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Synergistic effects of telomerase reverse transcriptase and regulator of telomere elongation helicase 1 on aggressiveness and outcomes in adrenocortical carcinoma
Huiyang Yuan ª, Yujiao Wub, Jing Wang , Xin Qina, Yongsheng Huangª, Lei Yanª,”, Yidong Fana a, b, c,d, e,f,g, ** , Jan Zedenius d, e, C. Christofer Juhlin1,8, Catharina Larsson8, Weng-Onn Lui &, Dawei Xu b,*
a Department of Urology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan 250012, China
b Department of Medicine, Division of Hematology, Bioclinicum and Center for Molecular Medicine, Karolinska Institutet and Karolinska University Hospital Solna, Stockholm SE-171 64, Sweden
” Department of Urologic Oncology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230036, China
d Department of Molecular Medicine and Surgery, Karolinska Institutet, Karolinska University Hospital Solna, Stockholm SE-171 64, Sweden
e Department of Breast, Endocrine Tumors and Sarcoma, Karolinska University Hospital Solna, Stockholm SE-171 64, Sweden
‘ Department of Pathology and Cancer Diagnostics, Karolinska University Hospital Solna, Stockholm SE-171 64, Sweden
% Department of Oncology-Pathology, Karolinska Institutet and Karolinska University Hospital Solna, Bioclinicum, Stockholm SE-171 64, Sweden
ARTICLE INFO
Keywords: ACC Prognostic factors RTEL1 Telomere TERT
ABSTRACT
Adrenocortical carcinoma (ACC) is one of the deadliest endocrine malignancies and telomere maintenance by activated telomerase is critically required for ACC development and progression. Because telomerase reverse transcriptase (TERT) and regulator of telomere elongation helicase 1 (RTEL1) play key roles in telomere ho- meostasis, we determined their effect on ACC pathogenesis and outcomes. Analyses of TCGA and GEO datasets showed significantly higher expression of RTEL1 but not TERT in ACC tumors, compared to their benign or normal counterparts. Furthermore, gains/amplifications of both TERT and RTEL1 genes were widespread in ACC tumors and their expression correlated with their gene copy numbers. Higher expression of either TERT or RTEL1 was associated with shorter overall and progression-free survival (OS and PFS) in the TCGA ACC patient cohort, and higher levels of both TERT and RTEL1 mRNA predicted the shortest patient OS and PFS. However, multi- variate analyses showed that only RTEL1 independently predicted patient OS and PFS. Gene set enrichment analysis further showed enrichments of wnt/ß-catenin, MYC, glycolysis, MTOR, and DNA repair signaling pathways in ACC tumors expressing high TERT and RTEL1 mRNA levels. Taken together, TERT and RTEL1 promote ACC aggressiveness synergistically and may serve as prognostic factors and therapeutic targets for ACC.
1. Introduction
The telomeric structure, composed of TTAGGG repetitive sequences and their associated factors or shelterin, is required to maintain genomic stability and integration of human linear chromosomes [1,2]. Progres- sive telomere shortening occurs in normal human somatic cells with cell replication or increased age, and critically shortened (dysfunctional) telomeres induces cellular senescence (permanent growth arrest) and/or apoptosis [1,3]. Therefore, telomere attrition serves as a mitotic clock to
record the number of cell divisions and to limit the cellular lifespan [1,3, 4]. In sharp contrast, cancer cells proliferate infinitely without telomere loss, which is predominantly achieved by activation of telomerase, an RNA-dependent DNA polymerase that synthesizes telomeric DNA and lengthens telomeres [2]. Telomerase reverse transcriptase (TERT) is the catalytic, rate-limiting subunit of telomerase. While telomerase is silent in most normal human cells due to the tight repression of TERT tran- scription, induction of TERT expression is required to activate telome- rase during oncogenesis [2]. Indeed, numerous studies in the last
* Corresponding authors.
** Corresponding author at: Department of Urology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan 250012, China. E-mail addresses: yanlei5309@sdu.edu.cn (L. Yan), fanyd@sdu.edu.cn (Y. Fana), Dawei.Xu@ki.se (D. Xu).
https://doi.org/10.1016/j.biopha.2022.112796
Available online 10 March 2022
0753-3322/ 2022 The Authors. Published by Elsevier Masson SAS. This is an open access article under the CC BY license
decades have shown important roles for TERT in malignant trans- formation and cancer progression.
