The putative effects of carboplatin based neoadjuvant chemotherapy on tumor microenvironment of epithelial ovarian carcinoma

The putative effects of carboplatin based neoadjuvant chemotherapy on tumor microenvironment of epithelial ovarian carcinoma | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The putative effects of carboplatin based neoadjuvant chemotherapy on tumor microenvironment of epithelial ovarian carcinoma Yunyun Li, Fei Li, Yao Li, Xue Liu, Cuiying Zhang, Li-na Hu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3900539/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract For late-stage epithelial ovarian carcinoma (EOC) patients, carboplatin based neoadjuvant chemotherapy (NACT) followed interval debulking surgery (IDS) could be alternative choice. The failure of immune checkpoint inhibitors combining chemotherapy for EOC patients promote us to comprehensively understand the impact of NACT on the tumor mircroenvironment (TME) of EOC. Methods : The RNA-sequencing profiles of EOC patients before and after NACT were downloaded from the Gene Expression Omnibus (GEO) databases. Differentially expressed genes (DEGs) were calculated and further analyzed using GO and KEGG analyses. The variation of immune cell infiltration upon NACT was analyzed by CIBERSORT and further identified using immunohistochemistry and multi-immunofluorescence assays. Results : A total of 6 GEO datasets were included in our study, and 1138 DEGs were found compared the pre-NACT with post-NACT groups. The inflammation-related IL-17 signaling pathway and the apoptosis-related P53 signaling pathway were the most enriched signaling pathways in post-NACT tissues. A diagnostic pattern using the 6 hub genes, figured out by protein network analysis, could efficiently distinguish the normal ovarian tissues from the gynecology malignancies, including OC. Upon NACT, the phenotype of immune cells in the TME was more complex. Infiltrating follicular helper T (Tfh) cells and M1 macrophages significantly decreased, while the proportion of resting NK cells significantly increased. Although total M2 macrophages did not change significantly, the morphology and phenotype of relative macrophages changed, especially the lymphatic vessel endothelial hyaluronan receptor 1 (LYVE1) + macrophages. LYVE1 + macrophages co-expressed with CD206 but not CD68+, and they formed multicellular “nest” structures in the stroma, which might be related to chemotherapy sensitivity of EOC. Conclusion: The alterations in the TME of EOC following NACT exposure were complex and dynamic. Not only the tumor cells, but also immunological factors are involved in mediating the chemotherapeutic response. The LYVE1 + CD206 + perivascular TAMs were identified in EOC patients, and this specific subtype TAMs might be correlated with chemotherapeutic response, which will allow for the future development of novel immunologic therapies to combat chemoresistance. Epithelial ovarian carcinoma neoadjuvant chemotherapy immune system macrophage lymphatic vessel endothelial hyaluronan receptor 1. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Epithelial ovarian carcinoma (EOC) is the most lethal gynecological malignancy. Only 45% of patients survive 5 years after diagnosis [ 1 ]. Complete cytoreduction is associated with the best survival, irrespective of the surgical strategy; however, for many advanced patients, complete resection cannot be achieved since many patients are widely diagnosed with tumor metastasis. Thus, for these patients, neoadjuvant chemotherapy (NACT) followed by interval debulking surgery (IDS) is an alternative choice, as patients with complete cytoreduction after NACT fared better than those with residual disease after primary debulking surgery (PDS) [ 2 ]. The interaction between the immune system and the tumor is likely based on an equilibrium between immune recognition and tolerance through immune cells in tumor microenvironment (TME) [ 3 ]. TME is composed of many different cellulars, containing tumor and immune cells, and acellular components that together driving tumor growth, invasion, metastasis and response to chemotherapy [ 4 ]. For patients with EOC, a putative immunosuppressive TME was reported to accompany most patients [ 5 ]. Consequently, immuno-therapy is increasingly being employed as a treatment modality for OC patients with advanced stages. Among the immunotherapy, immune checkpoint inhibitors (ICIs) were the most common kind since its significant value achieved in the treatment of other kinds of cancers [ 6 ]. ICIs were introduced in the treatment combing with conventional chemotherapy for OC patients for the primary hypothesis that chemotherapy agents could active the TME of cancers, and provide a broad-acting immune stimulus function. However, the survival time of combing therapy group in this clinical trail did not present obvious advantages [ 7 ], wondering us the exact effects of adjuvant chemotherapy on TME of EOC. In general, cytotoxic chemotherapy is assumed to be immunosuppressive because of its toxicity to dividing cells in the bone marrow and peripheral lymphoid tissues. However, increasing evidence highlights that NACT might act as an immune modulator in OC. For the local immune system, lymphocyte-related immune reactions are supposed to increase, as T-activated cells are found to be enhanced, and T-regulatory cell density decreases upon NACT [ 8 ]. In addition, carboplatin could alter the subtype of macrophages, which might induce the activation of antitumor immunity in some cancer models [ 9 ]. However, the putative effects of NACT on TME of EOC were controversial. A comprehensive dynamic understanding of the effects of NACT on the TME might provide more novel reference to evaluate the prognosis of EOC patients and provide new strategies for immuno-therapy. Accordingly, based on the published bioinformatics content, we integratedly analyzed the differentially expressed genes (DEGs) in EOC tissues upon neoadjuvant chemotherapy, and conducted functional enrichment analysis of these DEGs. The differences of immune cells in TME upon NACT were clustered and analyzed according to specific immune cell marker genes. Herein, a special subtype of tumor associated macrophages (TAMs) was found to be significantly suppressed post NACT and might be correlated with chemotherapeutic sensitivity of EOC. This might provide a new target for tumor immuno-therapy in the future. Materials and Methods 2.1. Patients’ information We retrospectively searched the electronic medical records of all patients who were first diagnosed with primary ovarian malignancy at the Department of Gynecology, Yongchuan Hospital of Chongqing Medical University, from Jan. 2014 to July. 2019. Patients were clinically staged according to the International Federation of Gynecology and Obstetrics (FIGO) staging criteria. All patients received no other surgeries or chemotherapy before surgery. Among the patients included, 13 patients received 2–3 cycles of neoadjuvant chemotherapy (NACT) and interval cytoreductive surgery. The chemotherapy regimen was carboplatin (AUC = 5) plus paclitaxel (175 mg/m 2 ). The following information was extracted from the medical records of the eligible patients: 1) patient demographics, 2) final pathology report, 3) preoperative complete blood counts, and 4) review of past medical history and medications prior to surgical staging. All patients received 3–4 cycles of chemotherapy after the IDS. After standard treatment administration, all patients were subjected to routine follow-up, including gynecological examination, CT scan, and CA125 and HE4 assessments every 3–6 months. If needed, PET-CT should be considered for some patients. During the follow-up, once the tumor biomarker level was elevated combined with imaging evidence of new tumor growth, tumor recurrence was considered. PFS was defined as the time interval from initial diagnosis to the date of the first recurrence. The last follow-up date for all patients was Feb. 2022. The follow-up time ranged from 32 to 97 months. 2.2. Immunohistochemistry For the analysis of tumor-infiltrated immune cells, tissue sections from the first biopsy surgery and interval cytoreductive surgery were collected from the Pathology Department of Chongqing Medical University. Among the patients, one underwent IDS at another hospital. Thus, 12 matched pre- and post-NACT biopsy samples were included for further experiments. Sections were dewaxed, dehydrated, and incubated in antigen unmasking solution in a microwave for 20 minutes. The sections were incubated in 0.3% H 2 O 2 in methanol for 10 minutes and in blocking buffer (2.5% BSA and 2.5% goat serum in PBS) for 60 minutes at room temperature. Primary antibodies against the following proteins were incubated in blocking buffer overnight at 4°C: anti-CD8 (Proteintech, USA, 1:500 dilution) and anti-CD68 (Proteintech, USA, 1:500 dilution). The next day, the sections were washed with PBS supplemented with 0.1% Tween-20 (PBST). The sections were incubated with a vector impact kit for 1 hour at room temperature and washed 3 times in PBST before the addition of the DAB chromogen for 20 seconds. Then, the sections were imaged using scanner software. The cutoff value for high expression was set as the mean value. The levels of CD68 and CD8 were calculated manually at high magnification (40x objective) in 5 randomly selected fields for each sample, and the mean values of each marker were recorded. The results were reviewed with the oversight of a pathologist, Dr. Yao Li. 2.3. Immunofluorescence Sections of OC tissues were fixed in 4% paraformaldehyde in PBS (Gibco) for 10 min at room temperature. The sections were incubated with 0.2% Triton X-100 in blocking buffer, BSA (10% rabbit serum for goat-derived primary antibodies and 3% BSA for other sources of primary antibodies) was added, and the samples were blocked for 30 minutes. The following antibodies were used at relative dilutions unless stated otherwise: anti-CD8 (Proteintech, USA, 1:1000), anti-CD68 (Proteintech, USA, 1:3000), anti-CD206 (Bio-Rad, USA, 1:5000), and anti-LYVE1 (Abcam, 14917, 1:3000). Primary antibodies were detected using Cy™3 donkey anti-sheep IgG (1:100; Jackson ImmunoResearch, 1 mg/ml) overnight at 4°C with gentle agitation. The sections were washed with PBS and then incubated with HRP-labeled secondary antibodies (iF488-tyramide, Cy3-tyramide, iF647-tyramide, and FITC-tyramide) for 1 hour at room temperature. Nuclei were further stained with 4′,6-diamidino-2-phenylindole and dihydrochloride (DAPI). The sections were washed three times with PBST and imaged using the Nikon Eclipse C1 Imaging system and associated software 3DHISTECH (Pannoramic MIDI). 2.4. Datasets Acquisition and Analysis The datasets used in this study were downloaded from the Gene Expression Omnibus (GEO) database ( https://www.ncbi.nlm.nih.gov/geo/ ), a publicly available research project of the American National Cancer Institute. The keywords used for the search were neoadjuvant chemotherapy/chemotherapy and ovarian cancer/ovarian carcinoma. A total of nine datasets were found, among which one dataset was analyzed via single-cell RNA sequencing and two were analyzed via peripheral blood; thus, these datasets were excluded. The RNA-seq transcriptome data were subsequently normalized to the fragments per kilobase of exon model per million (FPKM, mean fragment per kilobase million). A total of 211 samples were obtained. Dynamic analysis of the gene expression profile data was performed through the GEPIA ( http://gepia.cancer-pku.cn/ ) website to obtain relative gene expression levels and survival analysis results for OC tissues. Principal component analysis (PCA) was also performed using the GEPIA website. 2.5. Differential gene expression and functional enrichment analysis Differential gene expression analysis was performed using the edgeR and limma R packages [ 7 ]. A generalized linear model with a 0 + group + batch design was used. The DAVID database ( https://david.ncifcrf.gov/summary.jsp ), an online bioinformatics resource that provides researchers with annotation tools for understanding the biological mechanisms involved in the function of a large number of genes or proteins, was used to perform GO function enrichment and KEGG pathway enrichment analysis of the DEGs. A P value < 0.05 and a number of genes greater than 10 were considered to indicate statistically significant results. 2.6. Construction of the PPI network and Hub Gene Screening The STRING database ( https://string-db.org ) and Cytoscape 3.7.1 software were used to construct a protein‒protein interaction network for the differentially expressed genes. The top 10 genes with the highest degree of upregulation and downregulation were plug-in as core genes. The hub genes were subjected to interaction analysis via the STRING database online analysis platform. An interaction analysis network was constructed between the hub genes. 2.7. Analysis of infiltrating immune cell components To estimate the immune cell components before and after neoadjuvant chemotherapy, CIBERSORT was used with the LM22 signature and 1000 permutations. We used a panel of 22 immune cells consisting of B cells, T cells, natural killer cells, macrophages, dendritic cells, and myeloid subsets. 