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Article

Monitoring Metastatic Colorectal Cancer Progression According to Reactive Oxygen Metabolite Derivative Levels

by
Katsuji Sawai
*,
Takanori Goi
,
Youhei Kimura
and
Kenji Koneri
First Department of Surgery, University of Fukui, Fukui 910-1193, Japan
*
Author to whom correspondence should be addressed.
Cancers 2023, 15(23), 5517; https://doi.org/10.3390/cancers15235517
Submission received: 25 October 2023 / Revised: 19 November 2023 / Accepted: 20 November 2023 / Published: 22 November 2023
(This article belongs to the Special Issue Predictive Biomarkers for Colorectal Cancer)
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Abstract

:

Simple Summary

Oxidative stress has been implicated in the development, proliferation, and metastasis of colorectal cancer. In this study, we investigated whether the rate of change in reactive oxygen metabolite derivatives (d-ROM)—serum markers of oxidative stress—could predict treatment response in metastatic colorectal cancer. We enrolled 53 patients with metastatic colorectal cancer who were treated with 3 months of chemotherapy. We measured d-ROM levels before and after chemotherapy and examined the change in d-ROM levels for each anticancer treatment. Factors influencing the d-ROM ratio (post-treatment: pre-treatment levels) were examined using linear regression analysis. d-ROM levels decreased in patients showing a partial response and increased in those showing disease progression. An increasing d-ROM ratio was associated with disease progression. Our study indicates that d-ROM levels are useful markers of tumor progression and that the d-ROM ratio is useful for predicting treatment response in patients with metastatic colorectal cancer.

Abstract

Oxidative stress has been implicated in the development, proliferation, and metastasis of colorectal cancer, but few studies have considered how oxidative stress changes in relation to treatment response. In this study, we investigated whether the rate of change in reactive oxygen metabolite derivatives (d-ROM)—serum markers of oxidative stress—could predict treatment response in metastatic colorectal cancer. We enrolled 53 patients with metastatic colorectal cancer who were treated with 3 months of chemotherapy. We measured d-ROM levels and performed computed tomography before and after chemotherapy, and we examined the change in d-ROM levels for each anticancer treatment. Factors influencing the d-ROM ratio (post-treatment: pre-treatment levels) were examined using linear regression analysis. d-ROM levels decreased in patients showing a partial response (p < 0.001) and increased in those showing disease progression (p = 0.042). An increasing d-ROM ratio was associated with disease progression (regression coefficient: 0.416, 95% confidence interval: 0.279–0.555, p < 0.001). Our study indicates that d-ROM levels are useful markers of tumor progression and that the d-ROM ratio is useful for predicting treatment response in patients with metastatic colorectal cancer.

1. Introduction

In Japan, the number of colorectal cancer cases increases annually, and the number of deaths from colorectal cancer currently exceeds 50,000, making it the second leading cause of cancer-related deaths [1]. Moreover, approximately 30% of patients with colorectal cancer die because of metastasis [2].
The prognosis of metastatic colorectal cancer has markedly improved with the recent introduction of new anticancer therapies, such as molecular-targeted agents [3]. However, effective long-term disease management requires the continued evaluation of treatment efficacy. Treatment efficacy could be determined using computed tomography (CT) imaging with the Response Evaluation in Solid Tumors (RECIST) criteria (recommended by the World Health Organization [WHO]) or by assessing changes in carcinoembryonic antigen (CEA) levels (recommended by the National Comprehensive Cancer Network [NCCN] guidelines) [4,5,6,7,8,9]. However, imaging has its limitations: it is not particularly cost-effective and is ineffective in the absence of measurable or disseminated lesions. CEA is also only an effective marker of treatment efficacy when its levels are abnormal before treatment [10,11].
Oxidative stress plays a critical role in various biological processes, particularly in cell signaling. Under normal conditions, it is balanced by cellular antioxidant defenses. However, an imbalance leading to oxidative stress overload can cause significant harm, including extensive DNA damage, and the oxidation of key lipids and proteins. These changes disrupt intracellular signaling, contributing to abnormal cell functioning. Such disruption is a major factor in promoting carcinogenesis, accelerating cell mutation, and impairing cell function, thereby facilitating the development and progression of various cancers [12,13]. Increased oxidative stress is often triggered by the activation of nicotinamide adenine dinucleotide phosphate (NADPH) oxidase in neutrophils’ plasma membranes and the production of lipid peroxidation products in cancer tissues [14,15,16]. This stress influences the activities of matrix metalloproteinases, adhesion molecules, and the epidermal growth factor and its receptor, which are instrumental in cancer progression and metastasis [17,18,19]. Specifically, oxidative stress has been linked to the development of colorectal cancer. The transformation of colorectal adenomas into colorectal cancer involves the activation of oncogenes, inactivation of tumor suppressor genes, and DNA methylation. These DNA mutations, primarily induced by oxidative stress, underscore its significant impact in the pathogenesis of cancer [20]. Quantitative methods for measuring stable oxidative stress in blood, such as via reactive oxygen metabolite derivative (d-ROM) measurement, have recently been developed [21]. Blood d-ROM levels tend to increase with tumor size and progression, and patients with high blood d-ROM levels have been associated with poor disease prognosis [16,18,22]. However, the effect of anticancer treatment on d-ROM levels in colorectal cancer is unclear.
In this study, we tested the hypothesis that d-ROM levels decrease in colorectal cancer patients with unresectable lesions when treatment is effective and increase when treatment is ineffective. Consequently, we verified the use of oxidative stress as a marker of treatment efficacy in patients with unresectable colorectal cancer.

