Home Medicine The ENST00000539930 transcript predicts sensitivity to PARP inhibitors and clinical prognosis in cancers
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The ENST00000539930 transcript predicts sensitivity to PARP inhibitors and clinical prognosis in cancers

  • Chi Xu , Cong Li , Wenna Wang , Yiqun Li , Ting Wang , Fangzhou Sun , Xiaoqi Yang , Chunxiao Li EMAIL logo and Fei Ma EMAIL logo
Published/Copyright: September 8, 2025
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Oncologie
From the journal Oncologie Volume 27 Issue 6

Abstract

Objectives

Synthetic lethality-based cancer therapy, particularly using PARP inhibitors, faces resistance challenges. This study aims to explore the role of gene-specific transcripts due to alternative splicing in PARP inhibitor resistance.

Methods

This study conducted transcript-level correlation analyses using CCLE and GDSC databases to identify associations between PARP2 splice variants and sensitivity to PARP inhibitors. This study validated the findings through in vitro overexpression experiments in MDA-MB-231 breast cancer cells and organoid models derived from ovarian cancer patients. Cytotoxicity assays were performed to assess olaparib sensitivity, and RNA sequencing was applied to examine transcriptomic changes. Functional enrichment analyses and clinical prognosis evaluations were conducted using TCGA datasets.

Results

The PARP2 splice variant ENST00000539930 showed a strong correlation with PARP inhibitors sensitivity, outperforming total PARP2 gene expression. Overexpression of ENST00000539930 in MDA-MB-231 cells significantly increased sensitivity to olaparib. Patient-derived ovarian cancer organoids confirmed this correlation (r=−0.43, p=0.04). Transcriptomic analysis revealed that ENST00000539930 expression was associated with activation of RNA splicing and inhibition of DNA repair pathways. Clinically, high ENST00000539930 expression predicted improved survival in breast, ovarian, and pancreatic cancer cohorts (HR=0.62–0.71, p<0.05).

Conclusions

ENST00000539930 is a splice variant of PARP2 that predicts enhanced sensitivity to PARP inhibitors and favorable clinical outcomes. This study highlights the potential of transcript-specific biomarkers and the importance of alternative splicing in cancer therapy response.

Keywords: alternative splicing; DNA damage repair; BRCA1/2 mutation; PARP inhibitors; drug sensitivity

Introduction

Poly (ADP-ribose) polymerases (PARPs) are a large family of enzymes that play an essential role in various cellular physiological processes. PARP2, along with PARP1 and PARP3, are the only known DNA damage-dependent PARPs and play a critical role in DNA damage signaling, DNA metabolism, chromatin remodeling, and transcriptional regulation [1], 2]. Following DNA damage, PARP2 is quickly recruited to single or double-stranded breaks and binds with single-stranded DNA, resulting in the recruitment of downstream DNA repair factors [3]. In contrast, during the S and G2 phases of the cell cycle, BRCA1 and BRCA2 are recruited to regulate pathways for double-strand break repair, a process known as homologous recombination (HR) repair [4], [5], [6]. In the absence of functional BRCA1/2, pharmacological inhibition of key enzymes in compensatory repair pathways, such as PARP2, leads to DNA damage, ultimately triggering genomic instability, mitotic catastrophe, and cell death [7], 8]. These findings suggest that there is a synthetic lethal relationship between PARPs and BRCA1/2.

A major goal of current therapy strategies is to target and kill tumor cells while sparing surrounding non-malignant cells. Given the critical interaction between BRCA1/2 and PARPs, several small-molecule inhibitors targeting the catalytic center of PARPs [9], such as olaparib, niraparib, and rucaparib, have been developed and approved for various clinical indications, especially for BRCA1/2-mutant tumors, including breast cancer [10], ovarian cancer [11], prostate cancer [12], and pancreatic cancer [13]. More recently, several studies demonstrated that patients with BRCA1/2-wild-type tumors who harbor deleterious variants in other DNA repair genes, such as ATM and CDK12, might also benefit from PARP inhibitors [14]. However, patients receiving PARP inhibitors, despite an initial and dramatic response, ultimately develop drug resistance, resulting in disease relapse. It is urgent to better understand the mechanisms of drug resistance, explore methods for overcoming this effect, and identify novel biomarkers for predicting drug sensitivity.

Alternative splicing is a post-transcriptional process in which non-coding introns are removed, and coding exons are joined to form mature mRNA transcripts encoding different protein isoforms [15]. Aberrant RNA splicing is a common event in cancers and contributes to oncogenic transformation, cancer progression, and therapeutic resistance [16], [17], [18]. Previous research has shown that alternative splicing can affect genes involved in various aspects of drug response in cancer cells, including drug uptake and efflux, drug metabolism, qualitative and quantitative alterations in drug targets, and drug inactivation [17], 19]. PARP has been implicated in the regulation of pre-mRNA splicing, and inhibiting its PARylation activity can alter alternative splicing patterns [20]. These changes can significantly affect the coding region of drug targets, leading to treatment resistance despite unchanged gene expression [21]. Therefore, it is more rational and accurate to assess drug sensitivity by analyzing alternative splicing isoforms. However, limited research has investigated this process’s role in PARP inhibitor treatment resistance.

In this study, we aimed to explore the relationship between alternative splicing of DNA damage repair genes and cellular sensitivity to PARP inhibitors. Specifically, we focused on identifying transcript-level biomarkers that may predict PARP inhibitor response, with a particular interest in PARP2 splice variants. We further sought to investigate the potential biological functions of key transcript isoforms and assess their clinical relevance in terms of prognosis and therapeutic response in cancers.

