CN113444796A - Biomarkers associated with lung cancer and their use in diagnosing cancer - Google Patents

Abstract

The invention discloses a biomarker related to lung cancer and application thereof in diagnosing cancer. Biomarkers of the invention include AIM2, RERGL, VSTM2L, and/or TREM 1. The invention provides an effective means for the diagnosis of lung cancer.

Description

Biomarkers associated with lung cancer and their use in diagnosing cancer

Technical Field

The present invention relates to the field of disease diagnosis, more specifically, the present invention relates to biomarkers associated with lung cancer and their use in diagnosing cancer.

Background

Lung cancer is currently one of the leading causes of cancer-related deaths worldwide. A Study has reported that the number of Global lung cancer deaths in 2010 is close to 150 million, accounting for 19% of the total number of cancer-related deaths in this year (Lozano R, Naghavi M, Formaman K, et al. Global and regional mortalities from 235 cases of death for 20 g groups in 1990and 2010: A systematic analysis for the Global Burden of Disease Study 2010[ J)]Lancet,2012,380(9859): 2095-. The incidence of lung cancer has increased significantly in the past 10 years due to increased environmental pollution and inadequate tobacco control (Chen WQ, Zheng RS, Zhang SW, Zeng HM and Zou XN. the incidences and mortalities of major cancers in China,2010[ J ] W]Chin J Cancer,2014,33: 402-. With the increasing aging of the global population, lung cancer is expected to be the sixth most common leading death disease by 2030. Based on histological phenotype and cell origin, lung cancer can be divided into 2 major categories, namely Small Cell Lung Cancer (SCLC) and non-small cell lung cancer (NSCLC), wherein the NSCLC accounts for 70-80% of lung cancer (Eseng ü l)

Uzel,Ufuk

Treatment of Early Stage Non-Small Cell Lung Cancer:Surgery or Stereotactic Ablative Radiotherapy？[J]Balkan Journal of Medical Genetics 2015,32(1): 8-16.). Among all patients with Lung Cancer, early-stage NSCLC patients can be treated better by surgical resection (Hamaji M, Ali S O, Burt B M.A Meta-Analysis of detected Metathronous Second Non-Small Cell Lung Cancer [ J]Annals of clinical Surgery 2015,99(4): 1470-. Therefore, over the years, in addition to traditional treatment means (surgical therapy, radiotherapy andchemotherapy, etc.), emerging therapeutic methods are being rapidly developed and put into clinical use, providing more options for lung cancer patients, such as immunotherapy, targeted therapy, etc., however, the phenomena of high recurrence rate and high mortality rate of lung cancer patients still remain to be significantly improved. Overall lung cancer cure rates remain low, with 5-year overall survival rates of less than 15% (Zhou C, Wu YL, Chen G, et al, erlotinib versatherapy as first-line traffic for properties with advanced EGFR mutation-positive non-small-cell lung cancer (OPTIMAL, CTONG-0802): a multiple, openlabel, randomised, phase 3study [ J]Lancet Oncol,2011,12: 735-. For improving the survival rate of lung cancer patients, the timely diagnosis and treatment of NSCLC play a critical role, and therefore, research on the occurrence and development mechanisms of NSCLC is urgent. In recent years, researchers have conducted extensive research on lung cancer, but they are still explaining the underlying molecular mechanisms of development of lung cancer. Therefore, aiming at the basic molecular mechanism of the occurrence and development of the lung cancer, the research on the related genes in the development process of the lung cancer has important significance for the early discovery and the targeted treatment of the lung cancer.

Disclosure of Invention

In order to make up for the defects of the prior art, the invention provides the following technical scheme:

the invention provides application of biomarkers comprising at least two of AIM2, RERGL, VSTM2L and TREM1 in preparation of products for diagnosing or predicting early lung cancer.

Further, the biomarkers are selected from any two of AIM2, RERGL, VSTM2L, TREM 1.

Further, the biomarker is selected from any three of AIM2, RERGL, VSTM2L, TREM 1.

Further, the biomarker is selected from AIM2, RERGL, VSTM2L, and TREM 1.

Further, the product comprises a reagent for detecting the presence, absence and/or amount of at least one biomarker or functional fragment thereof in the sample.

Further, the reagents include reagents for detecting the presence, absence and/or amount of a biomarker or functional fragment thereof in a sample by digital imaging techniques, protein immunization techniques, dye techniques, nucleic acid sequencing techniques, nucleic acid hybridization techniques, chromatography techniques, mass spectrometry techniques.

Further, the reagents for detecting the presence, absence and/or amount of a biomarker or a functional fragment thereof in a sample using protein immunoassay techniques include antibodies specific for an epitope of the biomarker or functional fragment thereof.

