CN113373220A

CN113373220A - Marker molecules associated with prognosis of non-small cell lung cancer

Info

Publication number: CN113373220A
Application number: CN202110502193.0A
Authority: CN
Inventors: 王艳; 黄蔚; 余和芬; 张竞尧
Original assignee: Capital Medical University
Current assignee: Capital Medical University
Priority date: 2021-05-08
Filing date: 2021-05-08
Publication date: 2021-09-10

Abstract

The invention relates to a marker molecule related to non-small cell lung cancer prognosis, which comprises at least one gene sequence shown as SEQ ID NO. 1, SEQ ID NO. 2, SEQ ID NO. 3, SEQ ID NO. 4 and SEQ ID NO. 5. The marker molecule provided by the invention can better reflect the prognosis of the patient with the non-small cell lung cancer, and provides a new strategy for the diagnosis and prognosis of the patient with the non-small cell lung cancer.

Description

Marker molecules associated with prognosis of non-small cell lung cancer

Technical Field

The invention relates to the field of cancer treatment and prognosis evaluation, in particular to a marker molecule related to non-small cell lung cancer prognosis.

Background

Lung Cancer is one of the most common malignancies, with Non-Small Cell Lung Cancer (NSCLC) accounting for approximately 85% of Lung Cancer cases. With the continuous development of bioinformatics technology and the continuous improvement of sequencing level in recent years, the existing domestic and foreign databases are utilized to carry out deep excavation, and the search of potential diagnosis, prognosis and drug targets of the non-small cell lung cancer is particularly important.

Transcription initiation by eukaryotic cells is a complex process in which transcription factors that play important regulatory functions are involved in the development of a variety of human tumors. In many types of cancer, the activity of transcription factors is altered by a variety of direct mechanisms, including chromosomal translocations, gene amplifications or deletions, point mutations, and altered expression, as well as indirectly by mutations in non-coding DNA that affect transcription factor binding. Transcription factors play a key role in tumors, such as affecting cell cycle, immune response, immune cell infiltration, and the like. Also, the transcription factor may be the product of an oncogene or an oncosuppressor gene, such as the role of an EMT-induced transcription factor in creating a tumor-promoting environment. Recently, the research of the transcription factor in the tumor is concerned, but the role played by the transcription factor in the tumor prognosis is not completely clear, and partial research shows that part of the transcription factor has the function of predicting the prognosis of malignant tumor patients, and recently, the research has found that key target genes and transcription factors with the prediction function comprise PHLDB1, ZBTB16 and the like by analyzing glioma data of a public database; cheng Q et al, using data mining using public databases to identify differentially expressed transcription factors in glioblastoma, found that a combined scoring model of four factors LHX2, MEOX2, SNAI2, ZNF22 predicted prognosis in glioblastoma patients. Three synergistic major factors (ETS2, HNF4a and Jun B) were identified by analysis of public data, ETS2, HNF4a and Jun B are associated with super enhancers, and the use of BRD4 inhibitors could abrogate TGF- β -induced epithelial endothelial cell transformation, and these regulators could regulate partial EMT state transitions while their overexpression is predictive of poor clinical outcome.

Weighted correlation network analysis (WGCNA) can be used to find highly correlated gene clusters (modules), which can evaluate the interrelationships between genes in the modules and convert gene expression profile data into co-expression modules, thereby constructing gene regulatory networks. The WGCNA is divided into 5 steps, a gene co-expression network, an identification module and an association module are constructed, the relationship between the association module and external information is researched, and key driving factors in an interested module are found out. WGCNA has wide application and strong function in the research of pancreatic cancer, lung cancer and other tumors.

The current research is mostly based on the research of the regulation mechanism of the transcription factor for regulating the occurrence and the development of the NSCLC, the research of the transcription factor as a prognosis target is relatively less, and no accurate marker molecule capable of predicting the prognosis of the NSCLC patient exists.

Disclosure of Invention

The invention aims to screen marker molecules highly related to prognosis of non-small cell lung cancer, and provides a new strategy for prevention and treatment of non-small cell lung cancer patients.

The first object of the present invention is to provide an isolated marker molecule associated with prognosis of non-small cell lung cancer, comprising: at least one gene sequence shown as SEQ ID NO. 1, SEQ ID NO. 2, SEQ ID NO. 3, SEQ ID NO. 4 and SEQ ID NO. 5.

Specifically, the marker molecule related to the prognosis of the non-small cell lung cancer provided by the invention can be any one sequence of SEQ ID NO. 1-SEQ ID NO. 5, and can also comprise any two, three, four or all five sequences.

