A novel prediction model to evaluate genotoxicity based on a gene signature in metabolically competent human HepaRG? cells

Abstract Background Colon cancer (CC) is one of the most common gastrointestinal malignant tumors with high mortality rate. Because of malignancy and easily metastasis feather, and limited treatments, the prognosis of CC remains poor. Glycolysis is a metabolic process of glucose in anoxic environments which is an important way to provide energy for tumor. The role of glycolysis in CC largely remains unknown and is necessary to be explored. Method In our study, we analyzed glycolysis related genes expression in CC, patients gene expression and corresponding clinical data were downloaded from GEO dataset, glycolysis related genes sets were collected from Msigdb. Through COX regression analysis, prognosis model based on glycolysis-related genes was established. The efficacy of gene model was tested by Survival analysis, ROC analysis and PCA analysis. Furthermore, the relationship between risk scores and clinical characteristic was researched. Results Our findings identified 13 glycolysis related genes (NUP107, SEC13, ALDH7A1, ALG1, CHPF, FAM162A, FBP2, GALK1, IDH1, TGFA, VLDLR, XYLT2 and OGDHL) consisted prognostic prediction model with relative high accuracy. The relationship between prediction model and clinical feathers were specifically studied, results showed age > 65years, TNM III-IV, T3-4, N1-3, M1 and high-risk score were independent prognostic risk factors with poorer prognosis. Finally, model genes were significantly expressed and EMT were activated in CC patients. Conclusion This study provided a new aspect to advance our understanding in the potential mechanism of glycolysis in CC.

Download Full-text

Stromal Score-based Gene Signature: A Prognostic Prediction Model for Colon Cancer

10.21203/rs.3.rs-125354/v1 ◽

2020 ◽

Author(s):

Jing Jia ◽

Yuhan Dai ◽

Qing Zhang ◽

Peiyu Tang ◽

Qiang Fu ◽

...

Keyword(s):

Colon Cancer ◽

Overall Survival ◽

Prediction Model ◽

Differentially Expressed Genes ◽

Gene Signature ◽

Rank Test ◽

Regression Analyses ◽

Log Rank Test ◽

Auc Value ◽

Prognostic Prediction

Abstract BackgroundGrowing evidence has revealed the crucial roles of stromal cells in the microenvironment of various malignant tumors. However, efficient prognostic signatures based on stromal characteristics in colon cancer have not been well-established yet. The present study aimed to construct a stromal score-based multigene prognostic prediction model for colon cancer.MethodStromal scores were calculated based on the expression profiles of a colon cancer cohort from TCGA database applying the ESTIMATE algorithm. Linear models were used to identify differentially expressed genes between low-score and high-score groups by limma R package. Univariate and multivariate CoxPH regression analyses were used successively to select prognostic gene signature. An independent dataset from GEO was used as a validation cohort.ResultsLow stromal score was demonstrated to be a favorable factor to overall survival of colon cancer patients in TCGA cohort (log-rank test p = 0.0046). Three hundred and seven stromal score-related differentially expressed genes were identified. Through univariate and multivariate CoxPH regression analyses, a gene signature consisting of LEP, SYT3, NOG and IGSF11 was recognized to build a prognostic prediction model. Based on the predictive values estimated by the established integrated model, patients were divided into two groups with significantly different overall survival outcomes (log-rank test p < 0.0001). Time-dependent Receiver operating characteristic curve analyses suggested the satisfactory predictive efficacy for 5-year overall survival of the model (AUC value = 0.736). A nomogram with great predictive performance combining the multigene prediction model and clinicopathological factors was developed. The established model was verified to be of significant prognostic value for different subgroups in an independent colon cancer cohort from GEO database, which was demonstrated to be especially accurate for young patients (AUC value = 0.752). ConclusionThe well-established model based on stromal score-related gene signature might serve as a promising tool for the prognostic prediction of colon cancer.

Download Full-text

Identification of Immune-Desert Phenotypes in Head and Neck Squamous Cell Carcinoma With a Nine-Gene Signature Prediction Model Using Bioinformatics Methods

SSRN Electronic Journal ◽

10.2139/ssrn.3738078 ◽

2020 ◽

Author(s):

Chenlu Zhang ◽

Bowen Yang ◽

Yujing Yang ◽

Jin Bai ◽

Chaoxu Zhang ◽

...

Keyword(s):

Squamous Cell Carcinoma ◽

Prediction Model ◽

Cell Carcinoma ◽

Head And Neck ◽

Squamous Cell ◽

Gene Signature ◽

Neck Squamous Cell Carcinoma

Download Full-text

A Novel Ferroptosis-Related Gene Signature Predicts Overall Survival of Breast Cancer Patients

Biology ◽

10.3390/biology10020151 ◽

2021 ◽

Vol 10 (2) ◽

pp. 151

Author(s):

Haifeng Li ◽

Lu Li ◽

Cong Xue ◽

Riqing Huang ◽

Anqi Hu ◽

...

