽  
Author(s):  
Jiahui Tian ◽  
yi wu ◽  
Xuan Zeng ◽  
Xiaoxiao Fang ◽  
Chunyan Fu
Keyword(s):  
Pancreatic Cancer ◽  
Roc Curve ◽  
Noncoding Rna ◽  
Prognostic Model ◽  
Cox Regression ◽  
Risk Group ◽  
Target Therapy ◽  
Clinical Prognosis ◽  
Cox Regression Analysis ◽  
Gene Sets

Abstract Purpose Pancreatic cancer(PC) is a common cancer with high lethality and low survival rate. Autophagy is involved in the biological process of PC. Thus, we intended to explore the function of autophagy-related long noncoding RNA signature for survival assessment in PC. Methods Based on 10 autophagy-related lncRNAs, the prognostic model was attained through univariate and multivariate Cox regression analysis. Subsequently, the relationship network of 10 lncRNAs was crystallized in co-expression network and Sankey diagram. Survival analysis and ROC curve were used to evaluate the signature. GSEA was utilized to screen enriched gene sets. Result The OS has significant deference in low-risk group and high-risk group(P < 0.001). The ROC curve further proved the potential utility of the signature(AUC = 0.815). GSEA was significantly enriched in cancer-related gene sets. Conclusion The signature has potential to evaluate clinical prognosis in PC. The 10 autophagy-related lncRNAs may achieve great development for PC in target therapy field.

scholarly journals Development and validation of an immune-related gene prognostic model for stomach adenocarcinoma

2020 ◽  
Vol 40 (10) ◽  
Author(s):  
Ming Wu ◽  
Yu Xia ◽  
Yadong Wang ◽  
Fei Fan ◽  
Xian Li ◽  
...  
Keyword(s):  
Prognostic Model ◽  
Clinical Decision Making ◽  
Cox Regression ◽  
Risk Group ◽  
Prognostic Models ◽  
Related Gene ◽  
Cox Regression Analysis ◽  
Model Based ◽  
Immune Related Gene ◽  
Stomach Adenocarcinoma

Abstract Purpose: Stomach adenocarcinoma (STAD) is one of the most common malignant tumors, and its occurrence and prognosis are closely related to inflammation. The aim of the present study was to identify gene signatures and construct an immune-related gene (IRG) prognostic model in STAD using bioinformatics analysis. Methods: RNA sequencing data from healthy samples and samples with STAD, IRGs, and transcription factors were analyzed. The hub IRGs were identified using univariate and multivariate Cox regression analyses. Using the hub IRGs, we constructed an IRG prognostic model. The relationships between IRG prognostic models and clinical data were tested. Results: A total of 289 differentially expressed IRGs and 20 prognostic IRGs were screened with a threshold of P&lt;0.05. Through multivariate stepwise Cox regression analysis, we developed a prognostic model based on seven IRGs. The prognostic model was validated using a GEO dataset (GSE 84437). The IRGs were significantly correlated with the clinical outcomes (age, histological grade, N, and M stage) of STAD patients. The infiltration abundances of dendritic cells and macrophages were higher in the high-risk group than in the low-risk group. Conclusions: Our results provide novel insights into the pathogenesis of STAD. An IRG prognostic model based on seven IRGs exhibited the predictive value, and have potential application value in clinical decision-making and individualized treatment.

scholarly journals Identification and Validation of a Prognostic Model Based on Three Autophagy-Related Genes in Hepatocellular Carcinoma

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Fanbo Qin ◽  
Junyong Zhang ◽  
Jianping Gong ◽  
Wenfeng Zhang
Keyword(s):  
Hepatocellular Carcinoma ◽  
Regression Analysis ◽  
Risk Score ◽  
Prognostic Value ◽  
Prognostic Model ◽  
Cox Regression ◽  
Cancer Genome ◽  
Clinical Parameters ◽  
Cox Regression Analysis ◽  
Model Based

