Comprehensive Analysis of Differentially Expressed Long Non-Coding RNA-mRNA in The Adenoma–Carcinoma Sequence of DNA Mismatch Repair Proficient Colon Cancer

Comprehensive Analysis of Differentially Expressed Long Non-Coding RNA-mRNA in The Adenoma–Carcinoma Sequence of DNA Mismatch Repair Proficient Colon Cancer | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Comprehensive Analysis of Differentially Expressed Long Non-Coding RNA-mRNA in The Adenoma–Carcinoma Sequence of DNA Mismatch Repair Proficient Colon Cancer Wenkun Li, Qian Li, Jiang Ge, Yun Wang, Nanshan Li, Yueqiong Lao, and 9 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-38704/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background DNA mismatch repair proficient colon cancer (pMMR CC) is the most common subtype of sporadic CC. However, the role of long non-coding RNAs (lncRNAs) in pMMR CC carcinogenesis has not been fully elucidated. Methods In the present study, we conducted transcriptomic analysis of lncRNAs-mRNAs in five low-grade intraepithelial neoplasia (LGIN), five high-grade intraepithelial neoplasia (HGIN), four pMMR CC, and five normal control (NC) tissues. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment pathway, and Co-Expression Network analyses were performed to elucidate the functions of lncRNAs and mRNAs as well as their interactions. Quantitative real-time polymerase chain reaction (qRT-PCR) was used to validate five dysregulated lncRNAs in a large set of colon tissues. Receiver-operating characteristic (ROC) curves were employed to evaluate the performance of the candidate lncRNAs. Results A set of 5783 differentially expressed lncRNAs and 4483 differentially expressed mRNAs were detected among the LGIN, HGIN, pMMR CC, and NC samples. These differentially expressed lncRNAs and mRNAs were assigned to 275 significant GO terms and 179 significant KEGG enriched pathways. qRT-PCR confirmed that the expression of five selected lncRNAs (ENST00000521815, ENST00000603052, ENST00000609220, NR_026543, and ENST00000545920) were consistent with the microarray data. ROC analysis showed that four lncRNAs (ENST00000521815, ENST00000603052, ENST00000609220, and NR_026543) had larger area under the ROC curve (AUC) values than serum carcinoembryonic antigens, thereby distinguishing NC from pMMR CC. Conclusions Several lncRNAs play various roles in the adenoma–carcinoma sequence and may serve as potential biomarkers for the early diagnosis of pMMR CC. Cancer Biology Colon cancer long non-coding RNA DNA mismatch repair proficient adenoma–carcinoma sequence microarray Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Background Colorectal cancer (CRC) is the third most commonly diagnosed cancer worldwide, comprising over 1.8 million new cases in 2018. The disease is also the second leading cause of cancer deaths [ 1 ] . More than 70% of CRC cases are located in the colon, which is also called colon cancer (CC) [ 2 ] . With the development of the economy and the westernization of diets and lifestyles, the incidence of CRC is likely to increase in Asia. According to the latest data from the Chinese National Cancer Center, CRC ranks fifth in both incidence and mortality among cancers in China [ 3 ] . CRC is a heterogeneous disease with a progressive accumulation of genetic and epigenetic alterations [ 4 – 6 ] . About two-thirds of CRC cases arise with unknown contributions from germline factors or a significant family history of cancer or inflammatory bowel disease, defined as sporadic CRC. Three major genetic mechanisms underlie the pathogenesis of sporadic CRC [ 7 ] , namely chromosomal instability (CIN), microsatellite instability (MSI), and the epigenomic CpG island methylator phenotype (CIMP). CIN, the first described and the most common pathway, accounts for 80%-85% of sporadic CRC [ 8 ] . CIN CRCs are non-hypermutated with DNA proficient mismatch repair (pMMR) functions [ 7 ] . The mechanism responsible for CIN is different from that responsible for MSI and CIMP. Understanding the molecular mechanisms underlying CRC is critically important for the clinical prognosis and therapeutic response. A meta-analysis of 63 eligible studies (10126 patients with CRC) showed a worse prognosis for CRC patients with CIN/pMMR. CIN and MSI status can be evaluated as a predictor of prognosis [ 9 ] . Additionally, stage II CRC patients with defective mismatch repair (dMMR) or high-level MSI (MSI-H) have better prognosis but no benefits from fluorouracil chemotherapy [ 10 , 11 ] . More than 90% of colorectal tumorigenesis cases follow the adenoma–carcinoma sequence (ACS), with multiple gene mutations and abnormal activation of signaling pathways [ 12 , 13 ] . Mutations in the adenomatous polyposis coli (APC) gene occur early during colorectal tumorigenesis, followed by the activation of the KRAS gene and the inactivation of the TP 53 gene, which are often associated with CIN [ 14 ] . Long non-coding RNAs (lncRNAs) are a class of non-coding RNAs with more than 200 nucleotides that are not translated into proteins. lncRNAs can regulate gene expression at different levels, including epigenetically, transcriptionally, and post-transcriptionally [ 15 ] . Existing studies have shown the aberrant expression of lncRNAs is associated with tumorigenesis, metastasis, and prognosis in various cancers. For instance, decreased expression of the lncRNA SATB2-AS1 promotes metastasis and influences the tumor microenvironment in CRC by regulating SATB2, thereby resulting in poor survival [ 16 ] . Overexpression of the lncRNA UCA1 contributes to the immune escape of cancer cells and protects PDL1 expression from miRNA repression in gastric carcinoma, serving as a potentially novel target for immunotherapy [ 17 ] . Aberrant expression of lncRNAs is involved in the evolution of several types of tumors; however, the differential expression and the role of lncRNAs in ACS for CRC remain to be elucidated. Previous studies on the lncRNA expression profiles of CC were generally performed on all cancers in the colon together including rectal cancer, thereby neglecting the heterogeneity of molecular subtypes of CC. Although often linked together, CC and rectal cancer are different when it comes to treatment. Thus far, the differential expression profile for lncRNAs in the normal colonic mucosa-pMMR adenoma-pMMR sporadic CC sequence has not been reported. In the present study, we aimed to characterize the expression profile of lncRNAs in the malignant evolution process for pMMR sporadic CC using transcriptome microarray technology. Subsequently, differentially expressed lncRNAs were investigated using bioinformatics analysis and validated with quantitative real-time polymerase chain reaction (qRT-PCR). The findings provide useful candidates for the diagnosis and treatment of CC. Materials And Methods Clinical samples A total of 244 sporadic colonic tissues, including 63 adenomas of low-grade intraepithelial neoplasia (LGIN), 32 adenomas of high-grade intraepithelial neoplasia (HGIN), 66 pMMR CC, eight dMMR CC, and 75 normal control (NC) samples from adjacent ( ≥10 cm) hyperplastic or inflammatory polyps were sampled at Beijing Shijitan Hospital (Beijing, China) from 2018 to 2019. The samples were immediately placed in RNALater solution (Cat. No. 76104, Qiagen Co, GmBH, Germany) for 24 hours at 4°C. Freshly frozen samples were stored at -80℃ for RNA extraction. None of the patients received targeted therapy, chemotherapy, radiotherapy, or intervention therapy. The study was approved by the ethics committee of Beijing Shijitan Hospital (No: 2018-59) and informed consent was obtained from each participant. Screening of pMMR samples and total RNA isolation As in our previous study [18] , multiplex PCR and immunohistochemistry were used to determine the MMR status. pMMR colonic tissues were defined as tissue samples with a low frequency of MSI (MSI-L) or microsatellite stability (MSS, no marker tested). Total RNA was extracted from the tissue samples using RNAiso Plus (Code No. 9109, TAKARA, Japan). RNA integrity was confirmed with a NanoDrop ND-2000 spectrophotometer (Thermo Fisher Scientific, Rochester, NY, USA) and Agilent Bioanalyzer 2100 (Agilent Technologies, Santa Clara, CA, US), and further purified with a RNeasy mini kit (Cat. # 74106, Qiagen, GmBH, Germany) and RNase-Free DNase Set (Cat. #79254, Qiagen Co, GmBH, Germany). RNA samples with integrity ≥ 7.0 and a 28S:18S ratio ≥ 0.7 were used for further experiments. Microarrays analysis The lncRNA and mRNA expression profiles in multistage colonic mucosa tissues (five NC, five LGIN, five HGIN, and four pMMR CC samples) were determined with the Agilent custom SBC Human (4*180 K) lncRNA Microarray V6.0 (Product No. G4862A-074348; Agilent Technologies, Santa Clara, CA). Total RNA was amplified and labeled with the Low Input Quick Amp WT Labeling Kit (Cat. No. 5190-2943, Agilent Technologies, Santa Clara, CA, US) following the manufacturer’s instructions. Labeled cRNAs were purified with the RNeasy mini kit (Cat. No. 74106, Qiagen, GmBH, Germany). Each slide was hybridized with 1.65 μg Cy3-labeled cRNA using a Gene Expression Hybridization Kit (Cat. No. #5188-5242, Agilent Technologies, Santa Clara, CA, US) in a hybridization oven (Cat. No. # G2545A, Agilent Technologies, Santa Clara, CA, US) for 17 h. The slides were washed in staining dishes (Cat. No. 121, Thermo Shandon, Waltham, MA, US) with the Gene Expression Wash Buffer Kit (Cat. No. #5188-5327, Agilent Technologies, Santa Clara, CA, US) following the manufacturer’s instructions and scanned with an Agilent Microarray Scanner (Cat. No. # G2565CA, Agilent Technologies, Santa Clara, CA, US) with default settings (Dye channel: Green, Scan resolution = 3 μm, PMT 100%, 20 bit). The data were extracted with Feature Extraction software 10.7 (Agilent Technologies, Santa Clara, CA, US). Data processing and identification of DEGs Series test of cluster (STC) analysis Raw data were normalized with the Quantile algorithm in GeneSpring Software 11.0 (Agilent Technologies, Santa Clara, CA, US). Cluster analysis was performed to systematically and intuitively display the relatedness between samples. Differential genes expression (DEGs) among the four groups were filtered with the F test in the random variance model (RVM) using P values < 0.05 and Benjamini-Hochberg false discovery rate (FDR) < 0.05 as the significant cutoff criteria [19] . STC analysis was employed to identify the most probable set of clusters responsible for the observed ACS series by profiling the dynamic nature of the temporal gene expression. NC, LGIN, HGIN, and pMMR CC were set as different points to identify significant trending models related to multistage colonic mucosa tissue and their associated DEGs by STC analysis. Clusters with P values < 0.05/26 were considered statistically significant. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analysis Gene ontology (GO) analysis was performed to annotate DEGs in three categories: molecular functions (MF), biological processes (BP), and cellular components (CC). Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis was performed to identify the functional pathways and pathways for molecular interactions, reactions, and relation networks. The statistically significant threshold was set as P value < 0.01 and FDR <0.05. Construction of co-expression and transcription factor (TF) regulatory network To identify co-expressed lncRNA-mRNA pairs and hub DEGS, the lncRNA-mRNA co-expression network was constructed based on the gene expression value and the Pearson's correlation coefficient (PCC) between the differentially expressed lncRNAs and mRNAs. |PCC| ≥ 0.8 and P value < 0.01 were considered as statistically significantly relevant. In order to distinguish the TF regulatory network in the stepwise process encompassing CC progression, the interactions were predicted starting from a TF by searching the conserved TF binding sites within a putative promoter area 2000 bp upstream and 500 bp downstream of the transcriptional initiation site of co-expressed genes. Validation by quantitative real-time PCR (qRT-PCR) The expression levels of five lncRNAs were examined by qRT-PCR in 225 colorectal tissue samples. qRT-PCR was performed with a QuantStudioTM 6 Flex Real Time PCR System (Thermo Fisher, Rochester, NY, USA). Primers are listed in Additional file 1 and glyceraldehyde 3-phosphate dehydrogenase (GAPDH) was used as the endogenous control. Each qRT-PCR reaction (in 10 μL) contained 5 μL 2X SYBR Green PCR Mix (ABI, USA), 0.4 μM each of the forward and reverse primers, and 5 ng of template cDNA. The PCR program was as follows: initial denaturing at 95 °C for 5 min followed by 40 cycles at 94°C for 30 s and 60°C for 1 min and fluorescence acquisition at 60°C for 1 min. PCR amplifications were performed in triplicate for each sample. Statistical analysis Computer-based calculations were conducted using SPSS version 20.0 (SPSS Inc., Chicago, IL, USA) and MedCalc version 19.1 (MedCalc Software bvba, Ostend, Belgium). The differences in the expression of selected lncRNAs among multiple groups were compared using ANOVA. Further pairwise comparisons were performed with the least significance difference (LSD) test. The differences in the expression of lncRNAs between the pMMR CC group and the dMMR CC group were compared with the nonparametric Mann–Whitney U test. The relationship between lncRNAs and their target coding genes were determined by Spearman correlation analysis. Receiver-operating characteristic (ROC) curves and the area under the ROC curve (AUC) were employed to evaluate the diagnostic accuracy of candidate lncRNAs. The data were presented as the mean with standard deviation (SD) for normally distributed data and the median with the interquartile range for skewed data. |Fold change| ≥ 5 was used as the significant threshold to screen differentially expressed lncRNA and mRNA. All P values were two-sided and FDR was calculated for multiple comparisons. Differences with P < 0.05 were considered as statistically significant. Results Basic characteristics of the microarray data The total RNA extracted from pMMR colonic tissue samples, consisting of five NC, five LGIN, five HGIN, and four pMMR CC samples, were hybridized to a transcriptome microarray. The quality control results for RNA/microarray are summarized in Supplementary Tables 1 and 2. Cluster analysis of normalized data indicated that the samples from each group were well-separated when all DEGs were considered (Fig.1), suggesting the microarray data were reliable for further bioinformatics analysis. Dysregulated lncRNAs and mRNAs in the colonic adenoma-carcinoma sequence A total of 5783 lncRNAs and 4483 mRNAs were differentially expressed among the NC, LGIN, HGIN, and pMMR CC samples. In order to generate dynamic and significant trending models of DEGs related to colon mucosa malignant transformation, STC analysis was used to further identify the clusters of DEGs with similar expression patterns. As shown in Fig. 2A, the differential expression profile consisted of 26 differentially expressed lncRNA clusters, among which 15 clusters (profile No. 0, 1, 2, 3, 4, 5, 9, 12, 17, 20, 21, 22, 23, 24, and 25) showed significant expression trends (P < 0.05). Moreover, the lncRNA expression levels in clusters 21, 22, 24, and 25 were stable or gradually elevated, while the lncRNA expression in clusters 0, 1, 3, 4, 9, and 12 showed a decreasing trend. Cluster 23 contained 207 lncRNAs that gradually increased from NC to adenoma and were suppressed in CC (Fig. 2 B). However, lncRNA expression in cluster 3 was opposite that in cluster 23. As shown in Fig. 2A, a total of nine mRNA expression clusters, specifically clusters 2, 4, 5, 13, 20, 21, 22, 24, and 25, showed significant expression trends (P < 0.05), among which clusters 21, 22, 24, and 25 showed a stable or gradual elevation. The results suggested that adenoma is an intermediate step from normal tissue to CC, and certain lncRNAs may play important roles during the dynamic process in colonic mucosal protruding lesions. Functional and pathway enrichment analysis DEGs from significant expression trends were subjected to GO and KEGG pathway analyses to identify their potential functions and mechanisms in the dynamic process for colonic mucosal protruding lesions (Fig. 3). . The top 20 GO terms included multicellular organismal development, cell division, cell adhesion, cell cycle, cell differentiation, mitosis, proteolysis, and angiogenesis. KEGG enrichment analysis revealed the DEGs were mainly involved in several signaling pathways, including metabolism, cancer, cell cycle, PI3K-Akt signaling, and transcriptional misregulation in cancer (Fig. 3B). Construction of the lncRNA -mRNA co-expression networks To discover the significant molecular mechanisms for the lncRNAs associated with tumorigenesis in colonic ACS, lncRNAs from elevated clusters (21, 22, 24, 25) or reduced clusters (0, 1, 3, 4, 9, 12) and mRNAs with significant GO terms and KEGG enrichment pathways were selected to construct elevated-lncRNA-mRNA co-expression networks (Co-expression network E) and decreased-lncRNAs-mRNA co-expression networks (Co-expression network D), respectively. The top 10 lncRNA /mRNA in terms by degree are listed in Tables 1 and 3. Co-expression network E contained 714 nodes and 1711 edges (Fig. 4A), among which the hub nodes with the highest degrees were NOTCH4 (mRNA, Degree=29), INHBA (mRNA, Degree=26), TSTA3 (mRNA, Degree=29), lnc-NPRL3-1:1 (lncRNA, Degree=22), NR_024431 (lncRNA, Degree=21), and ENST00000564984 (lncRNA, Degree=17). Co-expression network D contained 598 nodes and 1908 edges (Fig. 4B) and the DEGs with the highest degrees were AQP8 (mRNA, Degree=47), STRADB (mRNA, Degree=47), ENST00000435912 (lncRNA, Degree=15), NR_024431 (lncRNA, Degree=21), and ENST00000564984 (lncRNA, Degree=17) (Tables 1 and 3). Angiogenesis is a crucial step in tumor growth and progression. The angiogenic switch has been observed at the adenoma stage in ACS [20] . We constructed angiogenesis-related lncRNA-mRNA co-expression networks (A-co-expression networks E and D) with DEGs from significantly elevated/decreased clusters as well as GO terms and KEGG pathways related to angiogenesis. A-co-expression network E consisted of 649 nodes and 2299 edges (Fig. 4C) and the top hub genes were NOTCH4 (mRNA, Degree=35), MAD2L1 (mRNA, Degree=34), E2F7 (mRNA, Degree=34), lnc-ZBTB20-2:1 (lncRNA, Degree=22), lnc-CENPH-2:1 (lncRNA, Degree=20), and lnc-PAQR4-2:1 (lncRNA, Degree=19) (Tables 2 and 3). The top hub genes in A-co-expression network D (Fig. 4D) were CAMK2B (mRNA, Degree=61), STRADB (mRNA, Degree=60), LTK (mRNA, Degree=57), ENST00000513255 (lncRNA, Degree=12), NR_024605 (lncRNA, Degree=11), and NONHSAT057082 (lncRNA, Degree=11) (Tables 2 and 3). All the DEGs primarily participated in angiogenesis as the key genes in each network. Construction of TF regulatory networks To investigate the mechanism of gene regulation at the transcriptional level, we constructed two interaction networks (TF-DEG network and TF-angiogenesis-DEG network) between TFs and DEGs that originated from the co-expression network or A-co-expression network. The TF-DEG network contained 99 regulation models and 402 nodes involving 85 TFs (Fig. 4B), among which three TFs, namely LUN-1 (TF, Degree=218), Tel-2 (TF, Degree=22), and 1-Oct (TF, Degree=20), regulated over 20 nodes (Table 3). LUN-1 had the highest degree and might play more important roles in the TF regulatory networks. The lncRNAs regulated by the most TFs were NR_103548, lnc-ARRDC3-1:16, ENST00000513626, and ENST00000516496 (regulated by six TFs). In the TF-angiogenesis-DEG network, due to the important role of LUN-1 (TF, Degree=73) and Tel-2 (TF, Degree=18) in the progression of CC, a subnetwork was constructed showing their effects on angiogenesis functions (Fig. 3, Table 3). qRT-PCR verification for the candidate genes According to the probe signal value, type, DEG fold change, degree value, and protein coding ability, five LncRNAs were selected from the networks above for validation in the expanded clinical samples (70 NC, 58 LIGN, 27 HIGN, 62 pMMR CC, eight dMMRCC samples). LncRNAs ENST00000545920, ENST00000521815, ENST00000609220, and ENST00000603052 presented a sequentially increasing trend in expression with tumor progression (Fig. 6 A-D), whereas NR_026543 showed a sequentially decreasing trend (Fig. 6 E). No statistically significant differences were observed in the expression of NR_026543 and ENST00000521815 among LGIN, HGIN, and pMMR CC. However, the expression levels for each group were significantly elevated or decreased compared with that of the NC samples, suggesting that these genes could be used to distinguish normal from lesion tissue. Furthermore, the expression of ENST00000603052, ENST00000521815, and ENST00000609220 was significantly different in pMMR CC and dMMR CC tissue samples (Fig 6. F-J), indicating these three genes could distinguish pMMR CC from dMMR CC. Diagnostic efficacy of selected lncRNAs The diagnostic efficacy of the above selected five lncRNAs was evaluated as potential biomarkers for pMMR CC diagnosis compared to carcinoembryonic antigen (CEA), a traditional serum tumor biomarker. The specificities of ENST00000521815, ENST00000603052, ENST00000609220, NR_026543, ENST00000545920, and CEA were 0.981, 1, 0.962, 0.932, 0.981, and 0.904, and the sensitivities were 0.931, 0.864, 0.746, 0.846, 0.576, and 0.593, respectively. ROC analysis showed significantly higher AUC for lncRNAs ENST00000521815, ENST00000603052, ENST00000609220, and NR_026543 compared with CEA (0.785) (P=0.0001, 0.0002, 0.0028, and 0.0011, respectively) (Fig 7. A-D). No statistically significant difference in the AUC was observed between ENST00000545920 (0.794) and CEA (P=0.8734) (Fig 7. E). Discussion Among the human transcriptome, more than 90% are non-coding RNAs (ncRNAs), including microRNAs (miRNAs), lncRNAs, and circular RNAs (circRNAs) [ 21 ] . As a relatively new field, accumulated studies have uncovered the crucial roles of lncRNAs in oncogenicity [ 22 , 23 ] . With the improvement of microarray and next-generation sequencing technology, large-scale aberrantly expressed lncRNAs have been discovered in multiple cancers, including breast cancer [ 24 ] , gastric cancer [ 25 ] , CRC [ 26 ] , lung cancer [ 27 ] , and pancreatic cancer [ 28 ] . A large proportion of colon adenomas are able to transform into adenocarcinoma through a dynamic process with the accumulation of multiple mutations and preternatural activation of signaling pathways [ 13 ] . Altered lncRNA expression patterns for microsatellite-stable colorectal cancer indicated that several lncRNAs were continuously upregulated/downregulated during the adenoma-adenocarcinoma sequence [ 29 ] . Unlike previous studies that focused on one or more lncRNAs in CRC [ 30 – 32 ] , we first conducted a systematic and comprehensive analysis using microarrays to reveal the differentially expressed profiles of lncRNAs and mRNAs in the malignant evolutionary process for pMMR CC. Thousands of dysregulated transcripts were identified in the ACS, including 5783 lncRNAs and 4483 mRNAs. Subsequently, STC analysis revealed that adenoma is an intermediate step from normal tissue to CC and certain lncRNAs participate in this continuous process. As the dysregulation can be detected already in adenomas, these lncRNAs can be used as biomarkers for the screening and early detection of CC. GO annotation and KEGG pathway analysis were performed to determine the functions underlying the differentially expressed lncRNAs in ACS. GO annotation suggested that aberrant transcripts were predominantly enriched in multicellular organismal development, cell division, cell adhesion, cell cycle, cell differentiation, mitosis, proteolysis, and angiogenesis. KEGG enrichment analysis revealed that the dysregulated transcripts mainly participated in metabolic pathways, pathways in cancer, cell cycle, PI3K-Akt signaling pathway, and purine metabolism. Cell cycle involvement was identified in both analyses. The uncontrolled tumor cell cycle is an essential feature of cancer and is caused by aberrant cell cycle