Further, the antibody is a labeled antibody.

Further, the reagent for detecting the presence, absence and/or amount of a biomarker or functional fragment thereof in a sample using dye technology comprises a dye specific for the biomarker or functional fragment thereof.

Further, the reagents for detecting the presence, absence and/or amount of a biomarker or a functional fragment thereof in a sample using nucleic acid sequencing techniques include primers that bind to the sequence of the biomarker or functional fragment thereof.

Further, the reagents for detecting the presence, absence and/or amount of a biomarker or a functional fragment thereof in a sample using nucleic acid hybridization techniques include probes complementary to the sequence of the biomarker or functional fragment thereof.

Further, the probe is a labeled probe.

Further, the sample includes tissue, body fluid.

The invention provides a product for diagnosing or predicting early lung cancer, wherein the biomarker comprises a reagent for detecting the biomarker, and the biomarker comprises at least two of AIM2, RERGL, VSTM2L and TREM 1.

Further, the biomarkers are selected from any two of AIM2, RERGL, VSTM2L, TREM 1.

Further, the biomarker is selected from any three of AIM2, RERGL, VSTM2L, TREM 1.

Further, the biomarker is selected from AIM2, RERGL, VSTM2L, and TREM 1.

Further, the product comprises a chip and a kit.

Further, the kit comprises a qPCR kit, an immunoblotting detection kit, an immunochromatography detection kit, a flow cytometry kit, an immunohistochemical detection kit, an ELISA kit and an electrochemiluminescence detection kit.

Further, the kit may further comprise instructions for diagnosing or prognosing early stage lung cancer.

The present invention provides a system comprising:

a sample;

one or more probes and/or stains that bind to at least one biomarker and/or homologous sequence thereof; and

one or more devices capable of quantifying the presence, absence and/or amount of at least one probe or stain that binds to the biomarker and/or homologous sequence thereof.

The present invention provides a system/apparatus for diagnosing whether a subject has early stage lung cancer or is at risk for lung cancer,

the method comprises the following steps:

a processor;

an input module for inputting a level of a biomarker in a biological sample, the biomarker selected from the group consisting of; AIM2, RERGL, VSTM2L, and/or TREM 1;

a computer-readable medium containing instructions that, when executed by the processor, perform a first algorithm on input levels of at least two genes and/or their expression products; and

an output module that provides one or more markers based on the input levels of the at least two genes and/or their expression products, wherein the one or more markers indicate that the subject has lung cancer.

Further, the system further comprises an agent for the biomarker.

Further, the biomarkers of the system include: at least two of AIM2, RERGL, VSTM2L, TREM 1.

Further, any three of AIM2, RERGL, VSTM2L, TREM1 are included.

Further, AIM2, RERGL, VSTM2L, and TREM1 are included.

The present invention provides a computer-readable storage medium, characterized in that a computer program is stored thereon, which, when being executed by a processor, implements the system/apparatus as described above.

The invention provides application of biomarkers in preparing a pharmaceutical composition for treating lung cancer, wherein the biomarkers comprise one or more of AIM2, RERGL, VSTM2L and TREM 1.

Further, the pharmaceutical composition comprises an enhancer or an inhibitor that enhances or inhibits the expression level of the biomarker.

Further, the promoter promotes an expression level of a biomarker whose expression is down-regulated in lung cancer, and the inhibitor inhibits an expression level of a biomarker whose expression is up-regulated in lung cancer.

Further, the biomarker whose expression is down-regulated is selected from TREM1 or RERGL.

Further, the biomarker whose expression is up-regulated is selected from AIM2 or VSTM 2L.

Drawings

Fig. 1 shows the AIM2 gene mRNA differential expression profile, in which panel a: TCGA; and B: GEO;

FIG. 2 shows the TREM1 gene mRNA differential expression profile, where panel A: TCGA; and B: GEO;

FIG. 3 shows a graph of RERGL gene mRNA differential expression, where Panel A: TCGA; and B: GEO;

FIG. 4 shows the differential expression profile of VSTM2L gene mRNA, where Panel A: TCGA; and B: GEO;

fig. 5 shows ROC plots of the AIM2 gene for diagnosis of lung adenocarcinoma, in which panel a: TCGA; and B: GEO;

figure 6 shows ROC plots of TREM1 gene diagnosis of lung adenocarcinoma, where panel a: TCGA; and B: GEO;

figure 7 shows ROC plots for the diagnosis of lung adenocarcinoma by RERGL gene, where panel a: TCGA; and B: GEO;

fig. 8 shows ROC plots of VSTM2L gene diagnosis of lung adenocarcinoma, in which panel a: TCGA; and B: GEO;

FIG. 9 shows ROC plots for the combined diagnosis of lung adenocarcinoma by AIM2+ TREM1+ RERGL + VSTM2L, in which panel A: TCGA; and B: GEO.

