Integrative Analysis and Identification of an Excellent lncRNA Signature to Predict Prognosis in Patients with COAD

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Therefore We aimed to identify a lncRNA signature through an integrative analysis of copy number variation, mutation and transcriptome data to predict prognosis and explore its internal mechanism. Methods: The lncRNA expression profile were collected from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). TCGA data was randomly divided 3:1 intotraining andtesting cohort. In the training, weperformed integrated analyses of three candidate lncRNA sets that correlated with prognosis, copy number variations and mutations to establish a signature through Cox regression analysis. The robustness was determined in the testing and GEO. Results: An 11-lncRNA signature that was significantly associated with prognosiswas constructed in the training ( P <0.0001, HR=2.014) , And this signature was validated in the testing( P =0.0019, HR=3.374) and GSE17536( P =0.0076, HR=1.864). The signature is significantly related to MSI status and clinical prognostic factors. The prognostic-relatedrisk scores were significantly excellent than the other five models have been reported. Furthermore, GSEA suggested that the signature was involved in COAD development and metastasis-related pathways. Conclusions: We identifiedansignature has strong robustness and can stably predict the prognosis of COAD in different platformsand may be implicated in COAD pathogenesis and metastasis and applied clinically as a prognostic marker. Molecular Biology General Cell Biology & Physiology Long noncoding RNA Colon cancer Integrative analyses Prognostic signature Mechanism Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Introduction Colorectal cancer (CRC) is the third most commonly diagnosed cancer[ 1 ]. As the treatment technology for CRC continues to advance, its mortality rate has declined for several decades. However, CRC is still the second most common cause of cancer-related death worldwide because of its high rates of tumour recurrence and metastasis, which cause a poor prognosis[ 2 ]. Therefore, it is important to find a molecular model that can effectively identify and predict the risk of CRC recurrence and metastasis. Zhou Yiming [ 3 ] identified and structured a prognostic signature based on five candidate genes, REG1B, TGM6, NTF4, PNMA5, and HOXC13, that could recognize COAD patients at a high risk of metastasis. Long noncoding RNA (lncRNA) is a major type of noncoding RNA (ncRNA) defined as an RNA transcript of more than 200 base pairs in length. Recently, several studies have shown that the aberrant expression of lncRNAs is closely related to the development of many human tumours, including CRC[ 4 – 6 ]. After the lncRNA CCAT1 (CARLo-5) was identified as an oncogene in COAD[ 7 ], a number of lncRNAs that are dysregulated in COAD have been identified[ 8 ], and the role of lncRNAs has recently received increasing attention. lncRNAs are characterized as oncogenes or tumour suppressor genes [ 9 , 10 ] and play a role in different biological processes in COAD. lncRNAs participate in transcriptional and epigenetic regulation by interacting with genomic DNA, transcription factors, chromatin, spliceosomes, chromatin regulators and other nuclear proteins[ 11 ], and lncRNAs are always involved in posttranscriptional, translational and posttranslational regulatory processes[ 12 ]. Various lncRNAs are involved in carcinogenesis and progression by regulating COAD cell proliferation, migration and invasion[ 7 , 13 ]. High-through put multi-omics sequencing data have laid a solid foundation for identifying genes associated with cancer prognosis. Multi-omics data analysis can reveal the mechanism of cancer development from multiple perspectives. To improve the predictive value and accuracy for COAD prognosis, we need to identify a robust lncRNA signature through an integrative analysis of prognosis-related lncRNA, copy number variation, mutation and transcriptome data with the help of multi-omics data analysis technology. In this study, We collected the data of the copy number variation, mutation and transcriptome of COAD tissues from The Cancer Genome Atlas (TCGA, n = 478) and the GEO(GSE17536 dataset, n = 177). A signature was established through integrated analyses, and validated in the diferent platforms of the testing cohort and the GSE17536 cohort. The microsatellite instability (MSI) status and clinical independence of the lncRNA signature were analysed. We also explored the pathways associated with the development and metastasis of COAD enriched in the lncRNA signature (Fig. 1 ). Furthermore, Compared with other rognostic-related risk signatures, the signature was better of predicting prognosis in COAD patients. Results 1 Comprehensive analysis of multi-omics data to obtain lncRNAs related to COAD prognosis 1.1 Lncrnas That Are Closely Related To Coad Prognosis According to the univariate Cox regression analyse, we identified a total of 483 candidate prognostic lncRNAs from the training cohort, and information on the top 20 lncRNAs is shown in Table 2 . Table 2 Information on the top 20 candidate prognostic lncRNAs lncRNA ID P -value HR Low 95% CI High 95% CI ENSG00000274925 5.78E-06 1.156 1.086 1.231 ENSG00000272512 1.29E-05 1.219 1.115 1.333 ENSG00000275494 3.19E-05 1.051 1.027 1.077 ENSG00000258053 3.59E-05 1.114 1.059 1.173 ENSG00000260563 6.04E-05 1.086 1.043 1.131 ENSG00000245281 8.09E-05 0.362 0.219 0.600 ENSG00000247095 0.000138199 1.035 1.017 1.053 ENSG00000235245 0.000149251 1.104 1.049 1.162 ENSG00000272555 0.000187134 0.437 0.283 0.675 ENSG00000229380 0.000188209 1.309 1.136 1.507 ENSG00000273456 0.000197802 1.159 1.073 1.253 ENSG00000279148 0.000207384 0.422 0.268 0.666 ENSG00000228288 0.000246995 1.077 1.035 1.120 ENSG00000269680 0.000250415 1.253 1.110 1.413 ENSG00000227947 0.000294015 0.591 0.445 0.786 ENSG00000271781 0.000314732 1.068 1.031 1.108 ENSG00000238113 0.000381295 1.301 1.125 1.505 ENSG00000230641 0.000382509 0.476 0.316 0.717 ENSG00000226659 0.000402587 1.176 1.075 1.287 ENSG00000251637 0.000431394 0.495 0.335 0.732 1.2 lncRNAs that are closely related to gene copy number variation We obtained lncRNAs that are closely related to gene copy number variation. A total of 137 lncRNAs that were significantly amplified on each fragment of the COAD genome (Fig. 2 A), including LINC00392 on the 13q22.1 segment (q = 8.17E-12), LINC01598 on the 20q11.21 segment (q = 5.75E-09) and LOC730183 on the 16p11.2 segment (q = 0.0014485), were identified. On the other hand, a total of 261 lncRNAs that were significantly deleted on each fragment in the COAD genome (Fig. 2 B), including LINC00681 in the 8p22 segment (q = 2.74E-45), LOC101928728 in the 1p36.11 segment (q = 2.02E-09), and LINC00491 in the 5q22.2 segment (q = 1.48E-05), were identified. 1.3 lncRNAs that are closely related to gene mutations Through MutSig2, we identified a total of 41 genes with significant mutation frequencies. The distribution of synonymous mutations, missense mutations, framework insertions or deletions, framework movements, nonsense mutations, cleavage sites and other nonsynonymous mutations in these 41 genes in TCGA COAD patient samples are shown in Fig. 3 . We identified 41 genes, some of which have been reported to be closely related to the development of cancer, such as KRAS, TP53, APC, PIK3CA, and FBXW7. Among these 41 genes, we identified lncRNAs associated with gene mutations using each of the genes to mutate into a tag, and a rank-sum test was used to detect the difference between the expression of each lncRNA in the mutant and nonmutant groups. lncRNAs with a P- value < 0.01 were considered to be associated with a gene mutation; thus, we obtained 2712 lncRNAs related to gene mutations. 2 Identification Of An 11-lncrna Signature For Coad Survival The comprehensive analysis revealed 147 lncRNAs associated with amplifications, deletions, and mutations from a total of 483 candidate prognostic lncRNAs. We analysed the change trajectory of each independent variable (Fig. 4 A). It can be seen that with the gradual increase in lambda, the number of independent coefficients becomes closer to zero. We used three-fold cross-validation to build the model. The confidence interval under each lambda is shown in Fig. 4 B. As shown in the figure, the model was optimal when lambda = 0.04078231. For this reason, we selected the lncRNAs obtained when lambda = 0.04078231 as the target lncRNAs to construct the model. After Lasso Cox regression narrowed the scope, we obtained 21 target lncRNAs that were used to construct the model. A multivariate Cox survival analysis was performed on 21 lncRNAs, and the 11 lncRNAs with the lowest AIC values (AIC = 767.27) were used to construct the final model. Details of the 11 lncRNAs are shown in Table 3 . The 11-lncRNA signature was then tested for its ability to predict survival in COAD patients. Table 3 Eleven lncRNAs identified as significantly associated with OS in the training cohort Ensembl Gene ID Symbol Coef HR Z-score P- value Low 95% CI High 95% CI ENSG00000269680 AC008760.1 2.2664 9.645 3.516 0.000438 2.727 34.115 ENSG00000215039 CD27-AS1 0.3273 1.387 2.03 0.042341 1.011 1.903 ENSG00000249550 LINC01234 0.4492 1.567 2.418 0.015587 1.089 2.255 ENSG00000247095 MIR210HG 0.3796 1.462 3.788 0.000152 1.201 1.779 ENSG00000180139 ACTA2-AS1 0.9405 2.561 3.111 0.001866 1.416 4.632 ENSG00000256546 AC156455.1 0.6567 1.928 2.736 0.006223 1.205 3.087 ENSG00000260805 AC092803.2 1.5048 4.503 2.02 0.043372 1.046 19.391 ENSG00000273576 AC009283.1 0.1432 1.154 2.15 0.031533 1.013 1.315 ENSG00000246627 CACNA1C-AS1 2.7294 15.323 4.146 3.39E-05 4.216 55.69 ENSG00000238113 LINC01410 2.0232 7.563 3.026 0.00248 2.039 28.045 ENSG00000235560 AC002310.1 1.3988 4.05 2.372 0.017675 1.275 12.863 3 Determination and analysis of the 11-lncRNA signature in the training cohort The 11-lncRNA signature was then established using a multivariate Cox regression analysis with the following model: RiskScore 11 = 2.2664*exp AC008760.1 +0.3273*exp CD27−AS1 +0.4492*exp LINC01234 + 0.3796*exp MIR210HG +0.9405*exp ACTA2−AS1 +0.6567*exp AC156455.1 + 1.5048*exp AC092803.2 +0.1432*exp AC009283.1 +2.7294*exp CACNA1C−AS1 + 2.0232*exp LINC01410 +1.3988*exp AC002310.1 The risk score was calculated as the sum of the above gene expression values * the ordinal, and then we selected 0.9892846 as the cutoff (median risk score) and divided the samples into high-risk and low-risk groups. Finally, 247 patients were classified as low risk, and 110 patients were classified as high risk; significantly different OS rates were observed between the two groups in the training cohort (log-rank P < 0.0001, HR = 2.014) Fig. 5 C). We acquired a five-year AUC of 0.83 according to the ROC curve for predicting survival in COAD patients (Fig. 5 B). As the patient’s risk score increased, the OS rate significantly decreased, and the number of deaths in the high-risk group increased significantly (Fig. 5 A). According to the changes in the expression levels of the 11 different lncRNAs in the signature observed with increases in the risk score, the expression of ENSG00000246627 was correlated with a low risk of COAD, and the other 10 lncRNAs were identified as risk factors based on their high expression and correlation with a high risk of COAD. 4 Validation of the 11-lncRNA signature in the testing and GSE17536 cohorts First, the 11-lncRNA signature was validated in the testing cohort; 88 patients were classified as low risk, and 31 patients were classified as high risk. There was a significant difference in OS between the two groups (log-rank P = 0.0019, HR = 3.374) (Fig. 6 C). The five-year AUC was 0.66 according to the ROC curve (Fig. 6 B). The results of the testing cohort were similar to those of the training cohort; as the patient’s risk score increased, the OS time decreased significantly, and the number of deaths in the high-risk group increased significantly (Fig. 6 A). Moreover, 10 lncRNAs (all lncRNAs except ENSG00000246627) were identified as risk factors. Similarly, we used the same model and the same cut-off from the training cohort and verified the model’s robustness using an external independent data cohort (GSE17536). Ultimately, 99 patients were classified as low risk, and 78 patients were classified as high risk. A significant difference in OS was observed between the two groups (log-rank P = 0.0076, HR = 1.864) (Fig. 7 C). The five-year AUC was 0.71 according to the ROC curve (Fig. 7 B). The GSE17536 cohort showed similar results to the TCGA training cohort. As the risk score increased, the survival time decreased significantly, and the number of deaths in the high-risk group increased. The expression of the 11 different signature lncRNAs also increased with the increase in the risk score, indicating that high expression of the 11 lncRNAs is associated with a high risk of COAD and could serve as a risk factor (Fig. 7 A). 