Translational Biomedicine

Keywords

Ovarian cancer; Protein oxidative modification; Cytokines

Introduction

Ovarian cancer (OC) remains the leading cause of death from cancer among gynecologic tumors [1]. According to EuroCare-5 Study statistics, the 5-year survival rate for OC is 30% [2]. 11,700 ovarian malignant tumors and 7,300 deaths from them are registered annually in Russia [3]. Low survival rate of ovarian cancer patients results from late diagnosis. At present, 70% of cases are diagnosed advanced disease [4]. The observed increase in 5-year survival of patients with malignant ovarian tumors in Europe by 3% and in the U.S. by 4% over the past decade can be explained not so much by diagnostics improvement, as by effective application of platinum-based chemotherapy in a treatment of disseminated forms of ovarian cancer and germ cell tumors.

Economic loss resulting from the consequences of malignant tumors incidence is about 90 billion rubles per year and, according to some authors, is going to increase [5]. Therefore it is necessary to correlate the response to treatment with economic factors to determine the cost efficiency of the management of patients with OC. At present, there is a view of chemotherapy futility for cancer patients in terminal stages [6-9]. In this regard, the urgency of clarifying diagnostics methods of advanced (III-IV) disease stage is enhancing.

Ovarian malignant tumors are characterized by absence of symptoms. There are no pathognomonic symptoms of gonadal neoplasia. According to National guidelines [10], diagnosis of OC includes not only precise history taking, full physical, bimanual, recto-vaginal examination, abdominal and transvaginal ultrasound and CT scan, but also chemistry panel and detailed biochemical and clinical blood tests, and determination of the level of tumor markers. For final diagnosis appropriate surgical staging is performed. Due to the complexity of diagnosis and staging of ovarian cancer, the search is conducted for markers that would help to clarify the stage of the tumor process.

Protein oxidative modification is considered one of the earliest and reliable indicators of redox homeostasis disruption [11]. The aim of our research was to develop a clarifying diagnosis method of progressive OC forms on the basis of determining the blood plasma POMP level.

Methods

We studied 300 patients with primary OC stage III-IV according to the classification of the International Federation of Obstetrics and Gynecology (FIGO) and the TNM (7th edition 2010), who were treated the first time during the period from January 2010 till August 2013 in Ulyanovsk Regional Clinical Oncology Center. FIGO staging of ovarian cancer changed from 1 January 2014. However, a study was carried out on the basis of the previous FIGO classification, which has been used on the territory of the Russian Federation until 2015. FIGO stage based on surgical data and TNM categories based on clinical and/or pathological classification. Using the standardized methods, 21 indicators were defined in blood plasma of each patient: leukocyte count, neutrophil count, levels of metalloproteinases MMP-2 and MMP- 9 in neutrophils and blood serum, levels of cytokines IL-1β, IL-6, IL-10, IFN-γ, TNF-α, levels of Her-2/neu and CA125, 4 indicators of POMP in plasma: the level of malondialdehyde (MDA), the activity of glutathione reductase (GR), glutathione S-transferases (GTs), and catalase. The parameters are shown in Tables 1-4 and distributed randomly.

IndicatorStage	Le	PMN	Her-2/neu	MMP-2 PMN	-9 PMN
IIIstage	0,79675	0,596502	1,96315	0,650714	1,380432
IVstage	1,86213	0,8433268	1,94747	0,63294	2,034398

Table 1: Optimal values σ (Le, PMN, Her-2/neu, MMP-2, 9 in PMN) for stages III and IV ovarian cancer

IndicatorStage	POMP356nm	POMP 370 nm	POMP 430 nm	POMP 530nm	MDA	Catalase
III stage	5,25719	6,46599	3,93999	2,17439	0,996194	3,54199
IV stage	2,29001	2,63601	1,53241	0,53801	0,617518	0,28201

Table 2: Optimal values σ (POMP, MDA, Catalase) for stages III and IV ovarian cancer.

IndicatorStage	IL-6	IL-10	IL-1β	IFN-λ	CA-125
IIIstage	3,69995	0,88705	2,4446012	1,237234	1,20299
IVstage	1,35715	1,25999	2,304716	1,155144	1,18701

Table 3:Optimal values σ (IL-1β, IL-6, IL-10, IFN-λ, CA-125) for stages III and IV ovarian cancer

IndicatorStage	GR	GST	TNF-α	MMP-2 serum	MMP-9 serum
IIIstage	1,89599	1,57761	1,044316	2,0501928	3,932618
IVstage	4,01999	0,67055	0,9374848	1,780624	3,879402

Table 4:Optimal values σ (GR, GST, TNF-α, MMP-2, 9 in serum) for stages III and IV ovarian cancer.

For each indicatorχi, i = 1, …, N ,functions

were constructed, where each function describes probability density of reaching each stage out of two under consideration.

On the basis of these functions, likelihood ratio was constructed for each indicator (P.410) [3].

                           (1)

Thus, for each indicator , , intervals were defined, where ,correspondingly (D-threshold level). We united intervals into the multitude into the multitude , correspondingly.

For each indicator xi,i=1,....21 characteristic values were simply ordered by a tenfold-hundredfold reduction in each disease stage.

