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Introduction

Colorectal cancer (CRC) ranks as the third most prevalent cancer in men and second in women. In 2012 alone, ~1,361,000 CRC diagnoses were made, with 694,000 deaths worldwide. Due to the asymptomatic nature of CRC in the early stages of the disease, the majority of CRC-related deaths are due to distant organ metastases and recurrence (1). Unfortunately, the lack of more reliable biomarkers for early CRC detection further contributes to increased mortality rates. Over the past few decades, CRC investigators have been dedicated to studying the genetic mutations particularly protein-coding genes, such as APC (2), DNA mismatch repair genes (3) and AXIN (4). To date, since CRC is a multi-factorial, multi-step disease, the molecular mechanisms underlying pathogenesis and progression are yet to be completely elucidated. To further understand this disease, we need to break away from traditional views and adopt a fresh perspective, in which case, long non-coding RNAs (lncRNAs) have attracted increased attention in recent years.

lncRNAs are a new class of non-coding RNAs which are longer than 200 nucleotides and lack protein-coding potential (5), and were once thought to be non-functional. However, a growing body of evidence indicates that lncRNAs play important roles in many aspects of cell activity. Indeed, lncRNAs are involved in almost all aspects of gene regulation, including chromosome dosage compensation, control of imprinting, epigenetic regulation, nuclear and cytoplasmic trafficking, transcription, mRNA splicing and translation (6). As a direct result of such biological functions, lncRNAs participate in cell differentiation, proliferation, growth, apoptosis, maintenance of pluripotency, and invasion and metastasis of tumor cells (7,8). Several lncRNAs have been reported to be abnormally expressed in CRC; and not only were they involved in tumor progression, but were also considered potential candidate biomarkers for prognosis and staging of CRC. For instance, colon cancer-associated transcript 1 (CCAT1) was found to be overexpressed in CRC, and its upregulation was also correlated with clinical stage, metastasis and patient survival time (9). HOX antisense intergenic RNA (HOTAIR) was found to have a significant effect on CRC cell migration and invasion, but not proliferation; it played a crucial role in cancer development and was considered as an effective predictor for poor prognosis (10).

Since the lncRNA expression profile in CRC is not yet completely understood, herein, we aimed to identify lncRNAs that are differentially expressed in CRC. We performed micro-array analysis to assess the expression profiles of lncRNAs in colorectal cancer tissues and matched adjacent normal samples. Bioinformatics analysis was subsequently used for functional annotation of the differentially expressed lncRNAs. Our findings demonstrated that lncRNAs were differentially expressed between CRC tissues and normal tissues, and the aberrant expression of lncRNAs may contribute to colon carcinogenesis.

Materials and methods

Patient samples

Samples from 5 colon adenocarcinoma patients including tumor and paired non-cancerous tissues were collected and immediately cryo-preserved in liquid nitrogen following surgery at The Sanming First Hospital Affiliated to Fujian Medical University from October 2014 to November 2014. All diagnoses of colon adenocarcinoma were confirmed by pathology, and the non-cancerous samples were obtained 5 cm away from the edge of the tumor and were free of tumor cells, as evaluated by a pathologist. Tumor stage was evaluated based on the tumor-node-metastasis (TNM) staging system of the International Union against Cancer (Table I). At the time of the study, no patient had received chemotherapy, radiotherapy or other preoperative treatments. The study procedures were reviewed and approved by the Ethics Committees of the Sanming First Hospital Affiliated to Fujian Medical University [permit number: 2013(96)]. Informed written consent was obtained from all of the subjects.

Table I Clinical parameters of the 4 colon adenocarcinoma patients.

Table I

Clinical parameters of the 4 colon adenocarcinoma patients.

Specimen no.	Gender	Age (years)	Histology	Histologic differentiation	TNM stage
No. 1	Male	69	Ulcerative	Moderate	T2N1M0
No. 2a	Male	43	Ulcerative	Moderate	T4bN1bM0
No. 3	Female	57	Ulcerative	Well-Moderate	T4aN0M0
No. 4	Female	61	Ulcerative	Well-Moderate	T4aN0M0
No. 5	Male	61	Ulcerative	Moderate	T4aN2M0

a No. 2: tissue from this patient was found unqualified in the subsequent RNA quantity and quality detection, so it was not involved in the microarray analysis.

