CN117214276A

CN117214276A - Application of plasma lipid molecules in lung cancer diagnosis

Info

Publication number: CN117214276A
Application number: CN202311169083.2A
Authority: CN
Inventors: 邱满堂; 王文香
Original assignee: Peking University Peoples Hospital
Current assignee: Peking University Peoples Hospital
Priority date: 2023-09-12
Filing date: 2023-09-12
Publication date: 2023-12-12

Abstract

The application relates to the field of biological information and biotechnology, in particular to application of lipid molecules in lung cancer diagnosis, wherein the lipid molecules comprise LPC 14:0, LPC 16:0, LPC 18:0, LPC 18:1, LPC 18:2, PC (16:0/18:1), PC (18:0/18:1), LPE 16:0, LPE 18:0 and LPE 18:1, and diagnosis based on the lipid molecules has the advantages of high sensitivity, good specificity and the like.

Description

Application of plasma lipid molecules in lung cancer diagnosis

Technical Field

The application relates to the technical field of biological information and molecular detection, in particular to a plasma lipid molecule screened by raw letter and application of the plasma lipid molecule in lung cancer diagnosis.

Background

Lung cancer is the leading cause of morbidity and mortality in global malignancies. Early screening and early treatment are important means for reducing the death rate of lung cancer, and after radical surgical excision of lung cancer, the survival rate of lung cancer in stage I in 5 years is as high as 68-92%, and the survival rate in stage II-IIIA is only 25-53%. Early diagnosis provides a critical advantage in improving survival, and Low Dose Computed Tomography (LDCT) has been proposed as the preferred screening strategy for lung cancer. Results of the national cancer screening test (National Cancer Screening Trial, NLST) show that LDCT screening is performed in the high risk group of lung cancer, and the death rate of lung cancer is reduced by 20% compared with that of a control group. LDCT screening has a number of drawbacks such as the enormous cost of screening, the risk of exposure to ionizing radiation, low positive predictive value, etc. Therefore, a minimally invasive technique with high sensitivity and specificity is needed for early screening and diagnosis of lung cancer.

Metabolic reprogramming is one of the hallmarks of malignancy, with cancer cells of different malignancy having different metabolic patterns. Metabonomics is a science of qualitatively and quantitatively analyzing biological samples (such as plasma, serum, urine, feces, saliva, etc.) or all small molecule metabolites (such as amino acids, fatty acids, lipids, etc.) in cells, and finding the relative relationship between the metabolites and pathophysiological changes. In particular, lipidomics are used as a branch of metabonomics, so that the whole lipid change of an organism can be systematically analyzed, and key lipid biomarkers in metabolic regulation are identified by comparing the change of lipid metabolism networks under different physiological states, so that the action mechanism of the lipid in various vital activities is finally revealed. Abnormality of lipid metabolism occurs in early stages of tumorigenesis, so searching for metabolite change characteristics of occurrence of lung cancer using lipidomic techniques has great potential.

Lung cancer diagnosis has been studied by current researchers using plasma lipidomic techniques, such as Klupczynska A, plewa S, kasprzyk M, dyszkiewicz W, kokot ZJ, matysiak J.serum lipidome screening in patients with stage I non-smallcell lun cancer.Clin Exp Med.2019nov;19 (4): 505-513) and Noreldeen HAA et al (Noreldeen HAA, du L, li W, liu X, wang Y, xu G.serum lipidomic biomarkers for non-small cell lung cancer in nonsmoking female parameters.Jpharm Biomed Anal 2020Jun 5;185 113220.). However, most of the researches only select a small amount of samples, and a series of lipid molecular markers obtained by screening are not verified in an external queue by using a universal chromatographic method, and only specific groups of lung cancer are targeted, so that the lipid molecular markers have no generalization and have very limited practical clinical significance.

Therefore, the large-scale clinical sample is adopted for plasma lipidomic research, and the search of lung cancer diagnosis plasma metabolic markers with high sensitivity and good specificity has important clinical application value. Through non-target metabonomics screening, target method verification and multi-center verification, the method combining large-scale metabonomic data and machine learning strategies can comprehensively analyze high-dimension samples, and has great efficacy on early diagnosis of lung cancer.

In view of this, the present application has been proposed.

Summary of The Invention

In order to solve the technical problems, the application discovers a group of lipid molecular marker combinations which can be used for diagnosing lung cancer through serial bioinformatics analysis and clinical sample actual examination, and the diagnosis specificity and the definition of the series of markers for lung cancer are far superior to those of the prior art, thus having remarkable clinical value.

Therefore, the present application has at least the following objects:

a first object of the present application is to find a screening method for lipid molecular markers;

a second object of the present application is to find a new use for lung cancer diagnosis;

a third object of the present application is to find a completely new product suitable for lung cancer diagnosis;

a fourth object of the present application is to find a completely new method for lung cancer diagnosis.

In order to achieve the above purpose, the present application proposes the following specific technical scheme:

the application firstly provides a plasma targeting lipid molecular marker letter generation screening method, which comprises the following steps:

1) Preparing a plasma sample;

2) Chromatographic and mass spectrometric detection of plasma samples and quality control samples;

3) Establishing a non-targeted lipidomic profile based on the chromatographic and mass spectrometric detection data; preferably, the chromatography is liquid chromatography;

4) PCA performs quality control and sample clustering;

5) OPLS-DA statistically compares lung cancer groups with control groups;

6) Screening potential lipid markers based on the differential expression metabolite profile;

7) Screening the targeted lipid molecular markers based on a random forest algorithm;

8) And (5) verifying the effectiveness of the lipid molecular markers in detecting early lung cancer by using a multi-center sample.

