Identifying novel ovarian tumor biomarkers through mining of the transcriptome of circulating immune cells: A proof‐of‐concept study

Abstract

Objective Treatment of high‐grade serous ovarian cancer (HGSOC) will benefit from early detection of cancer. Here, we provide proof‐of‐concept data supporting the hypothesis that circulating immune cells, because of their early recognition of tumors and the tumor microenvironment, can be considered for biomarker discovery. Methods Longitudinal blood samples from C57BL/6 mice bearing syngeneic ovarian tumors and peripheral blood mononuclear cells (PBMC) from healthy postmenopausal women and newly diagnosed for HGSOC patients were subjected to RNASeq. The results from human immune cells were validated using Affymetrix microarrays. Differentially expressed transcripts in immune cells from tumor‐bearing mice and HGSOC patients were compared to matching controls. Results A total of 1282 transcripts (798 and 484, up‐ and downregulated, respectively) were differentially expressed in the tumor‐bearing mice as compared with controls. Top 100 genes showing longitudinal changes in gene expression 2, 4, 7, and 18 days after tumor implantation were identified. Analysis of the PBMC from healthy post‐menopausal women and HGSOC patients identified 4382 differentially expressed genes and 519 of these were validated through Affymetrix microarray analysis. A total of 384 genes, including IL‐1R2, CH3L1, Infitm1, FP42, CXC42, Hdc, Spib, and Sema6b, were differentially expressed in the human and mouse datasets. Conclusion The PBMC transcriptome shows longitudinal changes in response to the progressing tumor. Several potential biomarker transcripts were identified in HGSOC patients and mouse models. Monitoring their expression in individual PBMC subsets can serve as additional discriminator for the diagnosis of HGSOC.

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“…The key genes must have high expression levels and significant differences and must have functions consistent with our research purposes. A common idea of quickly targeting genes is to look for "star" molecules, which requires literature collection, summarizing certain star genes from previous studies, and then focusing on the expression of these genes in our project and structural information [ 35 ].…”

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“…Affymetrix microarray is the most widely used high-throughput technology to measure gene expression analysis ( 13 ) and has successfully identified many biomarkers for molecular mechanisms, early diagnosis and prognostic evaluation in many diseases ( 14 – 17 ). The most distinguishing feature of Affymetrix GeneChip microarray is that their manufacture is by using light-controlled in situ synthesis of DNA microarrays.…”