scholarly journals Nomogram based on clinical characteristics and serological inflammation markers to predict overall survival of oral tongue squamous cell carcinoma patient after surgery

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Yi-Wei Lin ◽  
Wei-Piao Kang ◽  
Bin-Liang Huang ◽  
Zi-Han Qiu ◽  
Lai-Feng Wei ◽  
...  
Keyword(s):  
Overall Survival ◽  
Prognostic Factors ◽  
Clinical Characteristics ◽  
Cox Regression ◽  
Risk Group ◽  
Information Criterion ◽  
Tnm Stage ◽  
Inflammation Markers ◽  
Oral Tongue ◽  
Cox Regression Analysis

Abstract Background Oral tongue squamous cell carcinoma (OTSCC) is a prevalent malignant disease that is characterized by high rates of metastasis and postoperative recurrence. The aim of this study was to establish a nomogram to predict the outcome of OTSCC patients after surgery. Methods We retrospectively analyzed 169 OTSCC patients who underwent treatments in the Cancer Hospital of Shantou University Medical College from 2008 to 2019. The Cox regression analysis was performed to determine the independent prognostic factors associated with patient’s overall survival (OS). A nomogram based on these prognostic factors was established and internally validated using a bootstrap resampling method. Results Multivariate Cox regression analysis revealed the independent prognostic factors for OS were TNM stage, age, lymphocyte-to-monocyte ratio and immunoglobulin G, all of which were identified to create the nomogram. The Akaike Information Criterion and Bayesian Information Criterion of the nomogram were lower than those of TNM stage (292.222 vs. 305.480; 298.444 vs. 307.036, respectively), indicating a better goodness-of-fit of the nomogram for predicting OS. The bootstrap-corrected of concordance index (C-index) of nomogram was 0.784 (95% CI 0.708–0.860), which was higher than that of TNM stage (0.685, 95% CI 0.603–0.767, P = 0.017). The results of time-dependent C-index for OS also showed that the nomogram had a better discriminative ability than that of TNM stage. The calibration curves of the nomogram showed good consistency between the probabilities and observed values. The decision curve analysis also revealed the potential clinical usefulness of the nomogram. Based on the cutoff value obtained from the nomogram, the proposed high-risk group had poorer OS than low-risk group (P < 0.0001). Conclusions The nomogram based on clinical characteristics and serological inflammation markers might be useful for outcome prediction of OTSCC patient.

scholarly journals Nomogram Based On Clinical Characteristics and Serological Inflammation Markers To Predict Overall Survival of Tongue Squamous Cell Carcinoma Patient After Surgery

2021 ◽  
Author(s):  
Yi-Wei Lin ◽  
Wei-Piao Kang ◽  
Bin-Liang Huang ◽  
Zi-Han Qiu ◽  
Lai-Feng Wei ◽  
...  
Keyword(s):  
Overall Survival ◽  
Squamous Cell Carcinoma ◽  
Prognostic Factors ◽  
Clinical Characteristics ◽  
Cox Regression ◽  
Risk Group ◽  
Information Criterion ◽  
Tnm Stage ◽  
Inflammation Markers ◽  
Cox Regression Analysis

Abstract Background Tongue squamous cell carcinoma (TSCC) is a prevalent malignant disease that is characterized by high rates of metastasis and postoperative recurrence. The aim of this study was to establish a nomogram to predict the outcome of TSCC patients after surgery. Methods We retrospectively analyzed 169 TSCC patients who underwent treatments in the Cancer Hospital of Shantou University Medical College from 2008 to 2019. The Cox regression analysis was performed to determine the independent prognostic factors associated with patient’s overall survival (OS). A nomogram based on these prognostic factors was established and internally validated using a bootstrap resampling method. Results Multivariate Cox regression analysis revealed the independent prognostic factors for OS were TNM stage, age, lymphocyte-to-monocyte ratio and immunoglobulin G, all of which were identified to create the nomogram. The Akaike Information Criterion and Bayesian Information Criterion of the nomogram were lower than those of TNM stage (292.222 vs. 305.480; 298.444 vs. 307.036, respectively), indicating a better goodness-of-fit of the nomogram for predicting OS. The bootstrap-corrected of concordance index (C-index) of nomogram was 0.784 (95% CI: 0.708–0.860), which was higher than that of TNM stage (0.685, 95% CI: 0.603–0.767, P = 0.017). The results of time-dependent C-index for OS also showed that the nomogram had a better discriminative ability than that of TNM stage. The calibration curves of the nomogram showed good consistency between the probabilities and observed values. The decision curve analysis also revealed the potential clinical usefulness of the nomogram. Based on the cutoff value obtained from the nomogram, the proposed high-risk group had poorer OS than low-risk group (P < 0.0001). Conclusions The nomogram based on clinical characteristics and serological inflammation markers might be useful for outcome prediction of TSCC patient.