Further, the targeted lipid molecular markers include any or more or all of LPC 14:0, LPC 16:0, LPC 18:0, LPC 18:1, LPC 18:2, PC (16:0/18:1), PC (18:0/18:1), LPE 16:0, LPE 18:0, and LPE 18:1; preferably all.

The application also provides application of the lipid molecules as markers in diagnosing lung cancer, or application of a detection agent or a component for obtaining the lipid molecule level in a sample in preparing a product for diagnosing lung cancer; the lipid molecules include any or more or all of LPC 14:0, LPC 16:0, LPC 18:0, LPC 18:1, LPC 18:2, PC (16:0/18:1), PC (18:0/18:1), LPE 16:0, LPE 18:0 and LPE 18:1; preferably all.

In some aspects, the article of manufacture includes, but is not limited to, a kit form, a system device form, a computer readable medium, or a computer system form.

In some aspects, the lipid molecules are used as independent indicators of lung cancer diagnosis.

In some aspects, the level of lipid molecules in the obtained sample is obtained by mass spectrometry detection means.

In some aspects, a detection agent or component to obtain levels of other markers is also included in the product.

Preferably, the additional marker comprises a gene marker, a protein marker, or a lipid molecular marker;

the other markers are combined with the markers for detection and are used for lung cancer diagnosis.

In some aspects, the product further includes a sample processing reagent comprising at least one or more of a sample lysing reagent, a sample purifying reagent, and a sample extracting reagent.

In some aspects, the product further comprises at least one of a standard, a calibrator, a control, and a buffer.

In some aspects, the sample comprises tissue, cells, body fluids, serum, plasma, whole blood (peripheral blood), urine, semen, saliva, hydrothorax, ascites, cerebrospinal fluid, stool, or synovial fluid; preferred are serum, plasma, whole blood, urine, and the like.

In some aspects, the diagnosis includes, but is not limited to, screening for lung cancer, early diagnosis, auxiliary diagnosis, and definitive diagnosis.

The application also provides a product for diagnosing lung cancer comprising a detector or module for obtaining the level of said lipid molecules in a sample; the lipid molecules include any or more or all of LPC 14:0, LPC 16:0, LPC 18:0, LPC 18:1, LPC 18:2, PC (16:0/18:1), PC (18:0/18:1), LPE 16:0, LPE 18:0 and LPE 18:1; preferably all.

In some aspects, the product includes, but is not limited to: a product in the form of a kit, a device, a computer readable medium or a computer system.

Preferably, the additional marker comprises a gene marker, a protein marker, or a lipid molecular marker.

In some aspects, the diagnosis includes, but is not limited to, screening for lung cancer, early diagnosis, auxiliary diagnosis, definitive diagnosis, and the like.

The application also provides a method of diagnosing lung cancer in vivo or in vitro comprising the step of obtaining the level of lipid molecules in a sample from a subject.

In some aspects, the method comprises the steps of:

(i) Obtaining a level of lipid molecules in the subject sample;

(ii) Comparing the lipid molecular level with a control sample; wherein a significant difference in the level of lipid molecules in the subject sample and the control sample is indicative of the subject having lung cancer; or,

(ii) Comparing with a set threshold absolute amount; wherein a sample level of the subject above a threshold absolute amount is an indication that the subject has lung cancer.

The lipid molecules include any or more or all of LPC 14:0, LPC 16:0, LPC 18:0, LPC 18:1, LPC 18:2, PC (16:0/18:1), PC (18:0/18:1), LPE 16:0, LPE 18:0 and LPE 18:1; preferably all.

In some aspects, the diagnosis includes, but is not limited to, screening for lung cancer, early diagnosis, auxiliary diagnosis, definitive diagnosis, and the like; preferably for early diagnosis.

In some aspects, the subject is preferably a human.

The application also provides a method of detecting a marker in a subject having or suspected of having lung cancer in vivo or in vitro, the method comprising determining or detecting the level of a lipid molecule in a sample from the subject; the lipid molecules include any or more or all of LPC 14:0, LPC 16:0, LPC 18:0, LPC 18:1, LPC 18:2, PC (16:0/18:1), PC (18:0/18:1), LPE 16:0, LPE 18:0 and LPE 18:1; preferably all.

In some aspects, the methods may be for disease diagnosis uses, or may be for non-disease diagnosis uses.

The application also provides a method for evaluating or screening a diagnostic marker of lung cancer, comprising the step of correlating or concordant analysis of a potential biomarker with the lipid molecules described above, whereby the diagnostic value of the potential marker is validated by conclusion of the correlation or concordance. For example, the potential biomarker is suggested to be a candidate marker when the potential biomarker has a correlation or identity with a lipid molecule.

The present application also provides a method for screening a therapeutic agent for lung cancer in vivo or in vitro, comprising the step of performing the above-described evaluation of the levels of 10 lipid molecules on a sample treated with the therapeutic agent.

The application also provides a marker for diagnosing lung cancer, which comprises the 10 lipid molecules.

The beneficial technical effects of the application are as follows:

through screening and clinical verification, a group of plasma lipid molecule combinations are obtained: any or more or all of LPC 14:0, LPC 16:0, LPC 18:0, LPC 18:1, LPC 18:2, PC (16:0/18:1), PC (18:0/18:1), LPE 16:0, LPE 18:0, and LPE 18:1; preferably, the combination has the advantages of high prediction sensitivity and strong specificity in the lung cancer diagnosis process, and is suitable for clinical popularization and use.

Drawings

In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings that are needed in the description of the embodiments or the prior art will be briefly described, and it is obvious that the drawings in the description below are some embodiments of the present application, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.

