Identification an novel classification and nomogram of programmed cell-death patterns for investigating the immune microenvironment and prognosis of postoperative ovarian cancer patients

doi:10.21203/rs.3.rs-3245670/v1

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Identification an novel classification and nomogram of programmed cell-death patterns for investigating the immune microenvironment and prognosis of postoperative ovarian cancer patients

https://doi.org/10.21203/rs.3.rs-3245670/v1

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Background: Ovarian cancer (OV) is a highly aggressive and often fatal disease that frequently goes undetected until it has already metastasized. The classic treatment for ovarian cancer involves surgery followed by chemotherapy. However, despite the effectiveness of surgery, relapse is still a common occurrence. Unfortunately, there is currently no ideal predictive model for the progression and drug sensitivity of postoperative ovarian cancer patients. Cell death patterns play an important role in tumor progression and have the potential to be used as indicators of postoperative OV prognosis and drug sensitivity.

Materials and methods: A total of 12 PCD patterns were employed to construct novel classification and prognosis model. Bulk transcriptome, single-cell transcriptome, genomics, and clinical information were collected from TCGA-OV, GSE9891, GSE26712, GSE49997 and GSE63885. In addition, single-cell transcriptome data GSE210347 was procured from the Gene Expression Omnibus (GEO) database for subsequent analysis.

Results: In this study, a novel PCD classification has been employed to phenotype postoperative ovarian cancer patients, revealing that patients in cluster 1 exhibit heightened sensitivity to immune-based therapies. Meanwhile, a programmed cell death index (PCDI) was established using an 8-gene signature with the help of a machine learning algorithm. The PCDI was validated in four independent datasets, and it was found that patients with high-PCDI had a worse prognosis after surgery in OV. The investigation also revealed that PCDI is associated with chemokines, interleukins, interferons, and checkpoint genes, as well as important components of the immune microenvironment, as determined through a comprehensive analysis of bulk and single-cell transcriptomes. In addition, we also found that patients with low-PCDI values may exhibit sensitivity to immunotherapy, while those with high PCDI values may display increased responsiveness to tyrosine kinase inhibitors.

Conclusion: This study provides new insights into the significance of programmed cell death (PCD) patterns in ovarian cancer patients following surgery. Through a comprehensive analysis of different cell death patterns, we have developed a novel PCD model and nomogram that can effectively predict the clinical prognosis and drug sensitivity of OV patients post-surgery.

programmed cell death

ovarian carcinoma

classification

nomogram

drug sensitivity

Ovarian cancer (OV) is a significant global gynecological malignancy with the highest mortality rate compared to other gynecological malignancies[1]. In 2020, there were 313,959 new cases of OV and 207,252 deaths worldwide[2]. Unfortunately, due to the difficulty in detecting the early stages of ovarian cancer, more than half of the patients miss the opportunity for early diagnosis, which significantly impacts their prognosis[3]. Surgery combined with chemotherapy is a commonly used treatment for ovarian cancer. However, despite the effectiveness of surgery, relapse is still a common occurrence. Despite advancements in diagnostics and treatment, the ability to quickly diagnose and predict the prognosis of ovarian cancer patients who have undergone surgery is still inadequate. Therefore, molecular methods are considered crucial for early detection of this disease.

Programmed cell death (PCD) not only acts as a natural defense against tumorigenesis and progression, but also plays a significant role in the tumor microenvironment (TME)[4-6]. Pyroptosis is an inflammatory type of regulated cell death characterized by cell swelling and the release of abundant inflammatory cytokines[7]. Recently, cupronoptosis was identified as a novel PCD in the mitochondrial TCA cycle induced by copper-dependent proteotoxic stress[8]. Ferroptosis is an iron-dependent process that results in the accumulation of lipid hydroperoxides at lethal concentrations[9]. Interestingly, a recent study showed that inhibition of ferroptosis induced drug resistance in anti-PD-1/PD-L1 cells[10]. Furthermore, apoptotic vesicles are occupied by macrophages, which do not trigger an inflammatory response or interfere with neighboring cells[11]. The autophagy-dependent cell death process involves multiple lysosomal degradation steps that contribute to metabolic adaptation and nutrient cycling. PCD, known to affect the TME and antitumor immunity, affects ovarian cancer prognosis, TME, and chemotherapy outcomes.