Materials and Methods

Data collection

The percent spliced-in (PSI) data were downloaded from the TCGA spliceSeq database (https://bioinformatics.mdanderson.org/TCGASpliceSeq/). The alternative splice variants data and clinical pathological information of 993 ovarian cancer patients, 148 pancreatic cancer patients, and 298 breast cancer patients from TCGA were downloaded from the TSVdb database (https://www.tsvdb.com/index.html).

Drug sensitivity analysis

Matrix data of 611 cell lines and 342 drug IC50 values and matrix data of 532 cell lines and 147 drug IC50 values were downloaded from the Genomics of Drug Sensitivity in Cancer (GDSC) database (https://www.cancerrxgene.org/). Matrix data of cellular expression of 2,547 transcripts was collected from the Cancer Cell Line Encyclopedia (CCLE) database (https://portals.broadinstitute.org/ccle/). Then, a Spearman correlation analysis was conducted between the drug IC50 values from the GDSCs dataset and the corresponding transcript expression of target genes in the CCLE dataset.

Cluster functional analysis

GDSC1-matched CCLE data were used to screen for the co-expressed genes of PARP2 and ENST00000539930 transcript, respectively. Then, we selected the top 500 co-expressed genes for gene ontology biological process analysis (GOBP). The same analysis was conducted in the GDSC1-matched TCGA dataset for further validation. The functional enrichment analyses were based on the threshold of p-value<0.05.

Cell culture and transfection

MDA-MB-231 cells were purchased from the American Type Culture Collection (Manassas, VA, USA), and cultured using DMEM complete medium containing 10 % fetal bovine serum under conditions of 5 % CO2 at 37 °C. Routine cell culture is maintained at 30–90 % confluence, cells are washed with PBS, 0.25 % trypsinized, and cryopreservation solution is configured according to the volume of DMEM: serum: DMSO=7:2:1. Overexpressed PARP2 transcripts were constructed using pcDNA3.1 vectors. Using Lipofectamine 3,000 (Invitrogen, Carlsbad, CA, USA), plasmid transfection was performed as instructed.

Cytotoxicity assay

MDA-MB-231 cells were seeded in 96-well plates to adhere overnight. Cells were transfected with plasmids using Lipofectamine 3,000 (Thermo Fisher, Waltham, MA, USA) for 12 h. Then, cell culture medium with Lipofectamine 3,000 (Thermo Fisher, Waltham, MA, USA) was removed, and 400 μM DMEM diluted Olaparib was added with a 2-fold specific dilution and nine gradients. The 0 concentration of Olaparib and the empty DMEM medium group were used as controls. For the cytotoxicity assay, cells were treated with 20 μM Olaparib for 48 h. Cell viability was quantified by CCK8 assay. Cells were treated with the indicated agents and cultured in a 384-well plate for 72 h. CCK8 reagent (TargetMol, Boston, MA, USA) was added to each well and incubated for 1 h, and the absorbance was measured at 450 nm using the microplate reader. Three independent experiments were performed in each group.

RNA sequencing

Total RNA extracted from MDA-MB-231 cells overexpressing ENST00000539930 or ENST00000250416 transcripts was reversely transcribed into cDNA, created a library, and sequenced by Novaseq 6,000 (Illumina, San Diego, CA, USA), with PE150. Three duplicated samples in each group. The expression of genes was acquired using RSEM (v1.2.12). Subsequently, DEGs were identified by DESeq2 (v1.4.5) with a Q value≤0.05. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of the annotated DEGs were performed using Phyper, and a rigorous threshold of Q value≤0.05 was used to correct the significant levels of terms and pathways by Bonferroni. Biological functional clustering was analyzed by the DAVID database (https://davidbioinformatics.nih.gov). Images were generated from R Studio (v4.0.2) and ‘ggplot’. Sashimi plot was performed by IGV (v2.19.1).

Organoid culture

Ovarian cancer patient-derived organoids (PDOs) used in this study (n=24) were obtained from Beijing K2 Oncology Co., Ltd. (Beijing, China). These PDOs were acquired for experimental use under a material transfer agreement (MTA) from Beijing K2 Oncology Co., Ltd. (Beijing, China). All PDOs were derived from anonymized epithelial ovarian cancer tissues from consented patients, and their generation was approved by the respective institutional review boards (IRBs) of participating hospitals. The supplier ensured that informed consent was obtained from all tissue donors in accordance with ethical and legal guidelines. The research protocol was approved by the Ethics Committee of Chinese PLA General Hospital (S2021-566-03). Organoids were maintained in specialized ovarian cancer organoid culture medium (K2O-M-OA, K2 Oncology, Beijing, China) and used within 3–5 passages, which was described in previously published research [22] and public database(www.cytomap.com). For drug sensitivity assays, organoids were seeded into 96-well plates and treated with serial dilutions of olaparib for 5 days. Cell viability was assessed using the CellTiter-Glo® Luminescent Cell Viability Assay (Promega, USA). IC50 values were calculated based on a four-parameter logistic regression model. All experiments were conducted in triplicate.

Statistical analysis

All the statistical analyses were conducted using R software v4.0.2 and GraphPad Prism v7.0.0. Data are presented as mean ± standard deviation (SD). The quantitative variables were analyzed using a double unpaired t-test or a non-parametric Wilcoxon rank-sum test. The correlation analysis was conducted using Spearman’s coefficient, and the multiple groups were analyzed using one-way analysis of variance (ANOVA). The survival analysis was performed using the Kaplan-Meier (KM) method (log-rank test). Each experiment was performed in at least three independent rounds. Values with p<0.05 were considered to indicate statistical significance.