Detailed Description

Early and mid-stage lung cancer patients are insidious and have no obvious symptoms, more than half of the patients have already developed locally advanced stages or have developed metastasis of other organs at a distance when diagnosed, and therefore methods, products, systems/devices for improving the detection or prediction of lung cancer (lung adenocarcinoma) patients are needed.

biomarker/Gene expression products

The term "biomarker" refers to a measurable indication of a certain biological state or disease. In some cases, a biomarker may be a substance found in a subject, the amount of the substance, or some other indicator. For example, a biomarker may be the amount of protein and/or other gene expression products in a sample. In some embodiments, the biomarker is a full-length unmodified protein. In other embodiments, the biomarker is a spliced, post-translationally cleaved, or post-translationally chemically modified (e.g., methylated, phosphorylated, glycosylated, formylated, etc.) protein.

In particular embodiments of the invention, the biomarkers include AIM2, TREM1, RERGL and/or VSTM 2L.

In the present invention, biomarkers such as AIM2 (geneID: 9447), TREM1 (geneID: 54210), RERGL (geneID: 79785), VSTM2L (geneID: 128434) include genes and their encoded proteins and homologs, mutations, and isoforms. The term encompasses full-length, unprocessed biomarkers, as well as any form of biomarker that results from processing in a cell. The term encompasses naturally occurring variants (e.g., splice variants or allelic variants) of the biomarkers.

Sample(s)

Methods for detecting molecules (e.g., nucleic acids, proteins, etc.) in a subject to detect, diagnose, monitor, predict, or assess lung cancer status or outcome are described in the present disclosure.

The methods, products, and systems/devices disclosed herein may be used to classify one or more samples from one or more subjects. The sample can be any material comprising a tissue, cell, nucleic acid, gene fragment, expression product, protein, polypeptide, exosome, gene expression product, or gene expression product fragment of the subject to be tested. Samples may include, but are not limited to, tissue, cells, plasma, serum, or any other biological material from cells or derived from cells of an individual. The sample may be a heterogeneous or homogeneous population of cells or tissues. The sample may be a cell-free or cell-poor fluid (e.g., plasma or serum). In some cases, the sample is from a single patient. In some cases, the method includes analyzing multiple samples at a time, for example, by performing massively parallel, multiplex expression analysis on protein arrays and the like.

As used herein, "obtaining a sample" includes obtaining a sample directly or indirectly. In some embodiments, the sample is obtained from the subject by the same party (e.g., a testing laboratory) that subsequently obtains biomarker data from the sample. In some embodiments, the sample is received (e.g., by a testing laboratory) from another entity that collects the sample from a subject (e.g., a physician, nurse, phlebotomist, or medical caregiver). In some embodiments, the sample is taken from the subject by a medical professional under the direction of an isolated entity (e.g., a testing laboratory) and then provided to the entity (e.g., a testing laboratory). In some embodiments, the sample is collected at home by the subject or a caregiver of the subject, and then provided to a party (e.g., a testing laboratory) who obtains biomarker data from the sample.

Sample data

The methods, kits, and systems disclosed herein can include data relating to one or more samples or uses thereof. The data may represent the amount or concentration of one or more biomarkers (e.g., various proteins described herein). In other words, the data may be expression level data of a nucleic acid, protein or polypeptide. The expression level data for a biomarker described herein can be expression level data for a protein or polypeptide, and can be obtained by protein array, proteomics, expression proteomics, mass spectrometry (e.g., liquid chromatography-mass spectrometry (LC-MS), Multiple Reaction Monitoring (MRM), Selective Reaction Monitoring (SRM), scheduled MRM, scheduled SRM), 2D PAGE, 3D PAGE, electrophoresis, proteome chip, proteome microarray, Edman degradation, direct or indirect ELISA, immunoadsorption assay, immuno-PCR, proximity extension analysis, Luminex analysis or homogeneous analysis, time resolved fluorescence resonance energy transfer, Time Resolved Fluorescence (TRF), Fluorescence Oxygen Channel Immunoassay (FOCI), or luminescence oxygen channel immunoassay.

In some embodiments, the methods, products, devices/systems described herein utilize marker molecules in various sandwich, competitive or non-competitive assay formats to determine the expression levels of the biomarkers described herein. Such methods generate a signal related to the presence or amount of one or more of the proteins described herein. Suitable assay formats also include chromatography, mass spectrometry and western "blotting" methods. In addition, certain methods and devices, such as biosensors, optical immunoassays, immunoabsorbent assays, and enzyme immunoassays, can be used to determine the presence or quantity of an analyte without the need for a labeling molecule. Examples of Enzyme Immunoassays (EIA) include chemiluminescent enzyme immunoassays, electrochemiluminescent immunoassays (ECLIA), and enzyme-linked immunosorbent assays (ELISA).

