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Identification of gene expression models for laryngeal squamous cell carcinoma using co-expression network analysis.

Abstract One of the most common head and neck cancers is laryngeal squamous cell carcinoma (LSCC). LSCC exhibits high mortality rates and has a poor prognosis. The molecular mechanisms leading to the development and progression of LSCC are not entirely clear despite genetic and therapeutic advances and increased survival rates. In this study, a total of 116 differentially expressed genes (DEGs), including 11 upregulated genes and 105 downregulated genes, were screened from LSCC samples and compared with adjacent noncancerous. Statistically significant differences (log 2-fold difference > 0.5 and adjusted P-value < .05) were found in this study in the expression between tumor and nontumor larynx tissue samples. Nine cancer hub genes were found to have a high predictive power to distinguish between tumor and nontumor larynx tissue samples. Interestingly, they also appear to contribute to the progression of LSCC and malignancy via the Jak-STAT signaling pathway and focal adhesion. The model could separate patients into high-risk and low-risk groups successfully when only using the expression level of mRNA signatures. A total of 4 modules (blue, gray, turquoise, and yellow) were screened for the DEGs in the weighted co-expression network. The blue model includes cancer-specific pathways such as pancreatic cancer, bladder cancer, nonsmall cell lung cancer, colorectal cancer, glioma, Hippo signaling pathway, melanoma, chronic myeloid leukemia, prostate cancer, and proteoglycans in cancer. Endocrine resistance (CCND1, RAF1, RB1, and SMAD2) and Hippo signaling pathway (CCND1, LATS1, SMAD2, and TP53BP2) could be of importance in LSCC, because they had high connectivity degrees in the blue module. Results from this study provide a powerful biomarker discovery platform to increase understanding of the progression of LSCC and to reveal potential therapeutic targets in the treatment of LSCC. Improved monitoring of LSCC and resulting improvement of treatment of LSCC might result from this information.
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Mayor MeshTerms
Keywords
Journal Title medicine
Publication Year Start




PMID- 29443735
OWN - NLM
STAT- MEDLINE
DCOM- 20180222
LR  - 20180222
IS  - 1536-5964 (Electronic)
IS  - 0025-7974 (Linking)
VI  - 97
IP  - 7
DP  - 2018 Feb
TI  - Identification of gene expression models for laryngeal squamous cell carcinoma
      using co-expression network analysis.
PG  - e9738
LID - 10.1097/MD.0000000000009738 [doi]
AB  - One of the most common head and neck cancers is laryngeal squamous cell carcinoma
      (LSCC). LSCC exhibits high mortality rates and has a poor prognosis. The
      molecular mechanisms leading to the development and progression of LSCC are not
      entirely clear despite genetic and therapeutic advances and increased survival
      rates. In this study, a total of 116 differentially expressed genes (DEGs),
      including 11 upregulated genes and 105 downregulated genes, were screened from
      LSCC samples and compared with adjacent noncancerous. Statistically significant
      differences (log 2-fold difference &gt; 0.5 and adjusted P-value &lt; .05) were found
      in this study in the expression between tumor and nontumor larynx tissue samples.
      Nine cancer hub genes were found to have a high predictive power to distinguish
      between tumor and nontumor larynx tissue samples. Interestingly, they also appear
      to contribute to the progression of LSCC and malignancy via the Jak-STAT
      signaling pathway and focal adhesion. The model could separate patients into
      high-risk and low-risk groups successfully when only using the expression level
      of mRNA signatures. A total of 4 modules (blue, gray, turquoise, and yellow) were
      screened for the DEGs in the weighted co-expression network. The blue model
      includes cancer-specific pathways such as pancreatic cancer, bladder cancer,
      nonsmall cell lung cancer, colorectal cancer, glioma, Hippo signaling pathway,
      melanoma, chronic myeloid leukemia, prostate cancer, and proteoglycans in cancer.
      Endocrine resistance (CCND1, RAF1, RB1, and SMAD2) and Hippo signaling pathway
      (CCND1, LATS1, SMAD2, and TP53BP2) could be of importance in LSCC, because they
      had high connectivity degrees in the blue module. Results from this study provide
      a powerful biomarker discovery platform to increase understanding of the
      progression of LSCC and to reveal potential therapeutic targets in the treatment 
      of LSCC. Improved monitoring of LSCC and resulting improvement of treatment of
      LSCC might result from this information.
FAU - Yang, Chun-Wei
AU  - Yang CW
AD  - Department of Otorhinolaryngology Head and Neck Surgery, Tianjin Union Medical
      Center.
FAU - Wang, Shu-Fang
AU  - Wang SF
AD  - Intensive Care Unit, General Hospital Airport Hospital, Tianjin Medical
      University, Tianjin, China.
FAU - Yang, Xiang-Li
AU  - Yang XL
AD  - Department of Otorhinolaryngology Head and Neck Surgery, Tianjin Union Medical
      Center.
FAU - Wang, Lin
AU  - Wang L
AD  - Department of Otorhinolaryngology Head and Neck Surgery, Tianjin Union Medical
      Center.
FAU - Niu, Lin
AU  - Niu L
AD  - Department of Otorhinolaryngology Head and Neck Surgery, Tianjin Union Medical
      Center.
FAU - Liu, Ji-Xiang
AU  - Liu JX
AD  - Department of Otorhinolaryngology Head and Neck Surgery, Tianjin Union Medical
      Center.
LA  - eng
PT  - Journal Article
PT  - Meta-Analysis
PL  - United States
TA  - Medicine (Baltimore)
JT  - Medicine
JID - 2985248R
RN  - 0 (Biomarkers, Tumor)
RN  - 0 (RNA, Messenger)
SB  - AIM
SB  - IM
MH  - Biomarkers, Tumor/genetics
MH  - Carcinoma, Squamous Cell/*genetics
MH  - Disease Progression
MH  - Gene Expression/genetics
MH  - Gene Expression Profiling/*methods
MH  - Gene Expression Regulation, Neoplastic/genetics
MH  - Humans
MH  - Laryngeal Neoplasms/*genetics
MH  - Larynx/metabolism
MH  - Network Meta-Analysis
MH  - RNA, Messenger/genetics
MH  - Signal Transduction/*genetics
MH  - Survival Rate
EDAT- 2018/02/15 06:00
MHDA- 2018/02/23 06:00
CRDT- 2018/02/15 06:00
PHST- 2018/02/15 06:00 [entrez]
PHST- 2018/02/15 06:00 [pubmed]
PHST- 2018/02/23 06:00 [medline]
AID - 10.1097/MD.0000000000009738 [doi]
AID - 00005792-201802160-00009 [pii]
PST - ppublish
SO  - Medicine (Baltimore). 2018 Feb;97(7):e9738. doi: 10.1097/MD.0000000000009738.