Skip to main content
NCBI home page
Chinese Medical Journal logoLink to Chinese Medical Journal
. 2023 Nov 24;137(15):1844–1856. doi: 10.1097/CM9.0000000000002865

Oncogene goosecoid is transcriptionally regulated by E2F1 and correlates with disease progression in prostate cancer

Yue Ge 1, Sheng Ma 1, Qiang Zhou 2, Zezhong Xiong 1, Yanan Wang 1, Le Li 1, Zheng Chao 1, Junbiao Zhang 1, Tengfei Li 1, Zixi Wu 1, Yuan Gao 1, Guanyu Qu 1, Zirui Xi 1, Bo Liu 3, Xi Wu 4,✉, Zhihua Wang 1,✉
Editor: Yuanyuan Ji
PMCID: PMC12077560  PMID: 37997674

Abstract

Background:

Although some well-established oncogenes are involved in cancer initiation and progression such as prostate cancer (PCa), the long tail of cancer genes remains to be defined. Goosecoid (GSC) has been implicated in cancer development. However, the comprehensive biological role of GSC in pan-cancer, specifically in PCa, remains unexplored. The aim of this study was to investigate the role of GSC in PCa development.

Methods:

We performed a systematic bioinformatics exploration of GSC using datasets from The Cancer Genome Atlas, Genotype-Tissue Expression, Gene Expression Omnibus, German Cancer Research Center, and our in-house cohorts. First, we evaluated the expression of GSC and its association with patient prognosis, and identified GSC-relevant genetic alterations in cancers. Further, we focused on the clinical characterization and prognostic analysis of GSC in PCa. To understand the transcriptional regulation of GSC by E2F transcription factor 1 (E2F1), we performed chromatin immunoprecipitation quantitative polymerase chain reaction (qPCR). Functional experiments were conducted to validate the effect of GSC on the tumor cellular phenotype and sensitivity to trametinib.

Results:

GSC expression was elevated in various tumors and significantly correlated with patient prognosis. The alterations of GSC contribute to the progression of various tumors especially in PCa. Patients with PCa and high GSC expression exhibited worse progression-free survival and biochemical recurrence outcomes. Further, GSC upregulation in patients with PCa was mostly accompanied with higher Gleason score, advanced tumor stage, lymph node metastasis, and elevated prostate-specific antigen (PSA) levels. Mechanistically, the transcription factor, E2F1, stimulates GSC by binding to its promoter region. Detailed experiments further demonstrated that GSC acted as an oncogene and influenced the response of PCa cells to trametinib treatment.

Conclusions:

GSC was highly overexpressed and strongly correlated with patient prognosis in PCa. We found that GSC, regulated by E2F1, acted as an oncogene and impeded the therapeutic efficacy of trametinib in PCa.

Keywords: Goosecoid, Prostate cancer, Prognosis, E2F transcription factor 1, Trametinib

Introduction

Prostate cancer (PCa), which accounts for a significant number of cancer-related deaths in men worldwide,[1,2] follows a sequential progression from prostate intraepithelial neoplasia to adenocarcinoma and metastatic disease.[3] The development of PCa is influenced by genetic changes, particularly mutations in oncogenes and tumor suppressor genes.[4–6] Phosphatase and tensin homolog (PTEN) loss and hyperactivated phosphatidylinositol-3-hydroxykinase/protein kinase B (PI3K/AKT) signaling are recognized as key drivers of PCa pathogenesis.[7] Additionally, BRCA1/BRCA2 are essential for maintaining genomic stability and repairing damaged DNA. Therefore, mutations of BRCA1/BRCA2 may contribute to the progression of PCa.[8] Despite significant advancements in understanding the mechanisms underlying PCa development, the reason why a small subset of patients with PCa experience rapid progression and poor prognosis remains to be fully elucidated.[4] Therefore, there is an urgent need to identify promising biomarkers for PCa prognosis.

Goosecoid (GSC) is a conserved transcription factor[9] that plays a crucial role in embryonic development, particularly in gastrulation[10,11] and neural crest[12] formation. Accumulating evidence has suggested that GSC shows significant effects on tumor migration and invasion,[9,13] and it is highly associated with multiple metastasis-related biological processes, including epithelial-mesenchymal transition, transforming growth factor (TGF)-β, and Wnt/β-catenin signaling pathways.[14–16] These findings strongly indicate the involvement of GSC in malignant tumor progression. Consistent with this, strong evidence indicates a poorer prognosis for patients with high GSC expression in different types of tumors, including ovarian, liver, and breast cancer.[9,13,14] Although prior studies on PCa have shown that GSC expression is influenced by downstream methylation and the cyclin D1 network,[17,18] it remains unclear whether different clinical stages and outcomes are associated with GSC in PCa, requiring further investigation.

In this study, we conducted a comprehensive bioinformatics analysis to investigate the biological roles and prognostic importance of GSC in various tumors with a particular focus on PCa. The functional significance of alterations in GSC expression in PCa was systematically investigated in relation to prognosis, genetic alterations, immune cell infiltration, and drug responses. Our analysis revealed that GSC was the top-ranking gene concerning genetic alterations. We performed detailed in vitro and in vivo experiments to elucidate the oncogenic functions of GSC in PCa.

Methods

Analysis of GSC expression

Genome-wide mRNA expression data for 33 types of cancer in The Cancer Genome Atlas (TCGA) cohort were obtained from the University of California, Santa Cruz (UCSC) Cancer Genomics Browser (https://genome-cancer.ucsc.edu/). We used the Tumor Immune Estimation Resource 2.0 (TIMER2) website (http://timer.cistrome.org/) to compare GSC expression between tumors and adjacent tissues in the TCGA cohort.[19] Additionally, we utilized SangerBox (http://SangerBox.com/Tool) to analyze the expression of GSC in tumors and their corresponding normal tissues by combining the TCGA and Genotype-Tissue Expression (GTEx) cohorts.

Survival prognosis analysis

To retrieve the overall survival (OS) and progression-free survival (PFS) data from the TCGA database, we accessed the UCSC online website. The tumor patients were divided into high and low GSC expression groups based on the median GSC expression. Kaplan–Meier survival plots for various tumors were generated using the R package "survival". We used the log-rank test to compare survival curves and performed Cox regression analysis to calculate the hazard ratio (HR) using the SangerBox online platform.

In addition, we obtained RNA-Sequence data and the corresponding clinical data for 105 PCa samples from the German Cancer Research Center (Deutsches Krebsforschungszentrum, DKFZ) cohort using the cBioPortal for Cancer Genomics (http://cbioportal.org).[20,21] We used R studio (https://www.r-project.org/) to analyze the relationship between GSC expression and biochemical recurrence (BCR) in patients with PCa. We also analyzed the differences in GSC expression among PCa patients with different clinical characteristics, such as T stage, N stage, and Gleason scores. For all statistical analyses, a two-tailed P-value <0.05 was considered statistically significant.

