CN116243002B - Method for discovering genes related to oral cancer lipid metabolism and application of genes - Google Patents
Method for discovering genes related to oral cancer lipid metabolism and application of genes Download PDFInfo
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Abstract
Provided are a method for discovering a gene related to lipid metabolism of oral cancer and an application of the gene, which comprises the following steps: step one, performing non-targeted lipidomic detection on an oral cancer patient to obtain a differential lipid metabolite between the oral cancer patient and a healthy person; step two, screening lipid metabolism genes related to pathogenesis and prognosis of oral cancer from differential lipid metabolites by combining with bioinformatics means; and thirdly, verifying the expression of the lipid metabolism genes in the oral cancer cell line by adopting a WesternBlot method. The invention explores the gene mechanism of the regulation and control of the lipid metabolism of OSCC, and provides a solid foundation for improving the clinical diagnosis and treatment mode of OSCC by regulating the lipid metabolism to assist in diagnosing OSCC.
Description
Technical Field
The invention relates to the technical field of biomedical science, in particular to a method for discovering genes related to lipid metabolism of oral cancer and application of the genes.
Background
Oral cancer is a common malignancy of the head and neck, with oral squamous cell carcinoma (oral squamous cell carcinomas, OSCC) being the major pathological type, accounting for more than 90% of oral cancer cases. The first mode of OSCC treatment is surgical treatment, often supplemented with radiation and chemotherapy in the late stages. Of these, 40% of OSCC patients have already been associated with lymph node metastasis at the time of diagnosis. The 5-year survival rate for non-metastatic OSCC patients is about 90%, whereas the 5-survival rate for metastatic OSCC patients is only around 40%. Thus, the development of therapeutic targets specific to oral cancers has become a hotspot in research in this area. More and more literature reports reveal that mutations, structural variations and abnormal expression of some genes are closely related to the occurrence and development of oral cancers. However, since oral cancer is a multi-factor, multi-stage and multi-mechanism complex pathological change process, no specific relevant diagnosis and treatment target aiming at the oral cancer exists at present.
Lipids play a vital role in signal transduction, maintaining cell membrane integrity, regulating energy metabolism, etc. Lipid metabolism is part of metabolic reprogramming and is a hallmark of many cancers. Early stages of cancer progression increase de novo synthesis and uptake of lipids. Lipids involved in various cancer malignant transformations, such as Glycerophospholipids (GPLs), have been shown to regulate various functions such as adhesion, migration, apoptosis and signal transduction; cholesterol is an integral part of lipid rafts and also plays a key role in cell signaling, controlling oncogenic, drug-resistant and metastatic pathways. Reprogramming of lipid metabolism provides energy and cellular structural molecules to tumor cells to meet the need for rapid proliferation and metastasis of tumor cells. Studies have shown that increased adipogenesis in OSCC is associated with increased cellular invasiveness. Currently, regulation of lipid metabolism in OSCC progression is not yet clear.
Therefore, systematic research on lipid mass spectra of OSCC is performed, and exploration of molecular mechanisms of OSCC lipid metabolism regulation is a technical problem to be solved by those skilled in the art.
Disclosure of Invention
In order to solve the above problems, the present invention provides a method for finding a gene related to lipid metabolism in oral cancer, comprising:
step one, performing non-targeted lipidomic detection on an oral cancer patient to obtain a differential lipid metabolite between the oral cancer patient and a healthy person;
step two, screening lipid metabolism genes related to pathogenesis and prognosis of oral cancer from the differential lipid metabolites by combining with bioinformatics means;
and thirdly, verifying the expression of the lipid metabolism gene in an oral cancer cell line by adopting a Western Blot method.
In certain embodiments, the method of finding a gene associated with lipid metabolism of oral cancer, wherein the lipid metabolism gene is a DGKG gene.
In some embodiments, in the method for finding a gene related to lipid metabolism of oral cancer, the first step further comprises:
acquiring a training case set, wherein subjects in the training case set comprise diseased subjects and healthy subjects;
a serum sample of the subject is collected and a plurality of differential lipid metabolites of the subject are analyzed using multivariate statistics.
In some embodiments, in the method for finding a gene related to lipid metabolism of oral cancer, the second step further comprises:
retrieving a plurality of first genes associated with a plurality of said differential lipid metabolites in an HMDB database;
analyzing in a TCGA database a plurality of second genes differentially expressed between head and neck squamous cell carcinoma and healthy people;
a third gene having a plurality of the first genes and a plurality of the second genes as candidates;
and carrying out single-factor cox regression analysis, multi-factor cox regression analysis and T test on the third gene to determine the lipid metabolism gene.
