LU502513B1

LU502513B1 - Breast cancer prognosis evaluation method and system based on autophagy-related incrna model

Info

Publication number: LU502513B1
Application number: LU502513A
Authority: LU
Inventors: Jinniu Guo; Zhe Yang; Ruyue Zhang; Yu Jia; Qingwen Zhu; Tianshuo Yang; Daidi Zhang; Jinxiu Guo; Jiali Zhang
Original assignee: Univ Zhengzhou
Priority date: 2021-07-26
Filing date: 2022-07-15
Publication date: 2023-01-16
Also published as: CN113593648A

Abstract

The present invention discloses a breast cancer prognosis evaluation method and system based on an autophagy-related lncRNA model. The breast cancer prognosis evaluation method based on the autophagy-related lncRNA model is applied to prognosis of breast cancer. The breast cancer prognosis evaluation method based on the autophagy-related lncRNA model comprises the following steps: determining autophagy-related lncRNA information according to RNA information of a plurality of breast cancer samples and RNA information of a plurality of normal breast samples; analyzing the autophagy-related lncRNA information and clinical data of the plurality of breast cancer samples, and establishing an autophagy-related lncRNA prognosis model; and carrying out prognosis based on the autophagy-related lncRNA prognosis model. With the adoption of the breast cancer prognosis evaluation method and system based on the autophagy- related lncRNA model of the present invention, the prognosis of the breast cancer samples can be accurately judged.

Description

BREAST CANCER PROGNOSIS EVALUATION METHOD AND SYSTEM BASED ON AUTOPHAGY-RELATED INCRNA MODEL Technical Field The present invention relates to the technical field of biomedical detection, and particularly relates a breast cancer prognosis evaluation method and system based on an autophagy-related IncRNA model.

Background Art Breast cancer is the most common malignant tumor in the world and is also the main cause of female cancer-related death, its morbidity and mortality reach 24.2% and 15.0% respectively. At present, the treatment of breast cancer is usually to combine surgery with various adjuvant treatments, such as chemotherapy, radiotherapy, endocrine therapy, targeted therapy and immunotherapy. Most patients respond to preliminary treatment within a period of time, but some breast cancer, especially triple-negative breast cancer, still may develop into a more invasive tumor form, and consequently prognosis is poor.

In order to carry out prognosis on breast cancer samples, in the prior art, the researches usually focus on the action mechanism and clinical value of a single biomarker in breast cancer. The inventor finds that breast cancer has high heterogeneity, and an existing breast cancer prognosis evaluation method based on an autophagy-related IncRNA model is not accurate enough in prognosis results of the breast cancer samples with a large deviation.

The information disclosed in Background Art is only for enhancing the understanding to the general background of the present invention and should not be taken as an acknowledgement or any form of suggestion that this information forms the prior art already known to those of ordinary skill in the art.

Summary of the Present Invention An objective of the present invention is to provide a breast cancer prognosis evaluation method and system based on an autophagy-related IncRNA model, which can accurately judge the prognosis of breast cancer samples.

In order to achieve the above objective, the present invention provides the breast cancer prognosis evaluation method based on an autophagy-related IncRNA model, which is applied to prognosis of breast cancer, and comprises the following steps: determining autophagy-related IncRNA information according to RNA information of a plurality of breast cancer samples and RNA information of a plurality of normal breast samples; analyzing the autophagy-related IncRNA information and clinical data of the plurality of breast cancer samples, and establishing an 1 autophagy-related IncRNA prognosis model; and carrying out prognosis based on the autophagt#502513 related IncRNA prognosis model. In one embodiment of the present invention, the step of determining the autophagy-related IncRNA information according to the RNA information of the plurality of breast cancer samples and the RNA information of the plurality of normal breast samples comprises: extracting the RNA information and the clinical information of the plurality of breast cancer samples, and extracting the RNA information of the plurality of normal breast samples; extracting each IncRNA information and each mRNA information related to human autophagy genes from the RNA information of the plurality of breast cancer samples, extracting each IncRNA information from the RNA information of the plurality of normal breast samples, performing differential analysis on the IncRNA information of the plurality of breast cancer samples and each IncRNA information of the plurality of normal breast samples, and extracting each IncRNA information of which the differential expression value is within a preset range; analyzing the correlation between each IncRNA information of which the differential expression value is within the preset range and each mRNA information related to the human autophagy genes, and extracting all IncRNAs with the correlation meeting a preset condition; comparing all extracted IncRNAs with the correlation meeting the preset condition with all IncRNAs in a GSE20685 data set, and extracting the IncRNAs with the correlation meeting the preset condition and existing in the GSE20685 data set; analyzing the IncRNAs with the correlation meeting the preset condition and existing in the GSE20685 data set, and extracting all IncRNAs related to prognosis and corresponding significance level values; and analyzing IncRNA with the correlation meeting the preset condition and existing in the GSE20685 data set, and extracting all IncRNAs related to prognosis, wherein the significance level values of all IncRNAs related to prognosis are less than a preset threshold value, and all IncRNAs related to prognosis refer to the autophagy-related IncRNA information.