proteins including cyclin-dependent kinases, Aurora kinases, and Polo-like kinases. Exploiting key molecules in the cell cycle may provide a new perspective for cancer therapy [ 33 ] . To further explore the underlying functions of lncRNAs in ACS, co-expression of lncRNAs with their associated coding genes was performed based on the degree of correlation. Among the top 10 elevated and decreased lncRNAs according to the degree, NR_029374 with 12 degrees was upregulated, consistent with the results obtained by Sun et.al [ 34 ] , who reported that NR_029374 was upregulated in CC and correlated with poor overall survival. NR_029374 promoted the migration, invasion, and metastasis of CC through the miR-30-5p/SOX9 axis and this finding was supported by previous results [ 35 ] . NR_029374 was upregulated in hepatocellular carcinoma (HCC), markedly promoting HCC proliferation, migration, and angiogenesis [ 35 ] . Angiogenesis plays a key role in tumorigenesis and development by delivering nutrients and evacuating metabolic wastes [ 36 ] . Recently, several lncRNAs and mRNAs were demonstrated to participate in tumor angiogenesis. Upregulation of the lncRNA FLANC promoted angiogenesis by upregulating and prolonging the half-life of phosphorylated STAT3/VEGFA in CRC [ 37 ] . In the present study, we discovered that NOTCH4 (mRNA, Degree = 35), a member of the Notch family of transmembrane receptors, had the highest degree. Wu et al. reported the association of overexpressed NOTCH4 with CRC survival [ 38 ] . The NOTCH4 expression level was especially higher in non-small cell lung cancer (NSCLC) tissues than in the NC tissues, related to the tumor size and TNM stage [ 39 ] . Active NOTCH4 might inhibit endothelial sprouting in vitro and vivo [ 40 ] . Further study is required to determine the expression level of NOTCH4 in a large population of pMMR CC patients and the underlying interactions with lncRNAs in angiogenesis. Several lncRNAs have demonstrated interactions with TFs in tumorigenesis and progression. For instance, lncRNA SNHG15 obstructed the ubiquitination and degradation of Slug, a fast-turnover TF critical for controlling cancer cell invasion and metastasis, thereby promoting CC progression [ 41 ] . The transcription of lncRNA LINC01503 was activated by TF TP63, resulting in shorter survival times for patients with esophageal squamous cell carcinoma [ 42 ] . In the current study, the lncRNA–TF network analysis revealed that LUN-1 had the highest degree both in the TF-DEG network and the TF-angiogenesis-DEG network. qRT-PCR analysis showed the upregulation of ENST00000545920, ENST00000521815, ENST00000609220, and ENST00000603052 and the downregulation of NR_026543 with tumor progression. Furthermore, ENST00000603052, ENST00000521815, and ENST00000609220 can be used to identify pMMR CC and dMMR CC. Previous studies revealed the upregulation of ENST00000545920, also known as lnc-SNHG1, in CRC [ 41 ] , non-small cell lung cancer [ 43 ] , gastric cancer [ 44 ] , and glioma [ 45 ] to promote cancer cell growth by interacting with EZH2 in the nucleus and miR-154-5p in the cytoplasm [ 46 ] . Similar results have been observed in other tumors, including ENST00000521815, also known as CASC19, which possesses an oncogenic function through targeting miR-140-5p/CEMIP in CRC progression [ 47 ] . The downregulation of NR_026543 was associated with liver metastasis and poor prognosis for CC by binding with the miR-203 promoter [ 48 ] . ENST00000609220 was found to be highly expressed in CRC tissue, promoting CRC growth and metastasis through the miR-206/YAP1 axis [ 49 ] . To date, there is little knowledge on the function of ENST00000603052 in cancer. Ultimately, the diagnostic efficacy of the identified lncRNAs was evaluated against serum CEA. Most of the selected lncRNAs achieved a slightly higher AUC in distinguishing CC from normal tissue. Further study is needed to validate these lncRNAs as biomarkers for the early diagnosis of CC by measuring their expression in serum samples to assess the consistency in tissue samples. Monitoring expression changes in these genes may also provide a new strategy to assess disease progression. There are certain limitations in the present study. First, the sample size was relatively small, thereby limiting result reliability. We increased the number of clinical samples to confirm the function of the lncRNAs. Second, we only constructed expression profiles for colon tissue. Additional studies are required to evaluate tissues from other tumors. Last, compared with high-throughput sequencing, microarray is incapable of identifying novel lncRNAs. Conclusions In conclusion, we verified a series of differentially expressed lncRNAs and mRNAs in a normal mucosa-adenoma-pMMR CC sequence in the current study. The potential roles of these RNAs were predicted through bioinformatics analyses. The findings may provide novel insights for the diagnosis and therapeutic strategy of pMMR CC. Further studies are required to provide robust validation evidence for the functions of lncRNAs in CC. Declarations Ethics approval and consent to participate The study was approved by the ethics committee of Beijing Shijitan Hospital (No: 2018-59) and informed consent was obtained from each participant. Consent for publication Not applicable Availability of data and materials The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests. Funding This work was supported by the Digestive Medical Coordinated Development Center of Beijing Hospitals Authority under Grant No. XXZ015, Medical and Health Public Foundation of Beijing under Grant No. YWJKJJHKYJJ-B17262-067, and the Science and Technology Development Project of China State Railway Group under Grant No. N2019Z004. Authors’ contributions Study concept and design: Jing Wu, Qian Li, Wenkun Li; recruitment of patient: Qian Li, Wenkun Li, Jiang Ge, acquisition of data: Qian Li, Wenkun Li, Wang Yun, Jiang Ge; statistical analysis and drafting of manuscript: Wenkun Li, Qian Li; acquisition of tissue samples: Wenkun Li, Qian Li, Jiang Ge, Yueqiong Lao, Nanshan Li, Kuiliang Liu, Yandan Wang, Chunmei Guo , Wu Lin, Guojun Jiang, Nan Wei, Canghai Wang, Hong Liu; critical review of manuscript: Jing Wu, Kuiliang Liu, Yadan Wang. All authors read and approved the final manuscript. 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Tables Table 1 The top 10 lncRNAs with the highest degree in co-expression network E/D Gene NC LGIN HGIN CC p-value FDR Type Degree lncRNAs of elevated profiles lnc-NPRL3-1:1 1625.59 2834.43 2901.7 2777.36 0.0037928 0.0253 sense 22 NR_024431 7.42 18.65 19 18.6 0.0035591 0.0242 intergenic 21 ENST00000564984 9.17 18.63 19.15 18.83 0.002729 0.0204 antisense 17 lnc-CLEC3A-9:1 10892.39 20059.59 19686.96 19862.6 0.0007135 0.00894 sense 13 NR_029374 17.3 41.96 42.92 39.14 0.0064708 0.0358 antisense 12 lnc-MKI67IP-8:1 25.14 43.39 43.53 40.98 0.0044777 0.0282 antisense 12 NONHSAT064233 60.28 118.49 113.3 273.65 1.68e-005 0.000976 antisense 11 lnc-DLX2-9:1 13.74 32.64 32.78 30.29 0.0038192 0.0254 antisense 11 ENST00000545920 202.78 259.09 260.38 421.62 0.0019042 0.0162 bidirectional 10 lnc-MAP3K9-9:1 406.91 663.73 501 1392.28 0.0010763 0.0114 antisense 10 lncRNAs of descending profiles ENST00000435912 35.42 19.61 19.43 19.98 0.0083754 0.0425 antisense 15 lnc-PDZK1-1:1 1838.67 149.47 119.91 220.16 0.0004298 0.00669 antisense 14 lnc-MRPL54-2:1 200.94 123.77 123.05 125.75 0.0106357 0.0495 sense 14 ENST00000364025 7.73 3.84 3.78 3.92 0.0026283 0.0199 sense 14 lnc-CCNB3-2:1 617.57 101.05 95.87 115.2 4.63e-005 0.00179 intergenic 14 lnc-CCDC14-2:2 68.74 7.14 5.92 10.93 5.71e-005 0.00201 sense 13 NR_003064 29637.97 1140.93 703.58 1579.91 6e-007 0.000143 intergenic 13 lnc-SRSF5-1:1 8.49 3.84 3.77 4.14 1.61e-005 0.000956 intergenic 13 NR_110552 472.62 9.51 10.64 39.1 0.0001868 0.00406 antisense 13 lnc-ATF6B-1:4 41.99 5.78 5.88 8.03 3.64e-005 0.00154 sense 13 Table 2 The top 10 lncRNAs with the highest degree in A-co-expression network E/D Gene NC LGIN HGIN CC p-value FDR Type Degree lncRNAs of elevated profiles lnc-ZBTB20-2:1 32.95 48.38 41.53 94.12 0.0001576 0.00367 sense 22 lnc-CENPH-2:1 718.1 1438.07 1182.1 3286.29 0.0003228 0.0056 sense 20 lnc-PAQR4-2:1 450.98 753.46 692.77 1489.34 0.0032726 0.0229 sense 19 lnc-SRGN-1:2 3.78 6.76 5.07 16.83 0.0059561 0.034 intergenic 19 NR_045669 94.62 135.86 126.36 246.74 8.6e-006 0.000666 antisense 18 lnc-GRASP-2:2 5 13.63 12.86 40.9 0.0012449 0.0125 sense 18 NR_036480 90.08 160.88 152.83 351.49 0.0001743 0.00391 antisense 18 lnc-BDKRB1-3:1 1199.21 1623.17 1474.82 2825.3 0.0005168 0.00739 sense 18 lnc-TXNDC3-1:1 146.23 288.55 263.33 624.69 0.001605 0.0146 intergenic 17 ENST00000455309 140.37 210.07 197.67 369.84 0.0093281 0.0455 antisense 17 lncRNAs of descending profiles ENST00000513255 193.19 58.84 55.95 67.08 < 1e-07 5.34e-005 antisense 12 NONHSAT057082 28.11 6.37 6.24 7.91 2.31e-005 0.00117 _sense 11 lnc-SRSF5-1:1 8.49 3.84 3.77 4.14 1.61e-005 0.000956 intergenic 11 lnc-CCDC14-2:2 68.74 7.14 5.92 10.93 5.71e-005 0.00201 sense 11 ENST00000617921 173.45 13.12 9.11 22.58 1.7e-006 0.00027 intergenic 11 lnc-RAG1-5:1 427.82 137.02 132.72 154.63 0.000499 0.00724 sense 10 lnc-TMTC3-5:4 84.52 4.05 4.46 8.36 < 1e-07 < 1e-07 intergenic 10 lnc-SLC10A6-5:1 32.91 12.69 12.13 13.98 0.0044594 0.0281 sense 10 lnc-GCNT1-6:1 24.59 4.32 4.44 5.47 < 1e-07 < 1e-07 sense 10 NR_126337 101.45 35.03 33.64 38.33 0.0048936 0.0299 intergenic 10 Table 3 The top 10 mRNAs with the highest degree in co-expression networks co-expression network E co-expression network D A-co-expression network E A-co-expression network D NOTCH4 29 AQP8 47 NOTCH4 35 CAMK2B 61 INHBA 26 STRADB 47 MAD2L1 34 STRADB 60 TSTA3 24 GRIA3 46 E2F7 34 LTK 57 RFC3 24 SCN7A 46 CCNE1 32 PDZK1 56 POLR3K 24 AGTR1 46 INHBA 32 FGF18 55 CCNE1 24 EEF2K 46 RNASEH2A 30 SEMA3E 54 ZYX 23 ALPI 46 RFC3 29 SCIN 54 ALYREF 23 CAMK2B 44 CLSPN 29 SST 53 BRCA2 22 CNTN1 44 NME1 29 IGF1 53 MAD2L1 21 IGF1 44 ERCC6L 28 BMP3 52 Table 4 Key transcription factors (TFs) in regulatory networks TFs Degree TFs Degree TF-DEGs network TF-angiogenesis -DEGs network LUN-1 218 LUN-1 73 Tel-2 22 Tel-2 18 1-Oct 22 MEF-2 13 RSRFC4 20 POU3F2 9 POU3F2 20 1-Oct 9 MEF-2 20 RSRFC4 7 STE11 19 PPARalpha 5 Evi-1 18 MADS-A 4 STAT5B 16 Brachyury 4 IRF-1 12 STAT5A 3 Supplementary Files Sumpplement.docx Sumpplement.docx 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. 