In some embodiments, the methods, products, devices/systems described herein utilize any reliable method to measure levels or quantities in a sample. Generally, detection and quantification can be from a sample (including fractions thereof), such as a sample of isolated RNA, by various known methods for mRNA, including, for example, amplification-based methods (e.g., Polymerase Chain Reaction (PCR), real-time polymerase chain reaction (RT-PCR), quantitative polymerase chain reaction (qPCR), rolling circle amplification, etc.), hybridization-based methods (e.g., hybridization arrays (e.g., microarrays), NanoString analysis, Northern Blot analysis, branched dna (bdna) signal amplification, in situ hybridization, etc.), and sequencing-based methods (e.g., next generation sequencing methods, e.g., using Illumina or iontorrentt platform). Other exemplary techniques include Ribonuclease Protection Assay (RPA) and mass spectrometry.

Reagent kit

In some embodiments, the present disclosure provides an assay kit for assaying any of the set of biomarkers for detecting lung adenocarcinoma included herein. In certain instances, the assay kit comprises one or more detection reagents. The detection reagent includes but is not limited to a probe, a primer, and an antibody.

Probes or primers may include standard (A, T or U, G and C) bases, or modified bases. Modified bases include, but are not limited to, AEGIS bases. In certain aspects, the bases are linked by natural phosphodiester bonds or different chemical linkages. Different chemical bonds include, but are not limited to, peptide bonds or Locked Nucleic Acid (LNA) bonds.

In certain embodiments, one or more primers in an amplification reaction may comprise a label. In still further embodiments, the different probes or primers comprise detectable labels that are distinguishable from each other. In some embodiments, a nucleic acid, such as a probe or primer, may be labeled with two or more distinguishable labels.

In some aspects, the label is attached to one or more probes and has one or more of the following properties: (i) providing a detectable signal; (ii) interact with the second label to modify a detectable signal provided by the second label, e.g., FRET (fluorescence resonance energy transfer); (iii) stable hybridization, e.g., formation of duplexes; and (iv) providing a member of a binding complex or affinity group, e.g., affinity, antibody-antigen, ionic complex, hapten-ligand (e.g., biotin-avidin). In still other aspects, the use of labels can be accomplished using any of a number of known techniques employing known labels, bonds, linkers, reagents, reaction conditions, and analytical and purification methods.

As used herein, the term "probe" refers to any molecule capable of selectively binding to a particular intended target biomolecule. In some embodiments, the term "probe" herein refers to any molecule that can bind to or be associated with any substrate and/or reaction product and/or protease disclosed herein, either indirectly or directly, covalently or non-covalently, and which association or binding can be detected using the methods disclosed herein. In some embodiments, the probe is a fluorescent probe, an antibody, or an absorbance-based probe. In the case of absorbance-based probes, the chromophore pNA (p-nitroaniline) can be used as a probe for detecting and/or quantifying the target nucleic acid sequence disclosed herein. In some embodiments, a probe may be a nucleic acid sequence comprising a fluorescent molecule or substrate that becomes fluorescent upon exposure to an enzyme, and the nucleic acid sequence is complementary to a fragment of one nucleic acid sequence.

The term "primer" or "probe" encompasses an oligonucleotide having a specific sequence or an oligonucleotide having a specific sequence. In other embodiments, the nucleic acid is detected by an indirect detection method. For example, biotinylated probes can be combined with streptavidin-conjugated dyes to detect bound nucleic acids. The streptavidin molecules bind the biotin labels on the amplified biomarkers, and the bound biomarkers are detected by detecting dye molecules attached to the streptavidin molecules. In one embodiment, the streptavidin-conjugated dye molecule comprises PHYCOLINK. Streptavidin R-phycoerythrin (PROzyme). Other conjugated dye molecules are known to those skilled in the art.

As used herein, the term "antibody" includes, but is not limited to, monoclonal or polyclonal antibodies, antigen-binding fragments of antibodies (e.g., Fab', f (ab)2, f (abc)2, or Fv fragments), or antibody derivatives (e.g., diabodies, or scfvs).

In some embodiments, provided assay products, such as kits, are suitable for use in multiplex homogeneous biomarker assays, suitable for detecting all analytes in a single reaction (e.g., in the same solution chamber). In such assays, multiple antibodies or antigen detection reagents to the same analyte/biomarker are provided that bind different epitopes, and the detection of simultaneous binding/interaction of two antibodies with the same molecular analyte/biomarker can be used to detect the analyte/biomarker in the sample. Thus, such kits provide two antibodies or antigen binding reagents for each analyte.

