CN115505644A - Kit for predicting chemotherapeutic effect of head and neck squamous cell carcinoma and application thereof - Google Patents

Kit for predicting chemotherapeutic effect of head and neck squamous cell carcinoma and application thereof Download PDF

Info

Publication number
CN115505644A
CN115505644A CN202211303022.6A CN202211303022A CN115505644A CN 115505644 A CN115505644 A CN 115505644A CN 202211303022 A CN202211303022 A CN 202211303022A CN 115505644 A CN115505644 A CN 115505644A
Authority
CN
China
Prior art keywords
lipid metabolism
model
prognosis
gene
kit
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
Application number
CN202211303022.6A
Other languages
Chinese (zh)
Inventor
苗湘琬
向明亮
叶斌
范翠
丁锐
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Ruinjin Hospital Affiliated to Shanghai Jiaotong University School of Medicine Co Ltd
Original Assignee
Ruinjin Hospital Affiliated to Shanghai Jiaotong University School of Medicine Co Ltd
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Ruinjin Hospital Affiliated to Shanghai Jiaotong University School of Medicine Co Ltd filed Critical Ruinjin Hospital Affiliated to Shanghai Jiaotong University School of Medicine Co Ltd
Priority to CN202211303022.6A priority Critical patent/CN115505644A/en
Publication of CN115505644A publication Critical patent/CN115505644A/en
Pending legal-status Critical Current

Links

Images

Classifications

    • 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
    • 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
    • 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
    • G16H70/00—ICT specially adapted for the handling or processing of medical references
    • G16H70/40—ICT specially adapted for the handling or processing of medical references relating to drugs, e.g. their side effects or intended usage
    • 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
    • C12Q2600/106—Pharmacogenomics, i.e. genetic variability in individual responses to drugs and drug metabolism
    • 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)
  • Chemical & Material Sciences (AREA)
  • Engineering & Computer Science (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Public Health (AREA)
  • Organic Chemistry (AREA)
  • Proteomics, Peptides & Aminoacids (AREA)
  • Pathology (AREA)
  • General Health & Medical Sciences (AREA)
  • Medical Informatics (AREA)
  • Genetics & Genomics (AREA)
  • Analytical Chemistry (AREA)
  • Zoology (AREA)
  • Immunology (AREA)
  • Wood Science & Technology (AREA)
  • Epidemiology (AREA)
  • Primary Health Care (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Toxicology (AREA)
  • Pharmacology & Pharmacy (AREA)
  • Physics & Mathematics (AREA)
  • Biophysics (AREA)
  • Biotechnology (AREA)
  • Microbiology (AREA)
  • Molecular Biology (AREA)
  • Medicinal Chemistry (AREA)
  • Oncology (AREA)
  • Hospice & Palliative Care (AREA)
  • Biochemistry (AREA)
  • General Engineering & Computer Science (AREA)
  • Biomedical Technology (AREA)
  • Data Mining & Analysis (AREA)
  • Databases & Information Systems (AREA)
  • Measuring Or Testing Involving Enzymes Or Micro-Organisms (AREA)

Abstract

The invention relates to an application of a reagent for detecting the expression level of a lipid metabolism gene in preparing a kit for predicting a head and neck squamous cell carcinoma chemotherapy efficacy prognosis model, wherein the lipid metabolism gene is selected from the following 8 gene combinations: ACSBG2, APOB, IKB, MAPK9, MOGAT2, PLA2G10, PIK3R3, and SREBF1. The invention also provides a model and a kit for predicting the pharmacodynamic prognosis of head and neck squamous cell carcinoma chemotherapy, wherein a prediction model is constructed according to 8 gene expression levels and clinical information, and appropriate evaluation and selection criteria are provided, and the wind fraction of the model is (= (-2.7486) × ACSBG2+ (1.7158) × APOB + (-0.3216) × IKBKB + (0.4612) × MAPK9+ (-0.8421) × MOGAT2+ (0.6413) × PLA2G10+ (-0.2157) (-PIK 3R3+ (-0.2355) (-SREBF 1. In addition, the LMRS model has excellent diagnosis efficiency on the drug effect evaluation and the overall survival prognosis diagnosis of HNSCCs systemic treatment drugs, has an important relation with the distribution and expression of immune cells, has a potential prognosis value for HNSCCs patients, and provides an important reference value for the research and development of new drugs.

