Transcriptomic identification of differentially expressed genes in Levonorgestrel resistant endometrial cancer cell lines

Endometrial cancer (EC) is the most common gynecologic malignancy in the world and incidence is steadily increasing. The Levonorgestrel Intrauterine System (LNG‐IUS) is an alternative conservative treatment for early‐stage EC, however, Levonorgestrel (LNG) resistance occurs for 1 in 3 people. This study aimed to present potential LNG resistance mechanisms and identify differentially expressed genes (DEGs) in EC cell lines. Two LNG resistant cell lines were developed through long term culture in LNG (MFE296R and MFE319R). Whole transcriptome sequencing was carried out on triplicate RNA samples. EdgeR v3.32.1 was used to identify differentially DEGs. Blast2go V6.0 (BioBam software) was used for functional annotation and analysis of genomic datasets. Protein interactions were investigated using the STRING database, including the identification of genes with high levels of interaction (HUB genes). Select DEGs and HUB genes were validated by quantitative real‐time polymerase chain reaction (qRT‐PCR) and Western blot. Fifteen DEGs were identified according to FDR < 0.05 and logFC < 2. Protein analysis identified six HUB genes with a degree of connectivity > 10. Relative mRNA expression of MAOA, MAOB, THRSP, CD80, NDP, LINC01474, DUSP2 and CXCL8 was significantly upregulated in both LNGR cell lines. Relative protein expression of GNAO1 and MAOA were significantly upregulated in both LNGR cell lines. This research identified novel markers of resistance in LNGR cell lines. We discussed potential mechanisms of LNG resistance including dedifferentiation and immunostimulation. The next step for this research is to validate these findings further in both translational and clinical settings.

treatment of EC consists of a total laparoscopic hysterectomy and bilateral salpingooophorectomy. 3 However, the growing incidence of premenopausal people being burdened with this disease, 4 alongside the direct association of EC with high body mass index 5,6 is complicating the surgical approach to the disease and increases the need for conservative management.
The Levonorgestrel Intrauterine System (LNG-IUS) is gaining traction as an alternative treatment for early-stage EC for people that are unable to undergo primary surgery. [7][8][9][10] However, evidence appears to show a recalcitrance in response to treatment in one in three people. [7][8][9][10] Therefore, while the LNG-IUS is being utilized clinically, the lack of predictive biomarkers limits the certainty of recommendation for the treatment, 11 and invasive endometrial biopsies every 3−6 months are required to monitor treatment response. 3 While both molecular and clinicopathological predictors of response have been investigated previously, there are still no clinically utilized predictive biomarkers to guide LNG-IUS treatment. 12 Alongside this, the current knowledge around LNG resistance is scarce.
Whole transcriptome sequencing first became available with the advent of next-generation sequencing technology and plays an essential role in the understanding of the genome as a whole and how it responds to diseases, pathogens and environmental challenges. 13 This study aimed to carry out RNA sequencing and subsequent protein analysis on LNG resistant and matched sensitive early-stage EC cell lines (MFE296 and MFE319). This may elucidate potential mechanisms of LNG resistance in EC cells and identify potential predictive biomarkers for LNG-IUS treatment of earlystage EC.

| METHODS
A schematic of the methods can be seen in (Figure 1).

| Development of LNG resistant cell lines
LNG resistant Endometrioid EC cell lines MFE296 R and MFE319 R were developed as previously described. 14  Thermo Fisher Scientific #11875-093) containing 20% FBS. All media was supplemented with 100U/ml penicillin/streptomycin (Gibco; Thermo Fisher Scientific, #15070-063). Cells were grown in 5% CO 2  for 40 s) for a total of 40 cycles and then 95°C for 60 s, followed by melt curve analysis. C t values were analyzed using the normalization method against three reference genes: (succinate dehydrogenase complex flavoprotein subunit A (SDHA), 90-kDa heat shock protein 1 beta (HSPCB) and 60 S ribosomal protein L13a (RPL13A) to calculate the ΔC t (ΔC t mRNA C t -geomean endogenous reference genes). ΔΔC t was then calculated relative to sensitive controls (ΔC t (sample1)-ΔC t (sample2)). Fold change was then calculated.   Table 2. Membranes were imaged and quantified using Pierce™ SuperSignal™ West Pico PLUS Chemiluminescent Substrate (Thermofisher) and the iBright CL100 (Invitrogen). Band size is described as relative optical density using α-Tubulin as a loading control. Blots were carried out in biological triplicate.

