CN114934117A

CN114934117A - Product for evaluating recurrence risk of lung cancer patient

Info

Publication number: CN114934117A
Application number: CN202210610966.1A
Authority: CN
Inventors: 饶皑炳
Original assignee: Shenzhen Luwei Biotechnology Co ltd
Current assignee: Shenzhen Luwei Biotechnology Co ltd
Priority date: 2022-05-31
Filing date: 2022-05-31
Publication date: 2022-08-23
Also published as: WO2023231280A1

Abstract

The invention discloses application of a reagent for detecting a marker in preparation of a product for evaluating the recurrence risk of a lung cancer patient. The application establishes different models about the recurrence risk for lung cancer patients who receive adjuvant chemotherapy and do not receive adjuvant chemotherapy, thereby distinguishing different adjuvant therapy means in the lung cancer patients, predicting the recurrence risk of the patients through two groups of markers, and selecting a proper adjuvant therapy means from the prognosis. For relapse risks higher with adjuvant chemotherapy than without, other treatment regimens are preferred; and the adjuvant chemotherapy is first selected for the patients with higher relapse risk without adjuvant chemotherapy.

Description

Product for evaluating recurrence risk of lung cancer patient

Technical Field

The application relates to the technical field of molecular diagnosis, in particular to a product for evaluating the recurrence risk of a lung cancer patient.

Background

The lung cancer is the malignant tumor with the highest morbidity and mortality in the world, the radical cure of the lung cancer is difficult, and the recurrence phenomenon still easily occurs after radical surgical resection is performed on part of the lung cancer types, which is also an important reason for death of lung cancer patients. The recurrence of lung cancer includes different types such as local recurrence, regional recurrence and distant recurrence, and therefore, postoperative adjuvant therapy is an important means for reducing the risk of recurrence and metastasis and improving prognosis. Currently, the clinical gold standard for recurrence monitoring of lung cancer is imaging examination, including ultrasound, X-ray chest radiography, CT, etc., but these traditional diagnostic techniques have hysteresis in recurrence monitoring of lung cancer, i.e., it is determined that a tumor is formed in the primary or other parts, which often misses the optimal treatment time window. Therefore, there is a need to provide markers that can accurately assess the risk of relapse.

Disclosure of Invention

The present application is directed to solving at least one of the problems in the prior art. Therefore, the application provides the application of the reagent for detecting the marker in preparing the product for evaluating the recurrence risk of the lung cancer patient.

In a first aspect of the present application, there is provided a use of an agent for detecting a marker in the preparation of a product for assessing risk of recurrence in a patient with lung cancer, the marker comprising at least one positive integer selected from AGFG2, CCND2, CDC42BPA, CFH, DHCR7, ECI2, GPX2, KIAA0355, KIAA1109, MAOB, PGGT1B, PIK3CG, RHEB, SNRPB, UTP CG, ZKSCAN CG, ADAM CG, ANGPT CG, CAMP, CPE, CYP1a CG, DDR CG, dope y CG, E2F CG, EP300, EPHA CG, FANCL, GABPA, KRT CG, MIS18 MIS CG, NDRG CG, NOTCH CG, nxct CG, OXCT CG, PBRM CG, PITRM CG, RNF CG, SCGB2a CG, STC 39a CG, STC 72, tsch CG, tsxt CG, and tun 3643.

According to the application of the embodiment of the application, at least the following beneficial effects are achieved:

the application establishes a model about the recurrence risk for lung cancer patients who receive adjuvant chemotherapy and do not receive adjuvant chemotherapy, so that different adjuvant therapy means in the lung cancer patients are distinguished, and the recurrence risk of the patients is predicted through different markers, so that a proper adjuvant therapy means can be selected. For relapse risks higher with adjuvant chemotherapy than without, other treatment regimens are preferred; and the adjuvant chemotherapy is first selected for the patients with higher relapse risk without adjuvant chemotherapy. The two tests can be selected by doctors and patients, thereby being used as the basis for doctors to establish more accurate treatment schemes. For patients undergoing adjuvant chemotherapy or not undergoing adjuvant chemotherapy after surgery, the recurrence risk assessment product using the above detection reagent can improve and optimize the existing treatment regimen for the patient.

Among them, AGFG2(ArfGAP With FG Repeats 2) is a protein 2 gene containing the Arf GAP domain and FG Repeats, which is a member of the HIV-1Rev binding protein (HRB) family, encoding a protein containing one Arf-GAP zinc finger domain, several phe-gly (FG) motifs and four NPF motifs.

CCND2(Cyclin D2) is a Cyclin D2 gene, the protein encoded by which belongs to the highly conserved Cyclin family, is capable of forming a complex with CDK4 or CDK6, and functions as a regulatory subunit of the complex, the activity of which is essential for the G1/S turnover of the cell cycle. In addition, it is able to interact with the tumor suppressor protein Rb and participate in its phosphorylation.

CDC42BPA (CDC42 Binding Protein Kinase Alpha) is a Cdc42 Binding Protein Kinase Alpha gene encoding a Protein that is a member of the serine/threonine Protein Kinase family. This kinase contains multiple functional domains and its kinase domain is highly similar to myotonic dystrophy protein kinase (DMPK).

Cfh (complement Factor H) is complement Factor H, a member of the complement activation Regulator (RCA) gene cluster, encoding a protein with 20 Short Consensus Repeat (SCR) domains, which plays a crucial role in the regulation of complement activation.

DHCR7 (7-dehydrocholestrol Reductase) is a 7-Dehydrocholesterol Reductase gene that encodes an enzyme that removes the C (7-8) double bond in the B ring of sterols and catalyzes the conversion of 7-dehydrosterols to cholesterol. The gene is localized to the inner plasma membrane and the outer membrane.

ECI2(Enoyl-CoA Delta Isomerase 2) is the Enoyl-COA Delta Isomerase 2 gene which encodes a member of the hydratase/Isomerase superfamily, the protein which encodes it is a key mitochondrial enzyme involved in the beta-oxidation of unsaturated fatty acids.

GPX2 (glutaminone Peroxidase 2) is a Glutathione Peroxidase 2 gene encoding a protein belonging to the Glutathione Peroxidase family, the members of which catalyze the reduction of organic hydrogen peroxide and hydrogen peroxide by Glutathione to protect cells from oxidative damage.

KIAA0355 (also known as GARRE1, granular Associated Rac And RHOG Effector 1) is a particle-Associated Rac And RHOG Effector 1 gene, which activates CCR4-NOT complex binding activity And small GTPase binding activity, And is involved in Rac protein signaling.

The KIAA1109 gene is positioned on the long arm of the No. 4 chromosome, and the coded protein is related to the differentiation of spermatocytes and adipocytes. The C-terminus of this protein is also similar to C.elegans protein and plays a role in lipid storage. It is postulated that this protein may play a role in regulating epithelial growth and differentiation and tumor development in mammals.

MAOB (monoamine Oxidase B) is the monoamine Oxidase B gene, the encoded protein of which belongs to the flavin monoamine Oxidase family. The enzyme is located in the outer mitochondrial membrane, catalyzes the oxidative deamination of biogenic and xenogenic amines, and plays an important role in the metabolism of neuroactive and vasoactive amines in the central nervous system and peripheral tissues.

PGGT1B (Protein Geranylgeranyltransferase Type I Subunit Beta) is a Protein flavanthromycin Type I Subunit Beta, which consists of a 48kD alpha Subunit and a 43kD Beta Subunit, which transfers the flavanoylgroup to the cysteine residue of a candidate Protein containing the C-terminal CAAX motif.

PIK3CG (phospholipidosol-4, 5-Bisphosphate 3-Kinase Catalytic Subunit Gamma) is Phosphatidylinositol-4,5-Bisphosphate 3-Kinase Catalytic Subunit Gamma, which, together with other class I Catalytic subunits (p110 α, p110 β and p110 δ), binds to the p85 regulatory Subunit to form PI3K, PI3K phosphorylates inositol lipids and participates in immune reactions.

RHEB (Ras homo log, MTORC1 Binding) is the RAS Homolog MTORC1 Binding gene, a member of the GTPase superfamily, encoding a lipid anchored cell membrane protein with five repeats of the RAS associated GTP-Binding domain, important in regulating growth and cell cycle progression.

SNRPB (Small Nuclear Ribonucleoprotein Polypeptides B And B1) is a Small Ribonucleoprotein polypeptide B And B1 gene that encodes a protein that is one of several Nuclear proteins commonly found in the granule of micronuclein (SNRNP), is involved in pre-mRNA splicing, And plays a role in the structure of SNRNP.

UTP20(UTP20 Small Subunit Process Component) is a Component of the UTP20 Small Subunit process, a Component of the U3 Small nucleolar RNA (snorNA) protein complex, involved in 18S rRNA processing.

The ZKSCAN7(Zinc Finger With KRAB And SCAN Domains 7) is a Zinc Finger gene With KRAB And scanning domain 7, participates in the transcription regulation of RNA polymerase II according to the predicted activity of initiating DNA binding transcription factor, RNA polymerase II specificity And RNA polymerase II cis-regulatory region sequence-specific DNA binding activity, And is active in the nucleus.

ADAM8(ADAM Metallopeptidase Domain 8) is an ADAM Metallopeptidase Domain 8 gene, encoding an ADAM (disintegrin and metalloprotease Domain) family member. Members of this family are membrane-anchored proteins structurally related to snake venom disintegrin and are involved in a variety of biological processes involving cell-cell and cell-matrix interactions.

ANGPT2(Angiopoietin 2) is an angiogenic 2 gene and belongs to the Angiopoietin family of growth factors. The encoded protein of the gene is an angiogenin 1 antagonist, can affect angiogenesis in the process of embryogenesis and tumorigenesis, destroys the vascular remodeling capability of angiogenin 1, and possibly induces endothelial cell apoptosis.

CAMP (Cathelicidin Antimicrobial peptide) is a Cathelicidin Antimicrobial peptide gene which encodes a member of the family of Antimicrobial peptides. The protein plays an important role in innate immune defense against viruses, and also has functions of cell chemotaxis, induction of immune mediators and regulation of inflammatory response.