In some embodiments, provided assay products, such as kits, are suitable for use in multiplex heterogeneous biomarker assays that are suitable for detecting all analytes in separate reactions (e.g., in separate solution chambers). In some embodiments of such assays (e.g., sandwich ELISA), antibodies or antigen binding reagents directed against a relevant set of biomarkers are provided linked to a substrate (e.g., in a well of a multi-well plate or in a lateral flow assay channel). Also provided is a second free antibody to each of the provided biomarkers linked to a substrate. The antibody may be labeled (e.g., with a fluorescent dye, chemiluminescent enzyme, or luminescent enzyme) or unlabeled. Where an unlabeled antibody is provided, a second labeled (e.g., with a fluorescent dye, chemiluminescent enzyme, or luminescent enzyme) antibody or antigen binding reagent having binding specificity for the second free antibody is provided.

Arrangements or systems

The products and systems/devices disclosed herein may comprise an algorithm or use thereof. One or more algorithms may be used to classify one or more samples from one or more subjects. One or more algorithms may be applied to data from one or more samples. The data may include biomarker expression data.

The products and systems/devices disclosed herein may include assigning a classification to one or more samples from one or more subjects. Assigning a classification to a sample may include applying an algorithm to the expression level. In some cases, the gene expression level is input to a data analysis system that includes a trained algorithm. For differentiating samples into normal controls and lung cancer (lung adenocarcinoma); in some embodiments, the algorithm may, as part of its execution, calculate an index of the sample and compare the index of the sample to a threshold; the predetermined relationship may indicate a likelihood that the sample belongs to a particular classification.

The algorithm may provide a record of its output, including the classification and/or confidence of the sample. In some cases, the output of the algorithm may be the likelihood that the subject has lung adenocarcinoma.

The data analysis system may be a trained algorithm. The algorithm may include a linear classifier. In some cases, the linear classifier includes one or more of a linear discriminant analysis, fisher linear discriminant, naive bayes classifier, logistic regression, perceptron, support vector machine, or a combination thereof. The linear classifier may be a Support Vector Machine (SVM) algorithm. The algorithm may include a bi-directional classifier. The bi-directional classifier may include one or more decision trees, random forests, bayesian networks, support vector machines, neural networks, or logistic regression algorithms.

The algorithm may include one or more of Linear Discriminant Analysis (LDA), basic perceptron, elastic network, logistic regression, (kernel) Support Vector Machine (SVM), Diagonal Linear Discriminant Analysis (DLDA), Golub classifier, Parzen-based, (kernel) fisher discriminant classifier, k nearest neighbor, iterative mitigation, classification tree, maximum likelihood classifier, random forest, nearest centroid, micro computer Program Array (PAM) prediction analysis, k median clustering, fuzzy C-means clustering, gaussian mixture model, ranked response (GR), Gradient Boosting Method (GBM), elastic network logistic regression, or combinations thereof. The algorithm may comprise a Diagonal Linear Discriminant Analysis (DLDA) algorithm. The algorithm may include a nearest centroid algorithm. The algorithm may comprise a random forest algorithm. In some embodiments, logistic regression, random forest and gradient enhancement methods (GBM) perform better than Linear Discriminant Analysis (LDA), neural networks and Support Vector Machines (SVMs) in order to distinguish lung adenocarcinomas from normal controls.

The article of manufacture, system, or apparatus disclosed herein may comprise at least one computer program or use thereof. The computer program may include a sequence of instructions written to perform specified tasks that are executable in the CPU (i.e., processor) of the digital processing apparatus. Computer readable instructions may be implemented as program modules, e.g., functions, objects, Application Programming Interfaces (APIs), data structures, etc., that perform particular tasks or implement particular abstract data types. Based on the disclosure provided herein, one of ordinary skill in the art will recognize that a computer program may be written in various versions of various languages.

The functionality of the computer readable instructions may be combined or distributed as desired in various environments. The computer program will typically provide a series of instructions from one location or a plurality of locations.

Further disclosed herein are systems for classifying (or excluding from classification) one or more samples and uses thereof. The system may include: (a) a digital processing device comprising an operating system and a storage device configured to execute executable instructions; (b) a computer program comprising instructions executable by a digital processing device to classify a sample from a subject, comprising: (i) a first software module configured to receive a biomarker expression profile of one or more biomarkers from a sample from a subject; (ii) a second software module configured to analyze a biomarker expression profile from a subject; (iii) a third software module configured to classify a sample from the subject based on the classification system. In some embodiments, the classification system includes two categories. In other embodiments, the classification system includes two or more categories. At least two categories may be selected from lung adenocarcinoma, normal control. Analyzing the biomarker expression profile from the subject may include applying an algorithm. Analyzing the biomarker expression profile may comprise normalizing the biomarker expression profile from the subject.