2. Materials and Methods

2.1. Study Population

We analyzed data for 53 patients with unresectable metastatic colorectal cancer and unresectable recurrent colorectal cancer who had undergone chemotherapy or management therapy at the department of the University of Fukui between January 2020 and December 2021. Patients with synchronous or heterochronic cancer, coexisting infectious disease, steroid therapy, and/or lack of follow-up data were excluded. Written informed consent was obtained from participants prior to the study.

2.2. Treatment and Response Evaluation by CT

Chemotherapy was administered according to the colorectal cancer treatment guidelines and included two-drug combination therapy, three-drug combination therapy, oral anticancer drugs, and molecular-targeted drugs [1]. Four patients were not treated with anticancer drugs. To evaluate treatment efficacy (for both patients treated with and without anticancer drugs), CT scans were performed every 3 months, and images before and 3 months after the start of treatment were evaluated according to the RECIST criteria.

2.3. Measurement of d-ROM Levels

Using blood samples collected during routine examinations, d-ROM levels were measured before and 3 months after the start of treatment.
Serum d-ROM levels were determined using the FREE radical analyzer system by Wismerll Co., Ltd., Tokyo, Japan, which includes a spectro-photometric device. This process involves using both a reader and specific measurement kits (d-ROMs test, Wismerll Co., Ltd.), all of which are calibrated for optimal performance with the FREE Carpe Diem System, as per the guidelines provided by the manufacturer. Initially, a 20 µL sample of serum is combined with 1 mL of a specially formulated buffered solution in a cuvette. Subsequently, 20 µL of a chromogenic substrate is introduced into this mixture. Upon thorough mixing, the cuvette is then inserted into the thermostatic block of the analyzer. The sample is incubated here for 5 min at a controlled temperature of 37 °C. Post-incubation, the absorbance is measured at a wavelength of 505 nm. The results from this procedure are presented in U.CARR units, with one unit equating to 0.8 mg/L of hydrogen peroxide. Typically, the normal range for these measurements is between 250 to 300 U.CARR. Any d-ROM level that equals or exceeds 300 U.CARR is indicative of serum oxidative stress, a condition attributed to the excessive production of free radicals in the body [23,24,25,26].

2.4. Assessment of d-ROM Levels

We examined the pre- and post-treatment changes in d-ROM levels in the partial response (PR), stable disease (SD), and progressive disease (PD) groups using the RECIST criteria. We examined the d-ROM ratio (i.e., the ratio of d-ROM levels before and after treatment) and determined the influencing factors using linear regression analysis. We set a cutoff value for the d-ROM ratio to predict disease progression and assessed the positive predictive value, negative predictive value, and diagnostic accuracy.

2.5. Statistical Analysis

Changes in d-ROM levels and CT image RECIST scores before and after treatment were examined using a Wilcoxon signed rank test. We performed a multiple linear regression analysis to identify factors affecting the d-ROM ratio, including age, sex, treated or not, number of metastatic organs, use of molecular-targeted drug, use of oxaliplatin-based regimens, and the RECIST criteria. We assessed the normality of residuals obtained in the final model using a normal probability plot. Receiver operating characteristic (ROC) curve analysis was performed to estimate the optimal sensitivity and specificity of the d-ROM ratio in predicting PD based on the RECIST score. All statistical analyses were performed using IBM SPSS software version 21.0 (IBM Japan, Ltd., Tokyo, Japan). Differences were considered significant at p < 0.05.

3. Results

3.1. Patient Characteristics

The patient group (n = 53, 30 males and 23 females) had a median age of 69 years (range: 38–93 years). Regarding treatments, 44 patients (83%) had a resection of the primary tumor, 47 (88.7%) had concurrent metastases, and 37 (69.8%) had no prior anticancer therapy. Regarding the metastatic organ count, 28 patients (52.8%) had one metastatic organ, and 25 (47.2%) had more than one (Table 1).

3.2. Anticancer Drug Treatment and Effects

A doublet oxaliplatin-based anticancer regimen was applied for 33 patients, doublet irinotecan-based regimen for 5 patients, triplet regimen for 4 patients, 5-fluorouracil-based oral anticancer drugs for 5 patients, and trifluridine/tipiracil hydrochloride for 2 patients. A total of 44 patients received molecular-targeted agents (Table 2). According to the RECIST criteria, 26 patients (49%) had a PR, 10 patients (18.9%) had an SD, and 17 patients (32.1%) had a PD (Table 3). The median d-ROM level before treatment was 424 U.CARR (range: 283–636), and the median d-ROM level after 3 months was 375 U.CARR (range: 168–664) (Table 4).

3.3. Relationship between Treatment Response by the RECIST Criteria and Changes in d-ROM Levels

In PR patients, d-ROM levels significantly decreased after treatment compared to those before treatment (p < 0.001). d-ROM levels in PD patients increased (p = 0.042), while those in SD patients were unchanged (p = 0.139). In non-PD patients (PR and SD patients combined), d-ROM levels significantly decreased after treatment compared to those before (p < 0.001) (Figure 1). Non-PD patients (median: 437 U.CARR) had higher pre-treatment d-ROM levels than PD patients (median: 381 U.CARR; p = 0.029). Post-treatment d-ROM levels did not differ between non-PD (median: 362 U.CARR) and PD (median: 423) patients (p = 0.051).

