Twenty-six CRC patients who were surgically treated at Second Affiliated Hospital of Nanjing Medical University from June 2011 to June 2012 were included in our study. All patients recruited in this study received neither chemotherapy nor radiotherapy before the surgery. Written informed consent was obtained from all patients and permission for this study was obtained from the ethics committee of Second Affiliated Hospital of Nanjing Medical University. A set of lymph node and tumor tissues were collected during the operation and stained with HE by two experienced pathologists. Lymph nodes were divided into MLN and NLN according to the HE staining results. All samples were frozen in liquid nitrogen and stored at -80 °C until further analysis. Out of 87 samples, 9 (3 MLN, 3 NLN and, 3 tumor tissues) from 3 patients were used for microarray analysis of lncRNAs and the others were used for an extra evaluation.

Total RNAs were extracted from 26 snap frozen subsets of MLN, NLN, and tumor tissues using TRIzol reagent (Invitrogen, Carlsbad, CA, United States) according to the manufacturer’s protocol. RNA quantity and quality were measured with a NanoDrop ND-1000 spectrometer and RNA integrity was assessed by standard denaturing agarosegel electrophoresis.

Sample labeling and array hybridization were performed according to the Agilent one-color microarray-based gene expression analysis protocol (Agilent Technology) with minor modifications. Briefly, mRNA was purified from total RNA after removal of rRNA (mRNA-ONLY™ eukaryotic mRNA isolation kit, Epicentre). Then, each sample was amplified and transcribed into fluorescent cRNA along the entire length of the transcripts without 3’ bias utilizing a random priming method. The labeled cRNAs were purified by RNeasy Mini Kit (Qiagen). The concentration and specific activity of the labeled cRNAs (pmol Cy3/μg cRNA) were measured with a NanoDrop ND-1000 spectrometer. Each labeled cRNA (1 μg) was fragmented by adding 5 μL 10 × Blocking Agent and 1 μL of 25 × Fragmentation Buffer, then the mixture was heated at 60 °C for 30 min, and finally 25 μL 2 × GE Hybridization Buffer was added to dilute the labeled cRNA. Fifty microliters of hybridization solution was dispensed into the gasket slide and assembled to the lncRNA expression microarray slide. The slides were incubated for 17 h at 65 °C in an Agilent hybridization oven. The hybridized arrays were washed, fixed and scanned using an Agilent DNA microarray scanner (part number G2505C).

Agilent feature extraction software (version 11.0.1.1) was used to analyze the array images. Quantile normalization and subsequent data processing were performed using the GeneSpring GX v11.5.1 software package (Agilent Technologies). A normalized value is a relative number that comes from the ratio of the raw data value of the listed probe to that of the control. After quantile normalization of the raw data, lncRNAs and mRNAs with 9 samples that have flags in present or marginal were chosen for further data analysis. Differentially expressed lncRNAs and mRNAs with statistical significance were identified through Volcano Plot filtering and fold change filtering. Hierarchical clustering was performed using the Agilent GeneSpring GX software (Version 11.5.1). GO analysis and pathway analysis were performed using the standard enrichment computation method. The microarray work was performed by KangChen Bio-tech Shanghai, China.

Quantitative real-time polymerase chain reaction (qRT-PCR) was used to verify differential expression of 4 lncRNAs that were detected to be specifically expressed in MLN by the lncRNA expression microarray. The first strand cDNA was synthesized using SuperScript™ III Reverse Transcriptase (Invitrogen), RNase Inhibitor (Epicentre), and 1.25 mmol/L dNTPs Mix. Each qRT-PCR reaction (in 10 μL) contained 2 × Super Array PCR master mix 5 μL, 10 μmol/L PCR forward primer 0.5 μL, 10 μmol/L PCR reverse primer 0.5 μL, diluted first strand cDNA synthesis reaction 2 μL, and ddH2O 2 μL. The cycling conditions consisted of an initial, single cycle of 10 min at 95 °C, followed by 40 cycles of 10 s at 95 °C, 60 s at 60 °C, and 15 s at 95 °C. For each sample, we performed qRT-PCR for target genes and a housekeeping gene. A standard curve was constructed using serial 10-fold dilutions (from 1 to 10⁶) of the PCR products to quantify the results. According to the standard curve, the gene concentration of each sample is generated directly using Rotor-Gene Real-Time Analysis Software 6.0. For each sample, the relative amount of the target gene is determined by calculating the ratio between the concentration of the target gene and that of the housekeeping gene. For each target gene, we took the relative amount of the control sample as 1, and then the relative amount of the other samples as n fold of the control sample. The lncRNA expression differences between two groups were analyzed using Student’s t test. P < 0.05 was considered statistically significant.

Clinical and pathological information of 3 patients were used for microarray analysis. Briefly, more than 15 lymph nodes were harvested and at least one was confirmed to be metastatic by HE staining in each patient (Figure 1).

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Figure 1 Metastatic lymph node and tumor tissues of colorectal cancer revealed by HE staining. A: Metastatic lymph node; B: Tumor tissue.