In addition to telomerase or TERT, many other factors significantly contribute to genomic stability and telomere maintenance. One such factor is regulator of telomere elongation helicase 1 (RTEL1), an (Fe-S) cluster-containing helicase specifying 5’-3’ helicase activity. RTEL1 plays an important part in telomere homeostasis by facilitating repli- cation at telomeres [5,6]. Rtel(-/-) murine embryonic stem cells exhibit substantial telomere loss and chromosome breaks/fusions upon differ- entiation [6]. Moreover, germline mutations in RTEL1 result in Dys- keratosis Congenita and the related Hoyeraal-Hreidarsson syndrome, characterized by short telomeres and other phenotypes in humans [7,8]. More importantly, telomerase even induced telomere loss or telomere catastrophe in RTEL1-deficient cells, which indicates that TERT and RTEL1 co-operate for appropriate telomere homeostasis [9]. The role of RTEL1 in cancer development is largely unclear [10], although its gene amplification was observed in gastrointestinal cancers [11], and RTEL1 single nucleotide polymorphisms were shown to be associated with increased risk of several types of cancer [12-14].
Adrenocortical carcinoma (ACC), originating from the cortex of the adrenal gland, is a rare disease, but one of the deadliest endocrine ma- lignancies [15]. In the United States, the incidence of ACC is approxi- mately 0.72 per million inhabitants per year, which leads to 0.2% of all cancer-related deaths [15]. Recent breakthroughs in next-generation sequencing technologies have greatly promoted the discovery in alter- ations of the genetic, epigenetic and transcriptomic or multiomic land- scapes of ACC, thereby gaining deep insights into their molecular pathogenesis for precision intervention of the disease [16-18]. Never- theless, it is still highly necessary to improve ACC early diagnosis, risk stratification or prognostication, care or managements, as well as to identify new therapeutic targets. Like other malignancies, ACC tumors have been shown to express TERT and telomerase activity [19-22], however, the occurrence of RTEL1 expression and its implications in ACC is presently not known. The present study is designed to determine TERT and RTEL1 expression and their biological/clinical relevance in ACCs.
2. Materials and methods
2.1. Data collection and processing
The data for The Cancer Genome Atlas (TCGA) cohort of 91 ACC patients [17] (Table S1) were downloaded at https://www.canc ergenome.nih.gov in November 2021. We collected transcriptome (n = 76), mutation, and copy number alterations (CNAs) (n = 76) data and clinical-pathological information [survival status, stage, grade, Weiss system score and Adrenocortical differentiation score (ADS)] (Table S1). For RNA sequencing data, mRNA abundances were expressed as RSEM (RNA-Seq by Expectation Maximization). Expression data of TERT and RTEL1 included in the GSE10927 cohort were downloaded from the Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih. gov/geo) in November 2021. The GSE10927 cohort contained speci- mens of 33 ACCs, 22 adrenocortical adenomas (ACAs), and 10 normal adrenocortical tissues (NATs), and mRNA expression profiling had been performed on all the samples using the Affymetrix platform [23]. Telomere length (TL) data for the TCGA-ACC tumors were downloaded from Barthel et al. [24]. Gene set enrichment analysis (GSEA) (version 4.2.1 www.broadinstitute.org/gsea) was used to determine enrichments of signaling pathways related to TERT and/or RTEL1 expression. Adjusted P values < 0.05 were defined as the activation or inhibition of signaling pathways.
2.2. ACC patients and tumors in the local cohort
Twenty-one patients with ACC treated at Karolinska University Hospital between July 1986 and July 2009 were included in the present
study [20,21]. Tumor samples were collected in direct connection to surgery and kept frozen in - 80 ℃ until use, following an established procedure [20,21]. Patient information is detailed in Table S2. The study was approved by the Swedish Ethical Review Authority, and informed consent was given prior to sample collection.
2.3. RNA extraction and quantitative real-time PCR (qPCR)
Total cellular RNA was extracted from ACC tumors using TRIzol reagent (Thermo Fisher Scientific) according to the manufacturer’s protocol. RNA was reversely transcribed into cDNA using High Capacity cDNA Reverse Transcription Kit (Thermo Fisher Scientific). qPCR re- actions were carried out with QuantStudio 7 Flex Real-Time PCR system (Applied Biosystems, Walthan, MA, USA) using SYBR Green PCR Master Mix (Thermo Fisher Scientific). The levels of TERT and RTEL1 mRNAs were expressed as arbitrary units according to the CT values normalized against ß-2M gene expression. PCR primer sequences include for TERT: 5’-CGGAAGAGTGTCTGGAGCAA-3’ (forward) and 5’-GGATGAAGCG- GAGTCTGGA-3’ (reverse); For RTEL1: 5’-GTGGCAAGTCGCTCCTGTCA- 3’ (forward) and 5’-CCAGCTCCTGCTCCAGGCT-3’ (reverse); and for ß-2M: 5’-GAATTGCTATGTGTCTGGGT-3’ (forward) and 5’-CATCTT- CAAACCTCCATGATG-3’ (reverse).
