CN116312788A - Colorectal cancer prognosis analysis method, system and device - Google Patents

Colorectal cancer prognosis analysis method, system and device Download PDF

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CN116312788A
CN116312788A CN202310346212.4A CN202310346212A CN116312788A CN 116312788 A CN116312788 A CN 116312788A CN 202310346212 A CN202310346212 A CN 202310346212A CN 116312788 A CN116312788 A CN 116312788A
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童云广
杨凡
马雪燕
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Aoming Hangzhou Biomedical Co ltd
China Jiliang University
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China Jiliang University
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Abstract

The invention discloses a colorectal cancer prognosis analysis method, a colorectal cancer prognosis analysis system and a colorectal cancer prognosis analysis device, wherein the colorectal cancer prognosis analysis method comprises the following steps: firstly, acquiring RNA expression data and clinical information of colorectal cancer patients, and removing zero value genes in samples; secondly, performing differential analysis on RNA expression data and clinical information of colorectal cancer patients meeting the requirements in the first step, and screening colorectal cancer prognosis differential genes; thirdly, performing cross analysis on the differential gene obtained in the second step and the iron death gene to obtain the iron death gene related to colorectal cancer prognosis; fourthly, establishing a colorectal cancer prognosis model based on the iron death related genes screened in the third step through LASSO-Cox regression analysis; and fifthly, carrying out prognosis analysis on RNA expression data and clinical information of colorectal cancer patients based on the prognosis model constructed in the fourth step. Through verification, the prognosis model constructed by the invention can be used for prognosis of colorectal cancer patients.

Description

Colorectal cancer prognosis analysis method, system and device
Technical Field
The invention relates to the technical field of medical evaluation, in particular to colorectal cancer prognosis analysis method, system and device based on iron death related genes.
Background
Colorectal Cancer (CRC) is one of the most common malignancies worldwide (CA Cancer J. Clin 68 (6), 394-424, 2018; CA Cancer J Clin 70 (3), 145-64, 2020). The morbidity and mortality are the third and second of the global Cancer morbidity and mortality, respectively (CA Cancer J. Clin 68 (6), 394-424, 2018; CA Cancer J Clin 70 (3), 145-64, 2020). In recent years, the prevention, diagnosis and treatment of colorectal cancer have progressed greatly, but survival rates for 5 years are still low (Lancet. 383 (9927), 1490-1502, 2014). For early CRC patients, chemotherapy and molecular targeted therapy can heal, but survival is closely related to distant metastasis and recurrence in situ (Treat Options Oncol.2 (6), 459-471, 2001). Since early symptoms of colorectal Cancer patients are not obvious, most patients have reached middle and late stages at the time of diagnosis, early diagnosis and treatment are extremely important for improving prognosis of colorectal Cancer patients (World j. Surg. 35 (2), 424-429, 2011; int. J. Color. Dis. 30 (2), 205-212, 2015; BMC Cancer 14, 810, 2014; cancer epidemic 49, 92-100, 2017).
Iron death is a recently discovered form of regulated cell death, associated with excessive accumulation of iron-dependent lipid reactive oxygen species (L-ROS). Since the advent of the iron death mechanism, iron death has been found to play a role in the development and progression of a variety of cancers, such as hepatocellular carcinoma (Frontiers in Genetics, 614888, 2020), clear Cell renal Cell carcinoma (Aging 12, 1493-14948, 2020), lung adenocarcinoma (Future oncol. 17 (12), 1533-1544, 2021), glioma (Frontiers in Cell and Developmental Biology, 538, 2020; frontiers in Oncology 10, 590861, 2020), esophageal adenocarcinoma (Cancer Cell int. 21, 124, 2021) and other cancers (Science 23, 101302, 2020). Studies have shown that iron death is also closely related to colorectal cancer (Cell rep 20 (7), 1692-1704, 2017; nature 572 (7769), 402-406, 2019), which may provide a new direction for the treatment of colorectal cancer. For example, GPx4, as a central regulator of iron death, uses GSH as an co-substrate, catalyzes the conversion of lipid hydrogen peroxide to the corresponding lipid alcohol, limiting the excessive accumulation of L-ROS, preventing the onset of iron death (Cell 156 (1-2), 317-331, 2014). In prior studies, a variety of molecules, such as cisplatin (Cancer res. Treat. 50 (2), 445-460, 2018), RAS-selective lethality 3 (rsl 3) (Frontiers in Pharmacology, 1371, 2018) have been found to inhibit GPx4 in colorectal Cancer cells, induce the occurrence of cellular iron death, and then inhibit progression of colorectal Cancer.
Acyl-coa synthetase long chain family member 4 (ACSl 4) is a key cofactor for the synthesis of phosphatidylethanolamine (res. Commun. 478 (3), 1338-1343, 2016). ACSl4 is expressed more in iron death-sensitive cells. In KRAS mutated human colorectal cancer cells, ACSl4 expression is high, bromelain can stimulate ACSl4 expression, induce iron death and inhibit tumor progression. This suggests that the KRAS gene may be an upstream regulator of iron death and promotes the expression of its effector ACSl4, resulting in iron death (Animal Cells and Systems (5), 334-340, 2018). Heme Oxygenase (HO) can degrade heme into biliverdin, CO and iron, which are the major sources of iron in the body. HO-1 is a major member of the heme oxygenase family. Overexpression of HO-1 can promote an increase in intracellular iron content (Cancer letters 416, 124-137, 2018). Excessive iron can disrupt the redox balance, ultimately leading to iron death. Experimental observations show that after the birch bark methanol extract is treated, the expression of HO-1 in colorectal cancer cells is increased, lipid ROS are excessively accumulated (int. J. Mol. Sci. 20 (11), 2723, 2019), the activity of the cells is reduced, and even the death of HO-1 can induce the colorectal cancer cells to generate iron death in vivo, and the anticancer effect of the colorectal cancer cells is yet to be further confirmed. The P53 gene is an important oncogene that generally initiates cell cycle arrest, cell senescence or apoptosis by transcriptional or non-transcriptional mechanisms, thereby playing a role in tumor suppression. However, recent studies have found that induction of iron death by tumor cells may be another important pathway for p53 to exert anti-tumor effects. Unlike other tumor cells, p53 induces iron death in colorectal cancer cells primarily through non-transcriptional mechanisms (Nature 520 (7545), 57-62, 2015). The P53 gene can mediate the expression of iron death inhibitor scl7a11c, and the P53 gene deletion can improve the anticancer activity of ilostin in vivo (Cell Rep.20 (7), 1692-1704, 2017). P53 is a potential target for future colorectal cancer treatment. However, it is still unclear whether these genes associated with iron death are associated with prognosis of colorectal cancer patients.
