JP2011047715A - Method for detecting cancer based on comprehensive analysis of mineral components in hair - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide a new method for cancer detection using an extremely noninvasive ultramicroanalytic means for the comprehensive ultramicroscopic analysis of a plurality of a very small amount of mineral components in hair. <P>SOLUTION: The concentration of a mineral component such as iodine, arsenic, zinc, iron, sodium, selenium, potassium, manganese or the like in the hair of examinee is measured and a value imparting a statistically significant change is obtained from the obtained measured value and the age and distinction of sex of the examinee by statistical analysis such as multiple logistic regression analysis or the like. Cancer is detected on the basis of the obtained value. <P>COPYRIGHT: (C)2011,JPO&INPIT

Description

The present invention relates to a cancer detection method based on comprehensive analysis of mineral components in hair.

Cancer is the leading cause of death in developed countries, and more than 10 million new cancer patients are found each year. To date, there have been many empirical and epidemiological research reports on identifying potential risk factors for cancer, including research on carcinogenic metals such as cadmium, nickel, beryllium or arsenic ( Non-patent documents 1 to 4).

However, exhaustive detailed analysis studies on the relationship between the risk of carcinogenesis and a plurality of biological mineral components have not been made so far.

The inventors of the present invention have comprehensively analyzed mineral components in hair for a large number of specimens, and have analyzed and studied the relationship between these values and various physical or mental disorders (Non-Patent Documents). 5-7). For example, from the results of multivariate analysis of the relationship between minerals in hair and the degree of obesity and cancer risk, it was found that there is a positive or negative correlation between the concentration of several minerals and the degree of obesity and cancer risk ( Non-patent documents 7, 8 and 9).

Navarro Silvera SA, et al., Cancer Causes Conrol. 2007; 18: 7-27 Drasch G, et al., Biol. Trace Elem. Res. 2005; 103: 103-7 Cantor KP, et al., Toxicol. Appl. Pharmacol., 2007; 222: 252-7 Boffetta P., Scand. J. Work Environ. Health, 1993; 19 (suppl 1): 67-70 Yasuda H., et al., Biomed. Res. Trace Elem., 2005; 16: 39-45 Yasuda H., et al., Biomed. Res. Trace Elem., 2005; 16: 285-91 Yasuda H., et al., Biomed. Res. Trace Elem., 2006; 17: 316-21 Hiroshi Yasuda et al., Nihon Magazine (Jpn. J. Hyg.) 62 (2): 514, March 2007 Hiroshi Yasuda et al., Jpn. J. Hyg. 63 (2): 548, March 2008

Accordingly, an object of the present invention is to provide a new method for detecting cancer based on the amount of a plurality of mineral components in hair.

As a result of intensive studies by the present inventors in order to achieve the above-mentioned object, by detailed statistical analysis using multiple logistic regression analysis, the presence or absence of cancer, particularly solid cancer, and essential and harmful minerals in hair are determined. The present inventors have found that a plurality of mineral component concentrations to be included are statistically significantly correlated, and based on this, the present invention has been completed.

That is, the present invention includes the following aspects.

[Aspect 1]
A method of measuring cancer by measuring a mineral component concentration in a subject's hair and using the obtained measured value and the age and sex of the subject as indices.
[Aspect 2]
Measure the mineral component concentration in the subject's hair, obtain a value that gives a statistically significant change from the measured value obtained and the age and sex of the subject, and detect cancer based on that value A method according to aspect 1.
[Aspect 3]
The method according to embodiment 1 or 2, wherein multiple logistic regression analysis is used as the statistical analysis.

According to the method of the present invention, cancer, particularly solid cancer, is detected with a high accuracy (accuracy) of about 84% by a very non-invasive ultra-trace analysis means of comprehensive trace trace analysis of trace mineral components in hair. It is possible to detect and diagnose cancer or to screen cancer patients.

The result of the logistic regression analysis which shows the positive correlation with the iodine density | concentration in hair and cancer risk, a receiver operating characteristic (Receiver Operating Characteristic: ROC) curve, and the area under an ROC curve (AUC) value are shown. The iodine concentration showed a significant positive correlation (p <0.0001) with cancer risk with correlation coefficients r = 0.31, R ² = 0.096, and the AUC value was 0.704. The result of the logistic regression analysis which shows the positive correlation with the arsenic density | concentration in hair and a cancer risk, a receiver operating characteristic (Receiver Operating Characteristic: ROC) curve, and the area under an ROC curve (AUC) value are shown. The arsenic concentration showed a significant positive correlation (p = 0.0004) with cancer risk with correlation coefficients r = 0.210 and R ² = 0.044, with an AUC value of 0.647. The result of the logistic regression analysis which shows the positive correlation with the zinc density | concentration in hair and cancer risk, a receiver operating characteristic (Receiver Operating Characteristic: ROC) curve, and the area under an ROC curve (AUC) value are shown. Zinc concentration showed a significant positive correlation (p <0.0001) with cancer risk with correlation coefficients r = 0.243 and R ² = 0.059, with an AUC value of 0.662. The result of the logistic regression analysis which shows the negative correlation tendency of the selenium density | concentration in hair and a cancer risk, a receiver operating characteristic (Receiver Operating Characteristic: ROC) curve, and the area under an ROC curve (AUC) value are shown. The selenium concentration was negatively correlated with correlation coefficients r = -0.10 and R ² = 0.010 (p = 0.10), and the AUC value was 0.558. Results of multiple logistic regression analysis showing the correlation between the value of multiple linear regression obtained using equation (1) and cancer risk, the receiver operating characteristic (Receiver Operating Characteristic: ROC) curve, and the ROC curve Area (AUC) values and other various statistics are shown. The correlation coefficient was r = 0.661, R ² = 0.437, indicating a significant positive correlation (p <0.0001), and the AUC value was also high at 0.918. Compared to Figure 1-4, the area under the ROC curve (AUC) is significantly improved, even at Sensitivity: 87.1%, Specificity: 79.1%, and Accuracy: 83.8% It was suggested that this is a highly reliable evaluation method.

