Next Article in Journal
Learning the Biochemical Basis of Axonal Guidance: Using Caenorhabditis elegans as a Model
Previous Article in Journal
Evaluation of Bone Regenerative Capacity in Rabbit Femoral Defect Using Thermally Disinfected Bone Human Femoral Head Combined with Platelet-Rich Plasma, Recombinant Human Bone Morphogenetic Protein 2, and Zoledronic Acid
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Biomedicines
Volume 11
Issue 6
10.3390/biomedicines11061730
biomedicines-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Blood-Derived Exosomal hTERT mRNA in Patients with Lung Cancer: Characterization and Correlation with Response to Therapy

by
Ofer Rotem
1,
Alona Zer
1,2,
Lilach Yosef
1,
Einat Beery
3,
Hadar Goldvaser
4,
Anna Gutkin
2,3,
Ron Levin
5,
Elizabeth Dudnik
1,
Tamar Berger
2,6,
Meora Feinmesser
2,7,
Adva Levy-Barda
7,
Meir Lahav
2,3,6,
Pia Raanani
2,3,6 and
Orit Uziel
2,3,6,*
1
Davidoff Cancer Center, Rabin Medical Center, Petah Tikva 49100, Israel
2
Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 6997801, Israel
3
The Felsenstein Medical Research Center, Rabin Medical Center, Petah Tikva 49100, Israel
4
Shaare Zedek Medical Center, Faculty of Medicine, Hebrew University, Rehovot 7612001, Israel
5
Sheba Medical Center, Ramat Gan 5262000, Israel
6
Institute of Hematology, Rabin Medical Center, Petah Tikva 49100, Israel
7
Biobank, Department of Pathology, Rabin Medical Center, Petah Tikva 49100, Israel
*
Author to whom correspondence should be addressed.
Biomedicines 2023, 11(6), 1730; https://doi.org/10.3390/biomedicines11061730
Submission received: 25 April 2023 / Revised: 12 June 2023 / Accepted: 14 June 2023 / Published: 16 June 2023
(This article belongs to the Section Cancer Biology and Oncology)
Browse Figures
Review Reports Versions Notes

Abstract

:
Background: Telomerase (human telomerase reverse transcriptase (hTERT) is considered a hallmark of cancer, being active in cancer cells but repressed in human somatic cells. As such, it has the potential to serve as a valid cancer biomarker. Exosomal hTERT mRNA can be detected in the serum of patients with solid malignancies but not in healthy individuals. We sought to evaluate the feasibility of measuring serum exosomal hTERT transcripts levels in patients with lung cancer. Methods: A prospective analysis of exosomal hTERT mRNA levels was determined in serum-derived exosomes from 76 patients with stage III–IV lung cancer (11 SCLC and 65 NSCLC). An hTERT level above RQ = 1.2 was considered “detectable” according to a previous receiver operating characteristic curve (ROC) curve. Sequential measurements were obtained in 33 patients. Demographic and clinical data were collected retrospectively from patients’ charts. Data on response to systemic therapy (chemotherapy, immunotherapy, and tyrosine kinase inhibitors) were collected by the treating physicians. Results: hTERT was detected in 53% (40/76) of patients with lung cancer (89% of SCLC and 46% of NSLCC). The mean hTERT levels were 3.7 in all 76 patients, 5.87 in SCLC patients, and 3.62 in NSCLC patients. In total, 25 of 43 patients with sequential measurements had detectable levels of hTERT. The sequential exosomal hTERT mRNA levels reflected the clinical course in 23 of them. Decreases in hTERT levels were detected in 17 and 5 patients with partial and complete response, respectively. Eleven patients with a progressive disease had an increase in the level of exosomal hTERT, and seven with stable disease presented increases in its exosomal levels. Another patient who progressed on the first line of treatment and had a partial response to the second line of treatment exhibited an increase in exosomal hTERT mRNA levels during the progression and a decrease during the response. Conclusions: Exosomal hTERT mRNA levels are elevated in over half of patients with lung cancer. The potential association between hTERT levels and response to therapy suggests its utility as a promising cancer biomarker for response to therapy. This issue should be further explored in future studies.

Graphical Abstract

1. Introduction

Lung cancer is the most often diagnosed malignancy in the world and the most frequent cause of cancer death. About 2.2 million new cases of lung cancer were diagnosed worldwide in 2021 [1]. It is estimated that 3 million patients with lung cancer will die by 2035 [2,3]. The prognosis for lung cancer is relatively poor, with a 5-year survival rate varying from 4% to 17%, depending on the stage of the disease at the time of diagnosis [3]. Unfortunately, 75% of patients are diagnosed at an advanced stage of the disease [4]. Although novel medications continue to improve outcome, prognosis remains poor, with a median overall survival (OS) of less than 3 years in most subpopulations of patients with advanced lung cancer [4]. The currently recommended screening for high-risk populations, a low-dose CT scan, improved early detection rates; however, the majority of the patients were still diagnosed with advanced disease [5]. Serum biomarkers have been investigated in lung cancer for two main purposes: early detection (in adjunct to CT screening) or as surrogate markers for response to therapy. The analysis of genomic epithelial serum tumor markers such as squamous cell carcinoma antigen (SCCA), carcinoembryonic antigen (CEA), or neuron-specific enolase (NSE) and cytokeratin 19 (CYFRA 21-1) [6], which have been reported to be elevated, may detect the disease only at advanced stages [7,8]. Other markers such as modulations of microsatellites, DNA hypermethylation in several genes (BCAT1, CDO1, TRIM58, ZNF177, and CRYGD), the mutational status of the p53 and KRAS gene, and the expression of microRNA may improve early-stage diagnosis, but they are still under investigation [9]. Similarly, several other methods based on the analysis of volatile organic compounds and exhaled breath condensation analysis were also studied but are not yet implemented in clinical practice [10]. Due to these drawbacks, there is a need to search for other markers for early diagnosis for the effective treatment of the disease.
Telomeres, repetitive sequences located at the two ends of chromosomes, protect genome integrity by masking chromosomal ends from being recognized as double-strand breaks that need to be repaired. Telomeres gradually shorten with repeated cycles of cell division accompanied to DNA synthesis until they reach a threshold which signals the cells to stop dividing, undergo senescence, and die [11,12]. Telomerase (human Telomerase Reverse Transcriptase, hTERT) is a reverse transcriptase which maintains telomere lengths and thus prevents cellular senescence and cell death due to telomere shortening. In this way, telomerase provides dividing cells with a limitless lifespan. Because telomerase is absent in almost all human somatic cells but is ubiquitously expressed in more than 90% of cancer cells, it serves as an attractive biomarker that distinguishes normal and neoplastic cells [13]. Due to its specificity, mainly to cancer cells, and its essentiality for providing them with limitless replicative potential, telomerase was defined as a hallmark of cancer [14]. The role of telomerase activity and regulation was deeply studied in a plethora of cancers, and its activity was mostly correlated with the aggressiveness of the studied malignancies [15].
Lung cancer cells have been shown by many reports to be heavily dependent on telomerase activity both in vitro and ex vivo [16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32]. Moreover, similarly to other types of cancers, a positive correlation was described between the levels of telomerase activity or regulation and the aggressiveness of the disease [19,20,21]. Subsequently, studies were focused around using telomerase as a valid anti-cancer target in lung carcinoma. Importantly, several inhibitors of the enzyme were developed and tested, showing that the inhibition of the enzyme’s activity successfully eliminated lung cancer cells [32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51].
Telomerase activity is mainly regulated by the transcriptional level. In most studies that aimed at deciphering the regulatory mechanism of telomerase in various settings, the transcript of telomerase, hTERT, was found to be well correlated with the activity of the enzyme; therefore, in most cases, its levels are considered to reflect its activity [39,52].
Detecting the hTERT transcript in cells derived from the lung is not feasible and is only doable when biopsies are taken for clinical purposes. Therefore, non-invasive blood-sample-based liquid biopsies seem like the preferable solution for this problem.
One use for liquid biopsies is the analysis of the content of exosomes that originate from neoplastic cells and travel in the blood. There are three types of vesicles that are shredded from cells and circulate in the blood: exosomes, microparticles, and apoptotic bodies. In our study, we decided to use exosomes, the most investigated extracellular vesicles, as a liquid-biopsy-related approach to identify hTERT. Carrying a molecular cargo of various types of nucleic acids, proteins, and lipids, exosomes circulate in blood and other body fluids and may deliver their cargo to bystander cells [53]. Previously, we have shown that various types of cancer cells secrete exosomes that carry the hTERT mRNA transcript [54,55]. Whether the hTERT transcript is detected in exosomes from patients with lung carcinoma is currently unknown. Our study addressed the possible future use of exosomal hTERT as a diagnostic marker for lung carcinoma and for assessment response to systemic therapy.

