The Cancer Genome Atlas (TCGA) dataset on ovarian cancer RNA sequencing and survival data was retrieved from the UCSC Xena database (13). Additionally, 12 GEO cohorts focusing on ovarian cancer (GSE13876, GSE138866, GSE140082, GSE14764, GSE17260, GSE18520, GSE19829, GSE26712, GSE31245, GSE49997, GSE63885, GSE9891) were acquired, each containing detailed survival information.

The research gathered various sets of data from groups of patients treated with anti-PD-L1/PD-1 medications to investigate the correlation between cancer stem cell characteristics and the effectiveness of immunotherapy. The cohorts included the following: Rose TL cohort (14) (GSE176307: ICB treated metastatic urothelial cancer), Jung H cohort (15) (GSE135222: anti-PD-1/PD-L1 treated non-small cell lung carcinoma), Riaz N cohort (16) (GSE91061: anti-CTLA4 and PD-1 treated melanoma), Liu/VanAllen cohort (phs000452.v3: anti-PD1/CTLA4-treated metastatic melanoma) from the dbGaP database, Necchi cohort (17) (IMvigor210: Atezolizumab treated advanced or metastatic urothelial carcinoma) obtained using the “IMvigor210CoreBiologies” R package, Wang GY cohort (anti-PD-1/PD-L1 treated melanoma), and Braun DA cohort (anti-PD-1 treated advanced clear cell renal cell carcinoma). Gene expression and clinical data were also gathered for these immunotherapy-treated datasets. The details of all cohorts used in this study can be found in Supplementary Table S1 .

Gene expression profiles of single cell OV dataset were preprocessed and retrieved from the GEO database with accession number GSE184880 (18). The dataset consisted of five non-malignant tissues and seven high-grade serous ovarian cancer tissues. Moreover, an examination was conducted on a melanoma cohort to explore the correlation between cancer cell stemness and the efficacy of immunotherapy. This cohort comprised data on both ICI response and single-cell RNA sequencing, sourced from GEO under accession number GSE115978 (12).

The CRISPR screening of OV cells at a genome-wide level was acquired through the DepMap portal (https://depmap.org/portal/download). Utilizing the CERES algorithm, dependency scores were computed for about 17,000 potential genes (19). Genes deemed essential demonstrated a CERES score below -1 in 75% of the OV cell lines (n = 73).

An innovative pipeline was developed to construct a predictive model for Cancer stem cells (CSC), illustrated in Figure 1A . Initially, by utilizing the CERES algorithm with cell line data, we pinpointed 687 mRNAs that displayed an association with the survival and progression of ovarian cancer cells. Subsequently, we computed mRNA stemness indices (10) across 12 GEO datasets, the TCGA-OV dataset and evaluated the relationship between total mRNA and mRNA expression-based stemness index (mRNAsi). We then identified the mRNAs showing significantly positive correlations in at least half of the cohorts (7 out of 13) as mRNAsi-associated mRNAs (Cor>0.2 and P<0.01), resulting in the discovery of 60 mRNAsi-associated mRNAs ( Supplementary Tables S2, S3 ).

Finally, 8 ovarian cancer (OV) cohorts were used in the creation of a predictive model for CSCs. To accomplish this, a variety of machine learning techniques were employed, including random forest (RSF), elastic net (Enet), gradient boosting (GBM), ridge regression, Stepcox, plsRcox, CoxBoost, and SuperPC.

To evaluate the effectiveness of PD-1/CTLA4 immunotherapy, our first step involved the calculation of scores for tumor immune dysfunction and exclusion (TIDE). This analysis was performed using the adjusted expression data collected from patients with ovarian cancer. The resultant matrix of expression profiles was then submitted to the TIDE database website (http://tide.dfci.harvard.edu/) to assess the response of the patients (20). Next, we employed the submap algorithm available on the GenePattern website to determine the differences in response likelihood between the low- and high-CSI groups.

By analyzing gene expression profiles, drug sensitivity prediction in cell lines was achieved using the ‘oncoPredict’ R package and the calcPhenotype method. To estimate drug IC50, a ridge regression model was developed utilizing gene expression profiles of cell lines acquired from GDSC through the pRRophetic algorithm (21).

