CN115097034A - Marker for identifying oligospermia, screening method and application thereof - Google Patents
Marker for identifying oligospermia, screening method and application thereof Download PDFInfo
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- CN115097034A CN115097034A CN202210702748.0A CN202210702748A CN115097034A CN 115097034 A CN115097034 A CN 115097034A CN 202210702748 A CN202210702748 A CN 202210702748A CN 115097034 A CN115097034 A CN 115097034A
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- oligospermia
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Links
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Abstract
The invention provides a marker for identifying oligospermia, a screening method and application thereof, belongs to the technical field of biochemistry, and comprises PE (O-16:0/22:6), TAG (54:8) _ FA22:6, HexCel (18:1/18:1), TAG (50:2) _ FA16:0, TAG (52:7) _ FA22:6, FFA (22:6), TAG (56:8) _ FA22:6, TAG (54:7) _ FA22:6, TAG (56:8) _ FA16:0, SM (18:1), FFA (24:1) and DAG (16:0/18:0), and can solve the technical problems that false positive markers still exist in the current clinical diagnosis of oligospermia and the diagnosis mechanism is ambiguous.
Description
Technical Field
The invention belongs to the technical field of biochemistry, and particularly discloses a marker for identifying oligospermia, a screening method and application thereof.
Background
In recent years, the increase of infertility is a major health problem currently facing the world. The decline of male fertility becomes a major public health problem which seriously puzzles the global reproductive health, and is one of the major problems to be solved urgently in the world. Understanding the pathogenesis of idiopathic male infertility not only lays a foundation for clinical idiopathic male infertility treatment, but also has important significance for public health prevention of steady population growth in the world.
Metabonomics is a brand new omics technology developed after genomics and proteomics, is an important component of system biology, and is a method for identifying key biomarkers in disease states by using advanced instruments such as LC-MS (liquid chromatography-mass spectrometry) with high separation rate, high sensitivity and low detection limit, comparing the information change of metabolic maps of endogenous small molecules in organisms under different physiological states, and performing information extraction, mathematical dimension reduction and other methods. The metabonomics analysis method comprises a targeted metabonomics and a non-targeted metabonomics, wherein the non-targeted metabonomics is a method for carrying out system identification and analysis on the metabonomics of the whole life body and finding out differential metabolites; targeted metabolomics is more targeted than non-targeted metabolomics, attention is paid to a specific class of metabolites, and repeatability, sensitivity and accuracy are higher. Lipidomics are a targeted metabonomics analysis method for comprehensively and systematically analyzing and identifying lipids and molecules interacting with the lipids in organisms, tissues and cells so as to further disclose lipid metabolism and physiological and pathological processes of organisms. Quantitative lipidomics is the accurate identification and absolute quantification of lipids. The technology is a lipid group full-quantitative library established based on Stable Isotope Labeling Internal Standard (SILIS) and Response Factors (RF), and can accurately determine the nature and the absolute quantity of various lipids.
Some of the literature reports fatty acid metabolic profiles of sperms of patients with oligospermia, asthenospermia and varicocele, and finds that stearic acid in semen is in negative correlation with sperm motility, and docosahexaenoic acid (DHA) is in positive correlation with sperm motility and discloses complete fatty acid profiles of different patients, but does not reflect the characteristics of metabolite changes of quantitative targeted lipidomics. Although there are also non-patent documents analyzing the seminal plasma metabolome of 20 patients with oligozoospermia and finding that metabolites such as phosphatidylcholine, sphingomyelin, acylcarnitine, L-carnitine, polyunsaturated fatty acids, amino acids and biogenic amines are significantly altered, there is a possibility of false positive markers due to the small number of samples and not systematically analyzed with quantitative targeted lipidomics, and the clinical diagnostic availability and involved influence mechanism for key metabolites are not disclosed.
Disclosure of Invention
The first purpose of the present invention is to provide a marker for identifying oligospermia, which can solve the technical problems that false positive markers still exist in the current clinical diagnosis of oligospermia and the diagnosis mechanism is not clear.
The second objective of the present invention is to provide a screening method for identifying markers of oligospermia, which uses multivariate statistical analysis to perform layer-by-layer screening analysis on data of differential metabolites, thereby improving the accuracy of obtaining the markers.