scholarly journals Identification of the angiogenesis related genes for predicting prognosis of patients with gastric cancer

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Sheng Zheng ◽  
Zizhen Zhang ◽  
Ning Ding ◽  
Jiawei Sun ◽  
Yifeng Lin ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Overall Survival ◽  
High Efficiency ◽  
Cox Regression ◽  
Risk Group ◽  
The Cancer Genome Atlas ◽  
Differentially Expressed ◽  
Lasso Regression ◽  
Sequencing Data ◽  
Cox Regression Analysis

Abstract Introduction Angiogenesis is a key factor in promoting tumor growth, invasion and metastasis. In this study we aimed to investigate the prognostic value of angiogenesis-related genes (ARGs) in gastric cancer (GC). Methods mRNA sequencing data with clinical information of GC were downloaded from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases. The differentially expressed ARGs between normal and tumor tissues were analyzed by limma package, and then prognosis‑associated genes were screened using Cox regression analysis. Nine angiogenesis genes were identified as crucially related to the overall survival (OS) of patients through least absolute shrinkage and selection operator (LASSO) regression. The prognostic model and corresponding nomograms were establish based on 9 ARGs and verified in in both TCGA and GEO GC cohorts respectively. Results Eighty-five differentially expressed ARGs and their enriched pathways were confirmed. Significant enrichment analysis revealed that ARGs-related signaling pathway genes were highly related to tumor angiogenesis development. Kaplan–Meier analysis revealed that patients in the high-risk group had worse OS rates compared with the low-risk group in training cohort and validation cohort. In addition, RS had a good prognostic effect on GC patients with different clinical features, especially those with advanced GC. Besides, the calibration curves verified fine concordance between the nomogram prediction model and actual observation. Conclusions We developed a nine gene signature related to the angiogenesis that can predict overall survival for GC. It’s assumed to be a valuable prognosis model with high efficiency, providing new perspectives in targeted therapy.

scholarly journals Identification of a Liver Progenitor Cell-Related Genes Signature Predicting Overall Survival for Hepatocellular Carcinoma

2021 ◽  
Vol 20 ◽  
pp. 153303382110414
Author(s):  
Xiaoyong Li ◽  
Jiaqong Lin ◽  
Yuguo pan ◽  
Peng Cui ◽  
Jintang Xia
Keyword(s):  
Hepatocellular Carcinoma ◽  
Overall Survival ◽  
Regression Analysis ◽  
Prognostic Model ◽  
Cox Regression ◽  
Risk Group ◽  
Gene Signature ◽  
Regression Analyses ◽  
Related Gene ◽  
Cox Regression Analysis