In some embodiments, in the method for finding a gene related to lipid metabolism of oral cancer, the third step is specifically:
human oral squamous cell carcinoma cell lines SCC9, SCC25, cal27 and human immortalized oral keratinocytes HaCat were cultured, and the difference in DGKG expression between OSCC cell lines and normal cell lines was further verified from protein levels by Western blot.
In another aspect, the present invention provides a method for aiding in the diagnosis of oral cancer, comprising applying the genes found in the above-described discovery method.
The beneficial effects are that:
the method for finding the genes related to oral cancer lipid metabolism comprises the steps of finding lipid metabolites related to oral cancer occurrence and development through lipidomic technology, finding the genes related to the lipid metabolites and the genes related to oral cancer occurrence and development and the genes related to survival through bioinformatics technology, obtaining genes-DGKG related to oral cancer lipid metabolism and related to survival after intersection of the genes and the genes, and confirming the expression of the DGKG genes in an oral cancer cell line through Western Blot technology. The lipid mass spectrum of OSCC is systematically researched, the molecular mechanism of the lipid metabolism regulation of OSCC is explored, and the lipid metabolism is effectively regulated to assist in diagnosing the OSCC, so that a solid theoretical basis is provided for improving the clinical diagnosis and treatment mode of the OSCC in the future.
Drawings
FIG. 1 is a thermogram analysis of 70 differential lipid metabolites found by lipidomic in the method for finding genes related to oral cancer lipid metabolism according to the present invention.
FIG. 2 shows the intersection of the oral cancer lipid metabolism-related gene and the oral cancer differential expression gene, which is the lipid metabolism gene related to the development of oral cancer, in the method for finding the oral cancer lipid metabolism-related gene according to the present invention.
FIG. 3 shows a one-factor COX regression analysis of lipid metabolism genes related to the development of oral carcinogenesis in the method for finding a lipid metabolism related gene for oral cancer according to the present invention.
FIG. 4 shows a multi-factor COX regression analysis of lipid metabolism genes related to the development of oral carcinogenesis in the method for finding a lipid metabolism related gene for oral cancer according to the present invention.
FIG. 5 is a graph showing expression columns of DGKG and CLC mRNA expression levels in 502 HNSC tissues and 44 paired tissues in the TCGA database according to the method for finding a gene associated with lipid metabolism of oral cancer according to the present invention.
FIG. 6 is a KM survival curve drawn according to survival information of oral cancer samples in the TCGA database in the method for finding a gene related to oral cancer lipid metabolism according to the present invention.
FIG. 7 shows the expression of DGKG in oral cancer tissues and healthy control tissues in human protein profile HPA database in the method for finding genes related to oral cancer lipid metabolism according to the present invention.
FIG. 8 is a bar graph showing the expression of DGKG in human oral cavity squamous cell carcinoma cell lines SCC9, SCC25, cal27 and human immortalized oral cavity keratinocyte HaCat according to Western Blot experiment in the method for finding genes related to oral cavity cancer lipid metabolism.
FIG. 9 is a bar graph showing the expression of DGKG in human oral squamous cell carcinoma cell lines SCC9, SCC25, cal27 and human immortalized oral keratinocytes HaCat, according to Western Blot experiments, in the method of finding genes related to lipid metabolism of organisms and oral cancers according to the present invention.
Detailed Description
Various exemplary embodiments of the invention will now be described in detail, which should not be considered as limiting the invention, but rather as more detailed descriptions of certain aspects, features and embodiments of the invention.
It is to be understood that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. In addition, for numerical ranges in the present invention, it is understood that the upper and lower limits of the ranges and each intermediate value therebetween are specifically disclosed. Every smaller range between any stated value or stated range, and any other stated value or intermediate value within the stated range, is also encompassed within the invention. The upper and lower limits of these smaller ranges may independently be included or excluded in the range.
Unless otherwise defined, 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 invention belongs. Although only preferred methods and materials are described herein, any methods and materials similar or equivalent to those described herein can be used in the practice or testing of the present invention. All documents mentioned in this specification are incorporated by reference for the purpose of disclosing and describing the methods and/or materials associated with the documents. In case of conflict with any incorporated document, the present specification will control.
As used herein, the terms "comprising," "including," "having," "containing," and the like are intended to be inclusive and mean an inclusion, but not limited to. As used herein, "and/or" includes any or all combinations of such things. Unless otherwise indicated,% refers to mass volume percent.
