scholarly journals Identification of Transcription Factor-Related Gene Signature and Risk Score Model for Colon Adenocarcinoma

2021 ◽  
Vol 12 ◽  
Author(s):  
Jianwei Lin ◽  
Zichao Cao ◽  
Dingye Yu ◽  
Wei Cai
Keyword(s):  
Risk Model ◽  
Cox Regression ◽  
Characteristic Curve ◽  
Differential Expression Analysis ◽  
Colon Adenocarcinoma ◽  
Gene Signature ◽  
Cox Regression Analysis ◽  
Diagnosis And Prognosis ◽  
Kaplan Meier ◽  
Cox Analysis

The prognosis of colon adenocarcinoma (COAD) remains poor. However, the specific and sensitive biomarkers for diagnosis and prognosis of COAD are absent. Transcription factors (TFs) are involved in many biological processes in cells. As the molecule of the signal pathway of the terminal effectors, TFs play important roles in tumorigenesis and development. A growing body of research suggests that aberrant TFs contribute to the development of COAD, as well as to its clinicopathological features and prognosis. In consequence, a few studies have investigated the relationship between the TF-related risk model and the prognosis of COAD. Therefore, in this article, we hope to develop a prognostic risk model based on TFs to predict the prognosis of patients with COAD. The mRNA transcription data and corresponding clinical data were downloaded from TCGA and GEO. Then, 141 differentially expressed genes, validated by the GEPIA2 database, were identified by differential expression analysis between normal and tumor samples. Univariate, multivariate and Lasso Cox regression analysis were performed to identify seven prognostic genes (E2F3, ETS2, HLF, HSF4, KLF4, MEIS2, and TCF7L1). The Kaplan–Meier curve and the receiver operating characteristic curve (ROC, 1-year AUC: 0.723, 3-year AUC: 0.775, 5-year AUC: 0.786) showed that our model could be used to predict the prognosis of patients with COAD. Multivariate Cox analysis also reported that the risk model is an independent prognostic factor of COAD. The external cohort (GSE17536 and GSE39582) was used to validate our risk model, which indicated that our risk model may be a reliable predictive model for COAD patients. Finally, based on the model and the clinicopathological factors, we constructed a nomogram with a C-index of 0.802. In conclusion, we emphasize the clinical significance of TFs in COAD and construct a prognostic model of TFs, which could provide a novel and reliable model for the prognosis of COAD.

scholarly journals Identification of a Pyroptosis-Related lncRNA Risk Model for Predicting Prognosis and Immune Response in Colon Adenocarcinoma

2022 ◽  
Author(s):  
Yuying Tan ◽  
Liqing Lu ◽  
Xujun Liang ◽  
Yongheng Chen
Keyword(s):  
Regression Analysis ◽  
Risk Model ◽  
Cox Regression ◽  
Malignant Tumors ◽  
Colon Adenocarcinoma ◽  
Sequencing Data ◽  
Cox Regression Analysis ◽  
Immune Infiltration ◽  
Cerna Network ◽  
Kaplan Meier

Abstract Background: Colon adenocarcinoma (COAD) is one of the most common malignant tumors and diagnosed at an advanced stage with poor prognosis in the world. Pyroptosis is involved in the initiation and progression of tumors. This research focused on constructing a pyroptosis-related ceRNA network to generate a reliable risk model for risk prediction and immune infiltration analysis of COAD.Methods: Transcriptome data, miRNA-sequencing data and clinical information were downloaded from the TCGA database. Firstly, differentially expressed mRNAs (DEmRNAs), miRNAs (DEmiRNAs), and lncRNAs (DElncRNAs) were identified to construct a pyroptosis-related ceRNA network. Secondly, a pyroptosis-related lncRNA risk model was developed applying univariate Cox regression analysis and least absolute shrinkage and selection operator method (LASSO) regression analysis. The Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analyses were utilized to functionally annotate RNAs contained in the ceRNA network. In addition, Kaplan-Meier analysis, receiver operating characteristic (ROC) curves, univariate and multivariate Cox regression, and nomogram were applied to validate this risk model. Finally, the relationship of this risk model with immune cells and immune checkpoint blockade (ICB) related genes were analyzed.Results: Totally 5373 DEmRNAs, 1159 DElncRNAs and 355 DEmiRNAs were identified. A pyroptosis-related ceRNA regulatory network containing 132 lncRNAs, 7miRNAs and 5 mRNAs was constructed and a ceRNA-based pyroptosis-related risk model including 11 lncRNAs was built. Tumor tissues were classified into high- and low- risk groups according to the median risk score. Kaplan-Meier analysis showed that the high-risk group had a shorter survival time; ROC analysis, independent prognostic analysis and nomogram further indicated the risk model was a significant independent prognostic factor which had excellent ability to predict patients’ risk. Moreover, immune infiltration analysis indicated that the risk model was related to immune infiltration cells (i.e., B cells naïve, T cells follicular helper, Macrophages M1, etc.) and ICB-related genes (i.e., PD-1, CTLA4, HAVCR2, etc).Conclusions: This pyroptosis-related lncRNA risk model possessed good prognostic value and the ability to predict the outcome of ICB immunotherapy in COAD.