FIG. 1, PCA score plot for reversed phase chromatography positive ion mode;

FIG. 2, PCA score plot for negative ion mode of reversed phase chromatography;

FIG. 3, OPLS-DA results for reverse chromatography positive ion mode;

FIG. 4, OPLS-DA results for reverse chromatography negative ion mode;

FIG. 5, differential metabolite profile screening results;

FIG. 6, RF-based multivariate exploratory ROC curve analysis results;

fig. 7, ROC evaluation curve results for clinical trial queue 1;

fig. 8, ROC evaluation curve results for clinical trial queue 2;

fig. 9, ROC evaluation curve results for clinical trial cohort 3.

Detailed Description

The present application discloses the use of a panel of lipid molecules in the diagnosis of lung cancer, and those skilled in the art will be able to practice the use thereof in light of the present disclosure, and it is specifically noted that all such substitutions and modifications as would be apparent to one skilled in the art are intended to be included in the present application. While the methods and applications of this application have been described in terms of preferred embodiments, it will be apparent to those skilled in the relevant art that the application can be practiced and practiced with modification and alteration of the methods and applications described herein, or with appropriate modification and combination, without departing from the spirit and scope of the application. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

The following basic terms or definitions are provided solely to aid in the understanding of the application. These definitions should not be construed to have a scope less than understood by those skilled in the art. Unless defined otherwise hereinafter, all technical and scientific terms used in the detailed description of the application are intended to be identical to what is commonly understood by one of ordinary skill in the art. While the following terms are believed to be well understood by those skilled in the art, the following definitions are set forth to better explain the present application.

As used herein, the terms "comprising," "including," "having," "containing," or "involving" are inclusive or open-ended and do not exclude additional unrecited elements or method steps. The term "consisting of …" is considered to be a preferred embodiment of the term "comprising". If a certain group is defined below to contain at least a certain number of embodiments, this should also be understood to disclose a group that preferably consists of only these embodiments.

The indefinite or definite article "a" or "an" when used in reference to a singular noun includes a plural of that noun.

The terms "about" and "substantially" in this application mean the range of accuracy that one skilled in the art can understand yet still guarantee the technical effect of the features in question. The term generally means a deviation of + -10%, preferably + -5%, from the indicated value.

Furthermore, the terms first, second, third, (a), (b), (c), and the like in the description and in the claims, are used for distinguishing between similar elements and not necessarily for describing a sequential or chronological order. It is to be understood that the terms so used are interchangeable under appropriate circumstances and that the embodiments of the application described herein are capable of operation in other sequences than described or illustrated herein.

The terms "or more", "at least", "exceeding", etc., such as "at least one" should be understood to include, but not be limited to, values of at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100 or 200, 300, 400, 600, 700, 900, or 5000. But also any larger numbers or scores therebetween.

Conversely, the term "no more than" includes every value that is less than the recited value. For example, "no more than 100 nucleotides" includes 100, 99, 98, 97, 96, 95, 94, 93, 92, 91, 90, 89, 88, 87, 86, 85, 84, 83, 82, 81, 80, 79, 78, 77, 76, 75, 74, 73, 72, 71, 70, 69, 68, 67, 66, 65, 64, 63, 62, 61, 60, 59, 58, 57, 56, 55, 54, 53, 52, 51, 50, 49, 48, 47, 46, 45, 44, 43, 42, 41, 40, 39, 38, 37, 36, 35, 34, 33, 32, 31, 30, 29, 28, 27, 26, 25, 24, 23, 22, 21, 20, 19, 18, 17, 16, 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1 and 0 nucleotides. But also any smaller numbers or scores therebetween.

The terms "plurality," "at least two," "two or more," "at least a second," and the like should be understood to include, but are not limited to, at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100 or 200, 300, 600, 700, 900, or more, 5000, or more. But also any larger numbers or scores therebetween.

Reference now will be made in detail to embodiments of the application, one or more examples of which are described below. Each example is provided by way of explanation, not limitation, of the application. It will be apparent to those skilled in the art that various modifications and variations can be made to the present application without departing from the scope or spirit of the application. For example, features illustrated or described as part of one embodiment can be used on another embodiment to yield still a further embodiment. Accordingly, it is intended that the present application cover such modifications and variations as fall within the scope of the appended claims and their equivalents. Other objects, features and aspects of the present application will be disclosed in or be apparent from the following detailed description. It is to be understood by one of ordinary skill in the art that the present discussion is a description of exemplary embodiments only, and is not intended as limiting the broader aspects of the present application.

Diagnostic use

According to the application, the lipid molecules which are differentially expressed by lung cancer patients and healthy control groups are screened out, and clinical sample evaluation shows that the lipid molecule combination has very high prediction sensitivity and specificity, so that the lipid molecule combination can be used for diagnosing clinical lung cancer.

Thus in aspects of the present disclosure there is provided diagnostic uses for lung cancer comprising a panel of lipid molecules as markers, and the use of a detector for detecting 10 lipid molecules in a sample in the manufacture of a product for diagnosing lung cancer.

The term "lipid molecule" as used herein refers to a class of substances that is generally known as fats and lipids, insoluble in water, but soluble in non-polar organic solvents such as diethyl ether, chloroform, benzene, and the like. It is one of the important nutrients needed by human body, and is an ingredient of human body cell tissue. For example, LPC stands for Lysophosphatidylcholine (lysophosphotidyline), a biologically active pro-inflammatory lipid produced by pathological activity, and is also the main phospholipid component of oxidized low density lipoproteins, playing an important role in atherosclerosis and inflammatory diseases. PC stands for Phosphatidylcholine (Phosphatidylcholine), also called lecithin, which belongs to a subclass of glycerophospholipids, is a major lipid in mammalian cells, and not only acts as a structural component on cell membranes, but also participates in cell signal transduction and gene regulation. As is clear in the art, exemplary LPC 14:0 refers to one of LPC, wherein 14 represents the number of C atoms in the pendant acyl chain and 0 represents the number of double bonds; PC (16:0/18:1) is one of PC, and 16:0/18:1 refers to the constitution of two fatty acid chains connected by a glycerol backbone respectively.

