scholarly journals Identification of Distinct Molecular Patterns and a Four-Gene Signature in Colon Cancer Based on Invasion-Related Genes

2021 ◽  
Vol 12 ◽  
Author(s):  
Yunfei Dong ◽  
Tao Shang ◽  
HaiXin Ji ◽  
Xiukou Zhou ◽  
Zhi Chen
Keyword(s):  
Gene Expression ◽  
Colon Cancer ◽  
Cancer Patients ◽  
Risk Group ◽  
Univariate Analysis ◽  
Gene Signature ◽  
The Cancer Genome Atlas ◽  
Training Dataset ◽  
Lasso Regression ◽  
Independent Predictive Factor

BackgroundThe pathological stage of colon cancer cannot accurately predict recurrence, and to date, no gene expression characteristics have been demonstrated to be reliable for prognostic stratification in clinical practice, perhaps because colon cancer is a heterogeneous disease. The purpose was to establish a comprehensive molecular classification and prognostic marker for colon cancer based on invasion-related expression profiling.MethodsFrom the Gene Expression Omnibus (GEO) database, we collected two microarray datasets of colon cancer samples, and another dataset was obtained from The Cancer Genome Atlas (TCGA). Differentially expressed genes (DEGs) further underwent univariate analysis, least absolute shrinkage, selection operator (LASSO) regression analysis, and multivariate Cox survival analysis to screen prognosis-associated feature genes, which were further verified with test datasets.ResultsTwo molecular subtypes (C1 and C2) were identified based on invasion-related genes in the colon cancer samples in TCGA training dataset, and C2 had a good prognosis. Moreover, C1 was more sensitive to immunotherapy. A total of 1,514 invasion-related genes, specifically 124 downregulated genes and 1,390 upregulated genes in C1 and C2, were identified as DEGs. A four-gene prognostic signature was identified and validated, and colon cancer patients were stratified into a high-risk group and a low-risk group. Multivariate regression analyses and a nomogram indicated that the four-gene signature developed in this study was an independent predictive factor and had a relatively good predictive capability when adjusting for other clinical factors.ConclusionThis research provided novel insights into the mechanisms underlying invasion and offered a novel biomarker of a poor prognosis in colon cancer patients.

scholarly journals The identification of a potential prognostic model of colon cancer using integrated bioinformatics analysis

2020 ◽  
Author(s):  
Zhengyu Fang ◽  
Sumei Xu ◽  
Yiwen Xie ◽  
Wenxi Yan
Keyword(s):  
Gene Expression ◽  
Colon Cancer ◽  
Prognostic Model ◽  
Predictive Power ◽  
Gene Signature ◽  
Gene Expression Omnibus ◽  
The Cancer Genome Atlas ◽  
Cancer Prognosis ◽  
Training Dataset ◽  
High Predictive Power

Abstract Background This study aimed to construct prognostic model by screening prognostic gene signature of colon cancer. Methods The gene expression profile data of colon cancer were obtained from The Cancer Genome Atlas (TCGA) and gene expression omnibus (GEO) and differently expressed genes (DEGs) between tumor and control samples were identified. Prognosis-associated genes were then identified and used for the construction of prognostic model. The independent factors that associated with the prognosis of colon in the TCGA cohort was identified. Results Totally, 1153 consistent DEGs were screened out between tumor and normal tissues in the TCGA cohort, GSE44861 and GSE44076 datasets. Among these genes, 12 DEGs were related to the prognosis of colon cancer and were used for constructing the prognostic model. This model presented a high predictive power for the prognosis of colon cancer both in the training dataset and in the validation datasets (AUC > 0.8). Statistical analysis showed that age, pathological T, tumor recurrence, and model status were the independent factors for prognosis of patients with colon cancer in TCGA. Conclusions The 12-gene signature prognostic model had a high predictive power for colon cancer prognosis.

scholarly journals Development and Validation of a Novel 13-Genes Prognostic Model for Colon Cancer