Digital processing apparatus or device

The articles, systems, or apparatus disclosed herein may include a digital processing device or a use of a digital processing device. In further embodiments, the digital processing device includes one or more hardware Central Processing Units (CPUs) that perform the functions of the device. In other embodiments, the digital processing device further comprises an operating system configured to execute the executable instructions. In some embodiments, the digital processing device is optionally connected to a computer network. In a further embodiment, the digital processing device is optionally connected to the internet, such that it accesses the world wide web. In other embodiments, the digital processing device is optionally connected to a cloud computing infrastructure. In other embodiments, the digital processing device is optionally connected to an intranet. In other embodiments, the digital processing device is optionally connected to a data storage device.

Suitable digital processing devices include, by way of non-limiting example, server computers, desktop computers, laptop computers, notebook computers, sub-notebook computers, netbook computers, nettablet computers, set-top computers, handheld computers, internet appliances, mobile smartphones, tablets, personal digital assistants, video game consoles, and vehicles, in accordance with the description herein. Those skilled in the art will recognize that many smart phones are suitable for use with the system described herein. Those skilled in the art will also recognize that selected televisions, video players, and digital music players with optional computer network connectivity are suitable for use with the system described herein. Suitable tablet computers include those known to those skilled in the art having booklets, tablets and convertible configurations.

The digital processing device will typically include an operating system configured to execute executable instructions. An operating system is, for example, software including programs and data that manages the hardware of the device and provides services for executing application programs.

The device typically includes a storage and/or memory device. The storage and/or memory device is one or more physical devices for temporarily or permanently storing data or programs. In some embodiments, the device is a volatile memory and requires power to maintain the stored information. In some embodiments, the device is a non-volatile memory and retains stored information when the digital processing device is not powered. In other embodiments, the non-volatile memory comprises flash memory. In some embodiments, the non-volatile memory comprises Dynamic Random Access Memory (DRAM). In some embodiments, the non-volatile memory comprises Ferroelectric Random Access Memory (FRAM). In some embodiments, the non-volatile memory includes phase change random access memory (PRAM). In other embodiments, the device is a storage device, which includes, by way of non-limiting example, CD-ROMs, DVDs, flash memory devices, disk drives, tape drives, optical disk drives, and cloud-based storage. In other embodiments, the storage and/or memory device is a combination of devices such as those disclosed herein.

Typically, a display that sends visual information to the user is initialized. Examples of displays include Cathode Ray Tubes (CRTs), Liquid Crystal Displays (LCDs), thin film transistor-liquid crystal displays (TFT-LCDs, Organic Light Emitting Diode (OLED) displays). In various other embodiments, on the OLED display is a passive matrix OLED (pmoled) or active matrix OLED (amoled) display. In some embodiments, the display may be a plasma display, a video projector, or a combination of devices such as those disclosed herein.

The digital processing device will typically include an input device to receive information from a user. The input device may be, for example, a keyboard, a pointing device (including, by way of non-limiting example, a mouse, trackball, trackpad, joystick, game controller, or stylus), a touch screen or multi-touch screen, a microphone for capturing voice or other sound input, a camera for capturing motion or visual input, or a combination of devices such as those disclosed herein.

Sensitivity, specificity, AUC and accuracy

The products, systems, or devices disclosed herein for identifying, classifying (or excluding classification) or characterizing a sample can be characterized by having a specificity of at least about 60% using the methods disclosed herein. In some embodiments, the specificity of the method is at least about 70%. In some embodiments, the specificity of the method is at least about 80%. In some embodiments, the specificity is at least about 90%. In some embodiments, the specificity is at least about 95%.

In some embodiments, the invention provides methods of identifying, classifying (or excluding classification) or characterizing a sample using the methods disclosed herein, which methods give a sensitivity of at least about 60%. In some embodiments, the method has a sensitivity of at least 70%. In some embodiments, the method has a sensitivity of at least 80%. In some embodiments, the method has a sensitivity of at least 90%. In some embodiments, the method has a sensitivity of at least 95%.

The methods, kits, and systems disclosed herein can improve AUC of methods currently monitoring or predicting the status or outcome of lung adenocarcinoma or determining or excluding the classification of a sample. The AUC of a method, product, system, or device disclosed herein can be at least about 50%, 53%, 55%, 57%, 60%, 63%, 65%, 67%, 70%, 72%, 75%, 77%, 78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99%, 99.1%, 99.2%, 99.3%, 99.4%, 99.5%, 99.6%, 99.7%, 99.8%, 99.9%, or any range between these values. The following detailed description of embodiments of the present application will be made with reference to the accompanying drawings. It should be understood that the detailed description and specific examples, while indicating the present application, are given by way of illustration and explanation only, and are not intended to limit the present application.