Background: Liver progenitor cells (LPCs) play significant roles in the development and progression of hepatocellular carcinoma (HCC). However, no studies on the value of LPC-related genes for evaluating HCC prognosis exist. We developed a gene signature of LPC-related genes for prognostication in HCC. Methods: To identify LPC-related genes, we analyzed mRNA expression arrays from a dataset (GSE57812 & GSE 37071) containing LPCs, mature hepatocytes, and embryonic stem cell samples. HCC RNA-Seq data from The Cancer Genome Atlas (TCGA) were used to explore the differentially expressed genes (DEGs) related to prognosis through DEG analysis and univariate Cox regression analysis. Lasso and multivariate Cox regression analyses were performed to construct the LPC-related gene prognostic model in the TCGA training dataset. This model was validated in the TCGA testing set and an external dataset (International Cancer Genome Consortium [ICGC] dataset). Finally, we investigated the relationship between this prognostic model with tumor-node-metastasis stage, tumor grade, and vascular invasion of HCC. Results: Overall, 1770 genes were identified as LPC-related genes, of which 92 genes were identified as DEGs in HCC tissues compared with normal tissues. Furthermore, we randomly assigned patients from the TCGA dataset to the training and testing cohorts. Twenty-six DEGs correlated with overall survival (OS) in the univariate Cox regression analysis. Lasso and multivariate Cox regression analyses were performed in the TCGA training set, and a 3-gene signature was constructed to stratify patients into 2 risk groups: high-risk and low-risk. Patients in the high-risk group had significantly lower OS than those in the low-risk group. Receiver operating characteristic curve analysis confirmed the signature's predictive capacity. Moreover, the risk score was confirmed to be an independent predictor for patients with HCC. Conclusion: We demonstrated that the LPC-related gene signature can be used for prognostication in HCC. Thus, targeting LPCs may serve as a therapeutic alternative for HCC.

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.

Clinical Characteristics, Pathology and Outcome of 237 Patients With Synovial Sarcoma: Single Center Experience

2021 ◽  
pp. 106689692110560
Author(s):  
Hao Cheng ◽  
Chi Yihebali ◽  
Hongtu Zhang ◽  
Lei Guo ◽  
Susheng Shi
Keyword(s):  
Overall Survival ◽  
Synovial Sarcoma ◽  
Tumor Size ◽  
Clinical Characteristics ◽  
Cox Regression ◽  
Survival Outcomes ◽  
Tumor Diameter ◽  
Single Center ◽  
Primary Tumors ◽  
Cox Regression Analysis

Background Synovial sarcoma (SS) is a rare soft tissue sarcoma. Available data regarding survival outcomes of patients with SS still remains limited. In this study, a single center retrospective analysis was performed to investigate the clinical characteristics, pathology and survival outcomes in patients with SS in China. Methods Patient data were systematically reviewed at the National Cancer Center from January 2015 to December 2020. The general information and treatment condition of patients were collected. Overall survival (OS) was evaluated using the Kaplan-Meier and Cox regression method. Results A total of 237 consecutive patients were included in this study (follow-up cut-off date: December, 2020). The median age of patients involved was 35 years (ranging from 5 to 83 years) and the mean tumor diameter was 5.3 cm (ranging from .2 to 26.0 cm). The main findings of the immunohistochemical staining analyses were EMA (111/156) (71%), keratin (32/64) (50.0%), keratin (12/20) (60%), keratin (42/70) (60%), S-100 (18/160) (11%), BCL-2 (128/134) (96%), CD99 (137/148) (93%) and TLE1 (23/26) (88%). It was found that 109 patients (66%) were presented with monophasic subtype and 55 (34%) with biphasic subtype. A total of 137 patients were tested by FISH method and 119 patients (87%) demonstrated SS18 rearrangement, whereas 18 patients (13%) did not show SS18 rearrangement. Generally, it was found that the 3-year OS rate was 86% and the 3-year DFS was 55%. Results of univariate analysis revealed that age, tumor size, tumor site, radiotherapy and targeted therapy were significantly correlated with the overall survival ( P < .05). Further, multivariate Cox regression analysis revealed that age, tumor size and radiotherapy were significantly associated with OS ( P < .05). Conclusions In conclusion, this study shows that the outcomes of patients with SS significantly decrease with age and tumor size. It was evident that radiotherapy is an independent and positive prognostic factor for patients with SS. In addition, it was shown that the prognosis of SS varies with tumor location. For instance, primary tumors in lower extremities have a higher prognosis, whereas tumors located in thorax have a lower prognosis.

scholarly journals Identification of IFN-β Associated Genes Signature Predicting Overall Survival for Glioblastoma

2021 ◽  
Author(s):  
Cheng Lijing ◽  
Yuan Meiling ◽  
Li Shu ◽  
Chen Junjing ◽  
Zhong Shupeng ◽  
...  
Keyword(s):  
Overall Survival ◽  
Clinical Decision Making ◽  
Cox Regression ◽  
Risk Group ◽  
Expression Profiles ◽  
Individual Treatment ◽  
Normal Brain ◽  
Data Set ◽  
Cox Regression Analysis ◽  
Prognosis Prediction