Description

Kit for predicting chemotherapeutic effect of head and neck squamous cell carcinoma and application thereof
Technical Field
The invention relates to the technical field of kits, in particular to a kit for predicting the chemotherapeutic effect of head and neck squamous cell carcinoma and application thereof.
Background
Head and Neck Squamous Cell Carcinoma (HNSCC) is a group of malignancies occurring in the head and neck, accounting for approximately 90% of head and neck tumors. HNSCC contains neck tumors, oral, maxillofacial tumors, and otorhinolaryngological tumors, and is frequently found in women. Of the patients diagnosed each year, about 60% are locally advanced, more than 85 ten thousand are diagnosed with the head HNSCC worldwide, and 44 ten thousand die each year, and although HPV (Human Papilloma virus) positive HNSCC patients are better at prognosis and OS (Overall Survival) is 70%, patients in stages III-IV still have extensive local invasion and treatment failure, and the 5-year poor-prognosis OS is about 40%. The treatment of HNSCC varies according to the pathological characteristics or clinical stage, and includes surgical treatment, concurrent chemoradiotherapy, targeted therapy, and immunotherapy. However, impaired swallowing and respiratory function due to extensive tissue resection, reconstruction and side effects of chemoradiotherapy severely impact the quality of life and survival of patients with HNSCC.
With the advent of immunodetection inhibitors, the role of systemic therapy in head and neck tumors is increasing, and the overall survival rate of HNSCCs is greatly improved. Patients who are sensitive to chemotherapy (cisplatin is the primary chemotherapeutic drug for use) not only have the opportunity to preserve the laryngeal organs, but also gain better overall survival and quality of life. However, most patients with HNSCCs are diagnosed at an advanced stage, and even if personalized comprehensive treatment is established, treatment failure still occurs. The most common cause of treatment failure is resistance to the drug. The existing treatment means has limited selection after platinum drug resistance, and few selectable targeted drugs are available except PD-1/PD-L1 (Programmed Cell Death 1/Programmed Cell Death 1ligand 1, programmed Death receptor 1/Programmed Cell Death-ligand 1) and EGFR (Epidermal growth factor receptor). Therefore, it would be of great value to be able to predict early efficacy of chemotherapy-based systemic treatments in HNSCC after definitive diagnosis. Identification of potentially critical drug resistance molecules and mechanisms will also provide an important reference basis for the formulation of comprehensive treatment regimens and the development of new drugs in patients with HNSCC.
Since metabolic remodeling is one of the important features of cancer, its role in HNSCC is also playing an increasingly important role. In addition to the Warburg effect and glutamine metabolism, lipid metabolic remodeling also has a large impact on the proliferation, metastasis and recurrence of HNSCC tumor cells. Most lipid metabolism enzymes are expressed elevated in HNSCC and are associated with poor prognosis. Its effects and potential effects on systemic administration of HNSCC are still poorly studied. Therefore, the invention analyzes the influence of lipid metabolism remodeling on systemic HNSCC administration and constructs a lipid metabolism characteristic prognosis model (LMRS model).
However, no report is found on the prognostic model of lipid metabolism characteristics constructed according to the present invention.
Disclosure of Invention
The first purpose of the present invention is to provide a kit for predicting the efficacy of chemotherapy for head and neck squamous cell carcinoma, which overcomes the shortcomings of the prior art.
The second purpose of the invention is to provide the application of the reagent for detecting the expression quantity of the lipid metabolism gene in the preparation of a prognosis model kit.
The third purpose of the invention is to provide a prognostic model system for predicting the efficacy of chemotherapy for head and neck squamous cell carcinoma.
In order to achieve the first purpose, the invention adopts the technical scheme that:
a kit for predicting a head and neck squamous cell carcinoma chemotherapy drug effect prognosis model comprises a reagent for detecting lipid metabolism gene expression level and a lipid metabolism characteristic risk score.
More preferably, the lipid metabolism gene is a combination of the following 8 genes: ACSBG2, APOB, IKB, MAPK9, MOGAT2, PLA2G10, PIK3R3, SREBF1.
More preferably, the kit is a risk score comprising a lipid metabolism profile: risk score = (-2.7486) × ACSBG2+ (1.7158) × APOB + (-0.3216) × IKBKB + (0.4612) × MAPK9+ (-0.8421) × MOGAT2+ (0.6413) 'PLA 2G10+ (-0.2157) × PIK3R3+ (-0.2355)' SREBF1.
In order to achieve the second object, the invention adopts the technical scheme that:
the application of the reagent for detecting the expression level of the lipid metabolism gene in preparing a kit for predicting the drug effect prognosis model of the head and neck squamous cell carcinoma chemotherapy is characterized in that the lipid metabolism gene is the combination of the following 8 genes: ACSBG2, APOB, IKB, MAPK9, MOGAT2, PLA2G10, PIK3R3, SREBF1, wherein the prognostic model is a risk score comprising lipid metabolism characteristics: risk score = (-2.7486) × ACSBG2+ (1.7158) × APOB + (-0.3216) × IKBKB + (0.4612) × MAPK9+ (-0.8421) × MOGAT2+ (0.6413) 'PLA 2G10+ (-0.2157) × PIK3R3+ (-0.2355)' SREBF1.
In order to achieve the third object, the invention adopts the technical scheme that:
a prognostic model system for predicting the efficacy of chemotherapy on head and neck squamous cell carcinoma, said system comprising information acquisition, calculation of risk score and prognostic assessment.
