CN112908406A - Application of PINK1 as diagnostic marker in construction of lung squamous cell carcinoma prognosis prediction model - Google Patents
Application of PINK1 as diagnostic marker in construction of lung squamous cell carcinoma prognosis prediction model Download PDFInfo
- Publication number
- CN112908406A CN112908406A CN202110150029.8A CN202110150029A CN112908406A CN 112908406 A CN112908406 A CN 112908406A CN 202110150029 A CN202110150029 A CN 202110150029A CN 112908406 A CN112908406 A CN 112908406A
- Authority
- CN
- China
- Prior art keywords
- pink1
- prognosis
- lung squamous
- expression level
- carcinoma
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000004393 prognosis Methods 0.000 title claims abstract description 35
- 201000005243 lung squamous cell carcinoma Diseases 0.000 title claims abstract description 25
- 101000605835 Homo sapiens Serine/threonine-protein kinase PINK1, mitochondrial Proteins 0.000 title claims abstract description 21
- 102100038376 Serine/threonine-protein kinase PINK1, mitochondrial Human genes 0.000 title claims abstract description 21
- 239000003550 marker Substances 0.000 title claims abstract description 9
- 238000010276 construction Methods 0.000 title description 4
- 206010041823 squamous cell carcinoma Diseases 0.000 claims abstract description 16
- 206010028980 Neoplasm Diseases 0.000 claims abstract description 15
- 210000004072 lung Anatomy 0.000 claims abstract description 14
- 238000011398 antitumor immunotherapy Methods 0.000 claims abstract description 10
- 238000001514 detection method Methods 0.000 claims abstract description 8
- 238000011156 evaluation Methods 0.000 claims abstract description 5
- 230000004083 survival effect Effects 0.000 claims description 20
- 238000009169 immunotherapy Methods 0.000 claims description 13
- 239000003153 chemical reaction reagent Substances 0.000 claims description 12
- 239000003112 inhibitor Substances 0.000 claims description 8
- 230000004043 responsiveness Effects 0.000 claims description 8
- 101100190541 Caenorhabditis elegans pink-1 gene Proteins 0.000 claims description 6
- 239000003814 drug Substances 0.000 claims description 6
- 230000009257 reactivity Effects 0.000 claims description 4
- 210000002966 serum Anatomy 0.000 claims description 4
- 230000002222 downregulating effect Effects 0.000 claims description 2
- 239000003937 drug carrier Substances 0.000 claims description 2
- 229920002521 macromolecule Polymers 0.000 claims description 2
- 239000013641 positive control Substances 0.000 claims description 2
- -1 small molecule compound Chemical class 0.000 claims description 2
- 239000000126 substance Substances 0.000 claims description 2
- 239000003795 chemical substances by application Substances 0.000 claims 1
- 229940079593 drug Drugs 0.000 claims 1
- 239000000546 pharmaceutical excipient Substances 0.000 claims 1
- 210000002865 immune cell Anatomy 0.000 abstract description 6
- 230000037361 pathway Effects 0.000 abstract description 5
- 230000008595 infiltration Effects 0.000 abstract description 4
- 238000001764 infiltration Methods 0.000 abstract description 4
- 238000010801 machine learning Methods 0.000 abstract description 3
- 229940076838 Immune checkpoint inhibitor Drugs 0.000 abstract description 2
- 108091008026 Inhibitory immune checkpoint proteins Proteins 0.000 abstract description 2
- 102000037984 Inhibitory immune checkpoint proteins Human genes 0.000 abstract description 2
- 239000012274 immune-checkpoint protein inhibitor Substances 0.000 abstract description 2
- 238000012216 screening Methods 0.000 abstract description 2
- 239000000090 biomarker Substances 0.000 abstract 1
- 238000012163 sequencing technique Methods 0.000 abstract 1
- 210000004881 tumor cell Anatomy 0.000 abstract 1
- 238000000034 method Methods 0.000 description 9
- 238000003559 RNA-seq method Methods 0.000 description 7
- 238000010219 correlation analysis Methods 0.000 description 6
- 208000020816 lung neoplasm Diseases 0.000 description 6
- 108090000623 proteins and genes Proteins 0.000 description 6
- 206010058467 Lung neoplasm malignant Diseases 0.000 description 5
- 230000000694 effects Effects 0.000 description 5