3.4. Accuracy of the d-ROM Ratio in Predicting Disease Progression

Linear regression analysis showed that the d-ROM ratio varied significantly with RECIST-rated PD (regression coefficient: 0.416, 95% confidence interval [CI]: 0.277–0.555, p < 0.001), oxaliplatin use (regression coefficient: 0.209, 95% CI: 0.063–0.354, p = 0.006), and female sex (regression coefficient: −0.111, 95% CI: −0.205 to 0.017, p = 0.021) (Table 5). Age, being treated or not, the number of metastatic organs, and use of molecular-targeted drugs did not affect the d-ROM ratio. The cutoff d-ROM ratio for predicting a PD was 1.006, corresponding to an area under the curve of 0.874 with a 95% CI of 0.0760–0.988 (p < 0.001) (Figure 2). Therefore, the cutoff value was set at 1.0. There were 36 patients with a d-ROM ratio ≤ 1, of which 33 were non-PD patients; 17 had a d-ROM ratio > 1, of which 14 were PD patients (Table 4). When predicting a PD with a d-ROM ratio > 1, the sensitivity was 82.4%, specificity was 91.7%, positive predictive value was 82.4%, negative predictive value was 91.7%, and diagnostic accuracy was 88.7% (Table 6).

4. Discussion

In this study, we examined the relationship between changes in d-ROM levels and chemotherapeutic treatment outcomes. Two important findings were obtained in this study. First, in patients with metastatic colorectal cancer, blood d-ROM levels decreased when treatment was effective and increased with tumor progression. Second, the d-ROM ratio was highly sensitive and specific in predicting disease progression and is therefore considered to be an effective marker for monitoring treatment outcomes in this patient population.
The treatment of non-resectable colorectal cancer is generally aimed at suppressing tumor growth with anticancer drugs, alleviating tumor-related symptoms, and, if possible, reducing tumor size to allow for resection. Therefore, new tumor markers need to be identified to address the shortcomings of traditional methods. Colon tissues are constantly exposed to intrinsic and extrinsic oxidative stress, which can promote carcinogenesis through protein denaturation, DNA damage, and lipid oxidation, promoting tumor growth, invasion, and metastasis through various metabolites [27,28,29,30]. Blood oxidative stress levels are higher in patients with colorectal cancer than in healthy individuals [15,31,32], and they show a strong association with tumor size, progression, and prognosis [16,18,22,32]. Oxidative stress levels are also higher in colon cancer tissues than in the surrounding tissues [15,32,33]. Although a close relationship between oxidative stress and colorectal cancer has been observed, only few studies have assessed changes in oxidative stress to determine the efficacy of treatment for colorectal cancer. The redox state in colorectal cancer is affected by inflammatory infiltration and the environmental characteristics of the tumor. Inflammatory reactions in cancer tissues activate NADPH oxidase in neutrophils, which amplifies oxidative stress. When the redox balance is disturbed in normal cells, antioxidant defense and repair are activated to ameliorate oxidative stress, but in cancer cells, antioxidant defense and repair are not fully activated, resulting in increased oxidative stress. Oxidative stress promotes the expression of matrix metalloproteinases, adhesion molecules, and epidermal growth factor and its receptor, which in turn promotes cancer progression and metastasis. Oxidative stress can promote oxidative damage to DNA, proteins, and lipids, resulting in the production of lipid peroxidation products, such as malondialdehyde (MDA) and oxidized low-density lipoprotein (ox-LDL). MDA severely damages DNA and inhibits its repair through direct interaction with DNA repair-related proteins [34]. ox-LDL is also a potent mitogen, promoting the expression of proteins that activate the cell cycle and releasing cytokines and growth factors, which in turn accelerate cancer progression [34,35,36,37,38]. Cancer cell proliferation requires increased levels of adenosine triphosphate (ATP), which allows cancer cells to absorb glucose at a faster rate and promotes glycolysis. However, inadequate oxygen supply leads to anaerobic metabolism, resulting in the formation of advanced glycation end products (AGEs) [39,40]. Increased production of AGEs can lead to tumor initiation, growth, and invasion through increased cell proliferation and cell migration [41,42]. Furthermore, it is believed that oxidative stress and cancer cell proliferation interact to exacerbate oxidative stress within the tumor.
Salehi et al. measured changes in the levels of the oxidative stress markers MDA, ox-LOL, and AGEs before and after tumor resection in 60 patients with stage I and II colorectal cancer and confirmed a decrease in the levels after resection. They also reported an increase in the levels of antioxidant factors, which was likely related to the resection of tumor tissues with high oxidative stress levels [15]. Acevedo-León et al. measured oxidative stress levels based on the disulfide-oxidized forms of the tripeptide L-γ-glutamyl-cysteinyl-glycine (GSSG) in 79 patients with stage I to IV colorectal cancer and reported a decrease after resection compared to those before [32]. Hristozov et al. measured MDA levels in red blood cells and found a marked decrease after surgery compared to those before [43]. In the present study, we examined changes in tumor response and oxidative stress related to anticancer drug treatment rather than tumor resection. d-ROM levels decreased after treatment in PR patients and increased in PD patients. We confirmed that a PD was strongly associated with an increased d-ROM ratio. Our findings are consistent with the decreased and increased oxidative stress levels following tumor resection and disease progression, respectively, observed in previous studies. However, the effect of anticancer drugs on oxidative stress levels remains inconclusive. Kadam and Abhang reported an increase in oxidative stress with anticancer drug administration in patients with breast cancer, while El-Hefny et al. found no difference in oxidative stress before and after chemotherapy [44,45,46]. Yokoyama et al. investigated the induction of intracellular oxidative stress by anticancer drugs using DLD-1, a colon cancer cell line, and reported that fluorouracil and oxaliplatin could not induce oxidative stress [47]. In the present study, we tested the effects of various anticancer drugs and used a multivariate analysis approach to address the bias associated with the different effects of each drug.
When using oxidative stress levels as tumor markers, a high sensitivity and specificity are needed. Kim et al. reported that the CEA level, currently recommended as a colorectal cancer marker, is not an effective indicator of therapeutic response unless the pre-treatment CEA level is above 5 ng/mL. Wang et al. reported a sensitivity of 81% and specificity of 88% for a 50% or greater increase in the CEA level as an effective predictor of disease progression [10,48]. Huang et al. defined disease progression as an increase in the CEA level above the pre-treatment value and reported a sensitivity of 67.7% and specificity of 78.4% in an analysis of 330 cases [49].
In this study, the d-ROM ratio cutoff value for PD prediction was determined to be 1, based on an ROC curve analysis. A d-ROM ratio > 1 meant that the sensitivity and specificity of PD prediction were 82.4% and 91.7%, respectively, which were superior compared to the sensitivity and specificity of CEA in various past studies. The d-ROM ratio is an easy-to-calculate marker of tumor progression that could aid in determining the efficacy of chemotherapy.
We acknowledge some limitations to our study. First, the study of the d-ROM ratio was limited to stage IV colorectal cancer, and it is unclear whether its performance would remain constant at all stages and among cancer types. Second, d-ROM levels may be affected by inflammatory reactions, such as co-infections. In addition, as we compared d-ROM levels in the same patient, the influence of lifestyle and other factors is considered to be minimal, but the influence of diet remains unclear.