2.8. Statistical analysis The data are reported as the mean and standard deviation of at least three independent experiments. Descriptive values of quantitative continuous variables, such as age and neutrophil, monocyte, and lymphocyte counts, were examined using standard descriptive statistical methods. Student’s t tests (for two groups) and variance analysis (for more than two groups) were used for the analyses. Comparisons of categorical variables were made by the chi-square test or Fisher’s exact test, depending on the state of case distributions. Statistical analyses were carried out using SPSS 20.0 and GraphPad Prism 7.0 software. Progression-free survival (PFS) and overall survival (OS) analyses of categorical variables were performed using the Kaplan‒Meier method, and significant differences between groups were identified using the log-rank test. Univariate and multivariate analyses were performed using Cox proportional hazards models. P values less than 0.05 were considered to indicate statistical significance. Results 1. Differentially expressed genes upon NACT for patients with EOC To better understand the putative alterations in the TME of EOC upon NACT, we searched the GEO database to retrieve information on the impact of NACT on ovarian cancer tissue. A total of 9 datasets were included in our analysis. Among these datasets, two studies explored the effects of NACT on peripheral blood, and one explored the effects of NACT using single-cell RNA sequencing; thus, these studies were excluded. Six final datasets (GSE143897, GSE71340, GSE158739, GSE201600, GSE181597 and GSE109934) were included for further research. In total, 211 samples were included for further experiment (99 pre-NACT samples and 112 post-NACT samples). The detailed distribution of the patients is summarized in Table I. The datasets were normalized to the Count value, and the differences in gene expression were summed for each of the 6 datasets. The absolute values were subsequently calculated, and a total of 1138 DEGs were found. As shown in Fig. 1 , the top 100 DEGs were selected based on their order of magnitude. The whole-genome sequencing (WGS) data from the GSE143897, GSE71340, and GSE158739 datasets were used; these data were more extensive than those from the GSE201600, GSE181597, and GSE109934 cohorts. The latter data included some missing genes with differential expression. The GSE158739 data were obtained from sorted macrophages, so the gene expression trend of these cells was quite different from that of the other 5 groups. Table I. The detailed information of GEO datasets included in our study, including the biopsies numbers, biopsies sites, whether the biopsies were matched, the immune and stroma scores before and after NACT, and relative significant change of immune cells infiltrated. *P < 0.05. **P < 0.01. No. Of GSE data Biopsies number Biopsies site Matched immune score stroma score Changes of immune cells infiltrating upon NACT GSE143897 18 pre- and 18 post- Primary tumor site Yes 0.068 0.019* M0 ↑ (but P = 0.09) GSE71340 11 pre- and 18 post- omentum Yes 0.076 0.146 CD8+ ↑ ( P = 0.028) GSE181597 25 pre- and 19 post- Primary tumor site No 0.124 0.157 Th ↓; M1 ↓; M2 ↑; Mast cell ↑; Eosinophils ↑; GSE201600 31 pre- and 31 post- Primary tumor site Yes 0.000725** 0.011** CD8+ ↑; Treg ↓; M1 ↓; mast cells ↑; GSE158739 5 pre- and 7 post- omentum Yes NA NA NA GSE109934 19 pre- and 19 post- Primary tumor site Yes NA NA NA The top 100 DEGs were subsequently analyzed for their expression in primary OC tissues using the TCGA database (GEPIA). Interestingly, the genes whose expression significantly decreased after NACT were consistently highly expressed in OC tissues, and some genes were closely related to patient disease-free survival (PFS) or overall survival (OS) (Fig. 1 A); however, those genes whose expression significantly increased after NACT had no significant trend in expression before chemotherapy. Among these genes, the five genes ( C7, EGR1, DUSP1, SFRP4, PPARG, CCL14 ) most significantly upregulated upon NACT were proposed to be suppressed in primary OC tissues (Fig. 1 B and 1 C). As shown in Supplement Table I, most of these genes participate in inflammatory responses and immune regulation, indicating that NACT activates inflammation and the immune response in OC tumor tissues. The five genes with the most significant decrease in expression were EPCAM, UBE2C, CCNB1, MKI67 , and BIRC5 . We also further explored their expression levels in the TCGA database. As expected, these genes were consistently highly expressed in OC tissues compared with normal tissues (Fig. 1 C and 1 D); these genes function as oncogenes by participating in the progression of tumors through cell cycle regulation, cell adhesion, and apoptosis (Supplement Table I). The suppression of the expression of these genes upon NACT suggested that chemotherapy mediated inhibition on the tumor cells. We further explored the prognostic value of these oncogenes and tumor suppressors on PFS and OS of patients with EOC. Except for BIRC5, none of these genes was supposed to be present effects on PFS and OS of patients with EOC (Supplement Figure). The OS of EOC patients with high expression of BIRC5 was longer than those patients with lower expression (Fig. 1 D). 2. GO and KEGG analyses of DEGs upon NACT We further performed a gene ontology (GO) analysis of the DEGs using the DAVID website, and the results are shown in Fig. 2 . The overall dataset-related DEG-enriched biological processes (BP) included regulating MAP kinase activity, cellular chemical toxic side effects, and myeloid leukocyte migration processes; the enriched cellular components (CC) included extracellular matrix containing collagen, cyclin-dependent kinase full-enzyme complex, and protein kinase complex; and the enriched molecular functions (MF) were chemokine activity, cyclin-dependent kinase tyrosine/serine kinase regulatory activity, and cytokine activity (Fig. 2 A). KEGG signaling pathway analysis of the DEGs revealed that the highest enrichment was in the inflammation-related IL-17 signaling pathway and the apoptosis-related P53 signaling pathway. The most significant and most enriched signaling pathways were the PI3K/AKT pathway, the cytokine‒cytokine receptor interaction pathway, and pathways related to cancer (Fig. 2 B). Therefore, the overall response of EOC after NACT was related to the toxic side effects of the corresponding chemotherapeutic drugs and the related cell cycle regulation. 3. Protein network analysis of DEGs upon NACT To understand the relationships between DEGs, the Search Tool for the Retrieval of Interacting Genes (STRING) was used to construct a protein‒protein interaction (PPI) network for the obtained DEGs. Each node in the network represents a protein, and the connection represents the interaction between proteins. A clear clustering between upregulated and downregulated proteins after NACT was found. The top 6 hub genes were selected based on the node connectivity calculated by Cytoscape software and included interleukin-6 (IL6), matrix metalloproteinase 9 (MMP9), CD8A, EZH2, protein tyrosine phosphatase receptor type C (PTPRC), CD8A, and cadherin 1 (CDH1). Among them, IL6, MMP9, CD8A and PTPRC exhibited increased expression after NACT, while EZH2 and CDH1 exhibited decreased expression after NACT. We further evaluated the expression of these hub genes in the TCGA OC-related database. MMP9 , EZH2 , and CDH1 were significantly upregulated in OC tissues compared to normal ovarian tissues (Supplement Fig. 1 B, 1 C and 1 E). IL6 , CD8A , and PTPRC were also upregulated in tumor tissues, but the difference was not significant (Supplemental Fig. 1A and 1D). IL6 is an important cytokine involved in the inflammatory response and B lymphocyte maturation. PTPRC is a member of the protein tyrosine phosphatase (PTP) family and is involved in the regulation of T and B lymphocyte antigen receptors. It can directly bind to antigen receptor complexes or activate different Src family kinases, which play important roles in antigen presentation. CD8A , also known as the CD8 antigen, is located on the surface of cytotoxic T cells and can recognize antigen-presenting cells and participate in immune responses. All these genes participate in the immune reaction. We further examined the impact of these six genes on the disease-free survival (DFS) and OS of patients with EOC with TCGA dataset. Except for CD8A and MMP9 , other four genes had no significant impact on OS or DFS of patients with EOC (Supplemental Fig. 1F and 1G). EOC patients with high expression of MMP9 or CD8A present to be with longer DFS compared with those patients with lower expression. The high expression of the above hub genes strongly suggested the activation of inflammatory responses in the body after NACT. Using the above hub genes, we performed a diagnostic model using principal component analysis for distinguish normal ovarian tissues from malignant tissues. Interestingly, these hub genes not only distinguished ovarian tumor tissues from normal tissues (Supplementary Fig. 2E) but also helped to distinguish ovarian normal tissues from gynecological malignancies (Fig. 2 D), indicating that these hub genes play important roles in the tumor progression of EOC. The relative ratios of diagnostic pattern was supplied in Supplement Fig. 2 F. 4. Analysis of the effects of NACT on infiltrating immune cell components in OC patients To determine the putative effects of NACT on immune cells in OC, the CIBERSORT algorithm was used to predict the differences in 22 kinds of infiltrating immune cells in tumor samples before and after NACT. The GSE158739 dataset was excluded because it included only sorted macrophages, the GSE109934 dataset was excluded because the data were incomplete and because the immune score could not be calculated. Thus, Only 4 datasets were finally included in the study. Four studies included 85 pre-NACT and 86 post-NACT samples. Detailed information on the tissue source, sample pairs, immune scores, stromal scores, and significant infiltrating immune cell changes before and after chemotherapy are provided in Table I. The distributions of immune cells in patients before and after chemotherapy are shown in Fig. 3 A. The five most common immune cell fractions of EOC were T-cell CD8 + cells, T-cell CD4 + memory resting cells, M0 macrophages, M1 macrophages and M2 macrophages. The total proportion of the five immune cells was more than 60% in the majority of the samples. We further explored the differences in the proportions of various immune cell infiltrates between the two groups. The analysis showed that the proportions of infiltrating follicular helper T (Tfh) cells and M1 macrophages significantly decreased after NACT, while the proportion of resting NK cells significantly increased (Fig. 3 B). However, there were no significant differences in the percentages of CD8 + T cells or B cells. Upon NACT, the ratio of CD4 + T memory resting cells also decreased, but the difference was not statistically significant. To comprehensively understanding the effects of NACT on the TME of EOC, immune score and stromal score were estimated to assess the proportion of immune and stroma components and tumor purity in TME of pre-NACT and post-NACT groups. Upon NACT, immune score and stroma score were significantly up-regulated, and corresponding ESTIMATE score increased (Fig. 3 C- 3 E). Correspondingly, tumor purity of tumor tissues in post-NACT groups reduced (Fig. 3 F), suggesting the immune phenotype in the TME is more complex. 5. The putative effects of NACT on infiltrated macrophages of EOC. Although the differences of M2 macrophages were not significant upon NACT, the change of M1 macrophages attracted our attention, as the dataset GSE158739 was analyzed using sorted tumor-associated macrophages in EOC tissues before and after NACT. The DEGs were quite different from those in other datasets (Fig. 1 A). 26 genes were upregulated and 126 genes were downregulated upon NACT (Fig. 4 A and 4 B). We further performed GO and KEGG signaling pathway analyses of these genes. The main biological processes enriched in macrophages after NACT included projection neuron structure (neuron projection organization), cell‒cell junction organization, and cell-substrate adhesion; enriched cellular components included the cell cortex, adherens junction, and collagen-containing extracellular matrix; and enriched molecular functions included extracellular matrix structural constituent, cadherin binding, and actin filament binding (Fig. 4 D). These findings suggested that NACT induced macrophages to respond to inflammatory conditions and regulates their cell adhesion and polarization. Consistent with these findings, further KEGG signaling pathway analysis revealed that the signaling pathways significantly enriched after NACT were involved in ECM-receptor interactions and adherens junctions, and the pathways significantly related to the inflammatory response included the human papillomavirus infection pathway and phosphatidylinositol 3-kinase/protein kinase B (PI3K/AKT) pathway (Fig. 4 C). It was supposed that the cell adhesion function of macrophages in the TME of OC patients changed significantly upon NACT. Among the genes significantly suppressed, lymphatic vessel endothelial hyaluronan receptor 1 (LYVE1) attracted our attention mostly (Fig. 4 B), as the expression of LYVE1 were also found to be consistently suppressed in another two datasets (Fig. 1 A). LYVE1 is an important glycoprotein located on the cell membrane that binds to hyaluronan and participates in hyaluronan metabolism in lymphatic endothelial cells. LYVE1 expressing macrophages could influences chemotherapy responses in murine breast cancer [ 10 ]. We further explored its expression in primary EOC tissues. The expression of LYVE1 was significantly suppressed in OC tissues compared with normal ovary tissues (Fig. 4 E), and the OC patients with lower LYVE1 expression had significantly prolonged OS (Fig. 4 F). 