5 Independent predictive power of the 11-lncRNA signature according to the MSI status, tumour stage and clinicopathological characteristics The patients were subdivided into a high-risk subgroup and a low-risk subgroup based on different MSI statues, and the 11-lncRNA signature was used to predict OS; the OS rate was significantly different between the high-risk and low-risk subgroups in the MSI-L and MSS groups (excluding MSH) (Fig. 8 A-C). Based on their tumour stage, patients were subdivided into a high-risk group and a low-risk group in each stage, and the 11-lncRNA signature revealed no significant difference in OS at the II, III and IV stages (all stages except stage I) between the two groups (Fig. 8 D-G). Furthermore, these results demonstrate that the 11-lncRNA signature model can better predict the OS of patients with different MSI statuses and tumour stages. We systematically analysed the clinical information of the TCGA and GSE17536 patients, including age, sex, lymph node invasion status, pathology (T, N, and M classifications), tumour stage, and the 11-lncRNA signature grouping information (Table 4 ). Table 4 Identification of the clinical factors and clinical independence associated with prognosis with univariate and multivariate Cox regression analyses Variables Univariate analysis Multivariable analysis HR 95% CI of HR P -value HR 95% CI of HR P -value TCGA training dataset 11-lncRNA risk score Risk score (High/Low) 4.96 3.14–7.82 5.14E-12 4.39 2.58–7.46 4.26E-08 Age 1.02 0.99–1.04 0.07 1.03 1.01–1.05 0.002 Sex(Male/Female) 1.14 0.72–1.82 0.57 0.97 0.60–1.58 0.921 T3/T4 vs T1/T2 5.77 1.81–18.37 0.002 3.59 0.84-15.219 0.082 N1/N2 vs N0 3.05 1.85–5.01 1.04E-05 0.31 0.091–1.07 0.065 M1 vs M 0 2.58 1.61–4.16 8.72E-05 1.48 0.88–2.49 0.136 Stage Ⅲ/Ⅳ vs Stage Ⅰ/Ⅱ 3.37 2.00-5.67 4.94E-06 7.56 1.94–29.47 0.003 TCGA validation dataset 11-lncRNA risk score Risk score (High/Low) 3.06 1.36–6.89 0.0066 3.02 1.13–8.08 0.027 Age 1.02 0.98–1.05 0.28 1.04 0.99–1.08 0.083 Sex (Male/Female) 0.93 0.42–2.05 0.86 0.55 0.21–1.44 0.226 T3/T4 vs T1/T2 0.99 0.29–3.39 0.99 0.76 0.13–4.34 0.756 N1/N2 vs N0 2.52 1.13–5.58 0.02 0.54 0.059–4.87 0.583 M1 vs M 0 8.03 3.31–19.51 4.17E-06 5.85 1.88–18.15 0.002 Stage Ⅲ/Ⅳ vs Stage Ⅰ/Ⅱ 2.86 1.24–6.56 0.01 5.05 0.41–61.95 0.205 GSE17536 validation dataset 11-lncRNA risk score Risk score (High/Low) 1.86 1.17–2.97 0.0085 1.65 1.02–2.67 0.039 Age 1.006 0.98–1.02 0.49 1.02 1.002–1.04 0.029 Sex (Male/Female) 1.104 0.69–1.76 0.67 1.17 0.71–1.91 0.521 Stage Ⅲ/Ⅳ vs Stage Ⅰ/Ⅱ 4.22 2.39–7.46 7.28E-07 4.226 2.36–7.56 1.21E-06 In the TCGA training cohort, we found significant survival-related correlations in the clinicopathological characteristics, with the exception of age and sex, according to the univariate Cox regression analysis, but we found that only the risk score (HR = 4.39, 95% CI = 2.58–7.46, P = 4.26E-08), age, and stage III/IV vs sage I/II were significantly related to survival according to the multivariate Cox regression analysis. The 11-lncRNA signature was verified to be clinically independent. In the TCGA testing cohort, the risk score, N1/N2 vs N0, M1 vs M0, and stage III/IV vs stage I/II were significantly associated with survival according to the univariate Cox regression analysis, but only the risk score (HR = 3.02, 95% CI = 1.13–8.08, P = 0.027) and M1 vs M0 were significantly related to survival according to the multivariate Cox regression analysis. The 11-lncRNA signature was also verified to be clinically independent. In the GSE17536 cohort, the risk score and stage III/IV vs stage I/II were significantly associated with survival according to the univariate Cox regression analysis, but only the risk score (HR = 1.65, 95% CI = 1.02–2.67, P = 0.039), age and stage III/IV vs stage I/II were significantly associated with survival according to the multivariate Cox regression analysis. In conclusion, the 11-lncRNA signature is a prognostic indicator independent of other clinical factors and shows independent predictive performance with clinical application value. 6 Identification of the 11-lncRNA signature-associated biological pathways in the training cohort The signalling pathways associated with the 11-lncRNA signature significantly enriched in the TCGA training cohort were detected by GSEA (Table 5 ). The signalling pathways that were significantly enriched in the high-risk and low-risk groups, were the Notch signalling pathway, the VEGF signalling pathway, the P53 signalling pathway and the cell cycle; all were significantly associated with the development and metastasis of COAD (Fig. 9 ). Table 5 KEGG pathways significantly enriched in the high-risk and low-risk groups detected by GSEA NAME SIZE ES NES NOM P-val FDR q-val FWER P-val KEGG_NOTCH_SIGNALING_PATHWAY 47 -0.468 -1.633 0.028 1.000 0.726 KEGG_VEGF_SIGNALING_PATHWAY 75 -0.354 -1.486 0.048 1.000 0.916 KEGG_P53_SIGNALING_PATHWAY 67 0.434 1.660 0.015 0.936 0.632 KEGG_ALANINE_ASPARTATE_AND_GLUTAMATE_METABOLISM 32 0.464 1.601 0.025 0.552 0.752 KEGG_RIBOSOME 87 0.757 1.773 0.031 0.747 0.387 KEGG_UBIQUITIN_MEDIATED_PROTEOLYSIS 133 0.418 1.658 0.035 0.632 0.638 KEGG_BASAL_TRANSCRIPTION_FACTORS 35 0.490 1.575 0.043 0.466 0.801 KEGG_CELL_CYCLE 124 0.459 1.609 0.050 0.657 0.745 7 Comparison of the 11-lncRNA signature with other COAD prognostic signatures The ROC and OS KM curves of the five models are shown in Fig. 10 . Significantly different OS rates were observed between the high-risk and low-risk groups using the six-lncRNA signature established by Zhao[ 17 ] (log-rank P = 0.0014,HR = 2.03) (Fig. 10 B). We acquired a five-year AUC of 0.65 and a ten-year AUC of 0.67 according to the ROC (Fig. 10 A). Significantly different OS rates were also observed between the two groups using the two-lncRNA signature established by Xue[ 18 ] (log-rank P = 0.018, HR = 1.73) (Fig. 10 D), and we obtained a five-year AUC of 0.54 and a ten-year AUC of 0.47 (Fig. 10 C). Significantly different OS rates were also observed with the 14-lncRNA signature reported by Xing[ 19 ](Fig. 10 F), and we found a five-year AUC of 0.66 and a ten-year AUC of 0.53 (Fig. 10 E). In addition, the six-lncRNA signature established by Fan[ 20 ](Fig. 10 H) yielded a five-year AUC of 0.64 and a ten-year AUC of 0.41(Fig. 10 G), and the 15-lncRNA signature obtained by Wang[ 21 ] (Fig. 10 J) resulted in a five-year AUC of 0.78 and a ten-year AUC of 0.67 (Fig. 10 I). The final comparison showed that our model was slightly better than the 15-lncRNA model and significantly better than the other four models. Discussion CRC is a common digestive tract tumour that is a serious threat to the health of patients. According to recent statistics, there are approximately 1.45 million new cases of CRC each year, which made it the third most prevalent cancer in 2018[ 1 ] and the second most prevalent cancer in American males in 2019[ 22 ]. Approximately 694,000 deaths have been reported every year, making the second most common cause of cancer-related death worldwide[ 2 ]. The recurrence and metastasis of CRC seriously affect the efficacy and prognosis of treatment. Identifying the risk of recurrence and metastasis can help us guide early intervention for the treatment of CRC, ultimately improving the prognosis. Therefore, it is very urgent to study and identify one or more efficient molecular models for predicting prognosis to guide treatment options and to improve the survival quality of CRC patients. lncRNAs are a major class of ncRNAs with a length of more than 200 base pairs and are not translated into proteins[ 23 , 24 ]. Over the past decade, it has become clear that certain lncRNAs have strong potential, and an in-depth study is needed to elucidate their mechanism of action. lncRNAs not only control the nuclear structure[ 25 ] but also regulate the expression of adjacent genes and act as amplifiers, with remarkable tissue specificity through various mechanisms[ 26 ]. lncRNAs have been shown to directly regulate gene expression at the transcriptional, posttranscriptional and epigenetic levels. lncRNAs have long nucleotide chains and intricate secondary structures and can interact with genomic DNA, chromatin, transcription factors, chromatin regulators, spliceosomes and other nuclear proteins[ 27 ]. lncRNAs are known to serve as tumour regulators and participate in complex networks of biological regulation[ 28 , 29 ]. Therefore, the identification of lncRNAs closely related to tumour prognosis and the establishment of a signature that can predict the risk of tumour prognosis will be helpful for improving the prevention and treatment of tumours. Zhang GH[ 15 ] identified a novel four-lncRNA signature using Cox regression analysis to identify lncRNAs that correlated with the prognosis of 111 laryngeal cancer patients from the TCGA. The signature was shown to predict the prognosis of patients with laryngeal cancer and may influence the prognosis of laryngeal cancer through many pathways, such as regulating immunity and tumour apoptosis. Jie Li[ 30 ] also identified a five-lncRNA signature to predict the risk of tumour recurrence in breast cancer (BC) patients and found that it was independent of clinical prognostic factors, such as BC subtypes and adjuvant treatments. Recently, an increasing number of researchers have focused on the role of lncRNAs in the development and progression of CRC and its significance to clinical prognosis[ 4 , 6 ]. Many lncRNAs, such as MALAT1 and HOTAIR, have also been used as biomarkers in CRC[ 31 , 32 ]. Several lncRNAs have not only been reported as markers in CRC diagnosis but also been shown to be correlated with patient prognosis (e.g., CCAL, PURPL and lnc-GNAT1-1)[ 33 – 35 ]. Therefore, this method was also used to identify a CRC-related lncRNA signature for predicting the prognosis of CRC[ 17 – 21 ] by analysing different datasets. At the same time, as high-throughput multi-omics sequencing data have laid a solid foundation for identifying genes associated with cancer prognosis, multi-omics data analysis can reveal the mechanisms of cancer development from multiple perspectives[ 36 ]. We identified closely related genomics, gene mutations, epigenetics, and functions of lncRNAs that are inherently regulated by COAD[ 4 , 6 ]. We show that dysregulated lncRNAs may be new prognostic and diagnostic biomarkers or therapeutic targets for clinical applications. Therefore, we identified a robust lncRNA signature through an integrative analysis of prognosis-related lncRNA, copy number variation, mutation and transcriptome data with the help of multi-omics data analysis technology. In this study, we fully integrated and analysed lncRNAs that are related not only to prognosis but also to copy number variations, mutations and transcriptome regulation in 359 COAD samples from the TCGA training cohort. We developed an 11-lncRNA signature that was validated in testing and GSE17536 cohorts. We also found that the signature had good robustness and good predictive ability of the prognosis risk in other independent datasets. Moreover, we compared the 11-lncRNA signature with other COAD prognostic signatures[ 17 – 21 ] to validate the good prediction of the prognosis risk of the 11-lncRNA signature. We found that the AUC of the 11-lncRNA signature was better than that of the other five signatures. Because the researchers did not confirm that the lncRNAs directly regulate gene expression at the transcriptional, post-transcriptional and epigenetic levels, we focused on the predictive role of lncRNAs in tumour prognosis and established lncRNA signatures to predict prognosis merely by analysing and identifying lncRNAs associated with prognosis. However, the intrinsic interactive relationship between lncRNAs, genomics, gene mutations and epigenetics was not investigated. Ultimately, the studies failed to identify a good lncRNA signature to predict the risk of CRC prognosis. Significant differences in OS outcomes between the high-risk and low-risk groups were obtained using the described method[ 37 ]. Furthermore, we found that the 11-lncRNA signature had independent predictive power from the MSI status and tumour stage (e.g., MSI-L and MSS patients and patients in stages II, III and IV, excluding stage I), which may be due to a good survival rate in COAD patients with stage I [ 22 ]. This study also showed that the 11-lncRNA signature was a prognostic indicator independent of other clinical factors and had independent predictive performance with clinical application value. Additionally, we identified the 11-lncRNA signature-associated biological pathways that were significantly enriched in COAD patients as detected by GSEA. In summary, we determined that the Notch signalling pathway, the VEGF signalling pathway, the P53 signalling pathway and the cell cycle are significantly associated with the development and metastasis of COAD[ 38 , 39 ]. In summary, we constructed a novel 11-lncRNA signature that can be used to predict the prognosis of patients with COAD and exploited the possible underlying mechanisms involved. The 11-lncRNA signature may indicate the potential roles of lncRNAs in COAD pathogenesis. The results will provide molecular diagnostic markers and therapeutic targets with clinical implications in COAD patients. Finally, we hope that all of the above results will be verified in basic experiments and clinical trials in further studies. Conclusion In our study, we integrated analysis of the lncRNAs were not only related to the prognosis, but also related to copy number variation, mutation and transcriptome data to identify and constructe a lncRNA signature to predict prognosis in COAD patients. And the signature was validated in the testing and GSE17536 cohorts and was independent of the MSI status and clinical prognostic factors. The signature was significantly better than the other five lncRNA signatures have been reported in the COAD. Materials And Methods 1 Patients and data collection We downloaded COAD RNA-Seq data, clinical follow-up information and copy number variation data from the SNP 6.0 chip in the TCGA database from the UCSC cancer browser ( https://xenabrowser.net/datapages/ ), and we downloaded the mutation comment file (MAF) from the GDC client. We also downloaded the GSE17536 