Identification of a gene set correlated with immune status in ovarian cancer by transcriptome-wide data mining

doi:10.21203/rs.3.rs-35960/v1

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Identification of a gene set correlated with immune status in ovarian cancer by transcriptome-wide data mining

https://doi.org/10.21203/rs.3.rs-35960/v1

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Background: Checkpoint block-mediated tumor immunotherapy has obtained huge success in treating various malignancies. However, the efficacy of immunotherapies against ovarian cancer (OC) was low largely due to limited information on the tumor immune microenvironment (TIME) of OC. This results in lacking reliable biomarkers to select the right patients for immunotherapy and prognosis prediction. Published studies have reported that high tumor mutation burden (TMB) can generate many neoantigens resulting in a higher degree of an anti-tumor immune response. However, the correlation between TMB and TIME of OC remains controversial let along a collection of TMB information is time and resource consuming. In this study, we considered the TMB and TIME of OC comprehensively to categorize patients based on their tumor local immune status and provide information that could guide further OC immunotherapy trials as well as the prognosis prediction.

Method: OC RNAseq data were downloaded from The Cancer Genome Atlas (TCGA) and divided into TMB^high and TMB^low subgroups according to TMB scores for comparison. OC cases were also grouped into immunity^high or immunity^low based on their profiles of tumor-infiltrating leukocytes (TILs) analyzed by ssGSEA (Single Sample Gene Set Enrichment Analysis). Besides, the function of genes enriched in both TMB^high and immunity^high was analyzed by Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes pathway (KEGG), and protein-protein interaction (PPI) network. The correlation of each gene with overall survival was also analyzed.

Results: Positive association was observed between the OC grade and the TMB. Both TMB^high and immunity^high were significantly correlated with a better prognosis. However, different from the findings of other studies, TMB of OC didn’t correlated with preferable TIME in our analysis. By comparing the up-regulated signature genes in TMB^high and immunity^high cases, we found 14 overlapped genes that were mainly involved in immune response-related pathways. We further analyzed the prognostic value of these 14 genes and found the upregulation of 4 of them, CXCL13, FCRLA, PLA2G2D, and MS4A1 are significantly associated with better survival. With available data collected form a melanoma cohort, we also found that these four genes are positively associated with better response to immune checkpoint blockade-based immunotherapy.

Conclusion: By comparing signature genes enriched in TMB^high and immunity^high subgroups, four genes, CXCL13, FCRLA, PLA2G2D, and MS4A1, were found positively correlated with both OC immune infiltration and better prognosis. Additionally, these four genes predicted the response to checkpoint blockade-based immunotherapy in a melanoma cohort indicating that they can also be used as biomarkers for further immunotherapy clinical trials of OC.

Neurobiology of Disease

ovarian cancer

TMB

immune cells infiltration

survival prognosis.

Ovarian cancer (OC) is the most fatal gynecological tumor around the world. Due to the lack of specific symptoms or effective screencing strategies[1], more than 70% of patients are in the middle and late stages at the time of diagnosis resulting in an increased risk of recurrence and poor prognosis[2]. Despite having various treatments, the five-year survival rate of late-stage patients was no more than 35%[3]. Traditional therapies to unsuitable patients, including chemotherapy and targeted therapy, up to 70% of patients with ovarian cancer still relapse after 12 to 18 months[4], highlighting the importance of exploring novel therapies.

As a rising novel treatment, PD-1/PD-L1 blockade based immunotherapy has demonstrated promising efficacy in treating various types of solid tumors[5]. Ample of clinical data indicate that pre-existing favorable tumor immune microenvironment (TIME), containing antigen-presenting cells, effector T cells, Th1 type cytokines and chemokines, and expression of PD-L1 are prerequisites for PD-1/PD-L1 blocking antibody-mediated immunotherapy. Current clinical trials on OC has shown that this immunotherapy can only benefit a small part of the patient population while a large number of patients have either limited or no response[6, 7]. Those nonresponsive cases are largely due to a far less understanding of the TIME of OCs that led to 1) missing targetable immune checkpoint molecules that are highly expressed in OC cells and inhibit anti-tumor immune response, or 2) lacking specific biomarkers to select the right patient population. Thus, it’s critical to analyze the TIME of OC extensively to categorize patients based on their tumor immune cell profiles and to identify potential therapeutic targets and/or molecular biomarkers to facilitate the design of novel immunotherapies.