Results

Transcripts expression predicts sensitivity to drugs targeting DNA damage repair genes

The CCLE and GDSC datasets were employed in our analysis, which sought to establish a correlation between DNA damage repair gene transcripts and drug susceptibility. This study screened the cell-drug IC50 matrix from the GDSC datasets and the cell-gene transcript expression matrix from the CCLE dataset. Then, we selected the expression of DNA damage repair-associated genes and the drug IC50 matrix of their targeted small molecular inhibitors, as shown in Figure 1A. Specifically, this study analyzed 232 transcripts of 17 genes, including ATM, ATR, CHEK1, CHEK2, FEN1, MDM2, MDM4, MRE11, PARP1, PARP2, PARP6, PARP7, PRKDC, TP53, TERT, USP1, and USP7, and their corresponding 37 targeted drugs’ IC50 values. By using a previously reported analysis protocol [21], it was found that the expression of ENST00000539930 transcript of the PARP2 gene and ENST00000541157 transcript of the MRE11 gene had better correlation with sensitivity to targeted drugs than the expression of the PARP2 and MRE11 genes (Figure 1B). Notably, it was observed that these transcripts exhibited significantly different expression patterns among cell lines in GDSC2, which might be associated with different drug sensitivity in cell lines (Figure 1C). Overall, these findings suggest that alternative transcripts might be a better predictor for sensitivity to drugs targeting DNA damage repair genes.

Figure 1: Transcripts expression predicts sensitivity to drugs targeting DNA damage repair genes. (A) The flow chart of screening the correlation between DNA damage repair genes/transcripts and drug susceptibility. (B) The correlation coefficient of MRE11 and PARP2 genes/transcripts expression and the IC50 of targeted drugs. (C) Transcript expression of ATR, CHEK1, CHEK2, MDM2, MRE11, PARP1, PARP2, PARP6, PARP7, TERT, TP53, USP1, USP7 in CCLE and the IC50 values of corresponding cells in GDSC2. The transcripts of each gene were displayed according to the IC50 of the gene-targeted drug. The columns in each set of genes represent one type of cell, and the rows represent one type of transcript.
Figure 1:

Transcripts expression predicts sensitivity to drugs targeting DNA damage repair genes. (A) The flow chart of screening the correlation between DNA damage repair genes/transcripts and drug susceptibility. (B) The correlation coefficient of MRE11 and PARP2 genes/transcripts expression and the IC50 of targeted drugs. (C) Transcript expression of ATR, CHEK1, CHEK2, MDM2, MRE11, PARP1, PARP2, PARP6, PARP7, TERT, TP53, USP1, USP7 in CCLE and the IC50 values of corresponding cells in GDSC2. The transcripts of each gene were displayed according to the IC50 of the gene-targeted drug. The columns in each set of genes represent one type of cell, and the rows represent one type of transcript.

Identification of the ENST00000539930 transcript for predicting drug sensitivity to PARPs inhibitors

To clarify further the role of PARP2 alternative transcripts in treatment resistance, this study evaluated the correlation between PARP2 transcript expression and Olaparib/Talazoparib response in ovarian cancer cells. Notably, it was found that the expression of ENST00000539930 transcript was tightly correlated with Olaparib/Talazoparib sensitivity (|r|>0.2, p<0.001), which exhibited significantly stronger correlation than total PARP2 expression (|r|<0.2, p<0.05) (Figure 2A). Interestingly, the ENST00000539930 transcript of PARP2 lacks exons 1–9 and retains the PARP catalytic domain, which serves as the major targeted domain of PARP2 inhibitors. To further validate the role of specific transcript-encoded protein domains in modulating drug sensitivity, this study performed Spearman correlation analysis between 11 types of PARP2 transcript expression and IC50 values of Olaparib in ovarian cancer cells using the CCLE database. A negative correlation was found between IC50 values and the expression of transcripts containing PARP catalytic domains, especially the ENST00000539930 transcript (Figure 2B). Subsequently, this study hierarchically clustered the expression of 11 PARP2 transcripts and gene groups in various cell lines and found an obviously different expression pattern in ENST00000539930 transcript (Figure 2C), which further explained the different IC50 correlation coefficient and determined the specific predictive value of the ENST00000539930 transcript. This evidence indicated that ENST00000539930 transcript might serve as a better biomarker, compared to gene expression level, for predicting drug sensitivity to PARP inhibitors.

Figure 2: Identification of the ENST00000539930 transcript for predicting drug sensitivity to PARP inhibitors. (A) The correlation of PARP2 gene and ENST00000539930 transcript expression with the drug susceptibility (IC50) to Olaparib and Talazoparib in ovarian cancer cells in the GDSC1 and GDSC2 databases. (B) The diagram of PARP2 gene transcripts and protein domain classification, and correlation coefficient of different transcripts expression and the IC50 to Olaparib in ovarian cancer cell lines. (C) The clustered heatmap of 11 PARP2 transcripts and the PARP2 gene in different cells in the CCLE dataset.
Figure 2:

Identification of the ENST00000539930 transcript for predicting drug sensitivity to PARP inhibitors. (A) The correlation of PARP2 gene and ENST00000539930 transcript expression with the drug susceptibility (IC50) to Olaparib and Talazoparib in ovarian cancer cells in the GDSC1 and GDSC2 databases. (B) The diagram of PARP2 gene transcripts and protein domain classification, and correlation coefficient of different transcripts expression and the IC50 to Olaparib in ovarian cancer cell lines. (C) The clustered heatmap of 11 PARP2 transcripts and the PARP2 gene in different cells in the CCLE dataset.

