Identification of Five Glycolysis-Related Gene Signature and Risk Score Model for Colorectal Cancer

Metabolic changes, especially in glucose metabolism, are widely established during the occurrence and development of tumors and regarded as biological markers of pan-cancer. The well-known ‘Warburg effect’ demonstrates that cancer cells prefer aerobic glycolysis even if there is sufficient ambient oxygen. Accumulating evidence suggests that aerobic glycolysis plays a pivotal role in colorectal cancer (CRC) development. However, few studies have examined the relationship of glycolytic gene clusters with prognosis of CRC patients. Here, our aim is to build a glycolysis-associated gene signature as a biomarker for colorectal cancer. The mRNA sequencing and corresponding clinical data were downloaded from TCGA and GEO databases. Gene set enrichment analysis (GSEA) was performed, indicating that four gene clusters were significantly enriched, which revealed the inextricable relationship of CRC with glycolysis. By comparing gene expression of cancer and adjacent samples, 236 genes were identified. Univariate, multivariate, and LASSO Cox regression analyses screened out five prognostic-related genes (ENO3, GPC1, P4HA1, SPAG4, and STC2). Kaplan–Meier curves and receiver operating characteristic curves (ROC, AUC = 0.766) showed that the risk model could become an effective prognostic indicator (P < 0.001). Multivariate Cox analysis also revealed that this risk model is independent of age and TNM stages. We further validated this risk model in external cohorts (GES38832 and GSE39582), showing these five glycolytic genes could emerge as reliable predictors for CRC patients’ outcomes. Lastly, based on five genes and risk score, we construct a nomogram model assessed by C-index (0.7905) and calibration plot. In conclusion, we highlighted the clinical significance of glycolysis in CRC and constructed a glycolysis-related prognostic model, providing a promising target for glycolysis regulation in CRC.

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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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A Novel Four-Gene Prognostic Signature for Prediction of Survival in Patients with Soft Tissue Sarcoma

Cancers ◽

10.3390/cancers13225837 ◽

2021 ◽

Vol 13 (22) ◽

pp. 5837

Author(s):

Changwu Wu ◽

Siming Gong ◽

Georg Osterhoff ◽

Nikolas Schopow

Keyword(s):

Soft Tissue ◽

Risk Score ◽

Cox Regression ◽

Soft Tissue Sarcomas ◽

Gene Signature ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Prognostic Models ◽

Malignant Tumours ◽

Free Survival

Soft tissue sarcomas (STS), a group of rare malignant tumours with high tissue heterogeneity, still lack effective clinical stratification and prognostic models. Therefore, we conducted this study to establish a reliable prognostic gene signature. Using 189 STS patients’ data from The Cancer Genome Atlas database, a four-gene signature including DHRS3, JRK, TARDBP and TTC3 was established. A risk score based on this gene signature was able to divide STS patients into a low-risk and a high-risk group. The latter had significantly worse overall survival (OS) and relapse free survival (RFS), and Cox regression analyses showed that the risk score is an independent prognostic factor. Nomograms containing the four-gene signature have also been established and have been verified through calibration curves. In addition, the predictive ability of this four-gene signature for STS metastasis free survival was verified in an independent cohort (309 STS patients from the Gene Expression Omnibus database). Finally, Gene Set Enrichment Analysis indicated that the four-gene signature may be related to some pathways associated with tumorigenesis, growth, and metastasis. In conclusion, our study establishes a novel four-gene signature and clinically feasible nomograms to predict the OS and RFS. This can help personalized treatment decisions, long-term patient management, and possible future development of targeted therapy.

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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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Identification and Development of a Novel 4-Gene Immune-Related Signature to Predict Osteosarcoma Prognosis

Frontiers in Molecular Biosciences ◽

10.3389/fmolb.2020.608368 ◽

2020 ◽

Vol 7 ◽

Author(s):

Mingde Cao ◽

Junhui Zhang ◽

Hualiang Xu ◽

Zhujian Lin ◽

Hong Chang ◽

...

