Clinical role of CYP1B1 gene polymorphism in prediction of postoperative chemotherapy efficacy in NSCLC based on individualized health model

3.1 Introduction to CYP1B1

CYPIBI is an important cytochrome oxidase located on chromosome 2 2p21-p22. It has three exons and two introns, and the open reading frame starts from the second exon, totaling 1,629 bp. It encodes an enzymatic protein consisting of 543 amino acids. In addition to participating in drug metabolism, CYP1B1 also participates in the metabolism of endogenous hormones, such as estrogen and testosterone, and plays an important role in the metabolism of some carcinogen. Defects or mutations in CYP1B1 may lead to the occurrence of some diseases, such as cataracts, glaucoma, tumors, etc. It has been found that CYPIBI has six polymorphic sites, four of which lead to amino acid changes, and its allele frequencies have ethnic differences. CYP1B1 is widely present in extrahepatic tissues, such as lung, breast, uterus, central nervous system, etc. It can oxidatively metabolize many pro-carcinogens (e.g., catalyze the activation of polycyclic aromatic hydrocarbons and aromatic amines) and body hormones (e.g., mediate the lightening of 17-estradiol to form 4-catechol estrogens). Compared with CYP1A1, 1A2, 3A3, and 3A4, CYP1B1 had the highest catalyzing activity of estradiol 4-light, which is the main enzyme of 4-light metabolism. Some studies suggest that CYP1B1 is specifically expressed in tumor tissues. Therefore, CYP1B1 is not only an important enzyme in the bioactivation of environmental carcinogens, but also in the regulation of steroid hormones. The generation of some toxic steroid hormone metabolites is also of great significance. In cells, CYP1B1 can metabolize estrogen and convert it into secondary metabolites, thereby affecting the biological effects of estrogen.

Normally, activation/deactivation of pro-carcinogens is in a state of homeostasis [5,6]. When one or both of these two processes are changed, it leads to some activated intermediates such as epoxides, semi-alkaloids, etc., which bind to DNA bases and become final carcinogens. For example, when AHH enzyme (aromatic hydrocarbon lightenase) activity increases, it disrupts this balance [7,8]. If the GSTMI and other genes in the reaction are deleted, it will not have the activity of the enzyme, and the ability to prevent the formation of DNA adducts will be lost. It prevents the intermediate products from being excreted from the body, and combines with DNA to form carcinogens, which can easily lead to normal cells becoming cancerous. It is currently believed that foreign pro-carcinogens enter the body and are activated into metabolically active intermediates under the action of phase I metabolic enzymes such as cytochrome P450. It then enters the phase reaction and is excreted in combination with phase II metabolizing enzymes such as glutathione-S-transferase (GSTS). This process is deactivation or inactivation. Members of the GSTS family use different thiol auxiliary substrates such as glutathione to bind pre-carcinogenic metabolites to them, making them easier to excrete from the body.

Cytochrome P450 (referred to as CYP450) represents a large family of autooxidizable heme proteins, belonging to a class of monooxygenases. Due to the wide variety and subtypes of metabolic substances involved in CYP450, it has broad clinical application value. For example, many drugs need to be metabolized through the CYP450 enzyme in order to achieve therapeutic effects. It is named for its specific absorption peak at 450 nm. It is involved in the metabolism of endogenous substances and exogenous substances including drugs and environmental compounds. As an extrahepatic metabolic enzyme, CYP1B1, on the one hand, forms DNA adducts in the metabolic activation environment of polycyclic aromatic hydrocarbons, heterocyclic amines, aromatic amines, etc., leading to the formation of tumors. On the other hand, it produces semiquinones and quinones in 17β-E2 in metabolically oxidized tissues, which have toxic effects on DNA and induce carcinogenesis. CYP1B1 is a relatively conservative gene sequence, its gene is located on chromosome 2p21-22, and its full length is 12 kb. In the long-term human evolution, there are mainly four loci affecting enzyme activity, namely R48G, A119S, L432V, and N453S. Among them, R48G, A119S, and L432V site mutations can increase the catalytic activity of CYP1B1; however, N453S may have reduced activity in the metabolism of pro-carcinogens, such as polycyclic aromatic hydrocarbons. Many studies have shown that CYP1B1 gene locus polymorphism can increase the susceptibility of hormone-dependent tumors such as breast cancer, endometrial cancer, and prostate cancer. However, these tumor susceptibility may be related to ethnicity and geography [9,10]. Therefore, it is presumed that CYP1B1 key locus gene polymorphisms can increase the susceptibility of NSCLC, which may explain individual differences. Geographical differences can lead to sensitivity results in NSCLC. The cytochrome p450 and CYP1B1 genes and their structures are shown in Figures 1 and 2.