The third purpose of the invention is to provide the application of the marker for identifying oligospermia in preparing the medicine for preventing or treating oligospermia, and provide a key targeting guideline for preparing the medicine for preventing or treating oligospermia.
The fourth object of the present invention is to provide a kit for diagnosing oligospermia, which has a low false positive rate and high diagnostic accuracy and can be used for clinical examination.
Compared with the prior art, the invention at least has the following advantages and positive effects:
the invention provides a marker for identifying oligospermia, a screening method and application thereof, wherein the marker can reduce the false positive probability and improve the accuracy of oligospermia detection; the screening method combines the advantages of non-targeted high coverage, full-scanning accurate molecular weight determination and targeted multi-reaction monitoring (MRM) accurate quantification, and has the characteristics of simplicity, convenience, sensitivity and accuracy.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.
FIG. 1 is a schematic diagram of a screening procedure for identifying markers of oligospermia according to the present invention;
FIG. 2 is a PCA analysis chart of the normal control group and the oligospermia seminal plasma lipid metabolism group in the present invention;
FIG. 3 is a graph showing the analysis of OPLS-DA in the normal control group and the oligospermia seminal plasma lipid metabolism group according to the present invention.
FIG. 4 is a graph of differential metabolite screening volcano in the oligospermia group and the control group of the present invention;
FIG. 5 is a hierarchical clustering analysis thermodynamic diagram of the differential metabolites of the oligospermia group and the control group in accordance with the present invention;
FIG. 6 is a diagram of the enrichment pathway for differential metabolites in the present invention;
FIG. 7 is a graph of ROC analysis of potential markers in the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below. The examples, in which specific conditions are not specified, were conducted under conventional conditions or conditions recommended by the manufacturer. The reagents or instruments used are conventional products which are not indicated by manufacturers and are commercially available.
It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present invention will be described in detail below with reference to specific examples.
The invention provides a marker for identifying oligospermia, which comprises PE (O-16:0/22:6), TAG (54:8) _ FA22:6, HexCER (18:1/18:1), TAG (50:2) _ FA16:0, TAG (52:7) _ FA22:6, FFA (22:6), TAG (56:8) _ FA22:6, TAG (54:7) _ FA22:6, TAG (56:8) _ FA16:0, SM (18:1), FFA (24:1) and DAG (16:0/18: 0).
Wherein PE represents phosphatidylethanolamine; TAG represents a triglyceride; FA represents a fatty acid; HexCer denotes hexosylceramide; FFA represents free fatty acids; SM represents sphingomyelin; DAG for diglyceride; HexCer (18:1/18:1) indicates that the hexosylceramide comprises two carbon chains, one of which consists of 18 carbon atoms and contains 1 double bond; the other chain is made up of 18 carbon atoms and contains 1 double bond, SM (18:1) indicates that sphingomyelin comprises a carbon chain containing 18 carbon atoms with one double bond thereon, and so on. PE (O-16:0/22:6) indicates that the phosphatidylethanolamine contains 2 carbon chains, wherein 1 carbon chain consists of 16 carbon atoms and does not contain a double bond, and also contains one oxygen, and the other carbon chain consists of 22 carbon atoms and contains 6 double bonds.
