A risk score model with five long non-coding RNAs for predicting prognosis in gastric cancer: an integrated analysis combining TCGA and GEO datasets
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 pred...
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PeerJ Inc.
2021-02-01
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| author | Yiguo Wu Junping Deng Shuhui Lai Yujuan You Jing Wu |
| author_facet | Yiguo Wu Junping Deng Shuhui Lai Yujuan You Jing Wu |
| author_sort | Yiguo Wu |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T08:06:34Z |
| format | Article |
| id | doaj.art-8af46514982b48dd9d93bffad12a61cb |
| institution | Directory Open Access Journal |
| issn | 2167-8359 |
| language | English |
| last_indexed | 2024-03-09T08:06:34Z |
| publishDate | 2021-02-01 |
| publisher | PeerJ Inc. |
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| series | PeerJ |
| spelling | doaj.art-8af46514982b48dd9d93bffad12a61cb2023-12-02T23:48:32ZengPeerJ Inc.PeerJ2167-83592021-02-019e1055610.7717/peerj.10556A risk score model with five long non-coding RNAs for predicting prognosis in gastric cancer: an integrated analysis combining TCGA and GEO datasetsYiguo Wu0Junping Deng1Shuhui Lai2Yujuan You3Jing Wu4Department of Medicine, Nanchang University, Nan Chang, ChinaDepartment of General Surgery, The First Affiliated Hospital of Nanchang University, Nan Chang, ChinaDepartment of Medicine, Nanchang University, Nan Chang, ChinaDepartment of Anesthesiology, The Second Affiliated Hospital of Nanchang University, Nan Chang, ChinaShenzhen Prevention and Treatment Center for Occupational Diseases, Shen Zhen, ChinaBackground 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.https://peerj.com/articles/10556.pdfLong non-coding RNAGastric cancerPrognosisLINC00205TRHDE-AS1OVAAL |
| spellingShingle | Yiguo Wu Junping Deng Shuhui Lai Yujuan You Jing Wu A risk score model with five long non-coding RNAs for predicting prognosis in gastric cancer: an integrated analysis combining TCGA and GEO datasets PeerJ Long non-coding RNA Gastric cancer Prognosis LINC00205 TRHDE-AS1 OVAAL |
| title | A risk score model with five long non-coding RNAs for predicting prognosis in gastric cancer: an integrated analysis combining TCGA and GEO datasets |
| title_full | A risk score model with five long non-coding RNAs for predicting prognosis in gastric cancer: an integrated analysis combining TCGA and GEO datasets |
| title_fullStr | A risk score model with five long non-coding RNAs for predicting prognosis in gastric cancer: an integrated analysis combining TCGA and GEO datasets |
| title_full_unstemmed | A risk score model with five long non-coding RNAs for predicting prognosis in gastric cancer: an integrated analysis combining TCGA and GEO datasets |
| title_short | A risk score model with five long non-coding RNAs for predicting prognosis in gastric cancer: an integrated analysis combining TCGA and GEO datasets |
| title_sort | risk score model with five long non coding rnas for predicting prognosis in gastric cancer an integrated analysis combining tcga and geo datasets |
| topic | Long non-coding RNA Gastric cancer Prognosis LINC00205 TRHDE-AS1 OVAAL |
| url | https://peerj.com/articles/10556.pdf |
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