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pmid:32208363

A nine-gene signature related to tumor microenvironment predicts overall survival with ovarian cancer.

Qi Ding · Shanshan Dong · Ranran Wang · Keqiang Zhang · Hui Wang · Xiao Zhou · Jing Wang · Kee Wong · Ying Long · Shuai Zhu · Weigang Wang · Huayi Ren · Yong Zeng

Aging · 2020 doi:10.18632/aging.102914

Paper

complete

Experiment

partial

Data

partial

Analysis

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Execution

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Record details sci_version · id · kind · revision · content_hash · schema · created_at · updated_at · generator
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pmid:32208363
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science-record
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2026-11-01T15:40:58Z
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2026-11-01T21:23:02Z
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paper object · 10 fields
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title
A nine-gene signature related to tumor microenvironment predicts overall survival with ovarian cancer.
doi
10.18632/aging.102914
pmid
32208363
authors array · 13 items
[0]
Qi Ding
[1]
Shanshan Dong
[2]
Ranran Wang
[3]
Keqiang Zhang
[4]
Hui Wang
[5]
Xiao Zhou
[6]
Jing Wang
[7]
Kee Wong
[8]
Ying Long
[9]
Shuai Zhu
[10]
Weigang Wang
[11]
Huayi Ren
[12]
Yong Zeng
journal
Aging
year
2020
abstract
Mounting evidence suggests that immune cell infiltration within the tumor microenvironment (TME) is a crucial regulator of carcinogenesis and therapeutic efficacy in ovarian cancer (OC). In this study, 593 OC patients from TCGA were divided into high and low score groups based on their immune/stromal scores resulting from analysis utilizing the ESTIMATE algorithm. Differential expression analysis revealed 294 intersecting genes that influencing both the immune and stromal scores. Further Cox regression analysis identified 34 differentially expressed genes (DEGs) as prognostic-related genes. Finally, the nine-gene signature was derived from the prognostic-related genes using a Least Absolute Shrinkage and Selection Operator (LASSO) and Cox regression. This nine-gene signature could effectively distinguish the high-risk patients in the training (TCGA database) and validation (GSE17260) cohorts (all p < 0.01). A time-dependent receiver operating characteristic (ROC) analysis showed that the nine-gene signature had a reasonable predictive accuracy (AUC = 0.707, AUC =0.696) in both cohorts. In addition, this nine-gene signature is associated with immune infiltration in TME by Gene Set Variation Analysis (GSVA), and can be used to predict the survival of patients with OC.
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Subsequently, these 34 candidate markers were used to construct a prognostic model with a Lasso-Cox proportional hazards regression.
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sections[6] "Derivation of prognostic DEGs and construction of a gene risk score model" paragraph 2
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Subsequently, these 34 candidate markers were used to construct a prognostic model with a Lasso-Cox proportional hazards regression.
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Clinical characteristics of the study patients
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Baseline characteristics of study patients.
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[2]
Validation cohort No. (%)
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465
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109
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46 (41.8%)
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[2]
92(84.5%)
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[2]
17(15.5%)
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40(37.3%)
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397(85.4%)
[2]
43(39.1%)
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Venous invasion
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[2]
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[2]
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Nine prognostic genes significantly associated with OS in the training cohort.
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Derivation of prognostic DEGs and construction of a gene risk score model
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Nine prognostic genes significantly associated with OS in the training cohort.
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Derivation of prognostic DEGs and construction of a gene risk score model
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Univariate and multivariate Cox proportional hazards regression analyses in the training cohort.
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0.669
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, and can be used to predict the survival of patients with OC.
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By comparing the gene expression profiles of patients with high immune scores against those with low im
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paper_text_order:1327
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Subsequently, these 34 candidate markers were used to construct a prognostic model with a Lasso-Cox proportional hazards regression.
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Complete file
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