2.4. TERT copy number and promoter mutation in ACCs from the local cohort
The TERT copy numbers were determined using the TaqMan assays (Applied Biosystems; Thermo Fisher Scientific, Inc., Waltham, MA, USA) Hs 01237576_CN for TERT, and Hs 4403326_C for the RNaseP gene used as an endogenous control, as previously analyzed in Svahn et al. [21]. The copy numbers were estimated using CopyCaller v2.0 (Applied Bio- systems; Thermo Fisher Scientific, Inc.) after calibrating with NAT DNA. The TERT promoter mutation analyses were previously performed in Liu et al. [20].
2.5. Statistical analysis
All statistical analyses were performed using R package version 4.0.5. Wilcoxon Rank test and Kruskal-Wallis sum test were used for analysis of differences between two groups and among three or more multi groups, respectively. Spearman’s Rank-Order Correlation coeffi- cient was applied to determine correlation coefficients R between two variables. Survival analyses were performed with log-rank test. Overall survival (OS, time to death from any causes after ACC diagnosis) and progression-free survival (PFS, time to local progression or distant metastasis after ACC diagnosis) were visualized with Kaplan-Meier plots. Univariate and multivariate Cox regression analyses were used to determine the effect of various quantitative predictor variables (including TERT and RTEL1) on OS and PFS. P values < 0.05 were considered as statistically significant.
3. Results
3.1. TERT and RTEL1 expression in ACC tumors, NATs and ACAs
Because the TCGA cohort of ACCs did not contain data from normal adrenal cortical tissues, we determined TERT and RTEL1 expression in ACC and ACA tumors, and NATs by analyzing mRNA data from the GSE10927 cohort. This cohort included NAT specimens from 10 in- dividuals, 22 from ACA patients and 33 from ACC patients. As expected, TERT mRNA expression in NATs and ACAs was consistently low, but varied robustly between ACC tumors: some were much higher while others were lower than in NATs and ACAs (Fig. 1A, left). Taking all of them together, there was no significant difference in TERT expression between NATs or ACAs and ACC tumors (expressed as log(probe-set value); ACC: 2.32 ± 0.20; ACA: 2.24 ± 0.08, NAT: 2.26 ± 0.04; P >
A
TERT expression
3.0
P = 0.3901
RTEL1 expression
P = 0.0009
3.0
2.5
2.0
2.5
ACC
Adenoma
Normal
ACC
Adenoma
Normal
B
RTEL1 expression log2(RSEM+1)
.
R = 0.29, P= 0.0112
RTEL1 expression
30
R = 0.4878, P = 0.0249
20
10
2.0
0
1.0
2.0
3.0
4.0
0
TERT expression log2(RSEM+1)
0
2
4
6
8
10
TERT expression
C
2.
1.
0
0
20
40
60
79%
TERT
Alterations
Deletion
Amplification
62%
RTEL1
Gain
D
TERT expression log2(RSEM+1)
4.0
R=0.21,P=0.0723
RTEL 1 expression log2(RSEM+1)
R=0.25 ,P=0.0332
3.0
Shallow Deletion
Diploid
4.0
Gain
2.0
00
Amplification
3.0
1.0
Shallow Deletion
2.0
Diploid
0
Gain
Amplification
0
1.0
2.0
1.0
2.0
TERT: Log2 copy number values
3.0
0
RTEL1: Log2 copy number values
E
TERT expression log2(RSEM+1)
4.0
3.0
R =- 0.21 , P = 0.0709
2.0
1.0
៛
0
3
-2
-1
TLratio
0
1
2
RTEL 1 expression log2(RSEM+1)
R = 0.14 , P = 0.2266
4.0
2.0
-3
-2
-1
0
1
2
TLratio
0.05). RTEL1 mRNA levels in ACCs also differed substantially but were significantly higher than in NATs and ACAs (expressed as log(probe-set value); ACC: 2.58 ± 0.11; ACA: 2.50 ± 0.12, NAT: 2.47 ± 0.07; P < 0.05) (Fig. 1A, right). No significant difference in RTEL1 levels was observed between ACAs and NATs (P > 0.05).
We then analyzed TERT and RTEL1 expression in the TCGA cohort of ACC tumors. Fifty-two of 76 ACC tumors (68.4%) expressed TERT mRNA, while all of them had RTEL1 expression. The mRNA levels of both TERT and RTEL1 varied substantially and were positively corre- lated with each other (Fig. 1B, left panel). We further determined TERT and RTEL1 mRNA expression in our local cohort of 21 ACC specimens. TERT expression was detectable in 16 of them (76%); while RTEL1 mRNA was expressed in all 21 ACC tumors (Table S2). TERT and RTEL1 expressions were positively correlated with each other (R = 0.488, P < 0.05) (Fig. 1B, right panel). Taken together these results showed similar expression patterns in both cohorts of ACCs.