Background. Accumulating studies have demonstrated that autophagy plays an important role in hepatocellular carcinoma (HCC). We aimed to construct a prognostic model based on autophagy-related genes (ARGs) to predict the survival of HCC patients. Methods. Differentially expressed ARGs were identified based on the expression data from The Cancer Genome Atlas and ARGs of the Human Autophagy Database. Univariate Cox regression analysis was used to identify the prognosis-related ARGs. Multivariate Cox regression analysis was performed to construct the prognostic model. Receiver operating characteristic (ROC), Kaplan-Meier curve, and multivariate Cox regression analyses were performed to test the prognostic value of the model. The prognostic value of the model was further confirmed by an independent data cohort obtained from the International Cancer Genome Consortium (ICGC) database. Results. A total of 34 prognosis-related ARGs were selected from 62 differentially expressed ARGs identified in HCC compared with noncancer tissues. After analysis, a novel prognostic model based on ARGs (PRKCD, BIRC5, and ATIC) was constructed. The risk score divided patients into high- or low-risk groups, which had significantly different survival rates. Multivariate Cox analysis indicated that the risk score was an independent risk factor for survival of HCC after adjusting for other conventional clinical parameters. ROC analysis showed that the predictive value of this model was better than that of other conventional clinical parameters. Moreover, the prognostic value of the model was further confirmed in an independent cohort from ICGC patients. Conclusion. The prognosis-related ARGs could provide new perspectives on HCC, and the model should be helpful for predicting the prognosis of HCC patients.

Development and validation of stemness associated LncRNA based prognostic model for lung adenocarcinoma patients

2021 ◽  
pp. 1-11
Author(s):  
Annesha Chatterjee ◽  
Seema Khadirnaikar ◽  
Sudhanshu Shukla
Keyword(s):  
Lung Adenocarcinoma ◽  
Prognostic Model ◽  
Cox Regression ◽  
Prognostic Score ◽  
Differentiation Markers ◽  
Critical Determinant ◽  
Cox Regression Analysis ◽  
Novel Biomarkers ◽  
Tcga Dataset ◽  
Development And Validation

BACKGROUND: An increasing number of studies are indicating that the stemness phenotype is a critical determinant of the Lung adenocarcinoma (LUAD) patient’s response. Thus, it is crucial to identify novel biomarkers for stemness determination. OBJECTIVE: Here, we aim to develop a robust LncRNAs based prognostic signature with a stemness association for the LUAD patients. METHODS: RNA-seq and clinical data were downloaded from the existing database. The data were analysed using Cox regression, KM-plot, GSEA, and T-test. RESULTS: Initially, we used the TCGA dataset to characterize the stemness phenotype in LUAD. The commonly expressed LncRNAs in TCGA and MCTP cohort were then used as input for the Cox-regression analysis. The top three LncRNAs were selected to build a prognostic model, which was the best prognosticator in multivariate analysis with stage and previously published prognosticators. The characterization of poor surviving patients using various analysis showed high stemness properties and low expression of differentiation markers. Furthermore, we validated the prognostic score in an independent MCTP cohort of patients. In the MCTP cohort, prognostic score significantly predicted survival independent of stage and previous prognosticators. CONCLUSION: Taken together, in this study, we have developed and validated a new prognostic score associated with the stemness phenotype.

The impact of previous tracheostomy in patients with locally advanced squamous cell carcinoma of the larynx submitted to concurrent chemotherapy and radiation

2007 ◽  
Vol 25 (18_suppl) ◽  
pp. 6004-6004
Author(s):  
F. L. Dias ◽  
D. Herchenhorn ◽  
I. A. Small ◽  
C. M. Araújo ◽  
C. G. Ferreira ◽  
...  
Keyword(s):  
Prognostic Factor ◽  
Treatment Response ◽  
Cox Regression ◽  
Performance Status ◽  
Locally Advanced ◽  
Treatment Strategies ◽  
Concurrent Chemotherapy ◽  
Cancer Specific Survival ◽  
Cox Regression Analysis ◽  
The Impact

6004 Background: The combination of chemotherapy and radiotherapy is a standard treatment for locally advanced larynx cancer. Patients presenting with previous tracheostomy due to aiway obstruction have a worse clinical outcome when submitted to a total laryngectomy or radiotherapy; the impact of previous tracheostomy is not clear in patients submitted to chemotherapy combined with radiation. Methods: A single-institutional study, patients with stage III and IV laryngeal carcinoma were prospectively selected from 2000 to 2003. Treatment consisted of Cisplatin 100 mg/m2 every three weeks for 3 cycles concurrent with radiotherapy to a total dose of 70.2 Gy. Prognostic factors like stage, age, performance status, chemotherapy completion, treatment response and previous tracheostomy were correlated on univariate and multivariate analysis with treatment response, progression-free and overall survival. Results: Forty-nine patients were selected, previous tracheostomy was performed in 12 (24,5%) before chemo/radiation therapy. Patients with tracheostomy had an inferior median overall cancer-specific survival (12 months versus 56 months), HR 2.37 (CI 95% 1.43–3.93) p=0.001, progression free-survival HR 2.8 (CI 95% 1.61–4.89) p<0.001 and lower rates of complete responses (40 versus 75%). The impact of previous tracheostomy was not altered when adjusted by number of chemotherapy cycles, tumor stage, performance status, age or treatment response. On a cox regression analysis for overall cancer-specific survival it was the strongest prognostic factor HR 7.75 (CI 95% 2.75–21.84) p<0.001. Conclusions: Previous tracheostomy is an independent negative prognostic factor for patients submitted to chemotherapy combined with radiation. Tracheostomty should be considered in the design of future studies and to select patients to different treatment strategies. No significant financial relationships to disclose.