Among them, the gene sequence shown in SEQ ID NO. 1 (the protein coded by the gene sequence is also named ETV1) has research that when the EGFR pathway is damaged, the oncogene Ets factor ETV1 regulated by the transcription inhibitor CIC can be activated, thereby promoting the growth of NSCLC cells and further leading to gefitinib resistance. At present, no report related to the prognosis of the non-small cell lung cancer exists in the gene sequence or the corresponding protein.

The gene sequence shown in SEQ ID NO. 2 (the protein coded by the gene sequence is named as SCML4), SCML4 plays an important role in stem cell plasticity, cell fate determination and cancer through forming a complex, and research shows that SCML4 has a potential function of regulating immune response and participates in adaptive immunity. It has also been shown that inhibition of SCML4 expression exacerbates endothelial dysfunction and vascular remodeling. At present, no report related to the prognosis of the non-small cell lung cancer exists in the gene sequence or the corresponding protein.

The gene sequence shown in SEQ ID NO. 3 (the protein coded by the gene sequence is also named as SETDB2), researches show that histone H3K9 methyltransferase SETDB2 participates in cell cycle disorder of acute leukemia, plays a carcinogenic function in gastric cancer, and researches show that SETDB2 plays an important role in breast cancer dryness maintenance. SETDB2 may act by up-regulating genes of the Hedgehog pathway downstream of Δ Np63 α. At present, no report related to the prognosis of the non-small cell lung cancer exists in the gene sequence or the corresponding protein.

The gene sequence shown in SEQ ID NO. 4 (the protein coded by the gene sequence is also named as Snail3), and Snail3 is one of the Snail family members and is positioned on chromosome 16, and researches show that Snail3 is an inducer for malignant epithelial mesenchymal transition, and Snail3 is abnormally expressed in breast cancer. At present, no report related to the prognosis of the non-small cell lung cancer exists in the gene sequence or the corresponding protein.

The gene sequence shown as SEQ ID NO. 5 (the protein coded by the gene sequence is also named ZNF540), ZNF540 is a zinc finger protein and is positioned on chromosome 19, and researches show that ZNF540 can interact with MVP to inhibit the transcriptional activity of an ERK signal channel. At present, no report related to the prognosis of the non-small cell lung cancer exists in the gene sequence or the corresponding protein.

The marker molecule provided by the invention is verified to be highly related to non-small cell lung cancer prognosis, and can be used as a biomarker for clinical non-small cell lung cancer patient prognosis evaluation, so that the life quality of patients is improved.

The second objective of the present invention is to provide a test kit for evaluating the prognostic effect of non-small cell lung cancer, which comprises: a reagent for detecting the expression level of a marker molecule associated with prognosis of non-small cell lung cancer; the marker molecule comprises: at least one gene sequence shown as SEQ ID NO. 1, SEQ ID NO. 2, SEQ ID NO. 3, SEQ ID NO. 4 and SEQ ID NO. 5.

Specifically, the marker molecule can be any one of the sequences of SEQ ID NO. 1 to SEQ ID NO. 5, and can also comprise any two, three, four or all five of the sequences. The expression quantity is the mRNA content or protein content corresponding to the marker molecule.

As a preferable scheme of the invention, the kit is used for detecting the expression quantity of the mRNA of the marker molecule, the reagent comprises a reverse transcription primer pair aiming at the marker molecule, and preferably also comprises a specific fluorescent probe aiming at the marker molecule, and the expression quantity of the marker molecule in a patient sample is detected quantitatively by monitoring a fluorescent signal in a PCR system in real time.

The third purpose of the invention is to provide an evaluation model of the non-small cell lung cancer prognosis effect, wherein the evaluation model specifically comprises the following steps:

RiskScore＝∑(ZNF540×0.015+ETV1×(-0.013)+SNAIL3×0.075+SCML4×0.005+SETDB2×0.025)；

wherein ZNF540 represents the expression level of the gene sequence shown as SEQ ID NO. 5 in the sample, ETV1 represents the expression level of the gene sequence shown as SEQ ID NO. 1in the sample, SNAIL3 represents the expression level of the gene sequence shown as SEQ ID NO. 4 in the sample, SCML4 represents the expression level of the gene sequence shown as SEQ ID NO. 2 in the sample, and SETDB2 represents the expression level of the gene sequence shown as SEQ ID NO. 3 in the sample; the expression quantity is the mRNA content corresponding to the gene sequence.

The evaluation model provided by the invention can be used for predicting the prognosis of the patient with the non-small cell lung cancer.

It is a fourth object of the present invention to provide an evaluation system for the prognostic effect of non-small cell lung cancer, comprising: (1) the data input module is used for inputting the expression quantity detection result of the marker molecules related to the prognosis of the non-small cell lung cancer into the model calculation module; (2) the model calculation module is used for calculating and processing the input detection result to obtain the prognosis effect data of the detected patient; (3) and the result output module is used for evaluating the prognosis effect data of the detected patient according to the non-small cell lung cancer prognosis effect evaluation standard and outputting an evaluation result.