3.2. Widespread gain/amplification of TERT and RTEL1 gene copy numbers in ACC tumors
The results above suggest that TERT expression is frequently increased in ACC tumors and that RTEL1 expression is up-regulated in the vast majority of ACC tumors. It is known that TERT gene amplifi- cation contribute to its transcription or expression in ACCs [19,21,22]. Therefore, we analyzed the TCGA cohort of ACCs to determine the relationship between TERT expression and copy numbers. TERT gene amplification and gain occurred in 12 (16%) and 46 (61%) of 76 ACC tumors, respectively (Fig. 1C). TERT mRNA expression tended to correlate with its copy numbers with a significance at a borderline (R = 0.21, P = 0.072) (Fig. 1D). Copy number gain of TERT was similarly observed in our cohort of ACCs (11/20, 55%) (Table S2), but there was no correlation between copy number and mRNA levels (R = 0.0871, P > 0.05). In addition, two of these 21 ACC tumors carried a TERT promoter mutation C228T (Table S2).
We then determined RTEL1 expression and copy numbers in the TCGA cohort of ACCs. RTEL1 gene amplification was only observed in one case, but its copy gain occurred in most ACCs (43/76, 57%) (Fig. 1C). In addition, three tumors carried homozygous deletion of the RTEL1 locus. Nevertheless, RTEL1 expression was positively correlated with its copy numbers (R = 0.25, P < 0.05) (Fig. 1D). Interestingly, RTEL1 copy number gain co-occurred with TERT in most tumors (Fig. 1C).
3.3. TERT and RTEL1 expression and relation to telomere length in ACC tumors
TERT and RTEL1 are required for telomere homeostasis, and there- fore their relationship with telomere length (TL) in ACC tumors was evaluated. TL was arbitrarily expressed as log(Tumor TL/Control self Normal TL) calculated based on whole exome sequencing (WES) [24]. TERT expression levels were inversely correlated with TL with a P value at the borderline (R = - 0.21, and P = 0.071). However, no significant correlation was observed between RTEL1 expression and TL (R = 0.14, and P > 0.05) (Fig. 1E).
3.4. Association between TERT or RTEL1 expression and clinical- pathological and molecular variables
We next evaluated whether TERT and/or RTEL1 expression was associated with clinical-pathological variables by analyzing the TCGA cohort of ACCs. Patient age and gender did not significantly affect TERT and RTEL1 expression. TERT expression increased with advanced dis- ease stages but became lower at AJCC stage IV (expressed as Log2 (RSEM), stage I: 0.04 ± 1.13; II: 0.42 ± 0.84; III: 0.90 ± 1.41; IV: 0.24 ± 0.37. P < 0.05). RTEL1 expression did not differ significantly among the four different disease stages (expressed as Log2(RSEM), stage I: 2.85
± 0.71; II: 3.15 ± 0.58; III: 3.15 ± 0.56; IV: 3.23 ± 0.73. P > 0.05) (Fig. 2A). The Weiss scoring system has long been used for ACC diag- nosis [25,26], and therefore, we analyzed the relationship between Weiss score and TERT or RTEL1 expression in ACCs. The total Weiss score was found to be correlated with TERT (R = 0.28, and P < 0.05) but not with RTEL1 expression (Fig. 2B). However, among the individual Weiss criteria only necrosis and mitosis showed significant associations to TERT and/or RTEL1 levels. TERT expression was only observed in tumors with necrosis (with vs. without necrosis, P < 0.01) and was correlated with mitosis counts (R = 0.41, P < 0.05) (Fig. S1), whereas RTEL1 expression was significantly associated with presence of necrosis (P < 0.05) (Fig. S1).
Unexpectedly, TERT expression tended to increase with cellular differentiation (adrenocortical differentiation score, ADS), although a significance was not reached (R = 0.17, P > 0.05), whereas the level of RTEL1 expression was positively correlated with ADS (R = 0.25, P < 0.05) (Fig. 2C). In addition, TP53 inactivation and wnt/ß-catenin hyperactivity play a key role in ACCs, while both were previously shown to regulate TERT transcription [27-29]. Therefore, we further analyzed TERT expression in tumors with and without TP53 and ß-catenin gene mutations. No association was observed between TERT mRNA levels and TP53 or ß-catenin mutation statuses (Fig. S2). This was also the case for RTEL1 (Fig. S2).
3.5. TERT and RTEL1 as prognostic factors for ACC patient survival
We then sought to determine whether TERT and RTEL1 expression could serve as prognostic markers for ACC patients. TERT and RTEL1 were first tested separately for their predicative values in overall and progression-free survival (OS and PFS) of the TCGA-ACC cohort. Pa- tients were divided into TERThigh and TERTlow groups based on median TERT levels in their tumors. As shown in Fig. 3A, the TERTlow group had significantly longer OS and PFS (both P < 0.01). The same analysis of RTEL1 obtained similar results: lower RTEL1 expression (bottom 25%) was significantly associated with longer patient OS and PFS (both P < 0.05, Fig. 3B). Given the finding above, we further determined the combined effect of TERT and RTEL1 on patient survival by dividing patients into the following four groups (median values as cut-off): TERThigh /RTEL1 high, TERThigh/RTEL]low, TERTlow/RTEL1high and TER- Tlow/RTEL1low. As expected, the TERThigh/RTEL1high patient group survived for the shortest periods (both P < 0.01 for OS and PFS) among these four groups (Fig. 3C). To evaluate whether TERT and RTEL1 are independent prognostic factors, we further performed Cox regression analyses that included stage, Weiss score, age, TERT and RTEL1 expression. As shown in Figs. 3D and 3E, disease stage and RTEL1 were the only variables that independently associated with both OS and PFS.