Keyword(s):

Risk Model ◽

Cox Regression ◽

Expression Profiles ◽

External Validation ◽

Treatment Strategies ◽

Enrichment Analysis ◽

Gene Signature ◽

Gene Set Enrichment Analysis ◽

Functional Enrichment ◽

Immune Related Genes

Osteosarcoma (OS) is a malignant disease that develops rapidly and is associated with poor prognosis. Immunotherapy may provide new insights into clinical treatment strategies for OS. The purpose of this study was to identify immune-related genes that could predict OS prognosis. The gene expression profiles and clinical data of 84 OS patients were obtained from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database. According to non-negative matrix factorization, two molecular subtypes of immune-related genes, C1 and C2, were acquired, and 597 differentially expressed genes between C1 and C2 were identified. Univariate Cox analysis was performed to get 14 genes associated with survival, and 4 genes (GJA5, APBB1IP, NPC2, and FKBP11) obtained through least absolute shrinkage and selection operator (LASSO)-Cox regression were used to construct a 4-gene signature as a prognostic risk model. The results showed that high FKBP11 expression was correlated with high risk (a risk factor), and that high GJA5, APBB1IP, or NPC2 expression was associated with low risk (protective factors). The testing cohort and entire TARGET cohort were used for internal verification, and the independent GSE21257 cohort was used for external validation. The study suggested that the model we constructed was reliable and performed well in predicting OS risk. The functional enrichment of the signature was studied through gene set enrichment analysis, and it was found that the risk score was related to the immune pathway. In summary, our comprehensive study found that the 4-gene signature could be used to predict OS prognosis, and new biomarkers of great significance for understanding the therapeutic targets of OS were identified.

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Construction of an RNA Binding Protein-based Model for Prognosis Prediction of Colorectal Cancer

10.21203/rs.3.rs-35100/v1 ◽

2020 ◽

Author(s):

Ting li ◽

Wenjia Hui ◽

Halina Halike ◽

Feng Gao

Keyword(s):

Colorectal Cancer ◽

Prognostic Factor ◽

Risk Score ◽

Rna Binding ◽

Cox Regression ◽

Prognostic Significance ◽

Gene Signature ◽

Independent Prognostic Factor ◽

Time Dependent ◽

Incidence And Mortality

Abstract Background: Colorectalcancer (CRC) is a prevalent gastrointestinal tumor with high incidence and mortality. Dysregulation of RNA binding proteins (RBPs) has been found in a variety of cancers and is related to oncogenesis and progression. This study aimed to develop and validate new biomarkers related to CRC prognosis by a series of bioinformatics analysis.Methods: We mined the gene expression data of 510 CRC samples from The Cancer Genome Atlas (TCGA) database, differentially expressed genes were screened and prognosis-related genes were identified. Furthermore, gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were carried out. A prognosis-related gene signature was constructed by univariate and multivariate Cox analysis. Kaplan–Meier curves and time-dependent receiver operating characteristic (ROC) curves were utilized to evaluate the signature,The test set was used to validate the RBPs risk score model.Survival analysis was carried out to determine the independent prognostic significance of the signature. A nomogram combined with the gene signature was constructed.Results: A total of 224 aberrantly expressed RBPs were obtained, comprising 78 downregulated and 146 upregulatedRBPs. 13 RBPs with p < 0.005 were revealed in univariateCox regression analysis of train group, then stepwise multivariate Cox regression was applied for constituting an eight- RBP (BRCA1, TERT, TDRD7, PPARGC1A, LUZP4, CELF4, ZC3H12C, PNLDC1) signature prognostic biomarkers. Further analysis demonstrated that high risk score for patients was significantly related to poor overall survival according to the model. The area under the time-dependent receiver operator characteristic curve of the prognostic model was 0.730 at 5 years. The signature-based risk score was an independent prognostic factor in CRC patients. We also established a nomogram based on eight RBPs and internal validation in the train set, which displayed a favorable discriminating ability for Colorectal cancer.Conclusions: The established eight-RBP signature may serve as a novel independent prognostic factor that could be an important tool to predict the prognostic outcome of CRC patients. However, the specific biological mechanism needs further verification.