Keyword(s):

Breast Cancer ◽

Overall Survival ◽

Prediction Model ◽

Risk Score ◽

Cox Regression ◽

Gene Prediction ◽

Clinical Information ◽

Risk Groups ◽

Gene Signature ◽

Breast Cancer Patients

Breast cancer is the second leading cause of death in women, thus a reliable prognostic model for overall survival (OS) in breast cancer is needed to improve treatment and care. Ferroptosis is an iron-dependent cell death. It is already known that siramesine and lapatinib could induce ferroptosis in breast cancer cells, and some ferroptosis-related genes were closely related with the outcomes of treatments regarding breast cancer. The relationship between these genes and the prognosis of OS remains unclear. The data of gene expression and related clinical information was downloaded from public databases. Based on the TCGA-BRCA cohort, an 8-gene prediction model was established with the least absolute shrinkage and selection operator (LASSO) cox regression, and this model was validated in patients from the METABRIC cohort. Based on the median risk score obtained from the 8-gene model, patients were stratified into high- or low-risk groups. Cox regression analyses identified that the risk score was an independent predictor for OS. The findings from CIBERSORT and ssGSEA presented noticeable differences in enrichment scores for immune cells and pathways between the abovementioned two risk groups. To sum up, this prediction model has potential to be widely applied in future clinical settings.

Download Full-text

Stromal Score-Based Gene Signature: A Prognostic Prediction Model for Colon Cancer

Frontiers in Genetics ◽

10.3389/fgene.2021.655855 ◽

2021 ◽

Vol 12 ◽

Author(s):

Jing Jia ◽

Yuhan Dai ◽

Qing Zhang ◽

Peiyu Tang ◽

Qiang Fu ◽

...

Keyword(s):

Colon Cancer ◽

Overall Survival ◽

Prediction Model ◽

Differentially Expressed Genes ◽

Cox Regression ◽

Gene Signature ◽

Differentially Expressed ◽

Model Based ◽

Auc Value ◽

Prognostic Prediction

BackgroundGrowing evidence has revealed the crucial roles of stromal cells in the microenvironment of various malignant tumors. However, efficient prognostic signatures based on stromal characteristics in colon cancer have not been well-established yet. The present study aimed to construct a stromal score-based multigene prognostic prediction model for colon cancer.MethodsStromal scores were calculated based on the expression profiles of a colon cancer cohort from TCGA database applying the ESTIMATE algorithm. Linear models were used to identify differentially expressed genes between low-score and high-score groups by limma R package. Univariate, LASSO, and multivariate Cox regression models were used successively to select the prognostic gene signature. Two independent datasets from GEO were used as external validation cohorts.ResultsLow stromal score was demonstrated to be a favorable factor to the overall survival of colon cancer patients in TCGA cohort (p = 0.0046). Three hundred and seven stromal score-related differentially expressed genes were identified. Through univariate, LASSO, and multivariate Cox regression analyses, a gene signature consisting of LEP, NOG, and SYT3 was recognized to build a prognostic prediction model. Based on the predictive values estimated by the established integrated model, patients were divided into two groups with significantly different overall survival outcomes (p < 0.0001). Time-dependent Receiver operating characteristic curve analyses suggested the satisfactory predictive efficacy for the 5-year overall survival of the model (AUC value = 0.733). A nomogram with great predictive performance combining the multigene prediction model and clinicopathological factors was developed. The established model was validated in an external cohort (AUC value = 0.728). In another independent cohort, the model was verified to be of significant prognostic value for different subgroups, which was demonstrated to be especially accurate for young patients (AUC value = 0.763).ConclusionThe well-established model based on stromal score-related gene signature might serve as a promising tool for the prognostic prediction of colon cancer.

Download Full-text

Development of a Bile Acid-Related Gene Signature for Predicting Survival in Patients with Hepatocellular Carcinoma

10.21203/rs.3.rs-215296/v1 ◽

2021 ◽

Author(s):

