scholarly journals Prognostic factors in patients with thyroid carcinoma: a competing-risks analysis

2020 ◽  
Author(s):  
Junhu Wang ◽  
Lisong Heng ◽  
Jie Yang ◽  
Feng Tian ◽  
Xiaojun Liang ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Thyroid Cancer ◽  
Prognostic Factors ◽  
Thyroid Carcinoma ◽  
Competing Risks ◽  
Tumor Size ◽  
Hazard Function ◽  
Cox Regression ◽  
Regional Lymph Nodes ◽  
Cox Regression Analysis

Abstract Background: Cox proportional-hazards models are widely used to describe survival trends and identify prognostic factors for thyroid carcinoma, but the prognostic model is not accurate enough. This study therefore used a competing-risks model to identify the significant prognostic factors for different types of thyroid carcinoma.Methods: We identified 38,444 eligible patients in the SEER (Surveillance, Epidemiology, and End Result) database. The potential prognostic factors for thyroid carcinoma were analyzed by Cox regression analysis, cause-specific hazard function (CS) analysis, and subdistribution hazard function (SD).Results: Cox regression analysis, CS analysis and SD analysis found identifying age, being unmarried, no regional lymph nodes examined, AJCC-6 II, III, IV vs I , having follicular, medullary, anaplastic vs Papillary carcinomas, no surgery, no radioiodine, liver metastasis, and lung metastasis as the significant risk factors for thyroid carcinoma, while being female was protective factor. However, the results from the three multivariate models for being black, tumor size >1 cm, and brain metastasis were inconsistent.Conclusion: In addition to finding that age, pathological type, tumor size, AJCC-6 stage, surgery status, radioiodine status, metastasis as common factors affected the prognosis, we also found that women, being unmarried and had their regional lymph nodes examined can improve the prognosis of thyroid cancer. The discovery of these factors will provide evidences for the prevention and treatment of thyroid cancer.

scholarly journals Prognostic factors in patients with thyroid carcinoma: a competing-risks analysis

2020 ◽  
Author(s):  
Junhu Wang ◽  
Lisong Heng ◽  
Jie Yang ◽  
Feng Tian ◽  
Xiaojun Liang ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Prognostic Factors ◽  
Thyroid Carcinoma ◽  
Competing Risks ◽  
Hazard Function ◽  
Cox Regression ◽  
Multivariate Analyses ◽  
Regional Lymph Nodes ◽  
Cox Regression Analysis ◽  
Competing Risks Analysis

Abstract Background Cox proportional-hazards models are widely used to describe survival trends and identify prognostic factors for thyroid carcinoma, but they have significant limitations and deficiencies. This study therefore used a competing-risks model to identify the significant prognostic factors for thyroid carcinoma. Methods We identified 38,444 eligible patients in the SEER (Surveillance, Epidemiology, and End Result) database. The potential prognostic factors for thyroid carcinoma were analyzed by competing-risks analysis using both univariate and multivariate analyses. Results The univariate analysis showed that age, sex, race, marital state, insurance status, tumor size, whether regional lymph nodes were examined, AJCC stage, histology, surgery status, radiation status, chemotherapy status, bone metastasis, brain metastasis, liver metastasis, and lung metastasis were prognostic factors for death caused by thyroid carcinoma. The multivariate analyses that comprised Cox regression analysis, the cause-specific hazard function analysis, and subdistribution hazard function (SD) analysis produced different results, identifying age, being unmarried, no regional lymph nodes examined, AJCC stages II, III, and IV, having follicular, medullary, and anaplastic carcinomas, no surgery, no radiation, liver metastasis, and lung metastasis as the significant risk factors for thyroid carcinoma, while being female and not receiving chemotherapy were protective factors. The results from the three multivariate models for being black, tumor size >1 cm, and brain metastasis were inconsistent. Conclusion This study had produced information about the significant prognostic factors for thyroid carcinoma using a competing-risks model that is more accurate than that obtained using Cox regression analysis. The SD model seems to be preferable for establishing a more accurate prognostic model of this disease aimed at guiding clinical treatments and improving prognoses.

scholarly journals Prognostic Factors and Diseases-Specific Survival Outcome in Patients with Glioblastoma: A Population-Based Study