In this study, we used a multi-omics approach that involved analyzing bulk transcriptome and single-cell datasets. Our aim was to characterize the prognosis of PCD-related genes, the TME, and the underlying immune landscapes across distinct OV patient subgroups. Additionally, we investigated treatment strategies for different groups of OV patients. Besides, we created a programmed cell-death index (PCDI) using a machine learning algorithm and conducted multi-omics analysis to investigate its prognostic value and associated molecular alterations. Our results demonstrate that the PCDI is a reliable predictor of prognosis, as confirmed by external validation datasets. Patients with higher PCDI levels exhibited longer survival durations. Our study found that patients with low-PCDI have increased levels of chemokines, immunomodulatory cytokines, and IFNs. Based on our results, it is possible that low-PCDI patients may respond better to immunotherapy, while targeted therapies may be more effective for those with high-PCDI. Additionally, our findings suggest that OV patients with high-PCDI who undergo surgery may be resistant to tyrosine kinase inhibitors. Our analysis of single-cell transcriptomes showed that the level of PCDI was higher in adjacent tissues compared to tumor tissues. Additionally, we found that the proportion of cycling cells had a significant negative correlation with PCDI, indicating their potential role in hindering tumor progression.

Transcriptome and single cell datasets collection

Transcriptome datasets combined with clinical data (e.g, Stage T stage, overall survival) of ovarian carcinoma patients were downloaded from the TCGA data portal (https://portal.gdc.cancer.gov/)[12]. In addition, GEO datasets including GSE9891[13], GSE26712[14], GSE49997[15] and GSE63885[16] were obtained from Gene-Expression Omnibus (GEO) (https://www.ncbi.nlm.nih.gov/geo/) to verify the precision of the model. Raw data of single cell from 10 ovarian carcinoma patients were retrieved from GEO under accession numbers GSE210347[17] (39316 single cells isolated from 3 adjacent samples and 7 tumors samples). The data was analyzed using R (version 4.1.1) and R Bioconductor packages.

Immunothreapy results calculation

Tumor Immune Dysfunction and Exclusion (TIDE) score was calculated by TIDE website (http://tide.dfci.harvard.edu/)[18].

The immune signatures calculation

To explore the immune landscapes of ovarian carcinoma patients, we acquired 25 classical immune signatures (e.g, Antigen-presenting cell (APC) co inhibition, APC co stimulation, Cytokine-cytokine receptor (CCR) and human leukocyte antigen (HLA) from the study by He et al[19].

Pathway enrichment analysis

In order to study the functional differences among OV patients, we used "GSVA" R package[20] to conduct GSVA enrichment analysis. HALLLMARK gene set was retrieved from the MSigDB database (http://software.broadinstitute.org/gsea/msigdb/index.jsp). Pathway enrichment analysis was performed using the “fgsea” package, an algorithm for fast preranked gene set enrichment analysis using cumulative statistic calculation. In addition, "clusterProfiler" R package was used to conduct GO enrichment analysis to comprehend the biological processes and molecular functions of the differential genes and KEGG enrichment analysis to identify potential related biological pathways.

The construction of machine learning model based on programmed cell death genes

The PCD related genes, including the key regulatory genes of 12 PCD patterns, were collected by Zou Y et al[21]. The “Limma”[22] package was subsequently used to screen out differentially expressed genes (DEGs) with the criterion of the adjusted P < 0.05 and |log2FC| > 1. We also performed univariate Cox proportional hazard regression to explore masked prognosis related PCD genes among ovarian carcinoma patients and finally we selected 28 PCD genes for future analysis, which related with the progress and prognosis of ovarian carcinoma patients. To predict the prognosis of ovarian carcinoma patients using these PCD-related genes, we applied a machine learning algorithm to establish the model.

Single cell data processing and cell type annotation

The data normalization, dimensionality reduction, and clustering for the ovarian carcinoma (GSE210347) datasets were performed using Seurat v.3.0.0[23] with default parameters. Cells were filtered based on the criterion of expressing at least 200 genes and having UMIs < 6000 during the preprocessing stage. Cells with more than 20% mitochondrial gene expression contribution were also removed. The cell clusters were annotated using classical cell signatures with SingleR[24].

Prediction of drug sensitivity in high and low-PCDI groups

The calcPhenotype function in the R package 'pRRophetic' was utilized to predict the half maximal inhibitory concentration (IC50) of drugs based on gene expression profiles in cell lines. A P value of less than 0.05 was used as the threshold for selecting drugs deemed favorable in the context of the study[25].