CPE (Carboxypeptidase E) is a carboxypeptidase E gene that encodes a member of the M14 family of metallocarboxypeptidases. The coding protein decomposes the C-terminal amino acid residue and is involved in the biosynthesis of peptide hormones and neurotransmitters, including insulin. Meanwhile, the protein can also play a role independently of peptidase activity, and can be used as a neurotrophic factor for promoting the survival of neurons and the like.

CYP1A1(Cytochrome P450 Family 1Subfamily A Member 1) is a Cytochrome P450 Family 1Subfamily A Member 1 gene, which is localized to the endoplasmic reticulum, and the expression of which is induced by some Polycyclic Aromatic Hydrocarbons (PAHs), but the endogenous substrates are unknown. In addition, it is capable of metabolizing some polycyclic aromatics to carcinogenic intermediates.

DDR2(Discoidin Domain Receptor Tyrosine Kinase 2) is a Discoidin Domain Receptor Tyrosine Kinase 2 gene, and the encoded protein is a collagen-induced Receptor, expressed in a variety of cell types, that activates signal transduction pathways involved in cell adhesion, proliferation and extracellular matrix remodeling, and may be involved in wound repair, regulation of tumor growth and invasion.

DOPE Y2 (also known as DOP1B, DOP1 Leucine Zipper Like Protein B) is a DOP1 Leucine Zipper-Like Protein B gene, which is involved in cognition, located in early endosomal membranes, and may play a role in regulating membrane trafficking of cargo proteins.

E2F3(E2F Transcription Factor 3) is the E2F Transcription Factor 3 gene, which encodes a member of a small family of Transcription factors, encodes a protein that recognizes specific sequence motifs in DNA and interacts directly with retinoblastoma protein (pRB) to regulate the expression of genes involved in the cell cycle.

EP300(E1A Binding Protein P300) is the E1A Binding Protein P300 gene which codes for the adenovirus E1A-related cellular P300 transcriptional coactivator Protein. It acts as a histone acetyltransferase, regulates transcription through chromatin remodeling, and has an important role in cell proliferation and differentiation.

EPHA2(EPH Receptor a2) is the EPH Receptor a2 gene, which belongs to the ephrin Receptor subfamily of the protein tyrosine kinase family, whereas EPH and EPH-related receptors are involved in mediating developmental events, especially in the nervous system.

Fancl (FA Complementation Group L) is the FA complement Group L gene, which encodes ubiquitin ligase and is a member of the Fanconi anemia Complementation Group (FANC). This protein mediates monoubiquitination of FANCD2 and FANCI.

GABPA (GA Binding Protein Transcription Factor Subunit alpha) is a GA-Binding Protein Transcription Factor Subunit alpha gene that encodes one of three GA-Binding Protein Transcription Factor subunits and functions as a DNA-Binding Subunit. This subunit may be involved in the activation of cytochrome oxidase expression and nuclear control of mitochondrial function.

KRT17(Keratin 17) is a Keratin 17 gene encoding type I intermediate filament chain Keratin 17, expressed in the nail bed, hair follicles, sebaceous glands and other epidermal appendages.

MIS18A (MIS18 Kinetochore Protein ｃA) is the MIS18 Kinetochore Protein ｃA gene, whose encoded Protein initiates the same Protein binding events, localizes in the cytoplasm and nucleoplasm, is predicted to be involved in CENP- ｃA containing chromatin assembly and chromosome segregation, and acts on upstream or internal regulation of dnｃA methylation.

NDRG1(N-Myc Downstream Regulated 1) is a Downstream regulatory 1 gene of N-Myc, and the encoded protein is a cytoplasmic protein involved in stress response, hormonal response, cell growth and differentiation, and is essential for p 53-mediated caspase activation and apoptosis.

NOTCH1(NOTCH Receptor 1) is a NOTCH Receptor 1 gene, the encoded proprotein is hydrolyzed across the golgi network to generate two polypeptide chains, which heterodimerize to form the mature cell surface Receptor. This receptor plays a role in the development of many cell and tissue types.

NXT2(Nuclear Transport Factor 2Like Export Factor 2) is a Nuclear Transport Factor 2-Like Export Factor 2 gene, and the encoded protein contains the Nuclear Transport Factor 2(NTF2) domain, which plays an important role in the Transport of large, ionic and small molecules between the cytoplasm and the nucleus. The protein may also play a role in nuclear export of mRNA.

OXCT1(3-Oxoacid CoA-Transferase 1) is a 3-oxoacetate CoA Transferase 1 gene, and the encoded protein is a homodimeric mitochondrial matrix enzyme that plays a central role in extrahepatic ketone body catabolism by catalyzing the reversible transfer of CoA from succinyl CoA to acetoacetate.

PBRM1(Polybromo 1) encodes a subunit of ATP-dependent chromatin remodeling complex. The encoded protein is a component of a complex necessary for nuclear hormone receptor ligand-dependent transcriptional activation. The mutation at this site is associated with primary clear cell renal carcinoma.

PITRM1(Pitrilysin Metalopeptidase 1) is a pituitary lysin Metallopeptidase 1 gene, and its encoded protein is an ATP-dependent metalloprotease, which degrades cleaved mitochondrial transit peptide, and can both bind zinc and degrade amyloid β A4 protein.

RNF43(Ring Finger Protein 43) is a Ring Finger Protein 43 gene encoding a Protein that is a cyclic E3 ubiquitin ligase that has a negative regulatory effect on Wnt signaling, and expression of this gene results in increased ubiquitination of the frizzled receptors, altered subcellular distribution, and reduced surface levels of these receptors.

SCGB2A2(Secretoglobin Family 2A Member 2) is a Secretoglobin Family 2A Member 2 gene which is predicted to participate in androgen receptor signaling pathway, is localized to the extracellular region, and is active in the extracellular space.

SLC39a6 (solvent Carrier Family 39Member 6) is a Solute Carrier Family 39Member 6 gene that encodes a protein belonging to a protein subfamily that displays structural features of the zinc transporter.

STC1(Stanniocalcin 1) encodes a secreted homodimeric glycoprotein, which is expressed in various tissues and may have autocrine or paracrine functions. The protein may play a role in regulating renal and intestinal and phosphate transport, cellular metabolism or regulation of cell/phosphate homeostasis.

STC2(Stanniocalcin 2) encodes a secreted homodimeric glycoprotein that may play a role in regulating renal and intestinal calcium phosphate transport, cellular metabolism or cellular calcium phosphate homeostasis. Overexpression in mice results in pre-and postnatal growth restriction, decreased skeletal and skeletal muscle growth, and organ hypertrophy.

TSPYL5(TSPY Like 5) is a TSPY-Like 5 gene that is predicted to activate chromatin binding and histone binding activities. Participate in a variety of processes, including cellular response to gamma radiation, positive regulation of protein kinase B signals, positive regulation of protein ubiquitination, and the like.

Tub 1(Tubulin Gamma 1) is a Tubulin Gamma 1 gene that encodes a member of the Tubulin superfamily, and the encoded protein is localized to the central body where it binds to microtubules as part of a complex known as the Gamma-Tubulin loop complex. This protein mediates microtubule nucleation and is essential for microtubule formation and cell cycle progression.

Recurrence refers to the appearance of new lesions in the lymph nodes around or near the original lesion, or in the bone, brain, adrenal gland, or liver, etc., after lung cancer has been cured, for example, by surgery. The risk of recurrence refers to the probability of the patient developing the at least one recurrence after the cure, for example, a high risk of recurrence refers to a high probability of developing the at least one recurrence.

In some embodiments of the present application, the marker includes at least N of AGFG2, CCND2, CDC42BPA, CFH, DHCR7, ECI2, GPX2, KIAA0355, KIAA1109, MAOB, PGGT1B, PIK3CG, RHEB, SNRPB, UTP20, and ZKSCAN7 in the presence of adjuvant chemotherapy ₁ A plurality of;

markers include at least N of ADAM8, ANGPT2, CAMP, CPE, CYP1A1, DDR2, DOPEY2, E2F3, EP300, EPHA2, FANCL, GABPA, KRT17, MIS18A, NDRG1, NOTCH1, NXT2, OXCT1, PBRM1, PITRM1, RNF43, SCGB2A2, SLC39A6, STC1, STC 632, TSPYL5, and TUBG1 in the absence of adjuvant chemotherapy ₂ A plurality of;

wherein, N ₁ A positive integer selected from 1 to 16, N ₂ Any positive integer from 1 to 27.

With adjuvant chemotherapy is meant that the lung cancer patient is undergoing or is scheduled to receive adjuvant chemotherapy, while without adjuvant chemotherapy is meant that the lung cancer patient has never received adjuvant chemotherapy or is scheduled not to receive adjuvant chemotherapy. In some embodiments, the lung cancer patient is a lung cancer patient who has undergone surgery, for example, lung cancer patients at different time intervals of 1min, 2min, 5min, 30min, 1h, 2h, 3h, 5h, 12h, 24h, 2d, 3d, 5d, 7d, 14d, 30d, etc. after surgery. A particular regimen for adjuvant chemotherapy may be a regimen comprising cisplatin (P), and in particular a dual-drug regimen comprising cisplatin, such as TP (paclitaxel + cisplatin), GP (gemcitabine + cisplatin), DP (docetaxel + cisplatin), AP (pemetrexed + cisplatin), LP (paclitaxel liposome + cisplatin), and the like, it being understood that carboplatin may be used instead for patients who are partially intolerant to cisplatin.

In some embodiments of the present application, the marker comprises at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least eleven, at least twelve, at least thirteen, at least fourteen, at least fifteen, and all sixteen of AGFG2, CCND2, CDC42BPA, CFH, DHCR7, ECI2, GPX2, KIAA0355, KIAA1109, MAOB, PGGT1B, PIK3CG, RHEB, SNRPB, UTP20, and ZKSCAN7 in the presence of adjuvant chemotherapy.

In some embodiments of the present application, a marker comprising at least one, at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, of AGFG2, PGGT1B, ZKSCAN7, KIAA0355, SNRPB, MAOB, UTP20, DHCR7, CFH, GPX2 is indicative of a higher risk of relapse when levels are elevated in the presence of adjuvant chemotherapy.

In some embodiments of the present application, a decrease in the level of at least one, at least two, at least three, at least four, at least five, at least six of the markers including CDC42BPA, ECI2, CCND2, RHEB, KIAA1109, PIK3CG in the presence of adjuvant chemotherapy indicates a higher risk of relapse.

