Identification of a Novel Tumor Microenvironment Prognostic Signature for Advanced-Stage Serous Ovarian Cancer
Abstract
:Simple Summary
Abstract
1. Introduction
2. Materials and Methods
2.1. Data Download and Preprocessing
2.2. Identification of Molecular Subtypes Using Non-Negative Matrix Factorization (NMF) Algorithm
2.3. Comparison of Immune Scores between Clusters
2.4. Sample Preparation
2.5. Lasso Regression Analysis
2.6. Construction of Nomogram Combined with Risk Score (RS) and Clinical Features
2.7. Prediction Model Evaluation
2.8. Immunotherapy Prediction
3. Results
3.1. Immune Scores Indicate Different Patterns According to Molecular Subtypes in OC
3.2. Establishment of a Predictive RS Based on TME-Related Genes
−(5.9449 × SNRPA1)
− (6.9887 × CCL19)
− (4.4685 × CXCL11)
− (6.9226 × CDC5L)
− (6.1777 × APCDD1)
− (8.9229 × LPAR2)
+ (0.2541 × PI3)
+ (1.7480 × PLEKHF1)
+ (5.4819 × CCDC80)
+ (0.3243 × CPXM1)
+ (0.7416 × CTAG2).
3.3. RS Assessment in Subgoups Presenting Different Clinical Features or Mutation Statuses
3.4. TME-Related Genes Correlate with Clinical Outcome
3.5. The 11-Gene Signature Risk Model as a Novel Predictive RS in OC
3.6. The 11-Gene Signature Risk Model Validation in Another Gynecological Cancer
(NES = 0.4600, q = 0.0049),
KEGG_T_CELL_RECEPTOR_SIGNALING_PATHWAY
(NES = 0.5604, q = 0.0030),
REACTOME_ADAPTIVE_IMMUNE_SYSTEM
(NES = 0.4685, q = 0.0010),
REACTOME_CYTOKINE_SIGNALING_IN_IMMUNE_SYSTEM
(NES = 0.4100, q = 0.0030),
REACTOME_METABOLISM_OF_LIPIDS
(NES = 0.4862, q = 0.0010).
3.7. Prediction of Response to Immunotherapy Based on 11-Gene Risk Model
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
(q-value) | adjusted p-value |
(CESC) | cervical squamous cell carcinoma |
(CR) | complete response |
(C-index) | concordance index |
(CI) | confidence interval |
(C1/2/3) | cluster 1/2/3 |
(DCA) | decision curve analysis |
(EMT) | epithelial–mesenchymal transition |
(ESTIMATE) | Estimation of Stromal and Immune cells in Malignant Tumor tissues using Expression data |
(FIGO) | Fédération Internationale de Gynécologie et d’Obstétrique |
(HR) | hazard ratio |
(HRG) | high-risk group |
(IC-score) | immune cell score |
(ICGC) | International Cancer Genome Consortium |
(KM) | Kaplan–Meier |
(LRG) | low-risk group |
(MCP) | Microenvironment Cell Populations |
(NEO) | neoantigen |
(NMF) | non-negative matrix factorization |
(NES) | normalized enrichment score |
(OC) | ovarian cancer |
(OS) | overall survival |
(PR) | partial response |
(PD-L1) | programmed death ligand 1 |
(PFS) | progression-free survival |
(PD) | progressive disease |
(ROC) | receiver operating characteristic |
(rss) | residual sum of squares |
(RMS) | restricted mean survival |
(RS) | risk score |
(SOC) | serous ovarian cancer |
(SD) | stable disease |
(TCGA) | The Cancer Genome Atlas |
