
==== Front
Transl Oncol
Transl Oncol
Translational Oncology
1936-5233
Neoplasia Press

S1936-5233(24)00224-9
10.1016/j.tranon.2024.102097
102097
Original Research
GTF2H5 Identified as a crucial synthetic lethal target to counteract chemoresistance in colorectal cancer
Nie Junjie a1
Liu Xinwei a1
Xu Mu a
Chen Xiaoxiang a
Hu Shangshang a
Gu Xinliang a
Sun Huiling a
Gao Tianyi a
Pan Yuqin panyuqin01@163.com
a⁎
Wang Shukui sk_wang@njmu.edu.cn
ab⁎
a General Clinical Research Center, Nanjing First Hospital, Nanjing Medical University, Nanjing 210000, Jiangsu, China
b Jiangsu Collaborative Innovation Center on Cancer Personalized Medicine, Nanjing Medical University, Nanjing 210000, Jiangsu, China
⁎ Corresponding authors at: Nanjing First Hospital, Nanjing Medical University, Nanjing 210000, Jiangsu, China. panyuqin01@163.comsk_wang@njmu.edu.cn
1 These authors contributed equally to this work.

21 8 2024
11 2024
21 8 2024
49 10209726 9 2023
3 6 2024
11 8 2024
© 2024 Published by Elsevier Inc.
2024

https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Highlights

• Multi-omic Analysis allowed us to uncover crucial relationships between five SL genes, mutations (particularly TP53 and KRAS), and poor patient prognosis.

• The dynamic elevation of GTF2H5 and SORBS1 expression during the transition from sensitivity to resistance in CRC cells was identified by time series analysis.

• The established 3D co-culture system successfully replicated the effects of chemotherapy on tumor cells in vivo, where combined chemotherapeutic agents FOLFOX led to a significant increase in tumor cell mortality when GTF2H5 expression was inhibited.

Background

Synthetic lethality (SL) emerges as a novel concept being explored to combat cancer progression and resistance to conventional therapy. Despite the efficacy of chemotherapy in select cases of colorectal cancer (CRC), a substantial proportion of patients encounter challenges, leading to an adverse prognosis of CRC patients. CRC-related SL genes offer a potential avenue for identifying therapeutic targets.

Methods

CRC-related SL genes were obtained from the SynLethDB database. The bulk RNA sequencing data, mutation data, and clinical information for treated and untreated CRC patients were enrolled from the UCSC and GEO databases. The Tumor Immunology Single Cell Center database served as the repository for collecting and analyzing single-cell RNA sequencing data. The synergistic killing effect of SL genes and chemotherapeutic drugs on resistant cells was experimentally verified.

Results

In the present study, pivotal SL genes associated with chemoresistance identified by using WGCNA and CRC patients categorized into two groups based on these genes. Variations between the groups were most pronounced in pathways associated with extracellular matrix remodeling. Further by integrating mutation data, five potential SL genes were discerned, which were highly expressed in the presence of TP53 or KRAS mutations, leading to a severely poor prognosis. Subsequent time series analysis revealed that the expression of GTF2H5 was gradually elevated at different stages of the transition from sensitive to resistant in CRC cells. Finally, it was preliminarily verified by experiments that GTF2H5 may play a key role in driving the drug-resistant transition within CRC cells.

Conclusions

The identification of SL genes that collaboratively interact with chemotherapeutic agents could provide new insights into solving the issue of chemotherapy resistance in CRC patients. And GTF2H5 wields a fundamental influence in inducing chemoresistance in CRC, which provided a potential therapeutic target for CRC.

Keywords

Synthetic lethality
Chemoresistance
FOLFOX
GTF2H5
3D co-culture system
Abbreviations

CRC Colorectal cancer

SL Synthetic lethality

RNA-seq RNA sequencing

TCGA The Cancer Genome Atlas

WGCNA Weighted Gene Co-expression Network Analysis

GO Gene Ontology

KEGG Kyoto Encyclopedia of Genes and Genomes

GSEA Gene Set Enrichment Analysis

5-FU 5-Fluorouracil

AML Acute myeloid leukemia

HCC Hepatocellular carcinoma

TME Tumor immune microenvironment

CDF Cumulative Distribution Function

ECM Extracellular matrix

EMT Epithelial-mesenchymal transition

TFIIH Multi-subunit transcription/repair factor IIH
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pmcIntroduction

Colorectal cancer (CRC) has long been characterized by high and deteriorating rates of both incidence and mortality, with the third highest incidence and second mortality rate among all tumors globally [1]. The main approach to treating CRC involves surgery, accompanied by chemotherapy or radiotherapy, contributing to a substantial increase in the survival of patients with CRC [2,3]. FOLFOX is a classic chemotherapy regimen for CRC, which mixes 5-fluorouracil (5-FU), leucovorin and oxaliplatin in precise proportions. Frequently, it is adopted after surgical intervention for CRC, particularly in cases of high-risk presentation, wherein it has significantly reduced the risk of recurrence [4,5]. However, the overall efficacy of chemotherapy in certain patients proves inadequate owing to disease advancement, which leads to failure of the chemotherapy and the emergence of chemoresistance, etc [5]. Despite advances in the treatment of CRC and the continuous evolution of novel approaches aimed at countering drug resistance development, the overall prognosis for patients remains poor [6,7]. Several mechanisms have been identified that lead to chemoresistance in CRC cells, including disrupted DNA repair, modifications in drug metabolic pathways, and inhibition of cell death [8,9]. Hence, there is an urgent need to explore the underlying drivers of drug resistance in CRC.