RNA isolation

Total RNA from all 8 samples were extracted using TRIzol reagent (Invitrogen Life Technologies) following the manufacturer's protocol. RNA quantity and quality were assessed by a NanoDrop ND-1000 spectrometer (NanoDrop Technologies), and RNA integrity was evaluated by standard denaturing agarose gel electrophoresis methods.

Microarray

Arraystar Human LncRNA Microarray V3.0, which is designed for the global profiling of human lncRNAs and protein-coding transcripts, was used in this study. Microarray V3.0 was updated from the previous V2.0, and approximately 30,586 lncRNAs and 26,109 coding transcripts can be detected by this third-generation lncRNA microarray. All the sequences were collected from authoritative data sources, such as National Center for Biotechnology Information (NCBI) RefSeq, University of California-Santa Cruz (UCSC), Ultra Conserved Regions (UCRs), and related literature. Each transcript is represented by a specific exon or splice junction probe which can accurately distinguish between individual transcripts. The microarray hybridization and bio-information analysis was performed by KangChen Bio-tech (Shanghai, China).

RNA labeling and array hybridization

Sample labeling and array hybridization were performed according to the Agilent One-Color Microarray-Based Gene Expression Analysis protocol (Agilent Technologies) with minor modifications. Briefly, mRNA was purified from total RNA using mRNA-ONLY Eukaryotic mRNA isolation kit (Epicentre) after removal of rRNA. Subsequently, each specimen was magnified and transcribed into fluorescent cRNA along the entire length of the transcripts without 3′ bias using Arraystar Flash RNA labeling kit (Arraystar), which is a random priming method. The labeled cRNAs were depurated using the RNeasy Mini kit (Qiagen). The concentration and specific activity of the labeled cRNAs (pmol Cy3/µg cRNA) were measured by NanoDrop ND-1000 (NanoDrop Technologies). One microgram of each labeled cRNA was fragmented by adding 5 µl 10X blocking agents and 1 µl 25X fragmentation buffer, and the mixture was heated at 60°C for 30 min. Next, 25 µl 2X GE hybridization buffer was added to dilute the labeled cRNA. Fifty microliters of hybridization solutions 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).

Data analysis

Acquired array images were analyzed using Agilent Feature Extraction software (version 11.0.1.1). Quantile standardization and subsequent data processing were performed by GeneSpring GX v12.1 software package (Agilent Technologies). Differentially expressed lncRNAs and mRNAs with statistical significance between the two groups were identified through p-value/FDR filtering and fold change filtering (fold change ≥2.0). Hierarchical clustering and combined analysis were performed using homemade scripts.

Co-expression network

Gene co-expression networks were built according to the normalized signal intensity of specifically expressed genes. The network construction procedures included the following: i) Preprocess data: the same mRNAs with different transcripts taking the median value represent the gene expression values, without special treatment of the lncRNA expression value; ii) screen data: remove the subset of data according to the lists showing the differential expression of lncRNAs and mRNAs; iii) calculate the Pearson's correlation coefficient and use R-value to calculate the correlation coefficient between lncRNAs and mRNAs; and iv) screen by Pearson's correlation coefficient: select the Pearson's correlation coefficient greater than 0.98 as the meaningful value and draw the lncRNA/mRNA co-expression network by using the Cytoscape program. To make a visual representation, only the strongest correlations (≥0.9994 for DKK4, MMP7, SFRP4 and WIF1; ≥0.994 for MYC, CTNNB1 and PLCB4; ≥0.9975 for SFRP2 and WNT11; ≥0.98 for LEF1) were included in these renderings.

Quantitative real-time PCR (qRT-PCR)

Total RNA was extracted using TRIzol reagent (Invitrogen) according to the manufacture's protocol. The first strand cDNA was synthesized using SuperScript™ III First Strand kits (Invitrogen). GAPDH was used as an internal control. The difference in expression of lncRNA was calculated using the 2−ΔCt method described by Schmittgen and Livak (11). Primers used in this study are listed in Table II.

Table II Primers used in qRT-PCR.

Table II

Primers used in qRT-PCR.

