Prediction of colon cancer prognosis and treatment based on immune-related lncRNAs and tumor microenvironment

Prediction of colon cancer prognosis and treatment based on immune-related lncRNAs and tumor microenvironment | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Prediction of colon cancer prognosis and treatment based on immune-related lncRNAs and tumor microenvironment yixuan wang, Yujie Qin, Shihong Zhou, Yifan Wu, Jianzheng he This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4006759/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Increasing evidence suggests that long non-coding RNAs (lncRNAs) are closely related to the development of tumors, and no exception is made in colon cancer, which plays a crucial role in the development, metastasis, and prognosis of colon cancer. In this study, we analyzed the relationship between immune-related genes and lncRNAs and the prognosis of colon cancer based on the tumor microenvironment, and further explored possible small molecule inhibitors in order to provide more directions for clinical medication. Method We used transcriptomic and clinical data from colon cancer patients from The Cancer Genome Atlas (TCGA). Using weighted gene co-expression network analysis(WGCNA) correlation analysis, the relationship between relevant clinical traits and prognosis was analyzed, and risk-related genes and lncRNAs were identified. Subsequently, we constructed a prognostic correlation model using LASSO regression and validated the model using univariate and multivariate Cox regression analysis. In addition, we performed correlation analyses of risk scores and characteristics for survival and prognosis. Results We found 147 immune genes and lncRNAs associated with prognosis by WGCNA analysis, and then used LASSO regression analysis and cross-validation to find 37 immune genes and lncRNAs corresponding to the points with the smallest errors for the construction of prognosis-related models, risk scoring was performed for each sample, and patients were divided into two groups according to the median value: low-risk group and high-risk group. Then differential analysis, survival analysis, prognostic analysis and bioinformatics analysis of tumor microenvironment were performed for genes and lncRNAs in the high and low risk groups to obtain differences in expression levels as well as risk scores in the high and low risk groups, which had a good predictive ability for the prognosis of colon cancer patients compared with other clinical biomarkers. By enrichment analysis, we found that these prognostically relevant genes were associated with multiple biological functions and multiple signaling pathways. In addition, small molecule drugs that can inhibit high-risk genes were found by analyzing genes in the CMAP database in high and low risk groups. Conclusion Our study systematically assessed the role of immune-related lncRNAs in the tumor microenvironment and colon cancer prognosis. Risk scores based on genes and lncRNAs can reflect the survival and prognosis of patients with colon cancer. Model validation also shows that the corresponding genes and lncRNAs can be used as reliable biomarkers for predicting the prognosis and treatment response of patients with colon cancer. colon cancer lncRNA immune analysis tumor microenvironment prognosis drug prediction Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 1. Introduction In 2020, the International Agency for Research on Cancer conducted a statistical update on the incidence and mortality of cancer in the world [ 1 ] . The survey found that, worldwide, colorectal cancer has become the third most common malignant tumor in men and the second in women tumor.Colon cancer is a common malignant tumor of the digestive tract that occurs in the colon, most commonly at the junction of the rectum and the sigmoid colon. According to the latest report of the Chinese Cancer Center, the incidence and death of colon cancer are increasing year by year, and the new cases of colon cancer in 2020 ranked second in the country. From 2008 to 2018, The incidence and death of colon cancer did not show a downward trend. [ 2 ] .Not only in my country,the incidence of colon cancer has remained high worldwide,from 2008 to 2018,the incidence and death of colon cancer did not show a downward trend [ 3 ] . The treatment methods for colon cancer include surgery/polypectomy, chemotherapy, radiotherapy and combination therapy, immunotherapy, etc [ 4 ] , and advanced treatment methods include gene therapy, cell therapy, targeted immunotherapy, etc, which are currently in a large number of research stages [ 5 ] .At present, the research progress of colon cancer treatment has been relatively in-depth, and the discovery of some new nanotechnology or targets provides a wider space for the diagnosis and treatment of colon cancer. For example, LBX2-AS1 is a prognostic factor for colon cancer, which can be adjusted by regulating miR-627-5p/RAC1/PI3K/AKT pathway to regulate colon cancer progression [ 6 ] ; PNCs (polymer nanocarriers) have also transformed colon cancer diagnosis and treatment, with a special focus on PNCs in improving cellular uptake, drug targeting and chemical The effect of co-administration of therapeutic agents [ 7 ] . At present, the common inspection and diagnosis of colon cancer relies on colonoscopy biopsy. However, due to the insidious onset of colon cancer, it is not easy to attract attention in the early stage, and it has progressed to the middle and late stages when it is discovered. At this time, it is particularly important for the prognosis of colon cancer [ 8 ] . The immune environment in the tumor microenvironment is thought to play an important role in cancer development, recurrence, and cancer response and prognosis to therapy [ 9 ] . Liang Yanling explored the infiltration pattern of immune cells in the microenvironment of colon cancer and its prognosis evaluation, and constructed a prognostic model to help clinical selection of potential targets for immunotherapy [ 10 ] .Long non-coding RNAs (lncRNAs) are a class of non-protein-coding transcripts involved in the regulation of gene expression in mammalian cells [ 11 ] and play an important role in the occurrence and development of tumors [ 12 ] , and its abnormal expression is associated with the pathogenesis, metastasis and prognosis of colon cancer [ 13 ] .A study conducted a comprehensive analysis and prognosis prediction of necrosis-related long non-coding RNA (lncRNA) immune infiltration in colon cancer patients, and found 6 necrosis-related lncRNAs that can be used for prognostic analysis [ 14 ] .Of course, some studies have found that lncRNAs play an important role in the immune microenvironment of COAD(colon adenocarcinoma) by constructing risk models and Gene Set Enrichment Analysis (GSEA), combined with polymerase chain reaction (qt-PCR) for verification, and have great potential to become prognostic biomarkers [ 15 , 16 ] . Bioinformatics is a new discipline formed by the combination of life science and computer science. It reveals the inherent information contained in large and complex biological data by comprehensively utilizing biology,computer science and information technology [ 17 ] .In recent years, bioinformatics methods have become more and more important in academic research, especially in the early prediction of some difficult clinical problems and the analysis of survival prognosis, which often have unexpected effects [ 18 , 19 ] . The Cancer Genome Atlas (TCGA) is a National Cancer Institute project to analyze cases across different tumor types using genomic platforms and make these raw and processed data available to all researchers [ 20 ] .Here, we focus on the use of bioinformatics, combined with the TCGA database to analyze the clinical data of colon cancer, through the annotation and screening of genes and lncRNAs related to survival, risk, prognosis, etc., to predict the small molecules of colon cancer therapeutic drugs, to provide new methods and ideas for its clinical treatment. 2. Materials and methods 2.1 Data download In TCGA ( https://www.cancer.gov/about-nci/organization/ccg/research/structural-genomics/tcga ) In the database, find the data download section "Access TCGA Data" (Fig. https://portal.gdc.cancer.gov/ ), 524 sample files could be obtained with "colon" and "TCGA" as qualifiers in Case and "transcriptome profiling" and Gene Expression Quantification "in Files, and all files were added to cart files, Manifest files and Cart files were downloaded, and then Ensembl (Fig. https://asia.ensembl.org/index.html ), the profile"human.Gtf ", sample file collation and id transformation were performed using perl language to obtain gene expression files in samples.Similarly, for clinical data download, only the qualifiers in Files need to be changed to "clinical" and "bcrxml", and the other conditions are consistent with transcriptome data, resulting in 461 clinical data files, and the file information is extracted using perl language to obtain the corresponding clinical data of each sample. 2.2 Collating and extracting transcriptome and clinical data We need to distinguish between mRNAs and lncRNAs in collated transcriptome data to obtain a matrix of the expression of mRNAs encoding proteins in samples. In IMMPORT (Fig. https://www.immport.org/home ), the database obtains the immune-related gene list file, combined with the previously obtained expression matrix, and extracts the immune-related gene and lncRNA expression in the sample using the R language toolkit "limma". The survival time, survival status, age, gender, grade and stage in the clinical data files were collated. 2.3 Analysis of WGCNA and Construction of Prognostic Model Installation from Bioconductor using Rx64 4.1.2 ( http://www.bioconductor.org/ ) R Language Toolkit for Open Source Websites "GO.db""PreprocessCore""impute""limma""WGCNA", etc., the clinical data and immune-related gene expression matrix were analyzed by formaldehyde gene co-expression network analysis (WGCNA) module to obtain survival-related data. Prognostic immune genes and lncRNAs were then searched by the R language toolkit "survival" and univariate Cox method, and genes with Pvalue < 0.05 were prognostic genes and lncRNAs, and HR values (hazard ratios) were visualized to draw forest plots. The samples were divided into Train group and Test group, and the model construction of lasso regression was performed, and the results were cross-validated, requiring R language packages such as "survival" caret "glmnet" survminer "timeROC". A good model was constructed for clinical correlation analysis, survival analysis, ROC curve, and independent prognosis analysis. 2.4 Risk gene differential analysis and enrichment The mean values of genes in the high and low risk groups were integrated using the R language limma" package, and genes were classified bounded by the size relationship between logFc and 1, which was divided into high-risk genes (logFc > 1) and low-risk genes (logFc < 1). The R language toolkit colorspacestringiggplot2' combined with the DAVID database (Fig. https://david.ncifcrf.gov/home.jsp ), and the String online analysis platform (Fig. https://cn.string-db.org/ ), genes related to prognosis were enriched and their enrichment on different functions was analyzed. 