2020 ◽  
Author(s):  
Lihua Wu ◽  
Jianbo Song ◽  
Junping Zhang ◽  
Wenhui Yang ◽  
Mengxian Zhang ◽  
...  
Keyword(s):  
Risk Factors ◽  
Regression Analysis ◽  
Prognostic Factors ◽  
Tumor Size ◽  
Cox Regression ◽  
Cubic Spline ◽  
Age At Diagnosis ◽  
Lasso Regression ◽  
Cox Regression Analysis ◽  
Risk Effect

Abstract ObjectiveThis study aimed to determine the prognostic factors for disease-specific survival (DSS) of glioblastoma (GBM) and establish a corresponding effective nomogram for clinical prediction.Methods This study was based on Surveillance, Epidemiology, and End Results database between 2004 and 2015. Kaplan-Meier survival analysis was used to evaluate the effect of various prognostic factors on DSS. Lasso regression was used to determine the independent prognostic factors of DSS and multivariate cox regression analysis was performed correspondingly. Additional restricted cubic spline cox regression was used to analyze the trend of the risk effect (hazard ratio) of continuous variables on DSS. Based on the multivariate cox regression model, a nomogram was established to predict DSS. ResultsThe average age at diagnosis of all enrolled patients was 59.8±12.2 years, of which 40.5% were women and 59.5% were men. Lasso regression analysis showed that age at diagnosis, sex, marital status, race, tumor size, primary site, laterality, surgery, radiotherapy and chemotherapy, radiotherapy sequence with surgery, and year of diagnosis were independent prognostic factors for DSS. Multivariate cox regression analysis showed that elderly, males, unmarried status, larger tumors were all risk factors for DSS. Restricted cubic spline cox regression showed that the risk of death from GBM was significantly increased for the elderly, especially older than 75 years. When the tumor was smaller than 75mm, an increasing risk linearly was associated with DSS, but the risk effect remained constant after 75mm. Constructing the nomogram to predict the DSS probability of 1-, 3- and 5-year respectively, and its good predictive performance was proved by the calibration curve.ConclusionThe advanced age was one of the significant risk factors for GBM. How the change of tumor size affected DSS needed further study and discussion. The established nomogram was robust in predicting 1-, 3-, and 5- year DSS.

scholarly journals Identification of Prognostic RBPs in Osteosarcoma

2021 ◽  
Vol 20 ◽  
pp. 153303382110049
Author(s):  
Bei Li ◽  
Long Fang ◽  
Baolong Wang ◽  
Zengkun Yang ◽  
Tingbao Zhao
Keyword(s):  
Regression Analysis ◽  
Prognostic Factors ◽  
Differential Expression ◽  
Ribosome Biogenesis ◽  
Rna Binding ◽  
Cox Regression ◽  
Rna Binding Proteins ◽  
Malignant Tumors ◽  
Differential Expression Analysis ◽  
Cox Regression Analysis

Osteosarcoma often occurs in children and adolescents and causes poor prognosis. The role of RNA-binding proteins (RBPs) in malignant tumors has been elucidated in recent years. Our study aims to identify key RBPs in osteosarcoma that could be prognostic factors and treatment targets. GSE33382 dataset was downloaded from Gene Expression Omnibus (GEO) database. RBPs extraction and differential expression analysis was performed. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were performed to explore the biological function of differential expression RBPs. Moreover, we constructed Protein-protein interaction (PPI) network and obtained key modules. Key RBPs were identified by univariate Cox regression analysis and multiple stepwise Cox regression analysis combined with the clinical information from Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database. Risk score model was generated and validated by GSE16091 dataset. A total of 38 differential expression RBPs was identified. Go and KEGG results indicated these RBPs were significantly involved in ribosome biogenesis and mRNA surveillance pathway. COX regression analysis showed DDX24, DDX21, WARS and IGF2BP2 could be prognostic factors in osteosarcoma. Spearman’s correlation analysis suggested that WARS might be important in osteosarcoma immune infiltration. In conclusion, DDX24, DDX21, WARS and IGF2BP2 might play key role in osteosarcoma, which could be therapuetic targets for osteosarcoma treatment.

scholarly journals Prognostic Significance of PD-L1 and mTOR Expression in Oral Squamous Cell Carcinoma

2020 ◽  
Author(s):  
Nattinee Charoen ◽  
Kitti Jantharapattana ◽  
Paramee Thongsuksai
Keyword(s):  
Squamous Cell Carcinoma ◽  
Regression Analysis ◽  
Prognostic Factors ◽  
Oral Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Squamous Cell ◽  
Cox Regression ◽  
Prognostic Significance ◽  
P Value ◽  
Cox Regression Analysis