In some embodiments of the present application, markers comprising at least one, at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, elevated levels of AGFG2, PGGT1B, ZKSCAN7, KIAA0355, SNRPB, MAOB, UTP20, DHCR7, CFH, GPX2, and reduced levels of at least one, at least two, at least three, at least four, at least five, at least six, CDC42BPA, ECI2, CCND2, RHEB, KIAA1109, PIK3CG, indicate a higher risk of relapse when adjuvant chemotherapy is present.

In some embodiments of the present application, in the absence of adjuvant chemotherapy, the marker comprises ADAM8, ANGPT2, CAMP, CPE, CYP1a1, DDR2, dope y2, E2F3, EP300, EPHA2, FANCL, GABPA, KRT17, MIS18A, NDRG1, NOTCH1, NXT2, OXCT1, PBRM1, PITRM1, RNF43, SCGB2a2, SLC39a6, STC1, STC2, TSPYL5, and tub 1 of at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least eleven, at least twelve, at least thirteen, at least fourteen, at least fifteen, at least sixteen, at least seventeen, at least twenty-five, at least twenty-six, at least twenty-three, at least twenty-five, and all twenty-six.

In some embodiments of the present application, markers comprising PBRM1, GABPA, DDR2, SCGB2a2, CAMP, ADAM8, RNF43, PITRM1, STC2, TUBG1, E2F3, ANGPT2, NXT2, NDRG1, CYP1a1, dope y2, SLC39A, OXCT1, at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least eleven, at least twelve, at least thirteen, at least fourteen, at least fifteen, at least sixteen, at least seventeen, at least eighteen, at least nineteen, indicate a higher risk of relapse when levels of the at least two, at least three, at least four, at least five, KRT17 are increased in the absence of adjuvant chemotherapy.

In some embodiments of the present application, a decreased level of at least two, at least three, at least four, at least five, at least six, at least seven, at least eight of the markers including CPE, TSPYL5, STC1, FANCL, EPHA2, EP300, NOTCH1, MIS18A, indicates a higher risk of relapse when there is no adjuvant chemotherapy.

In some embodiments of the present application, the markers include PBRM1, GABPA, DDR2, SCGB2a2, CAMP, ADAM8, RNF43, PITRM1, STC2, TUBG1, E2F3, ANGPT2, NXT2, NDRG1, CYP1a1, dope y2, SLC39A, OXCT1, at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least eleven, at least twelve, at least thirteen, at least fourteen, at least fifteen, at least sixteen, at least seventeen, at least eighteen, at least nineteen, and CPE, TSPYL5, STC1, FANCL, EPHA 5, EP300, NOTCH1, MIS 24, at least two of seven, at least seven, 573six, at least four, at least eight, at least seven, at least nine, and at least six of the recurrence of the risk is indicated without adjuvant chemotherapy.

In some embodiments of the present application, the agent detects mRNA expression levels of the marker.

In some embodiments of the present application, the agent detects the protein expression level of the marker.

In some embodiments of the present application, the lung cancer is selected from at least one of small cell lung cancer, non-small cell lung cancer.

In some embodiments of the present application, the non-small cell lung cancer is selected from at least one of squamous cell carcinoma, adenocarcinoma, large cell carcinoma.

In some embodiments of the present application, the sample is selected from at least one of blood, tissue, stool, urine.

In a second aspect of the present application, there is provided a product for assessing a risk of recurrence in a patient with lung cancer, the product comprising a reagent for detecting a marker, the marker comprising at least one positive integer selected from AGFG2, CCND2, CDC42BPA, CFH, DHCR7, ECI2, GPX2, KIAA0355, KIAA1109, MAOB, PGGT1B, PIK3CG, RHEB, SNRPB, UTP CG, ZKSCAN CG, ADAM CG, ANGPT CG, CAMP, CYP1a CG, DDR CG, dope y CG, E2F CG, EP300, EPHA CG, FANCL, GABPA, KRT CG, MIS18 CG, ndtsrg CG, NOTCH CG, nxct CG, OXCT CG, PBRM CG, pirm CG, RNF CG, SCGB2a CG, STC a 3639, STC 72, bg CG, STC CG, agx CG, pyxt CG, tuxt CG, and tuxt CG.

In some embodiments of the present application, the marker comprises AGFG2, CCND2, CDC42BPA, CFH, DHCR7, ECI2, GPX2, KIAA0355, KIAA1109, MAOB, PGGT1B, PIK3CG, RHEB, SNRPB, UTP CG, ZKSCAN CG, ADAM CG, ANGPT CG, CAMP, CPE, CYP1a CG, DDR CG, dope y CG, E2F CG, EP300, EPHA CG, FANCL, gancl, gagb 2a CG, KRT CG, MIS18 BPA CG, nd3672, NOTCH CG, NXT CG, OXCT CG, PBRM CG, PITRM CG, RNF CG, SCGB2a CG, STC CG, pybg, tsbg, at least twenty five SLC CG, twenty five, twenty ten, twenty five, twenty, eleven, twenty, eleven, twenty, eleven, twenty, eleven, twenty, eleven, twenty, seven, twenty, eleven, twenty, eleven, seven, twenty, seven, twenty, eleven, twenty, seven, twenty, eleven, twenty, eleven, twenty, eleven, twenty, eleven, twenty, eleven, twenty, eleven, twenty, eleven, twenty, eleven, seven, twenty, eleven, seven, eleven, twenty, seven, eleven, seven, twenty, seven, twenty, at least five, twenty, eleven, at least five, eleven, seven, twenty, at least five, twenty, at least five, eleven, seven, eleven, twenty, eleven, seven, twenty, seven, twenty, at least, At least twenty-eight, at least twenty-nine, at least thirty-one, at least thirty-two, at least thirty-three, at least thirty-four, at least thirty-five, at least thirty-six, at least thirty-seven, at least thirty-eight, at least thirty-nine, at least thirty-ten, at least forty-one, at least forty-two, and all forty-three.

In some embodiments of the present application, the marker comprises at least N of AGFG2, CCND2, CDC42BPA, CFH, DHCR7, ECI2, GPX2, KIAA0355, KIAA1109, MAOB, PGGT1B, PIK3CG, RHEB, SNRPB, UTP20, and ZKSCAN7 ₁ N is ₁ Any positive integer from 1 to 16.

In some embodiments of the present application, the marker comprises at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least eleven, at least twelve, at least thirteen, at least fourteen, at least fifteen, and all sixteen of AGFG2, CCND2, CDC42BPA, CFH, DHCR7, ECI2, GPX2, KIAA0355, KIAA1109, MAOB, PGGT1B, PIK3CG, RHEB, SNRPB, UTP20, and ZKSCAN 7.

In some embodiments of the present application, the marker comprises ADAM8, ANGPT2, CAMP, CPE, CYP1a1, DDR2, dope y2, E2F3, EP300, EPHA2, FANCL, GABPA, KRT17, MIS18A, NDRG1, NOTCH1, NXT2, OXCT1, PBRM1, PITRM1, RNF43, SCGB2a2, SLC39a6, STC1, STC2, TSPYL5, and TUBG1At least N of ₂ N is ₂ Any positive integer from 1 to 27.

In some embodiments of the present application, the marker comprises ADAM8, ANGPT2, CAMP, CPE, CYP1a1, DDR2, dope y2, E2F3, EP300, EPHA2, FANCL, GABPA, KRT17, MIS18A, NDRG1, NOTCH1, NXT2, OXCT1, PBRM1, PITRM1, RNF43, SCGB2a2, SLC39a6, STC1, STC2, TSPYL5, and TUBG1 at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least eleven, at least twelve, at least thirteen, at least fourteen, at least fifteen, at least sixteen, at least seventeen, at least eighteen, at least nineteen, at least twenty-seven, at least twenty-two, twenty-three, twenty-five, twenty-at least twenty-six, and all.

In some embodiments of the present application, the marker comprises at least one, at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, and/or at least one, at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least ten, and/or CDC42BPA, ECI2, CCND2, RHEB, KIAA1109, PIK3CG of AGFG2, PGGT1B, ZKSCAN7, KIAA0355, SNRPB, MAOB, UTP20, DHCR7, CFH, GPX 2.

In some embodiments of the present application, the marker comprises PBRM1, GABPA, DDR2, SCGB2a2, CAMP, ADAM8, RNF43, PITRM1, STC2, TUBG1, E2F3, ANGPT2, NXT2, NDRG1, CYP1a1, dope y2, SLC39A, OXCT1, at least two of KRT17, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least eleven, at least twelve, at least thirteen, at least fourteen, at least fifteen, at least sixteen, at least seventeen, at least eighteen, at least nineteen, and/or CPE, TSPYL5, 1, FANCL, EPHA2, EP300, NOTCH1, MIS18, at least two of STC 24, at least three, seven, at least four, at least five, at least eight, at least six, at least eight, at least nine, and/or more.

In a third aspect of the present application, there is provided a marker for use in assessing risk of recurrence in a patient with lung cancer, the marker comprising at least one positive integer selected from AGFG2, CCND2, CDC42BPA, CFH, DHCR7, ECI2, GPX2, KIAA0355, KIAA1109, MAOB, PGGT1B, PIK3CG, RHEB, SNRPB, UTP CG, ZKSCAN CG, ADAM CG, ANGPT CG, CAMP, CPE, CYP1a CG, DDR CG, dope y CG, E2F CG, EP300, EPHA CG, FANCL, GABPA, KRT CG, MIS18 CG, NDRG CG, NOTCH CG, NXT CG, OXCT CG, PBRM CG, pirrm CG, RNF CG, SCGB2a CG, SLC39a CG, STC CG, bg CG, tsst CG, tsn CG, and tn 3643, tun 3643;

in some embodiments of the present application, the marker includes at least N of AGFG2, CCND2, CDC42BPA, CFH, DHCR7, ECI2, GPX2, KIAA0355, KIAA1109, MAOB, PGGT1B, PIK3CG, RHEB, SNRPB, UTP20, and ZKSCAN7 in the presence of adjuvant chemotherapy ₁ N is ₁ Any positive integer from 1 to 16; such as at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least eleven, at least twelve, at least thirteen, at least fourteen, at least fifteen, and all sixteen thereof.