Analysis of genetic alteration

To study the genetic mutation ratios of GSC in different cancers, we utilized the "Gene_Mutation" module of the TIMER2 online platform. Subsequently, we investigated the characteristics of genetic alterations in GSC using the cBioPortal database. For this analysis, we selected the TCGA Pan Cancer Atlas Studies and Prostate Adenocarcinoma (TCGA and PanCancer Atlas) datasets. Somatic mutations and copy number alterations were obtained from UCSC Xena. In the TCGA PCa cohort, we divided the patients into two groups based on the median GSC expression. To examine copy number alterations in the high and low GSC expression groups, we used the R package "maftools."

Analysis of GSC-related genes and immune cell infiltration

To identify potential GSC-binding proteins, we evaluated the STRING database (https://string-db.org/).[22] "GSC" was queried in the "protein name" module and the key parameters were set as follows: network edges with "evidence," minimum required interaction score of "medium confidence (0.400)," and a maximum of five interactors shown in the first shell. As a result, the five available candidates were selected. Subsequently, we investigated the correlation between the mRNA expression of GSC and the five genes encoding GSC-binding proteins in various tumors using TIMER2. In addition, we utilized the R program to analyze GSC-related genes in the TCGA and DKFZ databases, focusing on the top 100 genes that showed positive or negative links with GSC expression. To comprehensively understand the functions of GSC-related genes, we conducted Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses of PCa using the SangerBox portal. To explore the relationship between GSC expression and immune-related cells in PCa, we used the XCell portal (https://xcell.ucsf.edu).[23]

Human tissues

PCa tissues and their corresponding adjacent normal prostate tissues were obtained from the Department of Urology at Tongji Hospital, Huazhong University of Science and Technology, Wuhan, China. Fifty tissue pairs were collected for further experimental verification using real-time quantitative polymerase chain reaction (RT-qPCR). After undergoing radical prostatectomy surgery, all samples were immediately stored in liquid nitrogen. Before participation, informed consent was obtained from all patients. The research protocol was approved by the Ethics Committee of Tongji Medical College, Huazhong University of Science and Technology (No. 2019CR101).

Chromatin immunoprecipitation qPCR (ChIP-qPCR)

ChIP-qPCR experiments were conducted using the ChIP assay kit (Cat#9003, Cell Signal Technology, Boston, USA). Antibodies against E2F1 (66515-1-Ig, Proteintech, Wuhan, China) and a non-specific negative control rabbit IgG (Santa Cruz, Dallas, USA) were used. Supplementary Table 1 (http://links.lww.com/CM9/B753) lists the primers specific to the GSC promoter.

Statistical analyses

All statistical analyses used GraphPad Prism (V.8.0.3, San Diego, USA). The paired Student's t-tests were used to test GSC expression difference between tumor tissues and matched normal tissues. Kaplan–Meier survival analysis was used to assess the association between gene expression and the survival probability of multiple tumors. P-values, HRs, and 95% confidence intervals (CIs) were determined using the log-rank test. Student's t-tests were used to compare the mean values from two groups and gene co-expression was tested using Spearman's correlation analysis. For the functional experiments, the figure legends in the relevant figures denote the specific statistical tests used. P-values <0.05 were considered statistically significant.

Results

GSC is overexpressed and closely related to the prognosis in multiple tumors

To explore the role of GSC in the development of various tumors, we initially analyzed the expression levels of GSC from 33 cancer types in the TCGA dataset. Comparing the expression in tumor tissues with the corresponding adjacent tissues using TIMER2.0, we observed significant upregulation of GSC in multiple cancer types, including cholangiocarcinoma (CHOL), colon adenocarcinoma (COAD), esophageal carcinoma (ESCA), glioblastoma multiforme (GBM), head and neck squamous cell carcinoma (HNSC), kidney renal clear cell carcinoma (KIRC), liver hepatocellular carcinoma (LIHC), lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), prostate adenocarcinoma (PRAD), rectum adenocarcinoma (READ), and uterine corpus endometrial carcinoma (UCEC). Conversely, GSC expression was downregulated in breast invasive carcinoma (BRCA), kidney chromophobe (KICH), and thyroid carcinoma (THCA) [Figure 1A]. To validate these findings, we expanded our analysis by including normal samples from the GTEx database, further confirming the aberrant upregulation of GSC in most cancer types [Figure 1B]. Further, a matched sample t-test between cancer and adjacent tissues revealed abnormal expression of GSC in several tumors, including THCA, KIRC, LUSC, BRCA, CHOL, HNSC, and LIHC [Figure 1C and Supplementary Figure 1, http://links.lww.com/CM9/B752].

Figure 1.

Figure 1

Open in a new tab

GSC expression levels in various cancers. (A) Expression levels of GSC in TIMER2.0 database. *P <0.05; †P <0.01; ‡P <0.001. (B) GSC mRNA expression levels in 23 different tumor types from TCGA and GTEx database. †P <0.01; ‡P <0.001. (C) Differential expression levels of GSC in tumors and corresponding normal tissues in THCA, KIRC, and LUSC. Data were presented as the median (minimum to maximum). P-values were calculated using a paired two-sided Student's t-test. BRCA: Breast invasive carcinoma; CESE: Cervical squamous cell carcinoma and endocervical adenocarcinoma; CHOL: Cholangiocarcinoma; COAD: Colon adenocarcinoma; ESCA: Esophageal carcinoma; GBM: Glioblastoma multiforme; GSC: Goosecoid; GTEx: Genotype-Tissue Expression; HNSC: Head and neck squamous cell carcinoma; KICH: Kidney chromophobe; KIRC: Kidney renal clear cell carcinoma; KIRP: Kidney renal papillary cell carcinoma; LIHC: Liver hepatocellular carcinoma; LUAD: Lung adenocarcinoma; LUSC: Lung squamous cell carcinoma; PRAD: Prostate adenocarcinoma; READ: Rectum adenocarcinoma; TCGA: The Cancer Genome Atlas; THCA: Thyroid carcinoma; TIMER2: Tumor Immune Estimation Resource 2.0; TPM: Transcripts per million reads; UCEC: Uterine corpus endometrial carcinoma.

Subsequently, we investigated the effect of GSC expression on cancer patient survival. We compared the rates of OS and PFS in patients with high or low GSC expression [Figure 2A,B]. Our analysis revealed that high GSC expression was significantly associated with worse OS in adrenocortical carcinoma (ACC) (P <0.0001, HR = 1.35), kidney renal papillary cell carcinoma (KIRP) (P = 0.037, HR = 1.23), LIHC (P = 0.017, HR = 1.07), and KIRC (P = 0.029, HR = 1.10) [Figure 2C and Supplementary Figure 2A, http://links.lww.com/CM9/B752]. In addition, PFS was significantly attenuated in the ACC (P = 0.002, HR = 1.30), PRAD (P <0.0001, HR = 1.45), KIRP (P = 0.002, HR = 1.22), cervical squamous cell carcinoma and endocervical adenocarcinoma (CESE) (P = 0.011, HR = 1.09), and KIRC (P = 0.032, HR = 1.12) patients with high GSC expression [Figure 2D and Supplementary Figure 2B, http://links.lww.com/CM9/B752]. Notably, the highest HR value was observed in PCa based on PFS analysis. These findings indicate that GSC expression is significantly upregulated in multiple cancer types and is closely associated with patient prognosis in various tumors.