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

10.21203/rs.3.rs-29859/v1 ◽

2020 ◽

Author(s):

Ang Li ◽

Sinan Hou ◽

Jian Chen ◽

Huimin Hou ◽

Yanfang Jiang

Keyword(s):

Regression Analysis ◽

Pancreatic Adenocarcinoma ◽

Risk Score ◽

Cox Regression ◽

Risk Groups ◽

Gene Signature ◽

Gene Set Enrichment Analysis ◽

Survival Prediction ◽

Cox Regression Analysis ◽

Related Risk

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

10.21203/rs.3.rs-42550/v1 ◽

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 Purpose: 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. Material and 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.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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Identification of a novel glycolysis-related signature to predict the prognosis of patients with breast cancer

World Journal of Surgical Oncology ◽

10.1186/s12957-021-02409-w ◽

2021 ◽

Vol 19 (1) ◽

Author(s):

Menglin He ◽

Cheng Hu ◽

Jian Deng ◽

Hui Ji ◽

Weiqian Tian

Keyword(s):

Breast Cancer ◽

Risk Score ◽

Cox Regression ◽

Risk Group ◽

Gene Signature ◽

Low Risk ◽

Gene Set Enrichment Analysis ◽

Cox Regression Analysis ◽

Expression Levels ◽

Incidence And Mortality

Abstract Background Breast cancer (BC) has a high incidence and mortality rate in females. Its conventional clinical characteristics are far from accurate for the prediction of individual outcomes. Therefore, we aimed to develop a novel signature to predict the survival of patients with BC. Methods We analyzed the data of a training cohort from the Cancer Genome Atlas (TCGA) database and a validation cohort from the Gene Expression Omnibus (GEO) database. After the applications of Gene Set Enrichment Analysis (GSEA) and Cox regression analyses, a glycolysis-related signature for predicting the survival of patients with BC was developed; the signature contained AK3, CACNA1H, IL13RA1, NUP43, PGK1, and SDC1. Furthermore, on the basis of expression levels of the six-gene signature, we constructed a risk score formula to classify the patients into high- and low-risk groups. The receiver operating characteristic (ROC) curve and the Kaplan-Meier curve were used to assess the predicted capacity of the model. Later, a nomogram was developed to predict the outcomes of patients with risk score and clinical features over a period of 1, 3, and 5 years. We further used Human Protein Atlas (HPA) database to validate the expressions of the six biomarkers in tumor and sample tissues, which were taken as control. Results We constructed a six-gene signature to predict the outcomes of patients with BC. The patients in the high-risk group showed poor prognosis than those in the low-risk group. The area under the curve (AUC) values were 0.719 and 0.702, showing that the prediction performance of the signature is acceptable. Additionally, Cox regression analysis revealed that these biomarkers could independently predict the prognosis of BC patients with BC without being affected by clinical factors. The expression levels of all six biomarkers in BC tissues were higher than that in normal tissues; however, AK3 was an exception. Conclusion We developed a six-gene signature to predict the prognosis of patients with BC. Our signature has been proved to have the ability to make an accurate prediction and might be useful in expanding the hypothesis in clinical research.

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A Five-lncRNAs Signature-Derived Risk Score Based on TCGA and CGGA for Glioblastoma: Potential Prospects for Treatment Evaluation and Prognostic Prediction

Frontiers in Oncology ◽

10.3389/fonc.2020.590352 ◽

2020 ◽

Vol 10 ◽

Author(s):

Xuegang Niu ◽

Jiangnan Sun ◽

Lingyin Meng ◽

Tao Fang ◽

Tongshuo Zhang ◽

...