More preferably, the information acquisition is to acquire gene expression information of each head and neck squamous cell carcinoma patient.
More preferably, the risk score is calculated by substituting the expression level of the lipid metabolism mRNA into a prognosis model formula.
More preferably, the prognostic assessment is to group patients according to risk score, with a median value of-2.15128 for the low score group and-1.09682 for the high score group.
More preferably, the prognostic model system is also useful for predicting immune cell infiltration and immune checkpoint expression.
The invention has the advantages that:
1. better understanding of the source of heterogeneity and its interrelationships in HNSCC is a key goal in head and neck oncology, and has broad implications for diagnosis and treatment.
2. Our invention further supports lipid metabolism remodeling, especially fatty acid-related metabolism, has important reference value for survival prognosis of HNSCC patients, especially for response to systemic treatment, and the model obtains satisfactory effect by integrating risk score, age, sex, tumor stage, pathological typing and smoking condition. It has both short-term and intermediate diagnostic value, and shows better diagnostic threshold value compared with common clinical indicators, such as gender and age.
3. The model test panel established by the present invention provides appropriate evaluation and selection criteria to screen as far as possible patients that can receive the greatest benefit from systemic therapy and to allow patients who are unlikely to benefit to quickly transition to other therapies.
Drawings
FIG. 1 is a design flow chart of the method.
FIG. 2 shows the result of significant enrichment of lipid-associated genes after screening of CRISPR/cas9 library.
FIG. 3 shows the prognosis of a patient treated systemically with HNSCC using the lipid metabolism gene classification.
FIG. 4 shows the diagnostic prognosis model establishment and evaluation of 8 lipid-associated genes.
FIG. 5 shows the results of the gene prognosis and the DCA diagnostic curve.
FIG. 6 shows the results of the genetic model immune-related assessment.
FIG. 7 shows the results of model validation and functional analysis of HNSCC data in the GEO database.
FIG. 8 is a prognostic diagnostic test result of the LMRS model in clinical specimens.
Detailed Description
As an example of a preferred embodiment, the following example is performed according to the steps of the design flow chart of FIG. 1. The present invention will be further described with reference to the following 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.
In this embodiment, the sequencing service is performed by the andeda company, and the sequencing gene is an sgRNA sequence of the human complete group gene and is contained in the GECKO 2.0 library. The value carried in by the gene in the risk score is the mRNA expression value in the TCGA database. Extracting TPM-formatted data of each gene from the database, then carrying out normalization processing log2 (TPM + 1), and finally reserving a sample with RNAseq data and clinical information.
Example 1
1 materials and methods
1.1 instruments and reagents
Instrument
Tissue microtome
Fluorescence microscope Zeiss zen3.3
Hatching box
Super clean bench
Reagent
Figure BDA0003905579100000041
Figure BDA0003905579100000051
1.2 model building
1.2.1 CRISPR/cas9 library screening
CRISPR/cas9 library screening was performed according to the references (Tian X, wang X, cui Z, liu J, huang X, shi C, et al. A Fifteen-Gene Classifer to Predict New adoajuvant Chemotherapy Responses in Patients with Stage IB to IIB Squamous scientific cancer. Advanced science (2021) 8 (10): 2001978.). Lentivirus products are available from and Meta Biotechnology (Shanghai) Inc. To determine the resistance of cells to drugs following gene suppression, we screened the hypopharyngeal carcinoma cell lines Fadu and Detrot-562 (purchased from cell banks of Chinese academy of sciences) from human primary hypopharynx cancer and human pleural effusion metastasis. 50 ten thousand cells were seeded on 100mm dishes and after 12 hours of starvation the next day, the virus library was transfected with infection efficiencies (MOI) of 0.3, 0.4 and 0.5. After 12-18 hours, cells were screened with 2ug/ml puromycin for 3 days to screen for successful gene knock-out cells. The knocked-out genes were divided into control and drug screening groups, with drug IC20 as the screening concentrations, cisplatin (Detroit-562 at 5ug/ml; fadu at 1 ug/ml) and 5-FU (Detroit-562 at 5ug/ml; fadu at 1 ug/ml), each group was repeated 3 times. Fresh high-glucose DMEM medium containing the drug was replaced every 3 to 5 days. After 2 weeks, cells are respectively collected, gDNA is extracted for PCR amplification, and the sgRNA coding region is subjected to sequencing analysis. sgRNA-encoding primers were aligned to the human reference genome (hg 19) using MAGeCK (v0.5.7). Differential analysis of differential sgrnas of cisplatin, 5-FU-screened group and control group was performed using robust rank polymerization (RRA) analysis of MAGeCK. The selection criteria for the enriched sgRNAs for further analysis were as follows, for all independently replicated FDRs <0.25, for sgRNAs with p-value <0.05 in the 1.DDP and 5-FU groups, or for any independently replicated fold change <0.05, for sgRNAs with p-value <0.05 in the 2.DDP and 5-FU groups. sgRNA data analysis after CRISPR/cas9 library screening was done by Azenta Life Science.
1.2.2 CRISPR/cas9 library screening
Differential expression sgRNAs screened based on the CRISPR/cas9 library were subjected to GO and KEGG enrichment analysis using cluster profile (version 3.14.3) and org.hs.eg.db (version 3.10.0) packages in the R language environment to analyze differences in Bioprocess (BP), cellular Components (CC), molecular Functions (MF) and pathways. And (4) carrying out multiple test corrections by adopting a BH method.
