A novel gene-pair signature for relapse-free survival prediction in colon cancer

Chen, Pengfei; Wang, Fan; Zhang, Zi-xiong; Nie, Jiayan; Liu, Lan; Feng, Jianhua; Zhou, Rui; Wang, Hongling; Liu, Jing; Zhao, Qiu

doi:10.2147/cmar.s176260

Cited by 6 publications

(9 citation statements)

References 27 publications

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“…Recent advances in high-throughput gene testing technology have provided an opportunity to define the genetic landscape of colon cancer and led to the development of many molecular signatures for prognosis prediction and personalisation of treatment paradigms [[8], [9], [10], [11]]. However, some of these signatures were frequently ill-defined, having been generated from unspecified genetic backgrounds.…”

Section: Introductionmentioning

confidence: 99%

A robust panel based on tumour microenvironment genes for prognostic prediction and tailoring therapies in stage I–III colon cancer

et al. 2019

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Background: Tumour microenvironment (TME) is critical for the regulation of cancer development as well as therapy. The objective of the current study was the development of a robust prognostic model based on TMErelevant genes. Methods: Five public microarray datasets providing clinical information were obtained. The least absolute shrinkage and selection operator regression method was used to reduce the dimensionality of robust prognostic genes identified via the bootstrap method. Findings: We established a prognostic panel, designated as tumour microenvironment risk score (TMRS), consisting of 100 genes. With specific risk score formulae, the TMRS panel possesses a strong ability to predict relapse-free survival and overall survival through both univariate and multivariate analyses. Compared with the TNM stage, the TMRS panel showed much higher predictive accuracy. Further analysis revealed that patients with higher TMRS scores exhibited no therapeutic benefits from adjuvant chemotherapy, probably due to the activation of stromal relevant pathways and infiltration of stromal cells. Besides colon cancer, the TMRS panel was also revealed to be a reliable tool for prognostic prediction and chemotherapeutic decision-making in gastric cancer. Its value in predicting immunotherapy outcomes was also confirmed in two other cohorts consisting of metastatic urothelial carcinoma patients and melanoma patients. Interpretation: Our TMRS panel may be an effective tool for survival prediction and treatment guidance in patients with stage I-III colon cancer.

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Section: Introductionmentioning

confidence: 99%

A robust panel based on tumour microenvironment genes for prognostic prediction and tailoring therapies in stage I–III colon cancer

et al. 2019

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“…ROC curve analysis indicated that this 5-mRNA signature panel can accurately predict the prognosis for patients with RC, with the AUC of 0.915 and 0.827 for the training and validation datasets, respectively. The prognostic performance of our risk score seemed to be better than that of previously reported signature panels developed for CRC (such as 4 genes: AUC = 0.722 for the external dataset and 0.607 for the internal dataset [ 4 ]; 6 genes: AUC = 0.683 [ 7 ]; and 9 genes: AUC = 0.741 [ 23 ]) or CC (16 gene pairs: AUC = 0.724 [ 9 ]; 9 genes: AUC = 0.676 [ 22 ]). In addition, in the study of Zuo et al [ 7 ], they performed a subgroup analysis to confirm whether the 6-gene signature panel was effective for colon adenocarcinoma (COAD) and READ.…”

Section: Discussionmentioning

confidence: 69%

“…Sun et al developed a 12-gene expression signature panel to precisely predict the prognosis for CC patients, which could distinguish poor from good prognosis patients within stage II/III [ 8 ]. Chen et al found that the signature panel consisting of 16 gene pairs formed by 24 genes had a better prognostic ability than the TNM stage (AUC: 0.724 vs. 0.703; concordance index (C-index): 0.869 vs. less than 0.8) at 5 years [ 9 ]. Although there were also studies to identify mRNAs associated with the prognosis of RC patients [ 10 – 13 ], no reports specifically focused on the signature panel and compared its prognostic values with clinical factors.…”

Section: Introductionmentioning

confidence: 99%

Identification of a 5-Gene-Based Scoring System by WGCNA and LASSO to Predict Prognosis for Rectal Cancer Patients