Tumor mutation burden (TMB) has been demonstrated as a potential biomarker to predict responses to immunotherapy in several types of solid tumors[8-10]. It was defined as the total number of replacement and insert/deletion mutations per mega-base in the exon coding region of the genome examined in tumor samples. It’s proposed that tumors with high TMB may produce more neoantigens that can be recognized by the immune system and in turn elicit broader anti-tumor immune response[11]. Consistently, melanoma patients with higher TMB tumors manifested better clinical response to immune checkpoint inhibitors and long-term survival[12]. However, the clinical application of TMB as a prognostic marker was both time and financial consuming and the predictive significance of TMB has been questioned by some other studies[13, 14].

TIME has been considered to have essential impacts on tumor immunity[15-18]. It is composed of cancer cells and a variety of stromal cells, cytokines, chemokines, etc. Favorable TIME, containing antigen-presenting cells, effector T cells, Th1 type cytokines, and chemokines, is a positive factor for immunotherapy[19, 20]. However, the roles of the TMB combined with immunoactivity-related gene sets in predicting the biomarkers for prognosis of OC was unclear. Thus considering the correlation between the TMB, the immune cell types and the efficacy of cancer treatment, so the TMB and the immunoactivity-related gene sets were used to predict the biomarkers associated with the prognosis of OC was the focus of our studies. Therefore, our findings will accelerate the validation and the realization of TMB and TIME as the significant predictive markers for OC’s clinical and prognosis. Altogether, understanding the relationship between the combination of TMB and immune microenvironment and the survival prognosis and clinical outcomes of certain malignancies was very favorable and promising for future immunotherapy and could provide a mechanistic explanation and treatment direction for the survival and prognosis of clinical research.

Genome-wide gene expression profiling provides an extremely valuable tool to unwind complicated biological processes. In this study, we sought to identify prognostic biomarkers for OC by an integrated TMB and TIME analysis using the OC expression profile data and mutation annotation files downloaded from The Cancer Genome Atlas (TCGA) database. All OC cases were classed into TMB^high group and TMB^low group based on the OC’s somatic mutation data. Additionally, we also used ssGSEA (Single Sample Gene Set Enrichment Analysis) scores of immune cell types to conduct unsupervised clustering of OC patients by the profiles of tumor-infiltrating leukocytes (TILs) and obtained three different immune infiltrating clusters, here some of them were defined as the low immunity group and the high immunity group. We further compared upregulated genes enriched in either TMB^high or immunity^high patients and found 14 genes were shared by both of these two subgroups. We further sorted the function of these genes by Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes pathway (KEGG), protein-protein interaction (PPI) networks, and found that they were involved in various immune-related pathways. Immune infiltration and survival analysis indicated that four of these 14 genes, CXCL13, FCRLA, PLA2G2D, and MS4A1, were significantly correlated with better prognosis. Data of a published melanoma cohort also suggested that these four genes were associated with better response to immune checkpoint blockade-based immunotherapy. Altogether, our analysis strongly indicated that those four signature genes can be applied to predict prognosis and select OC patients for anti-PD-1/PD-L1 treatment (Figure1).

Database

RNAseq and mutation annotation files for OC patients were gained from the TCGA database portal (https://portal.gdc.cancer.gov/). Clinical data such as staging type, survival status and time were also downloaded from the TCGA database.

Implementation of ssGSEA

We obtained the marker gene sets for immune cell types from the TCGA database. Then we used the ssGSEA program to derive the enrichment scores of each immune-related term. The ssGSEA applies gene signatures expressed by immune cell populations to individual cancer samples. The computational approach used in our study included immune cell types that are involved in innate immunity and adaptive immunity. Tumors with qualitatively different immune cell infiltration patterns were grouped using hierarchical clustering.

Heatmaps and clustering analysis

Heatmaps and clustering were generated using the R package.

Identification of differentially expressed genes (DEGs)

TMB group: Top 200 upregulated genes of OC patients were selected by R package limma, FDR< 0.05, and |log2 fold change (FC)| ≥ 1. A total of 427 DEGs consisting of all upregulated genes of OC patients were selected by R package limma, FDR< 0.05, and |log2 fold change (FC)| ≥ 1.