Statistical analysis

In our study, we conducted a Wilcoxon test to compare the various features between the two groups of OV patients. To determine the correlation between the PCD index and immune associated features, we combined Drug IC50 data and calculated the Spearman's correlation coefficient. InferCNV[26] was applied to infer malignant epithelial cells from epithelial cell group. To examine the association between different groups of ovarian carcinoma patients and survival, Kaplan-Meier survival analysis was applied using the 'survminer' R package. Additionally, univariate Cox proportional hazard regression was used to assess the association between PCDI and overall survival. Multivariate Cox regression was used to assess the independent prognostic value of PCDI in comparison to other clinical factors. A P value of less than 0.05 was considered statistically significant.

Data preprocessing

For training and validation cohorts, we collected 379 patients from the TCGA, 285 patients from GSE9891, 185 patients from GSE26712, 101 patients from GSE63885, and 266 patients from GSE49997, among which, 76 patients from the TCGA, 285 patients from GSE9891, 185 patients from GSE26712, 101 patients from GSE63885, and 266 patients from GSE49997 who underwent surgery and have survival features met the inclusion criteria. As there was a lack of normal ovarian patient data in the TCGA database, we obtained transcriptome data from 88 ovarian normal tissues in the GTEx database. After integration, we identified 680 programmed cell death genes that were expressed in both the training and validation cohorts, which were then used for further analysis. Additionally, we obtained single-cell RNA transcriptome data from 10 patients, which included 3 adjacent samples and 7 tumor samples from GSE210347.

Genotyping and clinical features of OV patients after surgery

Given the adequacy of sample size and the comprehensiveness of corresponding clinical information, we employed GSE9891 for phenotyping ovarian cancer patients. By performing Nonnegative Matrix Factorization (NMF) analysis using the expression matrix of 285 prognosis associated cell death-related genes defined by FSbyCox algorithm, we obtained two different subtypes of OV (Fig. 1A). These OV clusters exhibited significant differences in overall survival (OS, P < 0.001). Among which, cluster 2 showed favorable survival, while cluster 1 showed the worst survival (Fig. 1B). Additionally, we assessed the clinical characteristics of patients in different clusters. We found that 64% of patients in cluster 1 were diagnosed with grade 3. Meanwhile, the 97% of patients in cluster 1 were diagnosed with stage III/IV, while stage I/II predominated in patients in clusters 1. Besides, all patients in cluster 1 were diagnosed with Malignant and all low malignant potential (LMP) patients were in cluster 2, indicating that cluster 1 was characterized by advanced pathological stages, which is consistent with a faster decline in its survival curve (Fig. 1C). We further investigated Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment pathways between these clusters. We found that cluster 1 showed significantly enriched in pathways associated with tumor invasion and metastasis containing PI3K-Akt signaling pathway, JAK-STAT signaling pathway, Wnt signaling pathway, TGF-beta signaling pathway, TNF signaling pathway and NF-kappa B signaling pathway (Fig. 1D). Additionally, we performed a GSEA analysis based on differentially expressed genes between C1 and C2 groups. Pathways associated with treatment resistance and tumor invasion (e.g., Hypoxia, Mitotic Spindle, EMT and Myogenesis) were significantly upregulated in the cluster 1 group (Fig. 1E). Taken together, our results suggested that this classification demonstrates excellent prognostic potential for predicting postoperative outcomes in OV patients undergo surgery. Enrichment of pathways associated with treatment resistance and tumor invasion in the cluster 1 group is potential cause of its poor prognosis.

Tumor microenvironment and chemotherapy response in different clusters

Given the prominent role of immunotherapy as a highly promising therapeutic strategy for ovarian cancer, and recognizing the crucial involvement of cell death in activating antitumor immune responses, we conducted an analysis of the tumor microenvironment (TME) within these two identified clusters. Tertiary lymphoid structures (TLS) are acknowledged as germinal centers for immune cells in the TME, and thus we assessed the expression levels of a range of chemokines implicated in TLS formation. Intriguingly, we observed significantly higher expression of most chemokines responsible for regulating immune infiltration in cluster 1 (Fig. 2A). Furthermore, several interferons, including their receptors (e.g., IFNAR2, IFNGR2), and the majority of interleukins and their receptors were associated with transcripts linked to immune activation. Consistent with the higher expression of chemokines and increased immune cell infiltration within the TME of cluster 1, we noted elevated levels of these interferons, interleukins, and their receptors in this particular cluster (Fig. 2B, 2C).