Seqname	Primers
GAPDH	F: 5′-GGGAAACTGTGGCGTGAT-3′;	R: 5′-GAGTGGGTGTCGCTGTTGA-3′
NR_045617	F: 5′-GGATGAGGAGAAGAAGCCAA-3′;	R: 5′-TGATGCGTCATTACCACTTTG-3′
uc002ywy.3	F: 5′-TTACATAGGTGTCCAGCCATC-3′;	R: 5′-GCAACTGAAGGGGCAATCT-3′
uc003dtq.3	F: 5′-GAAAAGCAAAGGCATAGAAGG-3′;	R: 5′-CACACAATGAGGTTTTTCCCA-3′
ENST00000502076	F: 5′-GGCTAAATGCCTGCTACACA-3′; R:	5′-TGGGCGACAGAACAGACTC-3′
NR_037661	F: 5′-AGTGCAGGTGGAAACCATCTC-3′;	R: 5′-GAGACCGCTGTACTGTCACC-3′
ENST00000440498	F: 5′-CCGCTTGCTGAGTCTTTCT-3′;	R: 5′-GAGGTCCTAAGTCAGGGTCG-3′
ENST00000552364	F: 5′-TTGGTGTGTTTCAGGTCATCC-3′;	R: 5′-CAGTGTTCTTGCTTTGGTTCC-3′

[i] F, forward; R, reverse.

Statistical analysis

Statistical analysis was performed using SPSS 17.0 software package. Significant differential expression levels of lncRNAs or mRNAs were analyzed by Student's t-test and FDR filtering was used for comparative analysis. The p-value ≤0.0.5 (two-tailed) and fold change ≥2.0 were considered statistically significant.

Results

Overview of the microarray data

To distinguish the abnormality of lncRNAs in CRC, we contrasted the expression profile between CRC tissues and adjacent non-cancerous tissues from 4 colon adenocarcinoma patients (Fig. 1). Microarray data showed that a total of 3,523 lncRNAs and 2,515 mRNAs were consistently differentially expressed in the CRC tissues in comparison to the non-cancerous tissues. Among them, 1,989 lncRNAs were upregulated with 1,534 lncRNAs downregulated and 1,471 mRNAs were upregulated with 1,044 mRNAs downregulated (see additional file 1 and 2; http addresses are shown in Fig. 1 legend). The top 15 upregulated and top 15 downregulated lncRNAs and mRNAs are listed in Tables III and IV. Furthermore, 156 lncRNAs (108 upregulated and 48 downregulated) and 99 mRNAs (60 upregulated and 39 downregulated) were highly differentially expressed with an absolute fold change >10 [see additional file 1 and 2].

Figure 1 lncRNA and mRNA profile comparison between colon cancer tissues and para-cancer tissues. Scatter-plots were used for assessing the (A) lncRNA and (B) mRNA expression variation between colon cancer tissues and para-cancer tissues. The values for the x-axis and y-axis in the scatter-plot were the normalized signal values of each sample (log2 scaled). The green lines are fold change lines (the default fold change value given is 2.0). lncRNAs above the top green line and below the bottom green line indicate >2.0-fold change in expression of lncRNAs between cancer tissues and para-cancer tissues. Hierarchical clustering based on (C) lncRNA and (D) mRNA expression value showed a distinguishable gene expression profiling among samples. 'Green' indicates high relative expression and 'red' indicates low relative expression. See additional file 1, https://www.researchgate.net/publication/282291289_additional_file_1. Differentially_Expressed_LncRNAs and additional file 2, https://www.researchgate.net/publication/282291544_additional_file_2.Differentially_Expressed_mRNAs?ev=prf_pub.

Table III The top 15 differentially expressed lncRNAs [cancer (C) vs. normal (N)].

Table III

The top 15 differentially expressed lncRNAs [cancer (C) vs. normal (N)].

Upregulated lncRNAs		Downregulated lncRNAs
Seqname	Fold change (C/N)	Seqname	Fold change (C/N)
TCONS_00022031	157.90	ENST00000434839	1049.22
AA558434	149.01	ENST00000421322	651.81
ENST00000438158	122.61	uc004ebm.1	463.49
ENST00000447898	114.53	uc002yjs.3	51.76
ENST00000559321	105.64	ENST00000548051	26.97
uc002zku.3	93.45	TCONS_00020621	22.01
NR_034119	85.35	TCONS_00003800	21.17
NR_051996	75.45	ENST00000483245	18.81
NR_051996	75.45	ENST00000572964	16.82
NR_036484	68.59	ENST00000497872	16.60
NR_036484	68.59	TCONS_00003799	14.12
ENST00000529081	56.37	TCONS_00019318	13.91
ENST00000504989	48.23	NR_047535	13.18
ENST00000426240	48.03	NR_047535	13.18
NR_046711	43.91	NR_047538	13.17

Table IV The top 15 differentially expressed mRNAs [cancer (C) vs. normal (N)].