The analysis of our cohort of 21 ACC patients showed no significant association of OS with TERT (P > 0.05) or RTEL1 (P > 0.05) mRNA expression (Fig. S3).
3.6. TERT- and RTEL1-related pathway enrichments in ACC tumors
To better understand the underlying molecular mechanisms by which TERT and RTEL1 contribute to poor outcomes, we sought to determine the involvement of potential signaling pathways. Thus, GSEA analyses were performed to address this issue. For ACC tumors with high TERT expression, the enriched pathways included cell cycle, DNA replication, various DNA repairs and spliceosomes, while RTEL1-high tumors showed overrepresentation of pathways related to glycosami- noglycan biosynthesis heparan/keratan sulfate, basal cell carcinoma, small cell lung cancer, NOTCH, p53, wnt signalings, n-glycan biosyn- thesis and glycerophospholipid metabolism (Fig. 4A and B). The analysis of tumors that were both TERT- and RTEL1-high revealed enrichment of important oncogenic pathways, including for example wnt/ß-catenin, MYC, G2M checkpoint, E2F targets, MTORC1, mitotic spindle, DNA
A
P = 0.0124
RTEL1 expression log2(RSEM+1)
6.0
TERT expression log2(RSEM+1)
P = 0.6176
1.5
1.0
4.0
2.0
0
stage i
stage ii
stage iii
stage iv
stage i
stage ii
stage iii
stage iv
B
Weiss
TERT
Expression (scaled)
Weiss
RTEL1
4
9
TOGA-OR-ASJA-01
TOGA-OR-A5JG-01
TCGA-OR-A5JM-01
TOGA-OR-ASJU-01
TCGA-OR-A5KO-01
TCGA-OR-A5JC-01
TCGA-OR-ASLJ-01
TCGA-OR-A5JE-01
TCGA-OR-A5J8-01
TOGA-OR-A5KZ-01
TOGA-OR-A5JK-01
TCGA-PK-A5HA-01
TCGA-OR-A5LT-01
TCGA-OR-A5J2-01
TCGA-OR-A5L4-01
TOGA-PK-A5H8-01
TOGA-OR-A5LM-01
TCGA-OR-ASLO-01
TCGA-OR-A5L6-01
TCGA-OR-A5KX-01
TOGA-OR-ASLL-01
TCGA-OR-A5JB-01
TCGA-OR-A519-01
TOGA-OR-A5LN-01
TCGA-OR-A5J3-01
TCGA-OR-A5J7-01
TOGA-OR-A5JP-01
TCGA-OR-ASLS-01
TCGA-OR-A5J5-01
TCGA-OR-A5LG-01
TCGA-OR-ASLA-01
TCGA-OR-ASLP-01
TCGA-OR-A5K6-01
TCGA-OR-A5JL-01
TCGA-OR-ASLE-01
TCGA-OR-A5LK-01
TOGA-OR-A5LD-01
TCGA-P6-A5OG-01
TCGA-OR-A5L5-01
TCGA-OR-A5LB-01
TOGA-OR-A5JF-01
TCGA-OR-A5JQ-01
TCGA-OR-ASK8-01
TCGA-P6-ASOF-01
TCGA-PK-A5H9-01
TOGA-OR-A5L8-01
TCGA-OR-A5KV-01
TOGA-OR-ASLC-01
TCGA-OR-A5L9-01
TOGA-OR-A5J0-01
TOGA-OR-A5L3-01
TCGA-OR-A5J6-01
TCGA-OR-A5KY-01
TCGA-OR-A5JD-01
TCGA-OR-A5JI-01
TCGA-OR-A5J1-01
TOGA-OR-A5LH-01
TCGA-OR-ASKU-01
TCGA-OR-A5KT-01
TCGA-OU-ASPI-01
2
0
-2
-4
2
TERT and Weiss Score
RTEL1 and Weiss Score
TERT expression log2(RSEM+1)
4.0
R = 0.28 , P = 0.0298
RTEL1 expression log2(RSEM+1)
R = 0.03 , P = 0.8492
4.0
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2.0
3.0
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$
1.0
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2.0
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.
.