ENST00000539930 transcript reveals a better predictive value of PARP inhibitor resistance in patients

Based on the potential predictive value of the ENST00000539930 transcript in public datasets and cell lines, we sought to further validate these findings in an ex vivo model system derived directly from cancer patients. Given the shared reliance on homologous recombination deficiency and PARPi treatment strategies in breast and ovarian cancers, this study employed a cohort of patient-derived ovarian cancer organoids (PDOs) to test whether ENST00000539930 expression similarly correlates with olaparib response in a clinically relevant, heterogeneous setting. Then, the PARPs transcripts/genes expression level and Olaparib response were analyzed through RNA sequencing and drug sensitivity experiments in ovarian cancer patient-derived organoids (n=24) within 3–5 passages, two of which harbor BRCA1/2 mutation. It was found that there was no significant correlation between the expression of PARP1, PARP2, and PARP3 genes and the IC50 of Olaparib (Figure 3A–C). Remarkably, the expression of ENST00000539930 exhibited highly positive relevance to Olaparib sensitivity (r=−0.43, p=0.04) (Figure 3D). However, the ENST00000250416 transcript showed negative relevance to drug sensitivity (r=0.48, p=0.02) (Figure 3E), and other transcripts exhibited no significant relevance (Figure 3F–L). These results indicate that the ENST00000539930 transcript has an improved predictive value for drug sensitivity to PARP inhibitors in clinical patients.

Figure 3: ENST00000539930 transcript reveals a better predictive value of PARP inhibitor resistance in ovarian cancer patients. (A–C) The correlation of drug susceptibility (IC50) to Olaparib and the expression of (A) PARP1, (B) PARP2, (C) PARP3 genes in ovarian cancer patients derived organoids. (D–L) The correlation of Olaparib IC50 and the expression of (D) ENST00000539930, (E) ENST00000250416, (F) ENST00000429687, (G) ENST00000532299, (H) ENST00000530598, (I) ENST00000534664, (J) ENST00000527915, (K) ENST00000529465, (L) ENST00000527384 transcript. Three independent biological replicates were used for IC50 calculations.
Figure 3:

ENST00000539930 transcript reveals a better predictive value of PARP inhibitor resistance in ovarian cancer patients. (A–C) The correlation of drug susceptibility (IC50) to Olaparib and the expression of (A) PARP1, (B) PARP2, (C) PARP3 genes in ovarian cancer patients derived organoids. (D–L) The correlation of Olaparib IC50 and the expression of (D) ENST00000539930, (E) ENST00000250416, (F) ENST00000429687, (G) ENST00000532299, (H) ENST00000530598, (I) ENST00000534664, (J) ENST00000527915, (K) ENST00000529465, (L) ENST00000527384 transcript. Three independent biological replicates were used for IC50 calculations.

Expression of the ENST00000539930 transcript is correlated with clinical prognosis

The role of the ENST00000539930 transcript in predicting the response to PARP2 inhibitors has led us to investigate its correlation with clinical prognosis in cancer patients. Using the TSVdb database, this study divided 993 ovarian cancer patients from TCGA into ENST00000539930 transcript-high and ENST00000539930 transcript-low groups. Patients with a higher level of ENST00000539930 transcript had a significantly longer survival time compared to the ENST00000539930 transcript-low group (HR=0.71, 95 % CI [0.51, 0.99], p=0.04) (Figure 4A). Similarly, it was found a similar prognostic trend was found in 148 pancreatic cancer patients (HR=0.53, 95 % CI [0.33, 0.85], p<0.01), and 298 breast cancer patients (HR=0.62, 95 % CI [0.40, 0.96], p=0.03) (Figure 4B and C). However, the expression of the total PARP2 transcript failed to predict the prognosis of breast cancer patients (HR=0.81, 95 % CI [0.52, 1.26], p=0.35) (Figure 4D), indicating that the ENST00000539930 transcript was a better predictor than the total expression of PARP2. Consistently, in breast tumor tissues, reduced expression of the ENST00000539930 transcript, as opposed to PARP2, was found in tumors, especially in metastatic patients, compared to normal samples (Figure 4E and F). More importantly, tumor tissues from triple-negative breast cancer (TNBC), known for their highly malignant, aggressive, and poor prognosis, exhibited an obvious decrease in ENST00000539930 transcript levels, compared to estrogen receptor positive (ER+) and human epidermal growth factor receptor two positive (HER2+) breast cancer tissues (Figure 4G). Together, these findings suggest that increased expression of ENST00000539930 in tumor tissues might predict a better prognosis in cancer patients.

Figure 4: Expression of the ENST00000539930 transcript is correlated with clinical prognosis. (A–C) The Kaplan-Meier survival curve of (A) ovarian cancer, (B) pancreatic cancer, and (C) breast cancer patients with high or low ENST00000539930 transcript expression. D. The Kaplan-Meier survival curve of breast cancer patients with high or low PARP2 gene expression. (E, F) The expression of (E) PARP2 gene and (F) ENST00000539930 transcript in normal, tumor, and metastatic breast cancer tissues. (G) The expression of the ENST00000539930 transcript in hormone receptor positive (HR+), human epidermal growth factor 2 receptor positive (HER2+), and triple negative breast cancer (TNBC) tumor tissues.
Figure 4:

Expression of the ENST00000539930 transcript is correlated with clinical prognosis. (A–C) The Kaplan-Meier survival curve of (A) ovarian cancer, (B) pancreatic cancer, and (C) breast cancer patients with high or low ENST00000539930 transcript expression. D. The Kaplan-Meier survival curve of breast cancer patients with high or low PARP2 gene expression. (E, F) The expression of (E) PARP2 gene and (F) ENST00000539930 transcript in normal, tumor, and metastatic breast cancer tissues. (G) The expression of the ENST00000539930 transcript in hormone receptor positive (HR+), human epidermal growth factor 2 receptor positive (HER2+), and triple negative breast cancer (TNBC) tumor tissues.