In some embodiments of the present application, in the absence of adjuvant chemotherapy, the marker comprises at least N of ADAM8, ANGPT2, CAMP, CPE, CYP1a1, DDR2, dope y2, E2F3, EP300, EPHA2, FANCL, GABPA, KRT17, MIS18A, NDRG1, NOTCH1, NXT2, OXCT1, PBRM1, PITRM1, RNF43, SCGB2a2, SLC39a6, STC1, STC2, TSPYL5, and TUBG1 ₂ N is ₂ Any positive integer from 1 to 27; for example at least two, at least three, at least four, at least five, at least six, at least seven thereof,At least eight, at least nine, at least ten, at least eleven, at least twelve, at least thirteen, at least fourteen, at least fifteen, at least sixteen, at least seventeen, at least eighteen, at least nineteen, at least twenty-one, at least twenty-two, at least twenty-three, at least twenty-four, at least twenty-five, at least twenty-six, and all twenty-seven.

In some embodiments of the present application, the marker comprises at least one, at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, and/or at least one, at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least ten, and/or CDC42BPA, ECI2, CCND2, RHEB, KIAA1109, PIK3CG, AGFG2, PGGT1B, ZKSCAN7, KIAA0355, SNRPB, MAOB, UTP20, DHCR7, CFH, GPX 2.

In some embodiments of the present application, the marker comprises PBRM1, GABPA, DDR2, SCGB2a2, CAMP, ADAM8, RNF43, PITRM1, STC2, TUBG1, E2F3, ANGPT2, NXT2, NDRG1, CYP1a1, dope y2, SLC39A, OXCT1, KRT17, at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least eleven, at least twelve, at least thirteen, at least fourteen, at least fifteen, at least sixteen, at least seventeen, at least eighteen, at least nineteen, and/or CPE, pyl5, 1, FANCL, EPHA2, EP300, not 1, MIS18, at least two of the three, at least four, at least five, at least eight.

In a fourth aspect of the present application, a computer-readable storage medium is provided that stores computer-executable instructions for causing a computer to:

step 1: obtaining information on the level of expression of markers from a sample from a lung cancer patient, the markers including AGFG2, CCND2, CDC42BPA, CFH, DHCR7, ECI2, GPX2, KIAA0355, KIAA1109, MAOB, PGGT1B, PIK3CG, RHEB, SNRPB, UTP20, ZKSCAN7, ADAM8, ANGPT2, CAMP, CPE, CYP1A1, 63DDR 2, DOPEEY 2, E2F3, EP300, EPHA2, FANCL, GANCBPA KRT17, MIS18A, NDRG1, NOTCH1, N2, OXCT1, PBRM1, PITRM1, RNF43, SCGB2A2, SLC 6, STC1, STC2, TSBG 5, and at least one positive TUN integer selected from SLC 1, TUN 1-1, SLC 6;

and 2, step: mathematically correlating the expression levels to obtain a score; the score is used to indicate the risk of recurrence in a lung cancer patient.

In some embodiments of the present application, the marker comprises AGFG2, CCND2, CDC42BPA, CFH, DHCR7, ECI2, GPX2, KIAA0355, KIAA1109, MAOB, PGGT1B, PIK3CG, RHEB, SNRPB, UTP CG, ZKSCAN CG, ADAM CG, ANGPT CG, CAMP, CPE, CYP1a CG, DDR CG, dope y CG, E2F CG, EP300, EPHA CG, FANCL, gancl, gaxt, KRT CG, MIS18 BPA CG, NDRG CG, NOTCH CG, NXT CG, OXCT CG, PBRM CG, PITRM CG, RNF CG, SCGB2a CG, sc39 a CG, STC CG, pystc CG, pysl CG, tsbg, twenty-seven, twenty-two-five-seven-eight-five-seven-twenty-five-twenty-five-eight-twenty-five-eight-nine-twenty-five-twenty-five-nine-five-twenty-five-nine-eight-nine-five-nine-eight-nine-five-eight-nine-eight-nine-eight-nine-five-nine-five-nine-five-nine-eight-five-nine-eight-nine-eight-nine-five-nine-eight-nine-five-eight-nine-five-nine-eight-nine-five-nine-five-nine-five-nine-five-nine-five-nine, At least twenty-eight, at least twenty-nine, at least thirty-one, at least thirty-two, at least thirty-three, at least thirty-four, at least thirty-five, at least thirty-six, at least thirty-seven, at least thirty-eight, at least thirty-nine, at least thirty, at least forty-two, and all forty-three.

In some embodiments of the present application, the marker comprises at least N of AGFG2, CCND2, CDC42BPA, CFH, DHCR7, ECI2, GPX2, KIAA0355, KIAA1109, MAOB, PGGT1B, PIK3CG, RHEB, SNRPB, UTP20, and ZKSCAN7 ₁ N is one ₁ Any positive integer from 1 to 16.

In some embodiments of the present application, the marker comprises at least N of ADAM8, ANGPT2, CAMP, CPE, CYP1a1, DDR2, dope y2, E2F3, EP300, EPHA2, FANCL, GABPA, KRT17, MIS18A, NDRG1, NOTCH1, NXT2, OXCT1, PBRM1, PITRM1, RNF43, SCGB2a2, SLC39a6, STC1, STC2, TSPYL5, and TUBG1 ₂ N is ₂ Any positive integer from 1 to 27.

In some embodiments of the present application, the marker comprises ADAM8, ANGPT2, CAMP, CPE, CYP1a1, DDR2, dope y2, E2F3, EP300, EPHA2, FANCL, GABPA, KRT17, MIS18A, NDRG1, NOTCH1, NXT2, OXCT1, PBRM1, PITRM1, RNF43, SCGB2a2, SLC39A6, STC1, STC2, TSPYL5, and TUBG1 of at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least eleven, at least twelve, at least thirteen, at least fourteen, at least fifteen, at least sixteen, at least seventeen, at least eighteen, at least nineteen, at least twenty-seven, at least twenty-two, twenty-five, twenty-three, twenty-at least twenty-six, and all-six.

In some embodiments of the present application, mathematically correlating the expression levels in step 2 to obtain a score comprises mathematically correlating the expression levels of at least one of ADAM8, ANGPT2, CAMP, CPE, CYP1a1, DDR2, dope y2, E2F3, EP300, EPHA2, FANCL, GABPA, KRT 2, MIS18 2, NDRG 2, NOTCH 2, NXT2, OXCT 2, pbha 2, pirrm 2, RNF 2, SCGB2a2, SLC39 a2, STC2, tsl 2, and TUBG 2 to obtain a recurrence score, mathematically correlating AGFG2, CCND2, CDC42BPA, CFH, DHCR 2, ECI2, x2, KIAA0355, KIAA1109, MAOB, pg1, pgfg 2, ptb 2, hrsg 2, rpha 2, ptb 2, and optionally comparing the recurrence score of the patient based on at least one recurrence score of recurrence score obtained from chemotherapy. It is understood that the difference between the two can be used to determine whether to use adjuvant chemotherapy. In particular, assuming adjuvant chemotherapy is employed, in which case the recurrence score obtained by correlating a particular set (one or more) of markers therein reflects a greater risk of recurrence than that obtained by correlating a particular set (one or more) of markers in the absence of adjuvant chemotherapy, the latter is obviously employed, i.e., taking into account other adjuvant therapies; in the opposite case, where the risk of recurrence is lower with adjuvant chemotherapy, it is clear that adjuvant chemotherapy is used.

Guiding a personalized accurate medical treatment scheme taking reduction of the recurrence risk as a clinical target according to the obtained recurrence risk score, and adopting other auxiliary treatment schemes with higher recurrence risk of auxiliary chemotherapy, except the auxiliary chemotherapy, to select; adjuvant chemotherapy should be first selected when there is a higher risk of relapse without receiving adjuvant chemotherapy. The two parts of detection contents can be selected by doctors and patients, and can be detected simultaneously and verified mutually, so that a more accurate basis for making a treatment scheme is provided. For patients undergoing adjuvant chemotherapy or not yet undergoing adjuvant chemotherapy post-operatively, assessment of risk of recurrence provides a tool to improve and optimize existing treatment regimens.

In some embodiments of the present application,

a _i is the expression level of a marker, b _i Setting weight for the markers, wherein N is the number of the markers and is less than or equal to N.

In some embodiments of the present application,

a _i is the expression level of a marker, b _i Setting weight for the markers, N is the number of the markers, N is less than or equal to N ₁ The marker is selected from AGFG2, CCND2, CDC42BPA, CFH, DHCR7, ECI2, GPX2, KIAA0355, KIAA1109, MAOB, PGGT1B, PIK3CG, RHEB, SNRPB, UTP20 and ZKSCAN 7.

In some embodiments of the present application,

a _i is the expression level of a marker, b _i Setting weight for markers, N is the number of markers, N is less than or equal to N ₂ The marker is selected from ADAM8, ANGPT2, CAMP, CPE, CYP1A1, DDR2, DOPEY2, E2F3, EP300, EPHA2, FANCL, GABPA, KRT17, MIS18A, NDRG1, NOTCH1, NXT2, OXCT1, PBRM1, PITRM1, RNF 243. SCGB2a2, SLC39a6, STC1, STC2, TSPYL5 and TUBG 1.

In some embodiments of the present application, when the score is above a set value, a lung cancer patient is indicated as having a higher risk of recurrence. Wherein, the set value can be at least a set threshold value based on a specific scoring formula, and the set threshold value can effectively distinguish patients with high recurrence risk from patients with low recurrence risk. For example, there is a significant difference in the risk of relapse between two groups of patients above and below the set point, above which the patients are at a higher risk of relapse; when below the set value, the patient is at a lower risk of relapse. The above-mentioned set value is understood as a cutoff value (cutoff value), i.e. a limit value between positive and negative, in the present case a limit value for a high or low risk of relapse. It is to be understood that in some embodiments, there is no general set point/cut-off value, and therefore, it is necessary to determine the set point/cut-off value in advance through pre-experiments and the like when performing the above-mentioned operations. The setting/cutoff value can be determined by conventional techniques in the art, for example, by comparing the accuracy (e.g., detection rate, false positive rate, false negative rate, sensitivity, specificity, etc.) of the pre-test samples under different setting/cutoff values, and adjusting the cutoff value up or down according to the detection effect until a better or optimal detection effect is achieved. It will be appreciated that pre-experimental samples may be obtained by taking tests from a particular sample, or obtaining samples from a relational database that meet criteria.