Examples Gene markers associated with diagnosis of Lung cancer

1. Data download

The expression profile data of lung adenocarcinoma in the database was retrieved with gene expression integrated database GEO (https:// www.ncbi.nlm.nih.gov/GEO /) and cancer genomic profile database TCGA (https:// cancer. Excluding samples with missing clinical information, including matched carcinoma and paracarcinoma samples, including 59 paracarcinoma samples and 500 lung adenocarcinoma samples for TCGA; GEO included 20 paracarcinoma tissue samples and 226 lung adenocarcinoma samples.

2. Data processing and analysis

For the data set in TCGA, RNA sequencing data (FPKM values) and clinical information for gene expression were downloaded from UCSC Xena (https:// gdc.xenahubs.net) and FPKM values were converted to transcripts with million per kilobase (TPM) values.

For the GEO database, the original "CEL" file of GSE31210 was downloaded, and the background adjustment and quantile normalization were performed using RMA algorithm in the affy software package. And annotating the gene expression matrix file by using an annotation file, taking an average value of a plurality of probes corresponding to the same gene as the expression quantity of the gene expression matrix file, and then obtaining the gene expression matrix file.

The TCGA data set is used as a discovery queue, and the GEO data set is used as a verification queue.

3. Differential expression analysis

Using a 'limma' packet in R software, fitting a linear model to each gene of the normalized gene expression data through a weighting or generalized least square method, calculating a proper t statistic value, a proper F statistic value and a proper differential expression value through empirical Bayes, and finally obtaining a differential gene analysis result, wherein the screening standard is as follows: maximum threshold value of adj.Pvalue is 0.05; log (log)₂The FC minimum absolute threshold is 1.

4. Diagnostic efficacy analysis

The Receiver Operating Curve (ROC) is drawn by using the R package 'pROC', the AUC value, the sensitivity and the specificity of the differential expression gene serving as a detection variable are analyzed, and the diagnosis efficiency of the indicators alone or in combination is judged.

When the diagnosis efficiency of the index combination is judged, firstly, glmnet is used for conducting Logistic regression on genes, an established Logistic regression model is used for predicting data, an ROC curve of a prediction result is drawn, the area under the curve is calculated, and sensitivity and specificity are analyzed.

5. Results

1) Differential expression of genes

The differential expression of AIM2, TREM1, RERGL and VSTM2L in TCGA and GEO databases is shown in figures 1-4, AIM2 and VSTM2L are up-regulated in lung cancer patients, TREM1 and RERGL are down-regulated in lung cancer patients, and the difference has statistical significance.

2) ROC curve analysis

For diagnostic efficacy data for AIM2, TREM1, RERGL, VSTM2L, and combinations see table 1, table 2, and figures 5-9.

TABLE 1 TCGA diagnostic Performance analysis

Index (I)	AUC	Specificity of	Sensitivity of the composition
				AIM2	0.798	0.881	0.69
TREM1	0.778	0.864	0.674
				RERGL	0.787	0.864	0.642
VSTM2L	0.745	0.966	0.522
				AIM2+TREM1	0.879	0.831	0.852
AIM2+RERGL	0.857	0.847	0.778
				AIM2+VSTM2L	0.841	0.915	0.708
TREM1+RERGL	0.858	0.966	0.682
				TREM1+VSTM2L	0.879	0.864	0.794
RERGL+VSTM2L	0.839	0.814	0.778
				AIM2+TREM1+RERGL	0.903	0.881	0.842
AIM2+TREM1+VSTM2L	0.927	0.831	0.902
				AIM2+RERGL+VSTM2L	0.886	0.966	0.726
TREM1+RERGL+VSTM2L	0.914	0.966	0.778
				AIM2+TREM1+RERGL+VSTM2L	0.944	0.881	0.926

TABLE 2 GEO diagnostic efficacy analysis

The preferred embodiments of the present application have been described in detail with reference to the accompanying drawings, however, the present application is not limited to the details of the above embodiments, and various simple modifications can be made to the technical solution of the present application within the technical idea of the present application, and these simple modifications are all within the protection scope of the present application.

It should be noted that, in the foregoing embodiments, various features described in the above embodiments may be combined in any suitable manner, and in order to avoid unnecessary repetition, various possible combinations are not described in the present application.

In addition, any combination of the various embodiments of the present application is also possible, and the same should be considered as disclosed in the present application as long as it does not depart from the idea of the present application.