5. Conclusions

A significant correlation was demonstrated between the rate of change in d-ROM levels and treatment response in patients with metastatic colorectal cancer. This insight opens avenues for further research into the role of oxidative stress in cancer treatment and the potential utility of d-ROM levels as a biomarker in clinical practice.

Author Contributions

K.S. and T.G. were involved in the study design and data interpretation. Y.K. and K.K. were involved in the data analysis. All authors critically revised the report, commented on drafts of the manuscript, and approved the final report. All authors have read and agreed to the published version of the manuscript.

Funding

The authors declare that no external funding was received for this study.

Institutional Review Board Statement

The protocol for this research project was approved by a suitable Institutional Ethics Committee and conformed to the provisions of the Declaration of Helsinki. The Research Ethics Committee of the University of Fukui approved the study (Approval No. 20200058).

Informed Consent Statement

Written informed consent was obtained from participants prior to the study.

Data Availability Statement

All data included in this study are available upon request by contact with the corresponding author.

Acknowledgments

The authors thank Masae Saitoh for their assistance in patients’ data collection and administrative support.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Hashiguchi, Y.; Muro, K.; Saito, Y.; Ito, Y.; Ajioka, Y.; Hamaguchi, T.; Hasegawa, K.; Hotta, K.; Ishida, H.; Ishiguro, M.; et al. Japanese Society for Cancer of the Colon and Rectum (JSCCR) guidelines 2019 for the treatment of colorectal cancer. Int. J. Clin. Oncol. 2020, 25, 1–42. [Google Scholar] [CrossRef]
  2. Biller, L.H.; Schrag, D. Diagnosis and Treatment of Metastatic Colorectal Cancer: A Review. JAMA 2021, 325, 669–685. [Google Scholar] [CrossRef]
  3. Xie, Y.H.; Chen, Y.X.; Fang, J.Y. Comprehensive review of targeted therapy for colorectal cancer. Signal Transduct. Target. Ther. 2020, 5, 22. [Google Scholar] [CrossRef]
  4. El-Shami, K.; Oeffinger, K.C.; Erb, N.L.; Willis, A.; Bretsch, J.K.; Pratt-Chapman, M.L.; Cannady, R.S.; Wong, S.L.; Rose, J.; Barbour, A.L.; et al. American Cancer Society Colorectal Cancer Survivorship Care Guidelines. CA Cancer J. Clin. 2015, 65, 428–455. [Google Scholar] [CrossRef]
  5. Meyerhardt, J.A.; Mangu, P.B.; Flynn, P.J.; Korde, L.; Loprinzi, C.L.; Minsky, B.D.; Petrelli, N.J.; Ryan, K.; Schrag, D.H.; Wong, S.L.; et al. Follow-up care, surveillance protocol, and secondary prevention measures for survivors of colorectal cancer: American Society of Clinical Oncology clinical practice guideline endorsement. J. Clin. Oncol. 2013, 31, 4465–4470. [Google Scholar] [CrossRef]
  6. Duffy, M.J.; van Dalen, A.; Haglund, C.; Hansson, L.; Holinski-Feder, E.; Klapdor, R.; Lamerz, R.; Peltomaki, P.; Sturgeon, C.; Topolcan, O. Tumour markers in colorectal cancer: European Group on Tumour Markers (EGTM) guidelines for clinical use. Eur. J. Cancer 2007, 43, 1348–1360. [Google Scholar] [CrossRef]
  7. Locker, G.Y.; Hamilton, S.; Harris, J.; Jessup, J.M.; Kemeny, N.; Macdonald, J.S.; Somerfield, M.R.; Hayes, D.F.; Bast, R.C. ASCO 2006 update of recommendations for the use of tumor markers in gastrointestinal cancer. J. Clin. Oncol. 2006, 24, 5313–5327. [Google Scholar] [CrossRef] [PubMed]
  8. Benson, A.; Venook, A.P.; Al-Hawary, M.M.; Arain, M.A.; Chen, Y.J.; Ciombor, K.K.; Cohen, S.; Cooper, H.S.; Deming, D.; Farkas, L.; et al. Colon Cancer, Version 2.2021. J. Natl. Compr. Cancer Netw. 2021, 19, 329–359. [Google Scholar] [CrossRef] [PubMed]
