Development and Validation of a Novel Glycolysis-Related Risk Signature for Predicting Survival in Pancreatic Adenocarcinoma

Abstract Background Pancreatic adenocarcinoma (PAAD) is one of the leading causes of cancer death worldwide. Through data mining, an increasing number of biomarkers have been identified for predicting survival of PAAD. However, the ability of single gene biomarkers to predict patient survival is still insufficient. This study aimed to develop a novel risk signature for predicting survival of PAAD. Methods mRNA expression profiling was performed in a large PAAD cohort (N = 177) from The Cancer Genome Atlas (TCGA) database. Gene Set Enrichment Analysis (GSEA) was analyzed to detect whether the gene sets showed statistically differences between PAAD and adjacent normal tissues. Univariate Cox regression analysis was used to analyze and identify genes related to overall survival (OS), then subjected to multivariable Cox regression to further confirm the prognostic genes and obtain the coefficients. The expression level of selected genes weighted by their coefficients through linearly combining, we constructed a risk score formula for prognostic prediction. The three-mRNA signature for survival prediction is validated by Kaplan–Meier curve analysis. Results We demonstrated that a set of three genes (KIF20A, CHST2, and MET) were significantly associated with OS. Based on this three-gene signature, 177 PAAD patients were classified into high-risk groups and low-risk groups using the median risk score as cut off value. Additionally, multivariate Cox regression analysis revealed that the three-gene signature had independent prognostic value. Conclusions To our best knowledge, we first develop a glycolysis-related risk signature for predicting survival of pancreatic adenocarcinoma. The findings provide insight into identification of patients with poor prognosis in PAAD and improve novel therapy targets for this disease.

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A glycolysis-based three-gene signature predicts survival in patients with lung squamous cell carcinoma

BMC Cancer ◽

10.1186/s12885-021-08360-z ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Guichuan Huang ◽

Jing Zhang ◽

Ling Gong ◽

Yi Huang ◽

Daishun Liu

Keyword(s):

Squamous Cell Carcinoma ◽

Cell Carcinoma ◽

Risk Score ◽

Squamous Cell ◽

Cox Regression ◽

Lung Squamous Cell Carcinoma ◽

Gene Signature ◽

Gene Set Enrichment Analysis ◽

Related Gene ◽

Cox Regression Analysis

Abstract Background Lung cancer is one of the most lethal and most prevalent malignant tumors worldwide, and lung squamous cell carcinoma (LUSC) is one of the major histological subtypes. Although numerous biomarkers have been found to be associated with prognosis in LUSC, the prediction effect of a single gene biomarker is insufficient, especially for glycolysis-related genes. Therefore, we aimed to develop a novel glycolysis-related gene signature to predict survival in patients with LUSC. Methods The mRNA expression files and LUSC clinical information were obtained from The Cancer Genome Atlas (TCGA) dataset. Results Based on Gene Set Enrichment Analysis (GSEA), we found 5 glycolysis-related gene sets that were significantly enriched in LUSC tissues. Univariate and multivariate Cox proportional regression models were performed to choose prognostic-related gene signatures. Based on a Cox proportional regression model, a risk score for a three-gene signature (HKDC1, ALDH7A1, and MDH1) was established to divide patients into high-risk and low-risk subgroups. Multivariate Cox regression analysis indicated that the risk score for this three-gene signature can be used as an independent prognostic indicator in LUSC. Additionally, based on the cBioPortal database, the rate of genomic alterations in the HKDC1, ALDH7A1, and MDH1 genes were 1.9, 1.1, and 5% in LUSC patients, respectively. Conclusion A glycolysis-based three-gene signature could serve as a novel biomarker in predicting the prognosis of patients with LUSC and it also provides additional gene targets that can be used to cure LUSC patients.

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Immune-related gene based prognostic signature for the risk stratification analysis of breast cancer

Current Bioinformatics ◽

10.2174/1574893616666211005110732 ◽

2021 ◽

Vol 16 ◽

Author(s):

Dongqing Su ◽

Qianzi Lu ◽

Yi Pan ◽

Yao Yu ◽

Shiyuan Wang ◽

...