Abstract Background: Brain glioblastoma (GBM) is the most common primary malignant tumor of intracranial tumors. The prognosis of this disease is extremely poor. While the introduction of IFN-β regimen in the treatment of gliomas has significantly improved the outcome of patients, the underlying mechanism remains to be elucidated. Materials and methods: mRNA expression profiles and clinicopathological data were downloaded from TCGA-GBM and GSE83300 data set from the GEO. Univariate Cox regression analysis and lasso Cox regression model established a novel four‐gene IFN-β signature (including PRDX1, SEC61B, XRCC5, and BCL2L2) for GBM prognosis prediction. Further, GBM samples (n=50) and normal brain tissues (n=50) were then used for real-time polymerase chain reaction (PCR) experiments. Gene Set Enrichment Analyses (GSEA) was performed to further understand the underlying molecular mechanisms. Pearson correlation was applied to calculate the correlation between the lncRNAs and IFN-β associated genes. A lncRNA with a correlation coefficient |R2| > 0.3 and P < 0.05 was considered to be an IFN-β associated lncRNA.Results: Patients in the high‐risk group shown significantly poorer survival than patients in the low‐risk group. The signature was found to be an independent prognostic factor for GBM survival. Furthermore, GSEA revealed several significantly enriched pathways, which might help explain the underlying mechanisms. Our study identified a novel robust four‐gene IFN-β signature for GBM prognosis prediction. The signature might contain potential biomarkers for metabolic therapy and treatment response prediction in GBM.Conclusions: Our study established a novel IFN-β associated genes signature to predict overall survival of GBM, which may help in clinical decision making for individual treatment.

scholarly journals Signature Based on Immune-Related LncRNA Can Predict Overall Survival of Osteosarcoma Patients

2020 ◽  
Author(s):  
Longqing Li ◽  
Lianghao Zhang ◽  
Manhas Adbul Khader ◽  
Yan Zhang ◽  
Xinchang Lu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Overall Survival ◽  
Immune Checkpoint ◽  
High Throughput Sequencing ◽  
Immune Cell ◽  
Cox Regression ◽  
Risk Group ◽  
Low Risk ◽  
Checkpoint Gene ◽  
Cox Regression Analysis

Abstract Background: Osteosarcoma is a malignant bone tumor common in children and adolescents. Metastatic status remains the most important guideline for classifying patients and making clinical decisions. Despite many efforts, newly diagnosed patients receive the same therapy that patients have received over the last 4 decades. With the development of high-throughput sequencing technology and the rise of immunotherapy, it is necessary to deeply explore the immune molecular mechanism of osteosarcoma.Methods: We obtained RNA-seq data and clinical information of osteosarcoma patients from TCGA database and TARGET database. With the help of co-expression analysis we identified immune-related lncRNA and then by means of univariate Cox regression analysis prognostic-related lncRNA was screened out. And also by using least absolute shrinkage and selection operator regression method a model based on immune-related lncRNA was constructed. The differences in overall survival, immune infiltration, immune checkpoint gene expression, and tumor microenvironmental immunity type between the two groups were evaluated.Results: We constructed a signature consisting of 13 lncRNA. Our results show that signatures can reliably predict the overall survival of patients with osteosarcoma and can bring net clinical benefits. Further more, the signatures can be used for further risk stratification of the metastasis patients. Patients in the low-risk group had higher immune cell infiltration and immune checkpoint gene expression. The results from gene set variation analysis show that patients in low-risk group are closely related to immune-related pathways when compared with patients in high-risk group. Finally, patients in the low-risk group are more likely to be classified as TMIT I and hence more likely to benefit from immunotherapy.Conclusion: Our signature may be a reliable marker for predicting the overall survival of patients with osteosarcoma.

scholarly journals Construction of a nomogram to predict overall survival for patients with lung large cell neuroendocrine carcinoma: analysis of the SEER database