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Weiss Score
6
8
2
4
Weiss Score
6
8
C
TERT expression log2(RSEM+1)
4.0
·
RTEL1 expression log2(RSEM+1)
6.0
R = 0.17 , P = 0.1403
R = 0.25 , P = 0.0276
3.0
4.0
2.0
2.0
1.0
0
0
3
-2
-1
0
1
ADS
-3
-2
-1
1 0
1
ADS
A
B
OS (ACC, TCGA provisional)
PFS (ACC, TCGA provisional)
OS (ACC, TCGA provisional)
PFS (ACC, TCGA provisional)
100
+ TERT- high
100
100
100
Percent survival
+ TERT- high
+ RTEL1- high
+ TERT- low
Percent survival
- TERT- low
Percent survival
+ RTEL1-low
Percent survival
+ RTEL1- high
+ RTEL1-low
50
50
50
50
P = 0.0009
P= 0.0013
P = 0.0149
P = 0.0487
0
50
100
150
200
Month
0
50
100
150
200
Month
0
50
100
150
200
0
50
100
Month
Month
150
200
C
OS (ACC, TCGA provisional)
PFS (ACC, TCGA provisional)
100
100
Percent survival
Percent survival
- TERT-high RTEL1-high
- TERT-high RTEL1-high
50
- TERT-high RTEL1-low
50
- TERT-high RTEL1-low
- TERT-low RTEL1-high
- TERT-low RTEL1-high
- TERT-low RTEL1-low
- TERT-low RTEL1-low
P = 0.0002
P = 0.0004
0
50
100
150
200
0
50
100
150
200
Month
Month
D
Univariate analysis of overall survival
Univariate analysis of progression-free survival
Variables
Pvalue
Hazard ratio
Variables
Pvalue
Hazard ratio
Age
0.306
1.013(0.988-1.040)
Age
0.663
1.005(0.984-1.026)
Gender
0.826
1.091(0.501-2.376)
4
Gender
0.292
0.703(0.365-1.354)
Stage
<0.001
6.008(2.472-14.602)
Stage
<0.001
3.821(1.939-7.529)
Weiss
0.006
1.354(1.092-1.679)
Weiss
0.050
1.188(1.000-1.411)
ADS
0.630
0.909(0.616-1.341)
ADS
0.159
1.288(0.905-1.832)
H
TERT
0.356
0.758(0.421-1.364)
TERT
0.330
1.153(0.866-1.536)
RTEL1
0.019
2.289(1.143-4.583)
RTEL1
0.024
1.888(1.085-3.284)
0
2
4
6
8
10
12
0
1
2
3
4
5
6
7
Hazard ratio
Hazard ratio
E
Multivariate analysis of overall survival
Multivariate analysis of progression-free survival
Variables Pvalue
Hazard ratio
Variables
Pvalue
Hazard ratio
Stage
0.004
4.649(1.620-13.341)
Stage
<0.001
4.907(1.924-12.515)
Weiss
0.146
1.173(0.946-1.454)
Weiss
0.950
1.006(0.842-1.201)
RTEL1
0.004
3.008(1.414-6.396)
RTEL1
0.030 2.028(1.071-3.843)
0
2
4
6
8
10
0
2
4
6
8
10
Hazard ratio
Hazard ratio
A
Cell Cycle
124
P53 Signaling Pathway
67
MYC Targets V2
58
DNA Replication
36
Folate Biosynthesis
10
E2f Targets
195
Homologous Recombination
26
Nucleotide Excision Repair
44
G2m Checkpoint
190
Ribosome
88
Spliceosome
126
RNA Degradation
MYC Targets V1
56
194
Mismatch Repair
23
RNA Polymerase
28
DNA Repair
147
Base Excision Repair.
34
1.425
1.475
1.500
1.42 1.44 1.46 1.48 1.50 NES
1.450
NES
B
Glycosaminoglycan Biosynthesis Heparan Sulfate.
26
Wnt ß-Catenin Signaling
Basal Cell Carcinoma
42
55
Notch Signaling Pathway
47
Uv Response Dn
139
Glycosaminoglycan Biosynthesis Keratan Sulfate
15
Small Cell Lung Cancer
84
MYC Targets V2
58
Base Excision Repair
34
P53 Signaling Pathway
DNA Repair
67
147
Wnt Signaling Pathway
150
Mitotic Spindle
N Glycan Biosynthesis
198
46
Glycerophospholipid Metabolism
71
Apical Surface
43
Pyrimidine Metabolism
97
Glycosaminoglycan Degradation
21
Glycolysis
197
Glycosylphosphatidylinositol Gpi Anchor Biosynthesis
25
Pathways In Cancer
Notch Signaling
322
32
RNA Polymerase
28
Unfolded Protein Response 1
Hedgehog Signaling Pathway
107
56
Inositol Phosphate Metabolism
54
G2m Checkpoint
190
Prostate Cancer
89
Snare Interactions In Vesicular Transport
Androgen Response 96
38
Cell Cycle 124
1.40
1.50
1.40 1.45 1.50 1.55 1.60
NES
1.60
NES
C
Cell Cycle
124
Wnt ß-Catenin Signaling
42
P53 Signaling Pathway
67
Nucleotide Excision Repair
MYC Targets V2
58
44
Base Excision Repair
34
DNA Repair
147
Spliceosome
126
Proteasome
45
G2m Checkpoint
190
RNA Degradation
56
Glycerophospholipid Metabolism
E2f Targets
71
195
RNA Polymerase
28
MYC Targets V1 194
One Carbon Pool By Folate
17
1.35
1.40
1.45
1.50
1.32
1.40
1.48
NES
NES
repair, cholesterol homeostasis and glycolysis (Fig. 4C).