Synthetic lethality (SL) refers to the concurrent anomalies in the expression of two or more genes with functional interrelationships, encompassing mutations or suppression, which culminate in rapid cellular death, whereas abnormalities in solitary genes exert a minor impact on cellular viability [10,11]. This phenomenon was first identified in Drosophila and is gradually being applied to the research of cancer therapy [10]. Pan et al. revealed that simultaneous activation of p53 and inhibition of Bcl-2 surmounts the obstacle of apoptosis resistance, leading to a substantial extension of survival in three mouse models of resistant acute myeloid leukemia (AML) [12]. Utilizing a comprehensive genome-wide CRISPR-Cas9-based knockout screen, Fang et al. identified DOCK1 as a synthetic lethal vulnerability in metformin-based interventions for hepatocellular carcinoma (HCC) and the abundance of DOCK1 determined the therapeutic effect of metformin against tumors [13]. The advantage of therapy based on SL lies in its ability to selectively eliminate cancer cells while minimizing detrimental effects on normal cells, which frequently lack mutations in pivotal genes. Moreover, even if a therapeutic agent targets a crucial protein or pathway, normal cells can ensure their survival by compensating through alternative gene operations [14]. Olaparib, a PARP inhibitor, stands as the pioneering anticancer therapeutic devised on the foundation of "synthetic lethality" principle, which has gained widespread authorization for the treatment of ovarian or breast cancer, and it has demonstrated favorable clinical outcomes [15]. The concept of SL also provides new ideas for drug resistance caused by conventional chemotherapy or targeted therapy, such as active synthetic lethality caused by combinations of drugs.

However, there exists a paucity of studies employing the concept of SL to combat chemoresistance in CRC and deserve further investigation. In this study, a comprehensive screening of 22 pivotal SL genes was conducted utilizing Weighted Gene Co-expression Network Analysis (WGCNA) and we categorized CRC patients based on the expression patterns of these genes. The resulting disparity between these patient groups was most pronounced in pathways related to the remodeling of the extracellular matrix. We further combined mutational data and identified 5 potentially SL genes (SORBS1, CAMK2G, ABCG2, TPM1 and GTF2H5), the high expression of which led to severely poor prognosis in the presence of TP53 or KRAS mutations, but their expression was generally downregulated in CRC. Furthermore, scRNA-seq analysis revealed that these 5 SL genes were mainly enriched in intestinal epithelial cells, fibroblasts and myofibroblast populations.

Recent research has highlighted the significance of specific SL genes in the development of chemoresistance in cancer cells. SORBS1, which is downregulated in numerous malignancies, plays a key role in regulating cell adhesion, cytoskeletal structure, and metastasis [16,17]. Moreover, SORBS1 can inhibit the epithelial-mesenchymal transition (EMT) in breast cancer cells by upregulating p53 protein levels, thus increasing their sensitivity to cisplatin [18]. GTF2H5 is a critical component of the transcription/repair factor IIH (TFIIH) complex, essential for DNA repair processes [19]. In CRC, GTF2H5 is found to be hypermethylated and downregulated, resulting in compromised transcription and DNA repair functions [20]. Additionally, lower expression levels of GTF2H5 correlate with better outcomes in high-grade serous ovarian cancer (HGSOC) patients, and its downregulation enhances the sensitivity of HGSOC cell lines to cisplatin [21]. However, we observed that their expression is restored under chemotherapeutic drug treatment, yet their exact roles in mediating CRC chemoresistance remain unclear. Finally, it was verified by 3D co-culture and other experiments that GTF2H5 is elevated in response to chemotherapeutic agents, helping CRC cells to resist stress and survive. Consequently, GTF2H5 emerges as a promising new target for overcoming chemoresistance in CRC, and our efforts will provide new perspectives in tackling this challenge.

Materials and methods

Dataset source

The UCSC database (http://xena.ucsc.edu) was utilized to obtain bulk RNA-seq data, mutation data, and clinical characteristics of patients diagnosed with COAD and READ. Additional CRC related cohorts (GSE69657, GSE19860, GSE106584, GSE35602, GSE218588 and GSE81005) were sourced from the GEO database for further analysis. To ensure data comparability, expression data was standardized to the transcripts per million (TPM) formats. Differentially expressed genes were identified by the “limma” package with a criterion of |log2FC|≥1 and p < 0.05. Patient mutation data was retrieved in the "maf" file format, and waterfall plots were generated employing the “Maftools” package to visually represent and encapsulate gene mutations.

Single-cell RNA sequencing analysis

The scRNA sequencing data of CRC tissues were extracted from two GEO datasets (GSE166555 and EMTAB8107) and subjected to analyze in the Tumor Immune Single-cell Hub (TISCH) database (http://tisch.comp-genomics.org/home/). The single-cell level expression matrix is subjected to normalization using the “NormalizeData” method within the “Seurat” package, aiming to standardize the raw counts in each cell. For each dataset, a consistent analytical algorithm was employed, encompassing processes such as quality control, clustering, and cell-type annotation. Specifically, the GSE166555 dataset comprises 66,050 cells originating from 12 patients with CRC and the EMTAB8107 dataset comprises 23,176 cells sourced from 7 patients with CRC.

Retrieval of synthetic lethality genes

We obtained 251 CRC-related SL genes along with 27 SL genes involved in the effects of commonly employed chemotherapeutic drugs for CRC (5-Fluorouracil, Leucovorin, Oxaliplatin) from the SynLethDB database under the catalog of disease types and compound, respectively, which is the comprehensive database that harbors a large set of synthetic lethality gene pairs collected from biochemical assays, other related databases and computational predictions (https://synlethdb.sist.shanghaitech.edu.cn/). This collection encompasses pivotal genes with the potential to mitigate chemotherapy resistance in patients with CRC.

Extraction of chemoresistance-associated genes through WGCNA

We applied R package “WGCNA” to identify genes linked to drug resistance in patients with CRC in GSE69657. Employing the "PickSoftThreshold" function, an automatic soft threshold value was chosen, followed by the execution of scale-free and average connectivity analysis across modules spanning diverse power values. Consequently, dissimilarity matrix (1-TOM) and topological overlap matrix (TOM) were obtained, which classified the genes into different modules. By conducting Pearson correlation analysis on co-expression modules pertaining to two clinical manifestations: drug resistance and sensitivity. We singled out modules exhibiting statistical significance as candidate CRC chemotherapy resistance-related genes with correlation >0.3 and p < 0.05 as screening criteria.

Identification of chemotherapy drug-related synthetic lethality gene in CRC

The overlap of the previously mentioned two SL gene sets resulted in four genes, followed by a subsequent search in SynLethDB database for matched synthetic lethal gene pairs associated with these four pharmacologically active genes and this iterative process culminated in the assembly of a gene set comprising 315 genes. Following this, these genes were cross-referenced with genes associated with chemotherapy identified through WGCNA, enabling the identification of synthetic lethality gene pairs that hold the potential to counteract chemotherapy drug resistance in CRC.