1.2.3 TCGA and GEO data acquisition
Clinical data, TCGA RNA-seq data and probe annotation files for HNSCC patients were downloaded from the TCGA dataset (https:// portal.gdc.com), excluding samples without clinical data. The GSE32877 and GSE10300 data sets were downloaded from the Gene Expression Omnibus (GEO) database in R using the R package "GEOquery".
1.2.4 construction of Gene prediction model
Gene count data received from systemic treatment TCGA (The Cancer Genome Atlas) HNSCC (Head and neck squamous cell Cancer) was converted to TPM and The data log2 (TPM + 1) was normalized while retaining samples containing clinical information. Finally, there were a total of 173 samples for subsequent analysis.
1.2.5 subgroup analysis
The identity analysis used the consensus clusterierplus R package (v 1.54.0), the number of clusters was 2, 80% of the total samples were plotted 100 times, clusterialg = "hc", innerLinkage = 'ward.d2'. Cluster heatmap rendering was performed using the R software package, pheatmap (v1.0.12). Gene expression heatmap retained genes with SD > 0.1. If the number of the input genes exceeds 1000, the SD is ranked and the first 25% of the genes are extracted. Principal component analysis was performed using the prcomp function in the R environment.
1.2.6 Construction of LMRS (Lipid metabolism correlated score) feature model
The two groups of drug screening groups have 751 genes related to lipid metabolism. In combination with the results of the CRISPR/cas9 library screening, only the top 50 genes among the lipid metabolism-related genes were used. Feature selection was then performed using least absolute contraction selection operator (LASSO) regression algorithm and 10-fold cross validation, and analysis was performed using R-package glmnet. And finally, calculating the risk score of each patient by using a formula obtained by regression coefficient weighted analysis, and dividing the sample into a high risk group or a low risk group according to the median risk score.
1.3 validation of the model
1.3.1 prognostic diagnostic assay
For the Kaplan-Meier curve, the risk ratio (HR) for the p-value and 95% Confidence Interval (CI) was generated by the log-rank test. Single and multifactor cox regression analyses were performed to determine the appropriate conditions for building nomogram information maps. Forest map then shows the P-value, HR and 95% CI of each variable by the 'forest plot' R package.
Establishing a nomogram risk prediction model based on the result of multivariate cox proportional risk analysis, and predicting the overall relapse in 1 year, 3 years and 5 years. The Nomogram provides a graphical representation of the factors that can be used to calculate the risk of recurrence for each patient, with the associated score for each risk factor calculated by the "rms" R-package.
The prediction accuracy was evaluated using a time-dependent receiver operating characteristic curve (ROC) and an area under the curve (AUC). And (3) comparing the prediction accuracy of each gene, subgroup, LMRS model and risk score by adopting timeROC (v 0.4) package analysis. When the area under the curve is >0.7, it is considered to have better diagnostic efficiency.
And analyzing the R package-ggDCA by adopting a decision curve to construct a 1-year, 3-year and 5-year diagnosis model.
1.3.2 immune-related functional assays
To obtain a reliable immune score assessment, immineeconv, which is a software package integrating six latest algorithms, including TIMER, xCell, MCP-counter, CIBERCORT, EPIC, and qualtiseq, was used. These algorithms are all benchmark tested; each with unique advantages.
SIGLEC15, TIGIT, CD274, HAVCR2, PDCD1, CTLA4, LAG3 and PDCD1LG2 are selected as related transcripts of immune check points, and the expression values of the 8 genes are extracted from a TCGAHNSCC queue. The potential response of the ICB is predicted using the TIDE algorithm. The correlation between gene expression and immune score was plotted using the R software ggstatsplat package, and the multigene correlation was plotted using the R software pheatmap package.
Correlation analysis between gene expression and immune scores Spearman correlation analysis was used to describe the correlation between quantitative variables that did not fit a normal distribution.
1.3.2 GEO validation
And extracting the expression profiles of the characteristic genes from the GEO data sets GSE32877, GSE10300 and GSE41613 for verifying the characteristic model. After calculating the risk score of each group, further analyzing the relationship between the risk score and the response result and survival outcome of the system treatment.
1.3.3 sample Collection and immunohistochemistry
Primary tissue samples (HNSCC, n = 20) were obtained anonymously according to the rules of the rekins hospital review board. After the samples were fixed in 4% paraformaldehyde, embedded in paraffin blocks, and then cut into 4 μm thick sections. After HE staining, another tumor section was incubated with SREBF1 (Sterol Regulatory Element Binding Transcription Factor 1), PIK3R3 (Phosphoinositide-3-Kinase Regulatory Subunit 3, phosphoinosine-3-Kinase Regulatory Subunit 3), MAPK9 (Mitogen-activated protein Kinase 9), IKK2, APOB (Apolipoprotein, B), ACSBG2 (Acyl synthesis Kinase peptide Kinase family membrane 2, long chain fatty acid CoA ligase 2), MOGAT2 (Monoacylglycerol-O-acyltransferase 2 ) and PLA2G10 (phosphopase A2X, phosphopase Group 2) at 4 ℃ overnight with Phospholipase a4 h antibody and then diluted with secondary antibody at 100 ℃ for 1 h, 2 h. Sections were viewed under a Zeiss Zen 3.3 system and then analyzed using Image-J analysis software.
1.4 data analysis