- 201000005202 lung cancer Diseases 0.000 description 5
- 238000004458 analytical method Methods 0.000 description 4
- 208000002154 non-small cell lung carcinoma Diseases 0.000 description 4
- 208000029729 tumor suppressor gene on chromosome 11 Diseases 0.000 description 4
- 230000008901 benefit Effects 0.000 description 3
- 238000010199 gene set enrichment analysis Methods 0.000 description 3
- 230000035945 sensitivity Effects 0.000 description 3
- 108010082399 Autophagy-Related Proteins Proteins 0.000 description 2
- 108010074708 B7-H1 Antigen Proteins 0.000 description 2
- 102000008096 B7-H1 Antigen Human genes 0.000 description 2
- 101000687905 Homo sapiens Transcription factor SOX-2 Proteins 0.000 description 2
- 108091008036 Immune checkpoint proteins Proteins 0.000 description 2
- 102000037982 Immune checkpoint proteins Human genes 0.000 description 2
- 206010054949 Metaplasia Diseases 0.000 description 2
- 238000010220 Pearson correlation analysis Methods 0.000 description 2
- 206010041826 Squamous cell carcinoma of lung Diseases 0.000 description 2
- 102100024270 Transcription factor SOX-2 Human genes 0.000 description 2
- 239000002131 composite material Substances 0.000 description 2
- 230000002596 correlated effect Effects 0.000 description 2
- 230000007547 defect Effects 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 230000015689 metaplastic ossification Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000007637 random forest analysis Methods 0.000 description 2
- 210000003171 tumor-infiltrating lymphocyte Anatomy 0.000 description 2
- 238000012795 verification Methods 0.000 description 2
- 206010006482 Bronchospasm Diseases 0.000 description 1
- 101100257359 Caenorhabditis elegans sox-2 gene Proteins 0.000 description 1
- 102100039498 Cytotoxic T-lymphocyte protein 4 Human genes 0.000 description 1
- 238000009007 Diagnostic Kit Methods 0.000 description 1
- 101000889276 Homo sapiens Cytotoxic T-lymphocyte protein 4 Proteins 0.000 description 1
- 101100257363 Mus musculus Sox2 gene Proteins 0.000 description 1
- 206010035664 Pneumonia Diseases 0.000 description 1
- 101710089372 Programmed cell death protein 1 Proteins 0.000 description 1
- 238000007792 addition Methods 0.000 description 1
- 238000011226 adjuvant chemotherapy Methods 0.000 description 1
- 210000000270 basal cell Anatomy 0.000 description 1
- 210000000424 bronchial epithelial cell Anatomy 0.000 description 1
- 230000007885 bronchoconstriction Effects 0.000 description 1
- 238000004422 calculation algorithm Methods 0.000 description 1
- 201000011510 cancer Diseases 0.000 description 1
- 230000005773 cancer-related death Effects 0.000 description 1
- 231100000504 carcinogenesis Toxicity 0.000 description 1
- 230000001684 chronic effect Effects 0.000 description 1
- 210000004081 cilia Anatomy 0.000 description 1
- 150000001875 compounds Chemical class 0.000 description 1
- 230000000875 corresponding effect Effects 0.000 description 1
- 230000006378 damage Effects 0.000 description 1
- 210000002919 epithelial cell Anatomy 0.000 description 1
- 230000007717 exclusion Effects 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 206010020718 hyperplasia Diseases 0.000 description 1
- 230000036737 immune function Effects 0.000 description 1
- 230000008629 immune suppression Effects 0.000 description 1
- 230000036039 immunity Effects 0.000 description 1
- 238000001325 log-rank test Methods 0.000 description 1
- 230000007774 longterm Effects 0.000 description 1
- 230000036210 malignancy Effects 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 239000000203 mixture Substances 0.000 description 1
- 210000004877 mucosa Anatomy 0.000 description 1
- 230000000414 obstructive effect Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 230000004044 response Effects 0.000 description 1
- 238000013058 risk prediction model Methods 0.000 description 1
- 230000000391 smoking effect Effects 0.000 description 1
- 230000000638 stimulation Effects 0.000 description 1
- 238000001356 surgical procedure Methods 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
- 238000002560 therapeutic procedure Methods 0.000 description 1
- 210000000115 thoracic cavity Anatomy 0.000 description 1