Construction of SL-related subtypes

Building upon the pivotal synthetic lethality genes, we employed the R package "ConsensusClusterPlus" to delineate the molecular subtypes among CRC patients in the TCGA database. Specifically, the upper limit for subtypes was set at nine, while the maximum iteration count was stipulated as 1000, thereby ensuring the robustness of the statistical analysis.

Functional enrichment analysis

The sample cohort was classified into two distinct subtypes, further culminating in the identification of genes exhibiting notable discrepancies between these subtypes with characterized by fold change (FC) threshold >2 and false discovery rate < 0.05. Following this, we predicted the biological functions and pathways of differentially expressed genes (DEGs) between the SL subtypes by using the "ClusterProfile" package. Furthermore, the protein-protein interactions (PPIs) among DEGs were constructed using the STRING database (https://string-db.org/). GSEA analysis was employed to scrutinize the pathways potentially assuming pivotal roles in distinguishing between resistant and sensitive responses among colorectal cancer patients. The significantly enriched gene sets were selected with a criterion of NES>1 and p-value < 0.05. It carried out based on the MSigDB database (http://www.gsea-msigdb.org/gsea/msigdb), and the reference gene sets (c2.cp.kegg. v2023.1.Hs. symbols) for this analysis were obtained from this database.

Tumor immune microenvironment of SL-related subtypes

The ESTIMATE algorithm facilitates the computation of stromal and immune scores, which in turn prognostically contrast the extent of infiltration by stromal and immune cells across subgroups. Utilizing the CIBERSORT algorithm, the gene expression matrix was transformed into the expression profiles of 22 immune cell types for comparative analysis. Employing both the ESTIMATE and CIBERSORT algorithms, we undertook an evaluation of variances within the tumor immune microenvironment (TME) among CRC patients. To illustrate the disparities in TME between the two subtypes, we showed the outcomes through bar graphs and violin plots.

Time series gene expression analysis

The Mfuzz algorithm is applied to execute expression pattern clustering analysis on transcriptome data across distinct time intervals. Leveraging the "ClusterGVis" R package, we scrutinized and illustrated alterations in gene expression throughout the course of drug treatment, unveiling persistently upregulated and downregulated cohorts of genes.

Cell culture and establishment of FOLFOX resistant cells

HCT8 and HCT15 cells were sourced from the American Type Culture Collection (USA) and subjected to authentication through short tandem repeat (STR) analysis and cultured in Roswell Park Memorial Institute medium (RPMI-1640, KeyGEN) containing 10 % fetal bovine serum. The cell culture flasks were incubated at 37 °C with 5 % CO2 and the medium was renewed every two days. To establish chemotherapeutic drug-resistant cell lines, CRC cells underwent a progressive increase in drug concentration across consecutive exposures. Specifically, HCT8 and HCT15 cells were treated with a medium containing 0.1 μg/mL of the drug. Once cell survival surpassed 80 % and continued growth was sustainable, the dosage was escalated by a factor of 2, and this process was iterated until cells could viably propagate in medium with a concentration of 5 μg/mL. For FOLFOX (5-Fluorouracil: leucovorin: oxaliplatin = 25:5:1) treatments, 5-Fu final concentration was maintained at 75μM, 125μM, 150μM.

RNA extraction and quantitative real-time PCR

We performed Real-time PCR to evaluate the expression of three genes-SORBS1, GTF2H5 and TPM1-under the influence of diverse drug concentration treatments. The total RNA was isolated from the designated cells, facilitated through the application of RNA-easy Isolation Reagent (Vazyme) and reverse transcribed into cDNA using a HiScript Reverse Transcriptase kit (Vazyme) with determined by the QuantStudio Q5 System (Applied Biosystems, USA). Quantification of each gene was effectuated utilizing the 2−ΔΔCt methodology, followed by data normalization employing β-actin as a standardized loading control. Primers used for detecting above RNAs were all synthesized from Ribobio company (Guangzhou). This Real-time PCR experiment was conducted independently, with the procedure being reiterated threefold. Primers for GTF2H5 (forward, 5’-ACATGGCTTTACCAGTATGACCC-3’; and reverse, 5’-CAATCTGCTCTTGCTGTGACTG-3’), SORBS1 (forward, 5’-CGCCTTCCTATGT ATGACGAAA-3’; and reverse, 5’- CCGCCAGTTAGTTGCACTTG- 3’) and TPM1 (forward, 5’-GCGTCTGGCAACAGCTTTG-3’, and reverse, 5’-TGTGCTTGGCCTC TTTCAGTT-3’) were used for qPCR.

Collecting CRC tissues and isolating fibroblasts

The primary cancer tissues were collected from colorectal cancer patients at the Nanjing First Hospital. Each patient provided informed consent, and the acquisition of tissue specimens for our research was sanctioned by the Internal Review and Ethics Boards at Nanjing First Hospital (KY20220124-04). Fibroblasts were isolated from freshly obtained tissues following established protocols [22], as adopted, and successfully implemented by our research group [23]. In brief, finely minced tissue samples underwent enzymatic digestion utilizing a mixture comprising 1mg/mL collagenase, Dulbecco's modified Eagle's medium (KeyGen, Nanjing), and 10 % fetal bovine serum (Sigma-Aldrich, USA) over a 2-hour incubation period at 37 °C under agitation. Following centrifugation, cellular pellets were resuspended and subsequently subjected to filtration through a 100 μm cell strainer. Verification of fibroblast identification encompassed the detection of two affirmative markers: α-SMA and Vimentin.

Cell transfection

Cellular inoculation took place within six-well plates, with transfection procedures conducted upon the attainment of a cellular density ranging from 30 % to 50 %. Procurement of small interfering RNA (siRNA), a negative control, and transfection reagents occurred through Ribobio, and the transfection protocols were meticulously adhered to as prescribed by the manufacturer's directives.

Western blot analysis

The cells were lysed using RIPA lysis buffer (Beyotime, Shanghai) and determination of protein concentrations was carried out via the BCA Protein Concentration Assay Kit (Beyotime, Shanghai), followed by the implementation of sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) to segregate of cellular lysates. The samples were subsequently transferred onto polyvinylidene difluoride membranes (Millipore, USA). These membranes underwent overnight incubation at 4 °C with primary antibodies targeting GTF2H5 (1:1000; Affinity, Changzhou) and Tubulin serving as a reference (1:6,000; Proteintech, Wuhan). Following this, the membranes were subjected to a 2-hour incubation at room temperature with horseradish peroxidase-conjugated anti-mouse or anti-rabbit secondary antibodies (1:10000; Proteintech, Wuhan). Detection of signals was facilitated through the utilization of the Enhanced Chemiluminescence Kit (NCM Biotech, Suzhou) by chemiluminescence imaging system (Tanon, Shanghai).