scholarly journals Identification of crucial genes of pyrimidine metabolism as biomarkers for gastric cancer prognosis

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Zhengxin Wu ◽  
Jinshui Tan ◽  
Yifan Zhuang ◽  
Mengya Zhong ◽  
Yubo Xiong ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Regression Analysis ◽  
Survival Analysis ◽  
Risk Model ◽  
Cox Regression ◽  
Clinical Samples ◽  
Pyrimidine Metabolism ◽  
Cox Regression Analysis ◽  
Kaplan Meier ◽  
Cox Analysis

Abstract Background Metabolic reprogramming has been reported in various kinds of cancers and is related to clinical prognosis, but the prognostic role of pyrimidine metabolism in gastric cancer (GC) remains unclear. Methods Here, we employed DEG analysis to detect the differentially expressed genes (DEGs) in pyrimidine metabolic signaling pathway and used univariate Cox analysis, Lasso-penalizes Cox regression analysis, Kaplan–Meier survival analysis, univariate and multivariate Cox regression analysis to explore their prognostic roles in GC. The DEGs were experimentally validated in GC cells and clinical samples by quantitative real-time PCR. Results Through DEG analysis, we found NT5E, DPYS and UPP1 these three genes are highly expressed in GC. This conclusion has also been verified in GC cells and clinical samples. A prognostic risk model was established according to these three DEGs by Univariate Cox analysis and Lasso-penalizes Cox regression analysis. Kaplan–Meier survival analysis suggested that patient cohorts with high risk score undertook a lower overall survival rate than those with low risk score. Stratified survival analysis, Univariate and multivariate Cox regression analysis of this model confirmed that it is a reliable and independent clinical factor. Therefore, we made nomograms to visually depict the survival rate of GC patients according to some important clinical factors including our risk model. Conclusion In a word, our research found that pyrimidine metabolism is dysregulated in GC and established a prognostic model of GC based on genes differentially expressed in pyrimidine metabolism.

scholarly journals A prognostic risk model constructed by proteins in predicting prognosis for ovarian cancer

2021 ◽  
Author(s):  
yan rong ◽  
Liangchen Niu ◽  
Li Li
Keyword(s):  
Ovarian Cancer ◽  
Risk Score ◽  
Risk Model ◽  
Cox Regression ◽  
The Cancer Genome Atlas ◽  
Cox Regression Analysis ◽  
Kaplan Meier ◽  
Cox Analysis ◽  
Survival Risk ◽  
Related Proteins

Abstract BackgroundsOvarian cancer is the most lethal malignant tumor in gynecological cancers worldwide. Approximately 70% of patients have a poor prognosis, who experienced progression or recurrence within 5 years. The aim of this study attempts is to screen out the potential prognosis-related proteins and establish a prognostic risk model for predicting the prognostic risk for patients with ovarian cancer.MethodData were obtained from the Cancer Proteome Atlas (TCPA) and the Cancer Genome Atlas (TCGA). The proteins significantly related to survival risk in ovarian cancer patients were screened out by Kaplan-Meier test and COX regression analysis. A prognostic risk model was constructed based on the optimal proteins selected by multivariate Cox analysis. The prognostic risk model was validated in different clinical characteristics. The sankyl diagram was used to visualize the relationship between the prognosis-related proteins and their co-expression proteins.ResultsA prognostic risk model consisting of seven proteins that significantly related to prognosis was established. Patients with high risk score were associated with poor survival and relative protein expression. In the multivariate cox regress analysis, only age and the risk score were the independence prognosis factors. The AUC for the risk score was 0.721 in ROC curve for patients under 70 years old. Pearson’s correlation analysis showed that 25 co-expression proteins correlated with the prognosis-related proteins.ConclusionOur study demonstrated that a novel prognostic risk model constructed by proteins could predict prognosis for patients with ovarian cancer.