Keyword(s):

Breast Cancer ◽

Regression Analysis ◽

Cancer Patients ◽

Risk Score ◽

Cox Regression ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Breast Cancer Patients ◽

Cox Regression Analysis ◽

Score Model

Background: Breast cancer has plagued women for many years and caused many deaths around the world. Method: In this study, based on the weighted correlation network analysis, univariate Cox regression analysis and least absolute shrinkage and selection operator, 12 immune-related genes were selected to construct the risk score for breast cancer patients. The multivariable Cox regression analysis, gene set enrichment analysis and nomogram were also conducted in this study. Results: Good results were obtained in the survival analysis, enrichment analysis, multivariable Cox regression analysis and immune-related feature analysis. When the risk score model was applied in 22 breast cancer cohorts, the univariate Cox regression analysis demonstrated that the risk score model was significantly associated with overall survival in most of the breast cancer cohorts. Conclusion: Based on these results, we could conclude that the proposed risk score model may be a promising method, and may improve the treatment stratification of breast cancer patients in the future work.

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Prognostic Value of an Immune-Related Gene Signature in Oral Squamous Cell Carcinoma

Frontiers in Oncology ◽

10.3389/fonc.2021.776979 ◽

2021 ◽

Vol 11 ◽

Author(s):

Chao Zhu ◽

Liqun Gu ◽

Mianfeng Yao ◽

Jiang Li ◽

Changyun Fang

Keyword(s):

Overall Survival ◽

Squamous Cell Carcinoma ◽

Regression Analysis ◽

Oral Squamous Cell Carcinoma ◽

Risk Score ◽

Cox Regression ◽

Gene Signature ◽

Related Gene ◽

Cox Regression Analysis ◽

Immune Related Gene

The prognosis and immunotherapy response rates are unfavorable in patients with oral squamous cell carcinoma (OSCC). The tumor microenvironment is associated with tumor prognosis and progression, and the underlying mechanisms remain unclear. We obtained differentially expressed immune-related genes from OSCC mRNA data in The Cancer Genome Atlas (TCGA) database. Overall survival-related risk signature was constructed by univariate Cox regression analysis and LASSO Cox regression analysis. The prognostic performance was validated with receiver operating characteristic (ROC) analysis and Kaplan–Meier survival curves in the TCGA and Gene Expression Omnibus (GEO) datasets. The risk score was confirmed to be an independent prognostic factor and a nomogram was built to quantify the risk of outcome for each patient. Furthermore, a negative correlation was observed between the risk score and the infiltration rate of immune cells, as well as the expression of immunostimulatory and immunosuppressive molecules. Functional enrichment analysis between different risk score subtypes detected multiple immune-related biological processes, metabolic pathways, and cancer-related pathways. Thus, the immune-related gene signature can predict overall survival and contribute to the personalized management of OSCC patients.

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Identification of Long Noncoding RNA Biomarkers for Hepatocellular Carcinoma Using Single-Sample Networks

BioMed Research International ◽

10.1155/2020/8579651 ◽

2020 ◽

Vol 2020 ◽

pp. 1-12

Author(s):

Xiaoqing Yu ◽

Jingsong Zhang ◽

Rui Yang ◽

Chun Li

Keyword(s):

Hepatocellular Carcinoma ◽

Regression Analysis ◽

Risk Score ◽

Cox Regression ◽

Single Sample ◽

Survival Prediction ◽

Prediction Ability ◽

Cox Regression Analysis ◽

Score Model ◽

Function Enrichment

Objective. Many studies have found that long noncoding RNAs (lncRNAs) are differentially expressed in hepatocellular carcinoma (HCC) and closely associated with the occurrence and prognosis of HCC. Since patients with HCC are usually diagnosed in late stages, more effective biomarkers for early diagnosis and prognostic prediction are in urgent need. Methods. The RNA-seq data of liver hepatocellular carcinoma (LIHC) were downloaded from The Cancer Genome Atlas (TCGA). Differentially expressed lncRNAs and mRNAs were obtained using the edgeR package. The single-sample networks of the 371 tumor samples were constructed to identify the candidate lncRNA biomarkers. Univariate Cox regression analysis was performed to further select the potential lncRNA biomarkers. By multivariate Cox regression analysis, a 3-lncRNA-based risk score model was established on the training set. Then, the survival prediction ability of the 3-lncRNA-based risk score model was evaluated on the testing set and the entire set. Function enrichment analyses were performed using Metascape. Results. Three lncRNAs (RP11-150O12.3, RP11-187E13.1, and RP13-143G15.4) were identified as the potential lncRNA biomarkers for LIHC. The 3-lncRNA-based risk model had a good survival prediction ability for the patients with LIHC. Multivariate Cox regression analysis proved that the 3-lncRNA-based risk score was an independent predictor for the survival prediction of patients with LIHC. Function enrichment analysis indicated that the three lncRNAs may be associated with LIHC via their involvement in many known cancer-associated biological functions. Conclusion. This study could provide novel insights to identify lncRNA biomarkers for LIHC at a molecular network level.