Functional annotation and pathway analysis of DEGs

The online tool, Database for Annotation, Visualization and Integrated Discovery (DAVID, https://david.ncifcrf.gov/) provided full-scale information for a list of genes by GO and KEGG pathway analysis. The ClusterProfiler and enrich plot packages to predict the function of DEGs. GO and KEGG pathway analysis with the standard of p < 0.05, the results were downloaded in text format. Gene set variation analysis (GSVA) is an open-source software package for R and can be downloaded on the website, then condensing information from Up-DEGs and Down-DEGs expression profiles respectively into KEGG pathway. Compared with single-gene analysis, the advantages of this method include noise and dimension reduction[21]. Moreover, Unsupervised hierarchical clustering of these top KEGG pathways was able to distinguish between normal ovaries group (GTEx datasets) and OC (TCGA datasets ).

PPI Networks Construction and Module Analysis

In order to analyze the links between these proteins, DEGs were uploaded to the Search Tool for the Retrieval of Interacting Genes (STRING, https://string-db.org/). Only single networks with more than ten nodes were included for further analysis.

Survival Analysis of Hub Genes

Download the datum matrix containing all ovarian patients’ prognosis information from the GEO database. The data was processed by GraphPad Prism 8 and then exported the results. Kaplan-Meier plots were drawn to demonstrate the relationship between patients’ overall survival and gene expression levels of DEGs. The relationship was tested by the log-rank test.

Kaplan-Meier plotter [Ovarian Cancer]

The online Kaplan-Meier plotter (http://www.kmplot.com) was used to assess the effect of four selected characteristic genes on ovarian cancer prognostic. The ovarian cancer patients were split into high and low expression groups by mRNA expression of auto-selected best cutoff. The Kaplan-Meier survival plot showed Hazard ratio (HR), 95% confidence intervals (CI) log-rank p-value for each corresponding gene, and the number at risk below each plot.

TIMER (Tumor Immune Estimation Resource)

TIMER (https://cistrome.shinyapps.io/timer/) is an interactive web server that can systematically analyze immune infiltrates in cancer with its own algorithm. We analyzed the correlation between four selected characteristic genes and the abundance of immune infiltrates in OC, including purity, B cells, CD4+ T cells, CD8+ T cells, by gene modules. The correlation analysis is the Spearman’s correlation statistical significance with p < 0.05 was indicated with *, p < 0.01 was indicated with **, p <0.001 were indicated with *** and p < 0.0001 were indicated with ****.

Statistical and computational analyses

SPSS 20.0 software was used for statistical analysis. p < 0.05 was considered statistically significant in all statistical tests. All the statistical and computational analyses were performed using the R statistical software environment.

TMB scores and the immunoactivity-related gene sets are conjoined with OC’s overall survival

To answer if a high load of somatic mutation is associated with enhanced tumor immune cell infiltration and result in prolonged overall survival, datasets comprising RNAseq from 379 TCGA tumors and mutation annotation files from 436 TCGA tumors (272 which overlapped with RNAseq data) were obtained from the TCGA Data Portal in March 2019 (https://portal.gdc.cancer.gov/). We evaluated all OC cases with complete gene expression data and clinical information in TCGA. For mutation annotation files, we presented the Word Cloud of gene symbols identified in OC which the mutation sites > 5 based on the WordArt (https://wordart.com). We found that TP53 was the most significant (90% cases) mutated gene[22] (Figure S1A). We then screened the top 25 genes and employed the DISCOVER (Discrete Independence Statistic Controlling for Observations with Varying Event Rates)[23] algorithm to examine co-occurrence and mutual exclusion among these genes and found that TTN and LRP2 are significantly mutual exclusive (p < 0.001) (Figure S1B). In order to better visualized the mutually exclusive genes pairs, we selected 420 (96.33%) of 436 samples and showed in a waterfall plot that the most frequently mutated genes were TP53 and TTN (p < 0.05, FDR < 0.01) (Figure S1C). These analyses of OC samples’ mutation can be used to guide further TMB analysis.