Apoptosis assay (flow cytometry)

Apoptosis levels were assessed using Annexin V/PI (KeyGEN, Nanjing) according to the manufacturer's instructions.

Cell proliferation assays

The proliferation capacity of the cells was evaluated using the Cell Counting Kit-8 (Vazyme, Nanjing) following the manufacturer's protocol.

Development of co-culture system for CRC cells and fibroblasts

Following transfection of small interfering RNA, CRC cells were plated in 6-well plates and allowed to grow for 24 h. Subsequently, these cells were embedded in 100 μL of a 10 % methacrylate-based gelatin, placed onto a curing ring for 3D culture, and subjected to 1 min of irradiating with a blue light at 405 nm for solidification. The solidified constructs were then transferred to a 24-well plate and cultured for an additional 24 h in standard culture medium. About 1 × 106 cancer-associated fibroblasts (CAFs) sourced from CRC patients were seeded onto each gelatin along with CRC cells. The culture medium was modified to suit CAF growth while supporting tumor cell growth. Co-cultures were maintained for 24 h, with the CRC cell-to-CAF ratio ranging from 2:1 to 3:1. On the third day after transfection, the FOLFOX chemotherapeutic drug was introduced to the co-culture system, achieving a drug concentration of 150 μM and undergoing a 48-hour treatment period. After thorough washing of CAFs adhered to the gelatin surface with PBS, fluorescent dyes (Calcein-AM for live cells; PI for dead cells) was applied to evaluate any alterations in tumor cell chemotherapeutic sensitivity through confocal laser microscopy-mediated 3D scanning (NIKON ECLIPSE TI, Nikon, Japan).

Statistical analysis

Statistical analysis was carried out using GraphPad Prism 9.0 (GraphPad, USA) and R 4.2.0 software. Kaplan-Meier (KM) curves with log-rank tests were applied to contrast overall survival disparities within the two patient groups characterized by mutations. Student's t test was employed to evaluate statistical distinctions between the two datasets. Statistical significance was associated with P values < 0.05 and N.S. for no statistical significance. (*P < 0.05, ⁎⁎P < 0.01, and ⁎⁎⁎P < 0.001).

Results

Identification of potentially essential synthetic lethality genes

To identify genes that wield a pivotal influence over the escalating failure of chemotherapeutic interventions in patients with CRC, we commenced by scrutinizing the gene ensemble harbored within cohorts marked by differential responses to the FOLFOX chemotherapy, utilizing a systematic WGCNA analysis. In the GSE69657 cohort, the soft threshold for network construction was set to 16, and a total of 32 gene modules were constructed (Fig. 1A–C). Then each module eigengenes (MEs) was correlated with the responsiveness of the patients in the GSE69657 cohort to the drugs by Pearson's correlation analysis. We discerned exclusive and statistically significant associations between patient drug responses and the red (r = 0.41, p = 0.02) and darkolivegreen gene modules (r = 0.46, p = 0.01). The red module, inclusive of 730 genes, and the darkolivegreen module, encompassing 249 genes, emerged as the noteworthy candidates. This gene pool, earmarked for further scrutiny, holds latent potential to serve as a target in devising efficacious strategies to counteract drug resistance conundrums prevalent among colorectal cancer patients.Fig. 1 Acquisition of SL genes associated with chemotherapeutic responsiveness. A Dendrogram of the clustering of genes using the dissimilarity measure (1-TOM). B Selection of the optimal scale-free fit index for various soft-thresholding powers (β). C Heatmap of the correlation between SL genes and chemotherapy response. D Venn diagram of 4 SL genes bound by FOLFOX. E Sankey diagram for screening of critical 22 SL genes.

Fig 1

Subsequently, the SynLethDB database was employed to discern prospective synthetic lethality genes implicated in CRC. There are a total of 251 genes that are closely related to CRC, while there are 27 genes that bind to the FOLFOX treatment. Through the overlapping of these gene cohorts, a quartet of pivotal genes, namely ALB, TYMS, ABCG2, and GSTP1, emerged (Fig. 1D). These genes are experimentally verified or computationally predicted to be inhibited by the drug when it works and can effectively kill tumor cells. Moreover, considering the robust counteractive response mounted by tumor cells to ensure survival upon drug exposure, we embarked on an extended exploration within the database to identify partner genes intricately linked with the 4 genes based on the principle of synthetic lethality, and took the intersection with the results of WGCNA. Ultimately, the MEs of red exhibited the most optimal alignment with the synthetic lethal genes. We screened a total of 18 genes, and briefly demonstrated our screening process with a Sankey diagram (Fig. 1E).

Generation and functional assessment of novel subtypes with CRC patients

The "ConsensusClusterPlus" R package was harnessed to stratify CRC patients into distinct molecular subtypes, a classification predicated upon the expression of 22 screened synthetic lethal genes. Via an empirical Cumulative Distribution Function (CDF) plot, the optimal cluster number was ascertained, culminating in the most effective partitioning at K = 2 (Fig. 2A). Further, we identified the DEGs between the two subtypes of patients (group A and B) and the PPI networks of these differential genes were meticulously curated utilizing the STRING database (Fig. 2B, C). To determine the potential molecular mechanisms and biological activities of the SL subtypes using the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. GO analysis showed that the DEGs were mainly involved in extracellular matrix organization, collagen fibril organization, extracellular matrix structural constituent and complex of collagen trimers (Fig. 2D, E). KEGG analysis unveiled a predominant enrichment of DEGs within pathways linked with cancer, notably encompassing the PI3K-Akt signaling pathway and TGF-beta signaling pathway. And DEGs additionally involved in extracellular matrix (ECM)-associated pathways, including ECM-receptor interaction and Focal adhesion (Fig. 2F).Fig. 2 Construction and functional analysis of SL gene-based subtypes of CRC patients. A Diagram showing the best consensus clustering results for k = 2. B Volcano map of DEGs expression levels. C The protein–protein interaction (PPI) network of DEGs. D-F The GO and KEGG signaling pathway analysis terms of DEGs.