Subsequently, we proceeded to calculate the immune score and ESTIMATE score using the ESTIMATE algorithm within the different clusters. A higher immune score signifies greater infiltration of immune cells, while a higher ESTIMATE score indicates lower tumor purity. Notably, we observed significantly elevated immune scores and ESTIMATE scores in cluster 1 compared to cluster 2, indicative of increased immune cell infiltration within cluster 1 (Fig. 2D). Additionally, we analyzed the immune-related function scores for each sample, revealing that most immune-related function scores were lower in cluster 2, suggesting suppression of these functions within this cluster (Fig. 2E).

Given that the expression of immune checkpoints serves as a crucial foundation for immune checkpoint inhibitors (ICIs) therapy, we proceeded to evaluate the expression levels of immune checkpoints within the two identified clusters. Remarkably, cluster 1 exhibited higher expression of most immune checkpoints, including CD274/PD-L1, CTLA4, HAVCR2/TIM-3, ICOS, IDO1, PD-L2, LAG3, and TIGIT, in comparison to cluster 2. This observation suggests that cluster 1 may potentially derive greater benefit from immunotherapy interventions (Fig. 2F). Furthermore, we conducted an additional assessment of the response to immune checkpoint inhibitors (ICIs) across the different clusters. Notably, patients in cluster 1 displayed higher TIDE scores, indicating a more favorable response to immunotherapy, whereas cluster 2 exhibited relative resistance. This finding aligns with the expression patterns of immune checkpoints observed (Figure 2G). Overall, the tumor microenvironment (TME) in cluster 1 appears to support anti-tumor immunity and exhibits higher expression of immune checkpoints, thereby suggesting potential benefits from ICIs therapy.

Variant landscape of programmed cell death genes in OV patients

Then, we used the TCGA-OV and GTEx cohort and applied the Limma algorithm to identify 239 differentially expressed genes (DEGs) associated with 12 programmed cell death (PCD) patterns. The DEGs were selected based on an adjusted p-value less than 0.05 and |LogFC| > 1. A comprehensive list of these DEGs can be found in Table S1, while their corresponding volcano plot is depicted in Fig. S1A. The KEGG was used to perform an enrichment analysis of the differentially expressed genes (DEGs), revealing their important role in regulating pathways associated with cancer processes. Specifically, the DEGs were found to be involved in the NF-kappa B signaling pathway, PI3K-Akt signaling pathway, and mTOR signaling pathway. Furthermore, the Gene Ontology (GO) enrichment analysis revealed that the differentially expressed genes (DEGs) are involved in crucial biological pathways such as regulating apoptotic signaling, autophagy, cellular response to hydrogen peroxide, and cell death in response to oxidative stress (Fig. S1B). Subsequent analysis using the UniCox method revealed that 28 of the identified genes were significantly correlated with the prognosis of OV patients Supplementary (Fig. S1C). The Circos plot of the expression of these 28 PCD genes demonstrated that CTSB was expressed at higher levels in OV prognostic effecta patients who underwent surgery (Fig. S1D). Additionally, we screened the CNV level of these features (Fig. S1E).

Establishment and evaluation of a programmed cell-death index in OV

To examine the prognosis of PCD-related mRNAs, we created a programmed cell-death index (PCDI) using Lasso and multiple Cox regression analysis. In contrast to conventional approaches, we employed multiple iterations (1000 times) to mitigate the impact of random errors in Lasso regression and reduce contingency issues. The process involved five steps: (i) screening of 28 PCD-related genes associated with prognosis, and removal of those with low variance (Var < 5), (ii) dimensional reduction of selected PCD-related genes using Lasso regression, repeated for 1000 times, and counting the total number of occurrences of each gene, (iii) retention of genes with a total number of occurrences of more than 999 times among the PCD-genes, (iv) the model with the maximum AUC value was selected as final prognostic model through multiple repetitions, (v) calculate the programmed cell-death index (PCDI) for each sample.

