A seven-gene prognostic signature for rapid determination of head and neck squamous cell carcinoma survival

Shen, Sipeng; Bai, Jianling; Wei, Yongyue; Wang, Guanrong; Li, Qingya; Zhang, Ruyang; Duan, Weiwei; Yang, Sheng; Du, Mulong; Zhao, Yang; Christiani, David C.; Chen, Feng

doi:10.3892/or.2017.6057

Cited by 27 publications

(23 citation statements)

References 51 publications

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“…Multiple stepwise Cox regression analysis was performed for potentially relevant genes in the preliminary screening, and the impact of these molecules on the survival time and the survival outcome was also analyzed. The screened molecules were used to construct the prognostic index (PI) model: (β 1 Exp 1 +β 2 Exp 2 +…+β x Exp x ) [16][17][18][19][20][21]. The larger the PI was, the greater the patient's risk and the worse the prognosis.…”

Section: Prognostic Factors Of Left-and Right-sided Colon Cancers Andmentioning

confidence: 99%

Distinguishable Prognostic Signatures of Left- and Right-Sided Colon Cancer: a Study Based on Sequencing Data

Liang

Zeng

Qin

et al. 2018

Cell Physiol Biochem

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Key WordsLeft-and right-sided • Colon cancer • Prognosis • TCGA Abstract Background/Aims: Left-and right-sided colon cancers are considered to be two different diseases and have altered outcomes. However, specific molecules to predict the prognosis of left-and right-sided colon cancers are currently lacking. Methods: Expression profiling of colon cancer were downloaded from The Cancer Genome Atlas (TCGA). Differentially expressed genes (DEGs) of left-and right-sided colon cancers were compared by DESeq analysis. The prognostic values of DEGs were assessed by univariate and multivariate Cox regression. Prognostic index models of two side colon cancers were conducted with prognostic values genes, respectively. Interaction of DEGs was then analyzed by the protein-protein interaction (PPI). Different biology function of two sides of colon cancer was assessed by Gene Set Enrichment Analysis (GSEA). Results: A total of 167 DEGs were identified between left-and right-sided colon cancers based on TCGA data. Using univariate COX regression analysis, five genes (PHACTR3, CKMT2, CYP2W1, ERFE, HOXC4) were related to overall survival in left-sided, and eight distinguishable genes (EREG, ERFE, HOXC6, SLC22A31, TFF1, GFI1, ZG16, RASL10B) in right-sided. Further, left-sided prognostic model was established with PHACTR3 and CKMT2 (HR=2.040; 95%CI=1.004-4.145; P=0.049). Distinguishable prognostic signature for right-sided colon cancer was established based on EREG, ERFE, GFI1, and RASL10B (HR=3.530; 95%CI: 1.934-6.444; P<0.001) in multivariate analysis. PPI analysis of 167 DEGs showed that CCL5, GNG4, GNLY, GZMH, DRD2, and FASLG genes were at the core of interaction network. In GSEA function analysis, four pathways, including antigen processing and presentation, natural killer cell mediated cytotoxicity, intestinal immune network for Iga production, and type I diabetes mellitus, were significantly enriched in the DEGs of the right-sided colon cancer. Conclusions: This study constructs a panel of potential prognostic model of left-and right-sided colon cancers, respectively. We also provide molecular biological alterations between left-and rightsided colon cancers.

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Section: Prognostic Factors Of Left-and Right-sided Colon Cancers Andmentioning

confidence: 99%

Distinguishable Prognostic Signatures of Left- and Right-Sided Colon Cancer: a Study Based on Sequencing Data

Liang

Zeng

Qin

et al. 2018

Cell Physiol Biochem

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“…In addition, LIS is correlated with treatment efficacy of the MAGE-A3 immunotherapy. In practice, LIS can be easily computed by measuring the expression levels of these 25 genes using simple platforms such as RT-PCR or a custom gene panel (29–31). This signature provides a practical assay that can be used to improve the treatment efficacy of melanoma.…”