Arraystar human lncRNA microarray V2.0 is designed for the global profiling of human lncRNAs and protein-coding transcripts. A total of 33045 lncRNAs and 30215 coding transcripts can be detected by this second-generation lncRNAs microarray. The lncRNAs are carefully collected from the most authoritative databases such as RefSeq_NR, UCSC_Known Genes, Ensembl, H-invDB, UCR, lincRNA, and the related literature. Each transcript is represented by a specific exon or splice junction probe which can identify individual transcript accurately. Positive probes for housekeeping genes and negative probes are also printed onto the array for hybridization quality control.

After quantile normalization of the raw data, lncRNAs with 9 samples that have flags in present or marginal were chosen for differentially expressed lncRNA screening. Out of 33045 lncRNAs, 20705 were in present or marginal in all 9 samples.

The box-plot is a convenient way to quickly visualize the distributions of a dataset. It is commonly used for comparing the distributions of the intensities from all samples. After normalization, the distributions of expression values for the samples are nearly the same, indicating that it is suitable for further analysis. The scatter-plot is a visualization method used for assessing the lncRNA expression variation (or reproducibility) between the two compared samples or two compared sample groups.

Initially, we performed a volcano plot filtering between two groups to identify differentially expressed lncRNAs with statistical significance (fold change ≥ 2.0, P≤ 0.05). From the data of microarray, variations of lncRNA and mRNA expression were shown in Tables 1 and 2. Briefly, compared with the NLN group, 873 probe sets representing 633 lncRNAs were down-regulated, while 260 probe sets representing 197 lncRNAs were up-regulated in the MLN group. Compared with the tumor tissue group, 85 probe sets representing 53 lncRNAs were down-regulated, while 460 probe sets representing 337 lncRNAs were up-regulated in the MLN group. Compared with the tumor tissue group, 623 probe sets representing 471 lncRNAs were down-regulated, while 2038 probe sets representing 1396 lncRNAs were up-regulated in the NLN group. Then, we performed a fold change filtering between every two samples in each patient to identify differentially expressed lncRNAs (fold change ≥ 2.0). The number of up-regulated and down-regulated lncRNAs varied in different patients compared with that of different samples. Interestingly, we found that the number of differently expressed lncRNAs between NLN and tumor tissues was much greater than those of differently expressed lncRNAs between MLN and NLN, and between MLN and tumor tissues (Figure 2), which indicated that NLN was quite different from tumor tissue.

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Figure 2 Counts of up-regulated and down-regulated long noncoding RNAs. Up-regulated and down-regulated long noncoding RNAs (lncRNAs) varied in different patients when comparing different samples: many lncRNAs were determined to be significantly up-regulated or down-regulated in metastatic lymph nodes (MLN), normal lymph node (NLN) and tumor tissues of colorectal cancer (CRC) in three patients by microarray analysis. The number of differently expressed lncRNAs between NLN and tumor tissue was much larger than those of differently expressed lncRNAs both between MLN and NLN and between MLN and tumor tissue. M: Metastatic lymph nodes; N: Normal lymph nodes; T: Tumor tissue of colorectal cancer.

Next, we collected the specifically expressed lncRNAs in MLN by comparing MLN with NLN and tumor tissues, respectively. Only 9 probe sets representing 5 lncRNAs were specifically down-regulated and 19 probe sets representing 14 lncRNAs specifically up-regulated in the MLN group compared with the NLN group and tumor tissue group (fold change ≥ 2.0, P≤ 0.05). Of these, AK021444 (absolute fold change MLN/NLN = 7.763431269) was the most significantly up-regulated one, while BC042589 (MLN/NLN = 4.323467267) was the most significantly down-regulated one in the MLN group (Table 3). In each patient, there were 548, 193, and 134 specifically down-regulated probe sets (representing 424, 144, 113 lncRNAs), and 862, 135, and 693 specifically up-regulated ones (representing 593, 111, 522 lncRNAs) in MLN, NLN and tumor tissues, respectively.

Additionally, we determined whether lncRNAs changed gradually from tumor tissue to MLN and then from MLN to NLN. We summarized the gradually increased and decreased lncRNAs from tumor tissue to MLN then to NLN. These lncRNAs may also play an important role in facilitating the CRC tumor cell transfer from the primary tumor to lymph nodes.

As reported, lncRNAs exerted important roles in gene expression, especially their nearby coding genes. The possible relationships of lncRNAs with their nearby coding genes included natural antisense[18-20], exon sense-overlapping[21], intron sense-overlapping, intronic antisense[22,23], bidirectional[24-26], and intergenic[27]. We analyzed the relationship between lncRNAs and their nearby coding genes and each lncRNA was annotated by their associated genes and proteins.

Hierarchical clustering is one of the most widely used clustering methods for analyzing lncRNA expression data. Cluster analysis arranges samples into groups based on their expression levels, which allows us to hypothesize the relationships among samples. Here, hierarchical clustering was performed based on “differentially expressed lncRNAs”. The result of hierarchical clustering showed distinguishable lncRNAs expression profiles among samples which were divided into two main subgroups, signifying two different tissues.