Through this process, we finally selected 8 PCD-related genes to establish model corresponding to the maximum AUC (5-year AUC = 0.81) (Fig. 3A - 3C), including FBXO7, NFATC4, PIK3R1, PPP1R15A, SIAH1, STXBP1, TIMP3 and UNC5B. Among them, FBXO7 and SIAH1 were positively correlated with survival rate while NFATC4, PIK3R1, PPP1R15A, SIAH1, STXBP1, TIMP3 and UNC5B were negatively correlated with survival rate (Fig. S2A). The programmed cell death index (PCDI) of each patient was exported using the following formula: PCDI = (-0.120880 * FBXO7 exp.) + (0.020864 * NFATC4 exp.) + (0.069234 * PIK3R1 exp.) + (0.008061 * PPP1R15A exp.) + (-0.179442 * SIAH1 exp.) + (0.032142 * STXBP1 exp.) + (0.066505 * TIMP3 exp.) + (0.013387 * UNC5B exp.). The PCDI was calculated using the formula above and patients were categorized into a high or low-PCDI group based on the best cut-off defined by the 'survminer' R package. In the TCGA cohort, 51 OV patients were in the high-PCDI group and 25 OV patients were in the low-PCDI group. Results from K-M analysis indicated that patients in the high-PCDI group had a lower overall survival rate compared to those in the low-PCDI group (P < 0.05) (Fig. 3B). Additionally, a low PCDI was associated with a prolonged survival time (Fig. 3D). Time-dependent receiver operating characteristic (ROC) curves were drawn using R software, and the area under the curve (AUC) was calculated at various time points to estimate the predictive performance of the prognostic model in the TCGA-OV cohorts. The ROC curve indicated that the PCDI possessed notable credibility and predictive value, with an AUC of 0.810 at 3 years and 0.774 at 5 years in the TCGA cohorts (Fig. 3C). Univariate Cox analysis revealed that only PCDI was associated with the survival rate of ovarian patients who underwent surgery (Fig. 3E). Furthermore, the multivariate Cox model identified PCDI as the only independent predictive factor with a p-value less than 0.001 (Fig. 3F).

External validation of the accuracy of programmed cell-death index

To assess the effectiveness and precision of the prognostic model in practical clinical use, we proceeded with external validation analysis. Our validation cohorts included GSE9891, GSE26712, GSE63885, and GSE49997. We determined the best cut-off using the 'survminer' R package and divided the patients into two groups. Specifically, 285 patients from GSE9891, 185 patients from GSE26712, 101 patients from GSE63885, and 266 patients from GSE49997 were included in the analysis. The results of the Kaplan-Meier analysis indicated that patients in the high-PCDI group had a significantly shorter overall survival and higher death rates (P < 0.05), as shown in Fig. 4A. Finally, multivariable Cox and logistic regression analyses were involved to establish a nomogram model in the OV cohort to estimate the overall survival. Sample type, Stage, Grade, and PCDI were included in the model (Fig. 4B).

Immune and functional landscapes of low- and high- PCDI groups

The immune system plays a crucial role in fighting tumors, and its effectiveness is influenced by various components within the TME. These components include the major histocompatibility complex (MHC) molecules that are responsible for presenting antigens to the immune system and interferons (IFNs) that help eradicate cancer cells. Furthermore, immune checkpoints, interleukins (ILs), cytokines, and their receptors represent essential TME components, with chemokines responsible for immune cell migration into the TME, and IFNs regulating the anti-tumor activity of effector T cells[27-29]. To explore the reasons behind the lower survival rate of patients with high-PCDI, a comprehensive analysis of the TCGA cohort was carried out. The study involved comparing immune functions of both groups to identify any discrepancies.

GSVA analysis was performed to examine the correlation between HALLMARK pathways and PCDI. Our results revealed that patients with high-PCDI exhibited an enrichment of pathways that support tumor progression and metastasis, including Epithelial mesenchymal transition, Hypoxia, Myogenesis and TGF BETA signaling (Fig. 5A). On the other hand, immune associated pathways such as IFN-α response, IFN-γ response, and inflammatory response were found to be enriched in the low-PCDI group (Fig. 5B).

Additionally, we found a strong negative correlation between PCDI and the expression of various chemokines and receptors, such as CCL5, CCL8, CCL18, CCR7, CXCL9, CXCL10, CXCL11, CXCL13, and XCL1 (Fig. 5C). Additionally, we observed a negative correlation between PCDI and the expression of chemokines and their receptors, including IFNs and their receptors (Fig. 5D and 5E). Moreover, our findings indicate that low-PCDI patients exhibited a significant improvement in classical immune function associated signatures, such as cytolytic activity, inflammation-promoting, T cell co-stimulation, and TIL. This suggests that these patients may have an increased response to immunotherapy, as shown in Figure 5F.