Based on the somatic mutation data across all the OC cases in this study, TMB scores range from 0.0263 to 6.5260. Interestingly, the TMB scores of OC cases at Grade 3 & Grade 4 were significantly higher than those of Grade 1 & Grade 2 OC cases (Figure 2A, n=417, except for the blank values, p=0.048). And then based on the ssGSEA scores of immune cell types, we divided the immunoactivity-related gene sets into three immune infiltration clusters by unsupervised clustering, and some of which were defined as immunity^high clusters (n=193) and immunity^low clusters (n=149) (Figure 2B). Besides, the tumor purity in immunity^high group was significantly lower than that in immunity^low group validating the reliability of our classification (Figure S1D). Moreover, the PD-L1 expression level of the immunity^high group is significantly higher than that of immunity^low group (Figure 2B). We further accessed if the TMB or immune infiltration profile could affect the overall survival. Kaplan-Meier survival curves showed that the overall survival time of TMB^high or immunity^high group was significantly longer than TMB^low or immunity^low group (Figure 2C 2.58 years vs. 1.92 years, p=0.0295 in the log-rank test. Figure 2D 2.50 years vs. 2.45 years, p=0.00082 in log-rank test). We then asked if there is any association between the TMB group and the immunity group. However, we did not see any correlation between these two factors across 379 OC cases (Figure 2E).

We further studied the relationship between the immunity group and the immune cell infiltration in OC. So We further studied the abundance of 22 different immune cells subpopulations in the high&low immunity group or the TMB group of cancers. However, only memory B cells and NK cells were increased in TMB^high group while there are seven immune cell subpopulations were enriched in high immune scores group, that was most of these immune cells have a higher infiltration degree in immunity^high group than in immunity^low group of OC (Figure S2A and 2B).

KEGG pathway and GO term analysis of DEGs associated with OC.

Next, as to reveal the correlation between the TMB, the immunoactivity-related gene sets, and global gene expression profiles, and wonder if individual genes that are upregulated in both TMB^high and immune^high groups are involved in anti-tumor related biological pathways. To this end, we overlapped TMB^high gene expression signature (top 200 genes) with immunity^high gene expression signature (427 genes) by Venn diagrams and finally obtained 14 differentially expressed genes (DEGs) that may be closely predicted the response to checkpoint blockade-based OC immunotherapy (Figure 3A). Heatmaps of the expression levels of 14 up-regulated DEGs in the TMB group and immunity group were shown in Figure S2C. Next, we conducted unsupervised hierarchical clustering of KEGG pathways related to Up-DEGs (Figure 3B) and found that most of these KEGG pathways are immune-related, such as “Cytokine cytokine receptor interaction”, “Chemokine signaling pathway”, as well as “Primary immunodeficiency”. Moreover, in order to summarize the potential function of the DEGs, we performed functional enrichment analysis of the 14 up-regulated genes between TMB^high and immunity^high group. GO terms and KEGG pathway analysis also demonstrate that the Up-DEGs function in the immune and inflammatory response, cytokine activities and T cell proliferation (Figure 3C and 3D). These results indicated that the gene sets shared by both TMB^high and immune^high groups have active immune functions.

Protein-protein interactions among genes associated with prognosis in OC

In order to understand the interactions between the identified DEGs, we performed a PPI networking study using the Search Tool for the Retrieval of Interacting Gene (STRING) tool. All 14 DEGs were uploaded to STRING as a whole Up-DEGs module (Figure 4) to construct PPI networks. A network consisting of 9 nodes and 10 edges were generated when the minimum required interaction score was set 1.5 and disconnected nodes were hidden in the network in the STRING website (Figure 4A). By counting the number of connections (Figure 4B), it shows that CXCL13 has the most key link with other members of the module in the network. Besides CXCL13, several key genes associated with T cell proliferation, inflammation, and immune response were located at the center of the module, such as PLA2G2D and FCRLA. These results indicated that PLA2G2D, MS4A1, ADAMDEC1, FCRLA, CXCL13 interact more with other molecules, suggesting that these genes are more likely to be key genes in regulating tumor restricting functions in OCs.

Correlation of expression of individual DEGs in overall survival

Since both TMB^high group and immunity^high group were correlated with better survival, we reasoned that there could be potential prognosis predicting biomarkers among the 14 shared DEGs defined above. To this end, we analyzed the association of individual DEGs with overall survival time. Among them, four genes, including PLA2G2D, CXCL13, FCRLA, and MS4A1, were validated to be significantly relevant to good survival and prognosis outcomes (log-rank test, p < 0.05) (Figure 5A-D). To validate our results, we downloaded and analyzed gene expression data from a different cohort from the GEO database. Meta-analysis further indicated that OC cases that have high expression of these four genes showed a significant survival benefit and all the four genes are independent predictors for the prognosis of OCs (Figure 5E-H). These results indicated that CXCL13, FCRLA, PLA2G2D, and MS4A1 could be a useful biomarkers for predicting responses to immunotherapy of OC.

