scholarly journals Identification and Clinical Validation of Genes Signatures With Grade and Survival in Head and Neck Carcinomas

2020 ◽  
Author(s):  
Wei Ma ◽  
Qing Cao ◽  
Wandong She
Keyword(s):  
High Risk ◽  
Head And Neck ◽  
Risk Score ◽  
Cox Regression ◽  
Expression Profiles ◽  
Low Risk ◽  
Low Grade ◽  
High Grade ◽  
Head And Neck Carcinomas ◽  
Risk Patients

Abstract Background: The mechanism of transition from low-grade to high-grade head and neck carcinomas (HNC) still remains unclear. The aim of this study was to explore the genes expression profiles that drive malignancy from low to high-grade HNC, as well as analyze their correlations with the survival.Methods: Gene expressions and clinical data of HNC were downloaded from the Gene Expression Omnibus (GEO) repository. The significantly differential genes (SDGs) between low and high-grade HNC were screened by GEO2R and R software. Bioinformatics functions of SDGs were investigated by the enrichment analyses. Univariate and multivariate cox regressions were performed to identify prognostic SDGs of progression free survival (PFS) and disease specific survival (DSS). ROC curve was established to evaluate the ability to predict the prognosis. Then, the correlations between SDGs and clinical features were evaluated. The genes were experimentally validated by RT-PCR in clinical specimens’ tissues at last.Results: Thirty-five SDGs were identified in 47 low-grade and 30 high-grade HNC samples. Enrichment analysis showed these SDGs were mainly enriched in the DNA repair pathway and the regulation of I−kappaB kinase/NF−kappaB signaling pathway. Cox regression analyses showed that CXCL14, SLC44A1 and UBD were significantly associated with DSS, and PPP2R2C and SLC44A1 were associated with PFS. Patients at a high-risk or low-risk for prognosis were established based on genes signatures. High-risk patients had significantly shorter DSS and PFS than low-risk patients (P=0.033, 0.010 respectively). Multivariate cox regression showed HPV (P=0.033), lymph node status (P=0.032) and residual status (P＜0.044) were independent risk factors for PFS. ROC curves showed the risk score had better efficacy to predict survival both for DSS and PFS (AUC=0.858, 0.901 respectively). In addition, we found UBD, PPP2R2C and risk score were significantly associated with HPV status (all P＜0.05). The experiment results showed CXCL14 and SLC44A1 were significantly overexpressed in the HNC grade I/II tissues and the UBD were overexpressed in the HNC grade III/IV tissues.Conclusions: Our results suggested that SDGs had different expression profiles between the low-grade and high-grade HNC, and these genes may serve as prognostic biomarker to predict the survival.

scholarly journals Identification and Clinical Validation of Genes Signatures with Grade and Survival in Head and Neck Carcinomas

2020 ◽  
Author(s):  
Wei Ma ◽  
Qing Cao ◽  
Wandong She
Keyword(s):  
High Risk ◽  
Head And Neck ◽  
Risk Score ◽  
Cox Regression ◽  
Expression Profiles ◽  
Low Risk ◽  
Low Grade ◽  
High Grade ◽  
Head And Neck Carcinomas ◽  
Risk Patients

