SERPINE2 Identified as Metastasis Target in Advanced Renal Cell Carcinoma through Single-cell RNA-seq and Multi-omics
Multi-omics and scRNA-seq for metastatic target identification.
Introduction
Chen et all (2023) present a multi-omics approach characterising mechanisms of advanced Renal Cell Carcinoma (RCC) regulated by the tumour microenvironment (TME). Spatial assessment of the TME in primary and metastatic RCC conducted using single cell RNA-Seq (scRNA-Seq), 3D high-throughput chromosome conformation capture (Hi-C), IHC and an assay for transposable accessible chromatin by high-throughput sequencing (ATAC-Seq). Results were compared to normal tissue. SERPINE2 identified as a potential predictor of RCC metastasis. Microarray and cell studies validated the biomarker discovery. qPCR validated SERPINE2 function in RCC. Expression Levels of SERPINE2 is an indicator of survival.
Main Points
- Tumour development and progression encompasses a highly complex environment, driving tumour growth and promoting metastasis.
- Targeted therapies have improved survival rates in RCC but despite this more data is required to fully characterise the tumour state allowing for personalised and appropriate treatment.
- The TME is characterised using techniques across the proteomic and transcriptomic spectrum.
- Diseased and normal tissue were used to analyse the differences between tumorigenic and normal tissue/cell states, with cell viability assay via knockdown/over-expression transfected cells and microarray data from historic samples bolstering findings.
- Cell-cell communication of primary cell clusters was assessed using CellphoneDB and iTalk, along with cell clustering analysis. When grouped into cellular classes, malignant cells clustered away from non-malignant cells, including cancer associated fibroblasts (CAFs), myofibroblasts (previously identified mechanisms of TME) and T-Cells. Known tumour markers such as PDGFRB, ACTA2 and TCF7 respectively were detected.
- Heterogeneity within the clusters of malignant cells was assessed. 60 gene signatures were applied to hierarchical clustering analysis and used as input into meta-signatures. This showed a concordance of heterogeneous patterns across tumours.
- Elevated expression of SERPINE2 was noted in Caki-1 cell lines (RCC metastatic tumour derived) in comparison to HK-2(normal cell line). Hi-C, ATAC-Seq and RNA-Seq showed that SERPINE2 is integral for tumour and metastatic progression of RCC.
- Bioinformatic interrogation of The Genomics of Drug Sensitivity in Cancer database identified SERPINE2 as pivotal in RCC drug response. Increased expression of SERPINE2 in Caki-1 via assessment of knockdown (Caki-1) and overexpression (786-O) cells was noted via Western blot and qPCR.
- IHC validated data from tissue microarrays, SERPINE2-High was linked to patients with poor clinical outcomes and is a key predictor of RCC metastasis.
Concluding Remarks
The multi-omic approach, along with bioinformatic analysis of historic datasets and microarray validation via IHC allowed the authors to envisage the modality and interactions of both cellular and genetic controls of the TME. Accurate categorisation of the mechanisms driving RCC tumour progression was achieved and more importantly identified SERPINE2 as an integral component of disease control.
Epistem Services
Epistem regularly employs multi-omic assessment studies which cover a wide spectrum of tissues and cells. These studies utilise the vast expanse of knowledge base available from the expert scientists to interrogate both genetic and cellular matrices as well as compound interactions in-vivo and ex-vivo.
- Histochemical Profiling and Transcriptomic Analysis: Isolated cells undergo downstream analysis via single cell RNA-Seq (scRNA-Seq) and histochemical profiling. Fixed cells can be further analysed for chromatin activity using downstream processes such as ATAC-Seq and Hi-C.
- High Resolution Precision: This advanced methodology allows for precise cell state classification, akin to the resolution achieved.
- Addressing Cell Heterogeneity: The complexity arising from cell population heterogeneity underscores the importance of high resolution.
- Powerful bioinformatic analysis relating current studies to historic datasets to create robust gene-signatures.