2. Materials and Methods

2.1. Patients’ Characteristics

The clinical characteristics of the patients and the corresponding levels of exosomal hTERT mRNA are depicted in Table 1.

2.2. Study Population and Procedures

Patients with advanced lung cancer were enrolled prior to the initiation of anti-cancer therapy at the Davidoff Cancer Center at the Rabin Medical Center, Beilinson campus. After signing an informed consent form approved by the local IRB (Helsinki committee of Rabin Medical Center), 10 mL of blood in a serum separation tube was obtained from 81 healthy volunteers and 76 consecutive untreated cancer patients in the Davidoff Cancer Center. In total, 44 patients were followed during treatment to observe the kinetics of exosomally derived hTERT mRNA. The blood samples were centrifuged at 2500 R.P.M. for 10 min; the serum was collected, divided into 1 mL aliquots, and kept in −20 °C for exosomes and mRNA isolation. The hTERT levels obtained from 81 healthy volunteers served as controls.
Patients with more than one primary solid malignancy were excluded. Patients’ demographic and clinical data were extracted from the electronic medical records. The response to systemic anti-cancer therapy was assessed and categorized as complete response, partial response, stable disease, and progression of disease, as defined by the treating physicians.

2.3. Exosomes’ Purification

Exosomes were isolated from patients’ sera by using the Total Exosome Isolation Kit (Invitrogen, Carlsbad, CA, USA) according to the manufacturer’s instructions. The purity and concentration of exosomes were analyzed by using the NanoSight tracking device.

2.4. RNA Purification

RNA from exosomes was purified with a Total Exosome RNA and Protein Isolation Kit (Invitrogen, Waltham, MA, USA) according to the provided manual.

2.5. cDNA Formation

mRNA was reverse transcribed using the High-Capacity cDNA Reverse Transcription Kit (Applied Biosystems, Foster City, CA, USA) according to the manufacturer’s instructions.

2.6. hTERT Expression by Real-Time PCR

The expression of hTERT was measured relative to that of HPRT-1 as a reference gene. Gene amplification was executed using the following sets of primers (HyLabs, Rehovot, Israel).
  • hTERT: Forward, 5′-GTACTTTGTCAAGGTGGATGTGA-3′
  • Reverse, 5′-GCTGGAGGTCTGTCAAGGTAGAG-3′.
  • HPRT-1: Forward, 5′-TCAGGCAGTATAATCCAAAGATGGT-3′
  • Reverse, 5′-CTTCGTGGGGTCCTTTTCAC-3′.
Polymerase chain reactions were prepared with the TaqMan fluorophore-labeled primers (Applied Biosystems, Waltham, MA, USA) and run and analyzed on the Step One Detection System (Applied Biosystems). Reactions were performed using 50 cycles; a normal value (no expression of hTERT) was arbitrarily defined as 1 for further calculation purposes.

2.7. Statistical Analysis

The statistical analysis was generated using the SAS Software, Version 9.4, 2002–2012 (SAS Institute Inc., Cary, NC, USA). Continuous variables were presented as mean ± std and median (minimum–maximum), and categorical variables were presented as (n, %). The normality of distribution for the continuous variables was assessed graphically and using a Kolmogorov–Smirnov test. If deemed normal, ANOVA (or t-test for two groups) was used to compare the value of continuous variables between study groups; if not deemed normal, Wilcoxon’s test was used. χ2 and/or Fisher’s exact test were used in the analysis of categorical variables between study groups. Pearson’s correlation was used to assess the association between continuous variables. Two-sided p-values <0.05 were considered statistically significant.

3. Results

3.1. Patients

In total, 76 patients were enrolled, 65 with NSCLC and 11 with SCLC. Out of those, 49 patients had adenocarcinoma, 17 had squamous cell carcinoma and 10 had large-cell carcinoma. Patients’ characteristics are presented in Table 1.

3.2. The Iolation of Exosomes

Basically, all living cells secrete vesicles, functioning as cell–cell communicators. There are several types of extra vesicles, differing in their cellular origin and biogenesis, size, release mechanism, molecular cargo, and function type. These include microvesicles (MVs), exosomes, and apoptotic bodies [53]. To show that our isolated vesicles are exosomes, we used NanoSight tracking analysis (NTA) and transmission electron microscopy (TEM). Based on Brownian motion, NTA identifies the size and concentration of vesicles of interest. The NTA results showed that a large concentration of the vesicles was at the expected size of exosomes (30–150 nm), and the TEM image indicated a similar size (Supplementary Figure S1).

3.3. The Dynamics of hTERT at Time of Evaluation Regarding the Status of Metastasis

hTERT was hardly detected in the healthy control cohort (7.4%) but was detected in 53% (40/76) of patients with lung cancer (89% of SCLC and 46% of NSLCC). The difference between the average values of the control group and those of the cancer patient group was statistically significant (p = 2.2 × 10−5). The mean hTERT levels were 3.7 (±0.79) in all 76 patients, 5.87 (±1.6) in SCLC patients, and 3.62 (±0.29) in NSCLC with metastases (METs), large-cell neuroendocrine carcinomas (LCNECs), or sarcomatoid patients (Table 1, Figure 1). There were no differences between the NSCLC and the SCLC groups in this regard. The difference between the average values of the exosome-derived hTERT transcripts in patients with NSCLCs and those of patients with NSCLCs, LCNECs or sarcomatoids was statistically significant (p = 0.003).
No correlation was found between the extent of the disease and the levels of exosomal hTERT (p = 0.68). This lack of correlation between metastatic and non-metastatic disease suggests that the levels of exosomal hTERT cannot be differentiated between the two.

3.4. The Levels of Exosomal hTERT Transcript throughout Follow-Up of the Disease

We were able to follow 49 patients and measure the levels of exosomal hTERT transcripts in more than one sample throughout their disease. The results of this analysis are shown in Figure 2. As shown, the levels of the exosomal hTERT transcript decreased in most patients during the follow-up measurement, and the differences between the first measurement (at diagnosis) and the second one reached statistical significance (p = 0.047). Interestingly, out of 32 patients of which we had the clinical details regarding their response to treatment, in 23 of them (>72%), whether the patients were benefiting from therapy or not, the clinical outcome was in line with the levels of exosomal hTERT., e.g., when the response was defined as progressive disease, the levels of the exosomal hTERT transcript were increased. Two cases in which the dynamics of the levels of the exosomal hTERT transcript matched the clinical outcomes are presented in Figure 3.
The correlation between response to treatment and values of the hTERT transcripts in exosomes (Supplementary Table S1) approached clinical significance (p = 0.064).