The term "diagnosis" as used herein refers to a process of identifying a medical condition or disease (e.g., lung cancer) by its signs, symptoms, and especially the results of various diagnostic processes, including detecting a set of lipid molecular levels in a biological sample (e.g., serum or urine) obtained from an individual. Moreover, the term "diagnosis" as used herein includes screening for a disease, early diagnosis, auxiliary diagnosis, diagnosis (determination of the presence or absence of a disease), prediction or determination of the severity of a disease, assessment of disease activity, monitoring treatment, e.g., monitoring disease progression or recurrence during treatment, evaluating the efficacy of a treatment for a disease, selection of a given treatment regimen, and the like. Without limitation, in some specific embodiments, the present application has fully demonstrated that lipid-based molecular levels can be used to determine the presence or absence of lung cancer in a subject, and thus can be used in screening, early diagnosis, auxiliary or definitive diagnosis, and the like.

The terms "sample," "specimen," "test sample," "subject sample," and the like as used herein include various sample types obtained from a patient, individual, or subject and useful in diagnostic or monitoring assays. Patient samples may be obtained from healthy subjects, diseased patients, or patients with symptoms associated with lung cancer. Furthermore, a sample obtained from a patient may be divided into parts, and only a part may be used for diagnosis. In addition, the sample or a portion thereof may be stored under conditions that hold the sample for subsequent analysis. This definition specifically includes blood and other liquid samples of biological origin (including but not limited to tissues, cells, body fluids, serum, plasma, whole blood, urine, semen, saliva, hydrothorax, ascites, cerebrospinal fluid, stool, synovial fluid, and the like). In a specific embodiment, the sample comprises a blood sample. In a specific embodiment, the sample comprises a plasma sample. The definition also includes samples that are manipulated in any way after sample retrieval, such as by centrifugation, filtration, precipitation, dialysis, chromatography, treatment with reagents, washing or enrichment of certain cell populations. These terms also include clinical samples, and also include cells in culture, cell supernatants, tissue samples, organs, and the like. The sample may be tested immediately after collection, stored at RT, 4 degrees celsius, -20 degrees celsius, or-80 degrees celsius, and tested after 24 hours, 1 week, 1 month, 1 year, 10 years, or up to 30 years of storage.

The terms "individual," "subject," and "patient" are used interchangeably herein and refer to any mammalian subject, particularly a human, in need of diagnosis, treatment, or therapy.

The term "ROC" or "ROC curve" as used herein may refer to a receptor operating profile. The ROC curve may be a graphical representation of the performance of a binary classifier system. For any given method, the ROC curve may be generated by plotting sensitivity for specificity at various threshold settings. Further, as long as at least one of three parameters (e.g., sensitivity, specificity, and threshold setting) is provided, and the ROC curve may determine the value or expected value of any unknown parameter. The unknown parameters may be determined using a curve fit to the ROC curve.

The term "AUC" or "ROC-AUC" as used herein generally refers to the area under the receptor operating profile. This metric, taking into account the sensitivity and specificity of the method, can provide a measure of the diagnostic utility of the method. Typically, ROC-AUCs are in the range of 0.5 to 1.0, with values closer to 0.5 indicating a method with limited diagnostic utility (e.g., lower sensitivity and/or specificity) and values closer to 1.0 indicating a method with greater diagnostic utility (e.g., higher sensitivity and/or specificity). See, e.g., pepe et al, "Limitations of the Odds Ratio in Gauging the Performance of aDiagnostic, prognotic, or Screening Marker", am. J. Epidemic 2004,159 (9): 882-890, which is incorporated herein by reference in its entirety. Other methods of characterizing diagnostic utility using likelihood functions, odds ratios, information theory, predictors, calibrations (including goodness of fit), and reclassification measures are summarized according to Cook, "Use and Misuse of the Receiver Operating Characteristic Curve inRisk Prediction", circulation 2007,115:928-935, which is incorporated herein by reference in its entirety.

A detection agent or component for obtaining a level of a lipid molecule in a sample may, as understood, include a detection agent that directly obtains a set of levels of lipid molecules in a sample, or a component that indirectly obtains a set of levels of lipid molecules in a sample (e.g., a computer program directly obtains an indication of a set of levels of lipid molecules in a sample that has been detected).

The detection means is not limited, but any means that can be used to directly or indirectly obtain the level of lipid molecules in a sample is suitable for the present application. In one embodiment of the application, it is obtained by mass spectrometry.

According to experimental data of the application, the 10 lipid molecular markers can be used as independent indicators for lung cancer diagnosis, and effective diagnosis or prediction of lung cancer can be realized only according to the group of lipid molecules.

It will be appreciated that, in order to further enhance the diagnostic effect on lung cancer, the biomarkers detected by the reagent may be other biological materials which are known to those skilled in the art to have diagnostic effect, such as nucleic acid fragments, proteins, other metabolites, etc. which can be used as markers, and by this way, the combination of multiple biomarkers is achieved, and the combination of the multiple biomarkers is matched with each other to achieve a better diagnostic effect on lung cancer patients, thereby achieving a more advantageous diagnostic structure. Therefore, the 10 lipid molecular markers can be used as independent indicators for lung cancer diagnosis, and can be combined with other known diagnostic markers for detection, so that the diagnostic efficiency is improved.

