A long non-coding RNA signature to improve prognosis prediction of colorectal cancer

Hu, Ye; Chen, Hao Yan; Chen, Yu; Xu, Jie; Wang, Jilin; Qian, Jin; Zhang, Xi; Fang, Jing

doi:10.18632/oncotarget.1895

Cited by 146 publications

(129 citation statements)

References 40 publications

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“…The expression profiles of lncR-NA have been characterized in many diseases by microarray and RNA-seq. recently, researchers have demonstrated that annotation of published microarray data is a feasible and reliable method to profile lncRNA expression [27,28]. In the current study, we re-annotate the HG U133Plus2.0 microarrays and analyzed lncRNA expression profile of lymphatic endothelial cells in diabetic and non-diabetic patients.…”

Section: Discussionmentioning

confidence: 97%

Analysis of Long Non-Coding RNA Expression of Lymphatic Endothelial Cells in Response to Type 2 Diabetes

Qi¹,

Zhou²,

Zeng³

et al. 2017

Cell Physiol Biochem

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Background: Recent evidence has indicated that long non-coding RNA (lncRNA) is involved in the pathogenesis of type 2 diabetes, but nothing is known about lncRNA expression changes of lymphatic endothelial cells in response to type 2 diabetes. Methods: The GSE38396 dataset was downloaded from the Gene Expression Omnibus database and the probe sets of Human Gnome U133 Plus2.0 microarray were annotated for lncRNA. Differentially expressed lncRNAs between diabetic and non-diabetic lymphatic endothelial cells were calculated. Results: Compared with lymphatic endothelial cells in non-diabetic patients, 31 lncRNAs were down-regulated and 79 lncRNAs were up-regualted in lymphatic endothelial cells of type 2 diabetic patients. Several known lncRNAs were found, such as H19, GAS5, UCA1, CRNDE, GAS5, and LINC00312. Co-expression network of differentially expressed lncRNAs and mRNAs were constructed. Based on genomic regions of these lncRNAs, we found that binding sites of MAF and TCF3 were enriched and these lncRNAs may be related to insulin reporter signaling pathway and response to insulin stimulus. Conclusions: In a word, we found a set of lncRNAs were differentially expressed in lymphatic endothelial cells in response to type 2 diabetes and these lncRNAs may be involved in the pathogenesis of diabetes-related complications.

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Section: Discussionmentioning

confidence: 97%

Analysis of Long Non-Coding RNA Expression of Lymphatic Endothelial Cells in Response to Type 2 Diabetes

Qi¹,

Zhou²,

Zeng³

et al. 2017

Cell Physiol Biochem

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“…LncRNAs are frequently found to be aberrantly expressed in cancers, yet only a few studies developed lncRNA-based prognosis signature [29,30]. In this study, we constructed the prognosis signature for predicting CRC DFS based on the resulting risk score of every sample, whose lncRNA profiling data was obtained from previous published studies.…”

Section: Discussionmentioning

confidence: 99%

A Six-LncRNA Expression Signature Associated with Prognosis of Colorectal Cancer Patients

Zhao

Shang

et al. 2018

Cell Physiol Biochem

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Background/Aims: Colorectal cancer (CRC) is one of the most common malignant tumor with high migration and invasion capacity. Long non-coding RNAs (lncRNAs) have been identified to influence multiple cancers progression through competitively binding microRNAs (miRNAs). In this study, we proposed to develop a lncRNA-based signature for CRC survival outcomes. Methods: LncRNA expression profiles of CRC patients were extracted from the Gene Expression Omnibus (GEO) data sets GSE38832 (training set) and GSE29621 (testing set) . Associations between lncRNA expression and CRC disease free survival (DFS) were evaluated through univariate Cox regression analysis, and prognosis signature constructed by combination of weighted lncRNA expression values were obtained through multivariate Cox regression analysis. Robustness of the prognosis signature was evaluated through receiver operating characteristics analysis in the testing set. Results: A weighted prognosis signature of six lncRNAs, including LINC01583, LINC00276, LUNAR1, DKFZp434J0226, SFTA1P and OGFOD3, was yielded from multivariate Cox regression analysis. Samples with significantly different DFS dislayed distinct signatures, indicating considerable predictory accuracy of this expression signature. Conclusion: Robustness of the prognosis signature was evaluated in the testing set through Kaplan-Meier and receiver operating characteristics (ROC) analysis. Furthermore, functional enrichment analysis of lncRNAs suggested significant enrichment of cancer related pathways. Our results revealed the promise of lncRNAs as prognostic biomarkers.

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“…The same results were observed in an independent testing dataset GSE57317. Similar approaches on improving prognosis prediction using lncRNA have been studied in many cancers; [38][39][40][41][42] however, few in MM. Top 20 out of 176 lncRNAs were most significantly expressed associated with survival of MM patients.…”

Section: Discussionmentioning

confidence: 99%

Potential prognostic long non-coding RNA identification and their validation in predicting survival of patients with multiple myeloma

Huang

Zhang

et al. 2017

Tumour Biol.

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Multiple myeloma, a typical hematological malignancy, is characterized by malignant proliferation of plasma cells. This study was to identify differently expressed long non-coding RNAs to predict the survival of patients with multiple myeloma efficiently. Gene expressing profiles of diagnosed patients with multiple myeloma, GSE24080 (559 samples) and GSE57317 (55 samples), were downloaded from Gene Expression Omnibus database. After processing, survival-related long non-coding RNAs were identified by Cox regression analysis. The prognosis of multiple myeloma patients with differently expressed long non-coding RNAs was predicted by Kaplan-Meier analysis. Meanwhile, stratified analysis was performed based on the concentrations of serum beta 2-microglobulin (S-beta 2m), albumin, and lactate dehydrogenase of multiple myeloma patients. Gene set enrichment analysis was performed to further explore the functions of identified long non-coding RNAs. A total of 176 long non-coding RNAs significantly related to the survival of multiple myeloma patients (p < 0.05) were identified. In dataset GSE24080 and GSE57317, there were 558 and 55 patients being clustered into two groups with significant differences, respectively. Stratified analysis indicated that prediction of the prognoses with these long non-coding RNAs was independent from other clinical phenotype of multiple myeloma. Gene set enrichment analysis-identified pathways of cell cycle, focal adhesion, and G2-M checkpoint were associated with these long noncoding RNAs. A total of 176 long non-coding RNAs, especially RP1-286D6.1, AC008875.2, MTMR9L, AC069360.2, and AL512791.1, were potential biomarkers to evaluate the prognosis of multiple myeloma patients. These long non-coding RNAs participated indispensably in many pathways associated to the development of multiple myeloma; however, the molecular mechanisms need to be further studied.

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A long non-coding RNA signature to improve prognosis prediction of colorectal cancer

Cited by 146 publications

References 40 publications

Analysis of Long Non-Coding RNA Expression of Lymphatic Endothelial Cells in Response to Type 2 Diabetes

Analysis of Long Non-Coding RNA Expression of Lymphatic Endothelial Cells in Response to Type 2 Diabetes

A Six-LncRNA Expression Signature Associated with Prognosis of Colorectal Cancer Patients

Potential prognostic long non-coding RNA identification and their validation in predicting survival of patients with multiple myeloma

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