4. Discussion

For the first time, our study shows the presence of the transcript of hTERT in exosomes derived from the sera of patients with lung cancer. The use of exosomes as diagnostic markers in early cancer detection, progression, response to treatment, and the identification of minimal residual disease (MDR) has been highly expanded in recent years [56]. As in several other types of malignancies, most patients diagnosed with primary lung carcinoma are already in the metastatic lethal stage of the disease upon the appearance of symptoms. Therefore, early diagnosis and treatment are critical to improve the survival of these patients.
As mentioned above, the current methodology of low-dose spiral CT is associated with a high rate of false positives and exposure to radiation and requires a solid biopsy of highly heterogenous tumors. To circumvent these associated drawbacks, liquid biopsies have been increasingly suggested as an alternative preferable diagnostic strategy, enabling the accurate diagnosis of lung cancer, the identification of the specific type of the malignancy, and the reveal of the mutational landscape of the tumor. Since multiple liquid biopsies can be taken throughout the course of the disease, the treatment of which is stage-dependent, patients’ stratification, the monitoring of responses to the disease treatments, and the detection of minimal residual disease post treatment are also possible, thus addressing precision oncology to improve clinical care [56].
The detection and analysis of circulating free DNA (cfDNA), circulating tumor cells (CTCs), and exosomes are the strategies used in most studies of liquid biopsies in cancer [57,58]. Traveling in the blood, cfDNA refers to DNA derived from all cell origins, whereas ctDNA only refers to DNA fragments originated from the tumor cells. As such, apart from DNA of genomic origin, cfDNA includes mitochondrial, fetal, extrachromosomal circular, and even microbial types of DNA ([56] and references therein). As the majority of cfDNA is of hematopoietic origin, some of which may contain benign clonal hematopoietic mutations which may be attributed to neoplastic cells, it is of high importance to differentiate between the two, based on their inherent differences. In the light of the sensitivity of the analysis of cfDNA, several studies reported impressive findings regarding the deciphering of the mutational makeup of lung cancer patients.
Interestingly, cfDNA levels in NSCLC patients are higher than those in patients with chronic respiratory inflammation or the healthy population, with very high specificity (90% and 80.5%, [59]), especially in those with stage II–IV NSCLC. Several studies were conducted comparing tissue to liquid-biopsy-derived analysis results. For example, a study of the ctDNA of cancer patients to identify mutations for relevant treatment (the TARGET study) demonstrated 78% similarity to mutations originated from the cognate tissues [60]. The detection of the EGFR mutation status, the most frequent driver mutation in NLSCS, using cfDNA analysis and the subsequent description of its positive association with the aggressiveness of the disease has been reported recently [61]. A similar study showed the relevance of this mutation upon the analysis of ctDNA [61,62]. In addition, numerous lung-cancer-related mutations (e.g., in ALK, EGFR, and pmKRAS) as well as the loss of heterozygosity, microsatellite instability, and gene methylation, are also readily detected in these cfDNA [62,63]. The recurrence and prognosis of lung carcinoma was also reported to be efficiently detected via cfDNA analysis, contributing to rationalized clinical decisions [64]. Along these lines, the identification of non-invasive early lung cancer in most pre-treated patients was achieved by using the deep sequencing of cfDNA, demonstrating its validity as a diagnostic lung cancer marker. Moreover, the somatic mutations found accurately reflected clonal hematopoiesis and were non-recurrent in these patients and in risk-matched controls [65]. The authors further developed a machine-learning-based platform for the accurate diagnosis of early-stage lung cancer using data obtained from cfDNA analysis [66].
The identification of alternative mutations in the RGFR gene in response to treatment was also reported (reviewed in ref. [56]). Other NSCLC-related mutations, including MET, KRAS, RET, BRAF PI3K assessed in response to treatments, were also detected successfully by analyzing cfDNA [67,68,69].
Another study successfully used a combination of both CTCs and cfDNA in the diagnosis of NSCLC [70].
Still, most studies point to the need for highly sensitive and standardized detection technologies for the valid widespread use of cfDNA analysis in clinical applications.
CTC counts correlated to tumor progression in patients with early lung adenocarcinoma and successfully detected typical mutant genes (Notch1, IGF2, EGFR, and PTCH1) [71,72] and genes in smokers as predictive biomarkers [73]. The precision treatment of patients with lung cancer was decided based on their tatus of the ALK gene mutation, detected via sequencing of their cognate CTC DNA content [74,75].
As for the cfDNA methodology, CTC detection still requires more effective enrichment and quantification methods.
As described in the introduction, exosome analysis has been reported as a diagnostic liquid-biopsy-based tool for lung carcinoma [53,54,55]. Most studies identifying exosomal cargo in plasma or sera derived from patients with lung carcinoma describe certain microRNA signatures as diagnostic markers [76,77,78,79,80,81,82]. All in all, the reported implication of exosomal cargo in the aggressiveness of lung cancer suggests that the inhibition of exosome formation and release may be a potential new strategy for the treatment of lung cancer [82]. Similarly to CTCs and cfDNA, the establishment and standardization of a reliable exosome extraction method is still needed for their use in clinical applications [83].
In our study, the levels of the hTERT transcript significantly differed between all lung cancer patients and the healthy control groups; however, a lack of correlation between these levels and disease staging was observed, suggesting that they do not correlate with the extent or burden.
These results are in contrast with other studies conducted in our laboratory showing that the exosomal hTERT transcript may be defined as a diagnostic marker. For example, in brain tumors, patients with glioblastoma multiforme (GBM) exhibited significantly higher levels of hTERT transcripts compared to patients with meningiomas, non-malignant brain tumors, or healthy controls (submitted). Likewise, the levels of the hTERT transcript were correlated with levels of carcinoembryonic antigen (PSA) in patients with colorectal cancer and significantly differed from those of patients with polyps that served as a control group. In this study, we also measured the levels of the hTERT transcript in patients with Lynch syndrome, which is associated with a predisposition to colorectal cancer, and found positive levels of hTERT transcripts in several carriers [84].
Although they did not solely address the levels of hTERT exosomal transcripts in lung cancer, a few recent studies demonstrated the use of lung-derived exosomal content as diagnostic, prognostic, and potentially therapeutic agents [85,86]. A relevant study demonstrated that tumor-associated exosomes promoted the aggressiveness of lung cancer by increasing metastasis formation [87].
Of note, the engulfment of nucleic acids and other molecules in exosomes is actively mediated, mainly by the endosomal sorting complex required for transport (ESCRT) protein complex. This engulfment of certain molecules may affect their concentration in exosomes, which may differ from their original expression in the mother cells [88]. This ESCRT-mediated process is only partially elucidated [89] and may differ among individual patients, affecting the expression of certain mRNAs such as the hTERT in exosomes. In addition, the correlation between the activity of telomerase and the burden of disease in lung cancer is controversial. Whereas many studies reported a positive correlation between the activity of the enzyme and disease progression [32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51], one study showed the opposite: patients with low telomerase activity in their mononuclear cells were at a significantly increased risk of lung cancer compared to patients with high telomerase activity [90]. Another recent study showed that liquid biopsies based on the detection of circulating tumor cells’ hTERT can effectively diagnose pulmonary nodules to improve efficient CT diagnosis in patients with lung cancer [91]. Telomerase-positive circulating tumor cells were shown to be associated with poor prognosis in glioma [92].
We have shown that CTC capturing based on membranal telomerase-derived peptides both in solid and hematological malignancies is feasible [93]. In another study, the inhibition of telomerase in lung cancer cell lines suppressed their growth by negatively regulating telomerase expression [94]. Along these lines, the ectopic expression of hTERT promoted epithelial–mesenchymal transition in lung cancer cells [95].
This study bears a major limitation, which is related to the number of analyzed patients. Since this was a pilot study, the number of patients was not large enough to draw more solid conclusions.
All in all, it seems that a differential expression of the transcript exists in different pathological cases and biological setting. The combination assessments of other markers, preferentially in exosomes representing efficient liquid biopsies, will probably shed more light on defining valid diagnostic markers for upregulating the treatment of patients with lung cancer.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/biomedicines11061730/s1, Figure S1. Characterization of the isolated exosomes. Left panel—NanoSight tracking analysis of the isolated extravesicles’ sizes and concentrations. Right panel—electron microscopy image of the extravesicles’ sizes and shapes; Table S1. Patients’ characteristics and the levels of exosomal hTERT mRNA.