Cluster analysis of co-expressed genes of the PARP2 gene and the ENST00000539930 transcript

This study compared the differences in cellular biological functions between the ENST00000539930 transcript and the PARP2 gene. The CCLE dataset matched with GDSC1 was used to screen the co-expressed genes of ENST00000539930 and PARP2. Then, the top 500 genes with the highest correlation were selected to conduct GOBP cluster analysis. The analysis revealed that while the main function of the PARP2 gene focuses on the regulation of chromosome segregation, nuclear division, and cell cycle processes (Figure 5A), in contrast, ENST00000539930 primarily regulates RNA splicing, mRNA processing, and RNA metabolic processes (Figure 5B). These findings suggest that overexpression of the ENST00000539930 transcript in tumor samples is closely associated with RNA cleavage function activation. Additionally, we analyzed the 10 most interconnected genes within the co-expressed gene set of ENST00000539930 in the CCLE database. The top 10 genes were SRSF6, SNRNP70, LUCL7L3, HNRNPDL, SRSF5, SRSF11, PNISR, RBM25, RBM39, and HNRNPH1 (Figure 5C), which are major oncogenic splicing factors, suggesting a potential association with increased drug sensitivity and better prognosis in cancer patients.

Figure 5: Cluster analysis of co-expressed genes of PARP2 and the ENST00000539930 transcript. (A) The top 10 gene Ontology biological process (GOBP) enrichment of the top 500 co-expressed genes with high correlation with PARP2 and ENST00000539930 transcript in the CCLE dataset. (B) The top 10 of GOBP enrichment of the top 500 co-expressed genes with high correlation with PARP2 and ENST00000539930 transcript in the TCGA dataset. (C) The 10 most interconnected genes within the co-expressed gene set of ENST00000539930 in the CCLE database.
Figure 5:

Cluster analysis of co-expressed genes of PARP2 and the ENST00000539930 transcript. (A) The top 10 gene Ontology biological process (GOBP) enrichment of the top 500 co-expressed genes with high correlation with PARP2 and ENST00000539930 transcript in the CCLE dataset. (B) The top 10 of GOBP enrichment of the top 500 co-expressed genes with high correlation with PARP2 and ENST00000539930 transcript in the TCGA dataset. (C) The 10 most interconnected genes within the co-expressed gene set of ENST00000539930 in the CCLE database.

ENST00000539930 transcript increased drug sensitivity to PARPs inhibitors through RNA splicing activation and metabolic processing

In order to further investigate the role of ENST00000539930 transcript in regulating PARP inhibitor sensitivity, we established ENST00000539930 overexpressed breast cancer MDA-MB-231 cell lines, and the ENST00000250416 (PARP2 full-length transcript) overexpressed MDA-MB-231 cells as a control (Figure 6A). Then, the Olaparib sensitivity was analyzed in ENST00000539930 overexpressed MDA-MB-231 cells and PARP2 control cells, and it was found that the Olaparib IC50 was significantly lower in ENST00000539930 overexpressing cells (Figure 6B). We further treated ENST00000539930 overexpressed or control MDA-MB-231 cells with 20 μM Olaparib for 48 h, and analyzed the cells’ viability. Consistently, we found that ENST00000539930 overexpressing cells were more sensitive to Olaparib and exhibited significantly lower cell viability than control cells (Figure 6C). Further, we performed transcriptome sequencing analysis in ENST00000539930 overexpressed or control MDA-MB-231 cells, and obtained a total of 283 significantly differentially expressed genes (Figure 6D). GOBP enrichment analysis showed that the main functions of differentially expressed genes regulated by ENST00000539930 transcript overexpression were in the fields of ribonucleoprotein complex biogenesis, ribosome biogenesis, RNA metabolism, and RNA splicing processing (Figure 6E). Moreover, gene set enrichment analysis (GSEA) indicated ENST00000539930 overexpression was negatively related to DNA repair function (Figure 6F), which might be associated with the increased sensitivity to DNA damage repair targeted drugs, PARP inhibitors, in ENST00000539930 overexpressed cells. Moreover, ENST00000539930 was also negatively related to the function of MYC targets (Figure 6G and H), which is a key regulatory molecule in cancer stemness and RNA splicing. Together, these results suggested that ENST00000539930 transcript regulated drug sensitivity to PARP inhibitors through comprehensive mechanism patterns, which might be associated with RNA splicing, metabolism, and cell fate regulation.

Figure 6: ENST00000539930 transcript increased drug sensitivity to PARP inhibitors through RNA splicing activation and metabolic processing. (A) The expression of ENST00000539930 transcript in the vector and ENST00000539930 overexpressed MDA-MB-231 cells. (B) The IC50 curve of Olaparib in ENST00000539930 and ENST00000250416 (PARP2 full-length transcript control) overexpressed MDA-MB-231 cells. (C) The cell cytotoxicity of Olaparib (20 μM for 48 h) in ENST00000539930 and ENST00000250416 (PARP2 full-length transcript control) overexpressed MDA-MB-231 cells. (D) The volcano plot of differentially expressed genes in ENST00000539930 and ENST00000250416 (PARP2 full-length transcript control) overexpressed MDA-MB-231 cells. (E) GOBP enrichment of differentially expressed genes in ENST00000539930 and ENST00000250416 (PARP2 full-length transcript control) overexpressed MDA-MB-231 cells. (F–H) The GSEA enrichment analysis of biological regulation in (F) DNA repair, (G, H) MYC targets. *p<0.05, ***p<0.001.
Figure 6:

