scRNA-seq Can Identify Different Cell Populations in Ovarian Cancer Bulk RNA-seq Experiments

• Published in: International journal of molecular sciences Vol. 26; no. 15; p. 7512
• Main Authors: Gabrilovich, Sofia, Devor, Eric, Cardillo, Nicholas, Bender, David, Goodheart, Michael, Gonzalez-Bosquet, Jesus
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 04.08.2025; MDPI
• Subjects: Aged; Annotations; Cancer therapies; Cells; Classification; Clinical outcomes; Datasets; Fallopian tubes; Female; Fibroblasts; Gene expression; Gene Expression Profiling; Gene Expression Regulation, Neoplastic; Humans; Hypotheses; Medical prognosis; Middle Aged; Ovarian cancer; Ovarian Neoplasms - genetics; Ovarian Neoplasms - pathology; Patients; RNA-Seq - methods; Sequence Analysis, RNA - methods; Single-Cell Analysis - methods; Single-Cell Gene Expression Analysis; Transcriptome; Tumors; RNA sequencing; single-cell RNA sequencing; genetic variation; ovarian cancer
• ISSN: 1422-0067; 1661-6596; 1422-0067
• DOI: 10.3390/ijms26157512

High-grade serous ovarian cancer (HGSC) is a heterogeneous disease. RNA sequencing (RNAseq) of bulk solid tissue is of limited use in these populations due to heterogeneity. Single-cell RNA-seq (scRNA-seq) allows for the identification of diverse genetic compositions of heterogeneous cell populations. New computational methodologies are now available that use scRNAseq results to estimate cell type proportions in bulk RNAseq data. We performed bulk RNA-seq gene expression analysis on 112 HGSC specimens and 12 benign fallopian tube (FT) controls. We identified several publicly available scRNAseq datasets for use as annotation and reference datasets. Deconvolution was performed with MUlti-Subject SIngle Cell Deconvolution (MuSiC) to estimate cell type proportions in the bulk RNA-seq data. Datasets from the Cancer Genome Atlas (TCGA). HGSC repositories were also evaluated. Clinical variables and percentages of cell types were compared for differences in clinical outcomes and treatment results. Pathway enrichment analysis was also performed. Different annotations for referenced scRNA-seq datasets used for deconvolution of bulk RNA-seq data revealed different cellular proportions that were significantly associated with clinical outcomes; for example, higher proportions of macrophages were associated with a better response to primary chemotherapy. Our deconvolution study of bulk RNAseq HGSC samples identified cell populations within the tumor that may be associated with some of the observed clinical outcomes.