Author Contributions

Conceptualization, A.Z., O.R., M.L. and O.U.; methodology, L.Y., A.G. and E.B.; software, T.B.; formal analysis, H.G.; investigation, M.F. and A.L.-B.; resources, P.R., M.L. and A.Z.; data curation, R.L. and E.D.; writing—original draft preparation, O.U. and O.R.; writing—review and editing, O.U., O.R. and A.Z.; supervision, O.U. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board of Rabin Medical Center (0038-12-RMC, date of approval: 14 December 2016).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. World Health Organization International Agency for Research on Cancer. Globocan 2020: Lung Cancer. International Agency for Research on Cancer. Available online: http://gco.iarc.fr/today/data/factsheets/cancers/15-Lung-fact-sheet.pdf (accessed on 2 March 2021).
  2. Choi, S.H.; Yoo, S.S.; Lee, S.Y.; Park, J.Y. Anti-angiogenesis revisited: Reshaping the treatment landscape of advanced non-small cell lung cancer. Arch. Pharmacal Res. 2022, 45, 263–279. [Google Scholar] [CrossRef] [PubMed]
  3. The Cancer Atlas. Lung Cancer Remains the Most Commonly Diagnosed Cancer and the Leading Cause of Cancer Death Worldwide because of Inadequate Tobacco Control Policies. Available online: https://canceratlas.cancer.org/the-burden/lung-cancer/ (accessed on 1 April 2023).
  4. NCCN Guidelines. Treatment by Cancer Type. Available online: https://www.nccn.org/guidelines/category_1 (accessed on 1 April 2023).
  5. Lu, T.; Yang, X.; Huang, Y.; Zhao, M.; Li, M.; Ma, K.; Yin, J.; Zhan, C.; Wang, Q. Trends in the incidence, treatment, and survival of patients with lung cancer in the last four decades. Cancer Manag. Res. 2019, 11, 943–953. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  6. Jonas, D.E.; Reuland, D.S.; Reddy, S.M.; Nagle, M.; Clark, S.D.; Weber, R.P.; Enyioha, C.; Malo, T.L.; Brenner, A.T.; Armstrong, C.; et al. Screening for Lung Cancer with Low-Dose Computed Tomography. JAMA 2021, 325, 971–987. [Google Scholar] [CrossRef] [PubMed]
  7. Cainap, C.; Pop, L.A.; Balacescu, O.; Cainap, S.S. Early diagnosis and screening in lung cancer. Am. J. Cancer Res. 2020, 10, 1993–2009. [Google Scholar] [PubMed]
  8. Wadowska, K.; Bil-Lula, I.; Trembecki, Ł.; Śliwińska-Mossoń, M. Genetic Markers in Lung Cancer Diagnosis: A Review. Int. J. Mol. Sci. 2020, 21, 4569. [Google Scholar] [CrossRef] [PubMed]
  9. El-Zein, R.A.; Abdel-Rahman, S.; Santee, K.J.; Yu, R.; Shete, S. Identification of Small and Non-Small Cell Lung Cancer Markers in Peripheral Blood Using Cytokinesis-Blocked Micronucleus and Spectral Karyotyping Assays. Cytogenet. Genome Res. 2017, 152, 122–131. [Google Scholar] [CrossRef] [Green Version]
  10. Van Mastrigt, E.; De Jongste, J.C.; Pijnenburg, M.W. The analysis of volatile organic compounds in exhaled breath and biomarkers in exhaled breath condensate in children—Clinical tools or scientific toys? Clin. Exp. Allergy 2015, 45, 1170–1188. [Google Scholar] [CrossRef]
  11. Aksenova, A.Y.; Mirkin, S.M. At the Beginning of the End and in the Middle of the Beginning: Structure and Maintenance of Telomeric DNA Repeats and Interstitial Telomeric Sequences. Genes 2019, 10, 118. [Google Scholar] [CrossRef] [Green Version]
  12. Chan, S.R.W.L.; Blackburn, E.H. Telomeres and telomerase. Philos. Trans. R. Soc. B Biol. Sci. 2004, 359, 109–122. [Google Scholar] [CrossRef]
  13. Phatak, P.; Burger, A.M. Telomerase and its potential for therapeutic intervention. Br. J. Pharmacol. 2007, 152, 1003–1011. [Google Scholar] [CrossRef] [Green Version]
  14. Low, K.C.; Tergaonkar, V. Telomerase: Central regulator of all of the hallmarks of cancer. Trends Biochem. Sci. 2013, 38, 426–434. [Google Scholar] [CrossRef]
  15. Lantuejoul, S.; Salon, C.; Soria, J.-C.; Brambilla, E. Telomerase expression in lung preneoplasia and neoplasia. Int. J. Cancer 2007, 120, 1835–1841. [Google Scholar] [CrossRef] [PubMed]
  16. Kawai, T.; Hiroi, S.; Nakanishi, K.; Meeker, A.K. Telomere length and telomerase expression in atypical adenomatous hyperplasia and small bronchioloalveolar carcinoma of the lung. Am. J. Clin. Pathol. 2007, 127, 254–262. [Google Scholar] [CrossRef] [PubMed]
  17. Mavrogiannou, E.; Strati, A.; Stathopoulou, A.; Tsaroucha, E.G.; Kaklamanis, L.; Lianidou, E.S. Real-Time RT-PCR Quantification of Human Telomerase Reverse Transcriptase Splice Variants in Tumor Cell Lines and Non–Small Cell Lung Cancer. Clin. Chem. 2007, 53, 53–61. [Google Scholar] [CrossRef] [Green Version]
  18. Kumaki, F.; Kawai, T.; Hiroi, S.; Shinomiya, N.; Ozeki, Y.; Ferrans, V.J.; Torikata, C. Telomerase activity and expression of human telomerase RNA component and human telomerase reverse transcriptase in lung carcinomas. Hum. Pathol. 2001, 32, 188–195. [Google Scholar] [CrossRef]
  19. Frías, C.; García-Aranda, C.; De Juan, C.; Morán, A.; Ortega, P.; Gómez, A.; Hernando, F.; López-Asenjo, J.-A.; Torres, A.-J.; Benito, M.; et al. Telomere shortening is associated with poor prognosis and telomerase activity correlates with DNA repair impairment in non-small cell lung cancer. Lung Cancer 2008, 60, 416–425. [Google Scholar] [CrossRef]
  20. Fan, Y.-B.; Ye, L.; Wang, T.-Y.; Wu, G.-P. Correlation between morphology and human telomerase gene amplification in bronchial brushing cells for the diagnosis of lung cancer. Diagn. Cytopathol. 2009, 38, 402–406. [Google Scholar] [CrossRef]
  21. Hashim, M.; Sayed, M.; Samy, N.; Elshazly, S. Prognostic significance of telomerase activity and some tumor markers in non-small cell lung cancer. Med. Oncol. 2010, 28, 322–330. [Google Scholar] [CrossRef] [PubMed]
  22. Targowski, T.; Jahnz-Różyk, K.; Owczarek, W.; Raczka, A.; Janda, P.; Szkoda, T.; Płusa, T. Telomerase activity and serum levels of p53 protein as prognostic factors of survival in patients with advanced non-small cell lung cancer. Respir. Med. 2010, 104, 1356–1361. [Google Scholar] [CrossRef] [Green Version]
  23. Wang, L.; Wang, L.E.; Mao, L.; Spitz, M.R.; Wei, Q. A functional variant of tandem repeats in human telomerase gene was associated with survival of patients with early stages of non-small cell lung cancer. Clin. Cancer Res. 2010, 16, 3779–3785. [Google Scholar] [CrossRef] [Green Version]
  24. Catarino, R.; Araújo, A.; Coelho, A.; Gomes, M.; Nogueira, A.; Lopes, C.; Medeiros, R.M. Prognostic Significance of Telomerase Polymorphism in Non–Small Cell Lung Cancer. Clin. Cancer Res. 2010, 16, 3706–3712. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  25. Aras, G.; Kanmaz, D.; Ürer, N.; Purisa, S.; Kadakal, F.; Yentürk, E.; Tuncay, E. Immunohistochemical expression of telomerase in patients with non-small cell lung cancer: Prediction of metastasis and prognostic significance. Anticancer Res. 2013, 33, 2643–2650. [Google Scholar] [PubMed]
  26. Yuan, P.; Huang, S.; Bao, F.C.; Cao, J.L.; Sheng, H.X.; Shi, L.; Lv, W.; Hu, J. Discriminating association of a common telomerase reverse transcriptase promoter polymorphism with telomere parameters in non-small cell lung cancer with or without epidermal growth factor receptor mutation. Eur. J. Cancer 2019, 120, 10–19. [Google Scholar] [CrossRef] [PubMed]
  27. Xing, Y.-L.; Liu, F.; Li, J.-F.; Lin, J.-C.; Zhu, G.-D.; Li, M.; Zhang, C.-R.; Niu, Y.-Y. Case-Control Study on Impact of the Telomerase Reverse Transcriptase Gene Polymorphism and Additional Single Nucleotide Polymorphism (SNP)- SNP Interaction on Non-Small Cell Lung Cancers Risk in Chinese Han Population. J. Clin. Lab. Anal. 2016, 30, 1071–1077. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  28. Fernández-Marcelo, T.; Gómez, A.; Pascua, I.; de Juan, C.; Head, J.; Hernando, F.; Jarabo, J.R.; Calatayud, J.; Torres-García, A.J.; Iniesta, P. Telomere length and telomerase activity in non-small cell lung cancer prognosis: Clinical usefulness of a specific telomere status. J. Exp. Clin. Cancer Res. 2015, 34, 78. [Google Scholar] [CrossRef] [Green Version]