Table IV

The top 15 differentially expressed mRNAs [cancer (C) vs. normal (N)].

Upregulated mRNAs		Downregulated mRNAs
Seqname	Fold change (C/N)	Seqname	Fold change (C/N)
NM_015515	203.83	NM_001048	74.70
NM_033260	151.46	ENST00000372581	57.54
NM_019844	125.16	NM_005478	34.00
ENST00000375825	93.85	NM_005396	31.79
NM_002423	76.24	ENST00000328886	30.99
NM_015424	57.47	NM_002054	29.88
NM_001445	53.51	NM_033553	24.69
NM_003247	52.28	NM_016602	23.57
ENST00000304749	49.95	ENST00000399889	22.68
NM_002422	34.13	ENST00000425175	22.46
NM_205841	33.74	NM_000717	21.43
NM_170736	31.58	ENST00000432364	21.29
NM_153488	29.34	NM_001159710	20.88
NM_003014	28.57	NM_002196	20.54
NM_001012512	27.97	NM_030667	16.52

Gene Ontology (GO) analysis

To deduce the function of differentially expressed lncRNAs and mRNA transcripts, GO analysis was used. GO enrichment analysis annotated the biological processes, cellular components and molecular functions of the transcripts (12). P-value assessment was performed to estimate the significance of GO term enrichment in the differentially expressed lncRNAs and mRNAs; a lower p-value indicated that the GO term was more significant than a higher value (p-value ≤0.05 was considered to be statistically significant). Our data showed that the most enriched GO terms were DNA metabolic processes (Fig. 2A), non-membrane-bound organelles (Fig. 2B) and protein binding (Fig. 2C) in upregulated transcripts (GO: 0006259 under biological process, p=4.71932E-11; GO: 0043228 under cellular components, p=7.26282E-14 and GO: 0005515 under molecular function, p=1.36123E-10, respectively). In downregulated transcripts, the most enriched GO terms were organic hydroxyl compound metabolic processes (Fig. 2D), extracellular regions (Fig. 2E) and steroid binding (Fig. 2F) (GO: 1901615 under biological processes, p=2.57819E-10; GO: 0005576 under cellular components, p=8.92426E-08 and GO: 0005496 under molecular function, p=1.19953E-05, respectively).

Figure 2 Top three most highly enriched GO terms. (A–C) Most highly enriched GO terms for the upregulated transcripts: (A) biological process (BP); (B) cellular components (CC); (C) molecular function (MF). (D–F) Most highly enriched GO terms for the downregulated transcripts: (D) biological process (BP); (E) cellular components (CC); (F) molecular function (MF).

KEGG pathway analysis

KEGG pathway analysis indicated that 22 pathways were related to the upregulated transcripts, with the most enriched pathway identified as hsa05322 (Systemic lupus erythematosus-Homo sapiens) (Fig. 3A), and 22 transcripts were associated with this pathway [see additional file 3, http address shown in Fig. 3A legend]. Moreover, 24 pathways correlated with the downregulated transcripts, with the most enriched pathway being hsa00982 (Drug metabolism-cytochrome P450-Homo sapiens) (Fig. 3B), and 13 transcripts were associated with this pathway [see additional file 4, http address shown in Fig. 3B legend). Among the top 10 upregulated pathways, the deregulation of the 'Wnt signaling pathway' was previously identified as a crucial initiation step of colorectal cancer (13), and was reported to be correlated with prognosis in CRC patients (14) (Fig. 3C). The gene category 'lupus erythematosus' has been proven to be associated with several types of cancer, including non-Hodgkin's lymphoma, leukemia, vulva, thyroid, lung, and possibly liver cancers (15). Moreover, the gene categories 'chemokine signaling pathway' (14) and 'PI3K-Akt signaling pathway' (16) have been shown to be associated with CRC. Among the top 10 downregulated pathways, the gene category 'Retinol metabolism' has been reported to be involved in colorectal cancer metastatic multiplicity and the gene category 'Steroid hormone biosynthesis' was found to play important roles in reducing the risk of CRC in many research studies (17) (Fig. 3D).