In some embodiments of the application, a test sample according to the application may be selected from the group consisting of tissue, cells, body fluid serum, plasma, whole blood (peripheral blood), urine, semen, saliva, hydrothorax, ascites, cerebrospinal fluid, stool, and synovial fluid; in a preferred embodiment, the test sample is selected from any one of serum, plasma, whole blood or urine.

Diagnostic method

Applicants have found that the levels of a set of lipid molecules in a patient with lung cancer are higher than the levels of a set of lipid molecules in a healthy person, and thus that a more accurate prediction of diagnosis (particularly early diagnosis) of lung cancer can be made based on the levels (e.g., expression levels) of a set of lipid molecules.

The core of the diagnostic method comprises the step of detecting or determining 10 lipid molecules in a sample of a subject;

in some specific embodiments, the method comprises the steps of:

(i) Detecting or determining the level of 10 lipid molecules in the test sample;

(ii) Comparison of the levels of 10 lipid molecules with the control sample; wherein the presence of a significant difference in the level of 10 lipid molecules in the test sample and the control sample is indicative of the subject having lung cancer;

or,

In some embodiments, the expression "level" herein includes, but is not limited to, a level such as abundance or concentration.

It will be appreciated that the control sample may be selected according to actual needs, for example, in disease diagnosis, the control sample is a normal population sample, and when used for prognosis evaluation, the control sample may be a control sample of different prognosis.

In some embodiments, a set value of the expression level of the lipid molecule may be given, which may be determined based on the expression level of the lipid molecule in normal samples of normal humans and/or non-lung cancer patients, e.g., selecting an average value of the expression level of the lipid molecule in normal samples of a suitable number of samples, or setting a reasonable multiple, such as 0.9-fold, 0.8-fold, 0.7-fold, 0.6-fold, 0.5-fold, etc., based on the average value, when the expression level of the lipid molecule in the subject is higher than the set value, lung cancer is determined. It will be appreciated that the need for a set point determined based on the mean, or a multiple of the mean, has a good classification meaning, and that known samples can be tested by conventional statistical testing methods based on the classification of the set point, and that the set point can be used as a criterion when the result has a statistical meaning. Where the level of a lipid molecule is a value indicating that this biomarker for a subject is derived, either directly or further indirectly from direct measurement, which is typically derived at least in part from the abundance or concentration of the biomarker in the sample of the subject. Wherein the indirectly derived values are derived by applying a function to the measured value of this biomarker. Direct measurements include, but are not limited to, the value of the biomarker as determined by biological detection means.

Product(s)

According to the core diagnostic use of the application, the procedure for detecting a set of lipid molecular levels can be configured as a corresponding product for diagnosis or prognosis of lung cancer. The product includes reagents or components for detecting a set of lipid molecular biomarkers. It is to be understood that such product forms are numerous and include, but are not limited to, kit forms, system devices, computer readable media, or computer system forms.

Kit form

In some embodiments of the application, kits for detecting or analyzing a set of lipid molecular levels to predict lung cancer are also disclosed herein. Such a kit may comprise reagents for detecting the level of the marker and instructions for predicting the corresponding disease based on the detected level.

The kit may comprise a set of reagents for generating a data set by at least one assay. The set of reagents is capable of detecting and quantifying the lipid molecule levels. The set of reagents may also further detect other marker levels.

In some embodiments, such kits may include a carrier, package, or container that is compartmentalized to receive one or more containers, such as vials, tubes, and the like, each of the containers comprising one of the individual elements to be used in the method. Kits of the application may include a container as described above as well as one or more other containers containing materials required from a commercial end-user standpoint, including buffers, diluents, filters, and package insert with instructions for use.

In some embodiments, the kit further includes a sample processing reagent, which may include at least one of a sample lysing reagent, a sample purifying reagent, and a sample extracting reagent.

In some embodiments, the product further comprises at least one of a standard, a calibrator, a control, and a buffer. Wherein the reference substance is a control substance for checking the validity of the experiment and is used as a comparison of the judging result. The buffer may be any solution known in the art that provides suitable buffer conditions during the detection process.

In addition to the components described above, the kit will further include instructions for practicing the subject methods. These instructions may be present in the subject kits in a variety of forms. One form in which these instructions may be present is that they are printed information on a suitable medium or substrate, for example, one or more sheets of paper on which the information is printed, in the packaging of the kit, in the form of package inserts, and the like.

Form of the device

A device configured to hold a detection reagent and a test sample mixture. For example, the device may house detection reagents and test samples for determining lipid molecule levels by mass spectrometry detection. The mixture of reagents and test samples may be provided to the device by a variety of containers, examples of which include wells of an orifice plate, vials or tubes, and the like. As such, the device may have an opening (e.g., slot, cavity, opening, sliding tray) that can receive a container containing a reagent test sample mixture and read to generate a quantitative representation of the soluble medium.

Computer system or computer readable medium form

In some embodiments of the application, the methods or uses of the application may be performed on a computer, including detecting or analyzing the lipid molecular levels by a computer to predict lung cancer disease.

The computer system communicates with the device to receive lipid molecular level values, the computer system analyzing the level values and determining a likelihood of a disease activity event in the subject by applying a predictive model.

For example, the construction and execution of the predictive model for generating the score (e.g., LFPI score) may be implemented in hardware or software or a combination of both. In one embodiment, a readable storage medium is provided, such as the medium comprising data storage material encoded with machine readable data, the data storage material being capable of displaying any dataset and execution and results of a predictive model of the application when used with a machine programmed with instructions for utilizing the data. Such data may be used for a variety of purposes, such as lung cancer patient monitoring, diagnosis, therapeutic considerations, and the like. Embodiments of the above-described methods may be implemented in a computer program executing on a programmable computer comprising a processor, a data storage system, a graphics adapter, a network adapter, at least one input device, and at least one output device, etc. A display is coupled with the graphics adapter. Program code is applied to the input data to perform the functions described above and generate output information. The output information is applied to one or more output devices in a known manner. The computer may be, for example, a personal computer, a microcomputer or a workstation of conventional design.