dataset, which included COAD expression profile data and clinical information, from the Gene Expression Omnibus (GEO) database ( https://www.ncbi.nlm.nih.gov/geo/ ). We preprocessed the downloaded data, downloaded the fragments per kilobase of transcript per million mapped reads (FPKM) RNA-Seq data from the TCGA. The R package "caret" was used to randomly divide the samples into the training cohort (359 samples) and the testing cohort (119 samples). The lncRNA expression profile data is extracted according to the Ensemble ID of lncRNA in the GENCODE database. SeqMap was used to compare GSE17536 expression data (no mismatch was allowed). A total of 5,076 probes were annotated on the lncRNAs. All selected expression datasets in the training, testing and GSE17536 cohorts were log 2 transformed for standardization. Ultimately, a total of 478 samples from the TCGA were randomly divided at a ratio of 3:1 into a training cohort (n = 359) and a testing cohort (n = 119), and 177 samples were obtained from the GEO database (GSE17536). We obtained clinical pathology data, including the patient age, survival status, sex, lymph node metastasis status, T classification, N classification, M classification and tumour stage (Table 1 ), from the three cohorts and found no significant differences among the groups. Table 1 Clinical pathology data of the three cohorts Characteristic TCGA training cohort (n = 359) TCGA testing cohort (n = 119) P -value GSE17536 (n = 177) Age (years) ≤ 60 103 31 0.724 59 > 60 225 76 118 Survival Status Living 276 91 0.949 104 Dead 81 28 73 Gender female 157 45 0.349 81 male 171 62 96 T T 1 7 4 0.188 -- T 2 63 12 -- T 3 214 84 -- T 4 43 7 -- N N 0 184 71 0.172 -- N 1 82 20 -- N 2 62 16 -- M M 0 234 87 0.027 -- M 1 90 17 -- Tumour stage Stage Ⅰ 60 13 0.131 24 Stage Ⅱ 114 53 57 Stage Ⅲ 95 29 57 Stage Ⅳ 51 9 39 2 Identification of lncRNAs that are closely related to the prognosis of COAD We performed a univariate Cox regression analysis to establish the correlation of lncRNA expression with overall survival (OS) in the TCGA training cohort, and the lncRNAs with significant P values ( P < 0.01) were selected as candidates. 3 Identification of lncRNAs that are closely related to gene copy number variation We used GISTIC 2.0 to identify genes with significant amplification or deletion from copy number variation data in the TCGA training cohort. We set a parameter threshold for fragments with amplification or deletion lengths greater than 0.1 and significant P- values ( P < 0.05). The significantly amplified fragments in the genome and the genes amplified on each of the fragments were recorded and the significantly deleted fragments in the genome and the genes that were significantly deleted on each fragment were recorded and incorporated to establish the lncRNAs associated with copy number variation. 4 Identification of lncRNAs that are closely related to gene mutations We used MutSig2 to identify genes with significant mutations from the mutation annotation data of the TCGA training cohort, and with a threshold of P < 0.05. We identified lncRNAs associated with gene mutations using the rank-sum test to detect the difference in the expression of each lncRNA between the mutant and nonmutant groups, and each gene mutation was used as a label. lncRNAs with significant P values ( P < 0.01) were considered to be associated with a gene mutation. Finally, the lncRNA dataset related to gene mutations was established. 5 Development and validation of the robustness of the lncRNA signature By intersecting these three lncRNA sets (Prognosis-related lncRNAs, copy number variation-related lncRNAs and mutation-related lncRNAs), we obtained the targeted lncRNAs. The least absolute shrinkage and selection operator (Lasso) method, which is a compression estimate, was used to narrow the lncRNA range. We used the R package glmnet for the Lasso Cox regression analysis[ 14 , 15 ]. Furthermore, we performed a multivariate Cox regression analysis, and stepwise regression was used to reduce the number of lncRNAs again. The lowest Akaike information criterion (AIC) value as the final model. RiskScore = coefficient*exp lncRNA1 +oefficient*exp lncRNA2 +oefficient*exp lncRNA3 +....+coefficient*exp lncRNAn . The risk score of each sample in the TCGA training cohort was then calculated. Based on the median risk score, the COAD patients were divided into two groups: a high-risk group and a low-risk group. A receiver operating characteristic (ROC) curve was used to test the accuracy of lncRNA signature to predict prognosis. Finally, the lncRNA signature was verified in the testing cohort. The GSE17536 cohort used the same model and the same cutoff as the TCGA training cohort. 6 Independent predictive power of the lncRNA signature based on different MSI statuses, tumour stages and clinicopathological characteristics We divided the TCGA cohort into MSI-high (MSI-H), MSI-low (MSI-L) and microsatellite stable (MSS) groups according to the MSI phenotype information described by the TCGA network study[ 16 ]. We further analyze the relationship between this lncRNA signature and MIS status. To identify the lncRNA signature model for clinical applications, we analysed the relationship between clinical information (including age, sex, lymph node invasion status, pathology (T, N, and M classifications), tumour stage) and lncRNA signature through univariate and multivariate Cox regression analyses. 7 Identification of the lncRNA signature-associated biological pathways with gene set enrichment analysis (GSEA) We used GSEA and the “cluster profile R” package to conduct Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Significantly enriched pathways in the high-risk and low-risk groups in the TCGA training cohort were identified. The selected gene set was c2.cp.kegg.v6.0.symbols, which contained the KEGG pathways. In KEGG pathway analysis, P < 0.05 was considered to indicate statistical significance. 8 Comparison of the lncRNA signature with other COAD prognostic signatures By reviewing the literature, we identified five prognostic-related risk models, a six-lncRNA signature (PMID: 30396175)[ 17 ], a two-lncRNA signature (PMID: 29254165)[ 18 ], a 14-lncRNA prognostic signature (PMID: 29565464)[ 19 ], a six-lncRNA signature (PMID: 29227531)[ 20 ] and a 15-lncRNA signature (PMID: 30510449)[ 21 ], for comparison with our lncRNA signature. To make the models comparable, we performed a multivariate Cox regression analysis to calculate the risk scores of the training set samples based on the corresponding genes in the three models. We evaluated the ROC of the five models and then divided the samples into high-risk and low-risk groups according to the median risk score and analysed the difference in OS between the two groups. Declarations Acknowledgements The authors are grateful to Ruiqing Chen and Lengxi Fu for technical assistance. Disclosure statement No potential conflict of interest was reported by the authors. Authors’ contributions ZC, YL and SL conceived this experiment; ZC, YL and JG performed the experiments and dataanalysis; ZC, YL, JG and SC wrote and revisedthe manuscript. All authors approved the final version of themanuscript. Funding This work was supported by grants from the Fujian health youth research project (NO. 2019-2-20), the Fujian natural fund project (NO. 2019J01448) and the Fujian science and technology innovation joint fund Project (NO. 2019Y9133). Data Availability Statement The data that support the findings of this study are available on request from the corresponding author. Ethics approval and consent to participate The research used the published database data to conduct secondary mining research, and the research does not involve ethical approval. Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. References Siegel Rebecca L,Miller Kimberly D,Jemal Ahmedin. Cancer statistics. 2019.CA Cancer J Clin. 2019; 69:7-34. Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2018;68:394-424. Zhou Yiming,Zang Yiwen,Yang Yi, Xiang J, Chen Z. Candidate genes involved in metastasis of colon cancer identified by integrated analysis.Cancer Med. 2019;8: 2338-2347. Kim T, Croce CM. Long noncoding RNAs: Undeciphered cellular codes encrypting keys of colorectal cancer pathogenesis. Cancer Lett. 2018;417:89-95. Tang X, Qiao X, Chen C, Liu Y, Zhu J, Liu J. Regulation Mechanism of Long Noncoding RNAs in colon cancer Development and Progression. Yonsei Med J. 2019;60: 319-325. 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Li XL, Subramanian M, Jones MF, Chaudhary R, Singh DK, Zong X, Gryder B, Sindri S, Mo M. Long Noncoding RNA PURPL Suppresses Basal p53 Levels and Promotes Tumorigenicity in Colorectal Cancer.Cell Rep. 2017; 20: 2408-2423. Ye C, Shen Z, Wang B, Li Y, Li T, Yang Y, Jiang K, Ye Y, Wang S. A novel long non-coding RNA lnc-GNAT1-1 is low expressed in colorectal cancer and acts as a tumor suppressor through regulating RKIP-NF-κB-Snail circuit.J Exp Clin Cancer Res. 2016; 35: 187. Archer TC, Ehrenberger T, Mundt F, Gold MP, Krug K, Mah CK, Mahoney EL, Daniel CJ, LeNail A. Proteomics, Post-translational Modifications, and Integrative Analyses Reveal Molecular Heterogeneity within Medulloblastoma Subgroups.Cancer Cell. 2018; 34: 396-410. Luo W, Wang M, Liu J, Cui X, Wang H. Identification of a six lncRNAs signature as novel diagnostic biomarkers for cervical cancer.J Cell Physiol. 2019;7:1-8. Sepulveda AR, Hamilton SR, Allegra CJ, Grody W, Cushman-Vokoun AM, Funkhouser WK. Molecular Biomarkers for the Evaluation of Colorectal Cancer: Guideline From the American Society for Clinical Pathology, College of American Pathologists, Association for Molecular Pathology, and the American Society of Clinical Oncology.J Clin Oncol.2017;35:1453-1486. Fearon Eric R. Molecular genetics of colorectal cancer.Annu Rev Pathol.2011;6: 479-507. 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. 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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-641736","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":37755541,"identity":"e273c1e2-f673-4619-bea4-412d649b1bc1","order_by":0,"name":"ZhiHua Chen","email":"","orcid":"","institution":"The first Affiliated Hospital of Fujian Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"ZhiHua","middleName":"","lastName":"Chen","suffix":""},{"id":37755542,"identity":"da6b3170-1ea1-4756-986d-8d659f076054","order_by":1,"name":"YiLin Lin","email":"","orcid":"","institution":"The first Affiliated Hospital of Fujian Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"YiLin","middleName":"","lastName":"Lin","suffix":""},{"id":37755543,"identity":"2052e0e5-de5f-410b-a1ae-20286fea7876","order_by":2,"name":"SuYong Lin","email":"","orcid":"","institution":"The first Affiliated Hospital of Fujian Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"SuYong","middleName":"","lastName":"Lin","suffix":""},{"id":37755544,"identity":"ca7d01de-3c0e-47af-9c80-345b6e4a63a0","order_by":3,"name":"Ji Gao","email":"","orcid":"","institution":"Fujian Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ji","middleName":"","lastName":"Gao","suffix":""},{"id":37755545,"identity":"9ed7015f-ff8f-429a-bfd2-510e73923031","order_by":4,"name":"Shao-Qin Chen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+ElEQVRIiWNgGAWjYDACCSBmbAASzAwMBz4Y1MixsbcfIFoL48EZBceM+XjOJBCpBajpMMcH5sR5Eg4GeHXwz24+9vDnDjs5vuO8Bw4zGLClt0kwJDD8qNiG25I7x9KNec8kG0se5ks4XGAgk9sm3XiAsefMbZxaDCRyzKQZ25gTNxzmMTg8w4Att03mQAIzYxs+LfnfJH+21UO08Bgwp7NJJBgQ0JLDJsHbdhiuJYGgFokbaWbSvG3HgX7hMTg4w+CYYRswkA/i8wv/jORnQIdVy/GdP2P84cOfGnn59vaDD35U4NaCAAdwsInUMgpGwSgYBaMAGQAA6pdZE8mF1Z8AAAAASUVORK5CYII=","orcid":"","institution":"The first Affiliated Hospital of Fujian Medical University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Shao-Qin","middleName":"","lastName":"Chen","suffix":""}],"badges":[],"createdAt":"2021-06-20 15:44:03","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-641736/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-641736/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":11312570,"identity":"bd4c4ada-00aa-426b-8024-0f9fc760fd0b","added_by":"auto","created_at":"2021-07-09 19:49:05","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":103685,"visible":true,"origin":"","legend":"Workflow of identifying a COAD survival-related 11-lncRNA signature.","description":"","filename":"fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-641736/v1/960025de30d29cc03a97c35f.png"},{"id":11312565,"identity":"d283d1d9-a311-45f5-b780-f04220501217","added_by":"auto","created_at":"2021-07-09 19:49:05","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":112900,"visible":true,"origin":"","legend":"lncRNAs that are closely related to gene copy number variation. (A): A fragment that was significantly amplified in the COAD genome (p\u003c0.05). (B): A fragment that was significantly deleted in the COAD genome (p\u003c0.05). ","description":"","filename":"fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-641736/v1/fd6dc7e1679dd358fa66bfcd.png"},{"id":11312839,"identity":"26552a0f-74d7-4505-b8fb-95110d608971","added_by":"auto","created_at":"2021-07-09 19:55:05","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":228571,"visible":true,"origin":"","legend":"A total of 41 genes with significant mutation frequencies were identified through MutSig2. The upper histogram shows the total number of synonymous and nonsynonymous mutations in 41 genes per patient, and the right histogram shows the number of samples in which the 41 genes were