2.5 Small Molecule Screening Perl language combined GEO database platform( https://www.ncbi.nlm.nih.gov/geo/ ) files, the risk difference genes were analyzed, and up- and down-regulated id files were obtained after finding the gene probe id corresponding to the gene and imported into the cMAP database ( https://clue.io/ ), running to find relevant small molecules, drugs that inhibit high-risk gene expression were screened with Pvalue < 0.05 and enrichment fraction < 0 and in pubchem ( https://pubchem.ncbi.nlm.nih.gov/ ), the database finds small molecule related structures. 3. Result Weighted gene co-expression network analysis (WGCNA) is a systems biology method used to describe gene association patterns between different samples, which can be used to identify gene sets with highly synergistic changes, and identify candidate biomarker genes or therapeutic targets based on the interconnectivity of gene sets and the association between gene sets and phenotypes [ 21 ] . As an efficient systems biology approach, WGCNA can analyze a variety of RNA-seq data including lncRNAs [ 22 ] .LASSO is a modified form of least squares regression that penalizes complex models with a regularization parameter [ 23 ] ,selects the value with the smallest cross-validation error through cross-validation, and then refits the model with all data according to the obtained values [ 24 ] . 3.1 Immune-related genes and lncRNAs in colon cancer We extracted 2483 associated expressed genes in 42 normal colon tissues and 482 colon cancer tissues from the TCGA database, with ImmPort (Fig. https://www.immport.org/home ), immune-related gene co-expression screening, combined with WGCNA module analysis (Fig. 1 A – C), immune-related genes in colon cancer were selected by comparing survival time with survival status (p < 0.05), and the results were visualized using univariate cox analysis to obtain a forest plot as shown (Fig. 1 D), indicating 438 immunogenes associated with prognosis, and high-risk genes and low-risk genes were represented in red and green, respectively (HR > 1 represents high-risk genes, p < 0.05). Then the correlation test was performed with the "limma" toolkit in R language to obtain the pvalue values of the correlation coefficient and correlation test and read 1415 immune-related lncRNAs; univariate cox analysis was performed using these immune-related lncRNAs, and the expression of lncRNAs was compared with survival time and survival status using the R language toolkit to obtain 142 prognostically relevant lncRNAs, and high-risk lncRNAs and low-risk lncRNAs were represented in red and green, respectively (HR > 1 represented high-risk lncRNAs, p < 0.05), and finally statistical visualization analysis was performed with forest plots, as shown (Fig. 1 E). 3.2 Construction of a prognostic model 3.2.1 Lasso regression model Using the prognosis-related genes and lncRNAs found above, the corresponding two files were intersected to obtain the samples required for the construction of the model, which were divided into Train group and Test group (50% each), and the lasso regression model was constructed and cross-validated to find the point with the smallest cross-validation error, and the corresponding number was the number of genes and lncRNAs involved in the model construction, as shown in Table 1 , including 21 genes (BDNF, IFNE, VIP, TRIM27, FGF23, NRG1, CXCL11, ICAM2, PROCR, CD1B, IL17RB, FGF9, XCL2, CCRL2, PTN, LCK, SCG2, PDGFRA, COLF, CD1A, TAFA1) and 16 lncRNAs (AP001469.3, AC156455.1, AC010973.2, AC004080.1, ACK1-AS, AC002310.1, 2-DT, AC009237.14, AC027307.2, AC027307.2, AC01774, AC00105). Table 1 Genes and lncRNAs involved in model building Gene Coef AP001469.3 0.186074954 AC156455.1 0.304975337 AC010973.2 0.246343281 AC004080.1 0.159718963 PINK1-AS -0.143450017 AC002310.1 0.342866606 HK2-DT 0.179633959 AC009237.14 0.180750073 AC027307.2 0.411537684 AC073896.3 -0.788532462 LINC02474 0.337701959 AC005520.2 0.067915755 CDC37L1-DT 0.408901825 COLCA1 0.01913132 AC105219.1 0.145266851 AL031058.1 0.523665188 PDGFRA -0.060083356 TRIM27 0.161827811 TAFA1 -0.320900314 CCRL2 -0.060604378 BDNF 0.587576156 FGF23 0.210563605 FGF9 0.688932252 NRG1 -0.228610855 PTN -0.222152697 SCG2 0.24149009 VIP 0.086340209 IL17RB -0.004003965 CD1A -0.154711017 CD1B -0.462977356 PROCR -0.181863489 CXCL11 -0.009735265 XCL2 -0.600415459 MANF -0.232789694 IFNE 0.379144005 ICAM2 0.239593639 LCK 0.216932972 3.2.2 Difference analysis For whether there are differences in the expression of genes and lncRNAs involved in model construction between high and low risk groups, we use boxplots to indicate that the abscissa represents the genes and lncRNAs involved in model construction, the ordinate represents the corresponding expression levels, the blue represents the low-risk patients, the red represents the high-risk patients, * represents the difference in the expression of genes and lncRNAs in the patients in the high and low risk groups, and can visually see the difference in the expression of genes and lncRNAs between different groups and statistical significance, for example, the expression of CD1A, CD1B, PROCR, CXCL11, XCL2, MANF in the low-risk group was significantly higher than that in the high-risk group and statistically significant (* **), while the expression of AC002310.1, HK2 DT, AC009237.14, AC027307.2 in the high-risk group was significantly higher than that in the low-risk group, and there was also statistical significance (**-***). 3.2.3 Clinical correlation analysis We can judge the difference of clinical traits between high and low risk groups and patients by clinical correlation analysis between Train group and Test group. Chi-square test can find that age and gender are not different between high and low risk groups (p > 0.05), while grade and stage are significantly different between high and low risk groups (p < 0.05). As shown in Tables 1 , 2 , 3 . Table 2 Analysis of clinical relevance in the test group Covariates Risk Total High Low Pvalue age 65 138(60.79%) 78(66.1%) 60(55.05%) gender FEMALE 111(48.9%) 52(44.07%) 59(54.13%) 0.1669 gender MALE 116(51.1%) 66(55.93%) 50(45.87%) stage Stage I-II 137(60.35%) 59(50%) 78(71.56%) 0.0051 stage Stage III-IV 80(35.24%) 51(43.22%) 29(26.61%) stage unknow 10(4.41%) 8(6.78%) 2(1.83%) T T1-2 48(21.15%) 22(18.64%) 26(23.85%) 0.4644 T T3-4 177(77.97%) 94(79.66%) 83(76.15%) T unknow 2(0.88%) 2(1.69%) 0(0%) M M0 172(75.77%) 82(69.49%) 90(82.57%) 0.066 M M1 26(11.45%) 18(15.25%) 8(7.34%) M unknow 29(12.78%) 18(15.25%) 11(10.09%) N N0 145(63.88%) 65(55.08%) 80(73.39%) 0.0099 N N1-3 80(35.24%) 51(43.22%) 29(26.61%) N unknow 2(0.88%) 2(1.69%) 0(0%) Table 3 Analysis of clinical relevance in the train group Covariates Risk Total High Low Pvalue age 65 132(58.15%) 69(60.53%) 63(55.75%) gender FEMALE 106(46.7%) 53(46.49%) 53(46.9%) 1 gender MALE 121(53.3%) 61(53.51%) 60(53.1%) stage Stage I-II 117(51.54%) 44(38.6%) 73(64.6%) 2.00E-04 stage Stage III-IV 107(47.14%) 68(59.65%) 39(34.51%) stage unknow 3(1.32%) 2(1.75%) 1(0.88%) T T1-2 41(18.06%) 12(10.53%) 29(25.66%) 0.0052 T T3-4 186(81.94%) 102(89.47%) 84(74.34%) M M0 161(70.93%) 72(63.16%) 89(78.76%) 0.0425 M M1 37(16.3%) 24(21.05%) 13(11.5%) M unknow 29(12.78%) 18(15.79%) 11(9.73%) N N0 124(54.63%) 48(42.11%) 76(67.26%) 2.00E-04 N N1-3 103(45.37%) 66(57.89%) 37(32.74%) Table 4 Analysis of clinical relevance in all groups Covariates Risk Total High Low Pvalue age 65 270(59.47%) 147(63.36%) 123(55.41%) gender FEMALE 217(47.8%) 105(45.26%) 112(50.45%) 0.311 gender MALE 237(52.2%) 127(54.74%) 110(49.55%) stage Stage I-II 254(55.95%) 103(44.4%) 151(68.02%) 0 stage Stage III-IV 187(41.19%) 119(51.29%) 68(30.63%) stage unknow 13(2.86%) 10(4.31%) 3(1.35%) T T1-2 89(19.6%) 34(14.66%) 55(24.77%) 0.0107 T T3-4 363(79.96%) 196(84.48%) 167(75.23%) T unknow 2(0.44%) 2(0.86%) 0(0%) M M0 333(73.35%) 154(66.38%) 179(80.63%) 0.0046 M M1 63(13.88%) 42(18.1%) 21(9.46%) M unknow 58(12.78%) 36(15.52%) 22(9.91%) N N0 269(59.25%) 113(48.71%) 156(70.27%) 0 N N1-3 183(40.31%) 117(50.43%) 66(29.73%) N unknow 2(0.44%) 2(0.86%) 0(0%) 3.2.4 Survival analysis The survival rate of patients was analyzed, and the survival rate was represented by the abscissa representing the time (unit is) ordinate. It was found that the survival rate of patients showed a downward trend with the extension of time (Fig. 2 F-H). According to the median risk score, patients were divided into two groups: low risk group (blue) and high risk group (red). According to the risk curve of high and low risk groups, it could be seen that there was a statistically significant difference in survival between low risk group and high risk group (p < 0.05). 3.2.5 ROC curve The abscissa of the ROC curve represents the false positive rate (1-specificity), and the ordinate represents the true positive rate (sensitivity), and the larger the area under the curve (AUC) represents the higher accuracy of predicting the survival of patients through our model. As shown in Fig. 3 A- 3 C, the AUC of each group of patients was more than 0.5, representing that our model had some accuracy in predicting the prognosis of patients. 3.2.6 Risk Curve The abscissa of the risk curve represents the patient, the risk of the patient from left to right increases in turn, the ordinate represents the risk score, the patient is divided into high and low risk groups according to the median value of the risk score, green represents the low risk group, red represents the high risk group; the ordinate of the survival state diagram represents the survival time, the green point represents the surviving patient, the red point represents the dead patient, with the increase of the patient 's risk, the number of dead patients increases; the ordinate of the risk heat map represents the high and low expression of the gene in the patient, with the increase of the patient' s risk, the expression of the gene increases, it proves that this gene is a high risk gene, and vice versa, as shown in Fig. 3 D- 3 L, the expression of all genes and lncRNAs in the high and low risk groups can be visually seen. 3.2.7 Independent Prognostic Analysis By independent prognostic analysis we could see whether our constructed model could act as an independent prognostic factor independent of other clinical traits, including univariate and multivariate independent prognostic analysis. Univariate prognostic analysis is to compare each factor with survival time and survival status, multivariate prognostic analysis is to compare all factors with survival time and survival status at one time, visualize each group through forest plots and find that the pvalue values are less than 0.05 (Fig. 4 ), demonstrating that our model can be used as an independent prognostic factor. 3.3 The tumor microenvironment The tumor microenvironment (TME) plays a key role in tumorigenesis and is composed of different components, including tumor cells, stromal cells, and immune cells, where the relationship between each component involved in TME construction can be explored by exploring each Secreted or expressed factors of cells to explain. In cancer, stromal cells influence extracellular matrix (ECM) formation and tumor progression through a variety of mediators, and immune cells respond to tumor cells by causing cytotoxic or inflammatory responses [ 25 ] . 