In one embodiment of the present invention, the step of extracting the RNA information and the clinical information from the plurality of breast cancer samples comprises: extracting the RNA information and the clinical information of the plurality of breast cancer samples from a TCGA database as training data of the IncRNA prognosis model.

In one embodiment of the present invention, the step of analyzing the correlation between each IncRNA information of which the differential expression value is within the preset range and each mRNA information related to the human autophagy genes, and extracting all IncRNAs with the correlation meeting the preset condition comprises: analyzing the correlation between each IncRNA information of which the differential expression value is within the preset range and each mRNA information related to the human autophagy genes through a Pearson correlation analysis method; and extracting the IncRNA of which the square of a correlation coefficient in the Pearson correlation analysis result is larger than a first threshold value and the significance level is less than a second threshold value.

In one embodiment of the present invention, the step of analyzing the IncRNA with the correlation meeting the preset condition and existing in the GSE20685 data set comprises: analyzing the 2

IncRNAs with the correlation meeting the preset condition and existing in the GSE20685 data se#502513 through a single-factor cox regression analysis method; and the step of analyzing the autophagy- related IncRNA information and the clinical data of the plurality of breast cancer samples comprises: analyzing all the IncRNAs related to prognosis and the clinical data of the plurality of breast cancer samples through a multi-factor cox risk regression analysis method.

In one embodiment of the present invention, the autophagy-related IncRNA prognosis model comprises three IncRNA related to prognosis of breast cancer, namely USP30-AS1, MIR205HG and LINCO1087. In one embodiment of the present invention, the autophagy-related IncRNA prognosis model is as follows: prognosis score Z = (-0.360 x USP30-AS1) + (-0.144 x MIR205HG) + (-0.120 X LINCO1087). Based on the same inventive concept, the present invention further provides a breast cancer prognosis evaluation system based on an autophagy-related IncRNA model, which is applied to prognosis of breast cancer and comprises an autophagy-related IncRNA determination module, a prognosis model establishing module and a prognosis module.

The autophagy-related IncRNA determination module is used for determining autophagy-related IncRNA information according to the RNA information of the plurality of breast cancer samples and the RNA information of the plurality of normal breast samples; the prognosis model establishing module is coupled with the autophagy-related IncRNA determination module and is used for analyzing the autophagy-related IncRNA information and the clinical data of the plurality of breast cancer samples, and establishing the autophagy-related IncRNA prognosis model; and the prognosis module is coupled with the prognosis model establishing module and used for carrying out prognosis based on the autophagy- related IncRNA prognosis model.

Based on the same inventive concept, the present invention further provides a storage medium, for storing computer executable instructions, wherein the computer executable instructions are used for executing the breast cancer prognosis evaluation method based on the autophagy-related IncRNA model in any one of the above embodiments.

Based on the same inventive concept, the present invention further provides electronic equipment, which comprises at least one processor and a memory in communication connection with at least one processor, wherein the memory stores an instruction which can be executed by at least one processor, and the instruction is executed by at least one processor, so that at least one processor can execute the breast cancer prognosis evaluation method based on the autophagy-related IncRNA model in any one of the embodiments.

Compared with the prior art, according to the breast cancer prognosis evaluation method and system based on the autophagy-related IncRNA model of the present invention, the important significance of IncRNA and autophagy in breast cancer biology is considered, the autophagy- related IncRNA model is determined to predict prognosis of the breast cancer samples to provide a theoretical basis for diagnosis and treatment of breast cancer, and thus prognosis of the breast 3 cancer samples can be accurately judged. Preferably, by more accurately extracting the autophagt#502513 related IncRNA and verifying in internal and external databases, the selected autophagy-related IncRNA can reflect the conditions of most breast cancer samples, the final prognosis model has universality and can benefit more patients, and the prognosis accuracy of breast cancer can be further improved.