mutated.","description":"","filename":"fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-641736/v1/10163e1090d35510660a5310.png"},{"id":11312684,"identity":"92df1baf-fa3e-4a6c-8136-1842f9513b99","added_by":"auto","created_at":"2021-07-09 19:52:05","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":120756,"visible":true,"origin":"","legend":"Target lncRNAs were identified and obtained by Lasso Cox regression. (A): The trajectory of each independent variable; the horizontal axis represents the log value of the independent lambda, and the vertical axis represents the coefficient of the independent variable; (B): The confidence interval under each lambda.","description":"","filename":"fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-641736/v1/235a1d1e0fe6eb12f041b072.png"},{"id":11312841,"identity":"8956e119-0d0b-41b0-9abe-c22a5c035174","added_by":"auto","created_at":"2021-07-09 19:55:05","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":254340,"visible":true,"origin":"","legend":"Determination and analysis of the 11-lncRNA signature in the training cohort. (A): Risk score, survival time, survival status and expression of the 11 lncRNAs in the TCGA training cohort. (B):11-lncRNA signature classification ROC curve and AUC. (C): 11-lncRNA signature Kaplan-Meier (KM) survival curve distribution in the TCGA training cohort.","description":"","filename":"fig5.png","url":"https://assets-eu.researchsquare.com/files/rs-641736/v1/eb3309d40b586d99278e9ec8.png"},{"id":11313031,"identity":"c1086335-2b40-41ec-9a69-6191dfae004c","added_by":"auto","created_at":"2021-07-09 19:58:05","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":226981,"visible":true,"origin":"","legend":"Validation of the 11-lncRNA signature in the testing cohort. (A): Risk score, survival time, survival status and expression of the 11 lncRNAs in the TCGA testing cohort. (B):11-lncRNA signature classification ROC curve and AUC. (C): 11-lncRNA signature KM survival curve distribution in the TCGA testing cohort.","description":"","filename":"fig6.png","url":"https://assets-eu.researchsquare.com/files/rs-641736/v1/1db26b59daf2ecbd34ac3738.png"},{"id":11312687,"identity":"b02df26b-4284-434a-9de5-28a05a411025","added_by":"auto","created_at":"2021-07-09 19:52:05","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":240306,"visible":true,"origin":"","legend":"Validation of the 11-lncRNA signature in the GSE17536 cohort. (A): Risk score, survival time, survival status and expression of the 11 lncRNAs in the GSE17536 cohort. (B): The 11-lncRNA signature classification ROC curve and AUC in the GSE17536 cohort. (C): The 11-lncRNA signature KM survival curve distribution in the GSE17536 cohort.","description":"","filename":"fig7.png","url":"https://assets-eu.researchsquare.com/files/rs-641736/v1/20e07ae32b28ee9dc3364088.png"},{"id":11312566,"identity":"1cfd3a5b-b959-45cd-92a5-1f1eca50265f","added_by":"auto","created_at":"2021-07-09 19:49:05","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":137298,"visible":true,"origin":"","legend":"11-lncRNA signature KM survival curve distribution according to the MSI status and tumour stage. (A): 11-lncRNA signature KM survival curve distribution in the MSS group. (B): 11-lncRNA signature KM survival curve distribution in the MSI-L group. (C): 11-lncRNA signature KM survival curve distribution in the MSI-H group. (D): 11-lncRNA signature KM survival curve distribution in the stage I group. (E): 11-lncRNA signature KM survival curve distribution in the stage II group. (F): 11-lncRNA signature KM survival curve distribution in the stage III group. (G): 11-lncRNA signature KM survival curve distribution in the stage IV group.","description":"","filename":"fig8.png","url":"https://assets-eu.researchsquare.com/files/rs-641736/v1/e4310eab2a8f5a811c34507e.png"},{"id":11312689,"identity":"2f1b5cfb-d251-4cbe-a687-231f0987feb3","added_by":"auto","created_at":"2021-07-09 19:52:05","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":475073,"visible":true,"origin":"","legend":"Signalling pathways associated with the 11-lncRNA signature were \nsignificantly enriched in the TCGA training cohort according to the GSEA. GSEA validated the enhanced activity of (A): “KEGG NOTCH SIGNALING PATHWAY”, (B): “ KEGG VEGF SIGNALING PATHWAY”, (C): “ KEGG P53 SIGNALING PATHWAY”, (D): “KEGG ALANINE ASPARTATE AND GLUTAMATE METABOLISM”, (E): “KEGG RIBOSOME”, (F): “KEGG UBIQUITIN MEDIATED PROTEOLYSIS”, (G): “KEGG BASAL RANSCRIPTION FACTORS”, and (H): “ KEGG CELL CYCLE”.\n","description":"","filename":"fig9.png","url":"https://assets-eu.researchsquare.com/files/rs-641736/v1/9787c4fdd30a5ec62804f9f8.png"},{"id":11312573,"identity":"7904a285-ef95-4bd8-b5e4-e70429ada55b","added_by":"auto","created_at":"2021-07-09 19:49:05","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":264380,"visible":true,"origin":"","legend":"Comparison of the 11-lncRNA signature with other signatures. (A):The Zhao’s 6-lncRNA signature classification ROC curve and AUC. (B): The Zhao’s 6-lncRNA signature KM survival curve distribution.(C):The Xue’s 2-lncRNA signature classification ROC curve and AUC. (D): The Xue’s 2-lncRNA signature KM survival curve distribution.(E):The Xing’s 14-lncRNA signature classification ROC curve and AUC. (F): The Xing’s 14-lncRNA signature KM survival curve distribution.(G):The Fan’s 6-lncRNA signature classification ROC curve and AUC. (H): The Fan’s 6-lncRNA signature KM survival curve distribution.(I):The Wang’s 15-lncRNA signature classification ROC curve and AUC. (J): The Wang’s 15-lncRNA signature KM survival curve distribution.","description":"","filename":"fig10.png","url":"https://assets-eu.researchsquare.com/files/rs-641736/v1/a69b9e71b6ff438b5c6fd5b7.png"},{"id":13703140,"identity":"44281586-0be4-4284-9873-55600a526664","added_by":"auto","created_at":"2021-09-17 13:39:33","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2986867,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-641736/v1/bf2715c9-7f66-4208-8c41-a62d4d27c294.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eIntegrative Analysis and Identification of an Excellent lncRNA Signature to Predict Prognosis in Patients with COAD\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eColorectal cancer (CRC) is the third most commonly diagnosed cancer[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. As the treatment technology for CRC continues to advance, its mortality rate has declined for several decades. However, CRC is still the second most common cause of cancer-related death worldwide because of its high rates of tumour recurrence and metastasis, which cause a poor prognosis[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Therefore, it is important to find a molecular model that can effectively identify and predict the risk of CRC recurrence and metastasis. Zhou Yiming [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] identified and structured a prognostic signature based on five candidate genes, REG1B, TGM6, NTF4, PNMA5, and HOXC13, that could recognize COAD patients at a high risk of metastasis.\u003c/p\u003e \u003cp\u003eLong noncoding RNA (lncRNA) is a major type of noncoding RNA (ncRNA) defined as an RNA transcript of more than 200 base pairs in length. Recently, several studies have shown that the aberrant expression of lncRNAs is closely related to the development of many human tumours, including CRC[\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. After the lncRNA CCAT1 (CARLo-5) was identified as an oncogene in COAD[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], a number of lncRNAs that are dysregulated in COAD have been identified[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], and the role of lncRNAs has recently received increasing attention. lncRNAs are characterized as oncogenes or tumour suppressor genes [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] and play a role in different biological processes in COAD. lncRNAs participate in transcriptional and epigenetic regulation by interacting with genomic DNA, transcription factors, chromatin, spliceosomes, chromatin regulators and other nuclear proteins[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], and lncRNAs are always involved in posttranscriptional, translational and posttranslational regulatory processes[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Various lncRNAs are involved in carcinogenesis and progression by regulating COAD cell proliferation, migration and invasion[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHigh-through put multi-omics sequencing data have laid a solid foundation for identifying genes associated with cancer prognosis. Multi-omics data analysis can reveal the mechanism of cancer development from multiple perspectives. To improve the predictive value and accuracy for COAD prognosis, we need to identify a robust lncRNA signature through an integrative analysis of prognosis-related lncRNA, copy number variation, mutation and transcriptome data with the help of multi-omics data analysis technology. In this study, We collected the data of the copy number variation, mutation and transcriptome of COAD tissues from The Cancer Genome Atlas (TCGA, n\u0026thinsp;=\u0026thinsp;478) and the GEO(GSE17536 dataset, n\u0026thinsp;=\u0026thinsp;177). A signature was established through integrated analyses, and validated in the diferent platforms of the testing cohort and the GSE17536 cohort. The microsatellite instability (MSI) status and clinical independence of the lncRNA signature were analysed. We also explored the pathways associated with the development and metastasis of COAD enriched in the lncRNA signature (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Furthermore, Compared with other rognostic-related risk signatures, the signature was better of predicting prognosis in COAD patients.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e \u003cb\u003e1 Comprehensive analysis of multi-omics data to obtain lncRNAs related to COAD prognosis\u003c/b\u003e \u003c/p\u003e\n\u003ch2\u003e1.1 Lncrnas That Are Closely Related To Coad Prognosis\u003c/h2\u003e\n\u003cp\u003eAccording to the univariate Cox regression analyse, we identified a total of 483 candidate prognostic lncRNAs from the training cohort, and information on the top 20 lncRNAs is shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2\u003c/span\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 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eInformation on the top 20 candidate prognostic lncRNAs\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003elncRNA ID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLow 95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHigh 95% CI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eENSG00000274925\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.78E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.086\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.231\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eENSG00000272512\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.29E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.219\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.333\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eENSG00000275494\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.19E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.077\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eENSG00000258053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.59E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.173\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eENSG00000260563\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.04E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.086\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.131\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eENSG00000245281\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.09E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.362\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.219\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.600\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eENSG00000247095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.000138199\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.053\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eENSG00000235245\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.000149251\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.162\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eENSG00000272555\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.000187134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.437\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.283\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.675\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eENSG00000229380\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.000188209\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.309\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.507\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eENSG00000273456\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.000197802\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.253\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eENSG00000279148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.000207384\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.422\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.268\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.666\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eENSG00000228288\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.000246995\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.120\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eENSG00000269680\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.000250415\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.253\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.413\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eENSG00000227947\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.000294015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.591\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.445\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.786\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eENSG00000271781\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.000314732\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.108\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eENSG00000238113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.000381295\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.301\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.505\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eENSG00000230641\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.000382509\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.476\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.316\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.717\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eENSG00000226659\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.000402587\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.287\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eENSG00000251637\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.000431394\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.495\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.335\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.732\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e1.2 