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Identification and validation of potential prognostic lncRNA biomarkers for predicting survival in patients with multiple myeloma

Overview of attention for article published in Journal of Experimental & Clinical Cancer Research, September 2015
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  • Above-average Attention Score compared to outputs of the same age (57th percentile)
  • Good Attention Score compared to outputs of the same age and source (75th percentile)

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4 tweeters

Citations

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127 Dimensions

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49 Mendeley
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Title
Identification and validation of potential prognostic lncRNA biomarkers for predicting survival in patients with multiple myeloma
Published in
Journal of Experimental & Clinical Cancer Research, September 2015
DOI 10.1186/s13046-015-0219-5
Pubmed ID
Authors

Meng Zhou, Hengqiang Zhao, Zhenzhen Wang, Liang Cheng, Lei Yang, Hongbo Shi, Haixiu Yang, Jie Sun

Abstract

Dysregulated long non-coding RNAs (lncRNAs) have been found to have oncogenic and/or tumor suppressive roles in the development and progression of cancer, implying their potentials as novel independent biomarkers for cancer diagnosis and prognosis. However, the prognostic significance of expression profile-based lncRNA signature for outcome prediction in patients with multiple myeloma (MM) has not yet been investigated. LncRNA expression profiles of a large cohort of patients with MM were obtained and analyzed by repurposing the publically available microarray data. An lncRNA-focus risk score model was developed from the training dataset, and then validated in the testing and another two independent external datasets. The time-dependent receiver operating characteristic (ROC) curve was used to evaluate the prognostic performance for survival prediction. The biological function of prognostic lncRNAs was predicted using bioinformatics analysis. Four lncRNAs were identified to be significantly associated with overall survival (OS) of patients with MM in the training dataset, and were combined to develop a four-lncRNA prognostic signature to stratify patients into high-risk and low-risk groups. Patients of training dataset in the high-risk group exhibited shorter OS than those in the low-risk group (HR = 2.718, 95 % CI = 1.937-3.815, p <0.001). The similar prognostic values of four-lncRNA signature were observed in the testing dataset, entire GSE24080 dataset and another two independent external datasets. Multivariate Cox regression and stratified analysis showed that the prognostic power of four-lncRNA signature was independent of clinical features, including serum beta 2-microglobulin (Sβ2M), serum albumin (ALB) and lactate dehydrogenase (LDH). ROC analysis also demonstrated the better performance for predicting 3-year OS. Functional enrichment analysis suggested that these four lncRNAs may be involved in known genetic and epigenetic events linked to MM. Our results demonstrated potential application of lncRNAs as novel independent biomarkers for diagnosis and prognosis in MM. These lncRNA biomarkers may contribute to the understanding of underlying molecular basis of MM.

Twitter Demographics

The data shown below were collected from the profiles of 4 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 49 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Bulgaria 1 2%
Denmark 1 2%
Unknown 47 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 14 29%
Researcher 9 18%
Student > Bachelor 4 8%
Student > Master 4 8%
Professor > Associate Professor 3 6%
Other 8 16%
Unknown 7 14%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 14 29%
Medicine and Dentistry 14 29%
Agricultural and Biological Sciences 5 10%
Sports and Recreations 1 2%
Business, Management and Accounting 1 2%
Other 3 6%
Unknown 11 22%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 24 May 2016.
All research outputs
#3,530,657
of 7,747,098 outputs
Outputs from Journal of Experimental & Clinical Cancer Research
#95
of 409 outputs
Outputs of similar age
#97,365
of 230,237 outputs
Outputs of similar age from Journal of Experimental & Clinical Cancer Research
#8
of 37 outputs
Altmetric has tracked 7,747,098 research outputs across all sources so far. This one has received more attention than most of these and is in the 54th percentile.
So far Altmetric has tracked 409 research outputs from this source. They receive a mean Attention Score of 1.5. This one has done well, scoring higher than 76% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 230,237 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 57% of its contemporaries.
We're also able to compare this research output to 37 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 75% of its contemporaries.