| Identification and validation of DEGs from LNG resistant MFE296 and MFE319 cells
RNAseq analysis was carried out to identify DEGs between LNG resistant and LNG sensitive early-stage EC commercial cell lines (MFE296 and MFE319). High throughput sequencing generated a total of 1.62E 07 high quality reads and about 98% of these sequenced reads mapped successfully to the Homo sapiens, hg38 build genome (Table 1). An overlap between the mapped reads and annotated features was observed with an average of 65% of mapped reads assigned to at least one gene body ( Table 1)

| PPI network construction and hub genes screening
The STRING network was adopted to produce a PPI of the 218 protein-coding DEGs using the threshold value of confidence > 0.4 and connectivity degree ≥ 1. If a protein has a high degree in a PPI network, it will be defined as playing a critical role in the whole module. 94 nodes were removed from the PPI network due to having no edges (direct connections). This PPI network contains 124 nodes with 209 edges and has a PPI enrichment value of <1.0e- 16 .
Cytoscape software was used for visual representation and analysis of the STRING PPI network ( Figure 4A). The color of the node represents the FDR of the gene, with the darkest purple representing the lowest FDR. The width of the node border represents the connectivity degree, with the thickest border representing the highest degree of connectivity and the width of the edge displays the combined score.
Cytoscape software CytoHubba was used to identify the top 10 nodes ranked by the degree of connectivity. Hub genes are shown with larger offset labels. The top 10 hub genes according to the degree of connectivity can be seen in Table 3 clusters with a score > 4 were identified. Cluster one contained 7 nodes and 19 edges and had an MCODE score of 6.33 ( Figure 4B).
Cluster one contained 5 of 10 Hub genes. Cluster two contained 5 nodes and 10 edges and had an MCODE score of 5.0 ( Figure 4C).
Cluster three contained 4 nodes and 6 edges and had an MCODE score of 4.0 ( Figure 4D). can be seen in Figure 5.

| DISCUSSION
The incidence of early stage EC is increasing at a rapid rate globally. people. [7][8][9][10]20,21  to certain drugs. 22 RNA sequencing is rapid, precise and has high sensitivity which allows for detection of low abundance transcripts. 23 By using a discovery based approach, we were able to identify novel genes that may be implicated in LNG resistance.   to tumorigenic progression of many cancers. 33 It has been proposed that the increased immune infiltration in the tumor microenvironment creates refuge for cancer cells and protects them from cytotoxic stimuli, often leading to better treatment outcomes. 34 The CXCL8 gene encodes a proinflammatory chemokine that has been previously identified as elevated in EC 35 and is also significantly upregulated in LNG resistant EC cells in this study. CXCL8 promotes immune infiltration and angiogenesis through acting on the CXCR1 and CXCR2 receptors of leukocytes and endothelial cells, establishing a gateway for cancer cell metastasis. 36 Progesterone is a wellrecognized suppressor of the endometrial inflammatory response. 37 In particular, the use of LNG-IUS has been observed to significantly increase immune modulators including CXCL8 and T cells in the endometrium of healthy people. 38

| CONCLUSION
The identification of predictive biomarkers to guide conservative treatment of early stage EC is essential due to the rising incidence of disease in younger people. A predictive signature comprised of genes or proteins implicated in progesterone resistance will be the most appropriate form of biomarker to aid in the EC conservative treatment pathway clinically. This study has presented suitable predictive biomarker candidates such as MAOA, MAOB, ALDH1A1, CXCL8 and CD80 that may be implicated in a multitude of drug resistance mechanisms such as dedifferentiation and immunostimulation. These markers warrant further investigation in both translational and clinical settings.

AUTHOR CONTRIBUTIONS
Molly Dore, under the supervision of Claire Henry and Sara Filoche carried out all differential expression analysis, functional analysis and in-vitro experiments. Molly Dore analyzed and interpreted the data.
Ngonidzashe Faya carried out quality assessment, filtering, genome alignment and provided feedback on bioinformatic analysis carried out by Molly Dore. Molly Dore was the major contributor to the preparation of the manuscript. Claire Henry made substantial contributions to the conception and design of the study. Sara Filoche advised on the design of the study and provided manuscript feedback. All authors read and approved the final manuscript.