The marker molecules relevant to the prognosis of the non-small cell lung cancer comprise: at least one gene sequence shown as SEQ ID NO. 1, SEQ ID NO. 2, SEQ ID NO. 3, SEQ ID NO. 4 and SEQ ID NO. 5.

Specifically, the marker molecule can be any one of the sequences of SEQ ID NO. 1 to SEQ ID NO. 5, and can also comprise any two, three, four or all five of the sequences.

As a preferred embodiment of the present invention, the model used by the model calculation module specifically includes:

And in the result output module, evaluating the Risk according to the Risk Score value, and accordingly evaluating the prognosis effect data of the tested patient.

Before clinical actual evaluation is carried out, a large amount of patient data can be collected in a big data mode, the median obtained by statistics after the large amount of data are collected is a cutoff value, samples are divided into high-risk groups and low-risk groups, and accordingly, the prognosis effect data of a detected patient are evaluated. I.e. high Risk if the patient's Risk Score value is above the cut-off value; if the patient's Risk Score is below this cut-off, then there is a low Risk.

The fifth purpose of the invention is to provide the application of the marker molecule, the kit, the evaluation model or the evaluation system in the diagnosis of the non-small cell lung cancer.

The sixth purpose of the invention is to provide the application of the marker molecule, the kit, the evaluation model or the evaluation system in the prognosis evaluation of the non-small cell lung cancer patient.

The invention obtains differential genes through differential expression analysis of diseases and normality based on TCGA-NSCLC gene expression profiles, and obtains transcription factor genes related to NSCLC through a transcription factor gene set obtained by integrating a transcription factor database. In the WGCNA module analysis of transcription factor genes associated with NSCLC disease, three similar modules were found to be associated with overall survival and time to survival, from which 118 transcription factor genes associated with the survival phenotype were obtained. GO and KEGG enrichment analysis showed that these genes were enriched in multiple transcription factor activation pathways. Then 5 transcription factor genes related to survival and corresponding regression factors are obtained through Cox single factor analysis and Lasso regression analysis dimensionality reduction, and a transcription factor prognosis Risk Score (TF Risk Score) model is constructed based on the genes. The model score is validated for performance based on validation data. The model was observed to have a degree of prognostic independence by multifactor regression analysis. Finally look at the target regulatory networks of these risk transcription factor genes and look at the immune microenvironment signature of the high and low risk groups in the TCGA NSCLC data. Finally, 5 characteristic genes relevant to the NSCLC prognosis are screened from the NSCLC-related transcription factors, and can be used as biological indicators for evaluating the survival prognosis of NSCLC patients. When the risk score is used for predicting the prognosis of a patient, the prognosis of a high-risk group is poor, the prognosis of a low-risk group is good, and the two groups have obvious difference in immune infiltration, which indicates that the 5 genes can better reflect the prognosis of a non-small cell lung cancer patient. Provides a new strategy for the diagnosis and prognosis of the non-small cell lung cancer patients.

Drawings

FIG. 1 is a chart of differentially expressed genes and their expression profiles in normal and NSCLC tumor samples; A. differentially expressed genes in normal and NSCLC tumor samples; B. a differentially expressed gene clustering heatmap;

FIG. 2 is a WGCNA co-expression network module; A. selecting a soft threshold; B. clustering the gene dendrograms according to topological overlap, and giving similar module colors; C. correlation heat map visualization between modules; D. clustering the gene dendrograms according to topological overlap, and giving similar module colors; co-expression analysis between Gene Significance and Module Membership of Green, Brown and Yellow modules;

FIG. 3 is the GO and KEGG enrichment analysis results of candidate transcription factors associated with prognosis; A. a biological process; B. a cellular component; C. an enrichment result plot of molecular function; enriched pathway results for kegg;

FIG. 4 shows the results of Cox single-factor regression analysis; a, a Cox single-factor regression analysis result forest map; a KM curve, wherein the p value is a KM test significance result; lamda selection of variable regression (left) (right) in Lasso regression analysis; displaying factor values corresponding to related prognostic genes in the Lasso regression result;

FIG. 5 is a cluster heatmap of candidate gene expression for high and low risk groups;

FIG. 6 is a ROC curve for 3-year survival and 5-year survival for high and low risk group samples;

FIG. 7 is a comparison of immune cell infiltration in 22 calculated by CIBERSORT; A. the immune cell infiltration ratio is compared in the Risk scoring groups of high and low TF Risk Score; B. obtaining the infiltration proportion difference of different immune cell types in the groups by the high-low TF Risk Score; p <0.05, p <0.01, p <0.001, p < 0.0001.