6. Macrophages in post-chemotherapy was correlated with PFS of patients with EOC. Otherwise, LYVE1 is thought to play an important role in iron deposition in macrophages and might promote a switch toward a proinflammatory phenotype during inflammatory conditions [ 11 ]. Combined with the results that M1 macrophages were suppressed in post-NACT tissues. We further explored the putative change of macrophages and lymphocytes in TME of EOC patients upon NACT. We retrospectively collected the patients with primary malignant EOC who were initially treated at the Department of Gynecology, Yongchuan Hospital of Chongqing Medical University, from January 2014 to July 2019. Among of them, 13 patients received the therapy of NACT combining IDS. The detailed clinical characteristics of these patients were summarized in Supplement Table II. Since 1 patient did IDS in other hospital, we collected 12 paired sections of tumor tissues from initial biopsy and internal reduction surgery from EOC patients. 11 patients relapsed during our observation phase; 3 patients recurrent in 6 months (platinum-resistant recurrent), and 8 patients recurrent within 2 years after completing therapy. According to the CRS score reported [ 12 ], one patient had CRS 1, eight patients had CRS 2, and three patients had CRS 3. We also analyzed the effects of the CRS on the prognosis of OC patients. As shown in the Supplement Fig. 2 A and 2 B, patients with CRS3 tended to have significantly longer PFS than patients with CRS2 or CRS1. After 2–3 cycles of NACT, there was a significant change in the morphology of the infiltrated macrophages, from large, round, and polymorphic to small, fibroinflammatory (Fig. 5 A). Only one patient showed upregulation of macrophage counts. Although macrophages were suppressed to some degree upon NACT, the difference was not significant (Fig. 5 B). The impact on infiltrated lymphocytes was quite different. There was no significant change in the number of local infiltrated lymphocytes, and 3 patients even showed a slight increase in the number of local infiltrated lymphocytes postchemotherapy; however, the difference in overall number of infiltrated lymphocytes was not statistically significant (Fig. 5 C). We further explored the putative correlations between CD68 + and CD8 + density and the PFS and OS of patients, albeit in a small cohort of patients. As shown in the survival curves, OS was greater in patients with high CD68 + density than in those with low CD68 + density following NACT, and a trend toward improved PFS was found in patients with high CD68 + density after NACT. For CD8 + cell infiltration, no significant difference was found between the high- and low-density groups. Although the difference between the two groups was not significant, patients with high CD8 + density had better OS and PFS (Fig. 5 D, supplemental Fig. 2C and 2D). Thus, high infiltration of CD68 + macrophages and CD8 + lymphocytes post-chemotherapy seems to be a better prognostic indicator for EOC patients with IDS. 7. LYVE1 + CD206 + PvTAMs might be correlated to chemotherapy sensitivity of EOC. Although the total expression of classical marker of M2 macrophages–CD68 was not changed significantly upon NACT in TME of EOC, the huge difference of gene expression among the macrophages before and after NACT, combining with the significant morphological changes suggesting the change of phenotypic diversity during the process of NACT. We used multiple immunofluorescence experiments to detect the difference of LYVE1 and relative M2 macrophage markers CD68 and CD206 to figure out the their putative correlation. Notably, the expression level and spatial location in TME of LYVE1 was significantly changed in EOC tissues compared with normal ovary tissues (Fig. 6 A). The expression level of LYVE1 was suppressed in EOC tissues compared to normal ovarian tissues and that LYVE1 expression decreased even more significantly in tissues after chemotherapy (Fig. 6 B). Moreover, LYVE1 + macrophages were almost co-expressed with CD206 but partially co-expressed with CD68 no matter in normal ovarian tissues or EOC tissues. In normal ovarian tissues, LYVE1 + CD206 + macrophages were rather evenly distributed across the tissue in vascular areas (Fig. 6 A and 6 D). But in tumor tissues, these LYVE1 + and CD206 + TAM subsets were heterogeneously distributed along the endothelium and clustered into discrete regions where these cells were either lining or appearing in bunches proximal to the vasculature, which were supposed to be the perivasular TAMs (PvTAMs) for their spatial proximity to the vasculature. However, macrophages with only CD68-positive cells infiltrated the central tumor epithelial tissues (Fig. 6 D). Recently, Joanne et al . reported that LYVE1 + TAMs could form coordinated multicellular “nest” structures that are heterogeneously distributed proximal to the vasculature in tumors in a spontaneous murine model of breast cancer, which was quite consistent with our results. They found that blocking the development of LYVE1 + TAMs or their nest structures could help to enhance the response to chemotherapy [ 10 ]. We also observed the PvTAMs in EOC tissues, co-expressing LYVE1, CD206, and partial CD68 in tumor stroma. Thus, we further analyzed the intensity of LYVE1 + CD206 + TAMs in patients with different types of CRS. The suppression of LYVE1 + CD206 + TAMs in patients with CRS3 was more significant than that in patients with CRS1 or CRS2, suggesting the putative effects of LYVE1 + CD206 + TAMs on the chemotherapeutic sensitivity of EOC (Fig. 6 C). Discussion RNA sequencing (RNA-seq) is an important genomic technology used to quantitatively understand molecular pathogenesis. Cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT) is a new algorithm for calculating the quantity of immune cells based on RNA transcript datasets, providing us with a new and microscopic way to assess immune cells in TME [ 13 ]. Multiple tumor types harbor complex ecosystems that might play a critical role in tumor progression and treatment response. Thus, the putative effects of NACT on the TME of EOC is controversial [ 14 ]. Based on RNA-seq technology, the variation of phenotypic and functional heterogeneity of TME in EOC during NACT could be comprehensively understood. In the present study, we collected the information of public published datasets, concerning on the effects of NACT on OC. In total, 6 datasets and 211 samples were included for further experiments (99 pre-NACT samples and 112 post-NACT samples). 1138 DEGs were found compared the pre-NACT with post-NACT groups. The five genes mostly depressed upon NACT were EPCAM, UBE2C, CCNB1, MKI67 , and BIRC5 , whose expression were consistently increased in primary EOC tissues compared with normal ovary tissues. All of these genes functioned as oncogenes by participating in the progression of cell cycle regulation, cell adhesion, apoptosis, respectively. Concurrently, NACT significantly up-regulated a cohort of suppressive genes, containing C7, EGR1, DUSP1, SFRP4, PPARG , and CCL14 , which were under-expressed in primary tumor tissues and play important function in transcriptional regulation, MAPK signaling pathway, Wnt signaling pathway, as well as mediating inflammation and immune response. KEGG signaling pathway analysis of the DEGs revealed that the pathways related to the greatest enrichment of genes affected by NACT were the inflammation-related IL-17 signaling pathway and the apoptosis-related P53 signaling pathway. Thus, our data demonstrated that NACT on one hand regulated genes and pathways important for mediating cytotoxic effects, such as through the regulation of proliferation and DNA damage, and on the other hand, up-regulated genes and pathways that activate the body's inflammatory and immune responses to the drug. Based on the protein network analysis, the top 6 hub genes, IL6, MMP9, CD8A, EZH2, PTPRC , and CDH1 , were figure out. All genes were reported to be up-regulated in primary OC tissues and participate in the immune reaction of the host in different ways. Among these genes, the expression of IL6, MMP9, CD8A and PTPRC exhibited significant increase after NACT. The diagnostic pattern using these hub genes could efficiently distinguish the normal ovarian tissues from the gynecology malignancies, including OC. While, the DEGs in macrophages after NACT were quite different from those in total tumor tissues. KEGG signaling pathway analysis revealed that the signaling pathways significantly enriched in macrophages after NACT were involved in ECM-receptor interactions and adherens junctions. Combined with the metamorphosis of macrophages, identified by our further immunohistochemistry assays, these findings suggested that NACT significantly affected the adhesion of macrophages in the TME of EOC patients. As we mentioned, the morphology of the infiltrated CD68 + macrophages, changed from large, round, and polymorphic to small, fibroinflammatory. Many papers only identified the change of CD68 expression during NACT, but ignored the metamorphosis of macrophages, suggesting the change of phenotype and function of macrophages. Chemical response system (CRS) is a kind of evaluation system used to assess the chemotherapeutic sensitivity of cancer cells and relative TME cells [ 15 ]. Depending on the morphological changes in cancer cells and regression-associated fibroinflammatory changes, the omentum tissues of IDS were divided into CRS1 (total/nearly total nonresponse), CRS2 (partial response) and CRS3 (good response) groups. The scoring system was supposed to be more important than debulking status for the prognosis of EOC patients, as patients who were evaluated with CRS3 had a higher ratio of complete resection and a lower probability of primary platinum-resistant disease [ 16 ]. Although the system was supposed to assess the change in omentum upon chemotherapy, the metamorphosis of cancer cells and regression-associated fibroinflammatory changes also could be found in primary tumor tissues. We also found that patients with CRS1 had worst PFS compared to patients with CRS2/3. The lymphocytes that infiltrated the tumor tissues were small and rounded, and the changes in the morphology of the infiltrated lymphocytes were not significant compared with macrophages. In the present study, although the difference of CD68 + macrophages and CD8 + lymphocytes in TME of EOC before and after chemotherapy were not significant, which were consistent with Owen et al . results [ 9 ], patients with high infiltration of CD68 + density or CD8 + density after NACT tended to have a better prognosis. As a typical marker of M2 macrophage, although the difference of expression intensity of CD68 was not significant during NACT, the obvious morphological changes promote us to further identify putative change of phenotypic diversity during NACT. According to the DEGs, LYVE1 attracted our attention. LYVE1 is widely expressed on the lymphatic endothelium, in subsets of vascular endothelial cells, and on some subgroups of macrophages, such as tumor-infiltrated PvTAMs [ 17 ]. The molecular functions of LYVE1 are diverse. In addition to participating in endocytosis and scavenging, it has also been implicated in the adhesion and migration of immune and tumor cells. One of the putative mechanisms involved is that LYVE1 plays an important role in the iron deposition of macrophages, which might promote a switch toward a proinflammatory phenotype during inflammatory conditions, such as antitumor immunity [ 11 , 18 ]. On the other hand, LYVE1 is also thought to participate in leukocyte adhesion to lymphatic endothelial cells [ 19 ], which might allow it to participate in tumor metastasis. In this study, we found that LYVE1 co-expressed with CD206 in both normal and tumor tissues, and this subtype of macrophage was heterogeneously distributed along the endothelium and clustered into discrete regions where these cells were either lining or appearing in bunches proximal to the vasculature; these cells were called “nest” structures by Joanne et al . Blocking the development of this “nest” structure could help to enhance the response to chemotherapy in murine breast cancer [ 10 ]. While CD68 + macrophages, in addition to being co-expressed with LYVE1 + CD206 + macrophages in the stroma, also infiltrated central tumor epithelial tissues, suggesting that these two kinds of subtypes might perform different functions in TME of EOC. As the expression of CD68 + was not found after NACT, but the suppression of LYVE1 + macrophages in patients with CRS3 was more significant than that in patients with CRS1, suggesting that the putative effects of LYVE1 + CD206 + macrophages on the chemotherapy sensitivity of EOC. Otherwise, Nan et al . also reported that LYVE1 hi mesothelial macrophages could drive tumor growth independently of the omentum, as syngeneic epithelial ovarian tumor growth was strongly reduced following ablation of LYVE1 hi macrophages in vivo , including in mice that received omentectomy to prevent the release of these macrophages from omental macrophages [ 20 ]. However, its effects on tumor metastasis might be highly tumor specific, as Anna et al . reported that LYVE1 deficiency could enhance the influence of the premetastatic hepatic immune microenvironment on early liver metastasis [ 21 ], but this influence was not found in syngeneic colorectal carcinoma in mice. The “nest” structure of LYVE1 + CD206 + macrophages were significantly found in our study, further experiments are needed to identify its function in EOC. LYVE1 + CD206 + macrophages, also be thought as PvTAMs, are a specialized and highly polarized TAM phenotype. PvTAMs have been demonstrated to shape the process of neo-angiogenesis [ 22 ], and recruitment of cytotoxic lymphocytes CD8+ [ 10 ]. The change in specific subtype rather than the overall M2 macrophages urges us to further refine the research on the function of different subtypes of immune cells. Herein, immune score, stroma score and relative ESTIMATE score in post-NACT group were all significantly higher than the pre-NACT group, implying that the TME of post-NACT was