Identification of prognostic long intergenic non‑coding RNAs as competing endogenous RNAs with KRAS mutations in colorectal cancer
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- Published online on: August 9, 2021 https://doi.org/10.3892/ol.2021.12978
- Article Number: 717
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Copyright: © Xu et al. This is an open access article distributed under the terms of Creative Commons Attribution License.
Abstract
Introduction
Colorectal cancer (CRC) is a common malignant tumor in the gastrointestinal tract; its morbidity and fatality rates are behind only gastric, esophageal and primary liver cancer in malignant tumors of the digestive system (1,2). Globally, the incidence and mortality of CRC rank fifth among all malignant tumors (3). The incidence of CRC is associated with factors such as age, region and sex. CRC primarily occurs in middle-aged and elderly people (>40 years old), and the incidence of CRC in men and women is relatively similar (4). Previously, the incidence and mortality rates of CRC have demonstrated a notable upward trend (5). Numerous studies have identified KRAS mutations as poor prognostic biomarkers correlated with poor CRC survival outcomes (6,7). Taking advantage of synthetic lethal interactions with KRAS mutation may represent a target for effective therapeutic strategies in patients with KRAS-mutant CRC (8). Thus, detection methodology should be used to identify the key prognostic biomarkers for CRC, specifically KRAS-mutant CRC.
Human coding genes account for <2% of the total genome. The number of transcripts in the genome is very large; 70% of the whole human genome is stably transcribed into RNAs, and the majority of these are non-coding RNAs (9), including microRNAs (miRNAs/miRs) and long non-coding RNAs (lncRNAs). lncRNAs refer to transcripts >200 nucleotides in length that do not contain a protein-coding sequence (10). Long intergenic non-coding RNAs (lincRNAs) are the largest class of lncRNA molecules (11). lincRNAs can serve as transcriptional regulators and influence gene transcription, acting as decoys to bind proteins or miRNAs (9,12). Previously, numerous studies have reported that lincRNAs serve tumor-suppressive or tumor-promoting roles. For instance, lincRNAs correlated with CRC include CCAT1, CCAT2, CRNDE, HULC and MALAT1 (13,14).
In the past several years, certain studies have reported crosstalk between lincRNAs and miRNAs during cancer progression, specifically the hypothesis of competing endogenous RNAs (ceRNAs) (14,15). The ceRNA hypothesis model states that all coding and non-coding RNAs sharing common miRNA response elements may inhibit and indirectly regulate the expression of each other by competing for miRNA binding sites (16). These models are critical for human cancer. For instance, CCAT1 epigenetically downregulates c-Myc by serving as a ceRNA for miR-155, which downregulates c-Myc expression (16). lincRNAs are included in the regulatory network of CLDN4 via ceRNA-mediated miRNA evasion in gastric cancer (17). Thus, ceRNAs comprising lincRNAs, miRNAs and mRNAs can serve as important prognostic biomarkers in human cancer.
Targeting KRAS-driven cancer is an effective strategy that selectively inhibits cancer growth while unharmed normal cells (18). Thus, it is necessary to explore and identify key lincRNAs in KRAS-mutant CRC. However, there appear to be few related previous studies. In the present study, RNA sequencing (RNA-Seq) and clinical data from The Cancer Genome Atlas (TCGA) were employed to identify key survival lincRNAs associated with KRAS mutations in CRC. Furthermore, RNA-Seq and miRNA-Seq data from TCGA were used to construct ceRNA models among lincRNAs, miRNAs and mRNAs in CRC. The current study may provide a new understanding of KRAS-mutated CRC and help to interpret the mechanisms underlying the functions of these lincRNAs in CRC.
Materials and methods
Raw data
RNA-Seq, miRNA-Seq, copy number variation (CNV) and clinical data of CRC (COAD + READ) were downloaded from the TCGA (https://portal.gdc.cancer.gov). The RNA-Seq data are presented as fragments per kilobase million (FPKM). The miRNA-seq data were presented as reads per million miRNA mapped data.