Human ovarian cancer cell lines, specifically OVCAR-3, A2780, and SK-OV-3, were supplied by the Cell Bank of the Committee for Conservation of Typical Cultures, which is part of the Chinese Academy of Sciences. These cell lines were cultured using Dulbecco’s Modified Eagle Medium (DMEM) from Gibco (New York, USA) and enriched with 10% fetal bovine serum. Furthermore, the culture medium was supplemented with 100 IU/mL penicillin and streptomycin, both of which were also procured from Gibco (New York, USA).

Following the removal of paraffin, the sections embedded in paraffin were subjected to a treatment with 3% hydrogen peroxide at 26°C for 10 minutes to suppress the activity of endogenous peroxidase. Next, the sections were blocked using 10% goat serum to avoid non-specific binding. Afterward, the sections were incubated overnight at 4°C with primary antibodies. Subsequently, rabbit secondary antibodies were applied to the sections, which were then stained using DAB.

Lentiviral vectors designed for the overexpression of SNRPE were sourced from Genechem in Shanghai, China. Stable transfection of cells with these SNRPE-overexpressing lentiviruses, along with the corresponding control plasmids, was performed to induce puromycin resistance. Following the instructions provided by the manufacturer, selection of stable transfectants was carried out using 2 μg/mL puromycin over a period of 7 days to establish stable SNRPE-overexpressing cell lines. Biotend Co., Ltd. synthesized the siRNA targeting SNRPE. The siRNA, at a concentration of 50 nM, was transfected into cells using the Lipofectamine 3000 transfection kit provided by Thermo Fisher Scientific in Waltham, Massachusetts, USA, with a 24-hour incubation period.

To conduct western blot analysis, cells in culture were washed with ice-cold PBS before extracting total cell protein lysates at 4°C with RIPA lysis buffer (Beyotime, Shanghai, China) supplemented with 1% protease inhibitor cocktail (MedChemExpress, New Jersey, USA). Following centrifugation at 12,000 g for 20 minutes at 4°C, the supernatant containing proteins was gathered and mixed with loading buffer. The samples were then subjected to separation by 10% SDS-PAGE and transfer onto a PVDF membrane. The membrane was then blocked for 2 hours at room temperature with 5% skim milk before incubating overnight at 4°C with primary antibodies. After rinsing with Tris Buffered Saline, the membrane was exposed to secondary antibodies for detection of protein bands using enhanced chemiluminescence reagents (Beyotime, Shanghai, China). Antibodies used in the analysis included SNRPE (20407-1-AP, Proteintech, Wuhan, China) and GAPDH (60004-1-Ig, Proteintech, Wuhan, China).

To evaluate cell proliferation, 2×10^3 cells were introduced into each well of a 96-well plate and maintained for the required period. Afterward, each well was treated with 10 μl of CCK-8 reagent (Dojindo Molecular Technologies, Kumamoto, Japan) and left to incubate for one hour. The absorbance was then recorded at a wavelength of 450 nm (OD450) for further analysis.

To assess the ability of colonies to form, a range of 500 to 2000 cells were placed in each well of a 6-well plate and left to incubate for around one week. Upon detection of colonies with over 50 cells, they were treated with 0.2% crystal violet for a duration of 30 minutes. Following three rounds of washing with PBS, the colonies were both captured in pictures and tallied for measurement.

To evaluate the migratory potential of cells, a total of 40,000 cells were suspended in 200 μL of culture medium and placed in the upper compartment of Transwell plates from BD Biosciences. At the same time, 600 μL of culture medium with 10% FBS was introduced into the lower compartment. After an overnight incubation at 37°C, the cells located beyond the Transwell membrane were fixed using 4% paraformaldehyde for half an hour and subsequently stained with 0.25% crystal violet for an additional 30-minute period. Following the removal of cells from the interior of the chamber, the migrated cells outside the membrane were visualized and quantified.

We utilized the Wilcoxon test to assess various attributes of the high- SNRPE and low- SNRPE groups. The Chisq test was employed to scrutinize the variability in immunotherapy response among the high-CSI and low-CSI groups. The correlation between mRNA and mRNAsi was investigated through the calculation of Pearson’s correlation coefficient. Kaplan-Meier survival analysis was performed to explore the connection between CSI, SNRPE, and survival, utilizing the log-rank test to determine the significance of observed distinctions. To assess the prognostic and immunotherapy advantages of CSI, time-dependent receiver operating characteristic (ROC) curves were generated with the assistance of the ‘pROC’ R package (22) being utilized. Key factors influencing immunotherapy efficacy were identified using xGboost, a scalable tree boosting system. Patients were grouped based on the optimal threshold established by the ‘survminer’ R package. Statistical significance was defined by a significance level of P or adjP < 0.05.