more complex. Enhanced immune phenotypes with low tumor purity was independently correlated with reduced survival time in patients with glioma. Moreover, macrophages and neutrophils were enriched in low purity glioma and could be served as robust indicators for poor prognosis [ 23 ]. Thus, Identifying the immune cell fraction might provide a new way to improve the diagnosis, prognosis and treatment response to immune therapy in patients with malignancies. Conclusion This study highlights the complex alterations in the TME of EOC following NACT exposure and reveals how immunological factors are involved in mediating the chemotherapeutic response. The LYVE1 + CD206 + PvTAMs were identified in EOC patients and might be correlated with chemotherapeutic response, which will allow for the future development of novel immunologic therapies to combat chemoresistance. However, there are still many limitations in this study. Due to the retrospective nature of the study, we collected only tumor tissue sections, thus we did not analyze the RNA sequence of our own tissues before or after NACT. Otherwise, the number of patients who received neoadjuvant chemotherapy included in this study was small, and it is necessary to increase the sample size to increase the statistical persuasiveness of the results. Declarations Acknowledgments Not applicable. Funding This work was supported by grants from the National Natural Science Foundation of China (Grant No. 81902645) and the Scientific Research Project of Yongchuan Hospital of Chongqing Medical University (YJLC202115). Conflicts of interests The authors declare that they have no competing interests. Authors’ contributions Investigation, writing-original draft preparation, Yunyun Li; methodology and resources and data curation, Fei Li; Pathological section diagnosis and evaluation, Yao Li and Xue Liu; software and supervision, Cuiying Zhang; writing—review and editing and funding acquisition, Li-na Hu. Availability of data and materials The data generated in the present study may be requested from the corresponding author. Ethics approval and consent to participate The present retrospective study was approved by the Institutional Ethics Committee of Yongchuan Hospital of Chongqing Medical University (2022109). 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3900539","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":269596886,"identity":"af3c1991-8722-4e82-a857-251eadb09748","order_by":0,"name":"Yunyun Li","email":"","orcid":"","institution":"Second Affiliated Hospital of Chongqing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yunyun","middleName":"","lastName":"Li","suffix":""},{"id":269596887,"identity":"ffff3b0b-5c81-437e-aa81-4190fb6401ff","order_by":1,"name":"Fei Li","email":"","orcid":"","institution":"the Yongchuan Hospital of Chongqing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Fei","middleName":"","lastName":"Li","suffix":""},{"id":269596888,"identity":"3b409d8c-b9b3-4259-b984-9243c3fbd969","order_by":2,"name":"Yao Li","email":"","orcid":"","institution":"the Yongchuan Hospital of Chongqing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yao","middleName":"","lastName":"Li","suffix":""},{"id":269596889,"identity":"01b14ca5-ff4a-4c8e-b408-a94934c8f9c5","order_by":3,"name":"Xue Liu","email":"","orcid":"","institution":"the Yongchuan Hospital of Chongqing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xue","middleName":"","lastName":"Liu","suffix":""},{"id":269596890,"identity":"227ac461-044c-44f3-9faa-a2c28296af73","order_by":4,"name":"Cuiying Zhang","email":"","orcid":"","institution":"the Yongchuan Hospital of Chongqing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Cuiying","middleName":"","lastName":"Zhang","suffix":""},{"id":269596891,"identity":"ce888619-624f-4fca-8d56-b26c39fc9b99","order_by":5,"name":"Li-na Hu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAz0lEQVRIiWNgGAWjYDCCA2DSgoeBAYg+GNjIEaOFsYGBQQKshXFGQZox0VoYQFqYeT4cTiSog+948/MHH/dIyJiznz0mbWPAnMDAfvjoBnxaJM8cM2yc8UyCx7InL006x4Atj4EnLe0GPi0GN3IYm3kOSPAYHMgxA2rhKQb6y4xILeffmElbGEgkNhCv5QbQFgYDA8JaQH6ZOQOs5Y2xZY9BgjEbIb8AQ+zBhw8HbOwNzucY3vjx578cP/vhY3i1YAI20pSPglEwCkbBKMAGAJTCR/ec+ulZAAAAAElFTkSuQmCC","orcid":"","institution":"the Second Affiliated Hospital of Chongqing Medical University","correspondingAuthor":true,"prefix":"","firstName":"Li-na","middleName":"","lastName":"Hu","suffix":""}],"badges":[],"createdAt":"2024-01-26 16:44:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3900539/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3900539/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":50387586,"identity":"f7f21a77-f768-423f-a8e1-e23361505d37","added_by":"auto","created_at":"2024-01-30 17:57:54","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":716890,"visible":true,"origin":"","legend":"\u003cp\u003eThe differentially expressed genes of OC tissues before and after NACT with public published Gene Expression Omnibus (GEO) datasets. A). Heatmap analysis of the top 100 DEGs among pre-NACT and post-NACT samples. Heatmap was generated to depict the mean expression intensity of the mRNA transcripts. Genes marked green are suppressed in OC tissues compared with normal tissues identified by the TCGA datasets; Genes marked red are increased in OC tissues compared with normal tissues in the TCGA datasets. The genes marked with symbols mean its influence the prognosis of patients with OC was significant. B). Relative expression levels of EPCAM, UBE2C, CCNB1 and MKI67 in OC tissues compared to normal ovary tissues in TCGA datasets. C. Relative expression levels of BIRC5, C7, DUSP1 and SFRP4 in OC tissues compared to those in normal ovary tissues according to TCGA datasets. D. Relative expression levels of PPARG and CCL14 in OC tissues compared to those in normal ovary tissues according to TCGA datasets. E. K‒M survival curves of BIRC on the overall survival of OC patients. Log-rank \u003cem\u003ep\u003c/em\u003e values are shown. * \u003cem\u003eP\u003c/em\u003e\u0026lt;0.05.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-3900539/v1/09899e05d463d4b839116d74.png"},{"id":50388320,"identity":"eed708a3-7bd6-4b56-9353-cc9b22fc32b3","added_by":"auto","created_at":"2024-01-30 18:05:54","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":554021,"visible":true,"origin":"","legend":"\u003cp\u003eFunctional enrichment analysis of differential expressed genes (DEGs) of OC tissues before and after NACT. A). Gene ontology (GO) analysis of DEGs upon NACT treatment with different functions. BP: biological process; CC: cellular component; MF: molecular function. B). Kyoto Encyclopedia of Genes and Genomes (KEGG) Pathway enrichment analysis showing the pathways enriched in DEGs after chemotherapy. C). Protein‒protein interaction (PPI) network for the DEGs. Each node in the network represents a protein, and the connection represents the interaction between proteins. Genes marked red were increased upon NACT, and genes marked green were supppressed upon NACT. D). Principal component analysis (PCA) of the diagnostic pattern by 6 hub genes in distinguish the normal ovarian tissues from gynecological malignancies. OV_tumor: ovarian cancer; UCEC_tumor: endometrial cancer; UCS_tumor: cervical cancer.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-3900539/v1/31103cb19cc3170fcf9ff3d3.png"},{"id":50388318,"identity":"92430b5c-1305-4bfe-9f3b-a77bd5dfb4ac","added_by":"auto","created_at":"2024-01-30 18:05:54","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1126870,"visible":true,"origin":"","legend":"\u003cp\u003eThe differences of infiltrating immune cells in TME upon NACT were clustered and analyzed according to specific immune cell marker genes. A). Clustering of infiltrating immune cells in TME compared pre-NACT with post-NACT samples. Stacked bar charts of samples ordered by clustering assignment. B). Comparison of 22 kinds of immune cells between pre-NACT and post-NACT OC patients according to specific immune cell marker genes. C-F). Comparison of immune score (C), stroma score (D), ESTIMATE score (E) and relative tumor purity (F) of OC tissues in pre-NACT and post-NACT groups; *\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05. **\u003cem\u003eP\u003c/em\u003e\u0026lt;0.01.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-3900539/v1/77cf5dee8e621b97a9c58a35.png"},{"id":50388756,"identity":"548d58a6-f7a9-423c-be7f-af62009ddfcf","added_by":"auto","created_at":"2024-01-30 18:13:54","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":625011,"visible":true,"origin":"","legend":"\u003cp\u003eFunctional enrichment analysis of DEGs of macrophages sorted from OC tissues pre-NACT and post-NACT. A). Heatmap analysis of the top DEGs in sorted tumor associated macrophages in OC tissues of pre-NACT and post-NACT groups. B). Volcano plot visualizing the DEGs of macrophages; C). KEGG Pathway enrichment analysis showing the pathways enriched in DEGs of macrophages after chemotherapy compared with before chemotherapy; D). GO analysis of DEGs of macrophages after chemotherapy compared with before chemotherapy; E). Relative expression level of LYVE1 mRNA in OC tissues compared with that in normal ovarian tissues in TCGA datasets. F). K‒M survival curves showing the association between LYVE1 expression and OS in OC patients. Log-rank \u003cem\u003ep\u003c/em\u003e values are shown. * \u003cem\u003eP\u003c/em\u003e\u0026lt;0.05.\u003c/p\u003e","description":"","filename":"FIgure4.png","url":"https://assets-eu.researchsquare.com/files/rs-3900539/v1/2ed98d87b3d9bb6bd38be27a.png"},{"id":50387587,"identity":"16c7069e-424f-421c-a285-493dba20ea96","added_by":"auto","created_at":"2024-01-30 17:57:54","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1031771,"visible":true,"origin":"","legend":"\u003cp\u003eThe putative effects of NACT on infiltrating immune cells. A). Representative images of IHC staining of CD68+ macrophages and CD8+ lymphocytes in pre-NACT and post-NACT samples from patients with different chemotherapy response systems (CRSs) of high-grade serous epithelial ovarian carcinoma. B). Changes in the mean CD68+ macrophage count per HPF in paired pre-NACT and post-NACT patients. C). Changes in the mean CD8+ macrophage count per HPF in paired pre-NACT and post-NACT patients. D). K‒M survival curves showing the association of CD68+ and CD8+ macrophages in post-NACT OC tissues with OS. Log-rank \u003cem\u003ep\u003c/em\u003e values are shown. The cutoff values were set as the mean counts per group.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-3900539/v1/a429afcf0f81503de2d6b23d.png"},{"id":50387582,"identity":"efcd6d62-0559-4a18-ba45-8e87ad467c54","added_by":"auto","created_at":"2024-01-30 17:57:54","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":2235121,"visible":true,"origin":"","legend":"\u003cp\u003eLYVE1+ macrophages were suppressed in OC tissues. A). Whole-mount multi immunofluorescence images of the relative CD68+, CD206+, LYVE1+ macrophages and CD8+ lymphocytes. Scale bar, 200 µm; The upper images present the OC tissues before and the middle images present the OC tissus after NACT, respectively. The bottom image present the normal ovarian tissues with relative markers expression; B). Comparison of quantification of LYVE1 expression in paired OC tissues before and after chemotherapy; The expression level of LYVE1 was present as the max intensity of immunofluorescence. C). Comparison of quantification of LYVE1 expression in OC tissues with different CRSs; D). Enlarged multi immunofluorescence images of the relative CD68+, CD206+, LYVE1+ macrophages in epithelial and stroma of OC tissues. Scale bar, 50 µm for lower images presenting nested macrophages. The data are representative of three independent experiments (n = 3 per genotype; mean ± SEM). Macrophages were quantified in multiple regions. *\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05. **\u003cem\u003eP\u003c/em\u003e\u0026lt;0.01.