2020 ◽  
Author(s):  
Dong Han ◽  
Fei Gao ◽  
Nan Li ◽  
Hao Wang ◽  
Qi Fu
Keyword(s):  
Overall Survival ◽  
Survival Rate ◽  
Neuroendocrine Carcinoma ◽  
Cox Regression ◽  
Risk Group ◽  
Multivariable Analysis ◽  
Large Cell ◽  
Large Cell Neuroendocrine Carcinoma ◽  
Seer Database ◽  
Cox Regression Analysis

Abstract Background Lung large cell neuroendocrine carcinoma (L-LCNEC) has a poor prognosis with lower survival rate than other NSCLC patients. The estimation of an individual survival rate is puzzling. The main purpose of this study was to establish a more accurate model to predict the prognosis of L-LCNEC.Methods Patients aged 18 years or older with L-LCNEC were identified from the Surveillance, Epidemiology and End Results (SEER) database from 2004 to 2015. Cox regression analysis was used to identify factors associated with survival time. The results were used to construct a nomogram to predict 1-year, and 3-year survival probability in L-LCNEC patients. Overall survival (OS) were compared between low risk group and high risk group by the Kaplan–Meier analysis.Results A total of 3216 patients were included in the study. We randomly divided all included patients into 7:3 training and validating groups. In multivariable analysis of training cohort, age at diagnosis, sex, stage of tumor, surgical treatment, radiotherapy and chemotherapy were independent prognostic factors for OS. All these factors were incorporated to construct a nomogram, which was tested in the validating cohort.Conclusions we constructed a visual nomogram prognosis model, which had the potential to predict the 1-year and 3-year survival rate of L-LCNEC patients, and could be used as an assistant prediction tool in clinical practice.

scholarly journals Development and Validation of Nomograms to Predict Prognosis of Oral and Oropharyngeal Mucoepidermoid Carcinoma: A SEER-Based Study

2020 ◽  
Author(s):  
muyuan liu ◽  
Litian Tong ◽  
Manbin Xu ◽  
Xiang Xu ◽  
Bin Liang ◽  
...  
Keyword(s):  
Overall Survival ◽  
Prognostic Factors ◽  
Mucoepidermoid Carcinoma ◽  
Cox Regression ◽  
Seer Database ◽  
Disease Specific Survival ◽  
Cox Regression Analysis ◽  
T Stage ◽  
N Stage ◽  
Disease Specific

Abstract Background: Due to the low incidence of mucoepidermoid carcinoma, there lacks sufficient studies for determining optimal treatment and predicting prognosis. The purpose of this study was to develop prognostic nomograms, to predict overall survival and disease-specific survival (DSS) of oral and oropharyngeal mucoepidermoid carcinoma patients, using the National Cancer Institute’s Surveillance, Epidemiology, and End Results (SEER) database. Methods: Clinicopathological and follow-up data of patients diagnosed with oral and oropharyngeal mucoepidermoid carcinoma between 2004 and 2017 were collected from the SEER database. The Kaplan-Meier method with the log-rank test was employed to identify single prognostic factors. Multivariate Cox regression was utilized to identify independent prognostic factors. C-index, area under the ROC curve (AUC) and calibration curves were used to assess performance of the prognostic nomograms. Results: A total of 1230 patients with oral and oropharyngeal mucoepidermoid carcinoma were enrolled in the present study. After multivariate Cox regression analysis, age, sex, tumor subsite, T stage, N stage, M stage, grade and surgery were identified as independent prognostic factors for overall survival. T stage, N stage, M stage, grade and surgery were identified as independent prognostic factors for disease-specific survival. Nomograms were constructed to predict the overall survival and disease-specific survival based on the independent prognostic factors. The fitted nomograms possessed excellent prediction accuracy, with a C-index of 0.899 for OS prediction and 0.893 for DSS prediction. Internal validation by computing the bootstrap calibration plots, using the validation set, indicated excellent performance by the nomograms. Conclusion: The prognostic nomograms developed, based on individual clinicopathological characteristics, in the present study, accurately predicted the overall survival and disease-specific survival of patients with oral and oropharyngeal mucoepidermoid carcinoma.

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.

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