Fig 2

Building on the insights furnished by GO and KEGG analysis, which unveiled a noteworthy enrichment of pathways associated with the extracellular matrix within group A and B, we further characterized the tumor microenvironment profiles and immune cell infiltration between the two subtypes. The results showed that patients in group B exhibited markedly elevated ESTIMATE scores, stromal scores, and immune scores compared to their counterparts in group A (Fig. 3A). Concomitant with this divergence, the infiltration levels of several immune cell types were found to exhibit significant differences, with high-abundant of CD8+ T cells, CD4 memory T cells, follicular helper T cells, and NK cells in group A, while the infiltration levels were obviously reduced in group B (Fig. 3B). As such, we postulated that the augmented stromal cell population within group B could potentially impede the infiltration of immune cells.Fig. 3 Immune and stromal cell landscape of different SL subtypes. A Violin plots of stromal, immune, and ESTIMATE scores of two subtypes. B Box plots of level of infiltration of 21 immune cells between SL subtypes. C The shared significantly enriched gene sets in CRC patients with chemoresistance based on the GSE19860, GSE69657 and GSE106584 datasets.

Fig 3

Our primary objective was to identify pivotal genes or pathways governing the alteration in reaction to chemotherapeutic agents among CRC patients. Consequently, we procured three sets of data pertaining to CRC patients subjected to chemotherapeutic treatments and conducted GSEA utilizing the GSE69657, GSE19860, GSE106584 datasets. The pathways impacted by the co-enrichment of these three datasets encompassed ECM-receptor interaction, Focal adhesion, Graft Versus Host Disease and Type I Diabetes Mellitus (Fig. 3C). It is noteworthy that these findings aligned with the pathways enriched among patients previously classified based on SL genes. This reinforces the hypothesis that the identified 22 SL genes might wield a significant influence over the modulation of drug resistance in CRC cells.

Discovery of synthetic lethality genes closely correlated with CRC mutation

Tumor mutation frequently intertwine with the development of drug resistance [24]. We conducted an evaluation of the prognostic significance of the identified SL genes in CRC patients, as well as the relationship between these genes and CRC mutation status using the TCGA database. The results revealed TP53 and KRAS as the predominantly mutated genes in CRC, whereas the mutation prevalence of the 22 identified SL genes was comparatively limited. Among them, KDR exhibited a mutation frequency of approximately 5 % in CRC, which was the most prominent (Fig. 4A). To determine whether the effect of SL genes on the survival of CRC patients depended on TP53 or KRAS mutations, we divided the cohort into TP53 or KRAS wild-type and TP53 or KRAS mutant cases. Kaplan-Meier curves showed that the expression of ABCG2, CAMK2G, SORBS1, and GTF2H5 were not associated with the survival of CRC patients (Fig. 4B-E), whereas patients with high expression of TPM1 had a better prognosis (Fig. 4F). However, categorizing the cohort into patients with (n = 316) or without (n = 301) TP53 mutations showed that high ABCG2, CAMK2G, SORBS1, and TPM1 expression was associated with low survival in TP53 mutant CRC. The cohort would also be categorized into patients with (n = 221) or without (n = 396) KRAS mutations, and the results showed that patients with high GTF2H5 expression had a poorer prognosis in the presence of KRAS mutations (Fig. 4E). However, these genes had a better or irrelevant prognostic impact in patients with wild-type TP53 or KRAS. The remaining other SL genes did not fit the above pattern and were not further analyzed. Therefore, we propose that the expression of ABCG2, CAMK2G, SORBS1, TPM1, and GTF2H5 could serve as independent prognostic factors in predicting poor outcome among CRC patients with TP53 mutant or KRAS mutant.Fig. 4 Identification of SL genes responsible for poor prognosis in mutant CRC patients. A Oncoprint display of frequently mutated genes and SL genes in CRC. B-F The expression of ABCG2 (B), CAMK2G (C), SORBS1 (D), GTF2H5 (E) and TPM1 (F) predicted CRC patient survival. Stratification of the cohort according to TP53 or KRAS status showed that high expression of these SL genes predicted poor survival in TP53 or KRAS mutant, but not wild-type, CRC patients.

Fig 4

Exploration of expression and function of core synthetic lethality genes through single-cell sequencing

Next, we analyzed the expression levels of these five characteristic SL genes in CRC tissues based on the GEPIA database. The results unveiled a significant decrease in the expression of CAMK2G, SORBS1, and TPM1 in CRC tissues when contrasted with normal tissues, whereas the expression patterns of ABCG2 and GTF2H5 remained relatively unchanged (Fig. 5A). Since prior analysis have indicated pathways associated with the extracellular matrix, we conducted a supplementary assessment of these five SL genes using the GSE35602 dataset. This dataset encompasses gene expression profiles derived from CRC paired cancer and adjacent tissues, divided into two distinct components: stromal and epithelial tissues. The results demonstrated a down-regulation in GTF2H5 gene expression, coupled with a significant elevation in TPM1 expression compared to normal stromal tissues. Conversely, the expression levels of the other genes exhibited no appreciable disparities (Fig. 5B). In the context of cancerous epithelial tissues, there was a markedly reduction in the gene expression of ABCG2 and TPM1, while the expression levels of the remaining genes showed no substantial variations (Fig. 5C).Fig. 5 The expression profile of SL genes under oncological conditions. A The expression of ABCG2, CAMK2G, SORBS1, GTF2H5 and TPM1 in CRC tissues and normal tissues deposited in the GEPIA database. B The expression levels of five identified SL genes in the paired stroma tissue in CRC based on the GSE35602 dataset. C The expression levels of five identified SL genes in the paired epithelial tissue in CRC based on the GSE35602 dataset.