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09:02:57","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-38704/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-38704/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":1479868,"identity":"f6443137-8116-43b2-8caa-65c54e2610bc","added_by":"auto","created_at":"2020-07-02 15:45:04","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":42123,"visible":true,"origin":"","legend":"Cluster of samples. Purple represents NC group, yellow represents LGIN group, green represents HGIN group, and pink represents CC group.","description":"","filename":"Fig1.JPG","url":"https://assets-eu.researchsquare.com/files/rs-38704/v1/Fig1.JPG"},{"id":1479869,"identity":"cd4be362-ff78-4ab1-967e-f5e2a2215a55","added_by":"auto","created_at":"2020-07-02 15:45:04","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":73169,"visible":true,"origin":"","legend":"STC analysis of the dysregulated DEGs in multistage colonic mucosa tissues. (A) Twenty-six clusters for the expression pattern of DEGs, 15 lncRNA expression patterns, and nine mRNA expression patterns showed significant P values (colored boxes, P \u003c 0.05/26). Each box represents a model expression profile, the lower number in the profile box is the P value and the upper number is the model profile number. (B) Cluster 25 (173 lncRNAs) gradually increased, cluster 0 (87 lncRNAs) showed a decreasing trend, cluster 23 contained 207 lncRNAs that gradually increased from NC to adenoma, and then decreased in CC, and cluster 25 (146 mRNAs) gradually increased. Horizontal axis represents different stages of colonic mucosal protruding lesions and the vertical axis shows the time series for the lncRNA/mRNA expression levels. r normalized with Log transformation.","description":"","filename":"Fig2.JPG","url":"https://assets-eu.researchsquare.com/files/rs-38704/v1/Fig2.JPG"},{"id":1479870,"identity":"66f4fa7a-1ce0-42dc-a6d7-54ed43bf2a0e","added_by":"auto","created_at":"2020-07-02 15:45:05","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":61030,"visible":true,"origin":"","legend":"Overview of Go annotation and KEGG enrichment pathways. (A) Top 20 GO terms for DEGs. (B) Top 20 pathways corresponding to DEGs. P value \u003c 0.05 and FDR \u003c 0.05.","description":"","filename":"Fig3.JPG","url":"https://assets-eu.researchsquare.com/files/rs-38704/v1/Fig3.JPG"},{"id":1479871,"identity":"8f22a060-d507-4bf8-8f9f-ab260905cc4b","added_by":"auto","created_at":"2020-07-02 15:45:05","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":69625,"visible":true,"origin":"","legend":"lncRNA-mRNA co-expression networks. (A-B) lncRNAs with elevated or decreased profiles and mRNAs for significant terms from GO and KEGG enrichment analysis were respectively selected to construct co-expression networks E and D in colonic ACS. (C-D) lncRNAs with significantly elevated/decreased model profiles and angiogenesis-related GO and KEGG items were respectively selected to construct A-co-expression networks E and D in colonic ACS. Nodes represent DEGs (green for mRNAs and yellow for lncRNAs). Lines represent interactions and node size represents degree value.","description":"","filename":"Fig4.JPG","url":"https://assets-eu.researchsquare.com/files/rs-38704/v1/Fig4.JPG"},{"id":1479872,"identity":"cc4a46cd-24c3-406d-8b7a-2a2940a4c568","added_by":"auto","created_at":"2020-07-02 15:45:05","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":81297,"visible":true,"origin":"","legend":"Transcription factor (TF) regulatory network. (A) Interaction networks between TFs and DEGs that originated from significant model profiles. (B) Interaction networks between TFs and DEGs that originated from angiogenesis-related model profiles. The yellow-colored nodes represent TFs, the red-colored nodes represent lncRNAs, and the green-colored nodes represent mRNAs.","description":"","filename":"Fig5.JPG","url":"https://assets-eu.researchsquare.com/files/rs-38704/v1/Fig5.JPG"},{"id":1479873,"identity":"32aea5c7-89cf-45f2-84b6-cad2d07bb293","added_by":"auto","created_at":"2020-07-02 15:45:05","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":69106,"visible":true,"origin":"","legend":"qRT-PCR verification of the candidate genes. ENST00000545920 (A), ENST00000521815 (B), ENST00000609220 (C), and ENST00000603052 (D) presented a sequentially ascending trend in expression with tumor progression, whereas NR_026543 (E) showed a sequentially decreasing trend. The expression of ENST00000521815 (F), ENST00000603052 (H), and ENST00000609220 (I) showed statistically significant differences between pMMR CC and dMMR CC.","description":"","filename":"Fig6.JPG","url":"https://assets-eu.researchsquare.com/files/rs-38704/v1/Fig6.JPG"},{"id":1479874,"identity":"04bd8e87-793d-4c5f-9550-7fc3fe7e77cc","added_by":"auto","created_at":"2020-07-02 15:45:05","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":60998,"visible":true,"origin":"","legend":"Diagnostic efficacy of selected lncRNAs. Compared with CEA, ENST00000521815 (A), ENST00000603052 (B), ENST00000609220 (C), and NR_026543 (D) had significantly larger AUCs (AUCs 0.957, 0.958, 0.933, 0.949 vs. 0.785, P=0.0001, 0.0002, 0.0028, and 0.0011, respectively). However, there were no statistically significant differences between the AUC of ENST00000545920 (0.794) and that of CEA (P=0.8734) (E). The ROC curve for CEA is shown in blue, while the ROC curves for the validation of the lncRNAs are shown in green.","description":"","filename":"Fig7.JPG","url":"https://assets-eu.researchsquare.com/files/rs-38704/v1/Fig7.JPG"},{"id":13547855,"identity":"829480c1-46c7-41b8-b1b7-92cfb979031c","added_by":"auto","created_at":"2021-09-17 02:15:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":901254,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-38704/v1/57381ee8-1ad1-4bfa-95c0-822cd61f7518.pdf"},{"id":1479876,"identity":"8498f984-2046-4b7f-b94d-1a080fe54b09","added_by":"auto","created_at":"2020-07-02 15:45:05","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":22216,"visible":true,"origin":"","legend":"","description":"","filename":"Sumpplement.docx","url":"https://assets-eu.researchsquare.com/files/rs-38704/v1/Sumpplement.docx"},{"id":1479867,"identity":"aba0d137-452c-4dc5-9002-a1537ab1b037","added_by":"auto","created_at":"2020-07-02 15:45:05","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":22216,"visible":true,"origin":"","legend":"","description":"","filename":"Sumpplement.docx","url":"https://assets-eu.researchsquare.com/files/rs-38704/v1/Sumpplement.docx"}],"financialInterests":"","formattedTitle":"\u003cp\u003eComprehensive Analysis of Differentially Expressed Long Non-Coding RNA-mRNA in The Adenoma–Carcinoma Sequence of DNA Mismatch Repair Proficient Colon Cancer\u003c/p\u003e","fulltext":[{"header":"Background","content":" \u003cp\u003eColorectal cancer (CRC) is the third most commonly diagnosed cancer worldwide, comprising over 1.8\u0026nbsp;million new cases in 2018. The disease is also the second leading cause of cancer deaths \u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. More than 70% of CRC cases are located in the colon, which is also called colon cancer (CC) \u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. With the development of the economy and the westernization of diets and lifestyles, the incidence of CRC is likely to increase in Asia. According to the latest data from the Chinese National Cancer Center, CRC ranks fifth in both incidence and mortality among cancers in China \u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eCRC is a heterogeneous disease with a progressive accumulation of genetic and epigenetic alterations \u003csup\u003e[\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. About two-thirds of CRC cases arise with unknown contributions from germline factors or a significant family history of cancer or inflammatory bowel disease, defined as sporadic CRC. Three major genetic mechanisms underlie the pathogenesis of sporadic CRC \u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e, namely chromosomal instability (CIN), microsatellite instability (MSI), and the epigenomic CpG island methylator phenotype (CIMP). CIN, the first described and the most common pathway, accounts for 80%-85% of sporadic CRC \u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. CIN CRCs are non-hypermutated with DNA proficient mismatch repair (pMMR) functions \u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. The mechanism responsible for CIN is different from that responsible for MSI and CIMP. Understanding the molecular mechanisms underlying CRC is critically important for the clinical prognosis and therapeutic response. A meta-analysis of 63 eligible studies (10126 patients with CRC) showed a worse prognosis for CRC patients with CIN/pMMR. CIN and MSI status can be evaluated as a predictor of prognosis \u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. Additionally, stage II CRC patients with defective mismatch repair (dMMR) or high-level MSI (MSI-H) have better prognosis but no benefits from fluorouracil chemotherapy \u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eMore than 90% of colorectal tumorigenesis cases follow the adenoma\u0026ndash;carcinoma sequence (ACS), with multiple gene mutations and abnormal activation of signaling pathways \u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. Mutations in the adenomatous polyposis coli (APC) gene occur early during colorectal tumorigenesis, followed by the activation of the KRAS gene and the inactivation of the \u003cem\u003eTP\u003c/em\u003e53 gene, which are often associated with CIN \u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eLong non-coding RNAs (lncRNAs) are a class of non-coding RNAs with more than 200 nucleotides that are not translated into proteins. lncRNAs can regulate gene expression at different levels, including epigenetically, transcriptionally, and post-transcriptionally \u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. Existing studies have shown the aberrant expression of lncRNAs is associated with tumorigenesis, metastasis, and prognosis in various cancers. For instance, decreased expression of the lncRNA SATB2-AS1 promotes metastasis and influences the tumor microenvironment in CRC by regulating SATB2, thereby resulting in poor survival \u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. Overexpression of the lncRNA UCA1 contributes to the immune escape of cancer cells and protects PDL1 expression from miRNA repression in gastric carcinoma, serving as a potentially novel target for immunotherapy \u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. Aberrant expression of lncRNAs is involved in the evolution of several types of tumors; however, the differential expression and the role of lncRNAs in ACS for CRC remain to be elucidated.\u003c/p\u003e \u003cp\u003ePrevious studies on the lncRNA expression profiles of CC were generally performed on all cancers in the colon together including rectal cancer, thereby neglecting the heterogeneity of molecular subtypes of CC. Although often linked together, CC and rectal cancer are different when it comes to treatment. Thus far, the differential expression profile for lncRNAs in the normal colonic mucosa-pMMR adenoma-pMMR sporadic CC sequence has not been reported. In the present study, we aimed to characterize the expression profile of lncRNAs in the malignant evolution process for pMMR sporadic CC using transcriptome microarray technology. Subsequently, differentially expressed lncRNAs were investigated using bioinformatics analysis and validated with quantitative real-time polymerase chain reaction (qRT-PCR). The findings provide useful candidates for the diagnosis and treatment of CC.\u003c/p\u003e "},{"header":"Materials And Methods","content":"\u003cp\u003e\u003cstrong\u003eClinical samples\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 244 sporadic colonic tissues, including 63 adenomas of low-grade intraepithelial neoplasia (LGIN), 32 adenomas of high-grade intraepithelial neoplasia (HGIN), 66 pMMR CC, eight dMMR CC, and 75 normal control (NC) samples from adjacent ( \u0026ge;10 cm) hyperplastic or inflammatory polyps were sampled at Beijing Shijitan Hospital (Beijing, China) from 2018 to 2019. The samples were immediately placed in RNALater solution (Cat. No. 76104, Qiagen Co, GmBH, Germany) for 24 hours at 4\u0026deg;C. Freshly frozen samples were stored at -80℃ for RNA extraction. None of the patients received targeted therapy, chemotherapy, radiotherapy, or intervention therapy. The study was approved by the ethics committee of Beijing Shijitan Hospital (No: 2018-59) and informed consent was obtained from each participant.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eScreening of pMMR samples and total RNA isolation \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs in our previous study \u003csup\u003e[18]\u003c/sup\u003e, multiplex PCR and \u003ca href=\"https://www.googleadservices.com/pagead/aclk?sa=L\u0026amp;ai=DChcSEwj8uNetyvLpAhUUi8gKHa_-DB8YABABGgJxdQ\u0026amp;ohost=www.google.com\u0026amp;cid=CAESQOD2qdfTLcTrG14_anVwnFuNdB_0VTM268FFjGZ9L_8B2EUzQP4EhUMcC4ATkcHc4eR_SSoKJv37RHRjLuhhiy8\u0026amp;sig=AOD64_02pZGVLxc0Zf9QIb7S46e62qKbqw\u0026amp;q=\u0026amp;ved=2ahUKEwju6sqtyvLpAhUdonIEHa7BBn0Q0Qx6BAgnEAE\u0026amp;adurl=\"\u003eimmunohistochemistry \u003c/a\u003ewere used to determine the MMR status. pMMR colonic tissues were defined as tissue samples with a low frequency of MSI (MSI-L) or microsatellite stability (MSS, no marker tested). Total RNA was extracted from the tissue samples using RNAiso Plus (Code No. 9109, TAKARA, Japan). RNA integrity was confirmed with a NanoDrop ND-2000 spectrophotometer (Thermo Fisher Scientific, Rochester, NY, USA) and Agilent Bioanalyzer 2100 (Agilent Technologies, Santa Clara, CA, US), and further purified with a RNeasy mini kit (Cat. # 74106, Qiagen, GmBH, Germany) and RNase-Free DNase Set (Cat. #79254, Qiagen Co, GmBH, Germany). RNA samples with integrity \u0026ge; 7.0 and a 28S:18S ratio \u0026ge; 0.7 were used for further experiments.