Figure 2.

Figure 2

Open in a new tab

Prognosis value of GSC in tumors. (A) Forest plot of the relationship between GSC expression and OS in tumors. (B) Analyses of correlation between GSC expression and PFS. (C) K–M survival curves showed that patients with high GSC expression levels tended to have worse OS in KIRP and ACC. P-values were calculated using the log-rank test. (D) Kaplan–Meier survival curves showed the association between GSC expression levels and PFS in PRAD and ACC. P-values were calculated using the log-rank test. ACC: Adrenocortical carcinoma; CI: Confidence interval; GSC: Goosecoid; HR: Hazard ratio; KIRC: Kidney renal clear cell carcinoma; KIRP: Kidney renal papillary cell carcinoma; OS: Overall survival; PFS: Progression-free survival; PRAD: Prostate adenocarcinoma.

GSC alterations contribute to the progression of various tumors, especially in PCa

Considering the significant impact of genetic alterations on tumor progression and the expression of protein-coding genes,[24] we analyzed five different types of GSC alterations (mutation, structural variant, amplification, deep deletion, and multiple alterations) using TCGA datasets through the cBioPortal. Our findings revealed that amplification was the most common alteration in LUSC (1.4%), followed by deep deletion in CHOL (2.8%), mutations in thymoma, multiple alterations in bladder urothelial carcinoma (0.2%), and structural variant in PRAD (0.2%) [Figure 3A]. Additionally, we divided the samples from the TCGA database into GSC-altered and unaltered groups and compared the mutation counts and tumor mutation burden between the two groups. The altered group exhibited higher mutation counts and tumor mutation burden than the unaltered group [Figure 3B]. Further, analysis of the cBioPortal database indicated that missense mutations were the most common type of GSC mutations in cancers [Figure 3C].

Figure 3.

Figure 3

Open in a new tab

GSC alterations contribute to the progression of various tumors, especially in PCa. (A) The cBioPortal tool was used to examine GSC genetic alteration characteristics (mutation, structural variant, multiple alterations, amplification, and deep deletion) in 32 distinct cancers from the TCGA database. (B) Differences in mutation count and TMB between the GSC-altered and unaltered groups in the TCGA database. (C) The mutation sites of GSC in multiple tumors through the cBioPortal tool. (D,E) Detection of the top 20 genes with the highest mutation rates in the TCGA-PRAD cohort, including GSC high group (D) and GSC low group (E). (F) Differences in gene expression between GSC-altered and unaltered groups according to TCGA PRAD cohort. (G) The expression level of GSC in the mutant group was higher than that in the non-mutant group. Data were presented as the median (minimum to maximum). P-value was calculated using a two-sided Student's t-test. (H) Kaplan–Meier survival analysis showed that the altered group was related to the poorer prognosis in patients with PCa. P-value was calculated using the log-rank test. GSC: Goosecoid; PCa: Prostate cancer; PRAD: Prostate adenocarcinoma; TCGA: The Cancer Genome Atlas; TMB: Tumor mutation burden.

Subsequently, we divided PCa patients from the TCGA database into high and low GSC expression groups and analyzed gene mutations in both groups. TP53 (17%), SPOP (12%), and TTN (12%) showed the highest mutation frequencies in the high GSC expression group, whereas the low GSC expression group showed a high frequency of mutations in SPOP (11%), TTN (8%), and FOXA1 (6%) [Figure 3D,E]. Moreover, we compared gene expression differences between the GSC-altered and unaltered groups in the TCGA-PRAD cohort and intriguingly found that GSC was the top-ranking gene with the most significant P-value in the altered group [Figure 3F]. Importantly, the GSC expression level was significantly higher in the altered group than in the unaltered group [Figure 3G]. Additionally, Kaplan–Meier survival analysis revealed a poorer prognosis in patients with PCa in the altered group [Figure 3H]. Overall, GSC alterations were observed in multiple tumors, which were closely associated with the expression levels of GSC and the prognosis of patients with PCa.

GSC is highly associated with PCa clinical characteristics and potentiates PCa progression in vitro and in vivo

In PCa, we observed a significant overexpression of GSC in tumor tissues compared with their corresponding normal tissues in multiple independent datasets, including TCGA [Figure 4A], GEO (GSE 46602, Figure 4B), and our own PCa tissues [Figure 4C]. To investigate the clinical implications of GSC expression in PCa, we examined its potential correlation with clinical characteristics using three independent datasets: TCGA, DKFZ, and our PCa tissues. These findings demonstrated that patients with PCa with a high Gleason score had significantly higher GSC expression [Figure 4D,G and Supplementary Figure 3A, http://links.lww.com/CM9/B752]. Moreover, a robust correlation was observed between the advanced tumor stage and high GSC expression [Figure 4E,H and Supplementary Figure 3B, http://links.lww.com/CM9/B752]. High GSC expression was also strongly associated with lymph node metastasis [Figure 4F] and elevated prostate-specific antigen (PSA) levels [Figure 4I] in TCGA and our own PCa cohorts, respectively. These findings indicated a close relationship between GSC and PCa progression. Consistent with these findings, higher GSC expression levels were also associated with an increased risk of BCR in patients with PCa [Figure 4J–L], and receiver operating characteristic (ROC) analysis revealed that GSC expression levels could serve as a reliable prognostic indicator for patients with PCa [Supplementary Figure 3C,D, http://links.lww.com/CM9/B752].

Figure 4.

Figure 4

Open in a new tab

Clinical characteristics of GSC in PCa. (A–C) GSC was significantly overexpressed in prostate tumor tissues compared with normal tissues from multiple independent databases, including the TCGA cohort, GEO (GSE46602) cohort, and our PCa tissues. Data are presented as the median (minimum to maximum). P-values were calculated using a two-sided Student's t-test in TCGA and GEO PCa samples, whereas it was calculated using a paired two-sided Student's t-test in our PCa tissues. (D–F) Elevated GSC expression correlated with higher Gleason score, advanced primary tumor stage, and lymph node metastasis in the TCGA cohort. P-values were determined by the two-sided Student's t-test. (G–I) High GSC expression correlated with higher Gleason score, advanced primary tumor stages, and elevated PSA level in our PCa samples. P-values were determined by the two-sided Student's t-test. (J–L) Kaplan–Meier plots suggested increased BCR risks of PCa patients with tumors expressing higher GSC levels in three independent cohorts. BCR: Biochemical recurrence; GSC: Goosecoid; PCa: Prostate cancer; PRAD: Prostate adenocarcinoma; PSA: Prostate-specific antigen; TCGA: The Cancer Genome Atlas.