Figure 3 Pathways corresponding to differentially expressed transcripts and two schematic diagram of gene category. (A) Pathways corresponding to the upregulated transcripts. See additional file 3, https://www.researchgate.net/publication/282291299_additional_file_3.hsa_pathwayResult?ev=prf_pub. (B) Pathways corresponding to the downregulated transcripts. See additional file 4, https://www.researchgate.net/publication/282291372_additional_file_4.hsa_pathwayResult?ev=prf_pub. (C) Schematic diagram of the gene category 'Wnt signaling pathway' for upregulated transcripts (referenced from Kanehisa Laboratories). (D) Schematic diagram of the gene category 'Retinol metabolism' for downregulated transcripts (referenced from Kanehisa Laboratories). 'Red' indicates upregulation, 'yellow' indicates downregulation and 'green' indicates unchanged.

Coding-non-coding gene co-expression network construction

As mentioned above, the 'Wnt signaling pathway' was upregulated in CRC tissues compared to non-cancerous tissues. Therefore, we aimed to ascertain whether these differentially expressed Wnt signaling pathway components were correlated with these differentially expressed lncRNAs. To explore this problem, coding-non-coding gene co-expression network (CNC network) was constructed between the top 10 differentially expressed 'Wnt signaling pathway' genes and all differentially expressed lncRNAs. We found that the co-expression network was composed of 1,468 nodes and 9,940 connections between 1,458 lncRNAs and 10 coding genes, including 6,004 positively correlated and 3,936 negatively correlated lncRNA-mRNA pairs in the cancer/para-cancer groups [see additional file 5, http address is shown in Fig. 4 legend). These results suggested that the expression of Wnt signaling pathway genes correlated closely with lncRNAs in CRC, and that a possible functional correlation existed between these lncRNAs and Wnt signaling pathway genes in CRC. The representative co-expression networks are shown in Fig. 4.

Figure 4 Co-expression sub-networks of Wnt signaling pathway genes and lncRNAs. Each node represents a gene. Genes colored in green are protein-coding RNAs, and genes colored in red are lncRNAs. Solid line represents a positive correlation, and dotted line represents a negative correlation. See additional file 5 https://www.researchgate.net/publication/282291303_additional_file_5.cnc_network_pairs?ev=prf_pub.

qRT-PCR validation

To verify the microarray results, we randomly selected 4 upregulated lncRNAs and 3 downregulated lncRNAs from the differentially expressed lncRNAs in the microarray (Table II). qRT-PCR method was used to confirm the expression levels of the selected lncRNAs in the CRC tissues and paired adjacent normal tissues used in the microarray test. As shown in Fig. 5, despite the fact that the fold change of each selected lncRNA in the qRT-PCR test was not in full accord with the microarray data, the variation tendency of each lncRNA was consistent with the result of the micro-array. H19, an lncRNA which was reported to be upregulated in multiple solid tumors such as breast, bladder, esophageal and lung was also found to be increased by an average fold of 3.6 in CRC samples compared to normal tissues.

Figure 5 qRT-PCR confirmation results of 7 differentially expressed lncRNAs. Four upregulated lncRNAs and three downregulated lncRNAs were verified by qRT-PCR. The y-axis represents the log-transformed median fold changes (C/N). Bars represent standard deviation.

Discussion

In the present study, we identified that lncRNAs and mRNAs were differentially expressed between colon cancer tissues and paired adjacent normal tissues. To address the underlying biology of those differentially expressed transcripts, GO analysis and KEGG pathway analysis were performed in our study.

GO analysis suggested that upregulated transcripts were predominantly enriched in DNA metabolic processes, non-membrane-bound organelles and protein binding. DNA metabolic processes consist of any cellular metabolic process involved in deoxyribonucleic acid, including replication, repair, recombination and so on. Dysregulation of each metabolic pathway may play a different role in the malignant transformation of pre-cancerous colorectal lesions (18). Non-membrane-bounded organelles include the ribosome, the cytoskeleton and chromosome, of which ribosome biogenesis is considered as a new pathway linking inflammation to CRC based on the inflammatory cytokine IL-6-mediated down-regulation of P53 (19).