4. Discussion
Telomerase and TERT have been investigated in ACC tumors by several groups [19-22], but the role for RTEL1 in ACC pathogenesis and its clinical implications are currently unclear. In the study presented herein, we analyzed both TERT and RTEL1 expression in the TCGA and GEO (GSE10927) datasets, and in our own local cohort of ACC patients. Our results showed that TERT and RTEL1 dysregulation occurred widely in ACC tumors and that copy number gains or amplification were a key driving-force to upregulate their expression. Moreover, TERT and RTEL1 were both associated with OS and PFS, and patients with TERThigh plus RTEL1 high in their tumors had the shortest OS and PFS.
Telomere maintenance via TERT induction and telomerase activation is required for ACC development and progression. Previous studies and TCGA analyses have identified important mechanisms underlying TERT expression in ACC tumors, which include TERT gene amplification, rearrangements, promoter mutations, and others [19-22]. The TERT gene is haploinsufficient and additional copy numbers thus leads to upregulation of TERT expression [30]. Rearrangements of the TERT locus frequently results in hijacking of enhancers facilitating TERT transcription and expression [2]. In addition, hotspot TERT promoter mutations occurred in 2 of 21 ACC tumors in the present study, and the previous report identified 12% of ACC tumors with the mutation [20]. Such mutations create de novo ETS binding motifs in the TERT promoter that are recognized by the GABP transcription factors, thereby activating TERT transcription [31,32]. Of note, we observed an inverse correlation between TERT expression and telomere length in those tumors, which suggests that telomere dysfunction drives these genetic alterations. Consequently, various genetic events observed above induce aberrant upregulation of TERT expression in ACC tumors. Moreover, higher TERT expression was significantly associated with shorter patient OS and PFS. However, both univariate and multivariate Cox regression analyses failed to show a significant effect of TERT independently. Likely, more aggressive tumors express higher levels of TERT, or TERT expression was correlated with ACC stages, and its effect was masked by strong influ- ence of advanced stages on survival.
Unexpectedly, we did not observe significant differences in TERT expression between ACCs and NATs or ACAs based on the analysis of the GSE10927 cohort. In accordance with the present result, Pereira et al. even documented much higher nuclear TERT protein staining in non- functional ACAs (45.5%) than in ACC tumors (26.6%) using immuno- histochemistry [22]. It is currently unclear about the mechanism un- derlying TERT expression in ACAs, and one potential explanation could be the presence of alternative splicing of TERT mRNA [33]. Such a scenario was previously observed in renal cell carcinomas (RCCs) [33]. Normal renal tissues expressed TERT splicing variants, but lacked full-length TERT mRNA that encoded a functional TERT protein, while this full-length transcript was readily detectable in RCC tumors [33]. Further studies are required to define whether normal adrenal cortical tissues and ACAs only express splicing variants of TERT transcripts.
Both TERT and RTEL1 are required for appropriate telomere ho- meostasis [9], and we thus simultaneously analyzed RTEL1 expression in ACC tumors. RTEL1 has been shown to function as either a tumor-suppressor or oncogenic factor in mouse cancer models [10,34], however, little is known about RTEL1 in human cancer including ACC. Wang et al. recently observed that RTEL1 over-expression promoted proliferation, survival and invasion of glioma cells [10]. The RTEL1 gene is localized in chromosomal region 20q13.33, and previous studies showed its amplification in a fraction of gastrointestinal cancer [11]. These results collectively suggest that RTEL1 is cancer-promoting factor and that the RTEL1 locus is an oncogenic target. By analyzing the dataset of TCGA-ACC tumors, we observed that 58% of the tumors harbored increased RTEL1 copies, and copy number gains or amplification was positively correlated with its expression. These results suggest that
up-regulation of RTEL1 expression is required for ACC pathogenesis or progression. Higher RTEL1 expression was significantly associated with OS and PFS in TCGA patients with ACC. Moreover, when combining TERT and RTEL1 together, tumors with high expression of both factors predicted the shortest patient OS and PFS. Importantly, the multivariate Cox regression analysis further showed that RTEL1 was an independent prognostic factor for ACC patients. However, we did not observe sig- nificant effects of TERT and RTEL1 on patient survival in our own cohort of ACC patients, which is likely related to the limited number of cases included. Nevertheless, further studies with recruitments of more pa- tients are required to address this issue.