ENST00000539930 transcript increased drug sensitivity to PARP inhibitors through RNA splicing activation and metabolic processing. (A) The expression of ENST00000539930 transcript in the vector and ENST00000539930 overexpressed MDA-MB-231 cells. (B) The IC50 curve of Olaparib in ENST00000539930 and ENST00000250416 (PARP2 full-length transcript control) overexpressed MDA-MB-231 cells. (C) The cell cytotoxicity of Olaparib (20 μM for 48 h) in ENST00000539930 and ENST00000250416 (PARP2 full-length transcript control) overexpressed MDA-MB-231 cells. (D) The volcano plot of differentially expressed genes in ENST00000539930 and ENST00000250416 (PARP2 full-length transcript control) overexpressed MDA-MB-231 cells. (E) GOBP enrichment of differentially expressed genes in ENST00000539930 and ENST00000250416 (PARP2 full-length transcript control) overexpressed MDA-MB-231 cells. (F–H) The GSEA enrichment analysis of biological regulation in (F) DNA repair, (G, H) MYC targets. *p<0.05, ***p<0.001.

ENST00000539930 transcript overexpression is associated with the DNA repair process and cell stress response

According to the previous results, ENST00000539930 transcript mainly participated in the RNA splicing process. This study further conducted a gene-splicing pattern analysis of transcriptome sequencing data from MDA-MB-231 cells overexpressing ENST00000539930 and the PARP2 full-length transcript ENST00000250416. Notably, the ENST00000539930 overexpressed cells showed a significant differential expression pattern of characteristic PARP2 splicing transcripts, compared to the PARP2 reference transcript group (Figure 7A). Further, this study performed differential expression and GOBP clustering analysis for transcriptome sequencing at the transcript level and found that ENST00000539930 overexpression-associated transcripts were closely related to DNA repair, apoptosis, nucleic acid metabolism, and macromolecule assembly function (Figure 7B). Consistently, it was found that the poly(A) binding protein cytoplasmic 1 (PABPC1) was remarkably increased in ENST00000539930 overexpressed cells (Figure 7C), which shuttles between the nucleus and cytoplasm and binds to the 3′ poly(A) tail of eukaryotic messenger RNAs via RNA-recognition motifs. The binding of this protein to poly(A) promotes ribosome recruitment and translation initiation. Besides, the expression of genes that regulate DNA repair and chromatin structure is also increased, including ARID1A、SMARCA4 (Figure 7D and E). The differentially expressed PFKFB3, LDHA, and HSP90AB1 (Figure 7F–H) indicated that ENST00000539930-associated RNA splicing could also influence cells’ glycolysis, metabolism, and stress response. These results indicated that ENST00000539930 transcript overexpression associated with drug sensitivity might be attributed to altering the DNA repair process and cell stress response.

Figure 7: ENST00000539930 transcript overexpression is associated with the DNA repair process and cell stress response. (A) The sashimi diagram of PARP2 alternative splicing pattern in ENST00000250416 and ENST00000539930 overexpressed MDA-MB-231 cells. (B) The GOBP analysis of differentially expressed transcripts in ENST00000250416 and ENST00000539930 overexpressed MDA-MB-231 cells. (C–H) The relative expression of (C) PABPC1, (D) SMARCA4, (E) ARID1A, (F) PFKFB3, (G) LDHA, and (H) HSP90AB1 in ENST00000250416 and ENST00000539930 overexpressed MDA-MB-231 cells. *p<0.05, **p<0.01, ***p<0.001.
Figure 7:

ENST00000539930 transcript overexpression is associated with the DNA repair process and cell stress response. (A) The sashimi diagram of PARP2 alternative splicing pattern in ENST00000250416 and ENST00000539930 overexpressed MDA-MB-231 cells. (B) The GOBP analysis of differentially expressed transcripts in ENST00000250416 and ENST00000539930 overexpressed MDA-MB-231 cells. (C–H) The relative expression of (C) PABPC1, (D) SMARCA4, (E) ARID1A, (F) PFKFB3, (G) LDHA, and (H) HSP90AB1 in ENST00000250416 and ENST00000539930 overexpressed MDA-MB-231 cells. *p<0.05, **p<0.01, ***p<0.001.

Discussion

Alternative splicing is a pre-mRNA maturation process that generates multiple mRNA isoforms, leading to the production of several proteins with distinct functions from a single gene [23], [24], [25]. Prior research has demonstrated that cancers exhibit transcriptome-wide abnormal splicing relative to normal tissues [26], [27], [28], [29]. For instance, a comprehensive global study analyzing splicing profiles across 32 cancer types from TCGA (comprising over 8,700 cases) reported that malignant tissues display roughly 30 % more alternative splice events than normal tissues [30]. Recent evidence shows that cancer-related alternative splicing events can significantly impact protein domains, which are likely to disrupt essential protein-protein interactions in cancer pathways, including chromatin and histone binding, protein kinase binding, and transcription factor binding [31].