Brief Description of the Figures FIG. 1 shows steps of a breast cancer prognosis evaluation method based on an autophagy-related IncRNA model according to one embodiment of the present invention; FIG. 2 shows an extraction step of autophagy-related IncRNA information according to one embodiment of the present invention; FIG. 3 is a differential expression distribution volcanic diagram of IncRNA according to one embodiment of the present invention; FIG. 4 shows 5 IncRNA-related information obtained by single-variable cox regression analysis according to one embodiment of the present invention; FIG. 5 shows 3 IncRNA-related information obtained by multi-variable cox regression analysis according to one embodiment of the present invention; FIG. 6 shows Kaplan-Meier survival analysis curves of breast cancer samples in a TCGA training group according to one embodiment of the present invention; FIG. 7 shows ROC curves of breast cancer samples in a TCGA training group according to one embodiment of the present invention; FIG. 8 shows risk scores of prognosis characteristics, survival state and an expression heat map of breast cancer samples in a TCGA training group according to one embodiment of the present invention; FIG. 9 shows Kaplan-Meier survival analysis curves of breast cancer samples in a GSE20685 verification group according to one embodiment of the present invention; FIG. 10 shows ROC curves of breast cancer samples in a GSE20685 verification group according to one embodiment of the present invention; FIG. 11 shows risk scores of prognosis characteristics, survival state and an expression heat map of breast cancer samples in a GSE20685 verification group according to one embodiment of the present invention; FIG. 12 shows single-factor and multi-factor Cox regression analysis results of a TCGA training group according to one embodiment of the present invention; FIG. 13 shows single-factor and multi-factor Cox regression analysis results of a GSE20685 verification group according to one embodiment of the present invention; 4

FIG. 14 shows a gene set enrichment analysis result according to one embodiment of the preseh#502513 invention; and FIG. 15 shows module composition of a breast cancer prognosis evaluation system based on an autophagy-related IncRNA model according to one embodiment of the present invention.

Detailed Description of the Present Invention The specific embodiments of the present invention will be described in detail below with reference to the figures, but it should be understood that the protection scope of the present invention is not limited by the specific embodiments.

Unless expressly stated otherwise, the term “including” or its conjugations such as “containing” or “comprising” and the like throughout the specification and claims will be understood to include the stated elements or components, and other elements or other components are not excluded.

In order to solve the problem that an existing breast cancer prognosis evaluation method based on an autophagy-related IncRNA model could not achieve accurate judgment, the inventor, after thought and research, found that the breast cancer had strong heterogeneity, a multi-parameter signal was more valuable than a single biomarker in predicting prognosis of breast cancer, and the autophagy is closely related to the occurrence, development and metastasis of tumors.

LncRNA is a small RNA longer than 200 bp and could not encode proteins, although the LncRNA could not be translated into proteins, it participates in the process of protein translation, and LncRNA plays a key role in a complex autophagy regulation network by adjusting the biological effects of various autophagy-related DNA, RNA or proteins.

For example, autophagy drugs such as sirolimus and arsenic trioxide could induce autophagy of tumor cells and improve prognosis of certain cancer patients, but only a few patients benefited from autophagy-based treatment.

Therefore, based on the abovementioned thought and research, the present invention provides a prognosis strategy, namely, by integrating a plurality of autophagy IncRNAs closely related to prognosis of breast cancer and establishing the model, breast cancer sample prognosis could be accurately judged, and thus an optimal treatment scheme could be selected to increase the survival rate of the patients.

FIG. 1 showed the breast cancer prognosis evaluation method based on the autophagy-related IncRNA model according to one embodiment of the present invention.

The breast cancer prognosis evaluation method based on the autophagy-related IncRNA model was applied to prognosis of breast cancer, and the breast cancer prognosis evaluation method based on the autophagy-related IncRNA model comprised steps S1-S3. In the step S1, the autophagy-related IncRNA (long-chain non-coding ribonucleic acid molecules) information was determined according to the RNA (ribonucleic acid) information of the plurality of breast cancer samples.

In the step S2, the autophagy-related IncRNA information and the clinical data of the plurality of breast cancer samples were analyzed, and an autophagy-related IncRNA prognosis model was 5 established. LU502513 In the step S3, prognosis was carried out based on the autophagy-related IncRNA prognosis model. Therefore, in this embodiment, the important significance of the IncRNAs and autophagy in breast cancer biology was considered, the autophagy-related IncRNA model was determined to predict prognosis of the breast cancer samples, a theoretical basis was provided for diagnosis and treatment of breast cancer, and compared with the prior art, the prognosis result was more accurate. In order to more accurately extract autophagy-related IncRNAs of the breast cancer samples, in a preferred embodiment of the present invention, the step S1 specifically included steps S101-S105, as shown in FIG. 2.

Inthe step S101, the RNA information and the clinical information of the plurality of breast cancer samples were extracted, and the RNA information of the plurality of normal breast samples was extracted; Optionally, the RNA information and the clinical information of the plurality of breast cancer samples could be extracted from a TCGA database (The Cancer Genome Atlas database) as training data of the prognosis model. Wherein the clinical information could include age, gender, clinical staging, TNM and other data. The verification data of the prognosis model could be selected from a GSE20685 data set in an external GEO database. Wherein TNM was a staging form of tumors in oncology, T represented primary focus, N represented lymph node, and M represented distant metastasis.