Correlation of four genes expression with immune infiltration level in OC and pathway analysis.

Tumor purity was usually taken into account when obtaining immunotherapy expression markers from transcriptome data. Some studies have shown that tumor samples with low purity tend to have more immune cells and a higher mutation load, because inflammatory responses induced by immune cells can increase the mutation rate of tumor cells, and the effect of immunotherapy may be better[24, 25]. Therefore, to study if the four signature genes can predict tumor immune infiltration, we used TIMER (Tumor Immune Estimation Resource, https://cistrome.shinyapps.io/timer/), an online database, to analyze the infiltration of different immune cells in tumor tissues based on available RNA-seq data. Our results demonstrated that CXCL13, FCRLA, MS4A1, and PLA2G2D have a negative correlation with tumor purity in OC (Figure 6A-D). Besides CXCL13 expression showed a very weak relationship with B cell infiltration level in OC (Figures 6A) while FCRLA, MS4A1, PLA2G2D have no significant correlations with B cell infiltration level in OC (Figure 6B-D). Moreover, there were moderate to strong positive relationships between the expression levels of PLA2G2D, FCRLA, MS4A1, CXCL13, and infiltration level of CD4⁺ T cells, as well as have a prominent positive correlation between expression level and infiltration level of CD8⁺ T cells in OC, especially CXCL13, MS4A1, PLA2G2D, FCRLA (Figures 6A-D, Table S1). In conclusion, CXCL13, FCRLA, MS4A1, and PLA2G2D might have multiple and closely function in immune infiltration.

Next, we conducted gene set enrichment analysis (GSEA) to analyze the four genes and found that all of four genes were related to immune cell signaling pathways. They were all associated with B cell receptor, chemokine, cytokine-cytokine receptor interaction, natural killer cell-mediated cytotoxicity and T cell receptor signaling pathway (Figure 6E-H). Interestingly, CXCL13, FCRLA, and PLA2G2D were also associated with primary immunodeficiency (Figure 6E-H). MS4A1 was associated with autoimmune thyroid disease (Figure 6G). All of these findings revealed that CXCL13, FCRLA, MS4A1 and PLA2G2D’s expression was closely related to immune function were enriched for hallmarks of OC. More information can be got from Table S2. These results indicated that CXCL13, FCRLA, PLA2G2D, and MS4A1 were validated as prognostic biomarkers for the immunotherapy of OC. These results indicate that CXCL13, FCRLA, PLA2G2D, and MS4A1 are positively correlated with immune cell infiltration, and these genes are enriched to be associated with immune pathways by GSEA enrichment method.

Prognostic evaluation of four genes in melanoma patients treated with immunocheckpoint inhibitors.

Immune infiltration and active inflammations are prerequisites of anti-tumor immune reaction and better response to immunotherapy. We have shown that CXCL13, FCRLA, PLA2G2D, and MS4A1 are upregulated in the immune^high group and involved in inflammation and T cell function. We then asked if these four genes can be used as biomarkers to predict responses to immune checkpoint blockade-based immunotherapy. Since the data of OC response to immunotherapy are not available, to address that, we explored an online database, TIDE (Tumor Immune Dysfunction and Exclusion, http://tide.dfci.harvard.edu/query/) and analyzed the data collected from melanoma. As shown in Figure 7, our results showed that among the melanoma patients treated with anti-PD-L1 or anti-CTLA4, the CXCL13, MS4A1, FCRLA, and PLA2G2D were positively correlated with the number of CTL infiltration. The two-sided t-test p values for correlations in CXCL13, MS4A1, FCRLA, PLA2G2D and mean are1.43×10^-10, 8.12×10^-2, 6.6×10^-10 and 2.1×10^-4, respectively. And the high expression levels of these genes were associated with the better response of melanoma patients.

Furthermore, we also investigated the protein expression of CXCL13, FCRLA, and MS4A1 in OC and normal ovarian tissues based on the IHC (immunohistochemistry) staining in The Human Protein Atlas (https://www.proteinatlas.org). We found that the staining intensity of CXCL13 and MS4A1 was slightly different in normal ovarian tissues and OC tissues, the protein expression levels in cancer were slightly lower than in adjacent tissues, but the difference was not significant. And the level of FCRLA protein was up-regulated in OC tissues compared to the normal tissues (Figure S3). There are no PLA2G2D IHC data available in the database currently. Considering the positive association between PLA2G2D mRNA level and prognosis data in OC, it is worth developing antibodies for future studies.