The invention also provides a screening method for identifying the marker of oligospermia, which comprises the steps of taking the semen of an oligospermia patient as a sample to be detected, and centrifuging the sample to be detected at 3000-3500rpm for 8-12min to obtain a seminal plasma sample; adopting ultra-high performance liquid chromatography-tandem mass spectrometry to detect seminal plasma samples,obtaining original data; filtering a single peak deviated in the original data according to the relative standard deviation and the variation coefficient, only reserving peak area data with a single group of null values not more than 50% or all the group of null values not more than 50%, and filling missing values in the original data according to a numerical simulation method with a minimum value of one half to obtain sorted data; carrying out logarithmic transformation and UV formatting treatment on the sorted data by using SIMCA software to obtain treated data, and carrying out principal component analysis and orthogonal partial least square method-discriminant analysis on the treated data to obtain differential metabolite data; analyzing the differential metabolite data by using a multivariate variable statistical analysis method to obtain analysis data, wherein the calorie value standard in the multivariate variable statistical analysis method is that the P value of a t test is less than 0.05 and the variable projection importance of a first main component of an OPLS-DA model is greater than 1; visually displaying the analysis data in the forms of volcano graphs and differential metabolite hierarchical clustering heat maps and carrying out signal path analysis by utilizing a KEGG database and/or a Metabioanalyser database; and performing ROC analysis on the difference genes by using Graph pad 9.0 software, and mapping and analyzing metabolites with significant differences to complete screening. Wherein the website of the Kegg database ishttp://www.genome.jp/kegg/The address of the MetabioAnalyst database ishttp:// www.metaboanalyst.ca/. The mass spectrum adopts an electrospray ionization scanning detection mode and a mass spectrum scanning mode of multi-reaction monitoring (MRM); wherein the ion spraying voltage is 5.5kv, the ion source temperature is 500 ℃, the atomizing air pressure is 50psi, and the auxiliary air pressure is 5 psi. The method combines the advantages of non-targeted high coverage, full-scanning accurate molecular weight determination and targeted Multiple Reaction Monitoring (MRM) accurate quantification, and has the characteristics of simplicity, convenience, sensitivity and accuracy.
The mobile phase A in the ultra performance liquid chromatography-tandem mass spectrometry comprises tetrahydrofuran, methanol, water and ammonium formate, wherein the volume ratio of the tetrahydrofuran to the methanol to the water is (28-32): (18-22): (45-55); the mobile phase B in the ultra-high performance liquid chromatography-tandem mass spectrometry comprises tetrahydrofuran, methanol, water and ammonium formate, wherein the volume ratio of the tetrahydrofuran to the methanol to the water is (70-80): (18-22): (3-7), the concentration of ammonium formate is 8-12 mmol/L.
The sample injection amount of the ultra-high performance liquid chromatography-tandem mass spectrometry is 1.8-2.2 mu L, and the column temperature is 52-58 ℃.
The mobile phase gradient of the ultra performance liquid chromatography-tandem mass spectrometry is 0min, namely 100% of mobile phase A and 0% of mobile phase B; for 1min, 80% of mobile phase A and 20% of mobile phase B; 3.0min, 60% of mobile phase A and 40% of mobile phase B; for 3.5min, 45% of mobile phase A and 55% of mobile phase B; 7.5min, 25% of mobile phase A and 75% of mobile phase B; 9.0min, 0% mobile phase A, 100% mobile phase B; 11min, 0% of mobile phase A and 100% of mobile phase B; 11.5min, 100% mobile phase A, 0% mobile phase B; 13.0min, 0% mobile phase A, 100% mobile phase B; or the mobile phase gradient is 0min, namely 100% of mobile phase A and 0% of mobile phase B; 80% mobile phase A and 20% mobile phase B for 1.0 min; 3.0min, 60% mobile phase A and 40% mobile phase B; for 3.5min, 45% of mobile phase A and 55% of mobile phase B; 7.5min, 25% of mobile phase A and 75% of mobile phase B; 9.0min, 0% mobile phase A, 100% mobile phase B; 11.0min, 0% mobile phase A, 100% mobile phase B; and (11.5 min) 98% of mobile phase A and 2% of mobile phase B. The time zone not shown represents the same mobile phase ratio as the previous time zone.
The invention also provides application of the marker for identifying oligospermia in preparing a medicament for preventing or treating oligospermia.
The invention also provides a medicament for preventing or treating oligospermia, which comprises the marker for identifying oligospermia.
The invention also provides application of the marker for identifying oligospermia in preparing a kit for diagnosing oligospermia.
The invention also provides a kit for diagnosing oligospermia, which comprises the marker for identifying oligospermia.
Examples
Main equipment and materials:
ultra high performance liquid chromatograph and triple quadrupole mass spectrometer (UPLC-QqQ-MS/MS) were purchased from ABI, cat #: ABI 6500;
kinetex C18 column was purchased from Phenomenex, model number: 4.6 mm. times.100 mm, 2.6 μm.