As shown in fig. 1 to 9, the present invention provides a method for finding a gene associated with lipid metabolism of oral cancer, comprising:
step one, performing non-targeted lipidomic detection on an oral cancer patient to obtain a differential lipid metabolite between the oral cancer patient and a healthy person; the method comprises the following specific steps:
1. sample collection
Blood samples of 78 OSCC patients and 80 healthy people in a fasting state were collected between 6:00 and 7:00 a.m., collected in an inert separation gel coagulation tube, centrifuged at 3000rpm for 10 minutes at 4 ℃, and the supernatant (serum) was collected and stored in a refrigerator at-80 ℃ for later use.
2. Sample pretreatment
Extracting lipid by methyl tert-butyl ether (MTBE) -methanol-water system, wherein a serum sample is taken out from a refrigerator at-80 ℃, and after the serum sample is placed at room temperature for thawing, 80uL of the serum sample is taken out in a 4mL EP tube, 600uL of methanol is added, and the mixture is vortexed for 1min to precipitate proteins in the serum sample; then adding 2mL of MTBE, swirling for 10min, and extracting lipid components in the serum sample into an organic solvent; then 500uL of water was added and vortexed for 5min, and the mixture was centrifuged at 3000 Xg for 10min at 4℃to separate the organic phase from the aqueous phase, wherein the lipid-containing organic phase was located on the top. Respectively taking 800uL of supernatant in two 2mL centrifuge tubes, drying under nitrogen, wherein one part is used for positive ion detection and the other part is used for negative ion detection. Re-dissolving the blow-dried sample with 70uL of mixed solvent of isopropanol/acetonitrile (9:1 volume ratio), swirling for 10min, fully dissolving the concentrated lipid component in an organic solvent, centrifuging for 10min at 13000 Xg at 4 ℃, taking 5uL of each sample for preparing a quality control sample, and transferring the rest samples into a sample injection vial for mass spectrum detection.
3. Preparation of quality control samples
5uL of each sample to be tested is extracted and concentrated in 12 mL centrifuge tubes, vortex oscillation is carried out for 1min, after the samples are uniformly mixed, the mixture is centrifuged for 10min at 13000 Xg at the temperature of 4 ℃, and the supernatant is taken out in a sample injection vial for subsequent mass spectrometry. During detection, 6 QC samples are detected firstly, and the pressure change before and after each sample injection and the change of the retention time of the main peak of the total ion flow diagram are monitored. After confirming that the instrument is stable, the analysis of the sample is started. One needle of QC samples was inserted after every 10 samples tested. After each QC sample was tested, a needle of pure water solution was inserted to avoid contaminating the other samples.
4. Serum samples were analyzed using an ultra high performance liquid chromatography-quadrupole/electrostatic field Orbitrap tandem high resolution mass spectrometer (UHPLC-Q-Orbitrap HRMS).
5uL of each sample was pipetted into a chromatographic column at 40 ℃. The chromatographic column is ACQUITYCSH C18 column (1.7 mm. Times.100 mm,1.8 μm); the mobile phase is (B): acetonitrile isopropanol solution (acetonitrile: isopropanol=1:9), gradient elution, elution procedure is 0-2 min (30% b), 2-25 min (30% -100% b), 25-30.0 min (100% b); the flow rate was 0.3mL/min.
High resolution mass spectrometry was serially connected to a UHPLC system using a heated electrospray ion source (Heated electrospray ionization, HESI). The temperature of the auxiliary gas is 300 ℃, and the flow is 10 mu L/min; the ion transport tube temperature was 320 ℃. The scanning range is 80.00-1200.00 m/z under the positive and negative ion modes, and the spraying voltage and the sheath air flow rate are respectively 3.50kV, 40 mu L/min and 2.80kV, 38 mu L/min. The resolution of the secondary mass spectrum is 17500, and the width of the mass-to-charge ratio window is set to be 2. The gradient collision energies were 20eV, 40eV and 60eV, respectively. The samples are placed on a sample feeding disc according to a random number method, and sample feeding is carried out one by one according to the sequence.
5. Data processing
Raw data obtained from LC-MS analysis of all samples were processed using the Lipid Search v4.0 software (Thermo Fisher Scientific, united States), then the data for each sample was normalized to peak area, and all data on variables (including retention time RT and charge-to-mass ratio m/z), sample numbers, and normalized peak intensities were imported into SIMCA14.1 software (immetrics, sweden). Obtaining variable weight importance projection values (Variable importance in projection, VIP) of each metabolite through OPLS-DA analysis, performing independent sample t-test on lipid metabolites with VIP >1, and screening out differential lipid metabolites with statistically significant differences according to P values and fold differences (FC). The multivariate statistical analysis results showed significant differences in lipid metabolism characteristics of OSCC and HC. Volcanic images drawn according to P-values, FC-values (FC) can screen for differential metabolites between the two groups. With the points of |log2fc| >1.0, vip >1 and P <0.05 as candidate differential metabolites, 70 differential lipid metabolites, mainly including sphingolipids, glycerides, glycerophospholipids, were finally identified and a thermal map of the differential lipids was drawn.