OC has been one of the major threats to women’s health for decades[26, 27]. Current treatments for late-stage OC patients are far less satisfying. Immune checkpoint blockade based immunotherapy, especially PD-1/PD-L1 blocking antibody, has been approved for clinical treatment to multiple solid tumors[28-30]. Accumulating clinical data indicated that inflammatory tumors with the high expression level of PD-L1 are more likely to respond[31]. However, the PD-L1 pathway blocking antibody has not been approved for treating OC. The reasons are likely due to scarce information of the TIME of OC resulting in missing targetable immune-modulatory molecules as well as promising biomarkers for choosing the right patient population for immunotherapy.

TMB represents the overall amount of somatic mutations that could generate neoantigens to elicit immune response[32-34]. Consistently, tumors with high TMB scores contain more TILs[35] and are associated with better response to immunotherapy[36], such as melanoma[37] and NSCLC[38], with high TMB have shown better response and treatment results in immune checkpoint blockade treatment. In our analysis, we found that late-stage OCs tend to have higher TMB, which has not been described before. We also found that a high TMB score is positively associated with a better prognosis (Figure 2).

The extent of tumor immune cell infiltration has been evaluated in many different ways. We analyzed the TIME of OCs by the ssGSEA and assigned each case an immune score. Consistent with other studies, high immune scores significantly associated with better prognosis. However, we did not found any correlation between TMB and immune scores (Figure 2). This may be due to genetic differences between tumor cells, and the inconsistency between TMB and immune infiltration has been reported in other studies, such as YardenaSamuels and EytanRuppin’s research team found that in melanin, intra-tumor heterogeneity (ITH) played a decisive role in tumor immune infiltration compared with other factors, like TMB and CNV[39]. Additionally, we also studied the correlation between TMB and the infiltration of specific immune cells and found TMB scores only associated with the infiltration of memory B cells and NK cells (Figure S2). These results indicate that OC has other layers of regulation upon the innate immune system, such as antigen uptake and presentation, that inhibit the activation of T cells. Alternatively, other mechanisms that inhibit immune cell accumulating might present in the microenvironment of OCs. Further studies are warranted to explore these possibilities.

Transcriptome studies provide genome-wide gene expression data. By performing the differential gene expression analysis, we found that most genes that are upregulated in the immune^high group are associated with an immune or inflammatory signaling pathway, which validates our analysis (Figure 3). However, we did not see a significant difference in gene expression between TMB^high and TMB^low groups. Our analysis did not suggest any group-specific or enriched mutation, which partially explains the similar transcriptome profiles between these two groups (Figure S2).

Given TMB^high and Immune^high are both correlated with better survival, we focused on 14 genes that are upregulated in immune^high group and also enriched in TMB^high group. These 14 genes are involved in many immune regulatory pathways, as shown by GO and KEGG terms analysis (Figure 3). Consistently, PPI network analysis also suggested that several key genes related to immune response were located in the center of the module (Figure 4). Nevertheless, these predictions could be biased by previous knowledge[40, 41]. The real biology of these genes in regulating the anti-tumor immune response in OCs need more investigations.

We further performed an overall survival analysis upon each of the 14 DEGs, and identified four genes, PLA2G2D, CXCL13, FCRLA, and MS4A1, were associated with better prognosis in OC patients. We also validated the four genes related to the prognosis of OC patients from the GEO database (Figure 5). In addition, multiple reports have shown that these four genes were involved in immune reactions, such as CXCL13 has recently been linked with TFH cell infiltration and improved survival[42]. FCRLA has been shown to be a potential target gene in immunotherapy for B-cell lymphoma[43]. MS4A1 played a vital role in the apoptosis of B-cell lymphoma Ramos cells[44]. And PLA2G2D involved in inflammation and immune response and may be used to treat inflammatory diseases[45]. The association of these four genes and immune cell infiltration was further validated by an independent algorithm presented in the TIMER database (Figure 6). However, positive association does not mean these genes are causative factors that affect the tumor inflammatory status, which takes more studies.