Isopropyl alcohol was purchased from Fisher, cat #: a451-4;
methanol was purchased from Merck, cat #: 1.06035.1000, respectively;
ammonium formate was purchased from Sigma, cat # cat: 70221-25G-F;
HPLC chloroform from Merck, cat #: 1.02444.4000, respectively;
HPLC tetrahydrofuran purchased from Merck, cat #: 1.08101.4008;
SIMCA version 14.1 was purchased from umemetrics, sweden;
LipidSearch software was purchased from Thermo Fisher Scientific;
graphpad version 9.0;
software, version: 3.6.0.
the main consumables are as follows: semen collection cup, centrifuge tube, external screw cap cryopreservation tube (1.8mL), white paper cryopreservation box (9X 9), label paper.
Acquiring an object: adult males of the reproductive age of 3-5 days are contraindicated.
The collection place comprises: seminal fluid collection chamber of Ningxia medical university's reproductive center.
(1) Transferring the remaining semen after the semen parameters are detected by the reproductive center to a sample processing chamber;
(2) respectively placing the centrifuge tubes with semen in a low-speed centrifuge, and centrifuging at 3200rpm for 10 min;
(3) in the centrifugation process, a sample processing professional prepares a label corresponding to the serial number of the sample, and the label is stuck to a used 1.8mL EP tube body and a tube cover;
(4) after the centrifugation is finished, the liquid can be obviously layered, and seminal plasma is collected to a 1.8mL freezing tube and placed in a refrigerator at the temperature of minus 80 ℃ for freezing and testing.
Preparation of seminal plasma lipid samples:
internal standard sample Avanti comprises 17:1LPA (lysophosphatidic acid), 17:1LPC (lysophosphatidylcholine), 17:0-14:1PA (phosphatidic acid), 17:0-14:1PC (phosphatidylcholine), 17:0-14:1PE (phosphatidylethanolamine), 17:0-14:1PG (phosphatidylglycerol), 17:0-14:1PI (phosphatidylinositol), 17:0-14:1PS (phosphatidylserine), 19:0 Cholesterol Ester (CE), sphingosine (D17:1), C17 ceramide (D18:1/17:0), Lyso SM (D17:1), 17:0SM (D18:1/17:0), cholesterol (D7), cardiolipin mixture I, deuterated TG mixture I and deuterated DG mixture I. The preparation method of the semen lipid sample comprises the following specific steps:
(1) taking 80 mu L of sample (serum) to a 4mL glass centrifuge tube core tube;
(2) add 10. mu.L of internal standard (-20 ℃ for storage);
(3) adding 300 μ L methanol (Merck liquid chromatography grade), and vortex mixing for 60 s;
(4) adding 500 mu L chloroform, and uniformly mixing by vortex for 60 s;
(5) adding 250 μ l of water (Drech distilled water), and mixing by vortex for 60 s;
(6) centrifuging at normal temperature at 3000r for 10 min;
(7) collecting the lower chloroform layer into a 2mL EP tube;
(8) adding 600 mu L of chloroform into the glass centrifuge tube, and repeating the steps 6 and 7;
(9) drying with nitrogen, and storing in a refrigerator at-80 deg.C;
(10) redissolving: taking 100 mul of isopropanol/methanol (50:50) compound solution, and resuspending;
(11) the supernatant was vortexed for 15s and after centrifugation at 4000rpm for 15min at 4 ℃, the supernatant was pipetted into a brown sample vial for LC-MS/MS analysis.