Huang

Chen

et al. 2021

Analytical Cellular Pathology

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Background. Although accumulating evidence suggested that a molecular signature panel may be more effective for the prognosis prediction than routine clinical characteristics, current studies mainly focused on colorectal or colon cancers. No reports specifically focused on the signature panel for rectal cancers (RC). Our present study was aimed at developing a novel prognostic signature panel for RC. Methods. Sequencing (or microarray) data and clinicopathological details of patients with RC were retrieved from The Cancer Genome Atlas (TCGA-READ) or the Gene Expression Omnibus (GSE123390, GSE56699) database. A weighted gene coexpression network was used to identify RC-related modules. The least absolute shrinkage and selection operator analysis was performed to screen the prognostic signature panel. The prognostic performance of the risk score was evaluated by survival curve analyses. Functions of prognostic genes were predicted based on the interaction proteins and the correlation with tumor-infiltrating immune cells. The Human Protein Atlas (HPA) tool was utilized to validate the protein expression levels. Results. A total of 247 differentially expressed genes (DEGs) were commonly identified using TCGA and GSE123390 datasets. Brown and yellow modules (including 77 DEGs) were identified to be preserved for RC. Five DEGs (ASB2, GPR15, PRPH, RNASE7, and TCL1A) in these two modules constituted the optimal prognosis signature panel. Kaplan-Meier curve analysis showed that patients in the high-risk group had a poorer prognosis than those in the low-risk group. Receiver operating characteristic (ROC) curve analysis demonstrated that this risk score had high predictive accuracy for unfavorable prognosis, with the area under the ROC curve of 0.915 and 0.827 for TCGA and GSE56699 datasets, respectively. This five-mRNA classifier was an independent prognostic factor. Its predictive accuracy was also higher than all clinical factor models. A prognostic nomogram was developed by integrating the risk score and clinical factors, which showed the highest prognostic power. ASB2, PRPH, and GPR15/TCL1A were predicted to function by interacting with CASQ2/PDK4/EPHA67, PTN, and CXCL12, respectively. TCL1A and GPR15 influenced the infiltration levels of B cells and dendritic cells, while the expression of PRPH was positively associated with the abundance of macrophages. HPA analysis supported the downregulation of PRPH, RNASE7, CASQ2, EPHA6, and PDK4 in RC compared with normal controls. Conclusion. Our immune-related signature panel may be a promising prognostic indicator for RC.

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“…The AUC and c-index in the TCGA set and the other three independent sets were similar. Additionally, the method of gene pair was based on a relative ranking of gene expression level to make pairwise comparison and generate the score in the same patient, which could eliminate the shortcomings, such as the batch effect of different platforms 12,13,18 . www.nature.com/scientificreports/ Nowadays, emerging studies show the tumor microenvironment (TME) is critical for the initiation, progression, and metastasis of cancers, and therapy targeting the TME seems to be an encouraging method to overthrow therapeutic escape issues 19,20 .…”

Section: Discussionmentioning

confidence: 99%

Development of an immune-related gene pairs signature for predicting clinical outcome in lung adenocarcinoma

Mao

2021

Sci Rep

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Lung adenocarcinoma (LUAD) is the main pathological subtype of Non-small cell lung cancer. We downloaded the gene expression profile and immune-related gene set from the TCGA and ImmPort database, respectively, to establish immune-related gene pairs (IRGPs). Then, IRGPs were subjected to univariate Cox regression analysis, LASSO regression analysis, and multivariable Cox regression analysis to screen and develop an IRGPs signature. The receiver operating characteristic curve (ROC) was applied for evaluating the predicting accuracy of this signature by calculating the area under ROC (AUC) and data from the GEO set was used to validate this signature. The relationship of 22 tumor-infiltrating immune cells (TIICs) to the immune risk score was also investigated. An IRGPs signature with 8 IRGPs was constructed. The AUC for 1- and 3-year overall survival in the TCGA set was 0.867 and 0.870, respectively. Similar results were observed in the AUCs of GEO set 1, 2 and 3 (GEO set 1 [1-year: 0.819; 3-year: 0.803]; GEO set 2 [1-year: 0.834; 3-year: 0.870]; GEO set 3 [1-year: 0.955; 3-year: 0.827]). Survival analysis demonstrated high-risk LUAD patients exhibited poorer prognosis. The multivariable Cox regression indicated that the risk score was an independent prognostic factor. The immune risk score was highly associated with several TIICs (Plasma cells, memory B cells, resting memory CD4 T cells, and activated NK cells). We developed a novel IRGPs signature for predicting 1- and 3- year overall survival in LUAD, which would be helpful for prognosis assessment of LUAD.

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A novel gene-pair signature for relapse-free survival prediction in colon cancer

Cited by 6 publications

References 27 publications

A robust panel based on tumour microenvironment genes for prognostic prediction and tailoring therapies in stage I–III colon cancer

A robust panel based on tumour microenvironment genes for prognostic prediction and tailoring therapies in stage I–III colon cancer

Identification of a 5-Gene-Based Scoring System by WGCNA and LASSO to Predict Prognosis for Rectal Cancer Patients

Development of an immune-related gene pairs signature for predicting clinical outcome in lung adenocarcinoma

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