- 210000001519 tissue Anatomy 0.000 description 1
- 238000012549 training Methods 0.000 description 1
- 230000003827 upregulation Effects 0.000 description 1
- 238000012049 whole transcriptome sequencing Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
- G16B5/00—ICT specially adapted for modelling or simulations in systems biology, e.g. gene-regulatory networks, protein interaction networks or metabolic networks
-
- C—CHEMISTRY; METALLURGY
- C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
- C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
- C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
- C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
- C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
- C12Q1/6883—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material
- C12Q1/6886—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
- G16B40/00—ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16C—COMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
- G16C20/00—Chemoinformatics, i.e. ICT specially adapted for the handling of physicochemical or structural data of chemical particles, elements, compounds or mixtures
- G16C20/50—Molecular design, e.g. of drugs
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment
-
- C—CHEMISTRY; METALLURGY
- C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
- C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
- C12Q2600/00—Oligonucleotides characterized by their use
- C12Q2600/118—Prognosis of disease development
-
- C—CHEMISTRY; METALLURGY
- C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
- C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
- C12Q2600/00—Oligonucleotides characterized by their use
- C12Q2600/158—Expression markers
Landscapes
- Health & Medical Sciences (AREA)
- Engineering & Computer Science (AREA)
- Life Sciences & Earth Sciences (AREA)
- Chemical & Material Sciences (AREA)
- Physics & Mathematics (AREA)
- Bioinformatics & Cheminformatics (AREA)
- General Health & Medical Sciences (AREA)
- Medical Informatics (AREA)
- Proteomics, Peptides & Aminoacids (AREA)
- Theoretical Computer Science (AREA)
- Spectroscopy & Molecular Physics (AREA)
- Public Health (AREA)
- Organic Chemistry (AREA)
- Bioinformatics & Computational Biology (AREA)
- Biotechnology (AREA)
- Pathology (AREA)
- Biophysics (AREA)
- Evolutionary Biology (AREA)
- Data Mining & Analysis (AREA)
- Analytical Chemistry (AREA)
- Databases & Information Systems (AREA)
- Genetics & Genomics (AREA)
- Epidemiology (AREA)
- Molecular Biology (AREA)
- Immunology (AREA)
- Wood Science & Technology (AREA)
- Zoology (AREA)
- Hospice & Palliative Care (AREA)
- Biochemistry (AREA)
- General Engineering & Computer Science (AREA)
- Physiology (AREA)
- Microbiology (AREA)
- Oncology (AREA)
- Primary Health Care (AREA)
- Biomedical Technology (AREA)
- Medicinal Chemistry (AREA)
- Pharmacology & Pharmacy (AREA)
- Crystallography & Structural Chemistry (AREA)
- Computing Systems (AREA)
- Artificial Intelligence (AREA)
Abstract
The invention relates to the technical field of biomedicine, in particular to application of PINK1 as a diagnostic marker in constructing a lung squamous cell carcinoma prognosis prediction model, which is combined by a biomarker PINK1 and other clinical indexes, the invention can assist in making lung squamous carcinoma prognosis detection, and can efficiently and accurately predict the prognosis condition of a patient with lung squamous carcinoma by screening and constructing the lung squamous carcinoma sample based on large-sample anti-tumor immunotherapy after full transcriptome sequencing and machine learning, meanwhile, according to the relevance of the risk of the tumor cell, different immune cell infiltration levels, immune-related pathways, the expression level of a key immune checkpoint inhibitor and the like, the comprehensive evaluation of a tumor immune microenvironment is realized, effective guidance opinions are provided for clinicians on treatment decisions of patients with squamous cell lung carcinoma, and the occurrence of ineffective treatment is reduced, so that the treatment cost and discomfort experience of the patients are reduced.