Considering the potential influence of cancer stemness on the resistance to ICIs, a comprehensive analysis was conducted on 13 transcriptome datasets related to OV obtained from the GEO and TCGA databases. The mRNAsi was calculated for each patient (10). By utilizing the Pearson correlation coefficient, mRNAs that exhibited a significant relationship with mRNAsi across multiple samples (Cor>0.2 & P<0.01) were identified. Subsequently, 253 mRNAs that detected in more than 50% of the datasets (7 out of 13) were considered as tumor stemness-associated mRNAs. Moreover, to pinpoint crucial candidate genes involved in OV malignancy, an in-depth examination of CRISPR-based loss-of-function screens was undertaken on a global scale based on DepMap database. As a result, a total of 687 genes essential for the survival of 73 OV cell lines (CERE score < -1 in 75% OV malignant cells) were identified. Of these, 60 mRNAs were selected through the overlap of OV mRNAsi-associated mRNAs with the mRNAs highlighted by CRISP ( Supplementary Table S3 ). All these genes associated with cancer stemness were chosen for further study. To validate the influence of cancer stemness associated genes on immunotherapy effectiveness, a previously published scRNA-seq dataset of PD1 ICI-treated patients with melanoma (SKCM) was initially employed to investigate the correlation between cancer stemness and ICI responses. After excluding individuals lacking data on malignant cells, a total of 23 patients from this cohort were included, comprising 10 non-responders (NR) and 13 treatment-naïve (TN) patients. Ideally, a comparison of cancer stemness between responders (R) and NR to ICI treatment would have been preferred. However, the dataset available did not contain specific data on responders. Given that treatment-naïve patients may consist of both potential responders and non-responders, our next step was to compare the stemness levels between the NR group and the TN group, as previously described. As depicted in Figures 1B, C , the NR subcategory displayed a higher frequency of cancer cells with increased stemness rankings. Further investigation revealed that individuals from the NR subgroup had significantly higher levels of stemness (P < 0.001, Figure 1D ), indicating an inverse relationship between cancer intrinsic driver and stemness with immune checkpoint inhibitor outcomes. Furthermore, we also found that these cancer stemness related genes significantly enriched Cell proliferation-related pathways, including Cell cycle and DNA replication ( Figure 1E ), indicating that tumor stemness-related genes may stimulate tumor cell proliferation.

To further develop a prediction model for CSI, nine machine learning algorithms were used with a combination of six GEO OV datasets and the OV TCGA dataset. Subsequently, we calculated the risk score for each sample in the eight cohorts, which included survival data, using these predictors. The performance was evaluated by determining the average C-index for each algorithm. Interestingly, most of these predictors showed a considerably high average C-index ( Figure 2A ). This finding can be partly attributed to the exceptional quality of our cancer stemness markers. Among all the models, random forest (RSF) demonstrated the highest level of precision (average C-index = 0.922, Figure 2A ) and was chosen as the definitive CSI. Additionally, through univariate cox analysis, a significant correlation between high CSI in the seven cohorts and poor survival outcomes was established (P<0.05, Figure 2B ).

The progress in next-generation sequencing and large-scale data mining technologies has facilitated the thorough investigation and advancement of gene expression-derived markers that are able to forecast prognosis results. To thoroughly assess the effectiveness of the CSI in comparison to alternative markers, we methodically compiled previously published markers from the past decade. A total of 79 markers were analyzed in this research ( Supplementary Table S4 ). Notably, the reliability of the CSI in predicting survival outcomes exceeded that of all other models across eight different OV cohorts, achieving an average AUC > 0.9 in the mentioned cohorts ( Figure 2C ).

Given the importance of cancer stemness mRNAs in predicting the success of tumor immunotherapy, we utilized the submap algorithm available on GenePattern website to predict the probability of immune therapy response based on high- and low- CSI groups. A noteworthy finding was revealed when individuals from the low CSI group exhibited a significant reaction to PD-1 immunotherapy (P = 0.007, Bonferroni corrected P < 0.001, Figure 2D ), demonstrating the remarkable predictive power of the CSI model in the context of PD-1 immunotherapy efficacy.