In the step S102, each IncRNA information and each mRNA information related to human autophagy genes were extracted from the RNA information of the plurality of breast cancer samples, each IncRNA information was extracted from the RNA information of the plurality of normal breast samples, differential analysis is performed on the IncRNA information of the plurality of breast cancer samples and each IncRNA information of the plurality of normal breast samples, and each IncRNA information of which the differential expression value was within a preset range was extracted.

Specifically, when extracting the mRNA information related to the autophagy genes, the autophagy genes were extracted from human autophagy data according to annotations provided by GENCODE (gene codes), and each mRNA related to the autophagy genes in the RNA of the breast cancer was extracted. In addition, when carrying out differential analysis, each IncRNA information of which the differential expression value was within the preset range in the breast cancer samples and the normal breast samples could be screened through a DESeq2 tool, wherein each IncRNA information of which the differential expression value was within a preset range could be obtained by setting parameters | log2 (difference multiple) | > 0.5 and p < 0.05. The IncRNA screened by the differential analysis screening mode was used for correlation analysis in subsequent steps, thus more accurate autophagy-related IncRNA could be extracted, and the accuracy of final model prognosis could be improved.

In the step S103, the correlation between each IncRNA information of which the differential expression value was within the preset range and each mRNA information related to the human 6 autophagy genes was analyzed, and all IncRNAs with the correlation meeting a preset conditidd/502513 were extracted. Optionally, the correlation between each IncRNA information of which the differential expression value was within the preset range and each mRNA information related to the human autophagy genes could be analyzed through a Pearson correlation analysis method; and IncRNA of which the square of a correlation coefficient R in the Pearson correlation analysis result was larger than a first threshold value and the significance level P1 was less than a second threshold value was extracted. Wherein the first threshold could be set to be 0.4 or above, and the second threshold could be set to be 0.05 or below. The IncRNA extracted in the step S103 was the primary autophagy-related IncRNA.

In the step S104, all IncRNAs with the correlation meeting the preset condition were compared with all the IncRNAs in the GSE20685 (breast cancer gene chip database) data set, and the IncRNAs with the correlation meeting the preset condition and existing in the GSE20685 data set were extracted. Wherein all the extracted IncRNAs were normalized through log2 conversion. In the step S104, the intersection of the primary autophagy-related IncRNAs and the IncRNAs in the GSE20685 data set was solved, thus the selected autophagy-related IncRNAs could reflect the conditions of most breast cancer samples, and the final prognosis model had universality and could benefit more patients.

In the step S105, the IncRNAs with the correlation meeting the preset condition and existing in the GSE20685 data set were analyzed, all IncRNAs related to prognosis were extracted, wherein the significance level values P2 of all the IncRNAs related to prognosis were less than a preset threshold value, and all the IncRNAs related to prognosis referred to the autophagy-related IncRNA information and the corresponding significance level values P2. Optionally, a single- factor cox regression analysis method could be used for analyzing the IncRNAs with the correlation meeting the preset condition and existing in the GSE20685 data set. Optionally, the preset threshold value could be set to be 0.05 or below. In the step S105, the IncRNAs selected in the step S104 were further screened, all the IncRNAs which were more accurate and related to prognosis were selected, and thus the accuracy of the prognosis model could be further improved. In conclusion, in the optional embodiment, the autophagy-related IncRNAs of the breast cancer samples could be extracted more accurately, and the prognosis accuracy and universality of breast cancer were further improved.

In order to establish a more accurate prognosis model, in one embodiment of the present invention, the autophagy-related IncRNA information and the clinical data of the plurality of breast cancer samples were analyzed in the step S2, and the autophagy-related IncRNA prognosis model was established and comprised: analyzing all IncRNAs related to prognosis and the clinical data of the plurality of breast cancer samples through a step-by-step multi-factor cox proportional risk regression analysis method. In order to avoid overfitting, an Akaike Information Criterion (AIC) was adopted to obtain an optimal model, wherein the model risk score = B IncRNA1 X exprineRNAI +B IncRNA2 X exprincrnaz + … + B IncRNAn X exprincrnan. PB Was a regression coefficient of the corresponding IncRNA; expr was an expression level of the IncRNA with the unit of FPKM; and 7 the risk scores of the breast cancer samples were calculated.

Wherein the obtained optimal modeW502513 was the autophagy-related IncRNA prognosis model.

The prognosis performed based on the optimal model achieved the accuracy and universality.

In addition, it needed to be explained that during prognosis, a nomograph could be constructed based on the autophagy-related IncRNA in the model, and points in the nomograph were distributed to all variables in a multi-variable Cox analysis model.