scholarly journals Identification of Crucial Genes of Pyrimidine Metabolism as Biomarkers for Gastric Cancer Prognosis

2021 ◽  
Author(s):  
Zhengxin Wu ◽  
Jinshui Tan ◽  
Yifan Zhuang ◽  
Mengya Zhong ◽  
Yubo Xiong ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Regression Analysis ◽  
Survival Analysis ◽  
Risk Model ◽  
Cox Regression ◽  
Clinical Samples ◽  
Pyrimidine Metabolism ◽  
Cox Regression Analysis ◽  
Kaplan Meier ◽  
Cox Analysis

Abstract Background Metabolic reprogramming has been reported in various kinds of cancers and is related to clinical prognosis, but the prognostic role of pyrimidine metabolism in gastric cancer (GC) remains unclear. Methods Here, we employed DEG analysis to detect the differentially expressed genes (DEGs) in pyrimidine metabolic signaling pathway and used univariate Cox analysis, Lasso-penalizes Cox regression analysis, Kaplan-Meier survival analysis, univariate and multivariate Cox regression analysis to explore their prognostic roles in GC. The DEGs were experimentally validated in GC cells and clinical samples by quantitative real-time PCR. Results Through DEG analysis, we found NT5E, DPYS and UPP1 these three genes are highly expressed in GC. This conclusion has also been verified in GC cells and clinical samples. A prognostic risk model was established according to these three DEGs by Univariate Cox analysis and Lasso-penalizes Cox regression analysis. Kaplan-Meier survival analysis suggested that patient cohorts with high risk score undertook a lower overall survival rate than those with low risk score. Stratified survival analysis, Univariate and multivariate Cox regression analysis of this model confirmed that it is a reliable and independent clinical factor. Therefore, we made nomograms to visually depict the survival rate of GC patients according to some important clinical factors including our risk model. Conclusion In a word, our research found that pyrimidine metabolism is dysregulated in GC and established a prognostic model of GC based on genes differentially expressed in pyrimidine metabolism.

scholarly journals Construction and Validation of a Prognostic Risk Model Based on Autophagy-Related Genes in Hepatocellular Carcinoma Cells

2021 ◽  
Author(s):  
Rui Feng ◽  
Jian Li ◽  
Weiling Xuan ◽  
Hanbo Liu ◽  
Dexin Cheng ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Regression Analysis ◽  
Differential Expression ◽  
Prognostic Model ◽  
Risk Model ◽  
Cox Regression ◽  
Univariate Analysis ◽  
Differential Expression Analysis ◽  
Cox Regression Analysis ◽  
Dismal Prognosis

Abstract Background Hepatocellular carcinoma (HCC) is a prevalent primary liver cancer and the main cause of cancer mortality. Its high complexity and dismal prognosis bring dramatic difficulty to treatment. Due to the disclosed dual functions of autophagy in cancer development, understanding autophagy-related genes devotes into seeking novel biomarkers for HCC. Methods Differential expression of genes in normal and tumor groups was analyzed to acquire autophagy-related genes in HCC. GO and KEGG pathway analyses were conducted on these genes. Genes were then screened by univariate regression analysis. The screened genes were subjected to multivariate Cox regression analysis to build a prognostic model. The model was validated by ICGC validation set. Results Altogether, 42 autophagy-related differential genes were screened by differential expression analysis. Enrichment analysis showed that they were mainly enriched in pathways including regulation of autophagy and cell apoptosis. Genes were screened by univariate analysis and multivariate Cox regression analysis to build a prognostic model. The model was constituted by 6 feature genes: EIF2S1, BIRC5, SQSTM1, ATG7, HDAC1, FKBP1A. Validation confirmed the accuracy and independence of this model in predicting HCC patient’s prognosis. Conclusion A total of 6 feature genes were identified to build a prognostic risk model. This model is conducive to investigating interplay between autophagy-related genes and HCC prognosis.

scholarly journals Association between lactate/albumin ratio and all-cause mortality in patients with acute respiratory failure: A retrospective analysis