Claims (10)

1. Use of a biomarker for the manufacture of a product for diagnosing or prognosing early stage lung cancer, wherein the biomarker comprises at least two of AIM2, RERGL, VSTM2L, TREM 1;

preferably, the biomarker is selected from any two of AIM2, RERGL, VSTM2L, TREM 1;

preferably, the biomarker is selected from any three of AIM2, RERGL, VSTM2L, TREM 1;

preferably, the biomarker is selected from AIM2, RERGL, VSTM2L, and TREM 1.

2. Use according to claim 1, wherein the product comprises a reagent for detecting the presence, absence and/or amount of at least one biomarker or functional fragment thereof in a sample.

3. Use according to claim 2, wherein the reagents comprise reagents for detecting the presence, absence and/or amount of a biomarker or functional fragment thereof in a sample by digital imaging techniques, protein immunization techniques, dye techniques, nucleic acid sequencing techniques, nucleic acid hybridization techniques, chromatography techniques, mass spectrometry techniques;

preferably, the reagent for detecting the presence, absence and/or amount of a biomarker or a functional fragment thereof in a sample using protein immunoassay comprises an antibody specific for an epitope of the biomarker or a functional fragment thereof;

preferably, the antibody is a labeled antibody;

preferably, the reagent for detecting the presence, absence and/or amount of a biomarker or functional fragment thereof in a sample using dye technology comprises a dye specific for the biomarker or functional fragment thereof;

preferably, the reagents for detecting the presence, absence and/or amount of a biomarker or functional fragment thereof in a sample using nucleic acid sequencing techniques comprise primers that bind to the sequence of the biomarker or functional fragment thereof;

preferably, the reagent for detecting the presence, absence and/or amount of a biomarker or a functional fragment thereof in a sample using nucleic acid hybridization techniques comprises a probe that is complementary to the sequence of the biomarker or functional fragment thereof;

preferably, the probe is a labeled probe.

4. Use according to claim 2 or 3, wherein the sample comprises tissue, body fluid.

5. A product for diagnosing or prognosing early lung cancer, wherein the biomarkers comprise reagents for detecting biomarkers comprising at least two of AIM2, RERGL, VSTM2L, TREM 1;

preferably, the biomarker is selected from any two of AIM2, RERGL, VSTM2L, TREM 1;

preferably, the biomarker is selected from any three of AIM2, RERGL, VSTM2L, TREM 1;

preferably, the biomarker is selected from AIM2, RERGL, VSTM2L, and TREM 1.

6. The product of claim 5, wherein the product comprises a chip, a kit;

preferably, the kit comprises a qPCR kit, an immunoblotting detection kit, an immunochromatography detection kit, a flow cytometry kit, an immunohistochemical detection kit, an ELISA kit and an electrochemiluminescence detection kit;

preferably, the kit further comprises instructions for diagnosing or prognosing early stage lung cancer.

7. A system, comprising:

a sample;

one or more probes and/or stains that bind to at least one biomarker and/or homologous sequence thereof; and

one or more devices capable of quantifying the presence, absence and/or amount of at least one probe or stain that binds to the biomarker and/or homologous sequence thereof.

8. A system/apparatus for diagnosing whether a subject has early stage lung cancer or is at risk of having lung cancer, comprising:

a processor;

an input module for inputting a level of a biomarker in a biological sample, the biomarker selected from the group consisting of; AIM2, RERGL, VSTM2L, and/or TREM 1;

a computer-readable medium containing instructions that, when executed by the processor, perform a first algorithm on input levels of at least two genes and/or their expression products; and

an output module that provides one or more markers based on the input levels of the at least two genes and/or their expression products, wherein the one or more markers indicate that the subject has lung cancer;

preferably, the system further comprises an agent for the biomarker;

preferably, the biomarkers of the system comprise: at least two of AIM2, RERGL, VSTM2L, TREM 1; preferably, any three of AIM2, RERGL, VSTM2L, TREM1 are included; preferably, AIM2, RERGL, VSTM2L and TREM1 are included.

9. A computer-readable storage medium, having stored thereon a computer program which, when executed by a processor, implements the system/apparatus of claim 8.

10. Use of a biomarker for the manufacture of a pharmaceutical composition for the treatment of lung cancer, wherein the biomarker comprises one or more of AIM2, RERGL, VSTM2L, TREM 1; preferably, the pharmaceutical composition comprises an enhancer or inhibitor that enhances or inhibits the level of expression of the biomarker; preferably, the promoter promotes the level of expression of a biomarker whose expression is down-regulated in lung cancer, and the inhibitor inhibits the level of expression of a biomarker whose expression is up-regulated in lung cancer; preferably, the biomarker whose expression is down-regulated is selected from TREM1 or RERGL; preferably, the biomarker whose expression is upregulated is selected from AIM2 or VSTM 2L.

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