Empowering Precision Oncology: Advancing Cancer Research by Augmenting Single-cell RNA and Bulk RNA Sequencing for Confronting the Heterogeneity in Ovarian Cancer

ABSTRACT

Ovarian cancer presents significant challenges in clinical oncology due to its high prevalence, heterogeneity, and late-stage diagnosis. Our study explores the application of single-cell RNA sequencing (scRNA-Seq) and bulk RNA sequencing to enhance our understanding of ovarian cancer at the molecular level. We highlight the diagnostic strategies and emphasize the critical role of scRNA-Seq in unraveling the intricate cellular composition and phenotypic plasticity within ovarian tumors. We also discuss the identification of rare cell subtypes, characterization of distinct cell types, and elucidation of molecular features, signaling pathways, and dysregulated networks using scRNA-Seq. Furthermore, our study showcases how scRNA-Seq aids in the discovery of novel biomarkers for diagnosis, prognosis, and treatment response prediction, as well as identifying therapeutic targets and pathways associated with drug resistance.

Ovarian cancer (OC), a prevalent malignancy characterized by high morbidity and mortality, originates within the ovarian follicles, endowing these crucial female reproductive organs with gametogenesis and hormonal homeostasis. Despite its pervasiveness, OC represents a formidable challenge in clinical oncology. Remarkably, it stands as the eighth most prevalent neoplasm afflicting women worldwide. The incipient stages of this insidious disease manifest surreptitiously, underscoring the urgent need for improved diagnostic strategies. The etiology of OC remains elusive; however, certain risk factors have been firmly established, including familial predisposition to ovarian or breast cancer, inherited mutations in tumor suppressor genes (e.g., BRCA1 and BRCA2), advanced age, obesity, hormone replacement therapy, and specific fertility treatments. These factors synergistically contribute to an augmented susceptibility to ovarian malignancies. The prodromal phase of OC is typically characterized by indistinct and inconspicuous symptoms, rendering early detection and subsequent intervention arduous tasks. As the disease progresses, discernible clinical manifestations emerge, encompassing abdominal or pelvic pain, pelvic and peritoneal distension, dysphagia or early satiety, increased urinary frequency, alterations in bowel habits, fatigue, unexplained weight fluctuations, and abnormal vaginal bleeding. Although these symptoms present with variability and nonspecificity, their recognition remains pivotal for timely diagnosis and management. The diagnostic armamentarium for OC entails a multimodal approach, synergistically combining various methodologies to maximize sensitivity and specificity.[1] This comprehensive diagnostic schema encompasses meticulous physical examination, thorough pelvic assessment, state-of-the-art imaging modalities such as ultrasound, computed tomography scans, and magnetic resonance imaging, comprehensive serological profiling for tumor markers, notably cancer antigen 125, a well-established biomarker for ovarian malignancies. Definitive confirmation of the presence of cancerous cells is achieved through histopathological analysis of biopsy specimens. This integrative diagnostic paradigm optimizes accuracy and facilitates informed clinical decision-making. Prognostic determinants in OC are inherently linked to the stage of disease at the time of diagnosis and the subsequent therapeutic response. Unfortunately, the advanced presentation of ovarian malignancies often precludes early detection due to the paucity of specific symptoms.[2] Five-year survival rates exhibit remarkable heterogeneity, ranging from approximately 30% to 90%, further underscoring the critical importance of timely diagnosis and intervention in improving patient outcomes.