Collectively, these results suggest that CSI could function as predictive markers for the prognosis of ovarian cancer, with higher robust than other models. In addition, CSI can also be used as a prognostic indicator of PD1 immunotherapy response.

To further verify the predictive performance of CSI on the therapeutic effect of PD-1 ICIs, we collected different datasets associated with PD-1/PD-L1 immune checkpoint inhibitors. Our results consistently indicated that patients diagnosed with specific cancers (such as SKCM, UC, KIRC, or metastatic urothelial carcinoma) who had lower CSI scores experienced notably enhanced overall survival (OS) or progression-free survival (PFS) following immunotherapy compared to those with higher CSI scores ( Figures 3A, B ). This suggests that higher CSI scores may impede the benefits of PD-1 immunotherapy. Furthermore, the response to PD-1/PD-L1 ICI therapy varied between patients with high and low CSI scores. Individuals with higher CSI scores exhibited suboptimal response to the treatment, whereas over half of those with lower CSI scores responded positively ( Figure 3C ). More specifically, the group with higher CSI scores predominantly displayed no response (progressive disease or stable disease), whereas the group with lower CSI scores mainly demonstrated a response (complete response or partial response). Significantly, our analysis indicated that CSI serves as a reliable predictor of PD-1/PD-L1 ICI immunotherapy response, as demonstrated by the area under the curve (AUC) values. The AUC curve portrayed outstanding predictive performance, with an average AUC > 0.8 across the six cohorts examined ( Figure 3D ). Additionally, we performed further analysis using the IMvigor210 dataset and observed that even upon excluding samples with incomplete clinical data, CSI remained a robust predictor of immunotherapy outcomes. Intriguingly, it held greater significance compared to parameters such as PD-L1 expression in tumor cells (TC), immune phenotype, ECOG score, Stage, or tumor mutation burden (TMB), as indicated by a multivariate Cox regression analysis (refer to Figure 3E ). To extend the clinical utility of our model, we explored the potential benefits of combining CSI with other commonly utilized markers of immunotherapy response. Specifically, we investigated the synergistic effects of CSI and TMB, a well-known indicator of immunotherapy effectiveness. Our findings revealed that patients exhibiting low CSI scores and high TMB levels experienced the most favorable outcomes with immunotherapy treatment, whereas those with elevated CSI scores demonstrated the least benefits from such therapies ( Figure 3F ). Additionally, we conducted a comparative analysis of CSI with established signatures for predicting immunotherapy response. Notably, CSI outperformed various signatures, including IFNG.Sig (23), Immune.Sig (23), ImmuneCells.Sig (16), PDL1.Sig (24), LRRC15.CAF.Sig (25), NLRP3.Sig (26), Stem.Sig (11), and CYT.Sig (27) in six PD1/PD-L1 immunotherapy cohorts, while the majority of these signatures exhibited optimal performance in only one or two cohorts ( Figure 3G ).

In conclusion, our research offers important insights into the predictive significance of CSI on immunotherapy results. Elevated CSI levels could potentially hinder the advantages of immunotherapy, whereas lower CSI levels have been linked to better survival rates and treatment responses. Integrating CSI with TMB could potentially improve the classification of patients for immunotherapy.

Examining somatic mutations and copy number variations (CNVs) as factors influencing both antitumor immunity and tumor advancement (28), we analyzed the most commonly mutated genes in various CSI categories (see Supplementary Figure S1A ). Among OV patients, TP53 exhibits the greatest mutation rate, trailed by TTN and CSMD3. TMB stands for the tally of somatic non-synonymous mutations in a specific genetic area, usually expressed as mutations per megabase (mut/Mb). Prior research has demonstrated a negative correlation between TMB and the efficacy of immunotherapy (29). Notably, our investigation revealed that the TMB levels were greater in patients from the low-CSI category compared to those in the high-CSI group ( Supplementary Figure S1B , P<0.05). Consistent with this, the SNV neoantigens and rate of nonsilent mutations were notably elevated in the low-CSI group compared to the high-CSI cohort. ( Supplementary Figures S1C, D , P<0.05). In addition, Homologous Recombination Repair (HRR), a key mechanism for repairing DNA double strand breaks in cells, plays a critical role in maintaining the stability and integrity of the genome. HRD refers to conditions that occur when this repair mechanism is impaired, which may be due to genetic mutations in key repair proteins (such as BRCA1 and BRCA2) or dysfunction of other regulators. Prior research has indicated that targeting HRD defects can be an effective strategy for combating cancer. This includes not only conventional treatments like chemotherapy and radiotherapy, which cause DNA damage, but also newer approaches such as targeted therapies and immunotherapies (30, 31). Consistently, our research revealed that the Homologous Recombination Defects rating was notably elevated in the low CSI group compared to the high CSI group ( Supplementary Figure S1E , P <0.001). Furthermore, to assess the prevalence of CNV across various CSI groups, we subsequently utilized the Genomic Identification of Significant Targets in Cancer (GISTIC) algorithm. Notably, we observed that the amplification GISTIC score was greater in patients from the low CSI group than in those from the high CSI group ( Supplementary Figure S1F ).