  BEST MODE FOR CARRYING OUT THE INVENTION The best embodiment of the present invention will be described in detail below, but the present invention can be implemented in many other different forms obvious to those skilled in the art. And it is not limited to description of an Example.

The biological sample of the subject used as the specimen in the method of the present invention is hair, and can be obtained from the subject by extremely noninvasive means. About 0.1-0.3 g is sufficient as the amount, and in order to ensure the accuracy of the method of the present invention, it is preferable to use a portion (about 2-3 cm in length) collected from the root as close as possible to the scalp. .

Measurement of the amount (concentration) of mineral components in the hair sample can be performed by any method known to those skilled in the art. Preferably, as described in the examples of the present specification, it is preferable to perform measurement using an inductively coupled plasma mass spectrometer after performing a suitable dissolution treatment using alkali or nitric acid after the washing treatment.

In the method of the present invention, a value giving a statistically significant change is obtained by appropriate statistical analysis from the measured value of the mineral component concentration in the hair of the subject and the age and sex of the subject, and cancer is detected based on the value. . Examples of statistical analysis include, for example, multiple logistic regression analysis and multiple regression analysis. It is preferable to use the correlation in the multiple logistic regression analysis between the value obtained by multiple linear regression and the cancer risk for each specimen. Mineral components include essential minerals and harmful minerals. In addition, it is preferable to include several kinds of mineral components having a high positive correlation with cancer risk and mineral components having a high negative correlation with cancer risk, in particular, iodine, arsenic, zinc, iron, sodium, selenium, potassium, And it is preferable to use 8 mineral concentrations of manganese.

In particular, the following equation (1) for multiple linear regression:
Multiple linear regression (value) = + 0.70 (for men: (+), for women (-)) + 0.023 x age + 1.75 x LogI + 3.59 x LogAs + + 5.38 x LogZn + 6.67 x LogFe + 2.04 x LogNa- 6.47 x LogSe-1.48 x LogK-1.47 x LogMn-50.88,
Is preferably used.

There are no particular restrictions on the type or primary site of cancer that is the object of detection.

As a value giving a statistically significant change obtained by the appropriate statistical analysis described above, a “cutoff value” can be mentioned. The “cut-off value” is a value set for the purpose of detecting a specific disease. Such a “cut-off value” can be set by means known to those skilled in the art. For example, based on the correlation in the multiple logistic regression analysis between the value obtained by the multiple linear regression equation (1) and the cancer risk, a ROC (Receiver Operating Characteristic) curve is created using commercially available statistical analysis software, Find the optimum sensitivity and specificity, and for the purpose of detection, for example, give priority to the higher sensitivity in the inspection for the purpose of primary screening, etc., and set the cut-off value that increases the specificity in the inspection for inspection purposes. It is possible to set.

124 patients undergoing cancer treatment at Hakuoji Clinic (28 breast cancer, 22 stomach cancer, 11 lung cancer, 10 colon cancer, 9 prostate cancer, 9 prostate cancer, 7 liver cancer, pancreas Cancer patients (including male): 5 cases, uterine cancer: 5 cases, ovarian cancer: 4 cases, esophageal cancer: 4 cases, malignant lymphoma: 4 cases, kidney cancer: 3 cases, and thyroid cancer: 2 cases Based on informed consent, hair samples (approximately 0.2 g) were obtained from 52 people: average age 60.9 + 12.2, women: 72 people: average age 54.9 + 10.6), and also live in Tokyo as a control sample Hair samples were obtained in the same manner from 86 healthy people aged 20-80 (53 men: average age 51.6 + 11.9, women: 33: average age 53.7 + 16.0).

A hair sample (75 mg) is precisely weighed and placed in a plastic container (50 ml), and the washing operation recommended by the Hair Analysis Standards Committee (Cranton EM, et al., Standardization and interpretation of human hair for elemental concentrations. According to Holistic Med. 1982; 4: 10-20), it was washed with acetone and then with a Triton solution (0.01%).

