A 2-Protein Signature Predicting Clinical Outcome in High-Grade Serous Ovarian Cancer

Jin, Chengjuan; Xue, Yingfeng; Li, Yingwei; Bu, Hualei; Yu, Hongfeng; Zhang, Tao; Zhang, Zhiwei; Shi, Yan; Lü, Nan; Kong, Beihua

doi:10.1097/igc.0000000000001141

Cited by 16 publications

(11 citation statements)

References 35 publications

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“…25,26 Protein signatures in ovarian cancer have been proposed as serum biomarkers, as well as to predict prognosis and chemosensitivity. [27][28][29] Similar biomarker and prognostic markers have been proposed in lung cancer and breast cancer. [30][31][32] Additional investigation into protein signatures in PDAC can provide similar utility in prognosis and chemosensitivity and deserves consideration.…”

Section: Discussionmentioning

confidence: 89%

Protein Signatures and Tissue Diagnosis of Pancreatic Cancer

Underwood

Gerber

Nguyen

et al. 2020

Journal of the American College of Surgeons

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BACKGROUND: Endoscopic ultrasound-guided fine-needle aspiration fails to diagnose up to 25% of patients with pancreatic ductal adenocarcinoma (PDAC). Proteomics can help to overcome this clinical dilemma. We hypothesized that soluble protein signatures can differentiate PDAC from benign tissues. STUDY DESIGN: Tissues were obtained from resected surgical specimens, lysed, and homogenates collected for analysis with a 41-protein multiplex assay. Analyte concentrations were normalized to total protein. Statistical analysis was performed to evaluate for differences in PDAC vs benign tissue. RESULTS: Tissues were obtained from 159 patients, 82 patients with PDAC naïve to therapy and 77 with benign pancreatic pathology. Fourteen analytes had a receiver operating characteristic curve area of >0.75 for predicting PDAC vs benign tissue. A recursive partitioning model using only 2 analytes, interleukin 1 receptor antagonist and transforming growth factor-a, provided an accuracy, sensitivity, and specificity of 91.2%, 90.2%, and 92.2%, respectively. A penalized logistic regression model found 12 analytes that provide diagnostic value to a protein signature. The mean area under the receiver operating characteristic after 50 tenfold cross-validations was 0.951. Accuracy, sensitivity, and specificity of this model were 91.2%, 87.8%, and 94.8%, respectively. Applying the scenario of 80% disease prevalence in patients undergoing endoscopic ultrasound with fine-needle aspiration for a pancreatic head mass, positive predictive value is 98.5% (95% CI 93.0% to 99.7%) and negative predictive value is 66.0% (95% CI 54.9% to 75.6%). CONCLUSIONS: Protein signatures from pancreatic specimens can differentiate PDAC from benign tissue. Additional work to validate these findings in a unique sample set is warranted.

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Section: Discussionmentioning

confidence: 89%

Protein Signatures and Tissue Diagnosis of Pancreatic Cancer

Underwood

Gerber

Nguyen

et al. 2020

Journal of the American College of Surgeons

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“…CXCL11 levels were also correlated with the stroma signature in the mesenchymal and differentiated subtypes, supporting a more context‐dependent role of CXCL11. In line with such role, CXCL11 has recently been identified as a predictive marker for OC clinical outcome (Jin et al ., ). In addition, when analyzed in the mesenchymal subtype, both CXCR7 and CXCL11 strongly correlate with genes associated with ECM and OC invasion phenotype.…”

Section: Discussionmentioning

confidence: 97%

Feedback control of the CXCR7/CXCL11 chemokine axis by estrogen receptor α in ovarian cancer

et al. 2018

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Ovarian cancer (OC) is one of the most intractable diseases, exhibiting tremendous molecular heterogeneity and lacking reliable methods for screening, resulting in late diagnosis and widespread peritoneal dissemination. Menopausal estrogen replacement therapy is a well‐recognized risk factor for OC, but little is known about how estrogen might contribute to this disease at the cellular level. This study identifies chemokine receptor CXCR7/ACKR3 as an estrogen‐responsive gene, whose expression is markedly enhanced by estrogen through direct recruitment of ERα and transcriptional active histone modifications in OC cells. The gene encoding CXCR7 chemokine ligand I‐TAC/CXCL11 was also upregulated by estrogen, resulting in Ser‐118 phosphorylation, activation, and recruitment of estrogen receptor ERα at the CXCR7 promoter locus for positive feedback regulation. Both CXCR7 and CXCL11, but not CXCR3 (also recognized to interact with CXCL11), were found to be significantly increased in stromal sections of microdissected tumors and positively correlated in mesenchymal subtype of OC. Estrogenic induction of mesenchymal markers SNAI1, SNAI2, and CDH2 expression, with a consequent increase in cancer cell migration, was shown to depend on CXCR7, indicating a key role for CXCR7 in mediating estrogen upregulation of mesenchymal markers to induce invasion of OC cells. These findings identify a feed‐forward mechanism that sustains activation of the CXCR7/CXCL11 axis under ERα control to induce the epithelial–mesenchymal transition pathway and metastatic behavior of OC cells. Such interplay underlies the complex gene profile heterogeneity of OC that promotes changes in tumor microenvironment and metastatic acquisition.

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“…It was also reported that its downregulation can inhibit tumor angiogenesis in epithelial OV (38). High CXCL11 expression was determined to predict worse OS in high-grade serous OV (39). STAT4 (signal transducer and activator of transcription 4) is a member of the STAT family.…”

Section: Discussionmentioning

confidence: 99%

Development and Verification of an Immune-Related Gene Pairs Prognostic Signature in Ovarian Cancer

Wang

Bao

Nie

et al. 2020

Preprint

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Background: Ovarian cancer (OV) is the most common gynecological cancer and is a major cause of cancer-related death among women worldwide. Immunotherapy has recently been of great interest, and it has been proven to be an effective treatment strategy. For this reason, the work here attempts to produce a prognostic immune-related gene pair (IRGP) signature to estimate OV patient survival. Methods: The Gene ExpressionOmnibus (GEO) and the Cancer Genome Atlas (TCGA) databases provided the genetic expression profiles and clinical data of OV patients, whose samples were divided into training, validation, and testing sets. The InnateDB database and the least absolute shrinkage and selection operator (LASSO) regression model, were used to build an IRGP signature. The functional enrichment analysis was performed to investigate the biological activities of IRGPs. Then, the correlation between risk scores and clinicopathological parameters was further analyzed. Finally, we compared our signature and four existing signatures for OV. Results: We first identified a 17-IRGP signature significantly associated with survival based on LASSO regression model. The average area under the curve (AUC) values of the training, validation, and all

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A 2-Protein Signature Predicting Clinical Outcome in High-Grade Serous Ovarian Cancer

Abstract: CXCL11 combined with HMGA2 signature was a clinically applicable prognostic model that could precisely predict an HGSOC patient's OS and tumor recurrence. This model could serve as an important tool for risk assessment of HGSOC prognosis.

Cited by 16 publications

References 35 publications

Protein Signatures and Tissue Diagnosis of Pancreatic Cancer

Protein Signatures and Tissue Diagnosis of Pancreatic Cancer

Feedback control of the CXCR7/CXCL11 chemokine axis by estrogen receptor α in ovarian cancer

Development and Verification of an Immune-Related Gene Pairs Prognostic Signature in Ovarian Cancer

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