Objective: Programmed cell death ligand 1 (PD-L1) and mammalian target of rapamycin (mTOR) are key players in host immune evasion and oncogenic activation, respectively. Evidence of the prognostic role in oral squamous cell carcinoma (OSCC) is conflicting. This study examined the associations of PD-L1 and mTOR expression with 5-year overall survival in OSCC patients. Material and Methods: The expressions of PD-L1 and mTOR proteins were immunohistochemically evaluated on tissue microarrays of 191 patients with OSCC who were treated by surgery at Songklanagarind Hospital, Thailand from 2008 to 2011. Cox regression analysis was used to determine independent prognostic factors. Results: PD-L1 expression was observed in 14.1% of cases while mTOR expression was present in 74.3% of cases. Females were more likely to have tumors with PD-L1 (p-value=0.007) and mTOR expressions (p-value=0.003) than males. In addition, lower clinical stage and well differentiated tumor are more likely to have mTOR expression (p-value= 0.038 and p-value<0.001, respectively). Cox regression analysis showed that age, tumor stage, nodal stage, combined surgical treatment with radiation or chemoradiation therapy, surgical margin status, PD-L1 expression and mTOR expression are independent prognostic factors. High PD-L1 expression (hazard ratio (HR) 3.14, 95% confidence interval (CI), 1.26–7.79) and high mTOR expression (HR 1.69, 95% CI, 1.00–2.84) are strong predictors of poor outcome. Conclusion: A proportion of OSCC expressed PD-L1 and mTOR proteins. Expression of PD-L1 and mTOR proteins are strong prognostic factors of OSCC.

scholarly journals Incidence, Survival, and Associated Factors Estimation in Osteosarcoma Patients with Lung Metastasis: A Single-center Experience of 11 Years in Tianjin, China

2021 ◽  
Author(s):  
Chao Zhang ◽  
Haixiao Wu ◽  
Guijun Xu ◽  
Wenjuan Ma ◽  
Lisha Qi ◽  
...  
Keyword(s):  
Risk Factors ◽  
Overall Survival ◽  
Regression Analysis ◽  
Prognostic Factors ◽  
Lung Metastasis ◽  
Associated Factors ◽  
Cox Regression ◽  
Cancer Center ◽  
Single Center ◽  
Cox Regression Analysis

Abstract Background: Osteosarcoma is the most common primary malignant bone tumor. The current study was conducted to describe the general condition of patients with primary osteosarcoma in a single cancer center in Tianjin, China and to investigate the associated factors in osteosarcoma patients with lung metastasis. Methods: From February 2009 to October 2020, patients from Tianjin Medical University Cancer Institute and Hospital, China were retrospectively analyzed. The Kaplan–Meier method was used to evaluate the overall survival of osteosarcoma patients. Prognostic factors of patients with osteosarcoma were identified by the Cox proportional hazard regression analysis. Risk factor of lung metastasis in osteosarcoma were investigated by the logistic regression model. Results: A total of 203 patients were involved and 150 patients were successfully followed up for survival status. The 5-year survival rate of osteo-sarcoma patients was 70.0%. Surgery, bone and lung metastasis were the significant prognostic factors in multivariable Cox regression analysis. Twenty-one (10.3%) patients showed lung metastasis at the diagnosis of osteosarcoma and 67 (33%) lung metastases during the later course. T3 stage (OR=11.415, 95%CI 1.362-95.677, P=0.025) and synchronous bone metastasis (OR=6.437, 95%CI 1.69-24.51, P=0.006) were risk factors of synchronous lung metastasis occurrence. Good necrosis (≥90%, OR=0.097, 95%CI 0.028-0.332, P=0.000) and elevated Ki-67 (≥50%, OR=4.529, 95%CI 1.241-16.524, P=0.022) were proved to be significantly associated with metachronous lung metastasis occurrence. Conclusion: The overall survival, prognostic factors and risk factors for lung metastasis in this single center provided insight about osteosarcoma management.

scholarly journals Application value of nomogram and prognostic factors of gastric cancer patients who underwent D2 radical lymphadenectomy