Each program may be implemented in a high level procedural or object oriented programming language to communicate with a computer system. However, the program(s) can be implemented in assembly or machine language, if desired. In any case, the language may be a compiled or interpreted language. Each such computer program is preferably stored on a storage medium or device (e.g., ROM or magnetic disk) readable by a general or special purpose programmable computer, for configuring and operating the computer when the storage medium or device is read by the computer to perform the procedures described herein. The system may also be considered to be embodied in the form of a computer-readable storage medium, configured with a computer program, where the storage medium so configured causes a computer to operate in a specific and predefined manner to perform the functions described herein.

Feature patterns and their databases may be provided in various media to facilitate their use. The "medium" refers to an article of manufacture containing the characteristic pattern information of the present application. The database of the present application may be recorded on a computer readable medium, such as any medium that can be directly read and accessed by a computer. Such media include, but are not limited to: magnetic storage media such as floppy disks, hard disk storage media, and magnetic tape; optical storage media such as CD-ROM; electronic storage media such as RAM and ROM; and mixtures of these classes, such as magnetic/optical storage media. Those skilled in the art will readily understand how any presently known computer readable medium may be used in the manufacture of a record containing current database information. "recording" refers to the process of storing information on a computer readable medium using any such method known in the art. Any convenient data storage structure may be selected based on the manner in which the stored information is accessed. A variety of data processor programs and formats may be used for storage, such as word processing text files, database formats, and the like.

Other aspects of use

It will be appreciated that, based on the belief that the lipid molecules may be used as diagnostic markers for lung cancer, one skilled in the art may evaluate or screen for other potential diagnostic markers for lung cancer accordingly, and generally evaluate the potential biomarkers with respect to the lipid molecules by correlation, and help to demonstrate the value of the potential diagnostic markers based on the conclusion of a positive correlation.

The application may also relate to a method of detecting a marker in a subject suffering from or suspected of suffering from lung cancer in vivo or in vitro, comprising determining or detecting the level of a set of lipid molecules in a biological sample from the subject, in particular an in vitro detection method, which may comprise non-disease diagnostic uses such as purely scientific nature.

Embodiments of the present application will be described in detail with reference to examples.

Example 1 evaluation and screening of plasma lipid molecular markers

1. Plasma sample preparation

176, -80 degree cryopreserved plasma samples were screened from the biological sample library, with 112 lung cancer, 14 benign nodules, 50 healthy subjects, and healthy and benign nodule patients collectively referred to as Controls (Controls). The preparation process of the plasma detection sample is as follows:

1) Samples were taken and placed at 4℃for thawing for 30min.

2) 40. Mu.L of serum was placed in a 2.0mL centrifuge tube, 300. Mu.L of methanol was added, and vortexed and shaken for 1min. 1000. Mu.L of methyl tert-butyl ether was added and the mixture was shaken for 1h. 250. Mu.L of ultrapure water was added thereto and the mixture was shaken for 1min.

3) The centrifuge tube was placed in a low temperature centrifuge and centrifuged at 12000rpm at 4℃for 10min.

4) 400 μl of the supernatant was placed in a 1.5mL centrifuge tube and evaporated in vacuo at low temperature. 100. Mu.L of isopropanol/acetonitrile (1:1) solution was added and the mixture was reconstituted with shaking.

5) Transfer to 250 μl of inner cannula to be tested.

6) The quality control sample is obtained by uniformly mixing samples with equal volumes.

2. Plasma sample and quality control sample chromatography and mass spectrometry

1) Chromatographic conditions

Analysis was performed using a Thermo Scientific U3000 flash liquid chromatography and reverse phase chromatography column. The chromatographic conditions were as follows:

chromatographic column: waters ACQUITY UPLC BEH C8 (1.7 μm,2.1 mm. Times.100 mm);

positive ion mode:

mobile phase: phase A (acetonitrile/water 6:4,0.1% formic acid, 5mM ammonium formate), phase B (acetonitrile/isopropanol 1:9,0.1% formic acid, 5mM ammonium formate); elution gradient: see table 1; flow rate: 0.26mL/min; sample injection amount: 4.0 μl; column temperature: 55 ℃.

TABLE 1 Positive ion mode reverse phase chromatography elution procedure

Negative ion mode: mobile phase: phase a (acetonitrile/water 1:10,0.04% acetic acid, 1mM ammonium acetate), phase B (acetonitrile/isopropanol 1:1) elution gradient: see table 1; flow rate: 0.3mL/min; sample injection amount: 4.0 μl; column temperature: 55 ℃.

TABLE 2 anion mode reverse phase chromatography elution procedure

2) Mass spectrometry conditions

Quadrupole orbitrap mass spectrometer (Q exact) with a thermoelectric spray ion source was used ^TM ) Mass spectrometry was performed. The ion source voltages of positive and negative ions are 3.7kV and 3.5kV respectively; the heating temperature of the capillary tube is 320 ℃; sheath gas pressure is 30psi, auxiliary gas pressure is 10psi; the solvent heating evaporation temperature is 300 ℃; the sheath gas and the auxiliary gas are nitrogen; the collision gas was nitrogen at a pressure of 1.5mTorr. The primary full scan parameters are: resolution 70000, automatic gain control target 1×10 ⁶ Maximum isolation time is 50ms, and mass-to-charge ratio scanning range is 150-1500. And calibrating a mass spectrum mass axis by adopting an external standard method, wherein the mass error is 5ppm. The calibration positive ions were 74.09643, 83.06037, 195.08465, 262.63612, 524.26496 and 1022.00341; the negative ions are 91.00368, 96.96010, 112.98559, 265.14790, 514.28440 and 1080.00999. Metabolite identification was performed using dd-MS2 scan mode (data dependent scan mode), the parameters were as follows: resolution 17500, automatic gain control target 1×10 ⁵ Maximum isolation time 50ms, secondary fragmentation (dynamic exclusion) of up to 10 ions, mass separation window 2, collision energy 30V, intensity limit 1×10 ⁵ . The liquid system was controlled using Xcalibur 2.2SP1.48 software and data acquisition was performed.