\u003c/p\u003e","description":"","filename":"FIgure6.png","url":"https://assets-eu.researchsquare.com/files/rs-3900539/v1/2fe36fff2d2f6651f8b38386.png"},{"id":50428575,"identity":"20b70d49-5301-454b-b1bf-5e356d9ee772","added_by":"auto","created_at":"2024-01-31 11:22:32","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4650478,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3900539/v1/8621964a-bd2f-429f-9644-11f3ee3cfa0d.pdf"},{"id":50388317,"identity":"5fec1948-8003-4502-9194-8f93188e5aba","added_by":"auto","created_at":"2024-01-30 18:05:54","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":13024,"visible":true,"origin":"","legend":"","description":"","filename":"supplementtable1.docx","url":"https://assets-eu.researchsquare.com/files/rs-3900539/v1/0ab4c0a68ead8cf5f7dfa7ea.docx"},{"id":50387581,"identity":"b812242c-e4d6-467b-b864-fdba49b1ac0a","added_by":"auto","created_at":"2024-01-30 17:57:54","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":14761,"visible":true,"origin":"","legend":"","description":"","filename":"Table2informationofpaitents.docx","url":"https://assets-eu.researchsquare.com/files/rs-3900539/v1/8772fe2b077f1cdb9e46ebbf.docx"},{"id":50387591,"identity":"4d1b41ae-6404-493c-a49d-e9ce4f88b92a","added_by":"auto","created_at":"2024-01-30 17:57:55","extension":"tif","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":3473748,"visible":true,"origin":"","legend":"","description":"","filename":"supplementfigure1.tif","url":"https://assets-eu.researchsquare.com/files/rs-3900539/v1/603d5d5132e13c7c45e709e4.tif"},{"id":50388321,"identity":"a93d3d4b-1cb7-469c-b30b-a93fb54920b4","added_by":"auto","created_at":"2024-01-30 18:05:55","extension":"tif","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":2867432,"visible":true,"origin":"","legend":"","description":"","filename":"supplementfigure2.tif","url":"https://assets-eu.researchsquare.com/files/rs-3900539/v1/15e3fa4c7f83fbe52da39f94.tif"}],"financialInterests":"No competing interests reported.","formattedTitle":"The putative effects of carboplatin based neoadjuvant chemotherapy on tumor microenvironment of epithelial ovarian carcinoma","fulltext":[{"header":"Introduction","content":"\u003cp\u003eEpithelial ovarian carcinoma (EOC) is the most lethal gynecological malignancy. Only 45% of patients survive 5 years after diagnosis [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Complete cytoreduction is associated with the best survival, irrespective of the surgical strategy; however, for many advanced patients, complete resection cannot be achieved since many patients are widely diagnosed with tumor metastasis. Thus, for these patients, neoadjuvant chemotherapy (NACT) followed by interval debulking surgery (IDS) is an alternative choice, as patients with complete cytoreduction after NACT fared better than those with residual disease after primary debulking surgery (PDS) [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe interaction between the immune system and the tumor is likely based on an equilibrium between immune recognition and tolerance through immune cells in tumor microenvironment (TME) [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. TME is composed of many different cellulars, containing tumor and immune cells, and acellular components that together driving tumor growth, invasion, metastasis and response to chemotherapy [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. For patients with EOC, a putative immunosuppressive TME was reported to accompany most patients [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Consequently, immuno-therapy is increasingly being employed as a treatment modality for OC patients with advanced stages. Among the immunotherapy, immune checkpoint inhibitors (ICIs) were the most common kind since its significant value achieved in the treatment of other kinds of cancers [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. ICIs were introduced in the treatment combing with conventional chemotherapy for OC patients for the primary hypothesis that chemotherapy agents could active the TME of cancers, and provide a broad-acting immune stimulus function. However, the survival time of combing therapy group in this clinical trail did not present obvious advantages [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], wondering us the exact effects of adjuvant chemotherapy on TME of EOC.\u003c/p\u003e \u003cp\u003eIn general, cytotoxic chemotherapy is assumed to be immunosuppressive because of its toxicity to dividing cells in the bone marrow and peripheral lymphoid tissues. However, increasing evidence highlights that NACT might act as an immune modulator in OC. For the local immune system, lymphocyte-related immune reactions are supposed to increase, as T-activated cells are found to be enhanced, and T-regulatory cell density decreases upon NACT [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. In addition, carboplatin could alter the subtype of macrophages, which might induce the activation of antitumor immunity in some cancer models [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. However, the putative effects of NACT on TME of EOC were controversial. A comprehensive dynamic understanding of the effects of NACT on the TME might provide more novel reference to evaluate the prognosis of EOC patients and provide new strategies for immuno-therapy.\u003c/p\u003e \u003cp\u003eAccordingly, based on the published bioinformatics content, we integratedly analyzed the differentially expressed genes (DEGs) in EOC tissues upon neoadjuvant chemotherapy, and conducted functional enrichment analysis of these DEGs. The differences of immune cells in TME upon NACT were clustered and analyzed according to specific immune cell marker genes. Herein, a special subtype of tumor associated macrophages (TAMs) was found to be significantly suppressed post NACT and might be correlated with chemotherapeutic sensitivity of EOC. This might provide a new target for tumor immuno-therapy in the future.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Patients\u0026rsquo; information\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eWe retrospectively searched the electronic medical records of all patients who were first diagnosed with primary ovarian malignancy at the Department of Gynecology, Yongchuan Hospital of Chongqing Medical University, from Jan. 2014 to July. 2019. Patients were clinically staged according to the International Federation of Gynecology and Obstetrics (FIGO) staging criteria. All patients received no other surgeries or chemotherapy before surgery.\u003c/p\u003e \u003cp\u003eAmong the patients included, 13 patients received 2\u0026ndash;3 cycles of neoadjuvant chemotherapy (NACT) and interval cytoreductive surgery. The chemotherapy regimen was carboplatin (AUC\u0026thinsp;=\u0026thinsp;5) plus paclitaxel (175 mg/m\u003csup\u003e2\u003c/sup\u003e). The following information was extracted from the medical records of the eligible patients: 1) patient demographics, 2) final pathology report, 3) preoperative complete blood counts, and 4) review of past medical history and medications prior to surgical staging. All patients received 3\u0026ndash;4 cycles of chemotherapy after the IDS. After standard treatment administration, all patients were subjected to routine follow-up, including gynecological examination, CT scan, and CA125 and HE4 assessments every 3\u0026ndash;6 months. If needed, PET-CT should be considered for some patients. During the follow-up, once the tumor biomarker level was elevated combined with imaging evidence of new tumor growth, tumor recurrence was considered. PFS was defined as the time interval from initial diagnosis to the date of the first recurrence. The last follow-up date for all patients was Feb. 2022. The follow-up time ranged from 32 to 97 months.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Immunohistochemistry\u003c/h2\u003e \u003cp\u003eFor the analysis of tumor-infiltrated immune cells, tissue sections from the first biopsy surgery and interval cytoreductive surgery were collected from the Pathology Department of Chongqing Medical University. Among the patients, one underwent IDS at another hospital. Thus, 12 matched pre- and post-NACT biopsy samples were included for further experiments. Sections were dewaxed, dehydrated, and incubated in antigen unmasking solution in a microwave for 20 minutes. The sections were incubated in 0.3% H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e in methanol for 10 minutes and in blocking buffer (2.5% BSA and 2.5% goat serum in PBS) for 60 minutes at room temperature. Primary antibodies against the following proteins were incubated in blocking buffer overnight at 4\u0026deg;C: anti-CD8 (Proteintech, USA, 1:500 dilution) and anti-CD68 (Proteintech, USA, 1:500 dilution). The next day, the sections were washed with PBS supplemented with 0.1% Tween-20 (PBST). The sections were incubated with a vector impact kit for 1 hour at room temperature and washed 3 times in PBST before the addition of the DAB chromogen for 20 seconds. Then, the sections were imaged using scanner software. The cutoff value for high expression was set as the mean value. The levels of CD68 and CD8 were calculated manually at high magnification (40x objective) in 5 randomly selected fields for each sample, and the mean values of each marker were recorded. The results were reviewed with the oversight of a pathologist, Dr. Yao Li.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Immunofluorescence\u003c/h2\u003e \u003cp\u003eSections of OC tissues were fixed in 4% paraformaldehyde in PBS (Gibco) for 10 min at room temperature. The sections were incubated with 0.2% Triton X-100 in blocking buffer, BSA (10% rabbit serum for goat-derived primary antibodies and 3% BSA for other sources of primary antibodies) was added, and the samples were blocked for 30 minutes. The following antibodies were used at relative dilutions unless stated otherwise: anti-CD8 (Proteintech, USA, 1:1000), anti-CD68 (Proteintech, USA, 1:3000), anti-CD206 (Bio-Rad, USA, 1:5000), and anti-LYVE1 (Abcam, 14917, 1:3000). Primary antibodies were detected using Cy\u0026trade;3 donkey anti-sheep IgG (1:100; Jackson ImmunoResearch, 1 mg/ml) overnight at 4\u0026deg;C with gentle agitation. The sections were washed with PBS and then incubated with HRP-labeled secondary antibodies (iF488-tyramide, Cy3-tyramide, iF647-tyramide, and FITC-tyramide) for 1 hour at room temperature. Nuclei were further stained with 4\u0026prime;,6-diamidino-2-phenylindole and dihydrochloride (DAPI). The sections were washed three times with PBST and imaged using the Nikon Eclipse C1 Imaging system and associated software 3DHISTECH (Pannoramic MIDI).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Datasets Acquisition and Analysis\u003c/h2\u003e \u003cp\u003eThe datasets used in this study were downloaded from the Gene Expression Omnibus (GEO) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/geo/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/geo/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), a publicly available research project of the American National Cancer Institute. The keywords used for the search were neoadjuvant chemotherapy/chemotherapy and ovarian cancer/ovarian carcinoma. A total of nine datasets were found, among which one dataset was analyzed via single-cell RNA sequencing and two were analyzed via peripheral blood; thus, these datasets were excluded. The RNA-seq transcriptome data were subsequently normalized to the fragments per kilobase of exon model per million (FPKM, mean fragment per kilobase million). A total of 211 samples were obtained. Dynamic analysis of the gene expression profile data was performed through the GEPIA (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://gepia.cancer-pku.cn/\u003c/span\u003e\u003cspan address=\"http://gepia.cancer-pku.cn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) website to obtain relative gene expression levels and survival analysis results for OC tissues. Principal component analysis (PCA) was also performed using the GEPIA website.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Differential gene expression and functional enrichment analysis\u003c/h2\u003e \u003cp\u003eDifferential gene expression analysis was performed using the edgeR and limma R packages [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. A generalized linear model with a 0\u0026thinsp;+\u0026thinsp;group\u0026thinsp;+\u0026thinsp;batch design was used. The DAVID database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://david.ncifcrf.gov/summary.jsp\u003c/span\u003e\u003cspan address=\"https://david.ncifcrf.gov/summary.jsp\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), an online bioinformatics resource that provides researchers with annotation tools for understanding the biological mechanisms involved in the function of a large number of genes or proteins, was used to perform GO function enrichment and KEGG pathway enrichment analysis of the DEGs. A \u003cem\u003eP\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and a number of genes greater than 10 were considered to indicate statistically significant results.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6. Construction of the PPI network and Hub Gene Screening\u003c/h2\u003e \u003cp\u003eThe STRING database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://string-db.org\u003c/span\u003e\u003cspan address=\"https://string-db.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and Cytoscape 3.7.1 software were used to construct a protein‒protein interaction network for the differentially expressed genes. The top 10 genes with the highest degree of upregulation and downregulation were plug-in as core genes. The hub genes were subjected to interaction analysis via the STRING database online analysis platform. An interaction analysis network was constructed between the hub genes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7. Analysis of infiltrating immune cell components\u003c/h2\u003e \u003cp\u003eTo estimate the immune cell components before and after neoadjuvant chemotherapy, CIBERSORT was used with the LM22 signature and 1000 permutations. We used a panel of 22 immune cells consisting of B cells, T cells, natural killer cells, macrophages, dendritic cells, and myeloid subsets.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8. Statistical analysis\u003c/h2\u003e \u003cp\u003eThe data are reported as the mean and standard deviation of at least three independent experiments. Descriptive values of quantitative continuous variables, such as age and neutrophil, monocyte, and lymphocyte counts, were examined using standard descriptive statistical methods. Student\u0026rsquo;s t tests (for two groups) and variance analysis (for more than two groups) were used for the analyses. Comparisons of categorical variables were made by the chi-square test or Fisher\u0026rsquo;s exact test, depending on the state of case distributions. Statistical analyses were carried out using SPSS 20.0 and GraphPad Prism 7.0 software. Progression-free survival (PFS) and overall survival (OS) analyses of categorical variables were performed using the Kaplan‒Meier method, and significant differences between groups were identified using the log-rank test. Univariate and multivariate analyses were performed using Cox proportional hazards models. \u003cem\u003eP\u003c/em\u003e values less than 0.05 were considered to indicate statistical significance.