PMID- 31827637
OWN - NLM
STAT- MEDLINE
DCOM- 20200501
LR  - 20220411
IS  - 1875-8630 (Electronic)
IS  - 0278-0240 (Print)
IS  - 0278-0240 (Linking)
VI  - 2019
DP  - 2019
TI  - A Genomic-Clinicopathologic Nomogram Predicts Survival for Patients with 
      Laryngeal Squamous Cell Carcinoma.
PG  - 5980567
LID - 10.1155/2019/5980567 [doi]
LID - 5980567
AB  - BACKGROUND: Long noncoding RNAs (lncRNAs), which have little or no ability to 
      encode proteins, have attracted special attention due to their potential role in 
      cancer disease. We aimed to establish a lncRNA signature and a nomogram 
      incorporating the genomic and clinicopathologic factors to improve the accuracy 
      of survival prediction for laryngeal squamous cell carcinoma (LSCC). METHODS: A 
      LSCC RNA-sequencing (RNA-seq) dataset and the matched clinicopathologic 
      information were downloaded from The Cancer Genome Atlas (TCGA). Using 
      univariable Cox regression and least absolute shrinkage and selection operator 
      (LASSO) analysis, we developed a thirteen-lncRNA signature related to prognosis. 
      On the basis of multivariable Cox regression analysis results, a nomogram 
      integrating the genomic and clinicopathologic predictors was built. The 
      predictive accuracy and discriminative ability of the inclusive nomogram were 
      confirmed by calibration curve and a concordance index (C-index), and compared 
      with the TNM staging system by C-index and receiver operating characteristic 
      (ROC) analysis. Decision curve analysis (DCA) was conducted to evaluate the 
      clinical value of our nomogram. RESULTS: Thirteen overall survival- (OS-) related 
      lncRNAs were identified, and the signature consisting of the selected thirteen 
      lncRNAs could effectively divide patients into high-risk and low-risk subgroups, 
      with area under curves (AUC) of 0.89 (3-year OS) and 0.885 (5-year OS). 
      Independent factors derived from multivariable analysis to predict survival were 
      margin status, tumor status, and lncRNA signature, which were all assembled into 
      the nomogram. The calibration curve for the survival probability showed that the 
      predictions based on the nomogram coincided well with actual observations. The 
      C-index of the nomogram was 0.82 (0.77-0.87), and the area under curve (AUC) of 
      the nomogram in predicting overall survival (OS) was 0.938, both of which were 
      significantly higher than the traditional TNM stage. Decision curve analysis 
      further demonstrated that our nomogram had larger net benefit than TNM stage. 
      CONCLUSION: An inclusive nomogram for patients with LSCC, comprising genomic and 
      clinicopathologic variables, generates more accurate estimations of the survival 
      probability when compared with TNM stage alone, but more data are needed before 
      the nomogram is used in clinical practice.
CI  - Copyright (c) 2019 Jie Cui et al.
FAU - Cui, Jie
AU  - Cui J
AD  - Department of Head and Neck Surgery, Affiliated Cancer Hospital & Institute of 
      Guangzhou Medical University, Guangzhou 510095, Guangdong Province, China.
FAU - Wen, Qingquan
AU  - Wen Q
AD  - Department of Head and Neck Surgery, Affiliated Cancer Hospital & Institute of 
      Guangzhou Medical University, Guangzhou 510095, Guangdong Province, China.
FAU - Tan, Xiaojun
AU  - Tan X
AD  - Department of Pathology, Affiliated Cancer Hospital & Institute of Guangzhou 
      Medical University, Guangzhou 510095, Guangdong Province, China.
FAU - Chen, Zhen
AU  - Chen Z
AUID- ORCID: 0000-0002-8114-107X
AD  - Department of Intensive Care Unit, Shunde Hospital, Southern Medical University 
      (The First People's Hospital of Shunde), Foshan 528308, Guangdong Province, 
      China.
FAU - Liu, Genglong
AU  - Liu G
AUID- ORCID: 0000-0002-9163-4396
AD  - Department of Pathology, Affiliated Cancer Hospital & Institute of Guangzhou 
      Medical University, Guangzhou 510095, Guangdong Province, China.
LA  - eng
PT  - Journal Article
DEP - 20191120
PL  - United States
TA  - Dis Markers
JT  - Disease markers
JID - 8604127
RN  - 0 (Biomarkers, Tumor)
RN  - 0 (RNA, Long Noncoding)
SB  - IM
MH  - Adult
MH  - Aged
MH  - Aged, 80 and over
MH  - Biomarkers, Tumor/*genetics
MH  - Carcinoma, Squamous Cell/genetics/*mortality/pathology
MH  - Case-Control Studies
MH  - Female
MH  - Follow-Up Studies
MH  - Genomics/*methods
MH  - Humans
MH  - Laryngeal Neoplasms/genetics/*mortality/pathology
MH  - Male
MH  - Middle Aged
MH  - Neoplasm Staging
MH  - *Nomograms
MH  - RNA, Long Noncoding/*genetics
MH  - ROC Curve
MH  - Risk Assessment/*methods
MH  - Survival Rate
PMC - PMC6886334
COIS- The authors declare that they have no competing interests.
EDAT- 2019/12/13 06:00
MHDA- 2020/05/02 06:00
PMCR- 2019/11/20
CRDT- 2019/12/13 06:00
PHST- 2019/08/23 00:00 [received]
PHST- 2019/11/04 00:00 [accepted]
PHST- 2019/12/13 06:00 [entrez]
PHST- 2019/12/13 06:00 [pubmed]
PHST- 2020/05/02 06:00 [medline]
PHST- 2019/11/20 00:00 [pmc-release]
AID - 10.1155/2019/5980567 [doi]
PST - epublish
SO  - Dis Markers. 2019 Nov 20;2019:5980567. doi: 10.1155/2019/5980567. eCollection 
      2019.