3. Non-targeted lipidomic profile analysis

The collected data is processed by Progenesis QI software, and the steps of raw data importing, peak alignment, peak extraction and normalization are sequentially carried out, so that a table of retention time, mass-to-charge ratio and peak intensity is finally formed. Various adduct ions (e.g., hydrogenation, sodium addition, etc.) are deconvoluted to each ion feature. In addition, ion characteristics with a variation coefficient of >15% in the quality control sample are eliminated to obtain reliable and repeatable metabolites. And adopting a human metabolome database and a lipid database to carry out primary molecular weight matching, and identifying the metabolites.

4. PCA analysis (for clustering between QC and Sample)

Principal component analysis of samples using the unsupervised technique PCA (principal component analysis) can generally reflect the metabolic differences between samples and the magnitude of variability between samples within a group. Prior to analysis using metaanalysis 5.0, the data sets were normalized, including median normalization, logarithmic transformation (base 10) and scaling, to obtain more intuitive and reliable results, with the purpose of scaling all variables (certain digital features, such as mean and standard deviation) on the same scale, to avoid masking of certain too high or too low metabolite signals resulting from large differences in concentration of different metabolites in complex biological samples, and thus affecting the identification of biomarkers.

For the analysis of an unsupervised model such as PCA, the main parameter for judging whether the model is good or bad is R2X, the value represents the interpretation rate of the model, and Q2 represents the predictable variable of the model. PCA analysis is an unsupervised model analysis method that can classify data based on their similarity, so that PCA can more truly reflect inter-group differences and identify intra-group variations than supervised model analysis methods such as PLS-DA and OPLS-DA analysis. The following tables 3 and FIGS. 1-2 show the results of reverse-phase chromatography positive ions and reverse-phase chromatography negative ions in sequence.

Table 3PCA analysis model parameters

5. OPLS-DA analysis (comparison of Lung cancer group and control group)

To obtain metabolite information that leads to this significant difference, the present application further employs a supervised multidimensional statistical method, partial least squares discriminant analysis (OPLS-DA), to statistically analyze both sets of samples.

The following Table 4 and FIGS. 3-4 show the results of reverse chromatography positive ions and reverse chromatography negative ions in order.

TABLE 4OPLS-da analytical model parameters

6. Identification and establishment of potential lipid markers

The differential expression metabolite profile was found using a multifactor analysis of the VIP (Variable Importance in the Projection) value of the OPLS-DA model (threshold > 1) and a single factor analysis of the p value of t-test (p < 0.05), and the results are shown in FIG. 5.

Then further screening according to Fold change (FC >2 or < 0.5), finally determining 60 difference features and then performing characterization. The qualitative method comprises the following steps: 1. an online database (HMDB) is searched (compare mass to charge ratio m/z of mass spectrum or exact molecular mass, error limit 0.01da,2. Combine local databases for characterization.

7. Screening and determination of targeted lipid molecular markers

Multivariate exploratory ROC was performed according to the 60 screened differential metabolite features, random Forest (Random Forest) model building was performed using different feature numbers, and the distinguishing efficacy of the model in lung cancer and control group under different features was compared, and the results are shown in fig. 6. Finally, the qualitative results of the differential metabolite features, the non-targeted mass spectrum modeling results and the difficulty level of the targeted detection method development are combined, and finally 10 targeted lipid molecular markers are determined (table 5).

TABLE 5 final determination of 10 target lipid molecular markers

Example 2 establishment of a method for detection of a target lipidome based on a lipid molecular marker

1. Preparation of targeted lipidome detection sample

1. The extraction of the test samples was performed using an acetonitrile/isopropanol extraction protocol and used for targeted lipid quantification.

1) Samples were taken and placed at 4℃for thawing for 30min.

2) 10. Mu.L of serum was placed in a 1.5mL centrifuge tube, 40. Mu.L of an internal standard solution (acetonitrile/isopropanol 1:1 formulation, concentration 10. Mu.g/mL) was added, and vortexed and shaken for 10min.

4) Taking 10 mu L of supernatant, transferring to a liquid phase small bottle (containing 250 mu L of inner tube), adding 90 mu L of methanol, shaking and mixing for 1min, and measuring.

2. Targeted lipid detection method

1) Targeted lipid chromatography conditions:

the chromatographic separation is carried out by adopting Waters ACQUITY UPLC I-CLASS ultra-high performance liquid chromatography under the following conditions:

chromatographic column: waters UPLC BEH C8 (2.1 mm (column inner diameter). Times.100 mm (column length), particle size 1.7 μm);

mobile phase: phase a (acetonitrile: water=6:4, 0.1% formic acid, 5mM ammonium formate), phase B (isopropanol: acetonitrile=9:1, 0.1% formic acid, 5mM ammonium formate); elution gradient: see table 6; flow rate: 0.26mL/min; sample injection amount: 1.0. Mu.L; column temperature: 55 ℃.