In addition to their telomere maintenance functions, both TERT and RTEL1 exhibit other biological activities. TERT is involved in the regu- lation of mitochondrial function, DNA damage repair, and gene tran- scription. [35-42], and by doing so, TERT stimulates proliferation, epithelial mesenchymal transformation, stemness and angiogenesis of cancer cells [24,40]. On the other hand, RTEL1 acts as a DNA helicase to disassemble various DNA secondary structures and is required to maintain not only telomere homeostasis/integrity, but also global genomic stability during DNA replication, repair and recombination [9]. RTEL1 was further shown to interact with different factors to exert specific functions in different tissues during oncogenesis [11,34]. Conceivably, high TERT and RTEL1 expression promotes aggressiveness of ACCs. Indeed, our GSEA analyses revealed enrichment of diverse oncogenic pathways such as wnt/ß-catenin, MYC and E2F targets, cholesterol homeostasis and glycolysis. Interestingly, in addition to cholesterol homeostasis and glycolysis, higher TERT and/or RTEL1 levels were also associated with alterations in other metabolic pathways that include folate and one carbon pool by folate, glycosaminoglycan, N-glycan, glycosylphosphatidylinositol (gpi) anchor, inositol phosphate and glycerophospholipid biogenesis or metabolisms. Indeed, there is emerging data that TERT expressing/mutated tumors have disparate metabolic function. In a recent study, TERTp mutated follicular thyroid carcinomas were found to display a unique mRNA pattern compared to TERT negative cases, of which the most significantly altered transcrip- tional pathways were associated to mitochondrial transmembrane transport, carnitine shuttle, and fatty acid transmembrane transport [43]. A similar pattern of altered metabolic activity has also been shown in gliomas and glioblastomas with aberrant TERT [44]. It is currently unclear how TERT and RTEL1 regulate metabolic reprograming in ACCs, and underlying mechanisms calls for further studies. Nevertheless, these metabolic alterations associated with TERT and RTEL1 may be a driving-force for aggressive phenotypes of ACCs and other types of cancer [45].
The aberrant WNT/ß-catenin signaling activation plays a key role in ACC development and progression [15]. We did not observe differences in RTEL1 expression between CTNNB1 wildtype and mutated tumors, but those tumors expressing higher RTEL1 were significantly enriched with the WNT/ß-catenin pathway, suggesting an intimate association of the activated WNT/ß-catenin signaling with RTEL1 expression. It is currently unclear how WNT/B-catenin and RTEL1 affect each other. Mechanistic insights into this relationship will contribute to a better understanding of the ACC pathogenesis.
5. Conclusions
Our study shows that aberrations in TERT gene copy numbers and expression are widespread and may be associated with poor patient outcomes in ACC tumors, which is consistent with previous reports. Importantly, we also observed widespread overexpression of RTEL1 in ACC tumors, which is the first study in this type of cancer. Moreover, higher RTEL1 expression resulted from its copy number gains and was associated with shorter patient survival. The combined analysis of TERT and RTEL1 could identify those patients with shortest OS and PFS, which might enable a risk-adapted approach and thereby help to provide ACC patients with improved personalized interventions.
Funding
This work was supported by grants from National Natural Science Foundation of China (No. 82103557, and 81972475), Shandong Pro- vincial Natural Science Foundation (No. ZR2020QH245), the Swedish Cancer Society, Swedish Research Council, the Cancer Society in Stockholm and Karolinska Institutet Foundation. The funding sources had no roles in the study.
CRediT authorship contribution statement
Conceptualization, HY, LY, YF and DX; methodology, HY, YW, JW, XQ and YH; validation, HY, YW, and XQ; formal analysis, HY, YW, JW, XQ and YH; investigation, HY, LY and DX; resources, HY, CL, JZ, CCJ, W- OL and YF; data curation, HY, LY, W-OL, CCJ, JZ, YF and DX; wri- ting-original draft preparation, HY and DX; writing-review and editing, HY, LY, YF, CL, JZ, CCJ, W-OL and DX; visualization, HY; su- pervision, LY, YF and DX; project administration, LY, YF and DX; funding acquisition, LY, YF, CL, W-OL and DX. All authors have read and agreed to the published version of the manuscript.
Conflict of interest statement
The authors declare that there are no conflicts of interest.
Data Availability
The analyzed datasets presented in the current study are available in the TCGA (https://cancergenome.nih.gov), and GEO (https://www. ncbi.nlm.nih.gov/geo).
Appendix A. Supporting information
Supplementary data associated with this article can be found in the online version at doi:10.1016/j.biopha.2022.112796.
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