Detailed Description

The following examples are intended to illustrate the invention but are not intended to limit the scope of the invention.

Examples

In this example, the non-small cell lung cancer RNA-seq data in the TCGA database and the transcription factor database are used to screen the transcription factors related to the prognosis of non-small cell lung cancer from the transcription factor gene set. 5 transcription factors closely related to prognosis of non-small cell lung cancer are screened by WGCNA co-expression network analysis, PCA analysis, Cox single-factor regression analysis, Lasso regression analysis, multifactor Cox regression, ROC curve analysis and risk scoring.

The method comprises the following steps:

1. data of

A total of 1076 transcriptome-expressed RNA-seq data and clinical data for lung adenocarcinoma and lung squamous carcinoma were downloaded from UCSC Xena (https:// Xena. UCSC. edu /), which contained 1017 cancer samples and 59 normal control samples.

Non-small cell lung cancer patient expression profile data, as well as clinical data including overall survival, were downloaded from the UCSC database (https:// ucscpublic. And converting the probe ID into Gene Symbols through affyU133 chip probe information, and averaging the expression values of a plurality of probes as the expression value of the Gene when the plurality of probes correspond to one Gene.

The personal transcription factors 1942 (after redundancy removal) were co-downloaded from the trruist (795), CISBP (1639), JASPAR (595) databases for subsequent analysis.

2. Method of producing a composite material

(1) Data preprocessing and differential gene screening

Firstly, carrying out differential gene screening on NSCLC expression profile data of 1076 samples in TCGA data, screening 8840 differential expression genes (figure 1) according to | log2Foldchange | 1.5 and a significance threshold value p <0.05, and taking intersection with transcription factors downloaded from a transcription factor database Trrut v2, CISBP and JASPAR to obtain 725 differential expression transcription factors which are considered to be NSCLC related transcription factors.

(2) Co-expression network

Construction of WGCNA co-expression network using 725 candidate disease-associated transcription factors. Constructing a co-expression network and dividing modules, selecting a non-scale network evaluation coefficient threshold value >0.9, and selecting the optimal beta as 6 by using a soft threshold value as known from fig. 2A. The constructed co-expression network results show 6 modules (fig. 2B, 2C). And analyzing the correlation between each module and each clinical data according to the corresponding clinical data in the TCGA, thereby finding a transcription factor module related to the important clinical data information survival (figure 2D), analyzing the correlation coefficient, the p value and the consistency between the module characteristic relation diagram and the scatter diagram of the three modules (figure 2E), and screening 118 genes contained in the three modules as candidate survival-related transcription factors for further analysis. The above results are shown in FIG. 2.

The 118 candidate transcription factors obtained above were subjected to functional enrichment analysis, and the biological process (fig. 3A), cellular components (fig. 3B) and molecular functions in GO enrichment were respectively subjected to enrichment analysis (fig. 3C). Wherein the gene molecular functions relate to DNA-Binding Transcription Activator Activity, RNA Polymerase II Transcription Factor Binding, and KEGG pathway enrichment analysis shows that the gene is mainly related to Herpes Simplex Virus 1Infection (Herpes Simplex Virus 1Infection) and Transcription disorder In tumor (Transcription mix-Regulation In Cancer) pathway (FIG. 3D).

(3) Cox regression analysis and Lasso regression analysis

Cox single-factor regression analysis was performed on the 118 key transcription factors and a prognostic transcription factor (p <0.05) was selected that was significantly associated with survival, with the results of the single-factor Cox analysis shown in FIGS. 4A and 4B. 9 prognostic transcription factors related to survival were obtained, namely SETDB2, SNAIL3, ZNF831, SCML4, ZNF763, ZNF441, ZNF442, ZNF540 and ZNF682 respectively. Because the screened prognostic related transcription factors are less, in order to construct a more accurate prognostic model, the invention increases the following transcription factors with potential carcinogenic functions, which are reported in the literature: the method comprises the following steps of performing joint analysis on BARX1, DLX6, ELF3, EN1, ETV1, FOXE1, HOXB7, IRX4, IRX5 and SALL1, and aiming at obtaining a prediction model with better prediction capability. The final TF-related survival prognosis scores were obtained using the obtained regression coefficients shown as 5 prognostic transcription factors (SETDB2, SNAI3, SCML4, ZNF540, ETV1) after screening and their corresponding Lasso regression coefficients (fig. 4C, 4D), and the model was constructed as follows (TF: transcription factor): TF Risk Score ═ Σ (ZNF540 × 0.015+ ETV1 × -0.013+ SNAIL3 × 0.075+ SCML4 × 0.005+ SETDB2 × 0.025).