Collectively, these findings further elucidate the rationale behind the improved immunotherapeutic efficacy observed in patients with lower tumor stemness and demonstrate that CSI could serve as a prognostic indicator for predicting the therapeutic benefits of ICIs in ovarian cancer.

To explore the clonal architecture and cell origins of ovarian malignant cells, we initially obtained a single-cell RNA profile from ovarian carcinoma. After filtering cells with a minimum expression of 200 genes and excluded those with over 20% expression of mitochondrial genes, we grouped the residual cells into eight major cell types based on traditional biomarkers. As described in Figures 4A, B all cells divided into diverse cell populations including B cells (MS4A1, and CD79A), Endothelial cells (PLVAP, and VWF), Epithelial cells (KRT18, and EPCAM), Fibroblasts (COL1A1, and ACTA2), Myeloid cells (LYZ, and CD14), NK cells (KLRD1, and TRDC), Plasmablast cells (JCHAIN, and MZB1), as well as T cells (CD3D, and IL7R).

Subsequently, the inferCNV algorithm was utilized to assess copy number variations (CNV) and clonality in ovarian malignant cells derived from epithelial cells (ECs). Among the 2807 ECs from THCA tissues, 6230 displayed high CNV scores, indicating malignancy ( Figure 4C ). Notably, amplifications in chromosomal 8q were identified as specific driving variations in ovarian cancer, with SNRPE amplification in chromosomal 8q being linked to cancer stemness and essential for the survival of ovarian cell ( Figure 4D ). Prior studies have indicated SNRPE’s role in promoting cell growth and advancing high-grade prostate cancer by controlling the expression of the androgen receptor [22740892]. Consistently, patients exhibiting high SNRPE expression consistently demonstrated poorer prognoses compared to those with low SNRPE expression ( Figure 4E ). Additionally, we observed that elevated SNRPE expression was specifically linked to tumor stage progression ( Figure 4F ).

Given that tumor heterogeneity and stemness play a crucial role in immune evasion and response to immunotherapy, we analyzed the TME among high and low SNRPE patients categorized based on the optimal threshold established by the ‘survminer’ R package. Tertiary lymphoid structures (TLS), which serve as germinal centers for immune cells, were analyzed for the expression of various interleukins associated with the formation of TLS and the activation of immune responses. Our findings indicate that the majority of these interleukins exhibited elevated expression levels in the group with high SNRPE. Specifically, we found that patients with low SNRPE expression showcased increased expression of IL10RA, IL10RB, IL18, IL21R, IL2RA, IL2RB, IL2RG and IL9R ( Figure 5A ). Additionally, numerous interferons along with their receptors (for example, IFNE, IFNG, IFNAR2, IFNGR2) as well as the majority of interleukins and their corresponding receptors were linked to immune-activating transcripts. Our discovery revealed that the levels of these interferons and receptors were elevated in the low SNRPE group, a pattern that aligns with the inverse relationship of interleukins within the tumor microenvironment ( Figure 5B ). Furthermore, recognizing the importance of immune checkpoint presence as a critical element in immunotherapy with ICIs, we carried out an additional investigation into the levels of immune checkpoints within two distinct groups. It is worth mentioning that the expression levels of several checkpoints (such as HAVCR2/TIM-3, ICOS, LAG3, LGALS9, PDCD1/PD-1, and PDCD1LG2/PD-L2) were significantly higher in the low SNRPE group compared to the high SNRPE group, indicating that higher expression SNRPE patients may benefit from immunotherapy benefit ( Figure 5C ). We also analyzed classical immune signatures in each sample, and we found that the most immune signatures were lower in high SNRPE group, suggesting that these immune cell and immune function were suppressed ( Figure 5D ). These results indicated that SNRPE might impact the effectiveness of immunotherapy by regulating the expression of immune checkpoints and immune microenvironment factors.