  9. Therasse, P.; Arbuck, S.G.; Eisenhauer, E.A.; Wanders, J.; Kaplan, R.S.; Rubinstein, L.; Verweij, J.; Van Glabbeke, M.; van Oosterom, A.T.; Christian, M.C.; et al. New guidelines to evaluate the response to treatment in solid tumors. European Organization for Research and Treatment of Cancer, National Cancer Institute of the United States, National Cancer Institute of Canada. J. Natl. Cancer Inst. 2000, 92, 205–216. [Google Scholar] [CrossRef]
  10. Kim, G.; Jung, E.J.; Ryu, C.G.; Hwang, D.Y. Usefulness of Carcinoembryonic Antigen for Monitoring Tumor Progression during Palliative Chemotherapy in Metastatic Colorectal Cancer. Yonsei Med. J. 2013, 54, 116–122. [Google Scholar] [CrossRef] [PubMed]
  11. de Haas, R.J.; Wicherts, D.A.; Flores, E.; Ducreux, M.; Levi, F.; Paule, B.; Azoulay, D.; Castaing, D.; Lemoine, A.; Adam, R. Tumor Marker Evolution: Comparison with Imaging for Assessment of Response to Chemotherapy in Patients with Colorectal Liver Metastases. Ann. Surg. Oncol. 2010, 17, 1010–1023. [Google Scholar] [CrossRef]
  12. Bardelcíková, A.; Soltys, J.; Mojzis, J. Oxidative Stress, Inflammation and Colorectal Cancer: An Overview. Antioxidants 2023, 12, 901. [Google Scholar] [CrossRef] [PubMed]
  13. Perse, M. Oxidative Stress in the Pathogenesis of Colorectal Cancer: Cause or Consequence? Biomed. Res. Int. 2013, 2013, 725710. [Google Scholar] [CrossRef] [PubMed]
  14. Panday, A.; Sahoo, M.K.; Osorio, D.; Batra, S. NADPH oxidases: An overview from structure to innate immunity-associated pathologies. Cell. Mol. Immunol. 2015, 12, 5–23. [Google Scholar] [CrossRef] [PubMed]
  15. Salehi, S.S.; Mirmiranpour, H.; Rabizadeh, S.; Esteghamati, A.; Tomasello, G.; Alibakhshi, A.; Najafi, N.; Rajab, A.; Nakhjavani, M. Improvement in Redox Homeostasis after Cytoreductive Surgery in Colorectal Adenocarcinoma. Oxid. Med. Cell Longev. 2021, 2021, 8864905. [Google Scholar] [CrossRef]
  16. Boakye, D.; Jansen, L.; Schottker, B.; Jansen, E.; Schneider, M.; Halama, N.; Gao, X.; Chang-Claude, J.; Hoffmeister, M.; Brenner, H. Blood markers of oxidative stress are strongly associated with poorer prognosis in colorectal cancer patients. Int. J. Cancer 2020, 147, 2373–2386. [Google Scholar] [CrossRef]
  17. Inokuma, T.; Haraguchi, M.; Fujita, F.; Tajima, Y.; Kanematsu, T. Oxidative Stress and Tumor Progression in Colorectal Cancer. Hepato Gastroenterol. 2009, 56, 343–347. [Google Scholar]
  18. Inokuma, T.; Haraguchi, M.; Fujita, F.; Torashima, Y.; Eguchi, S.; Kanematsu, T. Suppression of Reactive Oxygen Species Develops Lymph Node Metastasis in Colorectal Cancer. Hepato Gastroenterol. 2012, 59, 2480–2483. [Google Scholar] [CrossRef]
  19. Wu, R.; Feng, J.F.; Yang, Y.W.; Dai, C.M.; Lu, A.Y.; Li, J.; Liao, Y.; Xiang, M.; Huang, Q.M.; Wang, D.; et al. Significance of Serum Total Oxidant/Antioxidant Status in Patients with Colorectal Cancer. PLoS ONE 2017, 12, 13. [Google Scholar] [CrossRef]
  20. Carini, F.; Mazzola, M.; Rappa, F.; Jurjus, A.; Geagea, A.G.; Al Kattar, S.; Bou-Assi, T.; Jurjus, R.; Damiani, P.; Leone, A.; et al. Colorectal Carcinogenesis: Role of Oxidative Stress and Antioxidants. Anticancer. Res. 2017, 37, 4759–4766. [Google Scholar] [CrossRef]
  21. Kilk, K.; Meitern, R.; Härmson, O.; Soomets, U.; Hõrak, P. Assessment of oxidative stress in serum by d-ROMs test. Free Radic. Res. 2014, 48, 883–889. [Google Scholar] [CrossRef] [PubMed]
  22. Sawai, K.; Goi, T.; Sakamoto, S.; Matsunaka, T.; Maegawa, N.; Koneri, K. Oxidative stress as a biomarker for predicting the prognosis of patients with colorectal cancer. Oncology 2022, 100, 612–619. [Google Scholar] [CrossRef] [PubMed]
  23. Trotti, R.; Carratelli, M.; Barbieri, M. Performance and clinical application of a new, fast method for the detection of hydroperoxides in serum. Panminerva Medica 2002, 44, 37–40. [Google Scholar] [PubMed]
  24. Pasquini, A.; Luchetti, E.; Marchetti, V.; Cardini, G.; Iorio, E.L. Analytical performances of d-ROMs test and BAP test in canine plasma. Definition of the normal range in healthy Labrador dogs. Vet. Res. Commun. 2008, 32, 137–143. [Google Scholar] [CrossRef]