In order to better service the prognosis of colorectal cancer patients, it is therefore highly desirable to develop a method, system and device for more accurate prognosis estimation of colorectal cancer based on iron death-related genes for auxiliary reference of colorectal cancer patient treatment regimen selection and prognosis.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a colorectal cancer prognosis analysis method, a colorectal cancer prognosis analysis system and a colorectal cancer prognosis analysis device based on iron death related genes, which are used for screening colorectal cancer iron death related genes with obvious prognosis and constructing a prognosis model so as to provide effective and accurate prognosis evaluation.
In order to overcome the technical defects, the invention discloses a colorectal cancer prognosis analysis method based on iron death related genes, which comprises the following steps: firstly, acquiring RNA expression data and clinical information of colorectal cancer patients, and removing zero value genes in samples; secondly, performing differential analysis on RNA expression data and clinical information of colorectal cancer patients meeting the requirements in the first step, and screening colorectal cancer prognosis differential genes; thirdly, performing cross analysis on the differential gene obtained in the second step and the iron death gene to obtain the iron death gene related to colorectal cancer prognosis; fourthly, establishing a colorectal cancer prognosis model based on the iron death related genes screened in the third step through LASSO-Cox regression analysis; and fifthly, carrying out prognosis analysis on RNA expression data and clinical information of colorectal cancer patients based on the prognosis model constructed in the fourth step.
Preferably, the proportion of zero value genes removed from the sample is 80% and above.
Preferably, the threshold value of the false discovery rate of the differential gene between colorectal cancer patients and normal persons is set to be less than 0.05.
Preferably, the correlation between each prognosis-related gene expression of colorectal cancer patients and the overall survival of colorectal cancer patients and the false discovery rate threshold are both set to less than 0.05.
Preferably, the correlation and false discovery rate threshold for the cross-correlation of colorectal cancer patient prognosis-related genes and iron death-related genes are both set to less than 0.05.
Preferably, the stroke score calculation formula in the colorectal cancer prognosis model based on the iron death-related gene is:
Figure SMS_1
。
preferably, colorectal cancer patients are divided into a high risk group and a low risk group according to the threshold of the average risk score, and the correlation P-value threshold is set to be less than 0.05.
Preferably, the iron death-related gene comprises any one or a combination of the following genes: ACSL4, KR1C1, AKR1C2, AKR1C3, ALOX15, ALOX5, ALOX12, ATP5MC3, CARS, CBS, CD, CHAC1, CISD1, CS, DPP4, FANCD2, GCLC, GCLM, GLS2, GPX4, GSS, HMGCR, HSPB1, CRYAB, LPCAT3, MT1G, NCOA4, PTGS2, RPL8, SAT1, SLC7A11, FDFT1, TFRC, TP53, EMC2, AIFM2, PHKG2, HSBP1, ACO1, FTH1, STEAP3, NFS1, ACSL3, ACCA, PEBP1, ZEB1, SQLE, FADS2, NFE2L2, KEAP1 NQO1, NOX1, ABCC1, SLC1A5, GOT1, G6PD, PGD, IREB2, HMOX1, ACSF2, DUSP1, NOS2, NCF2, MT3, UBC, ALB, TXNRD1, SRXN1, GPX2, BNIP3, OXSR1, SELENOS, ANGPTL7, DDIT4, ASNS, TSC22D3, DDIT3, JDP2, SESN2, SLC1A4, PCK2, TXNIP, VLDLR, GPT2, PSAT1, LURAP1L, SLC A5, herprad 1, XBP1, ATF3, SLC3A2, ATF4, ZNF419, KLHL24, trie 3, ZFP69B, ATP V1G2, VEGFA, GDF15 TUBE1, ARRDC3, CEBPG, SNORA16A, RGS, BLOC1S5-TXNDC5, EIF2S1, KIM-1, IL6, CXCL2, RELA, HSD17B11, AGPAT3, SETD1B, TF, FTL, MAFG, IL, SLC40A1, HAMP, DRD5, DRD4, MAP3K5, MAPK14, SLC2A1, SLC2A3, SLC2A6, SLC2A8, SLC2A12, SLC2A14, EIF2AK4, HMGB1, ELAVL1, TFAP2C, SP1, HBA1, NNMT, PLIN4, HIC1, STMN1, HAMP 2, CAPG, HNF4A, NGB, YWHAE, GABPB1 AURKA, RIPK1, PRDX1, ALOX15B, ATP5G3, ALOX12B, ALOX5AP, CARS1, ACSL5, ACSL6, ANO6, ATG5, ATG7, BAP1, BECN1, CDKN1A, CDKN2A, CFTR, CP, EPAS1, FH, G3BP1, HELLS, HILPDA, HSPA5, ITGA6, LAMP2, LINC00472, LOX, MAP1LC3A, MAP LC3B, MAP1LC3C, MUC1, MYC, OTUB1, PCBP2, PRKAA1, PRKAA2, PRNP, RB1, SAT2, SLC11A2, SLC39A14, SLC39A8, SOCS1.
Preferably, the differential iron death-related gene comprises any one or a combination of the following genes: CS, HSPB1, GDF15, MAPK14, ACO1, OXSR1, HMGCR, G3BP1, GPX4, PCK2, NOS2, CXCL2, SLC2A3, GCLM, DDIT3, ATP6V1G2, HAMP, CDKN2A, CRYAB, PLN4.