Figure 1

Cytochrome p450.

Figure 2

Structure of CYP1B1.

3.2 CYP1B1 oncogenic pathway

3.2.2 Formation of activated semiquinones and quinones

Estrogen (estrone E1, estradiol E2), dopamine, and benzene all contain a benzene ring structure, which can be oxidized to semiquinones and quinones. Under the catalysis of CYP1A1 and CYP1B1, estrogens (E1, E2) are converted into catechol estrogens (2-OHE1(E2), 4-OHE1(E2)) and then gradually oxidized to form semiquinones and quinones (E1(E2)-2,3-Q, E1(E2)-3,4-Q). Among them, 4-OHE1(E2) exerts oncogenic effects in all animal models, while 2-OHE1 (E2) is not a carcinogenic or only forms borderline tumors [15]. In addition, benzene can also form quinones and semiquinones under the catalysis of CYP2E1. Carcinogens metabolized in the body react with DNA to form two types of DNA adducts. Among them, less than 1% of the DNA adducts are stable adducts, i.e., they are not dissociated unless during DNA repair. In addition, more than 99% are depurinated adducts. It can be separated after binding to DNA, making DNA appear apurine sites. The mechanism is that the electrophilic reaction of carcinogenic DNA adducts occurs on adenine (Ade) and guanine (Gua) of the outer ring amino group. When the adduct binds to N3, adenylate at N7, and guanylate at N7, the stability of the glycosidic bond decreases, forming DNA depurine adducts. At the same time, there is no purine site in the DNA, which makes the DNA site mutated and induces canceration. Its mutagenic 4-OHE1(E2) and E1(E2)-3,4-Q can both form depurine adducts with DNA. The reaction of E1(E2)-3,4-Q with DNA occurred at N3Ade and N7Gua. Studies have shown that after injection of E2-3,4-Q into the mammary glands of female ACI mice, the levels of N3Ade and N7Gua adducts are approximately the same, and it detected mainly H-ras gene mutation (AT/GC). The rate of depurination of its N3Ade adduct occurs instantaneously, whereas depurination of the N7Gua site requires a half-life of several hours. The mutation rate of A base in H-ras gene is high, while a small part is G site mutation, which may be due to the faster depurination rate of N3Ade than N7Gua adducts. In addition, both estrogen metabolites and their depurinated DNA adducts were detected in human urine. Therefore, this adduct can serve as an important biomarker for tumorigenesis risk, as shown in Figure 3.

Figure 3

SCLC and NSCLC.

3.3 NSCLC

The difference between small cell lung cancer (SCLC) and NSCLC is that SCLC has a higher degree of malignancy and requires chemotherapy to treat the disease, but the disease may not be completely cured. SCLC is a highly malignant lung cancer that accounts for approximately 10–15% of all lung cancer cases. SCLC cells have a relatively small morphology, high ratio of nucleus to cytoplasm, active nuclear division, fast growth rate, and are prone to invasion of surrounding tissues and distant metastasis. Therefore, SCLC often enters an advanced stage when detected and requires urgent treatment. NSCLC has a low degree of malignancy, and the disease can be treated by surgery, chemotherapy, radiotherapy, and targeted drugs. SCLC and NSCLC are shown in Figure 3. The so-called first-generation chemotherapy drugs for NSCLC refer to drugs with a single-drug effective rate of about 5%, such as doxorubicin (ADM), vincristine, and fluoropyrimidine (5-FU). Second-generation drugs refer to drugs that appeared after the 1970s and have a single-drug efficacy rate of about 10%, such as DDP, CBP, VDS, and IFO. The third-generation chemotherapeutic drugs refer to new drugs that appeared after the 1990s, and the single-drug efficacy rate exceeds 15%, such as NVB, Gemzar, Taxol, and Docitaxol. The structural diagram of these three generations of chemotherapy drugs is shown in Figure 4.

Figure 4

Structural diagram of the third-generation chemotherapeutic drugs.

According to the world’s major research and exploration on the efficacy of adjuvant chemotherapy after radical resection of NSCLC in the past 20 years, with the emergence and clinical promotion of the second and third generation chemotherapy drugs, the effectiveness of adjuvant chemotherapy and the compliance of patients with chemotherapy regimens have been confirmed and improved, respectively, future clinical trials should focus on the clinical practice of a new generation of chemotherapy drugs. Adjuvant chemotherapy refers to the chemotherapy method used to eliminate small residual tumors and prevent tumor recurrence and metastasis after surgical resection of malignant tumors such as NSCLC. It compensates for the shortcomings of surgical resection in controlling local lesions and treating systemic lesions. It provides stronger evidence and guidelines for the clinical use of new drugs. In addition, since 2000, many scholars and research institutions have paid attention to the evaluation of the efficacy of adjuvant chemotherapy after radical resection of stage I NSCLC. This is certainly helpful for deepening our understanding of adjuvant preventive measures. However, it should not ignore the exploration of patients with locally advanced NSCLC as the trial population. Only by comprehensively examining the entire disease population can this article gain a better understanding of the relationship between disease and interventions, so that decisions that are most beneficial to patients in clinical practice can be made.