Dysregulated lincRNA analysis
Dysregulated gene analysis was conducted with R software version 4.0.3, and the Mann-Whitney U test was performed to define significantly dysregulated lincRNAs between tumor and normal samples. To decrease background noise, any lincRNAs with 50th percentile FPKM=0 in tumor or normal samples were eliminated. Significantly dysregulated lincRNAs were defined as a logarithmic transformed fold-change (|log2 (FC)|) ≥1 and a false discovery rate (FDR) ≤0.05. Furthermore, lincRNAs with a mean FPMK value >1 in tumor samples were considered upregulated, and lincRNAs with a mean FPMK value >1 in normal samples were considered downregulated.
Predictive value of lincRNAs for survival in patients with KRAS mutations
Receiver operating characteristic (ROC) curves were used to analyze the association between lincRNAs and the survival status of patients with mutant or wild-type KRAS at 10 and 5 years. ROC curves were determined to evaluate the sensitivity and specificity of the expression level of each lincRNA in predicting mortality in patients (19). Forest plots were used to demonstrate the result of ROC. An area under the curve >0.6 and a P<0.05 were the threshold values to indicate significance.
Identification of key survival-related lincRNAs with KRAS mutations
Robust likelihood-based survival models were constructed to identify the key lincRNAs influencing the prognosis of CRC using rbsurv (https://bioconductor.org/packages/rbsurv) in R software version 4.0.3. The lincRNAs with the highest frequency were selected as the final feature lincRNAs. The effect of key lincRNA expression on patient survival was assessed by the Kaplan-Meier survival curves and the log-rank test.
Prediction of target lincRNAs and mRNAs of miRNAs
Target lincRNAs and mRNAs were predicted for miRNAs using the online tool DIANA (http://diana.imis.athena-innovation.gr/DianaTools/index.php?r=tarbase/index). The cut-off values used to identify a significant correlation were Pearson correlation coefficient (PCC) of <-0.1 and P<0.05. Furthermore, a maximal information coefficient (MIC) >0.17 was set as a cut off for significant correlation (20). PCC and MIC were performed with Rsoftware version 4.0.3.
Functional enrichment analysis
Functional enrichment analysis was performed with Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Ontology (GO) and Reactome under Metascape (http://metascape.org) (21). P<0.05 was set as the cut-toff.
Regulatory network
Regulatory network lincRNAs were directly connected to miRNAs, and mRNAs were directly connected to the miRNAs, constituting a regulatory network. The network was constructed and displayed using Cytoscape 3.7.2 (https://cytoscape.org).
Reverse transcription-quantitative (RT-q)PCR
A total of 12 pairs of CRC tissues and normal tissues (normal tissues were 2–3 cm away from cancer tissue) were obtained from 12 patients (age range, 42–53 years; median age, 48 years; eight men and four women) who underwent radical resection at The First College of Clinical Medical Science, China Three Gorges University (Yichang, China) between July 2020 and September 2020. The patient was diagnosed as CRC by imaging and pathological examination, and did not receive chemotherapy or radiotherapy before operation. All samples are stored in −80°C refrigerator for use. Total RNA was extracted using TRIzol® reagent (Invitrogen; Thermo Fisher Scientific, Inc.). RNA was reverse transcribed into cDNA using the PrimeScript 1st Strand cDNA Synthesis Kit Starter kit (Takara Bio, Inc.), according to the manufacturer's protocol. Relative expression levels of lincRNAs and mRNAs were quantified using the TB Green® Premix Ex Taq™ II kit (Takara Bio, Inc.), according to the manufacturer's protocol. Relative expression levels of miRNAs were quantified using the Mir-X miRNA First-Strand Synthesis kit (Takara Bio, Inc.), according to the manufacturer's protocol. The primer sequences are listed in Table SI. Expression levels of lincRNAs and mRNAs relative to β-actin and expression of miRNAs relative to U6 snRNA were determined using the 2−ΔΔCq method (22). All experimental procedures were approved by the Ethics Committee of The First College of Clinical Medical Science, China Three Gorges University. Written informed consent was provided by all patients prior to the study.