Preferably, the significant iron death-related genes associated with colorectal cancer prognosis include HSPB1, G3BP1 and HAMP.
Preferably, the prognostic assay comprises a 1-12 year survival assay.
Preferably, the prognostic model includes a signaling pathway and an immunoassay.
The invention also provides a system for realizing the method, which comprises an acquisition module, a first screening module, a second screening module, a model construction module and an analysis module.
The acquisition module is used for acquiring RNA expression data and clinical information of colorectal cancer patients and removing zero value genes in the samples.
The first screening module is used for carrying out differential analysis on RNA expression data and clinical information of colorectal cancer patients meeting requirements and screening colorectal cancer prognosis differential genes.
The second screening module the analysis module is used for carrying out prognosis analysis on RNA expression data and clinical information of colorectal cancer patients according to the prognosis model. For cross analysis of the colorectal cancer prognosis differential gene and the iron death gene selected, and screening the colorectal cancer prognosis related significant gene based on iron death.
The model construction module is used for constructing a prognosis model according to the RNA expression data and clinical information of the significant genes.
The analysis module is used for carrying out prognosis analysis on RNA expression data and clinical information of colorectal cancer patients according to the prognosis model.
Preferably, the second screening module obtains the significant genes by LASSO-Cox regression analysis.
Preferably, the significant genes include HSPB1, G3BP1 and HAMP, and the prognostic model includes signaling pathways and immunoassays.
The invention also provides a colorectal cancer prognosis analysis device, which comprises a processor and a memory, wherein the memory is used for storing programs and the prognosis model; the program includes instructions for performing a prognostic analysis of RNA expression data and clinical information of colorectal cancer patients according to the prognostic model.
The invention also provides a colorectal cancer prognosis analysis device, which comprises a processor and a memory, wherein the memory is used for storing a program and the prognosis model; the program comprises instructions for performing a prognostic analysis of case data for colorectal cancer according to the prognostic model.
Further, the operation efficiency of the prognosis model requires that the account management login success/logout success operation time of colorectal cancer patients is less than 5 seconds under the lowest configuration operation environment of the colorectal cancer prognosis analysis device.
Further, the colorectal cancer prognosis analysis device has the advantages that under the lowest configuration running environment, when the prognosis analysis of the colorectal cancer prognosis model is carried out in a full-load running analysis, the running time is less than 600 minutes; when the prognosis analysis of the colorectal cancer prognosis model is performed through half-load operation analysis, the operation time is less than 400 minutes; prognostic analysis of colorectal cancer prognostic model when run on a single sample, the run time was less than 100 minutes.
Further, the colorectal cancer prognosis analysis device has the advantage that the calling running time of the colorectal cancer prognosis model on the result report of the patient prognosis analysis is less than 5 seconds under the lowest configuration running environment.
Further, the hardware and operating system configuration of the colorectal cancer prognosis analysis device is divided into front-end configuration and back-end configuration of a prognosis model.
Further, the front end of the prognosis model is configured into 2 CPU cores and 8 core/core; the main frequency is 2.0GHz and above, the memory is 16G and above, the hard disk is 300G and above, the resolution of the display is 1366 x 768, the display card is VGA Asus Turbo GTX 1080TI, and the operating system is WINDOWS server 2012 R2 operating system; OFFICE 2010, firefox 65, C relative risk ome 73 and versions thereof; the network architecture is a B/S architecture (browser/server) and the network type is a local area network; the bandwidth is gigabit network and above.
Further, the rear end of the prognosis model is configured into 2 CPU cores and 24 core/core; the main frequency is 2.5GHz and above, the memory is 64G and above, the hard disk is 6TB and above, the resolution of the display is 1366×768, the display card is VGA Asus Turbo GTX 1080TI, the operating system is a linux 64-bit operating system CentOS 6.8, and GNOME2.28.2; the supporting software is gridinine-6.2u5; the network architecture is a B/S architecture (browser/server) and the network type is a local area network; the bandwidth is gigabit network and above.
Compared with the prior art, the invention has the beneficial effects that: the iron death related genes are significant genes of colorectal cancer patient prognosis analysis, which are also called independent risk factors, and the prognosis model constructed by the invention can carry out prognosis evaluation of colorectal cancer patients through verification and detection analysis.
Drawings
FIG. 1 is a flow chart of a method for prognosis analysis of colorectal cancer based on iron-death-related genes of the present invention.
FIG. 2 is a graph of a prognostic model versus risk profile for 372 satisfactory colorectal cancer patients from the TCGA database.
FIG. 3 is a graph of prognosis model versus survival of 372 colorectal cancer patients from the TCGA database.
FIG. 4 is a Kaplan-Meier plot of the prognosis model versus the survival probability of 372 colorectal cancer patients from the TCGA database.
Fig. 5 is a time-dependent ROC graph of prognostic model versus 372 colorectal cancer patients from TCGA database.
FIG. 6 is a Kaplan-Meier plot of prognosis model versus survival probability for 232 colorectal cancer patients from GEO database/GSE 17538.
FIG. 7 is a graph of the time dependent ROC of the prognostic model on 232 colorectal cancer patients from the GEO database/GSE 17538.
FIG. 8 is a one-factor regression curve of prognosis model versus risk score for 372 colorectal cancer patients from the TCGA database, clinical parameters and overall patient survival.
Figure 9 is a multi-factor regression curve of prognosis model versus risk score for 372 colorectal cancer patients from TCGA database, between clinical parameters and overall patient survival.
Figure 10 is a one-factor regression curve between clinical parameters and overall patient survival for the prognosis model versus risk score for 232 colorectal cancer patients from GEO database/GSE 17538.
FIG. 11 is a multi-factor regression curve between clinical parameters and overall patient survival for the prognostic model versus risk scores for 232 colorectal cancer patients from the GEO database/GSE 17538.
FIG. 12 is a KEGG enrichment curve for a prognostic model.
FIG. 13 is a gene enrichment curve of a prognostic model on 16 immunocytes single sample from 372 colorectal cancer patients from the TCGA database.