2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Guang-Chuan Mu ◽  
Yuan Huang ◽  
Zhi-Ming Liu ◽  
Xiang-Hua Wu ◽  
Xin-Gan Qin ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Regression Analysis ◽  
Prognostic Factors ◽  
Cancer Patients ◽  
Cox Regression ◽  
Term Survival ◽  
Long Term Survival ◽  
Cox Regression Analysis ◽  
Gastric Cancer Patients

Abstract Background The aim of this study was to explore the prognostic factors and establish a nomogram to predict the long-term survival of gastric cancer patients. Methods The clinicopathological data of 421 gastric cancer patients, who were treated with radical D2 lymphadenectomy by the same surgical team between January 2009 and March 2017, were collected. The analysis of long-term survival was performed using Cox regression analysis. Based on the multivariate analysis results, a prognostic nomogram was formulated to predict the 5-year survival rate probability. Results In the present study, the total overall 3-year and 5-year survival rates were 58.7 and 45.8%, respectively. The results of the univariate Cox regression analysis revealed that tumor staging, tumor location, Borrmann type, the number of lymph nodes dissected, the number of lymph node metastases, positive lymph nodes ratio, lymphocyte count, serum albumin, CEA, CA153, CA199, BMI, tumor size, nerve invasion, and vascular invasion were prognostic factors for gastric cancer (all, P < 0.05). However, merely tumor staging, tumor location, positive lymph node ratio, CA199, BMI, tumor size, nerve invasion, and vascular invasion were independent risk factors, based on the results of the multivariate Cox regression analysis (all, P < 0.05). The nomogram based on eight independent prognostic factors revealed a well-degree of differentiation with a concordance index of 0.76 (95% CI: 0.72–0.79, P < 0.001), which was better than the AJCC-7 staging system (concordance index = 0.68). Conclusion The present study established a nomogram based on eight independent prognostic factors to predict long-term survival in gastric cancer patients. The nomogram would be beneficial for more accurately predicting the prognosis of gastric cancer, and provide important basis for making individualized treatment plans following surgery.

scholarly journals Age as a Prognostic Factor in Anaplastic Thyroid Cancer

2014 ◽  
Vol 2014 ◽  
pp. 1-5 ◽  
Author(s):  
Vladan Zivaljevic ◽  
Katarina Tausanovic ◽  
Ivan Paunovic ◽  
Aleksandar Diklic ◽  
Nevena Kalezic ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Thyroid Cancer ◽  
Prognostic Factor ◽  
Cox Regression ◽  
Postoperative Radiotherapy ◽  
Distant Metastases ◽  
Anaplastic Thyroid Cancer ◽  
Multivariate Regression Analysis ◽  
Rank Test ◽  
Cox Regression Analysis

Background.Anaplastic thyroid cancer (ATC) is one of the tumors with the shortest survival in human medicine.Aim.The aim was to determine the importance of age in survival of patients with ATC.Material and Methods. We analyzed the data on 150 patients diagnosed with ATC in the period from 1995 to 2006. The Kaplan-Meier method and log-rank test were used to determine overall survival. Prognostic factors were identified by univariate and multivariate Cox regression analysis.Results.The youngest patient was 35 years old and the oldest was 89 years old. According to univariate regression analysis, age was significantly associated with longer survival in patients with ATC. In multivariate regression analysis, patients age, presence of longstanding goiter, whether surgical treatment is carried out or not, type of surgery, tumor multicentricity, presence of distant metastases, histologically proven preexistent papillary carcinoma, radioiodine therapy, and postoperative radiotherapy were included. According to multivariate analysis, besides surgery (P=0.000, OR = 0.43, 95% CI = 0.29–0.63), only patients age (P=0.023, OR = 0.68, 95% CI = 0.49–0.95) was independent prognostic factor of favorable survival in patients with ATC.Conclusion. Age is a factor that was independently associated with survival time in ATC. Anaplastic thyroid cancer has the best prognosis in patients younger than 50 years.

scholarly journals Influence of Old Age on Risk of Lymph Node Metastasis and Survival in Patients With T1 Colorectal Cancer: A Population-Based Analysis

2021 ◽  
Vol 11 ◽  
Author(s):  
Hua Ye ◽  
Bin Zheng ◽  
Qi Zheng ◽  
Ping Chen
Keyword(s):  
Colorectal Cancer ◽  
Regression Analysis ◽  
Lymph Node ◽  
Elderly Patients ◽  
Old Age ◽  
Tumor Size ◽  
Cox Regression ◽  
Cox Regression Analysis ◽  
T1 Colorectal Cancer ◽  
Well Differentiated