Abstract Background: The mechanism of transition from low-grade to high-grade head and neck carcinomas (HNC) still remains unclear. This study aimed to explore the genes expression profiles that drive malignancy from low to high-grade HNC, as well as analyze their correlations with survival.Methods: Gene expressions and clinical data of HNC were downloaded from the Gene Expression Omnibus (GEO) repository. The significantly different genes (SDGs) between low and high-grade HNC were screened by GEO2R tool and R software. Bioinformatics functions of SDGs were investigated by the enrichment analyses. Univariate and multivariate cox regressions were performed to identify prognostic SDGs of progression-free survival (PFS) and disease-specific survival (DSS). Receiver operating curve (ROC) was established to evaluate the ability to predict the prognosis. Then, the correlations between SDGs and clinical features were evaluated. The genes were experimentally validated by RT-PCR in clinical specimens.Results: 35 SDGs were identified in 47 low-grade and 30 high-grade HNC samples. Enrichment analysis showed these SDGs were enriched in the DNA repair pathway and the regulation of I−kappaB kinase/NF−kappaB signaling pathway. Cox regression analyses showed that CXCL14, SLC44A1 and UBD were significantly associated with DSS, and PPP2R2C and SLC44A1 were associated with PFS. Patients at a high-risk or low-risk for prognosis were established based on genes signatures. High-risk patients had significantly shorter DSS and PFS than low-risk patients (P=0.033, 0.010 respectively). Multivariate cox regression showed human papillomavirus (HPV) (P=0.033), lymph node status (P=0.032) and residual status (P＜0.044) were independent risk factors for PFS. ROC curves showed the risk score had better efficacy to predict survival both for DSS and PFS (AUC=0.858, 0.901 respectively). In addition, we found UBD, PPP2R2C and risk score were significantly associated with HPV status (all P＜0.05). The experiment results showed CXCL14 and SLC44A1 were significantly overexpressed in the HNC grade I/II tissues and the UBD was overexpressed in the HNC grade III/IV tissues.Conclusions: Our results suggested that SDGs had different expression profiles between the low-grade and high-grade HNC, and these genes may serve as prognostic biomarker to predict the survival.

scholarly journals Development of prognosis model for colon cancer based on autophagy-related genes

2020 ◽  
Vol 18 (1) ◽  
Author(s):  
Xu Wang ◽  
Yuanmin Xu ◽  
Ting Li ◽  
Bo Chen ◽  
Wenqi Yang
Keyword(s):  
Colon Cancer ◽  
Regression Analysis ◽  
High Risk ◽  
Cancer Patients ◽  
Risk Score ◽  
Cox Regression ◽  
Patient Survival ◽  
Expression Profiles ◽  
Cox Regression Analysis ◽  
Risk Patients

Abstract Background Autophagy is an orderly catabolic process for degrading and removing unnecessary or dysfunctional cellular components such as proteins and organelles. Although autophagy is known to play an important role in various types of cancer, the effects of autophagy-related genes (ARGs) on colon cancer have not been well studied. Methods Expression profiles from ARGs in 457 colon cancer patients were retrieved from the TCGA database (https://portal.gdc.cancer.gov). Differentially expressed ARGs and ARGs related to overall patient survival were identified. Cox proportional-hazard models were used to investigate the association between ARG expression profiles and patient prognosis. Results Twenty ARGs were significantly associated with the overall survival of colon cancer patients. Five of these ARGs had a mutation rate ≥ 3%. Patients were divided into high-risk and low-risk groups based on Cox regression analysis of 8 ARGs. Low-risk patients had a significantly longer survival time than high-risk patients (p < 0.001). Univariate and multivariate Cox regression analysis showed that the resulting risk score, which was associated with infiltration depth and metastasis, could be an independent predictor of patient survival. A nomogram was established to predict 1-, 3-, and 5-year survival of colon cancer patients based on 5 independent prognosis factors, including the risk score. The prognostic nomogram with online webserver was more effective and convenient to provide information for researchers and clinicians. Conclusion The 8 ARGs can be used to predict the prognosis of patients and provide information for their individualized treatment.

scholarly journals Development of prognosis model for colon cancer based on autophagy-related genes

2020 ◽  
Author(s):  
Xu Wang ◽  
Yuanmin Xu ◽  
Ting Li ◽  
Bo Chen ◽  
Wenqi Yang
Keyword(s):  
Colon Cancer ◽  
Regression Analysis ◽  
High Risk ◽  
Cancer Patients ◽  
Risk Score ◽  
Cox Regression ◽  
Patient Survival ◽  
Expression Profiles ◽  
Cox Regression Analysis ◽  
Risk Patients