3.3.1 Differential analysis of tumor microenvironment Do stromal cells and immune cells in the tumor microenvironment differ between patients in the high and low risk groups? Grading stromal cells and immune cells by the R language toolkits "ggpubr" and "limma" revealed that stromal cells and immune cells were different between patients in the high and low risk groups (Fig. 5 A – 5 C), and the content of immune cells was higher in patients in the high risk group, with a more significant difference (p < 0.05). 3.3.2 Tumor Microenvironment Survival Analysis We visually analyzed the tumor microenvironment, with abscissa as the survival time and ordinate as the survival rate, and found that the survival rate of patients decreased over time. According to the scoring of tumor microenvironment, the patients were divided into high hit group and low hit group, and then whether there was a difference in survival between high and low hit groups was compared. It was found that although the survival of patients in low hit group was better than that in high hit group, there was no difference in stromal cells and immune cells between high and low hit groups (p > 0.05), as shown in Fig. 5 D- 5 F. 3.3.3 Infiltration and analysis of immune cells Use R language toolkit "e1071" "preprocessCore" "limma" and configuration file "ref.txt", as well as running"CIBERSORT"yielded the result of immune cell infiltration, that is, the expression of immune cells in each sample. Subsequently, the results of immune cell infiltration were visualized with the R language toolkit "limma" "ggpubr" "vioplot" "pheatmap" to obtain a histogram, heat map, and violin map of the expression of the corresponding 22 immune cells in the high and low risk groups (Fig. 5 G- 5 I). 3.4 GO and KEGG enrichment analysis Enrichment analysis of risk genes revealed that GO enrichment analysis mostly involved biological functions such as inflammatory response, cell differentiation, cell division, cell chemotaxis, signal transduction, and immune response, and KEGG enrichment mainly involved Ras signaling pathway, MAPK signaling pathway, PI3K-Akt signaling pathway, Rap1 signaling pathway, and calcium signaling pathway. See Fig. 6 . 3.5 Drug screening Through risk analysis, we can identify drugs that inhibit the expression of high-risk genes, thereby reducing the risk of patients and improving the survival rate of patients. Through the online drug prediction platform CMap (Fig. https://clue.io/ ), input our resulting highly expressed genes and lowly expressed genes, perform drug screening to obtain drug small molecules that inhibit the expression of high-risk genes, and screen the top eight classes of small molecules with pvalue for visualization, as shown in Figs. 7 , 8 , 9 . 4. Discussion Colon cancer is one of the leading causes of cancer deaths worldwide. The 5-year survival rate of early-stage patients is over 90%, and that of advanced-stage patients is only about 10% [ 26 ] . In addition, because early symptoms are generally ignored, even though many markers have been developed [ 27 , 28 ] , half of colon cancer patients are diagnosed in the middle and late stages [ 29 ] , combined with the greater risk of late metastases, the majority of patients have poorer survival and prognosis [ 30 ] .Therefore, more and more studies are inclined to the development and prognosis of colon cancer. Some studies have found that a significant down-regulation of miR-876-3p was observed in colon cancer tissues and cells. TNM stage is closely related to peripheral infiltration, and miR-876-3p can be used as an independent indicator related to patient prognosis [ 31 ] .Based on the analysis of the clinical data and transcriptional data of colon cancer patients, multiple genes related to the development and prognosis of colon cancer were mined to provide the basis for clinical guidance [ 32 , 33 ] . The tumor microenvironment is the key to the occurrence and development of tumors [ 34 ] . Stromal cells and immune cells are the main components of the tumor microenvironment. Stromal cells influence the formation of the extracellular matrix (ECM) and tumor progression through various mediators. Immune cells respond to tumor cells by causing cytotoxic or inflammatory responses [ 35 – 37 ] . Studies have examined genes that affect stromal, immune cell infiltration, and the way they affect the prognosis of colon cancer patients, and these genes have the potential to be prognostic markers for colon cancer patients [ 38 , 39 ] .Immune-related long non-coding ribonucleic acids (irlncRNAs) are potential prognostic factors for colon cancer, and related genes can improve the sensitivity of prognostic models [ 40 ] . A feature model can be constructed by pairing the corresponding lncRNAs to better predict survival and chemotherapy efficacy and prognosis of colon cancer patients [ 41 , 42 ] . Our research is mainly based on bioinformatics technologies and methods, combined with clinical data and transcriptome data of colon cancer patients in the database, and constructs a prognostic model with related immune genes and lncRNAs, and validates the prognostic model to confirm its feasibility and Sensitivity can be used as a prognostic factor independent of other factors. Since the included clinical data is limited to a tumor-related database, there may be some differences with the latest clinical data, and no experiments have been performed to verify the differences at the molecular expression and transcription levels, which can be further explored in follow-up studies. Declarations Author Contribution JZ He and MH Xiu contributed to conception and design of the study. YX Wang, YJ Qin, YF Wu and SH Zhou performed the analyzed data. YX Wang wrote the paper. All authors contributed to manuscript revision and read and approved the submitted version. References Sung H, Ferlay J, Siegel RL et al (2021) Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries[J]. CA: A Cancer Journal for Clinicians, 71(3) Zheng R, Zhang S, Zeng H et al (2022) Cancer incidence and mortality in China, 2016[J]. 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Front Immunol 13:864739 Zhang X, Zhang H, Shen B et al (2019) Chromogranin-A Expression as a Novel Biomarker for Early Diagnosis of Colon Cancer Patients[J]. Int J Mol Sci, 20(12) Qi L, Ding Y (2018) Screening of Differentiation-Specific Molecular Biomarkers for Colon Cancer[J]. Cell Physiol Biochem 46(6):2543–2550 Ma Z, Li Z, Wang H et al (2022) [Screening of serum oxysterol biomarkers for colon cancer by liquid chromatography-tandem mass spectrometry][J]. Se Pu 40(6):541–546 Xiao S, Liu X, Yuan L et al (2021) A Ferroptosis-Related lncRNAs Signature Predicts Prognosis and Therapeutic Response of Gastric Cancer[J]. Front Cell Dev Biol 9:736682 Hur K, Toiyama Y, Takahashi M et al (2013) MicroRNA-200c modulates epithelial-to-mesenchymal transition (EMT) in human colorectal cancer metastasis[J]. Gut 62(9):1315–1326 Ma H, Li M, Jia Z et al (2021) miR-876-3p suppresses the progression of colon cancer and correlates the prognosis of patients[J]. Exp Mol Pathol 122:104682 Zhou Y, Zhou YN, Liu SX et al (2021) Effects of PIM3 in prognosis of colon cancer[J]. Clin Transl Oncol 23(10):2163–2170 Lu J, Chen Q Transcriptome-based identification of molecular markers related to the development and prognosis of Colon cancer[J]. Nucleosides, Nucleotides & Nucleic Acids, 2021,40(11). Satilmis B, Sahin TT, Cicek E et al (2021) Hepatocellular Carcinoma Tumor Microenvironment and Its Implications in Terms of Anti-tumor Immunity: Future Perspectives for New Therapeutics[J]. J Gastrointest Cancer 52(4):1198–1205 Mun JY, Leem SH, Lee JH et al (2022) Dual Relationship Between Stromal Cells and Immune Cells in the Tumor Microenvironment[J]. Front Immunol 13:864739 Guo S, Deng CX (2018) Effect of Stromal Cells in Tumor Microenvironment on Metastasis Initiation[J]. Int J Biol Sci 14(14):2083–2093 Mao X, Xu J, Wang W et al (2021) Crosstalk between cancer-associated fibroblasts and immune cells in the tumor microenvironment: new findings and future perspectives[J]. Mol Cancer 20(1):131 Wu Y, Jia H, Zhou H et al (2022) Immune and stromal related genes in colon cancer: Analysis of tumour microenvironment based on the cancer genome atlas (TCGA) and gene expression omnibus (GEO) databases[J]. Scand J Immunol 95(2):e13119 Luo R, Guo W, Wang H (2021) A comprehensive analysis of tumor microenvironment-related genes in colon cancer[J]. Clin Transl Oncol 23(9):1769–1781 Xu M, Li Q, Zhang J et al (2022) Identification of Immune-Related lncRNA Pairs and Construction and Validation of a New Prognostic Signature of Colon Cancer[J]. Can J Gastroenterol Hepatol 2022:5827544 Liu S, Peng X, Wu X et al (2022) Construction of a new immune-related lncRNA model and prediction of treatment and survival prognosis of human colon cancer[J]. World J Surg Oncol 20(1):71 Chen Y, Zhang Y, Lu J et al (2022) Characteristics of Prognostic Programmed Cell Death-Related Long Noncoding RNAs Associated With Immune Infiltration and Therapeutic Responses to Colon Cancer[J]. Front Immunol 13:828243 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4006759","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":276443288,"identity":"e2131171-6033-4d4f-9bd8-ee7003124baa","order_by":0,"name":"yixuan wang","email":"","orcid":"","institution":"Gansu University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"yixuan","middleName":"","lastName":"wang","suffix":""},{"id":276443289,"identity":"9325144e-37f8-44a3-af24-926e371b49df","order_by":1,"name":"Yujie Qin","email":"","orcid":"","institution":"Gansu University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Yujie","middleName":"","lastName":"Qin","suffix":""},{"id":276443290,"identity":"2259e13d-422a-42ba-97e3-c8915d3f96ac","order_by":2,"name":"Shihong Zhou","email":"","orcid":"","institution":"Gansu University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Shihong","middleName":"","lastName":"Zhou","suffix":""},{"id":276443291,"identity":"c1c47ddf-24d0-4bd2-a394-5aa322dd8eb6","order_by":3,"name":"Yifan Wu","email":"","orcid":"","institution":"Gansu University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Yifan","middleName":"","lastName":"Wu","suffix":""},{"id":276443292,"identity":"fc5f6de5-a6b5-4764-bcda-a7c0c801aaf7","order_by":4,"name":"Jianzheng he","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4ElEQVRIiWNgGAWjYBACNvnHx398qLCR4+dvSHyQUFFDWAsfQ1qC5IwzacaSMw48Nnhw5hhhLXIMOQbSvG2HEw0OJD6TfNjCTITDGA4YGM5sA1rVcDitIrGBjYG/vTsBvxbGhoSED+ds8viZ29JuJO6QYZA4c3YDfi3MDAcOzihLK5ZsOAPUcoaNwUAil4AWNsbGZh62w4kbDuR/K0hsYyZCCw8zMzNPG0hLQhoDcVokgPZAAzlZIuHMMR6CfpGfwf+NARaVH39U1Mjxt/fi14IBeEhTPgpGwSgYBaMAKwAAyrlPUSx2sxoAAAAASUVORK5CYII=","orcid":"","institution":"Gansu University of Chinese Medicine","correspondingAuthor":true,"prefix":"","firstName":"Jianzheng","middleName":"","lastName":"he","suffix":""}],"badges":[],"createdAt":"2024-03-02 15:16:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4006759/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4006759/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":52169171,"identity":"c70f365e-2322-4225-b8cd-b0f3b92489bf","added_by":"auto","created_at":"2024-03-07 15:55:43","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":393577,"visible":true,"origin":"","legend":"\u003cp\u003eScreening of immune-related genes and lncRNAs in colon cancer.