  25. Yagi, H.; Sumino, H.; Yoshida, K.; Aoki, T.; Tsunekawa, K.; Araki, O.; Kimura, T.; Nara, M.; Nakajima, K.; Murakami, M. Biological Antioxidant Potential Negatively Correlates With Carotid Artery Intima-Media Thickness. Int. Heart J. 2016, 57, 220–225. [Google Scholar] [CrossRef]
  26. Sugimoto, K.; Sakamoto, K.; Kawai, M.; Kawano, S.; Munakata, S.; Ishiyama, S.; Takahashi, M.; Kojima, Y.; Tomiki, Y. Serum oxidative stress is an independent prognostic marker in colorectal cancer. Transl. Cancer Res. 2019, 8, 1699–1708. [Google Scholar] [CrossRef]
  27. Wang, D.; Feng, J.F.; Zeng, P.; Yang, Y.H.; Luo, J.; Yang, Y.W. Total oxidant/antioxidant status in sera of patients with thyroid cancers. Endocr. Relat. Cancer 2011, 18, 773–782. [Google Scholar] [CrossRef]
  28. Feng, J.F.; Lu, L.; Zeng, P.; Yang, Y.H.; Luo, J.; Yang, Y.W.; Wang, D. Serum total oxidant/antioxidant status and trace element levels in breast cancer patients. Int. J. Clin. Oncol. 2012, 17, 575–583. [Google Scholar] [CrossRef]
  29. Eroglu, M.; Yilmaz, N.; Yalcinkaya, S.; Ay, N.; Aydin, O.; Sezer, C. Enhanced HDL-cholesterol-associated anti-oxidant PON-1 activity in prostate cancer patients. Kaohsiung J. Med. Sci. 2013, 29, 368–373. [Google Scholar] [CrossRef]
  30. Gào, X.; Wilsgaard, T.; Jansen, E.H.; Holleczek, B.; Zhang, Y.; Xuan, Y.; Anusruti, A.; Brenner, H.; Schöttker, B. Pre-diagnostic derivatives of reactive oxygen metabolites and the occurrence of lung, colorectal, breast and prostate cancer: An individual participant data meta-analysis of two large population-based studies. Int. J. Cancer 2019, 145, 49–57. [Google Scholar] [CrossRef]
  31. Byström, P.; Berglund, Å.; Nygren, P.; Wernroth, L.; Johansson, B.; Larsson, A.; Glimelius, B. Evaluation of predictive markers for patients with advanced colorectal cancer. Acta Oncol. 2012, 51, 849–859. [Google Scholar] [CrossRef] [PubMed]
  32. Acevedo-León, D.; Monzó-Beltrán, L.; Gómez-Abril, S.; Estañ-Capell, N.; Camarasa-Lillo, N.; Pérez-Ebri, M.L.; Escandón-Álvarez, J.; Alonso-Iglesias, E.; Santaolaria-Ayora, M.L.; Carbonell-Moncho, A.; et al. The Effectiveness of Glutathione Redox Status as a Possible Tumor Marker in Colorectal Cancer. Int. J. Mol. Sci. 2021, 22, 6183. [Google Scholar] [CrossRef] [PubMed]
  33. Pelicano, H.; Carney, D.; Huang, P. ROS stress in cancer cells and therapeutic implications. Drug Resist. Updates 2004, 7, 97–110. [Google Scholar] [CrossRef] [PubMed]
  34. Delimaris, I.; Faviou, E.; Antonakos, G.; Stathopoulou, E.; Zachari, A.; Dionyssiou-Asteriou, A. Oxidized LDL, serum oxidizability and serum lipid levels in patients with breast or ovarian cancer. Clin. Biochem. 2007, 40, 1129–1134. [Google Scholar] [CrossRef] [PubMed]
  35. Zabirnyk, O.; Liu, W.; Khalil, S.; Sharma, A.; Phang, J.M. Oxidized low-density lipoproteins upregulate proline oxidase to initiate ROS-dependent autophagy. Carcinogenesis 2010, 31, 446–454. [Google Scholar] [CrossRef]
  36. Crespo-Sanjuán, J.; Calvo-Nieves, M.D.; Aguirre-Gervás, B.; Herreros-Rodríguez, J.; Velayos-Jiménez, B.; Castro-Alija, M.J.; Muñoz-Moreno, M.F.; Sánchez, D.; Zamora-González, N.; Bajo-Grañeras, R.; et al. Early detection of high oxidative activity in patients with adenomatous intestinal polyps and colorectal adenocarcinoma: Myeloperoxidase and oxidized low-density lipoprotein in serum as new markers of oxidative stress in colorectal cancer. Lab. Med. 2015, 46, 123–135. [Google Scholar] [CrossRef]
  37. Zettler, M.E.; Prociuk, M.A.; Austria, J.A.; Massaeli, H.; Zhong, G.M.; Pierce, G.N. OxLDL stimulates cell proliferation through a general induction of cell cycle proteins. Am. J. Physiol. Heart Circ. Physiol. 2003, 284, H644–H653. [Google Scholar] [CrossRef]