In some embodiments of the present application, a marker scoring above a set value, e.g., 0.1-fold, 0.2-fold, 0.3-fold, 0.4-fold, 0.5-fold, 1-fold, 1.5-fold, 2-fold, 2.5-fold, 3-fold, 4-fold, 4.5-fold, 5-fold, 6-fold, 7-fold, 8-fold, 9-fold, 10-fold or more, indicates that it has a higher risk of relapse.

It will be appreciated that, since there are two sets of markers that characterize the risk of recurrence in different situations, the corresponding scores may be normalized to the two settings and the absolute values of the scores may be considered, or the relative values of the scores to the respective settings may be considered, and the selection of chemotherapy may be determined or an adjuvant therapy other than chemotherapy may be considered based on the comparison of the values.

In some embodiments of the present application, the score is 0.5931 × AGFG2+0.5562 × PGGT1B +0.4632 × ZKSCAN7+0.3192 × KIAA0355+0.2759 × SNRPB +0.2276 × MAOB +0.2107 × UTP20+0.145 × DHCR7+0.1276 × CFH +0.0613 × GPX2-0.212 × CDC42BPA-0.2196 × ECI2-0.2914 × CCND2-0.3073 × RHEB-0.405 × KIAA1109-0.4517 × PIK3CG, the abbreviations for the markers in the formula represent the normalized expression levels of the corresponding markers.

In some embodiments of the present application, a marker with a score of 0.4322 × PBRM1+0.3548 × GABPA +0.3147 × DDR2+0.3003 × SCGB2a2+0.275 × CAMP +0.2562 × ADAM8+0.2284 × RNF43+0.2239 × PITRM 0.2239 +0.222 × STC 0.2239 +0.2239 × TUBG 0.2239 +0.2239 × E2F 0.2239 +0.2239 × gpnt 0.2239 +0.2239 × nxnt 0.2239 +0.2239 × NDRG 0.2239 +0.2239 × CYP1a 0.2239 +0.2239 × doey 0.2239 +0.2239 × SLC39a 0.2239 +0.2239 × OXCT 0.2239 +0.2239 × KRT 0.2239-0.0657 × CPE-0.0665 × TSPYL 0.2239-0.157 × 0.2239-0.2239 × facsl-0.2239 × EP 72 × epnlc 0.2239 × EP 0.2239 × after normalization of the marker expression of the corresponding epstc formula epnlc 0.2239 × 300 × EP 0.2239-0.2239 × EP 0.2239 is expressed as a normalized marker.

In some embodiments of the present application, the normalized expression level is an expression level after sample normalization and gene normalization.

In a fifth aspect of the present application, there is provided an apparatus comprising a processor and a memory, the memory having stored thereon a computer program executable on the processor, the processor when executing the computer program implementing the following:

step 1: obtaining information on the expression levels of markers from a sample from a lung cancer patient, the markers comprising AGFG2, CCND2, CDC42BPA, CFH, DHCR7, ECI2, GPX2, KIAA0355, KIAA1109, MAOB, PGGT1B, PIK3CG, RHEB, SNRPB, UTP CG, ZKSCAN CG, ADAM CG, ANGPT CG, CAMP, CPE, CYP1A CG, DDR CG, DOPEY CG, E2F CG, EP300, EPHA CG, FANCL, GANCBPA KRT CG, NDP CG, NDRG CG, NOTCH CG, N3672, OXCT CG, PBRM CG, PITRM CG, RNF CG, SCGB2A CG, SLC39A CG, STC CG, PYTSBG CG, and at least one positive integer selected from TUN 43-TUN 3643;

step 2: mathematically correlating the expression levels to obtain a score; the score is used to indicate the risk of recurrence in a lung cancer patient.

In some embodiments of the present application, the marker in step 1 comprises at least N of AGFG2, CCND2, CDC42BPA, CFH, DHCR7, ECI2, GPX2, KIAA0355, KIAA1109, MAOB, PGGT1B, PIK3CG, RHEB, SNRPB, UTP20, and ZKSCAN7 ₁ N is ₁ Any positive integer from 1 to 16.

In some embodiments of the present application, the marker in step 1 comprises at least N of ADAM8, ANGPT2, CAMP, CPE, CYP1a1, DDR2, dope y2, E2F3, EP300, EPHA2, FANCL, GABPA, KRT17, MIS18A, NDRG1, NOTCH1, NXT2, OXCT1, PBRM1, PITRM1, RNF43, SCGB2a2, SLC39a6, STC1, STC2, TSPYL5, and TUBG1 ₂ N is ₂ Any positive integer from 1 to 27.

In some embodiments of the present application, mathematically correlating the expression levels in step 2 to obtain a score comprises mathematically correlating the expression levels of at least one of ADAM8, ANGPT2, CAMP, CPE, CYP1a1, DDR2, dope y2, E2F3, EP300, EPHA2, FANCL, GABPA, KRT 2, MIS18 2, NDRG 2, NOTCH 2, NXT2, OXCT 2, pbha 2, pirrm 2, RNF 2, SCGB2a2, SLC39 a2, STC2, tsl 2, and TUBG 2 to obtain a recurrence score, mathematically correlating AGFG2, CCND2, CDC42BPA, CFH, DHCR 2, ECI2, x2, KIAA0355, KIAA1109, MAOB, pg1, pgfg 2, ptb 2, hrsg 2, rpha 2, ptb 2, and optionally comparing the recurrence score of the patient based on at least one recurrence score of recurrence score obtained from chemotherapy. It is understood that the difference between the two can be used to determine whether to use adjuvant chemotherapy.

In some embodiments of the present application,

a _i is the expression level of a marker, b _i Setting weight of the markers, wherein N is the number of the markers and is less than or equal to N.

In some embodiments of the present application, the score is 0.5931 × AGFG2+0.5562 × PGGT1B +0.4632 × ZKSCAN7+0.3192 × KIAA0355+0.2759 × SNRPB +0.2276 × MAOB +0.2107 × UTP20+0.145 × DHCR7+0.1276 × CFH +0.0613 × GPX2-0.212 × CDC42BPA-0.2196 × ECI2-0.2914 × CCND2-0.3073 × RHEB-0.405 × KIAA1109-0.4517 × PIK3CG, the abbreviations for the markers in the formula indicate the expression levels of the corresponding markers.

In some embodiments of the present application, a marker having a score of 0.4322 × PBRM1+0.3548 × GABPA +0.3147 × DDR2+0.3003 × SCGB2a2+0.275 × CAMP +0.2562 × ADAM8+0.2284 × RNF43+0.2239 × PITRM 0.2239 +0.222 × STC 0.2239 +0.2239 × TUBG 0.2239 +0.2239 × E2F 0.2239 +0.2239 × gpnt 0.2239 +0.2239 × nxxt 0.2239 +0.2239 × NDRG 0.2239 +0.2239 × CYP1a 0.2239 +0.2239 × dope 0.2239 +0.2239 × SLC39a 0.2239 +0.2239 × OXCT 0.2239 +0.2239 × KRT 0.2239-0.0657 × CPE-0.0665 × TSPYL 0.2239-0.157 × 0.2239-0.2239 × fac-0.2239 × EP 72 × epnls 0.2239 × EP 0.2239 × eppyl 0.2239-0.2239 × EP 72 × epb 0.2239 × EP 72 is expressed in the expression formula.

The memory, as a non-transitory computer-readable storage medium, may be used to store a non-transitory software program and a non-transitory computer-executable program, such as the process of assessing risk of recurrence for a lung cancer patient as described in the embodiments of the present application. The processor implements an assessment of the risk of recurrence for a lung cancer patient by executing a non-transitory software program and instructions stored in memory.

The memory may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store a computer program for executing the above. Further, the memory may include high speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid state storage device.

In some embodiments of the present application, the memory optionally includes memory located remotely from the processor, and these remote memories may be connected to the processor through a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.

Non-transitory software programs and instructions needed to implement the above described evaluation are stored in the memory and, when executed by the one or more processors, perform the above described evaluation.

The above described implementation of the apparatus is merely illustrative, wherein the units illustrated as separate components may or may not be physically separate, i.e. may be located in one place, or may also be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment.

It will be understood that all or some of the steps, systems disclosed above may be implemented as software, firmware, hardware, or suitable combinations thereof. Some or all of the physical components may be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application specific integrated circuit. Such software may be distributed on computer readable media, which may include computer storage media (or non-transitory media) and communication media (or transitory media). It should be understood that computer storage media includes volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, Digital Versatile Disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can accessed by a computer.

In addition, it will be understood that communication media typically embodies computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media.

In a sixth aspect of the present application, there is provided a lung cancer patient recurrence risk assessment device, including:

an obtaining module for obtaining information on the expression level of a marker in a sample from a lung cancer patient, the marker comprising at least one positive integer selected from AGFG2, CCND2, CDC42BPA, CFH, DHCR7, ECI2, GPX2, KIAA0355, KIAA1109, MAOB, PGGT1B, PIK3CG, RHEB, SNRPB, UTP CG, ZKSCAN CG, ADAM CG, ANGPT CG, CAMP, CPE, CYP1a CG, DDR CG, dope y CG, E2F CG, EP300, EPHA CG, FANCL, GABPA, KRT CG, MIS18 CG, NDRG CG, NOTCH CG, NXT CG, OXCT CG, PBRM CG, pirm CG, RNF CG, SCGB2a CG, SLC 3639 a CG, STC CG, tsbg CG, and pytn 3643, and positive tun 3643;

a scoring module for mathematically correlating the expression levels to obtain a score indicative of a risk of recurrence for the lung cancer patient.