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMicroarrays analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe lncRNA and mRNA expression profiles in multistage colonic mucosa tissues (five NC, five LGIN, five HGIN, and four pMMR CC samples) were determined with the Agilent custom SBC Human (4*180 K) lncRNA Microarray V6.0 (Product No. G4862A-074348; Agilent Technologies, Santa Clara, CA). Total RNA was amplified and labeled with the Low Input Quick Amp WT Labeling Kit (Cat. No. 5190-2943, Agilent Technologies, Santa Clara, CA, US) following the manufacturer\u0026rsquo;s instructions. Labeled cRNAs were purified with the RNeasy mini kit (Cat. No. 74106, Qiagen, GmBH, Germany). Each slide was hybridized with 1.65 \u0026mu;g Cy3-labeled cRNA using a Gene Expression Hybridization Kit (Cat. No. #5188-5242, Agilent Technologies, Santa Clara, CA, US) in a hybridization oven (Cat. No. # G2545A, Agilent Technologies, Santa Clara, CA, US) for 17 h. The slides were washed in staining dishes (Cat. No. 121, Thermo Shandon, Waltham, MA, US) with the Gene Expression Wash Buffer Kit (Cat. No. #5188-5327, Agilent Technologies, Santa Clara, CA, US) following the manufacturer\u0026rsquo;s instructions and scanned with an Agilent Microarray Scanner (Cat. No. # G2565CA, Agilent Technologies, Santa Clara, CA, US) with default settings (Dye channel: Green, Scan resolution = 3 \u0026mu;m, PMT 100%, 20 bit). The data were extracted with Feature Extraction software 10.7 (Agilent Technologies, Santa Clara, CA, US).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData processing and identification of DEGs \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSeries test of cluster (STC) analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRaw data were normalized with the Quantile algorithm in GeneSpring Software 11.0 (Agilent Technologies, Santa Clara, CA, US). Cluster analysis was performed to systematically and intuitively display the relatedness between samples. Differential genes expression (DEGs) among the four groups were filtered with the F test in the random variance model (RVM) using P values \u0026lt; 0.05 and Benjamini-Hochberg false discovery rate (FDR) \u0026lt; 0.05 as the significant cutoff criteria \u003csup\u003e[19]\u003c/sup\u003e. STC analysis was employed to identify the most probable set of clusters responsible for the observed ACS series by profiling the dynamic nature of the temporal gene expression. NC, LGIN, HGIN, and pMMR CC were set as different points to identify significant trending models related to multistage colonic mucosa tissue and their associated DEGs by STC analysis. Clusters with P values \u0026lt; 0.05/26 were considered statistically significant.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analysis \u003c/strong\u003eGene ontology (GO) analysis was performed to annotate DEGs in three categories: molecular functions (MF), biological processes (BP), and cellular components (CC). Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis was performed to identify the functional pathways and pathways for molecular interactions, reactions, and relation networks. The statistically significant threshold was set as P value \u0026lt; 0.01 and FDR \u0026lt;0.05.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConstruction of co-expression and transcription factor (TF) regulatory network \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo identify co-expressed lncRNA-mRNA pairs and hub DEGS, the lncRNA-mRNA co-expression network was constructed based on the gene expression value and the Pearson's correlation coefficient (PCC) between the differentially expressed lncRNAs and mRNAs. |PCC| \u0026ge; 0.8 and P value \u0026lt; 0.01 were considered as statistically significantly relevant. In order to distinguish the TF regulatory network in the stepwise process encompassing CC progression, the interactions were predicted starting from a TF by searching the conserved TF binding sites within a putative promoter area 2000 bp upstream and 500 bp downstream of the transcriptional initiation site of co-expressed genes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eValidation by quantitative real-time PCR (qRT-PCR) \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe expression levels of five lncRNAs were examined by qRT-PCR in 225 colorectal tissue samples. qRT-PCR was performed with a QuantStudioTM 6 Flex Real Time PCR System (Thermo Fisher, Rochester, NY, USA). Primers are listed in Additional file 1 and glyceraldehyde 3-phosphate dehydrogenase (GAPDH) was used as the endogenous control. Each qRT-PCR reaction (in 10 \u0026mu;L) contained 5 \u0026mu;L 2X SYBR Green PCR Mix (ABI, USA), 0.4 \u0026mu;M each of the forward and reverse primers, and 5 ng of template cDNA. The PCR program was as follows: initial denaturing at 95 \u0026deg;C for 5 min followed by 40 cycles at 94\u0026deg;C for 30 s and 60\u0026deg;C for 1 min and fluorescence acquisition at 60\u0026deg;C for 1 min. PCR amplifications were performed in triplicate for each sample.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eComputer-based calculations were conducted using SPSS version 20.0 (SPSS Inc., Chicago, IL, USA) and MedCalc version 19.1 (MedCalc Software bvba, Ostend, Belgium). The differences in the expression of selected lncRNAs among multiple groups were compared using ANOVA. Further pairwise comparisons were performed with the least significance difference (LSD) test. The differences in the expression of lncRNAs between the pMMR CC group and the dMMR CC group were compared with the nonparametric Mann\u0026ndash;Whitney U test. The relationship between lncRNAs and their target coding genes were determined by Spearman correlation analysis. Receiver-operating characteristic (ROC) curves and the area under the ROC curve (AUC) were employed to evaluate the diagnostic accuracy of candidate lncRNAs. The data were presented as the mean with standard deviation (SD) for normally distributed data and the median with the interquartile range for skewed data. |Fold change| \u0026ge; 5 was used as the significant threshold to screen differentially expressed lncRNA and mRNA. All P values were two-sided and FDR was calculated for multiple comparisons. Differences with P \u0026lt; 0.05 were considered as statistically significant.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eBasic characteristics of the microarray data \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe total RNA extracted from pMMR colonic tissue samples, consisting of five NC, five LGIN, five HGIN, and four pMMR CC samples, were hybridized to a transcriptome microarray. The quality control results for RNA/microarray are summarized in Supplementary Tables 1 and 2. Cluster analysis of normalized data indicated that the samples from each group were well-separated when all DEGs were considered (Fig.1), suggesting the microarray data were reliable for further bioinformatics analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDysregulated lncRNAs and mRNAs in the colonic adenoma-carcinoma sequence\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;A total of 5783 lncRNAs and 4483 mRNAs were differentially expressed among the NC, LGIN, HGIN, and pMMR CC samples. In order to generate dynamic and significant trending models of DEGs related to colon mucosa malignant transformation, STC analysis was used to further identify the clusters of DEGs with similar expression patterns. As shown in Fig. 2A, the differential expression profile consisted of\u0026nbsp;26 differentially expressed lncRNA clusters, among which 15 clusters (profile No. 0, 1, 2, 3, 4, 5, 9, 12, 17, 20, 21, 22, 23, 24, and 25) showed significant expression trends (P \u0026lt; 0.05). Moreover, the lncRNA expression levels in clusters 21, 22, 24, and 25 were stable or gradually elevated, while the lncRNA expression in clusters 0, 1, 3, 4, 9, and 12 showed a decreasing trend. Cluster 23 contained 207 lncRNAs that gradually increased from NC to adenoma and were suppressed in CC (Fig. 2 B). However, lncRNA expression in cluster 3 was opposite that in cluster 23. As shown in Fig. 2A, a total of nine mRNA expression clusters, specifically clusters 2, 4, 5, 13, 20, 21, 22, 24, and 25, showed significant expression trends (P \u0026lt; 0.05), among which clusters 21, 22, 24, and 25 showed a stable or gradual elevation. The results suggested that adenoma is an intermediate step from normal tissue to CC, and certain lncRNAs may play important roles during the dynamic process in colonic mucosal protruding lesions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunctional and pathway enrichment analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDEGs from significant expression trends were subjected to GO and KEGG pathway analyses to identify their potential functions and mechanisms in the dynamic process for colonic mucosal protruding lesions (Fig. 3).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;. The top 20 GO terms included multicellular organismal development, cell division, cell adhesion, cell cycle, cell differentiation, mitosis, proteolysis, and angiogenesis. KEGG enrichment\u0026nbsp;analysis revealed the DEGs were mainly involved in several signaling pathways, including metabolism, cancer, cell cycle, PI3K-Akt signaling, and transcriptional misregulation in cancer (Fig. 3B).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConstruction of the \u003c/strong\u003e\u003cstrong\u003elncRNA\u003c/strong\u003e\u003cstrong\u003e-mRNA co-expression networks\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo discover the significant molecular mechanisms for the lncRNAs associated with tumorigenesis in colonic ACS, lncRNAs from elevated clusters (21, 22, 24, 25) or reduced clusters (0, 1, 3, 4, 9, 12) and mRNAs with significant GO terms and KEGG enrichment pathways were selected to construct elevated-lncRNA-mRNA co-expression networks (Co-expression network E) and decreased-lncRNAs-mRNA co-expression networks (Co-expression network D), respectively. The top 10 lncRNA /mRNA in terms by degree are listed in Tables 1 and 3. Co-expression network E contained 714 nodes and 1711 edges (Fig. 4A), among which the hub nodes with the highest degrees were NOTCH4 (mRNA, Degree=29), INHBA (mRNA, Degree=26), TSTA3 (mRNA, Degree=29), lnc-NPRL3-1:1 (lncRNA, Degree=22), NR_024431 (lncRNA, Degree=21), and ENST00000564984 (lncRNA, Degree=17). Co-expression network D contained 598 nodes and 1908 edges (Fig. 4B) and the DEGs with the highest degrees were AQP8 (mRNA, Degree=47), STRADB (mRNA, Degree=47), ENST00000435912 (lncRNA, Degree=15), NR_024431 (lncRNA, Degree=21), and ENST00000564984 (lncRNA, Degree=17) (Tables 1 and 3).\u003c/p\u003e\n\u003cp\u003eAngiogenesis is a crucial step in tumor growth and progression. The angiogenic switch has been observed at the adenoma stage in ACS \u003csup\u003e[20]\u003c/sup\u003e. We constructed angiogenesis-related lncRNA-mRNA co-expression networks (A-co-expression networks E and D) with DEGs from significantly elevated/decreased clusters as well as GO terms and KEGG pathways related to angiogenesis. A-co-expression network E consisted of 649 nodes and 2299 edges (Fig. 4C) and the top hub genes were NOTCH4 (mRNA, Degree=35), MAD2L1 (mRNA, Degree=34), E2F7 (mRNA, Degree=34), lnc-ZBTB20-2:1 (lncRNA, Degree=22), lnc-CENPH-2:1 (lncRNA, Degree=20), and lnc-PAQR4-2:1 (lncRNA, Degree=19) (Tables 2 and 3). The top hub genes in A-co-expression network D (Fig. 4D) were CAMK2B (mRNA, Degree=61), STRADB (mRNA, Degree=60), LTK (mRNA, Degree=57), ENST00000513255 (lncRNA, Degree=12), NR_024605 (lncRNA, Degree=11), and NONHSAT057082 (lncRNA, Degree=11) (Tables 2 and 3). All the DEGs primarily participated in angiogenesis as the key genes in each network.