Step two, screening lipid metabolism genes related to pathogenesis and prognosis of oral cancer from the differential lipid metabolites by combining with bioinformatics means;
the HMDB database was used to find differential lipid-related genes (298), and the TCGA database was used to analyze differentially expressed genes between head and neck squamous cell carcinoma (HNSC) patients and healthy persons, which were crossed to obtain 68 common genes. Namely the potential lipid metabolism related genes for the occurrence and the development of oral squamous cell carcinoma. A thermographic analysis of 68 differentially expressed genes revealed that there was significant differential expression of these 68 genes in HNSC and HC samples. The single factor cox regression analysis result shows that 12 genes in 68 genes are related to prognosis, wherein 2 HR >1 are risk genes, and 10 genes are protection genes; the multi-factor cox regression analysis results show that DGKG and CLC are independent risk factors for oral squamous cell carcinoma. According to the expression bar graph drawn by the expression levels of DGKG and CLC mRNA in 502 HNSC tissues and 44 matched tissues in the TCGA database, the result shows that the expression difference of DGKG and CLC has statistical significance, and the expression difference of DGKG is more obvious. Meanwhile, the T test of paired samples is carried out on the expression of DGKG and CLC, and the expression difference of DGKG has statistical significance. And drawing a KM curve according to survival information of the sample, and displaying that a DGKG high-expression group and a DGKG low-expression group have obvious prognosis difference. The human protein profile HPA database was used to verify the expression of DGKG, demonstrating that DGKG expression in OSCC tissue is significantly higher than that of normal oral mucosal epithelium.
New modes of oral cancer clinical pathology spectrum [ J ]. Pakistan journal of medicine, 2013, 29 (3): 783-7, describes that head and neck tumors are the sixth highest tumor in the world, including epithelial malignancy in the sinuses, nasal cavities, pharynx, larynx, oral cavity, etc., most head and neck squamous cell carcinoma is oral cancer, and head and neck squamous cell carcinoma (HNSC) data were used for differential analysis in TCGA database.
And thirdly, verifying the expression of the lipid metabolism gene in an oral cancer cell line by adopting a Western Blot method.
(1) Cellular protein extraction
Culturing corresponding cells in a six-hole plate until the fusion degree is 90%, removing the original culture medium, flushing for 3 times by using PBS, then adding pancreatin to digest the cells, adding a complete culture medium to neutralize pancreatin after digestion is completed, fully suspending the cells, and placing the uniformly mixed cell suspension in a 1.5mL EP tube for later use. The above cell suspension was centrifuged using a low temperature centrifuge at 4℃and 2000rpm for 3min, and after centrifugation was completed, the supernatant was removed and washed with PBS, the procedure was repeated three times, and finally the cell pellet was retained. Preparing protein understanding liquid, taking a certain volume to obtain RIPA (medium) lysate according to 980:10:10, a proper volume of cocktail was added and thoroughly mixed with the PMSF. 40. Mu.L of the above-prepared cell lysate was added to an EP tube containing cell pellet, and the mixture was allowed to stand on ice. The mixing was done every 10min and the process was repeated 3 times. The mixture was then centrifuged at 12000rpm for 5min at 4℃in a cryocentrifuge and the supernatant obtained was transferred to a fresh 1.5mL EP tube for further use.
(2) BCA protein quantification
(1) BCA protein quantification kit was prepared according to 49:1, fully mixing the solution A and the solution B in proportion, performing double dilution by using standard protein provided by the kit, and performing OD value measurement at the wavelength of 562nm to construct a protein standard curve;
(2) Taking a protein sample, fully mixing the protein sample (2 mu L), ultrapure water (18 mu L) and BCA mixed solution (200 mu L) according to the proportion, standing in a 37 ℃ incubator for 30min, and then measuring the OD value at the wavelength of 562 nm;
(3) Protein concentration was converted using the protein concentration standard curve and the OD values measured as described above, according to 4:1 in a ratio of 5 Xprotein loading buffer (loadingbuffer). Then heating in a dry thermostat at 100 ℃ for 10min for standby.