Dissection of tumor microenvironment based on single cell transcriptome

To investigate the differences in the tumor microenvironment between low- and high-PCDI groups, we analyzed single-cell mRNA profiles of ovarian carcinoma tissues from the GSE210347 dataset. We applied a filter to only include cells that expressed at-least 200 genes and removed cells with over 20% expression of mitochondrial genes. Following quality control, the filtered cells were classified into seven distinct cell types based on established biomarkers. These types include epithelial, identified by KRT8, KRT18, and EPCAM, T & NK, identified by CD3D, IL7R, NKG7 and GZMK cells, Cancer associated fibroblast (CAF), identified by COL1A1, DCN and COL1A2, Plasma, identified by JCHAIN, MZB1, and IGKC, Perivascular-like (PVL) cell, identified by TAGLN, MYH11, and MCAM, Macrophage, identified by SPP1, C1QC, S100A9 and CD14, Monocytes, identified by HLA-DRA and FCGR3A, Cycling cell, identified by MKI67 and CDK1, Cycling cell, identified by MKI67 and CDK1, Endothelial cell, identified by VWF and PECAM1, and B cell, identified by CD79A and MS4A1 (Fig. 6A, 6B and S3B). We utilized the lasso algorithm to evaluate the score of individual cells, using 8 PCD genes (Fig. 6C). Our findings indicate that the PCDI was significantly higher in the adjacent tissues as compared to the tumor tissues (Fig. 6D).

To overcome the limitations of our scRNA-seq dataset's sample size, we employed the use of BisqueRNA[30], a deconvolution algorithm, to simulate cell-type-specific gene expression profiles. This allowed us to predict the abundance of each cell type, as quantified by scRNA-seq, in larger scale datasets from four independent OV cohorts sourced from the GEO. The BisqueRNA tool's ability to accurately predict gene expression profiles specific to certain cell types was developed through training with our own single-cell RNA sequencing data. In order to better understand the relationships between various cell populations in the ovarian microenvironment, we conducted an analysis of pairwise Spearman correlations within infiltration patterns of ten major cell types across four independent ovarian cancer cohorts. We observed a strong negative correlation between cycling cells and PCDI across all groups studied and considered a spearman correlation coefficient [|Rs|] > 0.2 and P Value < 0.05 to indicate significant correlation. The correlation ranged from -0.2 in the GSE26712 dataset with 185 OV samples to -0.4 in the GSE49997 dataset with 266 OV samples (Fig. 6E). In addition, we found that the PCDI was higher in CAF, PVL, and endothelial cells, whereas it was almost zero in epithelial, T cell, plasma, and monocytes (Fig. S3A). Then, we utilized the Wilcoxon test to compare the proportion of Endothelial and CAF in low-PCDI samples to that in high-PCDI samples. To further analyze the changes in the immune microenvironment between the two groups, T&NK, macrophage, and monocytes were reaggregated (Fig. S3D, S3E). Additionally, infer-CNV was utilized to distinguish between malignant and normal epithelial cells (Fig. S3F).

Efficacy of programmed cell death index in predicting drug sensitivity

To investigate potential treatment strategies for high- and low-PCDI groups, we first explored the expression of immune checkpoints in the high- and low- PCDI groups, and revealed that OV patients with low-PCDI were more sensitive to immunotherapy drug, supported by the higher expression of immunotherapy targets such as BTLA, PD-L1 (CD274), CTLA4, ICOS and TIGIT in low-PCDI group (Fig. 7A). What’s more, by using TIDE webserver, we found that patients with high-PCDI responded poorly to checkpoint inhibitors (CPIs) therapy treatment, whereas the proportion of patients with low-PCDI responded to CPIs treatment was higher than patients with high-PCDI (Fig. 7B). In line with this, the TIDE score in OV patients was analyzed, revealing a higher TIDE score in high-PCDI patients and suggesting a significant positive correlation between the TIDE score and PCDI (Fig. 7B, 7C). Our results imply that OV patients with low-PCDI may benefit from immunotherapy.

To further investigate the correlation between the PCDI and drug sensitivity, we analyzed the IC50 values of each drug in OV samples to identify any significant variations. Our study revealed that patients in the high-PCDI group exhibited greater sensitivity to tyrosine kinase inhibitors, such as BMS.754807, AP.24534, OSI.906 and KIN001.135, as illustrated in Fig. 7D. Additionally, our findings indicate that OV patients with low-PCDI were more sensitive to Etoposide, a cell cycle specific antitumor agent, supported by the lower IC50 values of these drugs in the high-PCDI group (Fig. 7E).