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Development and Validation of a 7-Gene Prognostic Signature to Improve Survival Prediction in Pancreatic Ductal Adenocarcinoma

Frontiers in Molecular Biosciences ◽

10.3389/fmolb.2021.676291 ◽

2021 ◽

Vol 8 ◽

Author(s):

Zengyu Feng ◽

Hao Qian ◽

Kexian Li ◽

Jianyao Lou ◽

Yulian Wu ◽

...

Keyword(s):

Regression Analysis ◽

Pancreatic Ductal Adenocarcinoma ◽

Cox Regression ◽

Predictive Accuracy ◽

Disease Free Survival ◽

Roc Curves ◽

Gene Signature ◽

Ductal Adenocarcinoma ◽

Survival Prediction ◽

Cox Regression Analysis

Background: Previous prognostic signatures of pancreatic ductal adenocarcinoma (PDAC) are mainly constructed to predict the overall survival (OS), and their predictive accuracy needs to be improved. Gene signatures that efficaciously predict both OS and disease-free survival (DFS) are of great clinical significance but are rarely reported.Methods: Univariate Cox regression analysis was adopted to screen common genes that were significantly associated with both OS and DFS in three independent cohorts. Multivariate Cox regression analysis was subsequently performed on the identified genes to determine an optimal gene signature in the MTAB-6134 training cohort. The Kaplan–Meier (K-M), calibration, and receiver operating characteristic (ROC) curves were employed to assess the predictive accuracy. Biological process and pathway enrichment analyses were conducted to elucidate the biological role of this signature.Results: Multivariate Cox regression analysis determined a 7-gene signature that contained ASPH, DDX10, NR0B2, BLOC1S3, FAM83A, SLAMF6, and PPM1H. The signature had the ability to stratify PDAC patients with different OS and DFS, both in the training and validation cohorts. ROC curves confirmed the moderate predictive accuracy of this signature. Mechanically, the signature was related to multiple cancer-related pathways.Conclusion: A novel OS and DFS prediction model was constructed in PDAC with multi-cohort and cross-platform compatibility. This signature might foster individualized therapy and appropriate management of PDAC patients.

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A Novel Pyroptosis-related Prognostic Model for Hepatocellular Carcinoma

Frontiers in Cell and Developmental Biology ◽

10.3389/fcell.2021.770301 ◽

2021 ◽

Vol 9 ◽

Author(s):

Qianqian Wu ◽

Sutian Jiang ◽

Tong Cheng ◽

Manyu Xu ◽

Bing Lu

Keyword(s):

Hepatocellular Carcinoma ◽

Regression Analysis ◽

Risk Model ◽

Cox Regression ◽

Risk Groups ◽

Gene Set Enrichment Analysis ◽

Differentially Expressed ◽

Prognostic Biomarkers ◽

Cox Regression Analysis ◽

Public Data

Hepatocellular carcinoma (HCC) is the second most lethal malignant tumor because of its significant heterogeneity and complicated molecular pathogenesis. Novel prognostic biomarkers are urgently needed because no effective and reliable prognostic biomarkers currently exist for HCC patients. Increasing evidence has revealed that pyroptosis plays a role in the occurrence and progression of malignant tumors. However, the relationship between pyroptosis-related genes (PRGs) and HCC patient prognosis remains unclear. In this study, 57 PRGs were obtained from previous studies and GeneCards. The gene expression profiles and clinical data of HCC patients were acquired from public data portals. Least absolute shrinkage and selection operator (LASSO) Cox regression analysis was performed to establish a risk model using TCGA data. Additionally, the risk model was further validated in an independent ICGC dataset. Our results showed that 39 PRGs were significantly differentially expressed between tumor and normal liver tissues in the TCGA cohort. Functional analysis confirmed that these PRGs were enriched in pyroptosis-related pathways. According to univariate Cox regression analysis, 14 differentially expressed PRGs were correlated with the prognosis of HCC patients in the TCGA cohort. A risk model integrating two PRGs was constructed to classify the patients into different risk groups. Poor overall survival was observed in the high-risk group of both TCGA (p < 0.001) and ICGC (p < 0.001) patients. Receiver operating characteristic curves demonstrated the accuracy of the model. Furthermore, the risk score was confirmed as an independent prognostic indicator via multivariate Cox regression analysis (TCGA cohort: HR = 3.346, p < 0.001; ICGC cohort: HR = 3.699, p < 0.001). Moreover, the single-sample gene set enrichment analysis revealed different immune statuses between high- and low-risk groups. In conclusion, our new pyroptosis-related risk model has potential application in predicting the prognosis of HCC patients.