Description
Technical Field
The invention relates to the technical field of biomedicine, in particular to application of PINK1 as a diagnostic marker in construction of a squamous cell lung carcinoma prognosis prediction model.
Background
Lung cancer is the most common cause of cancer-related death in the world today, and 80% of them are non-small cell lung cancers (NSCLC). TNM staging is a currently widely accepted clinical staging system used to predict prognosis and to guide treatment of non-small cell lung cancer patients. However, the current TNM staging system is far from adequate to accurately predict prognosis in non-small cell lung cancer patients. For example, for lung cancer patients, the recurrence rate of lung cancer is as high as 35-50% even in the clinical stage I. In addition, a significant proportion of patients can be cured by surgery alone, and these patients should avoid the extremely strong side effects of adjuvant chemotherapy based on the current TNM system.
Squamous cell carcinoma of lung (also called squamous cell carcinoma of lung), accounts for 40% -51% of primary lung cancer, is commonly seen in middle-aged and elderly men, and has close relation with smoking. Is mainly formed by metaplasia of columnar epithelial cells of bronchial mucosa, including chronic stimulation and damage of bronchial epithelial cells, loss of cilia, squamous metaplasia or typical hyperplasia of basal cells and the like. Squamous cell lung cancer is common in central lung cancer, and tends to grow in the chest cavity, and early squamous cell lung cancer often causes bronchoconstriction or obstructive pulmonary inflammation. Squamous cell lung carcinoma has a large variation in malignancy, and generally, squamous cell carcinoma grows more slowly than other lung cancers, and the tumor grows larger when the squamous cell carcinoma is found.
At present, the main methods and technologies for prognosis prediction of tumor therapy immunotherapy are as follows: detecting markers (such as PD-L1, TMB, dMMR and the like); ② immune function assessment, such as Tumor Infiltrating Lymphocytes (TILs).
The existing lung squamous carcinoma related gene prediction model lacks comprehensive evaluation on different levels of tumor immune microenvironment, such as immune cell infiltration level/immune related pathway/immune molecule and the like. At present, the main technology has low specificity and sensitivity, and the detection method is unstable or has higher price, and has no clear guidance value for clinical tumor treatment. At present, clinical tumor immunity brings long-term benefit hope to partial patients, and the side effect and economic burden of immunotherapy limit clinical application. High specificity and sensitivity techniques are urgently needed in clinic.
Aiming at the defects, the invention provides a novel squamous cell lung carcinoma prognosis prediction model, which can predict the prognosis of patients with squamous cell lung carcinoma and realize the comprehensive evaluation of tumor immune microenvironment according to the correlation between the squamous cell lung carcinoma prognosis model and different immune cell infiltration levels, immune-related pathways, expression levels of key immune checkpoint inhibitors and the like, thereby providing guidance for immunotherapy selection of patients with squamous cell lung carcinoma. The application of PINK1 as a diagnostic marker in constructing a lung squamous cell carcinoma prognosis prediction model is not reported at present.
Disclosure of Invention
The invention aims to provide a lung squamous carcinoma prognosis prediction model and a kit aiming at the defects of the prior art.
In order to achieve the purpose, the invention adopts the technical scheme that:
in a first aspect, the invention provides an application of PINK1 as a diagnostic marker in constructing a lung squamous cell carcinoma prognosis prediction model.