Abstract Background: Autophagy is an orderly catabolic process for degrading and removing unnecessary or dysfunctional cellular components such as proteins and organelles. Although autophagy is known to play an important role in various types of cancer, the effects of autophagy-related genes (ARGs) on colon cancer have not been well studied.Methods: Expression profiles from ARGs in 457 colon cancer patients were retrieved from the TCGA database (https://portal.gdc.cancer.gov). Differentially expressed ARGs and ARGs related to overall patient survival were identified. Cox proportional-hazards models were used to investigate the association between ARG expression profiles and patient prognosis.Results: 20 ARGs were significantly associated with overall survival of colon cancer patients. Five of these ARGs had a mutation rate ≥3%. Patients were divided into high-risk and low-risk groups based on Cox regression analysis of 8 ARGs. Low-risk patients had a significantly longer survival time than high-risk patients (p<0.001). Univariate and multivariate Cox regression analysis showed that the resulting risk score, which was associated with infiltration depth and metastasis, could be an independent predictor of patient survival. A nomogram was established to predict 3- and 5-year survival of colon cancer patients based on 5 independent prognosis factors, including the risk score. The prognostic nomogram with online webserver was more effective and convenient to provide information for researchers and clinicians.Conclusion: The 8 ARGs can be used to predict the prognosis of patients and provide information for their individualized treatment.

scholarly journals Development of prognosis model for colon cancer based on autophagy-related genes

2020 ◽  
Author(s):  
Xu Wang ◽  
Yuanmin Xu ◽  
Ting Li ◽  
Bo Chen ◽  
Wenqi Yang
Keyword(s):  
Colon Cancer ◽  
Regression Analysis ◽  
High Risk ◽  
Cancer Patients ◽  
Risk Score ◽  
Cox Regression ◽  
Patient Survival ◽  
Expression Profiles ◽  
Cox Regression Analysis ◽  
Risk Patients

Abstract Background: Autophagy is an orderly catabolic process for degrading and removing unnecessary or dysfunctional cellular components such as proteins and organelles. Although autophagy is known to play an important role in various types of cancer, the effects of autophagy-related genes (ARGs) on colon cancer have not been well studied.Methods: Expression profiles from ARGs in 457 colon cancer patients were retrieved from the TCGA database (https://portal.gdc.cancer.gov). Differentially expressed ARGs and ARGs related to overall patient survival were identified. Cox proportional-hazards models were used to investigate the association between ARG expression profiles and patient prognosis.Results: 20 ARGs were significantly associated with overall survival of colon cancer patients. Five of these ARGs had a mutation rate ≥ 3%. Patients were divided into high-risk and low-risk groups based on Cox regression analysis of 8 ARGs. Low-risk patients had a significantly longer survival time than high-risk patients (p < 0.001). Univariate and multivariate Cox regression analysis showed that the resulting risk score, which was associated with infiltration depth and metastasis, could be an independent predictor of patient survival. A nomogram was established to predict 1-, 3- and 5-year survival of colon cancer patients based on 5 independent prognosis factors, including the risk score. The prognostic nomogram with online webserver was more effective and convenient to provide information for researchers and clinicians.Conclusion: The 8 ARGs can be used to predict the prognosis of patients and provide information for their individualized treatment.

scholarly journals Colorectal cancer risk following polypectomy in a multicentre, retrospective, cohort study: an evaluation of the 2020 UK post-polypectomy surveillance guidelines

Gut ◽  
2021 ◽  
pp. gutjnl-2020-323411
Author(s):  
Amanda J Cross ◽  
Emma C Robbins ◽  
Kevin Pack ◽  
Iain Stenson ◽  
Bhavita Patel ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
High Risk ◽  
General Population ◽  
Cox Regression ◽  
Population Based ◽  
Low Risk ◽  
High Grade Dysplasia ◽  
High Grade ◽  
High Risk Patients ◽  
Risk Patients