Statistical analysis
The Mann-Whitney U test was performed using GraphPad Prism (GraphPad Software, Inc.) version 9 software and R software version 4.0.3. Kaplan-Meier survival curves and log-rank tests were used to evaluate the effect of lincRNA expression on survival. Receiver operating characteristic (ROC) curves were generated using GraphPad Prism version 9 software. Data are presented as the mean ± SEM. P<0.05 was considered to indicate a statistically significant difference.
Results
Identification of significantly dysregulated lincRNAs in CRC
To identify the dysregulated lincRNAs in CRC, RNA-Seq data from 647 tumor and 51 normal (including 50 pairs) CRC samples from TCGA datasets were analyzed. Log2(FC) of lincRNA expression in tumor vs. normal >1 was classified as an upregulated gene, while <-1 was classified as a downregulated gene. In total, 7,369 lincRNAs were identified from the datasets. Subsequently, 96 upregulated lincRNAs and 51 downregulated lincRNAs were identified. Notably, these lincRNAs were also dysregulated in 50 tumor and adjacent normal samples (Fig. 1). These 147 lincRNAs were subsequently used to identify significant prognostic predictions.
Identification of independent prognostic lincRNAs with KRAS-mutant CRC
Patient 5- or 10-year survival rates are commonly used to represent statistical cure rates for those with cancer (23). Among the 647 tumor samples, 586 were identified as having 10-year survival data, and 545 were identified as having 5-year survival data. To determine whether the 147 dysregulated lincRNAs influencing the survival of patients with CRC depended on KRAS mutations, 217 and 198 samples were identified as having a KRAS mutation at 10 and 5 years, respectively. Using ROC curve analyses, 18 lincRNAs were identified as independent prognostic markers in mutant KRAS, rather than wild-type KRAS clinical samples (Fig. 2). These 18 lincRNAs were used to identify key lincRNAs with KRAS mutations.
Identification of key prognostic lincRNAs with KRAS mutations
Random data analysis was performed using robust likelihood-based modeling 1,000 times. Statistical frequency analysis of the significantly changed lincRNAs in KRAS-mutant samples suggested that all the selected lincRNAs had a high frequency. Two lincRNAs, LINC00265 (Gene Stable ID, ENSG00000188185) and AL390719.2 (Gene Stable ID, ENSG00000272141), were identified as key prognostic lincRNAs with KRAS mutations at both 10 years (Table I) and 5 years (Table II). LINC00106 was only significant in the 5-years survival group.
Oncogenicity of key prognostic lincRNAs with KRAS mutations
To examine whether the expression level of the key lincRNAs LINC00265 and AL390719.2 was correlated with a less favorable prognosis in CRC with KRAS mutations, 10- and 5-year overall survival (OS) rates were analyzed in the CRC TCGA dataset. To evaluate the clinical significance of the key lincRNAs LINC00265 and AL390719.2 in the survival of patients with CRC, as well as their associations with the KRAS mutation status, the prognostic significance of these lincRNAs was determined using TCGA datasets. The 10- and 5-year OS rates suggested that LINC00265 (Fig. 3A and B) and AL390719.2 (Fig. 3C and D) expression levels were associated with patient survival in CRC. High LINC00265 (Fig. 3A and B) and AL390719.2 (Fig. 3C and D) expression was significantly associated with less favorable survival in patients with CRC with mutant KRAS, but not in those with wild-type KRAS (Fig. 3). Hence, LINC00265 and AL390719.2 upregulation specifically predicts a poor prognosis and represents an independent prognostic marker in KRAS-mutant CRC.