（A-C）WGCNA Module Analysis.（D-E）Forest map of prognosis-related genes and risk genes.\u003c/p\u003e","description":"","filename":"floatimage1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4006759/v1/bdc49c3ebedd34b75f934842.jpg"},{"id":52169164,"identity":"c3703b87-f309-4b95-8175-6bc59799c05e","added_by":"auto","created_at":"2024-03-07 15:55:42","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":455788,"visible":true,"origin":"","legend":"\u003cp\u003eConstruction of prognostic models and risk analysis.（A）LASSO regression.（B）Cross-validation.（C-E）Difference analysis.（F-H）Survival analysis.(Ns represents no significance; *p \u0026lt; .05; **p \u0026lt; .01;***p \u0026lt; .001).\u003c/p\u003e","description":"","filename":"floatimage2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4006759/v1/967b5695c77139258bdba7a0.jpg"},{"id":52170223,"identity":"43146095-550b-46c7-a948-fd3d926d300b","added_by":"auto","created_at":"2024-03-07 16:03:42","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":564827,"visible":true,"origin":"","legend":"\u003cp\u003ePrognostic Model Analysis.（A-C）ROC curve.（D-F）All patient risk curve + survival status map + risk heat map.（G-I）Train group risk curve + survival status map + risk heat map.（J-L）Test group risk curve + survival status map + risk heat map.\u003c/p\u003e","description":"","filename":"floatimage3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4006759/v1/7ee7b27287c0b901b524d1bc.jpg"},{"id":52170225,"identity":"e96ce773-0d8b-4d3f-ba65-70330ea16670","added_by":"auto","created_at":"2024-03-07 16:03:42","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":163993,"visible":true,"origin":"","legend":"\u003cp\u003eIndependent Prognostic Analysis.（A-B）Univariate and multivariate independent prognostic analysis in Test group.（C-D）Univariate and multivariate independent prognostic analysis in Train group.（E-F）Univariate and multivariate independent prognostic analysis for all patients.\u003c/p\u003e","description":"","filename":"floatimage4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4006759/v1/b20a7e69dc4d0a3402a9b0ab.jpg"},{"id":52169163,"identity":"858c4bbe-6adf-4609-a30a-d1acac4173fc","added_by":"auto","created_at":"2024-03-07 15:55:42","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":385777,"visible":true,"origin":"","legend":"\u003cp\u003eThe tumor microenvironment.（A-C）Differential analysis of tumor microenvironment.（D-F）Tumor Microenvironment Survival Analysis.（G-I）Immune cell infiltration analysis.\u003c/p\u003e","description":"","filename":"floatimage5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4006759/v1/89b0f48357609ef43bae65d8.jpg"},{"id":52169168,"identity":"9f907284-6613-42aa-9b21-7140ddc28a1e","added_by":"auto","created_at":"2024-03-07 15:55:42","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":288579,"visible":true,"origin":"","legend":"\u003cp\u003eEnrichment analysis.（A）GO Enrichment Analysis Histogram.（B）KEGG Enrichment Analysis Bar Chart.\u003c/p\u003e","description":"","filename":"floatimage6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4006759/v1/3aebde26c7c7c536081de314.jpg"},{"id":52169165,"identity":"c79e8b27-977b-492b-bcae-7f142952c0f9","added_by":"auto","created_at":"2024-03-07 15:55:42","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":166875,"visible":true,"origin":"","legend":"\u003cp\u003eDrug structure.（1a-1d）PLK_INHIBITOR.（2）CORTICOSTEROID_AGONIST.（3a-3c）SULFONYLUREA.\u003c/p\u003e","description":"","filename":"floatimage7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4006759/v1/6513b5317afc14aae4463f9c.jpg"},{"id":52169170,"identity":"c06501b1-0bd3-4074-938f-96df3a10d2a7","added_by":"auto","created_at":"2024-03-07 15:55:42","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":157529,"visible":true,"origin":"","legend":"\u003cp\u003eDrug structure.（4a-4d）BENZODIAZEPINE_RECEPTOR_ANTAGONIST.（5a-5c）LEUCINE_RICH_REPEAT_KINASE_INHIBITOR.\u003c/p\u003e","description":"","filename":"floatimage8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4006759/v1/1a6f0351dc7b300bf6ba566e.jpg"},{"id":52170719,"identity":"10b7349e-c980-4b0f-8cc2-7845c61f1187","added_by":"auto","created_at":"2024-03-07 16:11:42","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":189629,"visible":true,"origin":"","legend":"\u003cp\u003eDrug structure.（6a-6c）TGF_BETA_RECEPTOR_INHIBITOR.（7a-7e）REACTOME_REGULATION_OF_INSULIN_SECRETION_BY_ACETYLCHOLINE.（8a-8b）TYROSINASE_INHIBITOR\u003c/p\u003e","description":"","filename":"floatimage9.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4006759/v1/c4d0b6a11dfae22437f83a86.jpg"},{"id":52294374,"identity":"e5424aa8-b71d-4c5e-ae32-530de3f0fc4d","added_by":"auto","created_at":"2024-03-08 17:31:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1483601,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4006759/v1/431a306b-30cf-4b2e-b00c-2b0078edc456.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Prediction of colon cancer prognosis and treatment based on immune-related lncRNAs and tumor microenvironment","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eIn 2020, the International Agency for Research on Cancer conducted a statistical update on the incidence and mortality of cancer in the world\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. The survey found that, worldwide, colorectal cancer has become the third most common malignant tumor in men and the second in women tumor.Colon cancer is a common malignant tumor of the digestive tract that occurs in the colon, most commonly at the junction of the rectum and the sigmoid colon. According to the latest report of the Chinese Cancer Center, the incidence and death of colon cancer are increasing year by year, and the new cases of colon cancer in 2020 ranked second in the country. From 2008 to 2018, The incidence and death of colon cancer did not show a downward trend.\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e.Not only in my country,the incidence of colon cancer has remained high worldwide,from 2008 to 2018,the incidence and death of colon cancer did not show a downward trend\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. The treatment methods for colon cancer include surgery/polypectomy, chemotherapy, radiotherapy and combination therapy, immunotherapy, etc\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e, and advanced treatment methods include gene therapy, cell therapy, targeted immunotherapy, etc, which are currently in a large number of research stages\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e.At present, the research progress of colon cancer treatment has been relatively in-depth, and the discovery of some new nanotechnology or targets provides a wider space for the diagnosis and treatment of colon cancer. For example, LBX2-AS1 is a prognostic factor for colon cancer, which can be adjusted by regulating miR-627-5p/RAC1/PI3K/AKT pathway to regulate colon cancer progression\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e; PNCs (polymer nanocarriers) have also transformed colon cancer diagnosis and treatment, with a special focus on PNCs in improving cellular uptake, drug targeting and chemical The effect of co-administration of therapeutic agents\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAt present, the common inspection and diagnosis of colon cancer relies on colonoscopy biopsy. However, due to the insidious onset of colon cancer, it is not easy to attract attention in the early stage, and it has progressed to the middle and late stages when it is discovered. At this time, it is particularly important for the prognosis of colon cancer\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe immune environment in the tumor microenvironment is thought to play an important role in cancer development, recurrence, and cancer response and prognosis to therapy\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. Liang Yanling explored the infiltration pattern of immune cells in the microenvironment of colon cancer and its prognosis evaluation, and constructed a prognostic model to help clinical selection of potential targets for immunotherapy\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e.Long non-coding RNAs (lncRNAs) are a class of non-protein-coding transcripts involved in the regulation of gene expression in mammalian cells\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e and play an important role in the occurrence and development of tumors\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e, and its abnormal expression is associated with the pathogenesis, metastasis and prognosis of colon cancer\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e.A study conducted a comprehensive analysis and prognosis prediction of necrosis-related long non-coding RNA (lncRNA) immune infiltration in colon cancer patients, and found 6 necrosis-related lncRNAs that can be used for prognostic analysis\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e.Of course, some studies have found that lncRNAs play an important role in the immune microenvironment of COAD(colon adenocarcinoma) by constructing risk models and Gene Set Enrichment Analysis (GSEA), combined with polymerase chain reaction (qt-PCR) for verification, and have great potential to become prognostic biomarkers\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eBioinformatics is a new discipline formed by the combination of life science and computer science. It reveals the inherent information contained in large and complex biological data by comprehensively utilizing biology,computer science and information technology\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e.In recent years, bioinformatics methods have become more and more important in academic research, especially in the early prediction of some difficult clinical problems and the analysis of survival prognosis, which often have unexpected effects\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. The Cancer Genome Atlas (TCGA) is a National Cancer Institute project to analyze cases across different tumor types using genomic platforms and make these raw and processed data available to all researchers\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e.Here, we focus on the use of bioinformatics, combined with the TCGA database to analyze the clinical data of colon cancer, through the annotation and screening of genes and lncRNAs related to survival, risk, prognosis, etc., to predict the small molecules of colon cancer therapeutic drugs, to provide new methods and ideas for its clinical treatment.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Data download\u003c/h2\u003e \u003cp\u003eIn TCGA ( \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cancer.gov/about-nci/organization/ccg/research/structural-genomics/tcga\u003c/span\u003e\u003cspan address=\"https://www.cancer.gov/about-nci/organization/ccg/research/structural-genomics/tcga\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) In the database, find the \u003cspan refid=\"Sec3\" class=\"InternalRef\"\u003edata download\u003c/span\u003e section \"Access TCGA Data\" (Fig. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://portal.gdc.cancer.gov/\u003c/span\u003e\u003cspan address=\"https://portal.gdc.cancer.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), 524 sample files could be obtained with \"colon\" and \"TCGA\" as qualifiers in Case and \"transcriptome profiling\" and Gene Expression Quantification \"in Files, and all files were added to cart files, Manifest files and Cart files were downloaded, and then Ensembl (Fig. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://asia.ensembl.org/index.html\u003c/span\u003e\u003cspan address=\"https://asia.ensembl.org/index.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), the profile\"human.Gtf \", sample file collation and id transformation were performed using perl language to obtain gene expression files in samples.Similarly, for clinical data download, only the qualifiers in Files need to be changed to \"clinical\" and \"bcrxml\", and the other conditions are consistent with transcriptome data, resulting in 461 clinical data files, and the file information is extracted using perl language to obtain the corresponding clinical data of each sample.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Collating and extracting transcriptome and clinical data\u003c/h2\u003e \u003cp\u003eWe need to distinguish between mRNAs and lncRNAs in collated transcriptome data to obtain a matrix of the expression of mRNAs encoding proteins in samples. In IMMPORT (Fig. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.immport.org/home\u003c/span\u003e\u003cspan address=\"https://www.immport.org/home\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), the database obtains the immune-related gene list file, combined with the previously obtained expression matrix, and extracts the immune-related gene and lncRNA expression in the sample using the R language toolkit \"limma\". The survival time, survival status, age, gender, grade and stage in the clinical data files were collated.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Analysis of WGCNA and Construction of Prognostic Model\u003c/h2\u003e \u003cp\u003eInstallation from Bioconductor using Rx64 4.1.2 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.bioconductor.org/\u003c/span\u003e\u003cspan address=\"http://www.bioconductor.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) R Language Toolkit for Open Source Websites \"GO.db\"\"PreprocessCore\"\"impute\"\"limma\"\"WGCNA\", etc., the clinical data and immune-related gene expression matrix were analyzed by formaldehyde gene co-expression network analysis (WGCNA) module to obtain survival-related data. Prognostic immune genes and lncRNAs were then searched by the R language toolkit \"survival\" and univariate Cox method, and genes with Pvalue\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were prognostic genes and lncRNAs, and HR values (hazard ratios) were visualized to draw forest plots. The samples were divided into Train group and Test group, and the model construction of lasso regression was performed, and the results were cross-validated, requiring R language packages such as \"survival\" caret \"glmnet\" survminer \"timeROC\". A good model was constructed for clinical correlation analysis, survival analysis, ROC curve, and independent prognosis analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Risk gene differential analysis and enrichment\u003c/h2\u003e \u003cp\u003eThe mean values of genes in the high and low risk groups were integrated using the R language limma\" package, and genes were classified bounded by the size relationship between logFc and 1, which was divided into high-risk genes (logFc\u0026thinsp;\u0026gt;\u0026thinsp;1) and low-risk genes (logFc\u0026thinsp;\u0026lt;\u0026thinsp;1). The R language toolkit colorspacestringiggplot2' combined with the DAVID database (Fig. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://david.ncifcrf.gov/home.jsp\u003c/span\u003e\u003cspan address=\"https://david.ncifcrf.gov/home.jsp\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), and the String online analysis platform (Fig. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cn.string-db.org/\u003c/span\u003e\u003cspan address=\"https://cn.string-db.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), genes related to prognosis were enriched and their enrichment on different functions was analyzed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Small Molecule Screening\u003c/h2\u003e \u003cp\u003ePerl language combined GEO database platform(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/geo/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/geo/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) files, the risk difference genes were analyzed, and up- and down-regulated id files were obtained after finding the gene probe id corresponding to the gene and imported into the cMAP database ( \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://clue.io/\u003c/span\u003e\u003cspan address=\"https://clue.io/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), running to find relevant small molecules, drugs that inhibit high-risk gene expression were screened with Pvalue\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and enrichment fraction\u0026thinsp;\u0026lt;\u0026thinsp;0 and in pubchem ( \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubchem.ncbi.nlm.nih.gov/\u003c/span\u003e\u003cspan address=\"https://pubchem.ncbi.nlm.nih.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), the database finds small molecule related structures.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Result","content":"\u003cp\u003eWeighted gene co-expression network analysis (WGCNA) is a systems biology method used to describe gene association patterns between different samples, which can be used to identify gene sets with highly synergistic changes, and identify candidate biomarker genes or therapeutic targets based on the interconnectivity of gene sets and the association between gene sets and phenotypes\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. As an efficient systems biology approach, WGCNA can analyze a variety of RNA-seq data including lncRNAs\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e.LASSO is a modified form of least squares regression that penalizes complex models with a regularization parameter\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e,selects the value with the smallest cross-validation error through cross-validation, and then refits the model with all data according to the obtained values\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Immune-related genes and lncRNAs in colon cancer\u003c/h2\u003e \u003cp\u003eWe extracted 2483 associated expressed genes in 42 normal colon tissues and 482 colon cancer tissues from the TCGA database, with ImmPort (Fig. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.immport.org/home\u003c/span\u003e\u003cspan address=\"https://www.immport.org/home\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), immune-related gene co-expression screening, combined with WGCNA module analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA \u0026ndash; C), immune-related genes in colon cancer were selected by comparing survival time with survival status (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and the results were visualized using univariate cox analysis to obtain a forest plot as shown (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD), indicating 438 immunogenes associated with prognosis, and high-risk genes and low-risk genes were represented in red and green, respectively (HR\u0026thinsp;\u0026gt;\u0026thinsp;1 represents high-risk genes, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Then the correlation test was performed with the \"limma\" toolkit in R language to obtain the pvalue values of the correlation coefficient and correlation test and read 1415 immune-related lncRNAs; univariate cox analysis was performed using these immune-related lncRNAs, and the expression of lncRNAs was compared with survival time and survival status using the R language toolkit to obtain 142 prognostically relevant lncRNAs, and high-risk lncRNAs and low-risk lncRNAs were represented in red and green, respectively (HR\u0026thinsp;\u0026gt;\u0026thinsp;1 represented high-risk lncRNAs, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and finally statistical visualization analysis was performed with forest plots, as shown (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Construction of a prognostic model\u003c/h2\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e3.2.1 Lasso regression model\u003c/h2\u003e \u003cp\u003eUsing the prognosis-related genes and lncRNAs found above, the corresponding two files were intersected to obtain the samples required for the construction of the model, which were divided into Train group and Test group (50% each), and the lasso regression model was constructed and cross-validated to find the point with the smallest cross-validation error, and the corresponding number was the number of genes and lncRNAs involved in the model construction, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, including 21 genes (BDNF, IFNE, VIP, TRIM27, FGF23, NRG1, CXCL11, ICAM2, PROCR, CD1B, IL17RB, FGF9, XCL2, CCRL2, PTN, LCK, SCG2, PDGFRA, COLF, CD1A, TAFA1) and 16 lncRNAs (AP001469.3, AC156455.1, AC010973.2, AC004080.1, ACK1-AS, AC002310.1, 2-DT, AC009237.14, AC027307.2, AC027307.2, AC01774, AC00105).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGenes and lncRNAs involved in model building\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoef\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAP001469.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.186074954\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAC156455.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.304975337\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAC010973.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.246343281\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAC004080.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.159718963\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePINK1-AS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.143450017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAC002310.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.342866606\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHK2-DT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.179633959\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAC009237.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.180750073\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAC027307.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.411537684\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAC073896.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.788532462\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLINC02474\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.337701959\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAC005520.