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e1. Differentially expressed genes upon NACT for patients with EOC\u003c/h2\u003e \u003cp\u003eTo better understand the putative alterations in the TME of EOC upon NACT, we searched the GEO database to retrieve information on the impact of NACT on ovarian cancer tissue. A total of 9 datasets were included in our analysis. Among these datasets, two studies explored the effects of NACT on peripheral blood, and one explored the effects of NACT using single-cell RNA sequencing; thus, these studies were excluded. Six final datasets (GSE143897, GSE71340, GSE158739, GSE201600, GSE181597 and GSE109934) were included for further research. In total, 211 samples were included for further experiment (99 pre-NACT samples and 112 post-NACT samples). The detailed distribution of the patients is summarized in Table I. The datasets were normalized to the Count value, and the differences in gene expression were summed for each of the 6 datasets. The absolute values were subsequently calculated, and a total of 1138 DEGs were found. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, the top 100 DEGs were selected based on their order of magnitude. The whole-genome sequencing (WGS) data from the GSE143897, GSE71340, and GSE158739 datasets were used; these data were more extensive than those from the GSE201600, GSE181597, and GSE109934 cohorts. The latter data included some missing genes with differential expression. The GSE158739 data were obtained from sorted macrophages, so the gene expression trend of these cells was quite different from that of the other 5 groups.\u003c/p\u003e \u003cp\u003eTable I. The detailed information of GEO datasets included in our study, including the biopsies numbers, biopsies sites, whether the biopsies were matched, the immune and stroma scores before and after NACT, and relative significant change of immune cells infiltrated. *P\u0026thinsp;\u0026lt;\u0026thinsp;0.05. **P\u0026thinsp;\u0026lt;\u0026thinsp;0.01.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo. Of GSE data\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBiopsies number\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBiopsies site\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMatched\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eimmune score\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003estroma score\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eChanges of immune cells infiltrating upon NACT\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGSE143897\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18 pre- and 18 post-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePrimary tumor site\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.019*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eM0 \u0026uarr; (but \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.09)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGSE71340\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11 pre- and 18 post-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eomentum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCD8+ \u0026uarr; (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.028)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGSE181597\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25 pre- and 19 post-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePrimary tumor site\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.157\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTh \u0026darr;; M1 \u0026darr;; M2 \u0026uarr;;\u003c/p\u003e \u003cp\u003eMast cell \u0026uarr;; Eosinophils \u0026uarr;;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGSE201600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31 pre- and 31 post-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePrimary tumor site\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000725**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.011**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCD8+ \u0026uarr;; Treg \u0026darr;; M1 \u0026darr;; mast cells \u0026uarr;;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGSE158739\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 pre- and 7 post-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eomentum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGSE109934\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19 pre- and 19 post-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePrimary tumor site\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe top 100 DEGs were subsequently analyzed for their expression in primary OC tissues using the TCGA database (GEPIA). Interestingly, the genes whose expression significantly decreased after NACT were consistently highly expressed in OC tissues, and some genes were closely related to patient disease-free survival (PFS) or overall survival (OS) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA); however, those genes whose expression significantly increased after NACT had no significant trend in expression before chemotherapy. Among these genes, the five genes (\u003cem\u003eC7, EGR1, DUSP1, SFRP4, PPARG, CCL14\u003c/em\u003e) most significantly upregulated upon NACT were proposed to be suppressed in primary OC tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB and \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). As shown in Supplement Table I, most of these genes participate in inflammatory responses and immune regulation, indicating that NACT activates inflammation and the immune response in OC tumor tissues. The five genes with the most significant decrease in expression were \u003cem\u003eEPCAM, UBE2C, CCNB1, MKI67\u003c/em\u003e, and \u003cem\u003eBIRC5\u003c/em\u003e. We also further explored their expression levels in the TCGA database. As expected, these genes were consistently highly expressed in OC tissues compared with normal tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC and \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD); these genes function as oncogenes by participating in the progression of tumors through cell cycle regulation, cell adhesion, and apoptosis (Supplement Table I). The suppression of the expression of these genes upon NACT suggested that chemotherapy mediated inhibition on the tumor cells. We further explored the prognostic value of these oncogenes and tumor suppressors on PFS and OS of patients with EOC. Except for BIRC5, none of these genes was supposed to be present effects on PFS and OS of patients with EOC (Supplement Figure). The OS of EOC patients with high expression of BIRC5 was longer than those patients with lower expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e2. GO and KEGG analyses of DEGs upon NACT\u003c/h2\u003e \u003cp\u003eWe further performed a gene ontology (GO) analysis of the DEGs using the DAVID website, and the results are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The overall dataset-related DEG-enriched biological processes (BP) included regulating MAP kinase activity, cellular chemical toxic side effects, and myeloid leukocyte migration processes; the enriched cellular components (CC) included extracellular matrix containing collagen, cyclin-dependent kinase full-enzyme complex, and protein kinase complex; and the enriched molecular functions (MF) were chemokine activity, cyclin-dependent kinase tyrosine/serine kinase regulatory activity, and cytokine activity (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003eKEGG signaling pathway analysis of the DEGs revealed that the highest enrichment was in the inflammation-related IL-17 signaling pathway and the apoptosis-related P53 signaling pathway. The most significant and most enriched signaling pathways were the PI3K/AKT pathway, the cytokine‒cytokine receptor interaction pathway, and pathways related to cancer (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Therefore, the overall response of EOC after NACT was related to the toxic side effects of the corresponding chemotherapeutic drugs and the related cell cycle regulation.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3. Protein network analysis of DEGs upon NACT\u003c/h2\u003e \u003cp\u003eTo understand the relationships between DEGs, the Search Tool for the Retrieval of Interacting Genes (STRING) was used to construct a protein‒protein interaction (PPI) network for the obtained DEGs. Each node in the network represents a protein, and the connection represents the interaction between proteins. A clear clustering between upregulated and downregulated proteins after NACT was found. The top 6 hub genes were selected based on the node connectivity calculated by Cytoscape software and included interleukin-6 (IL6), matrix metalloproteinase 9 (MMP9), CD8A, EZH2, protein tyrosine phosphatase receptor type C (PTPRC), CD8A, and cadherin 1 (CDH1). Among them, IL6, MMP9, CD8A and PTPRC exhibited increased expression after NACT, while EZH2 and CDH1 exhibited decreased expression after NACT. We further evaluated the expression of these hub genes in the TCGA OC-related database. \u003cem\u003eMMP9\u003c/em\u003e, \u003cem\u003eEZH2\u003c/em\u003e, and \u003cem\u003eCDH1\u003c/em\u003e were significantly upregulated in OC tissues compared to normal ovarian tissues (Supplement Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB, \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC and \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE). \u003cem\u003eIL6\u003c/em\u003e, \u003cem\u003eCD8A\u003c/em\u003e, and \u003cem\u003ePTPRC\u003c/em\u003e were also upregulated in tumor tissues, but the difference was not significant (Supplemental Fig.\u0026nbsp;1A and 1D). \u003cem\u003eIL6\u003c/em\u003e is an important cytokine involved in the inflammatory response and B lymphocyte maturation. \u003cem\u003ePTPRC\u003c/em\u003e is a member of the protein tyrosine phosphatase (PTP) family and is involved in the regulation of T and B lymphocyte antigen receptors. It can directly bind to antigen receptor complexes or activate different Src family kinases, which play important roles in antigen presentation. \u003cem\u003eCD8A\u003c/em\u003e, also known as the CD8 antigen, is located on the surface of cytotoxic T cells and can recognize antigen-presenting cells and participate in immune responses. All these genes participate in the immune reaction.\u003c/p\u003e \u003cp\u003eWe further examined the impact of these six genes on the disease-free survival (DFS) and OS of patients with EOC with TCGA dataset. Except for \u003cem\u003eCD8A\u003c/em\u003e and \u003cem\u003eMMP9\u003c/em\u003e, other four genes had no significant impact on OS or DFS of patients with EOC (Supplemental Fig.\u0026nbsp;1F and 1G). EOC patients with high expression of MMP9 or CD8A present to be with longer DFS compared with those patients with lower expression. The high expression of the above hub genes strongly suggested the activation of inflammatory responses in the body after NACT. Using the above hub genes, we performed a diagnostic model using principal component analysis for distinguish normal ovarian tissues from malignant tissues. Interestingly, these hub genes not only distinguished ovarian tumor tissues from normal tissues (Supplementary Fig.\u0026nbsp;2E) but also helped to distinguish ovarian normal tissues from gynecological malignancies (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD), indicating that these hub genes play important roles in the tumor progression of EOC. The relative ratios of diagnostic pattern was supplied in Supplement Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4. Analysis of the effects of NACT on infiltrating immune cell components in OC patients\u003c/h2\u003e \u003cp\u003eTo determine the putative effects of NACT on immune cells in OC, the CIBERSORT algorithm was used to predict the differences in 22 kinds of infiltrating immune cells in tumor samples before and after NACT. The GSE158739 dataset was excluded because it included only sorted macrophages, the GSE109934 dataset was excluded because the data were incomplete and because the immune score could not be calculated. Thus, Only 4 datasets were finally included in the study. Four studies included 85 pre-NACT and 86 post-NACT samples. Detailed information on the tissue source, sample pairs, immune scores, stromal scores, and significant infiltrating immune cell changes before and after chemotherapy are provided in Table I. The distributions of immune cells in patients before and after chemotherapy are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA. The five most common immune cell fractions of EOC were T-cell CD8\u0026thinsp;+\u0026thinsp;cells, T-cell CD4\u0026thinsp;+\u0026thinsp;memory resting cells, M0 macrophages, M1 macrophages and M2 macrophages. The total proportion of the five immune cells was more than 60% in the majority of the samples.\u003c/p\u003e \u003cp\u003eWe further explored the differences in the proportions of various immune cell infiltrates between the two groups. The analysis showed that the proportions of infiltrating follicular helper T (Tfh) cells and M1 macrophages significantly decreased after NACT, while the proportion of resting NK cells significantly increased (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). However, there were no significant differences in the percentages of CD8\u0026thinsp;+\u0026thinsp;T cells or B cells. Upon NACT, the ratio of CD4\u0026thinsp;+\u0026thinsp;T memory resting cells also decreased, but the difference was not statistically significant. To comprehensively understanding the effects of NACT on the TME of EOC, immune score and stromal score were estimated to assess the proportion of immune and stroma components and tumor purity in TME of pre-NACT and post-NACT groups. Upon NACT, immune score and stroma score were significantly up-regulated, and corresponding ESTIMATE score increased (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC-\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE). Correspondingly, tumor purity of tumor tissues in post-NACT groups reduced (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF), suggesting the immune phenotype in the TME is more complex.