  29. Flacco, A.; Ludovini, V.; Bianconi, F.; Ragusa, M.; Bellezza, G.; Tofanetti, F.R.; Pistola, L.; Siggillino, A.; Vannucci, J.; Cagini, L.; et al. MYC and Human Telomerase Gene (TERC) Copy Number Gain in Early-stage Non–small Cell Lung Cancer. Am. J. Clin. Oncol. 2015, 38, 152–158. [Google Scholar] [CrossRef] [Green Version]
  30. Wu, H.; Qiao, N.; Wang, Y.; Jiang, M.; Wang, S.; Wang, C.; Hu, L. Association between the telomerase reverse transcriptase (TERT) rs2736098 polymorphism and cancer risk: Evidence from a case-control study of non-small-cell lung cancer and a meta-analysis. PLoS ONE 2013, 8, e76372. [Google Scholar] [CrossRef]
  31. Antoniou, K.M.; Samara, K.D.; Lasithiotaki, I.; Margaritopoulos, G.A.; Soufla, G.; Lambiri, I.; Giannarakis, I.; Drositis, I.; Spandidos, D.A.; Siafakas, N.M. Differential telomerase expression in idiopathic pulmonary fibrosis and non-small cell lung cancer. Oncol. Rep. 2013, 30, 2617–2624. [Google Scholar] [CrossRef] [Green Version]
  32. Beisner, J.; Dong, M.; Taetz, S.; Piotrowska, K.; Kleideiter, E.; Friedel, G.; Schaefer, U.; Lehr, C.M.; Klotz, U.; Mürdter TEBeisner, J.; et al. Efficient telomerase inhibition in human non-small cell lung cancer cells by liposomal delivery of, 2′-O-methyl-RNA. J Pharm. Sci. 2009, 98, 1765–1774. [Google Scholar] [CrossRef]
  33. Liu, D.; Kojima, T.; Ouchi, M.; Kuroda, S.; Watanabe, Y.; Hashimoto, Y.; Onimatsu, H.; Urata, Y.; Fujiwara, T. Preclinical evaluation of synergistic effect of telomerase-specific oncolytic virotherapy and gemcitabine for human lung cancer. Mol. Cancer Ther. 2009, 8, 980–987. [Google Scholar] [CrossRef] [Green Version]
  34. Park, S.E.; Yoo, H.S.; Jin, C.-Y.; Hong, S.H.; Lee, Y.-W.; Kim, B.W.; Lee, S.H.; Kim, W.-J.; Cho, C.K.; Choi, Y.H. Induction of apoptosis and inhibition of telomerase activity in human lung carcinoma cells by the water extract of Cordyceps militaris. Food Chem. Toxicol. 2009, 47, 1667–1675. [Google Scholar] [CrossRef] [PubMed]
  35. Beisner, J.; Dong, M.; Taetz, S.; Nafee, N.; Griese, E.U.; Schaefer, U.; Lehr, C.M.; Klotz, U.; Mürdter, T.E. Nanoparticle mediated delivery of 2’-O-methyl-RNA leads to efficient telomerase inhibition and telomere shortening in human lung cancer cells. Lung Cancer 2010, 68, 346–354. [Google Scholar] [CrossRef] [PubMed]
  36. Wei, C.-W.; Lin, C.-C.; Yu, Y.-L.; Lin, P.-C.; Wu, M.-T.; Chen, C.-J.; Chang, W.; Lin, S.-Z.; Chen, Y.-L.S.; Harn, H.-J. n-Butylidenephthalide induced apoptosis in the A549 human lung adenocarcinoma cell line by coupled down-regulation of AP-2α and telomerase activity. Acta Pharmacol. Sin. 2009, 30, 1297–1306. [Google Scholar] [CrossRef]
  37. Zhang, J.F.; Wei, F.; Wang, H.P.; Li, H.M.; Qiu, W.; Ren, P.K.; Chen, X.F.; Huang, Q. Potent anti-tumor activity of telomerase-dependent and HSV-TK armed oncolytic adenovirus for non-small cell lung cancer in vitro and in vivo. J. Exp. Clin. Cancer Res. 2010, 29, 52. [Google Scholar] [CrossRef] [Green Version]
  38. Rao, Y.K.; Kao, T.-Y.; Wu, M.-F.; Ko, J.-L.; Tzeng, Y.-M. Identification of small molecule inhibitors of telomerase activity through transcriptional regulation of hTERT and calcium induction pathway in human lung adenocarcinoma A549 cells. Bioorganic Med. Chem. 2010, 18, 6987–6994. [Google Scholar] [CrossRef] [PubMed]
  39. Tuntiwechapikul, W.; Taka, T.; Songsomboon, C.; Kaewtunjai, N.; Imsumran, A.; Makonkawkeyoon, L.; Pompimon, W.; Lee, T.R.; Yaswen, P.; MacKenzie, K.L.; et al. Ginger Extract Inhibits Human Telomerase Reverse Transcriptase and c-Myc Expression in A549 Lung Cancer Cells. J. Med. Food 2010, 13, 1347–1354. [Google Scholar] [CrossRef]
  40. Dong, M.; Philippi, C.; Loretz, B.; Nafee, N.; Schaefer, U.F.; Friedel, G.; Ammon-Treiber, S.; Griese, E.U.; Lehr, C.M.; Klotz, U.; et al. Tissue slice model of human lung cancer to investigate telomerase inhibition by nanoparticle delivery of antisense 2′-O-methyl-RNA. Int. J. Pharm. 2011, 419, 33–42. [Google Scholar] [CrossRef]
  41. Serrano, D.; Bleau, A.-M.; Fernandez-Garcia, I.; Fernandez-Marcelo, T.; Iniesta, P.; Ortiz-De-Solorzano, C.; Calvo, A. Inhibition of telomerase activity preferentially targets aldehyde dehydrogenase-positive cancer stem-like cells in lung cancer. Mol. Cancer 2011, 10, 96. [Google Scholar] [CrossRef] [Green Version]
  42. Nafee, N.; Schneider, M.; Friebel, K.; Dong, M.; Schaefer, U.F.; Mürdter, T.E.; Lehr CMNafee, N. Treatment of lung cancer via telomerase inhibition: Self-assembled nanoplexes versus polymeric nanoparticles as vectors for 2′-O-methyl-RNA. Eur. J. Pharm. Biopharm. 2012, 80, 478–489. [Google Scholar] [CrossRef]
  43. Zhang, P.; Tan, J.; Yang, D.-B.; Luo, Z.-C.; Luo, S.; Chen, P.; Sun, P.; Zhou, Y.; Chen, X.-C.; Wei, Y.-Q.; et al. Gene therapy using the human telomerase catalytic subunit gene promoter enables targeting of the therapeutic effects of vesicular stomatitis virus matrix protein against human lung adenocarcinoma. Exp. Ther. Med. 2012, 4, 859–864. [Google Scholar] [CrossRef] [Green Version]
  44. Chiappori, A.A.; Kolevska, T.; Spigel, D.R.; Hager, S.; Rarick, M.; Gadgeel, S.; Blais, N.; Von Pawel, J.; Hart, L.; Reck, M.; et al. A randomized phase II study of the telomerase inhibitor imetelstat as maintenance therapy for advanced non-small-cell lung cancer. Ann. Oncol. 2015, 26, 354–362. [Google Scholar] [CrossRef] [PubMed]
  45. Chen, T.W.; Tsai, K.D.; Yang, S.M.; Wong, H.Y.; Liu, Y.H.; Cherng, J.; Chou, K.S.; Wang, Y.T.; Cuizon, J.; Cherng, J.M. Discovery of a Novel Anti-Cancer Agent Targeting Both Topoisomerase I & II as Well as Telomerase Activities in Human Lung Adenocarcinoma A549 Cells In Vitro and In Vivo: Cinnamomum verum Component Cuminaldehyde. Curr. Cancer Drug Targets 2016, 16, 796–806. [Google Scholar] [PubMed]
  46. Frink, R.E.; Peyton, M.; Schiller, J.H.; Gazdar, A.F.; Shay, J.W.; Minna, J.D. Telomerase inhibitor imetelstat has preclinical activity across the spectrum of non-small cell lung cancer oncogenotypes in a telomere length dependent manner. Oncotarget 2016, 7, 31639–31651. [Google Scholar] [CrossRef] [Green Version]
  47. Hu, W.; Jia, Y.; Xiao, X.; Lv, K.; Chen, Y.; Wang, L.; Luo, X.; Liu, T.; Li, W.; Li, Y.; et al. KLF4 downregulates hTERT expression and telomerase activity to inhibit lung carcinoma growth. Oncotarget 2016, 7, 52870–52887. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  48. Ding, X.; Cheng, J.; Pang, Q.; Wei, X.; Zhang, X.; Wang, P.; Yuan, Z.; Qian, D. BIBR1532, a Selective Telomerase Inhibitor, Enhances Radiosensitivity of Non-Small Cell Lung Cancer Through Increasing Telomere Dysfunction and ATM/CHK1 Inhibition. Int. J. Radiat. Oncol. Biol. Phys. 2019, 105, 861–874. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  49. Mender, I.; LaRanger, R.; Luitel, K.; Peyton, M.; Girard, L.; Lai, T.-P.; Batten, K.; Cornelius, C.; Dalvi, M.P.; Ramirez, M.; et al. Telomerase-Mediated Strategy for Overcoming Non–Small Cell Lung Cancer Targeted Therapy and Chemotherapy Resistance. Neoplasia 2018, 20, 826–837. [Google Scholar] [CrossRef]
  50. Chen, R.-J.; Wu, P.-H.; Ho, C.-T.; Way, T.-D.; Pan, M.-H.; Chen, H.-M.; Ho, Y.-S.; Wang, Y.-J. P53-dependent downregulation of hTERT protein expression and telomerase activity induces senescence in lung cancer cells as a result of pterostilbene treatment. Cell Death Dis. 2017, 8, e2985. [Google Scholar] [CrossRef] [Green Version]
  51. Brunsvig, P.F.; Guren, T.K.; Nyakas, M.; Steinfeldt-Reisse, C.H.; Rasch, W.; Kyte, J.A.; Juul, H.V.; Aamdal, S.; Gaudernack, G.; Inderberg, E.M. Long-Term Outcomes of a Phase I Study with UV1, a Second Generation Telomerase Based Vaccine, in Patients With Advanced Non-Small Cell Lung Cancer. Front. Immunol. 2020, 115, 72172. [Google Scholar] [CrossRef]