Having revealed the prognostic role of GSC in patients with PCa, we investigated its functional effects. Our experimental studies utilized specific GSC-targeting small hairpin RNAs (shRNAs) to achieve a stable knockdown of GSC expression in C4-2 and 22Rv1 PCa cells. The efficacy of the knockdown was confirmed using RT-qPCR and Western blotting [Figure 5A,B]. To further explore the potential impact of GSC on PCa, we conducted CCK-8, colony formation, and transwell assays to assess cell proliferation and migration abilities in vitro [Figure 5C–E]. These results demonstrated that GSC knockdown significantly suppressed the proliferation and migration of C4-2 and 22Rv1 cells in vitro. To evaluate the effect of GSC in vivo, we subcutaneously injected GSC-knockdown C4-2 and 22Rv1 cells into nude mice. Interestingly, the results revealed that GSC knockdown significantly inhibited the growth rate of xenograft tumors [Figure 5F]. The tumor weight in the GSC knockdown group was significantly lower compared to the control group [Figure 5G], consistent with the in vitro findings. These findings highlight the oncogenic effects of GSC on PCa in vitro and in vivo.

Figure 5.

Figure 5

Open in a new tab

The oncogenic functions of GSC in PCa. (A) RT-qPCR indicated the GSC transcript levels (normalized to GAPDH) in C4-2 and 22Rv1 cells transfected with shControl or shGSC plasmids. Data were shown as the mean ± SD. *P <0.01; †P <0.001 were calculated using a two-sided Student's t-test. (B) Western blotting indicated the GSC expression levels in C4-2 and 22Rv1 cells transfected with shControl or shGSC plasmids. (C) CCK-8 assays illustrated the change of cell viability in C4-2 and 22Rv1 cells stably transfected with shControl or shGSC. Data were shown as the mean ± SEM with five replicates. †P <0.001 compared with control by the two-sided Student's t-test. (D) Colony formation assays showed the proliferation of C4-2 and 22Rv1 cells stably transfected with shControl or shGSC. Data were shown as the mean ± SD. *P <0.01; †P <0.001 compared with shControl by the two-sided Student's t-test. (E) Transwell migration assays demonstrated the migration ability of C4-2 and 22Rv1 cells stably transfected with shControl or shGSC. Data were shown as the mean ± SD. †P <0.001 compared with shControl by a two-sided Student's t-test. (F) Representative images and growth curves of xenograft tumors derived from C4-2 and 22Rv1 cells with GSC suppression in nude mice. Data were shown as the mean ± SEM for six mice per group. †P <0.001 compared with shControl by the two-sided Student's t-test. (G) The weight of xenograft tumors derived from C4-2 and 22Rv1 cells with GSC suppression in nude mice. The results were shown as the mean ± SD for six mice per group. †P <0.01 compared with shControl by a two-sided Student's t-test. CCK-8: Cell Counting Kit-8; GAPDH: Glyceraldehyde-3-phosphate dehydrogenase;GSC: Goosecoid; PCa: Prostate cancer; RT-qPCR: Real-time quantitative polymerase chain reaction; SD: Standard deviation; SEM: Standard error of the mean; sh: Small harpin.

Transcription factor E2F1 drives the expression of GSC in PCa

To further investigate the potential tumorigenic mechanisms of GSC, STRING database analysis was implemented to identify GSC-binding proteins. Consequently, five GSC-binding proteins were selected: E2F1, E2F2, E2F3, SPI1, and RB1. The protein–protein interaction networks of these proteins are shown in Figure 6A. We analyzed the correlation between GSC and these five genes in 33 cancer types using the GEPIA2 database [Figure 6B]. The results indicated a strong correlation between these genes and GSC in multiple tumor types, including PCa. Additionally, we performed gene set enrichment analysis (GSEA) analysis to explore potential pathways associated with GSC in PCa. Interestingly, we discovered that GSC was likely a target of the E2F family [Supplementary Figure 4A,B, http://links.lww.com/CM9/B752], which is known to facilitate PCa development by activating downstream genes.[25] Therefore, we hypothesized that GSC was a downstream target of the E2F family in PCa.

Figure 6.

Figure 6

Open in a new tab

Transcription factor E2F1 drives GSC expression in PCa. (A) The potential GSC-binding proteins were illustrated using the STRING tool (E2F1, E2F2, E2F3, SPI1, and RB1). (B) The corresponding heatmap data of GSC and its potential binding proteins in multiple tumors using the TIMER2.0 tool. (C–E) The expression correlations of GSC with E2F1 were measured in the TCGA cohort, DKFZ cohort, and our patient samples with PCa. All P-values and R-values were calculated using the Spearman correlation analysis. (F) Real-time qPCR indicated the E2F1 and GSC transcript levels (normalized to GAPDH) in C4-2 and 22Rv1 cells transfected with shControl or shE2F1 plasmids. (G) Western blotting indicated the GSC and E2F1 expression levels in C4-2 and 22Rv1 cells transfected with shControl or shE2F1 plasmids. (H) Epigenetic track, obtained from publicly available E2F1 ChIP-seq data in LNCaP cells, revealed the enrichment of ChIP-seq signals at the GSC promoter region. (I) ChIP-qPCR results showed the significant recruitment of E2F1 at the GSC promoter region, and E2F1 recruitment attenuated after silencing E2F1 in C4-2 and 22Rv1 cells. Data were shown as the mean ± SD. *P <0.001 was calculated using the two-sided Student's t-test. ACC: Adrenocortical cancer; BLCA: Bladder urothelial carcinoma; BRCA: Breast invasive carcinoma; ChIP-qPCR: Chromatin immunoprecipitation qPCR; CESC: Cervical and endocervical cancer; CHOL: Cholangiocarcinoma; COAD: Colon adenocarcinoma; DLBC: Diffuse Large B-cell Lymphoma; E2F1: E2F transcription factor 1; ESCA: Esophageal carcinoma; GBM: Glioblastoma multiforme; GSC: Goosecoid; HNSC: Head and Neck squamous cell carcinoma; KICH: Kidney Chromophobe; KIRC: Kidney renal clear cell carcinoma; KIRP: Kidney renal papillary cell carcinoma; IgG: Immunoglobulin G; LGG: Lower Grade Glioma; LIHC: Liver hepatocellular carcinoma; LUAD: Lung adenocarcinoma; LUSC: Lung squamous cell carcinoma; MESO: Mesothelioma; OV: Ovarian serous cystadenocarcinoma; PAAD: Pancreatic adenocarcinoma; PCa: Prostate cancer; PCPG: Pheochromocytoma and Paraganglioma; PRAD: Prostate adenocarcinoma; READ: Rectum adenocarcinoma; SARC: Sarcomav; SD: Standard deviation; SKCM: Skin Cutaneous Melanoma; sh: Small harpin; STAD: Stomach adenocarcinoma; TCGA: The Cancer Genome Atlas; TIMER2: Tumor Immune Estimation Resource 2.0; TGCT: Testicular Germ Cell Tumors; THCA: Thyroid carcinoma; THYM: Thymoma; UCEC: Uterine Corpus Endometrial Carcinoma; UCS: Uterine Carcinosarcoma; UVM: Uveal Melanoma.