The washed hair sample was mixed with 10 ml of 6.25% tetramethylammonium hydroxide (Tama Chemical, Japan) and 50 μl of 0.1% gold solution (SPEX Certi Prep.) And dissolved at 75 ° C. for 2 hours with stirring. After cooling to room temperature, an internal standard (Sc, Ga and In) solution was added, the weight was adjusted, and the resulting solution was used for measurement. The mineral concentration was measured based on international standards using an inductively coupled plasma mass spectrometer (ICP-MS; Agilent-7500ce) (Yasuda H, et al., Anti-Aging Med., 2007; 4: 38- 42), the concentration was expressed in ng / g (hair). For the purpose of quality control in mineral analysis, a human hair environment standard sample (NIES CRM No. 13) provided by the National Institute for Environmental Studies was used (Yoshinaga J., et al., Fresenius J. Anal. Chem. 1997; 357: 279-83).

Since the mineral component concentration in the hair has a logarithmic normal distribution, a synergistic (geometric) average value is used as a representative value of the mineral concentration in the hair. Logarithmic values of mineral component concentrations were used for statistical analysis. The relationship between 24 kinds of mineral components and cancer risk was analyzed by multiple logistic regression analysis (JMP6; SAS Institute). The results are shown in Table 1.

The coefficient of determination (R ² ) in this multiple logistic regression analysis was 0.465. In Table 1, (Part.coeff.) And (Corr.coeff.) Indicate a partial correlation coefficient by multiple logistic regression analysis and a correlation coefficient in single regression analysis, respectively (* p <0.05, ** p <0.01, *** p <0.001, **** p <0.0001).

In addition, logistic regression for three typical minerals (iodine, arsenic, zinc) with positive correlation between hair concentration and cancer risk, and selenium with negative correlation trend. As a result of the analysis, the receiver operating characteristic (Receiver Operating Characteristic: ROC) curve, AUC, and various other statistical values are shown in FIGS.

Furthermore, the correlation between cancer risk and multiple linear regression values obtained using the following equation (1), with gender, age, and concentrations of harmful and essential minerals (total 8 types) as independent variables. It was determined by logistic regression analysis (R ² = 0.437, r = 0.661, p <0.0001). Furthermore, the area (AUC) of the area under the Receiver Operating Characteristic (ROC) curve was 0.918. These results are shown in FIG.

(Formula 1)
Multiple linear regression (value) = + 0.70 (for men: (+), for women (-)) + 0.023 x age + 1.75 x LogI + 3.59 x LogAs + + 5.38 x LogZn + 6.67 x LogFe + 2.04 x LogNa −6.47 x LogSe−1.48 x LogK−1.47 x LogMn−50.88

Further, analysis using a contingency table as shown in Table 2 (using the value of Formula 1 (horizontal axis) when the cancer probability (vertical axis) in FIG. 5 is “0.50” as the cutoff value), and According to χ ² test, sensitivity: 87.1%, specificity: 79.1%, and accuracy: 83.8% (108 + 68) / 210, χ ² = 99.1, p = 0.0001) It was shown that there is.

As shown by the above results, it was confirmed that there was a very significant correlation between the value of multiple linear regression of a plurality of trace mineral components as defined by Equation 1 and the cancer risk.

Using the detection method of the present invention, it becomes possible to detect cancer, particularly solid cancer, with high accuracy (accuracy) by extremely non-invasive means, to detect and diagnose cancer, or to screen cancer patients. A method is provided.

Claims (9)

A method of measuring cancer by measuring a mineral component concentration in a subject's hair and using the obtained measured value and the age and sex of the subject as indices. Measure the mineral component concentration in the subject's hair, obtain a value that gives a statistically significant change by statistical analysis from the obtained measured value and the age and sex of the subject, and detect cancer based on that value The method of claim 1. The method according to claim 1, wherein multiple logistic regression analysis is used as the statistical analysis. 4. The method according to claim 3, wherein, as a statistical analysis, a value giving a statistically significant change is obtained by using a correlation in a multiple logistic regression analysis between a value obtained by multiple linear regression and a cancer risk for each specimen. . The method as described in any one of Claims 1-4 which measures a harmful mineral and an essential mineral as a mineral component. The method as described in any one of Claims 1-5 containing the mineral component with a high positive correlation with a cancer risk as a mineral component, and the mineral component with a high negative correlation with a cancer risk. The method as described in any one of Claims 1-6 which measures the density | concentration of iodine, arsenic, zinc, iron, sodium, selenium, potassium, and manganese as a mineral component. The following equation (1) for multiple linear regression:
Multiple linear regression (value) = + 0.70 (for men: (+), for women (-)) + 0.023 x age + 1.75 x LogI + 3.59 x LogAs + + 5.38 x LogZn + 6.67 x LogFe + 2.04 x LogNa −6.47 x LogSe−1.48 x LogK−1.47 x LogMn−50.88
The method according to any one of claims 1 to 7, wherein The method according to any one of claims 1 to 8, wherein the cancer is a solid cancer.

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