Preferably, the prediction model further comprises a reagent for detecting the expression level of PINK 1.
Preferably, the high risk is when the PINK1 expression level is higher than 9.822569, and the low risk is when the PINK1 expression level is lower than 9.822569.
Preferably, the predictive model further comprises the following reagents: normal human serum and positive control serum.
In a second aspect, the invention provides an application of a reagent for detecting the expression level of PINK1 in preparing a kit for evaluating the anti-tumor immunotherapy reactivity and prognosis survival of squamous cell lung cancer, wherein the detection reagent is used as the only key component for evaluating the anti-tumor immunotherapy reactivity and prognosis survival function of squamous cell lung cancer, the kit further comprises an instruction book, and the instruction book records the following contents: when the expression level of PINK1 is higher than 9.822569, the prediction of the immunotherapy responsiveness and prognosis survival is represented as a high-risk group, and when the expression level of PINK1 is lower than 9.822569, the prediction of the immunotherapy responsiveness and prognosis survival is represented as a low-risk group.
Preferably, the sample detected using the kit is a fresh tissue tumor sample.
In a third aspect, the invention provides an application of an inhibitor in preparing a medicament for improving lung squamous carcinoma anti-tumor immunotherapy and prognosis survival, wherein the inhibitor is a substance for down-regulating the expression level of PINK 1.
Preferably, the inhibitor is selected from a small molecule compound or a biological macromolecule.
Preferably, the medicament also comprises other medicaments compatible with the inhibitor and pharmaceutically acceptable carriers and/or auxiliary materials.
The invention has the advantages that:
the invention is based on the whole transcriptome sequencing data of the lung squamous carcinoma specimen of the large-sample anti-tumor immunotherapy, screens and constructs by machine learning, can efficiently and accurately predict the response of the lung squamous carcinoma patient to receive the anti-tumor immunotherapy, and the experimental result shows that the model and the diagnostic kit have the advantages of high sensitivity, high specificity and high accuracy when being used clinically. Can provide effective guidance opinions for treatment decisions of the patients with the lung squamous carcinoma for clinicians, and reduce the occurrence of ineffective treatment, thereby reducing the treatment cost and discomfort experience of the patients.
Drawings
FIG. 1 shows the results of OS survival analysis of prognostic-related ARGs in 326 squamous cell lung carcinoma samples. (the results showed that the RNA level of PINK1 gene was significantly negatively correlated with prognosis).
FIG. 2 is the results of correlation analysis of the predicted gene ARGs with the expression levels of three key immune checkpoints (FIG. 2A-FIG. 2C).
Detailed Description
The invention will be further illustrated with reference to specific embodiments. It should be understood that these examples are for illustrative purposes only and are not intended to limit the scope of the present invention. Furthermore, it should be understood that various changes and modifications can be made by those skilled in the art after reading the disclosure of the present invention, and equivalents fall within the scope of the appended claims.
Example 1 model construction and Effect verification
1. Method of producing a composite material
1.1 training set 326 lung squamous carcinoma RNA sequencing data and clinical data from TCGA database, obtaining Autophagy-Related gene (ARGs) expression profiles, verifying and collecting 78 lung squamous carcinoma RNA sequencing data from GTO database, obtaining all RNA expression profiles and Autophagy-Related gene (ARGs) expression profiles.
1.2 screening ARGs related to prognosis through survival analysis, then obtaining the most key ARGs related to survival (PINK1) by adopting a random forest method, and constructing a risk prediction model based on the gene through a deep machine learning method of random forests. Patients were classified into low risk groups and high risk groups according to this model. The model is verified to be effective in predicting the prognosis of the squamous cell lung carcinoma patient by ROC analysis of a working characteristic curve of a subject and log-rank test verification of a KM survival curve.