FIG. 14 is a gene enrichment curve of a prognostic model on 13 immunocytes single sample from 372 colorectal cancer patients from the TCGA database.
FIG. 15 is a gene enrichment curve of a prognostic model on 16 immunocytes single sample from 232 colorectal cancer patients from GEO database/GSE 17538.
FIG. 16 is a graph of the prognostic model versus 13 immunocyte single sample gene enrichment for 232 colorectal cancer patients from GEO database/GSE 17538.
FIG. 17 is an immune gene network of a prognostic model.
Fig. 18 is a system logic block diagram of the present invention.
Description of the embodiments
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The present invention will be described in further detail with reference to the accompanying drawings.
A method of prognostic analysis of colorectal cancer, as shown in figure 1, the method comprising:
Step 101: RNA expression data and clinical information of colorectal cancer patients are acquired, wherein the RNA expression data and the clinical information can be obtained from a database of a hospital or a third-party clinical medicine detection laboratory, and can also be downloaded from a public platform (such as UCSC Xena browser, https:// xenabrowser. Net /). Pretreatment of RNA expression data and clinical information of colorectal cancer patients is prior art, and is not described in detail in this application, such as duplication removal, deletion of information loss data, etc. Wherein, in order to obtain a relatively accurate prognosis model, the proportion of zero value genes removed from the sample in this example is set to 85%;
step 102: performing differential analysis on RNA expression data and clinical information of colorectal cancer patients meeting requirements screened in the step 101, and screening colorectal cancer prognosis differential genes;
step 103: performing cross analysis on the differential gene and the iron death gene obtained in the step 102 to obtain the iron death gene related to colorectal cancer prognosis;
step 104: establishing a colorectal cancer prognosis model based on the iron death-related genes screened in the step 103 through LASSO-Cox regression analysis; wherein the prognostic model includes a signaling pathway and an immunoassay;
Step 105: performing a prognostic analysis on RNA expression data and clinical information of colorectal cancer patients based on the prognostic model constructed in step 104;
the statistical analysis shows that the iron death related genes HSPB1, G3BP1 and HAMP are obvious genes of colorectal cancer prognosis analysis, namely independent risk factors, and the constructed prognosis model can carry out prognosis evaluation of colorectal cancer patients through verification and detection analysis.
Example 1
Step S1, 434 pancreatic cancer patient case data are downloaded from UCSC Xena browser (https:// xenabrowser. Net /) updated in the latest time period, wherein files of important clinical pathology data, gene expression and the like of the patient are also included. Deleting some patient cases with information loss according to the need; and carrying out standardization and pretreatment treatment on the data; two categories of survival states were performed: 0 represents survival and 1 represents death. And (3) removing the normal tissue sample, and retaining the gene expression quantity of the tumor tissue sample, such as an RNA expression matrix. But the source of the data is not limited thereto. In operation of this embodiment, all data is publicly available. In other words, this is ethical. Meanwhile, this example works in compliance with access and release policies (https:// www.ncbi.nlm.nih.gov/GEO /);
Step S2: differential analysis was performed from RNA expression data and clinical information of colorectal cancer patients meeting the requirements, and the critical value of the false discovery rate was set to be less than 0.05, resulting in 12790 genes. Through further univariate Cox regression analysis, the correlation between the expression of each gene and the total survival rate (OS) of colorectal cancer patients is calculated, and 1836 colorectal cancer prognosis difference genes are screened according to a screening principle that the P value is smaller than 0.05. P values are test variables in the medical statistics, representing the significance of the differences, statistically considering P <0.05 as significant differences and P <0.01 as very significant differences;
the 172 genes associated with iron death include: ACSL4, KR1C1, AKR1C2, AKR1C3, ALOX15, ALOX5, ALOX12, ATP5MC3, CARS, CBS, CD, CHAC1, CISD1, CS, DPP4, FANCD2, GCLC, GCLM, GLS2, GPX4, GSS, HMGCR, HSPB1, CRYAB, LPCAT3, MT1G, NCOA4, PTGS2, RPL8, SAT1, SLC7A11, FDFT1, TFRC, TP53, EMC2, AIFM2, PHKG2, HSBP1, ACO1, FTH1, STEAP3, NFS1, ACSL3, ACCA, PEBP1, ZEB1, SQLE, FADS2, NFE2L2, KEAP1 NQO1, NOX1, ABCC1, SLC1A5, GOT1, G6PD, PGD, IREB2, HMOX1, ACSF2, DUSP1, NOS2, NCF2, MT3, UBC, ALB, TXNRD1, SRXN1, GPX2, BNIP3, OXSR1, SELENOS, ANGPTL7, DDIT4, ASNS, TSC22D3, DDIT3, JDP2, SESN2, SLC1A4, PCK2, TXNIP, VLDLR, GPT2, PSAT1, LURAP1L, SLC A5, herprad 1, XBP1, ATF3, SLC3A2, ATF4, ZNF419, KLHL24, trie 3, ZFP69B, ATP V1G2, VEGFA, GDF15 TUBE1, ARRDC3, CEBPG, SNORA16A, RGS, BLOC1S5-TXNDC5, EIF2S1, KIM-1, IL6, CXCL2, RELA, HSD17B11, AGPAT3, SETD1B, TF, FTL, MAFG, IL, SLC40A1, HAMP, DRD5, DRD4, MAP3K5, MAPK14, SLC2A1, SLC2A3, SLC2A6, SLC2A8, SLC2A12, SLC2A14, EIF2AK4, HMGB1, ELAVL1, TFAP2C, SP1, HBA1, NNMT, PLIN4, HIC1, STMN1, HAMP 2, CAPG, HNF4A, NGB, YWHAE, GABPB1 AURKA, RIPK1, PRDX1, ALOX15B, ATP5G3, ALOX12B, ALOX5AP, CARS1, ACSL5, ACSL6, ANO6, ATG5, ATG7, BAP1, BECN1, CDKN1A, CDKN2A, CFTR, CP, EPAS1, FH, G3BP1, HELLS, HILPDA, HSPA5, ITGA6, LAMP2, LINC00472, LOX, MAP1LC3A, MAP LC3B, MAP1LC3C, MUC1, MYC, OTUB1, PCBP2, PRKAA1, PRKAA2, PRNP, RB1, SAT2, SLC11A2, SLC39A14, SLC39A8, SOCS1. But is not limited thereto;
The screened 1836 colorectal cancer prognosis difference genes and 172 iron death related genes are crossed through the crossing function of R language, and 20 colorectal cancer prognosis difference iron death related genes are screened out. The differential iron death-related genes screened included the following: CS, HSPB1, GDF15, MAPK14, ACO1, OXSR1, HMGCR, G3BP1, GPX4, PCK2, NOS2, CXCL2, SLC2A3, GCLM, DDIT3, ATP6V1G2, HAMP, CDKN2A, CRYAB, PLN4;
