Development and internal validation of a nine-lncRNA prognostic signature for prediction of overall survival in colorectal cancer patients

Zhang, Zhiqiao; Liu, Qingbo; Wang, Peng; Li, Jing; He, Tingshan; Ouyang, Yanling; Huang, Yiyan; Wang, Weidong

doi:10.7717/peerj.6061

Cited by 11 publications

(12 citation statements)

References 19 publications

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“…It was calculated using binary lncRNA expression values according to the medians of original lncRNA expression values. This adjustment helps to improve the clinical application of the prognostic model in other study population (Zhang et al, 2018). In general, the higher is the risk score, the poorer is the prognosis, which is consistent with our analysis.…”

Section: Discussionsupporting

confidence: 89%

A risk score model with five long non-coding RNAs for predicting prognosis in gastric cancer: an integrated analysis combining TCGA and GEO datasets

Deng

Lai

et al. 2021

PeerJ

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Background Gastric cancer (GC) is one of the most common carcinomas of the digestive tract, and the prognosis for these patients may be poor. There is evidence that some long non-coding RNAs(lncRNAs) can predict the prognosis of patients with GC. However, few lncRNA signatures have been used to predict prognosis. Herein, we aimed to construct a risk score model based on the expression of five lncRNAs to predict the prognosis of patients with GC and provide new potential therapeutic targets. Methods We performed differentially expressed and survival analyses to identify differentially expressed survival-ralated lncRNAs by using GC patient expression profile data from The Cancer Genome Atlas (TCGA) database. We then established a formula including five lncRNAs to predict the prognosis of patients with GC. In addition, to verify the prognostic value of this risk score model, two independent Gene Expression Omnibus (GEO) datasets, GSE62254 (N = 300) and GSE15459 (N = 200), were employed as validation groups. Results Based on the characteristics of five lncRNAs, patients with GC were divided into high or low risk subgroups. The prognostic value of the risk score model with five lncRNAs was confirmed in both TCGA and the two independent GEO datasets. Furthermore, stratification analysis results showed that this model had an independent prognostic value in patients with stage II–IV GC. We constructed a nomogram model combining clinical factors and the five lncRNAs to increase the accuracy of prognostic prediction. Enrichment analysis based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) suggested that the five lncRNAs are associated with multiple cancer occurrence and progression-related pathways. Conclusion The risk score model including five lncRNAs can predict the prognosis of patients with GC, especially those with stage II-IV, and may provide potential therapeutic targets in future.

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Section: Discussionsupporting

confidence: 89%

A risk score model with five long non-coding RNAs for predicting prognosis in gastric cancer: an integrated analysis combining TCGA and GEO datasets

Deng

Lai

et al. 2021

PeerJ

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“…Long-chain non-coding RNA (lncRNA) is a kind of RNA with a length range of 200 nucleotides to 100,000 bases, which lacks an effective open reading framework and is transcribed by RNA polymerase II without protein coding function [7]. The main regulation modes of lncRNA include: (1) as a transcription regulator, interfering with the binding of transcription factors to promoters, interfering with gene transcription and chromatin remodeling; (2) as a regulator, influencing the transcription of target genes, up-regulating or down-regulating the expression of target genes; (3) binding to proteins through chaperone and regulating subcellular localization of proteins; and (4) binding to transcription to inhibit replication [8–10].…”

Section: Backgroudmentioning

confidence: 99%

Long non-coding RNA LINC00174 promotes glycolysis and tumor progression by regulating miR-152-3p/SLC2A1 axis in glioma