lncRNAs that are closely related to gene copy number variation\u003c/b\u003e \u003c/p\u003e \u003cp\u003eWe obtained lncRNAs that are closely related to gene copy number variation. A total of 137 lncRNAs that were significantly amplified on each fragment of the COAD genome (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA), including LINC00392 on the 13q22.1 segment (q\u0026thinsp;=\u0026thinsp;8.17E-12), LINC01598 on the 20q11.21 segment (q\u0026thinsp;=\u0026thinsp;5.75E-09) and LOC730183 on the 16p11.2 segment (q\u0026thinsp;=\u0026thinsp;0.0014485), were identified. On the other hand, a total of 261 lncRNAs that were significantly deleted on each fragment in the COAD genome (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB), including LINC00681 in the 8p22 segment (q\u0026thinsp;=\u0026thinsp;2.74E-45), LOC101928728 in the 1p36.11 segment (q\u0026thinsp;=\u0026thinsp;2.02E-09), and LINC00491 in the 5q22.2 segment (q\u0026thinsp;=\u0026thinsp;1.48E-05), were identified.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e1.3 lncRNAs that are closely related to gene mutations\u003c/h2\u003e \u003cp\u003eThrough MutSig2, we identified a total of 41 genes with significant mutation frequencies. The distribution of synonymous mutations, missense mutations, framework insertions or deletions, framework movements, nonsense mutations, cleavage sites and other nonsynonymous mutations in these 41 genes in TCGA COAD patient samples are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. We identified 41 genes, some of which have been reported to be closely related to the development of cancer, such as KRAS, TP53, APC, PIK3CA, and FBXW7. Among these 41 genes, we identified lncRNAs associated with gene mutations using each of the genes to mutate into a tag, and a rank-sum test was used to detect the difference between the expression of each lncRNA in the mutant and nonmutant groups. lncRNAs with a \u003cem\u003eP-\u003c/em\u003evalue\u0026thinsp;\u0026lt;\u0026thinsp;0.01 were considered to be associated with a gene mutation; thus, we obtained 2712 lncRNAs related to gene mutations.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch2\u003e2 Identification Of An 11-lncrna Signature For Coad Survival\u003c/h2\u003e\n\u003cp\u003eThe comprehensive analysis revealed 147 lncRNAs associated with amplifications, deletions, and mutations from a total of 483 candidate prognostic lncRNAs. We analysed the change trajectory of each independent variable (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). It can be seen that with the gradual increase in lambda, the number of independent coefficients becomes closer to zero. We used three-fold cross-validation to build the model. The confidence interval under each lambda is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB. As shown in the figure, the model was optimal when lambda\u0026thinsp;=\u0026thinsp;0.04078231. For this reason, we selected the lncRNAs obtained when lambda\u0026thinsp;=\u0026thinsp;0.04078231 as the target lncRNAs to construct the model.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAfter Lasso Cox regression narrowed the scope, we obtained 21 target lncRNAs that were used to construct the model. A multivariate Cox survival analysis was performed on 21 lncRNAs, and the 11 lncRNAs with the lowest AIC values (AIC\u0026thinsp;=\u0026thinsp;767.27) were used to construct the final model. Details of the 11 lncRNAs are shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The 11-lncRNA signature was then tested for its ability to predict survival in COAD patients.\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 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEleven lncRNAs identified as significantly associated with OS in the training cohort\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnsembl Gene ID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSymbol\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCoef\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eZ-score\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP-\u003c/em\u003evalue\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLow 95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eHigh 95% CI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eENSG00000269680\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAC008760.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.2664\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.645\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.516\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000438\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.727\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e34.115\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eENSG00000215039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCD27-AS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.3273\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.387\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.042341\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.903\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eENSG00000249550\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLINC01234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.4492\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.567\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.418\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.015587\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.089\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.255\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eENSG00000247095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMIR210HG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.3796\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.462\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.788\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000152\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.201\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.779\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eENSG00000180139\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eACTA2-AS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.9405\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.561\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.001866\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.416\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e4.632\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eENSG00000256546\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAC156455.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.6567\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.928\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.736\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.006223\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.205\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e3.087\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eENSG00000260805\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAC092803.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.5048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.503\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.043372\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e19.391\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eENSG00000273576\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAC009283.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.1432\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.031533\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.315\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eENSG00000246627\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCACNA1C-AS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.7294\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15.323\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.39E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4.216\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e55.69\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eENSG00000238113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLINC01410\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.0232\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.563\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.00248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e28.045\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eENSG00000235560\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAC002310.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.3988\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.372\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.017675\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.275\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e12.863\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e3 Determination and analysis of the 11-lncRNA signature in the training cohort\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe 11-lncRNA signature was then established using a multivariate Cox regression analysis with the following model:\u003c/p\u003e \u003cp\u003eRiskScore\u003csub\u003e11\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;2.2664*exp\u003csup\u003eAC008760.1\u003c/sup\u003e+0.3273*exp\u003csup\u003eCD27\u0026minus;AS1\u003c/sup\u003e+0.4492*exp\u003csup\u003eLINC01234\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e+\u0026thinsp;0.3796*exp\u003csup\u003eMIR210HG\u003c/sup\u003e+0.9405*exp\u003csup\u003eACTA2\u0026minus;AS1\u003c/sup\u003e+0.6567*exp\u003csup\u003eAC156455.1\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e+\u0026thinsp;1.5048*exp\u003csup\u003eAC092803.2\u003c/sup\u003e+0.1432*exp\u003csup\u003eAC009283.1\u003c/sup\u003e+2.7294*exp\u003csup\u003eCACNA1C\u0026minus;AS1\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e+\u0026thinsp;2.0232*exp\u003csup\u003eLINC01410\u003c/sup\u003e+1.3988*exp\u003csup\u003eAC002310.1\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThe risk score was calculated as the sum of the above gene expression values * the ordinal, and then we selected 0.9892846 as the cutoff (median risk score) and divided the samples into high-risk and low-risk groups. Finally, 247 patients were classified as low risk, and 110 patients were classified as high risk; significantly different OS rates were observed between the two groups in the training cohort (log-rank \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001, HR\u0026thinsp;=\u0026thinsp;2.014) Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). We acquired a five-year AUC of 0.83 according to the ROC curve for predicting survival in COAD patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). As the patient\u0026rsquo;s risk score increased, the OS rate significantly decreased, and the number of deaths in the high-risk group increased significantly (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). According to the changes in the expression levels of the 11 different lncRNAs in the signature observed with increases in the risk score, the expression of ENSG00000246627 was correlated with a low risk of COAD, and the other 10 lncRNAs were identified as risk factors based on their high expression and correlation with a high risk of COAD.