(3) Protein electrophoresis
A1 Xelectrophoretic solution was prepared, and 720mL of 10 Xelectrophoretic solution was added to 80mL of ultrapure water, followed by mixing to prepare a1 Xelectrophoretic solution. Subsequently, the prepared western gel was placed in an electrophoresis tank, the comb was pulled out, and an appropriate volume of 1×electrophoresis solution was added for use. Firstly, running for 10min under the constant pressure condition of 80V, and debugging the electrophoresis environment. And after the electrophoresis environment is debugged, carrying out protein sample application. Taking a prepared protein sample, carefully adding the prepared protein sample into a protein electrophoresis hole by using a pipette, and adding protein markers on two sides of the sample. After spotting, electrophoresis was performed at 80V for 20min until the protein samples passed through the gel concentrate, followed by electrophoresis at a constant pressure of 120V until the relevant target molecules were visualized.
(4) Transfer film
A1 Xtransfer solution was prepared, 700mL of 10 Xtransfer solution was added to 100mL of ultrapure water and 200mL of methanol, and the mixture was thoroughly mixed to prepare a1 Xtransfer solution. The gel after electrophoresis was placed in a transfer tank, a proper volume of 1 x transfer solution was added, and transfer was performed on ice. The film transfer condition is 400mA constant current for 80min.
(5) Strip closure
The blocking solution was prepared by mixing 2mg of skim milk with 40mL of PBST to prepare a 2% concentration blocking solution. After completion of transfer, the above bands were washed with PBST, repeated 3 times, and bands of appropriate molecular weight were placed in the corresponding primary antibody solution and incubated at 4℃for 16 hours. The secondary antibodies were configured using PBST in the appropriate ratio. After the primary antibody incubation was completed, the strips were washed 3 times with PBST for 10min each, then the corresponding strips were placed in the appropriate secondary antibody solution according to the antibody properties and incubated for 2h at room temperature for later use.
(6) Protein development
After the secondary incubation was completed, the strips were removed and washed 2 times with PBST for 10min each. And (3) preparing protein developing solution according to the requirements of the chemiluminescent kit, fully and uniformly smearing the prepared developing solution on the surface of the strip, placing the strip in a gel imaging analyzer for protein development, and storing proper pictures for subsequent analysis.
Human oral squamous cell carcinoma cell lines SCC9, SCC25, cal27 and human immortalized oral keratinocytes HaCat were cultured, and the difference in DGKG expression between OSCC cell lines and normal cell lines was further verified from protein levels by Westernblot. WB results showed that the protein expression levels of DGKG in SCC9, SCC25, cal27 were higher than HaCat, where the DGKG of SCC9 had significant statistical differences compared to normal control cells.
The invention also provides a method for assisting in diagnosing oral cancer, which is used for assisting in diagnosing the oral cancer by using the genes found in the technical scheme.
It will be apparent to those skilled in the art that various modifications and variations can be made in the specific embodiments of the invention described herein without departing from the scope or spirit of the invention. Other embodiments will be apparent to those skilled in the art from consideration of the specification of the present invention. The specification and examples are exemplary only.
Claims (1)
1. A method for finding a gene associated with lipid metabolism in oral cancer, comprising:
step one, performing non-targeted lipidomic detection on an oral cancer patient to obtain a differential lipid metabolite between the oral cancer patient and a healthy person; the method comprises the following specific steps: acquiring a training case set, wherein subjects in the training case set comprise diseased subjects and healthy subjects; collecting a serum sample from the subject, and analyzing a plurality of differential lipid metabolites of the subject using multivariate statistics;
step two, screening lipid metabolism genes related to pathogenesis and prognosis of oral cancer from the differential lipid metabolites by combining with bioinformatics means; the method comprises the following specific steps: retrieving a plurality of first genes associated with a plurality of said differential lipid metabolites in an HMDB database; analyzing in a TCGA database a plurality of second genes differentially expressed between head and neck squamous cell carcinoma and healthy people; a third gene having the same gene among the plurality of first genes and the plurality of second genes as a candidate; performing single-factor cox regression analysis, multi-factor cox regression analysis and T test on the third gene to determine the lipid metabolism gene; step three, verifying the lipid metabolism genes in an oral cancer cell line by adopting a Western Blot method
Is expressed by (a); culturing human oral squamous cell carcinoma cell lines SCC9, SCC25, cal27 and human immortalized oral keratinocyte HaCat, and further verifying the expression difference of DGKG between an OSCC cell line and a normal cell line from protein level by Western blot;
wherein the lipid metabolism gene is a DGKG gene.
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