By drawing a vertical line between a total point axis and each prediction axis, the estimated survival rate of the breast cancer samples over the next several years could be calculated, which helped professionals to make clinical decisions for the breast cancer samples.

In order to verify the effect of the present invention, in one embodiment, the whole prognosis process was as follows.

Firstly, according to the step 101, the RNA information of 1,109 breast cancer samples and 113 normal breast samples and related clinical information (such as age, gender, clinical staging and TNM staging) of the 1,109 breast cancer samples were extracted from the TCGA database to serve as the training group to construct the prognosis model; in order to verify the prediction capability and the universal applicability of the model, the GSE20685 data set in the external GEO database was used as the verification group, wherein the verification group contained 327 breast cancer samples; and 19,658 mRNAs and 14,142 IncRNAs were extracted from each of the RNA information of the 1,109 breast cancer samples and the 113 normal breast samples according to the step S102. According to the conditions that | log2 (difference multiple) | > 0.5 and p < 0.05, a “DESeq2” toolkit was utilized to screen 7,903 differentially expressed IncRNAs in the breast cancer samples and the normal breast samples, wherein 6,065 high- expression IncRNAs existed in breast cancer tissue, 1,838 low-expression IncRNAs existed in the breast cancer tissue.

FIG. 3 was the differential expression distribution diagram of the IncRNAs of this embodiment. 210 autophagy mRNAs were extracted from mRNAs of the breast cancer samples according to 222 autophagy genes in the human autophagy database; then 197 differential IncRNAs which were related to the autophagy genes, had the square of the correlation coefficient R > 0.4 and the significance level P < 0.05 in the Pearson correlation analysis result were extracted through the Pearson analysis method according to the step S103; then 26 common IncRNAs were obtained from 197 autophagy-related IncRNAs and 1,146 IncRNAs in the GSE20,685 according to the step S104 and used for subsequent analysis; 5 IncRNAs closely related to the OS of the patient were identified from the 26 autophagy-related IncRNA in the training data set by using single-variable cox regression analysis according to the step S105, wherein the P value was less than 0.05. FIG. 4 showed the risk ratio information corresponding to the 5 IncRNAs; then step-by- step cox proportion risk analysis was performed on the 5 candidate IncRNAs according to the step S2, and an optimal model was obtained through an Akaike information criterion to avoid overfitting; and finally, prognosis was performed according to the optimal model in the step S3. Wherein, the optimal model was 3 autophagy-related IncRNA models.

FIG. 5 showed the risk ratio information corresponding to the 3 IncRNA models.

Table 1 showed detailed information of the 3 IncRNA models in the prognosis model. 8

Table 1 LU502513 LncRNA cents tie mera Pale USP30.AS1 -0.36 0.698 0.544-0.894 0.004 MIR205HG -0.144 0.866 0.775-0.968 0.011 LINCO01087 -0.12 0.887 0.801-0.983 0.022 The prognosis model risk score formula was as follows. Prognosis score Z = (-0.360 x USP30- AS1) + (-0.144 x MIR205HG) + (-0.120 X LINCO1087). The prognosis model only had 3 autophagy-related IncRNAs, so that the detection cost was reduced, the detection efficiency was improved, patients could benefit from autophagy treatment, and a theoretical basis was provided for diagnosis and treatment of breast cancer. In order to further evaluate the prediction capability of the model to breast cancer prognosis, the risk score of each patient was calculated according to a risk score formula in the embodiment, and the patients were divided into a high-risk group and a low-risk group according to the median value of the risk scores. Kaplan-Meier survival analysis and ROC curve (receiver operator characteristic curve) analysis were carried out on the breast cancer samples in the TCGA training group. The analysis results were shown in FIG. 6 and FIG. 7 respectively. FIG. 6 showed that the OS (overall survival) of the breast cancer samples in the high-risk group was lower than that of the breast cancer samples in the low-risk group. FIG. 7 showed that the area AUC of the signed risk score under the ROC curve was 0.841, which indicated that the risk score had a credible diagnosis value. FIG. 8 showed the risk scores of the prognosis characteristics of the low-risk group and the high-risk group, the survival state and the expression heat map of the breast cancer samples. In order to evaluate the reliability of prognosis characteristics in OS prediction of the breast cancer samples, the same analysis was carried out on the GSE20685 data set, the result showed that the OS of a high-risk scoring patient was lower than that of a low-risk scoring patient, and the survival analysis curves were shown in FIG. 9. The AUC value of the ROC curve was