2021 ◽  
Author(s):  
Duo Yun ◽  
Zhirong Yang
Keyword(s):  
Gene Expression ◽  
Colon Cancer ◽  
Overall Survival ◽  
Cancer Patients ◽  
Prognostic Value ◽  
Risk Model ◽  
Malignant Tumors ◽  
Expression Profiles ◽  
The Cancer Genome Atlas ◽  
Pathway Enrichment

Abstract Colon cancer is one of the most common malignant tumors in the world. The purpose of this study is to explore the prognostic value of genes in colon cancer. After analyzing gene expression profiles, differential expressed genes between 39 normal tissues and 398 tumor tissues were identified from The Cancer Genome Atlas database. We use Cox and lasso regression to find genes related to prognosis. Through analysis, 13 genes were found to predict the overall survival of colon cancer patients. In addition, the external comparing of gene expression and the single prognostic gene survival analysis were made. Finally, pathway enrichment and mutation status of each gene were also analyzed. After a series of bioinformatics analysis, we select 13 survival-related signature and established a prognostic risk model based on these genes. The prognostic risk model was developed to comprehensively predict the overall survival of colon cancer patients. The prognostic value of the 13-genes (CLDN23,HAND1,IL23A,KLHL35,SIX2,UPK2,HOXC11,KRT6B,SRCIN1,TNNI3,TYRO3,MIR6835,LINC02474) related risk score for each colon cancer patent was calculated to predict the survival. Furthermore, five genes (SIX2 MIR6835 LINC02474 CLDN23 HOXC11) were significantly associated with overall survival (OS). The KEGG pathway enrichment results suggested that most of the pathways are related to the occurrence, metabolism, proliferation and invasion of the tumor cells. It was found that the expression of 13-genes signature can be used as prognostic indicator for colon cancer patients. The 13-genes signature predictive model may help clinicians provide a prognosis and personalized treatment for colon cancer patients.

Development of a clinically feasible molecular assay to predict recurrence of Dukes’ B colon cancer

2006 ◽  
Vol 24 (18_suppl) ◽  
pp. 20110-20110
Author(s):  
Y. Jiang ◽  
Y. Zhang ◽  
T. Briggs ◽  
D. Talantov ◽  
A. Mazumder ◽  
...  
Keyword(s):  
Colon Cancer ◽  
Multivariate Analysis ◽  
Cancer Patients ◽  
Therapeutic Option ◽  
Univariate Analysis ◽  
Clinical Importance ◽  
Gene Signature ◽  
Rt Pcr ◽  
Pcr Assay ◽  
Tissue Samples

20110 Background: The 5-year survival rate of Dukes’ B colon cancer patients is approximately 75%. In our earlier genome-wide measurements of gene expression we have identified a 23-gene signature that sub-classifies Dukes’ B tumors and may provide better means of risk assessment on an individual basis for these colon cancer patients. The aim of this study is to validate this gene signature in an independent and more diverse group of patients, and further develop this prognostic signature into a clinical feasible test using formalin-fixed paraffin-embedded (FFPE) tissue samples. Methods: Using Affymetrix U133a GeneChip we analyzed the expression of the 23 genes in total RNA of frozen tumor samples from 123 Dukes’ B patients who did not receive adjuvant systemic treatment. Furthermore, we developed a quantitative RT-PCR assay for this gene signature in order to perform the test with standard clinical FFPE samples. Results: In the independent validation set of 123 patients, the gene signature proved to be informative in identifying patients who would develop distant metastasis (hazard ratio, HR 2.56; 95% confidence interval CI, 1.01–6.48), even when corrected for the traditional prognostic factors in multivariate analysis (HR, 2.73; 95% CI, 0.97–7.73). The RT-PCR assay developed for this gene signature was also validated in an independent set of 114 patients as a strong prognostic factor for the development of distant recurrence (HR, 6.38; 95% CI, 2.88–14.2) in univariate analysis and in multivariate analysis (HR, 13.3; 95% CI, 5.13–34.4). Conclusions: Our data provide not only a validation of the pre-defined prognostic gene signature for Dukes’ B colon cancer patients but also a clear feasibility of testing the gene signature using RT-PCR with standard FFPE specimens. The ability of such a test to identify patients that have an unfavorable outcome demonstrates potential clinical importance that could lead clinicians to choose a more aggressive therapeutic option for the high-risk patients. [Table: see text]

scholarly journals A New Oxaliplatin Resistance-Related Gene Signature With Strong Predicting Ability in Colon Cancer Identified by Comprehensive Profiling