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e4 Validation of the 11-lncRNA signature in the testing and GSE17536 cohorts\u003c/b\u003e \u003c/p\u003e \u003cp\u003eFirst, the 11-lncRNA signature was validated in the testing cohort; 88 patients were classified as low risk, and 31 patients were classified as high risk. There was a significant difference in OS between the two groups (log-rank \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0019, HR\u0026thinsp;=\u0026thinsp;3.374) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC). The five-year AUC was 0.66 according to the ROC curve (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). The results of the testing cohort were similar to those of the training cohort; as the patient\u0026rsquo;s risk score increased, the OS time decreased significantly, and the number of deaths in the high-risk group increased significantly (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). Moreover, 10 lncRNAs (all lncRNAs except ENSG00000246627) were identified as risk factors.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSimilarly, we used the same model and the same cut-off from the training cohort and verified the model\u0026rsquo;s robustness using an external independent data cohort (GSE17536). Ultimately, 99 patients were classified as low risk, and 78 patients were classified as high risk. A significant difference in OS was observed between the two groups (log-rank \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0076, HR\u0026thinsp;=\u0026thinsp;1.864) (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC). The five-year AUC was 0.71 according to the ROC curve (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB). The GSE17536 cohort showed similar results to the TCGA training cohort. As the risk score increased, the survival time decreased significantly, and the number of deaths in the high-risk group increased. The expression of the 11 different signature lncRNAs also increased with the increase in the risk score, indicating that high expression of the 11 lncRNAs is associated with a high risk of COAD and could serve as a risk factor (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e\u003cb\u003e 5 Independent predictive power of the 11-lncRNA signature according to the MSI status, tumour stage and clinicopathological characteristics\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe patients were subdivided into a high-risk subgroup and a low-risk subgroup based on different MSI statues, and the 11-lncRNA signature was used to predict OS; the OS rate was significantly different between the high-risk and low-risk subgroups in the MSI-L and MSS groups (excluding MSH) (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA-C). Based on their tumour stage, patients were subdivided into a high-risk group and a low-risk group in each stage, and the 11-lncRNA signature revealed no significant difference in OS at the II, III and IV stages (all stages except stage I) between the two groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eD-G). Furthermore, these results demonstrate that the 11-lncRNA signature model can better predict the OS of patients with different MSI statuses and tumour stages.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe systematically analysed the clinical information of the TCGA and GSE17536 patients, including age, sex, lymph node invasion status, pathology (T, N, and M classifications), tumour stage, and the 11-lncRNA signature grouping information (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e4\u003c/span\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 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eIdentification of the clinical factors and clinical independence associated with prognosis with univariate and multivariate Cox regression analyses\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eUnivariate analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eMultivariable analysis\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eHR\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e95% CI of HR\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eP\u003c/span\u003e\u003cb\u003e-value\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eHR\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e95% CI of HR\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eP\u003c/span\u003e\u003cb\u003e-value\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTCGA training dataset\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e11-lncRNA risk score\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRisk score (High/Low)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.14\u0026ndash;7.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.14E-12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.58\u0026ndash;7.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.26E-08\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\u003e1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.99\u0026ndash;1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.01\u0026ndash;1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex(Male/Female)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.72\u0026ndash;1.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.60\u0026ndash;1.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.921\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT3/T4 vs T1/T2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.81\u0026ndash;18.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.84-15.219\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.082\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN1/N2 vs N0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.85\u0026ndash;5.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.04E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.091\u0026ndash;1.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.065\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM1 vs M\u003csub\u003e0\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.61\u0026ndash;4.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.72E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.88\u0026ndash;2.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.136\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage Ⅲ/Ⅳ vs Stage Ⅰ/Ⅱ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.00-5.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.94E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.94\u0026ndash;29.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTCGA validation dataset\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e11-lncRNA risk score\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRisk score (High/Low)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.36\u0026ndash;6.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.13\u0026ndash;8.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.027\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\u003e1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.98\u0026ndash;1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.99\u0026ndash;1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.083\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex (Male/Female)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.42\u0026ndash;2.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.21\u0026ndash;1.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.226\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT3/T4 vs T1/T2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.29\u0026ndash;3.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.13\u0026ndash;4.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.756\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN1/N2 vs N0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.13\u0026ndash;5.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.059\u0026ndash;4.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.583\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM1 vs M\u003csub\u003e0\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.31\u0026ndash;19.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.17E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.88\u0026ndash;18.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage Ⅲ/Ⅳ vs Stage Ⅰ/Ⅱ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.24\u0026ndash;6.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.41\u0026ndash;61.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.205\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGSE17536 validation dataset\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e11-lncRNA risk score\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRisk score (High/Low)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.17\u0026ndash;2.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0085\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.02\u0026ndash;2.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.039\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\u003e1.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.98\u0026ndash;1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.002\u0026ndash;1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex (Male/Female)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.69\u0026ndash;1.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.71\u0026ndash;1.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.521\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage Ⅲ/Ⅳ vs Stage Ⅰ/Ⅱ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.39\u0026ndash;7.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.28E-07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.226\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.36\u0026ndash;7.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.21E-06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn the TCGA training cohort, we found significant survival-related correlations in the clinicopathological characteristics, with the exception of age and sex, according to the univariate Cox regression analysis, but we found that only the risk score (HR\u0026thinsp;=\u0026thinsp;4.39, 95% CI\u0026thinsp;=\u0026thinsp;2.58\u0026ndash;7.46, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4.26E-08), age, and stage III/IV vs sage I/II were significantly related to survival according to the multivariate Cox regression analysis. The 11-lncRNA signature was verified to be clinically independent.\u003c/p\u003e \u003cp\u003eIn the TCGA testing cohort, the risk score, N1/N2 vs N0, M1 vs M0, and stage III/IV vs stage I/II were significantly associated with survival according to the univariate Cox regression analysis, but only the risk score (HR\u0026thinsp;=\u0026thinsp;3.02, 95% CI\u0026thinsp;=\u0026thinsp;1.13\u0026ndash;8.08, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.027) and M1 vs M0 were significantly related to survival according to the multivariate Cox regression analysis. The 11-lncRNA signature was also verified to be clinically independent.\u003c/p\u003e \u003cp\u003eIn the GSE17536 cohort, the risk score and stage III/IV vs stage I/II were significantly associated with survival according to the univariate Cox regression analysis, but only the risk score (HR\u0026thinsp;=\u0026thinsp;1.65, 95% CI\u0026thinsp;=\u0026thinsp;1.02\u0026ndash;2.67, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.039), age and stage III/IV vs stage I/II were significantly associated with survival according to the multivariate Cox regression analysis.\u003c/p\u003e \u003cp\u003eIn conclusion, the 11-lncRNA signature is a prognostic indicator independent of other clinical factors and shows independent predictive performance with clinical application value.\u003c/p\u003e \u003cp\u003e \u003cb\u003e6 Identification of the 11-lncRNA signature-associated biological pathways in the training cohort\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe signalling pathways associated with the 11-lncRNA signature significantly enriched in the TCGA training cohort were detected by GSEA (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The signalling pathways that were significantly enriched in the high-risk and low-risk groups, were the Notch signalling pathway, the VEGF signalling pathway, the P53 signalling pathway and the cell cycle; all were significantly associated with the development and metastasis of COAD (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\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 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eKEGG pathways significantly enriched in the high-risk and low-risk groups detected by GSEA\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNAME\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSIZE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eES\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNES\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNOM P-val\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFDR q-val\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFWER P-val\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKEGG_NOTCH_SIGNALING_PATHWAY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.468\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.633\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.726\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKEGG_VEGF_SIGNALING_PATHWAY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.354\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.486\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.916\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKEGG_P53_SIGNALING_PATHWAY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.434\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.660\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.936\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.632\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKEGG_ALANINE_ASPARTATE_AND_GLUTAMATE_METABOLISM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.464\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.601\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.552\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.752\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKEGG_RIBOSOME\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.757\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.773\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.747\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.387\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKEGG_UBIQUITIN_MEDIATED_PROTEOLYSIS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.418\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.658\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.632\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.638\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKEGG_BASAL_TRANSCRIPTION_FACTORS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.490\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.575\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.466\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.801\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKEGG_CELL_CYCLE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.459\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.609\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.657\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.745\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e7 Comparison of the 11-lncRNA signature with other COAD prognostic signatures\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe ROC and OS KM curves of the five models are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e. Significantly different OS rates were observed between the high-risk and low-risk groups using the six-lncRNA signature established by Zhao[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] (log-rank \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0014,HR\u0026thinsp;=\u0026thinsp;2.03) (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eB). We acquired a five-year AUC of 0.65 and a ten-year AUC of 0.67 according to the ROC (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eA). Significantly different OS rates were also observed between the two groups using the two-lncRNA signature established by Xue[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] (log-rank \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.018, HR\u0026thinsp;=\u0026thinsp;1.73) (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eD), and we obtained a five-year AUC of 0.54 and a ten-year AUC of 0.47 (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eC). Significantly different OS rates were also observed with the 14-lncRNA signature reported by Xing[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e](Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eF), and we found a five-year AUC of 0.66 and a ten-year AUC of 0.53 (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eE). In addition, the six-lncRNA signature established by Fan[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e](Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eH) yielded a five-year AUC of 0.64 and a ten-year AUC of 0.41(Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eG), and the 15-lncRNA signature obtained by Wang[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eJ) resulted in a five-year AUC of 0.78 and a ten-year AUC of 0.67 (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eI). The final comparison showed that our model was slightly better than the 15-lncRNA model and significantly better than the other four models.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eCRC is a common digestive tract tumour that is a serious threat to the health of patients. According to recent statistics, there are approximately 1.45\u0026nbsp;million new cases of CRC each year, which made it the third most prevalent cancer in 2018[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] and the second most prevalent cancer in American males in 2019[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Approximately 694,000 deaths have been reported every year, making the second most common cause of cancer-related death worldwide[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The recurrence and metastasis of CRC seriously affect the efficacy and prognosis of treatment. Identifying the risk of recurrence and metastasis can help us guide early intervention for the treatment of CRC, ultimately improving the prognosis. Therefore, it is very urgent to study and identify one or more efficient molecular models for predicting prognosis to guide treatment options and to improve the survival quality of CRC patients.\u003c/p\u003e \u003cp\u003elncRNAs are a major class of ncRNAs with a length of more than 200 base pairs and are not translated into proteins[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Over the past decade, it has become clear that certain lncRNAs have strong potential, and an in-depth study is needed to elucidate their mechanism of action. lncRNAs not only control the nuclear structure[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] but also regulate the expression of adjacent genes and act as amplifiers, with remarkable tissue specificity through various mechanisms[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. lncRNAs have been shown to directly regulate gene expression at the transcriptional, posttranscriptional and epigenetic levels. lncRNAs have long nucleotide chains and intricate secondary structures and can interact with genomic DNA, chromatin, transcription factors, chromatin regulators, spliceosomes and other nuclear proteins[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. lncRNAs are known to serve as tumour regulators and participate in complex networks of biological regulation[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Therefore, the identification of lncRNAs closely related to tumour prognosis and the establishment of a signature that can predict the risk of tumour prognosis will be helpful for improving the prevention and treatment of tumours. Zhang GH[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] identified a novel four-lncRNA signature using Cox regression analysis to identify lncRNAs that correlated with the prognosis of 111 laryngeal cancer patients from the TCGA. The signature was shown to predict the prognosis of patients with laryngeal cancer and may influence the prognosis of laryngeal cancer through many pathways, such as regulating immunity and tumour apoptosis. Jie Li[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] also identified a five-lncRNA signature to predict the risk of tumour recurrence in breast cancer (BC) patients and found that it was independent of clinical prognostic factors, such as BC subtypes and adjuvant treatments.\u003c/p\u003e \u003cp\u003eRecently, an increasing number of researchers have focused on the role of lncRNAs in the development and progression of CRC and its significance to clinical prognosis[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Many lncRNAs, such as MALAT1 and HOTAIR, have also been used as biomarkers in CRC[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Several lncRNAs have not only been reported as markers in CRC diagnosis but also been shown to be correlated with patient prognosis (e.g., CCAL, PURPL and lnc-GNAT1-1)[\u003cspan additionalcitationids=\"CR34\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Therefore, this method was also used to identify a CRC-related lncRNA signature for predicting the prognosis of CRC[\u003cspan additionalcitationids=\"CR18 CR19 CR20\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] by analysing different datasets.\u003c/p\u003e \u003cp\u003eAt the same time, as high-throughput multi-omics sequencing data have laid a solid foundation for identifying genes associated with cancer prognosis, multi-omics data analysis can reveal the mechanisms of cancer development from multiple perspectives[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. We identified closely related genomics, gene mutations, epigenetics, and functions of lncRNAs that are inherently regulated by COAD[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. We show that dysregulated lncRNAs may be new prognostic and diagnostic biomarkers or therapeutic targets for clinical applications. Therefore, we identified a robust lncRNA signature through an integrative analysis of prognosis-related lncRNA, copy number variation, mutation and transcriptome data with the help of multi-omics data analysis technology. In this study, we fully integrated and analysed lncRNAs that are related not only to prognosis but also to copy number variations, mutations and transcriptome regulation in 359 COAD samples from the TCGA training cohort. We developed an 11-lncRNA signature that was validated in testing and GSE17536 cohorts. We also found that the signature had good robustness and good predictive ability of the prognosis risk in other independent datasets. Moreover, we compared the 11-lncRNA signature with other COAD prognostic signatures[\u003cspan additionalcitationids=\"CR18 CR19 CR20\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] to validate the good prediction of the prognosis risk of the 11-lncRNA signature. We found that the AUC of the 11-lncRNA signature was better than that of the other five signatures. Because the researchers did not confirm that the lncRNAs directly regulate gene expression at the transcriptional, post-transcriptional and epigenetic levels, we focused on the predictive role of lncRNAs in tumour prognosis and established lncRNA signatures to predict prognosis merely by analysing and identifying lncRNAs associated with prognosis. However, the intrinsic interactive relationship between lncRNAs, genomics, gene mutations and epigenetics was not investigated. Ultimately, the studies failed to identify a good lncRNA signature to predict the risk of CRC prognosis. Significant differences in OS outcomes between the high-risk and low-risk groups were obtained using the described method[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFurthermore, we found that the 11-lncRNA signature had independent predictive power from the MSI status and tumour stage (e.g., MSI-L and MSS patients and patients in stages II, III and IV, excluding stage I), which may be due to a good survival rate in COAD patients with stage I [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. This study also showed that the 11-lncRNA signature was a prognostic indicator independent of other clinical factors and had independent predictive performance with clinical application value. Additionally, we identified the 11-lncRNA signature-associated biological pathways that were significantly enriched in COAD patients as detected by GSEA. In summary, we determined that the Notch signalling pathway, the VEGF signalling pathway, the P53 signalling pathway and the cell cycle are significantly associated with the development and metastasis of COAD[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn summary, we constructed a novel 11-lncRNA signature that can be used to predict the prognosis of patients with COAD and exploited the possible underlying mechanisms involved. The 11-lncRNA signature may indicate the potential roles of lncRNAs in COAD pathogenesis. The results will provide molecular diagnostic markers and therapeutic targets with clinical implications in COAD patients. Finally, we hope that all of the above results will be verified in basic experiments and clinical trials in further studies.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn our study, we integrated analysis of the lncRNAs were not only related to the prognosis, but also related to copy number variation, mutation and transcriptome data to identify and constructe a lncRNA signature to predict prognosis in COAD patients. And the signature was validated in the testing and GSE17536 cohorts and was independent of the MSI status and clinical prognostic factors. The signature was significantly better than the other five lncRNA signatures have been reported in the COAD.\u003c/p\u003e"},{"header":"Materials And Methods","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e1 Patients and data collection\u003c/h2\u003e \u003cp\u003eWe downloaded COAD RNA-Seq data, clinical follow-up information and copy number variation data from the SNP 6.0 chip in the TCGA database from the UCSC cancer browser (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://xenabrowser.net/datapages/\u003c/span\u003e\u003c/span\u003e), and we downloaded the mutation comment file (MAF) from the GDC client. We also downloaded the GSE17536 dataset, which included COAD expression profile data and clinical information, from the Gene Expression Omnibus (GEO) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/geo/\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWe preprocessed the downloaded data, downloaded the fragments per kilobase of transcript per million mapped reads (FPKM) RNA-Seq data from the TCGA. The R package \"caret\" was used to randomly divide the samples into the training cohort (359 samples) and the testing cohort (119 samples). The lncRNA expression profile data is extracted according to the Ensemble ID of lncRNA in the GENCODE database. SeqMap was used to compare GSE17536 expression data (no mismatch was allowed). A total of 5,076 probes were annotated on the lncRNAs. All selected expression datasets in the training, testing and GSE17536 cohorts were log\u003csub\u003e2\u003c/sub\u003e transformed for standardization.