  52. Cong, Y.-S.; Wright, W.E.; Shay, J.W. Human Telomerase and Its Regulation. Microbiol. Mol. Biol. Rev. 2002, 66, 407–425. [Google Scholar] [CrossRef] [Green Version]
  53. Zhang, Y.; Liu, Y.; Liu, H.; Tang, W.H. Exosomes: Biogenesis, biologic function and clinical potential. Cell Biosci. 2019, 9, 19. [Google Scholar] [CrossRef]
  54. Goldvaser, H.; Gutkin, A.; Beery, E.; Edel, Y.; Nordenberg, J.; Wolach, O.; Rabizadeh, E.; Uziel, O.; Lahav, M. Characterisation of blood-derived exosomal hTERT mRNA secretion in cancer patients: A potential pan-cancer marker. Br. J. Cancer 2017, 117, 353–357. [Google Scholar] [CrossRef] [Green Version]
  55. Gutkin, A.; Uziel, O.; Beery, E.; Nordenberg, J.; Pinchasi, M.; Goldvaser, H.; Henick, S.; Goldberg, M.; Lahav, M. Tumor cells derived exosomes contain hTERT mRNA and transform nonmalignant fibroblasts into telomerase positive cells. Oncotarget 2016, 7, 59173–59188. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  56. Filipska, M.; Rosell, R. Mutated circulating tumor DNA as a liquid biopsy in lung cancer detection and treatment. Mol. Oncol. 2021, 15, 1667–1682. [Google Scholar] [CrossRef] [PubMed]
  57. Li, W.; Liu, J.-B.; Hou, L.-K.; Yu, F.; Zhang, J.; Wu, W.; Tang, X.-M.; Sun, F.; Lu, H.-M.; Deng, J.; et al. Liquid biopsy in lung cancer: Significance in diagnostics, prediction, and treatment monitoring. Mol. Cancer 2022, 21, 25. [Google Scholar] [CrossRef] [PubMed]
  58. Li, Z.-B.; Chen, D.-D.; He, Q.-J.; Li, L.; Zhou, G.; Fu, Y.-M.; Deng, Y.; Niu, X.-X.; Chu, F.; Gao, X.-P.; et al. The LAC Score Indicates Significant Fibrosis in Patients with Chronic Drug-Induced Liver Injury: A Large Biopsy-Based Study. Front. Pharmacol. 2021, 12, 34090. [Google Scholar] [CrossRef]
  59. Szpechcinski, A.; Chorostowska-Wynimko, J.; Struniawski, R.; Kupis, W.; Rudzinski, P.; Langfort, R.; Puscinska, E.; Bielen, P.; Sliwinski, P.; Orlowski, T. Cell-free DNA levels in plasma of patients with non-small-cell lung cancer and inflammatory lung disease. Br. J. Cancer 2015, 113, 476–483. [Google Scholar] [CrossRef] [Green Version]
  60. Rothwell, D.G.; Ayub, M.; Cook, N.; Thistlethwaite, F.; Carter, L.; Dean, E.; Smith, N.; Villa, S.; Dransfield, J.; Clipson, A.; et al. Utility of ctDNA to support patient selection for early phase clinical trials: The TARGET study. Nat. Med. 2019, 25, 738–743. [Google Scholar] [CrossRef] [Green Version]
  61. Wu, Y.-L.; Sequist, L.V.; Hu, C.-P.; Feng, J.; Lu, S.; Huang, Y.; Li, W.; Hou, M.; Schuler, M.; Mok, T.; et al. EGFR mutation detection in circulating cell-free DNA of lung adenocarcinoma patients: Analysis of LUX-Lung 3 and 6. Br. J. Cancer 2016, 116, 175–185. [Google Scholar] [CrossRef] [Green Version]
  62. Nardo, G.; Carlet, J.; Marra, L.; Bonanno, L.; Boscolo, A.; Maso, A.D.; Bragadin, A.B.; Indraccolo, S.; Zulato, E. Detection of Low-Frequency KRAS Mutations in cfDNA from EGFR-Mutated NSCLC Patients After First-Line EGFR Tyrosine Kinase Inhibitors. Front. Oncol. 2021, 10, 607840. [Google Scholar] [CrossRef]
  63. Boldrin, E.; Nardo, G.; Zulato, E.; Bonanno, L.; Polo, V.; Frega, S.; Pavan, A.; Indraccolo, S.; Saggioro, D. Detection of Loss of Heterozygosity in cfDNA of Advanced EGFR- or KRAS-Mutated Non-Small-Cell Lung Cancer Patients. Int. J. Mol. Sci. 2019, 21, 66. [Google Scholar] [CrossRef] [Green Version]
  64. Almodovar, K.; Iams, W.T.; Meador, C.B.; Zhao, Z.; York, S.; Horn, L.; Yan, Y.; Hernandez, J.; Chen, H.; Shyr, Y.; et al. Longitudinal Cell-Free DNA Analysis in Patients with Small Cell Lung Cancer Reveals Dynamic Insights into Treatment Efficacy and Disease Relapse. J. Thorac. Oncol. 2017, 13, 112–123. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  65. Mayo-de-Las-Casas, C.; Jordana-Ariza, N.; Garzón-Ibañez, M.; Balada-Bel, A.; Bertrán-Alamillo, J.; Viteri-Ramírez, S.; Reguart, N.; Muñoz-Quintana, M.A.; Lianes-Barragan, P.; Camps, C.; et al. Large scale, prospective screening of EGFR mutations in the blood of advanced NSCLC patients to guide treatment decisions. Ann. Oncol. 2017, 28, 2248–2255. [Google Scholar] [CrossRef] [PubMed]
  66. Blakely, C.M.; Watkins, T.B.K.; Wu, W.; Gini, B.; Chabon, J.J.; E McCoach, C.; McGranahan, N.; A Wilson, G.; Birkbak, N.J.; Olivas, V.R.; et al. Evolution and clinical impact of co-occurring genetic alterations in advanced-stage EGFR-mutant lung cancers. Nat. Genet. 2017, 49, 1693–1704. [Google Scholar] [CrossRef]
  67. Chabon, J.J.; Hamilton, E.G.; Kurtz, D.M.; Esfahani, M.S.; Moding, E.J.; Stehr, H.; Schroers-Martin, J.; Nabet, B.Y.; Chen, B.; Chaudhuri, A.A.; et al. Integrating genomic features for non-invasive early lung cancer detection. Nature 2020, 580, 245–251. [Google Scholar] [CrossRef] [PubMed]
  68. Schoenfeld, A.J.; Chan, J.M.; Kubota, D.; Sato, H.; Rizvi, H.; Daneshbod, Y.; Chang, J.C.; Paik, P.K.; Offin, M.; Arcila, M.E.; et al. Tumor Analyses Reveal Squamous Transformation and Off-Target Alterations as Early Resistance Mechanisms to First-line Osimertinib in EGFR-Mutant Lung Cancer. Clin. Cancer Res. 2020, 26, 2654–2663. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  69. Beagan, J.J.; Bach, S.; van Boerdonk, R.A.; van Dijk, E.; Thunnissen, E.; van den Broek, D.; Weiss, J.; Kazemier, G.; Pegtel, D.M.; Bahce, I.; et al. Circulating tumor DNA analysis of EGFR-mutant non-small cell lung cancer patients receiving simertinib following previous tyrosine kinase inhibitor treatment. Lung Cancer 2020, 145, 173–180. [Google Scholar] [CrossRef]
  70. Cheng, Y.; Ma, L.; Liu, Y.; Zhu, J.; Xin, Y.; Liu, X.; Wang, Y.; Zhang, T.; Yang, C.; Wang, S.; et al. Comprehensive characterization and clinical impact of concomitant genomic alterations in EGFR-mutant NSCLCs treated with EGFR kinase inhibitors. Lung Cancer 2020, 145, 63–70. [Google Scholar] [CrossRef]
  71. Liu, H.E.; Vuppalapaty, M.; Wilkerson, C.; Renier, C.; Chiu, M.; Lemaire, C.; Che, J.; Matsumoto, M.; Carroll, J.; Crouse, S.; et al. Detection of EGFR Mutations in cfDNA and CTCs, and Comparison to Tumor Tissue in Non-Small-Cell-Lung-Cancer (NSCLC) Patients. Front. Oncol. 2020, 10, 572895. [Google Scholar] [CrossRef]
  72. Zhou, Q.; Geng, Q.; Wang, L.; Huang, J.; Liao, M.; Li, Y.; Ding, Z.; Yang, S.; Zhao, H.; Shen, Q.; et al. Value of folate receptor-positive circulating tumour cells in the clinical management of indeterminate lung nodules: A non-invasive biomarker for predicting malignancy and tumour invasiveness. Ebiomedicine 2019, 41, 236–243. [Google Scholar] [CrossRef] [Green Version]
  73. Lim, M.; Park, J.; Lowe, A.C.; Jeong, H.O.; Lee, S.; Park, H.C.; Lee, K.; Kim, G.H.; Kim, M.-H.; Cho, Y.-K. A lab-on-a-disc platform enables serial monitoring of individual CTCs associated with tumor progression during EGFR-targeted therapy for patients with NSCLC. Theranostics 2020, 10, 5181–5194. [Google Scholar] [CrossRef]
  74. Zhang, Z.; Eramnath, N.; Enagrath, S. Current Status of CTCs as Liquid Biopsy in Lung Cancer and Future Directions. Front. Oncol. 2015, 5, 209. [Google Scholar] [CrossRef]
  75. Hofman, P. ALK Status Assessment with Liquid Biopsies of Lung Cancer Patients. Cancers 2017, 9, 106. [Google Scholar] [CrossRef] [Green Version]
  76. Cazzoli, R.; Buttitta, F.; Di Nicola, M.; Malatesta, S.; Marchetti, A.; Rom, W.N.; Pass, H.I. microRNAs Derived from Circulating Exosomes as Noninvasive Biomarkers for Screening and Diagnosing Lung Cancer. J. Thorac. Oncol. 2013, 8, 1156–1162. [Google Scholar] [CrossRef] [Green Version]
  77. Cristiano, S.; Leal, A.; Phallen, J.; Fiksel, J.; Adleff, V.; Bruhm, D.C.; Jensen, S.Ø.; Medina, J.E.; Hruban, C.; White, J.R.; et al. Genome-wide cell-free DNA fragmentation in patients with cancer. Nature 2019, 570, 385–389. [Google Scholar] [CrossRef]