Gang Wang ◽

Jun Guan ◽

Qin Yang ◽

Fengtian Wu ◽

Junwei Shao ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Bile Acid ◽

Prediction Model ◽

Cox Regression ◽

De Novo ◽

Gene Prediction ◽

Gene Signature ◽

The Cancer Genome Atlas ◽

Tissue Samples ◽

Regulated Expression

Abstract Background: Hepatocellular carcinoma (HCC) is one of the most common diseases, threatening millions of patients annually. Increasing evidence supports that bile acid (BA) has an impact on HCC through inflammation, DNA damage or other mechanisms. Methods: With the data from The Cancer Genome Atlas portal, a total of 127 BA-associated genes were analyzed in HCC tumor and non-tumor samples, and then, using univariate and multivariate Cox regression, genes with correlations to the prognosis of HCC patients were identified. Then, a prediction model with identified genes was constructed for evaluating the risk of HCC patients for prognosis. Results: Twenty-six genes with differential expression between HCC and control tissue samples were identified, of which 19 genes had up-regulated expression and 7 genes had down-regulated expression in tumor samples. Three genes, NPC1, ABCC1 and SLC51B, were extrapolated to construct a prediction model for prognosis of HCC patients. Conclusion: The three-gene prediction model was more reliable than the pathological staging characters of the tumor for the prognosis and survival of HCC patients. Additionally, the up-regulated genes facilitating the transport of BAs are associated with poor prognosis of HCC patients and genes of de novo synthesis of BAs benefits HCC patients.

Download Full-text

Nonlinear Quantitative Radiation Sensitivity Prediction Model Based on NCI-60 Cancer Cell Lines

The Scientific World JOURNAL ◽

10.1155/2014/903602 ◽

2014 ◽

Vol 2014 ◽

pp. 1-11 ◽

Cited By ~ 1

Author(s):

Chunying Zhang ◽

Luc Girard ◽

Amit Das ◽

Sun Chen ◽

Guangqiang Zheng ◽

...

Keyword(s):

Prediction Model ◽

Cell Lines ◽

Latent Variables ◽

Predictive Performance ◽

Radiation Sensitivity ◽

Gene Signature ◽

Support Vector ◽

Cancer Pathways ◽

Different Types ◽

Input Variables

We proposed a nonlinear model to perform a novel quantitative radiation sensitivity prediction. We used the NCI-60 panel, which consists of nine different cancer types, as the platform to train our model. Important radiation therapy (RT) related genes were selected by significance analysis of microarrays (SAM). Orthogonal latent variables (LVs) were then extracted by the partial least squares (PLS) method as the new compressive input variables. Finally, support vector machine (SVM) regression model was trained with these LVs to predict the SF2 (the surviving fraction of cells after a radiation dose of 2 Gyγ-ray) values of the cell lines. Comparison with the published results showed significant improvement of the new method in various ways: (a) reducing the root mean square error (RMSE) of the radiation sensitivity prediction model from 0.20 to 0.011; and (b) improving prediction accuracy from 62% to 91%. To test the predictive performance of the gene signature, three different types of cancer patient datasets were used. Survival analysis across these different types of cancer patients strongly confirmed the clinical potential utility of the signature genes as a general prognosis platform. The gene regulatory network analysis identified six hub genes that are involved in canonical cancer pathways.

Download Full-text

Development of A Three-Gene Signature Prediction Model for Lymph Node Metastasis in Papillary Thyroid Cancer

10.21203/rs.3.rs-41157/v1 ◽

2020 ◽

Author(s):

Ziwei Huang ◽

Yuenan Liu ◽

Kehao Le ◽

Ming Xu ◽

Wenhui Li ◽

...

Keyword(s):

Thyroid Cancer ◽

Lymph Node ◽

Lymph Node Metastasis ◽

Prediction Model ◽

Papillary Thyroid Cancer ◽

Expression Patterns ◽

Gene Signature ◽

Rank Aggregation ◽

Papillary Thyroid ◽

Node Metastasis

Abstract Background: Thyroid cancer is one of the most prevalent endocrine cancers with a rising incidence rate over the past years. Papillary thyroid cancer (PTC) is the dominant historical type of thyroid cancer. Early lymph node metastasis happens frequently in PTC. However, some of the lymph node metastasis may be troublesome for detecting because of limited methods.Methods: Robust rank aggregation afforded us the shared differential expression genes among multiple datasets. Gene ontology analysis was performed to identify potential functions. Weighted gene co-expression network analysis was used to research the correlations between gene expression patterns with clinical characteristic. Protein-protein interaction network was performed to identify the hub genes. The least absolute shrinkage and selection operator and Logistic regression were performed to construct a prediction model.Results: We developed a three-gene signature prediction model for lymph node metastasis in PTC through transcriptomic analysis. After quality control, we collected 8 microarray datasets from GEO database and an RNA sequencing dataset from TCGA database. We found the transcriptome profiles were correlated with lymph node metastasis and 3 genes were verified to be independent prediction factors towards those statistic approach. Afterwards, we designed a predicable risk score system and effectively confirmed the model in two independent papillary thyroid cancer cohorts.Conclusions: We recommended a successful predicable model of lymph node metastasis in papillary thyroid cancer patients with moderate accuracy.

Download Full-text