In some embodiments of the present application, the scoring module mathematically correlating the expression levels to obtain a score comprises mathematically correlating the expression levels of at least one of ADAM8, ANGPT2, CAMP, CPE, CYP1a1, DDR2, dope y2, E2F3, EP300, EPHA2, FANCL, GABPA, KRT 2, MIS18 2, NDRG 2, NOTCH 2, NXT2, OXCT 2, PBRM 2, PITRM 2, RNF 2, SCGB2a2, SLC39 a2, STC2, TSPYL 2, and TUBG 2 to obtain a recurrence score, mathematically correlating the expression levels of at least one of aggb 2a2, CCND2, CDC42BPA, CFH, DHCR 2, ECI2, GPX2, pgkiaa 0355, pg1109, MAOB, kifg 361, rhfg 2, rtb 2, rtg 2, a recurrence score of the patient is obtained based on a comparison of the at least one recurrence score of the cancer, and a recurrence score of the cancer in the patient. It is understood that the difference between the two can be used to determine whether to use adjuvant chemotherapy.

It should be noted that, in the embodiments of the present application, not limited to the model of all 43 markers, or the model of all 16 markers, or the model of all 27 markers, selecting several markers from all 43, all 16, or all 17 markers can also construct other models for evaluating the risk of recurrence.

Compared with the existing imaging method for clinically diagnosing the recurrence of the lung cancer, the markers or the combinations thereof utilized in the embodiment of the application have no time lag in the evaluation of the risk of the recurrence of the lung cancer, can evaluate the risk of the recurrence immediately after the lung cancer operation, can respectively evaluate whether to adopt auxiliary chemotherapy, and provides a basis for selecting a treatment scheme for doctors and patients.

Linear models were constructed based on one or more of the above two sets of genes as markers to calculate the recurrence scores of both lung cancers with or without adjuvant chemotherapy, respectively. A personalized treatment regimen may be administered to the patient based on two relapse scores: adjuvant chemotherapy with a high recurrence score should be preferred over other treatment regimens other than adjuvant chemotherapy; adjuvant chemotherapy should be preferred if the relapse score is high without adjuvant chemotherapy. It is understood that the two lung cancer recurrence scores should be based on the same or similar criteria when compared to each other, for example, the absolute magnitudes of the two may be compared after transformation such as normalization by a set means, or the relative magnitudes of the two scores may be directly compared to the respective thresholds, and so on.

Additional aspects and advantages of the present application will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the present application.

Drawings

FIG. 1 is a Wien diagram of 4 groups of genes in clustering of data of NCHEMO in examples of the present application.

Figure 2 is a ROC curve corresponding to AUC maximum, median and minimum values for 20 replicates of a model cross-validation of NCHEMO in an embodiment of the present application.

Fig. 3 is a ROC curve that was verified in all samples by the model of NCHEMO in the examples of the present application.

Fig. 4 is a ROC curve corresponding to AUC maximum, median and minimum values of 20 replicates of model cross-validation of CHEMO in an embodiment of the present application.

Fig. 5 is a ROC curve that was verified in all samples by the model of CHEMO in the examples of the present application.

Fig. 6 is a ROC curve for a single marker of 19 positively weighted genes in the model of NCHEMO in the examples of the present application.

FIG. 7 is a ROC curve for a single marker of 8 negative weight genes in a model of NCHEMO in an example of the present application.

Fig. 8 is a ROC curve for a single marker for 10 positive weight genes in a model for CHEMO in an example of the application.

FIG. 9 is a ROC curve for a single marker for 6 negatively weighted genes in a model for CHEMO in an example of the present application.

Fig. 10 is a histogram of expression levels of 19 positive-weight genes in the model of NCHEMO in the example of the present application in different populations.

Fig. 11 is a histogram of the expression amounts of 8 negative weight genes in the model of NCHEMO in the example of the present application in different populations.

Fig. 12 is a histogram of expression amounts of 10 positive weight genes in a model of CHEMO in an example of the present application in different populations.

Fig. 13 is a histogram of expression amounts of 6 negative weight genes in different populations in the model of CHEMO in the example of the present application.

Detailed Description

The conception and the resulting technical effects of the present application will be clearly and completely described below in conjunction with the embodiments to fully understand the objects, features and effects of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, and not all embodiments, and other embodiments obtained by those skilled in the art without inventive efforts based on the embodiments of the present application belong to the protection scope of the present application.

The following detailed description of embodiments of the present application is provided for the purpose of illustration only and is not intended to be construed as a limitation of the application.

In the description of the present application, the meaning of a plurality is one or more, the meaning of a plurality is two or more, and the above, below, exceeding, etc. are understood as excluding the present number, and the above, below, within, etc. are understood as including the present number. If the first and second are described for the purpose of distinguishing technical features, they are not to be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated or implicitly indicating the precedence of the technical features indicated.

In the description of the present application, reference to the description of "one embodiment", "some embodiments", "illustrative embodiments", "examples", "specific examples", or "some examples", etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present application. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

Example 1

In this embodiment, the lung cancer markers screened by using the expression data of mRNA genes are defined as follows:

adjuvant chemotherapy population (CHEMO): refers to a post-operative lung cancer patient undergoing adjuvant chemotherapy (but not including adjuvant chemotherapy, i.e., neoadjuvant chemotherapy, which has already been started before surgery) or is scheduled to receive adjuvant chemotherapy, and is indicated by CHEMO (adjuvant chemotherapy);

non-adjuvant chemotherapy population (NCHEMO): this refers to a post-operative lung cancer patient who has not received Adjuvant chemotherapy or who is not planned to receive Adjuvant chemotherapy, and is referred to as NCHEMO (none Adjuvant chemotherapy).

First, data set preparation

1. A total of 46 lung cancer gene expression datasets were selected from the american gene expression integrated database (GEO) and the cancer genomic profile (TCGA) database, comprising full transcriptome data of 6597 lung cancer tissue sections. After gene transcription with extremely low expression (the number of non-zero expressed samples is not more than 10) is eliminated, miRNA and lncRNA are eliminated, and common genes of the data sets are selected to obtain the gene factor 8366.

2. Data normalization was performed on each data set, and samples and genes were performed in two steps:

(1) normalization was performed for each sample: respectively calculating the median of all gene expression levels of each sample, then subtracting the median of all gene expression levels of the sample from the original expression level of each gene of the sample to obtain the expression level of each gene of the sample after primary standardization, and removing the difference of mRNA input levels of the sample by the standardization mode;

(2) normalization was performed for each gene: and (2) further calculating the median of the expression quantity of each gene in all samples based on the gene expression data subjected to primary normalization in the step (1), and subtracting the median of the expression quantity of each gene in all samples from the expression quantity of each gene subjected to primary normalization in each sample to obtain the expression quantity of each gene subjected to secondary normalization in each sample.

And assembling the three normalized data sets into a comprehensive data set to form a comprehensive gene expression matrix, wherein the rows represent 6597 samples, and the columns represent 8400 variables. Wherein, 1-34 are listed as sample and patient information, including basic information such as age, sex and the like of patients, lung cancer pathological typing, mutation states of driving genes such as ALK, EGFR, KRAS and TP53, relapse-free survival (RFS), indexes of Overall Survival (OS) and long-term clinical prognosis information and the like.

Second, marker screening and modeling

Samples with non-null auxiliary chemotherapy markers and non-null recurrence markers were extracted from the gene expression matrix to obtain a total of 666 samples, which were derived from the data sets CONSORTIUM (308), GSE31210 (219), GSE37745 (96), GSE20875 (22) and GSE1037 (21). The patients who had undergone adjuvant chemotherapy were 129 of the CHEMO groups, and those who had not undergone adjuvant chemotherapy were 537 of the NCHEMO group, and the recurrence rate of the CHEMO group was 73.64% (95 patients), half of the recurrence rate of the NCHEMO group was 36.69% (197 patients), and the t-test p-value was 8.48E-15, indicating that the difference in recurrence rate between the two groups of patients was statistically significant. In addition, it should be noted that the adjuvant chemotherapy regimens received by the above 129 patients are not exactly the same, and it is counted that these adjuvant chemotherapy regimens cover a plurality of chemotherapy regimens including different drugs, including cisplatin and carboplatin, or cisplatin and carboplatin in combination with docetaxel, gemcitabine, paclitaxel, pemetrexed or vinorelbine, respectively, and each of the patients receives at least one of these chemotherapy regimens, so that the risk of recurrence of the global adjuvant chemotherapy can be reflected.

The results of the comparative analysis of the basic information such as age, sex and clinical pathology such as pathological staging (Stage), Grade (Grade), TNM grading, etc. in the relapsed and non-relapsed population are shown in Table 1, wherein the M grading is omitted due to the lack of samples. In both groups, the age, sex ratio, and grade were not statistically different from those of the relapse and relapse groups, but differed by less than two years despite the difference in age between the relapse and relapse groups in the NCHEMO group (p ═ 0.04). In addition, there was no statistical difference between the non-recurrent group and the recurrent group for Nx of NCHEMO and Tx of CHEMO, but the mean value of the recurrent group was 0.03 higher than that of the non-recurrent group for Tx of NCHEMO, which was statistically significant; for Nx of CHEMO, the mean value was 0.51 higher in the relapsing group than in the non-relapsing group, which was also statistically significant. Finally, the mean survival of OS and RFS were compared, respectively, in the NCHEMO group, the survival of OS was 24.96 months longer (n 340) and the survival of RFS was 42.55 months longer (n 202) than in the recurrence group; in the CHEMO group, the non-recurrence group had a longer survival time of 33.29 months (n: 34) and the RFS had a longer survival time of 51.04 months (n: 11) than the recurrence group. This suggests that recurrence is a fatal factor for the decline of OS and RFS in lung cancer patients, and that OS and RFS in non-relapsing populations (whether chemotherapy or not) is on average 2-4 years longer than in relapsing populations. Longitudinal comparisons revealed that CHEMO had a mean OS survival 10.03 months longer than NCHEMO and a mean RFS survival 11.38 months longer for the non-relapsing group; for the relapsing group, CHEMO had a mean OS survival that was only 1.7 months longer than NCHEMO, whereas the mean RFS survival was 1.89 months longer. This suggests that chemotherapy is clinically beneficial in non-relapsing populations in that both OS and RFS are prolonged by one year, but less than two months in relapsing populations.

TABLE 1 grouping test variable results

According to the results, the lung cancer recurrence risk assessment models can be respectively established according to the situation of whether the adjuvant chemotherapy is carried out or not, so that the recurrence risk of different treatment schemes (whether the adjuvant chemotherapy is carried out or not) of the lung cancer patient after the operation can be assessed, a proper treatment means can be selected, the survival time of the patient is prolonged, and the recurrence risk is reduced.