2021 ◽  
Vol 11 ◽  
Author(s):  
Qiu Lin ◽  
Li Luo ◽  
Hua Wang
Keyword(s):  
Gene Expression ◽  
Colon Cancer ◽  
Cancer Patients ◽  
Risk Score ◽  
Cox Regression ◽  
Differential Expression Analysis ◽  
Gene Signature ◽  
Single Individual ◽  
Related Gene ◽  
Specificity And Sensitivity

Numerous colon cancer cases are resistant to chemotherapy based on oxaliplatin and suffer from relapse. A number of survival- and prognosis-related biomarkers have been identified based on database mining for patients who develop drug resistance, but the single individual gene biomarker cannot attain high specificity and sensitivity in prognosis prediction. This work was conducted aiming to establish a new gene signature using oxaliplatin resistance-related genes to predict the prognosis for colon cancer. To this end, we downloaded gene expression profile data of cell lines that are resistant and not resistant to oxaliplatin from the Gene Expression Omnibus (GEO) database. Altogether, 495 oxaliplatin resistance-related genes were searched by weighted gene co-expression network analysis (WGCNA) and differential expression analysis. As suggested by functional analysis, the above genes were mostly enriched into cell adhesion and immune processes. Besides, a signature was built based on four oxaliplatin resistance-related genes selected from the training set to predict the overall survival (OS) by stepwise regression and least absolute shrinkage and selection operator (LASSO) Cox analysis. Relative to the low risk score group, the high risk score group had dismal OS (P < 0.0001). Moreover, the area under the curve (AUC) value regarding the 5-year OS was 0.72, indicating that the risk score was accurate in the prediction of OS for colon cancer patients (AUC >0.7). Additionally, multivariate Cox regression suggested that the signature constructed based on four oxaliplatin resistance-related genes predicted the prognosis for colon cancer cases [hazard ratio (HR), 2.77; 95% CI, 2.03–3.78; P < 0.001]. Finally, external test sets were utilized to further validate the stability and accuracy of oxaliplatin resistance-related gene signature for prognosis of colon cancer patients. To sum up, this study establishes a signature based on four oxaliplatin resistance-related genes for predicting the survival of colon cancer patients, which sheds more light on the mechanisms of oxaliplatin resistance and helps identify colon cancer cases with a dismal prognostic outcome.

scholarly journals A glycolysis-related gene expression signature in predicting recurrence of breast cancer

2020 ◽  
Author(s):  
Jianing Tang ◽  
Gaosong Wu
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
High Risk ◽  
Cancer Patients ◽  
Risk Group ◽  
Gene Signature ◽  
Low Risk ◽  
Individual Treatment ◽  
Breast Cancer Patients ◽  
Related Gene