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.067915755\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCDC37L1-DT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.408901825\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCOLCA1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.01913132\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAC105219.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.145266851\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAL031058.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.523665188\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePDGFRA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.060083356\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTRIM27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.161827811\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTAFA1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.320900314\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCCRL2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.060604378\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBDNF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.587576156\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFGF23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.210563605\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFGF9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.688932252\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNRG1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.228610855\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePTN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.222152697\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSCG2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.24149009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVIP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.086340209\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIL17RB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.004003965\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD1A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.154711017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD1B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.462977356\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePROCR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.181863489\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCXCL11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.009735265\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eXCL2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.600415459\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMANF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.232789694\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIFNE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.379144005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eICAM2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.239593639\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLCK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.216932972\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e3.2.2 Difference analysis\u003c/h2\u003e \u003cp\u003eFor whether there are differences in the expression of genes and lncRNAs involved in model construction between high and low risk groups, we use boxplots to indicate that the abscissa represents the genes and lncRNAs involved in model construction, the ordinate represents the corresponding expression levels, the blue represents the low-risk patients, the red represents the high-risk patients, * represents the difference in the expression of genes and lncRNAs in the patients in the high and low risk groups, and can visually see the difference in the expression of genes and lncRNAs between different groups and statistical significance, for example, the expression of CD1A, CD1B, PROCR, CXCL11, XCL2, MANF in the low-risk group was significantly higher than that in the high-risk group and statistically significant (* **), while the expression of AC002310.1, HK2 DT, AC009237.14, AC027307.2 in the high-risk group was significantly higher than that in the low-risk group, and there was also statistical significance (**-***).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e3.2.3 Clinical correlation analysis\u003c/h2\u003e \u003cp\u003eWe can judge the difference of clinical traits between high and low risk groups and patients by clinical correlation analysis between Train group and Test group. Chi-square test can find that age and gender are not different between high and low risk groups (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05), while grade and stage are significantly different between high and low risk groups (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). As shown in Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e,\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e,\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAnalysis of clinical relevance in the test group\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCovariates\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRisk\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePvalue\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;=65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e89(39.21%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40(33.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e49(44.95%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.1168\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e138(60.79%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e78(66.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e60(55.05%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003egender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFEMALE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e111(48.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e52(44.07%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e59(54.13%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.1669\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003egender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMALE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e116(51.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e66(55.93%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e50(45.87%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003estage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStage I-II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e137(60.35%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e59(50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e78(71.56%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0051\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003estage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStage III-IV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e80(35.24%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e51(43.22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29(26.61%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003estage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eunknow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10(4.41%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8(6.78%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2(1.83%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT1-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e48(21.15%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22(18.64%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26(23.85%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.4644\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT3-4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e177(77.97%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e94(79.66%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e83(76.15%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eunknow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2(0.88%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2(1.69%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0(0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e172(75.77%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e82(69.49%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e90(82.57%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.066\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26(11.45%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18(15.25%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8(7.34%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eunknow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29(12.78%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18(15.25%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11(10.09%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e145(63.88%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e65(55.08%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e80(73.39%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0099\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN1-3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e80(35.24%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e51(43.22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29(26.61%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eunknow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2(0.88%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2(1.69%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0(0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAnalysis of clinical relevance in the train group\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCovariates\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRisk\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePvalue\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;=65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e95(41.85%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e45(39.47%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e50(44.25%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.5522\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e132(58.15%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e69(60.53%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e63(55.75%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003egender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFEMALE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e106(46.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e53(46.49%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e53(46.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003egender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMALE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e121(53.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e61(53.51%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e60(53.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003estage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStage I-II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e117(51.54%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e44(38.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e73(64.