Statistical analysis and ggplot2 (v3.3.2) were done using R software v4.0.3 (RFoutput for Statistical Computing, vienna, austria). P <0.05 is statistically significant for the differences.
2 results of the experiment
2.1 The CRISPR/cas9 library screens out the isogenous enrichment related to lipid metabolism in chemotherapy drug-resistant genes.
Differential genes which are obviously related to HNSCC systemic treatment are screened by using a CRISPR/cas9 library screening technology, function enrichment and channel enrichment analysis are carried out, and a high-throughput CRISPR/cas9 knockout library GeCKO 2.0 (covering 3-6 sgRNAs per gene on average) based on a whole group of mRNA is transfected in a human throat cancer in-situ cell line Fadu and a transfer cell line Detroit-562 to carry out function loss screening (figure 2, A). Repeated screenings with cisplatin/pentafluorouracil narrowed the potential candidate genes to 6848 genes in total associated with drug resistance (FIG. 2, B), of which 731 genes were significantly different (639 for positive screening, 92 for negative screening; P <0.05 and FDR < 0.25). The highest ranked genes include several genes involved in tumorigenesis development and drug resistance, including AKT1, KLF6, NCOR2 and a number of genes significantly highly expressed in head and neck tumors, such as KRT5, TGFBRAP1. These are among the genes whose expression changes most seriously were found by in vitro screening, and functional enrichment and KEGG pathway analysis of these candidate genes revealed that lipid metabolism-related functions were significantly enriched (FIG. 2, C-F), especially genes in PPAR pathway, glycerolipid and fatty acid metabolism functions. The PPI (protein interaction) network analysis chart shows that a plurality of potential central node genes also exist in the genes related to lipid metabolism (FIG. 2, G). Lipid-associated gene heatmaps show that although there is significant enrichment of lipid metabolism genes, their function may vary from cell line to cell line and from chemotherapeutic drug (fig. 2, h).
2.2 construction of lipid metabolism characteristic prognostic model LMRS
The cases receiving systemic treatment systemically in the TCGA-HNSCC cohort (N = 173) were divided into two groups of subtypes C1 and C2 (Figure 3A) according to the expression difference of the lipid metabolism-associated gene set (751 genes). Survival analysis results combined with clinical information showed that the C1 group showed a poor clinical prognosis with median survival of only 2.3 years, whereas survival in the C2 group could be improved to 6.1 years (fig. 3, b). Both the C1 group (N = 117) and the C2 group (N = 56) exhibited opposite states in expression of various genes (fig. 3, C). Meanwhile, it was found in combination with clinical information that the proportion of female patients in the C1 group was higher (FIG. 3, D), the proportion of undifferentiated type was higher in pathological type, the proportion of intermediate type was lower, and the low differentiation was almost absent (FIG. 3, E). The groupings had no correlation with other clinical indicators (FIG. 3, D; table 1).
In combination with the results of the prior CRISPR/cas9 library screening and LASSO (LASSO algorithm) regression analysis in bioinformatics, we further narrowed the number of genes involved in this evaluation model. Feature selection was performed by Least Absolute Shrinkage and Selection Operator (LASSO) regression algorithm, and 10-fold cross validation was used to finally obtain 8 lipid metabolism-related genes (ACSBG 2, APOB, IKB, MAPK9, MOGAT2, PLA2G10, PIK3R3, SREBF 1) as features of this model (Figure 4A, B). The model calculation formula is as follows:
the risk score (= (-2.7486) × ACSBG2+ (1.7158) × APOB + (-0.3216) × IKBKB + (0.4612) × MAPK9+ (-0.8421) × MOGAT2+ (0.6413) (-PLA 2G10+ (-0.2157) × PIK3R3+ (-0.2355) (-SREBF 1).
2.3 Evaluation of LMRS prognostic models
For patients who received systemic treatment with TCGA-HNSCC, the risk score for each individual was calculated based on the status of each gene in the model and divided into a High-score LMRS-High group and a Low-score LMRS-Low group according to the score (FIG. 4, C, D; table 1). Survival analysis in combination with clinical data found that the High scoring LMRS-High group was associated with higher mortality (median survival =2.1 years) while the Low scoring LMRS-Low group showed better survival (median survival =6.1 years) similar to the previous evaluation of the subgroup classification of 751 genes (fig. 4, e). The prognostic evaluation models all had a diagnostic efficacy AUC greater than 0.7 for 1,3,5 years with better evaluation efficacy in the longer term group (fig. 4, f).
TABLE 1 characteristics of TCGA-type squamous cell carcinoma of head and neck patients (receiving chemotherapy)
Figure BDA0003905579100000091
Figure BDA0003905579100000101
Figure BDA0003905579100000111
2.4 comparison of diagnostic value of model with Single molecule, clinical indices
In all cases of TCGA-HNSCC, the individual genes in the model did not all have significant expression differences, with ACSBG2, MAPK9, PIK3R3, and SREBF1 showing expression differences only, and all were elevated (fig. 4, g). Prognostic nomograms constructed using these 8 genes showed better agreement with C-index =0.672 (0.651-0.692) (fig. 4, h, i). The results of the one-and multifactorial COX analyses revealed that ACSBG2, IKB (Inhibitor of kappa light polypeptide gene enhancer in B-cells, kinase epsilon, B cell kappa light peptide gene enhancer Inhibitor, kinase epsilon) and PIK3R3 were protective factors in HNSCC, while APOB and MAPK9 were risk factors (FIG. 4, J-K). The MOGAT2, PLA2G10 and SREBF1 molecules were not statistically significant. The diagnostic potency AUC of a single molecule in TCGA-HNSCC also only ACSBG2 and SREBF1 was >0.7 (fig. 4, l).