Some specific classes of lncRNAs, such as enhancer lncRNAs, rinn lincRNAs, HOX cluster, lincRNAs near coding genes, and enhancer lncRNAs near coding genes, have been reported to be involved in the development of many diseases, especially cancers[28-31]. Thus, we analyzed the expression levels of these lncRNAs in 9 samples. LncRNAs with enhancer-like function (LncRNA-a) are identified using GENCODE annotation of the human genes[28]. Depletion of these lncRNAs led to decreased expression of the neighboring protein-coding genes, including the master regulator of hematopoiesis, SCL (also called TAL1), Snai1, and Snai2. LncRNAs were demonstrated to be necessary for the activation of gene expression[29]. Nine hundred and one enhancer lncRNAs were identified to be expressed in samples by microarray. Rinn et al[31] characterized the transcriptional landscape of the four human Hox loci and identified a total of 407 discrete transcribed regions in the four human Hox loci. Transcription of these lncRNAs may demarcate chromosomal domains of gene silencing at a distance, which contains profiling data of all probes in the four human Hox loci.

17159 mRNAs out of 30215 coding transcripts could be detected in 9 samples. Generally, hundreds of mRNAs were statistically aberrantly expressed among the 3 different kinds of tissues. GO and pathway analyses showed that the different expressed mRNAs might be involved in T/B cell receptor signaling pathway and primary immunodeficiency, which indicated that lymph node metastasis of CRC was closely associated with deficiency of immune cells.

To verify the result of lncRNA array, we selected 4 specifically expressed lncRNAs in MLN to confirm their expression levels by qRT-PCR in 26 CRC patients with lymph node metastasis (Table 4). The data were in agreement with the results of the microarray analysis. Out of these 4 lncRNA, AK021444, ENST00000425785, and AK307796, 3 specifically up-regulated lncRNAs in MLN, were proved to be highly expressed in MLN while ENST00000465846 to be down-regulated in MLN. Interestingly, the fold changes of all of them were smaller than that of the microarray results, indicating that the microarray result may exaggerate the difference (Figure 3).

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Figure 3 Comparison between microarray data and quantitative real-time polymerase chain reaction results. AK021444, ENST00000425785, AK307796 and ENST00000465846 were determined to be differentially expressed by microarray in 3 colorectal cancer patients, and this result was validated by quantitative polymerase chain reaction result (qPCR). AK021444, ENST00000425785, and AK307796, 3 specifically up-regulated lncRNAs in metastatic lymph nodes (MLN), were confirmed to be highly expressed in MLN, while ENST00000465846 was confirmed to be down-regulated in MLN. The validation results for the four long noncoding RNAs (lncRNAs) indicated that the microarray data correlated well with the qPCR results. M: Metastatic lymph nodes; N: Normal lymph nodes; T: Tumor tissue of colorectal cancer.

Colorectal cancer (CRC) is one of the most common malignant tumors worldwide. The prime cause of death among CRC patients is metastasis such as blood metastasis and lymph node metastasis. Lymph node metastasis plays an important role in affecting the prognosis of CRC patients. Thus, how to detect lymph node metastasis at early stage and how to further study molecular mechanisms are essential for diagnosis and therapy of CRC. Long noncoding RNAs (lncRNAs) have been validated to have comprehensive functions in biological processes. When it comes to CRC, several lncRNAs have been reported to be oncogenic factors by inhibiting apoptosis and promoting cell proliferation and so on. However, the roles lncRNAs play in the progress of lymph node metastasis of CRC remain unknown. The authors findings indicated that lncRNAs may play a significant role in the process of lymph node metastasis of CRC.

LncRNAs have been validated to have comprehensive functions in biological processes such as inactivating X chromosome, regulating DNA metabolism, and activating transcription by recently published studies. Increasing evidence indicated that lncRNAs play important roles in many human diseases, including various types of cancer. LncRNAs were reported to be abnormally expressed in various cancers and were associated with tumor cell proliferation, growth, apoptosis, invasion, and metastasis.

In this study, authors have identified hundreds of differently expressed lncRNAs among the 3 different tissues. Thirteen lncRNAs and 5 lncRNAs were identified to be specifically up-regulated and down-regulated in metastatic lymph node (MLN), respectively. AK021444, ENST00000425785, AK307796, and ENST00000465846 were validated to be specifically expressed in MLN of CRC.

The findings suggest that lncRNAs may play important roles in lymph node metastasis of CRC. The clinical implications of our findings include the potential use of lncRNAs as molecular diagnosis markers and therapeutic targets.

LncRNAs, longer than 200 nucleotides (nt) in length, are a kind of RNAs that do not encode proteins.

LncRNAs was recently found to be frequently dysregulated in various diseases. Although previous data suggested that lncRNAs are likely to have biological effects, this possibility needs to be confirmed by specific hypothesis-driven studies. Understanding the precise molecular mechanisms by which lncRNAs function in various diseases will be critical for exploring new potential strategies for early diagnosis and therapy.