Fig 5

Considering the prognostic significance and deviant distribution patterns observed in these five SL genes within CRC tissues, our aim was to investigate the distinct cellular subtypes exhibiting enrichment for these genes through scRNA-seq. The datasets EMTAB8107 and GSE166555 underwent comprehensive analysis, revealing a total of 12 distinct cell types in the former and 13 cell types in the latter (Fig. 6A). Notably, we observed a prominent enrichment of all five SL genes within the epithelial cells, fibroblasts, and myofibroblasts. Moreover, there was a marginal expression of CAMK2G and GTF2H5 identified in T cells (Fig. 6B, C). Composing these genes into a signature accentuated the findings, with a marked enrichment evident within epithelial cells, fibroblasts, and myofibroblasts, exhibiting statistically significant distinctions (Fig. 6D). Further, GSEA analysis at the single-cell level revealed that gene sets such as the UV_RESPONSE_UP and EPITHELIAL_MESENCHYMAL_TRANSITION were closely associated with fibroblasts and myofibroblasts, whereas pathways such as MYC_TARGETS_V1, OXIDATIVE_PHOSPHORYLATION and E2F_TARGETS were significantly active in the malignant epithelial cell population (Fig. 7A). In contrast, the INFLAMMATORY_RESPONSE, INTERFERON_GAMMA_ RESPONSE and TNFA_SIGNALING _VIA_NFKB pathways were inhibited in normal and cancerous epithelial cells (Fig. 7B). In addition, the results of intercellular interactions showed that there were interactions between fibroblasts and malignant epithelial cells. Furthermore, the outcomes pertaining to intercellular interactions unveiled those fibroblasts in cluster 17, fibroblasts in cluster 31, and myofibroblasts in cluster 21 exhibited robust interactions with malignant epithelial cell clusters 11, 14, 18, and 29 (Fig. 7C, D).Fig. 6 Identification of five SL genes mainly located in epithelial cells and fibroblasts by Sc-RNA sequencing. A UMAP plots of different clusters of cells in CRC based on the GSE166555 and EMTAB8107 dataset. B, C The expression levels of ABCG2, CAMK2G, SORBS1, GTF2H5 and TPM1in the identified cell types based on the GSE166555 (B) and EMTAB8107 dataset (C). D Violin plots of expression levels of signature composed of SL genes in various cell clusters. ns: no significance, *P < 0.05, ⁎⁎P < 0.01, ⁎⁎⁎P < 0.001

Fig 6

Fig. 7 The single-cell level functional analysis and Time series gene expression analysis. A, B The enrichment pathways upregulated or downregulated in different cell clusters. C The cell-cell interaction among different cells. D The interactions between fibroblasts and other cells. E Variation in SL genes during different periods after drug treatment. F Expression of SL genes in organoids after drug treatment. ns: no significance, *P < 0.05, **P < 0.01, ***P < 0.001

Fig 7

Identify synthetic lethality genes potentially susceptible to the impact of chemotherapy in CRC

Derived from these findings, we have pinpointed these five SL genes as pivotal contributors in CRC. We want to go further to explore whether these genes mediate the responsiveness of CRC cells to chemotherapy. The Mfuzz algorithm was employed for assessing the expression patterns of SL genes in both sensitive and resistant CRC cells in response to chemotherapeutic drugs. We performed the analysis at six distinct stages to simulate the gradual transition of CRC cells from sensitive to resistant status. The findings unveiled ascending trends in the overall expression of GTF2H5 and SORBS1, accompanied by a declining pattern in the expression of ABCG2, while no discernible pattern was observed in the expression levels of CAMK2G and TPM1 (Fig. 7E). CRC organoids can more efficiently and realistically mimic all aspects of tumor properties in vitro and the results are closer to the in vivo conditions experienced by the patient [25]. We further analyzed and validated the expression of those genes in drug-resistant and sensitive organoids by utilizing data from the GSE218588 dataset. Consistently, the expression of these genes was generally upregulated in drug-resistant class organs, but only SORBS1 was statistically significant (Fig. 7F). Therefore, we need to follow up with experimental validation and focus on SORBS1 and GTF2H5.

GTF2H5 Potentially a crucial gene for tackling chemoresistance in CRC

CRC-resistant cell lines (HCT8 and HCT15) were successfully established, and their respective IC50 values were determined, reflecting a 14.59-fold and 24.67-fold increase in resistance compared to the sensitive cells (Fig. 8A, B). Next, we detected the expression changes of SORBS1, GTF2H5 and TPM1 by qPCR under different concentrations of FOLFOX, respectively. Consistent with the previous results, the expression of GTF2H5 was significantly elevated after FOLFOX treatment in both sensitive and resistant cells (Fig. 8C, D). While the expression of SORBS1 had the opposite trend in sensitive cells, it was instead decreased in HCT8 cells, while it was elevated in HCT15 cells. Previous survival analysis and scRNA-seq analysis revealed that TPM1 behaved in a special way, which was examined in conjunction with this analysis. The results showed that TPM1 expression was likewise significantly up-regulated in drug-resistant cells after FOLFOX treatment, while no significant changes were seen in sensitive HCT8 cells (Fig. 8C, D). Besides, the protein level of GTF2H5 was similarly elevated after treatment with different concentrations of FOLFOX (Fig. 9A). Since the expression level of GTF2H5 showed consistency across different cells, we further analyzed it. To explore the possibility that GTF2H5 might have affected the responsiveness of CRC cells to chemotherapy, we knocked down GTF2H5 in cells and explored the function of GTF2H5 in drug-resistant CRC cells (Fig. 9B). The knockdown of GTF2H5 has been shown to promote apoptosis and inhibit cell growth in glioma cells [21]. In contrast, our study reveals that while GTF2H5 knockdown does not markedly elevate apoptosis levels in CRC cells (Fig. 9C), it significantly inhibits cell proliferation, as determined by CCK8 assays (Fig. 9D). Ultimately, to simulate the cellular growth and extracellular matrix environment in vivo, we employed a gelatin-based 3D culture system, complemented by co-cultured with fibroblasts previously constructed by our group [23] (Fig. 9E and Fig. 10A). The results showed that compared with the negative control group, down-regulation of GTF2H5 notably increased the cell death under FOLFOX treatment, suggesting that GTF2H5 may exert a pivotal influence over the formation of drug resistance process in CRC cells (Fig. 10B–E). Collectively, the findings suggest that the inhibition of GTF2H5 in combination with FOLFOX treatment results in a synergistic effect, increasing cell death.Fig. 8 The verification of expression levels of SORBS1, GTF2H5 and TPM1 in sensitive or drug-resistant cells. A, B Dose-response curves of HCT8 (A) and HCT 15 cells (B) exposed to various concentrations of FOLFOX. C, D PCR experiments to verify the expression changes of SORBS1, GTF2H5 and TPM1 after 48 h of treatment with various concentrations of FOLFOX in sensitive (C) or drug-resistant (D) HCT8 or HCT15 cells.