scholarly journals Effects of marital status on overall and cancer-specific survival in laryngeal cancer patients: a population-based study

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Zhao Ding ◽  
Deshun Yu ◽  
Hefeng Li ◽  
Yueming Ding
Keyword(s):  
Marital Status ◽  
Cancer Patients ◽  
Laryngeal Cancer ◽  
Cox Regression ◽  
Group Analysis ◽  
Population Based ◽  
Cancer Specific Survival ◽  
Cox Regression Analysis ◽  
Prognostic Effect ◽  
The Difference

AbstractMarital status has long been recognized as an important prognostic factor for many cancers, however its’ prognostic effect for patients with laryngeal cancer has not been fully examined. We retrospectively analyzed 8834 laryngeal cancer patients in the Surveillance Epidemiology and End Results database from 2004 to 2010. Patients were divided into four groups: married, widowed, single, and divorced/separated. The difference in overall survival (OS) and cancer-specific survival (CSS) of the various marital subgroups were calculated using the Kaplan–Meier curve. Multivariate Cox regression analysis screened for independent prognostic factors. Propensity score matching (PSM) was also conducted to minimize selection bias. We included 8834 eligible patients (4817 married, 894 widowed, 1732 single and 1391 divorced/separated) with laryngeal cancer. The 5-year OS and CSS of married, widowed, single, and separated/divorced patients were examined. Univariate and multivariate analyses found marital status to be an independent predictor of survival. Subgroup survival analysis showed that the OS and CSS rates in widowed patients were always the lowest in the various American Joint Committee on Cancer stages, irrespective of sex. Widowed patients demonstrated worse OS and CSS in the 1:1 matched group analysis. Among patients with laryngeal cancer, widowed patients represented the highest-risk group, with the lowest OS and CSS.

scholarly journals Trends of incidence and prognosis of primary adenocarcinoma of the bladder

2021 ◽  
Vol 13 ◽  
pp. 175628722110180
Author(s):  
Haowen Lu ◽  
Weidong Zhu ◽  
Weipu Mao ◽  
Feng Zu ◽  
Yali Wang ◽  
...  
Keyword(s):  
Prediction Model ◽  
Marital Status ◽  
Cox Regression ◽  
Tnm Staging ◽  
Primary Site ◽  
Primary Adenocarcinoma ◽  
Cancer Specific Survival ◽  
Training Cohort ◽  
Cox Regression Analysis ◽  
Ajcc Stage

Background: Primary adenocarcinoma of the bladder (ACB) is a rare malignant tumor of the bladder with limited understanding of its incidence and prognosis. Methods: Patients diagnosed with ACB between 2004 and 2015 were obtained from the SEER database. The incidence changes of ACB patients between 1975 and 2016 were detected by Joinpoint software. Nomograms were constructed based on the results of multivariate Cox regression analysis to predict overall survival (OS) and cancer-specific survival (CSS) in patients with ACB, and the constructed nomograms were validated. Results: The incidence of ACB was trending down from 1991 to 2016. A total of 1039 patients were included in the study and randomly assigned to the training cohort (727) and validation cohort (312). In the training cohort, multivariate Cox regression showed that age, marital status, primary site, histology type, grade, AJCC stage, T stage, SEER stage, surgery, radiotherapy, and chemotherapy were independent prognostic factors for OS, whereas these were age, marital status, primary site, histology type, grade, AJCC stage, T/N stage, SEER stage, surgery, and radiotherapy for CSS. Based on the above Cox regression results, we constructed prognostic nomograms for OS and CSS in ACB patients. The C-index of the nomogram OS was 0.773 and the C-index of CSS was 0.785, which was significantly better than the C-index of the TNM staging prediction model. The area under the curve (AUC) and net benefit of the prediction model were higher than those of the TNM staging system. In addition, the calibration curves were very close to the ideal curve, suggesting appreciable reliability of the nomograms. Conclusion: The incidence of ACB patients showed a decreasing trend in the past 25 years. We constructed a clinically useful prognostic nomogram for calculating OS and CSS of ACB patients, which can provide a personalized risk assessment for ACB patient survival.

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