BackgroundWe aimed at determining the influence of old age on lymph node metastasis (LNM) and prognosis in T1 colorectal cancer (CRC).MethodsWe collected data from eligible patients in Surveillance, Epidemiology, and End Results database between 2004 and 2015. Independent predictors of LNM were identified by logistic regression analysis. Cox regression analysis, propensity score-matched analysis, and competing risks analysis were used to analyze the associations between old age and lymph node (LN) status and to validate the prognostic value of old age on cancer-specific survival (CSS).ResultsIn total, 10,092 patients were identified. Among them, 6,423 patients (63.6%) had greater than or equal to 12 examined lymph nodes (LNE ≥12), and 5,777 patients (57.7%) were 65 years or older. The observed rate of LNM was 4.6% (15 out of 325) in T1 CRC elderly patients, with tumor size &lt;3 cm, well differentiated, with negative carcinoembryonic antigen (CEA) level, and adenocarcinoma. Logistic regression models demonstrated that tumor size ≥3 cm (odds ratio, OR = 1.316, P = 0.038), poorly differentiated (OR = 3.716, P &lt; 0.001), older age (OR = 0.633 for ages 65–79 years, OR = 0.477 for age over 80 years, both P &lt;0.001), and negative CEA level (OR = 0.71, P = 0.007) were independent prognostic factors. Cox regression analysis demonstrated that CSS was not significantly different between elderly patients undergoing radical resection with LNE ≥12 and those with LNE &lt;12 (hazard ratio = 0.865, P = 0.153), which was firmly validated after a propensity score-matched analysis by a competing risks model.ConclusionsThe predictive value of tumor size, grading, primary site, histology, CEA level, and age for LNM should be considered in medical decision making about local resection. We found that tumor size was &lt;3 cm, well differentiated, negative CEA level, and adenocarcinoma in elderly patients with T1 colorectal cancer which was suitable for local excision.

scholarly journals A Novel Signature to Predict Thyroid Cancer Prognosis and Immune Landscape Using Immune-Related LncRNA Pairs

2021 ◽  
Author(s):  
BO SONG ◽  
Lijun Tian ◽  
Fan Zhang ◽  
Zheyu Lin ◽  
Boshen Gong ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Thyroid Cancer ◽  
High Risk ◽  
Small Molecule ◽  
Immune Cell ◽  
Cox Regression ◽  
Risk Group ◽  
High Risk Group ◽  
Immune Cell Infiltration ◽  
Cox Regression Analysis

Abstract Background: Thyroid cancer (TC) is the most common endocrine malignancy worldwide. The incidence of TC is high and increasing worldwide due to continuous improvements in diagnostic technology. TC is still often overtreated due to a lack of reliable diagnostic biomarkers. Therefore, determining accurate prognostic predictions to stratify TC patients is important.Methods: Raw data were downloaded from the TCGA database, and pairwise comparisons were applied to identify differentially expressed immune-related lncRNA (DEirlncRNA) pairs. Then, we used univariate Cox regression analysis and a modified Lasso algorithm on these pairs to construct a risk assessment model for TC. Next, TC patients were assigned to high- and low-risk groups based on the optimal cutoff score of the model for the 1-year ROC curve. We evaluated the signature in terms of prognostic independence, predictive value, immune cell infiltration, ICI-related molecules and small-molecule inhibitor efficacy. Results: We identified 30 DEirlncRNA pairs through Lasso regression, and 14 pairs served as the novel predictive signature. The high-risk group had a significantly poorer prognosis than the low-risk group. Cox regression analysis revealed that this immune-related signature can predict prognosis independently and reliably for TC. With the CIBERSORT algorithm, we found an association between the signature and immune cell infiltration. Additionally, several immune checkpoint inhibitor (ICI)-related molecules, such as PD-1 and PD-L1, showed a negative correlation with the high-risk group. We further found that some commonly used small-molecule inhibitors, such as sunitinib, were related to this new signature. Conclusions: We constructed a prognostic immune-related lncRNA signature that can predict TC patient survival without considering the technical bias of different platforms, and this signature also sheds light on TC overall prognosis and novel clinical treatments, such as ICB therapy and small molecular inhibitors.

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