ObjectiveColonoscopy surveillance aims to reduce colorectal cancer (CRC) incidence after polypectomy. The 2020 UK guidelines recommend surveillance at 3 years for ‘high-risk’ patients with ≥2 premalignant polyps (PMPs), of which ≥1 is ‘advanced’ (serrated polyp (or adenoma) ≥10 mm or with (high-grade) dysplasia); ≥5 PMPs; or ≥1 non-pedunculated polyp ≥20 mm; ‘low-risk’ patients without these findings are instead encouraged to participate in population-based CRC screening. We examined the appropriateness of these risk classification criteria and recommendations.DesignRetrospective analysis of patients who underwent colonoscopy and polypectomy mostly between 2000 and 2010 at 17 UK hospitals, followed-up through 2017. We examined CRC incidence by baseline characteristics, risk group and number of surveillance visits using Cox regression, and compared incidence with that in the general population using standardised incidence ratios (SIRs).ResultsAmong 21 318 patients, 368 CRCs occurred during follow-up (median: 10.1 years). Baseline CRC risk factors included age ≥55 years, ≥2 PMPs, adenomas with tubulovillous/villous/unknown histology or high-grade dysplasia, proximal polyps and a baseline visit spanning 2–90 days. Compared with the general population, CRC incidence without surveillance was higher among those with adenomas with high-grade dysplasia (SIR 1.74, 95% CI 1.21 to 2.42) or ≥2 PMPs, of which ≥1 was advanced (1.39, 1.09 to 1.75). For low-risk (71%) and high-risk (29%) patients, SIRs without surveillance were 0.75 (95% CI 0.63 to 0.88) and 1.30 (1.03 to 1.62), respectively; for high-risk patients after first surveillance, the SIR was 1.22 (0.91 to 1.60).ConclusionThese guidelines accurately classify post-polypectomy patients into those at high risk, for whom one surveillance colonoscopy appears appropriate, and those at low risk who can be managed by non-invasive screening.

scholarly journals Prognostic value of an autophagy-related gene expression signature for endometrial cancer patients

2020 ◽  
Author(s):  
Hui Wang ◽  
Xiaoling Ma ◽  
Jinhui Liu ◽  
Yicong Wan ◽  
Yi Jiang ◽  
...  
Keyword(s):  
Endometrial Cancer ◽  
High Risk ◽  
Risk Score ◽  
Cox Regression ◽  
Risk Group ◽  
Expression Profiles ◽  
Gene Expression Signature ◽  
Low Risk ◽  
Prognostic Signature ◽  
Test Set

Abstract Background: Autophagy is associated with cancer development. Autophagy-related genes play significant roles in endometrial cancer (EC), a major gynecological malignancy worldwide, but little was known about their value as prognostic markers. Here we evaluated the value of a prognostic signature based on autophagy-related genes for EC. Methods: First, various autophagy-related genes were obtained via the Human Autophagy Database and their expression profiles were downloaded from The Cancer Genome Atlas. Second, key prognostic autophagy-related genes were identified via univariat, LASSO and multivariate Cox regression analyses. Finally, a risk score to predict the prognosis of EC was calculated and validated by using the test and the entire data sets. Besides, the key genes mRNA expression were validated using quantitative real-time PCR in clinical tissue samples. Results: A total of 40 differentially expressed autophagy-related genes in EC were screened and five of them were prognosis-related (CDKN1B, DLC1, EIF4EBP1, ERBB2 and GRID1). A prognostic signature was constructed based on these five genes using the train set, which stratified EC patients into high-risk and low-risk groups (P<0.05). In terms of overall survival, the analyses of the test set and the entire set yielded consistent results (test set: p < 0.05; entire set: p < 0.05). Time-dependent ROC analysis suggested that the risk score predicted EC prognosis accurately and independently (0.674 at 1 year, 0.712 at 3 years and 0.659 at 5 years). A nomogram with clinical utility was built. Patients in the high-risk group displayed distinct mutation signatures compared with those in the low-risk group. For clinical sample validation, we found that EIF4EBP1and ERBB2 had higher level in EC than that in normal tissues while CDKN1B, DLC1 and GRID1 had lower level, which was consistent with the results predicted. Conclusions: Based on five autophagy-related genes (CDKN1B, DLC1, EIF4EBP1, ERBB2 and GRID1), our model can independently predict the OS of EC patients by combining molecular signature and clinical characteristics.

