Identification of Differentially Expressed Genes and Prognostic Biomarkers of Breast Cancer Based on RNA-Seq and KEGG Pathway Network

Zhang, S.M.; Gu, Yunyan; Wu, Sheng-Hai; Kang, Yu; Liu, S.; Zhang, D.

doi:10.5376/cge.2016.04.0002

Cited by 1 publication

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“…Both COL4A1 and COL4A2 were involved in the PI3K-Akt pathway and this signaling pathway has been demonstrated to be responsible for controlling metabolic reprogramming ( Lien, Lyssiotis & Cantley, 2016 ; Liu et al, 2020 ). All of five genes have been previously reported to have prognostic roles in several cancers, such as AARS in breast cancer and PCNA in colon adenocarcinoma ( Ho et al, 2017 ; Klupp et al, 2016 ; Zhang et al, 2016a ). Matrix metalloproteinases (MMPs) are important in colorectal cancer invasion and progression.…”

Section: Discussionmentioning

confidence: 99%

Transcriptome analysis reveals a reprogramming energy metabolism-related signature to improve prognosis in colon cancer

Zhang

Lan

et al. 2020

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Although much progress has been made to improve treatment, colon cancer remains a leading cause of cancer death worldwide. Metabolic reprogramming is a significant ability of cancer cells to ensure the necessary energy supply in uncontrolled proliferation. Since reprogramming energy metabolism has emerged as a new hallmark of cancer cells, accumulating evidences have suggested that metabolism-related genes may serve as key regulators of tumorigenesis and potential biomarkers. In this study, we analyzed a set of reprogramming energy metabolism-related genes by transcriptome analysis in colon cancer and revealed a five-gene signature that could significantly predict the overall survival. The reprogramming energy metabolism-related signature could distinguish patients into high-risk and low-risk groups with significantly different survival times (P = 0.0011; HR = 1.92; 95% CI [1.29–2.87]). Its prognostic value was confirmed in another two independent colon cancer cohorts (P = 5.2e–04; HR = 2.09, 95%; CI [1.37–3.2] for GSE17538 and P = 3.8e−04; HR = 2.08, 95% CI [1.37–3.16] for GSE41258). By multivariable analysis, we found that the signature was independent of clinicopathological features. Its power in promoting risk stratification of the current clinical stage was then evaluated by stratified analysis. Moreover, the signature could improve the power of the TNM stage for the prediction of overall survival and could be used in patients who received adjuvant chemotherapy. Overall, our results demonstrated the important role of the reprogramming energy metabolism-related signature in promoting stratification of high-risk patients, which could be diagnostic of adjuvant therapy benefit.

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