To evaluate if these four genes could help us to choose the right patients for future immunotherapy, we investigated the correlation between the expression of CXCL13, MS4A1, FCRLA, PLA2G2D and the response rate of immunocheckpoint inhibitors. Due to the lack of data on OC's response to immunotherapy, we analyzed the data collected from a melanoma cohort. It was found that in melanoma patients treated with anti-PD-L1 or anti-CTLA4, these four genes were positively correlated with the number of CTL infiltration, and their high expression was associated with a better prognosis of patients (Figure 7). Further effort will focus on validating antibodies or developing other diagnostic methods to detecting the expression of these genes in the tumor microenvironment, which allows us to pick the right patients for immune checkpoint blockade based immune therapy against OCs.

In summary, by a comprehensive study of TMB and TIME, we found some clues that provide additional data in decoding the complex interaction of TMB and TIME in OC. We also identified four genes signature that could be used as biomarkers to guide future immunotherapy on OC.

In our study, TMB was combined with TIME of OC to classify and analyze patients according to their local tumor immune status that can guide for further immunotherapy trials with OC. We found that higher TMB can promote immune cell infiltration and increases immune activity and have a better prognosis of OC. In summary, we found that CXCL13, FCRLA, PLA2G2D, and MS4A1 may be biomarkers for OC immunotherapy. Finally, through further study of these genes that may shed new light on the potential relationship between tumor microenvironment and OC prognosis. Therefore, our researches provided a new perspective on the relationship between TMB and tumor immune response in OCs.

TMB: Tumor Mutational Burden; Tumor Immune Microenvironment: TIME; OC: Ovarian Cancer; TCGA: The Cancer Genome Atlas; ssGSEA: Single-sample Gene Set Enrichment Analysis; Up-DEGs: Up-regulated Differential Expression Genes; GO: Gene Ontology; KEGG: Kyoto Encyclopedia of Genes and Genomes pathway; PPI: Protein-Protein Interaction; DAVID: Database for Annotation, Visualization, and Integrated Discovery; GSVA: Gene Set Variation Analysis; STRING: Search Tool for the Retrieval of Interacting Genes; DISCOVER: Discrete Independence Statistic Controlling for Observations with Varying Event Rates; GEO: Group on Earth Observations; OS: Overall Survival; TIMER: Tumor Immune Estimation Resource; GSEA: Gene Set Enrichment Analysis; ssGSEA: Single Sample Gene Set Enrichment Analysis; TIDE: Tumor Immune Dysfunction and Exclusion; IHC: immunohistochemistry.

Acknowledgments

The authors gratefully acknowledge contributions from the TCGA network, the GTEx network, and the CCLE Network. This study was supported by grants from the National Natural Science Foundation of China (No. 81572900). The Fundamental Research Funds for the Central Universities of Central South University (No. 502221804).

Authors’ contributions

Lili Fan conceived and designed this study, also crafted figures and tables, and was responsible for the critical reading of the manuscript. Also, she contributed to the data collection and analysis and the critical reading of the manuscript. Han Lei, Ying Lin, and Tianxiang Zhang were responsible for the writing and helped with the figures and tables. Han Lei, Zhengwei Zhou, Zhipeng Yan, Guang Shu, and Gang Yin supervised and contributed to the critical reading of the manuscript.

Funding

This work was supported by the National Natural Science Foundation of China [No. 81572900]; The Fundamental Research Funds for the Central Universities of Central South University [No. 502221804]; National Key R&D Program of China, Stem Cell, and Translation Research [No. 2016YFA0102000]

Availability of data and materials

All data generated or analyzed during the study period were included in this published article and its supplementary information.

Ethics approval and consent to participate

Not Applicable

Consent for publication

We have obtained consent to publish this paper from all the participants of this research.

Competing interests

The authors declare that they have no competing interests.

Author details

¹Department of Pathology, Xiangya Hospital, School of Basic Medical Sciences, Central South University, Changsha, Hunan Province, China. ²Department of Immunology, School of Basic Medical Sciences, Central South University, Changsha, Hunan Province, China. ³Department of Immunobiology, Yale University School of Medicine, New Haven, CT, USA. ⁴School of Basic Medical Sciences, Central South University, Changsha, Hunan Province, China. ⁵Hunan Cancer Hospital/the affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, Hunan Province, China.

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Figure S1. Database analysis.

Figure S2. Comparison of the infiltration levels of immune cells in the high and low immunity group.

Figure S3. The protein expressions of the three selected regulators.

Table S1. The correlation coefficients of four genes with immune infiltration in OC.

Table S2. The correlation coefficients of four genes with immune-related pathways in OC.

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Identification of a gene set correlated with immune status in ovarian cancer by transcriptome-wide data mining

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