(4) Data validation

The gene expression of the high-Risk group and the low-Risk group of TF Risk Score were observed in the clustering analysis using TCGA data, and the relationship between the high-Risk group and the low-Risk group and the clinical data of patients was analyzed (the results are shown in FIG. 5). In order to verify the reliability of the TF Risk Score, a group of data including 130 patients is used for verification, and the prognosis of the high-Risk group is poor, and the survival of the low-Risk group is better. In the ROC curve, the AUC for 3-year survival was observed to be 0.868 and the AUC for 5-year survival was 0.731 in the prognostic ROC analysis score for 5 years (results are shown in fig. 6).

(5) CIBERSORT immune microenvironment analysis and transcription factor protein interaction network

Immune survival Risk TF Risk Score was calculated from TCGA-NSCLC expression profiling data, and differences in immune microenvironment of high and low Risk Score groups were analyzed, and it was found that the two groups were significantly different in degree of infiltration of memory B cells, CD4+ naive T cells, CD4+ resting T cells, regulatory T cells, NK resting cells, NK activated cells, monocytes, macrophages (M0, M1), resting dendritic cells, activated dendritic cells, resting mast cells, activated mast cells, eosinophils, neutrophils calculated by cibers (results are shown in fig. 7).

Although the invention has been described in detail hereinabove by way of general description, specific embodiments and experiments, it will be apparent to those skilled in the art that many modifications and improvements can be made thereto based on the invention. Accordingly, such modifications and improvements are intended to be within the scope of the invention as claimed.