Subsequently, we analyzed the correlation between GSC and E2F transcription factors (E2F1, E2F2, and E2F3) using TCGA, DKFZ, and our own sample data. In the TCGA and DKFZ cohorts, we observed a positive correlation between the expression of E2F1 and GSC, with the correlation coefficient (R) value of E2F1 being more significant than that of E2F2 and E2F3 in both datasets [Figure 6C,D and Supplementary Figure 4C,D, http://links.lww.com/CM9/B752]. Consistently, we found a remarkable positive correlation between the expression of GSC and E2F1 in our own PCa tissue samples [Figure 6E]. Further, we stably knocked down the expression of E2F1 in C4-2 and 22Rv1 cells using two independent shRNAs. RT-qPCR and Western blotting experiments revealed a significant attenuation of GSC expression after E2F1 suppression in PCa cells [Figure 6F,G]. Importantly, analysis of publicly available ChIP-seq data from LNCaP cells[26] demonstrated a high enrichment of E2F1 ChIP-seq signals in the GSC promoter region [Figure 6H], which was further validated by ChIP-qPCR in C4-2 and 22Rv1 cells [Figure 6I]. In summary, these results suggest that the transcription factor E2F1 facilitates GSC expression in PCa.

GSC participates in regulating immune signaling and metabolism in PCa

To investigate the potential biological functions of GSC in PCa, we performed GO and KEGG enrichment analysis on the top 200 genes correlated with GSC in PCa. The enrichment distribution of GSC-related genes in the TCGA and DKFZ cohorts is illustrated in Figure 7A,B, respectively. Moreover, KEGG analysis indicated that GSC might regulate the erythroblastic oncogene B (ErbB) signaling pathway, retinol metabolism, metabolic pathway, and T cell receptor signaling pathway in the TCGA and DKFZ databases. Additionally, GSC might also be involved in the B cell receptor signaling pathway, T helper cell (Th)1 and Th2 cell differentiation, and IL-17 signaling pathway in the TCGA and DKFZ databases, respectively [Figure 7C,D]. These results suggest that GSC and related genes might be involved in metabolism and immune signaling regulation. Further, GO analysis results indicated that the mitogen-activated protein kinase (MAPK) cascade pathway was enriched in both TCGA and DKFZ databases [Figure 7E,F]. Consistently, GSEA analysis also demonstrated that GSC was closely associated with the regulation of MAPK cascade in PCa [Figure 7G,H].

Figure 7.

Figure 7

Open in a new tab

GSC-related genes enrichment analysis in PCa by TCGA and DKFZ databases. (A,B) Heatmap illustrated the enrichment patterns of the top 200 genes positively (100) or negatively (100) correlated with GSC expression of PCa in the TCGA and DKFZ databases. (C,D) KEGG pathway enrichment analyses were applied to the 200 GSC-related genes in the TCGA and DKFZ cohorts. (E,F) The top 200 GSC-related genes in the TCGA and DKFZ cohorts were used in GO pathway enrichment studies. (G,H) GSEA analyses showed that GSC was related to the regulation of MAPK cascade in PCa. E2F1: E2F transcription factor 1; GSC: Goosecoid; MAPK: Mitogen-activated protein kinase; PCa: Prostate cancer; TCGA: The Cancer Genome Atlas.

Given that the above results suggest a potential role for GSC in regulating the tumor microenvironment (TME), which affects PCa progression,[27] we further explored the potential connection between GSC and immune cell infiltration in PCa. We examined variations in immune cell infiltration between groups with high and low GSC expression levels. Interestingly, high GSC expression levels were associated with increased endothelial and mesenchymal stem cell infiltration, whereas low GSC expression levels were possibly linked to enhanced B cell and myocyte infiltration [Supplementary Figure 5A,B, http://links.lww.com/CM9/B752]. Additionally, in the TCGA cohort, the high GSC expression group showed significantly higher levels of CD8+ T cells, Th1 and Th2, and macrophage expression, while the low GSC expression group had higher a regulatory T cells level [Supplementary Figure 5A, http://links.lww.com/CM9/B752]. There was no significant difference in natural killer cell between the high and low GSC expression groups. In contrast, in the DKFZ cohort, there were no significant changes in the expression of these immune cells between the two groups [Supplementary Figure 5B, http://links.lww.com/CM9/B752], which may be attributed to the limited sample size of the DKFZ cohort. Taken together, these findings suggest that GSC plays a regulatory role in immune cell infiltration within the tumor microenvironment of PCa.

Inhibiting GSC enhances the therapeutic effects of trametinib in PCa

To gain a better understanding of the impact of GSC on drug responses in cancer, we analyzed 481 small molecules from the Cancer Therapeutics Response Portal (CTRP) database and examined the correlation between GSC expression level and the drug IC50 [Supplementary Table 2, http://links.lww.com/CM9/B754]. Interestingly, we found that trametinib (R = 0.24, FDR = 0.000124) showed the strongest positive correlation with GSC [Figure 8A]. Previous studies have reported the potential application of trametinib in PCa treatment.[28,29] Based on these findings, we further investigated whether the therapeutic effects of trametinib were influenced by GSC expression in PCa. Notably, CCK-8 and colony formation assays revealed that trametinib (2 μmol/L, following a previous study[29]) moderately suppressed the growth of PCa cells. However, a significantly stronger inhibitory effect on the proliferation of C4-2 and 22Rv1 cells was observed when GSC expression levels were significantly reduced [Figure 8B–E]. These results suggest that the inhibition of GSC expression could enhance the therapeutic effects of trametinib, thereby indicating the potential of combining trametinib with GSC-targeting drugs as a novel therapeutic strategy for PCa.

Figure 8.

Figure 8

Open in a new tab

Inhibiting GSC enhances the therapeutic effects of trametinib in PCa. (A) Bubble diagram showed the correlation between GSC expression and small molecules from the CTRP database. FDR and correlation coefficients were calculated using Spearman correlation analysis. (B,C) C4-2 and 22Rv1 cells stably transfected with shControl or shGSC-2 plasmids were treated with dimethyl sulfoxide (DMSO) or trametinib (2 μmol/L) and then subjected to CCK-8 cell proliferation assay. Data were shown as the mean ± SEM with five replicates. (D,E) C4-2 and 22Rv1 cells stably transfected with shControl or shGSC-2 plasmids were seeded in 6-well plates and incubated for 2 weeks. Cells were treated with DMSO or trametinib (2 μmol/L). Data were shown as the mean ± SD. *P <0.01; †P <0.001 compared with control using the two-sided Student's t-test. CTRP: The Cancer Therapeutics Response Portal; CCK-8: Cell Counting Kit-8; DMSO: Dimethyl sulfoxide; FDR: False discovery rate; GO: Gene Ontology; GSC: Goosecoid; KEGG: Kyoto Encyclopedia of Genes and Genomes; PCa: Prostate cancer; SD: Standard deviation; SEM: Standard error of the mean; sh: Small harpin.