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A Novel Glucose Metabolism-Related Gene Signature for Overall Survival Prediction in Patients with Glioblastoma

BioMed Research International ◽

10.1155/2021/8872977 ◽

2021 ◽

Vol 2021 ◽

pp. 1-13

Author(s):

Chaocai Zhang ◽

Minjie Wang ◽

Fenghu Ji ◽

Yizhong Peng ◽

Bo Wang ◽

...

Keyword(s):

Overall Survival ◽

High Risk ◽

Glucose Metabolism ◽

Cox Regression ◽

Gene Signature ◽

Low Risk ◽

Gene Set Enrichment Analysis ◽

Survival Prediction ◽

Related Gene ◽

Cox Regression Analysis

Introduction. Glioblastoma (GBM) is one of the most frequent primary intracranial malignancies, with limited treatment options and poor overall survival rates. Alternated glucose metabolism is a key metabolic feature of tumour cells, including GBM cells. However, due to high cellular heterogeneity, accurately predicting the prognosis of GBM patients using a single biomarker is difficult. Therefore, identifying a novel glucose metabolism-related biomarker signature is important and may contribute to accurate prognosis prediction for GBM patients. Methods. In this research, we performed gene set enrichment analysis and profiled four glucose metabolism-related gene sets containing 327 genes related to biological processes. Univariate and multivariate Cox regression analyses were specifically completed to identify genes to build a specific risk signature, and we identified ten mRNAs (B4GALT7, CHST12, G6PC2, GALE, IL13RA1, LDHB, SPAG4, STC1, TGFBI, and TPBG) within the Cox proportional hazards regression model for GBM. Results. Depending on this glucose metabolism-related gene signature, we divided patients into high-risk (with poor outcomes) and low-risk (with satisfactory outcomes) subgroups. The results of the multivariate Cox regression analysis demonstrated that the prognostic potential of this ten-gene signature is independent of clinical variables. Furthermore, we used two other GBM databases (Chinese Glioma Genome Atlas (CGGA) and REMBRANDT) to validate this model. In the functional analysis results, the risk signature was associated with almost every step of cancer progression, such as adhesion, proliferation, angiogenesis, drug resistance, and even an immune-suppressed microenvironment. Moreover, we found that IL31RA expression was significantly different between the high-risk and low-risk subgroups. Conclusion. The 10 glucose metabolism-related gene risk signatures could serve as an independent prognostic factor for GBM patients and might be valuable for the clinical management of GBM patients. The differential gene IL31RA may be a potential treatment target in GBM.

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Super-Enhancer Associated Nine-gene Prognostic Score Model for Prediction of Survival in Chronic Lymphoic Leukemia Patients

10.21203/rs.3.rs-929925/v1 ◽

2021 ◽

Author(s):

Xue Liang ◽

Ye Meng ◽

Cong Li ◽

Yangyang Wang ◽

Lianfang Pu ◽

...

Keyword(s):

Regression Analysis ◽

Risk Score ◽

Prognostic Model ◽

Prognostic Markers ◽

Cox Regression ◽

Prognostic Score ◽

Cox Regression Analysis ◽

Super Enhancer ◽

Mutational Status ◽

Related Risk

Abstract BackgroundChronic lymphocytic leukemia (CLL) is a group of highly heterogeneous mature B cell malignancy with various disease courses and diagnoses. Super-enhancer(SE) is a novel concept drew in recent years which is a cluster of enhancers involved in cell differentiation and tumorigenesis ,and is one of the promising therapeutic targets for cancer therapy. Although there is a multitude of prognostic markers in CLL, insights into the role of super-enhancer(SE)-related risk indicators are still lacking.MethodsThe CLL-related super-enhancers in training database were processed by Lasso penalized Cox regression analysis to screen a nine-gene prognostic model. And the associations between all of the individual markers and OS of CLL were assessed by Cox regression analysis. Besides, in order to understand the connection between individual genes and selected disease characteristics, like IGHV mutation status, FISH abnormality, and ZAP70 expression level, we performed correlation analysis.ResultsA nine-gene prognostic model was screened, including TCF7, VEGFA, MNT, GMIP, SLAMF1, TNFRSF25, GRWD1, SLC6AC, and LAG3. A SE-related risk score was further constructed and the robust predictive performance with 5-year survival and time-to-treatment (TTT) area under the curve(AUC): 0.997 and 1.000 in the training database and 0.628 and 0.673 in the testing database, respectively. Besides, a high correlation was found between the risk score and known prognostic markers of CLL, including the mutational status of immunoglobulin variable region loci (IGHV), chromosomal abnormalities, and ZAP70 expression. Meantime, we noticed that the expression of TCF7, GMIP, SLAMF1, TNFRSF25, and LAG3 in CLL were different from healthy donors(P<0.01), moreover, the risk score and LAG3 level of matched pairs before and after treatment samples varied significantly, although these results were not completely consistent in different datasets. ConclusionTherefore, the SE-associated nine-gene prognostic model developed here may be used to predict the prognosis and assist in the risk stratification of CLL patients in the future.