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConstruction of \u003c/strong\u003e\u003cstrong\u003eTF\u003c/strong\u003e\u003cstrong\u003e regulatory networks\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo investigate the mechanism of gene regulation at the transcriptional level, we constructed two interaction networks (TF-DEG network and TF-angiogenesis-DEG network) between TFs and DEGs that originated from the co-expression network or A-co-expression network. The TF-DEG network contained 99 regulation models and 402 nodes involving 85 TFs (Fig. 4B), among which three TFs, namely LUN-1 (TF, Degree=218), Tel-2 (TF, Degree=22), and 1-Oct (TF, Degree=20), regulated over 20 nodes (Table 3). LUN-1 had the highest degree and might play more important roles in the TF regulatory networks. The lncRNAs regulated by the most TFs were NR_103548, lnc-ARRDC3-1:16, ENST00000513626, and ENST00000516496 (regulated by six TFs). In the TF-angiogenesis-DEG network, due to the important role of LUN-1 (TF, Degree=73) and Tel-2 (TF, Degree=18) in the progression of CC, a subnetwork was constructed showing their effects on angiogenesis functions (Fig. 3, Table 3).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eqRT-PCR verification for the candidate genes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAccording to the probe signal value, type, DEG fold change, degree value, and protein coding ability, five LncRNAs were selected from the networks above for validation in the expanded clinical samples (70 NC, 58 LIGN, 27 HIGN, 62 pMMR CC, eight dMMRCC samples). LncRNAs ENST00000545920, ENST00000521815, ENST00000609220, and ENST00000603052 presented a sequentially increasing trend in expression with tumor progression (Fig. 6 A-D), whereas NR_026543 showed a sequentially decreasing trend (Fig. 6 E). No statistically significant differences were observed in the expression of NR_026543 and ENST00000521815 among LGIN, HGIN, and pMMR CC. However, the expression levels for each group were significantly elevated or decreased compared with that of the NC samples, suggesting that these genes could be used to distinguish normal from lesion tissue. Furthermore, the expression of ENST00000603052, ENST00000521815, and ENST00000609220 was significantly different in pMMR CC and dMMR CC tissue samples (Fig 6. F-J), indicating these three genes could distinguish pMMR CC from dMMR CC.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDiagnostic efficacy of selected lncRNAs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe diagnostic efficacy of the above selected five lncRNAs was evaluated as potential biomarkers for pMMR CC diagnosis compared to carcinoembryonic antigen (CEA), a traditional serum tumor biomarker. The specificities of ENST00000521815, ENST00000603052, ENST00000609220, NR_026543, ENST00000545920, and CEA were 0.981, 1, 0.962, 0.932, 0.981, and 0.904, and the sensitivities were 0.931, 0.864, 0.746, 0.846, 0.576, and 0.593, respectively. ROC analysis showed significantly higher AUC for lncRNAs ENST00000521815, ENST00000603052, ENST00000609220, and NR_026543 compared with CEA (0.785) (P=0.0001, 0.0002, 0.0028, and 0.0011, respectively) (Fig 7. A-D). No statistically significant difference in the AUC was observed between ENST00000545920 (0.794) and CEA (P=0.8734) (Fig 7. E).\u003c/p\u003e"},{"header":"Discussion","content":" \u003cp\u003eAmong the human transcriptome, more than 90% are non-coding RNAs (ncRNAs), including microRNAs (miRNAs), lncRNAs, and circular RNAs (circRNAs) \u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. As a relatively new field, accumulated studies have uncovered the crucial roles of lncRNAs in oncogenicity \u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e. With the improvement of microarray and next-generation sequencing technology, large-scale aberrantly expressed lncRNAs have been discovered in multiple cancers, including breast cancer \u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e, gastric cancer \u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e, CRC \u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e, lung cancer \u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e, and pancreatic cancer \u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eA large proportion of colon adenomas are able to transform into adenocarcinoma through a dynamic process with the accumulation of multiple mutations and preternatural activation of signaling pathways \u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. Altered lncRNA expression patterns for microsatellite-stable colorectal cancer indicated that several lncRNAs were continuously upregulated/downregulated during the adenoma-adenocarcinoma sequence \u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e. Unlike previous studies that focused on one or more lncRNAs in CRC \u003csup\u003e[\u003cspan additionalcitationids=\"CR31\" citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e, we first conducted a systematic and comprehensive analysis using microarrays to reveal the differentially expressed profiles of lncRNAs and mRNAs in the malignant evolutionary process for pMMR CC. Thousands of dysregulated transcripts were identified in the ACS, including 5783 lncRNAs and 4483 mRNAs. Subsequently, STC analysis revealed that adenoma is an intermediate step from normal tissue to CC and certain lncRNAs participate in this continuous process. As the dysregulation can be detected already in adenomas, these lncRNAs can be used as biomarkers for the screening and early detection of CC.\u003c/p\u003e \u003cp\u003eGO annotation and KEGG pathway analysis were performed to determine the functions underlying the differentially expressed lncRNAs in ACS. GO annotation suggested that aberrant transcripts were predominantly enriched in multicellular organismal development, cell division, cell adhesion, cell cycle, cell differentiation, mitosis, proteolysis, and angiogenesis. KEGG enrichment analysis revealed that the dysregulated transcripts mainly participated in metabolic pathways, pathways in cancer, cell cycle, PI3K-Akt signaling pathway, and purine metabolism. Cell cycle involvement was identified in both analyses. The uncontrolled tumor cell cycle is an essential feature of cancer and is caused by aberrant cell cycle proteins including cyclin-dependent kinases, Aurora kinases, and Polo-like kinases. Exploiting key molecules in the cell cycle may provide a new perspective for cancer therapy \u003csup\u003e[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eTo further explore the underlying functions of lncRNAs in ACS, co-expression of lncRNAs with their associated coding genes was performed based on the degree of correlation. Among the top 10 elevated and decreased lncRNAs according to the degree, NR_029374 with 12 degrees was upregulated, consistent with the results obtained by Sun et.al \u003csup\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e, who reported that NR_029374 was upregulated in CC and correlated with poor overall survival. NR_029374 promoted the migration, invasion, and metastasis of CC through the miR-30-5p/SOX9 axis and this finding was supported by previous results \u003csup\u003e[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/sup\u003e. NR_029374 was upregulated in hepatocellular carcinoma (HCC), markedly promoting HCC proliferation, migration, and angiogenesis \u003csup\u003e[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAngiogenesis plays a key role in tumorigenesis and development by delivering nutrients and evacuating metabolic wastes \u003csup\u003e[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e. Recently, several lncRNAs and mRNAs were demonstrated to participate in tumor angiogenesis. Upregulation of the lncRNA FLANC promoted angiogenesis by upregulating and prolonging the half-life of phosphorylated STAT3/VEGFA in CRC \u003csup\u003e[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/sup\u003e. In the present study, we discovered that NOTCH4 (mRNA, Degree\u0026thinsp;=\u0026thinsp;35), a member of the Notch family of transmembrane receptors, had the highest degree. Wu et al. reported the association of overexpressed NOTCH4 with CRC survival \u003csup\u003e[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]\u003c/sup\u003e. The NOTCH4 expression level was especially higher in non-small cell lung cancer (NSCLC) tissues than in the NC tissues, related to the tumor size and TNM stage \u003csup\u003e[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/sup\u003e. Active NOTCH4 might inhibit endothelial sprouting in vitro and vivo \u003csup\u003e[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]\u003c/sup\u003e. Further study is required to determine the expression level of NOTCH4 in a large population of pMMR CC patients and the underlying interactions with lncRNAs in angiogenesis. Several lncRNAs have demonstrated interactions with TFs in tumorigenesis and progression. For instance, lncRNA SNHG15 obstructed the ubiquitination and degradation of Slug, a fast-turnover TF critical for controlling cancer cell invasion and metastasis, thereby promoting CC progression \u003csup\u003e[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]\u003c/sup\u003e. The transcription of lncRNA LINC01503 was activated by TF TP63, resulting in shorter survival times for patients with esophageal squamous cell carcinoma \u003csup\u003e[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]\u003c/sup\u003e. In the current study, the lncRNA\u0026ndash;TF network analysis revealed that LUN-1 had the highest degree both in the TF-DEG network and the TF-angiogenesis-DEG network.\u003c/p\u003e \u003cp\u003eqRT-PCR analysis showed the upregulation of ENST00000545920, ENST00000521815, ENST00000609220, and ENST00000603052 and the downregulation of NR_026543 with tumor progression. Furthermore, ENST00000603052, ENST00000521815, and ENST00000609220 can be used to identify pMMR CC and dMMR CC. Previous studies revealed the upregulation of ENST00000545920, also known as lnc-SNHG1, in CRC \u003csup\u003e[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]\u003c/sup\u003e, non-small cell lung cancer \u003csup\u003e[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]\u003c/sup\u003e, gastric cancer \u003csup\u003e[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]\u003c/sup\u003e, and glioma \u003csup\u003e[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]\u003c/sup\u003e to promote cancer cell growth by interacting with EZH2 in the nucleus and miR-154-5p in the cytoplasm \u003csup\u003e[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]\u003c/sup\u003e. Similar results have been observed in other tumors, including ENST00000521815, also known as CASC19, which possesses an oncogenic function through targeting miR-140-5p/CEMIP in CRC progression \u003csup\u003e[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]\u003c/sup\u003e. The downregulation of NR_026543 was associated with liver metastasis and poor prognosis for CC by binding with the miR-203 promoter \u003csup\u003e[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]\u003c/sup\u003e. ENST00000609220 was found to be highly expressed in CRC tissue, promoting CRC growth and metastasis through the miR-206/YAP1 axis \u003csup\u003e[\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]\u003c/sup\u003e. To date, there is little knowledge on the function of ENST00000603052 in cancer.\u003c/p\u003e \u003cp\u003eUltimately, the diagnostic efficacy of the identified lncRNAs was evaluated against serum CEA. Most of the selected lncRNAs achieved a slightly higher AUC in distinguishing CC from normal tissue. Further study is needed to validate these lncRNAs as biomarkers for the early diagnosis of CC by measuring their expression in serum samples to assess the consistency in tissue samples. Monitoring expression changes in these genes may also provide a new strategy to assess disease progression.