1.3 based on RNA sequencing data, the relationship between high and low risks and immune-related pathways is researched through Gene Set Enrichment Analysis (GSEA), and the high-risk group is found to be related to the up-regulation of immune suppression-related pathways.
1.4 obtaining infiltration levels of 28 tumor infiltrating immune cells by single gene set enrichment analysis (ssGSEA) based on RNA sequencing data, and finding significant correlation with risk by correlation analysis.
1.5 correlation of risk with expression levels of key immune checkpoint molecules PD-1, PD-L1, CTLA4 was found by correlation analysis.
1.6 based on RNA sequencing data, tumor immune scores and stroma scores were obtained by the ESTIMATE algorithm, and correlations between risk and immune scores and mechanism scores were found.
The method for calculating the level of tumor infiltrating immune cells comprises the following steps: (ssGSEA method): immune cells and corresponding gene lists are obtained in the literature, and GSVAenrichment scales are evaluated according to RNA sequencing data and an R language GSVA package.
Correlation analysis method: pearson correlation analysis (correlation analysis methods in this study are all Pearson correlation analysis.
2 results
FIG. 1 is the results of the OS survival analysis of the ARGs with the highest prognosis in the 326 squamous cell lung carcinoma samples. (the results show that the level of gene RNA is significantly negatively correlated with prognosis).
FIGS. 2A-2C are the results of correlation analysis of the expression levels of the predicted genes ARGs with three key immune checkpoints.
Example 2 control test
1. Kit composition
Kit I
Comprises a detection kit instruction, and the detection reagent is a reagent for detecting the expression level of PINK 1.
The description content of the specification is as follows: when the expression level of PINK1 is higher than 9.822569, the prediction of the immunotherapy responsiveness and prognosis survival is represented as a high-risk group, and when the expression level of PINK1 is lower than 9.822569, the prediction of the immunotherapy responsiveness and prognosis survival is represented as a low-risk group.
Reagent kit II
The kit comprises a detection kit instruction, and the detection reagent is a reagent for detecting the expression quantity of the SOX 2.
The description content of the specification is as follows: when the expression level of SOX2 is higher than the normal value, it represents the prediction of the immunotherapy responsiveness and prognosis survival as the high-risk group, and when the expression level of SOX2 is lower than the normal value, it represents the prediction of the immunotherapy responsiveness and prognosis survival as the low-risk group.
2. Method of producing a composite material
2.1 patients with squamous cell lung carcinoma who received anti-tumor immunotherapy at Jinshan Hospital affiliated at the university of Compound Dane, the inclusion and exclusion criteria for the patients were as follows:
(1) patients with squamous cell lung carcinoma receiving immunotherapy with tumors;
(2) complete curative effect information and clinical follow-up information are provided;
(3) having whole transcriptome RNA sequencing data;
(4) patients with unknown tumor immunotherapy results or incomplete survival data were excluded.
2.2 78 patients meeting the above criteria were included in the study and were randomly divided into two groups, which were recorded by someone using kit one and kit two according to the instruction manual.
3. Results
The result shows that the accuracy rate of prediction by using the first kit is 68.7%, and the accuracy rate of prediction by using the second kit is 56.8%.
4. Conclusion
The results show that the marker of the invention has high prognosis prediction accuracy, and the inventor selects the best index based on abundant clinical and research experiences and a large number of cases in hospital for years, and confirms that the marker has excellent evaluation effect, can provide effective guidance for the treatment decision of a clinician on a patient with squamous cell lung carcinoma, reduces the occurrence of ineffective treatment, thereby reducing the treatment cost and discomfort experience of the patient, and has strong practicability.
The foregoing is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and additions can be made without departing from the principle of the present invention, and these should also be considered as the protection scope of the present invention.
Claims (9)
- The application of PINK1 as a diagnostic marker in constructing a lung squamous cell carcinoma prognosis prediction model.
- 2. The use of claim 1, wherein said predictive model further comprises reagents for detecting the expression level of PINK 1.
