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 Gene-expression signature of tumor recurrence in patients with stage II and III colon cancer treated with 5′fluoruracil-based adjuvant chemotherapy

2012 ◽  
Vol 132 (5) ◽  
pp. 1090-1097 ◽  
Author(s):  
María Dolores Giráldez ◽  
Juan José Lozano ◽  
Míriam Cuatrecasas ◽  
Virginia Alonso-Espinaco ◽  
Joan Maurel ◽  
...  
Keyword(s):  
Gene Expression ◽  
Colon Cancer ◽  
Adjuvant Chemotherapy ◽  
Tumor Recurrence ◽  
Gene Expression Signature ◽  
Stage Ii ◽  
Expression Signature

scholarly journals A Gene Expression Signature of Genetic Instability in Colon Cancer

2005 ◽  
Vol 65 (20) ◽  
pp. 9200-9205 ◽  
Author(s):  
Craig P. Giacomini ◽  
Suet Yi Leung ◽  
Xin Chen ◽  
Siu Tsan Yuen ◽  
Young H. Kim ◽  
...  
Keyword(s):  
Gene Expression ◽  
Colon Cancer ◽  
Genetic Instability ◽  
Gene Expression Signature ◽  
Expression Signature

scholarly journals A network‐based predictive gene expression signature for recurrence risks in stage II colorectal cancer

2019 ◽  
Vol 9 (1) ◽  
pp. 179-193 ◽  
Author(s):  
Wen‐Jing Yang ◽  
Hai‐Bo Wang ◽  
Wen‐Da Wang ◽  
Peng‐Yu Bai ◽  
Hong‐Xia Lu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Colorectal Cancer ◽  
Gene Expression Signature ◽  
Stage Ii ◽  
Expression Signature ◽  
Predictive Gene ◽  
Stage Ii Colorectal Cancer

scholarly journals Cross-platform Data Analysis Reveals a Generic Gene Expression Signature for Microsatellite Instability in Colorectal Cancer

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Anna Pačínková ◽  
Vlad Popovici
Keyword(s):  
Gene Expression ◽  
Colorectal Cancer ◽  
Colon Cancer ◽  
Microsatellite Instability ◽  
Gene Expression Signature ◽  
Data Sets ◽  
Rna Seq ◽  
Cancer Data ◽  
Expression Signature ◽  
Endometrial Cancers

The dysfunction of the DNA mismatch repair system results in microsatellite instability (MSI). MSI plays a central role in the development of multiple human cancers. In colon cancer, despite being associated with resistance to 5-fluorouracil treatment, MSI is a favourable prognostic marker. In gastric and endometrial cancers, its prognostic value is not so well established. Nevertheless, recognising the MSI tumours may be important for predicting the therapeutic effect of immune checkpoint inhibitors. Several gene expression signatures were trained on microarray data sets to understand the regulatory mechanisms underlying microsatellite instability in colorectal cancer. A wealth of expression data already exists in the form of microarray data sets. However, the RNA-seq has become a routine for transcriptome analysis. A new MSI gene expression signature presented here is the first to be valid across two different platforms, microarrays and RNA-seq. In the case of colon cancer, its estimated performance was (i) AUC = 0.94, 95% CI = (0.90 – 0.97) on RNA-seq and (ii) AUC = 0.95, 95% CI = (0.92 – 0.97) on microarray. The 25-gene expression signature was also validated in two independent microarray colon cancer data sets. Despite being derived from colorectal cancer, the signature maintained good performance on RNA-seq and microarray gastric cancer data sets (AUC = 0.90, 95% CI = (0.85 – 0.94) and AUC = 0.83, 95% CI = (0.69 – 0.97), respectively). Furthermore, this classifier retained high concordance even when classifying RNA-seq endometrial cancers (AUC = 0.71, 95% CI = (0.62 – 0.81). These results indicate that the new signature was able to remove the platform-specific differences while preserving the underlying biological differences between MSI/MSS phenotypes in colon cancer samples.