Shi

Zhang

Qin

et al. 2019

J Exp Clin Cancer Res

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Background Long non-coding RNA plays a crucial role in the occurrence and progression of glioma. We aimed to explore the function of LINC00174 in cell proliferation, apoptosis, migration, invasion and glycolysis of glioma cells, and investigate the molecular mechanism involved. Methods LINC00174 expression in glioma tissues and peritumoral brain edema (PTBE) tissues was examined by RT-qPCR and in situ hybridization. The CCK-8, TUNEL, wound healing, transwell, and ELISA assays were performed to identify the effects of LINC00174 knockdown on cell viability, apoptosis, migration, invasion, and glycolysis, respectively. RNA immunoprecipitation, dual-luciferase reporter, RNA pull down, and western blot assays were performed to explore the molecular mechanisms of LINC00174 in glioma cells. A nude mouse xenograft model was used to investigate the role of LINC00174 in xenograft glioma growth. Results LINC00174 was overexpressed in glioma tissues and cell lines. LINC00174 knockdown inhibited cell proliferation, migration, invasion and glycolysis of glioma cells, and LINC00174 exerted a tumorigenesis role. LINC00174 could interact with miR-152-3p/SLC2A1 axes. The miR-152-3p inhibitor or the SLC2A1 overexpression could rescue the anti-tumor effect of LINC00174 knockdown on glioma cells. Moreover, downregulation of LINC00174 also inhibited tumor volume and delayed the tumor growth in vivo. Conclusion LINC00174 accelerated carcinogenesis of glioma via sponging miR-1523-3p and increasing the SLC2A1 expression, which could be considered as a molecular target for glioma diagnosis and therapy.

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“…Individual genes often cannot accurately predict the prognosis of patients with CRC. Genes in most prognostic risk scores are screened via differential expression analyses [ 20 – 22 ]. Yet, there are few prognostic models associated with CRC immune infiltration.…”

Section: Discussionmentioning

confidence: 99%

Development of an Immune Infiltration-Related Eight-Gene Prognostic Signature in Colorectal Cancer Microenvironment

Tao

Yang

et al. 2020

BioMed Research International

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Objective. Stromal cells and immune cells have important clinical significance in the microenvironment of colorectal cancer (CRC). This study is aimed at developing a CRC gene signature on the basis of stromal and immune scores. Methods. A cohort of CRC patients (n=433) were adopted from The Cancer Genome Atlas (TCGA) database. Stromal/immune scores were calculated by the ESTIMATE algorithm. Correlation between prognosis/clinical characteristics and stromal/immune scores was assessed. Differentially expressed stromal and immune genes were identified. Their potential functions were annotated by functional enrichment analysis. Cox regression analysis was used to develop an eight-gene risk score model. Its predictive efficacies for 3 years, 5 years, overall survival (OS), and progression-free survival interval (PFI) were evaluated using time-dependent receiver operating characteristic (ROC) curves. The correlation between the risk score and the infiltering levels of six immune cells was analyzed using TIMER. The risk score was validated using an independent dataset. Results. Immune score was in a significant association with prognosis and clinical characteristics of CRC. 736 upregulated and two downregulated stromal and immune genes were identified, which were mainly enriched into immune-related biological processes and pathways. An-eight gene prognostic risk score model was conducted, consisting of CCL22, CD36, CPA3, CPT1C, KCNE4, NFATC1, RASGRP2, and SLC2A3. High risk score indicated a poor prognosis of patients. The area under the ROC curves (AUC) s of the model for 3 years, 5 years, OS, and PFI were 0.71, 0.70, 0.73, and 0.66, respectively. Thus, the model possessed well performance for prediction of patients’ prognosis, which was confirmed by an external dataset. Moreover, the risk score was significantly correlated with immune cell infiltration. Conclusion. Our study conducted an immune-related prognostic risk score model, which could provide novel targets for immunotherapy of CRC.

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Development and internal validation of a nine-lncRNA prognostic signature for prediction of overall survival in colorectal cancer patients

Cited by 11 publications

References 19 publications

A risk score model with five long non-coding RNAs for predicting prognosis in gastric cancer: an integrated analysis combining TCGA and GEO datasets

A risk score model with five long non-coding RNAs for predicting prognosis in gastric cancer: an integrated analysis combining TCGA and GEO datasets

Long non-coding RNA LINC00174 promotes glycolysis and tumor progression by regulating miR-152-3p/SLC2A1 axis in glioma

Development of an Immune Infiltration-Related Eight-Gene Prognostic Signature in Colorectal Cancer Microenvironment

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