\u003c/p\u003e \u003cp\u003eThere are certain limitations in the present study. First, the sample size was relatively small, thereby limiting result reliability. We increased the number of clinical samples to confirm the function of the lncRNAs. Second, we only constructed expression profiles for colon tissue. Additional studies are required to evaluate tissues from other tumors. Last, compared with high-throughput sequencing, microarray is incapable of identifying novel lncRNAs.\u003c/p\u003e "},{"header":"Conclusions","content":" \u003cp\u003eIn conclusion, we verified a series of differentially expressed lncRNAs and mRNAs in a normal mucosa-adenoma-pMMR CC sequence in the current study. The potential roles of these RNAs were predicted through bioinformatics analyses. The findings may provide novel insights for the diagnosis and therapeutic strategy of pMMR CC. Further studies are required to provide robust validation evidence for the functions of lncRNAs in CC.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was approved by the ethics committee of Beijing Shijitan Hospital (No: 2018-59) and informed consent was obtained from each participant.\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\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\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\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Digestive Medical Coordinated Development Center of Beijing Hospitals Authority under Grant No. XXZ015, Medical and Health Public Foundation of Beijing under Grant No. YWJKJJHKYJJ-B17262-067, and the Science and Technology Development Project of China State Railway Group under Grant No. N2019Z004.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStudy concept and design: Jing Wu, Qian Li, Wenkun Li; recruitment of patient: Qian Li, Wenkun Li, Jiang Ge, acquisition of data: Qian Li, Wenkun Li, Wang Yun, Jiang Ge; statistical analysis and drafting of manuscript: Wenkun Li, Qian Li; acquisition of tissue samples: Wenkun Li, Qian Li, Jiang Ge, Yueqiong Lao, Nanshan Li, Kuiliang Liu, Yandan Wang, Chunmei Guo , Wu Lin, Guojun Jiang, Nan Wei, Canghai Wang, Hong Liu; critical review of manuscript: Jing Wu, Kuiliang Liu, Yadan Wang. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank all members of the Department of Gastroenterology and Endoscopy Center for assistance in various aspects of this work.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e \u003cspan\u003eBray 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: a cancer journal for clinicians 2018, 68(6):394\u0026ndash;424.\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3322/caac.21492\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eBailey CE, Hu CY, You YN, Bednarski BK, Rodriguez-Bigas MA, Skibber JM, et al. 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Frontiers in oncology. 2020;10:160. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fonc.2020.00160\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e \u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":" \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe top 10 lncRNAs with the highest degree in co-expression network E/D\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLGIN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHGIN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFDR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eType\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eDegree\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003elncRNAs of elevated profiles\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnc-NPRL3-1:1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1625.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2834.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2901.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2777.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0037928\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0253\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003esense\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNR_024431\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0035591\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0242\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eintergenic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eENST00000564984\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.002729\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eantisense\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnc-CLEC3A-9:1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10892.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20059.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19686.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19862.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0007135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.00894\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003esense\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNR_029374\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e39.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0064708\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0358\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eantisense\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnc-MKI67IP-8:1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e43.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0044777\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0282\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eantisense\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNONHSAT064233\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e118.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e113.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e273.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.68e-005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000976\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eantisense\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnc-DLX2-9:1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0038192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0254\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eantisense\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eENST00000545920\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e202.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e259.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e260.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e421.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0019042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ebidirectional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnc-MAP3K9-9:1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e406.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e663.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e501\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1392.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0010763\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eantisense\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003elncRNAs of descending profiles\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eENST00000435912\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0083754\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0425\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eantisense\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnc-PDZK1-1:1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1838.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e149.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e119.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e220.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0004298\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.00669\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eantisense\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnc-MRPL54-2:1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e200.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e123.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e123.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e125.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0106357\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0495\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003esense\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eENST00000364025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0026283\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0199\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003esense\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnc-CCNB3-2:1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e617.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e101.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e115.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.63e-005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.00179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eintergenic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnc-CCDC14-2:2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e68.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.71e-005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.00201\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003esense\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNR_003064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29637.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1140.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e703.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1579.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6e-007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eintergenic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnc-SRSF5-1:1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.61e-005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000956\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eintergenic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNR_110552\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e472.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e39.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0001868\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.00406\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eantisense\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnc-ATF6B-1:4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.64e-005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.00154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003esense\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \n\n\u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe top 10 lncRNAs with the highest degree in A-co-expression network E/D\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLGIN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHGIN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFDR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eType\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eDegree\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003elncRNAs of elevated profiles\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnc-ZBTB20-2:1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e94.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0001576\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.00367\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003esense\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnc-CENPH-2:1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e718.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1438.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1182.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3286.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0003228\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003esense\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnc-PAQR4-2:1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e450.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e753.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e692.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1489.