Pathway analysis revealed that 22 pathways were associated with the upregulated transcripts, while 24 pathways were associated with the downregulated transcripts. The most enriched pathway was systemic lupus erythematosus-Homo sapiens in upregulated transcripts. The gene category in this pathway was previously reported to be associated with a variety of cancers, highlighting increased risks of non-Hodgkin's lymphoma, leukemia, vulva, thyroid, lung, and possibly liver cancers (20). Upregulation of the Wnt signaling pathway was also identified in pathway analysis, which has been shown to play an important role in colon carcinogenesis. However the majority of previous research has focused on the genetic mutations of signal transducers lying on the different level of this pathway (21–23), and the reports concerning the upregulation of this pathway at the mRNA level is rare so far. To our surprise, SRFP2, SRFP4, WIF and DKK4, which are known as Wnt signal antagonists, were also found to be upregulated in colon cancers in our study. There is increasing evidence indicating that these Wnt signal antagonists are associated with cancer biology. For example, SRFP2 and DKK4 have been proven to be associated with breast cancer angiogenesis (24) and colon cancer chemoresistance (25), respectively. SRFP4 serves as a tumor suppressor in a variety of human cancers (26–28), whose expression was also reported to be upregulated in colon cancer, although its exact role in the context of colon cancer is still unknown (29). 'Steroid hormone biosynthesis' was the fourth most enriched downregulated pathway in colon cancers in our study. In accordance with this, E2, the principal estrogen, which has been shown to play a vital role in reducing the risk of CRC (17) and to mediate the growth inhibition of colon cancer cells (30) was downregulated in CRC (31). More significantly, the expression of CYP19A1 and HSD3B1 (32), two estrogen synthesis gene, were decreased by 4.4- and 13.7-fold, respectively, in the colon cancer tissues compared to the adjacent normal tissues in our study.

Aberrant activation of the Wnt signaling pathway plays a central role in colon carcinogenesis (21). CNC co-expression analysis showed that the expression of Wnt signaling pathway genes is closely correlated with the expression of lncRNAs. Since genes with similar expression patterns in different conditions may have similar functions (33) or be involved in related biological pathways (34), we suggest that lncRNAs may play an important role during colon cancer initiation, progression or metastasis. The observations that the upregulation of Wnt signaling transducers at the mRNA level, and that lncRNAs have a functional correlation with this pathway may provide us with a novel mechanism, which is responsible for the aberrant activation of the Wnt signaling pathway in CRCs.

According to their genomic proximity to protein-coding genes, lncRNAs are mainly divided into five categories: intergenic, intronic, bidirectional, antisense and sense (35). Several studies indicate that the classification of lncRNAs may reflect functional characterization. For example, antisense lncRNAs can regulate the expression of neighboring genes in cis, or distant genes in trans, through base pair complementarity or through the ability to bind to proteins (36). Microarray data showed that 45 antisense lncRNAs were upregulated and 30 antisense lncRNAs were downregulated in cancer samples compared to paired normal samples. Among them, ENST00000496491 was upregulated, and its neighboring gene TM4SF1, which shows high expression in colon cancer, is closely related to the invasion and migration in CRC cells (37). On the other hand, Uc010fkt.3, which we found to be downregulated in CRC samples, is in close proximity to the gene PAX8, which was previously reported to have 100% negative expression in colon cancer (38). Another type of lncRNA is enhancer-like lncRNAs, which act as an enhancer-like element to influence the expression of nearby genes. In our data, ENST00000438158 was the highest upregulated enhancer-like lncRNA in cancer tissues compared to the normal tissues. ENST00000438158 is located downstream of the DUSP10 gene, which is upregulated in CRC (39), and can promote cell multiplication by inhibiting the JNK signaling pathway in pancreatic cancer cells (40). NR_034119 was another upregulated enhancer-like lncRNA, which is located downstream of the ARGLU1 gene. ARGLU1 was previously shown to have a significant stimulating effect on breast cancer cell growth (41).

While still a relatively new field, lncRNAs are now known to play many important roles in various types of cancers. Results of the microarray and bioinformatics analysis in our study strongly suggest that lncRNAs are likely to participate in colon cancer progression through different mechanisms and may be novel types of tumor markers for diagnosis or potential therapeutic targets for cancer treatment. Further studies are needed to investigate the exact biological functions and the underlining molecular mechanisms exerted by these abnormally expressed lncRNAs in CRC, and other cancers as well.

Acknowledgments

This study was supported by the Natural Science Foundation of Fujian Province, China (grant no. 2015J01566).

Abbreviations:

References