scholarly journals Development of prognosis model for colon cancer based on autophagy-related genes

2020 ◽  
Author(s):  
Xu Wang ◽  
Yuanmin Xu ◽  
Ting Li ◽  
Bo Chen ◽  
Wenqi Yang
Keyword(s):  
Colon Cancer ◽  
Regression Analysis ◽  
High Risk ◽  
Cancer Patients ◽  
Risk Score ◽  
Cox Regression ◽  
Patient Survival ◽  
Expression Profiles ◽  
Cox Regression Analysis ◽  
Risk Patients

Abstract Background: Autophagy is an orderly catabolic process for degrading and removing unnecessary or dysfunctional cellular components such as proteins and organelles. Although autophagy is known to play an important role in various types of cancer, the effects of autophagy-related genes (ARGs) on colon cancer have not been well studied.Methods: Expression profiles from ARGs in 457 colon cancer patients were retrieved from the TCGA database (https://portal.gdc.cancer.gov). Differentially expressed ARGs and AGRs related to overall patient survival were identified. Cox proportional-hazards models were used to investigate the association between ARG expression profiles and patient prognosis.Results: 20 ARGs were significantly associated with overall survival of colon cancer patients. Five of these ARGs had a mutation rate ≥3%. Patients were divided into high-risk and low-risk groups based on Cox regression analysis of 8 ARGs. Low-risk patients had a significantly longer survival time than high-risk patients (p<0.001). Univariate and multivariate Cox regression analysis showed that the resulting risk score, which was associated with infiltration depth and metastasis, could be an independent predictor of patient survival. A nomogram was established to predict 3- and 5-year survival of colon cancer patients based on 5 independent prognosis factors, including the risk score. The prognostic nomogram with online webserver was more effective and convenient to provide information for researchers and clinicians.Conclusion: The 8 ARGs can be used to predict the prognosis of patients and provide information for their individualized treatment.

scholarly journals Identification of Metastasis-Related Signature For Predicting Survival In Osteosarcoma

2021 ◽  
Author(s):  
Song Shi ◽  
Shuaijie Yang ◽  
Zhenyu Zhou ◽  
Kai Sun ◽  
Ran Tao ◽  
...  
Keyword(s):  
High Risk ◽  
Risk Score ◽  
Immune Cell ◽  
Cox Regression ◽  
Risk Group ◽  
Expression Profiles ◽  
Gene Signature ◽  
Low Risk ◽  
Risk Scores ◽  
Activation Markers

Abstract BackgroundRNA sequencing has become a powerful tool for exploring tumor recurrence or metastasis mechanisms. In this study, we aimed to develop a signature to improve the prognostic predictions of osteosarcoma.Materials and methodsBy comparing the expression profiles between metastatic and non-metastatic samples, we obtained 57 metastatic-related gene signatures. Then we constructed a 3‐gene signature to predict the prognostic risk of osteosarcoma patients by the Cox proportional hazards regression model. The risk score derived from this signature could successfully stratify osteosarcoma patients into subgroups with different survival outcomes.ResultsPatients in the low-risk group showed more prolonged overall survival than those in the high-risk group. And the performance was validated with another independent dataset. Multivariate cox regression revealed that the risk score served as an independent risk factor. Besides, we found that patients with low-risk scores had higher expression levels of immune-related signatures, suggesting an active immune status in those patients. Using the CIBERSORT database, we further systematically analyzed the relationships between the risk score and immune cell infiltration levels, as well as the immune activation markers. Higher infiltration of immune cells (CD8 T cells, monocytes, M2 macrophages, and memory B cells) and higher levels of immune cytotoxic markers (GZMA, GMZB, IFNG, and TNF) were observed in patients in the low-risk group.ConclusionsIn summary, this 3-gene signature could be a reliable marker for prognostic evaluation and help clinicians identify high‐risk patients.

scholarly journals A Five-Gene Risk Score Model for Predicting the Prognosis of Multiple Myeloma Patients Based on Gene Expression Profiles