(TME) | tumor microenvironment |
(TMB) | tumor mutation burden |
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Clinical Feature | TCGA | ICGC-Australian | |
---|---|---|---|
Event | Censored | 125 | 19 |
Dead | 222 | 74 | |
FIGO stage | III | 290 | 79 |
IV | 57 | 14 | |
Grade | G1 | 1 | |
G2 | 35 | ||
G3 | 302 | ||
G4 | 1 | ||
None | 8 | ||
Chemotherapy | Yes | 322 | |
No | 25 | ||
Recurrence | Yes | 186 | |
No | 161 | ||
Age | ≤60 | 192 | |
>60 | 155 | ||
Residual disease | No | 55 | |
1–10 mm | 25 | ||
11–20 mm | 164 | ||
>20 mm | 69 | ||
Unknown | 34 |
Clinical Feature | TCGA Training Cohort | TCGA Testing Cohort | p-Value | |
---|---|---|---|---|
Event | Censored | 92 | 33 | 0.3333 |
Dead | 151 | 71 | ||
FIGO stage | III | 201 | 89 | 0.6165 |
IV | 42 | 15 | ||
Grade | G1 | 1 | 0 | 0.5779 |
G2 | 22 | 13 | ||
G3 | 212 | 90 | ||
G4 | 1 | 0 | ||
None | 7 | 1 | ||
Chemotherapy | Yes | 227 | 95 | 0.6481 |
No | 16 | 9 | ||
Recurrence | Yes | 128 | 58 | 0.6803 |
No | 115 | 46 | ||
Age | ≤60 | 140 | 52 | 0.1913 |
>60 | 103 | 52 | ||
Residual disease | No | 41 | 14 | 0.8823 |
1–10 mm | 114 | 50 | ||
11–20 mm | 17 | 8 | ||
>20 mm | 46 | 23 | ||
Unknown | 25 | 9 |
Variables | Univariable Analysis | Multivariable Analysis | ||||
---|---|---|---|---|---|---|
HR | 95% CI | p-Value | HR | 95% CI | p-Value | |
FIGO stage | 1.16 | 0.82–1.66 | 0.408 | 1.24 | 0.86–1.78 | 0.251 |
Grade | 1.03 | 0.67–1.58 | 0.884 | 1.12 | 0.73–1.71 | 0.620 |
Chemotherapy | 0.29 | 0.18–0.48 | <0.001 | 0.35 | 0.20–0.59 | <0.001 |
Recurrence | 1.15 | 0.86–1.54 | 0.353 | 1.21 | 0.89–1.65 | 0.227 |
Age | 0.79 | 0.60–1.04 | 0.097 | 0.79 | 0.59–1.05 | 0.101 |
Residual disease | 2.06 | 1.29–3.26 | 0.001 | 1.70 | 1.06–2.73 | 0.027 |
RS | 2.19 | 1.53–3.15 | <0.001 | 1.73 | 1.17–2.55 | <0.006 |
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Zheng, M.; Long, J.; Chelariu-Raicu, A.; Mullikin, H.; Vilsmaier, T.; Vattai, A.; Heidegger, H.H.; Batz, F.; Keckstein, S.; Jeschke, U.; et al. Identification of a Novel Tumor Microenvironment Prognostic Signature for Advanced-Stage Serous Ovarian Cancer. Cancers 2021, 13, 3343. https://doi.org/10.3390/cancers13133343
Zheng M, Long J, Chelariu-Raicu A, Mullikin H, Vilsmaier T, Vattai A, Heidegger HH, Batz F, Keckstein S, Jeschke U, et al. Identification of a Novel Tumor Microenvironment Prognostic Signature for Advanced-Stage Serous Ovarian Cancer. Cancers. 2021; 13(13):3343. https://doi.org/10.3390/cancers13133343
Chicago/Turabian StyleZheng, Mingjun, Junyu Long, Anca Chelariu-Raicu, Heather Mullikin, Theresa Vilsmaier, Aurelia Vattai, Helene Hildegard Heidegger, Falk Batz, Simon Keckstein, Udo Jeschke, and et al. 2021. "Identification of a Novel Tumor Microenvironment Prognostic Signature for Advanced-Stage Serous Ovarian Cancer" Cancers 13, no. 13: 3343. https://doi.org/10.3390/cancers13133343