NSCLC refers to a type of malignant tumor that originates from lung tissue and belongs to the category of lung cancer. NSCLC accounts for approximately 85% of all lung cancers. Common types include adenocarcinoma, squamous cell carcinoma, and large cell carcinoma. The early symptoms of NSCLC are not obvious, while the late symptoms include persistent cough, expectoration, dyspnea, chest pain, weight loss, etc. The treatment goals of NSCLC are different according to the stage of NSCLC. For different patients, the goal of this article is cure. In contrast, for stage IV patients, the goal of palliative treatment is to relieve symptoms and prolong survival time. To achieve cure, the preferred intervention is radical surgery, including lobectomy and unilateral pneumonectomy. It is also combined with mediastinal lymph node dissection. Distant recurrence is the leading cause of death in patients with NSCLC within 5 years after radical resection, because even the tumor appears to be confined to the lung. However, some hard-to-find micrometastases are still the cause of distant recurrence. In this regard, many researchers try to eliminate the residual micrometastases after surgery by adjuvant chemotherapy after surgery, so as to achieve the purpose of reducing local recurrence and reducing distant metastasis. This article also reviews the research and attempts in adjuvant chemotherapy after radical resection of NSCLC in the past 20 years since 1992 in chronological order.

Cyclophosphamide, adriamycin, prednisone (CAP) adjuvant chemotherapy refers to a chemotherapy regimen used to treat NSCLC. CAP regimen consists of cyclophosphamide, adriamycin, and cisplatin, which is one of the most commonly used chemotherapy schemes in clinical practice. Adjuvant chemotherapy with CAP regimen is not expected to use for patients with stage I NSCLC after radical resection. This article attributes the low efficacy of the adjuvant chemotherapy regimen for CAP indicated in the conclusions to poor patient compliance with the chemotherapy regimen and the poor efficacy of the chemotherapy regimen itself. Compared with a similar study conducted in Finland, the negative results of this study suggest that new adjuvant chemotherapy regimens that are more effective, have fewer toxic side effects, and have higher patient compliance that are needed in the future.

3.4 SPSS algorithm

Assuming an algorithm capable of estimating the aggregate demand over a fairly long period T, total demand can be estimated based on the user’s plan or forecasted based on historical records. So this assumption is reasonable, let

It is the peak demand, then the demand d _t is divided into L tiers. The definition index d t l represents whether the lth layer has demand at time t, i.e., if d _t ≥ 1, then d t l = 1, otherwise it is 0. For example, Figure 5 depicts a demand curve.

Figure 5

Demand curve and tier-wise reservation method with three reserved instance periods.

d 3 4 = 0 because Tier 4 has no demand in billing cycle 3, and d 5 5 = 1, because Tier 5 has demand in billing cycle 5. In fact, each d t l corresponds to a small rectangular shaded block. d t l = 1 if the corresponding rectangle is below the curve, otherwise 0, obviously

A demand curve is said to be convex if

No other

Let

For example, in Figure 5, the demand curve at times 1–3 is convex. But the demand curve at times 4–9 is not convex because ∑ l = 1 L d 8 l is smaller than its neighbors.

y ljt is 1 if the request is served by a non- τ 0 instance τ j , otherwise 0. For example, in layer 2 of Figure 5, y ₂₂₃ = 1 because τ 2 is reserved at time 3. y ₂₀₉ = 1 because a real-time instance τ 0 is activated at time 9. All the rest y 2 jt = 0, the problem can be formalized as a mathematical programming 1 from a hierarchical perspective.

Because as long as τ j is reserved, c j will be charged, so the objective function is the total cost. In constraint (2-1),

It is the number of reserved instances that are still valid at level l up to time t. y lot is the number of real-time instances configured by layer l at time t. After calculating the sum of the instances of each layer. This number should not be less than the requirement d t .

Now, consider the following plan 0-1ILPForLevel corresponding to a single level:

Let y∗ be the optimal solution of planning 0-1ILPForLevel, and y be any feasible solution of planning 0-1ILPForLevel, then f(y) ≥ f(y∗), so

Then

4.1 Specimen source and related clinicopathological data

4.2 Main PCR reagents and methods

The experimental instruments and reagents are shown in Tables 2 and 3, and some of the instruments are shown in Figure 6:

Figure 6

Parts of the experimental equipment.

