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

Abstract 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 (pā€‰<ā€‰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 non-coding 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.
PMID
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Identification and validation of potential prognostic lncRNA biomarkers for predicting survival in patients with multiple myeloma.

Authors

Mayor MeshTerms
Keywords

Long non-coding RNA

gene set enrichment analysis

multiple myeloma

prognosis marker

survival rate

Journal Title tumour biology : the journal of the international society for oncodevelopmental biology and medicine
Publication Year Start




PMID- 28378636
OWN - NLM
STAT- MEDLINE
DA  - 20170405
DCOM- 20170410
LR  - 20170410
IS  - 1423-0380 (Electronic)
IS  - 1010-4283 (Linking)
VI  - 39
IP  - 4
DP  - 2017 Apr
TI  - Potential prognostic long non-coding RNA identification and their validation in
      predicting survival of patients with multiple myeloma.
PG  - 1010428317694563
LID - 10.1177/1010428317694563 [doi]
AB  - 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 &lt; 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 non-coding 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.
FAU - Hu, Ai-Xin
AU  - Hu AX
AD  - 1 Department of Orthopedic Surgery, People's Hospital of Three Gorges University,
      Yichang, China.
FAU - Huang, Zhi-Yong
AU  - Huang ZY
AD  - 2 PuAi Institute, Edong Healthcare Group, Huangshi Central Hospital, Huangshi,
      China.
FAU - Zhang, Lin
AU  - Zhang L
AD  - 3 Department of Spinal Surgery, The Affiliated Huai'an Hospital of Xuzhou Medical
      University and The Second People's Hospital of Huai'an, Huai'an, China.
FAU - Shen, Jian
AU  - Shen J
AD  - 4 Changzhou Hygiene Vocational Technology School, Changzhou, China.
LA  - eng
PT  - Journal Article
PL  - United States
TA  - Tumour Biol
JT  - Tumour biology : the journal of the International Society for Oncodevelopmental
      Biology and Medicine
JID - 8409922
RN  - 0 (RNA, Long Noncoding)
SB  - IM
MH  - Gene Expression Profiling
MH  - Humans
MH  - Kaplan-Meier Estimate
MH  - Multiple Myeloma/genetics/metabolism/*mortality
MH  - Prognosis
MH  - RNA, Long Noncoding/*metabolism
OTO - NOTNLM
OT  - Long non-coding RNA
OT  - gene set enrichment analysis
OT  - multiple myeloma
OT  - prognosis marker
OT  - survival rate
EDAT- 2017/04/06 06:00
MHDA- 2017/04/11 06:00
CRDT- 2017/04/06 06:00
AID - 10.1177/1010428317694563 [doi]
PST - ppublish
SO  - Tumour Biol. 2017 Apr;39(4):1010428317694563. doi: 10.1177/1010428317694563.

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