In our study, we performed phenotyping of postoperative ovarian cancer patients based on PCD-associated genes that are prognostically relevant. We investigated the prognostic outcomes among different subtypes and further analyzed the immune microenvironment characteristics in each subtype. Notably, we found that patients in cluster 1, despite having poorer prognosis, exhibited heightened sensitivity to immune-based therapies. Then, we also conducted a comprehensive analysis of twelve distinct PCD patterns in OV by constructing a PCD associated cluster and establishing a PCD signature using machine learning algorithm within the TCGA cohort and further validate its excellent performance in four other external cohorts (TCGA-OV, GSE9891, GSE26712, GSE49997 and GSE63885), demonstrating satisfactory results. We then explored the immune and functional landscapes of different patient groups based on both bulk and single-cell transcriptome data. Lastly, we examined the association between different patient subgroups, immunomodulators, tumor microenvironment, and drug sensitivity and provided different treatment strategies for high- and low-PCDI patients.

Surgery plus chemotherapy is a classic treatment for ovarian cancer. Nevertheless, despite the efficacy of surgery, relapse remains a frequent occurrence. Recent studies have connected PCD to the release of cancer antigens, IFN activity, and immune cell damage, involving various mechanisms[31-33]. PCD has been increasingly recognized as playing a critical role in biological processes, particularly in the development and metastasis of malignant tumors[31]. To investigate this further, we constructed an 8 genes PCD signature (FBXO7, NFATC4, PIK3R1, PPP1R15A, SIAH1, STXBP1, TIMP3 and UNC5B) using a lasso algorithm and categorized OV patients in the TCGA database into low- and high-PCDI groups. The model demonstrated satisfactory AUC values in the TCGA-cohort and four validation cohorts. In the Kaplan-Meier analysis, OV patients with low-PCDI displayed significantly better survival rates compared to those with high-PCDI. Additionally, we confirmed the constructed PCDI as an exclusive independent predictive factor for OV patients after surgery in both univariate and multivariate Cox regression analyses.

PCD has been associated with the release of cancer antigens, IFN activity, and immune cell damage[4]. Our findings reveal a robust negative correlation between PCDI and the expression of chemokines and their receptors, IFNs and their receptors and classical immune functional signatures. Cytokines and their receptors are essential components of TME, with chemokines responsible for the migration of immune cells into TME, ILs enriched in the TME as immunomodulatory cytokines, and IFNs mediating antitumor activity of effector T cells[27, 28]. We found that the expression of chemokines, including CCL5, CCL8, CCL18, CCR7, CXCL9, CXCL10, CXCL11, CXCL13 and XCL1 were higher in low-PCDI group than in high-PCDI group. It has been established that CCL5 plays an important role in attenuating T cell inflammation[34]. CXCL9 was found to be able to inhibit tumor growth and drive anti-PD-L1 therapy in ovarian cancer[35] as well as CXCL11[36]. In addition, CXCL13 also been proved to function as a biomarker for predicting the effect of immunotherapy[37]. In our study, IL2RG, IL27 and IFNB1 were also expressed higher in low-PCDI group. Previous studies have uncovered that IL-27 is a protein that plays a role in regulating the immune system. Specifically, it is involved in anti-inflammatory effects against spondylarthritis through the suppression of Th17 responses[38].

The role of immune cells in tumor development has been extensively documented, with varying subpopulations present in different types of tumors and even among patients with the same pathological type. To further explore this, we utilized single-cell analysis to examine the proportions of different cell types in patients with low- and high-PCDI and investigate the mechanisms of intercellular communication. We revealed that PCDI was higher in adjacent tissues than in tumor tissues, which was consistent with the result that PCDI was associated with lower survival rate in OV patients. Additionally, we utilized a deconvolution algorithm BisqueRNA to simulate the cell-type-specific gene expression profiles to predict the abundance of each cell types quantified by scRNA-seq in large scale datasets from 4 independent OV cohorts from the GEO and discovered that cycling cell were significantly negatively correlated with PCDI. Cycling cell refers to a cell that is able to divide and produce new cells. This is a natural process in our bodies that helps in growth and tissue repair. However, in cancer, cycling cells are often abnormal and can divide uncontrollably, leading to the formation of tumors. In our study, we also found that E2f target and Myc targets V1 pathways were significantly enriched in low-PCDI OV patients which may indicated that cell cycle processes were disrupted in patients with low-PCDI.