TABLE 6 elution gradient table

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2) Targeted lipid mass spectrometry conditions:

the collection mode is positive ions (ESI+), the ion source voltage is 3.0kV, and the temperature is 150 ℃; desolvation temperature 500 ℃ and desolvation gas flow rate 1000L/h; the taper hole voltage is 10.0V, and the gas flow rate is 10L/h. The mass spectrum collection information of the compounds is shown in Table 7.

Table 7 compound mass spectrometry information

3) Targeted data outcome analysis

TargetLynx quantitative software is adopted for calculation of the target data peak area, and the retention time is allowed to be 15s. And the concentration calculation adopts an internal standard external standard curve method or a single-point internal standard method to obtain a quantitative result.

Example 3 marker diagnostic Performance validation based on clinical samples

1. Clinical sample verification 1

59 and-80 degree frozen clinical plasma samples are screened from a clinical biological sample library, wherein 28 lung cancer samples and 31 healthy human samples are obtained, and the lung cancer samples are characterized in the following table:

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sample processing and detection were performed according to the detection method established in example 2. The clinical verification queue 1 is used as a training set, a linear SVM algorithm is used for establishing a lung cancer diagnosis model based on 10 lipid molecules, and model parameters c=8. The results are shown in FIG. 7, and it can be seen that AUC, sensitivity, specificity and accuracy in clinical validation cohort 1 can all reach 1.

2. Clinical sample verification 2

60-80-degree frozen clinical plasma samples are screened from a biological sample library, wherein the lung cancer samples are 30 cases, the healthy person samples are 30 cases, and the lung cancer samples are characterized in that:

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sample processing and detection were performed according to the detection method established in example 2. And (5) performing verification by using the established SVM model. The results are shown in fig. 8, with AUC 0.97111, specificity 0.866667, sensitivity 0.900000, and accuracy 88.3333% for clinical validation cohort 2.

3. Clinical sample verification 3

39-80 degree frozen plasma samples are screened from a biological sample library, wherein 12 lung cancer samples and 27 healthy human samples are obtained, and the lung cancer samples are characterized in that:

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sample processing and detection were performed according to the detection method established in example 2. Verification was performed using the SVM model established above, with an AUC of 1.000000, specificity of 1.000000, sensitivity of 0.833333, and accuracy of 94.8718% for clinical verification cohort 3.

From the verification results, the established molecular marker combination can distinguish clinical lung cancer and healthy samples with ultrahigh sensitivity and ultrahigh specificity, and the effect is superior to the combined prediction capability of other lipid molecular markers disclosed in the prior art, which has very important significance in clinical accuracy detection.

Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present application, and not for limiting the same; although the application has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the application.

Claims

1. A plasma targeting lipid molecular marker letter screening method is characterized by comprising the following steps:

1) Preparing a plasma sample;

3) Establishing a non-targeted lipidomic profile based on the chromatographic and mass spectrometry combined detection data;

4) PCA performs quality control and sample clustering;

5) OPLS-DA screening lung cancer group and control group for differentially expressed metabolites;

6) Screening potential lipid molecular markers by utilizing the characteristics of differentially expressed metabolites;

8) And verifying the effectiveness of the targeted lipid molecular markers in detecting early lung cancer by using a multi-center sample.

Preferably, the targeted lipid molecular markers include any or more or all of LPC 14:0, LPC 16:0, LPC 18:0, LPC 18:1, LPC 18:2, PC (16:0/18:1), PC (18:0/18:1), LPE 16:0, LPE 18:0 and LPE 18:1; preferably all.

2. The use of a detector or module for obtaining the level of a lipid molecule in a sample for the preparation of a product for diagnosing lung cancer, or the use of a lipid molecule as a marker for diagnosing lung cancer.

3. A product for diagnosing lung cancer comprising a detector or module for obtaining the level of lipid molecules in a sample.

4. The use of claim 2 or the product of claim 3, wherein the lipid molecules comprise any or all of LPC 14:0, LPC 16:0, LPC 18:0, LPC 18:1, LPC 18:2, PC (16:0/18:1), PC (18:0/18:1), LPE 16:0, LPE 18:0 and LPE 18:1; preferably all.

5. The use according to claim 2 or the product according to claim 3, wherein the product comprises, but is not limited to, a kit form, a device form, a computer readable medium or a computer system form.

6. The use according to claim 2 or the product according to claim 3, wherein the lipid molecular level in the obtained sample is obtained by mass spectrometry detection.

7. The use according to claim 2 or the product according to claim 3, further comprising a detector or module for obtaining other known marker levels for use in the joint diagnosis of lung cancer.

8. The use according to claim 2 or the product according to claim 3, wherein the sample comprises one or more of tissue, cells, body fluid, serum, plasma, whole blood, urine, semen, saliva, hydrothorax, ascites, faeces or synovial fluid; preferably, the sample is serum, plasma, whole blood or urine.

9. A method for diagnosing lung cancer in vivo or in vitro comprising the step of obtaining a level of lipid molecules in a sample from a subject;

preferably, the method comprises the steps of:

(i) Obtaining a level of lipid molecules in a sample of said subject in a sample;

(ii) Comparing with a set threshold absolute amount; wherein a sample level of the subject above a threshold absolute amount is an indication that the subject has lung cancer;

preferably, the lipid molecules include any or more or all of LPC 14:0, LPC 16:0, LPC 18:0, LPC 18:1, LPC 18:2, PC (16:0/18:1), PC (18:0/18:1), LPE 16:0, LPE 18:0 and LPE 18:1; preferably all.

10. A method for evaluating or screening for diagnostic markers of lung cancer, comprising the step of performing a correlation or identity analysis of potential biomarkers with lipid molecules; prompting the potential biomarker to be a candidate marker when the potential biomarker has correlation or consistency with a lipid molecule;