  38. Bitorina, A.V.; Oligschlaeger, Y.; Shiri-Sverdlov, R.; Theys, J. Low profile high value target: The role of OxLDL in cancer. Biochim. Et. Biophys. Acta Mol. Cell Biol. Lipids 2019, 1864, 158518. [Google Scholar] [CrossRef]
  39. van Heijst, J.W.; Niessen, H.W.; Hoekman, K.; Schalkwijk, C.G. Advanced glycation end products in human cancer tissues: Detection of Nepsilon-(carboxymethyl)lysine and argpyrimidine. Ann. N. Y. Acad. Sci. 2005, 1043, 725–733. [Google Scholar] [CrossRef]
  40. Romero-Garcia, S.; Lopez-Gonzalez, J.S.; Baez-Viveros, J.L.; Aguilar-Cazares, D.; Prado-Garcia, H. Tumor cell metabolism An integral view. Cancer Biol. Ther. 2011, 12, 939–948. [Google Scholar] [CrossRef]
  41. Sharaf, H.; Matou-Nasri, S.; Wang, Q.; Rabhan, Z.; Al-Eidi, H.; Al Abdulrahman, A.; Ahmed, N. Advanced glycation endproducts increase proliferation, migration and invasion of the breast cancer cell line MDA-MB-231. Biochim. Et. Biophys. Acta Mol. Basis Dis. 2015, 1852, 429–441. [Google Scholar] [CrossRef] [PubMed]
  42. Perrone, A.; Giovino, A.; Benny, J.; Martinelli, F. Advanced Glycation End Products (AGEs): Biochemistry, Signaling, Analytical Methods, and Epigenetic Effects. Oxidative Med. Cell. Longev. 2020, 2020, 3818196. [Google Scholar] [CrossRef] [PubMed]
  43. Hristozov, D.; Gadjeva, V.; Vlaykova, T.; Dimitrov, G. Evaluation of oxidative stress in patients with cancer. Arch. Physiol. Biochem. 2001, 109, 331–336. [Google Scholar] [CrossRef] [PubMed]
  44. Charushila, K.Y.; Subodhini, A.A. Evaluation of Serum Antioxidants during Adjuvant Chemotherapy of Breast Cancer—A Prospective Observational Study. Biochem. Anal. Biochem. 2015, 4, 1. [Google Scholar] [CrossRef]
  45. Rao, C.S.; Kumari, D.S. Changes in plasma lipid peroxidation and the antioxidant system in women with breast cancer. Int. J. Basic Appl. Sci. 2012, 1, 429–438. [Google Scholar]
  46. El-Hefny, M.A.; Karimova, S.T.; Afandiev, A.M. Lipid peroxidation and antioxidant status in breast cancer patients before and after therapy. Med. J. Cairo Univ. 2009, 77, 37–42. [Google Scholar]
  47. Yokoyama, C.; Sueyoshi, Y.; Ema, M.; Mori, Y.; Takaishi, K.; Hisatomi, H. Induction of oxidative stress by anticancer drugs in the presence and absence of cells. Oncol. Lett. 2017, 14, 6066–6070. [Google Scholar] [CrossRef]
  48. Wang, W.S.; Lin, J.K.; Lin, T.C.; Chiou, T.J.; Liu, J.H.; Fan, F.S.; Yen, C.C.; Chen, W.S.; Jiang, J.K.; Yang, S.H.; et al. Carcinoembryonic antigen in monitoring of response to systemic chemotherapy in patients with metastatic colorectal cancer. Int. J. Color. Dis. 2001, 16, 96–101. [Google Scholar] [CrossRef]
  49. Huang, S.C.; Lin, J.K.; Lin, T.C.; Chen, W.S.; Yang, S.H.; Wang, H.S.; Lan, Y.T.; Lin, C.C.; Jiang, J.K.; Chang, S.C. Concordance of Carcinoembryonic Antigen Ratio and Response Evaluation Criteria in Solid Tumors as Prognostic Surrogate Indicators of Metastatic Colorectal Cancer Patients Treated with Chemotherapy. Ann. Surg. Oncol. 2015, 22, 2262–2268. [Google Scholar] [CrossRef]
Cancers 15 05517 g001
Figure 1. Change in d-ROM after 3 months. The d-ROM values of patients with PR decreased after treatment, while those of patients with PD increased. d-ROM—derivatives of reactive oxygen metabolites; PR—partial response; PD—progressive disease.
Figure 1. Change in d-ROM after 3 months. The d-ROM values of patients with PR decreased after treatment, while those of patients with PD increased. d-ROM—derivatives of reactive oxygen metabolites; PR—partial response; PD—progressive disease.
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Figure 2. Receiver operating characteristic curve analysis of d-ROM ratio for predicting progression disease. Area under the curve = 0.874. d-ROM—derivatives of reactive oxygen metabolites.
Figure 2. Receiver operating characteristic curve analysis of d-ROM ratio for predicting progression disease. Area under the curve = 0.874. d-ROM—derivatives of reactive oxygen metabolites.