Remarkably, aberrant splicing of direct drug targets has been observed in various tumor types and reported to be associated with treatment resistance, including chemotherapy, targeted therapy, and immunotherapy. For instance, exon skipping of exon 12 or other exons, as well as intron retention, generates a non-functional FPGS enzyme that prevents antifolate drugs from being retained in cells, thus leading to resistance to methotrexate in acute lymphoblastic leukemia [32], 33]. The splice variant of BRAF carrying the V600E mutation and lacking exons 4–8 can eliminate the RAS-binding domain, contributing to acquired vemurafenib resistance in melanoma patients [34]. Moreover, full-length CD19 protein is the target for CART-19 immunotherapy, while alternative splicing of CD19 skipping exon two interferes with CART-19 recognition, leading to resistance to CART-19 immunotherapy [35]. The alternative splicing of targeted proteins, particularly their interaction and/or binding domains, as well as their catalytic domains, plays a crucial role in drug response. In this study, we initially analyzed the correlation between the transcript expression of DNA damage repair genes and corresponding target drug sensitivity. Particularly, we indicated that the expression of ENST00000539930 transcript and ENST00000541157 transcript had better correlation with sensitivity to targeted drugs than the expression of PARP2 and MRE11 genes. We further demonstrated that only transcripts containing the PARP catalytic domains were associated with enhanced response to PARP inhibitors, especially the ENST00000539930 transcript, which skips exons 1–9 but retains the catalytic domain, serving as the major targeted domain of PARP2 inhibitors. Our evidence highlights the critical role of PARP’s alternative splicing in counteracting resistance to PARP inhibitors and provides a new biomarker for evaluating drug sensitivity.

Tumor cells deficient in BRCA1/2 are sensitive to PARP inhibitors through the mechanism of synthetic lethality [36]. Recently, several PARP inhibitors, such as rucaparib, olaparib, niraparib, and talazoparib, which are oral drugs and generally well tolerated, have been approved for various ovarian and breast cancer indications [37], 38]. Despite the initial dramatic response, resistance to PARP inhibitors is common and develops through multiple mechanisms, including HR repair restoration, drug metabolism alteration, cell cycle arrest, and abnormal signaling activation [39]. Therefore, there is a significant need to better characterize patients who have potentially sustained responses to PARP inhibitors. A test for HR deficiency, assigning HR component mutations, is used as a criterion for patient stratification in several PARP inhibitor clinical trials. However, this test has several limitations, including prioritization, high false-positive rates, and minimal attention given to DNA methylation-induced gene silencing [40]. In contrast, the expression levels of PARP1/2 are ideal biomarkers, since Olaparib and veliparib are highly selective inhibitors of PARP1/2. Nevertheless, previous research evaluating PARP1 expression in 170 primary ovarian cancer samples indicated that the expression of PARP1 did not impact the clinical outcome of patients receiving PARP inhibitors [41], 42]. In contrast, a recent study utilized a PARP1 radiotracer to measure the expression as well as activity of PARP1 and suggested that PARP1 expression and activity significantly correlated with the response rates of PARP inhibitors [43]. Interestingly, our study indicated that higher expression levels of ENST00000539930 transcript were significantly correlated with increased response to PARP inhibitors and prolonged survival in several cancers, while the total expression of PARP2 did not impact tumor progression and clinical outcome of patients. Our results provide a promising biomarker for the prediction of potential response to PARP inhibitors and long-term prognosis of patients.

Given the limitations of previous studies, this study initially conducted an analysis of the differential alternative splicing patterns of PARP2 in the primary clinical indications for PARP inhibitors, including breast cancer, ovarian cancer, prostate cancer, and pancreatic cancer. Next, we conducted an analysis of 11 types of PARP2 transcript expression and the IC50 values of Olaparib in ovarian cancer cells using the CCLE database and identified ENST00000539930, which had a significant correlation to PARP inhibitor sensitivity. We also identified the clinical significance of ENST00000539930 for predicting PARP inhibitor resistance through RNA sequencing and drug sensitivity experiments in ovarian cancer patient-derived organoids. The concordance of results across breast cancer cell lines and ovarian cancer PDOs suggests a potentially universal role of this splice variant in modulating PARP inhibitor sensitivity in BRCA-deficient malignancies. We further demonstrated that expression of ENST00000539930 had a close association with clinical outcome in breast, ovarian, prostate, and pancreatic cancers. Based on these findings, we analyzed drug susceptibility associated with transcripts through cluster analysis of the top 500 co-expressed genes and revealed that ENST00000539930 was closely implicated in the regulation of RNA cleavage function and splicing factors activation. Further, we investigated the mechanism by which ENST00000539930 regulates drug sensitivity through overexpressing experiments and RNA sequencing analysis in breast cancer cells. We demonstrated that the ENST00000539930 overexpressed breast cancer cells exhibited different RNA splicing patterns, DNA damage repair, cell metabolism, and stress response processes.

Nevertheless, our study had several limitations: the analysis of drug susceptibility associated with transcripts was limited to the cellular level and lacked data validation for direct clinical treatment. In our study, we only used the ENST00000539930 overexpression plasmid to treat cells and observe the relationship between ENST00000539930 and drug sensitivity. Because the sequences of different PARP2 transcripts are highly similar, and the sequence of the ENST00000539930 transcript is contained within several other PARP2 transcripts, this study lacks knockdown or knockout experiments for ENST00000539930 in cells. Our experiments demonstrated that high expression of ENST00000539930 increases sensitivity to PARP inhibitors (PARPi). However, the regulatory mechanisms of ENST00000539930 expression and its mediated DNA repair and metabolic functions require further elucidation. Our study explored a further investigation direction in alternative transcripts for predicting drug resistance and clinical prognosis.

Conclusions

This study reveals that PARP inhibitors exhibit enhanced cytotoxicity in cells with high expression of the PARP2 splice variant ENST00000539930 compared to other PARP2 transcripts and overall PARP2 gene expression. This research identifies ENST00000539930 as a potential biomarker for PARP inhibitor efficacy, highlighting the role of alternative splicing in cancer therapy.

Corresponding authors: Chunxiao Li, Department of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China; and State Key Laboratory of Molecular Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China, E-mail: ; and Fei Ma, Department of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China, E-mail:
Chi Xu and Cong Li contributed equally to this work.