Establishing a model by using relevant data of the NCHEMO and CHEMO groups in the data sets, respectively taking each data set as training data, and taking a Recurrence index (Recurrence) as a target variable to construct the model:

1. determining genes associated with risk of relapse. Using t-test to find statistically significant (p <0.05) genes capable of distinguishing different target variables (Recurrence is 0, no Recurrence vs Recurrence is1, Recurrence) of the population, and initially obtain differentially expressed genes.

2. Genes were up-or down-regulated into groups. The differential expression genes are divided into two groups, and t in the t-test result is a positive number representing the genes which are expressed and down-regulated in the tissues of a patient; t is negative and represents a gene whose expression is up-regulated in the tissues of the patient. And respectively carrying out hierarchical association coefficient analysis on the two groups of genes.

3. And (5) analyzing a hierarchical association coefficient. And (3) carrying out hierarchical association coefficient clustering on the genomes with the expression up-regulated or down-regulated respectively, wherein the purpose is that in a given association coefficient horizontal interval, genes in each cluster need to be approximately pairwise associated with each other, the gene with the largest average association degree with other genes in the cluster is selected as a representative in each cluster, and the standard-reaching genes of all clusters form candidate genes of the marker model. Since the correlation coefficient clustering needs to process the cases of positive correlation and negative correlation separately, the following 4 combinations occur:

pp: performing positive correlation clustering on the down-regulated genome;

pn: performing negative correlation clustering on the down-regulated genomes;

np: carrying out positive correlation clustering on the up-regulated genome;

nn: and carrying out negative correlation clustering on the up-regulated genome.

And combining the 4 candidate genomes to obtain the final model candidate genome. FIG. 1 is a Wennen diagram of the 4 groups of genes of NCHEMO.

4. Iterative linear regression analysis determined the genome. For a genome whose expression is up-regulated or down-regulated, the number of genes(s) as model parameters is predetermined in the hierarchical association coefficient analysis, and iterative linear regression analysis is performed. And recycling different s values, searching the number of the optimal model parameter values, and determining the optimal model according to the maximum value of the corresponding R square value (rsq).

5. Pre-selecting 741 genes on the gene mutation map related to the cancer, and repeating the step 4 to obtain an optimal model;

6. the genes of 4 and 5 were combined and repeat 4 again to give the final model.

And performing cross validation after modeling is completed: and (3) averaging the data set according to the population of the target variable, wherein half of the data set is a training set, the other half of the data set is a verification set, ROC and AUC are calculated, and the operation is repeated for N times (20). And calculating the statistical characteristics of the AUC, such as minimum, maximum, median.

The final risk model for recurrence for NCHEMO in the absence of adjuvant chemotherapy was a model consisting of 27 markers: ADAM8, ANGPT2, CAMP, CPE, CYP1a1, DDR2, dope y2, E2F3, EP300, EPHA2, FANCL, GABPA, KRT17, MIS18A, NDRG1, NOTCH1, NXT2, OXCT1, PBRM1, PITRM1, RNF43, SCGB2a2, SLC39A6, STC1, STC2, TSPYL5 and TUBG 1.

The parameters of each gene in this model are shown in table 2 below:

TABLE 2.27 correlation parameters of individual genes in marker Linear regression model

Thus, the formula score for calculating the risk of lung cancer recurrence in the absence of adjuvant chemotherapy-0.4322 × PBRM1+0.3548 × GABPA +0.3147 × DDR2+0.3003 × SCGB2a2+0.275 × CAMP +0.2562 × ADAM8+0.2284 × RNF43+0.2239 × PITRM 0.2239 +0.222 × STC 0.2239 +0.2239 × TUBG 0.2239 +0.2239 × E2F 0.2239 +0.2239 × ANGPT 0.2239 +0.2239 × NXT 0.2239 +0.2239 × NDRG 0.2239 +0.2239 × CYP1a 0.2239 +0.2239 × dope 0.2239 +0.2239 × SLC39a 0.2239 +0.2239 × OXCT 0.2239 × KRT 0.2239-0.0657 × CPE-0.0665 × TSPYL 0.2239-0.157 × 0.2239-0.2239 × faccl 0.2239 × epncha-0.2239 × norpt-0.2239 × EP 72-0.2239 × EP-0.2239 × 0.2239-0.2239 × norp 0.2239 is a marker for normalization of the expression of the formula.

The ROC curve obtained by repeating the 27-marker model cross validation 20 times is shown in fig. 2, and the maximum value of AUC is 0.84, the minimum value is 0.71, and the median value is 0.76, which indicates that the model has good classification significance and can well separate the populations with different recurrence risks in lung cancer patients without adjuvant chemotherapy.

The ROC curve was plotted for the above-described overall sample of NCHEMO based on the established 27-gene linear regression model, and the model was evaluated for its ability to assess the risk of recurrence in patients, the results of which are shown in fig. 3. AUC was 0.832, and the optimum decision point on the ROC curve (shown as a dotted line) corresponded to a specificity (1-false positive rate) of 74% and a sensitivity of 78%.

The model of risk of recurrence for CHEMO in the case of the final adjuvant chemotherapy was a model consisting of 16 markers: AGFG2, CCND2, CDC42BPA, CFH, DHCR7, ECI2, GPX2, KIAA0355, KIAA1109, MAOB, PGGT1B, PIK3CG, RHEB, SNRPB, UTP20, and ZKSCAN 7.

The parameters of each gene in this model are shown in table 3 below:

TABLE 3.16 correlation parameters for each Gene in the marker Linear regression model

Thus the formula score for the risk of lung cancer recurrence in adjuvant chemotherapy cases is 0.5931 × AGFG2+0.5562 × PGGT1B +0.4632 × ZKSCAN7+0.3192 × KIAA0355+0.2759 × SNRPB +0.2276 × MAOB +0.2107 × UTP20+0.145 × DHCR7+0.1276 × CFH +0.0613 × GPX2-0.212 × CDC42BPA-0.2196 × ECI2-0.2914 × CCND2-0.3073 × RHEB-0.405 × KIAA1109-0.4517 × PIK3CG, the abbreviations for the markers in the formula represent the normalized values corresponding to the expression levels of the markers.

The ROC curve obtained by repeating the model cross validation for 20 times based on the 16 markers is shown in fig. 4, and the maximum value of AUC is 0.97, the minimum value is 0.65, and the median value is 0.9, which indicates that the model has good classification significance and can well separate the populations with different recurrence risks in the lung cancer patients under the condition of adjuvant chemotherapy.

An ROC curve was plotted for the above CHEMO whole body samples based on the established 16-gene linear regression model, and the model's ability to assess the risk of recurrence of the patient was evaluated, the results are shown in FIG. 5. AUC was 0.976, and the optimum decision point on the ROC curve (shown as the dotted line) corresponded to a specificity (1-false positive rate) of 94% and a sensitivity of 92%.

For the evaluation model for NCHEMO constructed above, ROC curves using a single gene as a marker in the combination of 27 genes are shown in FIGS. 6 to 7, respectively, and histograms of single gene expression levels are shown in FIGS. 10 to 11; for the CHEMO evaluation model, ROC curves using a single gene as a marker in a combination of 16 genes are shown in FIGS. 8 to 9, and histograms of single gene expression levels are shown in FIGS. 12 to 13.

For multiple genes in the CHEMO and NCHEMO models, K (2, 3 … … N) was randomly selected among the markers ₁ -1/N ₂ -1) genes, reconstructing the model according to the method and performing cross validation, wherein the minimum value, the median value and the maximum AUC of the NCHEMO submodel cross validation are shown in a table 4. The sub-model cross-validated minimum, median, maximum AUC for the CHEMO model is shown in table 5.

TABLE 4 results of cross-validation of NCHEMO submodels

TABLE 5 CHEMO submodel Cross-validation results

As can be seen from the data in the table, the reconstructed model by selecting the subset consisting of 2 or more genes in the 27 gene sets or 16 gene sets also has better diagnostic value, and the diagnostic value generally increases with the increase of the gene factors, so that any number of genes in the 27 gene sets or 16 gene sets selected can be used as the index for evaluating the recurrence risk of the lung cancer patients, and the closer to the total number of genes, the higher the diagnostic accuracy is possibly.

Example 2

The present embodiment provides a kit for assessing the risk of recurrence in a patient with lung cancer, which comprises reagents capable of quantitatively detecting mRNA levels of the following 43 genes: AGFG2, CCND2, CDC42BPA, CFH, DHCR7, ECI2, GPX2, KIAA0355, KIAA1109, MAOB, PGGT1B, PIK3CG, RHEB, SNRPB, UTP20, ZKSCAN7, ADAM8, ANGPT2, CAMP, CPE, CYP1A1, DDR2, DOPEEY 2, E2F3, EP300, EPHA2, FANCL, GABPA, KRT17, MIS18A, NDRG1, NOTCH1, NXT2, OXCT1, PBT 1, PITRM1, RNF43, SCGB2A2, SLC39A6, 5959 1, STC2, PYTSL 5 and TUBG1, which include reverse transcriptase, primers, Taq dyes, etc.

Example 3

The present embodiments provide an apparatus for assessing risk of recurrence in a patient with lung cancer, the apparatus comprising a processor and a memory, the memory having stored thereon a computer program executable by the processor. The method for assessing the risk of recurrence of a lung cancer patient using the device is as follows:

1. postoperative tissue slice samples from lung cancer patients were selected for mRNA extraction.

2. The extracted mRNA is sent to a detection device, and the following 24+8 genes are obtained: ADAM8, ANGPT2, CAMP, CPE, CYP1A1, DDR2, DOPE Y2, E2F3, EP300, EPHA2, FACCL, KRT17, MIS18A, NDRG1, NOTCH1, NXT2, PBRM1, PITRM1, RNF43, RNF 2,Information a quantitative expression levels of SCGB2A2, SLC39A6, STC1, STC2, TSPYL5 and CDC42BPA, ECI2, GPX2, PGGT1B, PIK3CG, RHEB, SNRPB, UTP20 _i 。

3. According to a scoring formula

Respectively substituting the expression levels of the first 24 genes and the last 8 genes to calculate a recurrence risk score; wherein b is _i The weight values of (a) are set as the weights of the corresponding models constructed by the first 24 genes and the last 8 genes in the example 1 or the weights are set according to the method in the example 1, and the respective threshold values are set to divide the relapse risk scores of the subjects into different risk groups. Comparing the risk of relapse assessed by the two models, and selecting adjuvant chemotherapy if the NCHEMO model is higher relative to the corresponding threshold; if the CHEMO model is higher relative to the corresponding threshold, then another adjunctive therapy modality is selected.