In addition, only MAPK9, MOGAT2, and PIK3R3 showed significant differences in prognosis of survival analysis among the eight molecules in TCGA-HNSCC data (FIG. 5, A-H). And both MOGAT2 and PIK3R3 were under-expressed showing worse prognosis. This not only indicates that a single gene in a single use model cannot be used for a good diagnostic evaluation of prognosis of TCGA-HNSCC, but also verifies that the survival prognosis of tumors with high expression of lipid metabolism related genes is relatively good. In combination with the results of the previous analyses, it is presumed that patients with a high expression of the lipid metabolism-related gene may benefit more from systemic treatment. In the research model of the project, the calculated weights of a plurality of genes are negative values, and the genes with relatively high expression can more easily obtain lower scores and are consistent with the predicted results. The results of the 1-5 year DCA decision curve also show that the 8-gene constructed prognostic diagnostic model with lipid metabolism characteristics has better evaluation effect compared with other common clinical indicators, such as age, sex, clinical stage, pathological typing and smoking status. Especially within 3 years, the diagnostic decision gain of the model was higher (FIG. 5, I-K). Therefore, the 8-gene-composed prognosis evaluation model with lipid metabolism characteristics also has good evaluation effect, and is consistent with the evaluation effect of 751 lipid-related genes.
2.5 Evaluation of LMRS model risk coefficient and HNSCC-related immune function
The analysis on the immune cell infiltration finds that the model has a better prediction effect on the immune cell infiltration. In particular in T cells, CIBERSORT results showed that the Low score LMRS-Low group showed a correlation with higher infiltration of T-helper, treg and M2 macrophages (FIG. 6, A, B). Nor can every gene in the model predict the infiltration of immune cells well (fig. 6, c). Therefore, when the model is applied, 8 genes need to be integrated for joint evaluation. PDCD1 (immunosuppressive receptor) and TIGIT (T cell regulation) were also found to be more highly expressed in the LMRS-Low group by prediction of the expression of immune checkpoint-related genes in the model. PDCD1LG2 (which interacts with PDCD1 to inhibit T cell proliferation) was less expressed in the LMRS-Low group (FIG. 6, D). The TIDE score also showed consistent results, i.e., the LMRS-High group showed a higher score, less effective immune checkpoint inhibition therapy and a short survival period after treatment (FIG. 6, E). In the case of specific cells, higher risk scores were seen, associated with more CD4+ T cells, fewer CTLs, M2 macrophages, NK cells and Treg cells (fig. 6, f-J), suggesting that the High score LMRS-High group is more difficult to benefit from immunotherapy. These results also further demonstrate that the model can better predict the curative effect of systemic therapy including immunotherapy in HNSCC, thereby evaluating the prognosis of HNSCC.
2.6 Verification of prognosis value of LMRS model in external HNSCC data set
Three sets of head and neck tumor datasets from GEO, including: a study containing an assessment of the efficacy of HNSCC patients receiving chemotherapy (GSE 32877, N =2; FIG. 7, A), a study containing information on the prognosis of survival of HNSCC patients (GSE 10300, N =42; FIG. 7, B) and a study containing information on the prognosis of treatment and survival (GSE 41613, N =97; FIG. 7, C) were the subjects of the study. After gene expression information of each case in the database is obtained, the corresponding LMRS risk score is calculated for each case according to the calculation formula of the model and analyzed (FIG. 7, C-F). The results show that the group with better response to chemotherapy (N = 13) showed a lower LMRS risk score in the cases receiving chemotherapy, and the case remaining alive (N = 27) also had a lower LMRS risk score. The survival prognosis results combined with GSE41613 further demonstrate that the prognosis is worse for patients with higher LMRS risk score. These results further demonstrate that the 8-gene LMRS model can better assess the efficacy of systemic treatment of HNSCC and serve as a diagnostic tool for prognostic assessment.
2.7 prognostic diagnostic validation of LMRS models in clinical specimens
In the collected HNSCC specimens (n =6; sensitive group =3, resistant group = 3), we also re-validated the assay efficiency of the LMRS model. As shown in figure 8, we combined the dual results of MRI and electronic laryngoscope, considering that after 2 induced chemotherapies, patients with <50% reduction in tumor volume are chemosensitive groups and those with >50% reduction in tumor volume are resistant groups. IHC or IF staining of 8 molecules in the LMRS model in both sets of specimens was consistent with mRNA expression in the TCGA and GEO databases (fig. 8, b). I.e., lower LMRS scores in the chemosensitive group and higher LMRS scores in the relative resistant group (fig. 8, c-D).
In conclusion, the invention analyzes the functions and genes affecting the systemic treatment of HNSCC by combining CRISPR/cas9 library screening technology, database mining and bioinformatics in depth, and determines the influence of lipid metabolism remodeling. The model test panel established by the present invention provides appropriate evaluation and selection criteria to screen as much as possible patients that can receive the greatest benefit from systemic therapy throughout the body and to allow patients who are unlikely to benefit to quickly transition to other therapies.
The above description is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, several modifications and additions can be made without departing from the method of the present invention, and these modifications and additions should also be regarded as the protection scope of the present invention.