Seminal plasma lipid sample analysis:
targeted lipidomic analysis was performed using an ultra-high performance liquid chromatograph and a triple quadrupole mass spectrometer (UPLC-QQQ-MS/MS). The chromatographic separation was carried out using a Kinetex C18 column, the column temperature being maintained at 55 ℃. Mobile phase a consisted of tetrahydrofuran/methanol/water (30: 20: 50, v/v) and 10mmol/L ammonium formate. The mobile phase B consisted of tetrahydrofuran/methanol/water (75: 20: 5, v/v) and 10mmol/l ammonium formate. The flow rate and the amount of sample were 0.6mL/min and 2. mu.L, respectively. The gradient program is as follows: initially, 100% mobile phase a; 0% mobile phase B. 1 minute, 80% mobile phase a; 20% mobile phase B. 3.0min, 60% mobile phase A; 40% mobile phase B. 3.5min, 45% mobile phase A; 55% mobile phase B. 7.5min, 25% mobile phase A; 75% mobile phase B. 9.0min, 0% mobile phase a; 100% mobile phase B. 11min, 0% mobile phase a; 100% mobile phase B. 11.5min, 100% mobile phase A; 0% mobile phase B. 13.0min, 0% mobile phase a; 100% mobile phase B. Wherein, the mass spectrum detection conditions are as follows: an electrospray ionization scanning detection mode and a mass spectrum scanning mode of multi-reaction monitoring (MRM) are adopted; the Ion spray voltage was 5.5kv, the Ion source temperature was 500 ℃, the atomization gas pressure (Ion source gas 1) was 50, and the assist gas pressure (Ion source gas 2) was 50. Based on the high energy collision resolution MS2 pattern of the negative data, the peaks were picked and aligned using LipidSearch software (Thermo Fisher Scientific, usa). The data were quantified by comparison with an internal standard and logarithmically transformed, and then further introduced into SIMCA software for multivariate statistical analysis. Differential lipid metabolites were obtained using student's t-test and VIP > 1. Further screening is carried out by using the absolute value of Log2 Fold Change >1, P <0.05 of correlation between main differential metabolite and sperm parameter and P <0.05 of receiver operating characteristic curve (ROC) to finally obtain the clinical potential biomarker.
And (3) analysis results:
the samples were analyzed according to the scheme shown in FIG. 1: according to the semen analysis report, 30 semen samples of each of the patients with oligospermia (sperm density: 10.4402 + -4.4278) and the normal population (sperm density: 114.0840 + -29.4012) were obtained by screening from the reproductive center. Screening conditions are as follows: the semen is not wanted for more than 4 days and the semen is analyzed for more than 3 times, the density of the semen is lower than 1.5 × 10 7 one/mL.
Collecting residual semen samples after clinical detection of two groups of people, and passing through ultra-high performance liquid chromatograph and triple quadrupole mass spectrometer (UHPLC-QQQ-MS, Exion LC-Sciex)
6500+) detection, and obtaining 310 effective Peak by processing the original data of 60 samples containing quality control samples, and then carrying out multivariate variable pattern recognition analysis on the effective Peak: principal Component Analysis (PCA) and orthogonal partial least squares-discriminant analysis (OPLS-DA). PCA can reveal the internal structure of the data to better interpret the data variables, where the abscissa PC and the ordinate PC represent the scores of the first and second ranked principal components, respectively, and each scatter represents a sample, with the results shown in fig. 2. Sit on the back in the figureMark t [1 ]]P represents the predicted principal component score for the first principal component, showing the difference between the sample groups, ordinate t [1 ]]O represents the orthogonal principal component score showing the intra-sample group differences, each scatter represents a sample, the scatter shape and color represent different experimental groupings, and the results are shown in fig. 3.
Screening is carried out according to the set calorie value standard (the P value of the student t test is less than 0.05, and the quantity projection importance (VIP) of the first main component of the orthogonal partial least square method-discriminant analysis model is more than 1), and finally 70 remarkable differential metabolites are obtained, and the obtained results are shown in Table 1.
TABLE 1 differential lipid metabolites with P <0.05 and VIP greater than 1
Wherein E-05 represents X10 -5 E-09 represents X10 -9 And the like.
Statistical analysis of the differential metabolites obtained in table 1 was performed, and the results were visualized in the form of volcano charts, and the results of the oligospermia group versus the normal control group are shown in fig. 4. Each point in the volcano plot represents a metabolite, the abscissa represents the fold change (base 2 logarithm) for the group of comparison substances, the ordinate represents the P-value (base 10 logarithm negative) of the student's t-test, and the scatter size represents the VIP value of the OPLS-DA model. Scatter color represents change in substance, significantly up-regulated metabolites are represented in red, significantly down-regulated metabolites are represented in blue, non-significantly different metabolites are in gray, dots in the upper left region in fig. 4 represent down-regulated differential metabolites; the dots in the upper right region represent up-regulated differential metabolites; the part below the dotted line represents metabolites with no significant difference. The size of the circle is proportional to its VIP value.
