PMID- 32596344
OWN - NLM
STAT- MEDLINE
DCOM- 20210312
LR  - 20220415
IS  - 2314-6141 (Electronic)
IS  - 2314-6133 (Print)
VI  - 2020
DP  - 2020
TI  - Identification of 5 Gene Signatures in Survival Prediction for Patients with Lung 
      Squamous Cell Carcinoma Based on Integrated Multiomics Data Analysis.
PG  - 6427483
LID - 10.1155/2020/6427483 [doi]
LID - 6427483
AB  - BACKGROUND: Lung squamous cell carcinoma (LSCC) is a frequently diagnosed cancer 
      worldwide, and it has a poor prognosis. The current study is aimed at developing 
      the prediction of LSCC prognosis by integrating multiomics data including 
      transcriptome, copy number variation data, and mutation data analysis, so as to 
      predict patients' survival and discover new therapeutic targets. METHODS: RNASeq, 
      SNP, CNV data, and LSCC patients' clinical follow-up information were downloaded 
      from The Cancer Genome Atlas (TCGA), and the samples were randomly divided into 
      two groups, namely, the training set and the validation set. In the training set, 
      the genes related to prognosis and those with different copy numbers or with 
      different SNPs were integrated to extract features using random forests, and 
      finally, robust biomarkers were screened. In addition, a gene-related prognostic 
      model was established and further verified in the test set and GEO validation 
      set. RESULTS: We obtained a total of 804 prognostic-related genes and 535 copy 
      amplification genes, 621 copy deletions genes, and 388 significantly mutated 
      genes in genomic variants; noticeably, these genomic variant genes were found 
      closely related to tumor development. A total of 51 candidate genes were obtained 
      by integrating genomic variants and prognostic genes, and 5 characteristic genes 
      (HIST1H2BH, SERPIND1, COL22A1, LCE3C, and ADAMTS17) were screened through random 
      forest feature selection; we found that many of those genes had been reported to 
      be related to LSCC progression. Cox regression analysis was performed to 
      establish 5-gene signature that could serve as an independent prognostic factor 
      for LSCC patients and can stratify risk samples in training set, test set, and 
      external validation set (p < 0.01), and the 5-year survival areas under the curve 
      (AUC) of both training set and validation set were > 0.67. CONCLUSION: In the 
      current study, 5 gene signatures were constructed as novel prognostic markers to 
      predict the survival of LSCC patients. The present findings provide new 
      diagnostic and prognostic biomarkers and therapeutic targets for LSCC treatment.
CI  - Copyright (c) 2020 Hongxia Ma et al.
FAU - Ma, Hongxia
AU  - Ma H
AD  - Pneumology Department, The Traditional Chinese Medicine Hospital Affiliated to 
      Xinjiang Medical University, Urumqi City, China.
FAU - Tong, Lihong
AU  - Tong L
AD  - Pneumology Department, The Traditional Chinese Medicine Hospital Affiliated to 
      Xinjiang Medical University, Urumqi City, China.
FAU - Zhang, Qian
AU  - Zhang Q
AD  - Pneumology Department, The Traditional Chinese Medicine Hospital Affiliated to 
      Xinjiang Medical University, Urumqi City, China.
FAU - Chang, Wenjun
AU  - Chang W
AD  - Pneumology Department, The Traditional Chinese Medicine Hospital Affiliated to 
      Xinjiang Medical University, Urumqi City, China.
FAU - Li, Fengsen
AU  - Li F
AUID- ORCID: 0000-0002-1213-2436
AD  - Pneumology Department, The Traditional Chinese Medicine Hospital Affiliated to 
      Xinjiang Medical University, Urumqi City, China.
LA  - eng
PT  - Journal Article
DEP - 20200608
PL  - United States
TA  - Biomed Res Int
JT  - BioMed research international
JID - 101600173
RN  - 0 (Biomarkers, Tumor)
SB  - IM
MH  - Biomarkers, Tumor/genetics
MH  - Carcinoma, Squamous Cell/*diagnosis/*genetics
MH  - DNA Copy Number Variations
MH  - Female
MH  - Gene Expression Profiling
MH  - Humans
MH  - Kaplan-Meier Estimate
MH  - Lung Neoplasms/*diagnosis/*genetics
MH  - Male
MH  - Middle Aged
MH  - Mutation Rate
MH  - Prognosis
MH  - ROC Curve
MH  - Transcriptome
PMC - PMC7298313
COIS- The authors stated they have no conflicts of interest.
EDAT- 2020/07/01 06:00
MHDA- 2021/03/13 06:00
PMCR- 2020/06/08
CRDT- 2020/06/30 06:00
PHST- 2019/11/08 00:00 [received]
PHST- 2020/02/14 00:00 [revised]
PHST- 2020/03/03 00:00 [accepted]
PHST- 2020/06/30 06:00 [entrez]
PHST- 2020/07/01 06:00 [pubmed]
PHST- 2021/03/13 06:00 [medline]
PHST- 2020/06/08 00:00 [pmc-release]
AID - 10.1155/2020/6427483 [doi]
PST - epublish
SO  - Biomed Res Int. 2020 Jun 8;2020:6427483. doi: 10.1155/2020/6427483. eCollection 
      2020.