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.00E-04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003estage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStage III-IV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e107(47.14%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e68(59.65%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e39(34.51%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003estage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eunknow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3(1.32%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2(1.75%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1(0.88%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT1-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e41(18.06%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12(10.53%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e29(25.66%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0052\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT3-4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e186(81.94%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e102(89.47%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e84(74.34%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e161(70.93%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e72(63.16%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e89(78.76%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0425\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e37(16.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24(21.05%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13(11.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eunknow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29(12.78%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18(15.79%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11(9.73%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e124(54.63%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e48(42.11%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e76(67.26%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.00E-04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN1-3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e103(45.37%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e66(57.89%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e37(32.74%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAnalysis of clinical relevance in all groups\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCovariates\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRisk\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePvalue\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;=65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e184(40.53%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e85(36.64%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e99(44.59%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.103\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e270(59.47%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e147(63.36%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e123(55.41%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003egender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFEMALE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e217(47.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e105(45.26%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e112(50.45%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.311\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003egender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMALE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e237(52.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e127(54.74%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e110(49.55%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003estage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStage I-II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e254(55.95%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e103(44.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e151(68.02%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003estage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStage III-IV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e187(41.19%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e119(51.29%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e68(30.63%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003estage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eunknow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13(2.86%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10(4.31%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3(1.35%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT1-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e89(19.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e34(14.66%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e55(24.77%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0107\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT3-4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e363(79.96%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e196(84.48%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e167(75.23%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eunknow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2(0.44%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2(0.86%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0(0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e333(73.35%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e154(66.38%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e179(80.63%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0046\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e63(13.88%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e42(18.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21(9.46%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eunknow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e58(12.78%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e36(15.52%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22(9.91%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e269(59.25%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e113(48.71%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e156(70.27%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN1-3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e183(40.31%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e117(50.43%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e66(29.73%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eunknow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2(0.44%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2(0.86%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0(0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e3.2.4 Survival analysis\u003c/h2\u003e \u003cp\u003eThe survival rate of patients was analyzed, and the survival rate was represented by the abscissa representing the time (unit is) ordinate. It was found that the survival rate of patients showed a downward trend with the extension of time (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF-H). According to the median risk score, patients were divided into two groups: low risk group (blue) and high risk group (red). According to the risk curve of high and low risk groups, it could be seen that there was a statistically significant difference in survival between low risk group and high risk group (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003e3.2.5 ROC curve\u003c/h2\u003e \u003cp\u003eThe abscissa of the ROC curve represents the false positive rate (1-specificity), and the ordinate represents the true positive rate (sensitivity), and the larger the area under the curve (AUC) represents the higher accuracy of predicting the survival of patients through our model. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA-\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC, the AUC of each group of patients was more than 0.5, representing that our model had some accuracy in predicting the prognosis of patients.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003e3.2.6 Risk Curve\u003c/h2\u003e \u003cp\u003eThe abscissa of the risk curve represents the patient, the risk of the patient from left to right increases in turn, the ordinate represents the risk score, the patient is divided into high and low risk groups according to the median value of the risk score, green represents the low risk group, red represents the high risk group; the ordinate of the survival state diagram represents the survival time, the green point represents the surviving patient, the red point represents the dead patient, with the increase of the patient 's risk, the number of dead patients increases; the ordinate of the risk heat map represents the high and low expression of the gene in the patient, with the increase of the patient' s risk, the expression of the gene increases, it proves that this gene is a high risk gene, and vice versa, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD-\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eL, the expression of all genes and lncRNAs in the high and low risk groups can be visually seen.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e \u003ch2\u003e3.2.7 Independent Prognostic Analysis\u003c/h2\u003e \u003cp\u003eBy independent prognostic analysis we could see whether our constructed model could act as an independent prognostic factor independent of other clinical traits, including univariate and multivariate independent prognostic analysis. Univariate prognostic analysis is to compare each factor with survival time and survival status, multivariate prognostic analysis is to compare all factors with survival time and survival status at one time, visualize each group through forest plots and find that the pvalue values are less than 0.05 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), demonstrating that our model can be used as an independent prognostic factor.