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 Identification and Verification of a 17 Immune-Related Gene Pair Prognostic Signature for Colon Cancer

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Qianshi Zhang ◽  
Zhen Feng ◽  
Yongnian Zhang ◽  
Shasha Shi ◽  
Yu Zhang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Colon Cancer ◽  
Poor Prognosis ◽  
Validation Cohort ◽  
Risk Group ◽  
High Risk Group ◽  
Related Gene ◽  
Prognostic Signature ◽  
Discovery Cohort ◽  
Immune Related Gene

Background. Colon cancer (CC) is a malignant tumor with a high incidence and poor prognosis. Accumulating evidence shows that the immune signature plays an important role in the tumorigenesis, progression, and prognosis of CC. Our study is aimed at establishing a novel robust immune-related gene pair signature for predicting the prognosis of CC. Methods. Gene expression profiles and corresponding clinical information are obtained from two public data sets: The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO, GSE39582). We screened out immune-related gene pairs (IRGPs) associated with prognosis in the discovery cohort. Lasso-Cox proportional hazard regression was used to develop the best prognostic signature model. According to this, the patients in the validation cohort were divided into high immune-risk group and low immune-risk group, and the prediction ability of the signature model was verified by survival analysis and independent prognostic analysis. Results. A total of 17 IRGPs composed of 26 IRGs were used to construct a prognostic-related risk scoring model. This model accurately predicted the prognosis of CC patients, and the patients in the high immune-risk group indicated poor prognosis in the discovery cohort and validation cohort. Besides, whether in univariate or multivariate analysis, the IRGP signature was an independent prognostic factor. T cell CD4 memory resting in the low-risk group was significantly higher than that in the high-risk group. Functional analysis showed that the biological processes of the low-risk group included “TCA cycle” and “RNA degradation,” while the high-risk group was enriched in the “CAMs” and “focal adhesion” pathways. Conclusion. We have successfully established a signature model composed of 17 IRGPs, which provides a novel idea to predict the prognosis of CC patients.

Identification of a gene expression signature predictive of clinical benefit in patients with advanced mucosal melanoma (MCM) treated with immune checkpoint blockade.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. 9546-9546
Author(s):  
Suthee Rapisuwon ◽  
Alexander Noor Shoushtari ◽  
Lee S. Gottesdiener ◽  
Douglas Buckner Johnson ◽  
Daniel Ying Wang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Immune Checkpoint ◽  
Chemokine Receptors ◽  
Tumor Response ◽  
Gene Expression Signature ◽  
Gene Signature ◽  
Anatomical Location ◽  
Expression Signature ◽  
Response Data ◽  
Treg Development

9546 Background: MCM is a rare melanoma subtype (only 1% of melanomas in the US). MCM has a lower tumor mutational burden than cutaneous melanoma (CM). While some patients (pts) with MCM respond to immune checkpoint inhibitor (ICI) therapy, predictive markers of response have not been established. We analyzed a cohort of MCM from pts treated with ICI to identify gene expression signatures associated with tumor response and clinical outcome. Methods: Fifty-eight MCM specimens were collected from 3 institutions. RNA was extracted from FFPE tissue slides and analyzed by NanoString 770 Immune Profiling Panel. Gene expression profiles were linked to anatomical location and disease outcome after ICI therapy: response as defined by RECIST v1.1 and median overall survival (mOS). Results: Fifty-one pts were treated with ICI - anti-CTLA-4 (n = 9), anti-PD1 (n = 38), or both (n = 5) ) and had tumor response evaluation. Three were without response data, 2 were without disease recurrence after surgery, 2 did not receive ICI. Among 51 pts with response data, seven were without long-term follow-up (1CR, 2PR, 3SD). The overall response rate (ORR) was 40.3%, similar to the prior study (Shoushtari et al, Cancer 2016). A signature involving differential expression of 87 immunoregulatory genes correlated with tumor response: ORR: 75% (12/16) signature high vs. 33.3% (7/21) signature low (p = 0.02, high vs. low) vs. 14.3% (2/14) signature average (p < 0.01; high vs. average). mOS for the whole population was 12.4 months. Pts with increased gene signature expression had superior mOS: signature-high: Not reached, signature-low: 8.2 months, (HR: 0.2; 95%CI: 0.07-0.55, p < 0.01). Transcript pathway analysis of the gene signature showed association with chemokine receptors, interleukin-10 signaling, and Treg development. Conclusions: We have identified a gene expression signature that involves chemokine receptors, IL-10 signaling, and Treg development gene sets, that appears to predict for tumor response and mOS in pts with advanced MCM treated with ICI. Further validation of these gene signatures is underway.

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