- 3. The use according to claim 2, characterized in that high risk is indicated when PINK1 expression level is higher than 9.822569 and low risk is indicated when PINK1 expression level is lower than 9.822569.
- 4. The use of claim 2, wherein the predictive model further comprises the following agents: normal human serum and positive control serum.
- 5. The application of the reagent for detecting the PINK1 expression quantity in preparing the kit for evaluating the lung squamous carcinoma anti-tumor immunotherapy reactivity and the prognosis survival is characterized in that the detection reagent is used as the only key component for realizing the evaluation of the lung squamous carcinoma anti-tumor immunotherapy reactivity and the prognosis survival function by using the kit, the kit further comprises an instruction book, and the instruction book records the following contents: when the expression level of PINK1 is higher than 9.822569, the prediction of the immunotherapy responsiveness and prognosis survival is represented as a high-risk group, and when the expression level of PINK1 is lower than 9.822569, the prediction of the immunotherapy responsiveness and prognosis survival is represented as a low-risk group.
- 6. The use of claim 5, wherein the sample to be tested using the kit is a fresh tissue tumor sample.
- 7. The application of an inhibitor in preparing a medicament for improving lung squamous carcinoma anti-tumor immunotherapy and prognostic survival is characterized in that the inhibitor is a substance for down-regulating the expression level of PINK 1.
- 8. The use according to claim 7, wherein the inhibitor is selected from a small molecule compound or a biological macromolecule.
- 9. The use of claim 7, wherein the medicament further comprises other drugs compatible with the inhibitor and pharmaceutically acceptable carriers and/or excipients.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110150029.8A CN112908406A (en) | 2021-02-03 | 2021-02-03 | Application of PINK1 as diagnostic marker in construction of lung squamous cell carcinoma prognosis prediction model |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110150029.8A CN112908406A (en) | 2021-02-03 | 2021-02-03 | Application of PINK1 as diagnostic marker in construction of lung squamous cell carcinoma prognosis prediction model |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN112908406A true CN112908406A (en) | 2021-06-04 |
Family
ID=76121880
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110150029.8A Pending CN112908406A (en) | 2021-02-03 | 2021-02-03 | Application of PINK1 as diagnostic marker in construction of lung squamous cell carcinoma prognosis prediction model |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112908406A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114480339A (en) * | 2022-01-13 | 2022-05-13 | 中山大学孙逸仙纪念医院 | Application of PINK1 as marker for predicting sensitivity of lung adenocarcinoma to MAPK inhibitor and lung adenocarcinoma recurrence |
| CN115713964A (en) * | 2022-10-16 | 2023-02-24 | 洛兮基因科技(杭州)有限公司 | Immune-related gene prognosis model for predicting overall survival rate of squamous cell lung carcinoma patients |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20090258925A1 (en) * | 2005-12-28 | 2009-10-15 | Claes Wahlestedt | Natural antisense and non-coding rna transcripts as drug targets |
| CN112133369A (en) * | 2020-08-26 | 2020-12-25 | 吴安华 | System for evaluating tumor patient prognosis based on active oxygen and drug sensitivity evaluation and improvement method |
-
2021
- 2021-02-03 CN CN202110150029.8A patent/CN112908406A/en active Pending
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20090258925A1 (en) * | 2005-12-28 | 2009-10-15 | Claes Wahlestedt | Natural antisense and non-coding rna transcripts as drug targets |
| CN112133369A (en) * | 2020-08-26 | 2020-12-25 | 吴安华 | System for evaluating tumor patient prognosis based on active oxygen and drug sensitivity evaluation and improvement method |
Non-Patent Citations (1)
| Title |
|---|