SEQUENCE LISTING

<110> university of capital medical science

<120> marker molecules associated with prognosis of non-small cell lung cancer

<130> RYP2011032.9

<160> 5

<170> PatentIn version 3.5

<210> 1

<211> 1434

<212> DNA

<213> Homo sapiens

<400> 1

atggatggat tttatgacca gcaagtgcct tacatggtca ccaatagtca gcgtgggaga 60

aattgtaacg agaaaccaac aaatgtcagg aaaagaaaat tcattaacag agatctggct 120

catgattcag aagaactctt tcaagatcta agtcaattac aggaaacatg gcttgcagaa 180

gctcaggtac ctgacaatga tgagcagttt gtaccagact atcaggctga aagtttggct 240

tttcatggcc tgccactgaa aatcaagaaa gaaccccaca gtccatgttc agaaatcagc 300

tctgcctgca gtcaagaaca gccctttaaa ttcagctatg gagaaaagtg cctgtacaat 360

gtcagtgcct atgatcagaa gccacaagtg ggaatgaggc cctccaaccc ccccacacca 420

tccagcacgc cagtgtcccc actgcatcat gcatctccaa actcaactca tacaccgaaa 480

cctgaccggg ccttcccagc tcacctccct ccatcgcagt ccataccaga tagcagctac 540

cccatggacc acagatttcg ccgccagctt tctgaaccct gtaactcctt tcctcctttg 600

ccgacgatgc caagggaagg acgtcctatg taccaacgcc agatgtctga gccaaacatc 660

cccttcccac cacaaggctt taagcaggag taccacgacc cagtgtatga acacaacacc 720

atggttggca gtgcggccag ccaaagcttt ccccctcctc tgatgattaa acaggaaccc 780

agagattttg catatgactc agaagtgcct agctgccact ccatttatat gaggcaagaa 840

ggcttcctgg ctcatcccag cagaacagaa ggctgtatgt ttgaaaaggg ccccaggcag 900

ttttatgatg acacctgtgt tgtcccagaa aaattcgatg gagacatcaa acaagagcca 960

ggaatgtatc gggaaggacc cacataccaa cggcgaggat cacttcagct ctggcagttt 1020

ttggtagctc ttctggatga cccttcaaat tctcatttta ttgcctggac tggtcgaggc 1080

atggaattta aactgattga gcctgaagag gtggcccgac gttggggcat tcagaaaaac 1140

aggccagcta tgaactatga taaacttagc cgttcactcc gctattacta tgagaaagga 1200

attatgcaaa aggtggctgg agagagatat gtctacaagt ttgtgtgtga tccagaagcc 1260

cttttctcca tggcctttcc agataatcag cgtccactgc tgaagacaga catggaacgt 1320

cacatcaacg aggaggacac agtgcctctt tctcactttg atgagagcat ggcctacatg 1380

ccggaagggg gctgctgcaa cccccacccc tacaacgaag gctacgtgta ttaa 1434

<210> 2

<211> 918

<212> DNA

<213> Homo sapiens

<400> 2

atgccctgcc agagaacagc ttggataggt tgcgacaggc agagaacccc cccattccac 60

tggagagaga tcaagtctcg ggttctcatg actcccttag ccctctcacc tccgcggagt 120

accccagagc ccgacctcag ctccatccct caggacgcag ccacggtccc cagcttggcg 180

gccccacagg ctctcacagt ctgcctctac atcaacaagc aggccaatgc ggggccctat 240

ctggagagga agaaggtgca gcagctcccg gagcattttg ggcccgagcg gccatcggcg 300

gtgctgcagc aggccgtcca agcctgcatc gactgcgccc accagcagaa gctggtcttc 360

tccctggtca agcagggcta tggtggtgag atggtgtcag tctcggcttc ctttgatggc 420

aaacagcacc tgcggagcct gcctgtggtg aacagcatcg gctatgtcct ccgcttcctc 480

gccaagctgt gccgaagcct cctgtgcgat gacctcttca gccaccagcc cttccccagg 540

ggctgcagtg cctctgagaa agtccaggag aaagaggaag ggaggatgga atcagtcaag 600

acagtcacca ccgaagagta cctggtgaac cctgtgggca tgaaccgcta cagcgtggac 660

acctccgcct ccacctttaa ccacaggggc tccttgcacc cctcctcctc gctgtactgc 720

aagaggcaga actctggaga cagccacctt gggggtggtc ctgctgccac cgctggtggt 780

ccccgcacta gccccatgtc ttctggtggc ccctcggcac ctgggctgag gcctccagcc 840

tccagcccca agagaaacac gacctctctt gaaggaaaca gatgtggtaa tgtaatgcat 900

gcatcagctt cccactga 918

<210> 3

<211> 2160

<212> DNA

<213> Homo sapiens

<400> 3

atgggagaaa aaaatggcga tgcaaaaact ttctggatgg agctagaaga tgatggaaaa 60

gtggacttca tttttgaaca agtacaaaat gtgctgcagt cactgaaaca aaagatcaaa 120

gatgggtctg ccaccaataa agaatacatc caagcaatga ttctagtgaa tgaagcaact 180

ataattaaca gttcaacatc aataaaggga gcatcacaga aagaagtgaa tgcccaaagc 240

agtgatccta tgcctgtgac tcagaaggaa caggaaaaca aatccaatgc atttccctct 300

acatcatgtg aaaactcctt tccagaagac tgtacatttc taacaacaga aaataaggaa 360

attctctctc ttgaagataa agttgtagac tttagagaaa aagactcatc ttcgaattta 420

tcttaccaaa gtcatgactg ctctggtgct tgtctgatga aaatgccact gaacttgaag 480

ggagaaaacc ctctgcagct gccaatcaaa tgtcacttcc aaagacgaca tgcaaagaca 540

aactctcatt cttcagcact ccacgtgagt tataaaaccc cttgtggaag gagtctacga 600

aacgtggagg aagtttttcg ttacctgctt gagacagagt gtaacttttt atttacagat 660

aacttttctt tcaataccta tgttcagttg gctcggaatt acccaaagca aaaagaagtt 720

gtttctgatg tggatattag caatggagtg gaatcagtgc ccatttcttt ctgtaatgaa 780