Consider three situations:

1) if is with the property that , ,then it is necessary to check that , . If there is no contradiction, then disease stage j can be diagnosed.

1) if is with the property that , then it is necessary to check that ,.If there is a contradiction, then D should be increased by 3%-10%.

3)if there is no with the property that , then one should calculate the value

                        (2)

If , then a corresponding disease stage can be diagnosed exactly. But if, then a patient’s disease stage can’t be determined exactly.

According to statistics, each parameter was divided into 5 groups. Statistics approximation of the distribution density was made by the least square method. Optimal values σ are presented in Tables 1-4 Suppose threshold level D =1, then indicators intervals are defined as follows (Tables 5-8).

IndicatorStage	Le	PMN	Her-2/neu	MMP-2 PMN	-9 PMN
IIIstage	[0;1,278]	[0;0,993]	(2,765;+∞]	(0,907;+∞]	[0;2,341]
IVstage	(1,278;+∞]	(0,993;+∞]	[0;2,765]	[0;0,907]	(2,341;+∞]

Table 5:Indicator intervals for stages III and IV ovarian cancer: Le, PMN, Her-2/neu, MMP-2,9 in PMN.

Indicator Stage	POMP 356 nm	POMP 370 nm	POMP 430nm	POMP530nm	Catalase	MDA
III stage	(4,638;+∞]	(5,469; +∞]	(3,232;+∞]	(1,312; +∞]	(0,900;+∞]	(1,088;+∞]
IV stage	[0;4,638]	[0;5,469]	[0;3,232]	[0;1,312]	[0;0,900]	[0;1,088]

Table 6:Indicator intervals for stages III and IV ovarian cancer: POMP, catalase, MDA.

IndicatorStage	IL-6	IL-10	IL-1β	IFN-λ	CA-125
IIIstage	(2,922;+∞]	[0;1,480]	(3,355;+∞]	(1,690;+∞]	(1,690;+∞]
IVstage	[0;2,922]	(1,480;+∞]	[0;3,355]	[0; 1,690]	[0;1,690]

Table 7:Indicator intervals for stages III and IV ovarian cancer: IL-1β, IL- 6, IL-10, CA-125 in serum

IndicatorStage	GR	GST	TNF-α	MMP-2 serum	MMP-9 serum
IIIstage	[0;3,728]	(1,370; +∞]	(1,398; +∞]	(2,697; +∞]	(5,523;+∞]
IVstage	(3,728;+∞]	[0; 1,370]	[0; 1,398]	[0;2,697]	[0;5,523)

Table 8:Indicator intervals for stages III and IV ovarian cancer: GR, GST, TNF-α, MMP-2,9 in serum.

Results

Let's consider a patient in disease stage III (Table 9).

Indicator	Value	Likelihood function, zi	Stage
Le	0,66	0,678	III
PMN	0,4818	0,588	III
POMP 356	3.5	2,04	IV
POMP 370	7.1	0,292	III
POMP 430	5.5	0,027	III
POMP 530	3.4	1,179E-07	III
Her2	2.05	1,007	IV
CA125	3.1	0,938	III
MMP-2 serum	2.4062	0,0007	IV
MMP-9 serum	6.258	1	III
MMP-2 PMN	0.403	1,06	IV
MMP-9 PMN	1.6141	0,992	III
IL-6	6,27	1,045	III
IL-10	0	0,665	III
IL-1 β	1.9942	1,079	IV
IFN- λ	0.9932	1,094	IV
TNF-α	0.8572	1,144	IV
MDA	1.109	1,083	III
Catalase	0	1	IV
GST	1	1,1111	III
GR	1.65	0,463	III

Table 9:Likelihood function value Z1 for diagnostics parameters of ovarian cancer (stage III).

With reference to the 13 parameters (? 1, 2, 4, 5, 6, 8, 10, 12, 13, 14, 18, 20 and 21)-stage III can be diagnosed, with reference to the rest 8 parameters (? 3, 7, 9, 11, 15, 16, 17 and 19)-stage IV. As it is impossible to diagnose the stage exactly in this case, then using formula (2) we get = 2, 74796E-13. Suppose ,then , consequently the patient has stage III of disease.

Let's consider a patient in disease stage IV (Table 10).

Indicator	Value	Likelihood function, zi	Stage
Le	1.28	1,001	IV
PMN	1.0624	1,1051	IV
POMP 356	1,1	4,7999	IV
POMP 370	2,9	3,632	IV
POMP 430	1,9	3,442	IV
POMP 530	1,0	3,227	IV
Her2	0,41	1,015	IV
CA125	1	1,017	IV
MMP-2 serum	2,2355	1,093	IV
MMP-9 serum	6,502	0,989	III
MMP-2 PMN	0,345	1,048	IV
MMP-9 PMN	2,1737	0,859	III
IL-6	2,08	2,689	IV
IL-10	0	1	III
IL-1β	2,4637	1,055	IV
IFN-λ	1,2837	1,062	IV
TNF-α	0,9715	1,1184	IV
MDA	0,516	2,099	IV
Catalase	0,1	157,65	IV
GST	4	0,2478	IV
GR	0,42	4,713	IV

Table 10:Likelihood function value, Z1 for diagnostics parameters of ovarian cancer (stage IV).