The differential metabolites obtained by analysis often have results and functional similarities/complements biologically or are positively/negatively regulated by the same metabolic pathway, and are expressed with similar or opposite expression characteristics among different experimental groups. Therefore, hierarchical clustering analysis is carried out according to the characteristics of the metabolites, the metabolites with the same characteristics are classified into one group, and the variation characteristics of the metabolites among experimental groups are combined. The quantitative values of the differential metabolites were subjected to Euclidean distance matrix calculation (Euclidean distance matrix), the differential metabolites were clustered by the complete linkage method, and presented in a thermodynamic diagram, and the results are shown in FIG. 5.
Through analysis of the KEGG database, differential metabolites are mainly enriched in glycerophospholipid metabolism, glycosylphosphatidylinositol synthesis, linoleic acid metabolism, alpha-linolenic acid metabolism, glycerolipid metabolism, arachidonic acid metabolism and the like, and as a result, as shown in FIG. 6, the ordinate in FIG. 6 is-ln P value, the higher the significance of the enrichment in the pathway is, the higher the abscissa is the influence, and the larger the influence is, the more the right the enrichment is.
According to the screening result, 12 metabolites with larger differences are obtained by taking the absolute value of Log FOLD CHANGE larger than 1 as the screening condition and are respectively PE (O-16:0/22:6), TAG (54:8) _ FA22:6, HexCel (18:1/18:1), TAG (50:2) _ FA16:0, TAG (52:7) _ FA22:6, FFA (22:6), TAG (56:8) _ FA22:6, TAG (54:7) _ FA22:6, TAG (56:8) _ FA16:0, SM (18:1), FFA (24:1) and DAG (16:0/18: 0).
Sperm concentrations and 12 lipid metabolites from 60 humans were subjected to Pearson correlation analysis, and the correlation coefficient (R) and significant variability (P-value) are shown in table 2. The correlation analysis results show that: sperm concentration and 7 metabolites DAG (16:0/18:0), FFA (22:6), FFA (24:1), HexCel (18:1/18:1), PE (O-16:0/22:6), SM (18:1), TAG (50:2) _ FA16:0 and TAG (54:8) _ FA22:6 were all significantly correlated, with the results shown in Table 2.
TABLE 2 correlation analysis of sperm concentration and metabolites
ROC analysis was performed on the obtained 8 significantly related metabolites DAG (16:0/18:0), FFA (22:6), FFA (24:1), HexCer (18:1/18:1), PE (O-16:0/22:6), TAG (50:2) _ FA16:0 and TAG (54:8) _ FA22:6, and the results are shown in FIG. 7, which show that 8 potential seminal plasma biomarkers of oligospermia were finally obtained, respectively: DAG (16:0/18:0), FFA (22:6), FFA (24:1), HexCor (18:1/18:1), PE (O-16:0/22:6), TAG (50:2) _ FA16:0, and TAG (54:8) _ FA22: 6.
In summary, the following steps:
the invention provides a marker for identifying oligospermia, a screening method and application thereof, wherein the marker can reduce the false positive probability and improve the accuracy of oligospermia detection; the screening method combines the advantages of non-targeted high coverage, full-scan accurate molecular weight characterization and targeted Multiple Reaction Monitoring (MRM) accurate quantification. Has the characteristics of simplicity, sensitivity and accuracy.
The embodiments described above are some, not all embodiments of the invention. The detailed description of the embodiments of the present invention is not intended to limit the scope of the invention as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be obtained by a person skilled in the art without inventive step based on the embodiments of the present invention, are within the scope of protection of the present invention.
Claims (9)
1. A marker for identifying oligospermia, comprising PE (O-16:0/22:6), TAG (54:8) _ FA22:6, HexCo (18:1/18:1), TAG (50:2) _ FA16:0, TAG (52:7) _ FA22:6, FFA (22:6), TAG (56:8) _ FA22:6, TAG (54:7) _ FA22:6, TAG (56:8) _ FA16:0, SM (18:1), FFA (24:1), and DAG (16:0/18: 0).