Claims (9)

1. A kit for predicting a head and neck squamous cell carcinoma chemotherapy efficacy prognosis model is characterized in that the kit comprises a reagent for detecting lipid metabolism gene expression level and a lipid metabolism characteristic risk score.
2. The kit according to claim 1, wherein the lipid metabolism gene is a combination of 8 genes: ACSBG2, APOB, IKB, MAPK9, MOGAT2, PLA2G10, PIK3R3, and SREBF1.
3. The kit of claim 1, wherein the risk score of the lipid metabolism signature risk score = (-2.7486) × ACSBG2+ (1.7158) × APOB + (-0.3216) × IKBKB + (0.4612) × MAPK9+ (-0.8421) × MOGAT2+ (0.6413)% PLA2G10+ (-0.2157) × PIK3R3+ (-0.2355) — SREBF1.
4. The application of the reagent for detecting the expression level of the lipid metabolism gene in the preparation of the kit for predicting the drug effect prognosis model of the head and neck squamous cell carcinoma chemotherapy is characterized in that the lipid metabolism gene is the combination of the following 8 genes: ACSBG2, APOB, IKBKB, MAPK9, MOGAT2, PLA2G10, PIK3R3 and SREBF1, the prognostic model being a risk score comprising a lipid metabolism feature: risk score = (-2.7486) × ACSBG2+ (1.7158) × APOB + (-0.3216) × IKBKB + (0.4612) × MAPK9+ (-0.8421) × MOGAT2+ (0.6413) 'PLA 2G10+ (-0.2157) × PIK3R3+ (-0.2355)' SREBF1.
5. A prognosis model system for predicting the efficacy of chemotherapy on head and neck squamous cell carcinoma is characterized in that the system comprises information acquisition, risk score calculation and prognosis evaluation.
6. The prognostic model system according to claim 5, wherein the information is obtained by obtaining gene expression information for each patient with head and neck squamous cell carcinoma.
7. The prognostic model system according to claim 5, wherein the risk score is calculated by substituting the expression level of the lipid metabolism gene into the prognostic model formula.
8. The prognostic model system according to claim 5, wherein the prognostic assessment is to group patients according to risk score with a median value of-2.15128 in the low scoring group and-1.09682 in the high scoring group.
9. The prognostic model system according to claims 5 to 8, wherein the prognostic model system is also used to predict immune cell infiltration and immune checkpoint expression.
CN202211303022.6A 2022-10-24 2022-10-24 Kit for predicting chemotherapeutic effect of head and neck squamous cell carcinoma and application thereof Pending CN115505644A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202211303022.6A CN115505644A (en) 2022-10-24 2022-10-24 Kit for predicting chemotherapeutic effect of head and neck squamous cell carcinoma and application thereof