Keyword(s):

High Risk ◽

Risk Score ◽

Therapeutic Efficacy ◽

Cox Regression ◽

Gene Signature ◽

Gene Set Enrichment Analysis ◽

Normal Brain ◽

Score Model ◽

Genome Atlas

Accumulating studies have confirmed the crucial role of long non-coding RNAs (ncRNAs) as favorable biomarkers for cancer diagnosis, therapy, and prognosis prediction. In our recent study, we established a robust model which is based on multi-gene signature to predict the therapeutic efficacy and prognosis in glioblastoma (GBM), based on Chinese Glioma Genome Atlas (CGGA) and The Cancer Genome Atlas (TCGA) databases. lncRNA-seq data of GBM from TCGA and CGGA datasets were used to identify differentially expressed genes (DEGs) compared to normal brain tissues. The DEGs were then used for survival analysis by univariate and multivariate COX regression. Then we established a risk score model, depending on the gene signature of multiple survival-associated DEGs. Subsequently, Kaplan-Meier analysis was used for estimating the prognostic and predictive role of the model. Gene set enrichment analysis (GSEA) was applied to investigate the potential pathways associated to high-risk score by the R package “cluster profile” and Wiki-pathway. And five survival associated lncRNAs of GBM were identified: LNC01545, WDR11-AS1, NDUFA6-DT, FRY-AS1, TBX5-AS1. Then the risk score model was established and shows a desirable function for predicting overall survival (OS) in the GBM patients, which means the high-risk score significantly correlated with lower OS both in TCGA and CGGA cohort. GSEA showed that the high-risk score was enriched with PI3K-Akt, VEGFA-VEGFR2, TGF-beta, Notch, T-Cell pathways. Collectively, the five-lncRNAs signature-derived risk score presented satisfactory efficacies in predicting the therapeutic efficacy and prognosis in GBM and will be significant for guiding therapeutic strategies and research direction for GBM.

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Integrated analysis of methylation-driven genes and pretreatment prognostic factors in patients with hepatocellular carcinoma

BMC Cancer ◽

10.1186/s12885-021-08314-5 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Dongsheng He ◽

Shengyin Liao ◽

Lifang Cai ◽

Weiming Huang ◽

Xuehua Xie ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Prognostic Factors ◽

Risk Score ◽

Prognostic Model ◽

Cox Regression ◽

The Cancer Genome Atlas ◽

Integrated Analysis ◽

Calibration Plot ◽

Clinical Parameters ◽

T Stage

Abstract Background The potential reversibility of aberrant DNA methylation indicates an opportunity for oncotherapy. This study aimed to integrate methylation-driven genes and pretreatment prognostic factors and then construct a new individual prognostic model in hepatocellular carcinoma (HCC) patients. Methods The gene methylation, gene expression dataset and clinical information of HCC patients were downloaded from The Cancer Genome Atlas (TCGA) database. Methylation-driven genes were screened with a Pearson’s correlation coefficient less than − 0.3 and a P value less than 0.05. Univariable and multivariable Cox regression analyses were performed to construct a risk score model and identify independent prognostic factors from the clinical parameters of HCC patients. The least absolute shrinkage and selection operator (LASSO) technique was used to construct a nomogram that might act to predict an individual’s OS, and then C-index, ROC curve and calibration plot were used to test the practicability. The correlation between clinical parameters and core methylation-driven genes of HCC patients was explored with Student’s t-test. Results In this study, 44 methylation-driven genes were discovered, and three prognostic signatures (LCAT, RPS6KA6, and C5orf58) were screened to construct a prognostic risk model of HCC patients. Five clinical factors, including T stage, risk score, cancer status, surgical method and new tumor events, were identified from 13 clinical parameters as pretreatment-independent prognostic factors. To avoid overfitting, LASSO analysis was used to construct a nomogram that could be used to calculate the OS in HCC patients. The C-index was superior to that from previous studies (0.75 vs 0.717, 0.676). Furthermore, LCAT was found to be correlated with T stage and new tumor events, and RPS6KA6 was found to be correlated with T stage. Conclusion We identified novel therapeutic targets and constructed an individual prognostic model that can be used to guide personalized treatment in HCC patients.

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