attgacagta gaaagctccc acagtttaag tacagaaaga ctgtgtggcc tcgagcatat 840

aatctaacca acttttccag catgtttact gattcctgtg actgctctga gggctgcata 900

gacataacaa aatgtgcatg tcttcaactg acagcaagga atgccaaaac ttcccccttg 960

tcaagtgaca aaataaccac tggatataaa tataaaagac tacagagaca gattcctact 1020

ggcatttatg aatgcagcct tttgtgcaaa tgtaatcgac aattgtgtca aaaccgagtt 1080

gtccaacatg gtcctcaagt gaggttacag gtgttcaaaa ctgagcagaa gggatggggt 1140

gtacgctgtc tagatgacat tgacagaggg acatttgttt gcatttattc aggaagatta 1200

ctaagcagag ctaacactga aaaatcttat ggtattgatg aaaacgggag agatgagaat 1260

actatgaaaa atatattttc aaaaaagagg aaattagaag ttgcatgttc agattgtgaa 1320

gttgaagttc tcccattagg attggaaaca catcctagaa ctgctaaaac tgagaaatgt 1380

ccaccaaagt tcagtaataa tcccaaggag cttactgtgg aaacgaaata tgataatatt 1440

tcaagaattc aatatcattc agttattaga gatcctgaat ccaagacagc catttttcaa 1500

cacaatggga aaaaaatgga atttgtttcc tcggagtctg tcactccaga agataatgat 1560

ggatttaaac caccccgaga gcatctgaac tctaaaacca agggagcaca aaaggactca 1620

agttcaaacc atgttgatga gtttgaagat aatctgctga ttgaatcaga tgtgatagat 1680

ataactaaat atagagaaga aactccacca aggagcagat gtaaccaggc gaccacattg 1740

gataatcaga atattaaaaa ggcaattgag gttcaaattc agaaacccca agagggacga 1800

tctacagcat gtcaaagaca gcaggtattt tgtgatgaag agttgctaag tgaaaccaag 1860

aatacttcat ctgattctct aacaaagttc aataaaggga atgtgttttt attggatgcc 1920

acaaaagaag gaaatgtcgg ccgcttcctt aatcatagtt gttgcccaaa tctcttggta 1980

cagaatgttt ttgtagaaac acacaacagg aattttccat tggtggcatt cttcaccaac 2040

aggtatgtga aagcaagaac agagctaaca tgggattatg gctatgaagc tgggactgtg 2100

cctgagaagg aaatcttctg ccaatgtggg gttaataaat gtagaaaaaa aatattataa 2160

<210> 4

<211> 879

<212> DNA

<213> Homo sapiens

<400> 4

atgccgcgct ccttcctggt gaaaacgcac tccagccaca gggtccccaa ctaccggcgg 60

ctggagacgc agagagaaat caatggtgcc tgctctgcct gtggggggct ggtggtgccc 120

ctcctccccc gagacaagga ggccccttct gtgcccggtg accttcccca gccctgggac 180

cgctcctcgg ccgtcgcctg catctccctg cccctcctgc cacggatcga ggaagctctg 240

ggggcctctg ggctggacgc cttggaagtc agcgaggtcg accctcgggc cagccgggcc 300

gccattgtac ccctcaaaga cagcctgaac cacctcaacc tgcccccact gctggtgctg 360

cccacacggt ggtccccgac cttgggccca gaccggcacg gggctccgga aaaactgctt 420

ggggctgagc ggatgccccg agccccgggc ggctttgagt gcttccactg ccacaaaccc 480

taccacacgc tggccgggct ggccaggcac cggcagctgc actgccacct gcaggtgggg 540

cgtgtcttca cctgcaagta ctgcgacaag gagtacacca gcctgggtgc cctcaagatg 600

cacatccgca ctcacacgct gccctgcacc tgcaagatct gtggcaaggc cttctccagg 660

ccctggttac tgcagggcca tgtccgcacc cacacagggg agaagcccta tgcctgctcg 720

cactgcagca gggcctttgc cgaccgctcc aaccttcggg cccatctgca aacgcactca 780

gacgccaaga agtaccggtg ccggcgctgc accaagacct tctcccgcat gtccctcctg 840

gcgcggcatg aggagtctgg ctgctgcccg ggcccctga 879

<210> 5

<211> 1983

<212> DNA

<213> Homo sapiens

<400> 5

atggcccatg cattggtgac gttcagggat gtggctatag acttctctca gaaggaatgg 60

gagtgcctgg acactaccca gaggaaattg tacagagatg tgatgttgga gaattataat 120

aacttggtct cactgggata ttctggctca aagccagatg tgattacctt actggagcaa 180

gggaaagagc cctgcgtggt ggcgagggat gtgacaggaa gacagtgccc cggtttgtta 240

tccaggcata agaccaagaa attatcttca gaaaaggaca ttcatgaaat cagtttatcc 300

aaagagagta taatagaaaa aagtaaaact cttcgtctga aaggatccat ttttagaaat 360

gagtggcaga acaaaagtga gtttgagggt caacagggac ttaaagaaag atctatcagt 420

caaaagaaaa tcgtctctaa aaaaatgtca actgatagaa aacgtccctc ttttactctg 480

aatcagagaa ttcacaatag tgagaaaagc tgtgactcac acttggttca acatgggaaa 540

atagattctg atgtgaaaca tgattgtaaa gaatgtggga gtacttttaa taatgtctat 600

cagcttactc tccatcagaa aattcatact ggtgaaaaat cctgtaaatg tgagaaatgt 660

gggaaagttt ttagtcatag ctatcaactt actctgcatc agagatttca tactggtgag 720

aaaccctatg aatgtcaaga atgtgggaag acctttactc tttacccaca acttaatcga 780

catcagaaaa ttcacactgg taaaaaaccc tatatgtgta agaaatgtga taagggtttt 840

tttagtagat tagaacttac tcaacataaa agaattcata ctggtaagaa atcttatgaa 900

tgtaaagaat gtggaaaagt ttttcaactt attttctact ttaaagaaca tgagagaatt 960

catacaggta agaaacccta tgaatgtaag gagtgtggga aagcttttag tgtatgcgga 1020

caacttaccc gtcatcagaa aattcatact ggtgtaaaac cctacgaatg taaggaatgt 1080

ggaaagacct ttagacttag tttttacctt actgaacaca gaagaactca tgcaggtaag 1140