\u003c/p\u003e \u003cp\u003e \u003cb\u003e5. The putative effects of NACT on infiltrated macrophages of EOC.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eAlthough the differences of M2 macrophages were not significant upon NACT, the change of M1 macrophages attracted our attention, as the dataset GSE158739 was analyzed using sorted tumor-associated macrophages in EOC tissues before and after NACT. The DEGs were quite different from those in other datasets (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). 26 genes were upregulated and 126 genes were downregulated upon NACT (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). We further performed GO and KEGG signaling pathway analyses of these genes. The main biological processes enriched in macrophages after NACT included projection neuron structure (neuron projection organization), cell‒cell junction organization, and cell-substrate adhesion; enriched cellular components included the cell cortex, adherens junction, and collagen-containing extracellular matrix; and enriched molecular functions included extracellular matrix structural constituent, cadherin binding, and actin filament binding (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD). These findings suggested that NACT induced macrophages to respond to inflammatory conditions and regulates their cell adhesion and polarization. Consistent with these findings, further KEGG signaling pathway analysis revealed that the signaling pathways significantly enriched after NACT were involved in ECM-receptor interactions and adherens junctions, and the pathways significantly related to the inflammatory response included the human papillomavirus infection pathway and phosphatidylinositol 3-kinase/protein kinase B (PI3K/AKT) pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). It was supposed that the cell adhesion function of macrophages in the TME of OC patients changed significantly upon NACT.\u003c/p\u003e \u003cp\u003eAmong the genes significantly suppressed, lymphatic vessel endothelial hyaluronan receptor 1 (LYVE1) attracted our attention mostly (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB), as the expression of LYVE1 were also found to be consistently suppressed in another two datasets (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). LYVE1 is an important glycoprotein located on the cell membrane that binds to hyaluronan and participates in hyaluronan metabolism in lymphatic endothelial cells. LYVE1 expressing macrophages could influences chemotherapy responses in murine breast cancer [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. We further explored its expression in primary EOC tissues. The expression of LYVE1 was significantly suppressed in OC tissues compared with normal ovary tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE), and the OC patients with lower LYVE1 expression had significantly prolonged OS (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF).\u003c/p\u003e \u003cp\u003e \u003cb\u003e6. Macrophages in post-chemotherapy was correlated with PFS of patients with EOC.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eOtherwise, LYVE1 is thought to play an important role in iron deposition in macrophages and might promote a switch toward a proinflammatory phenotype during inflammatory conditions [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Combined with the results that M1 macrophages were suppressed in post-NACT tissues. We further explored the putative change of macrophages and lymphocytes in TME of EOC patients upon NACT. We retrospectively collected the patients with primary malignant EOC who were initially treated at the Department of Gynecology, Yongchuan Hospital of Chongqing Medical University, from January 2014 to July 2019. Among of them, 13 patients received the therapy of NACT combining IDS. The detailed clinical characteristics of these patients were summarized in Supplement Table II. Since 1 patient did IDS in other hospital, we collected 12 paired sections of tumor tissues from initial biopsy and internal reduction surgery from EOC patients. 11 patients relapsed during our observation phase; 3 patients recurrent in 6 months (platinum-resistant recurrent), and 8 patients recurrent within 2 years after completing therapy. According to the CRS score reported [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], one patient had CRS 1, eight patients had CRS 2, and three patients had CRS 3. We also analyzed the effects of the CRS on the prognosis of OC patients. As shown in the Supplement Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB, patients with CRS3 tended to have significantly longer PFS than patients with CRS2 or CRS1.\u003c/p\u003e \u003cp\u003eAfter 2\u0026ndash;3 cycles of NACT, there was a significant change in the morphology of the infiltrated macrophages, from large, round, and polymorphic to small, fibroinflammatory (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). Only one patient showed upregulation of macrophage counts. Although macrophages were suppressed to some degree upon NACT, the difference was not significant (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). The impact on infiltrated lymphocytes was quite different. There was no significant change in the number of local infiltrated lymphocytes, and 3 patients even showed a slight increase in the number of local infiltrated lymphocytes postchemotherapy; however, the difference in overall number of infiltrated lymphocytes was not statistically significant (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). We further explored the putative correlations between CD68\u0026thinsp;+\u0026thinsp;and CD8\u0026thinsp;+\u0026thinsp;density and the PFS and OS of patients, albeit in a small cohort of patients. As shown in the survival curves, OS was greater in patients with high CD68\u0026thinsp;+\u0026thinsp;density than in those with low CD68\u0026thinsp;+\u0026thinsp;density following NACT, and a trend toward improved PFS was found in patients with high CD68\u0026thinsp;+\u0026thinsp;density after NACT. For CD8\u0026thinsp;+\u0026thinsp;cell infiltration, no significant difference was found between the high- and low-density groups. Although the difference between the two groups was not significant, patients with high CD8\u0026thinsp;+\u0026thinsp;density had better OS and PFS (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD, supplemental Fig.\u0026nbsp;2C and 2D). Thus, high infiltration of CD68\u0026thinsp;+\u0026thinsp;macrophages and CD8\u0026thinsp;+\u0026thinsp;lymphocytes post-chemotherapy seems to be a better prognostic indicator for EOC patients with IDS.\u003c/p\u003e \u003cp\u003e \u003cb\u003e7. LYVE1\u0026thinsp;+\u0026thinsp;CD206\u0026thinsp;+\u0026thinsp;PvTAMs might be correlated to chemotherapy sensitivity of EOC.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eAlthough the total expression of classical marker of M2 macrophages\u0026ndash;CD68 was not changed significantly upon NACT in TME of EOC, the huge difference of gene expression among the macrophages before and after NACT, combining with the significant morphological changes suggesting the change of phenotypic diversity during the process of NACT. We used multiple immunofluorescence experiments to detect the difference of LYVE1 and relative M2 macrophage markers CD68 and CD206 to figure out the their putative correlation. Notably, the expression level and spatial location in TME of LYVE1 was significantly changed in EOC tissues compared with normal ovary tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). The expression level of LYVE1 was suppressed in EOC tissues compared to normal ovarian tissues and that LYVE1 expression decreased even more significantly in tissues after chemotherapy (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). Moreover, LYVE1\u0026thinsp;+\u0026thinsp;macrophages were almost co-expressed with CD206 but partially co-expressed with CD68 no matter in normal ovarian tissues or EOC tissues. In normal ovarian tissues, LYVE1\u0026thinsp;+\u0026thinsp;CD206\u0026thinsp;+\u0026thinsp;macrophages were rather evenly distributed across the tissue in vascular areas (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA and \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD). But in tumor tissues, these LYVE1\u0026thinsp;+\u0026thinsp;and CD206\u0026thinsp;+\u0026thinsp;TAM subsets were heterogeneously distributed along the endothelium and clustered into discrete regions where these cells were either lining or appearing in bunches proximal to the vasculature, which were supposed to be the perivasular TAMs (PvTAMs) for their spatial proximity to the vasculature. However, macrophages with only CD68-positive cells infiltrated the central tumor epithelial tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD). Recently, Joanne \u003cem\u003eet al\u003c/em\u003e. reported that LYVE1\u0026thinsp;+\u0026thinsp;TAMs could form coordinated multicellular \u0026ldquo;nest\u0026rdquo; structures that are heterogeneously distributed proximal to the vasculature in tumors in a spontaneous murine model of breast cancer, which was quite consistent with our results. They found that blocking the development of LYVE1\u0026thinsp;+\u0026thinsp;TAMs or their nest structures could help to enhance the response to chemotherapy [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. We also observed the PvTAMs in EOC tissues, co-expressing LYVE1, CD206, and partial CD68 in tumor stroma. Thus, we further analyzed the intensity of LYVE1\u0026thinsp;+\u0026thinsp;CD206\u0026thinsp;+\u0026thinsp;TAMs in patients with different types of CRS. The suppression of LYVE1\u0026thinsp;+\u0026thinsp;CD206\u0026thinsp;+\u0026thinsp;TAMs in patients with CRS3 was more significant than that in patients with CRS1 or CRS2, suggesting the putative effects of LYVE1\u0026thinsp;+\u0026thinsp;CD206\u0026thinsp;+\u0026thinsp;TAMs on the chemotherapeutic sensitivity of EOC (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC).\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eRNA sequencing (RNA-seq) is an important genomic technology used to quantitatively understand molecular pathogenesis. Cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT) is a new algorithm for calculating the quantity of immune cells based on RNA transcript datasets, providing us with a new and microscopic way to assess immune cells in TME [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Multiple tumor types harbor complex ecosystems that might play a critical role in tumor progression and treatment response. Thus, the putative effects of NACT on the TME of EOC is controversial [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Based on RNA-seq technology, the variation of phenotypic and functional heterogeneity of TME in EOC during NACT could be comprehensively understood. In the present study, we collected the information of public published datasets, concerning on the effects of NACT on OC. In total, 6 datasets and 211 samples were included for further experiments (99 pre-NACT samples and 112 post-NACT samples). 1138 DEGs were found compared the pre-NACT with post-NACT groups. The five genes mostly depressed upon NACT were \u003cem\u003eEPCAM, UBE2C, CCNB1, MKI67\u003c/em\u003e, and \u003cem\u003eBIRC5\u003c/em\u003e, whose expression were consistently increased in primary EOC tissues compared with normal ovary tissues. All of these genes functioned as oncogenes by participating in the progression of cell cycle regulation, cell adhesion, apoptosis, respectively. Concurrently, NACT significantly up-regulated a cohort of suppressive genes, containing \u003cem\u003eC7, EGR1, DUSP1, SFRP4, PPARG\u003c/em\u003e, and \u003cem\u003eCCL14\u003c/em\u003e, which were under-expressed in primary tumor tissues and play important function in transcriptional regulation, MAPK signaling pathway, Wnt signaling pathway, as well as mediating inflammation and immune response. KEGG signaling pathway analysis of the DEGs revealed that the pathways related to the greatest enrichment of genes affected by NACT were the inflammation-related IL-17 signaling pathway and the apoptosis-related P53 signaling pathway. Thus, our data demonstrated that NACT on one hand regulated genes and pathways important for mediating cytotoxic effects, such as through the regulation of proliferation and DNA damage, and on the other hand, up-regulated genes and pathways that activate the body's inflammatory and immune responses to the drug.