Hitherto, the continuous advances in the immuno-oncology and the development of immunotherapeutic agents, such as checkpoint inhibitors (CPIs) and chimeric antigen receptor (CAR) T cell therapy, provide promising strategies to overcome tumors by activating endogenous immune system. In our study, we observed a significant increase in the expression of BTLA, PD-L1 (CD274), CTLA4, ICOS and TIGIT in OV patients with high-PCDI. Consistent with these results, we found that the TIDE score of low-PCDI patients was lower than that of high-PCDI patients. In line with this, the proportion of patients with low-PCDI responded to CPIs treatment was higher than patients with high-PCDI. These results suggest that patients with low-PCDI may have an anti-tumor microenvironment and be more sensitive to immunotherapy. Moreover, our findings revealed that OV patients with low-PCDI were more sensitive to Etoposide, a cell cycle specific antitumor agent, supported by the lower IC50 values of these drugs in the high-PCDI group, whereas patients in the high-PCDI group exhibited greater sensitivity to tyrosine kinase inhibitors, such as BMS.754807, AP.24534, OSI.906 and KIN001.135, implying that OV patients who undergo surgery with high-PCDI may exhibit tyrosine kinase inhibitors to standard tyrosine kinase inhibitors treatments but could be more resistant to immunotherapy and cell cycle specific anti-tumor agent drugs.

Despite the excellent performance of our model in both the training and validation cohorts, some limitations still need to be addressed. The absence of multi-omics data, such as tumor burden and methylation levels, hinders our ability to investigate the potential landscape of different PCDI groups. Besides, the biological function of some drug therapy strategies has not been investigated in OV cells, which is essential for further experimental research. Therefore, further validation of the results obtained requires more multi-center randomized controlled trials with high quality, large sample sizes, and adequate follow-up.

In summary, our research has developed a reliable PCD index that can be used to assess prognosis and determine drug therapy benefits. This signature has the potential to be a valuable tool for optimizing decision-making and surveillance protocols for individual OV patients who undergo surgery.

Acknowledgments

Not applicable.

Authors contributions

Y.P., A.J., and L.H. designed this project. Y.P. performed the bioinformatics analysis with the help of L.H.. Y.P., L.H., and F.W. wrote the manuscript and supervised the project. L.H. W. C., Q.Z., X.Z., H.X., B.Z., Y.W., performed the data review and modified manuscript. All authors read and approved the manuscript.

Funding

This work was supported by the Scientific Research Fund of Binzhou Medical University (BY2021KYQD34), Scientific Research Initiation Fund of Affiliated Hospital of Binzhou Medical University Hospital (2021-03).

Availability of data and materials

We obtained two datasets for our study. The GSE9891, GSE26712, GSE49997 and GSE63885 datasets are publicly available on the GEO database (http://www.ncbi.nlm.nih.gov/geo). We also utilized the ovarian cancer dataset from the TCGA repository (https://portal.gdc.cancer.gov/projects/TCGA), with accession code THCA. Raw data of single cell from 10 ovarian carcinoma patients were retrieved from GEO under accession numbers GSE210347. Both datasets are freely accessible for research purposes.

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Competing interests

The authors declare no competing interests.

Contributor Information

Jiajia An, Email: [email protected]

Yitong Pan, Email: [email protected]

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No competing interests reported.

FigureS1.jpg
Figure S1. Landscape of programmed cell death (PCD) gene variants in OV patients. (A) The volcano plot shows differentially expressed genes (DEGs) related to PCD in GTEx and TCGA-OV patients, with blue indicating down-regulation, red indicating up-regulation, and grey indicating no significant difference in TCGA-OV patients who undergo surgery. (B) GO and KEGG pathway enrichment analysis based on DEGs related to PCD. (C) Venn diagram showing the overlap between DEGs related to PCD and PCD genes associated with patient prognosis. (D) Circos plot shows the expression of 28 PCD genes in TCGA-OV, GSE9891, GSE26712, GSE49997 and GSE63885 cohorts (E) The location and CNV level of 28 PCD-related DEGs in the TCGA-OV cohort.
FigureS2.jpg
Figure S2. (A) Kaplan-Meier of 8 PCD associated genes in model.
FigureS3.jpg
Figure S3. (A) The score of PCD for each cell types. (B) Bar plot indicating the proportion of each cell type in each sample. (C) Box plots comparing the proportion of Endothelial and CAF in high- vs. low-PCDI groups. (D) UMAP plot showing the composition of three min cell types divided from T & NK cell group. (E) UMAP plot showing the composition of six min cell types divided from Macrophage & Monocytes cell group. (F) InferCNV identification malignant epithelial cells from epithelial cell group. (G) Box plots comparing different cell types in high- vs. low-PCDI groups.
TableS1.xls

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Identification an novel classification and nomogram of programmed cell-death patterns for investigating the immune microenvironment and prognosis of postoperative ovarian cancer patients

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