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A glycolysis-based three-gene signature predicts survival in patients with lung squamous cell carcinoma

10.21203/rs.3.rs-42550/v2 ◽

2020 ◽

Author(s):

Guichuan Huang ◽

Jing Zhang ◽

Ling Gong ◽

Yi Huang ◽

Daishun Liu

Keyword(s):

Squamous Cell Carcinoma ◽

Cell Carcinoma ◽

Risk Score ◽

Squamous Cell ◽

Cox Regression ◽

Lung Squamous Cell Carcinoma ◽

Gene Signature ◽

Gene Set Enrichment Analysis ◽

Related Gene ◽

Cox Regression Analysis

Abstract Background: Lung cancer is one of the most lethal and most prevalent malignant tumors worldwide, and lung squamous cell carcinoma (LUSC) is one of major histological subtypes. Although, numerous biomarkers were found to be associated with prognosis in LUSC, the prediction effect of a single gene biomarker is not sufficient, especially for glycolysis-related genes. Therefore, we aimed to develop a novel glycolysis-related gene signature to predict survival of patients with LUSC.Methods: The mRNA expression files and clinical information of LUSC were obtained from The Cancer Genome Atlas (TCGA) dataset.Results: Based on Gene set enrichment analysis (GSEA), we found 5 glycolysis-related gene sets were significantly enriched in LUSC tissues. Univariate and multivariate Cox proportional regression models were conducted to choose prognostic-related gene signature. Based on Cox proportional regression model, a risk score of three-gene signature (including HKDC1, ALDH7A1, and MDH1) was established to divide patients into high-risk and low-risk subgroups. We found that a risk score of three-gene signature was an independent of prognostic indicator in LUSC using multivariate Cox regression analysis. Additionally, based on the cBioPortal database, the rate of alterations in HKDC1, ALDH7A1, and MDH1 genes were 1.9%, 1.1%, and 5% in LUSC patients, respectively. Conclusion: In conclusion, a glycolysis-based three-gene signature could serve as a novel biomarker in predicting prognosis of patients with LUSC, which provided more gene targets to cure LUSC patients.

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A Novel Ferroptosis-related Gene Signature for Overall Survival Prediction in Patients with Bladder cancer

10.21203/rs.3.rs-333257/v1 ◽

2021 ◽

Author(s):

Liyuan Wu ◽

Feiya Yang ◽

Nianzeng Xing

Keyword(s):

Overall Survival ◽

Bladder Cancer ◽

Cox Regression ◽

Risk Group ◽

Risk Groups ◽

Gene Signature ◽

Survival Prediction ◽

Related Gene ◽

Cox Regression Analysis ◽

Prognostic Prediction

Abstract Background Bladder cancer (BC) is a highly heterogeneous disease, which makes the prognostic prediction challenging. Ferroptosis is related to a variety of biological pathways, including those involved in the metabolism of amino acids, lipids, and iron. However, the prognostic value of ferroptosis-related genes in BC remains to be further elucidated. Methods In this study, the mRNA expression profiles and corresponding clinical data of BC patients were downloaded from public databases. The least absolute shrinkage and selection operator (LASSO) Cox regression model was utilized to construct a multigene signature and validated it. Results Our results showed 12 differentially expressed genes (DEGs) were correlated with overall survival (OS) in the univariate Cox regression analysis (all adjusted P< 0.05). A 9-gene signature was constructed to stratify patients into two risk groups. Patients in the high-risk group showed significantly reduced OS compared with patients in the low-risk group (P < 0.001). The risk score was an independent predictor for OS in multivariate Cox regression analyses (HR> 1, P< 0.01). Receiver operating characteristic (ROC) curve analysis confirmed the signature's predictive capacity. Functional analysis revealed that immune-related pathways were enriched, and immune status were different between two risk groups, especially in humoral immune response process. Conclusion In conclusion, a novel ferroptosis-related gene signature can be used for prognostic prediction in BC. Targeting ferroptosis may be a therapeutic alternative for BC.

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