With reference to 3 parameters we can diagnose stage III, with reference to 18 parameters-stages IV. As it is impossible to diagnose the stage exactly in this case, then using formula (2), we get = 280189, 27. Suppose threshold level consequently the patient has stage IV.

Approximation accuracy

Approximation accuracy was identified while checking the hypothesis of correspondence of parameter practical value to the theoretical law of density distribution by Pearson criterion (P.444) [3]. For checking statistical data is introduced

                           (3)

Where N-the number of parameters groups, equals 5. The hypothesis is accepted when , where α-the level of significance, equal 0.05. Approximation accuracy is calculated as γ = 1-α, equal 0.95.

Discussion

We have analyzed chemistry and hematology panel values of 300 OC patients and stated the absence of statistically significant difference between these values in various stages of the disease, as well as the absence of significant correlation relationships between above mentioned values and OC stage [12]. Also, we found no correlation between ??-125 level and cancer stage in primary OC patients.

According to the cited literature, the change in oxygen metabolism plays an important role in carcinogenesis [13]. It is stated that metabolism change from oxidative phosphorylation to glycolysis is related to the higher production of reactive oxygen species (ROS) [14]. Furthermore, electron transfer through pentose phosphate pathway to NADPH and GSH is enhanced. As a result, redox homeostasis in neoplasia changes [15]. There is an opinion stating that high proliferative activity, drug resistance, apoptosis reduction and immortalization of tumor cells can be mediated by increasing of ROS levels, which results in changing of gene expression [16].

Protein oxidative modification products (POMP)-protein carbonyl derivatives-have long-term half-life; their number increases not only due to oxidative posttranslational modification, but also proteolytic destruction.

Applying assumed parameters of follow-up examination, stage clarification was performed in 300 patients with primary OC who were treated at Ulyanovsk Regional Clinical Oncology Center. In 21 patients the stage of cancer was changed: 9 patients had the stage changed from IV to III, 12 patients-from III to IV.

With reference to obtained results, patients with updated stage III received 6 courses of multiagent chemotherapy instead of 4; patients with updated stage IV received immunotherapy.

According to the obtained results, the level of protein oxidative modification products is an accurate indicator for differential diagnostics of ovarian cancer stage III and IV.

The patent was approved for this method of differential diagnostics of ovarian cancer.

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References

1. BakhidzeEV, Malek AV (2005) The significance of genom study method for diagnostics and treatment of ovarian cancer. Problems in oncology 51: 50-51.
2. Roberta De Agelisetal (2014) Cancer survival in Europe 1999–2007 by country and age: results of EUROCARE-5—a population-based study. The Lancet Onc 15: 23-24.
3. Mood AM, Graybill FA (1974) Introduction to the Theory of Statistics,(3rd Edn) McGraw-Hill 577p.
4. Charles N, ZandenJr, Michal J, Anil K (2008)Early stages of ovarian cancer pathogenesis. J Clinical Oncology 2: 149-160.
5. UrmancheyevaAF, Kutusheva GF (2001) Diagnostics and treatment of ovarian tumors. S Petersburg 311p.
6. Wright AA, Zhang B, Keating NL, Weeks JC, Prigerson HG (2014) Associations between palliative chemotherapy and adult cancer patients' end of life care and place of death: prospective cohort study. BMJ 348:1219.
7. Näppä U, Lindqvist O, Rasmussen BH, Axelsson B (2011) Palliative chemotherapy during the last month of life. Ann Oncol 22: 2375-2380.
8. Adam H, Hug S, Bosshard G (2014) Chemotherapy near the end of life: a retrospective single-centre analysis of patients' charts. BMC Palliat Care 13: 26.
9. Mehta DA, Hay JW (2014) Cost-effectiveness of adding bevacizumab to first line therapy for patients with advanced ovarian cancer. GynecolOncol 132: 677-683.
10. Chissov VI, Davydov MI (2008) Oncology. National guidelines. Moscow, 815 p.
11. Droge W (2002) Free radicals in the physiological control of cell function. Physiol Rev 82: 47-95.
12. Antoneeva II, Gening TP, AbakumovaTV, Arslanova DR, Gening SO (2012) Diagnostics algorithm of progressive ovarian cancer forms. Medical almanac 23: 29-31.
13. Zhang Y, Chen F (2004) Reactive oxygen species (ROS), troublemakers between nuclearfactor–B (NF-B) and c-JunNH2-terminalkinase (JNK). Cancer Res 64: 1902-1905.
14. Lopez LM (2010) A new view of carcinogenesis and an alternative approach to cancer therapy. Mol Med 16: 144-153.
15. Lopez LM (2007) Excessive superoxide anion generation plays a key role in carcinogenesis. Int J Cancer 120: 3075-3083.
16. Martinovich GG, Golubeva EN, Cherenkevich SN (2012) Redox-biotekhnologii as a basis for new strategy in antineoplastic therapies/Messenger of national academy of Sciences of Belarus. Series of medical sciences 2: 85-103.