Abstract Background Metabolic change is the hallmark of cancer. Even in the presence of oxygen, cancer cells reprogram their glucose metabolism to enhance glycolysis and reduce oxidative phosphorylation. In the present study, we aimed to develop a glycolysis-related gene signature to predict the prognosis of breast cancer patients.Methods Gene expression profiles and clinical data of breast cancer patients were obtained from the GEO database. Univariate, Lasso-penalized, and multivariate Cox analysis were performed to construct the glycolysis-related gene signature.Results A four-gene based signature (ALDH2, PRKACB, STMN1 and ZNF292) was developed to separate patients into high-risk and low-risk groups. Kaplan-Meier survival analysis demonstrated that patients in low-risk group had significantly better prognosis than those in the high-risk group. Time-dependent ROC analysis demonstrated that the glycolysis-related gene signature had excellent prognostic accuracy. We further confirmed the expression of the four prognostic genes in breast cancer and paracancerous tissues samples using qRT-PCR analysis. Expression level of PRKACB was higher in paracancerous tissues, while STMN1 and ZNF292 were overexpressed in tumor samples. No difference was found in ALDH2 expression. The same results were observed in the IHC data from the human protein atlas. Global proteome data of 105 TCGA breast cancer samples obtained from the Clinical Proteomic Tumor Analysis Consortium were used to evaluate the prognostic value of their protein levels. Consistently, high expression of PRKACB protein level was associated with better prognosis, while high ZNF292 and STMN1 protein expression levels indicated poor prognosis.Conclusions The glycolysis-related gene signature might provide an effective prognostic predictor and a new view for individual treatment of breast cancer patients.

scholarly journals Development and Clinical Validation of a Seven-Gene Prognostic Signature Based on Multiple Machine Learning Algorithms in Kidney Cancer

2021 ◽  
Vol 30 ◽  
pp. 096368972096917
Author(s):  
Mi Tian ◽  
Tao Wang ◽  
Peng Wang
Keyword(s):  
Gene Expression ◽  
Kidney Cancer ◽  
Cox Regression ◽  
Gene Signature ◽  
Machine Learning Algorithms ◽  
The Cancer Genome Atlas ◽  
Support Vector ◽  
Lasso Regression ◽  
Regression Analyses ◽  
Western Blots

About a third of patients with kidney cancer experience recurrence or cancer-related progression. Clinically, kidney cancer prognoses may be quite different, even in patients with kidney cancer at the same clinical stage. Therefore, there is an urgent need to screen for kidney cancer prognosis biomarkers. Differentially expressed genes (DEGs) were identified using kidney cancer RNA sequencing data from the Gene Expression Omnibus (GEO) database. Biomarkers were screened using random forest (RF) and support vector machine (SVM) models, and a multigene signature was constructed using the least absolute shrinkage and selection operator (LASSO) regression analysis. Univariate and multivariate Cox regression analyses were performed to explore the relationships between clinical features and prognosis. Finally, the reliability and clinical applicability of the model were validated, and relationships with biological pathways were identified. Western blots were also performed to evaluate gene expression. A total of 50 DEGs were obtained by intersecting the RF and SVM models. A seven-gene signature (RNASET2, EZH2, FXYD5, KIF18A, NAT8, CDCA7, and WNT7B) was constructed by LASSO regression. Univariate and multivariate Cox regression analyses showed that the seven-gene signature was an independent prognostic factor for kidney cancer. Finally, a predictive nomogram was established in The Cancer Genome Atlas (TCGA) cohort and validated internally. In tumor tissue, RNASET2 and FXYD5 were highly expressed and NAT8 was lowly expressed at the protein and transcription levels. This model could complement the clinicopathological characteristics of kidney cancer and promote the personalized management of patients with kidney cancer.

scholarly journals Establishment of a 12-gene expression signature to predict colon cancer prognosis

PeerJ ◽  
2018 ◽  
Vol 6 ◽  
pp. e4942 ◽  
Author(s):  
Dalong Sun ◽  
Jing Chen ◽  
Longzi Liu ◽  
Guangxi Zhao ◽  
Pingping Dong ◽  
...  
Keyword(s):  
Gene Expression ◽  
Colon Cancer ◽  
Poor Prognosis ◽  
Risk Group ◽  
Gene Expression Signature ◽  
Gene Signature ◽  
Good Prognosis ◽  
Stage Ii ◽  
Gene Score ◽  
Expression Signature