Funding source: Postdoctoral Innovative Talent Support Program of China

Award Identifier / Grant number: BX20230050

Funding source: Postdoctoral Fellowship Program of China Postdoctoral Science Foundation

Award Identifier / Grant number: GZC20242092

Funding source: Noncommunicable Chronic Diseases-National Science and Technology Major Project

Award Identifier / Grant number: 2023ZD0502200

Funding source: China Postdoctoral Science Foundation

Award Identifier / Grant number: 2023M730322

Award Identifier / Grant number: 2023M744031

Award Identifier / Grant number: 2024M760270

Funding source: National Natural Science Foundation of China

Award Identifier / Grant number: 82203185

Award Identifier / Grant number: 82230058

Award Identifier / Grant number: 82303912

Award Identifier / Grant number: 82403967

Award Identifier / Grant number: 82403982

Acknowledgments

The authors thank K2 ONCOLOGY for supporting patient-derived organoid experiments in this study.

  1. Research ethics: Approval of the research protocol by the Ethics Committee of Chinese PLA General Hospital (S2021-566-03). The experiments were performed in accordance with the guidelines of the Declaration of Helsinki.

  2. Informed consent: Ovarian cancer patient-derived organoids (PDOs) used in this study were obtained from Beijing K2 Oncology Co., Ltd. (Beijing, China) under a material transfer agreement (MTA). All PDOs were derived from anonymized epithelial ovarian cancer tissues from patients who provided written informed consent. The supplier, Beijing K2 Oncology Co., Ltd. (Beijing, China), ensured that informed consent was obtained from all tissue donors in accordance with ethical and legal guidelines.

  3. Author contributions: Fei Ma and Chunxiao Li designed research; Chi Xu, Cong Li, Wang WN, Yiqun Li, Ting Wang, Fangzhou Sun, Xiaoqi Yang performed research; Chi Xu, Cong Li and Chunxiao Li wrote the paper. All authors read and approved the final version of the manuscript. The underlying data was verified by Chunxiao Li and Fei Ma.

  4. Use of Large Language Models, AI and Machine Learning Tools: Not applicable.

  5. Conflict of interest: The authors state no conflict of interest.

  6. Research funding: This work was supported by the Noncommunicable Chronic Diseases-National Science and Technology Major Project (2023ZD0502200); National Natural Science Foundation of China (82230058, 82403982, 82303912, 82203185,82403967), Postdoctoral Innovative Talent Support Program of China (BX20230050); China Postdoctoral Science Foundation (2023M730322, 2024M760270, 2023M744031); Postdoctoral Fellowship Program of China Postdoctoral Science Foundation (GZC20242092).

  7. Data availability: The raw data can be obtained on request from the corresponding author.

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Received: 2025-03-25
Accepted: 2025-08-06
Published Online: 2025-09-08

© 2025 the author(s), published by De Gruyter on behalf of Tech Science Press (TSP)

This work is licensed under the Creative Commons Attribution 4.0 International License.

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https://doi.org/10.1515/oncologie-2025-0144
Keywords for this article
alternative splicing; DNA damage repair; BRCA1/2 mutation; PARP inhibitors; drug sensitivity
Creative Commons
BY 4.0

Articles in the same Issue

  1. Frontmatter
  2. Review Articles
  3. Liquid biopsy – a promising and effective method for surveying non-small cell lung cancer minimal residual diseases and anti-cancer drug response after treatment
  4. Current application status of proton beam therapy for gastrointestinal tumors
  5. Research progress on the regulation of cuproptosis-related genes by non-coding RNAs in tumors
  6. Deep learning in hepatic oncology imaging: a narrative review of computed tomography applications
  7. Synergistic approaches: a narrative mini-review of radiotherapy and immunotherapy in the treatment of lung cancer
  8. Research Articles
  9. Intravesical prostatic protrusion as a predictor of acute urinary retention following stereotactic body radiation therapy for localised prostate cancer: a retrospective study
  10. The differential effect of glutamine supplementation on the orthotopic and subcutaneous growth of two syngeneic murine models of glioma
  11. Intermittent afatinib treatment suppresses the growth of resistant T790M-H1975 cells in non-small cell lung cancer (NSCLC) co-culture
  12. Prognostic stratification of colorectal cancer by immune profiling reveals SPP1 as a key indicator for tumor immune status
  13. The activity of base excision repair is positively correlated with the infiltration of CD4+ T cells in melanoma
  14. Integrated analysis of immunity and ferroptosis related tumor microenvironment in a novel risk score model for lung adenocarcinoma prognosis
  15. Retrospective analysis of risk factors for early recurrence after hepatocellular carcinoma resection
  16. The ENST00000539930 transcript predicts sensitivity to PARP inhibitors and clinical prognosis in cancers
  17. VTA1 and breast cancer: a potential indicator for diagnostic and prognostic evaluation
  18. USP24 stabilizes VDAC2 via deubiquitination to promote apoptosis and ferroptosis in clear cell renal cell carcinoma (ccRCC)
  19. Clinicopathological characteristics, prognosis, and therapeutic implications in breast cancer with pathologically confirmed bone marrow metastases: an observational retrospective study
  20. Short Commentaries
  21. Cancer cell mitochondria: the missing puzzle in predicting response to PD-1/PD-L1 inhibitors
  22. From mitochondrial cristae pathobiology to metabolic reprogramming in cancer: the α and ω of Malignancies?
  23. Article Commentary
  24. Stopping SOAT1 sparks an immune attack on liver cancer: a metabolic-immune axis in hepatocellular carcinoma
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