2. A screening method for identifying markers of oligozoospermia according to claim 1, comprising the steps of: taking semen of a patient with oligospermia as a sample to be detected, and centrifuging the sample to be detected to obtain a seminal plasma sample; detecting the seminal plasma sample by adopting an ultra-high performance liquid chromatography-tandem mass spectrometry method to obtain original data; filtering the deviated single peak in the original data according to the relative standard deviation and the variation coefficient, only reserving peak area data with a single null value of not more than 50% or all the null values of not more than 50%, and filling missing values in the original data according to a numerical simulation method of one half of the minimum value to obtain sorted data;
carrying out logarithmic transformation and UV formatting treatment on the sorted data by using SIMCA software to obtain treated data, and carrying out principal component analysis and orthogonal partial least square method-discriminant analysis on the treated data to obtain differential metabolite data;
analyzing the differential metabolite data by using a multivariate variable statistical analysis method to obtain analysis data, wherein the calorific value standard in the multivariate variable statistical analysis method is that the P value of a student t test is less than 0.05 and the variable projection importance of a first main component of an OPLS-DA model is greater than 1;
visually displaying the analysis data in the forms of a volcano chart and a differential metabolite hierarchical clustering heat map and carrying out signal path analysis by utilizing a KEGG database and/or a Metabioanalyser database; and performing ROC analysis on the difference genes by using Graph pad 9.0 software, and mapping and analyzing metabolites with significant differences to complete screening.
3. The screening method for identifying markers of oligospermia according to claim 2, wherein the mobile phase A in the ultra high performance liquid chromatography-tandem mass spectrometry comprises tetrahydrofuran, methanol, water and ammonium formate, and the volume ratio of the tetrahydrofuran, the methanol and the water is (28-32): 18-22): 45-55;
the mobile phase B in the ultra performance liquid chromatography-tandem mass spectrometry comprises tetrahydrofuran, methanol, water and ammonium formate, wherein the volume ratio of the tetrahydrofuran to the methanol to the water is (70-80): (18-22): (3-7); and the concentration of the ammonium formate is 8-12 mmol/L.
4. The screening method for identifying a marker of oligospermia according to claim 2, wherein the sample size of the ultra high performance liquid chromatography-tandem mass spectrometry is 1.8 to 2.2 μ L, and the column temperature is 52 to 58 ℃.
5. The screening method for identifying markers of oligospermia according to any one of claims 2 to 4, wherein the mobile phase gradient of the ultra high performance liquid chromatography-tandem mass spectrometry is 0min: 100% mobile phase A, 0% mobile phase B; for 1min, 80% of mobile phase A and 20% of mobile phase B; 3.0min, 60% mobile phase A, 40% mobile phase B; for 3.5min, 45% of mobile phase A and 55% of mobile phase B; 7.5min, 25% of mobile phase A and 75% of mobile phase B; 9.0min, 0% mobile phase A, 100% mobile phase B; 11min, 0% of mobile phase A and 100% of mobile phase B; 11.5min, 100% mobile phase A, 0% mobile phase B; 13.0min, 0% mobile phase A, 100% mobile phase B;
or the mobile phase gradient is 0min, namely 100% of mobile phase A and 0% of mobile phase B; 80% mobile phase A and 20% mobile phase B for 1.0 min; 3.0min, 60% of mobile phase A and 40% of mobile phase B; for 3.5min, 45% of mobile phase A and 55% of mobile phase B; 7.5min, 25% of mobile phase A and 75% of mobile phase B; 9.0min, 0% mobile phase A and 100% mobile phase B; 11.0min, 0% mobile phase A and 100% mobile phase B; and 11.5min, 98% of mobile phase A and 2% of mobile phase B.
6. Use of a marker for identifying oligospermia according to claim 1 in the manufacture of a medicament for preventing or treating oligospermia.
7. A medicament for preventing or treating oligospermia, comprising the marker for identifying oligospermia of claim 1.
8. Use of a marker for identifying oligospermia according to claim 1 in the manufacture of a kit for diagnosing oligospermia.
9. A kit for diagnosing oligospermia, comprising the marker for identifying oligospermia of claim 1.
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