Example 4

The embodiment provides a device for evaluating the recurrence risk of a lung cancer patient, which adopts a centrifugal microfluidic chip for liquid biopsy to perform detection, the centrifugal microfluidic chip is provided with at least 25+10 detection grooves, the detection is performed by means of liquid biopsy, a dropped blood sample enters the detection grooves by means of centrifugation through the processes of combination, washing, elution and the like to react with reagents in the detection grooves, and information of the quantitative expression level of markers is obtained by fluorescence, and the markers comprise the following 25+10 genes: ADAM8, ANGPT2, CAMP, CPE, CYP1a1, DDR2, E2F3, EP300, EPHA2, GABPA, KRT17, MIS18A, NDRG1, NOTCH1, NXT2, OXCT1, PBRM1, PITRM1, RNF43, SCGB2a2, SLC39a6, STC1, STC2, TSPYL5, TUBG1 and CDC42BPA, GPX2, KIAA0355, KIAA1109, MAOB, PGGT1B, PIK3CG, RHEB, SNRPB, zcan 7.

The present application has been described in detail with reference to the embodiments, but the present application is not limited to the embodiments described above, and various changes can be made within the knowledge of those skilled in the art without departing from the gist of the present application. Furthermore, the embodiments and features of the embodiments of the present application may be combined with each other without conflict.

Claims

1. The application of the reagent for detecting the marker in preparing the product for evaluating the recurrence risk of the lung cancer patient is characterized in that:

the markers include AGFG2, CCND2, CDC42BPA, CFH, DHCR7, ECI2, GPX2, KIAA0355, KIAA1109, MAOB, PGGT1B, PIK3CG, RHEB, SNRPB, UTP CG, ZKSCAN CG, ADAM CG, ANGPT CG, CAMP, CPE, CYP1A CG, DDR CG, DOPEY CG, E2F CG, EP300, EPHA CG, FANCL, GABPA, KRT CG, MIS18 CG, NDRG CG, NOTCH CG, NXT CG, OXCT CG, PBRM CG, PITRM CG, RNF CG, SCGB2A CG, SLC39A CG, STC CG, PYL CG and TUBG CG, at least one of the N1 is selected from the positive integer 43-43;

preferably, the marker comprises at least N of AGFG2, CCND2, CDC42BPA, CFH, DHCR7, ECI2, GPX2, KIAA0355, KIAA1109, MAOB, PGGT1B, PIK3CG, RHEB, SNRPB, UTP20 and ZKSCAN7 in the presence of adjuvant chemotherapy ₁ A plurality of; in the absence of adjuvant chemotherapy, the markers include at least N of ADAM8, ANGPT2, CAMP, CPE, CYP1A1, DDR2, DOPEY2, E2F3, EP300, EPHA2, FANCL, GABPA, KRT17, MIS18A, NDRG1, NOTCH1, NXT2, OXCT1, PBRM1, PITRM1, RNF43, SCGB2A2, SLC39A6, STC1, STC 632, TSPYL5, and TUBG1 ₂ A plurality of; wherein N is ₁ A positive integer selected from 1 to 16, N ₂ Any positive integer from 1 to 27.

2. The use of claim 1, wherein the agent detects the expression level of the marker;

preferably, the expression level is the mRNA level of the marker.

3. The use according to claim 1, wherein the lung cancer is selected from at least one of small cell lung cancer, non-small cell lung cancer;

preferably, the non-small cell lung cancer is selected from at least one of squamous cell carcinoma, adenocarcinoma, large cell carcinoma.

4. The use according to any one of claims 1 to 3, wherein the sample is selected from at least one of tissue, blood, stool, urine.

5. A product for assessing the risk of recurrence in a lung cancer patient, comprising an agent for detecting a marker selected from the group consisting of AGFG2, CCND2, CDC42BPA, CFH, DHCR7, ECI2, GPX2, KIAA0355, KIAA1109, MAOB, PGGT1B, PIK3CG, RHEB, SNRPB, UTP CG, ZKSCAN CG, ADAM CG, ANGPT CG, CAMP, CPE, CYP1a CG, DDR CG, dope y CG, E2F CG, EP300, EPHA CG, FANCL, GABPA, KRT CG, MIS18 CG, NDRG CG, NOTCH CG, NXT CG, OXCT CG, PBRM CG, pirm CG, RNF CG, SCGB2a CG, SLC 3639 a CG, STC CG, tsbg CG, tsn CG, and pytn 3643, at least one positive integer selected from the group consisting of positive turn 3643 and tun CG;

preferably, the marker comprises at least N of AGFG2, CCND2, CDC42BPA, CFH, DHCR7, ECI2, GPX2, KIAA0355, KIAA1109, MAOB, PGGT1B, PIK3CG, RHEB, SNRPB, UTP20 and ZKSCAN7 ₁ N is one ₁ Any positive integer from 1 to 16;

preferably, the marker comprises at least N of ADAM8, ANGPT2, CAMP, CPE, CYP1a1, DDR2, dope y2, E2F3, EP300, EPHA2, FANCL, GABPA, KRT17, MIS18A, NDRG1, NOTCH1, NXT2, OXCT1, PBRM1, PITRM1, RNF43, SCGB2a2, SLC39a6, STC1, STC2, TSPYL5, and TUBG1 ₂ N is ₂ Any positive integer from 1 to 27;

preferably, the agent detects the level of expression of the marker;

preferably, the expression level is the mRNA level of the marker.

Preferably, the lung cancer is selected from at least one of small cell lung cancer and non-small cell lung cancer;

6. Markers for use in assessing the risk of recurrence in a lung cancer patient, wherein the markers comprise at least one positive integer selected from AGFG2, CCND2, CDC42BPA, CFH, DHCR7, ECI2, GPX2, KIAA0355, KIAA1109, MAOB, PGGT1B, PIK3CG, RHEB, SNRPB, UTP CG, ZKSCAN CG, ADAM CG, ANGPT CG, CAMP, CPE, CYP1a CG, DDR CG, dope y CG, E2F CG, EP300, EPHA CG, FANCL, GABPA KRT CG, ANGPT CG, NDRG CG, NOTCH CG, nxct CG, OXCT CG, PBRM CG, PITRM CG, RNF CG, scf CG, SCGB2a CG, SLC39a CG, STC bg CG, pyxt CG, and tuxt 3643;

7. A computer-readable storage medium having computer-executable instructions stored thereon for causing a computer to:

step 1: obtaining information on the level of expression of markers in a sample from a lung cancer patient, the markers including AGFG2, CCND2, CDC42BPA, CFH, DHCR7, ECI2, GPX2, KIAA0355, KIAA1109, MAOB, PGGT1B, PIK3CG, RHEB, SNRPB, UTP CG, ZKSCAN CG, ADAM CG, ANGPT CG, CAMP, CPE, CYP1A CG, DDR CG, DOPEEY CG, E2F CG, EP300, EPHA CG, FANCL, GANCL, BPA KRT CG, MIS CG, NDRG CG, NOTCH CG, N3672, OXCT CG, PBRM CG, PITRM CG, RNF CG, SCGB2A CG, STC 39A CG, STC CG, PYBG 72, PYTSBG 72, and at least one positive integer selected from SLC 1-6 SLC 3643 SLC CG, SLC 1-6N;

and 2, step: mathematically correlating said expression levels to obtain a score; the score is indicative of a risk of recurrence in the lung cancer patient;

preferably, the markers include at least N of AGFG2, CCND2, CDC42BPA, CFH, DHCR7, ECI2, GPX2, KIAA0355, KIAA1109, MAOB, PGGT1B, PIK3CG, RHEB, SNRPB, UTP20 and ZKSCAN7 in the presence of adjuvant chemotherapy ₁ N is ₁ Any positive integer from 1 to 16;

preferably, the markers include at least N of ADAM8, ANGPT2, CAMP, CPE, CYP1a1, DDR2, dope y2, E2F3, EP300, EPHA2, FANCL, GABPA, KRT17, MIS18A, NDRG1, NOTCH1, NXT2, OXCT1, PBRM1, PITRM1, RNF43, SCGB2a2, SLC39a6, STC1, STC2, TSPYL5, and TUBG1 in the absence of adjuvant chemotherapy ₂ N is ₂ Any positive integer from 1 to 27.

8. The computer-readable storage medium of claim 7,

a _i is the expression level of a marker, b _i Setting weight for the markers, wherein n is the number of the markers;

preferably, when the score is above a set value, a lung cancer patient is indicated as having a higher risk of recurrence.

9. An apparatus comprising a processor and a memory, the memory having stored thereon a computer program executable on the processor, the processor when executing the computer program performing the following:

step 2: mathematically correlating said expression levels to obtain a score; the score is used to indicate the risk of recurrence in a lung cancer patient.

10. A device for assessing the risk of recurrence of a patient with lung cancer, comprising:

an obtaining module for obtaining information on the expression level of a marker in a sample from a lung cancer patient, the marker comprising at least one positive integer selected from AGFG2, CCND2, CDC42BPA, CFH, DHCR7, ECI2, GPX2, KIAA0355, KIAA1109, MAOB, PGGT1B, PIK3CG, RHEB, SNRPB, UTP CG, ZKSCAN CG, ADAM CG, ANGPT CG, CAMP, CPE, CYP1a CG, DDR CG, dope y CG, E2F CG, EP300, EPHA CG, FANCL, gapa, KRT CG, MIS18 MIS CG, NDRG CG, NOTCH CG, nxct CG, OXCT CG, PBRM CG, PITRM CG, RNF CG, SCGB2a CG, STC 39a CG, STC CG, stxt CG, pyxt CG, and nttn 3643;

a scoring module for mathematically correlating the expression levels to obtain a score indicative of a risk of recurrence in a lung cancer patient.