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.3 The tumor microenvironment\u003c/h2\u003e \u003cp\u003eThe tumor microenvironment (TME) plays a key role in tumorigenesis and is composed of different components, including tumor cells, stromal cells, and immune cells, where the relationship between each component involved in TME construction can be explored by exploring each Secreted or expressed factors of cells to explain. In cancer, stromal cells influence extracellular matrix (ECM) formation and tumor progression through a variety of mediators, and immune cells respond to tumor cells by causing cytotoxic or inflammatory responses\u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e \u003ch2\u003e3.3.1 Differential analysis of tumor microenvironment\u003c/h2\u003e \u003cp\u003eDo stromal cells and immune cells in the tumor microenvironment differ between patients in the high and low risk groups? Grading stromal cells and immune cells by the R language toolkits \"ggpubr\" and \"limma\" revealed that stromal cells and immune cells were different between patients in the high and low risk groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA \u0026ndash; \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC), and the content of immune cells was higher in patients in the high risk group, with a more significant difference (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section3\"\u003e \u003ch2\u003e3.3.2 Tumor Microenvironment Survival Analysis\u003c/h2\u003e \u003cp\u003eWe visually analyzed the tumor microenvironment, with abscissa as the survival time and ordinate as the survival rate, and found that the survival rate of patients decreased over time. According to the scoring of tumor microenvironment, the patients were divided into high hit group and low hit group, and then whether there was a difference in survival between high and low hit groups was compared. It was found that although the survival of patients in low hit group was better than that in high hit group, there was no difference in stromal cells and immune cells between high and low hit groups (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05), as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD-\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eF.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section3\"\u003e \u003ch2\u003e3.3.3 Infiltration and analysis of immune cells\u003c/h2\u003e \u003cp\u003eUse R language toolkit \"e1071\" \"preprocessCore\" \"limma\" and configuration file \"ref.txt\", as well as running\"CIBERSORT\"yielded the result of immune cell infiltration, that is, the expression of immune cells in each sample. Subsequently, the results of immune cell infiltration were visualized with the R language toolkit \"limma\" \"ggpubr\" \"vioplot\" \"pheatmap\" to obtain a histogram, heat map, and violin map of the expression of the corresponding 22 immune cells in the high and low risk groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eG-\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eI).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e3.4 GO and KEGG enrichment analysis\u003c/h2\u003e \u003cp\u003eEnrichment analysis of risk genes revealed that GO enrichment analysis mostly involved biological functions such as inflammatory response, cell differentiation, cell division, cell chemotaxis, signal transduction, and immune response, and KEGG enrichment mainly involved Ras signaling pathway, MAPK signaling pathway, PI3K-Akt signaling pathway, Rap1 signaling pathway, and calcium signaling pathway. See Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Drug screening\u003c/h2\u003e \u003cp\u003eThrough risk analysis, we can identify drugs that inhibit the expression of high-risk genes, thereby reducing the risk of patients and improving the survival rate of patients. Through the online drug prediction platform CMap (Fig. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://clue.io/\u003c/span\u003e\u003cspan address=\"https://clue.io/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), input our resulting highly expressed genes and lowly expressed genes, perform drug screening to obtain drug small molecules that inhibit the expression of high-risk genes, and screen the top eight classes of small molecules with pvalue for visualization, as shown in Figs.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e,\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e,\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eColon cancer is one of the leading causes of cancer deaths worldwide. The 5-year survival rate of early-stage patients is over 90%, and that of advanced-stage patients is only about 10%\u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. In addition, because early symptoms are generally ignored, even though many markers have been developed\u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e, half of colon cancer patients are diagnosed in the middle and late stages\u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e, combined with the greater risk of late metastases, the majority of patients have poorer survival and prognosis\u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e.Therefore, more and more studies are inclined to the development and prognosis of colon cancer. Some studies have found that a significant down-regulation of miR-876-3p was observed in colon cancer tissues and cells. TNM stage is closely related to peripheral infiltration, and miR-876-3p can be used as an independent indicator related to patient prognosis\u003csup\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e.Based on the analysis of the clinical data and transcriptional data of colon cancer patients, multiple genes related to the development and prognosis of colon cancer were mined to provide the basis for clinical guidance\u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe tumor microenvironment is the key to the occurrence and development of tumors\u003csup\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e. Stromal cells and immune cells are the main components of the tumor microenvironment. Stromal cells influence the formation of the extracellular matrix (ECM) and tumor progression through various mediators. Immune cells respond to tumor cells by causing cytotoxic or inflammatory responses\u003csup\u003e[\u003cspan additionalcitationids=\"CR36\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/sup\u003e. Studies have examined genes that affect stromal, immune cell infiltration, and the way they affect the prognosis of colon cancer patients, and these genes have the potential to be prognostic markers for colon cancer patients\u003csup\u003e[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/sup\u003e.Immune-related long non-coding ribonucleic acids (irlncRNAs) are potential prognostic factors for colon cancer, and related genes can improve the sensitivity of prognostic models\u003csup\u003e[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]\u003c/sup\u003e. A feature model can be constructed by pairing the corresponding lncRNAs to better predict survival and chemotherapy efficacy and prognosis of colon cancer patients\u003csup\u003e[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eOur research is mainly based on bioinformatics technologies and methods, combined with clinical data and transcriptome data of colon cancer patients in the database, and constructs a prognostic model with related immune genes and lncRNAs, and validates the prognostic model to confirm its feasibility and Sensitivity can be used as a prognostic factor independent of other factors. Since the included clinical data is limited to a tumor-related database, there may be some differences with the latest clinical data, and no experiments have been performed to verify the differences at the molecular expression and transcription levels, which can be further explored in follow-up studies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eJZ He and MH Xiu contributed to conception and design of the study. YX Wang, YJ Qin, YF Wu and SH Zhou performed the analyzed data. YX Wang wrote the paper. All authors contributed to manuscript revision and read and approved the submitted version.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSung H, Ferlay J, Siegel RL et al (2021) Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries[J]. 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Front Immunol 13:828243\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"colon cancer, lncRNA, immune analysis, tumor microenvironment, prognosis, drug prediction","lastPublishedDoi":"10.21203/rs.3.rs-4006759/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4006759/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eIncreasing evidence suggests that long non-coding RNAs (lncRNAs) are closely related to the development of tumors, and no exception is made in colon cancer, which plays a crucial role in the development, metastasis, and prognosis of colon cancer. In this study, we analyzed the relationship between immune-related genes and lncRNAs and the prognosis of colon cancer based on the tumor microenvironment, and further explored possible small molecule inhibitors in order to provide more directions for clinical medication.\u003c/p\u003e\u003ch2\u003eMethod\u003c/h2\u003e \u003cp\u003eWe used transcriptomic and clinical data from colon cancer patients from The Cancer Genome Atlas (TCGA). Using weighted gene co-expression network analysis(WGCNA) correlation analysis, the relationship between relevant clinical traits and prognosis was analyzed, and risk-related genes and lncRNAs were identified. Subsequently, we constructed a prognostic correlation model using LASSO regression and validated the model using univariate and multivariate Cox regression analysis. In addition, we performed correlation analyses of risk scores and characteristics for survival and prognosis.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eWe found 147 immune genes and lncRNAs associated with prognosis by WGCNA analysis, and then used LASSO regression analysis and cross-validation to find 37 immune genes and lncRNAs corresponding to the points with the smallest errors for the construction of prognosis-related models, risk scoring was performed for each sample, and patients were divided into two groups according to the median value: low-risk group and high-risk group. Then differential analysis, survival analysis, prognostic analysis and bioinformatics analysis of tumor microenvironment were performed for genes and lncRNAs in the high and low risk groups to obtain differences in expression levels as well as risk scores in the high and low risk groups, which had a good predictive ability for the prognosis of colon cancer patients compared with other clinical biomarkers. By enrichment analysis, we found that these prognostically relevant genes were associated with multiple biological functions and multiple signaling pathways. In addition, small molecule drugs that can inhibit high-risk genes were found by analyzing genes in the CMAP database in high and low risk groups.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eOur study systematically assessed the role of immune-related lncRNAs in the tumor microenvironment and colon cancer prognosis. Risk scores based on genes and lncRNAs can reflect the survival and prognosis of patients with colon cancer. Model validation also shows that the corresponding genes and lncRNAs can be used as reliable biomarkers for predicting the prognosis and treatment response of patients with colon cancer.\u003c/p\u003e","manuscriptTitle":"Prediction of colon cancer prognosis and treatment based on immune-related lncRNAs and tumor microenvironment","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-07 15:55:38","doi":"10.21203/rs.3.rs-4006759/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"89faf82b-5c9b-4139-a27c-3007d82fe4a3","owner":[],"postedDate":"March 7th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-03-08T17:22:51+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-07 15:55:38","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4006759","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4006759","identity":"rs-4006759","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}