The extraction steps of genomic DNA in paraffin section lung cancer tissue are as follows. (1) Deparaffinization: it prepares 2–3 slices of paraffin tissue Section (5 μm), after removing excess paraffin around the tissue. The tissue section was put into a 1.5 mL Eppendorf tube, 1 mL of toluene was added, fully shaken, 56°C water bath for 15 min, inverted and mixed every 5 min, centrifuged at 12,000 rpm for 10 min, and the supernatant was removed. (2) Alcohol washing: adding 1 mL of absolute ethanol, mix well, centrifuge at 4°C for 5 min, and remove the supernatant. (3) Repeating dewaxing and alcohol washing once, and the steps are the same as above. (4) After the last alcohol wash, centrifuge at 4°C for 5 min, discard the supernatant, and dry at 37°C (20–30 min) until the pellet is completely dry. (5) After grinding the tissue with liquid nitrogen, transfer the tissue to a 2 mL EP tube. It was added with 1 mL of solution D and an equal volume of phenol/chloroform, vortexed for 10 s, and kept on ice for 10 min. Centrifuging at 12,000 rpm for 420 min, and transferring the supernatant (1,000 μL) to a new tube. (6) Adding an equal volume of cold isopropanol (800 μL), mixing well, and placed in a −20°C refrigerator overnight. (7) At 12,000 rpm for 410 min°C, discard the supernatant, washing twice with cold 75% ethanol, 12,000 rpm for 410 min°C, discarding the supernatant, and fully drying the DNA. (8) Resuspending with 30 μL sterilized triple-distilled water for later use.

4.3 Judgment of experimental results

The sequenced sequences were analyzed by DNAssitant software, and the sequencing results were compared with the EGFR and KRAS gene sequences in the gene bank with manual proofreading. If they were inconsistent, it was a mutation. Positive control: knowing positive specimen sections provided by the reagent company; negative control: using PBS instead of primary antibody as blank control. Expression location: positive staining for RRM1 protein indicated that brown-yellow granules of varying thickness were located in the cytoplasm, and positive staining for ERCC-1 protein indicated that the brown-yellow granules were located in the nucleus. Observation method: two or more attending pathologists observed the slices in a double-blind manner to determine the protein expression results. Positive judgment criteria: the staining intensity of cancer cells in the section: 0 points for no coloration of cells, 1 point for pale yellow, 2 points for brownish yellow, 3 points for tan (the staining depth should be compared with the background coloration). The number of positive cancer cells in the section is the percentage. It multiplied the positive staining intensity score by the percentage of positive tumor cells to obtain the final immunohistochemical score H value, which ranged from 0 to 300. The final median H value was used as the boundary, and the median H value greater than or equal to the median H value was judged as positive, and the median H value less than the median H value was judged as negative.

This article uses SPSS19.0 statistical software for analysis. The chi-square test was used to analyze the correlation, the Kaplan–Meier method was used to draw the disease-free survival curve, and the log-rank test was used between different stratification factors. Univariate and multivariate analyses were performed using COX proportional hazards regression model. Univariate analysis of statistically significant covariates was introduced into a multivariate COX proportional hazards regression model.

5.1 Overall characteristics of enrolled patients

In this study, March 1, 2013 was the cut-off date for follow-up, the follow-up rate was 95.6%, and the follow-up time was 9.1–51.8 months. The median follow-up time was 22.4 months. By the end of follow-up, 12 of 68 patients died, including 1 in stage I, 6 in stage II, and 5 in stage III. Recurrence or metastasis occurred in 27 cases, of which 12 cases recurred within 1 year after operation, and 10 cases recurred within 2 years after operation. The median recurrence-free survival time of all patients was 33.7 months, the 1-year disease-free survival rate was 80.9%, and the 2-year disease-free survival rate was 80.8%. The disease-free survival curves of all patients are shown in Figure 7.

Figure 7

Disease-free survival curve of all patients (m).

5.2 EGFR mutation and postoperative adjuvant chemotherapy in NSCLC

In the EGFR mutation-positive group, 14 of the 32 patients relapsed, whereas 13 of the 36 patients in the EGFR-negative group relapsed. The EGFR mutation-positive group had greater 1-year and 2-year disease-free survival rates than the EGFR mutation-negative group. The median time to progression was 33.7 months in the EGFR-positive group, which was not observed in the negative group. The P value for the comparison between the two groups was 0.519. The difference was not statistically significant, as shown in Figure 8.

Figure 8

Comparison of disease-free survival in patients with different EGFR mutation status.

According to different clinical stages, the disease-free survival rate of EGFR mutation status is quite different, as shown in Figure 9.

Figure 9

EGFR mutation status in different clinical stages.