  78. Jin, X.; Chen, Y.; Chen, H.; Fei, S.; Chen, D.; Cai, X.; Liu, L.; Lin, B.; Su, H.; Zhao, L.; et al. Evaluation of tumor-derived Exosomal miRNA as potential diagnostic biomarkers for early-stage non-small cell lung Cancer using next-generation sequencing. Clin. Cancer Res. 2017, 23, 5311–5319. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  79. Qin, X.; Yu, S.; Zhou, L.; Shi, M.; Hu, Y.; Xu, X.; Shen, B.; Liu, S.; Yan, D.; Feng, J. Cisplatin-resistant lung cancer cell–derived exosomes increase cisplatin resistance of recipient cells in exosomal miR-100–5p-dependent manner. Int. J. Nanomed. 2017, 12, 3721–3733. [Google Scholar] [CrossRef] [Green Version]
  80. Liu, A.; Xie, H.; Li, R.; Ren, L.; Yang, B.; Dai, L.; Lu, W.; Liu, B.; Ren, D.; Zhang, X.; et al. Silencing ZIC2 abrogates tumorigenesis and anoikis resistance of non-small cell lung cancer cells by inhibiting Src/FAK signaling. Mol. Ther. Oncolytics 2021, 22, 195–208. [Google Scholar] [CrossRef] [PubMed]
  81. Yang, D.; Feng, W.; Zhuang, Y.; Liu, J.; Feng, Z.; Xu, T.; Wang, W.; Zhu, Y.; Wang, Z. Long non-coding RNA linc00665 inhibits CDKN1C expression by binding to EZH2 and affects cisplatin sensitivity of NSCLC cells. Mol. Ther. Nucleic Acids 2021, 23, 1053–1065. [Google Scholar] [CrossRef]
  82. Huber, M.; Kepesidis, K.V.; Voronina, L.; Fleischmann, F.; Fill, E.; Hermann, J.; Koch, I.; Milger-Kneidinger, K.; Kolben, T.; Schulz, G.B.; et al. Author response: Infrared molecular fingerprinting of blood-based liquid biopsies for the detection of cancer. eLife 2021, 10, e68758. [Google Scholar] [CrossRef] [PubMed]
  83. Wu, S.H.; Hsieh, C.C.; Hsu, S.C.; Yao, M.; Hsiao, J.K.; Wang, S.W.; Lin, C.P.; Huang, D.M. RBC-derived vesicles as a systemic delivery system of doxorubicin for lysosomal- mitochondrial axis-improved cancer therapy. J. Adv. Res. 2020, 301, 85–96. [Google Scholar] [CrossRef] [PubMed]
  84. Laish, I.; Levi, Z.; Mahajna, H.; Albshesh, A.; Horesh, N.; Katz, E.; Feldman, D.; Shinar, N.; Picard, O.; Yavzori, M.; et al. Characterization of blood-derived exosomal hTERT mRNA as a biomarker for colon cancer and Lynch syndrome. Front. Oncol. 2022, 12, 62473. [Google Scholar] [CrossRef]
  85. Xia, Z.; Qing, B.; Wang, W.; Gu, L.; Chen, H.; Yuan, Y. Formation, contents, functions of exosomes and their potential in lung cancer diagnostics and therapeutics. Thorac. Cancer 2021, 12, 3088–3100. [Google Scholar] [CrossRef]
  86. Li, M.-Y.; Liu, L.-Z.; Dong, M. Progress on pivotal role and application of exosome in lung cancer carcinogenesis, diagnosis, therapy and prognosis. Mol. Cancer 2021, 20, 22. [Google Scholar] [CrossRef] [PubMed]
  87. Jiang, C.; Zhang, N.; Hu, X.; Wang, H. Tumor-associated exosomes promote lung cancer metastasis through multiple mechanisms. Mol. Cancer 2021, 20, 117. [Google Scholar] [CrossRef]
  88. Zhang, W.; Qin, T.; Yang, Z.; Yin, L.; Zhao, C.; Feng, L.; Lin, S.; Liu, B.; Cheng, S.; Zhang, K. Telomerase-positive circulating tumor cells are associated with poor prognosis via a neutrophil-mediated inflammatory immune environment in glioma. BMC Med. 2021, 19, 277. [Google Scholar] [CrossRef] [PubMed]
  89. Chen, R.; Xu, X.; Qian, Z.; Zhang, C.; Niu, Y.; Wang, Z.; Sun, J.; Zhang, X.; Yu, Y. The biological functions and clinical applications of exosomes in lung cancer. Cell Mol. Life Sci. 2019, 76, 4613–4633. [Google Scholar] [CrossRef]
  90. Jeon, H.-S.; Choi, J.E.; Jung, D.K.; Choi, Y.Y.; Kang, H.G.; Lee, W.-K.; Yoo, S.S.; Lim, J.-O.; Park, J.Y. Telomerase Activity and the Risk of Lung Cancer. J. Korean Med. Sci. 2012, 27, 141–145. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  91. Zhang, W.; Duan, X.; Zhang, Z.; Yang, Z.; Zhao, C.; Liang, C.; Liu, Z.; Cheng, S.; Zhang, K. Combination of CT and telomerase-positive circulating tumor cells improves diagnosis of small pulmonary nodules. J. Clin. Investig. 2021, 6, e148182. [Google Scholar] [CrossRef] [PubMed]
  92. Ha, D.; Yang, N.; Nadithe, V. Exosomes as therapeutic drug carriers and delivery vehicles across biological membranes: Current perspectives and future challenges. Acta Pharm. Sin. B 2016, 6, 287–296. [Google Scholar] [CrossRef] [Green Version]
  93. Karpov, O.; Lahav, M.; Wolach, O.; Raanani, P.; Peer, D.; Kaplan, T.; Uziel, O. Identification of Cancer Cells in the Human Body by Anti-Telomerase Peptide Antibody: Towards the Isolation of Circulating Tumor Cells. Int. J. Mol. Sci. 2022, 23, 12872. [Google Scholar] [CrossRef]
  94. Han, H.; He, C.; Chen, X.; Luo, Y.; Yang, M.; Wen, Z.; Hu, J.; Lin, F.; Han, M.; Yin, T.; et al. Shikonin N-benzyl matrinic acid ester derivatives as novel telomerase inhibitors with potent activity against lung cancer cell lines. Bioorganic Med. Chem. Lett. 2021, 57, 128503. [Google Scholar] [CrossRef] [PubMed]
  95. Prasad, R.R.; Mishra, D.K.; Kumar, M.; Yadava, P.K. Human telomerase reverse transcriptase promotes the epithelial to mesenchymal transition in lung cancer cells by enhancing c-MET upregulation. Heliyon 2021, 8, e08673. [Google Scholar] [CrossRef] [PubMed]
Biomedicines 11 01730 g001a 550Biomedicines 11 01730 g001b 550Biomedicines 11 01730 g001c 550
Figure 1. Levels of the exosomal hTERT transcript in patients with lung cancer. (A) Average values. (B) % of positive hTERT transcripts in patients with lung cancer. (C) Average values of exosomal hTERT in different lung malignancies; (D) % of positive hTERT transcripts in patients with different types of lung cancer. * denotes p < 0.05.
Figure 1. Levels of the exosomal hTERT transcript in patients with lung cancer. (A) Average values. (B) % of positive hTERT transcripts in patients with lung cancer. (C) Average values of exosomal hTERT in different lung malignancies; (D) % of positive hTERT transcripts in patients with different types of lung cancer. * denotes p < 0.05.
Biomedicines 11 01730 g001aBiomedicines 11 01730 g001bBiomedicines 11 01730 g001c
Biomedicines 11 01730 g002 550
Figure 2. Levels of the exosomal hTERT transcript in longitudinal follow-up during 2–4 measurements. (A) Values of individual patients. (B) Average values. * Denotes p < 0.05.
Figure 2. Levels of the exosomal hTERT transcript in longitudinal follow-up during 2–4 measurements. (A) Values of individual patients. (B) Average values. * Denotes p < 0.05.
Biomedicines 11 01730 g002
Biomedicines 11 01730 g003 550
Figure 3. Imaging analysis of two patients during follow-up and the corresponding values of exosomal hTERT transcript. (A) CT imaging over 4 months (patient #1); (B) PET CT imaging over 5 months (patient #2).
Figure 3. Imaging analysis of two patients during follow-up and the corresponding values of exosomal hTERT transcript. (A) CT imaging over 4 months (patient #1); (B) PET CT imaging over 5 months (patient #2).
Biomedicines 11 01730 g003
Table
Table 1. Patients’ characteristics and the levels of exosomal hTERT mRNA.
Table 1. Patients’ characteristics and the levels of exosomal hTERT mRNA.
AgeGenderRQ
nMedianRangeFMMean RQSEMMedianRangeTypeStage
positives4066.546–8528126.451.443.671.23–44.61NSCLC-30, SCLC-10IIIa-2, IIIb-3, IV-35
negatives366738–8822140.360.050.330–1.18NSCLC-30, SCLC-3, others-3IIIb-10, IV-26
Positives: exosomal hTERT levels ≥ 1.2; negatives: exosomal hTERT < 1.2, SEM—standard error mean, RQ—relative quantitation of hTERT levels.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Rotem, O.; Zer, A.; Yosef, L.; Beery, E.; Goldvaser, H.; Gutkin, A.; Levin, R.; Dudnik, E.; Berger, T.; Feinmesser, M.; et al. Blood-Derived Exosomal hTERT mRNA in Patients with Lung Cancer: Characterization and Correlation with Response to Therapy. Biomedicines 2023, 11, 1730. https://doi.org/10.3390/biomedicines11061730