Discussion

In this study, our findings revealed a significant upregulation of GSC mRNA expression in various malignancies, including PCa. Further, a comprehensive prognostic analysis demonstrated that high GSC expression was associated with worse survival outcomes in multiple cancer types. Specifically, in PCa, patients with high GSC expression exhibited poor progression-free and BCR survival. Moreover, the upregulation of GSC in patients with PCa consistently correlated with higher Gleason scores, advanced tumor stages, lymph node metastasis, and elevated PSA levels. These findings strongly support using GSC as an independent PCa diagnosis and prognosis marker. To further elucidate its functional role, detailed in vitro and in vivo experiments were conducted, which demonstrated that GSC could promote the proliferation and migration of PCa cells. Additionally, we reported, pioneeringly, a potential mechanism underlying GSC overexpression, whereby the transcription factor E2F1 bound to the GSC promoter region in PCa, which was confidently validated through ChIP-qPCR experiments. Moreover, our study revealed that alterations in GSC expression levels could influence the response of PCa cells to trametinib, highlighting the potential therapeutic value of targeting GSC in PCa.

GSC, a protein containing homeoboxes, was initially identified as a transcriptional repressor involved in the regulation of vertebrate embryo development.[30] In the context of malignant diseases, GSC had been implicated in the development and metastasis of several tumors. For instance, Kang et al[13] reported a significant association between elevated GSC expression, lymph node metastasis, and poor prognosis in ovarian serous carcinomas. Additionally, Hartwell et al[9] revealed that aberrant GSC expression enhanced the ability of breast cancer cells to develop pulmonary metastasis. Similarly, Xue et al[14] demonstrated that GSC overexpression promoted the migration and invasion of hepatocellular carcinoma cells through epithelial–mesenchymal transition in vitro. However, there remains a gap in our understanding of the comprehensive functional role of GSC in pan-cancer, particularly in PCa. Therefore, in this study, we systematically explored the functional involvement of GSC in various malignancies, with a specific focus on PCa. Our analysis encompassed differential expression, prognostic impact, genetic alterations, immune cell infiltration, and drug responses associated with GSC. Importantly, we experimentally investigated the effects of GSC on PCa cell proliferation, migration, and drug response, providing experimental evidence to support our previous bioinformatic analysis.

Accumulating evidence suggests that genetic alterations play a crucial role in driving the progression of normal cells from the hyperplastic and dysplastic stages to metastatic disease, including invasive cancer.[31,32] Therefore, the analysis of gene alterations is essential for gaining novel insights into the role of oncogenes in cancer development.[33] In line with this, we investigated the mutation types of GSC across various cancers and observed that missense mutations were the most frequently occurring mutation type in multiple cancer types. Further, in patients with PCa from the TCGA cohort, GSC ranked as the top gene with the most significant P-value in the GSC alteration group. Notably, we discovered a strong association between genetic alterations in GSC and poorer PFS in patients with PCa, indicating its involvement in PCa progression and highlighting its potential as an attractive target for PCa diagnosis and prognosis.

The E2F family consists of eight transcription factors that encode 10 proteins, which play critical roles in various biological processes such as genomic integrity, cell cycle development, differentiation, metabolism, and apoptosis.[34,35] These transcription factors are highly abundant in cancer and have been implicated as inducers of several human carcinomas, including breast, colon, oral, and PCa.[36] Among the E2F family, E2F1 is particularly essential for the development of PCa. For instance, Song et al[37] discovered that E2F1 may promote the neuroendocrine trans-differentiation of PCa by facilitating the expression of RACGAP1 and maintaining EZH2 expression levels through the ubiquitin-proteasome pathway. Additionally, Mandigo et al[38] demonstrated that the cooperation between AR and E2F1 elicited novel transcriptional networks that promoted cancer malignant phenotypes, especially those related to evading cell death in PCa. However, despite being a conserved transcription factor, the upstream regulatory mechanisms of GSC remained unclear. Interestingly, we discovered a positive correlation between the expression of E2F1 and GSC based on public databases and our tissue samples. Mechanistically, we performed ChIP-qPCR to validate whether E2F1 induced GSC expression by binding to its promoter.

To date, androgen deprivation therapy remains the most commonly used therapeutic approach for PCa, while targeted drug therapy has not been widely adopted to manage this disease.[39,40] Trametinib, primarily employed in treating melanoma and non-small cell lung cancer with specific genetic mutations,[41,42] is a targeted therapeutic drug for cancer treatment. Its primary mechanism of action is the suppression of mitogen-activated kinase (MEK) proteins, which are involved in the MAPK pathway.[43] By inhibiting MEK activity, trametinib can effectively impede the growth of cancer cells that rely on the MAPK pathway for survival. This property contributes to tumor shrinkage and improves patient outcomes.[44] Several studies have suggested trametinib as a potential candidate for the treatment of PCa. For example, Ciccarelli et al[28] found that trametinib successfully inhibited the growth of PCa cells in vitro. Further, a study by Li et al[29] strongly supported the utility of the MEK inhibitor trametinib for the treatment of enzalutamide-resistant PCa, and the combination of trametinib with enzalutamide was highly effective in managing castration-resistant PCa. However, the effect of trametinib on PCa treatment remained largely unknown. In this study, we provided novel insight into the therapeutic effect of trametinib in PCa. We demonstrated that the response of PCa cells to trametinib could be influenced by GSC, which was closely associated with MAPK pathway activation. These findings suggested that combining trametinib with drugs targeting GSC represents a promising and novel therapeutic strategy for PCa.

In conclusion, our study revealed that GSC was dysregulated and significantly associated with the prognosis of patients with PCa. We systematically investigated the functional significance of alterations in GSC expression in PCa, including their impact on prognosis, genetic alterations, immune cell infiltration, and drug response. Further, detailed experiments confirmed that GSC was regulated by the transcription factor E2F1 and functioned as an oncogene in PCa. Importantly, we demonstrated that suppression of GSC expression enhanced the therapeutic effects of trametinib in PCa. Taken together, our findings suggest that GSC can serve as an independent biomarker for PCa prognosis and as a potential target for therapeutic interventions.

Funding

This study was supported by the National Natural Science Foundation of China (Nos. 82173068, 81974400) and the Applied Basic Research Plan from Qinghai Provincial Department of Science and Technology (No. 2021-ZJ-723).

Conflicts of interest

None.

