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The Clinical Significance of CRNDE Gene Methylation, Polymorphisms, and CRNDEP Micropeptide Expression in Ovarian Tumors - PubMed

  • ️Mon Jan 01 2024

The Clinical Significance of CRNDE Gene Methylation, Polymorphisms, and CRNDEP Micropeptide Expression in Ovarian Tumors

Laura Aleksandra Szafron et al. Int J Mol Sci. 2024.

Abstract

CRNDE is an oncogene expressed as a long non-coding RNA. However, our team previously reported that the CRNDE gene also encodes a micropeptide, CRNDEP. The amino acid sequence of CRNDEP has recently been revealed by other researchers, too. This study aimed to investigate genetic alterations within the CRNDEP-coding region of the CRNDE gene, methylation profiling of this gene, and CRNDEP expression analysis. All investigations were performed on clinical material from patients with ovarian tumors of diverse aggressiveness. We found that CRNDEP levels were significantly elevated in highly aggressive tumors compared to benign neoplasms. Consistently, a high level of this micropeptide was a negative, independent, prognostic, and predictive factor in high-grade ovarian cancer (hgOvCa) patients. The cancer-promoting role of CRNDE(P), shown in our recent study, was also supported by genetic and epigenetic results obtained herein, revealing no CRNDEP-disrupting mutations in any clinical sample. Moreover, in borderline ovarian tumors (BOTS), but not in ovarian cancers, the presence of a single nucleotide polymorphism in CRNDE, rs115515594, significantly increased the risk of recurrence. Consistently, in BOTS only, the same genetic variant was highly overrepresented compared to healthy individuals. We also discovered that hypomethylation of CRNDE is associated with increased aggressiveness of ovarian tumors. Accordingly, hypomethylation of this gene's promoter/first exon correlated with hgOvCa resistance to chemotherapy, but only in specimens with accumulation of the TP53 tumor suppressor protein. Taken together, these results contribute to a better understanding of the role of CRNDE(P) in tumorigenesis and potentially may lead to improvements in screening, diagnosis, and treatment of ovarian neoplasms.

Keywords: CRNDE; CRNDEP; DNA methylation; DNA sequence variant; IHC; TP53; lncRNA; micropeptide; ovarian cancer.

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Conflict of interest statement

Author Arkadiusz M. Bonna was employed by the company Triple Helical Peptides Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1

Assessment of discriminating capabilities of the rs115515594 SNP in the CRNDE gene in BOTS. The ROC curves plotted for the multivariable and univariable Cox regression models (A) show that this SNP can serve as a predictor of poor prognosis (increasing the risk of relapse) in BOTS patients. The corresponding Kaplan–Meier survival curves (B), plotted for the univariable Cox model (presence (1) vs. absence (0) of the given CRNDE variant), demonstrate its discriminating value. The detailed outcome (significant results only) of the multivariable Cox regression analysis is shown in (C). RFS—relapse-free survival; HR—hazard ratio; CI—confidence interval; Ev.—events no.; AUC—area under a ROC curve; MOM—multivariable original model; UOM—univariable original model; MBM—multivariable bootstrapped model; UBM—univariable bootstrapped model; BT—best time (follow-up time in days with the highest AUC value for the MBM); Lower number of observations (N) in C than in B results from the fact that non-primary BOTS were excluded from the multivariable analysis, since FIGO stages were not assessed for such tumors. Low p-values are displayed in exponential notation (e−n), in which e (exponent) multiplies the preceding number by 10 to the minus nth power.

Figure 2
Figure 2

CpG methylation alterations in the CRNDE gene. Violin plots supplemented with boxplots showing differences in methylation patterns (beta values) in five different regions of the CRNDE gene (first exon (A), lncRNA (B), introns (C), proximal promoter (D), intron/exon boundaries (E) between BOTS without (BOT) and with the BRAF V600E mutation (BOT V600E), lgOvCa, and hgOvCa. Yellow triangles in the boxplots represent mean values, while gray horizontal bars denote medians. The results of the Kruskal–Wallis and Wilcoxon rank sum tests are also provided. In Figure (F), genomic locations (on GRCh37:chr16) of CpG sites included in each region are listed. All the CpG sites are located on the same DNA strand (minus (−)) as the CRNDE gene. The plots for the distal promoter (1–5 kb) were not included, as in this region, there were no statistically significant differences in methylation patterns between the analyzed groups of tumors. Low p-values are displayed in exponential notation (e−n), in which e (exponent) multiplies the preceding number by 10 to the minus nth power.