0.811 as shown in FIG. 10, which indicated that it had credible diagnosis value. FIG. 11 showed the risk scores of the prognosis characteristics of the low-risk group and the high-risk group, the survival state and the expression heat map of the breast cancer samples. The analysis result was consistent with the training set. Finally, single-factor and multi-factor Cox regression analysis results showed that whether under the training data set or the test data set, the risk score of the model could eliminate the influence of clinical features (gender, age, clinical staging and TNM staging), and the prognosis of the breast cancer samples could be remarkably predicted. FIG. 12 showed the single-factor and multi-factor Cox regression analysis results of the TCGA training group according to one embodiment of the present invention. FIG. 13 showed the single-factor and multi-factor regression analysis results of the GSE20685 verification group according to one embodiment of the present invention. In the FIG. 12 and the FIG. 13, A represented the single- factor Cox regression analysis result, and B represented the multi-factor Cox regression analysis result. The above results proved that the model could be regarded as an independent prediction factor to predict the prognosis of the breast cancer samples. In addition, according to the 9 embodiment, the clinical value of the model in the breast cancer samples was further evaluated 4502513 determining the correlation between the IncRNAs and the clinical characteristics (age, gender, clinical staging and TNM staging) of the breast cancer samples. The result showed that the risk score of the older patient tended to be remarkably increased, which prompted that the elderly, the later period and lymphatic metastasis were possibly related to the progress of the breast cancer, as shown in the Table 2. Table 2 Risk score Clinical Sample size Standard characteristics Mean value tan ar t value P value deviation Age <65 665 1.459 1.05 -2.062 0.04 >65 243 1.622 1.063 Gender Female 897 1.501 1.052 -0.294 0.774 Male 11 1.623 1.371 Staging Stage I-11 691 1.407 1.023 -4.729 0 Stage III-IV 217 1.806 1.101

T T1-2 774 1.471 1.039 -2.057 0.041 T3-4 134 1.686 1.133

M MO 891 1.489 1.048 -2.454 0.026 M1 17 2.211 1.204

N NO 449 1.432 1.063 -1.987 0.047 N1-3 459 1.571 1.044 In order to further explore the potential biological behavior of the prognosis model in the breast cancer samples, the embodiment of the present invention also adopted a gene set enrichment analysis method to determine 11 genomes in total, wherein the nominal P value of the genomes was < 0.05, and the FDR (false positive rate) q value was < 0.25. The result showed that the differentially expressed genes between the high-risk group and the low-risk group were enriched in cancer-related and autophagy-related pathways. Wnt, TGF (transforming growth factor) B and other carcinogenic pathways were obviously enriched in the high-risk group, and it was also found that glucose metabolism pathways were obvious in the high-risk group, such as glycolysis/gluconeogenesis pathway and TCA circulation pathway. Significantly, the TGF-b signal pathway and the glycometabolism pathway were closely related to autophagy. As shown in FIG. 14, the GSEA result disclosed that the autophagy-related genes were beneficial to carcinogenic activation pathways and autophagy-related pathways, which could provide enough evidence for targeted treatment of breast cancer. 10

Based on the same inventive concept, one embodiment also provided a breast cancer prognost#/502513 evaluation system based on an autophagy-related IncRNA model. As shown in FIG. 15, the breast cancer prognosis evaluation system based on the autophagy-related IncRNA model comprised an autophagy-related IncRNA determination module 10, a prognosis model establishing module 11 and a prognosis module 12. The autophagy-related IncRNA determination module 10 was used for determining the autophagy-related IncRNA information according to the RNA information of the plurality of breast cancer samples and the RNA information of the plurality of normal breast samples. The prognosis model establishing module 11 was coupled with the autophagy-related IncRNA determination module 10 and was used for analyzing the autophagy-related IncRNA information and clinical data of the plurality of breast cancer samples, and establishing the autophagy-related IncRNA prognosis model.

The prognosis module 12 was coupled with the prognosis model establishing module 11 and was used for carrying out prognosis based on the autophagy-related IncRNA prognosis model.

Therefore, in this embodiment, the important significance of the IncRNAs and autophagy in breast cancer biology was considered, the autophagy-related IncRNA model was determined to predict prognosis of the breast cancer samples, a theoretical basis was provided for diagnosis and treatment of breast cancer, and compared with the prior art, the prognosis result was more accurate.