Section: Introductionmentioning

confidence: 99%

A Leukocyte Infiltration Score Defined by a Gene Signature Predicts Melanoma Patient Prognosis

Zhao

Schaafsma

Gorlov

et al. 2019

Molecular Cancer Research

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Melanoma is the most aggressive type of skin cancer in the United States with an increasing incidence. Melanoma lesions often exhibit high immunogenicity, with infiltrating immune cells playing important roles in regression of tumors occurring spontaneously or caused by therapeutic treatment. Computational and experimental methods have been used to estimate the abundance of immune cells in tumors, but their applications are limited by the requirement of large gene sets or multiple antibodies. Although the prognostic role of immune cells has been appreciated, a systematic investigation of their association with clinical factors, genomic features, prognosis and treatment response in melanoma is still lacking. This study, identifies a 25-gene signature based on RNA-seq data from The Cancer Genome Atlas (TCGA)-Skin Cutaneous Melanoma (TCGA-SKCM) dataset. This signature was used to calculate sample-specific Leukocyte Infiltration Scores (LIS) in six independent melanoma microarray datasets and scores were found to vary substantially between different melanoma lesion sites and molecular subtypes. For metastatic melanoma, LIS was prognostic in all datasets with high LIS being associated with good survival. The current approach provided additional prognostic information over established clinical factors, including age, tumor stage, and gender. In addition, LIS was predictive of patient survival in stage III melanoma, and treatment efficacy of tumor-specific antigen vaccine. This study identifies a 25-gene signature that effectively estimates the level of immune cell infiltration in melanoma, which provides a robust biomarker for predicting patient prognosis.

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“…Prognostic signatures were baseline measurement to provide information about the long‐term outcome for cancer patients. Currently, the microarray or RNA‐sequencing data of gene mutation and expression were usually used to construct novel prognostic signatures by a Cox proportional hazards regression model . Well‐developed and validated prognostic signatures could help improve patient management in a personalized manner.…”

Section: Discussionmentioning

confidence: 99%

Identification of a novel gene pairs signature in the prognosis of gastric cancer

Peng

Zhou

et al. 2017

Cancer Medicine

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Current prognostic signatures need to be improved in identifying high‐risk patients of gastric cancer (GC). Thus, we aimed to develop a reliable prognostic signature that could assess the prognosis risk in GC patients. Two microarray datasets of GSE662254 (n = 300, training set) and GSE15459 (n = 192, test set) were included into analysis. Prognostic genes were screened to construct prognosis‐related gene pairs (PRGPs). Then, a penalized Cox proportional hazards regression model identified seven PRGPs, which constructed a prognostic signature and divided patients into high‐ and low‐risk groups according to the signature score. High‐risk patients showed a poorer prognosis than low‐risk patients in both the training set (hazard ratios [HR]: 6.086, 95% confidence interval [CI]: 4.341–8.533) and test set (1.773 [1.107–2.840]). The PRGPs signature also achieved a higher predictive accuracy (concordance index [C‐index]: 0.872, 95% CI: 0.846–0.897) than two existing molecular signatures (0.706 [0.667–0.744] for a 11‐gene signature and 0.684 [0.642–0.726] for a 24‐lncRNA signature) and TNM stage (0.764 [0.715–0.814]). In conclusion, our study identified a novel gene pairs signature in the prognosis of GC.

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A seven-gene prognostic signature for rapid determination of head and neck squamous cell carcinoma survival

Cited by 27 publications

References 51 publications

Distinguishable Prognostic Signatures of Left- and Right-Sided Colon Cancer: a Study Based on Sequencing Data

Distinguishable Prognostic Signatures of Left- and Right-Sided Colon Cancer: a Study Based on Sequencing Data

A Leukocyte Infiltration Score Defined by a Gene Signature Predicts Melanoma Patient Prognosis

Identification of a novel gene pairs signature in the prognosis of gastric cancer

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