Figure 3
Figure 3

Assessment of the CRNDE gene methylation. (A): Fragment of the CRNDE gene comprising the differentially methylated region (DMR, GRCh37:chr16:54962001-54963401), and locations of two primers, CRNDEmF4 and CRNDEmR4, used to verify methylation patterns in one CpG site (cg13471560, GRCh37:chr16:54960485) by PCR and Sanger sequencing. The DMR is highlighted in yellow, while the PCR primers are marked red and green. A detailed visualization of the DMR region is shown in (B). Apart from chromosomal localization and DNA methylation levels (beta-values), the DHSS (Dnase I hypersensitive sites) and TFBS (transcription factor binding sites) are also displayed in this plot. In (C), differences in methylation level (beta values) for the cg13471560 CpG site are presented in a dot plot, supplemented with significant results of linear regression modeling (only the BOT without BRAF V600E mutation vs. hgOvCa comparison). Red rectangles indicate three samples, which had their methylation levels validated by methylation-specific PCR and Sanger sequencing. In the sequencing results (D), the cytosine forming the relevant CpG site is marked with red rectangles. BH—Benjamini–Hochberg correction for multiple comparisons. Y in (D) stands for pyrimidine (C or T) according to IUPAC nucleotide codes.

Figure 4
Figure 4

Assessment of discriminating capabilities of methylation changes (beta values) in the DMR (GRCh37:chr16:54962001-54963401) within the CRNDE gene in patients with serous hgOvCa exhibiting TP53 accumulation in the tumors. The ROC curves plotted for the multivariable and univariable logistic regression models (A) show that methylation changes in the analyzed locus can serve as a predictor of hgOvCa sensitivity to chemotherapy (PS). In the corresponding boxplot (B), PS is categorized as either 0 (resistant) or 1 (sensitive) to treatment. The boxplot is supplemented with mean values (yellow triangles) and the result of the Wilcoxon rank sum test. The detailed outcome (significant results only) of the multivariable logistic regression analyses for the given DMR (comprising CpGs located either on both DNA strands or on the minus strand only) is shown in (C). DMR—differentially methylated region; m—minus DNA strand; p—plus DNA strand; RT—residual tumor size; TP53.acc. == 1—presence of the TP53 protein accumulation; OR—odds ratio; CI—confidence interval; Ev.—events no.; AUC—area under an ROC curve; MOM—multivariable original model; UOM—univariable original model; MBM—multivariable bootstrapped model; UBM—univariable bootstrapped model.

Figure 5
Figure 5

CRNDEP expression analysis in ovarian tumors. (A–C): exemplary IHC results depicting moderate (A) and strong (B) expression of CRNDEP in two ovarian tumors. Negative IHC staining in the “B” tumor after utilization of a blocking peptide is shown in (C). Pictures in (A–C) were all taken under 200× magnification. In (D), boxplots presenting differences in CRNDEP expression in serous ovarian tumors of different aggressiveness are shown. The plots are supplemented with the results of the Wilcoxon rank sum tests. Yellow triangles represent mean values.

Figure 6
Figure 6

Visualization of exemplary results of the Cox and logistic regression analyses. These results depict the prognostic and predictive meaning of altered CRNDEP expression in hgOvCa patients, treated with the taxane/platinum (TP) therapy, whose tumors exhibited the TP53 protein accumulation. Plots demonstrating the prognostic value of CRNDEP (estimation of overall survival (OS) and the risk of death) or its ability to predict complete remission (CR) are shown in (A–G), respectively. AUC plots for original uni- and multivariable models (A) and the corresponding models after bootstrap-based cross-validation of the set (B). A red dashed line in B indicates the same time point that was used to draw the time-dependent ROC curves (C). Optimal cutoff points in ROC curves (C,F) were calculated for the multivariable models using the Youden method. Discrimination sensitivity and specificity values for the given cutoff points are also provided in (C,F,D): Kaplan–Meier survival curves obtained for the patients divided into two categories (risk higher (high) or lower (low) than for the ROC curve (C)-estimated cutoff point, based on the risk of death calculated using the multivariable model). The Kaplan–Meier curves are supplemented with the result of the log-rank test as well. (E,G): boxplots showing CRNDEP expression changes in patients with a high or low risk of death (E), and with (1) or without (0) CR (G). Yellow triangles in boxplots represent mean values. Type—histological type; RT—residual tumor size. Low p-values are displayed in exponential notation (e−n), in which e (exponent) multiplies the preceding number by 10 to the minus nth power.

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