\u003c/p\u003e \u003cp\u003e Ultimately, a total of 478 samples from the TCGA were randomly divided at a ratio of 3:1 into a training cohort (n\u0026thinsp;=\u0026thinsp;359) and a testing cohort (n\u0026thinsp;=\u0026thinsp;119), and 177 samples were obtained from the GEO database (GSE17536). We obtained clinical pathology data, including the patient age, survival status, sex, lymph node metastasis status, T classification, N classification, M classification and tumour stage (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e1\u003c/span\u003e), from the three cohorts and found no significant differences among the groups.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClinical pathology data of the three cohorts\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTCGA training cohort (n\u0026thinsp;=\u0026thinsp;359)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTCGA testing cohort (n\u0026thinsp;=\u0026thinsp;119)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGSE17536 (n\u0026thinsp;=\u0026thinsp;177)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eAge (years)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.724\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e118\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eSurvival Status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLiving\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e276\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.949\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e104\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDead\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e73\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eGender\u003c/b\u003e\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\u003e157\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.349\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e81\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003eT\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.188\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eN\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN 0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e184\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eM\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM 0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003eTumour stage\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStage Ⅰ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStage Ⅱ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStage Ⅲ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStage Ⅳ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e2 Identification of lncRNAs that are closely related to the prognosis of COAD\u003c/b\u003e \u003c/p\u003e \u003cp\u003eWe performed a univariate Cox regression analysis to establish the correlation of lncRNA expression with overall survival (OS) in the TCGA training cohort, and the lncRNAs with significant P values (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) were selected as candidates.\u003c/p\u003e \u003cp\u003e \u003cb\u003e3 Identification of lncRNAs that are closely related to gene copy number variation\u003c/b\u003e \u003c/p\u003e \u003cp\u003eWe used GISTIC 2.0 to identify genes with significant amplification or deletion from copy number variation data in the TCGA training cohort. We set a parameter threshold for fragments with amplification or deletion lengths greater than 0.1 and significant \u003cem\u003eP-\u003c/em\u003evalues (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The significantly amplified fragments in the genome and the genes amplified on each of the fragments were recorded and the significantly deleted fragments in the genome and the genes that were significantly deleted on each fragment were recorded and incorporated to establish the lncRNAs associated with copy number variation.\u003c/p\u003e \u003cp\u003e4 \u003cb\u003eIdentification of lncRNAs that are closely related to gene mutations\u003c/b\u003e\u003c/p\u003e \u003cp\u003eWe used MutSig2 to identify genes with significant mutations from the mutation annotation data of the TCGA training cohort, and with a threshold of \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05. We identified lncRNAs associated with gene mutations using the rank-sum test to detect the difference in the expression of each lncRNA between the mutant and nonmutant groups, and each gene mutation was used as a label. lncRNAs with significant P values (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) were considered to be associated with a gene mutation. Finally, the lncRNA dataset related to gene mutations was established.\u003c/p\u003e \u003cp\u003e \u003cb\u003e5 Development and validation of the robustness of the lncRNA signature\u003c/b\u003e \u003c/p\u003e \u003cp\u003eBy intersecting these three lncRNA sets (Prognosis-related lncRNAs, copy number variation-related lncRNAs and mutation-related lncRNAs), we obtained the targeted lncRNAs. The least absolute shrinkage and selection operator (Lasso) method, which is a compression estimate, was used to narrow the lncRNA range. We used the R package glmnet for the Lasso Cox regression analysis[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Furthermore, we performed a multivariate Cox regression analysis, and stepwise regression was used to reduce the number of lncRNAs again. The lowest Akaike information criterion (AIC) value as the final model.\u003c/p\u003e \u003cp\u003eRiskScore\u0026thinsp;=\u0026thinsp;coefficient*exp \u003csup\u003elncRNA1\u003c/sup\u003e+oefficient*exp \u003csup\u003elncRNA2\u003c/sup\u003e+oefficient*exp \u003csup\u003elncRNA3\u003c/sup\u003e+....+coefficient*exp \u003csup\u003elncRNAn\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe risk score of each sample in the TCGA training cohort was then calculated. Based on the median risk score, the COAD patients were divided into two groups: a high-risk group and a low-risk group. A receiver operating characteristic (ROC) curve was used to test the accuracy of lncRNA signature to predict prognosis. Finally, the lncRNA signature was verified in the testing cohort. The GSE17536 cohort used the same model and the same cutoff as the TCGA training cohort.\u003c/p\u003e \u003cp\u003e \u003cb\u003e6 Independent predictive power of the lncRNA signature based on different MSI statuses, tumour stages and clinicopathological characteristics\u003c/b\u003e \u003c/p\u003e \u003cp\u003eWe divided the TCGA cohort into MSI-high (MSI-H), MSI-low (MSI-L) and microsatellite stable (MSS) groups according to the MSI phenotype information described by the TCGA network study[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. We further analyze the relationship between this lncRNA signature and MIS status.\u003c/p\u003e \u003cp\u003eTo identify the lncRNA signature model for clinical applications, we analysed the relationship between clinical information (including age, sex, lymph node invasion status, pathology (T, N, and M classifications), tumour stage) and lncRNA signature through univariate and multivariate Cox regression analyses.\u003c/p\u003e \u003cp\u003e \u003cb\u003e7 Identification of the lncRNA signature-associated biological pathways with gene set enrichment analysis (GSEA)\u003c/b\u003e \u003c/p\u003e \u003cp\u003eWe used GSEA and the \u0026ldquo;cluster profile R\u0026rdquo; package to conduct Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Significantly enriched pathways in the high-risk and low-risk groups in the TCGA training cohort were identified. The selected gene set was c2.cp.kegg.v6.0.symbols, which contained the KEGG pathways. In KEGG pathway analysis, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered to indicate statistical significance.\u003c/p\u003e \u003cp\u003e \u003cb\u003e8 Comparison of the lncRNA signature with other COAD prognostic signatures\u003c/b\u003e \u003c/p\u003e \u003cp\u003eBy reviewing the literature, we identified five prognostic-related risk models, a six-lncRNA signature (PMID: 30396175)[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], a two-lncRNA signature (PMID: 29254165)[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], a 14-lncRNA prognostic signature (PMID: 29565464)[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], a six-lncRNA signature (PMID: 29227531)[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] and a 15-lncRNA signature (PMID: 30510449)[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], for comparison with our lncRNA signature. To make the models comparable, we performed a multivariate Cox regression analysis to calculate the risk scores of the training set samples based on the corresponding genes in the three models. We evaluated the ROC of the five models and then divided the samples into high-risk and low-risk groups according to the median risk score and analysed the difference in OS between the two groups.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors are grateful to\u0026nbsp;Ruiqing Chen and Lengxi Fu\u0026nbsp;for technical assistance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisclosure statement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo potential conflict of interest was reported by the authors.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eZC,\u0026nbsp;YL and\u0026nbsp;SL\u0026nbsp;conceived this experiment;\u0026nbsp;ZC,\u0026nbsp;YL\u0026nbsp;and\u0026nbsp;JG\u0026nbsp;performed the experiments and dataanalysis;\u0026nbsp;ZC,\u0026nbsp;YL,\u0026nbsp;JG\u0026nbsp;and\u0026nbsp;SC\u0026nbsp;wrote and revisedthe manuscript. All authors approved the final version of themanuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by grants from the Fujian health youth research project (NO. 2019-2-20), the Fujian natural fund project (NO. 2019J01448) and the Fujian science and technology innovation joint fund Project (NO. 2019Y9133).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available on request from the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe research used the published database data to conduct secondary mining research, and the research does not involve ethical approval.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSiegel Rebecca L,Miller Kimberly D,Jemal Ahmedin. 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Molecular genetics of colorectal cancer.Annu Rev Pathol.2011;6: 479-507.\u003c/li\u003e\n\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":"Long noncoding RNA, Colon cancer, Integrative analyses, Prognostic signature, Mechanism","lastPublishedDoi":"10.21203/rs.3.rs-641736/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-641736/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackgroud: \u003c/strong\u003eTumour recurrence and metastasis lead to poor prognosis incolon cancer(COAD). Therefore We aimed to identify a lncRNA signature through an integrative analysis of copy number variation, mutation and transcriptome data to predict prognosis and explore its internal mechanism.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eThe lncRNA expression profile were collected from\u003ca href=\"http://www.baidu.com/link?url=p0JxpG6C4_hPGJBteNLsce1O1sPbFVx0a9tPEvY6Juv4orMZJBsVpCDYNLmxxBQD\" rel=\"noopener noreferrer\" target=\"_blank\"\u003eThe Cancer Genome Atlas\u003c/a\u003e (TCGA) and Gene Expression Omnibus (GEO). TCGA data was randomly divided 3:1 intotraining andtesting cohort. In the training, weperformed integrated analyses of three candidate lncRNA sets that correlated with prognosis, copy number variations and mutations to establish a signature through Cox regression analysis. The robustness was determined in the testing and GEO.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eAn 11-lncRNA signature that was significantly associated with prognosiswas constructed in the training (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.0001, HR=2.014) , And this signature was validated in the testing(\u003cem\u003eP\u003c/em\u003e=0.0019, HR=3.374) and GSE17536(\u003cem\u003eP\u003c/em\u003e=0.0076, HR=1.864). The signature is significantly related to MSI status and clinical prognostic factors. The prognostic-relatedrisk scores were significantly excellent than the other five models have been reported. Furthermore, GSEA suggested that the signature was involved in COAD development and metastasis-related pathways.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eWe identifiedansignature has strong robustness and can stably predict the prognosis of COAD in different platformsand may be implicated in COAD pathogenesis and metastasis and applied clinically as a prognostic marker.\u003c/p\u003e","manuscriptTitle":"Integrative Analysis and Identification of an Excellent lncRNA Signature to Predict Prognosis in Patients with COAD","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2021-07-09 19:49:03","doi":"10.21203/rs.3.rs-641736/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":"970a264d-e421-47fa-83b2-16fa8a1253c5","owner":[],"postedDate":"July 9th, 2021","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":5580825,"name":"Molecular Biology"},{"id":5580826,"name":"General Cell Biology \u0026 Physiology"}],"tags":[],"updatedAt":"2021-08-06T01:59:07+00:00","versionOfRecord":[],"versionCreatedAt":"2021-07-09 19:49:03","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-641736","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-641736","identity":"rs-641736","version":["v1"]},"buildId":"ehx78VzkSd0WSzXnipQa-","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}