To investigate the expression of the key lincRNAs LINC00265 and AL390719.2, the pan-cancer expression levels and CRC CNV profiles of these lincRNAs were analyzed in TCGA dataset. LINC00265 and AL390719.2 were identified as being expressed in cancer, including CRC (Fig. 4A and B). It was observed that LINC00265 CNV was amplified in 81.54% of CRC samples (Fig. 4C). LINC00265 expression was significantly higher in CRC with CNV amplification, and expression showed a positive correlation with CNV amplification (Fig. 4D). AL390719.2 expression was also significantly higher in CRC with CNV amplification (Fig. 4E), but CNV amplification was detected in 32.03% of CRC samples, and AL390719.2 expression was not associated with CNV amplification (Fig. 4F).
Regulatory modules of key prognostic lincRNAs with KRAS mutations
miRNA-lincRNA and miRNA-mRNA interactions were identified using tools from DIANA. PCC and MIC were used to evaluate the correlation of these interactions. The downregulated miRNAs were identified between tumor and normal samples with a log2 (FC) value ≤-1 and FDR <0.05. Finally, 2 miRNAs and 288 mRNAs connected to AL390719.2 (Fig. 5A) and 6 miRNAs and 415 mRNAs connected to LINC00265 (Fig. 5B) were identified. Functional enrichment analysis revealed that the mRNAs in the regulatory modules may be critical for the cell cycle in CRC (‘cell cycle’ from Reactome and KEGG, ‘regulation of cell cycle process’ from GO and ‘pathways in cancer’ from KEGG; Fig. 5C). To validate these findings, RT-qPCR was used to check lncRNAs, miRNAs and mRNAs (‘hsa05200 pathways in cancer’; orange nodes in Fig. 5 network) in 12 pairs of CRC samples. RT-qPCR demonstrated that LINC00265, AL390719.2 and 12 genes were upregulated in tumor samples, while 2 miRNAs were downregulated in tumor samples (Fig. 6A and C). Furthermore, RT-qPCR demonstrated that these two miRNAs were negatively correlated with lncRNAs and mRNAs (Fig. 6B and D).
Discussion
CRC is now the third most common malignancy worldwide. Oncogenic KRAS mutations initiate and sustain CRC progression. Numerous studies have assessed KRAS mutations associated with CRC outcomes (24–26). To provide prognostic lincRNAs to predict the outcomes of patients with CRC with KRAS mutations, 18 lincRNAs (LINC00265, AL390719.2, AL035461.2, AL117379.1, AC012615.1, AC020916.1, AL391244.3, AP002360.3, AC010719.1, AC015813.1, LINC00106, AP001453.4, AP005233.2, AL354707.1, AL133215.2, AL022322.1, AL139089.1 and LINC01748) were identified as independent prognostic lincRNAs in CRC with KRAS mutations. All these lincRNAs are upregulated in primary CRC tumors, and their increased expression is associated with a poor prognosis in patients with CRC with KRAS mutations. The expression levels of these lincRNAs were correlated with 5- and 10-year OS rates in patients with CRC. Furthermore, these 18 lincRNAs were independent prognostic markers in patients with CRC with mutant KRAS, but not in those with wild-type KRAS. The aforementioned results suggest that these lincRNAs may serve as prognostic biomarkers in CRC and correlate with CRC progression.
Robust likelihood-based survival models were used to identify two key lincRNAs, LINC00265 and AL390719.2, from 18 lincRNAs. All 18 lincRNAs were first identified as dysregulated and correlated with poor prognosis with KRAS mutations in human cancer. LINC00265 was reported to be differentially expressed and revealed to be a prognostic biomarker in the lung adenocarcinoma (LUAD) TCGA dataset (27). LINC00265 was also demonstrated to be upregulated in LUAD samples from TCGA. It was revealed that LINC00265 expression in CRC tumors was significantly associated with CNV amplification. Notably, CNV amplification was reported as an upstream mechanism to increase gene expression (28,29). The present results indicated that CNV amplification may cause LINC00265 overexpression in CRC. In addition, AL390719.2 was shown to be highly expressed in CRC tumors, but its increased expression was not caused by CNV amplification. Furthermore, Kaplan-Meier survival curves revealed that LINC00265 and AL390719.2 expression was associated with 5- and 10-year OS rates. High LINC00265 and AL390719.2 expression was correlated with less favorable 5- and 10-year OS rates in patients with mutant KRAS, but not in those with wild-type KRAS. Hence, LINC00265 and AL390719.2, as key prognostic lincRNAs in KRAS-mutant CRC, may be used to predict survival for patients with CRC.