\u003c/p\u003e \u003cp\u003eBased on the protein network analysis, the top 6 hub genes, \u003cem\u003eIL6, MMP9, CD8A, EZH2, PTPRC\u003c/em\u003e, and \u003cem\u003eCDH1\u003c/em\u003e, were figure out. All genes were reported to be up-regulated in primary OC tissues and participate in the immune reaction of the host in different ways. Among these genes, the expression of IL6, MMP9, CD8A and PTPRC exhibited significant increase after NACT. The diagnostic pattern using these hub genes could efficiently distinguish the normal ovarian tissues from the gynecology malignancies, including OC. While, the DEGs in macrophages after NACT were quite different from those in total tumor tissues. KEGG signaling pathway analysis revealed that the signaling pathways significantly enriched in macrophages after NACT were involved in ECM-receptor interactions and adherens junctions. Combined with the metamorphosis of macrophages, identified by our further immunohistochemistry assays, these findings suggested that NACT significantly affected the adhesion of macrophages in the TME of EOC patients. As we mentioned, the morphology of the infiltrated CD68\u0026thinsp;+\u0026thinsp;macrophages, changed from large, round, and polymorphic to small, fibroinflammatory. Many papers only identified the change of CD68 expression during NACT, but ignored the metamorphosis of macrophages, suggesting the change of phenotype and function of macrophages. Chemical response system (CRS) is a kind of evaluation system used to assess the chemotherapeutic sensitivity of cancer cells and relative TME cells [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Depending on the morphological changes in cancer cells and regression-associated fibroinflammatory changes, the omentum tissues of IDS were divided into CRS1 (total/nearly total nonresponse), CRS2 (partial response) and CRS3 (good response) groups. The scoring system was supposed to be more important than debulking status for the prognosis of EOC patients, as patients who were evaluated with CRS3 had a higher ratio of complete resection and a lower probability of primary platinum-resistant disease [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Although the system was supposed to assess the change in omentum upon chemotherapy, the metamorphosis of cancer cells and regression-associated fibroinflammatory changes also could be found in primary tumor tissues. We also found that patients with CRS1 had worst PFS compared to patients with CRS2/3. The lymphocytes that infiltrated the tumor tissues were small and rounded, and the changes in the morphology of the infiltrated lymphocytes were not significant compared with macrophages. In the present study, although the difference of CD68\u0026thinsp;+\u0026thinsp;macrophages and CD8\u0026thinsp;+\u0026thinsp;lymphocytes in TME of EOC before and after chemotherapy were not significant, which were consistent with Owen \u003cem\u003eet al\u003c/em\u003e. results [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], patients with high infiltration of CD68\u0026thinsp;+\u0026thinsp;density or CD8\u0026thinsp;+\u0026thinsp;density after NACT tended to have a better prognosis.\u003c/p\u003e \u003cp\u003eAs a typical marker of M2 macrophage, although the difference of expression intensity of CD68 was not significant during NACT, the obvious morphological changes promote us to further identify putative change of phenotypic diversity during NACT. According to the DEGs, LYVE1 attracted our attention. LYVE1 is widely expressed on the lymphatic endothelium, in subsets of vascular endothelial cells, and on some subgroups of macrophages, such as tumor-infiltrated PvTAMs [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The molecular functions of LYVE1 are diverse. In addition to participating in endocytosis and scavenging, it has also been implicated in the adhesion and migration of immune and tumor cells. One of the putative mechanisms involved is that LYVE1 plays an important role in the iron deposition of macrophages, which might promote a switch toward a proinflammatory phenotype during inflammatory conditions, such as antitumor immunity [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. On the other hand, LYVE1 is also thought to participate in leukocyte adhesion to lymphatic endothelial cells [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], which might allow it to participate in tumor metastasis. In this study, we found that LYVE1 co-expressed with CD206 in both normal and tumor tissues, and this subtype of macrophage was heterogeneously distributed along the endothelium and clustered into discrete regions where these cells were either lining or appearing in bunches proximal to the vasculature; these cells were called \u0026ldquo;nest\u0026rdquo; structures by Joanne \u003cem\u003eet al\u003c/em\u003e. Blocking the development of this \u0026ldquo;nest\u0026rdquo; structure could help to enhance the response to chemotherapy in murine breast cancer [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. While CD68\u0026thinsp;+\u0026thinsp;macrophages, in addition to being co-expressed with LYVE1\u0026thinsp;+\u0026thinsp;CD206\u0026thinsp;+\u0026thinsp;macrophages in the stroma, also infiltrated central tumor epithelial tissues, suggesting that these two kinds of subtypes might perform different functions in TME of EOC. As the expression of CD68\u0026thinsp;+\u0026thinsp;was not found after NACT, but the suppression of LYVE1\u0026thinsp;+\u0026thinsp;macrophages in patients with CRS3 was more significant than that in patients with CRS1, suggesting that the putative effects of LYVE1\u0026thinsp;+\u0026thinsp;CD206\u0026thinsp;+\u0026thinsp;macrophages on the chemotherapy sensitivity of EOC. Otherwise, Nan \u003cem\u003eet al\u003c/em\u003e. also reported that LYVE1\u003csup\u003ehi\u003c/sup\u003e mesothelial macrophages could drive tumor growth independently of the omentum, as syngeneic epithelial ovarian tumor growth was strongly reduced following ablation of LYVE1\u003csup\u003ehi\u003c/sup\u003e macrophages \u003cem\u003ein vivo\u003c/em\u003e, including in mice that received omentectomy to prevent the release of these macrophages from omental macrophages [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. However, its effects on tumor metastasis might be highly tumor specific, as Anna \u003cem\u003eet al\u003c/em\u003e. reported that LYVE1 deficiency could enhance the influence of the premetastatic hepatic immune microenvironment on early liver metastasis [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], but this influence was not found in syngeneic colorectal carcinoma in mice. The \u0026ldquo;nest\u0026rdquo; structure of LYVE1\u0026thinsp;+\u0026thinsp;CD206\u0026thinsp;+\u0026thinsp;macrophages were significantly found in our study, further experiments are needed to identify its function in EOC.\u003c/p\u003e \u003cp\u003eLYVE1\u0026thinsp;+\u0026thinsp;CD206\u0026thinsp;+\u0026thinsp;macrophages, also be thought as PvTAMs, are a specialized and highly polarized TAM phenotype. PvTAMs have been demonstrated to shape the process of neo-angiogenesis [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], and recruitment of cytotoxic lymphocytes CD8+ [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The change in specific subtype rather than the overall M2 macrophages urges us to further refine the research on the function of different subtypes of immune cells. Herein, immune score, stroma score and relative ESTIMATE score in post-NACT group were all significantly higher than the pre-NACT group, implying that the TME of post-NACT was more complex. Enhanced immune phenotypes with low tumor purity was independently correlated with reduced survival time in patients with glioma. Moreover, macrophages and neutrophils were enriched in low purity glioma and could be served as robust indicators for poor prognosis [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Thus, Identifying the immune cell fraction might provide a new way to improve the diagnosis, prognosis and treatment response to immune therapy in patients with malignancies.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study highlights the complex alterations in the TME of EOC following NACT exposure and reveals how immunological factors are involved in mediating the chemotherapeutic response. The LYVE1\u0026thinsp;+\u0026thinsp;CD206\u0026thinsp;+\u0026thinsp;PvTAMs were identified in EOC patients and might be correlated with chemotherapeutic response, which will allow for the future development of novel immunologic therapies to combat chemoresistance. However, there are still many limitations in this study. Due to the retrospective nature of the study, we collected only tumor tissue sections, thus we did not analyze the RNA sequence of our own tissues before or after NACT. Otherwise, the number of patients who received neoadjuvant chemotherapy included in this study was small, and it is necessary to increase the sample size to increase the statistical persuasiveness of the results.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eAcknowledgments\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis work was supported by grants from the National Natural Science Foundation of China (Grant No. 81902645) and the Scientific Research Project of Yongchuan Hospital of Chongqing Medical University (YJLC202115).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInvestigation, writing-original draft preparation, Yunyun Li; methodology and resources and data curation, Fei Li;\u0026nbsp;Pathological section diagnosis and evaluation, Yao Li and Xue Liu;\u0026nbsp;software and supervision, Cuiying Zhang; writing\u0026mdash;review and editing and funding acquisition, Li-na Hu.\u003c/p\u003e\n\u003cp\u003eAvailability of data and materials\u003c/p\u003e\n\u003cp\u003eThe data generated in the present study may be requested from the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe present retrospective study was approved by the Institutional Ethics Committee of Yongchuan Hospital of Chongqing Medical University (2022109). All procedures in studies involving human participants were performed in accordance with the ethical standards of the institutional and national research committee and the 1964 Helsinki declaration and its later amendments or comparable ethical standards.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePatient consent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe requirement for written informed consent was waived due to the retrospective design of the study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eArmstrong DK, Alvarez RD, Backes FJ, Bakkum-Gamez JN, Barroilhet L, Behbakht K, Berchuck A, Chen LM, Chitiyo VC, Cristea M, DeRosa M, Eisenhauer EL, Gershenson DM, Gray HJ, Grisham R, Hakam A, Jain A, Karam A, Konecny GE, Leath CA, Hang III, L (2022) NCCN Guidelines\u0026reg; Insights: Ovarian Cancer, Version 3.2022. 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Clin cancer research: official J Am Association Cancer Res 23(20):6279\u0026ndash;6291. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1158/1078-0432.CCR-16-2598\u003c/span\u003e\u003cspan address=\"10.1158/1078-0432.CCR-16-2598\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Epithelial ovarian carcinoma, neoadjuvant chemotherapy, immune system, macrophage, lymphatic vessel endothelial hyaluronan receptor 1.","lastPublishedDoi":"10.21203/rs.3.rs-3900539/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3900539/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eFor late-stage epithelial ovarian carcinoma (EOC) patients, carboplatin based neoadjuvant chemotherapy (NACT) followed interval debulking surgery (IDS) could be alternative choice. The failure of immune checkpoint inhibitors combining chemotherapy for EOC patients promote us to comprehensively understand the impact of NACT on the tumor mircroenvironment (TME) of EOC.\u003c/p\u003e \u003cp\u003e \u003cb\u003eMethods\u003c/b\u003e: The RNA-sequencing profiles of EOC patients before and after NACT were downloaded from the Gene Expression Omnibus (GEO) databases. Differentially expressed genes (DEGs) were calculated and further analyzed using GO and KEGG analyses. The variation of immune cell infiltration upon NACT was analyzed by CIBERSORT and further identified using immunohistochemistry and multi-immunofluorescence assays.\u003c/p\u003e \u003cp\u003e \u003cb\u003eResults\u003c/b\u003e: A total of 6 GEO datasets were included in our study, and 1138 DEGs were found compared the pre-NACT with post-NACT groups. The inflammation-related IL-17 signaling pathway and the apoptosis-related P53 signaling pathway were the most enriched signaling pathways in post-NACT tissues. A diagnostic pattern using the 6 hub genes, figured out by protein network analysis, could efficiently distinguish the normal ovarian tissues from the gynecology malignancies, including OC. Upon NACT, the phenotype of immune cells in the TME was more complex. Infiltrating follicular helper T (Tfh) cells and M1 macrophages significantly decreased, while the proportion of resting NK cells significantly increased. Although total M2 macrophages did not change significantly, the morphology and phenotype of relative macrophages changed, especially the lymphatic vessel endothelial hyaluronan receptor 1 (LYVE1)\u0026thinsp;+\u0026thinsp;macrophages. LYVE1\u0026thinsp;+\u0026thinsp;macrophages co-expressed with CD206 but not CD68+, and they formed multicellular \u0026ldquo;nest\u0026rdquo; structures in the stroma, which might be related to chemotherapy sensitivity of EOC.\u003c/p\u003e \u003cp\u003eConclusion:\u003c/p\u003e \u003cp\u003eThe alterations in the TME of EOC following NACT exposure were complex and dynamic. Not only the tumor cells, but also immunological factors are involved in mediating the chemotherapeutic response. The LYVE1\u0026thinsp;+\u0026thinsp;CD206\u0026thinsp;+\u0026thinsp;perivascular TAMs were identified in EOC patients, and this specific subtype TAMs might be correlated with chemotherapeutic response, which will allow for the future development of novel immunologic therapies to combat chemoresistance.\u003c/p\u003e","manuscriptTitle":"The putative effects of carboplatin based neoadjuvant chemotherapy on tumor microenvironment of epithelial ovarian carcinoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-30 17:57:46","doi":"10.21203/rs.3.rs-3900539/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"002a8d58-703e-4b3e-b95d-514c9cab2bd5","owner":[],"postedDate":"January 30th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-01-31T11:14:25+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-30 17:57:46","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3900539","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3900539","identity":"rs-3900539","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}