A robust and accurate gene expression signature is essential to assist oncologists to determine which subset of patients at similar Tumor-Lymph Node-Metastasis (TNM) stage has high recurrence risk and could benefit from adjuvant therapies. Here we applied a two-step supervised machine-learning method and established a 12-gene expression signature to precisely predict colon adenocarcinoma (COAD) prognosis by using COAD RNA-seq transcriptome data from The Cancer Genome Atlas (TCGA). The predictive performance of the 12-gene signature was validated with two independent gene expression microarray datasets:GSE39582includes 566 COAD cases for the development of six molecular subtypes with distinct clinical, molecular and survival characteristics;GSE17538is a dataset containing 232 colon cancer patients for the generation of a metastasis gene expression profile to predict recurrence and death in COAD patients. The signature could effectively separate the poor prognosis patients from good prognosis group (disease specific survival (DSS): Kaplan Meier (KM) Log Rankp= 0.0034; overall survival (OS): KM Log Rankp= 0.0336) inGSE17538. For patients with proficient mismatch repair system (pMMR) inGSE39582, the signature could also effectively distinguish high risk group from low risk group (OS: KM Log Rankp= 0.005; Relapse free survival (RFS): KM Log Rankp= 0.022). Interestingly, advanced stage patients were significantly enriched in high 12-gene score group (Fisher’s exact testp= 0.0003). After stage stratification, the signature could still distinguish poor prognosis patients inGSE17538from good prognosis within stage II (Log Rankp = 0.01) and stage II & III (Log Rankp= 0.017) in the outcome of DFS. Within stage III or II/III pMMR patients treated with Adjuvant Chemotherapies (ACT) and patients with higher 12-gene score showed poorer prognosis (III, OS: KM Log Rankp= 0.046; III & II, OS: KM Log Rankp= 0.041). Among stage II/III pMMR patients with lower 12-gene scores inGSE39582, the subgroup receiving ACT showed significantly longer OS time compared with those who received no ACT (Log Rankp= 0.021), while there is no obvious difference between counterparts among patients with higher 12-gene scores (Log Rankp= 0.12). Besides COAD, our 12-gene signature is multifunctional in several other cancer types including kidney cancer, lung cancer, uveal and skin melanoma, brain cancer, and pancreatic cancer. Functional classification showed that seven of the twelve genes are involved in immune system function and regulation, so our 12-gene signature could potentially be used to guide decisions about adjuvant therapy for patients with stage II/III and pMMR COAD.

scholarly journals Developing ZNF Gene Signatures Predicting Radiosensitivity of Patients with Breast Cancer

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Derui Yan ◽  
Mingjing Shen ◽  
Zixuan Du ◽  
Jianping Cao ◽  
Ye Tian ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Cancer Patients ◽  
Cox Regression ◽  
Molecular Taxonomy ◽  
Gene Signature ◽  
The Cancer Genome Atlas ◽  
Breast Cancer Patients ◽  
Expression Levels ◽  
The Relationship

Adjuvant radiotherapy is one of the main treatment methods for breast cancer, but its clinical benefit depends largely on the characteristics of the patient. This study aimed to explore the relationship between the expression of zinc finger (ZNF) gene family proteins and the radiosensitivity of breast cancer patients. Clinical and gene expression data on a total of 976 breast cancer samples were obtained from The Cancer Genome Atlas (TCGA) database. ZNF gene expression was dichotomized into groups with a higher or lower level than the median level of expression. Univariate and multivariate Cox regression analyses were used to evaluate the relationship between ZNF gene expression levels and radiosensitivity. The Molecular Taxonomy Data of the International Federation of Breast Cancer (METABRIC) database was used for validation. The results revealed that 4 ZNF genes were possible radiosensitivity markers. High expression of ZNF644 and low expression levels of the other 3 genes (ZNF341, ZNF541, and ZNF653) were related to the radiosensitivity of breast cancer. Hierarchical cluster, Cox, and CoxBoost analysis based on these 4 ZNF genes indicated that patients with a favorable 4-gene signature had better overall survival on radiotherapy. Thus, this 4-gene signature may have value for selecting those patients most likely to benefit from radiotherapy. ZNF gene clusters could act as radiosensitivity signatures for breast cancer patients and may be involved in determining the radiosensitivity of cancer.