Fig 8

Fig. 9 GTF2H5 is crucial for maintaining the proliferative capacity of CRC cells. A The protein expression levels of GTF2H5 in drug resistant HCT8 and HCT15 cells. B The protein expression of GTF2H5 was downregulated in drug resistant HCT8 and HCT15 cells determined by qPCR and Western blot. C, D Transfection of drug resistant CRC cells with si-GTF2H5 or control for 48 h, followed by assessment of apoptosis levels using flow cytometry (C) and cell proliferation using the CCK-8 assay (D). E Immunofluorescence assays of the positive markers α-SMA and Vimentin in fibroblasts.

Fig 9

Fig. 10 GTF2H5 plays a critical role in the formation of drug resistance process in CRC. A The illustration of co-culturing process of CRC cells with fibroblasts. B Under 2D cell culture conditions, drug-resistant HCT8 and HCT15 cells were treated with FOLFOX for 48 hours, with green fluorescence indicating live cells and red fluorescence indicating dead or dying cells. C 3D co-culturing experiments showing viability of drug resistant HCT8 or HCT15 cells after knockdown of GTF2H5; For each group, we randomly chose three fields of view, conducted live and dead cell counts, and subsequently calculated cell survival rates. D, E 3D layer scanning was performed by confocal microscopy, with green fluorescence representing live cells and red fluorescence representing dead or dying cells. Representative images of HCT 8 (D) and HCT 15 cells (E) are shown.

Fig 10

Discussion

The predominant pharmacotherapeutic strategy employed for the management of CRC patients centers around 5-Fu. Notably, the widely employed regimens, namely FOLFOX and FOLFIRI, have significantly ameliorated the prognosis for CRC patients, but the development of drug resistance and subsequent relapse is an inescapable phenomenon [3,5,26]. Although targeted therapies and immunotherapies exhibit efficacy in a subset of patients, but recurrence is still inevitable [4,5]. The emergence of chemotherapy resistance frequently fosters a favorable condition for the metastatic progression of CRC. It is also of concern that drugs are indiscriminately killing normal cells during treatment, leading to severe chemotherapy side effects and impeding recovery [27].

The concept of SL introduces a fresh avenue in the domain of tumor treatment, along with strategies to manage drug-resistant situations. It can be used to selectively kill tumor cells with little effect on normal cells. And it is promising that effective targets in combination with conventional chemotherapeutic agents can effectively break the dilemma when CRC patients develop drug resistance. For example, preclinical studies have shown that the ATR inhibitor Berzosertib was able to sensitize breast cancer patients to platinum-based chemotherapy [28,29], and that non-small-cell patients (including platinum-resistant patients) treated with the combination of topotecan experienced significant remission, with an objective remission rate of 36 % (9/25) [30]. Serra et al. found that the combination of PARPi with a WEE1 inhibitor showed enhanced antitumor activity, which presents another approach to address PARPi resistance in breast and ovarian cancer [31,32].

In our study, we commenced by conducting a preliminary screening of 22 SL genes correlated with the responsiveness of chemotherapy in patients with CRC by analyzing GEO data along with accessing the SynLethDB database. While targeting these genes might be able to potentially promote the therapeutic effect of FOLFOX. Further, CRC patients in the TCGA were divided into two groups based on the expression patterns of these genes. A substantial body of research has underscored the central role of ECM in fostering drug resistance. These mechanisms encompass heightened ECM matrix stiffness, compromised immune cell function, and alteration of collagen secretion to affect drug in distribution within the tumor, etc [33,34]. In a recent study, it was observed that the increased stiffness of the ECM in colorectal liver metastases may fostering therapeutic resistance to bevacizumab [35]. Moreover, ECM stiffness has been identified as a factor that promotes resistance in breast cancer cells to the HER2 inhibitor lapatinib and in melanoma cells to the BRAF inhibitor vemurafenib [36,37]. Peng et al. revealed that collagen exhibits efficacy in countering PD-1/PD-L1 immunosuppression by upregulating LAIR1 expression and initiating downstream signaling, leading to increased CD8+ T cell depletion [38]. As analyzed in our GO, KEGG and GSEA results, the pronounced dissimilarities observed between these groups with the functions connected to the remodeling of ECM, ECM-receptor interaction, and focal adhesion. Cell surface receptors serve as sensors that convey information regarding alterations in ECM composition and stiffness, as well as modulating the drug sensitivity of tumor cells [33]. Besides, focal adhesion to the ECM has been shown to essentially contribute to tumor cell resistance against both chemotherapy and targeted therapies [39].

Several studies have shown that cancer-associated fibroblasts, a predominant constituent within the tumor microenvironment [40], exert a potent influence on the ECM and actively enhance the process of EMT, which is marked by the loss of cell adhesion, heightened metastatic potential, and the facilitation of chemoresistance [33,41,42]. Furthermore, fibroblasts secrete a diverse array of cytokines that trigger signaling pathways aimed at assisting cancer cells in evading drug-induced cell death, thus promoting drug resistance in tumor cells [43]. The treatment of cetuximab resulted in the activation of cancer-associated fibroblasts, prompting the substantial secretion of epidermal growth factor, which mediates MAPK signaling and leading to cetuximab resistance [44]. Tang et al. observed that exposure to hypoxic conditions led to an elevation in the secretion of TGF-β by fibroblasts, which resulted in an enhanced resistance to chemotherapy in CRC [45]. It is noteworthy that our single-cell analysis also showed that five SL genes, including GTF2H5, were basically expressed only in epithelial cells and fibroblasts. Moreover, the fibroblasts exhibited a substantial upregulation of EMT pathway. These findings imply that these five SL genes may exert a notable influence on the phenotypic characteristics of fibroblasts, ECM remodeling, and the mediation of chemoresistance in CRC cells.