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202211303022.6A CN115505644A (en) 2022-10-24 2022-10-24 Kit for predicting chemotherapeutic effect of head and neck squamous cell carcinoma and application thereof

Publications (1)

Publication Number Publication Date
CN115505644A true CN115505644A (en) 2022-12-23

Family

ID=84511755

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202211303022.6A Pending CN115505644A (en) 2022-10-24 2022-10-24 Kit for predicting chemotherapeutic effect of head and neck squamous cell carcinoma and application thereof

Country Status (1)

Country Link
CN (1) CN115505644A (en)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116243002A (en) * 2023-01-13 2023-06-09 郑州大学第一附属医院 Method for discovering genes related to oral cancer lipid metabolism and application of genes

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116243002A (en) * 2023-01-13 2023-06-09 郑州大学第一附属医院 Method for discovering genes related to oral cancer lipid metabolism and application of genes
CN116243002B (en) * 2023-01-13 2024-02-06 郑州大学第一附属医院 Method for discovering genes related to oral cancer lipid metabolism and application of genes

Similar Documents

Publication Publication Date Title
Cho et al. Overexpression of glucose transporter-1 (GLUT-1) predicts poor prognosis in epithelial ovarian cancer
Reka et al. Epithelial–mesenchymal transition-associated secretory phenotype predicts survival in lung cancer patients
Wang et al. Development and experimental verification of a prognosis model for cuproptosis-related subtypes in HCC
CN103733065A (en) Molecular diagnostic test for cancer
Kawaguchi et al. Gene Expression Signature–Based Prognostic Risk Score in Patients with Primary Central Nervous System Lymphoma
Li et al. Gene sequencing and expression of Raf-1 in lymphatic metastasis of hypopharyngeal carcinoma
Zhang et al. Deciphering Treg cell roles in esophageal squamous cell carcinoma: a comprehensive prognostic and immunotherapeutic analysis
Zhang et al. Development and validation of a nomogram for predicting survival in patients with surgically resected lung invasive mucinous adenocarcinoma
Meng et al. Risk subtyping and prognostic assessment of prostate cancer based on consensus genes
CN115505644A (en) Kit for predicting chemotherapeutic effect of head and neck squamous cell carcinoma and application thereof
Pichler et al. A chemokine network of T cell exhaustion and metabolic reprogramming in renal cell carcinoma
US20210215700A1 (en) Personalized treatment of pancreatic cancer
Munkácsy et al. Gene expression-based prognostic and predictive tools in breast cancer
Hunt et al. Extensive intratumor proteogenomic heterogeneity revealed by multiregion sampling in high-grade serous Ovarian tumor specimens
Zhuge et al. Construction of the model for predicting prognosis by key genes regulating EGFR-TKI resistance
Dadiani et al. Tumor evolution inferred by patterns of microRNA expression through the course of disease, therapy, and recurrence in breast cancer
Ren et al. AQP9 and ZAP70 as immune-related prognostic biomarkers suppress proliferation, migration and invasion of laryngeal cancer cells
Zhang et al. Immune infiltration landscape on prognosis and therapeutic response and relevant epigenetic and transcriptomic mechanisms in lung adenocarcinoma
Ohara et al. The evolution of metastatic upper tract urothelial carcinoma through genomic-transcriptomic and single-cell protein markers analysis
Gao et al. A tumor microenvironment-related mRNA–ncRNA signature for prediction early relapse and chemotherapeutic sensitivity in early-stage lung adenocarcinoma
An et al. Sequential pembrolizumab cooperates with platinum/5FU to remodel the tumor microenvironment in advanced gastric cancer: a phase II chemoimmunotherapy trial
CN113862354B (en) System for predicting prognosis of patients with limited stage small cell lung cancer and application thereof
Zhang et al. Significance of Aneuploidy in Predicting Prognosis and Treatment Response of Uveal Melanoma
Wu et al. Immunological role and clinical prognostic significance of P2RY6 in lung adenocarcinoma: a multi-omics studies and single-cell sequencing analysis
Chen A Cancer Proliferation Gene Signature Supervised by Ki-67 Strata Specific to Luminal A, Estrogen Receptor-Positive, and HER2-Negative Ductal Carcinomas

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