To further explore the mechanism of SNRPE on ovarian malignant cells, we then divided malignant tumor cells into SNRPE+ malignant cells and SNRPE- malignant cells according to whether they expressed SNRPE. Through cell-cell interaction analysis, after eliminating common cell communication pairs, we found that SNRPE+ malignant cells had significantly higher specific cell communication with endothelial cells and fibroblasts than SNRPE-malignant cells ( Figure 5E ). Hence, we speculated that SNRPE+ malignant may promote tumor invasion and migration by promoting endothelial mesenchymal transformation. Interestingly, we found that NOTCH signaling pathway (DLL1_NOTCH1, JAG1_NOTCH2, NOTCH1_JAG1, and DLL4_NOTCH3), TGFβ1 signaling pathway (TGFB1_TGFBR3, TGFB1_TGFbeta receptor1, and TGFB2_TGFBR3) and VEGF signaling pathway (NRP1_VEGFA, and VEGFA_KDR) were significantly activated in cell communication pairs between SNRPE+ malignant cells and endothelial cells ( Figure 5F ). Then, we reclustered the tumor cells and found high expression of SNRPE in the C3 and C4 subpopulations ( Supplementary Figures S2A, B ). Using the HALLMARK pathway scoring, we identified that C3 can be defined as the EMT subpopulation, characterized by high activity in EMT signaling pathways. On the other hand, C4 can be defined as the proliferative subpopulation, characterized by the activation of proliferation-related pathways, including E2F_TARGETS, G2M_CHECKPOINT, MYC_TARGETS_V1, and MYC_TARGETS_V2 ( Supplementary Figure S2C ). Additionally, through analysis of cell-cell communications, we further discovered that the interaction intensity between SNRPE+ malignant cells and endothelial cells was significantly higher than that between SNRPE- malignant cells and endothelial cells ( Supplementary Figure S2D ). These findings further demonstrate how SNRPE enhances tumor cell proliferation and invasion. Furthermore, a univariate Cox regression analysis of the pan-cancer cohorts revealed that SNRPE expression was negatively correlated with prognosis across multiple cancer types, including Adrenocortical Carcinoma (ACC), Head and Neck squamous cell carcinoma (HNSC), Kidney Chromophobe (KICH), Kidney renal clear cell carcinoma (KIRC), liver hepatocellular carcinoma (LIHC), Brain Lower Grade Glioma (LGG), Lung adenocarcinoma (LUAD), and Pheochromocytoma and Paraganglioma (PCPG) ( Supplementary Figure S3A ).

Immunohistochemistry analysis revealed a marked increase in CSE1L expression in tumor tissues relative to the adjacent non-cancerous tissues ( Figure 6A ). To further validate SNRPE’s oncogenic role in ovarian cancer, SNRPE was knocked down in OVCAR-3 and A2780 cell lines, effectiveness confirmed at the protein-level through western blot analyses ( Figure 6B ). Significantly, SNRPE knockdown markedly suppressed cell proliferation in both OVCAR-3 and A2780 cell lines ( Figure 6C ), underscoring SNRPE’s contribution to promoting ovarian cancer cell growth. This was further supported by reduced clonogenic capacity in SNRPE knockdown cells compared to controls ( Figure 6D ), highlighting SNRPE’s involvement in fostering growth in ovarian cancer cells. Notably, transwell migration assays revealed decreased cell migration upon SNRPE depletion in OVCAR-3 and A2780 cell lines ( Figure 6E ).

Concurrently, SNRPE overexpression in SK-OV-3 cells was validated through western blot analyses ( Figure 6F ), demonstrating its significant enhancement of proliferation ( Figure 6G ). Furthermore, SNRPE overexpression notably boosted clonogenic potential and significantly increased migration capability in SK-OV-3 cells ( Figures 6H, I ).

In summary, the amplification of SNRPE can drive the progression of ovarian malignant cells and it may serve as an efficient biomarker in foreseeing the prognosis and immunotherapy response in ovarian cancer.