| LUMENG LUO ET AL.: "A Four-gene Autophagy-related Prognostic Signature and Its Association With Immune Landscape in Lung Squamous Cell Carcinoma", 《SQUAMOUS CELL CARCINOMA》 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114480339A (en) * | 2022-01-13 | 2022-05-13 | 中山大学孙逸仙纪念医院 | Application of PINK1 as marker for predicting sensitivity of lung adenocarcinoma to MAPK inhibitor and lung adenocarcinoma recurrence |
| CN114480339B (en) * | 2022-01-13 | 2024-05-14 | 中山大学孙逸仙纪念医院 | Application of PINK1 as marker for predicting sensitivity of lung adenocarcinoma to MAPK inhibitor and recurrence of lung adenocarcinoma |
| CN115713964A (en) * | 2022-10-16 | 2023-02-24 | 洛兮基因科技(杭州)有限公司 | Immune-related gene prognosis model for predicting overall survival rate of squamous cell lung carcinoma patients |
| CN115713964B (en) * | 2022-10-16 | 2023-08-15 | 洛兮基因科技(杭州)有限公司 | Method for predicting overall survival rate of lung squamous carcinoma patient based on immune related genes |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Chen et al. | Identification of ten serum microRNAs from a genome‐wide serum microRNA expression profile as novel noninvasive biomarkers for nonsmall cell lung cancer diagnosis | |
| Liu et al. | A five-microRNA signature identified from genome-wide serum microRNA expression profiling serves as a fingerprint for gastric cancer diagnosis | |
| CN109859801A (en) | A kind of model and method for building up containing seven genes as biomarker prediction lung squamous cancer prognosis | |
| US9249465B2 (en) | Molecular markers for lung and colorectal carcinomas | |
| Sivendran et al. | Dissection of immune gene networks in primary melanoma tumors critical for antitumor surveillance of patients with stage II–III resectable disease | |
| CN112813165A (en) | Lung squamous carcinoma prognosis prediction model and application thereof | |
| CN111394456B (en) | Early lung adenocarcinoma patient prognosis evaluation system and application thereof | |
| Ye et al. | Circulating tumor cells as a potential biomarker for postoperative clinical outcome in HBV-related hepatocellular carcinoma | |
| CN106834462A (en) | One group of application of stomach oncogene | |
| US20090220985A1 (en) | Rapid efficacy assessment method for lung cancer therapy | |
| CN113192560A (en) | Construction method of hepatocellular carcinoma typing system based on iron death process | |
| CN111676288B (en) | System for predicting lung adenocarcinoma patient prognosis and application thereof | |
| CN112908406A (en) | Application of PINK1 as diagnostic marker in construction of lung squamous cell carcinoma prognosis prediction model | |
| CN107523647A (en) | Detect the LncRNA combinations of early stage cancer of the esophagus prognosis situation and the kit containing the combination | |
| CN112614546B (en) | Model for predicting hepatocellular carcinoma immunotherapy curative effect and construction method thereof | |
| JP2013532489A5 (en) | ||
| JP2013532489A (en) | Prediction and monitoring of response to cancer treatment based on gene expression profiling | |
| Wu et al. | Low expression of keratin17 is related to poor prognosis in bladder cancer | |
| Han et al. | Plasma cell-free circRNAs panel act as fingerprint predicts the occurrence of laryngeal squamous cell carcinoma | |
| Bittla et al. | Exploring Circulating Tumor DNA (CtDNA) and Its Role in Early Detection of Cancer: A Systematic Review | |
| CN112921087A (en) | Application of CTSD (cytotoxic T lymphocyte) as diagnosis marker in construction of lung squamous cell carcinoma prognosis prediction model | |
| CN112961920A (en) | Method and product for predicting curative effect of combination of immune checkpoint inhibitor and targeted therapy of hepatobiliary tumor patient | |
| CN109735619B (en) | Molecular marker related to non-small cell lung cancer prognosis and application thereof | |
| Li et al. | Serum tRF-27-FDXXE6XRK45 as a Promising Biomarker for the Clinical Diagnosis in Gastric Cancer | |
| CN110331207A (en) | Adenocarcinoma of lung biomarker and related application |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20210604 |