scholarly journals Integrative Analysis of Identifying Methylation-Driven Genes Signature Predicts Prognosis in Colorectal Carcinoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Hao Huang ◽  
Jinming Fu ◽  
Lei Zhang ◽  
Jing Xu ◽  
Dapeng Li ◽  
...  
Keyword(s):  
Gene Expression ◽  
Risk Assessment ◽  
Risk Group ◽  
Integrative Analysis ◽  
Assessment Model ◽  
The Cancer Genome Atlas ◽  
Risk Assessment Model ◽  
Lasso Regression ◽  
Prognostic Signature ◽  
Training Set

BackgroundAberrant DNA methylation is a critical regulator of gene expression and plays a crucial role in the occurrence, progression, and prognosis of colorectal cancer (CRC). We aimed to identify methylation-driven genes by integrative epigenetic and transcriptomic analysis to predict the prognosis of CRC patients.MethodsMethylation-driven genes were selected for CRC using a MethylMix algorithm and LASSO regression screening strategy, and were further used to construct a prognostic risk-assessment model. The Cancer Genome Atlas (TCGA) database was obtained as the training set for both the screening of methylation-driven genes and the effect of genes signature on CRC prognosis. Then, the prognostic genes signature was validated in three independent expression arrays of CRC data from Gene Expression Omnibus (GEO).ResultsWe identified 143 methylation-driven genes, of which the combination of BATF, PHYHIPL, RBP1, and PNPLA4 expression levels was screened as a better prognostic model with the best area under the curve (AUC) (AUC = 0.876). Compared with patients in the low-risk group, CRC patients in the high-risk group had significantly poorer overall survival in the training set (HR = 2.184, 95% CI: 1.404–3.396, P < 0.001). Similar results were observed in the validation set. Moreover, VanderWeele’s mediation analysis indicated that the effect of methylation on prognosis was mediated by the levels of their expression (HRindirect = 1.473, P = 0.001, Proportion mediated, 69.10%).ConclusionsWe identified a four-gene prognostic signature by integrative analysis and developed a risk-assessment model that is significantly associated with patients’ survival. Methylation-driven genes might be a potential prognostic signature for CRC patients.

scholarly journals Identification of a ferroptosis-related gene signature predictive model in colon cancer

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Ye Wang ◽  
Heng-bo Xia ◽  
Zhang-ming Chen ◽  
Lei Meng ◽  
A-man Xu
Keyword(s):  
Colon Cancer ◽  
Cox Regression ◽  
Risk Group ◽  
Expression Profiles ◽  
Risk Groups ◽  
High Risk Group ◽  
Low Risk ◽  
The Cancer Genome Atlas ◽  
Lasso Regression ◽  
Cox Regression Analysis

Abstract Background The prognosis of colon cancer (CC) is challenging to predict due to its highly heterogeneous nature. Ferroptosis, an iron-dependent form of cell death, has roles in various cancers; however, the correlation between ferroptosis-related genes (FRGs) and prognosis in CC remains unclear. Methods The expression profiles of FRGs and relevant clinical information were retrieved from the Cancer Genome Atlas (TCGA) database. Cox regression analysis and the least absolute shrinkage and selection operator (LASSO) regression model were performed to build a prognostic model in TCGA cohort. Results Ten FRGs, five of which had mutation rates ≥ 3%, were found to be related to the overall survival (OS) of patients with CC. Patients were divided into high- and low-risk groups based on the results of Cox regression and LASSO analysis. Patients in the low-risk group had a significantly longer survival time than patients in the high-risk group (P < 0.001). Enrichment analyses in different risk groups showed that the altered genes were associated with the extracellular matrix, fatty acid metabolism, and peroxisome. Age, risk score, T stage, N stage, and M stage were independent predictors of patient OS based on the results of Cox analysis. Finally, a nomogram was constructed to predict 1-, 3-, and 5-year OS of patients with CC based on the above five independent factors. Conclusion A novel FRG model can be used for prognostic prediction in CC and may be helpful for individualized treatment.

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