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0255744
Author(s):  
Yan Lu ◽  
Haoyang Guo ◽  
Xuya Chen ◽  
Qiaohong Zhang
Keyword(s):  
Respiratory Failure ◽  
Acute Respiratory Failure ◽  
Medical Information ◽  
Cox Regression ◽  
Characteristic Curve ◽  
Severe Heart Failure ◽  
Albumin Ratio ◽  
Cox Regression Analysis ◽  
Kaplan Meier ◽  
All Cause Mortality

Previous studies have shown that lactate/albumin ratio (LAR) can be used as a prognostic biomarker to independently predict the mortality of sepsis and severe heart failure. However, the role of LAR as an independent prognostic factor in all-cause mortality in patients with acute respiratory failure (ARF) remains to be clarified. Therefore, we retrospectively analyzed 2170 patients with ARF in Medical Information Mart for Intensive Care Database III from 2001 to 2012. By drawing the receiver operating characteristic curve, LAR shows a better predictive value in predicting the 30-day mortality of ARF patients (AUC: 0.646), which is higher than that of albumin (AUC: 0.631) or lactate (AUC: 0.616) alone, and even higher than SOFA score(AUC: 0.642). COX regression analysis and Kaplan-Meier curve objectively and intuitively show that high LAR is a risk factor for patients with ARF, which is positively correlated with all-cause mortality. As an easy-to-obtain and objective biomarker, LAR deserves further verification by multi-center prospective studies.

scholarly journals The Prognostic Value of m6A-related LncRNAs in Patients with HNSCC

2021 ◽  
Author(s):  
Liu-qing Zhou ◽  
Jie-yu Zhou ◽  
Yao Hu
Keyword(s):  
Prognostic Value ◽  
Risk Model ◽  
Cox Regression ◽  
Predictive Ability ◽  
Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Consensus Clustering ◽  
Cox Regression Analysis ◽  
Kaplan Meier ◽  
Cancer Genome Atlas

Abstract Background: N6-methyladenosine (m6A) modifications play an essential role in tumorigenesis. m6A modifications are known to modulate RNAs, including mRNAs and lncRNAs. However, the prognostic role of m6A-related lncRNAs in head and neck squamous cell carcinoma (HNSCC) is poorly understood.Methods: Based on LASSO Cox regression, enrichment analysis, univariate and multivariate Cox regression analysis, a risk prognostic model, and consensus clustering analysis, we analyzed the 12 m6A-related lncRNAs in HNSCC samples data using the data from The Cancer Genome Atlas (TCGA) database.Results: We found twelve m6A-related lncRNAs in the training cohort and validated in all cohorts by Kaplan-Meier and Cox regression analyses, and revealing their independent prognostic value in HNSCC. Moreover, ROC analysis was conducted, confirming the strong predictive ability of this signature for HNSCC prognosis. GSEA and detailed immune infiltration analyses revealed specific pathways associated with m6A-related lncRNAs.Conclusions: In this study, a novel risk model including twelve genes (SAP30L-AS1, AC022098.1, LINC01475, AC090587.2, AC008115.3, AC015911.3, AL122035.2, AC010226.1, AL513190.1, ZNF32-AS1, AL035587.1 and AL031716.1) was built. It could accurately predict HNSCC prognosis and provide potential prediction outcome and new therapeutic target for HNSCC patients.

scholarly journals A Novel DNA Replication-Related Signature Predicting Recurrence After R0 Resection of Pancreatic Ductal Adenocarcinoma: Prognostic Value and Clinical Implications

2021 ◽  
Vol 9 ◽  
Author(s):  
Zengyu Feng ◽  
Kexian Li ◽  
Jianyao Lou ◽  
Mindi Ma ◽  
Yulian Wu ◽  
...  
Keyword(s):  
Dna Replication ◽  
Pancreatic Ductal Adenocarcinoma ◽  
Risk Model ◽  
Cox Regression ◽  
External Validation ◽  
Gene Signature ◽  
Ductal Adenocarcinoma ◽  
R0 Resection ◽  
Pathway Enrichment Analysis ◽  
Cox Regression Analysis

The aim of any surgical resection for pancreatic ductal adenocarcinoma (PDAC) is to achieve tumor-free margins (R0). R0 margins give rise to better outcomes than do positive margins (R1). Nevertheless, postoperative morbidity after R0 resection remains high and prognostic gene signature predicting recurrence risk of patients in this subgroup is blank. Our study aimed to develop a DNA replication-related gene signature to stratify the R0-treated PDAC patients with various recurrence risks. We conducted Cox regression analysis and the LASSO algorithm on 273 DNA replication-related genes and eventually constructed a 7-gene signature. The predictive capability and clinical feasibility of this risk model were assessed in both training and external validation sets. Pathway enrichment analysis showed that the signature was closely related to cell cycle, DNA replication, and DNA repair. These findings may shed light on the identification of novel biomarkers and therapeutic targets for PDAC.