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Table 1. Demographic and clinical characteristics of patients with stage IV colorectal cancer (n = 53).
Table 1. Demographic and clinical characteristics of patients with stage IV colorectal cancer (n = 53).
CharacteristicNumber of Patients (%)
Sex (%)
Male/female30 (56.6)/23 (43.4)
Median age, years (range)69 (38–93)
Location (%)
Colon/rectum30 (56.6)/23 (43.4)
Histological type (%)
Well/moderate/poor15 (28.3)/30 (56.6)/8 (15.1)
Primary tumor status (%)
Resected/not resected44 (83.0)/9 (17.0)
Metastatic presentation (%)
Metachronous/synchronous6 (11.3)/47 (88.7)
History of anticancer drug use (%)
No/yes37 (69.8)/16 (30.2)
Metastatic organs (%)
Single/multiple28 (52.8)/25 (47.2)
Table 2. Anticancer drug treatments.
Table 2. Anticancer drug treatments.
Anticancer DrugNumber of
Patients
Doublet oxaliplatin-based regimen (targeting agent used)33 (23)
Doublet irinotecan-based regimen (targeting agent used)5 (4)
Triplet regimen (targeting agent used)4 (3)
5-Fluorouracil-based oral drug (targeting agent used)5 (2)
Trifluridine/tipiracil hydrochloride (targeting agent)2 (2)
No treatment4
Table 3. Tumor RECIST evaluation after 3 months of treatment.
Table 3. Tumor RECIST evaluation after 3 months of treatment.
Evaluation by RECISTNumber of Patients (%)
Complete response0 (0)
Partial response26 (49.0)
Stable disease10 (18.9)
Progressive disease17 (32.1)
RECIST—Response Evaluation in Solid Tumors.
Table 4. d-ROM levels before and after 3 months of treatment.
Table 4. d-ROM levels before and after 3 months of treatment.
Total53 Cases
Levels at the start of treatment (median)283–636 (424)
Levels after 3 months of treatment (median)168–664 (375)
d-ROM ratio ≤ 136 cases
(RECIST non-PD: 33 cases; PD: 3 cases)
d-ROM ratio > 117 cases
(RECIST non-PD: 3 cases; PD: 14 cases)
d-ROM—derivatives of reactive oxygen metabolites; d-ROM ratio—post-treatment: pre-treatment d-ROM levels.
Table 5. Multivariate linear regression analysis of the d-ROM ratio in patients with metastatic colorectal cancer.
Table 5. Multivariate linear regression analysis of the d-ROM ratio in patients with metastatic colorectal cancer.
Independent
Variables		Multivariate Linear Regression
β (95% CI)p-Value
RECIST criteria
(Non-PD vs. PD)		0.416 (0.279–0.555)<0.001
d-ROM—derivatives of reactive oxygen metabolites; β—regression coefficient; CI—confidence interval; RECIST—Response Evaluation in Solid Tumors; PD—progressive disease.
Table 6. Accuracy of d-ROM ratio measurement for predicting disease progression.
Table 6. Accuracy of d-ROM ratio measurement for predicting disease progression.
SensitivitySpecificityPositive Predictive ValueNegative Predictive ValueDiagnostic
Accuracy
d-ROMs ratio > 182.4%91.7%82.4%91.7%88.7%
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Sawai, K.; Goi, T.; Kimura, Y.; Koneri, K. Monitoring Metastatic Colorectal Cancer Progression According to Reactive Oxygen Metabolite Derivative Levels. Cancers 2023, 15, 5517. https://doi.org/10.3390/cancers15235517

AMA Style

Sawai K, Goi T, Kimura Y, Koneri K. Monitoring Metastatic Colorectal Cancer Progression According to Reactive Oxygen Metabolite Derivative Levels. Cancers. 2023; 15(23):5517. https://doi.org/10.3390/cancers15235517

Chicago/Turabian Style

Sawai, Katsuji, Takanori Goi, Youhei Kimura, and Kenji Koneri. 2023. "Monitoring Metastatic Colorectal Cancer Progression According to Reactive Oxygen Metabolite Derivative Levels" Cancers 15, no. 23: 5517. https://doi.org/10.3390/cancers15235517

APA Style

Sawai, K., Goi, T., Kimura, Y., & Koneri, K. (2023). Monitoring Metastatic Colorectal Cancer Progression According to Reactive Oxygen Metabolite Derivative Levels. Cancers, 15(23), 5517. https://doi.org/10.3390/cancers15235517

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