Many of the presently available drugs, designed to target oncogenes or tumor suppressor genes, have exhibited effectiveness against various tumor types bearing specific genetic mutations and the most notable finding in SL is that tumor cells with BRCA1/2 mutations are hypersensitive to PARP inhibitors [10,46,47]. These PARP inhibitors, pioneering the concept of synthetic lethality in clinical applications, have obtained FDA approval for clinical use in the treatment of breast and ovarian cancer [48]. Therefore, we classified patients as TP53 or KRAS wild-type or mutant based on the mutational spectrum of CRC, not solely due to the higher prevalence of mutations in these two genes but also owing to their pivotal roles in tumor progression. Survival analyses yielded comparable differences, with high expression of GTF2H5 having a very poor prognosis in KRAS mutant patients, whereas in wild-type patients this gene had little impact on prognosis. Meanwhile, the other four SL genes (SORBS1, CAMK2G, TPM1 and ABCG2) showed opposite effects in TP53-mutant patients as in wild-type patients. Notably, the expression of these five SL genes exhibited a general down-regulation or remained unchanged in CRC, whereas in drug-resistant cells and CRC organoids, the expression of GTF2H5 and SORBS1 progressive increase during the development of drug resistance. These findings lead us to postulate that CRC cells proactively upregulate these genes in response to external stress to ensure their survival.

Genetic mutations affecting GTF2H5 have been identified as causative factors for severe genetic damage and a markedly elevated susceptibility to cancer [19]. After DNA damage occurs, cells mobilize TFIIH, a key player in opening the DNA double helix and excising DNA helix-destabilizing lesions that lack structural relevance [49]. A study has shown that low levels of GTF2H5 was linked to favorable prognosis in patients with high-grade serous ovarian cancer, and that silencing GTF2H5 expression contributed to sensitization to cisplatin in ovarian cancer patients [50]. Huan et al. previously determined that GTF2H5 is highly expressed in glioma tissues and promotes cell proliferation, mechanistically weakening pro-apoptotic signaling mainly through the p53-Bax/Bcl2 mitochondrial pathway [21]. Attention is drawn to the fact a significant portion of the drugs currently in development, following the principle of SL, are closely associated with functions related to DNA damage repair, for instance, inhibitors targeting genes such as ATR [51], DDR [52] and USP1 [53]. Utilizing qPCR, western blotting, and co-culture experiments, our investigation revealed that GTF2H5 expression became activated under the influence of the drug. In the presence of chemotherapeutic drugs, an incremental rise in concentration leads to a gradual increase in GTF2H5 expression within both sensitive and resistant CRC cells. FOLFOX combination therapy primarily operates by inhibiting and disrupting the DNA replication activity of tumor cells [54], while GTF2H5 plays a role in the repair of DNA damage. Therefore, the observed increase in GTF2H5 expression is consistent with its role in safeguarding against DNA damage. And we speculate that GTF2H5 activation serves to protect a fraction of tumor cells with facilitating their survival, and these resilient cells may contribute to the modification of the extracellular matrix, impeding the effective penetration of chemotherapeutic drugs, ultimately culminating in chemoresistance observed in CRC patients. There are also fewer studies on GTF2H5 in CRC, which may allow us to overlook an important target that can increase the efficacy of FOLFOX treatment. Furthermore, inhibiting GTF2H5 heightened the cytotoxicity of FOLFOX against tumor cells. These findings provide partial support for GTF2H5 as a potential therapeutic target, when combined with conventional chemotherapeutic agents, offering a potential avenue for the treatment of drug-resistant patients with CRC.

Our study introduces a fresh perspective on combatting chemotherapy resistance in patients with colorectal cancer. However, there are some constraints associated with our study. Firstly, all available data were obtained from public databases, and representative prospective data were lacking. Secondly, we used only one common chemotherapeutic drug for CRC for the experiment, and it is not clear whether other drugs used to treat CRC patients would have the same results. Moving forward, we are committed to the recruitment of a cohort, in accordance with our predefined criteria, for prospective investigations conducted at our institution, and to conduct in-depth mechanistic studies to explore the clinical value of the identified SL genes.

Conclusions

In conclusion, we have conclusively identified five specific SL genes within drug-resistant CRC patients, which significantly impact the prognosis of mutant CRC patients. And the co-culture system we established successfully replicated the in vivo impact of chemotherapy on tumor cells, and when GTF2H5 expression was suppressed, the combined chemotherapeutic drugs induced a noteworthy escalation in tumor cell mortality. Our study pinpointed GTF2H5 as a crucial transitional regulator in the development of drug resistance in CRC, offering a promising therapeutic target for CRC patients with chemoresistance.

Funding

This work was supported by grants from the National Natural Science Foundation of China (Grant No. 82272629 ), Jiangsu Provincial Medical Key Discipline Cultivation Unit (JSDW202239 ), Key Project of Science and Technology Development of Nanjing Medicine (ZKX21042 ), Postgraduate Research & Practice Innovation Program of Jiangsu Province (JX12014127 ), the research project of Jiangsu Health Development Research Center (JSHD2022057 ), Elderly Health Research Project of Jiangsu Province (Grant No. LR2021017 ), Specialized Cohort Research Project of Nanjing Medical University (NMUC2021013A ).

Availability of data and material

All data generated or analyzed during this study can be achieved from UCSC database (http://xena.ucsc.edu) as well as the Tumor Immune Single-cell Hub (TISCH) database (http://tisch.comp-genomics.org/home/). Further inquiries can be directed to the corresponding author.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki and approved by the Internal Review and Ethics Boards at Nanjing First Hospital. The approval number is KY20220124-04. Informed consent was obtained from patients at the time of genetic sample collection.

CRediT authorship contribution statement

Junjie Nie: Data curation, Formal analysis, Methodology, Validation, Visualization, Writing – original draft, Writing – review & editing. Xinwei Liu: Formal analysis, Investigation. Mu Xu: Conceptualization, Data curation, Methodology. Xiaoxiang Chen: . Shangshang Hu: Conceptualization, Data curation, Formal analysis. Xinliang Gu: Investigation, Methodology. Huiling Sun: Supervision, Validation, Writing – review & editing. Tianyi Gao: Conceptualization. Yuqin Pan: Supervision, Writing – review & editing, Project administration, Resources. Shukui Wang: Conceptualization, Funding acquisition, Supervision, Writing – review & editing, Project administration.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Appendix Supplementary materials

Image, application 1

Acknowledgements

We are very grateful to TCGA, GEO and TISCH database for sharing the CRC data. And we extend our sincere appreciation to BioRender (biorender.com) for their assistance in graphic production.

Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.tranon.2024.102097.
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