The advent of single-cell RNA sequencing (scRNA-Seq) has revolutionized molecular characterization, enabling in-depth profiling of individual cells. This powerful technique has found widespread utility in cancer research, including the intricate landscape of ovarian carcinoma, enabling comprehensive investigation of gene expression patterns at the cellular level. The profound heterogeneity inherent to OC poses significant challenges in terms of understanding disease biology and identifying therapeutic targets amenable to personalized interventions. Leveraging scRNA-Seq, researchers have made substantial progress in deciphering the complex cellular composition, unearthing rare cell subtypes, and identifying novel therapeutic avenues for combatting this disease. Of particular interest, the inhibition of the JAK/STAT pathway is being extensively investigated using scRNA-Seq to develop innovative therapeutic strategies capable of addressing the diverse molecular profiles exhibited by OC. Explorations employing scRNA-Seq have unveiled diverse cell populations within the fallopian tube epithelium, shedding light on the origins of intratumoral heterogeneity observed in serous OC.[3] This fundamental insight is instrumental in comprehending the intricate cellular makeup and phenotypic plasticity within ovarian tumors. With the ability to identify distinct cell types, scRNA-Seq provides invaluable information concerning the cellular states, functional diversity, and complex interactions within the tumor microenvironment. Moreover, scRNA-Seq has facilitated the characterization of various cell types, encompassing cancer cells, stromal cells, immune cells, and endothelial cells, in OC patients. Through the analysis of gene expression profiles, researchers can classify and delineate these cells, elucidating their molecular features, activation states, and intercellular communication networks.[4] This knowledge empowers a deeper understanding of tumor–stroma interactions, immune infiltration, and potential targets for immunotherapy. In the context of OC, scRNA-Seq has unraveled critical therapeutic targets by elucidating the gene expression profiles of individual cells and large cell populations. Furthermore, reconstruction of gene regulatory networks and signaling pathways using scRNA-Seq-derived gene expression data aids in selecting optimal treatment modalities tailored to individual patients. By profiling gene expression patterns across individual cells, crucial transcription factors, signaling molecules, and dysregulated pathways in specific cell types or disease progression stages can be identified with enhanced precision. These findings provide invaluable insights into the molecular mechanisms underpinning OC initiation and progression. Significantly, scRNA-Seq studies have made substantial contributions in the identification of novel biomarkers for OC diagnosis, prognosis, and treatment response prediction. Comparative analysis of gene expression profiles across tumors from diverse patients has facilitated the identification of genes or gene signatures that correlate with critical clinical outcomes, such as overall survival or response to specific therapies.[5] These molecular signatures hold promising potential for developing personalized treatment strategies. Furthermore, scRNA-Seq has facilitated the identification of molecular targets and pathways associated with drug resistance in OC. By examining gene expression profiles of treatment-resistant cells, pre- and posttreated cells, and cells undergoing therapeutic interventions, specific molecular alterations contributing to drug resistance can be discerned. However, considerable efforts and further investigations are warranted to establish the feasibility of implementing these strategies on a large scale for all OC patients.

To investigate and comprehend the intricate heterogeneity underlying OC, two distinct scRNA-Seq datasets, GSE154600 and GSE158937, were retrieved from the Gene Expression Omnibus database. To mitigate batch effects, data preprocessing was performed utilizing the SCTransform() function from the Seurat package. Nonlinear dimensional reduction was achieved using Uniform Manifold Approximation and Projection. Subsequently, clustering of the datasets was conducted using the FindClusters() function, relying on cell markers identified within the datasets. Notably, immune cell markers such as PTPRC, IL7R, CD8A, and NKG7 were instrumental in clustering natural killer cells, B-lymphocytes, and T-lymphocytes. Furthermore, clustering was performed on myeloid cells and fibroblasts utilizing markers including EPCAM (epithelial cell marker) and COL1A2 (fibroblast marker), among others. scRNA-Seq analysis enabled the identification and characterization of immune cell subsets, including T-cells and B-cells, unveiling distinct subpopulations such as naive, regulatory, memory, and exhausted phenotypes. In patients from the GSE154600 dataset, a considerable upregulation of M1 and M2 genes was observed, particularly within M2- and M1-like myeloid cells residing in the tumor microenvironment.[6] To complement the scRNA-Seq analysis, bulk RNA-Seq analysis was performed, providing a comprehensive dataset for the construction of prognostic models. Bulk sequencing facilitated the correlation and examination of clinical information across multiple samples, yielding precise and accurate insights. In-depth investigations were conducted on both OC cells and immune cells involved in OC pathogenesis. The analysis revealed that patients exhibiting an abundance of M1-like tumor-associated macrophages (M1-TAMs) displayed a higher survival rate, while the same correlation was not observed for M2-TAMs. This finding emphasizes the prognostic significance of M1-TAMs in OC. The CIBERSORT algorithm was employed to predict the proportion of 22 distinct immune cell populations based on the RNA-Seq count data, including the abundance of M1-TAMs. These results were consistent with the survival analysis, further substantiating the positive impact of M1-TAMs on patient outcomes.

Extensive investigations are currently underway to unravel the intricate mechanisms governing the etiology and progression of OC, utilizing state-of-the-art single-cell transcriptomic approaches.[7] Although conventional therapeutic modalities, including chemotherapy, radiation therapy, and surgery, have demonstrated partial efficacy in the treatment of OC, their success heavily relies on the stage at which the cancer is diagnosed. Regrettably, patients diagnosed with advanced-stage OC face abysmal survival rates due to the pervasive inter- and intratumoral heterogeneity inherent to this malignancy. OC poses a substantial threat to women’s health, and its prevalence has surged as a consequence of the modern era’s detrimental environmental conditions and lifestyles. Consequently, researchers and medical professionals worldwide are tirelessly striving to identify definitive curative strategies for various cancer types, and scRNA-Seq has emerged as a highly promising avenue for comprehending the complexities of this formidable disease.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.