In order to more accurately extract the autophagy-related IncRNAs of the breast cancer samples, in a preferred embodiment of the present invention, the autophagy-related IncRNA determination module 10 was specifically used for extracting the RNA information and the clinical information of the plurality of breast cancer samples, and extracting the RNA information of the plurality of normal breast samples; extracting each IncRNA information and each mRNA information related to human autophagy genes from the RNA information of the plurality of breast cancer samples, extracting each IncRNA information from the RNA information of the plurality of normal breast samples, performing differential analysis on the IncRNA information of the plurality of breast cancer samples and each IncRNA information of the plurality of normal breast samples, and extracting each IncRNA information of which the differential expression value was within the preset range; analyzing the correlation between each IncRNA information of which the differential expression value was within the preset range and each mRNA information related to the human autophagy genes, and extracting all IncRNAs with the correlation meeting the preset condition; comparing all extracted IncRNAs with the correlation meeting the preset condition with all IncRNAs in the GSE20685 data set, and extracting the IncRNAs with the correlation meeting the preset condition and existing in the GSE20685 data set; analyzing the IncRNA with the correlation meeting the preset condition and existing in the GSE20685 data set, and extracting all IncRNAs related to prognosis and corresponding significance level values; and extracting all IncRNAs related to prognosis, wherein all extracted IncRNAs related to prognosis referred to the autophagy- related IncRNA information. Therefore, in the optional embodiment, the autophagy-related IncRNAs of the breast cancer samples could be extracted more accurately, thus the prognosis 11 accuracy and universality of breast cancer were further improved. LU502513 It should be noted that various change modes and specific embodiments of the breast cancer prognosis evaluation method based on the autophagy-related IncRNA model in the above embodiment were also applicable to the breast cancer prognosis evaluation system based on the autophagy-related IncRNA model in this embodiment. Through above detailed description of the breast cancer prognosis evaluation method based on the autophagy-related IncRNA model, those skilled in the art could clearly know an implementation method of the breast cancer prognosis evaluation system based on the autophagy-related IncRNA model in this embodiment. For the brevity of the specification, it was not repeated here.

Based on the same inventive concept, one embodiment further provided a storage medium which was applied to storage of computer executable instructions, wherein the computer executable instructions were used for executing the breast cancer prognosis evaluation method based on the autophagy-related IncRNA model in any one of the above embodiments.

Based on the same inventive concept, one embodiment further provided electronic equipment, which comprised at least one processor and a memory in communication connection with at least one processor, wherein the memory stored an instruction which could be executed by at least one processor, and the instruction was executed by at least one processor, so that at least one processor could execute the breast cancer prognosis evaluation method based on the autophagy-related IncRNA model in any one of the embodiments.

Those skilled in the art should understand that the embodiments of the present invention might be provided as a method, a system, or a computer program product. Accordingly, the present invention might take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Moreover, the present invention could adopt a form of a computer program product implemented on one or more computer available storage media (including but not limited to a disk memory, a CD-ROM, an optical memory and the like) containing computer available program codes.

The present invention was described by referring to the method, equipment (system) and a flow chart and/or a block diagram of the computer program product according to the embodiment of the present invention. It should be understood that each flow and/or block in the flow chart and/or the block chart and the combination of the flow and/or the block in the flow chart and/or the block chart could be achieved through the computer program instructions. The computer program instructions could be provided for the processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing equipment to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing equipment generated a device for achieving the functions specified in one or more flows in the flow chart and/or one or more blocks in the block chart.

The computer program instructions could also be stored in a computer readable storage which could guide the computer or other programmable data processing equipment to work in a specific 12 mode, thus the instructions stored in the computer readable storage generated a manufacturé&/502513 product comprising the instruction device, and the instruction device achieved the functions specified in one or more flows in the flow chart and/or one or more blocks in the block chart. The computer program instructions could also be loaded to the computer or other programmable data processing equipment, thus a series of operation steps were executed on the computer or other programmable equipment to generate a computer-implemented process, and the instructions executed on the computer or other programmable equipment provided the steps for achieving the functions specified in one or more flows in the flow chart and/or one or more blocks in the block chart.

The above descriptions of specific exemplary embodiments of the present invention were to achieve the purposes of description and illustration. These descriptions were not intended to limit the present invention to the precise form disclosed, and obviously many changes and variations were possible in light of the above instructions. The exemplary embodiments were chosen and described for the purpose of explaining certain principles and practical applications of the present invention, thereby to enable those skilled in the art to make and utilize various exemplary embodiments of the present invention, as well as various choices and modifications. The scope of the present invention was intended to be defined by the claims and their equivalents.