To identify the function and molecular mechanism of LINC00265 and AL390719.2, it was determined whether these two key lincRNAs, as ceRNAs bound to miRNAs, regulate important genes. It was revealed that 2 miRNAs and 288 mRNAs were associated with AL390719.2, and that 6 miRNAs and 415 mRNAs were associated with LINC00265. Notably, functional enrichment analysis suggested that mRNAs in the regulatory modules were enriched in cell cycle biological processes. LINC00265 and AL390719.2 may serve as ceRNAs to bind these miRNAs and prevent the inhibitory effect of miRNAs on cell cycle genes, resulting in the upregulation of cell cycle genes and promoting CRC progression. AL390719.2, as a ceRNA, binds with hsa-mir-328-3p and hsa-let-7b-3p. LINC00265, as a ceRNA, binds to hsa-mir-1270, hsa-mir-139-3p, hsa-mir-149-3p, hsa-mir-377-5p, hsa-mir-378a-5p and hsa-mir-766-3p. These miRNAs were downregulated in CRC samples from TCGA. In addition, hsa-let-7b-3p, hsa-mir-328-3p, hsa-miR-139-3p, hsa-mir-149-3p and hsa-miR-378a-5p have been reported to be downregulated in CRC (30–34). Certain miRNAs have been reported to impact the cell cycle in human cancer. For example, hsa-let-7b has been reported to impact the cell cycle to inhibit prostate cancer cell proliferation in vitro (35). Moreover, hsa-mir-149 directly regulates the expression of cyclin-dependent kinase 4 (CDK4) cell lines, and hsa-mir-149 overexpression results in G0-G1 arrest and cell death in CRC (36). Also, CDK1 was the target gene of hsa-mir-378a-5p, and hsa-mir-378a-5p decreased CDK1 expression in hepatocytes (37). CDK1 is critical for regulating the G2-M transition during cell cycle progression (38). CDK4 controls cell cycle progression via pocket proteins and E2F transcription factors, and is correlated with cancer development and progression (39). Therefore, LINC00265 and AL390719.2 may serve as ceRNAs through competitive interactions with these miRNAs, resulting in the low expression of these miRNAs followed by upregulation of cell cycle genes in CRC. Moreover, RT-qPCR was used to validate LINC00265, AL390719.2 and related miRNAs and mRNAs in CRC samples.
In conclusion, 18 lincRNAs were identified that were upregulated and have potential as independent prognostic markers in CRC with KRAS mutations. From these lincRNAs, LINC00265 and AL390719.2 were identified as key lincRNAs that serve important roles in CRC progression, as ceRNAs for the regulation of lincRNA-miRNA-mRNA networks. The present findings may provide novel prognostic markers and therapeutic targets for CRC. However, the investigation of the regulatory models among these genes remains necessary.
Supplementary Material
Supporting Data
Acknowledgements
Not applicable.
Funding
No funding was received.
Availability of data and materials
The datasets generated and analyzed during the current study are available in The Cancer Genome Atlas [https://portal.gdc.cancer.gov].
Authors' contributions
JX conceptualized the present study, designed the research and performed bioinformatics analysis. JX and QYH performed the experiments. JX and CJG analyzed and interpreted the data. JX drafted and edited the manuscript, and QYH supervised the project. JX, QY and CJG confirmed the authenticity of all the raw data. All authors have read and approved the final manuscript.
Ethics approval and consent to participate
The present study was approved by the Ethics Committee of The First College of Clinical Medical Science, China Three Gorges University (Yichang, China; approval no. HEC-KYJJ-2019-056-01). Written informed consent was provided by all patients prior to the study start.
Patient consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
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