The effect of marital status on endometrial cancer-related diagnosis and prognosis: a Surveillance Epidemiology and End Results database analysis

2019 ◽  
Vol 15 (34) ◽  
pp. 3963-3976 ◽  
Author(s):  
Jia Dong ◽  
Qinjin Dai ◽  
Fan Zhang
Keyword(s):  
Endometrial Cancer ◽  
Marital Status ◽  
Cox Regression ◽  
Significant Prognostic Factor ◽  
Cox Regression Analysis ◽  
Diagnosis And Prognosis ◽  
Kaplan Meier ◽  
Risk Of Mortality ◽  
The Relationship ◽  
End Results

Aim: Marital status has been proved a significant prognostic factor for diagnosis and prognosis in various cancers, but the effect in endometrial cancer (EMC) is controversial. The research was designed to clarify the relationship between marital status and EMC. Methods: We identified 39,387 patients with EMC between 2004 and 2010 from the Surveillance Epidemiology and End Results database. Patients were categorized into four groups according to marital status. We used the logistic regression, the Kaplan–Meier method and Cox regression analysis to analyze the effect of marital status on EMC-related diagnosis and prognosis. Results: The study suggests that marriage benefits the diagnosis and prognosis of EMC. Widowed and unmarried patients had higher risk of mortality than other marital status.

Survival Impact of Postoperative Radiotherapy in Patients with Olfactory Neuroblastoma ： 513 Cases from the SEER Database

2021 ◽  
Author(s):  
Guo-Shuai Duo ◽  
Ji-Long Feng ◽  
En-Yi Zhang ◽  
Li-jun Wang
Keyword(s):  
Risk Model ◽  
Cox Regression ◽  
Postoperative Radiotherapy ◽  
Olfactory Neuroblastoma ◽  
Seer Database ◽  
Cox Regression Analysis ◽  
Kaplan Meier ◽  
Mortality Incidence ◽  
The Impact ◽  
Stage D

Abstract BackgroundTo evaluate the impact of postoperative radiotherapy (PORT) on survival in olfactory neuroblastoma (ONB) patients with different stages. MethodsPatients with ONB were selected in the Surveillance, Epidemiology, and End Results (SEER) database during 2004–2016. Survival analyses were performed using Kaplan Meier (K-M) method, Cox regression analysis, and competing risk model. ResultsA total of 513 patients were included in the study. Univariate and multivariate analysis results demonstrated that PORT was not an independent prognostic factor for overall survival (OS) of patients with modified Kadish stage A and B (p=0.699 and p=0.248, respectively). For C and D cases, patients who underwent PORT had significantly better OS than those who did not undergo PORT (p=0.03 and p< 0.0001, respectively). K-M curves illustrated that the 5- and 10-year OS rates according to radiotherapy (PORT vs. non-PORT) were 70.4% vs. 85.3% and 56.8% vs. 68.2% in stage C, respectively. For stage D patients, the 5-year OS rates were 42.6% and 70.7%, and 10-year OS rates were 29.5% and 53.4% in the PORT and non-PORT groups, respectively. The competitive risk model revealed that the 5-year cancer-specific cumulative mortality incidence decreased by 26.6% and the 10-year mortality incidence by 41.4% in patients with stage C who were treated using PORT; meanwhile, for patients with stage D who were treated with PORT, the 5- and 10-year mortality incidence reduced by 35.3% and 42.6%, respectively. Chemotherapy was not related to the prognosis of ONB (all p> 0.05).ConclusionsOur results indicate that PORT improved survival outcomes in ONB patients with modified Kadish stage C and D. However, for modified Kadish stage A and B cases, PORT may not affect survival. Chemotherapy was not recommended for ONB patients until more studies determine the role of chemotherapy.

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