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0032726\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0229\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003esense\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnc-SRGN-1:2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0059561\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eintergenic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNR_045669\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e94.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e135.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e126.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e246.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.6e-006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000666\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eantisense\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnc-GRASP-2:2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0012449\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003esense\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNR_036480\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e90.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e160.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e152.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e351.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0001743\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.00391\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eantisense\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnc-BDKRB1-3:1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1199.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1623.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1474.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2825.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0005168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.00739\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003esense\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnc-TXNDC3-1:1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e146.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e288.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e263.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e624.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.001605\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eintergenic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eENST00000455309\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e140.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e210.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e197.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e369.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0093281\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0455\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eantisense\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003elncRNAs of descending profiles\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eENST00000513255\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e193.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e55.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e67.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;1e-07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.34e-005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eantisense\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNONHSAT057082\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.31e-005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.00117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e_sense\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnc-SRSF5-1:1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.61e-005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000956\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eintergenic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnc-CCDC14-2:2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e68.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.71e-005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.00201\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003esense\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eENST00000617921\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e173.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.7e-006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.00027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eintergenic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnc-RAG1-5:1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e427.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e137.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e132.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e154.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000499\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.00724\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003esense\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnc-TMTC3-5:4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e84.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;1e-07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;1e-07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eintergenic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnc-SLC10A6-5:1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0044594\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0281\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003esense\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnc-GCNT1-6:1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;1e-07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;1e-07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003esense\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNR_126337\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e101.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e38.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0048936\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0299\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eintergenic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e\n\n\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\u003eThe top 10 mRNAs with the highest degree in co-expression networks\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eco-expression network E\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eco-expression network D\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eA-co-expression network E\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eA-co-expression network D\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNOTCH4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAQP8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNOTCH4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCAMK2B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e61\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eINHBA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSTRADB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMAD2L1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSTRADB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTSTA3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGRIA3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eE2F7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLTK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRFC3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSCN7A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCCNE1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePDZK1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePOLR3K\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAGTR1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eINHBA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFGF18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCCNE1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEEF2K\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRNASEH2A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSEMA3E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e 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align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNME1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eIGF1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e53\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMAD2L1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIGF1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eERCC6L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e 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\u003cp\u003eSTE11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePPARalpha\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEvi-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMADS-A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSTAT5B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBrachyury\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIRF-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSTAT5A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e "}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Colon cancer, long non-coding RNA, DNA mismatch repair proficient, adenoma–carcinoma sequence, microarray","lastPublishedDoi":"10.21203/rs.3.rs-38704/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-38704/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e DNA mismatch repair proficient colon cancer (pMMR CC) is the most common subtype of sporadic CC. However, the role of long non-coding RNAs (lncRNAs) in pMMR CC carcinogenesis has not been fully elucidated. \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e In the present study, we conducted transcriptomic analysis of lncRNAs-mRNAs in five low-grade intraepithelial neoplasia (LGIN), five high-grade intraepithelial neoplasia (HGIN), four pMMR CC, and five normal control (NC) tissues. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment pathway, and Co-Expression Network analyses were performed to elucidate the functions of lncRNAs and mRNAs as well as their interactions. Quantitative real-time polymerase chain reaction (qRT-PCR) was used to validate five dysregulated lncRNAs in a large set of colon tissues. Receiver-operating characteristic (ROC) curves were employed to evaluate the performance of the candidate lncRNAs. \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e A set of 5783 differentially expressed lncRNAs and 4483 differentially expressed mRNAs were detected among the LGIN, HGIN, pMMR CC, and NC samples. These differentially expressed lncRNAs and mRNAs were assigned to 275 significant GO terms and 179 significant KEGG enriched pathways. qRT-PCR confirmed that the expression of five selected lncRNAs (ENST00000521815, ENST00000603052, ENST00000609220, NR_026543, and ENST00000545920) were consistent with the microarray data. ROC analysis showed that four lncRNAs (ENST00000521815, ENST00000603052, ENST00000609220, and NR_026543) had larger area under the ROC curve (AUC) values than serum carcinoembryonic antigens, thereby distinguishing NC from pMMR CC. \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e Several lncRNAs play various roles in the adenoma–carcinoma sequence and may serve as potential biomarkers for the early diagnosis of pMMR CC.\u003c/p\u003e","manuscriptTitle":"Comprehensive Analysis of Differentially Expressed Long Non-Coding RNA-mRNA in The Adenoma–Carcinoma Sequence of DNA Mismatch Repair Proficient Colon Cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2020-07-02 15:44:56","doi":"10.21203/rs.3.rs-38704/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":"c9163cb9-16b3-4680-b4e9-c99209012c20","owner":[],"postedDate":"July 2nd, 2020","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":138771,"name":"Cancer Biology"}],"tags":[],"updatedAt":"2020-07-02T15:45:03+00:00","versionOfRecord":[],"versionCreatedAt":"2020-07-02 15:44:56","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-38704","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-38704","identity":"rs-38704","version":["v1"]},"buildId":"_2-kVJe1T_tPrBINL-cwx","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}