Data availability

Data used in this study are available from the corresponding author on reasonable request. The experimental details are available in the Supplementary Methods, http://links.lww.com/CM9/B753.

Supplementary Material

cm9-137-1844-s001.docx (1.5MB, docx)
Open in a new tab
cm9-137-1844-s002.docx (20.1KB, docx)
Open in a new tab
cm9-137-1844-s003.xlsx (29.5KB, xlsx)
Open in a new tab

Footnotes

Yue Ge and Sheng Ma contributed equally to this work.

How to cite this article: Ge Y, Ma S, Zhou Q, Xiong ZZ, Wang YN, Li L, Chao Z, Zhang JB, Li TF, Wu ZX, Gao Y, Qu GY, Xi ZR, Liu B, Wu X, Wang ZH. Oncogene goosecoid is transcriptionally regulated by E2F1 and correlates with disease progression in prostate cancer. Chin Med J 2024;137:1844–1856. doi: 10.1097/CM9.0000000000002865

References

  • 1.Sung H Ferlay J Siegel RL Laversanne M Soerjomataram I Jemal A, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 2021;71: 209–249. doi: 10.3322/caac.21660. [DOI] [PubMed] [Google Scholar]
  • 2.Xia C Dong X Li H Cao M Sun D He S, et al. Cancer statistics in China and United States, 2022: Profiles, trends, and determinants. Chin Med J 2022;135: 584–590. doi: 10.1097/CM9.0000000000002108. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3.Cha HR, Lee JH, Ponnazhagan S. Revisiting immunotherapy: A focus on prostate cancer. Cancer Res 2020;80: 1615–1623. doi: 10.1158/0008-5472.CAN-19-2948. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4.Bergengren O Pekala KR Matsoukas K Fainberg J Mungovan SF Bratt O, et al. 2022 update on prostate cancer epidemiology and risk factors-a systematic review. Eur Urol 2023;84: 191–206. doi: 10.1016/j.eururo.2023.04.021. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5.Jin K Qiu S Chen B Zhang Z Zhang C Zhou X, et al. DOK3 promotes proliferation and inhibits apoptosis of prostate cancer via the NF-κB signaling pathway. Chin Med J 2023;136: 423–432. doi: 10.1097/CM9.0000000000002251. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6.Gao P Xia JH Sipeky C Dong XM Zhang Q Yang Y, et al. Biology and clinical implications of the 19q13 aggressive prostate cancer susceptibility locus. Cell 2018;174: 576–589.e18. doi: 10.1016/j.cell.2018.06.003. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7.Ding Y Li N Dong B Guo W Wei H Chen Q, et al. Chromatin remodeling ATPase BRG1 and PTEN are synthetic lethal in prostate cancer. J Clin Invest 2019;129: 759–773. doi: 10.1172/JCI123557. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Tsujino T Takai T Hinohara K Gui F Tsutsumi T Bai X, et al. CRISPR screens reveal genetic determinants of PARP inhibitor sensitivity and resistance in prostate cancer. Nat Commun 2023;14: 252. doi: 10.1038/s41467-023-35880-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.Hartwell KA, Muir B, Reinhardt F, Carpenter AE, Sgroi DC, Weinberg RA. The Spemann organizer gene, Goosecoid, promotes tumor metastasis. Proc Natl Acad Sci U S A 2006;103: 18969–18974. doi: 10.1073/pnas.0608636103. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.Yasuo H, Lemaire P. Role of Goosecoid, Xnot and Wnt antagonists in the maintenance of the notochord genetic programme in Xenopus gastrulae. Development 2001;128: 3783–3793. doi: 10.1242/dev.128.19.3783. [DOI] [PubMed] [Google Scholar]
  • 11.Luu O, Nagel M, Wacker S, Lemaire P, Winklbauer R. Control of gastrula cell motility by the Goosecoid/Mix.1/ Siamois network: Basic patterns and paradoxical effects. Dev Dyn 2008;237: 1307–1320. doi: 10.1002/dvdy.21522. [DOI] [PubMed] [Google Scholar]
  • 12.Clouthier DE Hosoda K Richardson JA Williams SC Yanagisawa H Kuwaki T, et al. Cranial and cardiac neural crest defects in endothelin-A receptor-deficient mice. Development 1998;125: 813–824. doi: 10.1242/dev.125.5.813. [DOI] [PubMed] [Google Scholar]
  • 13.Kang KW Lee MJ Song JA Jeong JY Kim YK Lee C, et al. Overexpression of goosecoid homeobox is associated with chemoresistance and poor prognosis in ovarian carcinoma. Oncol Rep 2014;32: 189–198. doi: 10.3892/or.2014.3203. [DOI] [PubMed] [Google Scholar]
  • 14.Xue TC Ge NL Zhang L Cui JF Chen RX You Y, et al. Goosecoid promotes the metastasis of hepatocellular carcinoma by modulating the epithelial-mesenchymal transition. PLoS One 2014;9: e109695. doi: 10.1371/journal.pone.0109695. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.Watabe T Kim S Candia A Rothbächer U Hashimoto C Inoue K, et al. Molecular mechanisms of Spemann's organizer formation: Conserved growth factor synergy between Xenopus and mouse. Genes Dev 1995;9: 3038–3050. doi: 10.1101/gad.9.24.3038. [DOI] [PubMed] [Google Scholar]
  • 16.Moon RT, Kimelman D. From cortical rotation to organizer gene expression: Toward a molecular explanation of axis specification in Xenopus. Bioessays 1998;20: 536–545. doi: 3.0.CO;2-I">10.1002/(SICI)1521-1878(199807)20:7<536::AID-BIES4>3.0.CO;2-I. [DOI] [PubMed] [Google Scholar]
  • 17.Ju X Casimiro MC Gormley M Meng H Jiao X Katiyar S, et al. Identification of a Cyclin D1 network in prostate cancer that antagonizes epithelial–mesenchymal restraint. Cancer Res 2014;74: 508–519. doi: 10.1158/0008-5472.CAN-13-1313. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Camoriano M Kinney SR Moser MT Foster BA Mohler JL Trump DL, et al. Phenotype-specific CpG island methylation events in a murine model of prostate cancer. Cancer Res 2008;68: 4173–4182. doi: 10.1158/0008-5472.CAN-07-6715. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19.Li T Fu J Zeng Z Cohen D Li J Chen Q, et al. TIMER2.0 for analysis of tumor-infiltrating immune cells. Nucleic Acids Res 2020;48: W509–W514. doi: 10.1093/nar/gkaa407. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20.Gao J Aksoy BA Dogrusoz U Dresdner G Gross B Sumer SO, et al. Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Sci Signal 2013;6: pl1. doi: 10.1126/scisignal.2004088. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Cerami E Gao J Dogrusoz U Gross BE Sumer SO Aksoy BA, et al. The cBio cancer genomics portal: An open platform for exploring multidimensional cancer genomics data. Cancer Discov 2012;2: 401–404. doi: 10.1158/2159-8290.CD-12-0095. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Szklarczyk D Gable AL Nastou KC Lyon D Kirsch R Pyysalo S, et al. The STRING database in 2021: Customizable protein-protein networks, and functional characterization of user-uploaded gene/measurement sets. Nucleic Acids Res 2021;49: 10800. doi: 10.1093/nar/gkab835. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.Aran D, Hu Z, Butte AJ. xCell: Digitally portraying the tissue cellular heterogeneity landscape. Genome Biol 2017;18: 220. doi: 10.1186/s13059-017-1349-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Martincorena I Raine KM Gerstung M Dawson KJ Haase K Van Loo P, et al. Universal patterns of selection in cancer and somatic tissues. Cell 2017;171: 1029–1041.e21. doi: 10.1016/j.cell.2017.09.042. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Han Z Mo R Cai S Feng Y Tang Z Ye J, et al. Differential expression of E2F transcription factors and their functional and prognostic roles in human prostate cancer. Front Cell Dev Biol 2022;10: 831329. doi: 10.3389/fcell.2022.831329. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.McNair C Xu K Mandigo AC Benelli M Leiby B Rodrigues D, et al. Differential impact of RB status on E2F1 reprogramming in human cancer. J Clin Invest 2018;128: 341–358. doi: 10.1172/JCI93566. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Xiao J Sun F Wang YN Liu B Zhou P Wang FX, et al. UBC9 deficiency enhances immunostimulatory macrophage activation and subsequent antitumor T cell response in prostate cancer. J Clin Invest 2023;133: e158352. doi: 10.1172/JCI158352. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28.Ciccarelli C Di Rocco A Gravina GL Mauro A Festuccia C Del Fattore A, et al. Disruption of MEK/ERK/c-Myc signaling radiosensitizes prostate cancer cells in vitro and in vivo. J Cancer Res Clin Oncol 2018;144: 1685–1699. doi: 10.1007/s00432-018-2696-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.Li S Fong KW Gritsina G Zhang A Zhao JC Kim J, et al. Activation of MAPK signaling by CXCR7 leads to enzalutamide resistance in prostate cancer. Cancer Res 2019;79: 2580–2592. doi: 10.1158/0008-5472.CAN-18-2812. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.Niehrs C, Keller R, Cho KW, De Robertis EM. The homeobox gene goosecoid controls cell migration in Xenopus embryos. Cell 1993;72: 491–503. doi: 10.1016/0092-8674(93)90069-3. [DOI] [PubMed] [Google Scholar]
  • 31.Garnis C, Buys TP, Lam WL. Genetic alteration and gene expression modulation during cancer progression. Mol Cancer 2004;3: 9. doi: 10.1186/1476-4598-3-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Giannareas N Zhang Q Yang X Na R Tian Y Yang Y, et al. Extensive germline-somatic interplay contributes to prostate cancer progression through HNF1B co-option of TMPRSS2-ERG. Nature Commun 2022;13: 7320. doi: 10.1038/s41467-022-34994-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33.Hahn WC, Weinberg RA. Rules for making human tumor cells. N Engl J Med 2002;347: 1593–1603. doi: 10.1056/NEJMra021902. [DOI] [PubMed] [Google Scholar]
  • 34.Shrestha M, Wang DY, Ben-David Y, Zacksenhaus E. CDK4/6 inhibitors and the pRB-E2F1 axis suppress PVR and PD-L1 expression in triple-negative breast cancer. Oncogenesis 2023;12: 29. doi: 10.1038/s41389-023-00475-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.Nakajima R Zhao L Zhou Y Shirasawa M Uchida A Murakawa H, et al. Deregulated E2F activity as a cancer-cell specific therapeutic tool. Genes (Basel) 2023;14: 393. doi: 10.3390/genes14020393. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36.Kassab A, Gupta I, Moustafa AA. Role of E2F transcription factor in oral cancer: Recent insight and advancements. Semin Cancer Biol 2023;92: 28–41. doi: 10.1016/j.semcancer.2023.03.004. [DOI] [PubMed] [Google Scholar]
  • 37.Song Z Cao Q Guo B Zhao Y Li X Lou N, et al. Overexpression of RACGAP1 by E2F1 promotes neuroendocrine differentiation of prostate cancer by stabilizing EZH2 expression. Aging Dis 2023;Online ahead of print. doi: 10.14336/AD.2023.0202. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38.Mandigo AC Shafi AA McCann JJ Yuan W Laufer TS Bogdan D, et al. Novel oncogenic transcription factor cooperation in RB-deficient cancer. Cancer Res 2022;82: 221–234. doi: 10.1158/0008-5472.CAN-21-1159. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 39.Kishan AU Sun Y Hartman H Pisansky TM Bolla M Neven A, et al. Androgen deprivation therapy use and duration with definitive radiotherapy for localised prostate cancer: An individual patient data meta-analysis. Lancet Oncol 2022;23: 304–316. doi: 10.1016/S1470-2045(21)00705-1. [DOI] [PubMed] [Google Scholar]
  • 40.Wala J, Nguyen P, Pomerantz M. Early treatment intensification in metastatic hormone-sensitive prostate cancer. J Clin Oncol 2023;52: 3584–3590. doi: 10.1200/JCO.23.00723. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 41.Feng J Lian Z Xia X Lu Y Hu K Zhang Y, et al. Targeting metabolic vulnerability in mitochondria conquers MEK inhibitor resistance in KRAS-mutant lung cancer. Acta Pharm Sin B 2023;13: 1145–1163. doi: 10.1016/j.apsb.2022.10.023.023. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 42.Schreuer M Meersseman G Van Den Herrewegen S Jansen Y Chevolet I Bott A, et al. Quantitative assessment of BRAF V600 mutant circulating cell-free tumor DNA as a tool for therapeutic monitoring in metastatic melanoma patients treated with BRAF/MEK inhibitors. J Transl Med 2016;14: 95. doi: 10.1186/s12967-016-0852-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 43.Elkrief A Odintsov I Markov V Caeser R Sobczuk P Tischfield SE, et al. Combination therapy with MDM2 and MEK inhibitors is effective in patient-derived models of lung adenocarcinoma with concurrent oncogenic drivers and MDM2 amplification. J Thorac Oncol 2023;18;1165–1183. doi: 10.1016/j.jtho.2023.05.007. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 44.Sun K Jin L Karolová J Vorwerk J Hailfinger S Opalka B, et al. Combination treatment targeting mTOR and MAPK pathways has synergistic activity in multiple myeloma. Cancers (Basel) 2023;15: 2373. doi: 10.3390/cancers15082373. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

Data used in this study are available from the corresponding author on reasonable request. The experimental details are available in the Supplementary Methods, http://links.lww.com/CM9/B753.

Articles from Chinese Medical Journal are provided here courtesy of Wolters Kluwer Health

RESOURCES