aaaccttatg aatgtaagga gtgtgggaaa tcatttaatg tgcgtggaca gcttaatcgg 1200

cataaaacaa tccatactgg tataaaacct tttgcatgta aggtgtgtga gaaggctttt 1260

agttatagtg gtgacctcag agtacattct agaattcata ctggagagaa accatatgaa 1320

tgtaaggaat gcgggaaagc ctttatgctt cgttcagtcc ttactgaaca tcagagactt 1380

catactggtg tgaagcccta cgaatgtaag gaatgtggga agacctttcg agttcgttct 1440

caaattagtc tacataagaa aattcatact gatgtgaagc cctacaaatg tgtacgatgt 1500

gggaagacct ttagatttgg tttctacctt actgaacacc agagaattca cactggtgaa 1560

aagccctata aatgtaaaga atgtggaaag gcctttattc gtagagggaa tcttaaagaa 1620

catctgaaaa ttcattctgg tttaaaaccc tatgactgta aagaatgtgg gaagtccttt 1680

agtcggcgtg ggcagttcac tgaacatcag aaaattcata cgggtgtaaa accatacaaa 1740

tgtaaagaat gtgggaaggc ctttagtcgt agtgtagacc ttagaataca tcaaagaatt 1800

catactggtg agaaacccta tgagtgtaaa caatgtggga aggcctttag acttaattca 1860

caccttactg aacatcagag aattcacact ggtgagaaac cctatgagtg taaggtatgt 1920

agaaaggcct ttagacaata ttcacatctt tatcaacatc agaaaactca taatgtaatt 1980

taa 1983

Claims

1. The marker molecule related to the prognosis of the non-small cell lung cancer is characterized by comprising at least one gene sequence shown as SEQ ID NO. 1, SEQ ID NO. 2, SEQ ID NO. 3, SEQ ID NO. 4 and SEQ ID NO. 5.

2. The marker molecule according to claim 1, comprising any one, any two, any three, any four or all five of the gene sequences of SEQ ID No. 1 to SEQ ID No. 5.

3. A test kit for evaluating the prognosis effect of non-small cell lung cancer, which is characterized by comprising: a reagent for detecting the expression level of a marker molecule associated with prognosis of non-small cell lung cancer; the marker molecule comprises at least one gene sequence shown as SEQ ID NO. 1, SEQ ID NO. 2, SEQ ID NO. 3, SEQ ID NO. 4 and SEQ ID NO. 5; the expression quantity is the mRNA content or protein content corresponding to the marker molecule.

4. The kit according to claim 3, wherein the marker molecule comprises any one, any two, any three, any four or all five gene sequences of SEQ ID NO 1-SEQ ID NO 5.

5. The kit according to claim 3 or 4, wherein the reagent for detecting the expression level of the marker molecule comprises a reverse transcription primer pair for the marker molecule; preferably also specific fluorescent probes are included.

6. An evaluation model for the prognosis effect of non-small cell lung cancer, characterized in that the model is:

7. A system for assessing the prognostic effect of non-small cell lung cancer, the system comprising:

(1) the data input module is used for inputting the expression quantity detection result of the marker molecules related to the prognosis of the non-small cell lung cancer into the model calculation module; the expression quantity is the mRNA content or protein content corresponding to the marker molecule;

the marker molecule comprises at least one gene sequence shown as SEQ ID NO. 1, SEQ ID NO. 2, SEQ ID NO. 3, SEQ ID NO. 4 and SEQ ID NO. 5;

(2) the model calculation module is used for calculating and processing the input detection result to obtain the prognosis effect data of the detected patient;

(3) the result output module is used for evaluating the prognosis effect data of the detected patient according to the non-small cell lung cancer prognosis effect evaluation standard and outputting an evaluation result;

preferably, the model used by the model calculation module is specifically:

8. The evaluation system of claim 7, wherein the marker molecule comprises any one, any two, any three, any four, or all five of the gene sequences of SEQ ID NO 1 to SEQ ID NO 5.

9. Use of the marker molecule according to claim 1 or 2, the kit according to any one of claims 3 to 5, the evaluation model according to claim 6, or the evaluation system according to claim 7 or 8 for the diagnosis of non-small cell lung cancer.

10. Use of the marker molecule according to claim 1 or 2, the kit according to any one of claims 3 to 5, the evaluation model according to claim 6, or the evaluation system according to claim 7 or 8 for the prognostic evaluation of non-small cell lung cancer patients.