13

Claims

1. A breast cancer prognosis evaluation method based on an autophagy-related IncRNA model, applying to prognosis of breast cancer and comprising the following steps: determining autophagy-related IncRNA information according to RNA information of a plurality of breast cancer samples and RNA information of a plurality of normal breast samples; analyzing the autophagy-related IncRNA information and clinical information of the plurality of breast cancer samples, and establishing an autophagy-related IncRNA prognosis model; and carrying out prognosis based on the autophagy-related IncRNA prognosis model

2. The breast cancer prognosis evaluation method based on the autophagy-related IncRNA model according to claim 1, wherein the step of determining the autophagy-related IncRNA information according to the RNA information of the plurality of breast cancer samples and the RNA information of the plurality of normal breast samples comprises: extracting the RNA information and the clinical information of the plurality of breast cancer samples, and extracting the RNA information of the plurality of normal breast samples; extracting each IncRNA information and each mRNA information related to human autophagy genes from the RNA information of the plurality of breast cancer samples, extracting each IncRNA information from the RNA information of the plurality of normal breast samples, performing differential analysis on the IncRNA information of the plurality of breast cancer samples and each IncRNA information of the plurality of normal breast samples, and extracting each IncRNA information of which the differential expression value is within a preset range; analyzing the correlation between each IncRNA information of which the differential expression value is within the preset range and each mRNA information related to the human autophagy genes, and extracting all IncRNAs with the correlation meeting a preset condition; comparing all extracted IncRNAs with the correlation meeting the preset condition with all IncRNAs in a GSE20685 data set, and extracting the IncRNAs with the correlation meeting the preset condition and existing in the GSE20685 data set; and analyzing the IncRNAs with the correlation meeting the preset condition and existing in the GSE20685 data set, and extracting all IncRNAs related to prognosis, wherein the significance level value of all IncRNAs related to prognosis is less than a preset threshold value, and all IncRNAs related to prognosis refer to the autophagy-related IncRNA information.

3. The breast cancer prognosis evaluation method based on the autophagy-related IncRNA model according to claim 2, wherein the step of extracting the RNA information and the clinical information of the plurality of breast cancer samples comprises: 14 extracting the RNA information and the clinical information of the plurality of breast canck#/502513 samples from a TCGA database as training data of the IncRNA prognosis model.

4. The breast cancer prognosis evaluation method based on the autophagy-related IncRNA model according to claim 2, wherein the step of analyzing the correlation between each IncRNA information of which the differential expression value is within the preset range and each mRNA information related to the human autophagy genes, and extracting all IncRNAs with the correlation meeting the preset condition comprises: analyzing the correlation between each IncRNA information of which the differential expression value is within the preset range and each mRNA information related to the human autophagy genes through a Pearson correlation analysis method; and extracting the IncRNA of which the square of a correlation coefficient in the Pearson correlation analysis result is larger than a first threshold value and the significance level is less than a second threshold value.

5. The breast cancer prognosis evaluation method based on the autophagy-related IncRNA model according to claim 2, wherein the step of analyzing the IncRNA with the correlation meeting the preset condition and existing in the GSE20685 data set comprises: analyzing the IncRNA with the correlation meeting the preset condition and existing in the GSE20685 data set through a single-factor cox regression analysis method; and the step of analyzing the autophagy-related IncRNA information and the clinical data of the plurality of breast cancer samples comprises: analyzing all IncRNAs related to prognosis and the clinical data of the plurality of breast cancer samples through a step-by-step multi-factor cox proportional risk regression analysis method.

6. The breast cancer prognosis evaluation method based on the autophagy-related IncRNA model according to claim 1, wherein the autophagy-related IncRNA prognosis model comprises three IncRNAs related to prognosis of breast cancer, namely USP30-AS1, MIR205HG and LINCO1087.

7. The breast cancer prognosis evaluation method based on the autophagy-related IncRNA model according to claim 1, wherein the autophagy-related IncRNA prognosis model is: prognosis score Z = (-0.360 x USP30-AS1) + (-0.144 x MIR205HG) + (-0.120 x LINCO1087).

8. A breast cancer prognosis evaluation system based on an autophagy-related IncRNA model, applying to prognosis of breast cancer and comprising: an autophagy-related IncRNA determination module for determining autophagy-related IncRNA information according to RNA information of a plurality of breast cancer samples and RNA information of a plurality of normal breast samples; a prognosis model establishing module which is coupled with the autophagy-related IncRNA 15 determination module and used for analyzing the autophagy-related IncRNA information and th&/502513 clinical data of the plurality of breast cancer samples, and establishing an autophagy-related IncRNA prognosis model; and a prognosis module which is coupled with the prognosis model establishing module and used for carrying out prognosis based on the autophagy-related IncRNA prognosis model.

9. A storage medium, for storing computer executable instructions, wherein the computer executable instructions are used for executing the breast cancer prognosis evaluation method based on the autophagy-related IncRNA model according to any one of claims 1-7.

10. Electronic equipment, comprising at least one processor, and a memory in communication with at least one processor, wherein the memory stores an instruction which can be executed by at least one processor, and the instruction is executed by at least one processor, so that at least one processor can execute the breast cancer prognosis evaluation method based on the autophagy-related IncRNA model according to any one of claims 1-7. 16