several genes have been shown to be involved in drug-induced iron death in colorectal Cancer patient cells (Cancer res. Treat. 50 (2), 445-460, 2018;Frontiers in Pharmacology 9, 1371, 2018). However, the correlation between these iron death-related genes and colorectal cancer patient OS has not been revealed. In the Lasso-COX regression analysis, 10.4% of the iron-death-related genes were found to have prognostic characteristics for colorectal cancer, with 15% of the prognostic genes being OS-related. Clearly, it is possible to build a prognostic model based on iron death-related genes;
step S3: establishing a prognosis model
And (3) carrying out standardization treatment on the second expression matrix or the gene expression data by adopting a Z-score method to obtain a standardized expression matrix. Significant genes were further screened and confirmed by LASSO-Cox regression to obtain regression coefficients for the genes. Colorectal cancer prognosis models were constructed using the "glmnet" R software package according to LASSO-Cox regression analysis. Combining the standardized expression level and the regression coefficient, calculating the risk score of the patient by adopting the following formula:
Figure SMS_2
；
Based on the median of the risk scores, the model of the present invention will differentiate patients into high-risk and low-risk individuals based on the entered RNA expression data and clinical information of satisfactory colorectal cancer patients. In the distinguishing process, the model corrects the P value by using a BH (Benjamini & Hochberg) method, wherein the P value is a test variable in medical statistics and represents the difference significance, and the P <0.05 is statistically considered as a significant difference, and the P <0.01 is very significant difference. Meanwhile, the model of the invention gives survival analysis and time-dependent ROC curve analysis of colorectal cancer patients through 'surviviner' and 'survivinal ROC' in the R software package. In addition, the model of the invention not only combines Kyoto genes and genome database encyclopedia (KEGG, latest edition; www.KEGG.jp) to give the immunobiological action path of the patient iron death-related significant genes in the prognosis model (P value <0.05.P value is a test variable in medical statistics, representing difference significance, statistically considering P <0.05 as significant difference, P <0.01 as very significant difference), but also gives the protein-protein interaction (PPI) network and immune cell fraction (CIBERSORT assessment, https:// Cibersort.stan for d.edu/index php) between the patient iron death-related significant genes and the immune genes obtained from ImmPort shared data based on network PPI data (http:// www.pathwaycommons.org /);
Step S4: application verification of prognosis model
In order to verify the application reliability of the colorectal cancer prognosis model based on the iron death related genes constructed by the invention, RNA expression data and clinical information of 372 colorectal cancer patients meeting the requirements from a TCGA database are input into the model of the invention. The model of the invention classifies 372 patients into two groups according to the average risk score critical value, namely the median risk score value: one group is a high risk group containing 173 patients, and the other group is a low risk group containing 173 patients, as shown in fig. 2. As can be seen from fig. 3, the high risk group patients survived for a shorter period than the low risk group. Furthermore, the Kaplan-Meier analysis results of fig. 4 show that the overall survival rate of the high risk group patients is lower than that of the low risk group (log rank validation, p=2.36 e-05). From the time-dependent ROC curves of fig. 5, it can be seen that the area under the curve (AUC) at 365 days, 730 days and 1095 days are 0.69,0.68 and 0.64, respectively, and the survival prediction confidence level of the colorectal cancer prognosis model based on the risk score is 95%. These results demonstrate that the iron death-related gene-based colorectal cancer prognosis model constructed in the present invention is reliable.
In order to verify the universality of the model for prognosis of patients from different sources, the reliability of the colorectal cancer prognosis model based on the iron death-related genes, which is constructed by the invention, is further verified by using the RNA expression data and clinical information of 232 colorectal cancer patients meeting the requirements in a gene chip GSE17538 in a GEO database. During the verification process, the model of the invention divides 232 patients into 125 high risk groups and 107 low risk groups according to the median risk score value. The results of the validation showed that the high risk group patients survived for a shorter period than the low risk group (fig. 6, log rank validation, p= 0.00285). The time-dependent ROC curves can see areas under the curves (AUC) at 12, 24 and 36 months of 0.62,0.62 and 0.64, respectively (fig. 7), with a 95% confidence level for survival prediction for the colorectal cancer prognosis model based on risk scores. These results further demonstrate that the iron death-related gene-based colorectal cancer prognosis model constructed in the present invention is reliable and has versatility in patient sources.
Further, the results of the independent prognosis validation confirm that the colorectal cancer prognosis model based on the iron death-related gene constructed according to the present invention has an OS-independent prognostic value, as shown by the independent prognosis single-factor and multi-factor Cox regression analysis results (fig. 8, relative risk=2.72, 95% confidence interval=1.81-4.07, p=1.28 e-06; fig. 9: relative risk=2.20, 95% confidence interval=1.44-3.38, p < 0.001) of 372 patients from the TCGA database in fig. 8-11, or by the independent prognosis single-factor and multi-factor Cox regression analysis results (fig. 10, relative risk=7.71, 95% confidence interval=3.04-19.53, p=1.67 e-05; fig. 11: relative risk=4.8, 95% confidence interval=1.86-12.3, p=0.001) of 232 patients from the GSE 17538.
The 3 iron death-related genes in the predictive model were HSPB1, G3BP1 and HAMP, respectively. Among them, HSPB1 is a negative regulator of iron sagging in cancer cells (Oncogene 34, 5617-5625, 2015). G3BP1 regulates Ras and TGF-beta/Smad, src/FAK and p53 signaling pathways, promotes tumor cell proliferation and metastasis, inhibits apoptosis (J. Drug targeting.27 (3), 300-305, 2018). HAMP is a key gene for the regulation of iron metabolism homeostasis (Blood 133 (17), 1888-1898, 2019). HSPB1 is able to protect cells from iron death, whereas G3BP1 and HAMP work in opposite directions. However, in the prognostic model of the present invention, these three genes are all up-regulated in a high risk group of colorectal cancer patients. As there are few related studies, it is not clear to date how these three genes affect the iron death process in colorectal cancer patients.
KEGG enrichment analysis (fig. 12) indicated that parathyroid hormone synthesis, secretion and action, thermogenesis, alcoholism and arginine biosynthesis were associated with iron death in colorectal cancer patients. Abnormal synthesis, secretion and action of parathyroid hormone (PTH) can lead to calcium metabolic disorders (Lancet 394 (10204), 1145-1158, 2019; lancet Gastroenterology Hepatology, 4 (12), 913-933, 2019). For example, PTH is elevated in renal cell carcinoma and bronchogenic carcinoma patients (Tumour biol. 37 (9), 12823-12831, 2016). Clearly, the synthesis, secretion and role of the calcium signaling pathway and PTH play an important role in the pathogenesis of colorectal cancer. Heat generation can lead to tumor protein coagulation and necrosis (BioMed Research International, 5090852, 2016). The heat shock protein β1 phosphorylation mediated by erastin kinase C (PKC) can reduce iron uptake and promote iron death during treatment of HeLa cells by erastin in human cervical cancer (Oncogene 34, 5617-5625, 2015). At the same time, alcohol is metabolized to acetaldehyde by alcohol dehydrogenase and then to acetic acid by acetaldehyde dehydrogenase after entering the human body (Lancet Gastroenterology Hepatology, 4 (12), 913-933, 2019). Ethanol and acetic acid are relatively safe, but intermediate acetaldehyde is dangerous. Acetaldehyde can bind directly to DNA and induce genetic mutations (Nature 553 (7687), 171-177, 2018). In addition, spermine synthase (SMS) is overexpressed during colorectal Cancer progression (Cancer prev. Res. 3, 140-147, 2010). Targeted disruption of SMS in colorectal cancer patient cells results in accumulation of spermine, inhibiting acetylation of FOXO3a and allowing its subsequent transfer to the nucleus, and transcription induces expression of pro-apoptotic protein BIM (Am. j. Physiol. Cell physiol. 302, C587-596, 2012). However, this induction is limited by Myc-driven mir-19a and mir-19b expression, which inhibits BIM production (Nat. Cell biol. 12, 372-379, 2010). In SMS-deficient CRC cells, drug or gene inhibition Myc activity can significantly induce BIM expression and apoptosis and lead to tumor regression, but these effects are attenuated by BIM knockout (nat. Commun. 11, 3243, 2020). These results indicate that inhibition of polymerization of BIM expression by different signaling pathways mediated by SMS and myc is a key survival signal in colorectal cancer (nat. Commun. 11, 3243, 2020). Thus, the combined inhibition of SMS and myc signaling may be an effective method of treating colorectal cancer.
Furthermore, KEGG analysis indicated that cytokine-receptor interactions are an important signaling pathway. It is well known that cytokine-mediated cell signaling is initiated by the binding of cytokines and their receptors (j. Trans. Med. 18, 337, 2020). These cytokines contain tumor necrosis factor, a cytokine that directly causes death of tumor cells (j. Trans. Med. 18, 337, 2020). Clearly, alterations in cytokine-receptor interactions are one of the causes of colorectal cancer occurrence. Errors in endoplasmic reticulum protein processing can trigger stress responses in the endoplasmic reticulum, affecting the expression of specific genes (BBA mol. Dis. 1842 (9), 1444-1453, 2014). If endoplasmic reticulum function continues to be disturbed, the cell will eventually begin the apoptotic process (BBA mol. Dis. 1842 (9), 1444-1453, 2014). Meanwhile, disorders of gene expression are major markers of Cancer, and alterations in transcription factor activity have been demonstrated as driving factors for some of the most common subtypes of Cancer (Nat. Rev. Cancer 21, 22-36, 2021). That is, protein processing in the endoplasmic reticulum and transcriptional deregulation in cancer are an important factor affecting colorectal cancer pathogenesis. Mitochondrial calcium uptake is critical for cell survival and death. Under normal physiological conditions, the calcium signaling pathway may regulate aerobic metabolism. Reactive Oxygen Species (ROS), which act as important second messengers within cells, can activate a variety of key signaling pathways associated with tumor cell proliferation, apoptosis, metastasis and angiogenesis, ultimately leading to the development of malignant tumors.
It is clear that HSPB1, G3BP1 and HAMP and their co-expressed genes may be involved in cytokine-receptor interactions, endoplasmic reticulum protein processing, PTH synthesis, secretion and action, alcoholism, and arginine biosynthesis signaling pathways, etc. during the progression of iron-death-related colorectal cancer.
Further, iron death is associated with tumor immunity. The prognostic model of the present invention employs a single sample gene set enrichment analysis (ssGSEA) to give in vivo immunity in colorectal cancer patients, as shown in FIGS. 13-16 for 372 patients from the TCGA database (FIGS. 13 and 14, P < 0.05) or 232 patients from GSE17538 (FIGS. 15 and 16, P < 0.05). These results indicate that the high-risk group has greater macrophage immunity than the low-risk group, and that the high-risk group has similar immunity levels associated with type I interferon and side inflammatory responses. Tumor-associated macrophages are due to poor prognosis in colorectal cancer patients. Because it has an immune invasion function (cell. 179, 829-45 e20, 2019; gastroenterology. 150, 1646-58 e17, 2016). In addition, collateral inflammation is the expression of immune-signature genes activated by cancer cells and other non-immune cells (Genome biology 17, 145, 2016), which may drive tumor formation. Thus, immunosuppression may be one cause of poor prognosis in colorectal cancer patients (J.Immunol.206 (8), 1890-1900, 2021). At the same time, a high risk score for type I interferon means that the anti-tumor immunity is impaired, probably responsible for the poor prognosis of colorectal cancer patients.
Further, the prognostic model of the present invention gives a regulatory pathway of colorectal cancer in the immune microenvironment. As shown in fig. 17: there are 59 immunogens in the PPI network. In this network, the immune genes interact directly with HSPB1, HAMP and G3PB 1. Meanwhile, the number of T-cell type genes is the largest.
The invention also provides a system for realizing the method, as shown in fig. 18, which comprises an acquisition module 101, a first screening module 102, a second screening module 103, a model construction module 104 and an analysis module 105.
The acquisition module 101 is used for acquiring RNA expression data and clinical information of colorectal cancer patients and removing zero-value genes in samples.
The first screening module 102 is used for carrying out differential analysis on RNA expression data and clinical information of colorectal cancer patients meeting requirements and screening colorectal cancer prognosis differential genes.
The second screening module 103 is used for performing cross analysis on the colorectal cancer prognosis difference gene and the iron death gene to screen relevant significant genes based on colorectal cancer prognosis of iron death.
The model construction module 104 is configured to establish a prognosis model based on the RNA expression data and clinical information of the significant gene.
The analysis module 105 is used for performing a prognostic analysis on RNA expression data and clinical information of colorectal cancer patients according to the prognostic model.
Preferably, the second screening module 103 obtains the significant genes by LASSO-Cox regression analysis.
Preferably, the significant genes include HSPB1, G3BP1 and HAMP, and the prognostic model includes signaling pathways and immunoassays.
The invention also provides a colorectal cancer prognosis analysis device, which comprises a processor and a memory, wherein the memory is used for storing a program and the prognosis model; the program comprises instructions for performing a prognostic analysis of case data for colorectal cancer according to the prognostic model.
Further, the operation efficiency of the prognosis model requires that the account management login success/logout success operation time of colorectal cancer patients is less than 5 seconds under the lowest configuration operation environment of the colorectal cancer prognosis analysis device.
Further, the colorectal cancer prognosis analysis device has the advantages that under the lowest configuration running environment, when the prognosis analysis of the colorectal cancer prognosis model is carried out in a full-load running analysis, the running time is less than 600 minutes; when the prognosis analysis of the colorectal cancer prognosis model is performed through half-load operation analysis, the operation time is less than 400 minutes; prognostic analysis of colorectal cancer prognostic model when run on a single sample, the run time was less than 100 minutes.
Further, the colorectal cancer prognosis analysis device has the advantage that the calling running time of the colorectal cancer prognosis model on the result report of the patient prognosis analysis is less than 5 seconds under the lowest configuration running environment.
Further, the hardware and operating system configuration of the colorectal cancer prognosis analysis device is divided into front-end configuration and back-end configuration of a prognosis model.
Further, the front end of the prognosis model is configured into 2 CPU cores and 8 core/core; the main frequency is 2.0GHz and above, the memory is 16G and above, the hard disk is 300G and above, the resolution of the display is 1366 x 768, the display card is VGA Asus Turbo GTX 1080TI, and the operating system is WINDOWS server 2012 R2 operating system; OFFICE 2010, firefox 65, C relative risk ome 73 and versions thereof; the network architecture is a B/S architecture (browser/server) and the network type is a local area network; the bandwidth is gigabit network and above.
Further, the rear end of the prognosis model is configured into 2 CPU cores and 24 core/core; the main frequency is 2.5GHz and above, the memory is 64G and above, the hard disk is 6TB and above, the resolution of the display is 1366×768, the display card is VGA Asus Turbo GTX 1080TI, the operating system is a linux 64-bit operating system CentOS 6.8, and GNOME2.28.2; the supporting software is gridinine-6.2u5; the network architecture is a B/S architecture (browser/server) and the network type is a local area network; the bandwidth is gigabit network and above.
The colorectal cancer prognosis analysis device of the invention is applied in the actual clinical auxiliary
The RNA expression data and clinical information of 106 colorectal cancer patients in actual clinic are input into the colorectal cancer prognosis analysis device for clinical auxiliary application. The colorectal cancer prognosis analysis device can better carry out clinical danger grouping of colorectal cancer patients through return visit and treatment tracking of patients, and provide auxiliary treatment advice for doctors, so that prognosis of colorectal cancer patients can be better improved. The clinical auxiliary application result shows that: the prognosis of the control group (colorectal cancer patients for whom adjuvant treatment is not recommended in advance of the colorectal cancer prognosis analysis apparatus according to the present invention) is approximately 100% in agreement with the prognosis analysis of the colorectal cancer prognosis analysis apparatus according to the present invention; meanwhile, the total survival rate of colorectal cancer patients in a group which is subjected to adjuvant therapy according to the prognosis suggestion given by the colorectal cancer prognosis analysis device is obviously improved, and clinical adjuvant application experimental data show that the average total survival rate of colorectal cancer patients is improved by 67 percent. Therefore, the colorectal cancer prognosis analysis device is beneficial to providing effective advice for auxiliary diagnosis and treatment of doctors in hospitals, brings use benefits to colorectal cancer patients using the device, and has good market prospect and application.
The above is only a preferred embodiment of the present invention, and is not intended to limit the present invention, but various modifications and variations can be made to the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (9)

1. A method for prognosis analysis of colorectal cancer, characterized in that it comprises the steps of: firstly, acquiring RNA expression data and clinical information of colorectal cancer patients, and removing zero value genes in samples; secondly, performing differential analysis on RNA expression data and clinical information of colorectal cancer patients meeting the requirements in the first step, and screening colorectal cancer prognosis differential genes; thirdly, performing cross analysis on the differential gene obtained in the second step and the iron death gene to obtain the iron death gene related to colorectal cancer prognosis; fourthly, establishing a colorectal cancer prognosis model based on the iron death related genes screened in the third step through LASSO-Cox regression analysis; and fifthly, carrying out prognosis analysis on RNA expression data and clinical information of colorectal cancer patients based on the prognosis model constructed in the fourth step.
2. The method of claim 1, further comprising the step of risk grouping colorectal cancer patients: the gene expression data are standardized to obtain standardized expression quantity; obtaining LASSO-Cox regression coefficients of genes based on a regression analysis method; calculating the risk score of the colorectal cancer patient according to the standardized expression quantity and the LASSO-Cox regression coefficient:
Figure QLYQS_1
。
3. the method of claim 1, wherein the iron death-related gene comprises any one or a combination of the following genes: ACSL4, KR1C1, AKR1C2, AKR1C3, ALOX15, ALOX5, ALOX12, ATP5MC3, CARS, CBS, CD, CHAC1, CISD1, CS, DPP4, FANCD2, GCLC, GCLM, GLS2, GPX4, GSS, HMGCR, HSPB1, CRYAB, LPCAT3, MT1G, NCOA4, PTGS2, RPL8, SAT1, SLC7A11, FDFT1, TFRC, TP53, EMC2, AIFM2, PHKG2, HSBP1, ACO1, FTH1, STEAP3, NFS1, ACSL3, ACCA, PEBP1, ZEB1, SQLE, FADS2, NFE2L2, KEAP1 NQO1, NOX1, ABCC1, SLC1A5, GOT1, G6PD, PGD, IREB2, HMOX1, ACSF2, DUSP1, NOS2, NCF2, MT3, UBC, ALB, TXNRD1, SRXN1, GPX2, BNIP3, OXSR1, SELENOS, ANGPTL7, DDIT4, ASNS, TSC22D3, DDIT3, JDP2, SESN2, SLC1A4, PCK2, TXNIP, VLDLR, GPT2, PSAT1, LURAP1L, SLC A5, herprad 1, XBP1, ATF3, SLC3A2, ATF4, ZNF419, KLHL24, trie 3, ZFP69B, ATP V1G2, VEGFA, GDF15 TUBE1, ARRDC3, CEBPG, SNORA16A, RGS, BLOC1S5-TXNDC5, EIF2S1, KIM-1, IL6, CXCL2, RELA, HSD17B11, AGPAT3, SETD1B, TF, FTL, MAFG, IL, SLC40A1, HAMP, DRD5, DRD4, MAP3K5, MAPK14, SLC2A1, SLC2A3, SLC2A6, SLC2A8, SLC2A12, SLC2A14, EIF2AK4, HMGB1, ELAVL1, TFAP2C, SP1, HBA1, NNMT, PLIN4, HIC1, STMN1, HAMP 2, CAPG, HNF4A, NGB, YWHAE, GABPB1 AURKA, RIPK1, PRDX1, ALOX15B, ATP5G3, ALOX12B, ALOX5AP, CARS1, ACSL5, ACSL6, ANO6, ATG5, ATG7, BAP1, BECN1, CDKN1A, CDKN2A, CFTR, CP, EPAS1, FH, G3BP1, HELLS, HILPDA, HSPA5, ITGA6, LAMP2, LINC00472, LOX, MAP1LC3A, MAP LC3B, MAP1LC3C, MUC1, MYC, OTUB1, PCBP2, PRKAA1, PRKAA2, PRNP, RB1, SAT2, SLC11A2, SLC39A14, SLC39A8, SOCS1; differential iron death-related genes include any one or a combination of the following genes: CS, HSPB1, GDF15, MAPK14, ACO1, OXSR1, HMGCR, G3BP1, GPX4, PCK2, NOS2, CXCL2, SLC2A3, GCLM, DDIT3, ATP6V1G2, HAMP, CDKN2A, CRYAB, PLN4.
4. The method of claim 1, wherein the significant genes comprise HSPB1, G3BP1 and HAMP; establishing a prognosis model according to the expression quantity of the significant gene; the prognosis analysis includes a 1-12 year survival analysis.
5. The method of claim 1, wherein the prognostic model comprises a signaling pathway and an immunoassay.
6. A system for implementing the colorectal cancer prognostic assay of any of claims 1-5, comprising an acquisition module, a first screening module, a second screening module, a model building module, and an analysis module; the acquisition module is used for acquiring RNA expression data and clinical information of colorectal cancer patients and removing zero value genes in the samples; the first screening module is used for carrying out differential analysis on RNA expression data and clinical information of colorectal cancer patients meeting requirements and screening colorectal cancer prognosis differential genes; the second screening module is used for carrying out cross analysis on the colorectal cancer prognosis difference gene and the iron death gene from the screening, and screening relevant significant genes based on colorectal cancer prognosis of iron death; the model construction module is used for constructing a prognosis model according to the RNA expression data and clinical information of the significant gene; the analysis module is used for carrying out prognosis analysis on RNA expression data and clinical information of colorectal cancer patients according to the prognosis model.
7. The system of claim 6, wherein the second screening module obtains the significant genes by LASSO-Cox regression analysis.
8. The system of claim 7, wherein the salient genes comprise HSPB1, G3BP1 and HAMP and the prognostic model comprises signaling pathway and immune network analysis.
9. A colorectal cancer prognosis analysis device, characterized by comprising a processor and a memory for holding a program and a prognosis model established according to any one of claims 1 to 5; the program includes instructions for prognostic analysis of RNA expression data and clinical information of colorectal cancer patients according to the prognostic model.
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CN117476097A (en) * 2023-10-25 2024-01-30 中山大学附属第六医院 Colorectal cancer prognosis and treatment response prediction model based on tertiary lymphoid structure characteristic genes, and construction method and application thereof
CN117476097B (en) * 2023-10-25 2024-06-07 中山大学附属第六医院 Colorectal cancer prognosis and treatment response prediction model based on tertiary lymphoid structure characteristic genes, and construction method and application thereof

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