2021 ◽  
Vol 12 ◽  
Author(s):  
Xiaotong Chen ◽  
Lintao Liu ◽  
Mengping Chen ◽  
Jing Xiang ◽  
Yike Wan ◽  
...  
Keyword(s):  
Gene Expression ◽  
Multiple Myeloma ◽  
High Risk ◽  
Risk Score ◽  
Cox Regression ◽  
Risk Group ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Low Risk ◽  
Score Model

Multiple myeloma is a heterogeneous plasma cell malignancy that remains incurable because of the tendency of relapse for most patients. Survival outcomes may vary widely due to patient and disease variables; therefore, it is necessary to establish a more accurate prognostic model to improve prognostic precision and guide clinical therapy. Here, we developed a risk score model based on myeloma gene expression profiles from three independent datasets: GSE6477, GSE13591, and GSE24080. In this model, highly survival-associated five genes, including EPAS1, ERC2, PRC1, CSGALNACT1, and CCND1, are selected by using the least absolute shrinkage and selection operator (Lasso) regression and univariate and multivariate Cox regression analyses. At last, we analyzed three validation datasets (including GSE2658, GSE136337, and MMRF datasets) to examine the prognostic efficacy of this model by dividing patients into high-risk and low-risk groups based on the median risk score. The results indicated that the survival of patients in low-risk group was greatly prolonged compared with their counterparts in the high-risk group. Therefore, the five-gene risk score model could increase the accuracy of risk stratification and provide effective prediction for the prognosis of patients and instruction for individualized clinical treatment.

scholarly journals A Lipid Metabolism-Related Genes Prognosis Biomarker Associated With The Tumor Immune Microenvironment in Colorectal Carcinoma

2021 ◽  
Author(s):  
Chao YANG ◽  
Shuoyang Huang ◽  
Yongbin ZHENG ◽  
Fengyu CAO
Keyword(s):  
Lipid Metabolism ◽  
High Risk ◽  
Risk Score ◽  
Cox Regression ◽  
Expression Profiles ◽  
Low Risk ◽  
Metabolic Reprogramming ◽  
Immune Microenvironment ◽  
Potential Biomarker ◽  
Tumor Immune Microenvironment

Abstract Background: Lipid metabolic reprogramming was considered as a new hallmark of malignant tumors. It has been reported to play a crucial biological role in cell proliferation, energy homeostasis and signal-transduction. However, the important value of lipid metabolism-related genes(LMRGs) in prognostic prediction and the tumor immune microenvironment has not been explored by large sample studies in colorectal cancer(CRC). Methods: In this study, the lipid metabolism status of 1086 CRC samples was analyzed using RNA expression profiles and clinical data from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, of which the former was determined as training set and the latter as validation set. The risk signature was constructed by the univariate Cox regression and Least Absolute Shrinkage and Selection Operator(LASSO) COX regression. The patients were stratified into high- and low-risk groups according to the median value of the risk score. Immune and mutation landscape between low- and high-risk CRC patients were also explored. Additionally, we established a nomogram integrating the risk signature and clinical factors to improve risk assessment of CRC patients. Results: A four LMRGs signature, including PROCA1, CCKBR, CPT2 and FDFT1, was constructed to predict the prognosis of CRC. The risk signature as an independent prognostic factor for CRC was associated with a variety of parameters. Survival analysis showed that patients with low risk score had a better prognosis. There were different immune landscapes between low and high-risk CRC patients, especially in monocytes, dendritic cells, M0 and M2-like macrophages. Patients in the low-risk group were more likely to have higher tumor mutation burden, stem cell characteristics and level of PD-L1 expression. In addition, it was found that genes that played crucial biological functions in tumorigenesis (including TP53, PI3K and MUC16) had significant differences in mutation frequency between two groups. Conclusion: A lipid metabolism-related risk signature for predicting the prognosis of CRC was identified in this study. Furthermore, this prognostic signature may be a potential biomarker for predicting the efficacy of chemotherapy and anti-PD-L1 therapy in CRC.

Sign in / Sign up

Export Citation Format

Share Document