AMA Style

Rotem O, Zer A, Yosef L, Beery E, Goldvaser H, Gutkin A, Levin R, Dudnik E, Berger T, Feinmesser M, et al. Blood-Derived Exosomal hTERT mRNA in Patients with Lung Cancer: Characterization and Correlation with Response to Therapy. Biomedicines. 2023; 11(6):1730. https://doi.org/10.3390/biomedicines11061730

Chicago/Turabian Style

Rotem, Ofer, Alona Zer, Lilach Yosef, Einat Beery, Hadar Goldvaser, Anna Gutkin, Ron Levin, Elizabeth Dudnik, Tamar Berger, Meora Feinmesser, and et al. 2023. "Blood-Derived Exosomal hTERT mRNA in Patients with Lung Cancer: Characterization and Correlation with Response to Therapy" Biomedicines 11, no. 6: 1730. https://doi.org/10.3390/biomedicines11061730

APA Style

Rotem, O., Zer, A., Yosef, L., Beery, E., Goldvaser, H., Gutkin, A., Levin, R., Dudnik, E., Berger, T., Feinmesser, M., Levy-Barda, A., Lahav, M., Raanani, P., & Uziel, O. (2023). Blood-Derived Exosomal hTERT mRNA in Patients with Lung Cancer: Characterization and Correlation with Response to Therapy. Biomedicines, 11(6), 1730. https://doi.org/10.3390/biomedicines11061730

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop